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PubMed2026

Playing with Fire, Losing the Drive: Bidirectional Links Between Problematic Smartphone Use and Grit Dimensions.

This study examined bidirectional longitudinal associations between grit dimensions (consistency of interest [CI] and perseverance of effort [PE]) and problematic smartphone use (PSU) and tested cognitive flexibility as a mediating mechanism. A sample of 1,641 Chinese university students (55.2 percent female; Mage = 20.1 years) completed measures at two time points 6 months apart. A four-variable cross-lagged panel model revealed that CI and PSU negatively predicted each other over time, whereas PSU unidirectionally predicted decreased PE. Cognitive flexibility partially mediated the PSU-to-PE pathway (indirect effect = -0.004, 95 percent bootstrap CI [-0.010, -0.0001]). Competing models analysis confirmed this directionality: the forward mediation (PSU → cognitive flexibility → PE) was significant, whereas the reverse was not. These findings demonstrate that grit dimensions exhibit distinct longitudinal patterns with PSU and identify cognitive flexibility as a cognitive mechanism through which PSU specifically undermines effort persistence. Implications for dimensional approaches to grit and targeted interventions are discussed.

Humans↗
PubMed2026

Blood pressure management after endovascular thrombectomy in acute ischemic stroke: association with symptomatic intracranial hemorrhage and functional outcome at 3 months.

BACKGROUND: No clear consensus exists on ideal systolic blood pressure (SBP) targets after endovascular thrombectomy (EVT) following an acute ischemic stroke (AIS). This study investigated the association between SBP parameters within the first 24 h after EVT and 3-month functional outcomes and the risk of symptomatic intracranial hemorrhage (sICH). METHODS: We retrospectively collected and prospectively followed clinical, and radiological data for patients undergoing EVT for AIS from 2016 to 2024, including 2-hourly BP measurements during the first 24 h and SBP variability assessed by standard deviation (SD) and coefficient of variation (CV). Outcomes included 3-month functional status and sICH, and their associations with post-EVT BP metrics were analyzed. RESULTS: A total of 268 post EVT patients were included with a median age of 61 years (IQR, 51-69). Mean SBP was 129.67 ± 17.17 mm Hg, with SBP variability (SD 12.6 ± 5.4 mm Hg; CV 9.6 ± 3.8 %), while good functional outcome and sICH occurred in 39.7 % and 4.9 % of patients, respectively. Multivariate regression showed that higher admission NIHSS (>15) [0.90 (95 %CI, [0.86, 0.95], p = 0.000)], recanalization status [1.88 (95 %CI, [1.43, 2.48], p = 0.00)], and SBP-CV ≥ 10 [0.44 (95 %CI, [0.2, 0.94], p = 0.036)] was independently associated with poor 3-month functional outcome, while higher admission NIHSS (>15) [0.87 (95 %CI, [0.77,0.98], p = 0.02)] and diabetes [0.12 (95 %CI, [0.03, 0.54], p = 0.006)] predicted increased risk of sICH. CONCLUSIONS: The study showed that reduced BP variability during the first 24 h post-EVT was associated with better 3-month functional outcomes. A clear association between SBP and sICH risk was not demonstrated.

Humans↗
PubMed2026

TNFα-dependent modulation of WT1-MMP9 regulatory axis links developmental and inflammatory pathways in glaucoma.

Glaucomas are heterogeneous optic neuropathies associated with extracellular matrix dysregulation, abnormal ocular morphogenesis, and inflammatory signaling. Targeted deep sequencing of 586 primary congenital glaucoma (PCG) cases and 1,757 controls identified rare pathogenic variants in multiple genes, including WT1 and MMP9. Notably, WT1 variants clustered within the nuclear export sequence. Further, functional analyses showed that combined wt1-pax6 suppression in zebrafish disrupted ocular morphogenesis, highlighting developmental interdependence. In human trabecular meshwork cells, WT1 acted as a transcriptional repressor of MMP9, while TNF-α signaling triggered nitric oxide-dependent nuclear export of WT1, resulting in delayed MMP9 upregulation. This effect was reversible by inhibiting nuclear export or nitric oxide synthase. A patient-derived mutation in the nuclear-export region of WT1, disrupted this regulatory switch, causing abnormal MMP9 expression. These findings position WT1 as an important regulator linking developmental and inflammatory mechanisms in glaucoma pathogenesis.

anterior segment dysgenesis↗
PubMed2026

Triacylglycerol metabolism is a novel target to combat West Nile virus infection.

West Nile virus (WNV) is a zoonotic Orthoflavivirus transmitted by mosquitoes that is responsible for outbreaks of meningitis and encephalitis worldwide. Driven by climate change, WNV has expanded as a global public health concern, particularly in temperate regions. However, there are still no specific approved therapies, reinforcing the need for antiviral development. Previous works have documented that WNV multiplication strictly depends on certain cellular lipids. To identify novel lipid-related therapeutic targets, we analyzed the infection driven alterations in the CNS lipidome, the primary tissue supporting WNV replication. Our results indicated that the major alterations in the brain lipid content of WNV-infected mice corresponded to triacylglycerols (TAGs). Moreover, transcriptomic analysis showed that infected brains underwent changes in the expression of TAG metabolism. Supplementation with exogenous fatty acids increased lipid droplets (LD) content and promoted viral replication in cell culture models. On the contrary, pharmacological intervention in TAG metabolism using diacylglycerol acyltransferase inhibitors (DGATi) suppressed WNV multiplication in cell culture models. As a proof-of-concept of the therapeutic potential of DGATi, treatment of mice with A922500 reduced viral burden in the brain and proinflammatory cytokine production. Overall, our results unveil the importance of LDs and glycerolipid metabolism for WNV and highlight the potential of therapeutic interventions targeting this pathway to control viral replication and neuroinflammation.

West Nile virus; lipid↗
PubMed2026

Integrated bioinformatics analysis reveals cross-talking hub genes and therapeutic agents between sepsis and acute myocardial infarction.

BACKGROUND: Sepsis and acute myocardial infarction (AMI) are two significant diseases that may share overlapping etiological mechanisms. This study aims to systematically identify core genes common to both conditions and to explore their potential as therapeutic targets and drug candidates through an integrative analysis of clinical data and bioinformatics. METHODS: The AMI dataset was obtained from the GEO database, and RNA sequencing data were collected from blood samples of patients with sepsis at our hospital. Common genes were identified using differential expression gene analysis (DEG) and weighted gene co-expression network analysis (WGCNA). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were performed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified using the MCC/Degree algorithm. Diagnostic value was assessed via receiver operating characteristic curve analysis. Immune infiltration patterns, single-cell sequencing data, and molecular docking simulations were employed to evaluate immune relevance and identify potential therapeutic compounds. RESULTS: A total of 417 genes were identified between sepsis and AMI, with enrichment analysis revealing significant involvement in inflammatory responses. Three hub genes-JAK2, MYD88, and TIMP1-were selected for further investigation. ROC curves confirmed their strong diagnostic performance for both diseases. Immune infiltration analysis showed that these core genes were significantly correlated with the infiltration levels of various immune cell types. Molecular docking indicated that quercetin exhibited stable binding affinity with the proteins encoded by these genes. qPCR validation further confirmed the upregulation of these three genes, supporting the anti-inflammatory effects of quercetin as a potential targeted therapy. CONCLUSION: JAK2, MYD88, and TIMP1 were identified as shared core genes in sepsis and AMI. These genes not only serve as potential diagnostic biomarkers but also offer novel targets for developing common therapeutic strategies for both conditions. Furthermore, quercetin emerges as a promising candidate for targeted treatment.

Humans↗
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

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans↗
PubMed2026

Effects of apple phenolics on the human metabolome: modulation of key metabolic pathways.

Apples are widely recognized for their potential health benefits, partly attributed to their phenolic compounds. However, their impact on human metabolism remains incompletely understood. This study investigated metabolic effects of apple-derived phenolic compounds using untargeted metabolomics approach across multiple biofluids. In a crossover intervention study, 30 healthy men consumed a phenolic-rich apple juice or a placebo for two weeks. Blood, urine and saliva samples were collected before and after each intervention and analyzed by direct infusion ultra-high resolution mass spectrometry. Consumption of apple phenolic compounds resulted in significant alterations of the human metabolome, including increased levels of phenolic-derived degradation products and microbial-associated metabolites across all biofluids. Pathway enrichment analysis revealed pronounced effects on phenylalanine and tyrosine metabolism, as well as linoleic and arachidonic acid metabolism, Overall, these findings demonstrate that apple phenolic compounds induce measurable, microbiota-associated and systemic metabolic changes, providing new insights into their metabolic fate and biological relevance.

Humans↗
PubMed2026

Integrated miRNA-mRNA profiling reveals candidate regulatory relationships associated with high-fat diet-induced muscle lipid deposition in black seabream (Acanthopagrus schlegelii).

High-fat diets are increasingly used in aquaculture due to their protein-sparing effects; however, the post-transcriptional regulatory mechanisms of fish muscle in response to high-fat diets (HFD) remain unclear. In this study, juvenile black seabream were fed either a normal-fat diet (NFD) or a HFD to investigate the miRNA-mRNA regulatory network associated with diet-induced muscle lipid deposition. Oil Red O staining and biochemical analysis showed that high-fat diet feeding markedly increased lipid droplet accumulation and crude lipid content in muscle, indicating significant induction of muscle lipid deposition. Integrated mRNA and miRNA expression profiling revealed substantial transcriptomic and post-transcriptional responses to high-fat diet challenge. A total of 271 differentially expressed genes were identified, including 120 upregulated and 151 downregulated genes. Through combined target prediction and expression correlation analysis, thirteen candidate inverse miRNA-mRNA relationships were subsequently identified, and RT-qPCR supported the expression patterns of selected miRNAs and mRNAs. These pairs included miR-499-x-dmgdh, miR-499-y-gatm, miR-727-y-ass1, miR-4649-x-foxo4, miR-9129-z-myl7, and several novel miRNA-mediated interactions involving adk, chst11, lypla2, frem2, kcnc4, wars1, bag2, and capn2. Functional analysis suggested that these regulatory pairs were mainly associated with metabolic adaptation, structural remodeling, and cellular stress responses. In particular, gatm, dmgdh, ass1, and adk were associated with energy metabolism-related processes, including pathways previously linked to Ampk regulation, whereas myl7, frem2, and kcnc4 may contribute to muscle structural maintenance and excitability regulation. Overall, this study provides candidate miRNA-mRNA regulatory relationships potentially involved in high-fat diet-induced muscle lipid deposition and adaptive remodeling in black seabream, offering a basis for future functional studies on muscle metabolism and quality regulation in marine fish.

Animals↗
PubMed2026

Multi-Omics Genome-Wide to Explore the Formation and Development Targets for Intracranial Aneurysms.

Intracranial aneurysms (IAs) represent a significant and potentially life-threatening category of disease, and there is currently a lack of effective treatment options aimed at preventing the progression of the disease. Accordingly, this study is dedicated to exploring and identifying effective drug targets that can help in the prevention of both the formation and rupture of IAs, along with a detailed examination of the underlying potential mechanisms involved in these processes. The data related to IAs for this research was obtained from the ISGC Biobank and UK Biobank. Then, we investigated the possible biological functions and unintended consequences of targeting the specific genes that were highlighted in IAs by using mediation analysis, virtual knockout experiments, and PW-MR studies. A total of 5 unique potential drug targets for IAs (FKTN, MAP3K1, PSMA4, SLC22A4, ADAM17), 4 unique potential drug targets for SAH (PSMA4, ADAM17, GPR160, SLC22A4), and 2 unique potential drug targets for UIA (SLC22A4, PRCP) were identified across brain or blood samples. Among the various candidates identified, SLC22A4 has emerged as a promising potential drug target, showing significant expression levels in both blood and brain tissues. Additionally, phenome-wide MR of SLC22A4 across 32 selected phenotypes did not identify statistically significant adverse associations after FDR correction. Virtual knockout (KO) experiments on SLC22A4 revealed that SLC22A4 KO disrupted 81 genes, all of which are involved in IAs-related pathways. Besides, we recognized BRD-K85337334 as potential candidates for targeting SLC22A4. This research indicates that an increase in SLC22A4 gene expression within the blood or brain is directly linked to a heightened risk of IAs rupture, which will aid in prioritizing the development of drugs for IAs.

Humans↗
PubMed2026

Lipid metabolism is a key central, systemic and gut microbial feature of the decline in rat hippocampal function during middle age.

Middle age is emerging as a turning point in brain ageing, prognostic of future cognitive health and amenable to intervention. Metabolic and proteomic differences during this period are not yet fully understood and may potentially influence functions of the hippocampus, a brain area that regulates memory and anxiety. While the gut microbiota is implicated in brain ageing, the relationship between the gut microbiota, the metabolic state, and hippocampal proteome in middle age has not been investigated. We hypothesise that peripheral metabolic or protein features are associated with hippocampal vulnerability in middle age. Therefore, young adult and middle-aged rats were assessed for behavioural, proteomic, metabolic, and gut microbiota differences. Proteomic profiling of the hippocampus revealed differential expression of proteins indicative of altered synaptic signalling. Concurrently, adult hippocampal neurogenesis was decreased in middle age. Hippocampal microglia exhibited a lipid rich, inflammatory phenotype in middle age which correlated with poorer memory performance. CSF and serum proteomic and metabolomic analyses identified dysregulated lipid-related pathways potentially contributing to hippocampal vulnerability in middle age. Furthermore, 16S rRNA sequencing revealed reduced abundance of bacteria involved in lipid metabolism regulation. However, faecal microbiota transfer from young to middle aged rats was not sufficient to robustly improve hippocampus-dependent spatial memory. Together, these findings highlight dysfunctional lipid metabolism as a key feature of middle age that may contribute to decline in hippocampal function. Given that the scope for intervention is limited during older age, targeting biomarkers involved in metabolic and lipid homeostasis may be pivotal for the development of pharmacological or lifestyle-based interventions during middle age which could ultimately delay future cognitive ageing.

Animals↗
PubMed2026

Extent of resection as an independent predictor of survival for patients with glioblastoma as defined by the new WHO 2021 classification.

OBJECTIVE: Extent of resection (EOR) has previously been demonstrated to have an impact on survival in patients with glioblastoma (GBM). However, with the World Health Organization (WHO) 2021 reclassification of GBMs based on IDH-mutation status, patients with "IDH-mutant GBMs," who typically survive long term, were reclassified as WHO grade 4 IDH-mutant astrocytomas and removed from the GBM taxonomy. Therefore, it is unknown whether the previously reported impact of resection on survival was a false-positive result due to the inclusion of the less aggressive IDH-mutant tumors in previous datasets. This study aimed to determine the extent to which EOR remains an independent predictor of survival in patients with WHO 2021 GBM after the reclassification of IDH-mutant grade 4 astrocytomas. METHODS: All cases of GBM tumors (based on the pre-2021 GBM classification) that were newly diagnosed between 2005 and 2021 were identified in our institutional database and subsequently reclassified based on the updated WHO 2021 criteria using IDH status. Multivariable statistical analyses of demographic information, survival time, and EOR based on volumetric MRI were performed to determine the independent predictors of survival for the whole group of patients and for IDH-wildtype GBM patients exclusively. Additional analyses were performed to identify an EOR threshold for improvement in survival. RESULTS: Of the 523 tumors classified as GBM based on the pre-2021 taxonomy, 52 (9.9%) cases were reclassified as WHO grade 4 IDH-mutant astrocytomas, and the median survival of patients in this group was 7.9 years, whereas median survival of the IDH-wildtype GBM patients was 1.4 years. Multivariate analyses of the whole group demonstrated that IDH-mutant astrocytomas were associated with reduced hazard of death. In both the whole group (n = 523) and in IDH-wildtype GBMs (n = 471), higher EOR of the contrast-enhancing (CE) tumor was associated with reduced hazard of death, whereas older age or male sex was associated with increased hazard of death. Because most patients (90%) had high EOR values (> 81%), a statistically meaningful EOR threshold could not be established. CONCLUSIONS: These analyses demonstrated that EOR of the CE tumor is an independent predictor of survival and that greater EOR is associated with improved survival in WHO 2021 IDH-wildtype GBMs even after excluding grade 4 IDH-mutant astrocytomas. However, an absolute EOR threshold below which resection did not improve survival could not be established, raising concerns about prior cutoff assessments.

Humans↗
PubMed2026

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

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

Artificial intelligence↗
PubMed2026

Post-Breakup Instagram Surveillance: Attachment Style, Personality Traits, and Breakup Distress as Predictors.

The end of a romantic relationship is one of the most emotionally challenging life events. Social media platforms such as Instagram enable users to monitor an ex-partner, a behavior known as Interpersonal Electronic Surveillance (IES), which may complicate coping. This study examined associations with retrospectively reported IES on Instagram during the first 3 weeks post-breakup, focusing on attachment, personality, and breakup-related emotional distress. Previous studies suggest that higher anxious attachment and emotional distress are related to increased monitoring behaviors on Facebook. The present research extends this approach to Instagram, a popular platform among Generation Z, and additionally examines personality factors. Data from N = 232 participants (aged 18-27 years; 84 percent women), who had experienced a breakup within the past year and followed their ex-partner on Instagram, were collected using a cross-sectional online questionnaire. The survey included standardized measures and self-constructed items. Hierarchical regression analyses including breakup-related variables, mediation analyses, and independent-samples t-tests were conducted. Due to extremely low internal consistency, Agreeableness was excluded from inferential analyses. The analyses indicated that Extraversion was the only personality trait directly associated with increased IES. Attachment styles showed no direct associations after emotional distress was included in the model. Emotional distress emerged as the most consistent factor associated with IES, showing patterns consistent with indirect associations involving Neuroticism and anxious attachment, suggesting a central role of emotional distress in post-breakup surveillance behavior. These findings highlight digital monitoring as a potentially maladaptive coping strategy and underscore the importance of addressing social media use in post-breakup adjustment.

Humans↗
PubMed2026

Remotely Supervised, Home-Based Transcranial Direct Current Stimulation for Major Depressive Disorder: Systematic Review and Meta-Analysis.

BACKGROUND: Major depressive disorder affects over 280 million people worldwide, and access to effective treatment remains limited. Transcranial direct current stimulation (tDCS) is a noninvasive option, and portable devices now allow for home-based delivery under varying degrees of remote supervision. OBJECTIVE: This study aimed to systematically review and meta-analyze the efficacy, safety, feasibility, and acceptability of home-based and remotely supervised tDCS for depressive disorders. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 and PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension) guidelines, we searched MEDLINE, Embase, Web of Science, the Cochrane databases, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform up to July 2025, with backward and forward citation searching. Two reviewers independently screened records, extracted data, and assessed risk of bias (version 2 of the Cochrane risk-of-bias tool for randomized trials, Newcastle-Ottawa Scale for observational studies, and Critical Appraisal Skills Programme for qualitative studies) and certainty of evidence (Grading of Recommendations Assessment, Development, and Evaluation; GRADE). RESULTS: This review included 12 distinct studies (16 reports), of which 6 (50%) were randomized sham-controlled trials forming the meta-analytic pool. Active home-based tDCS produced a small, statistically significant improvement over sham (pooled Hedges g=0.36, 95% CI 0.06-0.66; P=.03; I2=34.3%). The effect was not robust to removal of the single largest positive trial (omitting the one study from 2025: g=0.39, 95% CI -0.12 to 0.91), and trial-level results were mixed: the 2 largest trials (one unsupervised [n=210] and one self-administered [n=141]) were negative on their primary depression outcomes, whereas the largest real-time supervised trial (n=174) was positive (between-group 95% CI 0.51-4.01; P=.01). This estimate was concordant in direction with an independent peer-reviewed meta-analysis of overlapping trials, which reported a pooled Montgomery-Åsberg Depression Rating Scale reduction (weighted mean difference -2.74, 95% CI -4.19 to -1.29) and Hamilton Depression Rating Scale reduction (weighted mean difference -2.24, 95% CI -4.16 to -1.49), attenuating to nonsignificance (P>.05) in major depressive disorder without comorbid cognitive impairment. The pooled effect fell at or near the minimal clinically important difference. GRADE certainty was moderate. Adverse events were predominantly mild: one pilot study was terminated early for skin lesions, and one nonfatal suicide attempt occurred in an unsupervised trial. CONCLUSIONS: Home-based and remotely supervised tDCS produces a small, statistically significant but clinically modest antidepressant effect that is sensitive to the inclusion of the largest positive trial, with the 2 largest trials being negative. The available controlled evidence does not establish supervision intensity as a determinant of efficacy. Current data are insufficient to recommend routine clinical adoption; adequately powered trials with standardized supervision and longer follow-up are needed.

Humans↗
PubMed2026

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

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

Biological Products↗
PubMed2026

Genome-Wide Characterization of β-Glucosidase (TaBGLU) Genes in Bread Wheat and Their Expression Under Drought, Cold, and Combined Stress.

Glycoside hydrolase 1 (GH1) β-glucosidases were known to activate hormone conjugates and defense metabolites, yet their genomic organization and stress-response dynamics in wheat remained incompletely defined. We therefore performed an integrated characterization of TaBGLUs spanning phylogeny, gene structure and conserved motifs, subcellular localization, promoter cis-elements, Gene Ontology enrichment, protein-protein interaction networks, and targeted expression profiling. Wheat TaBGLUs partitioned into well-supported clades that shared canonical GH1 catalytic residues and a largely conserved motif scaffold. Subcellular localization predictions indicated predominant nuclear and chloroplast targeting, with a smaller cohort directed to secretory or endomembrane compartments. Promoters were enriched for light-responsive, hormone-related (ABA, JA/SA, auxin, GA) and stress-associated (MYB/WRKY, heat, low temperature) cis-elements, and functional annotations were consistent with roles in carbohydrate and cell-wall metabolism, hormone homeostasis, and defense. Network analysis revealed a densely connected TaBGLU submodule embedded within broader carbohydrate and defense interaction networks, suggesting coordinated or cooperative functions. Expression profiling under cold, drought, and combined drought and cold demonstrated broad stress inducibility, with early activation detected by 6 h, cold-responsive maxima typically at 12 h, drought-responsive peaks predominating at 24 h, and combined stress eliciting both earlier and more sustained expression maxima between 12-24 h. Representative strongly responsive genes included TaBGLU20, TaBGLU44, TaBGLU6, and TaBGLU23, which showed pronounced late induction under combined stress, TaBGLU30, which exhibited an earlier combined-stress peak, and TaBGLU12, which displayed a marked late drought-specific response. Taken together, this integrated genomic, regulatory, and expression atlas refined the wheat BGLU repertoire relative to previous gene model inventories, highlighted candidate TaBGLUs with central network positions and strong stress inducibility, and provided concrete entry points for functional validation and breeding for improved stress resilience.

Triticum↗
PubMed2026

Online Social Anxiety in the Digital Age: Transitions, Predictors, and Mental Health Associations in Emerging Adulthood.

BACKGROUND: Online social anxiety (OSA), a multidimensional form of social evaluative anxiety in online social contexts, disproportionately affects emerging adults who constitute the largest active group of media users and face heightened psychological sensitivity due to growing pressures and immature sociocognitive regulation during the transition to adulthood. However, its heterogeneity, transitions, and longitudinal associations with mental health outcomes remain underexplored. METHODS: This study utilized data from two waves of a three-wave longitudinal survey, with 849 Chinese participants (Meanage = 21.6 years; 50.4 percent female) assessed at 4-month intervals. Individuals were classified using latent profile analysis and the stability and changes of profiles were assessed via latent transition analysis (LTA). Multinomial logistic regressions were conducted separately at baseline and follow-up to identify correlates of profile membership. Predictors of profile transitions were examined using manual three-step LTA models, and associations between latent transition patterns and follow-up mental health outcomes were examined using BCH-LTA distal outcome analyses controlling for the corresponding baseline symptom level. RESULTS: Four profiles of OSA were identified: low, privacy-sensitive, moderate-high, and high OSA. Extreme profiles (low/high OSA) showed high stability (80.4 percent and 79.1 percent), while privacy-sensitive OSA exhibited the lowest stability (55.1 percent). Profile memberships were influenced by social-cognitive biases and digital interaction, particularly fear of negative evaluation and online interpersonal trust, whereas profile transitions were mainly associated with anxiety. Transitions toward less severe OSA profiles were generally associated with better subsequent mental health, whereas transitions toward more severe profiles corresponded to poorer outcomes, particularly for offline social anxiety. CONCLUSION: OSA was heterogeneous in its manifestation, severity and transitions. Personalized and early interventions targeting profile-specific vulnerabilities are critical to prevent the worsening of OSA and mitigate its psychological burden.

Humans↗