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PubMed2026

Indigenous and local knowledge inclusion in forest fauna research: A systematic review in the tropics.

Indigenous and Local Knowledge (ILK) is an expression of biocultural diversity and is vital for inclusive and sustainable forest management and epistemic justice. We examine how researchers studying tropical forest fauna engage with ILK and the Indigenous Peoples and Local Communities (IPLC) who are holders of this knowledge. We conducted a systematic review of 62 articles that focus on tropical forest fauna and ILK. We used a category-based quantitative and qualitative content analysis on the types of forest fauna studied and how research engages with, defines and represents ILK. We also evaluated the varied forms of inclusion of IPLC in the research. We find that less than half of the reviewed studies (25) explicitly define ILK, and only four studies reported including IPLC in the decision-making processes. Our findings reveal that science has not fully acknowledged and understood the depth of ILK and we suggest ways to address this in future research.

Forests↗

PubMed2026

Integrative machine learning and transcriptomic analysis reveals molecular mechanisms underlying low survival rate in larval Chinese Bahaba (Bahaba taipingensis).

Chinese Bahaba (Bahaba taipingensis) is a Class I protected marine fish endemic to China. Low larvae survival during artificial breeding severely hinder population recovery. To investigate the molecular mechanism of high mortality in larval fish, this study performed RNA-seq on liver from naturally deceased (ND) and mass-dead (MD) individuals, combined with least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithms to screen for core signature genes. A total of 873 differentially expressed genes (DEGs) were identified, including 112 upregulated and 761 downregulated genes. GO and KEGG enrichment analyses revealed significant enrichment in amino acid metabolism disorders, one‑carbon folate pool impairment, PPAR signaling abnormalities, ECM-receptor interaction, focal adhesion pathway, indicating widespread metabolic suppression accompanied by extracellular matrix remodeling and signaling disturbances in the livers of MD fish. MAD pre-filtering combined with dual machine learning algorithms yielded 18 robust core signature genes, among which SLC38A4, MMP1, FADD, FKBP5, and APOB were consistently identified as high-frequency core genes by both algorithms. SLC38A4 exhibited the highest importance score in the RF model and was significantly downregulated, making it the primary molecule distinguishing ND from MD phenotypes. ROC curve analysis showed that both models achieved an AUC of 1.000 (95% CI lower bound: 0.610), confirming the precise discriminatory ability of the core genes. GSEA further demonstrated significant enrichment of this core gene set in ND samples. This study provides the first systematic elucidation of the molecular mechanisms underlying liver dysfunction in low survival rate B. taipingensis, characterized by amino acid transport impairment, metabolic reprogramming, and structural remodeling, offering theoretical foundations for health assessment, early mortality risk warning, and artificial breeding conservation of this species.

Animals↗

PubMed2026

Can't see the forest for the trees: The influence of marker type on inferred phylogenetic relationships in a cosmopolitan bat genus.

Fine-resolution information on species relationships and biological diversity is critically needed to guide conservation efforts amidst rapid environmental changes. Systematics, which forms the foundation of this knowledge, has been revolutionized by phylogenomics, utilizing genome-scale datasets. However, the use of diverse marker types, non-comparable taxon sampling, and outgroup selection can lead to conflicting phylogenetic hypotheses. These inconsistencies complicate study comparisons and hinder our ability to assess marker-specific impacts on phylogenetic resolution. The phylogenetic reconstruction of the bat genus Myotis, encompassing over 140 species and characterized by a rapid radiation in the last 20 million years, has been particularly influenced by these challenges. Achieving phylogenetic resolution in Myotis is particularly complex due to subtle interspecific differences in both morphological and molecular traits. Mitochondrial and nuclear markers often produce discordant trees, influenced by hybridization, introgression, and methodological variations. In this study, we employed a consistent taxonomic sample set of 44 Myotis taxa to evaluate the impact of five different genetic marker types on phylogenetic reconstruction. We observed significant discordance between topologies derived from conserved nuclear and mitochondrial markers and found that transposable elements were inadequate for resolving relationships across the entire genus. Our results also clarify the placement of previously problematic taxa within the genus. These findings emphasize the importance of aligning genetic marker choice with specific phylogenetic questions and highlight the influence of taxonomic and methodological variation on phylogenomic outcomes. This work provides a framework for improving phylogenetic inference in rapidly radiating groups and enhances our understanding of evolutionary history in Myotis.

Animals↗

PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Wildlife forensic DNA evidence links a suspected vehicle to a fatal lowland tapir (Tapirus terrestris) collision in Misiones, Argentina.

Vehicle collisions are recognized as a major driver of biodiversity loss, particularly in road-dense landscapes, exceeding the impact of invasive species and wildlife trafficking. For large-bodied, slow-reproducing, and low-abundance species, such as the lowland tapir (Tapirus terrestris), this threat can have major impacts. Here, we present a wildlife forensic investigation in Misiones, Argentina, involving a tapir, a species afforded the highest level of legal protection as a Provincial Natural Monument. The fatal hit-by-vehicle (HBV) incident occurred in northern Misiones on 31 March 2019 along Provincial Route 19, in a portion that bisects Parque Provincial Urugua-í, with the driver involved in the collision leaving the scene. The suspect was later located and claimed that the damage to the vehicle resulted from a collision with a horse (Equus caballus) rather than a tapir. To legally resolve the incident, DNA (hair and blood) recovered from the suspected vehicle's bumper (evidence) was compared with tissue samples from the tapir carcass (reference). Genetic confirmation of species identity used a 110-bp region of the mitochondrial cytochrome b gene, and individual identity was assessed using 12 species-specific microsatellite loci. These analyses confirmed that all evidence matched the tapir carcass at both species and individual levels, strongly supporting the association between the suspected vehicle and the HBV tapir, and refuting the alternative explanation proposed by the driver. This case demonstrates the value of using wildlife forensic genetics to reconstruct wildlife-vehicle collisions, supporting environmental law enforcement, and strengthening conservation efforts in the Atlantic Forest of Misiones, Argentina.

Animals↗

PubMed2026

Effects of transcutaneous electrical acupoint stimulation versus acupressure on the trajectories of multidimensional adverse reactions to chemotherapy in breast cancer patients: a secondary analysis of a randomized controlled trial.

BACKGROUND: Chemotherapy for breast cancer often induces multidimensional adverse reactions such as nausea and vomiting, anxiety, depression, and sleep disturbances. These symptoms are interrelated and may evolve dynamically, impacting patients' treatment outcomes and quality of life. As non-pharmacological interventions, transcutaneous electrical acupoint stimulation (TEAS) and self-acupressure (SA) have shown potential in alleviating symptoms. However, their long-term effects on the joint developmental trajectories of these multidimensional symptoms (nausea and vomiting, anxiety, depression, and sleep disturbances) remain unclear. OBJECTIVE: This study aimed to identify potential trajectory class of multidimensional adverse reactions in breast cancer patients undergoing chemotherapy and to explore the differential effects of TEAS and SA on different trajectory subgroups. METHODS: This was a secondary analysis of a randomized controlled trial. A total of 189 breast cancer patients receiving chemotherapy were included. The Group-Based Multi-Trajectory Model (GBMTM) was employed to identify joint developmental trajectories of acute/delayed chemotherapy-induced nausea and vomiting (CINV), anxiety, depression, and sleep quality during chemotherapy. Subsequently, causal forest was used to analyze the average treatment effects (ATE) of TEAS (vs. control group) and SA (vs. control group) on patients' symptom trajectory. RESULTS: Multidimensional adverse reactions were classified into two heterogeneous trajectories: a "High Symptom Burden-Persistent (HSBP)" type (n = 101) and a "Low Symptom Burden-Relieving (LSBR)" type (n = 88). The persistent high incidence of acute CINV contrasted sharply with the comprehensive relief of other symptoms in the latter group. Causal forest suggested that both TEAS and SA significantly increased the probability of patients being classified into the "LSBR" trajectory. The ATE was 0.147 (95% CI: 0.143, 0.151) for TEAS, slightly lower (P < 0.05) than 0.176 (95% CI: 0.162, 0.190) for SA.  CONCLUSION: Multidimensional adverse reactions in breast cancer patients undergoing chemotherapy exhibit heterogeneity in their trajectories. Both TEAS and SA were associated with a higher probability of patients being classified into a more favorable symptom trajectory-LSBR. The multidimensional trajectory identification with treatment effect estimation may serve as a useful analytical strategy for future longitudinal research in cancer chemotherapy-induced adverse reactions symptom management. CLINICAL TRIAL REGISTRATION: ChiCTR2300077667 (Chinese Clinical Trial Registry, https://www.chictr.org.cn/ ), Registered 15 November 2023.

Humans↗

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

Effectiveness of digital health technologies for post-discharge follow-up and management in older adults: a systematic review.

Older adults (≥65 years) are a rapidly growing population that are experiencing a higher number of hospitalisation admissions, longer hospital stays, and greater hospitalisation-related costs than younger adults. There is an important gap in post-discharge care for older adults, and digital technologies, such as video visits, mobile health apps, and remote patient monitoring, may support follow-up and management after hospital discharge. This systematic review examined the effectiveness, feasibility, acceptability, and impact (ie, effects on rehospitalisation, quality of life, mental health, adherence, and patient satisfaction) of technology-based interventions used for the follow-up and management of older adults after hospital discharge. MEDLINE (via PubMed), Scopus, and Web of Science were searched from database inception to January, 2026. The search identified 1972 records, of which 46 studies met the inclusion criteria: older adult populations (aged ≥65 years), a technology-based intervention, post-discharge follow-up or management, and empirical data. Overall, digital post-discharge interventions were reported to be feasible, with good engagement, adherence, compliance, and retention; low dropout rates; and positive patient satisfaction. However, mixed findings were reported regarding rehospitalisation rates and mental health outcomes for virtual care compared with those for traditional care. Digital health technologies might represent a promising step towards improving post-discharge health care and continuity of care for older adults.

Journal Article↗

PubMed2026

Comparison of paralog identification methods and their impact on species tree topologies in target capture phylogenomics within the Sindora clade (Detarioideae: Leguminosae).

Target capture is a common method of generating high throughput DNA sequencing data for phylogenetic reconstruction of species relationships, for which single copy genes are usually most informative. However, a pervasive problem with target capture is that putatively single copy genes may in fact be paralogs resulting from gene duplication, which are problematic for phylogenetic inference because their evolutionary history may differ from the divergence history of species. Here, we use as a case study a target enrichment dataset of 88 species of Detarioideae (Leguminosae) with a focus on the Sindora clade to examine approaches for handling paralogs, including the built-in paralog handling functions in HybPiper and CAPTUS, plus subsequent steps using Putative Paralog Detection and the tree-based Yang & Smith orthology inference approach. We compare the paralogs flagged using these methods and verify their performance with BLAST mapping against a reference genome sequence of Sindora glabra, and then subsequently compare the species tree topologies produced across these methods. Our comparisons of paralogs flagged across the Sindora clade show that the Putative Paralog Detection pipeline was the most accurate in identifying paralogs in terms of its similarity to the BLAST mapping, followed by the built-in paralog identification function of CAPTUS. However, the results we recovered for the Detarioideae subfamily suggest that the largest differences in species tree topology resulted from the use of paralog-filtered alignments (such as with the Putative Paralog Detection pipeline and the Yang & Smith orthology inference approaches) rather than just by removing the sequences of identified paralogous genes. This was the true for HybPiper-assembled datasets but was not seen in CAPTUS-assembled datasets. In all comparisons, the topological differences caused by different paralog handling methods tended to be confined to clades where processes such as hybridisation and introgression are prevalent. Our study provides a roadmap to establish the best approach to identify, eliminate or separate paralogs in the absence of a chromosomally contiguous reference genome for a study group, and highlights the importance of careful data inspection and processing in addition to understanding the extent of paralogy and paralog characteristics (e.g. sequence divergence between copies) for their study group.

Phylogeny↗

PubMed2026

Efficacy of the NMIC-150 system in identifying extended-spectrum beta-lactamases in clinical isolates.

Extended-spectrum beta-lactamases (ESBLs) are significant contributors to the growing global crisis of antimicrobial resistance. This study evaluated the performance of the NMIC-150 System for susceptibility testing of third-generation cephalosporins (3GCs) and assessed whether ceftazidime-avibactam and aztreonam-avibactam could identify ESBL-producing carbapenem-resistant Enterobacterales (CREs). A total of 278 non-duplicate clinical isolates (Klebsiella pneumoniae, E. coli, and Proteus mirabilis) were analyzed. Antimicrobial susceptibility was determined using reference broth microdilution (BMD) and the NMIC-150 System. ESBL production was defined as an ≥eight-fold reduction in the minimum inhibitory concentration (MIC) of 3GCs in the presence of clavulanic acid, according to CLSI criteria. Whole-genome sequencing was performed to characterize ESBL and carbapenemase genes among 3GC-resistant isolates. A Random Forest model was used to predict ESBL-producing isolates based on MIC values. The NMIC-150 System demonstrated over 90% categorical and essential agreement with BMD for ceftazidime and ceftriaxone, along with robust predictive performance via Random Forest analysis. These findings suggest that the NMIC-150 System is a reliable platform for 3GC susceptibility testing and that an ≥eight-fold MIC reduction with ceftazidime-avibactam or aztreonam-avibactam may serve as a phenotypic indicator of ESBL production in CRE isolates. In conclusion, the NMIC-150 System shows potential for routine antimicrobial resistance surveillance and may facilitate the rapid identification of ESBL-producing CREs in clinical settings.

Microbial Sensitivity Tests↗

PubMed2026

Endovascular intervention for acute stroke in the very late window: A meta-analysis of 90-day mRS and procedural outcomes.

OBJECTIVE: This study investigates the effectiveness of Endovascular Therapy (EVT) in very late windows (beyond 24 h) for patients with ischemic stroke, focusing on 90-day mortality, functional outcomes, and procedural success. METHODS: This systematic review and meta-analysis followed Cochrane and PRISMA guidelines to evaluate the effectiveness of endovascular therapy (EVT) in acute ischemic stroke patients. Literature from PubMed, Embase, Web of Science, and Scopus was reviewed using specific keywords. Studies included those after 2000 with outcomes such as functional independence (mRS 0-2), mortality, TICI scores, and neurological improvements. RESULTS: The data findings reveal mixed outcomes across studies on EVT beyond 24 h. Sarraj et al. (2023) found a significant improvement in functional independence with EVT (38% vs. 10%, p < 0.05), despite an increased risk of sICH. The forest plots for mRS and procedural outcomes show some significant results, such as Sarraj (2023) for mortality (p < 0.05), but most other studies indicated no statistically significant differences (p > 0.05). CONCLUSION: The study has concluded that Endovascular Therapy (EVT) beyond 24 h can improve functional outcomes and reduce mortality in selected ischemic stroke patients, though patient selection remains crucial.

Humans↗

PubMed2026

Association between seminal and serum iron parameters and male infertility: a systematic review and meta-analysis.

BACKGROUND: Iron is an essential trace element for normal spermatogenesis, yet excessive iron accumulation may impair male fertility. Preliminary studies imply a link between elevated iron levels and male infertility, but evidence remains limited without systematic quantitative synthesis. This metaanalysis assessed the association between iron concentrations and male infertility. METHODS: We systematically searched PubMed, CBM, CNKI and Cochrane Library. RevMan, Stata and R were used for data analysis. Randomeffects models pooled effect sizes, with forest and funnel plots generated to evaluate seminal and serum iron levels in male infertility. RESULTS: After screening studies published up to April 2025, a total of ten eligible articles involving 985 participants were finally included in this meta-analysis. Pooled results revealed that seminal and serum iron concentrations were notably higher in infertile males compared with fertile controls. Specifically, infertile men presented higher seminal iron levels (SMD = 0.44, 95% CI: 0.12-0.76, P < 0.05), as well as elevated serum iron levels (SMD = 3.77, 95% CI: 1.68-5.87, P < 0.05). The present results suggest that increased seminal and serum iron concentrations may be potentially correlated with male infertility risk.

Humans↗

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 weeks) and late laying (50 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 β-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

Dynamic evolution of chaperone-mediated autophagy is associated with tumor microenvironment remodeling and prognostic stratification in lung adenocarcinoma: insights from single-cell transcriptomics, ensemble machine learning, and experimental validation.

BACKGROUND: Lung adenocarcinoma (LUAD) shows prognostic heterogeneity, and tumor-node-metastasis (TNM) staging is limited for individualized management. Chaperone-mediated autophagy (CMA) maintains proteostasis, but its role during adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA)-invasive adenocarcinoma (IAC) progression remains unclear. METHODS: Single-cell RNA sequencing (scRNA-seq) data from GSE189357 and bulk transcriptomes from The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus (GEO) cohorts were integrated. CMA activity, cell-cell communication, weighted gene co-expression network analysis (WGCNA), tumor-normal differential expression, machine-learning survival modeling, tumor microenvironment (TME) features, drug sensitivity, and EPC1 function were analyzed. RESULTS: CMA-high tumor epithelial cells increased from AIS (58.1%) to MIA (65.7%) but declined in IAC (44.4%; p < 0.001). CMA-low cells preferentially received fibroblast-derived extracellular matrix cues. A CMA-negatively correlated module identified 69 core genes. Random survival forest (RSF) performed best among 117 machine-learning combinations (mean concordance index > 0.873). High-risk patients had worse survival across cohorts, and the risk score was independently associated with overall survival (hazard ratio = 16.013, 95% confidence interval: 9.579-26.768, p < 0.001). High-risk tumors showed proliferative activation and M0 macrophage enrichment, whereas low-risk tumors showed stronger immune-related signaling. EPC1 overexpression suppressed malignant phenotypes in A549 cells. CONCLUSION: CMA dynamics are associated with stromal and immune remodeling during LUAD progression. A CMA-based model provides robust prognostic stratification and may offer a basis for future TME-guided studies.

Chaperone-mediated autophagy↗

PubMed2026

Genome-wide SNP data support species boundaries in sympatric Polylepis Ruiz & Pav. (Rosaceae) species from Bolivia and Ecuador.

Species delimitation in the South American genus Polylepis is notoriously challenging due to high morphological similarity and phenotypic plasticity, likely driven by hybridization and gene flow. Previous phylogenetic studies suggested that genetic structure aligns more strongly with geography than with taxonomy, questioning existing species concepts and hampering conservation efforts. We used double-digest RAD sequencing (ddRADseq) to generate genome-wide SNP data for 11 Polylepis species sampled across multiple localities in Bolivia and Ecuador. Population genetic analyses, phylogenetic inference, and network approaches were combined to assess whether genetic structure aligns more closely with taxonomy or geography. Morphologically defined species formed largely cohesive genetic lineages across regions, with species identity explaining substantially more genetic variation than locality. While localized admixture and reticulation were detected among closely related taxa, widespread species showed strong genetic cohesion and clear separation from congeners. Our results indicate that the sampled Polylepis species from Bolivia and Ecuador maintain distinct genetic identities despite localized signals consistent with gene flow. This genome-wide support for current taxonomy highlights Polylepis as a valuable model for studying speciation under gene flow and indicates that multiple geographic sampling will be essential in reconstructing a robust phylogeny of the genus, with important implications for conservation planning in Andean montane forests.

Bolivia↗

PubMed2026

Dynamic lysine acetylation and succinylation of platelet proteins regulates platelet storage lesion: mechanistic insights from multi-omics.

OBJECTIVES: Platelet storage lesion (PSL) severely impairs platelet function during storage, presenting a major hurdle in transfusion medicine; however, the dynamic interplay between global proteomic changes and post-translational modifications (PTMs) underlying these functional deteriorations remains insufficiently characterized. Here, we report the first comprehensive multi-omics analysis integrating global proteomics, acetylomics, and succinylomics to dissect the molecular dynamics during platelet storage. METHODS: We performed quantification of global proteomics, acetylome and succinylome based on TMT-labeled LC-MS/MS analysis, combined with antibody-affinity enrichment and purification. Dynamic molecular changes and functional transformation of platelet were also characterized under proper conditions stored for 1, 3, 5, 7 days, respectively. RESULTS: We systematically characterized 3,609 proteins, 1,308 acetylation sites, and 1,947 succinylation sites across multiple storage time points (D1, D3, D5, D7). We distinct temporal patterns of post-translational modifications, with succinylation showing more extensive coverage than acetylation in platelets. Pathway enrichment analysis revealed extensive metabolic reprogramming involving complement activation, energy metabolism, and cellular detoxification processes. The identification of specific motif patterns provided mechanistic insights into the functional specificity of these modifications. Random forest machine learning identified 20 core regulatory proteins representing critical nodes in PSL development. Furthermore, we employed real - time quantitative polymerase chain reaction (RT - QPCR) to measure the expression levels of key genes related to platelet function and PTM - associated pathways. CONCLUSION: By mapping the interplay between proteomic abundance shifts and PTM dynamics, this study provides a multidimensional understanding of PSL, establishing a foundational framework for optimizing storage protocols and enhancing transfusion safety.

Blood Platelets↗

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↗