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  • SOX4 enhances tumor progression and cisplatin resistance in orthotopic mouse xenograft model of head and neck squamous cell carcinoma | BMC Cancer

    SOX4 enhances tumor progression and cisplatin resistance in orthotopic mouse xenograft model of head and neck squamous cell carcinoma | BMC Cancer

    Cell culture and transfection

    The HNSCC, FADU cell lines were purchased from the American Type Culture Collection (Manassas, VA, USA). FADU cell line were cultured in DMEM/F12 medium (GIBCO®, Invitrogen, Carlsbad, CA, USA) with 10% fetal bovine serum (FBS, GIBCO®, Invitrogen) and 1% penicillin/streptomycin. Cells were cultured in a Humidified incubator at 37°C with 5% CO2. To knock down endogenous SOX4 gene expression in FADU cells, small interfering RNAs (siRNAs) were utilized. FADU cells were seeded into 6-well plates at a density of 2.0 × 105 cells/well and transfected with a SOX4-specific (Bioneer, Daejeon, Korea) or a negative control siRNA (cat. no. 1027281, Qiagen, Germantown, MD, USA) using (Invitrogen) for 48 h at 37°C. The SOX4-specific si-RNA sequences were as follows: Sense, 5’- GAU AGA UGG CGC UAU CUU U-3’ and Antisense, 5’-AAA CAU AGC GCC AUC UAU C −3’. Subsequent analyses were conducted 48 h post-transfection.

    RNA isolation and reverse-transcription polymerase chain reaction

    Total RNA was extracted from FADU using TRIzol reagent (Invitrogen) following the manufacturer’s protocol. For reverse transcription (RT), total RNA (1 µg), M-MLV reverse transcriptase (Invitrogen), 1 µL of 2 mM dNTP mix (Enzynomics Co., Ltd., Daejeon, Korea), 2 µL of 0.1 M dithiothreitol (Invitrogen), 4 µL of 5× first strand buffer (Invitrogen), 1 µL of RNase inhibitor (Promega Corporation), and 1 µL of oligo dT (Bioneer Corporation, Daejeon, Korea) were used. Primers specific for SOX4 and Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH, Bioneer Corporation) were utilized to amplify the cDNA. Polymerase chain reaction (PCR) was performed using GoTaq DNA Polymerase and 5× Green GoTaq reaction buffer (Promega Corporation). The primer sequences employed were as follows: SOX4 forward, 5ʹ- GCA CAT GGC TGA CTA CCC C-3ʹ; SOX4 reverse, 5ʹ- GCC TTG TAC AGC GAG TGG TG-3ʹ; GAPDH forward, 5ʹ-ACC ACA GTC CAT GCC ATC AC-3ʹ; and GAPDH reverse, 5ʹ-TCC ACC CTG TTG CTG TA-3ʹ. PCR products were separated via electrophoresis on a 1% agarose gel containing ethidium bromide.

    Protein isolation and western blot analysis

    Cells were lysed using a radioimmunoprecipitation assay buffer (Biosesang Inc.). Protein concentrations were subsequently measured using a bicinchoninic acid assay. Protein lysates (20–30 µg per lane) were separated via sodium dodecyl sulfate-polyacrylamide gel electrophoresis on 10%–12% gels and electrophoretically transferred onto polyvinylidene fluoride membranes. The membranes were then incubated at room temperature for 1 h with 5% bovine serum albumin (BSA; Bioshop Canada Inc.) in Tris-buffered saline (TBS) containing 0.1% Tween-20. The membranes were then washed four times for 15 min each with TBS–0.1% Tween-20. Specific proteins were identified using primary antibodies targeting GAPDH (cat. no. sc-25778; Santa Cruz Biotechnology, Inc., Dallas, TX, USA), β-actin (cat. no. 3700 S; Cell signaling Technology, Inc.), SOX4 (cat. no. ab80261; Abcam, UK), Cleaved caspase-3 (cat. no. 9664; Cell Signaling Technology, Inc.), Cleaved caspase-7 (cat. no. 9491; Cell Signaling Technology, Inc.), X-linked inhibitor of apoptosis protein (XIAP, cat.no. sc-11426; Santa Cruze Biotechnology, Texas, USA), and Cleaved poly (ADP-ribose) polymerase (PARP; cat. no. 5625; Cell Signaling Technology, Inc.; cat. no. ab32064; Abcam). The primary antibodies were diluted at a 1:1,000 ratios in TBS–0.1% Tween-20 and incubated with the membranes for 24 h at 4 °C. Secondary antibodies, anti-rabbit (cat. no. 7074; Cell Signaling Technology, Inc.) or anti-mouse (cat. no. 7076; Cell Signaling Technology, Inc.), were diluted at 1:2,000 ratio, and incubated at room temperature for 1 h. Immunoreactive proteins were visualized using a LAS 4000 luminescence image analyzer (FUJIFILM Wako Pure Chemical Corporation) and an enhanced chemiluminescence detection system for HRP (EMD Millipore). Western blot analysis was independently conducted in triplicate.

    Cell proliferation assay

    Following of transfection, cells plated in 24-well plates (1 × 104 cells per well). After an additional 48 h incubation, cell viability was measured using an EZ-CyTox (WST-1) enhanced cell viability assay kit (cat. no. EZ-3000; Daeil Lab Inc.) at 37 °C for 1–2 h. Absorbance was measured at 460 nm using a microplate reader. Cell viability assays were performed in triplicate and repeated independently.

    Cell invasion assay

    The extent of cell invasion was evaluated by counting the number of cells that migrated through an 8.0 μm pore Transwell invasion chamber (cat. no. 3422; Costar, Inc.). The upper chamber was coated with a 1% gelatin solution and incubated for 12 h at 37 °C, followed by 12 h of drying at room temperature before the experiment. After 48 h of transfection, cells in the upper chamber were seeded at a density of 2 × 105 cells in 120 µl of 0.2% BSA (BioShop Canada, Inc.) in FBS-free DMEM. As a chemoattractant, 400 µl of 0.2% BSA in FBS-free DMEM containing fibronectin (cat. no.361635; Calbiochem, San Diego, CA, USA) was loaded into the lower chamber. After 24 h of incubation, cells that had migrated to the bottom of the Transwell membranes were stained with Diff-Quik solution (Sysmex Corporation). Using a Light microscope, the cells were then counted in five random fields at 100× magnification. Results were expressed as mean ± standard error of the number of cells per field from three independent experiments.

    Cell migration assay (wound healing assay)

    The cells were seeded into each well of Culture-Inserts (Ibidi GmbH) at 1.5 × 105 cells/well, following transfection. They were incubated for 24 h. Following that, each insert was detached, and the progression of cell migration was assessed by imaging at 0, 8, 12, and 24 h using an inverted microscope. Distances between gaps were normalized to 1 cm after three random sites were captured.

    Apoptosis assay

    Apoptosis was evaluated using an APC Annexin V assay. Following transfection, the cells were harvested via trypsinization, washed twice with phosphate-buffered saline, and resuspended in a binding buffer (cat. no. 556454; BD Biosciences, San Jose, CA, USA). After adding APC Annexin V (cat. no. 550474) and 7-amino-actinomycin D (cat. no. 559925; BD Biosciences, San Jose, CA, USA), the cells were incubated in the dark for 15 min. The samples were subsequently resuspended in 400 µl of binding buffer. They were analyzed using a FACSCalibur flow cytometer (BD Biosciences) and BD Cell Quest version 3.3 software (Becton Dickinson). Data analysis was performed using WinMDI version 2.9 (The Scripps Research Institute). Apoptosis assays were conducted independently in triplicate.

    Cell irradiation or cisplatin treatment

    After 48 h of transfection, the cells were maintained at 37 °C and exposed to γ-irradiation at varying doses (8–10 Gy, 137Cs, and 2.875 Gy/min) using a Gammacell 3000 Elan (Therathronics) at room temperature. A stock solution of cisplatin (10 mg/20 ml; Dong-A, Co., Ltd., Seoul, Korea) was prepared and diluted to 2.5 ~ 5 µg/ml concentrations. The cisplatin solution was incubated at 37 °C for 24 h before being used in experiments.

    Establishment of SOX4 overexpressing stable cell line and an orthotopic mouse xenograft model of head and neck squamous cell carcinoma

    A stable SOX4-overexpressing SCC VII mouse squamous cell carcinoma cell line was established. After transfection of pcDNA6/myc-SOX4 into SCC VII cells using Lipofectamine 2000 (Thermo Fisher Scientific, USA), transfected cells were selected by culturing in media containing blasticidin (cat. no. A11139-03; ThermoFisher, Massachusetts, USA) at 5 µg/ml concentration. The stable overexpression of SOX4 in selected clones was confirmed via Western blot analysis. Female C3H/HeJ syngeneic mice (6–8 weeks old) were purchased from OrientBio (Seongnam, South Korea), and randomly assigned to a control or SOX4 overexpression group. Furthermore, 1 × 106 of SOX4 overexpressed or controlled SCC VII cells were suspended in 70 µl of serum-free media and slowly injected into the floor of the mouth (FOM) of mice via an intraoral approach. These animal experimental procedures were approved by the Chonnam National University Animal Care and Use Committee (CNU IACUC-H-201624). All animal care, experiments and euthanasia were performed per protocols approved by the Chonnam National University Animal Research Committee. For the euthanasia of mice, mice were first rendered unconscious with isoflurane, and then death was confirmed after 5 to 10 min using carbon dioxide.

    Immunofluorescence

    Tumor tissue-mounted slides were subjected to a graded ethanol series for permeabilization, involving sequential immersion in 100%, 90%, 80%, 70%, and 60% ethanol for 5 min each. Antigen retrieval was then performed using citrate buffer (pH 6.0) for 15–20 min via heat-induced epitope retrieval (HIER). Slides were subsequently cooled under running water and treated with 0.1% Triton X-100 for 10 min at room temperature to reduce non-specific background staining. After three washes in PBS (5 min each), endogenous peroxidase activity was blocked using Endoblocker (Peroxidase-Blocking Solution, cat. no. S2023; Dako, Glostrup, Denmark) for 20 min at room temperature. Slides were again washed with PBS (3 × 5 min), and then incubated overnight at 4 °C with the primary antibody against Ki-67 (cat. no. ab16667; Abcam, Cambridge, UK), diluted 1:300 in blocking buffer. The following day, after three additional PBS washes, the sections were incubated with the secondary antibody, anti-rabbit Alexa Fluor 568 (cat. no. A1101; Invitrogen, California, USA), diluted 1:200, for 1 h at room temperature. Nuclear counterstaining was performed using DAPI (1:500 dilution; cat. no. D1306; Life Technologies, California, USA) for 10 min. After a final PBS wash (3 × 5 min), slides were mounted using Paramount Aqueous Mounting Medium (cat. no. S3025; Dako, Life Technologies, California, USA) and covered with a coverslip. Mounted slides were allowed to dry at room temperature for 24 h. Fluorescence images were acquired using the EVOS FL Imaging System (Invitrogen, California, USA). The number of Ki-67–positive cell per 100 tumor cells was used to determine the Ki-67 labeling proliferation index (%).

    Statistical analysis

    The significance of experimental differences was assessed using an unpaired Student’s t-test. Data are presented as mean ± standard error. All experimental assays were conducted independently in triplicate. Statistical analyses were conducted using SPSS version 21.0 (IBM Corp.). A p-value of < 0.05 was considered statistically significant.

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  • Marvell Adds Active Copper Cable Linear Equalizers to Its Connectivity Portfolio :: Marvell Technology, Inc. (MRVL)

    Marvell Adds Active Copper Cable Linear Equalizers to Its Connectivity Portfolio :: Marvell Technology, Inc. (MRVL)





    Marvell ACC Linear Equalizers Enable Longer Reach and Power-efficient Copper in High-speed, Scale-up Interconnects

    SANTA CLARA, Calif.–(BUSINESS WIRE)–
    2025 OCP Global Summit — Marvell Technology, Inc. (NASDAQ: MRVL), a leader in data infrastructure semiconductor solutions, today announced that it is expanding its industry-leading connectivity portfolio with the addition of Marvell® active copper cable (ACC) linear equalizers.

    The scale and complexity of today’s AI workloads are driving exponential growth in data center bandwidth, requiring new challenges in managing thermal and power efficiency. Copper remains the preferred solution for in-rack scale-up interconnects due to its low cost and ease of deployment. However, next-generation AI systems demand thinner copper-based interconnects within server racks to improve airflow and cooling. Meanwhile, as bandwidth and cable gauge requirements continue to rise, the signal transmission performance of direct attach copper (DAC) technology is increasingly limited.

    Analog ACC devices incorporate a signal equalizer, offering longer reach than traditional passive DAC cables while adding minimal latency. They are also more cost-effective and power-efficient than digital alternatives.

    Leveraging Marvell industry-leading PAM4 technology and expertise in 100G/lane and 200G/lane analog devices, the new Marvell ACC linear equalizers deliver superior gain, extending the reach of ACC compared to competitive ACC solutions at the same cable gauge. They support 800G and 1.6T copper interconnects and expand the Marvell scale-up interconnect portfolio, which includes chipsets for active electrical cables (AEC) and active optical cables (AOC).

    “Offering a full complement of ACC, AEC and AOC silicon technologies, Marvell is unique in the scale-up interconnect landscape, providing customers with a full range of solutions to meet their individual requirements,” said Xi Wang, senior vice president and general manager, Connectivity Business Unit at Marvell. “We are excited to work with our ecosystem of cable OEM partners and system vendors to provide end customers with high-performance, in-rack connectivity solutions to handle their most advanced AI workloads.”

    Marvell is showcasing its latest advancements in accelerated infrastructure at the OCP Global Summit this week, October 13 to 16, at the San Jose Convention Center in San Jose, California. More information about Marvell at OCP 2025 can be found here.

    Availability

    Marvell ACC linear equalizers are currently sampling to customers.

    About Marvell

    To deliver the data infrastructure technology that connects the world, we’re building solutions on the most powerful foundation: our partnerships with our customers. Trusted by the world’s leading technology companies for over 30 years, we move, store, process and secure the world’s data with semiconductor solutions designed for our customers’ current needs and future ambitions. Through a process of deep collaboration and transparency, we’re ultimately changing the way tomorrow’s enterprise, cloud and carrier architectures transform—for the better.

    Marvell and the M logo are trademarks of Marvell or its affiliates. Please visit www.marvell.com for a complete list of Marvell trademarks. Other names and brands may be claimed as the property of others.

    This press release contains forward-looking statements within the meaning of the federal securities laws that involve risks and uncertainties. Forward-looking statements include, without limitation, any statement that may predict, forecast, indicate or imply future events, results or achievements. Actual events, results or achievements may differ materially from those contemplated in this press release. Forward-looking statements are only predictions and are subject to risks, uncertainties and assumptions that are difficult to predict, including those described in the “Risk Factors” section of our Annual Reports on Form 10-K, Quarterly Reports on Form 10-Q and other documents filed by us from time to time with the SEC. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and no person assumes any obligation to update or revise any such forward-looking statements, whether as a result of new information, future events or otherwise.

    Media Contact:

    George Millington

    pr@marvell.com

    Source: Marvell Technology, Inc.

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  • Global government bonds rise as Trump slaps new 100% tariffs on China

    Global government bonds rise as Trump slaps new 100% tariffs on China

    Traders work on the floor of the New York Stock Exchange.

    NYSE

    Bond yields reflect borrowing costs for the governments who issue them, but can have an effect on mortgage rates, investment returns, the wider economy and personal borrowing.

    Certain markets have their own domestic issues at play. An uptick in unemployment in the U.K., political instability in France, and the ongoing U.S. government shutdown are also influencing investors in those respective markets, for example.

    However, market watchers told CNBC that Tuesday’s rally in sovereign bonds was largely due to a broad move into safer assets. Alongside bonds, gold, the Japanese yen and the Swiss franc — all typically regarded as safe haven assets in times of uncertainty or volatility — moved higher.

    Investors are seeking options to ride out fresh tariffs-induced volatility, according to Marc Ostwald, chief economist and global strategist at London’s ADM Investor Services.

    “The move lower in [developed markets] yields is broad based, and a function of flight to safety due to rising volatility in risk assets, even if a lot of this is very knee-jerk, and as we saw yesterday can turn on sixpence into renewed risk appetite,” he said in an email.

    Monday saw a brief reprieve for equities following Friday’s selloff, with Wall Street’s major averages clawing back some of the previous session’s losses, while European stocks also notched gains.

    “It is all tied to the now typical ambiguous and posturing headlines and measures from the U.S. and China in respect of trade relations and negotiations, and unlikely to dissipate in the near term,” Ostwald added on Tuesday.

    “Longer term concerns about political instability … and headwinds from the high level of government debt, which no DM government is doing anything to address, will tend to temper gains, [but] this week’s speeches at the IMF/World Bank … which may offer hints on relaxing bank capital rules with regards to purchases of [U.S. Treasurys] could also give bonds something of a tailwind,” he said in reference to the IMF and the World Bank’s Annual Meetings taking place in Washington, D.C., this week.

    Broader risk appetite

    Russ Mould, investment director at AJ Bell, agreed that the bond markets could be responding to a shift in overall sentiment.

    “Western sovereign bond yields are moving lower, and thus prices are moving higher. This may be the result of an easing in risk appetite – Asian and European headline equity indices are generally down today, thanks to ongoing worries over U.S.-China trade relations,” he told CNBC via email on Tuesday.

    Mould also pointed to broader concerns over the economy and key industries, with the high profile collapse of First Brands raising concerns and sending jitters through markets.

    “[These are] worries which will not ease in the context of a profit warning from another company which supplies the car industry, namely France’s Michelin,” he said. “Yield curves are flattening a touch, too, again to perhaps reflect concerns over economic softness and to price in further interest rate cuts from central banks.”

    Tim Hynes, head of credit research at Debtwire, also told CNBC on Tuesday that bonds were rallying due to concerns about the possible reignition of a Sino-U.S. trade war, attributing the market moves to “trade tension and growth fears.”

    “The renewed U.S.–China trade escalation is tilting sentiment toward risk-off,” he said. “Investors, fearing weaker demand, are piling into government bonds.”

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  • Hippo pathway suppression reprograms TNFα-primed glioblastoma extracellular vesicles transcripts cargo to drive mesenchymal stem/stromal cells vasculogenic mimicry | Cell Communication and Signaling

    Hippo pathway suppression reprograms TNFα-primed glioblastoma extracellular vesicles transcripts cargo to drive mesenchymal stem/stromal cells vasculogenic mimicry | Cell Communication and Signaling

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  • WFW advises Eiffel Investment Group on acquisition of 50% stake in 270 MW TotalEnergies Renouvelables France renewables portfolio

    WFW advises Eiffel Investment Group on acquisition of 50% stake in 270 MW TotalEnergies Renouvelables France renewables portfolio

    Watson Farley & Williams (“WFW”) advised Eiffel Investment Group on its acquisition of a 50% stake in a 270 MW French wind and solar portfolio from TotalEnergies Renouvelables France, valued €265m.

    TotalEnergies retains a 50% stake in the portfolio and will continue to operate the assets and distribute the majority of the energy produced.

    Eiffel Investment Group is a French asset management firm with approximately €7bn AUM. With an investor base comprising large institutional investors and retail investors via intermediated distribution, it delivers strong industrial expertise, particularly in the field of energy transition.

    Paris-headquartered TotalEnergies is a multi-energy company that puts sustainable development at the heart of all its projects and operations. With 30+ GW of gross renewable capacity, it aims to reach 35 GW by the end of 2025 and 100+ TWh net electricity production by 2030.

    The multidisciplinary WFW Paris team that advised Eiffel Investment Group was led by Regulatory and Public Law Partner Laurent Battoue, assisted by Partner Thomas Rabain on corporate and M&A matters. They were supported notably by Counsel Antoine Bois-Minot and Associate Lucile Mazoué. Finance expertise was provided by Partner Laurence Martinez-Bellet, with Partner Romain Girtanner advising on the tax aspects of the transaction.

    All the above partners were supported by their respective teams of counsel, senior associates and associates.

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  • WHO and the European Union launch collaboration to advance digitized health systems in sub-Saharan Africa

    WHO and the European Union launch collaboration to advance digitized health systems in sub-Saharan Africa

    The World Health Organization (WHO) and the European Union (EU) announced today a new agreement to support the digital transformation of health systems and wider adoption of WHO’s Global Digital Health Certification Network (GDHCN) in sub-Saharan Africa. This EU–WHO partnership will improve pandemic preparedness and accelerate progress towards better health and well-being for all.

    The agreement was announced at the World Health Summit 2025 by Dr Yukiko Nakatani, WHO Assistant Director-General for Health Systems, Access and Data; Dr Mohamed Yakub Janabi, WHO Regional Director for Africa; and Mr Martin Seychell, Deputy Director-General of the European Commission Directorate-General for International Partnerships.

    The GDHCN is a global system that enables countries to securely and reliably verify nationally approved digital health credentials across borders. The system builds on the European Union Digital COVID Certificate (EU DCC), which facilitated verification of vaccination, testing and recovery certification for international travelers connecting 76 countries and territories. However, only four countries from the WHO African Region—Benin, Cabo Verde, Seychelles and Togo—were able to join the EU DCC network.

    Since its transfer to the WHO in 2023, the GDHCN has shown strong potential to support the digitization of the International Certificate of Vaccination or Prophylaxis (ICVP), commonly known as the Yellow Card, in alignment with the updated International Health Regulations (IHR). Making the most of its potential could enhance global vaccination tracking, reduce fraud, and simplify international health requirements.

    Under the new joint agreement, which includes an €8 million EU grant spanning 2025 to 2028, WHO and the European Union will collaborate to bolster national efforts to advance the digital transformation of health systems in sub-Saharan Africa. WHO will provide technical and policy expertise, in collaboration with regional partners such as the Africa Centres for Disease Control and Prevention (Africa CDC).

    The EU investment is part of the Digital Health workstream of the Team Europe Initiative on the EU-AU Health Partnership, which brings together European and African stakeholders to build resilient digital health ecosystems across the continent, and aligned with the EU Global Gateway strategy.

    Empowering countries and people

    The GDHCN supports countries in building trusted, interoperable digital health systems that directly benefit people — providing secure, portable health records accessible wherever they travel, including during health emergencies. Personal health records are managed securely by each individual country or their health system. These records cannot be accessed by other parties, including WHO.

    The network is built on internationally recognized standards for privacy, data protection, and interoperability, and participation of countries is voluntary. The network fosters cross-border collaboration among countries and partners, strengthening health security today, while laying the foundation for more resilient, person-centered health systems for future generations.

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  • Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting | BMC Primary Care

    Machine learning models for the prediction of COVID-19 prognosis in the primary health care setting | BMC Primary Care

    This retrospective study analyzed over 2 million COVID-19 cases from March 2020 to September 2022 across multiple COVID-19 waves, with follow-up 90 days post-diagnosis or until death. Our study underscores the significant impact of COVID-19 on PHC in the initial years of this pandemic in Catalonia. Furthermore, using machine learning models (GLMs, Lasso, Gradient Boosting, SVMs), we identified key predictors of poor outcomes, such as age, social deprivation (MEDEA), blood pressure, and a history of either diabetes, COPD, cardiovascular disease, or obesity. The models showed strong predictive accuracy (AUC: 0.73, 95%CI (0.72;0.73)–0.95, 95%CI (0.94;0.95)). Finally, using these models, an interactive web app was developed for personalized risk estimation (https://dapcat.shinyapps.io/CovidScore).

    We found that the CFR was highest during the first wave of the pandemic, gradually decreasing in subsequent waves, with the second wave showing the highest incidence of cases. The higher comorbidity burden observed in the first wave may reflect limited testing availability during that period, which likely focused on more severe cases. The decrease in the CFR may be attributed to a combination of increased immunity (due to vaccination and SARS-CoV-2 infections), better identification of more severe cases, and the lower pathogenicity of recent variants like Omicron. Overall, these findings suggest changes in the SARS-CoV-2 virus, an adaptive response in healthcare, and improvements in the prevention (i.e., via vaccination) and treatment of complications as the pandemic progressed [21].

    Regarding predictors of poor prognosis at the time of COVID-19 diagnosis in PHC, we identified older age, epidemic wave, social deprivation, and a history of diabetes, obesity, chronic obstructive pulmonary disease (COPD), cardiovascular disease, hypertension, and dyslipidemia. While study results vary across different populations, numerous studies have identified advanced age and comorbidities such as hypertension, cardiovascular disease, COPD, and diabetes as predictors of increased COVID-19 severity [22,23,24]. Additionally, social deprivation indicators, such as the MEDEA index, have been associated with poorer outcomes in terms of severity and mortality, underscoring the multifactorial nature of COVID-19 outcomes. Understanding and addressing these predictive factors is crucial for improving management and outcomes in affected patients. The inclusion of’epidemic wave’reflects the changing contextual factors, such as virus variants, public health measures, and healthcare strain that significantly influenced outcomes [25]. However, this limits the direct application in future waves. In such cases, the most recent wave may be used as an initial reference, while outcome data are monitored to reassess and recalibrate the model accordingly. Ideally, prediction models should be dynamically updated to remain accurate and clinically useful during future epidemic scenarios.

    Conditions associated with low-grade chronic inflammation, such as obesity and diabetes mellitus, are also relevant at the metabolic level [26]. Several systematic reviews have provided consistent evidence that diabetes and obesity are associated with poorer COVID-19 outcomes, which agrees with our study [27]. Although the reasons for this association are not entirely clear, these conditions could exacerbate respiratory problems and/or affect immune responses. A systematic review and meta-analyses on high-risk phenotypes in people with diabetes determined that individuals with a more severe course of diabetes and pre-existing comorbidities had a poorer prognosis of COVID-19 than individuals with a milder course of the disease, highlighting the need for individualized and proactive management strategies for high-risk patients [28].

    At the hospital level, predictive models like the ISARIC 4 C have been developed to anticipate clinical deterioration (including mortality, ICU admission, or intubation), assessing age, gender, comorbidities, and nosocomial infection [29]. A similar study in the United Kingdom, utilizing computerized PHC medical records, developed predictive algorithms for COVID-19 mortality and hospital admission risk. Factors such as age, body weight, ethnicity, and social risk explained 73% of COVID-19 deaths and 58% of hospital admissions, suggesting periodic recalibration of these models to reflect the evolving nature of the pandemic [30]. An early pandemic study on PHC identified key risk factors for ICU admission and mortality, including advanced age, male gender, autoimmune disease, bilateral pulmonary infiltrates, and elevated LDH, D-dimer, and C-reactive protein. Protective factors included myalgias, arthralgias, and anosmia [31].

    Recent advances in AI and machine learning have significantly contributed to managing the COVID-19 pandemic by aiding in detection, treatment, mortality prediction, and infection modeling to reduce virus spread [32,33,34].

    In this study, we used machine learning to develop predictive models and then used these models to develop an app. The app provides comprehensive information to estimate the risk of COVID-19 prognosis outcomes for individuals based on their risk factors (e.g., age, sex, comorbidities, vaccination status, COVID-19 wave). This approach could be used in PHC to identify individuals needing closer monitoring and interventions to prevent serious complications and hospitalization. Moreover, model calibration was assessed using the Brier Score, which integrates both discrimination and calibration aspects. All models showed low Brier Scores, suggesting a high level of agreement between predicted and observed risks. Given the large, representative nature of the dataset, we expect limited calibration drift within this population. Nonetheless, we acknowledge that external validation with calibration plots and slope/intercept assessment would be useful to confirm generalizability in other settings. Although COVID-19 is now endemic, this study highlights the importance of early risk stratification in primary care based on routine data at diagnosis. These findings may inform preparedness for future COVID-19 waves or other infectious outbreaks, where identifying predictors of poor outcomes at diagnosis remains key to guiding timely care.

    Our study has limitations inherent to its retrospective design. Outcomes depend on the quality of existing clinical records not specifically collected for this research and have yet to undergo individual validation. Some outcomes may have been underreported or misclassified, especially if they were managed outside the hospital setting or not fully captured in the electronic health records. However, major outcomes such as mortality, hospitalization, and ICU admission are reliably recorded within the system. A notable limitation is the potential impact of vaccine implementation on epidemiology and prognosis, which underscores the need to recalibrate predictive models with post-vaccination data to maintain accuracy. Periodic updates with the latest available data are essential to ensure the continued relevance of these models. Although no predictive model is perfect for COVID-19 patients, our models serve as valuable tools to estimate the risk of complications, helping to identify patients who require closer monitoring. However, the accuracy of these models can be affected by variations in the detection and recording of symptoms and risk factors by different healthcare professionals. Moreover, mild COVID-19 cases may have gone unrecorded in PHC, potentially leading to their exclusion from our study population. Another limitation of our study is that the model was developed using subjects with confirmed COVID-19, whereas in primary care, clinicians often assess patients with suspected infection before test confirmation. This may limit applicability in early diagnostic uncertainty. Moreover, the model remains useful for risk stratification once a diagnosis is confirmed in the primary care setting. Additionally, the study benefits from using the SIDIAP database, which includes a substantial patient cohort and is a well-validated source for epidemiological and pharmaco-epidemiological studies within the Catalan primary care setting. This database not only provides standardized clinical data (including health issues, physical exams, lab results, and medication records) from pseudo-anonymized electronic health records but was also specifically updated to include COVID-19-related variables (such as diagnostic tests and procedures), enabling researchers to conduct targeted epidemiological studies. Despite the fact that we used a large, representative dataset and validated the models on a separate 25% test set, external validation was not performed. This may lead to overestimation of performance. External validation using more recent data or in other populations, such as from different regions, healthcare systems, or countries is needed to confirm the model’s generalizability and ensure robust performance of the predictive models across evolving clinical contexts.

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  • CLE14 peptide delays broccoli senescence by regulating chlorophyll metabolism and reactive oxygen species homeostasis | BMC Plant Biology

    CLE14 peptide delays broccoli senescence by regulating chlorophyll metabolism and reactive oxygen species homeostasis | BMC Plant Biology

    Plant materials and treatments

    Broccoli (Brassica oleracea L. var. Italica) was collected from Shulan Agricultueal Farm in Hangzhou, Zhejiang Province. ‘Naihanyouxiu’ broccoli cultivar was used as plant material in the study, which was provided from Sakata Seed Corporation. And the broccoli was harvested at commercial maturity, characterized by tightly closed florets and a deep green color. For this experiment, broccoli of the same size, consistent color, and no mechanical damage were selected.

    The CLE14 peptide was synthesized by Dongheng Biomedical Co., Ltd., China. A total of 300 broccoli heads were randomly assigned to 5 groups containing 60 broccoli heads each (three replicates, 20 broccoli heads per replicate) for the experimental treatments, and sprayed with 0 (control), 10, 50, 100 or 300 µM CLE14, respectively. After being dried overnight, the treated broccoli was packaged and stored at 4 ± 0.5℃ for 28 days. The appearance attributes were evaluated every 7 days. After each evaluation, the florets from the five broccoli groups were collected for subsequent gene expression and biochemical analyses.

    Broccoli yellowing index and color detection

    The broccoli yellowing index (%) was used to assess the yellowing area of broccoli surfaces using the scale described by Luo et al. [22]. The color parameters of broccoli were valued using a digital colorimeter (CR-400, Konica Minolta, Japan). The values of lightness (L*) and hue angle (H*) were measured at five points on each broccoli every 7 days.

    Measurement of nutritional quality of broccoli

    The soluble sugar content of broccoli was determined using a commercial assay kit (BC0030, Solarbio, China).

    The soluble protein content in broccoli was measured using a BCA protein assay kit (ml095490, Shanghai Enzyme-linked Biotechnology, China). The intracellular proteins in broccoli were extracted using PBS buffer (50 mM, pH 7.4), and the soluble protein content was calculated by measuring the absorbance at 760 nm using BSA as a standard.

    Broccoli endogenous Vitamin C content was detected using the solid blue salt colorimetric method descripted by Zhang and Huang [23]. Vitamin C content was calculated by measuring the absorbance at 420 nm.

    Glucosinolate levels were quantified using a glucosinolate assay kit (ml092776, Shanghai Enzyme-Linked Biotechnology). Total glucosinolate content was determined by measuring the absorbance at 505 nm.

    The phenolic and flavonoid contents were measured using the Folin-Ciocalteu method and NaNO2-Al(NO3)3-NaOH colorimetry method, respectively. First, broccoli samples (0.5 g) were mixed with 5 mL of methanol (80%) and subjected to ultrasonic treatment for 30 min. Supernatants were then collected for analysis. For phenolic content measurement, 200 µL of the supernatant was combined with 1 mL Folin-Ciocalteau and 800 µL Na2CO3 solution (75 g L−1), and the mixture was incubated at 25℃ for 1 h. The absorbance was then measured at 765 nm. For the total flavonoid content test, 1 mL of the supernatant was mixed with 1 mL 70% ethanol and 0.3 mL of 5% NaNO2. After mixing, 0.3 mL 10% Al(NO3)3 was added and the mixture was left to stand for 3 min at 25℃, 1 mL NaOH (1 mol L−1) was then added. The reaction mixture was detected at 510 nm after 10 min using rutin as a standard.

    Chlorophyll content

    The chlorophyll content was assessed following the method described by Xu et al. [24]. Approximately 100 mg of broccoli powder was added to 5 mL of acetone/ethanol (2:1) solution. Chlorophyll content was analyzed based on absorbance at 664 nm and 645 nm.

    RNA extraction and quantitative PCR analysis

    Total RNA was extracted from broccoli florets using an RNA Simple Total RNA Kit (Tiangen, China) with DNase I treatment. A ReverTra Ace quantitative (qPCR) reverse transcription (RT) kit (Toyobo, Japan) was used to synthesize cDNA. qRT-PCR assays were performed using the StepOne detection system (Thermo Fisher Scientific, USA) and SYBR Green PCR Master Mix Kit (Takara, Japan). Gene expression levels were calculated using the comparative ΔΔCt method. Briefly, the average Ct value for the target gene was normalized to the average Ct value of the endogenous reference gene (BoActin) to obtain the ΔCt value for each sample: ΔCt = Ct (target gene) – Ct (reference gene). The ΔΔCt value was then calculated as: ΔΔCt = ΔCt (test sample) – ΔCt (untreated sample). The relative fold change in gene expression was determined as 2^(-ΔΔCt). Three independent biological replicates were analyzed per experimental group. Sequences of primers are listed in Table S1.

    RNA-seq analysis

    Three independent repeats from the control and CLE14-treated broccoli at 0, 7, 14, 21, and 28 d post-harvest were used for RNA-seq analysis. RNA was extracted using the RNAprep Pure Plant Kit (Tiangen, China) and quantified using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). RNA integrity was determined using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system. Biomarker Technologies (Beijing, China) prepared Illumina libraries and sequenced them on an Illumina NovaSeq 6000 sequence platform (150 bases paired-end reads; Illumina, USA). The raw reads were further processed using the bioinformatics pipeline tool, and the BMKCloud (www.biocloud.net) online platform. Raw reads in fastq format were first processed through in-house Perl scripts, and the reads containing adapter or poly-N and low-quality reads were removed to obtain clean reads for further analyses. The proportion of clean reads in the samples (Q30) ranged from 94.62 to 97.82%. The cleaned reads were aligned to the Brassica oleracea reference genome from NCBI using hisat2 tools soft with default parameters. Gene expression levels were quantified by mapping fragments per kilobase of transcript per million fragments mapped (FPKM). Additional, differential expression analysis between treatments were performed using the DESeq2 package, and the genes with an adjusted P-value < 0.01 & Fold Change ≥ 2 were assigned as differentially expressed. Randomly chosen deferentially expressed genes were verified using qRT-PCR. The weighted gene co-expression network analysis (WGCNA) (min Module Size 30, and merge Cut Height, 0.25) was used to build a co-expression network. Data were analyzed using the online BMKCloud bioinformatics platform.

    Malondialdehyde (MDA), H2O2 content and O2
    •− production rate

    The MDA levels were measured following Xu et al. [24]. Broccoli floret powder was mixed with 5 mL trichloroacetic acid, and the supernatant was collected via centrifugation. The supernatant was combined with 2 mL of thiobarbituric acid (0.67%) and boiled for 20 min. The MDA content was analyzed by measuring the absorbance at 450, 532, and 600 nm.

    H2O2 content was measured using a Micro Hydrogen Peroxide (H2O2) Assay Kit (BC3590, Solarbio, China).

    The generation rate of superoxide anions (O2•−) was determined using hydroxylamine oxidization according to Shi et al. [25]. Briefly, broccoli (0.5 g) was mixed with 2 mL of PBS buffer, and the supernatant was collected. The supernatant was mixed with 100 µL NH2OH·HCl (1 mM), 1 mL PBS buffer and subjected to incubation at 25℃ for 1 h. Following this, 1 mL C6H7NO3S (17 mM) and 1 mL C10H7NH2 (7 mM) were added. The generation rate of O2•− was calculated based on absorbance at 530 nm after incubation at 25℃ for 20 min, using NaNO2 as the standard.

    Enzyme activities

    The activities of pheophorbide a oxygenase (PAO) and pheophytinase (PPH) were measured using Plant enzyme activity Kits (Enzyme-linked Biotechnology).

    Catalase (CAT) and superoxide dismutase (SOD) activity was measured as described by Cheng et al. [26]. Broccoli floret powder (0.5 g) was added to 3 mL of PBS buffer (50 mM, pH 7.8, with 0.2 mM EDTA, 2mM AsA, 25 mM HEPES, and 2% polyvinylpolypyrrolidone [w/v]). The supernatant was then collected for enzyme activity assay, which was performed using a SHIMADZU UV-2600 spectrophotometer (Shimadzu, Kyoto, Japan).

    Statistical analysis

    All data were analyzed using Statistical Analysis System version 8 (SAS Institute Inc., Cary, NC, USA). The significance of treatment differences was analyzed using ANOVA followed by Tukey’s test at the 5% significance level. Data were recorded as the mean ± standard errors (SE) of at least three independent biological replicates.

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