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PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Prognostic significance of NLRP-3 expression in solid cancers: a systematic review and meta-analysis.

BACKGROUND: The inflammasome is a critical immunological sensor comprised of NLRP-3, ASC, and CASPASE-1. Mutations in NLRP-3 are prevalent in inflammatory diseases. However, the role of NLRP-3 in cancer is controversial. This study investigates whether NLRP-3 expression is associated with clinical outcomes in patients with solid cancers. METHODS: PubMed (MEDLINE), Embase, Cochrane, and Google Scholar were searched for articles reporting NLRP-3 expression and disease outcome data in cancer patients. RevMan Review Manager was used to calculate pooled hazard ratios and Mantel-Haenszel pooled odds ratios. RNA sequencing datasets from the TCGA Pan-Cancer (PANCAN) were used for external validation. RESULTS: Patients with higher NLRP-3 expression showed a significant association with larger tumor size, advanced tumor grade, TNM stage, and presence of metastasis. High NLRP-3 expression has a significant association with poor OS (HR:2.12, 95% CI = 1.49-3.03), p < 0.0001) and DFS (HR:1.86, 95% CI = 1.30- 2.65, p = 0.0007). Subgroup analysis showed that higher NLRP-3 expression is associated with worse OS in head and neck cancer (HR: 2.77, 95% CI = 1.88-4.09, p < 0.00001), colorectal cancers (HR:2.14, 95% CI= 1.59- 2.87, p < 0.00001), and pancreatic cancer patients (HR: 3.19, 95% CI = 1.73-5.91, p = 0.0002). CONCLUSION: High NLRP-3 expression is associated with advanced disease and poor outcomes in many solid tumours.

Humans↗

PubMed2026

Beyond multidimensionality: a systematic review of recurrent frailty archetypes in community-dwelling older adults.

BACKGROUND: Frailty is a clinically heterogeneous geriatric syndrome commonly summarised using physical or multidomain severity scores. Whether person-centred analyses identify recurring within-frailty configurations has not been systematically examined in community-dwelling older adults. METHODS: We searched PubMed, Embase, MEDLINE, and CINAHL (January 2000-November 2025) for cross-sectional studies using latent class, latent profile, or analogous clustering methods to derive frailty subgroups. Quality was assessed using the AHRQ checklist and a purpose-built appraisal of person-centred model reporting. Study-derived classes were mapped in duplicate to a structured archetype framework developed through comparison of class-defining features across studies. RESULTS: Fourteen reports representing 12 independent datasets from eight countries were included. Six configurations were identified: minimally impaired reference, mobility-physical, nutritional-metabolic, cognitive-predominant, combined cognitive-physical, and psychosocial/mood-predominant. Convergence was measurement-dependent. The reference and mobility-physical configurations recurred across physical-only and multidomain indicator sets, while the combined cognitive-physical configuration appeared across several multidomain frameworks but required cognition to be measured. The remaining configurations emerged only when their defining domains were included. Evidence of prognostic value beyond aggregate frailty severity came from one deficit-index study. Collapsing shared-provenance reports and excluding the boundary-eligible study did not alter recurrence; excluding the Croatian dataset left five configurations recurrent, with the cognitive-predominant configuration supported by one independent dataset. CONCLUSIONS: Person-centred analyses identify recurring within-frailty configurations, but their apparent stability is partly measurement-dependent. A five-configuration core persisted after exclusion of the Croatian dataset, whereas the cognitive-predominant configuration remained weakly replicated. Harmonised indicators and rigorous external validation are needed before clinical application.

Humans↗

PubMed2026

Artificial intelligence-derived myocardial fibrosis on cardiac magnetic resonance for prognosis in cardiomyopathy: A systematic review of a sparse evidence base.

BACKGROUND: Myocardial fibrosis on cardiovascular magnetic resonance (CMR), assessed by late gadolinium enhancement (LGE) and parametric mapping, is an established predictor of adverse events in cardiomyopathy. We assessed whether artificial intelligence (AI) quantification of fibrosis adds independent prognostic value. METHODS: We searched six databases, a clinical-trials register, and a preprint server from inception to 13 June 2026. Eligible studies used AI to generate a fibrosis marker in adults with ischemic or nonischemic cardiomyopathy, with covariate-adjusted outcomes over ≥12 months. Risk of bias was assessed using PROBAST, PROBAST+AI, and QUIPS. Fewer than three comparable studies precluded meta-analysis; certainty was rated using GRADE. RESULTS: Of 448 records (381 after de-duplication), 18 full texts were reviewed and two included, one peer-reviewed and one preprint. In an ischemic-cardiomyopathy registry (Ghanbari et al.; n = 216 analytic, 26 events), AI-derived dense LGE scar predicted arrhythmic events (univariable hazard ratio [HR] 2.35, 95% CI 1.33-4.15), and AI-derived but not manual scar improved discrimination beyond guideline criteria (area under the curve 0.63 to 0.68; p = 0.02). In a nonischemic dilated-cardiomyopathy preprint (Kim et al.; n = 347, 119 events), automated extracellular volume ≥30% predicted cardiovascular death or heart-failure hospitalization (adjusted HR 2.00, 95% CI 1.32-3.03). Both were at high risk of bias, with data-derived thresholds and no external validation. CONCLUSIONS: Across only two studies, AI-derived fibrosis was independently associated with adverse cardiovascular events, but its added value over manual quantification remains unproven. Certainty was very low. The evidence base is sparse and not yet ready for clinical use.

Humans↗

PubMed2026

Non-parametric differential methylation analysis characterizes histotype-specific promoter regions in epithelial ovarian cancer.

Epithelial ovarian cancer (EOC) is a heterogenous disease with frequent late-stage diagnosis and high mortality rates, for which no reliable screening tests exist. In recent years, epigenetic biomarkers in the form of DNA methylation in CpG-rich regions have gained increased attention in the scientific community due to their robust nature and accessibility, allowing for diagnosis without the need for invasive surgery. In this study, we investigated the aberrant methylation of promoter regions in early stage EOC through non-parametric methods, with the purpose of characterizing candidate epigenetic biomarkers. The approach was used on a cohort of early stage EOC samples, and results were compared to existing programs for differential methylation. Significant regions were then used to construct a CpG panel for stratifying EOC histotypes through predictive classification in external data. Identified promoter regions were highly reproducible across cohorts, and the constructed CpG model stratified histotypes in external cohorts through predictive classification. Comparisons against other DMP and DMR callers showed a degree of homogeneity between results but also revealed promoter regions that were overlooked despite clear signs of aberrant methylation. Finally, EOC histotypes were found to differ in their methylation distribution types, and results indicate that methods sensitive to non-normally distributed data may be poorly suited to compare groups with different distribution types. The non-parametric approach identified aberrantly methylated promoter regions that were highly reproducible across cohorts. Results from predictive classification indicate that these regions may be useful for the purpose of EOC histotype stratification.

Humans↗

PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗

PubMed2026

Male accessory gland proteins in Grapholita molesta: Identification and reproductive functional validation of four accessory gland-specific lipases.

Accessory gland proteins (Acps), synthesized in the male accessory glands (AGs), are transferred to females via spermatophores during mating and elicit diverse post-mating physiological and behavioral responses. However, Acps have not been comprehensively characterized in Grapholita molesta, a cosmopolitan orchard pest. Here, using data-independent acquisition mass spectrometry, we describe an integrated proteomic approach combining comparative AG analyses (virgin vs. newly mated) with spermatophore profiling to identify Acps in G. molesta. According to the established screening criteria, we identified 83 confirmed Acps, which were classified into nine categories. Tissue-specific expression patterns of 20 randomly selected Acp genes were evaluated, revealing that these genes were specifically or highly expressed in male AGs. Among the 83 confirmed Acps, four Acps harbored the PLN02872 superfamily domain and were classified into the canonical lipase family. Notably, their transcripts were all highly expressed in the AGs during the pre-maturation stage. These four Acps were selected for preliminary validation of their male reproductive functions. RNAi-mediated knockdown of three out of four lipase genes in G. molesta males significantly decreased the fertility of mated females, with phenotypes including a significant reduction in egg production and egg hatching rate. This study provides a comprehensive catalog of high-confidence Acps, lays a foundation for subsequent in-depth functional characterization of these reproductive proteins, and offers promising molecular targets for the development of novel genetic regulation-based integrated pest management strategies.

Animals↗

PubMed2026

Critical insights on the application of the theory of planned behaviour to food handlers' food safety practices.

Foodborne diseases remain a significant public health concern, often linked to unsafe food-handling practices. The Theory of Planned Behaviour (TPB) is widely used to predict and explain food safety behaviours, yet its application in this field has not been systematically and in-depth evaluated. This review evaluated how the TPB has been applied to study food handlers' behaviour, focusing on methodological approaches, use of the TACT (Target, Action, Context, and Time) framework, validity, elicitation studies, and reliability. Seventeen studies were included following a systematic search of four databases (Scopus, Web of Science, Wiley Online Library, and Taylor & Francis Online). Data were extracted on behaviour definition, aim of study, main findings, use of indirect and direct TPB measures, use of elicitation studies, internal consistency, content validation, analytical methods used, and any extensions to the original TPB framework. Key elements related to adherence to core TPB principles and measurement practices were extracted using a Checklist. Most studies used direct measures of TPB constructs, and only a few reported procedures for content validation. Considerable variability was found in the reporting of key measurement and psychometric practices. Five studies fully applied the TACT framework, while nine incorporated additional factors such as knowledge and moral norms. Elicitation studies were conducted in five cases where indirect measures were employed. Analytical approaches were mainly based on multiple linear regression, with limited use of more advanced techniques such as structural equation modeling. Twelve studies reported internal consistency results. Overall, the review highlights opportunities to strengthen methodological practices in future TPB research on food safety. Greater attention to conducting and reporting content validation, full application of the TACT framework, reporting of internal consistency, and consistent inclusion of elicitation studies when using indirect measures may enhance transparency, reinforcing the credibility and trustworthiness of research findings. A major methodological limitation of this review was that screening and data extraction were conducted by a single reviewer and no formal quality or risk-of-bias assessment of the included studies was performed. Despite these limitations, the findings provide practical guidance for the development and validation of TPB-based questionnaires and may support more robust food safety research, interventions, and policy initiatives aimed at improving food handlers' practices.

Humans↗

PubMed2026

AI-driven snapshot hyperspectral imaging for on-line sorting systems in food industry: From real-time sensing to intelligent decision-making.

High-throughput food sorting requires rapid, non-destructive detection of external defects, foreign materials, and internal quality attributes in heterogeneous food matrices. Conventional scanning hyperspectral imaging may suffer from motion-induced spatial-spectral mismatches, whereas snapshot hyperspectral imaging (S-HSI) captures spectral images within a single integration time. However, its advantage is limited by trade-offs in resolution, signal-to-noise ratio (SNR), reconstruction uncertainty, and calibration stability, which are further amplified by variable tissue structure, surface reflection, moisture, and fat distribution in foods. This review critically examines artificial intelligence (AI)-driven S-HSI for on-line food sorting within a sensing-representation-decision-execution framework. Compact architectures are compared according to their physical constraints, food-sorting suitability, and ability to support mapping between spectral responses and physicochemical quality attributes. AI strategies are reviewed for spectral reconstruction, image restoration, spatial-spectral representation, band selection, uncertainty-aware decision-making, and edge implementation. AI can partially compensate for snapshot-specific limitations, but current evidence remains largely limited to laboratory or prototype studies. Future work should link system performance to food safety and quality outcomes by reporting throughput, decision latency, calibration drift, missed-detection risk, false-rejection cost, and closed-loop sorting success.

Hyperspectral Imaging↗

PubMed2026

Patient-reported outcome measures for depression or anxiety symptoms in patients with cardiovascular disease: A COSMIN systematic review.

BACKGROUND: Depression and anxiety are common in patients with cardiovascular disease (CVD), but the measurement quality of patient-reported outcome measures (PROMs) used in this population remains unclear. This review aimed to evaluate the methodological quality, measurement properties, and certainty of evidence for depression and anxiety PROMs in adults with CVD and to inform instrument selection. METHODS: Following COSMIN and PRISMA guidance, four databases were searched from inception to February 2026. Studies assessing measurement properties of PROMs in adults with CVD were included. Methodological quality was evaluated using the COSMIN Risk of Bias checklist, and certainty of evidence was graded using an adapted GRADE approach. RESULTS: Sixty-six studies assessing 38 PROMs were included, comprising 29 generic and 9 CVD-specific instruments. Six PROMs met COSMIN Category A criteria: Cardiac Depression Scale-Short Form, Patient Health Questionnaire-9, Beck Depression Inventory-II, Hospital Anxiety and Depression Scale, Generalized Anxiety Disorder-7, and Major Depression Inventory. Four instruments were classified as Category C because of insufficient structural validity. Content-validity evidence was largely indeterminate or of limited certainty. Only 24 studies used confirmatory factor analysis or Rasch analysis, and no study assessed measurement error or responsiveness. Cross-cultural validity evidence was scarce. CONCLUSIONS: Six PROMs met Category A criteria, but selection should remain purpose- and context-specific. Particular attention should be given to somatic symptom overlap and intended clinical use. Further validation should prioritize content validity, measurement invariance, responsiveness, measurement error, and clinimetric performance.

Humans↗

PubMed2026

A pragmatic randomized controlled trial of self-directed online writing interventions for posttraumatic stress symptoms in a real-world digital setting.

Background: Public health and other large-scale crises, such as the COVID-19 pandemic, have intensified the global mental health burden, creating unprecedented demand for accessible interventions for posttraumatic stress symptoms (PTSS).Objective: We evaluated the feasibility and effectiveness of two self-directed online writing interventions embedded within China's WeChat ecosystem during the COVID-19 pandemic through a pragmatic randomised controlled trial.Methods: Between December 2021 and August 2022, 1,526 adults were screened for PTSS via a Tencent Medinfo Mini-Program. Eligible participants (n = 211) were randomised to Guided Narrative Technique-Writing (GNT-W, n = 100) or Expressive Writing (EW, n = 111). Both interventions comprised three self-directed daily writing sessions delivered entirely online without human support. Primary outcome was PTSD symptom severity (PTSD Checklist-Short), assessed at baseline, post-intervention, 2-week, and 1-month follow-ups.Results: While initial engagement followed typical digital health patterns (64.5% overall attrition), participants who initiated treatment showed strong adherence (77% completion). Both interventions were associated with significant within-group reductions in PTSS severity (GNT-W: b = -0.43, p = .023, d = -0.43; EW: b = -0.60, p = .001, d = -0.58), with no significant between-group difference (group × time: b = 0.18, p = .48). GNT-W did not confer additional benefit over EW protocol on PTSS severity.Conclusions: Both self-directed writing interventions were associated with within-group reductions in PTSS; without an inactive control condition, however, these changes cannot be firmly attributed to the interventions. GNT-W showed no advantage over the simpler EW protocol. These findings offer preliminary support for embedding scalable, low-barrier writing interventions in widely used digital platforms.Chinese Clinical Trial Registry: ChiCTR2000034836.

Humans↗

PubMed2026

Aneurysmal subarachnoid hemorrhage care in a middle-income public healthcare system: A real-world neurocritical care cohort.

BACKGROUND AND PURPOSE: Although aneurysm treatment capacity has expanded worldwide, outcomes after aneurysmal subarachnoid hemorrhage (aSAH) remain strongly influenced by neurocritical care (NCC) delivery, referral pathways, and access to specialized treatment. Contemporary data describing real-world aSAH care in resource-limited healthcare systems remain scarce. We aimed to characterize treatment patterns, NCC delivery, complications, and outcomes in a large Brazilian public referral center. METHODS: This retrospective cohort study included consecutive adults with confirmed aSAH admitted between June 2018 and March 2022 to a high-volume Brazilian tertiary referral center. Only patients admitted within five days of symptom onset were included. Demographic, clinical, radiological, treatment, complication, and outcome data were extracted from institutional records. Primary outcomes were in-hospital mortality and 3-month functional outcome assessed by the modified Rankin Scale (mRS). RESULTS: Seventy-four patients were included. Disease severity was high, with 45% presenting WFNS grades 4-5, 73% modified Fisher grade 4 hemorrhage, and 64% hydrocephalus. Endovascular treatment was performed in 73% of cases, and median time from admission to aneurysm treatment was 1 day. Despite early treatment capability, only 28% of patients were admitted to an ICU within 48 h, while 38% never received ICU care. Delayed cerebral ischemia occurred in 43%, radiologic vasospasm in 58%, ventriculitis in 22%, and infectious complications in 57%. External ventricular drainage was required in 42%, and vasoactive drugs were used in 85%. In-hospital mortality was 42%, and 66% had unfavorable 3-month outcomes (mRS 4-6). CONCLUSIONS: This real-world cohort highlights the substantial neurocritical care burden of aSAH in a middle-income public healthcare system. Despite timely access to definitive aneurysm treatment, patients experienced frequent neurological and systemic complications, emphasizing that contemporary aSAH care extends well beyond aneurysm occlusion.

Humans↗

PubMed2026

Norgestrel drives mitochondrial collapse and plasma membrane impairment in Pacific oyster (Crassostrea gigas) sperm by triggering premature acrosome reaction.

The toxic mechanisms of norgestrel (NGT), an emerging marine pollutant, on the sperm from externally fertilized invertebrates remain elusive. This study employed an integrated physiological and multi-omics framework to elucidate how NGT (10 and 1000 ng/L) disrupts acrosome reaction (AR) signaling machinery, thereby impairing the functional integrity of Pacific oyster (Crassostrea gigas, also known as Magallana gigas) sperm. Exposure to NGT triggered a significant, dose-dependent premature AR, characterized by elevated acrosin activity and a loss of acrosomal integrity. Multi-omics integration supports a model in which this premature exocytosis is linked to signaling disturbances, including disruption of calcium signaling and reduced transcript abundance of calmodulin (CaM) and the primary recognition protein zonadhesin (Zan). This signaling interference induced an premature AR, subsequently driving a cascade of bioenergetic and structural failures. At the mitochondrial level, NGT induced abnormal mitochondrial permeability transition pore (mPTP) opening and elevated the transcript levels of antioxidant defense genes (e.g., peroxiredoxin-5, PRDX5). These alterations indicate the occurrence of mitochondrial collapse. Concurrently, scanning electron microscopy verified localized plasma membrane wrinkling and pore formation in sperm. In addition, NGT exposure decreased the transcript abundance of cytoskeleton-related genes, including solute carrier family 26 member 6 (SLC26A6), actin (ACT), and tubulin polymerization promoting protein family member 3 (TPPP3). These molecular changes further disrupted membrane phospholipid homeostasis, as represented by altered glycerophospholipid metabolism. At the same time, cumulative cellular stress was associated with decreased transcript abundance of cytoprotective factors (e.g., baculoviral IAP repeat-containing proteins, birc2) and changes in apoptosis-related genes consistent with activation of a caspase-8-mediated apoptotic programme. In conclusion, NGT, as a representative synthetic progestin, exerts reproductive toxicity by interfering with signaling mediators to induce premature AR, which subsequently exhausts metabolic energy and triggers plasma membrane impairment. These findings provide a critical mechanistic basis for the aquatic ecological risk assessment of synthetic progestins.

Animals↗

PubMed2026

From host response to genomic targets: electrochemical biosensing of tuberculosis biomarkers.

Tuberculosis (TB) remains one of the leading causes of death from a single infectious agent worldwide, with timely diagnosis continuing to be a major challenge, particularly in resource-limited settings. Conventional TB diagnostic methods are limited by low sensitivity, long turnaround times, and an inability to reliably differentiate latent from active disease. Biomarker-based diagnostic strategies have therefore gained increasing attention as they offer the potential to improve early detection, disease differentiation, and treatment monitoring. Herein, we examine electrochemical biosensing strategies for TB diagnostics using a biomarker-class-driven framework, covering host-response biomarkers (IFN-γ and TNF-α), pathogen-derived antigens (ESAT6, CFP10, CFP10-ESAT6, MPT64, Ag85, HspX and LpqH), cell-wall signatures and whole-cell markers (LAM and whole cell Mtb), and genomic markers (Mtb DNA and IS6110). Through structured comparison of recognition elements, biointerface designs, signal amplification strategies, electrochemical techniques, matrices, and validation levels, this review identifies the most promising technical approaches for different TB biomarker classes. It further highlights key translational bottlenecks, including limited clinical validation, buffer-based testing, complex multistep amplification, redox-probe dependence, matrix fouling, and insufficient evidence of manufacturability. This review therefore provides practical guidance for developing electrochemical TB biosensors that are analytically sensitive, clinically relevant, and suitable for decentralized diagnostic applications.

Biosensing Techniques↗

PubMed2026

Correlative analysis of endogenous miRNA expression profiles underlying brown planthopper adaptation to resistant rice.

The brown planthopper (Nilaparvata lugens Stål, BPH) is a major insect pest threatening global rice production. However, the molecular mechanisms underlying the adaptation of BPH populations with different virulence levels to resistant rice cultivars remain poorly understood. MicroRNAs (miRNAs), as key post-transcriptional regulators, play critical roles in host adaptation in herbivorous insects. In this study, we analyzed the miRNA expression profiles of a high-virulent population (IR56p) and a low-virulence population (TN1p) after feeding on susceptible (TN1) and resistant (IR56) rice cultivars. Our findings reveal distinct miRNA-mediated regulatory strategies employed by the two populations. The IR56p population showed downregulation of miRNAs including miR-10, miR-124, and miR-316, showing an inverse correlation with increased expression of predicted target genes involved in detoxification (carboxylesterase, UDP-glycosyltransferase) and effector function (calmodulin). In contrast, several miRNAs highly expressed in IR56p, including miR-307, miR-317, and miR-275, were predicted to target rice genes associated with hormone signaling, cell wall biosynthesis, and oxidative homeostasis, suggesting a possible but unproven inter-species regulatory role that requires functional validation. Collectively, these descriptive and correlative findings provide hypothesis generating insights into insect-plant coevolution and identifies candidate molecular targets for future functional validation and RNA interference-based pest management strategies.

Animals↗

PubMed2026

Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals↗

PubMed2026

Exploratory identification and cellular functional characterization of ppiabl as a candidate gene associated with growth traits in Paralichthys olivaceus.

The Japanese flounder (Paralichthys olivaceus) is an important mariculture species. However, the genetic mechanisms underlying its growth traits remain poorly understood. To explore the genetic basis of growth variation, whole-genome resequencing was performed in a cultured cohort of 60 individuals, followed by exploratory genome-wide association analysis and candidate-gene prioritization. The results revealed heritability estimates of 0.40 for body weight and 0.24 for body length, with substantial overlap in associated loci between the two traits. Exploratory association and variant-annotation analyses prioritized ppiabl, which carries a nonconservative missense variant, as a candidate gene for further investigation. Tissue expression analysis showed that ppiabl was highly expressed in muscle tissue. This gene encodes a protein belonging to the conserved peptidyl-prolyl cis-trans isomerase family. In Japanese flounder primary muscle cells, ppiabl knockdown was associated with altered expression of growth-related genes and an increased G1-phase fraction, whereas overexpression produced changes in the opposite direction. In line with this, fast-growing individuals were found to have significantly larger muscle fiber areas than slow-growing ones. These findings suggest that ppiabl may be involved in muscle-related cellular processes associated with growth variation in Japanese flounder, although its contribution to whole-animal growth requires further validation. Overall, this exploratory study prioritizes ppiabl as a candidate gene potentially associated with growth-related cellular processes in Japanese flounder, although validation in larger independent populations and in vivo models is required.

Animals↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗