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PubMed2026

Dual Transcranial Direct Current Stimulation Modulates Hierarchical Functional Network Organization in Post-Stroke Cognitive Impairment: A Randomized Controlled Trial.

OBJECTIVE: To evaluate the clinical efficacy of dual transcranial direct current stimulation (tDCS) in patients with post-stroke cognitive impairment (PSCI) and to explore the effects on the hierarchical organization of functional brain networks, ranging from regional synchronization to inter-regional connectivity and global network topology. METHODS: In this randomized, double-blind, sham-controlled trial, 74 PSCI patients received conventional therapy alongside either active dual-tDCS (n = 38) or sham stimulation (n = 36). Active tDCS targeted the dorsolateral prefrontal cortex (DLPFC) via anodal-left/cathodal-right nodes (2.0 mA, 20 min/day, 20 sessions). The primary outcome was the Montreal Cognitive Assessment (MoCA). Secondary outcomes included the Mini-Mental Status Examination (MMSE), Stroop Test (ST), Trail Making Test (TMT), Wechsler Memory Scale (WMS), and Barthel Index (BI). A subgroup of 36 participants (18 per group) underwent resting-state functional magnetic resonance imaging (rs-fMRI) to analyze regional homogeneity (ReHo), functional connectivity (FC), and network topology. Partial correlations assessed the association between neuroimaging alterations and clinical improvements. RESULTS: The tDCS group showed significantly greater improvements in MoCA scores (tDCS: 5.74 ± 2.76 vs. sham: 2.69 ± 2.69; t = 4.799, p < 0.001) as well as in attention and memory domains compared to the sham group. The rs-fMRI changes included increased ReHo in the right middle temporal gyrus (MTG) and the left inferior frontal gyrus (IFG), and reduced FC between the right MTG-left superior frontal gyrus and left IFG-cerebellum (p < 0.05, FWE-corrected). Additionally, small-worldness and global efficiency increased (p < 0.05) with these alterations correlating with clinical recovery. Adverse events were rare and self-limiting. CONCLUSION: Dual-tDCS over bilateral DLPFC safely improves cognitive recovery in PSCI. These clinical gains are associated with rs-fMRI alterations, specifically in regional synchronization, inter-regional connectivity, and global topology, which suggest a potential biomarker for monitoring tDCS efficacy, offering a rationale for precision neuromodulation in stroke rehabilitation.

Humans↗

PubMed2026

Temporal shifts in gyrA mutation types and sublineage replacement in ST11 Salmonella enterica serovar Enteritidis over a decade (2014-2023): A genomic epidemiological study in Guangxi, China.

The overuse or abuse of antibiotics drives the global health threat of antimicrobial resistance. Although bans on certain veterinary antibiotics, such as colistin, have proven effective, the impact of fluoroquinolone stewardship on the evolution of the foodborne pathogen Salmonella enterica serovar Enteritidis (S. Enteritidis) remains unclear. Here, we conducted a decade-long (2014-2023) retrospective longitudinal genomic epidemiological analysis of 441 ST11 S. Enteritidis isolates from Guangxi, China, alongside a global reference dataset of 4297 genomes. Our aim was to elucidate the effect of real-world antibiotic stewardship on the shift of gyrA point mutations and lineage distribution. Surveillance identified three global epidemic clade sublineages (GEC-L2, L3, L4), with the multidrug-resistant GEC-L4 (i.e., GC-c or MMC2), characterized by the gyrA mutation with amino acid substitution D87Y, being domestically dominant (70.07%, 309/441). Following China's 2016 ban on the veterinary use of critical fluoroquinolones, the proportion of the highly resistant GEC-L4 sublineage decreased continuously (from 86.84% in 2017 to 56.00% in 2023), while the less resistant GEC-L3 sublineage (i.e., GC-b or MMC1), mainly characterized by gyrA D87G, increased simultaneously (from 13.16% to 44.00%). This phenomenon might be attributed to the fact that the GEC-L4 sublineage exhibited a higher fitness cost compared with the GEC-L3 sublineage, as confirmed by the competition assay. A Random Forest Model validated that the gyrA mutation with amino acid substitution D87Y was the paramount feature for these sublineages' identification. In contrast, global data showed a continuous increase in gyrA mutations (from 8.63% in 2006 to 68.85% in 2024), primarily D87Y (from 1.44% to 31.15%) and D87N (from 4.32% to 22.95%), correlating with rising average fluoroquinolone consumption. This study provides direct genomic evidence that national-level antibiotic stewardship can drive the replacement of highly resistant sublineages with moderately resistant ones. These findings offer crucial scientific evidence for evaluating the impact of antibiotic management policies and inform strategies for the rational use of antimicrobials.

China↗

PubMed2026

Assessing the threat of Bacillus cereus: From toxin characterization to modern detection strategies.

Bacillus cereus is a spore-forming pathogen responsible for both diarrheal and emetic foodborne illnesses worldwide. Its significance in food safety has received growing attention. Recent advances, including the discovery of novel virulence factors and the development of emerging detection technologies, have provided new insights into its pathogenic mechanisms and surveillance strategies. This review critically examines the global burden of B. cereus infections, and molecular mechanisms of its major virulence factors, and the performance characteristics of current detection knowledge gaps such as the viable-but-non-culturable state and regulatory blind spots for emetic toxins, and discuss unresolved challenges in clinical management. By integrating epidemiological, microbiological, and technological perspectives with critical lens, this review aims to provide a valuable reference for future research and food safety practices.

Bacillus cereus↗

PubMed2026

Diffusion MRI radiomics in meningiomas: imaging correlates of tumor grade and intraoperative consistency.

OBJECTIVE: Despite advancements in imaging studies, the preoperative prediction of the biological behavior and intraoperative consistency of intracranial meningiomas remains limited. This study evaluated the association of volumetric diffusion-based and texture-derived radiomic features extracted from routine MRI with histopathological aggressiveness and intraoperative tumor consistency. METHODS: Ninety-seven intracranial meningiomas resected at two tertiary centers were retrospectively analyzed. Volumetric segmentation was performed on contrast-enhanced T1-weighted MRI and coregistered to apparent diffusion coefficient (ADC) maps. Data on first-order diffusion metrics and selected texture features were collected. The associations between World Health Organization (WHO) grade and Ki-67 index were assessed using nonparametric tests and Spearman correlation analysis. Independent factors associated with intraoperative tumor consistency (Zada grades 1-5) were evaluated via multivariate ordinal logistic regression analysis that adjusted for tumor volume, skull base location, calcification status, and WHO grade. Secondary receiver operating characteristic (ROC) curve analyses were performed to differentiate solid (Zada grades 4-5) from soft (Zada grades 1-2) tumors. ROC analyses were performed within the study cohort and were intended as exploratory assessments of discriminative performance. RESULTS: The mean ADC (ADCmean) and the 10th percentile of the ADC decreased significantly with increasing WHO grade (p < 0.001). ADCmean had a moderate inverse correlation with the Ki-67 index (r = -0.42, p < 0.001) and intraoperative tumor consistency (r = -0.45, p < 0.001). In the multivariate analysis, the ADCmean remained independently associated with increasing tumor firmness. Each 0.1 × 10-3 mm2/sec increase corresponded to a 38% reduction in the odds of belonging to a higher consistency category (OR 0.62, 95% CI 0.51-0.74, p < 0.001). The ROC analysis showed good discrimination for solid tumors (area under the curve 0.847, 95% CI 0.742-0.953) and soft tumors (area under the curve 0.824, 95% CI 0.714-0.935). Texture features had weaker associations with intraoperative tumor consistency. CONCLUSIONS: Volumetric diffusion-derived metrics, particularly ADCmean, are associated with both histopathological aggressiveness and intraoperative tumor firmness in meningiomas. Diffusion imaging may reflect a graded microstructural continuum rather than a purely dichotomous property, providing complementary preoperative insights into surgical complexity.

Humans↗

PubMed2026

Early and late CTCAE and patient-reported outcomes following lung cancer radiotherapy: A sex-stratified descriptive analysis from the REQUITE cohort.

BACKGROUND: Long-term prospective data on lung cancer patients treated with radiotherapy are limited, restricting understanding of outcomes and sex-specific characteristics. The multicentre REQUITE study provides standardized follow-up data from an international cohort. METHODS: We analysed longitudinal data from 530 lung cancer patients treated with radical radiotherapy (sequential or concurrent chemoradiotherapy, or stereotactic body radiation therapy [SBRT]) between 2014 and 2017 at 16 centres in Europe and the USA. Healthcare professionals prospectively recorded 21 pulmonary, oesophageal, neurological, cardiac, and skin adverse events using CTCAE v4.0. Patient-reported outcomes assessed symptoms, quality-of-life, fatigue, and physical activity. Adverse event incidence was stratified by sex, radiotherapy technique, chemotherapy administration, smoking status, and other clinical factors. RESULTS: At 12 months, 309 patients were evaluable, with 151 having follow-up beyond one year. Pulmonary adverse events were most frequent (40% grade ≥ 2), mainly dyspnoea and cough. Women had higher rates of oesophagitis, and more frequently reported dysphagia, and chest wall pain, while men experienced a higher frequency of cardiac adverse events. Exploratory subgroup analyses identified significant sex-related differences in grade ≥ 3 pulmonary adverse events following SBRT and in overall grade ≥ 2 oesophageal adverse events among patients with clinical stage I-II and those aged >70 years. Patient-reported outcomes showed persistent fatigue and reduced physical activity, particularly in females. Symptom prevalence and severity varied by sex, age, treatment modality, smoking status, and clinical stage. CONCLUSION: The REQUITE-Lung cohort provides prospectively collected real-world data on radiotherapy-related adverse events in lung cancer patients. This study describes patterns of adverse events and patient-reported outcomes according to sex, age, and treatment characteristics. These findings are hypothesis-generating and may support future validation in independent and pooled datasets.

Patient-reported outcomes↗

PubMed2026

Efficacy, tolerability, and threshold effect of atropine eye drops for myopia control: A systematic review and dose-response meta-analysis.

Atropine is an emerging therapy for myopia, yet the optimal concentration for prescription remains uncertain. We searched PubMed, Embase, Web of Science, Cochrane Library, World Health Organization International Clinical Trials, and ClinicalTrials.gov registry platforms. We included the randomized clinical trials (RCTs) that compared any dose of atropine against a placebo in myopic children. Among 3566 studies assessed, we identified 33 eligible RCTs involving 6301 children aged 4-18 years, with 10 different concentrations and a mean follow-up time of 19.5 ± 12.3 months. A nonlinear relationship was observed between atropine dosage and treatment efficacy (P < 0.001). Compared to placebo groups, the mean differences in reducing annual spherical equivalent refraction progression for atropine concentrations of 0.01%, 0.02%, 0.03%, 0.04%, and 0.05% were 0.21 diopters (D) (95% CI, 0.13-0.28), 0.35 D (95% CI, 0.23-0.46), 0.42 D (95% CI, 0.28-0.56), 0.45 D (95% CI, 0.30-0.60), and 0.46 D (95% CI, 0.32-0.61) respectively For higher concentrations, the estimates were 0.49 D (95% CI, 0.34-0.63) for 0.1% and 0.99 D (95% CI, 0.66-1.31) for 1%, although these were based on fewer and smaller trials. Higher doses of atropine were associated with decreased amplitude of accommodation (P = 0.02), increased pupil diameters (P = 0.01) and a higher frequency of photophobia (P = 0.02). Our findings suggest that the increase in treatment efficacy with higher concentrations may plateau beyond a certain range, and that the current practice of increasing atropine concentrations for children who show inadequate responses to lower doses should be confined to a specific concentration range. This analysis is limited by the number, design heterogeneity, and sample sizes of available trials for higher concentrations, and by the frequent lack of pre-intervention refractive history in included studies. Therefore, estimates-particularly for doses exceeding 0.1%-should be interpreted with caution.

Humans↗

PubMed2026

The identification of growth-promoting lncRNAs in oral cavity squamous cell carcinoma.

Oral Cavity Squamous Cell Carcinoma (OCSCC) is an aggressive tumor that develops within the mouth of patients. Tumor-suppressor gene loss and genomic arrangements fuel tumorigenesis and transcriptional reprogramming. Understanding how these alterations contribute to OCSCC growth and cell survival may identify new therapeutic vulnerabilities or biomarkers. We profiled the role of long non-coding RNAs (lncRNAs) in the growth of three OCSCC cell lines using a CRISPRi-screen and identified 19 lncRNAs that contribute to OCSCC proliferation. By comparing these lncRNAs to other screens, we find that these lncRNAs are uniquely required in OCSCC and not other malignancies. We show that these lncRNAs are abundantly expressed in OCSCC cells and tumors. Independent testing of candidate lncRNAs confirms their role in supporting OCSCC growth. Our results show that a novel subset of lncRNAs are required for the growth of OCSCC cancer cells and that these lncRNAs are cell lineage specific.

CRISPRi↗

PubMed2026

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2×2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I²=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗

PubMed2026

Molecular mechanisms underlying umami taste perception: A DIA-based proteomic analysis of Agrocybe aegerita peptides.

The mechanisms underlying the modulation of the salivary perception of umami peptides remain poorly understood. Herein, three umami peptides (DDL, DEL, and ENG) obtained from Agrocybe aegerita were used to investigate the regulatory role of saliva in umami taste perception via a combined approach involving sensory evaluation and proteomics analysis based on 4D-DIA technology. The results revealed that umami intensity peaked at 10 s after ingestion and was accompanied by a significant increase in saliva secretion (p < 0.05). Further proteomics analysis revealed that lactotransferrin and proline-rich proteins are closely associated with the sensory perception of umami peptides. Differentially expressed proteins were mainly enriched in pathways related to saliva secretion and proteasome function. This study provides new insights from the perspectives of salivary proteomics and dynamic salivary secretion, contributing to a deeper understanding of the mechanisms by which saliva regulates umami perception.

Humans↗

PubMed2026

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗

PubMed2026

Alternative genetic codes in bacteria and archaea identified with a fast k-mer-based algorithm.

The genetic code is conserved across all domains of life and is often described as universal. Nevertheless, many exceptions to the "universal" code have now been documented, most of these through manual or semiautomated inspection of highly conserved genes. Modern bioinformatics tools improved our ability to find alternative genetic codes but remain computationally expensive, preventing widespread use on thousands of new species identified by sequencing environmental samples. Here, I report a >100-fold accelerated method for inferring the genetic code directly from assembled genomes and apply it to thousands of previously uncharacterized assemblies from archaea and bacteria. I describe three candidate genetic code variations, one of which, an alternative genetic code used by a family of Asgard archaea, is a unique example of sense codon reassignments for this domain. Identifying genetic code variations is important for understanding evolution of the standard code and improving accuracy of protein databases and open reading frame identification.

Genetic Code↗

PubMed2026

Temporal proteomic analysis reveals a three-phase adaptation strategy in Phytophthora cinnamomi during salinity stress.

Phytophthora cinnamomi, a highly invasive hemibiotrophic oomycete, threatens global agriculture, forestry, and native ecosystems. Although drought and temperature effects on P. cinnamomi-host interactions are well studied, current knowledge of abiotic stress responses in P. cinnamomi remains largely centered on infection and phytopathology, with limited molecular insight into the pathogen's direct response to salinity independent of its host. To address this gap, we combined growth assays, time-resolved proteomics, and network analysis to define how P. cinnamomi responds and adapts to salinity exposure. Growth assays showed that NaCl-modified agar enhanced mycelial expansion in a concentration-dependent manner, with 100 mM NaCl significantly increasing growth at 48, 72, and 96 h compared with controls, while 50 mM NaCl remained comparable to control conditions. Temporal proteomic analysis of 100 mM NaCl treatment at 0, 1, 6, 12, and 24 h post treatment revealed dynamic shifts in protein abundance. Early induction of ROS (Reactive Oxygen Species)-detoxifying enzymes, including glutathione S-transferases and peroxidases, was consistent with ROS-specific staining assays. Network analysis identified modules enriched for redox regulation, ATP generation, ion transport, and translational control, highlighting multi-layered adaptation to elevated NaCl levels. Notably, clusters of conserved hypothetical proteins were strongly upregulated, indicating unexplored stress tolerance components in Phytophthora species. Here, we propose that P. cinnamomi rapidly activates a three-phase strategy involving metabolism readjustments, redox defenses, and cellular structure alterations under salinity conditions. With increasing soil salinization due to climate change, our study provides first mechanistic insights into P. cinnamomi's adaptive plasticity and ecological resilience to abiotic stress. SIGNIFICANCE: This study represents the first temporal proteomic analysis of salinity stress adaptation in Phytophthora cinnamomi, revealing a sophisticated three-phase adaptation strategy. This research fundamentally advances our understanding of how this globally destructive plant pathogen, P. cinnamomi, maintains environmental resilience. Our findings reveal proteome remodelling as a mechanistic framework for understanding stress tolerance in oomycetes, a group of microorganisms responsible for some of the world's most destructive agricultural and forest diseases. Our results show proteins involved in emergency damage control through metabolic recalibration to sustained adaptation. These findings have relevance for predicting pathogen behavior under climate change scenarios, where increasing soil salinity threatens agricultural productivity while simultaneously enhancing pathogen survival and virulence. Understanding how P. cinnamomi responds to prolonged salinity exposure may inform targeted biocontrol strategies and improve predictive models of disease pressure in salt-affected agricultural regions. The temporal analysis framework we present offers a broadly applicable approach for understanding microbial stress adaptation, with implications extending beyond plant pathology to environmental microbiology and biotechnology applications where stress tolerance is paramount.

Phytophthora↗

PubMed2026

Distinct functions of mammalian RAD51 paralogs in genome maintenance.

RAD51 paralogs (RAD51B, RAD51C, RAD51D, XRCC2, and XRCC3) are evolutionarily conserved essential proteins for cell survival and genome maintenance. RAD51 paralogs were originally identified to play a role in homologous recombination-mediated repair of DNA double-strand breaks (DSBs). However, investigations over the last decade have uncovered new roles of RAD51 paralogs beyond DSB repair in replication stress responses, including replication fork progression, fork stability, and its restart. Recent structural studies have not only uncovered the molecular architecture of previously known RAD51 paralog complexes but also identified novel paralog complex assemblies, providing mechanistic insights into their various genome-maintenance functions. Additionally, a role for RAD51 paralogs in resolving R-loops has been identified, and studies with cancer-associated variants suggest that RAD51 paralogs are potential determinants of cancer susceptibility and therapeutic responses. In the present review, we highlight the recently deciphered structures and novel functions of RAD51 paralog complexes and discuss the clinical and therapeutic implications.

Rad51 Recombinase↗

PubMed2026

Voluntary Knowledge Brokering to Promote Evidence-Based Nursing Practice: A Qualitative Study.

Knowledge brokering is a process of connecting knowledge producers with users to facilitate evidence-based practice through relationship building and information sharing. This descriptive qualitative study aimed to clarify knowledge brokering by nurses in Japanese hospitals. Twelve registered nurses in Japanese hospitals participated. They had over 5 years' clinical experience, including experience in conducting research, particularly staff research, and education. Data were collected through semi-structured individual interviews and analyzed using qualitative content analysis. The analysis revealed a central theme: continuous efforts to foster empathy among colleagues and spontaneously promote evidence-based practice: multifaceted brokering activities by clinical nurses. Findings identified 10 categories categorized into four interconnected gears: establishing the foundational ground, assessing clinical needs and staff readiness, tailoring and diffusing evidence, and sustaining and evolving evidence-based practice. Even nurses without formal titles voluntarily bridged the research-practice gap, providing new insights into informal brokering. Brokers communicated considerately, balanced evidence with clinical context, negotiated practical compromises, and fostered staff research competency.

Humans↗

PubMed2026

The Brazilian contribution to ant toxinology: challenges and perspectives.

Ant toxinology in Brazil is a small but growing field that has revealed wide biochemical diversity and clear potential for bioprospecting therapeutic molecules. This review compiles the Brazilian contribution, focusing on advances in the characterization of venoms from medically and ecologically important species of Dinoponera, Solenopsis, Paraponera, Pachycondyla, Neoponera, and Ectatomma. Brazilian groups applied omics approaches, including transcriptomics and proteomics, to resolve the composition of these venoms and identified peptide-rich arsenals with antimicrobial, antiparasitic, antitumor, and neuroactive activity. Studies of venom phenotypic plasticity showed ecological factors such as diet and seasonality shape venom composition. Two challenges persist: assigning function to still unidentified components and obtaining venom in the quantities that broad analysis requires. The near-term prospects are the rational design of peptide analogues with improved activity and continued bioprospecting of new species, which together position Brazil as a central contributor to ant toxinology.

Animals↗

PubMed2026

Loss, persistence and reversal of phenotypic traits.

The irreversibility of complex trait loss has long been a tenet of evolutionary biology. However, this idea is increasingly at odds with the numerous documented exceptions across the Tree of Life. We synthesise this growing body of evidence across a diverse array of taxa and traits, exploring the evolutionary conditions that enable evolutionary reversal. By integrating macroevolutionary, genetic, and developmental information, we argue that trait reversal is commonly fostered by some form of persistence in the generative developmental pathway of the lost trait. We identify three overarching modes of trait reversal and support them with multiple case studies: by pleiotropy (the involvement of the same generative components in other traits and/or functions), by plasticity (environment-dependent expression of the trait) and by hemiplasy (persistence in another lineage, followed by reticulate evolution). We also examine important affinities between trait reversal and evolutionary novelties, undermining a neat distinction between what is old and what is new in evolution. This survey may provide a useful framework for future explorations of the developmental mechanisms underlying these still overlooked macroevolutionary dynamics.

Phenotype↗

PubMed2026

AI-enabled viral genomics: from virus discovery to host prediction and emerging variant forecasting.

The rapid expansion of metagenomic sequencing has generated vast repositories of viral sequence data that far outpace our capacity to interpret them using conventional approaches. Highly divergent sequences, sparse functional annotation, and taxonomically uneven sampling present fundamental challenges for reference-dependent methods, which lose sensitivity precisely for novel and understudied viruses with high public health relevance. Artificial intelligence (AI) provides a new avenue to address these challenges by enabling predictive inference from viral genomes and proteins while reducing dependence on sequence similarity. In this Review, we discuss representative advances in AI for virus discovery, taxonomic classification and functional annotation, prediction of host range and zoonotic potential, and efforts toward forecasting emerging variants. These advances are transforming viral genomics from a largely descriptive discipline into one with increasing predictive capability. We also critically assess the major challenges that constrain current approaches, including the availability of high-quality and representative datasets, rigorous model evaluation, biological interpretability and responsible governance for increasingly capable AI models.

Artificial Intelligence↗

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↗