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PubMed2026

Engineered MXene-based nanozyme platform: NIR-II photothermal and dual enzyme-mimetic potentiated chemodynamic synergy for precision tumor eradication.

The antioxidant defense barrier in the tumor microenvironment, particularly glutathione (GSH), considerably restricts the therapeutic efficacy of chemodynamic therapy (CDT). Moreover, CDT generally exhibits relatively mild therapeutic efficacy owing to its intrinsic reaction kinetics, making it difficult to achieve complete tumor eradication within a short time. To address these issues, we construct a functionalized nanotherapeutic platform, Nb2CTx@Ru-PEG2000-FA (NCRPF), for tumor photothermal ablation and enhanced CDT resulting from GSH depletion. NCRPF possesses three key advantages: 1. Efficient near-infrared II photothermal conversion (η = 42.08%), raising the tumor temperature above 45 °C within 90 s for rapid ablation; 2. Dual peroxidase-like and glutathione peroxidase-like activities, simultaneously depleting GSH and generating a burst of ·OH to eliminate residual tumors; 3. Targeted tumor accumulation with 2.9-fold higher efficiency than passive diffusion. Both in vitro and in vivo results confirm that this combined strategy achieves complete tumor eradication with favorable biosafety. Collectively, the NCRPF nanotherapeutic system provides a powerful new paradigm with high translational potential for the complete eradication of breast cancer.

Animals↗
PubMed2026

Effect of walking training on blood glucose control and metabolic health in patients with type 2 diabetes: A systematic review and meta-analysis.

OBJECTIVE: To systematically evaluate the improvement effect of walking training on blood glucose control and metabolic health indicators in type 2 diabetes patients, and to explore the effect of different intervention program characteristics on the efficacy through subgroup analysis. METHOD: The system searched PubMed, Web of Science, EMBASE, Cochrane Library, and EBSCO databases, with a search period from the establishment of the database to March 15, 2026. Include a randomized controlled trial with the main intervention measures of walking behavior, with an intervention period of &#x2265;8&#xa0;weeks. Two researchers independently conducted literature screening, data extraction, and bias risk assessment. Meta-analysis was conducted using RevMan 5.4 software, with mean difference (MD) and its 95% confidence interval (CI) as effect measures for continuous variables. Select fixed effects model or random effects model for combined analysis based on heterogeneity size, and conduct subgroup analysis according to intervention program characteristics. RESULTS: Totally 5 randomized controlled trials were included, including 483 patients with type 2 diabetes (241 cases in the intervention group and 242 cases in the control group). Participants had a mean age of 54.2&#xa0;&#xb1;&#xa0;6.5&#xa0;years, BMI of 29.1&#xa0;&#xb1;&#xa0;3.2&#xa0;kg/m2, and 48.5% were male. The meta-analysis results showed that walking training significantly reduced glycated hemoglobin levels, with a combined effect of -0.48% (95% CI: -0.60 to -0.36, P&#xa0;<&#xa0;0.00001), There is moderate heterogeneity among the studies (I2&#xa0;=&#xa0;67%). Subgroup analysis showed that the "walking&#xa0;+&#xa0;other interventions" subgroup (combined effect size -0.58%, 95% CI: -0.96 to -0.20) and the "clear step target" subgroup (combined effect size -0.52%, 95% CI: -0.65 to -0.39) had larger effect sizes and lower heterogeneity within the subgroups. The bias risk assessment shows that the overall quality of the included research methodology is good. CONCLUSION: Walking training can significantly improve the blood glucose control in patients with type 2 diabetes. Combined with diet or behavioral intervention, setting clear goals for the number of steps may achieve better results. Walking training can be used as an effective auxiliary treatment for the management of type 2 diabetes in clinical promotion. Due to limitations in the number and quality of studies included, the above conclusions still require more high-quality research to validate.

Humans↗
PubMed2026

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence↗
PubMed2026

Machine learning-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.

Accurate detection of biogenic amines (BAs) in meat remains challenging due to their high structural similarity and co-occurrence. Herein, an Mn-N-C nanozyme was synthesized via a metal-organic framework confined pyrolysis strategy, possessing excellent oxidase (OXD)- and peroxidase (POD)-like activities. The dual enzyme-like activity showed Km values of 0.1584&#xa0;mM (OXD) and 0.1498&#xa0;mM (POD), respectively, in detection system. Leveraging these properties, a colorimetric sensor array was constructed, enabling the detection of four representative BAs within a concentration range of 2-10&#xa0;ppm with 100% classification accuracy. In addition, a concentration independent recognition model based on an artificial neural network was developed to address signal nonlinearity interference in meat. The integrated system achieved accurate trace-level identification of BAs in perishable fish, pork, and chicken, demonstrating its applicability for early-stage BAs monitoring and quality deterioration warning during storage and transportation.

Biogenic Amines↗
PubMed2026

Operationalizing Local Ecological Knowledge for Aquatic Biodiversity Conservation: A Systematic Review and Management Framework.

Effective conservation and management of aquatic biodiversity is severely constrained by the absence of long-term ecological data in small-scale, tropical, and data-poor fisheries, where roughly one-quarter to one-third of freshwater fish species and 37.5% of elasmobranchs are threatened with extinction once Data Deficient species are accounted for. Conventional monitoring and stock-assessment tools are often financially and technically inaccessible in these systems, leaving managers without the evidence needed to prioritize conservation action or implement precautionary governance. Local Ecological Knowledge (LEK) is a largely underutilized resource for natural resource management that can provide temporal depth, spatial resolution, and species-specific ecological insights unavailable from scientific records. We conducted a systematic review and bibliometric synthesis of 60 peer-reviewed studies (1997-2025) applying LEK to assess fish conservation status, examining how, where, and through what methods this knowledge has been used. Our analysis identifies four complementary pathways through which LEK informs conservation management: reconstructing multi-decadal population changes, documenting spatial contraction and habitat loss, detecting extreme rarity and local extirpation, and characterizing intrinsic sensitivity to exploitation based on life-history traits. Despite growing methodological rigor, freshwater systems and African fisheries remain critically underrepresented, and formal integration of LEK into fisheries governance and biodiversity assessment remains the exception rather than the rule. We propose a practical three-stage framework to operationalize LEK within existing management and conservation systems. Recognizing fishing communities as legitimate co-producers of ecological knowledge is both scientifically necessary and an equity imperative for achieving global biodiversity commitments under the Kunming-Montreal Global Biodiversity Framework.

Biodiversity↗
PubMed2026

Robust error-minimization in the genetic code across physicochemical metrics and variant codes: A graph-theoretic analysis in GF(2)6.

The standard genetic code reduces the impact of point mutations, but the robustness of this property across physicochemical metrics, naturally occurring variant codes, and codon-reassignment mechanisms remains incompletely quantified. Embedding the 64 codons in GF(2)6 represents the hypercube Q6 as a coordinate-dependent subgraph of the encoding-independent single-nucleotide mutation graph H(3,4), and enables continuous &#x3c1;-interpolation between the two. Under a quartet-pattern shuffle null (n=10,000), the standard code is significantly low-cost across four established, code-independent physicochemical distance metrics with partially overlapping content (Grant ham p=0.0062; Miyata p<0.001; Woese polar requirement p=0.003; Kyte-Doolittle hydropathy p=0.001), and the signal strengthens monotonically as &#x3c1; moves Q6&#x2192;H(3,4). A structure-aware sensitivity analysis under the alignment-derived ProtSub matrix (Jia & Jernigan 2021) yields the most extreme percentile of any measure tested (p=0.0004; all five p-values pass Bonferroni at &#x3b1;=0.05). Across the 27 NCBI translation tables, near-optimality is preserved: 11 of 12 informative-distance variants retain top-5% placement after BH-FDR correction. Natural codon reassignments avoid disrupting codon-family connectivity: under the encoding-independent H(3,4) adjacency, observed events are topology-breaking at relative risk 0.32 versus the candidate landscape (permutation p&#x2264;10-4). The H(3,4) result is stable by construction; the Q6 decomposition is representation-specific and fails to show depletion under 8 of 24 base-to-bit encodings, so we report H(3,4) as the primary test and Q6 as a sensitivity. Event-level conditional-logit modelling shows that topology avoidance and local physicochemical cost provide complementary, only weakly correlated signal (rs=0.15), and that topology adds explanatory value beyond physicochemistry under both Q6 and encoding-independent H(3,4) adjacency. Retrospective reanalysis of nine genome-recoding datasets is consistent with codon-family topology operating as an evolutionary-trajectory constraint distinct from acute engineering fitness. The contribution is the second axis: code evolution is jointly constrained by physicochemical smoothness and codon-family topological integrity, and these two constraints are partly independent.

Codon reassignment↗
PubMed2026

Emerging techniques of CRISPR/Cas system in antiviral therapy and diagnostics: Applications, limitations, and translational perspectives.

The CRISPR/Cas (clustered regularly interspaced short palindromic repeats) system is a versatile technology for developing antiviral medicines and editing viral genomes in both diagnostics and vaccine synthesis. Emerging insights into class 2 effectors, such as Cas9, Cas12, and Cas13, which target viral DNA and RNA, have revolutionized vaccines against viruses such as HIV, HPV, HBV, and EBV. Innovative diagnostic techniques such as SHERLOCK, DETECTR, and FELUDA have demonstrated system's diversity and accuracy in detecting the virus markers, supporting clinical decision-making, indicating adaptability and precision of CRISPR. This review critically evaluates CRISPR's role in RNA editing, emphasizing its importance for functional genomics and development of recombinant vaccines. Translational challenges are critically discussed, including off-target effects, delivery limitations, and ethical issues, for which unique approaches such as high-fidelity Cas variants, non-viral delivery systems, and bioethical frameworks are evaluated to address these limitations. This review also covers other social implications, such as accessibility and biosecurity risks, associated with CRISPR technologies Collectively, these advances underscore the transformative potential of CRISPR technologies in shaping next-generation antiviral diagnostics and therapeutics.

CRISPR-Cas Systems↗
PubMed2026

Targeting Gasdermins for Therapeutic Interventions in Central Nervous System Injury.

Central nervous system (CNS) injuries are the leading cause of permanent disability and premature death in adults worldwide, with their incidence continuing to rise amid social development. These injuries not only severely impair the quality of life but also impose a heavy burden on the global public health system. Current clinical interventions, such as decompression and thrombolysis, can alleviate primary injury but fail to effectively reverse the secondary neuroinflammatory damage. Traditional anti-inflammatory therapies, which cannot block the upstream source of the inflammatory cascade, have led to repeated failures in global clinical translation research over the past decades. Gasdermins were first characterized in studies of systemic inflammatory diseases. These proteins form transmembrane pores to drive the release of proinflammatory factors and inflammatory cell death, serving as key mediators of host innate immunity. Recent studies have revealed that gasdermins play critical roles in regulating the initiation and amplification of neuroinflammation following CNS injury. To clarify the therapeutic potential of gasdermins as targets for injury repair, this review systematically summarizes the structure and function of gasdermins, as well as their cell-specific activation and regulatory mechanisms. We further elaborate on their pathological roles in these injuries and the corresponding therapeutic strategies, aiming to provide a theoretical reference for basic research and clinical translation in this field.

Humans↗
PubMed2026

Hypothalamic-Pituitary Axis Involvement in Primary Central Nervous System Lymphoma.

CONTEXT: Primary central nervous system lymphoma (PCNSL) is a rare malignancy that may involve the hypothalamic-pituitary axis (HPA), leading to underrecognized but clinically significant endocrine dysfunction. OBJECTIVE: This work aims to characterize the spectrum and patterns of HPA-related endocrine disturbances in patients with PCNSL. DATA SOURCES: A systematic search was conducted in PubMed, EMBASE, Scopus, and Web of Science, supplemented by gray literature. The search concluded in February 2025. STUDY SELECTION: We included studies reporting adult PCNSL cases with documented dysfunction of at least one hormonal axis. Exclusion criteria were preexisting hypopituitarism or lack of endocrine data. DATA EXTRACTION: Data on demographics, tumor localization, hormonal axes affected, radiological findings, treatment, and outcomes were extracted. Risk of bias was assessed using JBI tools. RESULTS: Ninety-nine cases met the inclusion criteria. Diffuse large B-cell lymphoma accounted for 84% of cases. Endocrine dysfunction included isolated adenohypophyseal involvement (46%), neurohypophyseal (8%), and combined (45%). The most affected pituitary axes were the gonadal and thyroid axes, with 89.7% and 89.2% involvement, respectively. Hypothalamic tumors were strongly associated with combined dysfunction (odds ratio = 9.47; 95% CI, 3.76-23.86; P < .001). Persistent endocrinopathy was more frequent in progressive disease. No direct association was found between endocrine dysfunction and mortality. CONCLUSION: HPA dysfunction in PCNSL is frequent and often underdiagnosed. Hypothalamic involvement is associated broader hormonal impairment. Routine hormonal screening and multidisciplinary management should be standard in PCNSL care to minimize complications and improve outcomes.

Humans↗
PubMed2026

Trio-based whole-exome sequencing identifies convergent epithelial junction-related pathways in syndromic hidradenitis suppurativa.

INTRODUCTION: Hidradenitis suppurativa (HS)-related autoinflammatory syndromes, simply termed as syndromic HS (sHS), represent a group of rare immune-mediated inflammatory disorders in which HS coexists with systemic or cutaneous autoinflammatory features like PASH (pyoderma gangrenosum-PG-, acne and HS), PAPASH (PASH, pyogenic arthritis), PASS (PG, acne, HS, and ankylosing spondylitis), and SAPHO syndrome (synovitis, acne, pustulosis, hyperostosis, and osteitis). In recent years, genetic studies identified several novel pathogenic variants underlying sHS; however, most investigations rely exclusively on affected individuals sequencing and the absence of parental genomic information limits the possibility to determine inheritance patterns. METHODS: To address these gaps, we performed trio-based whole-exome sequencing (WES) on five individuals diagnosed with sHS and their unaffected parents. RESULTS: The pathway related to epidermal adhesion and desmosome organization was the most represented across our cohort, encompassing seven genes: DSC3, DSG1, FAT1, LAMA3, MICALL2, PLEC and TJP2. Integrin-extracellular matrix (ECM) adhesion signaling pathway, represented by ten genes (CSPG4, FERMT3, ITGA3, LAMA3, LAMA5, LIMS2, LTBP3, PLEC, TGM2, TNC) was also retrieved. Also, variants affecting innate immune pathways, including cytokine signalling and antigen presentation, have been observed. CONCLUSION: Our exploratory findings suggest that genetically heterogeneous variants in syndromic HS converge on biological processes involving epithelial junction organisation, extracellular matrix interactions and innate immune regulation. Although not establishing a unique pathogenic mechanism, these observations identify epithelial barrier biology as a candidate pathway warranting validation in larger cohorts and functional studies.

Journal Article↗
PubMed2026

Self-healing materials for food packaging: Design principles, activation mechanisms and implications for food safety.

Self-healing materials (SHMs), originally developed to restore mechanical integrity, have recently attracted growing interest in food packaging. By autonomously repairing physical damage, SHMs help preserve packaging integrity, barrier performance, food safety, and shelf-life during storage and transportation. This review summarizes recent advances in the design principles, activation mechanisms, material systems and food packaging applications of SHMs. Key healing strategies, including microencapsulation, dynamic covalent bond exchange, reversible non-covalent interactions and responsiveness to external stimuli such as temperature, pH, and humidity, are discussed. Representative material systems, including biopolymer-based films, hydrogels, nanocomposites, and stimuli-responsive polymers are evaluated with respect to their relevance to packaging animal-derived foods, fruits, and vegetables. Performance evaluation methods, sustainability implications, and food-contact safety concerns are addressed. Despite promising healing efficiency and mechanical resilience, challenges remain regarding production cost, food-grade safety, migration risks, trigger compatibility and stability under fluctuating environmental conditions. Future research should focus on scalable manufacturing, standardized evaluation protocols, repeated damage-healing safety assessment, regulatory compliance, and integration with intelligent packaging technologies.

Food Packaging↗
PubMed2026

Exploring Professional Experiences in Caring for Vulnerable Migrants in an Italian Rural Reception Centre: A Qualitative Study Using Multidimensional Textual Analysis-Professional Experiences in Rural Migrant Care.

AIM: This study aims to explore the experiences, strengths, challenges, and potential improvements for professionals in managing the complex needs of vulnerable migrants (VM) in an Italian rural reception centre. METHODS: A qualitative study using semi-structured interviews was conducted in April 2024. Data were analysed using the Automatic Analysis of Textual Data, based on Fraire's seven-step model for Exploratory Multidimensional Data Analysis. DATA SOURCES: Data were collected from 16 professionals working in a rural reception centre in southern Italy. Interviews were conducted and analysed using AATD in April 2024. FINDINGS: The analysis identified two main dimensions of professionals' roles: balancing systemic responsibilities with personal engagement and managing immediate needs versus long-term integration goals. Professionals face significant challenges, such as resource scarcity, bureaucratic inefficiencies, and emotional fatigue, which impact their well-being and the quality of care provided to migrants. Resilience, adaptability, and multidisciplinary collaboration were identified as key strengths. CONCLUSION: The study highlights the dual nature of professionals' work in reception centres, requiring them to balance operational tasks with emotional involvement in migrant care. Targeted interventions and systemic reforms are necessary to support professionals and enhance the quality of care for vulnerable migrants, particularly in resource-constrained rural settings. IMPLICATIONS FOR PRACTICE AND/OR PATIENT CARE: This study underscores the importance of providing targeted support to professionals working in reception centres, including training in intercultural competence, stress management, and coping strategies. Policies should address systemic challenges and provide resources to enhance healthcare delivery and social integration programs. REPORTING METHOD: This study adhered to the EQUATOR guidelines for reporting qualitative research (COREQ). The findings were reported in compliance with these guidelines, ensuring methodological rigour and transparency. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: This study highlights the critical need for targeted support and training for professionals working in reception centres, particularly in rural settings. To improve care for vulnerable migrants, professionals should receive training in intercultural competence, stress management, and coping strategies to better navigate the complex challenges they face. Furthermore, systemic changes are necessary to alleviate the pressures on reception centres, such as streamlining bureaucratic processes and enhancing healthcare infrastructure, particularly in rural areas where resources are limited. By addressing these needs, we can improve the well-being of both the professionals and the migrants they serve, fostering more effective support systems and better care outcomes. Additionally, fostering multidisciplinary collaboration and community engagement can contribute to more comprehensive and sustainable care models. PROTOCOL REGISTRATION: The Ethics Committee of the University of Rome Tor Vergata approved this study on 07/07/2021 (protocol registration number 160.21).

Humans↗
PubMed2026

Behaviourally informed text message reminders to increase cervical screening attendance in people with severe mental illness: the OPTIMISE pilot randomised controlled trial.

OBJECTIVES: This study assessed the feasibility of both the delivery and evaluation of 'enhanced' (behaviourally informed) text message reminders containing links to existing co-designed resources supporting decision-making for people with severe mental illness (SMI) regarding attendance of cervical screening. DESIGN: A pilot randomised controlled trial (RCT). SETTING: 13 General Practice (GP) practices in London were recruited. PARTICIPANTS: GP practices identified people with SMI aged 24-64 years who were overdue cervical screening. Target sample size was 120 participants (60 per arm) based on existing guidance for pilot trials. INTERVENTION: In March 2025, participants were randomised (1:1) to receive either the enhanced (intervention) or the standard (control) SMS reminder. PRIMARY AND SECONDARY OUTCOME MEASURES: 18 weeks later, feasibility outcomes were collected (primary outcomes) and data analysis for a definitive RCT was rehearsed (secondary outcome). RESULTS: Of the 150 participants across 13 GP practices that were randomised (n=75 per arm), 132 (88%) texts delivered (intervention n=64/75 (85%), control n=68/75 (91%)). 10 practices (76.9%) provided follow-up data for 102 participants (intervention n=50, control n=52). Five participants (intervention n=4, control n=1) attended screening within the trial period. Participant survey response rate was low (9/132 (7%), intervention n=5, control n=4). Both SMS messages were low cost, with the intervention SMS a 50% higher cost to deliver (7.5p vs 5p per SMS). Primary feasibility measures of recruitment rate of GP practices (27%), retention of GP practices (77%) and participants (100%), SMS delivery (88%) and data completeness (64%) indicated viability, although survey response rate (7%) did not. CONCLUSIONS: Achieving adequate recruitment and retention, data completeness and comparable groups is viable with some amendments, although an alternative method is required to assess fidelity. Behaviourally informed SMS reminders are feasible to deliver to people with SMI, although it is uncertain if the extra resources are accessed and used. With changes to data collection, a definitive trial could be feasible. Given the low observed cervical screening attendance, additional intervention is needed for this group. TRIAL REGISTRATION NUMBER: ISRCTN12558681.

Humans↗
PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗
PubMed2026

Revealing potential biomarkers and metabolic mechanisms of ovarian aging in hens during late laying period based on machine learning and metabolomics.

Ovarian function decline during the late laying period represents a major bottleneck for the economic efficiency of the global poultry industry. However, the underlying metabolic mechanisms and reliable early-warning biomarkers for ovarian aging remain poorly understood. In this study, we performed the first untargeted LC-MS/MS metabolomics analysis of ovarian tissues from Taihe silky fowls at peak laying (30&#xa0;weeks) and late laying (50&#xa0;weeks) stages, and employed an ensemble machine learning strategy integrating LASSO, random forest, and support vector machine (SVM) algorithms to identify high-confidence core biomarkers of ovarian aging. Gene expression analysis was further conducted to validate the potential molecular mechanisms. Our results showed that the metabolic profiles of ovarian tissues differed significantly between the two groups. A total of 6 core biomarkers were identified, 4 of which were long-chain acylcarnitines. Mechanistic analysis revealed that downregulation of key genes in the carnitine shuttle system led to impaired mitochondrial fatty acid &#x3b2;-oxidation, which in turn triggered excessive oxidative stress and compromised ovarian endocrine function. In conclusion, this study identifies long-chain acylcarnitines as potential metabolic biomarkers for ovarian aging in Taihe silky fowls. These findings provide novel insights into the metabolic basis of poultry ovarian aging and lay a theoretical foundation for the precise regulation of reproductive performance in indigenous poultry breeds.

Animals↗
PubMed2026

Proteomics-based analysis of the defense mechanisms of disease-resistant grass carp against Aeromonas veronii.

Sustainable aquaculture of grass carp (Ctenopharyngodon idella, GC) is consistently threatened by bacterial diseases, particularly those caused by Aeromonas veronii. A disease-resistant grass carp (DR-GC) has been developed by backcrossing female gynogenetic GC with normal male GC, exhibiting improved resistance. However, the systemic molecular mechanisms of DR-GC defending against Aeromonas veronii infection remain largely unexplored. Here, a label-free quantitative proteomics approach was employed to systematically compare proteomic profiles across five tissues (intestine, liver, muscle, skin, and kidney) in DR-GC and GC under healthy and infected conditions. The intestine was identified as the central defense tissue, exhibiting the highest number of differentially abundant proteins (DAPs). In DR-GC, A0A3N0YEK7 (small ribosomal subunit protein eS28), A0A3N0YGT8 (ATP synthase-coupling factor 6) and A0A3N0YNS7 (apolipoprotein A-I) were significantly upregulated in intestine, while D5KZW6 (GCHV-induced protein), A0A3N0Z0A1 and Q8JH84 (hemoglobin subunit alpha) were significantly dysregulated across multiple tissues, which playing the critical roles in defense mechanisms at the protein level. Furthermore, cytochrome P450-associated pathways, cytosolic DNA-sensing and RIG-I-like receptor signaling pathways were identified as crucial coordinators mediating immune and metabolic responses. This study provides the first comprehensive proteomic view of multi-tissue defense mechanisms in DR-GC, and identifies key DAPs and pathways for subsequent functional validation.

Animals↗