Category: 3. Business

  • Shaping the Nuclear Workforce through Data

    Shaping the Nuclear Workforce through Data

    After graduating, Chong quickly took up a job as an executive assistant to a local entrepreneur to repay her student loan. 

    “I learned the importance of not only placing the right people in the right jobs but also ensuring the workforce can adapt to evolving business needs,” she says. The experience also taught her how business resilience and care for employees can go hand in hand as she observed how her employer, conscious of the impact on people’s lives, prioritized the company’s workforce when responding to business challenges. 

    “I realized that HR decisions are more than just operational choices, but have also have the power to transform workplace culture and employee well-being” she says.

    Chong’s career in HR took off as she moved into specialized roles in various industries, from property and construction to logistics and supply chain management. She gained hands-on experience across the spectrum of HR functions, from recruitment and workforce engagement to organizational transformation. 

    “I was motivated by being able to contribute to different types of change, and with each move, I gained new perspectives on organizational growth and transition,” she explains. Her career spanned several countries in Southeast Asia, enriching her understanding of different workplace cultures. 

    After becoming a mother, Chong decided to focus on opportunities closer to home. In 2015, she joined the World Health Organization (WHO) in Malaysia, working in the service centre that processes contracts and benefits for its staff globally. Her role enabled the timely deployment of personnel, often during emergency situations such as disease outbreaks or global health initiatives. 

    After a year, she took on a more senior role as a team lead and resolved to continue a career in international organizations. “Working at an international organization was very motivating, as I felt the mandate was more meaningful than being profit centred. I felt I was contributing to something important,” she says.

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  • Boston Scientific announces results for third quarter 2025

    Boston Scientific announces results for third quarter 2025

    MARLBOROUGH, Mass., Oct. 22, 2025 /PRNewswire/ — Boston Scientific Corporation (NYSE: BSX) generated net sales of $5.065 billion during the third quarter of 2025, growing 20.3 percent on a reported basis, 19.4 percent on an operational1 basis and 15.3 percent on an organic2 basis, all compared to the prior year period. The company reported GAAP net income attributable to Boston Scientific common stockholders of $755 million or $0.51 per share (EPS), compared to $469 million or $0.32 per share a year ago, and achieved adjusted3 EPS of $0.75 for the period, compared to $0.63 a year ago.

    “We delivered another exceptional quarter of strong performance across businesses and regions thanks to the winning spirit of our global team,” said Mike Mahoney, chairman and chief executive officer, Boston Scientific. “As we shared at our recent Investor Day meeting, we are well-positioned for differentiated growth that is fueled by our category leadership strategy, relentless focus on innovation and commitment to scaling capabilities.”  

    Third quarter financial results and recent developments:

    • Reported net sales of $5.065 billion, representing an increase of 20.3 percent on a reported basis, compared to the company’s guidance range of 17 to 19 percent; 19.4 percent on an operational basis; and 15.3 percent on an organic basis, compared to the company’s guidance range of 12 to 14 percent, all compared to the prior year period.
    • Reported GAAP net income attributable to Boston Scientific common stockholders of $0.51 per share, compared to the company’s guidance range of $0.44 to $0.46 per share, and achieved adjusted EPS of $0.75 per share, compared to the guidance range of $0.70 to $0.72 per share.
    • Achieved the following net sales growth in each reportable segment, compared to the prior year period:
      • MedSurg: 16.4 percent reported, 15.6 percent operational and 7.6 percent organic
      • Cardiovascular: 22.4 percent reported, 21.5 percent operational and 19.4 percent organic
    • Achieved the following net sales growth/(declines) in each region, compared to the prior year period:
      • United States (U.S.): 27.0 percent reported and operational
      •  Europe, Middle East and Africa (EMEA): 2.6 percent reported and (2.0) percent operational
        • In the second quarter of 2025, management made the decision to discontinue worldwide sales of the ACURATE neo2™ and ACURATE Prime™ Aortic Valve Systems, which had prior year global sales of approximately $50 million per quarter
      • Asia-Pacific (APAC): 17.1 percent reported and 16.9 percent operational
      • Latin America and Canada (LACA): 10.4 percent reported and 9.6 percent operational
      • Emerging Markets4: 11.8 percent reported and 11.5 percent operational
    • Announced Pharmaceuticals and Medical Device Agency (PMDA) approval in Japan for expanded labeling of the FARAPULSE™ Pulsed Field Ablation (PFA) System to include treatment of drug refractory, symptomatic persistent atrial fibrillation (AF).
    • Commenced enrollment in the AGENT DCB STANCE trial to assess the safety and effectiveness of the AGENT Drug-Coated Balloon (DCB) in patients with previously untreated coronary lesions, compared to standard of care percutaneous coronary intervention (PCI) treatment with drug-eluting stents and/or balloon angioplasty.
    • Published in JAMA Neurology outcomes from the five-year INTREPID study demonstrating sustained benefits of deep brain stimulation in people with moderate to advanced Parkinson’s disease, including improved motor function and quality of life.
    • Completed asset acquisition with Elutia, Inc. to acquire the antibiotic-eluting EluPro™ BioEnvelope and the CanGaroo® Envelope, designed to prevent certain post-operative complications for devices such as pacemakers and defibrillators.
    • Announced agreement to acquire Nalu Medical, Inc., developer of the Nalu Neuromodulation System, which is designed to use peripheral nerve stimulation to deliver targeted relief for adults living with severe, intractable chronic pain of peripheral nerve origin — subject to customary closing conditions.

    1.

    Operational net sales growth excludes the impact of foreign currency fluctuations.

    2.

    Organic net sales growth excludes the impact of foreign currency fluctuations and net sales attributable to certain acquisitions and divestitures for which there are less than a full period of comparable net sales.

    3.

    Adjusted EPS excludes the impacts of certain charges (credits) which may include amortization expense, goodwill and other intangible asset impairment charges, acquisition/divestiture-related net charges (credits), investment portfolio net losses (gains) and impairments, restructuring and restructuring-related net charges (credits), certain litigation-related net charges (credits), European Union (EU) Medical Device Regulation (MDR) implementation costs, debt extinguishment net charges, deferred tax expenses (benefits) and certain discrete tax items.

    4.

    Our Emerging Markets countries include all countries except the United States, Western and Central Europe, Japan, Australia, New Zealand and Canada.

    Net sales for the third quarter by business and region:

    Increase/(Decrease)

    Three Months Ended
    September 30,

    Reported
    Basis

    Impact of
    Foreign
    Currency
    Fluctuations

    Operational

     Basis

    Impact of
    Certain
    Acquisitions
    /Divestitures

    Organic
    Basis

    (in millions)

    2025

    2024

       Endoscopy

    $          747

    $          678

    10.1 %

    (1.1) %

    9.0 %

    — %

    9.0 %

       Urology

    682

    532

    28.1 %

    (0.6) %

    27.5 %

    (22.1) %

    5.4 %

       Neuromodulation

    293

    268

    9.1 %

    (0.5) %

    8.6 %

    — %

    8.6 %

    MedSurg

    1,722

    1,479

    16.4 %

    (0.8) %

    15.6 %

    (8.0) %

    7.6 %

       Cardiology

    2,641

    2,129

    24.0 %

    (1.0) %

    23.1 %

    — %

    23.1 %

       Peripheral Interventions          

    702

    602

    16.7 %

    (0.9) %

    15.8 %

    (9.5) %

    6.3 %

    Cardiovascular

    3,343

    2,731

    22.4 %

    (1.0) %

    21.5 %

    (2.1) %

    19.4 %

    Net Sales

    $       5,065

    $       4,209

    20.3 %

    (0.9) %

    19.4 %

    (4.2) %

    15.3 %

     

    Increase/(Decrease)

    Three Months Ended

    September 30,

    Reported
    Basis

    Impact of
    Foreign
    Currency
    Fluctuations

    Operational

     Basis

    (in millions)

    2025

    2024

    U.S.

    $       3,294

    $       2,593

    27.0 %

    — %

    27.0 %

    EMEA

    793

    773

    2.6 %

    (4.6) %

    (2.0) %

    APAC

    802

    684

    17.1 %

    (0.2) %

    16.9 %

    LACA

    175

    159

    10.4 %

    (0.8) %

    9.6 %

    Net Sales

    $       5,065

    $       4,209

    20.3 %

    (0.9) %

    19.4 %

    Emerging Markets4

    $          765

    $          684

    11.8 %

    (0.2) %

    11.5 %

    Amounts may not add due to rounding. Growth rates are based on actual, non-rounded amounts and may not recalculate precisely.

    Net sales growth rates that exclude the impact of foreign currency fluctuations and/or the impact of certain acquisitions/divestitures are not prepared in accordance with U.S. GAAP.

    Guidance for Full Year and Fourth Quarter 2025
    The company estimates net sales growth for the full year 2025, versus the prior year period, to be approximately 20 percent on a reported basis and approximately 15.5 percent on an organic basis. Full year organic net sales guidance excludes the impact of foreign currency fluctuations and net sales attributable to certain acquisitions and divestitures for which there are less than a full period of comparable net sales. The company estimates EPS on a GAAP basis in a range of $1.97 to $2.01 and estimates adjusted EPS, excluding certain charges (credits), of $3.02 to $3.04.

    The company estimates net sales growth for the fourth quarter of 2025, versus the prior year period, to be in a range of approximately 14.5 to 16.5 percent on a reported basis, and 11 to 13 percent on an organic basis. Fourth quarter organic net sales guidance excludes the impact of foreign currency fluctuations and net sales attributable to certain acquisitions and divestitures for which there are less than a full period of comparable net sales. The company estimates EPS on a GAAP basis in a range of $0.48 to $0.52 and estimates adjusted EPS, excluding certain charges (credits), of $0.77 to $0.79.

    Conference Call Information
    Boston Scientific management will be discussing these results with analysts on a conference call today at 8:00 a.m. ET. The company will webcast the call to interested parties through its website: investors.bostonscientific.com. Please see the website for details on how to access the webcast. The webcast will be available for approximately one year on the Boston Scientific website.

    About Boston Scientific
    Boston Scientific transforms lives through innovative medical technologies that improve the health of patients around the world. As a global medical technology leader for more than 45 years, we advance science for life by providing a broad range of high-performance solutions that address unmet patient needs and reduce the cost of healthcare. Our portfolio of devices and therapies helps physicians diagnose and treat complex cardiovascular, respiratory, digestive, oncological, neurological and urological diseases and conditions. Learn more at www.bostonscientific.com and follow us on LinkedIn.

    Cautionary Statement Regarding Forward-Looking Statements 
    This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934. Forward-looking statements may be identified by words like “anticipate,” “expect,” “project,” “believe,” “plan,” “estimate,” “may,” “intend” and similar words. These forward-looking statements are based on our beliefs, assumptions and estimates using information available to us at the time and are not intended to be guarantees of future events or performance. These forward-looking statements include, among other things, statements regarding our expected net sales; reported, operational and organic revenue growth rates; reported and adjusted EPS for the fourth quarter and full year 2025; our financial performance; acquisitions; clinical trials; our business plans and product performance; and new and anticipated product approvals and launches. If our underlying assumptions turn out to be incorrect, or if certain risks or uncertainties materialize, actual results could vary materially from the expectations and projections expressed or implied by our forward-looking statements. These factors, in some cases, have affected and in the future (together with other factors) could affect our ability to implement our business strategy and may cause actual results to differ materially from those contemplated by the statements expressed in this press release. As a result, readers are cautioned not to place undue reliance on any of our forward-looking statements.

    Risks and uncertainties that may cause such differences include, among other things: economic conditions, including the impact of foreign currency fluctuations; future U.S. and global political, competitive, reimbursement and regulatory conditions, including changing trade and tariff policies; geopolitical events; manufacturing, distribution and supply chain disruptions and cost increases; disruptions caused by cybersecurity events; disruptions caused by public health emergencies or extreme weather or other climate change-related events; labor shortages and increases in labor costs; variations in outcomes of ongoing and future clinical trials and market studies; new product introductions; expected procedural volumes; the closing and integration of acquisitions; demographic trends; intellectual property; litigation; financial market conditions; the execution and effect of our business strategy, including our cost-savings and growth initiatives; and future business decisions made by us and our competitors. New risks and uncertainties may arise from time to time and are difficult to predict accurately and many of them are beyond our control. For a further list and description of these and other important risks and uncertainties that may affect our future operations, see Part I, Item 1A – Risk Factors in our most recent Annual Report on Form 10-K filed with the Securities and Exchange Commission, which we may update in Part II, Item 1A – Risk Factors in Quarterly Reports on Form 10-Q we have filed or will file hereafter. We disclaim any intention or obligation to publicly update or revise any forward-looking statements to reflect any change in our expectations or in events, conditions, or circumstances on which those expectations may be based, or that may affect the likelihood that actual results will differ from those contained in the forward-looking statements, except as required by law. This cautionary statement is applicable to all forward-looking statements contained in this press release.

    Note: Amounts reported in millions within this press release are computed based on the amounts in thousands. As a result, the sum of the components reported in millions may not equal the total amount reported in millions due to rounding. Certain columns and rows within tables may not add due to the use of rounded numbers. Percentages presented are calculated from the underlying unrounded amounts.

    Use of Non-GAAP Financial Information
    A reconciliation of the company’s non-GAAP financial measures to the corresponding GAAP measures, and an explanation of the company’s use of these non-GAAP financial measures, is included in the exhibits attached to this press release.

     

    BOSTON SCIENTIFIC CORPORATION

    CONSOLIDATED STATEMENTS OF OPERATIONS

    (Unaudited)

    Three Months Ended

    September 30,

    Nine Months Ended

    September 30,

    (in millions, except per share data)

    2025

    2024

    2025

    2024

    Net sales

    $        5,065

    $        4,209

    $      14,788

    $      12,186

    Cost of products sold (excluding amortization of intangibles)                                                                                             

    1,523

    1,312

    4,613

    3,791

    Gross profit

    3,542

    2,897

    10,175

    8,395

    Operating expenses:

    Selling, general and administrative expenses

    1,741

    1,562

    5,053

    4,372

    Research and development expenses

    514

    407

    1,483

    1,156

    Royalty expense

    12

    5

    40

    24

    Amortization expense

    225

    205

    669

    631

    Intangible asset impairment charges

    0

    46

    276

    Contingent consideration net expense (benefit)

    11

    (23)

    11

    (4)

    Restructuring net charges (credits)

    (8)

    8

    85

    12

    2,494

    2,164

    7,387

    6,467

    Operating income (loss)

    1,048

    733

    2,788

    1,928

    Other income (expense):

    Interest expense

    (87)

    (79)

    (259)

    (225)

    Other, net

    (23)

    14

    156

    (7)

    Income (loss) before income taxes

    939

    669

    2,685

    1,697

    Income tax expense (benefit)

    183

    200

    463

    413

    Net income (loss)

    755

    468

    2,222

    1,284

    Net income (loss) attributable to noncontrolling interests

    (0)

    (0)

    (4)

    (4)

    Net income (loss) attributable to Boston Scientific common
    stockholders

    $          755

    $          469

    $        2,226

    $        1,288

    Net income (loss) per common share – basic

    $         0.51

    $         0.32

    $         1.50

    $         0.88

    Net income (loss) per common share – diluted

    $         0.51

    $         0.32

    $         1.49

    $         0.87

    Weighted-average shares outstanding

    Basic

    1,481.7

    1,472.7

    1,479.6

    1,470.6

    Diluted

    1,495.5

    1,487.4

    1,494.0

    1,484.5

    Amounts may not add due to rounding.

     

    BOSTON SCIENTIFIC CORPORATION

    NON-GAAP NET INCOME AND NET INCOME PER SHARE RECONCILIATIONS

    (Unaudited)

    Three Months Ended September 30, 2025

    (in millions, except per share data)

    Gross
    Profit

    Operating 
    Expenses

    Operating 
    Income
    (Loss)

    Other 
    Income
    (Expense)

    Income 
    (Loss)
    Before
    Income
    Taxes

    Net 
    Income
    (Loss)

    Net Income 
    (Loss)
    Attributable to
    Noncontrolling
    Interests

    Net Income 
    (Loss)
    Attributable to
    Boston
    Scientific
    Common
    Stockholders

    Impact 
    per
    Share

    Reported

    $      3,542

    $      2,494

    $      1,048

    $       (110)

    $        939

    $        755

    $                  (0)

    $                755

    $     0.51

    Non-GAAP adjustments:

    Amortization expense

    (225)

    225

    225

    194

    2

    191

    0.13

    Goodwill and other intangible asset impairment  
    charges

    (0)

    0

    0

    0

    0

    0.00

    Acquisition/divestiture-related net charges
    (credits)

    23

    (76)

    99

    0

    99

    95

    95

    0.06

    Restructuring and restructuring-related net
    charges (credits)

    23

    (14)

    36

    36

    30

    30

    0.02

    Investment portfolio net losses (gains) and
    impairments

    (6)

    (6)

    (5)

    (5)

    (0.00)

    EU MDR implementation costs

    7

    (4)

    11

    11

    9

    9

    0.01

    Deferred tax expenses (benefits)

    47

    47

    0.03

    Discrete tax items

    1

    1

    0.00

    Adjusted

    $      3,595

    $      2,175

    $      1,419

    $       (116)

    $      1,303

    $      1,126

    $                    2

    $              1,124

    $     0.75

    Three Months Ended September 30, 2024

    (in millions, except per share data)

    Gross 
    Profit

    Operating 
    Expenses

    Operating 
    Income
    (Loss)

    Other 
    Income
    (Expense)

    Income 
    (Loss)
    Before
    Income
    Taxes

    Net 
    Income
    (Loss)

    Net Income 
    (Loss)
    Attributable to
    Noncontrolling
    Interests

    Net Income 
    (Loss)
    Attributable to
    Boston
    Scientific
    Common 
    Stockholders

    Impact 
    per
    Share

    Reported

    $      2,897

    $      2,164

    $        733

    $        (65)

    $        669

    $        468

    $                  (0)

    $                469

    $     0.32

    Non-GAAP adjustments:

    Amortization expense

    (205)

    205

    205

    177

    2

    175

    0.12

    Acquisition/divestiture-related net charges 
    (credits)

    27

    (116)

    143

    0

    144

    200

    200

    0.13

    Restructuring and restructuring-related net
    charges (credits)

    28

    (24)

    52

    52

    45

    45

    0.03

    Investment portfolio net losses (gains) and
    impairments

    (1)

    (1)

    (1)

    (1)

    (0.00)

    EU MDR implementation costs

    9

    (4)

    13

    13

    12

    12

    0.01

    Deferred tax expenses (benefits)

    38

    38

    0.03

    Adjusted

    $      2,962

    $      1,815

    $      1,147

    $        (65)

    $      1,082

    $        939

    $                    2

    $                937

    $     0.63

    An explanation of the company’s use of these non-GAAP financial measures is provided at the end of this document.

    Amounts may not add due to rounding.

     

    BOSTON SCIENTIFIC CORPORATION

    NON-GAAP NET INCOME AND NET INCOME PER SHARE RECONCILIATIONS

    (Unaudited)

    Nine Months Ended September 30, 2025

    (in millions, except per share data)

    Gross
    Profit

    Operating
    Expenses

    Operating
    Income
    (Loss)

    Other

    Income

    (Expense)

    Income
    (Loss)
    Before

     Income
    Taxes

    Net
    Income

    (Loss)

    Net Income
    (Loss)
    Attributable to
    Noncontrolling
    Interests

    Net Income
    (Loss)
    Attributable to
    Boston
    Scientific
    Common
    Stockholders

    Impact
    per
    Share

    Reported

    $    10,175

    $      7,387

    $      2,788

    $       (103)

    $      2,685

    $      2,222

    $                  (4)

    $              2,226

    $     1.49

    Non-GAAP adjustments:

    Amortization expense

    (669)

    669

    669

    576

    7

    570

    0.38

    Goodwill and other intangible asset impairment
    charges

    (46)

    46

    46

    37

    37

    0.02

    Acquisition/divestiture-related net charges
    (credits)

    159

    (226)

    385

    (229)

    156

    157

    157

    0.10

    Restructuring and restructuring-related net
    charges (credits)

    84

    (162)

    247

    247

    215

    215

    0.14

    Investment portfolio net losses (gains) and
    impairments

    (0)

    (0)

    (0)

    (0)

    (0.00)

    EU MDR implementation costs

    22

    (11)

    34

    34

    29

    29

    0.02

    Deferred tax expenses (benefits)

    139

    139

    0.09

    Discrete tax items

    1

    1

    0.00

    Adjusted

    $    10,440

    $      6,272

    $      4,168

    $       (332)

    $      3,836

    $      3,375

    $                    3

    $              3,372

    $     2.26

    Nine Months Ended September 30, 2024

    (in millions, except per share data)

    Gross
    Profit

    Operating

     Expenses

    Operating

     Income

     (Loss)

    Other

    Income

     (Expense)

    Income

     (Loss)
    Before

    Income

     Taxes

    Net
    Income

     (Loss)

    Net Income
    (Loss)
    Attributable to

    Noncontrolling

     Interests

    Net Income
    (Loss)
    Attributable to
    Boston
    Scientific
    Common

    Stockholders

    Impact
    per

    Share

    Reported

    $      8,395

    $      6,467

    $      1,928

    $       (231)

    $      1,697

    $      1,284

    $                  (4)

    $              1,288

    $     0.87

    Non-GAAP adjustments:

    Amortization expense

    (631)

    631

    631

    545

    7

    539

    0.36

    Goodwill and other intangible asset impairment
    charges

    (276)

    276

    276

    243

    243

    0.16

    Acquisition/divestiture-related net charges
    (credits)

    49

    (207)

    255

    1

    256

    315

    315

    0.21

    Restructuring and restructuring-related net
    charges (credits)

    83

    (65)

    149

    149

    129

    129

    0.09

    Investment portfolio net losses (gains) and
    impairments

    17

    17

    17

    17

    0.01

    EU MDR implementation costs

    27

    (12)

    39

    39

    34

    34

    0.02

    Deferred tax expenses (benefits)

    120

    120

    0.08

    Adjusted

    $      8,553

    $      5,275

    $      3,278

    $       (213)

    $      3,065

    $      2,685

    $                    2

    $              2,683

    $     1.81

    An explanation of the company’s use of these non-GAAP financial measures is provided at the end of this document.

    Amounts may not add due to rounding.

     

    BOSTON SCIENTIFIC CORPORATION

    Q4 and FY 2025 GUIDANCE RECONCILIATIONS

    (Unaudited)

    Net Sales

    Q4 2025 Estimate

    (Low)

    (High)

    Full Year 2025 Estimate

    Reported growth

    14.5 %

    16.5 %

    ~20.0%

    Impact of foreign currency fluctuations

    (2.0) %

    (2.0) %

    ~(1.0)%

    Operational growth

    12.5 %

    14.5 %

    ~19.0%

    Impact of certain acquisitions/divestitures

    (1.5) %

    (1.5) %

    ~(3.5)%

    Organic growth

    11.0 %

    13.0 %

    ~15.5%

    Earnings per Share

    Q4 2025 Estimate

    Full Year 2025 Estimate

    (Low)

    (High)

    (Low)

    (High)

    GAAP results

    $         0.48

    $         0.52

    $            1.97

    $            2.01

    Amortization expense

    0.13

    0.13

    0.51

    0.51

    Acquisition/divestiture-related net charges (credits)

    0.04

    0.04

    0.15

    0.15

    Restructuring and restructuring-related net charges                                                                                    
    (credits)

    0.07

    0.05

    0.22

    0.20

    Other adjustments

    0.04

    0.04

    0.18

    0.18

    Adjusted results

    $        0.77

    $        0.79

    $              3.02

    $              3.04

    Amounts may not add due to rounding.

    Use of Non-GAAP Financial Measures

    To supplement our unaudited consolidated financial statements presented on a GAAP basis, we disclose certain non-GAAP financial measures, including adjusted net income (loss), adjusted net income (loss) attributable to Boston Scientific common stockholders and adjusted net income (loss) per share (EPS) that exclude certain charges (credits); operational net sales, which exclude the impact of foreign currency fluctuations; and organic net sales, which exclude the impact of foreign currency fluctuations as well as the impact of certain acquisitions and divestitures with less than a full period of comparable net sales. These non-GAAP financial measures are not in accordance with generally accepted accounting principles in the United States and should not be considered in isolation from or as a replacement for the most directly comparable GAAP financial measures. Further, other companies may calculate these non-GAAP financial measures differently than we do, which may limit the usefulness of those measures for comparative purposes.

    To calculate adjusted net income (loss), adjusted net income (loss) attributable to Boston Scientific common stockholders and adjusted net income (loss) per share, we exclude certain charges (credits) from GAAP net income and GAAP net income attributable to Boston Scientific common stockholders, which include amortization expense, goodwill and other intangible asset impairment charges, acquisition/divestiture-related net charges (credits), investment portfolio net losses (gains) and impairments, restructuring and restructuring-related net charges (credits), certain litigation-related net charges (credits), EU MDR implementation costs, debt extinguishment net charges, deferred tax expenses (benefits) and certain discrete tax items. Amounts are presented after-tax using the company’s effective tax rate, unless the amount is a significant unusual or infrequently occurring item in accordance with Financial Accounting Standards Board Accounting Standards Codification Topic 740-270-30, “General Methodology and Use of Estimated Annual Effective Tax Rate.” In addition to the explanation below, please refer to Part II, Item 7. Management’s Discussion and Analysis of Financial Condition and Results of Operations in our most recent Annual Report on Form 10-K filed with the Securities and Exchange Commission or Part I, Item 2. Management’s Discussion and Analysis of Financial Condition and Results of Operations in any Quarterly Report on Form 10-Q that we have filed or will file thereafter for an explanation of each of these adjustments and the reasons for excluding each item. The following is an explanation of each incremental or revised adjustment type, since our most recent Annual Report on Form 10-K, that management excluded as part of these non-GAAP financial measures as well as the reason for excluding each item:

    • Restructuring and restructuring-related net charges (credits) – These adjustments primarily represent severance and other compensation-related charges, fixed asset write-offs, contract cancellations, project management fees, facility shut down costs, costs to transfer manufacturing lines between geographically dispersed facilities and other direct costs associated with our restructuring plans. These restructuring plans each consist of distinct initiatives that are fundamentally different from our ongoing, core cost reduction initiatives in terms of, among other things, the frequency with which each action is performed and the required planning, resourcing, cost and timing. Examples of such initiatives include the movement of business activities, facility consolidations and closures and the transfer of product lines between manufacturing facilities, which, due to the highly regulated nature of our industry, requires a significant investment in time and cost to create duplicate manufacturing lines, run product validations and seek regulatory approvals. Restructuring plans take place over a defined timeframe and have a distinct project timeline that requires, and begins subsequent to, approval by our Board of Directors. In contrast to our ongoing cost reduction initiatives, restructuring plans typically result in duplicative cost and exit costs over the defined timeframe and are not considered part of our core, ongoing operations. In addition, during the second and third quarter of 2025, we incurred restructuring and restructuring-related net charges (credits) associated with management’s decision to discontinue worldwide sales of the ACURATE neo2TM and ACURATE PrimeTM Aortic Valve Systems. These restructuring plans and activities are incremental to the core activities that arise in the ordinary course of our business. Restructuring and restructuring-related net charges (credits) are excluded from management’s assessment of operating performance and from our operating segments’ measures of profit and loss used for making operating decisions and assessing performance.

    The GAAP financial measures most directly comparable to adjusted net income (loss), adjusted net income (loss) attributable to Boston Scientific common stockholders and adjusted net income (loss) per share are GAAP net income (loss), GAAP net income (loss) attributable to Boston Scientific common stockholders and GAAP net income (loss) per common share – diluted, respectively.

    To calculate operational net sales growth rates, which exclude the impact of foreign currency fluctuations, we convert actual net sales from local currency to U.S. dollars using constant foreign currency exchange rates in the current and prior periods. To calculate organic net sales growth rates, we also remove the impact of certain acquisitions and divestitures with less than a full period of comparable net sales. The GAAP financial measure most directly comparable to operational net sales and organic net sales is net sales reported on a GAAP basis.

    Reconciliations of each of these non-GAAP financial measures to the corresponding GAAP financial measure are included in the accompanying schedules.

    Management uses these supplemental non-GAAP financial measures to evaluate performance period over period, to analyze the underlying trends in our business, to assess our performance relative to our competitors and to establish operational goals and forecasts that are used in allocating resources. In addition, management uses these non-GAAP financial measures to further its understanding of the performance of our operating segments. The adjustments excluded from our non-GAAP financial measures are consistent with those excluded from our operating segments’ measures of net sales and profit or loss. These adjustments are excluded from the segment measures reported to our chief operating decision maker that are used to make operating decisions and assess performance.

    We believe that presenting adjusted net income (loss), adjusted net income (loss) attributable to Boston Scientific common stockholders, adjusted net income (loss) per share, operational net sales growth rates and organic net sales growth rates, in addition to the corresponding GAAP financial measures, provides investors greater transparency to the information used by management for its operational decision-making and allows investors to see our results “through the eyes” of management. We further believe that providing this information assists our investors in understanding our operating performance and the methodology used by management to evaluate and measure such performance. 

    SOURCE Boston Scientific Corporation

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  • Employers urged to prepare as UK government ramps up clean energy jobs plan

    Employers urged to prepare as UK government ramps up clean energy jobs plan

    The new Clean Energy Jobs Plan published by the government at the weekend, with expectations that the number of clean energy sector jobs may double to 860,000 roles by 2030, looks to revamp the process to get more people into the workforce.

    This includes funding to get 16- to 19-year-olds skilled in clean energy sector work, investing in engineering higher education provision and the launch of five clean energy technical excellence colleges. Existing investment plans to boost the UK’s skilled construction workforce will supplement this with 10 construction technical excellence colleges. Skills England will also ensure apprenticeships and technical qualifications deliver skills needed across the clean energy sector.

    Up to £20 million will also be made available from the UK and Scottish governments to help upskill oil and gas industry workers to allow them to transition to roles in the renewables sector. Following on from a successful skills pilot in Aberdeen. The ‘energy skills passport’, which identifies routes for oil and gas workers to transition into roles in offshore wind, will also be updated to include nuclear and electricity grid roles.

    The government has already published plans for a ‘fair work charter’ to cover the wind industry, with new social value requirements also having come into effect this month as part of the national procurement plan which applies equally to the clean energy sector. Both initiatives are a drive to improve standards and training within the sector. The plan also signals that the government considers the fair work charter for the wind industry as a pilot and will explore extending the concept to other clean energy sectors. 

    Gillian Harrington, an employment law expert with Pinsent Masons, said the moves send a clear message to clean energy employers about the need to demonstrate commitment to improving employment standards.

    “The message to employers who want to engage in government backed clean energy projects is clear – your employment standards will be an important aspect of any evaluation of a funding request or procurement opportunity,” she said.

    “Employers in the clean energy sector may want to plan how any additional fair work standards can be factored into wider changes needed as the Employment Rights Bill is also gradually implemented.”

    The report draws together previously highlighted themes, including the government’s clean energy industries sector plan (90 pages / 9.5 MB), 10-year industrial strategy (160 pages / 15.6 MB) and the recent consultation over proposals to link financial incentives for offshore wind companies to fair work standards and skills training for staff as part of clean industry bonuses (CIB) allocation.

    The plan also looks to boost trade union involvement in the sector, with unions invited to negotiate the CIB fair work charter with the government and stakeholder employers, along with increasing union recognition and collective bargaining – with the plan noting: “For too long, parts of the clean energy sector have been a union-free zone”. The plan also announces that the government will, “work with industry and trade unions to explore a range of other initiatives, including the potential for Framework Agreements and sector specific arrangements to guide job quality standards for major infrastructure projects”.

    Anthony Convery, an expert in employment law with Pinsent Masons, said: “The Employment Rights Bill will make trade union recognition easier and will give even non-recognised unions rights to request physical and digital access to workplaces. The new plan puts clean energy sector employers clearly in the line of sight of unions who are eager to get a foothold in workplaces operating in this sector”.

    He added: “The plan for framework agreements also sounds somewhat like a form of sectoral collective bargaining for parts of the clean energy sector. The Employment Rights Bill only makes provision for sectoral collective bargaining in relation to adult social care and school support staff, but Labour’s Plan to Make Work Pay did say that it would assess how and to what extent sectoral collective bargaining could benefit other sectors and tackle labour market challenges”.

    The plan also covers extending employment rights to certain offshore workers, with the intention of reducing discrepancies that it notes can arise between offshore oil and gas workers and offshore renewable and low carbon energy workers. Further clarification of this proposed extension of the law will be needed to understand the potential impact, the experts said.

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  • Inside a Cloud-Based Gift Card Fraud Campaign

    Inside a Cloud-Based Gift Card Fraud Campaign

    Executive Summary

    We investigated a campaign waged by financially motivated threat actors operating out of Morocco. We refer to this campaign as Jingle Thief, due to the attackers’ modus operandi of conducting gift card fraud during festive seasons. Jingle Thief attackers use phishing and smishing to steal credentials, to compromise organizations that issue gift cards. Their operations primarily target global enterprises in the retail and consumer services sectors. Once they gain access to an organization, they pursue the type and level of access needed to issue unauthorized gift cards.

    The activity related to this campaign is tracked by Unit 42 as cluster CL‑CRI‑1032. The threat actors behind the activity target organizations that primarily rely on cloud-based services and infrastructure. They then exploit Microsoft 365 capabilities to conduct reconnaissance, maintain long-term persistence and execute large-scale gift card fraud. We assess with moderate confidence that the activity cluster we track as CL-CRI-1032 overlaps with the activity of threat actors publicly tracked as Atlas Lion and STORM-0539 [PDF].

    What makes the threat actor behind this activity particularly dangerous is the ability to maintain a foothold inside organizations for extended periods — sometimes over a year. During this time, they gain deep familiarity with the environment, including how to access critical infrastructure — making detection and remediation especially challenging. In April and May 2025, the threat actor behind the Jingle Thief campaign launched a wave of coordinated attacks across multiple global enterprises.

    This article presents an end-to-end analysis of the Jingle Thief campaign lifecycle, based on real-world incident telemetry and detections. We provide a clear view of the methods involved in this activity, and practical guidance for mitigating identity-based threats — attacks that target user accounts and credentials — in cloud environments. As identity increasingly replaces the traditional perimeter, understanding campaigns like Jingle Thief is essential to securing modern enterprise infrastructure.

    This activity was identified through behavioral anomalies detected by Cortex User Entity Behavior Analytics (UEBA) and Identity Threat Detection and Response (ITDR). Customers are better protected from this activity with the new Cortex Advanced Email Security module.

    If you think you might have been compromised or have an urgent matter, contact the Unit 42 Incident Response team.

    Who Is Behind the Jingle Thief Campaign?

    We assess with moderate confidence that the Jingle Thief campaign was created by financially motivated Morocco-based attackers who have been active since 2021. Their operations primarily target global enterprises in the retail and consumer services sectors. Although not affiliated with a nation-state, the activity we track as CL‑CRI‑1032 includes advanced tactics, persistence and operational focus.

    Unlike threat actors who rely on commodity malware or endpoint exploitation, the attackers behind CL‑CRI‑1032 operate almost exclusively in cloud environments once they obtain credentials through phishing. They exploit cloud-based infrastructure to impersonate legitimate users, gain unauthorized access to sensitive data and carry out gift card fraud at scale.

    Anatomy of the Jingle Thief Campaign

    In a campaign that we observed, threat actors maintained access for approximately 10 months and compromised over 60 user accounts within a single global enterprise. The activity involved the use of Microsoft 365 services, including SharePoint, OneDrive, Exchange and Entra ID. This demonstrated a high degree of adaptability and operational patience. Detecting this approach requires close observation of adversaries’ actions over an extended period. The threat actors behind the Jingle Thief campaign often align their activity with holiday periods, increasing operations during times of reduced staffing and heightened gift card spending.

    Having gained initial access, the threat actors conducted reconnaissance to map the environment, moved laterally to access more sensitive areas, and identified opportunities to execute large-scale financial fraud. Figure 1 illustrates the end-to-end attack lifecycle across Microsoft 365, highlighting how the threat actors progressed from phishing-based entry to persistent access through device registration.

    Figure 1. Jingle Thief phishing attack chain across Microsoft 365.

    The final attack step of device registration creates a foothold that the threat actors exploit to issue gift cards, which they then leverage for monetary gain.

    Why Gift Cards? The Prey of Choice

    Gift cards are highly attractive to financially motivated actors due to their ease of redemption and rapid monetization. Threat actors resell gift cards on gray-market forums at discounted rates, enabling near-instant cash flow.

    Additional factors that make gift cards attractive include:

    • Minimal personal information required for redemption
    • Difficult to trace, making fraud harder to investigate or recover
    • Accepted widely, often indistinguishable from legitimate use
    • Useful for low-risk money laundering, especially across jurisdictions
    • Frequently issued through systems with weak access controls, broad internal permissions, and limited monitoring or logging

    Retail environments are particularly vulnerable to this type of attack, as gift card systems are often accessible to a wide range of internal users, such as store employees. These systems may support multiple vendors or programs, making access pathways broader and more difficult to control.

    Gift card fraud combines stealth, speed and scalability, especially when paired with access to cloud environments where issuance workflows reside. To exploit these systems, the threat actors need access to internal documentation and communications. They can secure this by stealing credentials and maintaining a quiet, persistent presence within Microsoft 365 environments of targeted organizations that provide gift card services.

    In the campaign we observed, the attackers made repeated access attempts against multiple gift‑card issuance applications. They tried to issue high‑value cards across different programs in order to monetize them, and possibly to use the cards as collateral in money-laundering schemes — effectively turning digital theft into untraceable cash or short-term loans. These operations were staged in a way that minimizes logging and forensic traces, reducing the chance of rapid detection.

    Highly Targeted and Tailored Attacks

    The threat actors behind the Jingle Thief campaign invest heavily in reconnaissance before launching attacks. They gather intelligence on each target, including branding, login portals, email templates and domain naming conventions. This allows them to craft highly convincing phishing content that appears authentic to both users and security tools.

    Phishing URLs often include the organization’s name, a trusted third-party tool or software, and landing pages that closely mimic legitimate login screens. This highly customized social engineering approach increases the likelihood of compromise and highlights the actors’ use of sophisticated techniques.

    Figure 2 shows a credential phishing page crafted by the threat actors to impersonate a legitimate Microsoft 365 login portal, tailored to the victim organization’s branding.

    Screenshot of an "Account Sign On" interface with fields for User ID and Password, and buttons for "Sign In," "Forgot Password," and "Change Password."
    Figure 2. Fake Microsoft 365 login page tailored to the target organization.

    Initial Access: Phishing and Smishing for Cloud Credentials

    The threat actors behind the Jingle Thief campaign typically begin their operations with tailored phishing or SMS-based smishing attacks. These messages lure victims to counterfeit Microsoft 365 login portals that mimic legitimate sign-in pages. Some lures impersonate nonprofits or non-governmental organizations (NGOs), likely to give the appearance of credibility and increase victim engagement.

    Notably, many messages are delivered using self-hosted PHP mailer scripts, often sent from compromised or hijacked WordPress servers, which obscure the attackers’ origin and improve delivery.

    The threat actors also employ deceptive URL formatting, such as: https://organization[.]com@malicious.cl[/]workspace

    While the URL above appears to point to the legitimate organization’s domain (organization[.]com), browsers interpret everything before the @ as user credentials, and actually navigate to the domain after it (malicious.cl). This tactic helps disguise the true destination of the link and increases the likelihood of victims clicking.

    After harvesting credentials in the campaign that we observed, the attackers authenticated to Microsoft 365 directly and began navigating the environment, with no malware required. Figure 3 shows a smishing attempt used to harvest credentials, captured from a malicious PHP email send log from the attackers’ infrastructure. The message originated from a Moroccan IP address, and was sent to a Verizon SMS gateway (vtext.com).

    Screenshot of an email header displaying various metadata fields such as date, subject, and server information, indicating the use of Microsoft Windows and Linux operating systems. Two sections are highlighted in red boxes.
    Figure 3. Credential phishing via smishing, logged from attackers’ infrastructure.

    Cloud Reconnaissance: Mining SharePoint and OneDrive for Gift Card Intel

    After initial access, the attackers behind Jingle Thief perform extensive reconnaissance within the Microsoft 365 environment, particularly focusing on SharePoint and OneDrive. These services frequently contain internal documentation related to business operations, financial processes and IT workflows.

    The threat actors search for:

    • Gift card issuance workflows
    • Ticketing system exports or instructions
    • VPN configuration and access guides
    • Spreadsheets or internal tools used to issue or track gift cards
    • Organizational virtual machines, Citrix environments

    Figure 4 shows SharePoint files accessed by the threat actors after account compromise, revealing their focus on internal documentation tied to gift card workflows and remote access infrastructure.

    Screenshot showing a list of hyperlinks and document files.
    Figure 4. Internal SharePoint files accessed by Jingle Thief post-compromise.

    Rather than escalating privileges, the threat actors build situational awareness by accessing readily available data on compromised users. This discreet approach helps evade detection while laying the groundwork for future fraud.

    Internal Phishing for Lateral Moves

    Instead of deploying malware or post-exploitation frameworks, Jingle Thief relies on internal phishing to expand their foothold within target environments. In an attempted attack against one of our customers, after compromising a user’s Microsoft 365 account, the attackers sent phishing emails from the legitimate account to personnel inside the same organization. These messages mimicked IT service notifications or ticketing updates, often leveraging information gathered from internal documentation or previous communications to appear legitimate.

    Common lures:

    • Fake ServiceNow alerts: “INCIDENT REQ07672026 Has been completed”
    • IT access notifications: “ServiceNow Account Inactivity Notice”
    • Generic approval prompts: “Incident pending your review”

    These emails link to fake login portals branded with the organization’s identity, leveraging internal trust to evade suspicion and spread laterally.

    Figure 5 shows an internal phishing email sent from a compromised account, spoofing a ServiceNow inactivity notice to trick users into entering credentials.

    An email screenshot from ServiceNow titled "ServiceNow Account Inactivity Notice," alerting the recipient of detected inactivity on their account and asking them to verify their account activity within 48 hours to prevent deactivation. Some of the information is redacted for privacy concerns.
    Figure 5. Internal phishing email mimicking a ServiceNow notification.

    Ruling the Inbox for Silent Email Exfiltration

    To passively monitor internal communications, the attackers responsible for the Jingle Thief campaign often create inbox rules to automatically forward emails to attacker-controlled addresses.

    They monitor:

    • Gift card approvals
    • Financial workflows
    • IT ticketing or account changes

    This approach reduces the need for active attacker interaction and helps maintain stealth. Figure 6 shows an alert flagging the creation of a malicious inbox forwarding rule, which is one of the stealth tactics employed by these threat actors to monitor internal communications.

    Screenshot of a security alert from XDR Analytics indicating an "Exchange inbox forwarding rule configured" identified as an Identity Threat.
    Figure 6. Cortex XDR alert showing automatic email forwarding rule set by threat actors.

    Stealthy Email Activity: Hiding in Plain Sight

    To cover their tracks, the attackers actively manage mailbox folders:

    • Moving sent phishing emails immediately from Sent Items to Deleted Items
    • Moving replies from users from Inbox to Deleted Items

    This ensures that victims won’t see the phishing messages or responses, delaying discovery by both victims and defenders.

    The Exchange audit logs in Figure 7 show the attackers moving phishing email replies from the Inbox folder to the Deleted Items folder.

    Screenshot of an email inbox displaying multiple messages with the subject 'Automatic reply: INCIDENT [Set of numbers] has been completed', all from the sender 'MoveToDeclassifiedItems' and located in the 'Inbox' folder.
    Figure 7. Items moved from Inbox to Deleted Items.

    Dominating Rogue Devices for Persistence

    Most of the intrusions we observed in the Jingle Thief campaign relied on stolen credentials or session tokens for temporary access. However, the actors also demonstrated techniques for establishing longer-term persistence within compromised environments.

    In some intrusions, the threat actors took control of identity infrastructure by misusing legitimate user self-service and device enrollment mechanisms in Microsoft Entra ID. These tactics allowed them to maintain access even after passwords were reset or sessions were revoked.

    Tactics include:

    • Registering rogue authenticator apps to bypass MFA
    • Resetting passwords via self-service flows
    • Enrolling attacker-controlled devices in Entra ID

    Figure 8 shows the user interface for registering a device in Microsoft Entra ID using the Authenticator app. The attackers misused this legitimate process to silently enroll rogue devices and maintain MFA-resistant access.

    Screenshot of the Microsoft Authenticator app onboarding screen with an illustration featuring a person and a cat next to a mobile device displaying security features. There are options to 'Add account' and links for 'Begin recovery' and checking if the user already has a backup.
    Figure 8. Device registration flow in Microsoft Entra ID.

    The ultimate goal of these varied tactics – phishing, inbox control, mail exfiltration and rogue device registration – is to obtain and monetize gift cards at scale.

    Tracing Jingle Thief’s Moroccan Roots

    The campaign activities that we observed almost exclusively originated from IP addresses geolocated in Morocco. Across incidents, Microsoft 365 logs showed recurring device fingerprints and login behaviors associated with these IP addresses. Unlike many actors who hide behind VPNs, these threat actors often made no attempt to obscure their origin, and only sometimes used Mysterium VPN when accessing compromised accounts.

    Autonomous System Number (ASN) metadata from the connections also consistently matched Moroccan telecommunications providers, including:

    • MT-MPLS
    • ASMedi
    • MAROCCONNECT

    In addition to IP and ASN infrastructure, Jingle Thief reuses distinctive domain and URL structures across campaigns. These recurring patterns in domain naming and infrastructure further support attribution to a Morocco-based threat group.

    Conclusion

    The Jingle Thief campaign demonstrates a clear focus on major retailers’ gift-card issuance systems. The attackers targeted multiple issuance applications to generate high‑value cards, likely for resale on gray markets, or as fungible assets in money‑laundering chains. Gift-card systems are often under‑monitored and widely accessible internally, making them an attractive extension to identity‑based attacks: By compromising the right accounts, threat actors can issue and steal gift cards, while leaving almost no trace of their malicious operations.

    The cluster of activity behind the Jingle Thief campaign overlaps with the activity of threat actors publicly tracked as Atlas Lion. This cluster — tracked by Unit 42 as CL-CRI-1032 — favors identity misuse over malware, and leverages trusted cloud services rather than endpoint compromise. Their campaigns highlight how attackers can operate entirely within cloud environments, abusing legitimate features for phishing, persistence and fraud.

    By understanding the tactics used in the Jingle Thief campaign, defenders can better prioritize identity-based monitoring and adapt to the industry’s shift toward treating identity as the new security perimeter. Understanding user behavior, login patterns and identity misuse are increasingly essential for early detection and response.

    Palo Alto Networks customers are better protected from this activity with the new Cortex Advanced Email Security module, as well as Cortex UEBA and ITDR.

    If you think you may have been compromised or have an urgent matter, get in touch with the Unit 42 Incident Response team or call:

    • North America: Toll Free: +1 (866) 486-4842 (866.4.UNIT42)
    • UK: +44.20.3743.3660
    • Europe and Middle East: +31.20.299.3130
    • Asia: +65.6983.8730
    • Japan: +81.50.1790.0200
    • Australia: +61.2.4062.7950
    • India: 00080005045107

    Palo Alto Networks has shared these findings with our fellow Cyber Threat Alliance (CTA) members. CTA members use this intelligence to rapidly deploy protections to their customers and to systematically disrupt malicious cyber actors. Learn more about the Cyber Threat Alliance.

    Indicators of Compromise

    Moroccan Infrastructure (Attribution Signal)

    • 105.156.109[.]227
    • 105.156.234[.]139​​
    • 105.157.86[.]136
    • 105.158.226[.]49
    • 105.158.237[.]165
    • 160.176.128[.]242
    • 160.178.201[.]89
    • 160.179.102[.]157
    • 196.64.165[.]160
    • 196.65.139[.]51
    • 196.65.146[.]114
    • 196.65.172[.]48
    • 196.65.237[.]97
    • 196.74.125[.]243
    • 196.74.183[.]81
    • 196.77.47[.]232
    • 196.89.141[.]80
    • 41.141.201[.]19
    • 41.250.180[.]114
    • 41.250.190[.]104

    Associated ASN Organizations (Geolocated to Morocco)

    • MT-MPLS
    • ASMedi
    • MAROCCONNECT

    U.S. Infrastructure (Potential Proxy or Compromised Hosts)

    • 70.187.192[.]236
    • 72.49.91[.]23

    Phishing URL Patterns

    • hxxps://*.com.ng/*[brand-name].com/home/
    • hxxps://*.[brand-name].servicenow.*/*access
    • hxxps://[brand-name].com@*.*/portal/
    • hxxps://[brand-name].com@*.*/workspace
    • hxxps://*/home
    • hxxps://*/workspace/home

    Additional Resources

    Cortex XDR/XSIAM Alerts on Jingle Thief Activity

    Table 1 shows Cortex alerts for this activity, using Identity Analytics including behavioral indicators of compromise (BIOC) and the ITDR module.

    Alert Name Alert Source MITRE ATT&CK Technique
    Exchange inbox forwarding rule configured XDR Analytics BIOC, Identity Threat Module (ITDR) Hide Artifacts: Email Hiding Rules (T1564.008)
    User moved Exchange sent messages to deleted items XDR Analytics, Identity Threat Module (ITDR) Indicator Removal: Clear Mailbox Data (T1070.008)
    First connection from a country in organization XDR Analytics BIOC, Identity Analytics Compromise Accounts (T1586)
    First SSO access from ASN in organization XDR Analytics BIOC, Identity Analytics Valid Accounts: Domain Accounts (T1078.002)
    Impossible Traveler – SSO XDR Analytics, Identity Analytics Compromise Accounts (T1586)
    A user connected from a new country XDR Analytics BIOC, Identity Analytics Compromise Accounts (T1586)
    First SSO access from ASN for user XDR Analytics BIOC, Identity Analytics Valid Accounts: Domain Accounts (T1078.002)
    A user connected to a VPN from a new country XDR Analytics BIOC, Identity Analytics Compromise Accounts (T1586)
    VPN access with an abnormal operating system XDR Analytics BIOC, Identity Analytics Valid Accounts: Domain Accounts (T1078.002)
    First VPN access from ASN in organization XDR Analytics BIOC, Identity Analytics Valid Accounts: Domain Accounts (T1078.002)
    First SSO Resource Access in the Organization XDR Analytics BIOC, Identity Analytics Valid Accounts: Domain Accounts (T1078.002)
    Suspicious SSO access from ASN XDR Analytics BIOC, Identity Analytics Valid Accounts: Domain Accounts (T1078.002)
    A possible risky login to Azure XDR Analytics BIOC, Identity Analytics Compromise Accounts (T1586)
    User attempted to connect from a suspicious country XDR Analytics BIOC, Identity Analytics Compromise Accounts (T1586)
    SSO with new operating system XDR Analytics BIOC, Identity Analytics Valid Accounts: Domain Accounts (T1078.002)
    Massive file downloads from SaaS service XDR Analytics, Identity Threat Module (ITDR) Data from Cloud Storage (T1530)

    Table 1. Cortex XDR/XSIAM alerts on Jingle Thief campaign activity.

     

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  • Frankendough Dozen is a Monster Deal, Available Wednesday and Thursday, Oct. 22-23 – Krispy Kreme

    1. Frankendough Dozen is a Monster Deal, Available Wednesday and Thursday, Oct. 22-23  Krispy Kreme
    2. Krispy Kreme’s New Spooktacular Halloween Collection Has Arrived  People.com
    3. Horror Movie-Themed Donuts  Trend Hunter
    4. No Tricks, Just Sweet Treat Weekends: KRISPY KREME® Returns ‘Scary Sharies’ and Doubles Down on Halloween Fun! – Company Announcement – FT.com  Financial Times
    5. KRISPY KREME® Frankendough Dozen is a Monster Deal, Available Wednesday and Thursday, Oct. 22-23  Business Wire

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  • Disruption of low-frequency narrowband EEG microstate networks in Parkinson’s disease with mild cognitive impairment | Journal of NeuroEngineering and Rehabilitation

    Participants and study design

    This investigation enrolled a total of 67 individuals, comprising 47 individuals diagnosed with primary PD who were undergoing treatment at Beijing Rehabilitation Hospital, affiliated with Capital Medical University (25 males and 22 females), along with 20 individuals without any neurological conditions (healthy controls, HC) sourced from the local population, ensuring parity in gender and age. These controls had no past records of neurological impairments. Motor function was primarily appraised via the Movement Disorder Society Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS III). Concurrently, cognitive abilities were assessed through the Mini-Mental State Examination (MMSE) as well as the Montreal Cognitive Assessment (MoCA) scales [22]. The MoCA is the most frequently used standard screening tool for PD-MCI, with a specificity of 82%, a sensitivity of 41%, and a diagnostic accuracy of 68% while maintaining clinical relevance [23]. From the MoCA results, two groups emerged among the 47 participants with PD: those with normal cognition (PD-NC, score > 25) and those with mild cognitive impairment (PD-MCI, score ≤ 25). With the approval of the Ethics Committee of Beijing Rehabilitation Hospital, Capital Medical University, we collected the data for this study (Approval Number: 2023bkky044). The data acquisition adhered rigorously to the ethical principles outlined in the Declaration of Helsinki. Before participating, all participants gave written informed consent.

    EEG data acquisition and preprocessing

    EEG data were recorded using a 30-channel electrode system (actiCAP snap, Brain Products GmbH, Gilching, Germany). The electrode positions included frontal electrodes (Fp1, Fz, F3, F4, F7, F8), fronto-central electrodes (FT9, FT10, FC5, FC6, FC1, FC2), central electrodes (C3, Cz, C4), parietal electrodes (CP5, CP6, CP1, CP2, Pz, P3, P4, P7, P8), and occipital electrodes (O1, Oz, O2). Participants sat relaxed with eyes closed in dimly lit rooms, maintaining wakefulness. The impedance of all electrodes was kept under 5 kΩ. The EEG signals were sampled at 1000 Hz for 15 min per individual. A rigorous, multi-stage preprocessing pipeline was applied to the raw EEG data to ensure high data quality and mitigate the constraints of the acquisition setup. Data were originally recorded using an actiCAP system with TP9 as the online reference. The data were first filtered using a zero-phase bandpass filter (0.5–45 Hz; Hamming-windowed Finite Impulse Response filter in EEGLAB) to attenuate low-frequency drifts and high-frequency noise. As the online reference (TP9) is suboptimal for Independent Component Analysis (ICA), the data were offline-referenced to the average of the bilateral mastoids (TP9 and TP10). This approach established a pragmatic and stable non-cephalic reference point for ICA, consistent with established practices in the field [24]. Subsequently, the data were decomposed into 30 independent components using the Infomax ICA algorithm. To ensure the validity of the decomposition and the accuracy of artifact rejection, a robust, semi-automated component classification strategy was employed. An initial, data-driven labeling was performed using ICLabel [25], followed by a rigorous manual review conducted by two experienced researchers. Components unambiguously identified as ocular (blinks, saccades), cardiac, or muscle artifacts (along with other categories) were rejected. A 5-minute continuous portion of EEG data per participant was selected for the purpose of microstate analysis. Due to the intermittent nature of artifacts, this 5-minute segment was often composed of multiple concatenated artifact-free intervals. The artifact-free EEG data were down-sampled to 125 Hz and then filtered for the broadband range (1–30 Hz). This broadband range was divided into four narrow bands based on their frequency ranges: the 1–4 Hz delta band, the 4–7 Hz theta band, the 7–13 Hz alpha band, and the 13–30 Hz beta band. Each narrow-band signal was further filtered using a zero-phase Finite Impulse Response filter (Hamming window) to ensure precise frequency isolation and temporal fidelity.

    EEG spectral microstate analysis

    The core principle of EEG microstate analysis is to segment the continuous EEG stream into a sequence of quasi-stable topographical maps, achieved by clustering the EEG data at specific time points [26]. The global field potential (GFP) quantifies the aggregate field strength of EEG signals at varying time points, as depicted in formula (1):

    $$ \,GFP\left( t \right) = \sqrt {\frac{1}{N}\sum {\,_{{i = 1}}^{N} } (V_{i}^{{\prime \,}} (t))^{2} } $$

    (1)

    where \(\:{V}_{i}\left(t\right)\) denotes the potential of the \(\:i\)-th electrode at time\(\:\:t\), with \(\:N\) indicating the overall count of electrodes. Since the peak point of GFP typically coincides with the transition between microstates, it allows for the segmentation of microstate time points based on the GFP peak locations. GFP peaks were extracted with a minimum inter-peak distance of 10 ms and a maximum of 1000 peaks per subject. To mitigate noise, peaks with an amplitude exceeding one standard deviation above the mean GFP amplitude were excluded. Microstate templates were derived using a modified k-means clustering algorithm (modkmeans). The number of clusters (k) was fixed at 5, and the clustering procedure was repeated 50 times with random initializations to ensure solution stability. The optimal cluster set was selected based on the lowest cross-validation error, and the resulting maps were sorted by their global explained variance. Clustering iterations were limited to 1000 with a convergence threshold of 1e-6. Following template extraction, a back-fitting procedure was applied to assign a microstate label to each time point of the continuous EEG data for each subject. During this process, the polarity of the EEG signals was ignored to prevent label inversion. Subsequently, temporal smoothing was applied to remove microstate segments shorter than 30 ms, thereby improving the temporal continuity and physiological plausibility of the sequences. The refined, ongoing EEG data from each subject were sorted into their corresponding microstate types A, B, C, D, and E (abbreviated as MS-A, MS-B, MS-C, MS-D, and MS-E) by using the derived microstate type labels as templates. This analysis was performed on both the broadband data and data filtered into four classical frequency bands: delta, theta, alpha, and beta. For each of these five conditions, the preprocessed EEG data were independently matched against the set of microstate templates. From the resulting microstate sequences, several parameters were computed for each microstate class (Fig. 1A): (a) Coverage: The ratio of the duration a microstate is present to the total recording time. (b) Duration: Measures the length of each instance of a microstate. (c) Occurrence: The frequency of microstate occurrences throughout the recording period. (d) Transition Probability (TP): The chance that one microstate in the brain transforms into another.

    Fig. 1
    figure 1

    The scheme of the EEG microstate dynamics and spatiotemporal network analysis. A Steps for frequencyspectral microstate extraction, including bandpass filtering, global field power detection, microstate class identification, and microstate parameter analysis. B Steps for microstate-derived network analysis, including dynamic functional connectivity computation per microstate class by phase lag index, and quantification of temporal variability and spatial variability

    EEG spectral microstate network analysis

    The EEG microstate sequence for each individual was segmented into a series of discrete, non-overlapping intervals based on the assigned microstate labels (A-E). For each resulting microstate window, a functional connectivity network was constructed from 30 EEG electrodes using the Phase Lag Index (PLI). The PLI serves to measure the asymmetry level observed in the phase disparity between two signals [27]. Functional connectivity was estimated independently within each microstate segment, without concatenation or averaging across segments, resulting in a series of state-specific functional connectivity matrices. The segmentation algorithm adaptively defines the duration of each window, thereby avoiding arbitrary temporal partitioning and maintaining physiologically meaningful boundaries. Moreover, microstate segments, typically lasting between 60 and 300 ms, are considered quasi-stationary and have been empirically demonstrated to support reliable phase-based connectivity estimation [21]. Thus, the functional connectivity of paired EEG channels in the interval represented by the \(\:r\)-th microstate window was determined, as illustrated by formula (2),

    $$ F_{r} \left( {i,j} \right) = \left| {~\left\langle {sign~\left[ {~sin\left( {\Delta \phi \left( {t_{l} } \right)} \right)~} \right]} \right\rangle ~} \right|l = 1,2,…,m_{r} ,i \ne j $$

    (2)

    where \(\:{F}_{r}(i,j)\) represents the functional connectivity between electrode channels \(\:i\) and \(\:j\) in the r-th microstate epoch. The notation \(\:\left\langle.\right\rangle\) is employed to denote the average value over that period. \(\:\Delta\varphi\:\left({t}_{l}\right)\) represents the phase differential determined at the \(\:l\)-th time instance, while \(\:{m}_{r}\) stands for the total number of time points within the \(\:r\)-th microstate epoch. By aggregating these PLI values for all pairs of EEG channels, the 30 × 30 adjacency matrix was constructed, which forms the brain network \(\:{F}_{r}\) for the \(\:r\)-th microstate window.

    Subsequently, the functional connection networks corresponding to every distinct microstate category was divided into five different brain network sets (microstate networks A, B, C, D, and E). Utilizing these networks, the temporal and spatial variabilities were computed [21]. Temporal variability, by assessing the correlation between functional connectivity patterns of the same brain region across different time windows, captures the time-dependent changes of the functional architecture in specific brain areas, thereby providing insights into the temporal robustness of the region’s functional connectivity. The specific definition is shown in formula (3):

    $$\:{T}_{i}=1-\frac{1}{v(v-1)}\sum\:_{p,q=1,p\ne\:q}^{v}\text{c}\text{o}\text{r}\text{r}\left({F}_{p}(i,:),{F}_{q}(i,:)\right)$$

    (3)

    where \(\:v\) is the number of microstate windows, \(\:{F}_{p}(i,:)\) indicates the functional arrangement of the \(\:i\)-th channel within the \(\:p\)-th microstate window, while \(\:corr({F}_{p}\left(i,:\right),{F}_{q}\left(i,:\right))\) calculates the correlation coefficient that measures the temporal similarity between two separate functional connectivity patterns. Spatial variability, by evaluating the correlation between the functional connectivity sequences of a particular brain region and those of other regions, reflects both the dynamic changes in local spatial functional connectivity and the spatial consistency of connectivity between regions. The definition is shown in formula (4),

    $$\:{S}_{i}=1-\frac{1}{29\times\:28}\sum\:_{i,h=1,j\ne\:h\ne\:i}^{30}\text{c}\text{o}\text{r}\text{r}\left({F}_{s}(i,j),{F}_{s}(i,h)\right)$$

    (4)

    where \(\:corr({F}_{s}\left(i,j\right),{F}_{s}\left(i,h\right))\) denotes the correlation coefficient that quantifies the relationship between two different spatial functional connectivity patterns, \(\:{F}_{s}\left(i,j\right)\) and \(\:{F}_{s}\left(i,h\right)\). This metric is employed to assess the spatial consistency across all functional connectivity sequences associated with a particular scalp region (channel \(\:i\)) (Fig. 1B).

    Clinical effect prediction

    A feature set for classification was derived from microstate parameters, including coverage, duration, occurrence, and both temporal and spatial variability. These parameters were extracted across multiple frequency bands. To capture the spectral specificity of brain dynamics, these features were organized into distinct sets based on the following frequency bands: delta (1–4 Hz), theta (4–7 Hz), alpha (7–13 Hz), beta (13–30 Hz), and a broadband (1–30 Hz). A Random Forest classifier, implemented using a Bagging ensemble of decision trees, was trained for multi-class classification. To enhance model robustness and generalizability, hyperparameters (i.e., the number of trees, minimum leaf size, and maximum number of splits) were optimized via a Bayesian optimization algorithm. A nested cross-validation (CV) strategy was employed to prevent data leakage and overfitting. In this scheme, an inner 5-fold CV performed hyperparameter tuning, while an outer 5-fold CV provided an unbiased estimate of the model’s performance. To address potential class imbalance, class weights were set to be inversely proportional to class frequencies and applied during model training. The final model performance was determined by averaging the prediction metrics across the five outer folds of the nested CV. Classifier performance was comprehensively assessed using accuracy, sensitivity, and specificity. The results for each metric are reported as the mean ± standard deviation across these folds.

    Statistical analysis

    Data normality was evaluated by applying the Shapiro-Wilk test. The Mann-Whitney U test and independent t-tests were used to examine differences in demographic characteristics and clinical scores among the groups. Repeated measures Analysis of Variance was employed to evaluate group (PD-MCI, PD-NC, and HC) differences across narrowband and broadband microstate network metrics using four model specifications: (1) microstate parameters (3 groups × 3 parameters [coverage, duration, and occurrence] × 5 classes [MS-A, MS-B, MS-C, MS-D, and MS-E]), (2) transition probabilities (3 groups × 20 metrics [A to B/C/D/E, B to A/C/D/E, C to A/B/D/E, D to A/B/C/E, E to A/B/C/D]), (3) microstate network regional temporal or spatial variabilities (3 groups × 5 microstate levels × 30 channels), and (4) microstate network global temporal or spatial variabilities (3 groups × 5 classes [MS-A, MS-B, MS-C, MS-D, and MS-E]). Where significant main and interaction effects emerged (p < 0.05), post hoc analyses was conducted using Bonferroni-corrected paired-samples t-tests. Pearson correlations then quantified relationships between MoCA scores and group-discriminating features in PD subgroups (PD-MCI vs. PD-NC), with independent Bonferroni corrections applied to four metric families per frequency band: microstate parameters, transition probabilities, regional temporal or spatial variabilities, and global temporal or spatial variabilities.

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  • Re-examining the association between region-specific pain recurrence and muscle force strategies in patients with patellofemoral pain via OpenSim and artificial intelligence: a prospective cohort study toward targeted rehabilitation | Journal of Neur…

    Re-examining the association between region-specific pain recurrence and muscle force strategies in patients with patellofemoral pain via OpenSim and artificial intelligence: a prospective cohort study toward targeted rehabilitation | Journal of Neur…

    In this study, we developed and evaluated an artificial intelligence (machine learning) model to elucidate the muscle force-production characteristics associated with the recurrence of pain in the APP, MBP, and LBP regions. The findings support our hypothesis, indicating that the muscle force–related factors underlying PFP recurrence differ across anatomical regions. Among the three machine learning classifiers employed, the XGBoost model exhibited the highest accuracy, with predictive performance ranging from 95.9% to 98.3% in forecasting pain across these regions. Consequently, SHAP values were computed based on the XGBoost model to visually represent the importance of each feature, thereby delineating the key muscle force patterns that precipitate PFP in distinct regions.

    In addressing overfitting, it is noteworthy that XGBoost incorporates embedded regularization during training, specifically the L1 (lambda) and L2 (alpha) penalties, which effectively constrain model complexity and reduce the likelihood of overfitting to the training set [36]. In addition, we employed five-fold cross-validation within the training data to optimize hyperparameters, thereby avoiding the inappropriate use of the test set for model tuning and further mitigating the risk of overfitting. Nevertheless, given that the AUC achieved by the XGBoost models in this study was relatively high (>0.99), it remains possible that some degree of uncontrollable overfitting occurred. This observation highlights the need for future research to expand both sample size and feature dimensionality, as well as to improve group balance during testing, in order to further safeguard against overfitting.

    Muscle force production patterns associated with APP region pain

    Insufficient strength in the gracilis, tibialis anterior, and internal oblique muscles, combined with excessive strength in the adductor longus and tensor fascia latae, constitutes the principal precipitating factors for pain in the APP region. Although both the gracilis and adductor longus are located on the medial thigh, their distinct insertion sites result in different contributions to joint kinematics [37, 38]. Specifically, the gracilis inserts on the medial aspect of the proximal tibia, facilitating hip internal rotation, whereas the adductor longus terminates at the linea aspera of the femur and functions to externally rotate the hip [37, 38]. Consequently, insufficient strength of the gracilis, combined with excessive strength of the adductor longus, may result in exaggerated hip external rotation during gait. Cadaveric studies have demonstrated that a hip external rotation of 30° can significantly increase patellofemoral joint contact forces [39]. During ambulation, the contact force generated by hip external rotation is continuously loaded onto the anterior aspect of the patella, peaking at knee flexion angles between 30° and 60°. Over time, this repetitive loading may precipitate the recurrence of pain in the APP region [39]. It is noteworthy that recent studies suggest potential differences in neural innervation and functional roles between the proximal and distal regions of the same muscle [40]. For the gracilis, the proximal fibers, originating from the medial aspect of the ischiopubic ramus, may primarily contribute to hip joint motion. In contrast, the distal fibers extend across the knee joint, where they merge with the tendons of the sartorius and semitendinosus to form the pes anserinus, inserting onto the superior medial tibia [41]. This anatomical arrangement suggests that the distal fibers may be more directly involved in knee movement and tibial internal rotation. Considering that current evidence generally supports the association between gracilis insufficiency and the recurrence of PFP, primarily through alterations in hip joint mechanics as previously discussed [37,38,39], clinical practice should place particular emphasis on targeted activation and rehabilitation of the proximal gracilis. Such interventions may help prevent recurrent pain in the APP region.

    Our findings indicate that insufficient tibialis anterior strength is a primary precipitant of pain in the APP region. Notably, previous cohort studies have not clearly delineated the relationship between tibialis anterior strength and PFP. The present study’s ability to capture this association may be attributable to the employment of a machine learning model, which can discern more subtle inter-variable relationships that may be pivotal for PFP prevention. Inadequate tibialis anterior strength disrupts the balance between plantarflexor and dorsiflexor forces at the ankle, thereby impairing sagittal plane stability [42]. As a consequence, the ankle’s capacity for sagittal attenuation is markedly diminished, allowing unbuffered ground reaction forces to be transmitted proximally to the knee and patellofemoral joints, which leads to excessive localized loading on the patellar surface and ultimately precipitates APP pain [43]. Furthermore, the machine learning model revealed that diminished internal oblique strength is associated with APP pain, corroborating findings from previous cross-sectional studies [44]. Insufficient internal oblique strength increases trunk inclination and rotation during ambulation. In particular, when the internal oblique of the contralateral side is weak, the trunk tends to rotate and tilt toward the affected side. Given that the trunk accounts for over 50% of body weight, such a shift in mass distribution results in an increased overall load on the affected limb, thereby elevating patellofemoral joint stress [44]. This heightened joint stress exacerbates anterior patellar wear during knee flexion-extension cycles, ultimately culminating in APP pain [45].

    Excessive strength in the tensor fascia latae is also a major predictor of APP region pain. Previous studies have further reported that such overexertion of the tensor fasciae latae can induce hip flexion [46]. However, during gait, excessive hip flexion results in forward tilting of the trunk, thereby shifting the center of mass anteriorly and increasing the load on the anterior aspect of the knee, ultimately precipitating recurrent APP pain [47]. Moreover, excessive force of the tensor fasciae latae may disrupt the strength balance between the hip flexors and extensors [48], thereby undermining sagittal-plane core stability and consequently amplifying anterior–posterior displacement of the center of mass [49]. In response, the lower limb joints often adopt a “stiff” movement pattern to stabilize the center of mass and prevent falls. Nevertheless, in such a “stiff” gait pattern, energy absorption by tissues distal to the knee may be reduced, thereby increasing the mechanical load on the knee joint. This load may be repeatedly transmitted to the anterior patellar region, ultimately resulting in patellar cartilage damage and the recurrence of APP [47].

    Muscle force production patterns associated with MBP region pain

    The machine learning model identified that excessive strength in the rectus femoris, gracilis, gluteus maximus, and adductor longus muscles, coupled with insufficient strength in the semitendinosus, constitutes the primary mechanism underlying MBP region pain. Notably, the rectus femoris serves as a knee extensor, whereas the semitendinosus—being part of the hamstring group—functions as a knee flexor. Excessive rectus femoris force, combined with insufficient semitendinosus strength, may lead to an increased knee extension moment during gait. Prior investigations have demonstrated that when the knee extension moment reaches 240 Nm, the patellofemoral joint stress escalates by 3.9 MPa [50]. Furthermore, inadequate semitendinosus strength may compromise the dynamic control of the knee’s frontal plane, resulting in augmented lateral displacement of the patella, exacerbation of medial patellar wear, and ultimately the manifestation of MBP region pain in patients with PFP [51]. Recent studies have reported that the proximal and distal compartments of the semitendinosus exhibit distinct discharge rates and differences in the variability of neural drive [40]. It is speculated that the region of the semitendinosus located closer to the knee joint may be more directly involved in knee and patellofemoral joint loading and movement, and thus more strongly associated with the recurrence of MBP. Consequently, targeted stimulation and rehabilitation of this knee-adjacent region of the semitendinosus during clinical training may yield greater therapeutic benefits and improve the effectiveness of preventing MBP recurrence. However, this assumption remains speculative and requires confirmation through future cohort studies.

    During ambulation, excessive force generated by the gracilis and adductor longus muscles is a key precipitant of MBP region pain. In addition to mediating hip internal and external rotation, both muscles are part of the hip adductor group; their combined force production may lead to pronounced hip adduction [52]. Studies by McKenzie et al. [53] and Willson et al. [54] have shown that patients with PFP exhibit significantly greater hip adduction during functional activities—such as running, jump landing, and squatting—compared with healthy controls, thereby further validating these findings. Ultrasonographic data indicate that when hip adduction reaches 20°, lateral displacement of the patella increases by approximately 7.3 mm [55]. Repeated lateral displacement may exacerbate friction at the medial femoral condyle and medial patellar region, ultimately precipitating pain in the MBP region [55].

    Results from the machine learning model indicate that excessive gluteus maximus strength is one of the contributing factors to pain in the MBP region. Previous studies have demonstrated that excessive force production by the gluteus maximus during gait leads to heightened frontal-plane stiffness at the hip joint [56]. Given that the hip and knee joints serve as the primary sites for frontal-plane energy absorption during ground contact—accounting for 59.1% and 38.5% of energy absorption, respectively [57]—excessive hip stiffness markedly reduces its capacity to absorb energy. Consequently, unbuffered ground reaction forces are transferred to the knee joint [57], increasing the frontal-plane load and elevating medial patellar pressure [57]. Moreover, excessive force production by the gluteus maximus during gait may promote an overly upright trunk posture, thereby elevating the center of mass and increasing lateral trunk sway. The lateral forces generated by this trunk movement are repeatedly imposed on the knee joint, which is already burdened by high energy absorption demands [58], ultimately leading to repeated compression of the medial patellar margin and significant pain over time.

    Muscle force production patterns associated with LBP region pain

    Our explainable machine learning model revealed that insufficient strength in the rectus femoris, tensor fascia latae, and gluteus maximus, coupled with excessive force generated by the adductor longus and gracilis, constitutes the primary mechanism underlying pain in the LBP region. Among these, the rectus femoris—an integral component of the quadriceps—primarily generates the knee extension moment during gait [59]. Powers et al. [60] reported that PFP patients exhibit a knee extension moment of 23.6 N·cm/kg, compared to 30.4 N·cm/kg in healthy individuals; the inability of the rectus femoris to produce an adequate extension moment may therefore serve as a precipitating factor for PFP. Moreover, the quadriceps play a critical role in controlling patellar tracking during knee flexion-extension. Weakness in this muscle group can result in aberrant patellar kinematics, including increased frontal plane displacement of the patella and consequent excessive compression of its lateral edge, ultimately leading to pain [59]. It should be noted that previous studies have reported regional heterogeneity in the activation of the rectus femoris under different contraction tasks. Specifically, hip flexion predominantly activates the proximal region of the muscle, whereas knee extension primarily recruits its central and distal regions [61]. This task-dependent distribution is thought to arise from the relatively segregated organization of motor neuron pools within the spinal cord, where neurons innervating the proximal region are mainly located in the rostral part of the motor nucleus, while those serving the distal region are positioned more caudally [61]. Such topographical differentiation provides a mechanistic explanation for the distinct activation patterns of the proximal versus distal regions of the rectus femoris [61]. In the context of the present study, the distal region may be a critical contributor to LBP-related pain, as it is more directly associated with knee and patellofemoral joint function, suggesting that this region should receive particular attention during clinical interventions [61]. However, we did not directly examine the relationship between regional rectus femoris activation and LBP pain in this study. Accordingly, this interpretation remains speculative and requires systematic investigation in future research.

    Based on our findings, insufficient gluteus maximus strength predisposes individuals to LBP region pain. The gluteus maximus not only facilitates hip extension but also functions as a hip external rotator; when its strength is diminished, the hip and femur tend to exhibit excessive internal rotation. Powers et al. [62] demonstrated via magnetic resonance imaging that an 8° increase in femoral internal rotation can induce a 13° patellar external rotation, during which the lateral edge of the patella is repeatedly subjected to friction and compressive forces against the lateral femoral condyle. Moreover, owing to the inherent anatomy of the patellofemoral joint, the lateral force on the patella during gait (0.54 BW) far exceeds that on the medial side (0.15 BW) [63]. Collectively, this loading environment markedly increases the risk of LBP region pain. Furthermore, our machine learning results indicate that insufficient tensor fascia latae strength, combined with excessive force generated by the adductor longus and gracilis, further predisposes to LBP region pain. The tensor fascia latae, a principal component of the hip abductor group [64], contrasts with the adductor longus and gracilis, which are key elements of the hip adductor group. Reduced strength of the tensor fascia latae, combined with excessive force production by the adductor longus and gracilis, leads to pronounced hip adduction during ambulation [52, 65]. Ultrasonographic evidence indicates that, compared with a neutral position, 20° of hip adduction results in a 0.24-cm increase in lateral patellar displacement. Repeated lateral displacement of the patella may exacerbate wear and local compressive loading on its lateral margin, ultimately precipitating LBP region pain [66].

    Implications for preventive training

    Targeted neuromuscular training may alleviate regional PFP by correcting specific deficits in force-generation patterns. For the prevention of APP region pain, it is imperative to enhance the strength of the hip internal rotator group to counteract the excessive force generated by the adductor longus, which predisposes the hip to external rotation. In addition, incorporating resistance exercises for the tibialis anterior, alongside relaxation protocols for the tensor fascia latae to prevent its overcontraction during the stance phase, is recommended. Furthermore, targeted training of the contralateral internal oblique may help correct trunk lean during gait, thereby reducing unilateral knee loading.

    In the context of MBP region pain, emphasis should be placed on augmenting hamstring strength to restore a balanced knee flexion–extension moment and improve knee joint stability. Concurrently, targeted strengthening of the hip abductor group is recommended to counteract the aberrant force production by the gracilis and adductor longus during gait. Furthermore, relaxation training for the gluteus maximus may increase the hip joint’s frontal-plane mobility and its capacity for energy absorption, thereby reducing the overall load on the knee joint.

    For LBP region pain, priority should be given to reinforcing the rectus femoris and gluteus maximus. Concurrently, targeted strengthening of the hip abductor group (e.g., tensor fascia latae) should be implemented, while relaxation training for the hip adductor group (e.g., adductor longus and gracilis) is recommended to correct imbalances between hip adduction and abduction. This integrated approach is expected to enhance proximal joint stability and ultimately reduce lateral displacement and loading of the patellofemoral joint.

    It should be emphasized that although the SHAP dependence plots identified apparent “thresholds” for these risk factors, these values do not represent clinically meaningful cut-off points. Rather, they indicate the points at which the relationship between a given feature and the risk of PFP recurrence begins to change within the model. Accordingly, these thresholds cannot be directly applied in clinical practice and should instead be viewed as preliminary reference values that require further verification and refinement through systematic clinical studies.

    Looking ahead, our model could be integrated into clinical decision-support tools, such as OpenSim-based musculoskeletal modeling platforms or gait analysis systems, to enable direct identification of region-specific risks of PFP recurrence and the muscle strength deficits most in need of improvement. Such integration would not only facilitate the development of individualized intervention strategies but also provide more robust evidence to inform clinical decision making. Furthermore, review studies have indicated that different regions within the same muscle exhibit distinct levels of focal adhesion kinase (FAK) phosphorylation during eccentric and concentric training [67]. For instance, following eccentric training of the vastus lateralis, pY397-FAK levels in the distal region were approximately four times higher than those observed after concentric training, contributing to distal hypertrophy of the quadriceps [67]. Given that different training modalities induce region-specific muscle hypertrophy, future strength interventions for patients with PFP should first identify the specific muscle regions implicated in the condition and then select training strategies that effectively target these regions. This study provides only a preliminary intervention framework by treating the muscle as a single functional unit. Future research should build on these findings to investigate more refined, region-specific approaches.

    Limitations

    Although this study provides preliminary evidence of distinct muscle force strategies associated with PFP across different anatomical regions, several limitations should be acknowledged. First, although SHAP was used to preliminarily identify the points at which the relationship between muscle strength features and the risk of PFP recurrence begins to shift within the machine-learning model, their generalizability may be limited by individual variability and environmental context. To translate these thresholds into clinical practice, further clinical studies are needed for validation and refinement. Furthermore, although a six-month follow-up in a cohort of 299 participants yielded important initial findings, a longer observation window and a larger, more heterogeneous sample would likely offer a more comprehensive understanding of the temporal dynamics and recurrence patterns of PFP. Finally, recent studies have suggested that neural innervation and activation levels may vary across different regions (proximal versus distal) of the same muscle [40]. In the present study, however, the OpenSim modeling approach treated each muscle as a single unit, which made it difficult to examine how region-specific muscle activation might be related to the recurrence of PFP. This limitation underscores the need for future cohort studies to further investigate these region-dependent mechanisms underlying PFP recurrence. Finally, recent studies have demonstrated that neural innervation and activation levels can differ across distinct regions of the same muscle, such as proximal versus distal segments [40, 61]. This phenomenon is likely attributable to the differential spatial organization of motor neurons within the spinal cord that control the proximal and distal portions of the muscle [61]. However, in the present study, the OpenSim modeling approach treated each muscle as a single unit, limiting our ability to investigate the relationship between region-specific muscle activation and PFP recurrence. This limitation underscores the need for future cohort studies to examine the region-dependent mechanisms underlying PFP recurrence. In addition, evidence from training studies indicates that different muscle regions exhibit distinct molecular responses during concentric and eccentric exercises, which can lead to region-specific hypertrophy [67]. Because the present study considered muscles as whole units, it provides limited guidance for interventions targeting specific muscle regions, representing a key limitation of this work.

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  • International Conference on Medical Radiation Dosimetry

    International Conference on Medical Radiation Dosimetry

    Where Clinical Reality Meets Standards

    As countries around the world expand their clinical use of ionizing radiation, accurate measurement and calculation remain essential for the safe and effective use of radiation-based technologies. Primary and secondary standards laboratories provide reference measurements that allow medical professionals to trace their results directly to the International System of Units — ensuring global consistency. Dosimetry codes of practice reinforce this traceability and enable the optimized application of ionizing radiation in clinical settings. 

    “Recent technological developments — from new diagnostic approaches to cutting-edge computational methodologies that leverage Monte Carlo models and artificial intelligence (AI) — have shaped dosimetry standards, audit practices and quality assurance guidance,” said Mauro Carrara, IAEA Head of Dosimetry and Medical Radiation Physics and one of the symposia’s scientific secretaries. “For medical physicists, radiation metrologists and other scientists and researchers in the field, there is a critical need to comprehensively review innovations while addressing the growing complexity of available tools.” 

    “In building on the legacy of previous symposia since 1987, IDOS2026 will provide an international forum to discuss and disseminate the latest advances across radiation dosimetry, radiation medicine, radiation protection and their associated standards,” said Zakithi Msimang, IAEA medical radiation physicist and the event’s other scientific secretary. “Its proceedings and conclusions promise to provide relevant recommendations for the medical and scientific community.”

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  • Plastic packaging waste in the EU: 35.3 kg per person – News articles

    Plastic packaging waste in the EU: 35.3 kg per person – News articles

    In 2023, 79.7 million tonnes of packaging waste were generated in the EU, or 177.8 kg per inhabitant. While this marks a reduction of 8.7 kg per capita compared with 2022, the figure remains 21.2 kg higher than in 2013.

    Out of all the packaging waste generated, 40.4% was paper and cardboard, 19.8% was plastic, 18.8% glass, 15.8% wood, 4.9% metal and 0.2% other packaging. 

    An average of 35.3 kg of plastic packaging waste was generated in 2023 for each person living in the EU. Out of this, 14.8 kg were recycled. The amount of generated plastic waste decreased by 1.0 kg compared with 2022, while the amount of recycled plastic waste increased by 0.1 kg. Between 2013 and 2023, the amount of plastic packaging waste generated increased by 6.4 kg per capita, while the amount recycled increased by 3.8 kg.

    Source dataset: env_waspac

    This information comes from data on packaging waste published by Eurostat today. The article presents a handful of findings from the more detailed Statistics Explained article on packaging waste.

    Increase in plastic packaging waste recycling

    In 2023, the EU recycled 42.1% of all the generated plastic packaging waste, indicating an increase in the recycling rate compared with 2013 (38.2%).

    Belgium recorded the highest recycling rate at 59.5%, followed by Latvia (59.2%) and Slovakia (54.1%).

    In contrast, the lowest rates were recorded in Hungary (23.0%), France (25.7%) and Austria (26.9%).

    Recycling rate of plastic packaging waste, 2023 (%). Chart. See link to the full dataset below.

    Source dataset: env_waspac

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