Category: 3. Business

  • Disability weights measurement for 148 childhood health statuses in Hunan, China: a study based on face-to-face surveys | Population Health Metrics

    Disability weights measurement for 148 childhood health statuses in Hunan, China: a study based on face-to-face surveys | Population Health Metrics

    Study design

    This study utilized PC and PHE methods, which are comparable to those used in GBD 2010 and European surveys measuring adult DWs [17, 18]. However, we differed in our survey approach and survey instruments. We used a paper version of the questionnaire, tailored to the situation, as the survey instrument instead of an electronic questionnaire. Furthermore, we conducted a face-to-face survey instead of an online survey from March 2021 to October 2022.

    Research setting and participants

    The primary target of the sample for this study was people who had some knowledge or awareness of a particular health state in children. Participants were preferably close contacts of children or children themselves. Due to the face-to-face nature of the survey, the large amount of human, material, and financial resources required, and the limited time available due to the epidemic control during the survey period, the respondents of this study were selected from the parents of children in the birth cohort already established by the research group; the parents of children attending and being hospitalised in Hunan Provincial Children’s Hospital, Xiangya Hospital, Xiangya No. 2 Hospital, Xiangya No. 3 Hospital; and the paediatricians of the community and the general hospitals; The general population living in other urban areas of Hunan Province and the administrative districts of Changsha, as well as university students.

    The inclusion criteria for the respondents were: (1) be 18 years of age or older; (2) possess normal intelligence, a certain level of literacy, and the ability to comprehend the questionnaire; and (3) have an understanding of the content and purpose of the study, agree to participate, and voluntarily sign an informed consent form. Exclusion criteria include: (1) participants who did not meet the inclusion criteria; (2) incomplete questionnaire responses; and (3) participants who refused to cooperate or did not sign the informed consent form.

    The sample size was estimated in consultation with experts in the field of statistics and computer simulations revealed that pairwise comparisons, with more than five comparisons using Probit regression analysis, were able to identify differences, and we also referred to the published literature [19], where 206 illnesses and injuries were included in face-to-face surveys of 5,750 people. The number of disease and injury categories investigated in our study was 148. The number of pairs for two-by-two comparison is 148*147/2 = 10,878, each questionnaire in this study incorporates 16 PC method pairs, a total of 10,878/16 = 680 different questionnaires are needed for one round of survey, each questionnaire needs to be completed by one person independently, 8 rounds of survey are planned in this study, the sample size to be surveyed is 8*680 = 5440, the PC method of our study investigates a total of 5455 respondents in 8 rounds of survey.

    Since it was difficult for us to access high schools or middle schools to survey children under the age of 18 in person. Therefore, we surveyed a random sample of students in colleges and universities to make it more representative of the health preferences of this age group. Our survey involved health state descriptions and the anchoring tool, the PHE method, was more difficult to understand, requiring respondents to have some knowledge and equivalent measures of a certain health state, so we surveyed almost exclusively children’s parents in hospitals and neighbourhoods and required parents to have a certain level of cognition, resulting in an overall high level of education in the included population.

    Determination of health statuses

    After reviewing relevant literature, we identified 148 childhood health statuses based on the disease spectrum of Chinese children’s outpatient and inpatient services, the Global Burden of Childhood Diseases list, the WHO Children’s Disease Statistical List, and major childhood health statuses gained through interviews with children’s parents, as well as the disease spectrum of pediatric outpatient and inpatient services at comprehensive tertiary hospitals and children’s specialty hospitals (See Supplementary documents 1). The text describes six categories of children’s health statuses: birth defects and congenital disability diseases (24), acute infectious disease (31), chronic diseases and injuries (34), accidental injuries (36), mental and behavioral disorders diseases (14), and malignant neoplasm diseases (9). (See Supplement Table S1).

    Table 1 Background characteristics of respondents

    Lay description of health statuses

    The principles for describing children’s health statuses are to use concise, non-clinical vocabulary, to highlight the main functional consequences and symptoms associated with the health statuses, and to keep the description to 50 words or less. The same health statuses assessed in adult DWs were identified by referring to lay descriptions of adult health statuses and incorporating them for children [20,21,22]. In the first phase of lay disease descriptions, we measured the functional and symptomatic dimensions of the 148 childhood health statuses included, using the “International Classification of Functioning, Disability, and Health, (ICF)” (https://apps.who.int/iris/bitstream/handle/10665/42407/9241545429.pdf?sequence=1) assessment scale (See Supplement Table S2). This helps to characterise the specific health statuses of these manifestations. Where possible, descriptions were determined based on standard clinical professional classification systems to accurately reflect the severity of a particular condition. The research team extensively discussed and revised the functional health and symptom presentation of the 148 childhood health statuses before finalising a preliminary version. Pediatric experts and doctors from community health service centers were consulted to review and modify the preliminary textual descriptions. This was done to ensure that they accurately reflected the characteristics, common presentations, and duration of associated symptoms involving the sequelae of impaired functioning. Finally, we obtained versions of the textual descriptions of childhood health statuses suitable for use in general population surveys using the PC and PHE methods (See Supplement Table S1).

    Table 2 DW (95%UI) for 148 child health States

    Health status valuation

    A comprehensive evaluation of the 148 children’s health statuses was conducted using the PC and PHE methodologies. The PC technique is an ordinal measure that assesses relative differences in individual functioning and health by comparing pairs of children’s health statuses. It also captures the assessor’s preferred choices for these health statuses. The PHE technique is a group health benefit transformation method that requires the assessor to retrospectively assess two hypothetical health items. The first health item is to prevent 1,000 people from developing a disease that leads to rapid death, while the second health item is to prevent 1,500, 2,000, 3,000, 5,000, or 10,000 people (based on randomly selected bids for each question) from developing a disease that is not fatal (i.e., one of the 148 health statuses for children) but would experience the symptoms and durations mentioned in the descriptions. Evaluators are asked to choose which health program they believe produces greater overall population health benefits. The PHE method is utilised for the purpose of evaluating and comparing health statuses affecting entire populations. This is achieved by estimating the propensity for children to experience a loss of welfare due to different health statuses, and subsequently translating this into an equivalent value of welfare loss.

    The 148 childhood diseases were first paired two-by-two, for a total of 148*147/2 = 10,878 pairs. We arranged 16 pairs and 3 PHE questions per questionnaire, each questionnaire needed pairs without put back randomly selected from 10,878 pairs and 3 PHE questions without put back randomly selected from 148 childhood diseases until all pairs were included in the questionnaire. Each round of the survey required the completion of 10,878/16 = 680 different questionnaires (See Supplementary documents 2).

    Data collection procedures and instruments

    In this study, a paper-based questionnaire served as the primary survey instrument. The questionnaire was developed by the research team in consultation with experts and was finalized based on the pre-survey results. The questionnaire mainly consisted of basic socio-demographic information of the respondents (e.g., age, gender, usual address, type of household registration, marital status, education level, annual household income level, type of occupation, presence of children in the household, presence of medical background, etc.), as well as 16 PC questions and 3 PHE questions.

    Trained investigators conducted face-to-face interviews. Respondents were informed of the survey’s purpose and questionnaire content and asked to sign an informed consent form. The enumerator supervised respondents while they completed the questionnaire and answered any questions. The questionnaire was completed in full. Respondents did not receive payment for their participation in the survey. The investigator prompted respondents to imagine two children with the disease and to weigh who was healthier between the two. Two investigators worked in pairs to enter the completed questionnaires into a pre-developed computer program.

    Statistical analysis

    The statistical analyses for this study were conducted using R software (version 4.3.1), Stata MP software (version 17.0), and Microsoft Excel 2021. Probit regression models were used to estimate relative outcomes for childhood health statuses based on pooled PC data. The result reflects the relative differences in severity between childhood health statuses on a quantitative scale, and also shows participants’ choice preferences for each childhood health statuses [23]. The probit regression model was used to determine the selection of the first disease and injure as the healthier state in pairwise comparisons. A response variable of 1 was assigned to this selection, while a value of 0 was assigned to the alternative selection. The probit regression model incorporated indicator variables for each disease and injure, with a value of 1 assigned to the first state in a PC, −1 to the second state in a PC, and 0 to all states other than the pair being considered. Additionally, the interval regression model was used to analyze the pooled group health equivalence data. Finally, a linear regression model was used to anchor the estimates from the probit regression. Logit transformations based on the PHE responses were performed to map to a DW scale of 0 to 1. Finally, 1000 bootstrap iterations were used to calculate 95% uncertainty intervals (UIs) [17, 18].

    In addition, we conducted a trend analysis of the DWs of the childhood health statuses included in this study, to validate the logical soundness of the study and the reliability of the DW values. Trend analysis i.e. by plotting a trend line on the DW values of diseases with severity ratings to see if they logically fit the intuition. RSpearman’s correlation coefficient (rs) was used to test for correlations between the DW values obtained for different subgroups and to identify overall differences in DW values measured under different population characteristics. These factors included gender (male and female), age (≥ 35 and < 35 years), education level (bachelor’s degree/above and below), annual household income (high income ≥ 100,000 yuan and low income < 100,000 yuan), type of household registration (urban and rural), presence of a medical background, and physical labor status (manual and non-manual), whether there are children in the family (with child and without child), and whether there is a medical background (with medical background (MB) and without MB). Using the results of the ICF assessment of 148 children’s health statuses (See Supplement Table S3), we investigated the impact of various functional attributes and symptomatic manifestations on children’s DW values.

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  • US senators call for investigation of scam ads on Facebook and Instagram | US news

    US senators call for investigation of scam ads on Facebook and Instagram | US news

    US senators Josh Hawley and Richard Blumenthal have asked the heads of the Federal Trade Commission (FTC) and the Securities and Exchange Commission (SEC) to investigate revenue from ads on Facebook and Instagram that promote scams and banned goods.

    “The FTC and SEC should immediately open investigations and, if the reporting is accurate, pursue vigorous enforcement action where appropriate” to force Meta to disgorge profits, pay penalties and agree to cease running such advertisements, Hawley and Blumenthal wrote in a letter to the federal agencies.

    Earlier this month, Reuters reported that internal documents from late 2024 stated that that year – about $16bn – from illicit advertising. One document noted Meta, which owns Facebook and Instagram, earns $3.5bn in revenue from “higher risk” scam ads every six months. Other documents stated that Meta’s anti-fraud rules didn’t appear to apply to many ads that regulators and the company’s own staff believed “violated the spirit” of its rules against scam advertising.

    In response to the Reuters report, Meta said it had reduced user reports of scams by 58% over the last 18 months.

    The Hawley-Blumenthal letter “makes claims that are exaggerated and wrong”, Meta spokesman Andy Stone said. “We aggressively fight fraud and scams because people on our platforms don’t want this content, legitimate advertisers don’t want it and we don’t want it either.”

    Hawley, a Republican, and Blumenthal, a Democrat, expressed skepticism about Meta’s efforts to combat illicit advertising. They pointed to the company’s “ad library”, a publicly accessible database of advertising that appears on Meta’s social-media platforms.

    “Even a short review of Meta’s Ad Library at the time of this letter shows clearly identifiable advertisements for illicit gambling, payment scams, crypto scams, AI deepfake sex services, and fake offers of federal benefits,” they wrote.

    The senators cited Reuters reporting that Meta itself estimated its platforms were involved in a third of all scams in the US, and went on to note that the FTC estimates Americans lost $158.3bn to scams last year.

    “Scams have been allowed to take over Facebook and Instagram as Meta has drastically cut its safety staff, including for FTC mandated reviews, even as it dumps unimaginable sums into its generative AI projects.“

    Blumenthal and Hawley expressed particular concern about fake ads purporting to represent the US government or political figures. They cited an example of a bogus ad that claimed Donald Trump was offering $1,000 to recipients of food assistance.

    “While Meta has been warned about advertisement deepfakes impersonating politicians, it still continues to run fraudulent clips,” their letter states. “The beneficiaries of these scams are often cybercrime groups based in China, Sri Lanka, Vietnam and the Philippines.”

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  • Collagenase-mediated extracellular matrix targeting for enhanced drug penetration and therapeutic efficacy in nanoscale delivery systems for cancer therapy | Journal of Nanobiotechnology

    Collagenase-mediated extracellular matrix targeting for enhanced drug penetration and therapeutic efficacy in nanoscale delivery systems for cancer therapy | Journal of Nanobiotechnology

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  • Mierke CT. Extracellular matrix cues regulate mechanosensing and mechanotransduction of cancer cells. Cells. 2024;13:96. https://doi.org/10.3390/cells13010096.

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  • Secondary ocular syphilis in an immunocompromised patient: a case report | Journal of Medical Case Reports

    Secondary ocular syphilis in an immunocompromised patient: a case report | Journal of Medical Case Reports

    A 32-year-old Irish man was referred to our eye emergency department with a myriad of symptoms which included jaw pain, right-sided headache, scalp discomfort, and sudden-onset painless right visual loss over a duration of 3 days. He denied any significant antecedent trauma event as a lucid historian but cited pain following eye movement. He was initially and incorrectly managed by a medical team as suspected giant cell arteritis (GCA) with oral steroid therapy initiated. The jaw pain and scalp discomfort created a clinical picture suggestive of GCA, but this differential was weakened by the young age profile of the man. His right-sided visual acuity was reduced to “counting fingers” from a previous baseline of 6/6, while his left-sided visual acuity remained intact with 6/6 documented. Medical history included psoriatic arthritis that had been managed medically with monoclonal antibody therapy in the form of adalimumab administered via the subcutaneous route at a dose of 40 mg every 2 weeks. His family history was noncontributory. He worked as a hotel manager and is a nonsmoker. He was initially treated with oral prednisolone 60 mg once daily, for which he received 7 days of treatment.

    Clinical findings

    Family and personal history were unremarkable for any ophthalmic pathology. His intraocular pressures lay within an acceptable range bilaterally. Significant pain on eye movement that was most pronounced on lateral gaze was recognized during the physical examination. Clinical examination revealed blot hemorrhages observed superiorly in the right retina accompanied by gross optic nerve swelling with a flat retina. The left eye examination did not relevant significant pathology. The anterior segment findings were noncontributory in relation to both eyes examined. The anterior chamber was deep and quiet bilaterally. There was no evidence of flare or fibrin in each eye. The optical coherence tomography (OCT) macula scan was unremarkable bilaterally.

    Timeline

    Initially, the patient was admitted to hospital and treated as atypical optic neuritis, using intravenous methylprednisolone therapy. A thorough set of radiological and immunological tests were requested. The timeline for definitive diagnosis and treatment is illustrated in Table 1. Here, the treatment paradigm shifts amid the knowledge of an evolving diagnosis.

    Table 1 Sequence of events pertaining to definitive diagnosis and treatment

    Diagnostic assessment

    A battery of tests were requested on admission to hospital. Admission blood work illustrated a significant elevation in white cell count and neutrophils in addition to elevated inflammatory markers such as C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR). Initial white cell count was shown to be 17.0 × 10⁹/L alongside an elevated neutrophil count of 14.21 × 10⁹/L. A CRP of 59 mg/L and ESR of 58 mm/hour were reported by the hospital laboratory. Additional immunology testing revealed the patient to be negative for the following tests: quantiferon, anti-cyclic citrullinated peptide (anti-CCP), antinuclear antibody (ANA), anti-neutrophil cytoplasmic antibody (ANCA), and antimitochondrial antibody.

    Given the significant rise in acute inflammatory markers, a blood-borne viral screen was sent in addition to a complete infectious disease panel. Treponema pallidum was later detected, and the remaining screen was noncontributory. Serum protein electrophoresis was normal alongside normal immunoglobulin reports. The man was HIV negative on serological investigation. Liver autoantibody profile testing was also reassuring. The anti-parietal cell, anti-smooth muscle antibody, and anti-liver–kidney microsomal (anti-LKM) antibody results were negative. A lumbar puncture was performed and was proven to be noncontributory.

    The urea and electrolytes and liver function tests all lay within an acceptable range. Rheumatological panel testing was reported as normal. The patient was negative for myelin oligodendrocyte glycoprotein (MOG) antibodies and neuromyelitis optica (NMO) antibodies. Relevant imaging included an erect chest x-ray and magnetic resonance imaging (MRI) orbits including contrast. Radiological investigations were reported as entirely unremarkable. There were no financial concerns in relation to ordering tests owing to the level 4 nature of the clinical site. Additionally, the native English-speaking nature of the patient removed any language barriers.

    Differential diagnosis

    The ophthalmology team initially treated this case as an atypical optic neuritis presentation. The significant pain following eye movement accompanied with monocular visual loss rendered optic neuritis a reasonable differential and important component of the clinical workup. Reassuring optical coherence tomography (OCT) scans of the disc accompanied with normal retinal nerve fiber layers rendered optic neuritis an unlikely cause for pathology. MRI orbit with contrast was noncontributory and accompanied by negative blood test results for MOG antibodies and NMO antibodies, making this an unlikely cause for presentation.

    A wide variety of other differentials were considered, including intermediate uveitis, white dot syndromes, macular choroiditis, acute retinal necrosis (ARN), progressive outer retinal necrosis (PORN), and Behçet’s disease. The history and clinical examination findings excluded these listed differentials systematically.

    Laboratory tests revealed positive syphilis serology with detectable Treponema pallidum antibodies (TPHA) and rapid plasma reagin (RPR) at a titer of 1:128. In the aforementioned clinical context, this result was suggestive of ocular syphilis. At this point, the infectious disease team were invited for their expert oversight.

    Therapeutic intervention

    This man faced initial medical management with oral prednisolone 60 mg taken once daily for a duration of 7 days, prior to the timely referral to ophthalmology colleagues. A clinical decision was made to treat the patient on an inpatient basis. He was subsequently commenced on intravenous methylprednisolone therapy 1 g once daily for a period of 3 days. The intravenous methylprednisolone therapy provided analgesic effect but no change from presenting visual acuity. In total, the patient received 7 days of oral steroid therapy and 3 days of intravenous prior to antibiotic commencement.

    The visual acuity prior to antibiotic was “counting fingers.” Knowledge of the specific blood results prompted a shift toward intravenous antibiotic therapy in the form of intravenous benzylpenicillin 2.4 g four times daily, as advised by the infectious diseases team. A significant improvement in visual acuity was demonstrated following benzylpenicillin therapy with a visual acuity of 6/15 noted in the clinic review 3 weeks subsequently. He completed a 14-day course of intravenous benzylpenicillin and was later switched to a tapering course of oral steroids alongside this. He was discharged from hospital following completion of pharmacotherapy and reviewed in the outpatient clinic setting thereafter.

    Follow-up and outcomes

    He recorded right-sided visual acuity of 6/15 without any aid amid uncompromised color vision at the 2-month follow-up following his planned discharge. The OCT macula scan did not show signs of evolution, however the right-sided OCT disc highlighted issues related to ganglion cell loss and retinal nerve fiber layer decay, consistent with the infectious disease process.

    Follow-up clinical examination excluded right-sided papilledema. This represented a departure from the initial assessment. The formative pain following eye movement had subsided during the hospital admission. A gradual decline in symptom severity had been cited by the patient throughout the treatment course.

    At the 2-month follow-up, the patient was entirely asymptomatic. Right-sided visual acuity had stabilized to a revised baseline of 6/15 from an initial visual acuity of “counting fingers.” The RPR test revealed a titer of 16 at the follow-up review with an initial RPR titer of 128 noted. This reduction is an important marker for disease resolution.

    The most recent follow-up revealed a right visual acuity of 6/12 unaided with no improvement with pinhole test. His intraocular pressure stood at 15 mmHg in the right eye and 16 mmHg in the left eye. Optos wide-field imaging revealed a decrease in disc edema in the right eye consistent with clinical improvement in his condition. The patient had completed relevant antibiotic therapy and is being monitored by the infectious disease and ophthalmology team. This man will continue to be followed up by the ophthalmology clinical service on an annual basis.

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  • Human umbilical cord mesenchymal stromal cells derivatives in treating diabetic foot ulcers: a phase I/II safety and efficacy trial | Stem Cell Research & Therapy

    Human umbilical cord mesenchymal stromal cells derivatives in treating diabetic foot ulcers: a phase I/II safety and efficacy trial | Stem Cell Research & Therapy

    Diabetic foot ulcers (DFUs) are a major complication of DM, affecting up to 34% of diabetic patients. They contribute significantly to morbidity, healthcare costs, and lower limb amputations [1]. Current treatment often fails to achieve complete healing, particularly in chronic and ischemic ulcers [2]. Human umbilical cord-derived mesenchymal stromal cells (hUC-MSCs) are a promising therapeutic option due to their potent immunomodulatory, pro-angiogenic, and regenerative properties [4].

    Central to the regenerative efficacy of hUC-MSCD is their secretion of key growth factors. Epidermal Growth Factor (EGF), a potent mitogen and a mainstay in wound healing, stimulates keratinocyte proliferation and migration—essential drivers of re-epithelialization—and also activates fibroblast proliferation and migration, facilitating granulation tissue formation and extracellular matrix remodeling [16]. Physiologically, serum levels of EGF in healthy individuals hover around ~ 30 pg/mL, and may range from 30 to 60 pg/mL in diabetics, although endogenous wound fluid concentrations are significantly lower, likely due to local depletion and enzymatic degradation. Restoring local EGF via hUC-MSCD-mediated secretion could potentiate epithelial closure and matrix deposition.

    CXCL12 (SDF-1), another critical component in the hUC-MSCD secretome, plays a pivotal role in angiogenesis, recruitment of stem/progenitor cells, and immune modulation. CXCL12 signaling is essential for neovascularization and cellular recruitment during wound repair. Although systemic/homeostatic levels of CXCL12 have been recorded in the range of ~ 100–200 pg/mL, local levels in chronic wound environments are substantially lower, limiting effective healing [17]. Enhancing CXCL12 availability through hUC-MSCD may therefore rejuvenate vascular responses and cell trafficking to the wound.

    Transforming Growth Factor-β1 (TGF-β1) is a multifunctional cytokine integral to all phases of wound healing—modulating inflammation, recruiting immune cells (especially macrophages), activating fibroblasts, synthesizing ECM and collagen, inducing angiogenesis (via VEGF), and regulating keratinocyte behavior and integrin expression for re-epithelialization. In DFUs, wound fluid TGF-β1 levels around 115 pg/mL have been associated with healing outcomes; levels above this threshold may predict closure within 12 weeks. Conversely, non-healing ulcers often show deficient or dysregulated TGF-β1 activity [18]. By supplying balanced TGF-β1 via hUC-MSCD, you may help re-establish the essential inflammatory-proliferative equilibrium needed for healing [19].

    The term human umbilical cord mesenchymal stromal cell derivatives (hUC-MSCD) refers to bioactive components obtained from human umbilical cord mesenchymal stromal cells (UC-MSCs). These derivatives include exosomes, extracellular vesicles (EVs), and conditioned media, and they retain the therapeutic properties of UC-MSCs—most notably, immunomodulation and tissue regeneration—while being explored as cell-free therapies for a range of medical conditions [20].

    Recent studies indicate that hUC-MSC derivatives such as conditioned medium, EVs, and exosomes may offer distinct advantages over direct use of UC-MSCs in therapeutic applications [20]. A key benefit is their reduced risk of tumorigenicity and immune rejection compared with other stromal cell types. UC-MSCs themselves exhibit low immunogenicity, making their derivatives particularly appealing for allogeneic transplantation [21]. Importantly, these derivatives preserve the regenerative capabilities of their parent cells while minimizing the ethical concerns associated with direct stem cell therapies.

    Another significant advantage of hUC-MSCD lies in their paracrine activity, secreting bioactive molecules such as growth factors, cytokines, and EVs that promote tissue repair and modulate immune responses. These results validated the paracrine regenerative potential of the prepared hUC-MSCD. Studies have shown that hUC-MSC-derived exosomes display anti-inflammatory, pro-angiogenic, and anti-fibrotic properties, making them promising candidates for conditions including myocardial infarction, osteoarthritis, and neurodegenerative diseases [21]. Additionally, hUC-MSC-derived conditioned media have been shown to accelerate wound healing and reduce tissue damage by promoting cell migration and proliferation [22, 23]. These characteristics make hUC-MSCD a more accessible, scalable approach to regenerative medicine, avoiding the logistical and safety challenges associated with direct UC-MSC use—such as the need for extensive in-vitro expansion and concerns about long-term engraftment.

    To date, only one clinical study has investigated allogeneic hUC-MSCs in diabetic foot ulcers (DFUs) in humans [9]. This trial examined both topical and intravenous administration of hUC-MSCs in DFU patients with peripheral arterial disease (PAD). Fourteen patients received treatment, which was found to be safe and effective, achieving >95% ulcer closure in all cases within 1.5 months [9]. Remarkably, no amputations or ulcer recurrences were reported after three years of follow-up.

    In another study, stromal vascular fraction (SVF) derived from adipose-origin MSCs was tested in a phase I clinical trial involving 63 patients with type II diabetes and chronic non-healing DFUs [22]. Autologous adipose-derived SVF was delivered via local injections and was reported to be safe and effective: at 12 months, 50 patients achieved complete healing and four patients achieved ≥ 85% healing. Six patients died during follow-up and three underwent amputation.

    Our study offers the unique advantage of not using MSCs from any source. Instead, we utilized the unfractionated conditioned media derived fromhUC-MSCs, the complete secretome, encompassing soluble growth factors, cytokines, and naturally released extracellular vesicles (including exosomes). In this phase I/II study, we characterized sterility, endotoxin absence, and the presence of key soluble mediators (EGF, CXCL12/SDF-1, and TGF-β1) at therapeutic concentrations, confirming biological activity of the preparation. We acknowledge that we did not separately isolate or analyze the extracellular vesicle and exosome fractions in this trial. Future preclinical and clinical studies will focus on detailed characterization of these subcomponents to further delineate their individual contributions to wound healing.

    This product demonstrated excellent safety, with only mild short-term adverse events, and remarkable efficacy in accelerating DFU healing. The absence of ulcer recurrence during two years of follow-up further underscores its therapeutic promise. Moreover, the preparation can be stored as a ready-to-use product, without requiring additional cell culture or expansion prior to administration.

    Although no mortality, amputations, re-hospitalizations, or cardiovascular events were observed during the 24-month follow-up, the small sample size limits the strength of conclusions regarding long-term outcomes, which should be rigorously evaluated in larger, controlled trials. While these results are encouraging, the small sample size limits the generalizability of our findings. Future work should focus on larger, placebo-controlled clinical trials to confirm efficacy, optimize dosing strategies, and further validate hUC-MSCD as a potential off-the-shelf biologic therapy for DFUs.

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  • Ituran Signs New OEM Agreement with Renault in Latin America

    Ituran Signs New OEM Agreement with Renault in Latin America

    AZOUR, Israel, Nov. 24, 2025 /PRNewswire/ — Ituran Location and Control Ltd. (NASDAQ: ITRN), a global leader in vehicle telematics, today announced that it has signed an initial three-year service agreement with a new major European OEM, Renault. The contract covers multiple countries in the Latin American region and there is strong potential for expansion to additional markets globally as well as extended service periods.

    Eyal Sheratzky, co-CEO of Ituran commented, “We are thrilled to announce this new agreement with Renault, bringing Ituran’s service and tracking solutions to their Latin American customers. We are excited with the long-term potential of this agreement which allows us to more broadly sell our service offerings, strengthening our presence in Latin America. It also brings us new customers of another leading global OEM, potentially accelerating our long-term net subscriber growth.”

    André Mói, Purchasing Director at Renault, added, “Renault is committed to offering high-quality and innovative services to its customers. The agreement with Ituran is aligned with our strategy of working with leading suppliers in the market to ensure the best solutions for our customers.”

    About Ituran

    Ituran is a leader in the mobility technology field, providing value-added location-based services, including a full suite of services for the connected-car. Ituran offers Stolen Vehicle Recovery, fleet management as well as mobile asset location, management & control services for vehicles, cargo and personal security for the retail, insurance industry and car manufacturers. Ituran is the largest OEM telematics provider in Latin America. Its products and applications are used by customers in over 20 countries. Ituran is also the founder of the Tel-Aviv based DRIVE startup incubator to promote the development of smart mobility technology.

    Ituran’s subscriber base has been growing significantly since the Company’s inception to over 2.5 million subscribers using its location-based services with a market leading position in Israel and Latin America. Established in 1995, Ituran has approximately 2,800 employees worldwide, with offices in Israel, Brazil, Argentina, Mexico, Ecuador, Columbia, India, Canada and the United States.

    For more information, please visit Ituran’s website, at: www.ituran.com

    Company Contact

    Udi Mizrahi

    [email protected]

    Deputy CEO and VP Finance, Ituran

    (Israel) +972 3 557 1348

    International Investor Relations

    Ehud Helft

    [email protected]

    EK Global Investor Relations

    (US) +1 212 378 8040 

    Logo: https://mma.prnewswire.com/media/1972820/Ituran_logo.jpg

    SOURCE Ituran Location and Control Ltd.

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  • Development of a novel nomogram to predict the prognosis of acute pancreatitis in pregnancy | BMC Gastroenterology

    Development of a novel nomogram to predict the prognosis of acute pancreatitis in pregnancy | BMC Gastroenterology

    APIP is a rare but critical disease, which could lead to adverse outcome both in pregnant women and fetus [10]. In our study, 66.7% of NMAP patients were admitted into ICU and 61.1% NMAP patients end the pregnancy, with longer hospital stays than MAP patients. As a special type of AP, APIP is differ from other types of AP, owing to anatomical and physiological changes in pregnancy. Despite several literatures having reported the risk factors of APIP patients [11,12,13], there is still lack of study to develop a practical tool to predict the prognosis for APIP patients.

    To our knowledge, the unique physiological state of pregnancy could affect bile flow, bile composition, gallbladder contractility, gallbladder postprandially emptying and lipid metabolism, might lead to gallstone formation, cholestasis, hyperlipidemia and so on, which contribute to occurrence of APIP [14]. Our study showed that the etiology of APIP was mainly biliary (37.8%) and hypertriglyceridemia (15.6%). In Europe and America, gallstones are the most etiology of APIP consistent with our study [15]. However, the most common etiology of APIP in several Chinese research cohorts was hypertriglyceridemia [16, 17]. Obviously, though our data primarily originate from China, the etiological composition may still vary across different regions within the same country. In the future, it will be necessary to include populations from more regions and country for analysis. Interestingly, in our cohort, APIP patients with hypertriglyceridemia exhibited a higher proportion of NMAP group than MAP group (85.7% VS 14.3%), but biliary APIP was more common in MAP group than in NMAP group (82.4% VS 17.6%), which might suggest the risk of hypertriglyceridemia in APIP patients. The underlying mechanisms for this observed disparity are likely multifactorial. Pregnancy is characterized by physiological hyperlipidemia and a state of heightened inflammatory readiness [18, 19]. In hypertriglyceridemia-induced APIP, the massive release of serum fatty acid may induce severe pancreatic injury and a potent systemic inflammatory response, resulting in respiratory, kidney, and cardiovascular failure in AP patient [20]. On the other hand, the management of biliary APIP, often involving timely endoscopic intervention to relieve obstruction, may lead to the termination of disease process. It is recommended to pay more attention to the management of hypertriglyceridemia-induced APIP patients.

    To date, there are no standardized and special scoring systems to evolute the severity and prognosis of APIP. Computed tomography scan is main technique for AP prognostic estimation, while is unsuitable for pregnant woman. Risk stratification of APIP patients still rely on clinical experience. Previous studies showed that routine laboratory tests were useful predictors in the early assessment of the severity of AP [7, 21]. Several prognostic models have been constructed based on clinical laboratory tests for APIP patients. Tang’s team established a nomogram model for predicting the risk factors of APIP, which contained five indicators including diabetes, triglyceride, Body Mass Index, white blood cell, and C-reactive protein [22]. Yang et al. also constructed a predictive model based on four indicators including lactate dehydrogenase, triglyceride, cholesterol, and albumin [23]. However, these prediction models of APIP require more indicators, simpler and practical tools are still needed. Our nomogram incorporates only two readily available variables including ALB and BUN based on stepwise logistic regression and LASSO regression. This makes our model more accessible for rapid clinical decision-making. Besides, in terms of predictive accuracy, our model achieved an AUC of 0.920, which is competitive with the high AUC of 0.942 reported by Tang et al. and superior to the model by Yang et al. (AUC: 0.865). What’s more, as the ROC curves and calibration curves showed, the model also could effectively predict the probability of pregnant woman admitted ICU (AUC: 0.819). Notably, previous models have rarely been developed to predict ICU admission of APIP patients. In a word, our model not only maintains high predictive accuracy but also excels in simplicity and clinical usability.

    There were several scoring systems utilized for the assessment of severity in AP patients, such as Ranson and APACHE-II scoring systems. However, these scoring systems incorporate clinical, laboratory and radiographic data, usually demand at least 48 h to evaluate the severity. And the items of these scoring systems were too complex to be inconvenient for clinicians to use. Besides, BISAP and SIRS score were also used to evaluate severity in AP patients in the first 24 h. In this study, the model only contained two items in assessment of APIP severity and was visualized as nomogram, with robustness and accuracy. Figure 3A also showed that the AUC of nomogram was higher than BISAP score and SIRS score, which indicated nomogram may be more suitable for APIP patients.

    It is known that ALB is a plasma protein synthesized by the liver, which plays an important role in maintaining plasma colloid osmotic pressure, transporting substances, etc. Numerous studies have demonstrated that ALB and other serum nutritional biomarkers played a significant role in the disease prognosis prediction including cancer, abdominal sepsis and so on [24,25,26]. Studies reported that the synthesis of ALB usually decreases while patients suffer from AP [27]. Meanwhile, the inflammatory response leads to the rise of capillary permeability, resulting in a large loss of ALB and a decrease in serum ALB levels [28]. In this study, ALB obtained within the first 24 h after admission was found to be an independent risk factor of the severity of APIP. Previous studies also showed that AP patients with low level ALB usually had poor prognosis [29]. Our study showed that ALB exhibited moderate diagnosis values to predict APIP prognosis. Previous studies found that combination of ALB and other laboratory indicators could effectively enhance predictive performance [30, 31]. Present study also showed that the AUC of nomogram incorporating ALB and BUN was higher than single indicator. By integrating these predictors, the model could offer more reliable prediction results. Additionally, ALB could be easily detected from peripheral blood at a low cost, which could contribute to clinical evaluation for APIP. However, it is necessary to recognize that various factors could influence the levels of ALB, such as nutritional status, other complications (liver or kidney) and exogenous ALB [32].

    In addition, BUN was also selected in our prognostic model. In general, BUN is related to glomerular filtration and volume status. At the onset of AP, BUN is observed to be ascending because of the decrease of the intravascular volume, fluid loss in body and acute renal injury [33]. The level of BUN has been deemed to be one of the most valuable single routine laboratory tests for predicting mortality in AP, as well as included in BISAP and RANSON scoring systems [33]. Remarkably, BUN is also disturbed by various factors, including protein intake, gastrointestinal bleeding, corticosteroid use and so on, which might lead to interference in disease evaluation [34]. In this study, we identified BUN obtained within the first 24 h after admission as an independent risk factor after multivariate analysis. It implies that BUN could be an effective predictor of APIP, but the influence of other factors should be taken into consideration.

    There were several limitations to the present study. First, due to the rarity of APIP, the sample size of present study was small. More clinical centers should participate in statistics in the future, and the model still needs to verify in an external and larger cohort. Second, as a retrospective study, some clinical data was not available. Thus, comparison of other scoring systems cannot be achieved, such as Ranson and APACHE-II score. It is necessary to collect more data in next research.

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  • Sensor-based assessment of fertilizer strategies in soybean: linking SPAD, NDVI, plant height, and thermal imaging with biomass accumulation | BMC Plant Biology

    Sensor-based assessment of fertilizer strategies in soybean: linking SPAD, NDVI, plant height, and thermal imaging with biomass accumulation | BMC Plant Biology

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  • Mechanistic insights into yield enhancement of Chimonobambusa opienensis bamboo shoots driven by organic fertilizer combined with Azotobacter Chroococcum via soil nutrient-microbe-metabolite interactions | BMC Plant Biology

    Mechanistic insights into yield enhancement of Chimonobambusa opienensis bamboo shoots driven by organic fertilizer combined with Azotobacter Chroococcum via soil nutrient-microbe-metabolite interactions | BMC Plant Biology

    OFAc improved bamboo shoot yield while maintaining overall quality

    In the first year, C. opienensis bamboo shoot phenotype and daily bamboo shoot volume varied significantly across treatments. OFAc showed significantly higher bamboo shoot volume than the Control on day 5 (P < 0.05, ANOVA, Tukey HSD). However, total bamboo shoot volume did not differ significantly among treatments (Fig. 1A, B). Basal diameter differed significantly among treatments (P < 0.01, Tukey HSD, Fig. 1C), with the largest observed in Ba (3.61 ± 0.37 cm) and the smallest in Control (2.70 ± 0.16 cm). Fresh weight also varied significantly (P < 0.0001, Tukey HSD, Fig. 1D), with Ba producing the heaviest bamboo shoots (196.90 ± 27.13 g), a 95.03% increase over the Control (100.96 ± 15.74 g). Bamboo shoot height differed significantly (P < 0.01, Tukey HSD, Fig. 1E), with Ac producing the tallest shoots (33.80 ± 2.17 cm) and Control the shortest (29.20 ± 3.70 cm). Bamboo shoot yield was significantly higher in Ba (29.54 ± 6.96 kg) and OFAc (25.78 ± 2.81 kg) groups than in the Control group (P = 0.0035, Tukey HSD, Fig. 1F). To evaluate bamboo shoot quality, 38 key traits were analyzed (Fig. 1G–J, Table S3, Analysis of bamboo shoot quality after different fertilization treatments). Oxalic acid and tannin levels, which affect palatability, differed significantly among treatments (P = 0.0051 and P = 0.0004, respectively; Tukey HSD, Fig. 1G, H). Ba had the highest oxalic acid (2.47 ± 0.03 mg/g) and Control the lowest (2.11 ± 0.19 mg/g). Tannin content was highest in Ac (1.16 ± 0.09 mg/kg) and lowest in Ba (0.83 ± 0.02 mg/kg).

    Fig. 1

    Effects of fertilization treatments on the growth, development, and quality traits of C. opienensis bamboo shoots in the field. A Representative bamboo shoot phenotypes; B line graph of daily bamboo shoot production; histograms showing (C) basal diameter, D fresh weight, E bamboo shoot height, and (F) yield. Histograms of (G) oxalic acid, H tannin, I calcium (Ca), and (J) iron (Fe) contents at peak bamboo shoot emergence. C (Control, no fertilizer), OF (organic fertilizer), Ba (Bacillus amyloliquefaciens), Ac (Azotobacter chroococcum), OFBa (organic fertilizer + B. amyloliquefaciens), and OFAc (organic fertilizer + A. chroococcum). One-way ANOVA was used for statistical analysis. Different letters (P < 0.05) and asterisks (*P < 0.05) indicate significance level with Tukey HSD. C–E, number of replicates per treatment for each measurement (n) = 5; B, F–J n = 3

    Calcium and iron contents also varied significantly among treatments (P < 0.0001 for both, Tukey HSD, Fig. 1I, J). OFAc showed the highest Ca (289.00 ± 31.00 mg/g) and Fe (6.61 ± 0.44 mg/g) contents, whereas Control had the lowest (Ca: 178.50 ± 8.50 mg/g; Fe: 1.83 ± 0.02 mg/g). OF showed no significant difference from Control in 37 of 38 traits (Fig. 1G–J, Table S3). In Ba, Asp, Thr, Ser, and lignin levels decreased significantly, whereas Mg and Zn levels increased (29/38 traits unchanged). Ac showed significant reductions in multiple amino acids (Asp, Gly, Ala, Val, Ile, Leu, Tyr, Lys, His, Arg, and Pro), Cu, and fiber, whereas Mg and Na increased (19/38 traits unchanged). In OFBa, fiber and five amino acids (Asp, Ala, Lys, Arg, and Pro) decreased significantly, whereas K, Mg, and Zn increased (27/38 traits unchanged). OFAc showed reductions in fiber, Mg, and Cu, with no significant changes in 33 of 38 traits (Fig. 1G–J, Table S3).

    In the second year, basal diameter (P < 0.0001, Tukey HSD, Fig. S1A), fresh weight (P < 0.0001, Tukey HSD, Fig. S1B), and bamboo shoot length (P < 0.0001, Tukey HSD, Fig. S1C) again differed significantly among treatments. All fertilization treatments resulted in significantly higher values than Control. On day 5, OFAc and OFBa yielded significantly more than Control (P < 0.01, Tukey HSD, Fig. S1D). The yield trend mirrored that of the first year, with significantly higher bamboo shoot yields in Ba (15.99 ± 4.16 kg) and OFAc (13.05 ± 1.07 kg) than that in Control (P = 0.012, Student’s t test, Fig. S1E). In summary, Ba significantly increased bamboo shoot yield while also raising oxalic acid levels and significantly reduced some quality traits. In contrast, OFAc enhanced yield without compromising palatability and largely preserved nutritional value, providing empirical evidence for further exploration of the “fertilizer type–nutritional traits–yield” mechanism.

    Ac and Ba had the strongest impact on soil nutrients, followed by OFAc and OFBa

    Soil physicochemical properties varied significantly among fertilization treatments (P < 0.05, Tukey HSD, Table 1). Ac and Ba exhibited the lowest soil pH values, whereas OFBa and OFAc showed intermediate values, and OF and CK displayed the highest pH levels. Total nitrogen (TN) levels were highest in Ac and Ba, whereas OFAc and OFBa showed intermediate values. OFAc had the highest total phosphorus (TP), exceeding Control, OF, and Ac. Control and OF exhibited significantly higher total potassium (TK) than other treatments. Total organic carbon (TOC) and total organic matter (TOM) peaked in Ba-treated soils, followed by Ac and OFAc. Ammonium nitrogen (AN) did not differ significantly, except for elevated levels in Ac. Available phosphorus (AP) was highest in OFAc, and available potassium (AK) peaked in Ba and OFAc. Total carbon (TC) followed the gradient: Ba > Ac > OFAc > OFBa > Control > OF. These results indicate that single applications of Ac or Ba significantly improved several soil properties (TN, TP, TOC, TOM), while combined treatments (OFAc, OFBa) produced intermediate effects between single amendments and the Control, providing a basis for subsequent analysis of nutrient regulation pathways.

    Table 1 Soil chemical properties of different fertilization treatments

    Soil chemistry as a key factor in shaping the soil microbiota of C. opienensis under control and fertilization treatments

    To assess the impact of fertilization on microbial diversity, we analyzed 41,046 bacterial and 8,242 fungal ASVs in C. opienensis soils. Rarefaction curves based on observed ASVs approached saturation, indicating sufficient sequencing depth (Fig. S2A, B). Bacterial and fungal α-diversity differed significantly among treatments based on multiple metrics, including the Chao1 index (P < 0.05, Tukey HSD, Fig. 2A, B). Phylogenetic diversity also varied significantly (P < 0.05, Tukey HSD). OFBa showed significantly higher bacterial and fungal α-diversity than Control, whereas OF, Ac, Ba, and OFAc showed no significant differences (Fig. S2C–H). At the phylum level, bacterial communities were dominated by Proteobacteria (31.35 ± 5.02%) and Acidobacteriota (27.90 ± 6.39%), whereas fungal communities were dominated by Ascomycota (40.13 ± 5.51%) and Basidiomycota (26.72 ± 5.14%) (Dataset S1, Mean relative abundance of soil ASVs by phylum across all samples, n = 30). Only Actinobacteriota and Chloroflexi averaged more than 9% abundance among other bacterial phyla, whereas Mortierellomycota and Rozellomycota were the only other fungal phyla exceeding 12% (Fig. S2I, J). PCoA revealed distinct clustering of bacterial (R = 0.7889) and fungal (R = 0.8802) communities by fertilization treatment (P = 0.001 for both; ANOSIM, Fig. 2C, D), indicating significant compositional shifts.

    Fig. 2
    figure 2

    Effects of fertilization and Control treatments on soil microbial diversity and community composition in the rhizosphere of C. opienensis. Fused violin-box plots showing Chao1 indices of rhizosphere bacterial (A) and fungal (B) communities (P < 0.05, one-way ANOVA with Tukey’s test). Principal coordinates analysis (PCoA) of bacterial (C) and fungal (D) communities using the ANOSIM test. Different letters indicate significant differences at P < 0.05. ASV identification based on Bray–Curtis dissimilarity revealed clear clustering by treatment, and significance was evaluated using anosim in R. Effects of soil physicochemical properties on bacterial (E) and fungal (F) community variation were assessed using PERMANOVA with 999 Monte Carlo permutation test

    Redundancy analysis (RDA) and PERMANOVA revealed soil chemical properties as key factors shaping bacterial and fungal communities. For bacteria, the following significantly influenced community composition: pH (variance explained [VE] = 7.96%, P = 0.001, Monte Carlo permutation test), TC (9.20%), TN (9.06%), TP (7.71%), TK (9.66%), TOC and TOM (both 8.36%), AN (8.33%), AP (7.64%), and AK (5.62%, P = 0.003) (Fig. 2E; Dataset S2, Results of the PERMANOVA for exploring the variance in the bacterial and fungal communities explained by the soil properties). For fungi, pH (9.60%, P = 0.001, Monte Carlo permutation test), TC (12.35%), TN (11.93%), TP (13.60%), TK (13.32%), TOC and TOM (12.01%), AN (11.96%), AP (11.64%), and AK (8.88%) were significant drivers (Fig. 2F; Dataset S2). Soil properties explained 32.44% of bacterial and 50.40% of fungal community variance (Dataset S2). These findings indicate that soil chemical characteristics play a major role in shaping the rhizosphere microbiota of C. opienensis under different fertilizations, laying a foundation for elucidating how fertilization influences microbial community structure.

    Co-occurrence network of Ba was denser, whereas that of OFAc was sparser

    Besides differences in microbial diversity and community composition, the co-occurrence networks of the Ba and OFAc microbiomes differed significantly from the Control (Fig. 3A–D; Fig. S3C–D). Other treatments showed no significant differences (Fig. S3A, B, D–F, H). In Ba-treated samples, bacterial network degree (P < 0.05, Mann–Whitney U test, Fig. 3I) and closeness centrality (P < 0.01, Mann–Whitney U test, Fig. 3J) were significantly higher than in Control. Fungal network degree showed no significant change (Fig. 3K), but closeness centrality was significantly lower (P < 0.001, Mann–Whitney U test, Fig. 3L) than that in Control. Similarly, in OFAc-treated soils, bacterial network degree (P < 0.05, Mann–Whitney U test) and closeness centrality (P < 0.01, Mann–Whitney U test) were also significantly higher than Control (Fig. 3I, J), whereas fungal degree remained unchanged and closeness centrality decreased significantly (P < 0.001, Mann–Whitney U test, Fig. 3L). Separate analysis of bacterial and fungal co-occurrence networks in Ba and OFAc communities showed that the bacterial network in Ba was more aggregated and denser than in Control (Fig. S3K), whereas its fungal network was more isolated and sparser (Fig. 3O). In contrast, both bacterial and fungal networks in OFAc were more isolated and less dense than those in Control (Fig. 3E–H). Both Ba and OFAc significantly increased C. opienensis bamboo shoot yield. However, the contrasting network structures and topological features of OFAc compared to Ba and Control warranted further investigation, providing key insights for further exploring how different fertilizer combinations influence microbial interaction patterns and their effects on yield.

    Fig. 3
    figure 3

    Bacterial and fungal co-occurrence networks in C. opienensis soil under control and fertilization treatments. Bacterial co-occurrence networks are shown for Control (A, E) and OFAc (B, F), and fungal networks for Control (C, G) and OFAc (D, H). Nodes in panels A–D are colored by microbial modules, whereas nodes in panels E–H are colored by microbial taxonomy at the phylum level. Correlations were inferred from ASV abundance matrices using Spearman’s method. Only robust and significant correlations (correlation coefficient < − 0.7 or > 0.7, P < 0.05) were retained to construct the co-occurrence networks. Each node represents an ASV of bacteria or fungi, and edges indicate positive correlations (red lines) or negative correlations (blue lines). Degree (I) and closeness centrality (J) of bacterial networks, and degree (K) and closeness centrality (L) of fungal networks in Control and OFAc soils were compared (Mann–Whitney U test). Asterisks denote significance levels (*P < 0.05, **P < 0.01, ns no significance)

    A. chroococcum addition (Ac and OFAc treatments) caused the most significant shifts in community composition

    To evaluate the effect of A. chroococcum addition on specific ASVs and identify those driving treatment-level differences, we used DESeq2 to compare microbial communities across treatments. Differentially enriched ASVs were identified in OF, Ac, Ba, OFAc, and OFBa treatments relative to Control (Fig. 4A, B). The addition of A. chroococcum (in Ac and OFAc) caused the most significant shifts in community composition, influencing 44 and 66 bacterial ASVs (Fig. 4E and F), and 75 and 156 fungal ASVs, respectively (Fig. 4E and F). Additionally, Ba notably altered the fungal community, affecting 126 ASVs (Fig. 4F), whereas OF and OFBa had minimal effects. Although some ASVs were commonly affected across treatments (Fig. 4E and F), many were uniquely influenced by Ac, OFAc, or Ba. Specifically, 22 bacterial and 40 fungal ASVs were uniquely affected by both Ac and OFAc, highlighting the strong influence of A. chroococcum. Meanwhile, 64 fungal ASVs were uniquely affected by Ba treatment (Fig. 4F). In contrast, OF and OFBa treatments affected relatively few ASVs—2 and 4 bacterial ASVs and 35 and 13 fungal ASVs, respectively (Fig. 4E, F; Dataset S3, DESeq2 results for responsive ASVs, their taxonomy, and the treatments they responded to). Bacterial ASVs that increased in abundance after A. chroococcum addition (Ac and OFAc) were primarily from the Acidobacteria, including Acidobacteriales, Vicinamibacterales, and Solibacteraceae (Fig. 4C). Fungal ASVs that increased were mainly from Ascomycota and Basidiomycota, such as Sordariomycetes and Auricularia (Fig. 4C). In contrast, ASVs that decreased after A. chroococcum addition were taxonomically diverse, spanning over 21 bacterial and fungal phyla, primarily Ascomycota, Rozellomycota, Basidiomycota, Acidobacteriota, Proteobacteria, Chloroflexi, and Actinobacteriota (Fig. 4C, D; Dataset S3). Ba uniquely influenced fungal ASVs, mainly increasing members of Ascomycota, including Sordariomycetes, Helotiales, Sordariales, and Talaromyces (Fig. S3B). In OF, only 10 bacterial ASVs responded, with 4 increasing—three of which were Actinomycetes—and 6 decreasing, spanning five phyla. Among 38 fungal ASVs that increased, most belonged to Ascomycota (Fig. S3C, D). In OFBa, 10 bacterial ASVs responded positively compared to those in Control, including Subgroup_2 (log₂ fold change = 22.65). Most ASVs that decreased in abundance belonged to Acidobacteria (Fig. S3E). The addition of A. chroococcum (Ac and OFAc) significantly reshapes bacterial and fungal community composition, highlighting the pivotal role of specific functional microbes in fertilizer-driven community shifts and laying a foundation for subsequent metabolite–microbe interaction analyses.

    Fig. 4
    figure 4

    Differential effects of fertilization treatments on the microbial community structure in C. opienensis soil were assessed relative to the Control using DESeq2 (Adjust P < 0.01). A Number of upregulated (Up) and downregulated (Down) bacterial ASVs during microbial and/or organic fertilizer treatments (Ac, Ba, OF, OFAc, and OFBa) compared to those in Control, grouped by phylum. Bubble size indicates the number of responsive ASVs. B Top 50 bacterial ASVs showing increased (log2 fold change > 0) or decreased (log2 fold change < 0) abundance in response to treatments containing A. chroococcum (Ac and OFAc). ASVs are presented at the highest available taxonomic resolution and colored by class within each phylum. C Numbers of unique and shared ASVs identified during each treatment compared to those in the Control group

    A. chroococcum addition (Ac and OFAc) significantly increased the richness of soil metabolites in C. opienensis

    To investigate how A. chroococcum application alters the soil microbial community, liquid chromatography–mass spectrometry (LC-MS)-based metabolomics was used to profile metabolites in the root soil of C. opienensis under five fertilization treatments and Control. Differential metabolites were identified based on VIP scores from OPLS-DA, fold change, and P-values from univariate analysis. A total of 133 metabolites were detected (Dataset S4). Root soil metabolite composition varied across treatments, with Ac, OFAc, and OFBa showing the most pronounced differences compared to Control (Fig. S5A, B). Twenty-eight metabolites were significantly more abundant in Control than in all fertilization treatments (P < 0.05; Fig. 5A–D, Fig. S5C). Most were fatty acyls (n = 15), including fatty acids and conjugates (n = 11). The rest included organooxygen compounds (n = 7), lactones (n = 3), pteridines and derivatives (n = 2), and prenol lipids (n = 2), and others (Fig. 5A-D, Fig. S5C).

    Fig. 5
    figure 5

    Differential metabolite profiles of C. opienensis soil under five fertilization treatments compared to those under Control. Significantly enriched metabolites in Ac (A), OFAc (B), Ba (C), and OFBa (D) treatments compared to that in the Control group. Red circles represent metabolites significantly upregulated, whereas blue circles indicate downregulated metabolites. Scatter plots display the abundance of representative enriched metabolites in each treatment; horizontal lines indicate the mean, and dots represent individual samples. VIP, variable importance; OPLS-DA, orthogonal partial least squares-discriminant analysis. n = 5, evaluated using OPLS-DA (VIP > 1.5, P < 0.05; see Dataset S4)

    In contrast, A. chroococcum addition (Ac and OFAc) significantly increased the abundance of 70 soil metabolites, including 28 carboxylic acids and derivatives, notably amino acids, peptides, and analogues such as Gamma-Glu-Leu (Fig. 5A, B). Six organoheterocyclic compounds also increased, including benzopyrans, triazines, and imidazopyrimidines like N6-(Delta2-Isopentenyl)-adenine. A total of 31 and 5 metabolites were significantly increased and decreased in Ba treatment, respectively, compared to Control (Fig. 5C; Dataset S4). OFBa significantly increased 33 metabolites, including organic acids and derivatives (n = 7), lipids and lipid-like molecules (n = 7), organoheterocyclic compounds (n = 6), and others such as D-glucosaminide (Fig. 5C, D; Dataset S4). Changes in metabolite abundance show that Ac and OFAc treatments markedly enrich the rhizosphere metabolite pool, especially amino acids and organic acids, suggesting that metabolite enrichment may be an important mediator of microbially driven yield improvement.

    Relationship of metabolites and microbial ASV with ac and OFAc

    To assess metabolite–microbe interactions under A. chroococcum addition, Spearman rank correlation and hierarchical clustering were used to associate differentially abundant ASVs (identified by DESeq2) with metabolites from OPLS-DA. Clustering revealed two major groups. Cluster 1 comprised 45 microbial ASVs and 10 metabolites enriched in Control soils. ASVs mainly belonged to Proteobacteria, Chloroflexi, Rozellomycota, and Mortierellomycota, whereas metabolites included hydroxy acids and derivatives, fatty acyls, organooxygen compounds, and organonitrogen compounds such as 12-hydroxydodecanoic acid, 22-hydroxydocosanoic acid, 4-O-methylgalactinol, and arachidoyl ethanolamide (Fig. 6). Cluster 2 included 30 microbial ASVs and 65 root soil metabolites enriched in Ac and OFAc soils. Unlike Cluster 1, ASVs primarily belong to Acidobacteriota, Proteobacteria, Gemmatimonadota, Ascomycota, and Rozellomycota. The metabolites predominantly consisted of carboxylic acids and derivatives (17/65), organooxygen compounds (7/65), fatty acyls (5/65), and amino acids, peptides and analogues, carbohydrates, and carbohydrate conjugates (Fig. 6). Metabolite–microbe clustering analysis reveals that Ac and OFAc treatments induce the co-enrichment of specific metabolites and microbial taxa, providing empirical evidence for understanding fertilizer-regulated microbe–metabolite cooperation in the rhizosphere.

    Fig. 6
    figure 6

    Heatmap of co-varying microbial taxa and metabolites in C. opienensis soil under five fertilization treatments and Control conditions. Differentially abundant ASVs (identified using DESeq2; (n = 75)), showing more than three significant positive or negative correlations with metabolites (Spearman rank correlation, r > 0.7, P < 0.05). A total of 75 metabolites were identified as key associations between metabolites and ASVs. These metabolites had more than two significant positive or negative correlations with ASVs (Spearman rank correlation, |ρ| >0.7, P < 0.05). Hierarchical clustering revealed two metabolites–ASV correlation clusters. Cluster #1 (red line) represents metabolites and ASVs more abundant in Control rhizosphere soil, whereas Cluster #2 (brown line) includes metabolites and ASVs enriched in soils amended with Azotobacter chroococcum (Ac and OFAc treatments). Red indicates positive correlations, white indicates no correlation, and blue indicates negative correlations between metabolites and ASVs

    Metabolite–microbe networks in C. opienensis soil communities

    To identify co-occurring changes between metabolites and microbial ASVs, we constructed a correlation network using their relative abundances across all treatments. The root soil network (Fig. 7A) included 148 ASVs and 91 metabolites connected via 692 links—352 positive and 340 negative—with an average of six connections per node (Dataset S5, List of network connectors, module and network hubs, network topological features, and correlation strength between network nodes). We identified 11 connectivity hubs, 5 modular centers, and 1 central network cluster as potential keystone metabolites or microbes (Fig. 7B; Dataset S5). One fungal hub, Rozellomycota (ASV369), was predominantly negatively correlated with metabolites in Modules 3 and 4, showing negative links with 34 of 36 metabolites, but positive correlations with 4-O-methylgalactinol and Dibenzo-18-Crown-6. Module 2, the largest, was dominated by positive correlations between Rozellomycota ASVs and four key metabolites: 4-deoxyphysalolactone, Fa(18:3 + 1O), 3-hydroxybutyric acid, and diethylene glycol diacetate (27 of 52 links). Negative associations primarily involved bacterial and fungal ASVs from diverse phyla. Module 1 featured negative correlations between the module center Mortierellomycota (ASV151) and 19 metabolites, alongside positive correlations with 18 metabolites linked to ASVs from Proteobacteria, Acidobacteriota, Ascomycota, and Rozellomycota (99 of 118 links). Module 5 lacked a central hub and was dominated by metabolite nodes, including 1-methoxyindole-3-carbaldehyde and fungal ASVs from various phyla. The 11 network connectors included 7 metabolites and 4 microbial ASVs (Fig. 7C). These ASVs were linked to 41 metabolites via 45 positive and 16 negative associations. Bradyrhizobium (ASV2748) and Galerina (ASV1232) were positively correlated with 36 metabolites, primarily amino acids, peptides, and analogues. These connected with the three module centers and seven connectors in Module 2. In contrast, 14 of 16 negative links were formed by fungi (ASV385) and Rozellomycota (ASV234) with 13 metabolites. Approximately half (69 of 143) of ASVs in the network were identified as DESeq2-responsive; of these, 54 responded to Ac or OFAc, and 38 to Ba or OFBa (Dataset S3).

    Metabolite–microbe network analysis reveals complex positive and negative correlation modules and key nodes, providing a systematic perspective for elucidating the mechanisms of microbe–metabolite interactions under fertilization.

    Fig. 7
    figure 7

    Co-occurrence networks depicting the relation between soil metabolites and microbial ASVs in C. opienensis soil under five fertilization treatments and Control. A A total of 1,048 bacterial and fungal ASVs and 130 rhizosphere soil metabolites were first analyzed using Spearman coefficients. Network showing associations among 148 bacterial and fungal ASVs and 91 rhizosphere soil metabolites (|ρ| >0.70, P < 0.05). Circles indicate bacterial ASVs, triangles fungal ASVs, and squares metabolites. Edges represent Spearman correlations of relative abundances, with red for positive and blue for negative correlations. The network is organized into five major modules. Hub nodes within the network and modules display dense connections, while eleven central nodes serve as connectors linking different modules. B Subnetworks of module hubs formed by metabolites or ASVs and their adjacent nodes. C Subnetworks of connector metabolites or microbes and their adjacent nodes. Microbial ASVs are colored by phylum

    Soil nutrient changes induced by Ac and OFAc treatments drive increased C. opienensis shoot yields

    To evaluate the cascading effects of A. chroococcum addition on soil chemistry, microbial community structure, root metabolites, and bamboo shoot yield, a PLS-PM was constructed (Fig. 8). The model posits that fertilization directly alters soil chemical properties (e.g., pH, organic matter, AN), which subsequently influence microbial community structure and diversity. These microbial changes promote the enrichment of key taxa and modulate metabolite abundance, ultimately enhancing C. opienensis bamboo shoot yield. The final model demonstrated good overall fit (GoF = 0.77) and convergent validity (AVE > 0.50 for all latent variables) (Fig. 8; Dataset S6, List of parameters for the PLS-PM). A. chroococcum addition was identified as the strongest driver of soil nutrient changes (path coefficient = 0.9916, P < 0.001), and soil nutrients emerged as the primary drivers of yield (path coefficient = 0.8430, P < 0.001). Soil nutrients also positively influenced microbial community structure (0.6668, P < 0.001), indirectly contributing to yield improvement through effects on key microbial taxa and metabolite profiles. Overall, the model suggests that A. chroococcum addition enhances soil nutrient status, which in turn reshapes microbial community composition, regulates functional microbes and metabolite abundance, and ultimately promotes C. opienensis bamboo shoot yield, thereby laying the foundation for proposing a “soil nutrients–microbes–metabolites–yield” regulatory model.

    Fig. 8
    figure 8

    Partial least squares-path model depicting causal relationships among fertilization, soil properties, microbial community structure, keystone taxa, key metabolites, and yield. Solid and dashed arrows represent significant (P < 0.05) and non-significant (P > 0.05) effects, respectively. Blue arrows indicate positive influences, while red arrows indicate negative ones. Standardized path coefficients and their corresponding p-values are shown alongside the arrows; non-significant paths are not displayed. The values of R² denote the proportion of variance explained for each dependent variable

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  • Use of quasi-experimental studies to evaluate causal effects of public health interventions in Portugal: a scoping review | BMC Medical Research Methodology

    Use of quasi-experimental studies to evaluate causal effects of public health interventions in Portugal: a scoping review | BMC Medical Research Methodology

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