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PubMed2026

Integrated assessment of biocontrol potential and genome analysis of endophytic Bacillus velezensis MGL-B1 against mango stem-end rot.

Mango stem-end rot is a globally significant postharvest disease that severely threatens the mango industry, primarily caused by Botryosphaeria dothidea. However, information on biocontrol agents targeting this pathogen in mango remains limited. In this study, we isolated and identified a strain of Bacillus velezensis MGL-B1 from mango leaf tissues for the first time, which exhibited broad-spectrum antifungal activity. Both in vitro and in vivo assays demonstrated that MGL-B1 effectively inhibited the growth of B. dothidea, with an in vivo biocontrol efficacy reaching 83.72 ± 5.10%, comparable to that of the commonly used chemical fungicide thiabendazole. Further mechanistic analysis revealed that MGL-B1 acts by directly disrupting the integrity of the pathogen's mycelial cell membrane. In addition, its released volatile organic compounds (VOCs) also displayed significant antifungal activity, with components such as 2-nonanone, 2-nonanol, and phenylethyl alcohol being confirmed to exert antifungal effects in in vitro fumigation assays. qPCR analysis showed that MGL-B1 treatment significantly upregulated the transcriptional levels of genes involved in plant-pathogen interaction, phenylpropanoid biosynthesis, and antioxidant defense pathways in mango fruits, with upregulation folds of 16.32, 37.19, and 75.93, respectively; meanwhile, the expression of browning-related genes such as polyphenol oxidase (PPO) was markedly suppressed. Whole-genome sequencing further revealed 14 biosynthetic gene clusters for antimicrobial compounds, including five unknown gene clusters. Collectively, B. velezensis MGL-B1 represents a promising biocandidate strain with multiple antifungal mechanisms and excellent control efficacy, providing a valuable resource for green and sustainable management of mango diseases.

Mangifera↗
PubMed2026

Transcriptomic changes in the gut mucosa of fasting northern elephant seal pups reveal immune modulation during early microbiome establishment.

Fasting is an integral component of the life-history of many species. Following abrupt weaning, northern elephant seal pups (Mirounga angustirostris) undergo an extended post-weaning fast of approximately 60 days. During this period, enteric bacterial diversity increases, suggesting that host immune regulation may facilitate the establishment of microbial communities. However, the molecular processes occurring within the intestinal mucosa during this transition remain poorly understood. To investigate these mechanisms, we characterized transcriptional changes in the enteric mucosa of male and female northern elephant seal pups sampled at weaning and after one month of fasting. Total RNA isolated from rectal swabs was sequenced and aligned to the Mirounga angustirostris reference genome. Differential gene expression and gene set enrichment analyses were used to identify genes and pathways associated with fasting and sex-specific responses. Fasting was accompanied primarily by transcriptional downregulation, including genes involved in antimicrobial defense, inflammation, protein turnover, and epithelial remodeling. In contrast, several genes associated with B-cell activity and immune recognition were upregulated. Gene Set Enrichment Analysis revealed coordinated activation of immune-regulatory pathways indicating dynamic modulation of intestinal immunity rather than generalized immune suppression. Pronounced sex-specific differences were also observed. Male pups exhibited transcriptional patterns consistent with enhanced immune tolerance, whereas females showed broader immune-pathway activation, including enrichment of pro-inflammatory and stress-response pathways. Several non-coding RNAs also displayed sex-specific changes in expression. Together, these findings suggest that fasting induces transcriptional remodeling of the gut and may contribute to immune regulation during a critical period of microbiome establishment in northern elephant seal pups.

Animals↗
PubMed2026

Genetic regulation of CPEB3-mediated alternative polyadenylation associated with survival of patients with hepatocellular carcinoma.

BACKGROUND: Alternative polyadenylation (APA) is a key post-transcriptional mechanism that regulates gene expression by modulating 3'UTR length, its dysregulation has been implicated in carcinogenesis. How genetic variants influence APA to affect hepatocellular carcinoma (HCC) prognosis remains unclear. METHODS: Prognosis-APA quantitative trait loci (apaQTL) were performed using genotype and APA profiling from TCGA data. A two-stage survival analysis in 848 Chinese and 369 TCGA LIHC patients and functional validation were used to identify prognostic apaQTL in HCC progression. RESULTS: A total of 2,025 and 817 significant APA events were identified in Chinese and TCGA cohort, respectively. Besides, 859 events were associated with poor prognosis in HCC and enriched in RNA splicing / metabolism pathways. We detected 32,034 significant apaQTLs, predominantly enriched in 3'UTRs and RBP-binding regions. CPEB3 was prioritized as a key APA regulator RBP; its low expression correlated with poor patient survival and promoted proliferation, migration, and invasion in HCC cells. Notably, a functional apaQTL variant rs2037547, located in GSK3B and mediated by CPEB3, demonstrated a poor survival of HCC patients in both cohort (pooled HR=1.29, p=0.016). Mechanistically, rs2037547 promoted aberrant APA at proximal poly(A) sites of GSK3B through CPEB3, leading to increased expression of short 3'UTR isoform. This regulatory alteration enhanced HCC cell proliferation, invasion, and migration, and contributed to HCC progression. CONCLUSION: These findings elucidated the distinct role of apaQTL-mediated APA dysregulation in HCC prognosis, providing insights for prognostic stratification and potential targets for personalized therapy in HCC.

RNA-binding proteins↗
PubMed2026

Comprehensive source-risk assessment of organophosphate esters in surface water of the Dianchi Lake Basin, Yunnan, China.

Organophosphate esters (OPEs), widely used as flame retardants and plasticizers, have been increasingly detected in aquatic environments. However, investigations of their distribution in high-altitude plateau lakes remain scarce. Identifying and quantifying the sources and associated risks of OPEs are crucial for subsequent water environment management. In this study, an integrated source-risk analysis approach was employed by combining the Positive Matrix Factorization (PMF) model, the Geodetector (GD) model, and risk quotient (RQ). Analysis of 14 OPEs in surface waters of the Dianchi Lake Basin (DLB) revealed 12 detectable compounds, with total OPEs concentrations (ΣOPEs) ranging from not detected (ND)-64.6 ng/L during the wet season and ND-35.8 ng/L during the dry season. Elevated ΣOPEs were primarily observed at inflow sites in the northern part of the lake and in urban rivers. Source apportionment indicated four major contributing sources: agricultural films containing flame-retardant and plasticizer additives, traffic-related particulate emissions, releases from household and personal care products, and industrial production and applications of flame retardants in plastics, electronics, and related products (the predominant source). The ecological impact caused by OPEs ranges from no risk to low risk, with tris(2-chloroethyl) phosphate emitted from industrial source being the primary driver of potential environmental risk. These findings highlight the necessity of prioritizing industrial sources in future management strategies. Overall, this study provides a methodological framework for source apportionment and risk assessment of OPEs and offers scientific evidence to support environmental management of OPEs in the DLB.

Environmental Monitoring↗
PubMed2026

Emerging Principles in Spatial Functional Genomics.

Spatial transcriptomic and proteomic atlases have enabled mapping of gene programs within intact tissues, but these measurements remain largely descriptive and do not define the mechanisms controlling tissue biology. Pooled CRISPR screening provides scalable causal interrogation of gene function but remains largely confined to dissociated systems that lack spatial context. In vivo spatial functional genomics (SFG) bridges these approaches by integrating genetic perturbations with in situ transcriptomic and proteomic readouts to measure gene function within intact tissue ecosystems. By preserving spatial organization, SFG enables interpretation of perturbations through effects on cell-cell interactions, diffusible signals, multicellular niches, and tissue architecture. Here, we outline key design axes of SFG: perturbation strategy, barcoding strategy, and phenotypic readout. We discuss computational challenges, including spatial autocorrelation, neighborhood dependence, and context-aware null modeling, and highlight how SFG reveals non-cell-autonomous, architecture-dependent mechanisms of gene function, advancing toward predictive models of tissue organization and gene function.

Genomics↗
PubMed2026

The gut microbiota-obesity axis in the pathogenesis and prognosis of breast cancer.

BACKGROUND: Breast cancer (BC) remains a major global health concern, accounting for 11.7% of all cancer cases and ranking as the second leading cause of female cancer-related deaths worldwide. Increasing evidence highlights the interplay between gut microbiota (GM) dysbiosis and obesity-associated metabolic dysfunction in BC progression. This review aims to elucidate the role of GM in obese patients with BC. METHODS: A systematic literature search was conducted in PubMed and Web of Science databases for publications from July 2015 to January 2025. Search terms combined BC, GM, obesity, dysbiosis, immunity, and microbiome. Article selection prioritized studies investigating microbial alterations in BC patients, mechanistic links between obesity and cancer progression, and GM-targeted interventions. Both original studies and authoritative reviews were included, supplemented by manual reference screening. DISCUSSION: Obesity may trigger systemic inflammation, altered adipokine secretion, and disrupted steroid hormone metabolism via gut-derived β-glucuronidase activity, thereby exacerbating BC occurrence and recurrence. GM dysbiosis-driven metabolites such as branched-chain amino acids (BCAAs) and short-chain fatty acids (SCFAs) can activate oncogenic signaling pathways and immunosuppressive myeloid-derived suppressor cells (MDSCs), fostering tumor immune evasion. Conversely, dietary interventions, probiotics, and fecal microbiota transplantation (FMT) can alleviate dysbiosis, strengthen gut barriers, and restore anti-tumor immunity, improving chemotherapy response and reducing recurrence. However, challenges persist in deciphering BC subtype-related microbial signatures and optimizing microbiota-targeted therapies. CONCLUSION: Future longitudinal studies are needed to clarify causal relationships, validate microbial biomarkers, and translate preclinical findings into clinical applications. Addressing the gut-breast axis may offer transformative potential for precision oncology in obesity-driven BC.

Humans↗
PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗
PubMed2026

Optimizing focal vibration therapy for balance and gait: A systematic review.

OBJECTIVE: This systematic review evaluated the efficacy of focal (localized) vibration therapy (FVT) applied to muscles/tendons on balance, gait, and mobility, with a specific focus on defining optimal vibration protocols (frequency, amplitude, dosing) and muscle-targeting strategies to maximize sensorimotor recovery. METHODS: A systematic review was conducted across six databases (CINHAL, Embase, Medline, Web of Science, Scopus, CENTRAL) from January 2000 to May 2025. Studies were included if they involved human participants, applied FVT therapeutically, and reported balance, gait, or mobility outcomes. Data extraction included study characteristics, intervention protocols, and outcomes. Methodological quality was assessed using the PEDro scale. RESULTS: Sixty-two studies (n = 2090 participants) were included. Methodological quality assessment (PEDro scale) indicated 44% of studies met high-quality standards. Biomechanical analysis identified the quadriceps, gastrocnemius/soleus, and plantar muscles as the most effective vibration sites, given their critical roles in gait propulsion and postural stability. The synthesis of protocol data indicated a promising therapeutic window characterized by a vibration frequency of 80-120 Hz (primarily fixed sinusoidal waveforms at a single frequency) and an amplitude of 0.2-0.5 mm (reported only in 12 studies; amplitude was not reported in 23 studies), applied bilaterally for a minimum of 3 sessions per week over 4-12 weeks, which could lead to improved balance and gait performance with benefits sustained for up to 5 months. CONCLUSION: FVT shows potential to improve gait and balance, particularly when targeting lower-extremity muscles with optimized vibration parameters. To advance the field, future research must prioritize the development of standardized protocols and investigate neurophysiological mechanisms to refine FVT as a precision bioengineering solution for mobility deficits.

Humans↗
PubMed2026

Physical therapy for urinary incontinence in older women: A systematic review.

BACKGROUND: Urinary incontinence is highly prevalent among older women, affecting more than one-third of this population and significantly impairing quality of life, independence, and healthcare utilization. Older women often present with complex needs that may require broader rehabilitation strategies. METHODS: This systematic review evaluated randomized controlled trials of physical therapy interventions for urinary incontinence in older women. PubMed, Embase, and Scopus were searched to October 2025. Eligible studies included women ≥60 years and assessed interventions such as Pelvic floor muscle training (PFMT), bladder training, Yoga, Pilates, general resistance training, electrical stimulation, or multimodal programs. Methodological quality was appraised using the PEDro scale, and random-effects meta-analysis was performed where appropriate. RESULTS: Twenty studies involving 2002 women across 13 countries were included. Eleven trials were rated as good quality and nine as fair. Meta-analysis demonstrated that PFMT significantly reduced urinary incontinence severity compared with usual care (SMD = -1.27, 95% CI: -2.18 to -0.36, p = 0.006). Multimodal programs combining PFMT with mobility, strength, or fall-prevention training also showed significant benefits (SMD = -0.98, 95% CI: -1.60 to -0.36, p = 0.002). Comparative studies indicated that PFMT was similarly effective to Yoga and Pilates, while adjuncts such as general resistance training or tibial nerve stimulation provided additional improvements. CONCLUSION: Physical therapy interventions, particularly PFMT and multimodal programs, are effective in reducing urinary incontinence severity and improving functional outcomes among older women. These findings support prioritizing physical therapy as an important management strategy, with multimodal approaches offering added value for enhancing functional independence and fall prevention.

Humans↗
PubMed2026

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans↗
PubMed2026

Vaccine preferences and their role for vaccine confidence and uptake: a meta-ethnography.

Vaccine confidence and uptake are influenced by individuals' preferences regarding vaccine composition, quality, or administration pathways. However, literature synthesizing available qualitative insights into individuals' vaccine preferences remains limited. We therefore conducted a meta-ethnographic systematic review of the qualitative literature on vaccine preferences to identify opportunities for enhancing vaccine confidence and uptake. We implemented a comprehensive search strategy and screened 5,528 studies across seven research databases published between 2001 and 2023. We identified and synthesized 97 qualitative articles to delineate factors influencing consumers' vaccine preferences. Our findings revealed four primary domains shaping individuals' vaccine preferences: Product, Place, Price, and Promotion. First, individuals' preferences for vaccines often hinge on perceived quality and safety of the product itself, which can, for example, be associated with vaccine brand or origin, especially in the case of novel vaccines. Second, people prioritize convenience in terms of vaccination sites and delivery methods (wanting vaccinations offered at their doorstep or in local peripheral clinics); evidence regarding preferred groups to administer the vaccines was mixed. Third, the price of vaccines and the secondary costs associated with vaccination played a role in uptake considerations. Finally, both the sources of information (such as healthcare workers, community volunteers, and religious authorities) and the methods of promoting vaccine information (including face-to-face consultations during clinic visits and the distribution of leaflets or banners), emerged as crucial factors shaping decision-making processes. Overall findings highlight the importance of addressing multifaceted preferences to enhance vaccine confidence and uptake. By understanding individuals' vaccine preferences, strategic recommendations can be developed to optimize vaccination programs and ensure acceptability and utilization.

Humans↗
PubMed2026

Meta-analysis of growth and inactivation kinetics of Legionella.

Quantitative risk assessments intended to inform evidence-based water management plans and public health targets for Legionella in engineered water systems are constrained by fragmented and heterogeneous growth and inactivation kinetics. We conducted a meta-analysis of 25 growth and 39 thermal- and chemical-inactivation studies, fitting microbial persistence models to harmonize parameters. Nonlinear models outperformed first-order formulations, indicating that lag phases and resistant or protected subpopulations are central to Legionella persistence. Random forest analysis identified environmental and methodological drivers of variability based on 226 growth rates and reduction times for thermal (209) and chemical (135) inactivation. Growth was primarily governed by temperature, nutrient availability, and compatible Legionella-host pairings; thermal inactivation by quantification method, temperature, and turbidity; and chemical inactivation by inoculum size, disinfectant type, concentration, and host-associations. Accordingly, temperature-dependent growth parameters and exposure metrics for heat, free-chlorine, and monochloramine, expressed as TT (Temperature×time) and CT (Concentration×time), were derived as condition-specific inputs for predictive models. Growth optima around 37-40 °C, together with lag-time estimates, indicate that hot-water temperature setbacks and energy-saving practices may favor Legionella proliferation under repeated or prolonged lukewarm exposure. Culture- and viability-based TT differences highlight the need to consider viable‑but-non-culturable persistence in monitoring programs. CT comparisons suggest monochloramine may be advantageous because of its lower apparent sensitivity to host-associated protection. Although limited by restricted experimental conditions, the findings show that predictive models should account for microbial ecology, water matrix effects, and quantification endpoints. Future kinetic studies should prioritize realistic multi-host systems, strain pre-adaptation, complementary viability measurements, and standardized protocols and reporting to ensure reproducibility and enable robust system-level predictive modeling.

Legionella↗
PubMed2026

Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.

OBJECTIVE: To systematically review and evaluate published risk prediction models for perioperative blood transfusion in patients undergoing total hip or knee arthroplasty (THA/TKA). METHODS: We systematically searched PubMed, Web of Science, the Cochrane Library, and Embase from inception to May 31, 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability using the Prediction model Risk Of Bias Assessment Tool (PROBAST). The area under the receiver operating characteristic curve (AUC) values were pooled via a meta-analysis using Stata 18.0. RESULTS: d Fourteen studies containing 36 prediction models were included. The incidence of blood transfusion among THA/TKA patients ranged from 3.2% to 30.8%. Preoperative hemoglobin (Hb) level, tranexamic acid (TXA) use, operative duration, intraoperative blood loss, and age were the most frequently incorporated predictors. Model sensitivity ranged from 58% to 94.5%, and specificity ranged from 71.3% to 94%. Meta-analysis showed that the pooled AUC value of the 13 validated models was 0.87 (95% CI: 0.85-0.90), suggesting good discriminatory performance. All models were rated as having a high risk of bias. The applicability of four studies was rated as unclear. CONCLUSION: Although the included studies demonstrated promising discriminative ability of prediction models for blood transfusion in THA/TKA, all were assessed as having a high risk of bias using the PROBAST tool. Therefore, future research should prioritize the development of models with larger sample sizes, rigorous study designs, and multicenter external validation.

Humans↗
PubMed2026

A Pilot Study: Developing a Lactating Dairy Goat Model to Study Staphylococcus aureus Mastitis in Women.

INTRODUCTION: Lactational mastitis is common in lactating women, with Staphylococcus aureus as the most commonly isolated agent associated with infectious lactational mastitis. Currently, there are no evidence-based guidelines for antimicrobial treatment due to barriers in obtaining pharmacokinetic data from lactating women. To overcome this barrier, a suitable large animal model is needed. Goats are an ideal translational model for human mastitis due to their anatomical and physiological similarity to humans. The objective of this pilot study was to assess if goats would develop clinical mastitis following intramammary inoculation with a clinical human isolate of S. aureus with the goal of establishing an alternative in vivo model for future research. The hypothesis was that the infected mammary gland half would show similar clinical signs to women with mastitis and demonstrate a similar local immune response when compared to the control mammary gland half. METHODS: One half of the mammary gland of two healthy lactating does was inoculated with a clinical human isolate of S. aureus. The other half of the mammary gland was sham inoculated with sterile buffered saline. Physical examinations, mammary gland assessments, and sterile milk samples were collected every 12 hours post inoculation. At 96 hours post inoculation, the goats were euthanized, and the mammary glands were examined for pathological changes. RESULTS: Goats did not develop systemic signs of disease following inoculation. Focal infected mammary gland changes included warmth, swelling, redness, discoloration, and reduced milk production; the other mammary gland half remained normal throughout the study period. S. aureus was enumerated from only the infected mammary gland half. The microscopic findings of the infected half showed neutrophilic inflammation and cell necrosis consistent with acute mastitis. DISCUSSION: This pilot study demonstrated lactating does can develop clinical signs like those observed in women. Goats have the potential to be a promising animal model to study infectious lactational mastitis.

Animals↗
PubMed2026

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans↗
PubMed2026

The potential of clustering methods for pre-test triage in sleep medicine: A systematic review.

Sleep disorders exhibit substantial heterogeneity, and traditional classifications may not fully capture clinically relevant subtypes. Clustering techniques can identify patient subgroups that improve phenotypic characterization and may support personalized management. This systematic review evaluated the application of clustering in sleep medicine, with particular focus on its potential use as a pre-test triage tool prior to formal sleep testing. PubMed/MEDLINE, Embase, Web of Science, and Scopus were searched to February 2025. Eligible studies applied clustering to classify sleep disorders in adults. Two reviewers independently conducted screening, data extraction, and risk-of-bias assessment using QUADAS-2. The protocol was registered on PROSPERO. Fifty-one studies (1983-2025) were included, predominantly focused on obstructive sleep apnea (OSA) (n = 38, 74%). Hierarchical clustering (n = 20) and K-means clustering (n = 14) were the most frequently used techniques. Internal validation was reported in only 18% of studies, and external validation was reported in only 1 study. Seven studies relied exclusively on baseline clinical, demographic, or questionnaire data, representing pre-test scenarios, whereas most incorporated polysomnography-derived variables, limiting their applicability to early clinical stratification. Hierarchical clustering was the most commonly applied method; however, the overall lack of validation limits confidence in the robustness and clinical applicability of identified phenotypes. The potential role of clustering as a pre-test triage strategy remains largely unexplored, as most studies focused on post-diagnostic phenotyping and were affected by incorporation bias. Future research should prioritize pre-test clinical variables, rigorously validate internally and externally, and adopt standardized methodological and reporting practices to facilitate clinical translation.

Humans↗
PubMed2026

Comparative phylogenomics and transcriptional regulatory networks of AQPs, HSPs, and LEA proteins in salt-stressed Portulaca oleracea.

Soil salinization severely threatens global food security, necessitating systematic investigations of halophytes like Portulaca oleracea to decode the molecular mechanisms of environmental resilience. Utilizing an integrated framework of deep learning-based genome annotation (58,817 predicted genes; 96.5% BUSCO completeness), multi-tissue RNA-Seq, phylogenomics, and gene regulatory network (GRN) inference, the synergistic orchestration of 78 aquaporins (AQPs), 525 heat shock proteins (HSPs), and 119 late embryogenesis abundant (LEA) proteins was elucidated. The active transcriptome, encompassing 39,065 expressed loci, revealed a systemic growth-defense trade-off. Tissues displayed distinct adaptive mechanisms: leaves modulated intracellular water balance via specialized AQPs, whereas adult roots maintained proteostasis through robust HSP20/HSP70 induction. Phylogenomic clustering across 154 species demonstrated that salinity tolerance constitutes an evolutionary mosaic, identifying 81 halophyte-exclusive orthogroups and 1129 species-specific clusters. Comparative topology across six independent GRNs (4.2M-5.3 M edges) unmasked a highly modular transcriptional reprogramming strategy governed by a core apparatus of 22 stress-exclusive regulators, with functional enrichment heavily prioritizing protein dimerization and chromatin remodeling. Theoretically, the distinct convergence of Trihelix transcription factors with guard cell differentiation pathways offers a candidate transcriptomic framework to explain the plant's characteristic C4-CAM photosynthetic plasticity under severe osmotic pressure. Practically, these evolutionary blueprints and specific master switches transcend single-gene transgenic limitations. Utilizing these root-sustained and stress-inducible targets under localized promoters provides a naturally optimized, network-level precision engineering roadmap to transfer robust, compartmentalized halotolerance to sensitive glycophytic crops.

Gene Regulatory Networks↗
PubMed2026

From buffalo to human: Klebsiella pneumoniae in high-somatic cell count milk as an overlooked link in the one health chain.

High somatic cell count (SCC) is a critical indicator of udder health and milk quality in buffalo milk production. However, in many low-income regions, SCC monitoring is often underemphasized, allowing a proportion of high-SCC buffalo milk to enter the food chain and potentially compromising food safety and public health. Klebsiella pneumoniae (K. pneumoniae) is a common zoonotic pathogen found in high-SCC milk, yet systematic investigations into the prevalence and characteristics in high-SCC buffalo milk remain limited. In this study, 23 K. pneumoniae strains were screened out from 460 bacterial isolates obtained from high-SCC buffalo milk samples from Guangxi, China, with an isolation rate of 5.0%. These isolates were comprehensively characterized using whole-genome sequencing and comparative genomic analyses. The results revealed that 78.26% (18/23) of the isolates shared high genomic similarity with the human reference strain ATCC 13883, and the ST37 clone exhibited a pronounced potential of cross-species transmission. All isolates harbored core adhesion factors and intrinsic resistance genes. Notably, several strains displayed high-risk features: strain 419 carried the K1 capsular serotype, strain 326 possessed a complete yersiniabactin synthesis gene cluster, and strain 320 exhibited a multidrug-resistant phenotype. Phenotypic assays further demonstrated a positive correlation between biofilm formation capacity and virulence in Galleria mellonella. Metabolic pathway enrichment analyses suggested that K. pneumoniae has undergone substantial adaptation to the nutrient-rich buffalo milk environment. Collectively, these findings confirm that raw high-SCC buffalo milk serves as a significant reservoir for high-risk zoonotic K. pneumoniae. While industrial thermal processing effectively eliminates viable pathogens, the resilient antimicrobial resistance determinants within these isolates pose a persistent risk of horizontal gene dissemination along the food chain, providing critical evidence for enhancing pre-processing milk quality regulations within a One Health framework.

Animals↗