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PubMed2026

Quantitative assessment of the fingerprint evidential value using machine learning.

Fingerprints as physical evidence have long supported criminal investigation and adjudication. In practice, however, fingerprint identification relies mainly on examiners' experience. Furthermore, expert opinions tend to be categorical, even though the opinions with the same conclusion could differ substantially in evidential strength. To quantitatively assess fingerprint evidential value, this study proposes a machine learning-based framework as an interpretable decision-support tool. A lightweight residual one-dimensional convolutional neural network was constructed, incorporating channel recalibration and a similarity-driven attention mechanism to learn adaptive contribution weights for different matched minutiae (minutiae for short). Controlled experiments revealed that the predicted evidential value increased with the number of minutiae and was significantly influenced by the quality of minutiae. With 10 minutiae, the mean predicted scores were 4.49, 7.00, and 9.09 for blurred, moderately blurred, and clear minutiae, respectively. Multiple regression analysis indicated that replacing a pair of blurred minutiae with a pair of clear minutiae increased the score by 0.492, whereas replacing it with a pair of moderately blurred minutiae increased the score by only 0.216. By mapping predicted scores to graded levels of evidential strength, the framework contributes to a paradigm shift from categorical expert opinions to graded ones, helping courts evaluate fingerprint evidence more scientifically.

Humans↗

PubMed2026

Implicit and explicit statistical learning in reading: Evidence from a randomized controlled-learning study and computational modeling.

A key challenge in reading acquisition is understanding how learners extract the complex probabilistic mappings between print, meaning, and sound. Statistical learning (SL) theory offers a mechanistic account of how such mappings are acquired, whether implicitly through exposure or explicitly through instruction. We conducted a randomized controlled-learning study in Chinese, a writing system characterized by multiple sub-lexical regularities linking orthography, semantics, and phonology. Ninety-five 2nd-3rd graders with or at risk for dyslexia were randomly assigned to one of three groups: an implicit-SL training group exposed to repeated lexical and sublexical orthography-semantics-phonology associations, an explicit-SL training group receiving the same input plus explicit instruction on the sublexical print-sound mapping, and a no-SL control group. Both SL groups outperformed controls on the characters they were trained on, as well as on untrained characters that required generalization. However, only the explicit group demonstrated abstraction of print-sound mapping to novel items. Neural network simulations further revealed distinct mechanisms supporting implicit and explicit SL, consistent with a dual-system account of reading acquisition. Together, these findings (1) clarify how implicit and explicit learning distinctly support the discovery of statistical structure in written language and (2) underscore the implicit-explicit dual learning mechanism underlying reading acquisition.

Humans↗

PubMed2026

Inducible flocculation in Komagataella phaffii enables enhanced biomass separation for biopharmaceutical production.

Biomass separation represents a critical bottleneck in Komagataella phaffii-based biopharmaceutical processes, as typically high cell densities of 40 - 50 % create significant operational, technical and economic challenges for harvest operations. Yeast cell aggregation (flocculation) provides a solution to accelerate cell sedimentation by increasing particle size, thus allowing to improve biomass-supernatant separation efficiency during both natural gravity settling and (continuous) centrifugation operations. This study demonstrates successful engineering of K. phaffii strains with an inducible flocculation phenotype using CRISPR/Cas9-based genome editing to integrate the Saccharomyces cerevisiae FLO1 (ScFLO1) gene under control of various regulatory elements, including methanol-inducible and derepressible promoters. Flocculation strength could be enhanced by implementing transcriptional positive feedback circuits based on the methanol-inducible AOX1 promoter. To address methanol-free production requirements, we developed alternative systems to retrofit PAOX1-based ScFLO1 expression and exploited the derepressible PDF promoter, offering broader compatibility with biopharmaceutical manufacturing facilities. Flocculating cells cultivated in a bioreactor demonstrated significantly improved sedimentation behavior, with considerably lower supernatant turbidity after short low-speed centrifugation or gravity sedimentation compared to non-flocculating controls. Crucially, cell flocculation had no negative impact on product amount and quality when expressing a multivalent NANOBODY® VHH molecule with pharmaceutical relevance. Thus, this work establishes the first genetically engineered flocculation system in K. phaffii compatible with recombinant protein production, providing the basis for an innovative approach to streamline harvest operations in biopharmaceutical processes.

Flocculation↗

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

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

A Conserved 3'UTR Stem-loop Directs UPF1/eIF4AIII-Dependent Regulation of GABARAPL1 mRNA.

RNA-binding proteins (RBP) interact with mRNA untranslated regions containing cis-regulatory elements to govern mRNA localization, stability, and translational efficiency. Among these trans-regulatory factors, RNA helicase UPF1 is a central factor which play a role in multiple mRNA decay pathways, including nonsense-mediated mRNA decay (NMD). NMD is triggered when an exon-junction complex (EJC) is located downstream of a premature termination codon. However, in some cases, NMD can be activated in an EJC-independent manner through mechanisms involving the 3'UTR. In the present study, we focused on the GABARAPL1 3'UTR, as previous studies had shown that this region plays a key role in NMD targeting, although the underlying molecular mechanism had not yet been elucidated. Unlike canonical NMD targets such as SC35, we found that the chemical inhibition of eIF4AIII helicase activity did not affect GABARAPL1 transcript levels, indicating that this transcript is regulated through its 3'UTR via an EJC-independent mechanism. We therefore investigated the potential presence of cis-regulatory element within the 3'UTR of GABARAPL1 which can regulate mRNA and protein levels in a UPF1-dependent manner. Furthermore, we identified a conserved RNA region spanning nucleotides 364-421 involved in GABARAPL1 targeting and used biochemical analysis to demonstrate the direct binding of UPF1 and eIF4AIII to this RNA region, to analyse its secondary structure in solution, and to map the protein-binding sites. By complementing these approaches with molecular modelling, we showed that this stem-loop adopts a stable global fold but a local flexibility and dynamic behaviour properties. Together, our results support the role of UPF1 and eIF4AIII as specific regulators of GABARAPL1 transcript and reveal a novel RNA regulatory element within its 3'UTR, which provides a completely unexpected binding site for these factors.

3' Untranslated Regions↗

PubMed2026

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

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

RNA-binding proteins↗

PubMed2026

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

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

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

Reduced FOXP3 expression and its association with genetic ancestry in neuromyelitis Optica spectrum disorder: a Colombian cohort.

BACKGROUND: Autoimmune disorders are characterized by impaired immune tolerance, largely mediated by CD4⁺ regulatory T cells (Tregs), whose function depends on the transcription factor FOXP3. OBJECTIVES: To compare FOXP3 expression levels between patients with neuromyelitis optica spectrum disorder (NMOSD) and healthy controls from Bogotá, Colombia, and to explore their association with genetic ancestry. METHODS: FOXP3 expression was quantified from peripheral blood mononuclear cells using RNA-based analysis. Genomic ancestry proportions were estimated using ancestry-informative markers. RESULTS: FOXP3 mRNA expression was significantly reduced in NMOSD patients compared with controls (∼2.4-fold decrease in median expression; p = 0.027). Lower FOXP3 mRNA expression was associated with optic neuritis (p = 0.0059), but not with historical or current AQP4-IgG seropositivity. In a sensitivity analysis restricted to patients with documented historical AQP4-IgG seropositivity, the direction of reduced FOXP3 mRNA expression was preserved but did not reach statistical significance. In pre-specified exploratory ancestry-related analyses, African (p = 0.035) and Amerindian ancestry (p = 0.012) were associated with FOXP3 mRNA expression levels. CONCLUSIONS: Reduced FOXP3 mRNA expression in Peripheral Blood Mononuclear Cells (PBMCs) suggests an altered immune regulatory profile in NMOSD, potentially involving mechanisms beyond antibody-mediated immunity. The association between genetic ancestry and FOXP3 mRNA expression suggests that population-specific genetic background may influence immune regulatory pathways. These findings should be interpreted as exploratory and require validation in larger, clinically homogeneous cohorts.

Adolescent↗

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

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↗

PubMed2026

METTL14-mediated m6A modification of CCNE1 accelerates progression of myelodysplastic syndromes via MAPK-ERK and PI3K-AKT signaling pathways.

BACKGROUND: N6-methyladenosine (m6A) is the most common RNA modification and plays a key role in the initiation, progression, and relapse of multiple cancers, including hematologic malignancies. However, the role of m6A and m6A regulatory genes in myelodysplastic syndromes (MDS) remains unclear. This study aims to elucidate the function and molecular mechanism of methyltransferase METTL14 in MDS. METHODS: RT-qPCR was used to assess the expression of multiple m6A regulators, focusing on METTL14 in MDS patients and cell lines. METTL14 overexpressing and knockdown cell lines were established, and CCK-8, EdU, and flow cytometry assays were performed to explore the biological functions of METTL14.Dot blot, MeRIP-Seq, MeRIP-qPCR, RT-qPCR, and Western blot were employed to investigate the underlying molecular mechanism. RESULTS: Dysregulation of multiple m6A regulators was observed in MDS, among which METTL14 was upregulated. Elevated METTL14 expression increases MDS risk and adverse prognosis, emerging as a biomarker for poor prognosis. METTL14 promoted proliferation and cell-cycle progression of MDS cells while inhibiting apoptosis; corresponding changes were observed in cell cycle and apoptosis markers. METTL14 regulated cellular m6A levels. Downstream targets of METTL14 were enriched in cell cycle-related pathways, with CCNE1 identified as a critical target. Knockdown of METTL14, actinomycin D, or S-adenosylhomocysteine treatment reduced CCNE1 mRNA and protein levels. Furthermore, METTL14 activated MAPK-ERK and PI3K-AKT signaling via CCNE1 in an m6A-dependent manner, thereby promoting proliferative MDS cells' capacity. CONCLUSIONS: This study delineates a METTL14/m6A/CCNE1 signaling axis in MDS progression and suggests that METTL14-mediated m6A modification may be a potential therapeutic target for MDS.

Humans↗

PubMed2026

Perioperative Depression and Anxiety Care in Older Patients: A Randomized Clinical Trial.

IMPORTANCE: Depression and anxiety are common among older adults undergoing surgery and are associated with adverse postoperative outcomes. However, effective tailored perioperative mental health interventions are lacking. OBJECTIVE: To evaluate a perioperative intervention to optimize mental health. DESIGN, SETTING, AND PARTICIPANTS: A single-blind, hybrid, type 1, effectiveness-implementation randomized clinical trial was conducted (November 1, 2022, to March 31, 2025), with 3-month postoperative follow-up, at a US academic and community practice hospital network. Participants were 60 years or older; scheduled for cardiac, oncologic, or orthopedic surgery; and had clinically meaningful symptoms of depression and/or anxiety based on the Patient Health Questionnaire-Anxiety and Depressive Symptom (PHQ-ADS) scale. A total of 3159 patients were screened for eligibility, with 1518 ineligible, 1079 declining participation, and 236 excluded for other reasons. A total of 326 patients were enrolled and randomized (1:1), with 20 excluded after surgery cancelation. INTERVENTION: Participants were assigned to receive a perioperative intervention combining psychological management and pharmacologic optimization or enhanced usual care (materials for self-managing symptoms). MAIN OUTCOMES AND MEASURES: The primary outcome was change in PHQ-ADS score from baseline to 3 months after surgery. Other outcomes included persistent postsurgical pain, delirium, falls, quality of life, patient satisfaction, length of stay, and rehospitalizations. Implementability was evaluated through semistructured interviews and reach, acceptability, feasibility, appropriateness, and fidelity measures. RESULTS: A total of 306 older adults were included in analysis (mean [SD] age, 68.5 [6.1] years; 209 [68.3%] female; 153 randomized to intervention and 153 randomized to enhanced usual care): 102 cardiac, 100 oncologic, and 104 orthopedic patients. Participants' mean (SD) baseline PHQ-ADS score was 18.5 (7.4). At 3 months, there was a significant decrease in PHQ-ADS scores in the intervention group compared with the enhanced usual care group (mean difference, 2.20; 95% CI, 0.16-4.24; P = .03). Effects varied by surgical subgroups (oncologic patients: mean difference, 4.93; 95% CI, 1.51-8.36; P = .005; cardiac patients: mean difference, 2.68; 95% CI, -0.98 to 6.35; P = .15; and orthopedic patients: mean difference, -1.11; 95% CI, -4.62 to 2.40; P = .54). Patients and interventionists perceived the intervention as appropriate, with high-fidelity delivery and broad reach across the target population. CONCLUSIONS AND RELEVANCE: In this randomized clinical trial, psychological management and pharmacologic optimization reduced anxiety and depression in older adults undergoing surgery. Future studies should assess reproducibility and determine which patients benefit most. TRIAL REGISTRATION: ClinicalTrials.gov Identifiers: NCT05575128, NCT05685511, and NCT05697835.

Humans↗

PubMed2026

Epigenetic drift and LINE-1 activation in aging brain: Implications for neurodegenerative disease.

Brain aging and age-associated neurological diseases, such as Alzheimer's Disease (AD), Parkinson's Disease (PD), and Amyotrophic Lateral Sclerosis (ALS), are largely attributed to epigenetic drift which is characterized by the gradual accumulation of alterations in neural cell methylation patterns over time. These methylation changes are particularly evident in transposable element (TE)-derived sequences such as Long interspersed element-1 (LINE-1) which comprises approximately 17% of the human genome. During aging, LINE-1 elements gradually lose their methylation, as well as the regulatory safeguard mechanisms that usually keep them inactive. This repression loss can lead to LINE-1 reactivation, contributing to harmful effects including genomic instability, neuroinflammation, and more. Together these findings indicate that impaired epigenetic maintenance, especially in repetitive genome regions, plays a key role in biological aging of neurons and glial cells. In this narrative review, we discuss the methylation dynamics and regulatory mechanisms of LINE-1 retrotransposons, their activation processes during aging, and contribution to age-associated neurological diseases. We also highlight the potential of targeting LINE-1 methylation to restore methylation homeostasis, epigenetic stability and delay brain aging.

Humans↗

PubMed2026

Criteria for Safe Hospital Discharge in Bronchiolitis: A Systematic Review.

Bronchiolitis is the leading cause of hospital presentation and admission for infants in Australasia. We aimed to synthesise current evidence on the effect of discharge criteria for infants (aged < 12 months) who are presenting to or are admitted to hospital with bronchiolitis, to inform a binational guideline recommendation update. Systematic searches were conducted on MEDLINE, EMBASE, PubMed, Cochrane Library and CINAHL (last search 19 February 2025) for non-randomised studies evaluating hospital discharge criteria in bronchiolitis. The primary outcomes were length of stay (LOS) and readmission rates. The risk of bias (ROBINS-I) and certainty of the evidence (GRADE) were appraised, and findings were narratively synthesised. GRADE evidence-to-decision methodology, expert consensus voting and interest-holder consultation were used to finalise the recommendation update. Two retrospective observational studies were included (N = 2697) (low to very low quality), reporting on unique discharge criteria. In both studies, use of the discharge criteria was associated with a significant reduction in LOS relative to alternative protocols. There was no significant difference in readmission rates observed in either study. There was low to very low certainty evidence across outcomes due to risk of bias, indirectness and imprecision. The review findings informed a recommendation update for safe discharge criteria in the 2025 Australasian Bronchiolitis Guideline update. Updated, prescriptive discharge criteria and flow chart were developed, covering clinical stability, oxygen saturation/support, feeding difficulties, caregiver confidence and education on deterioration, social factors and follow-up. The revised criteria provide clinicians with increased certainty in decision-making in bronchiolitis, albeit with further research needed.

Humans↗