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PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2026

Early infantile developmental and epileptic encephalopathy: clinical spectrum, diagnosis, outcomes, and evolving treatment strategies.

Early infantile developmental and epileptic encephalopathy (EIDEE) is among the most severe epilepsy syndromes, with onset before three months of age and an estimated incidence of approximately 10 per 100,000 live births. The 2022 International League Against Epilepsy classification unified the historically distinct Ohtahara syndrome and early myoclonic encephalopathy under a single diagnostic framework defined by frequent drug-resistant tonic and/or myoclonic seizures, an abnormal neurological examination, and an abnormal interictal electroencephalogram-most characteristically a burst-suppression pattern. This narrative review synthesizes the clinical, electrophysiological, neuroimaging, genetic, and therapeutic literature within the EIDEE framework. The clinical phenotype is characterized by central hypotonia, postnatal microcephaly, cortical visual impairment, and age-dependent syndromic evolution toward infantile epileptic spasms syndrome or Lennox-Gastaut syndrome in the majority of patients. Electroencephalography remains essential for syndromic classification, while systematic metabolic screening and early trio whole-exome or whole-genome sequencing are central to the etiologic workup, achieving diagnostic yields of 60-65%. The most commonly identified genetic causes include STXBP1, KCNQ2, and SCN2A variants. Outcomes are poor overall and strongly etiology-dependent: vitamin-responsive disorders carry a substantially more favorable prognosis, whereas mortality reaches 25% in genetic cohorts. Genotype-guided pharmacotherapy is now applicable to a clinically meaningful subset of patients, with sodium channel blockers, potassium channel openers, and emerging antisense oligonucleotide therapies representing important therapeutic advances. Gene therapy trials are underway but have encountered early safety signals, underscoring the vulnerability of this population. Critical unmet needs include earlier molecular diagnosis, precision therapies targeting developmental outcomes beyond seizure control, and prospective international registries to characterize the long-term natural history of EIDEE.

Humans↗

PubMed2026

Longitudinal whole-genome analysis of bluetongue virus identifies conserved serotype-specific genomes and distinct genomic constellations within a Colorado sheep flock (2021-2023).

Bluetongue virus (BTV) is a segmented double-stranded RNA virus of ruminants transmitted by Culicoides spp. biting midges. Although the genome consists of ten segments, classification into serotypes is primarily based on genome segment 2. However, reassortment among genomic segments is a major driver of BTV evolution and diversity. This study used longitudinal whole-genome sequencing to characterize BTV genomes collected from 2021 to 2023 within a single sheep flock in Colorado, where multiple serotypes co-circulate. Whole-genome sequences were generated from fourteen blood samples representing four serotypes: BTV-6, -11, -13, and -17. Longitudinal sampling identified multiple BTV serotypes within individual sheep across consecutive years. Tanglegram analysis comparing segment phylogenies to the segment 2 tree demonstrated incongruent topologies across all genomic segments, suggestive of reassortment or the circulation of distinct genomic constellations. Nucleotide-level comparisons revealed high sequence homology among same-serotype samples from the same year, while the greatest genetic divergence was observed among BTV-17 genomes collected in different years. Additionally, all BTV-13 genomes contained a previously undescribed nonsynonymous substitution in segment 10 predicted to extend the encoded protein by three amino acids. Together, these findings demonstrate that highly conserved BTV genomes and distinct genomic constellations can be detected at the flock level across multiple years. This longitudinal whole-genome approach reveals the genetic complexity of endemic BTV populations, including novel variants and genomic patterns consistent with reassortment that are lost with conventional serotyped-based approaches, highlighting the need to integrate whole-genome characterization into endemic BTV monitoring programs.

Animals↗

PubMed2026

Understanding psychosocial adjustment in military-to-civilian transition: A latent profile analysis of ex-serving Australian Defence Force members.

Military-to-civilian transition is a critical life stage that can expose veterans to elevated risks of psychological distress, social difficulties, and reduced wellbeing. Although psychosocial factors are central to successful reintegration, little is known about distinct patterns of needs among ex-serving Australian Defence Force (ADF) members. This study used latent profile analysis (LPA) to identify psychosocial needs profiles across five domains of the Military-Civilian Adjustment and Reintegration Measure (M-CARM) in a sample of 725 ex-serving ADF members. The optimal three-class solution identified: a Low Adjustment Need (LAN) group (20.7%) reporting minimal reintegration challenges; a Cultural Adjustment Need (CAN) group (42.9%) characterized by cultural adaptation difficulties, particularly beliefs about civilians and regimentation; and a Cultural and Psychological Need (CPN) group (36.4%) showing broader challenges across beliefs about civilians, purpose and connection, regimentation, and resentment and regret. The CAN group was more likely to be male, have lower educational attainment, and have no combat deployment history. The CPN group was similarly male dominated with lower education, and was additionally characterized by Navy service, unemployment or not being in the labor force, and medical discharge. Compared with the LAN group, both CAN and CPN groups reported higher levels of depression, anxiety, posttraumatic stress, and nightmare distress, as well as poorer quality of life and greater functional impairment. These findings highlight persistent reintegration challenges among veterans and support the need for stratified support models, ranging from psychoeducation to intensive multidisciplinary care, to better address diverse psychosocial needs of ex-serving ADF members.

Australian defense force↗

PubMed2026

Integrative modeling of the genome structure and dynamics in fission yeast.

Genome organization in the nucleus is highly structured and dynamic. Recent advances in genomic technology have enabled the measurement of genome-wide architecture and locus-specific motion, yielding contact maps and live-cell trajectories. However, these outcomes are derived from different modalities and are not directly comparable, with their quantitative integration being a key challenge. Here we establish a genome-wide live-cell imaging platform in fission yeast Schizosaccharomyces pombe, tracking 131 chromosomal loci, along with the spindle pole body (SPB) and nucleolus, to construct a quantitative map of locus dynamics. By integrating these dynamics with contact data through polymer modeling of Hi-C data, we build a physics-based "digital twin" of the S. pombe genome consistent with the spatiotemporal dynamics of interphase chromatin. We validate it against genome-wide mobility patterns and known architectural features, including centromere and telomere clustering. The model also identifies distinct dynamical regimes: centromere- and telomere-proximal loci relax within [Formula: see text]150 s, whereas the remaining loci relax within [Formula: see text]70 s. We measure semiperiodic dynamics of SPB motion, including a characteristic peak near 225 s and [Formula: see text] fluctuations. We use the model with SPB-directed forcing to show how these low-frequency components propagate through the genome to drive genome-wide chromatin displacements. Together, this predictive physics-based modeling framework integrates genome structure and dynamics to reveal how nuclear mechanical driving forces shape chromosome motion, linking mechanically driven chromatin responses to genome maintenance and regulation.

Schizosaccharomyces↗

PubMed2025

Barriers and Facilitators of Help-Seeking for LGBTQ+ Survivors of Sexual Violence: A Systematic Review.

People who identify as LGBTQ+ (Lesbian, Gay, Bisexual, Transgender, Queer, plus) are known to experience similar or higher levels of sexual violence compared to their heterosexual cisgender counterparts. However, sexual violence research has largely focused on heterosexual female survivors of male perpetrated crime. Thus, the unique support needs and help-seeking patterns of LGBTQ+ survivors are poorly understood. This review addresses this gap by systematically exploring literature on barriers and facilitators to help-seeking for LGBTQ+ survivors of sexual violence. Four databases (PsycINFO, CINAHL, MEDLINE, and Web of Science) were searched to identify relevant material, with 35 articles (30 qualitative, 1 quantitative, and 4 mixed-methods) meeting the inclusion criteria. Data were extracted and analyzed using a narrative synthesis. The topic was investigated almost exclusively cross-sectionally. Barriers included discrimination experiences, myths and stereotypes, feelings of shame and self-blame, and rejection of victim status. Additional barriers were reported by survivors who hold multiple minority identities, in particular LGBTQ+ people of color and sex workers. Facilitators to help-seeking included the intrinsic need to connect with others, social encouragement and empowerment, and positive disclosure experiences. The Power Threat Meaning framework provides insight into these findings by presenting help-seeking behaviors as adaptive responses to increase a sense of safety following a traumatic experience. The analyzed data indicate several implications for the development and improvement services to support LGBTQ+ survivors. They further serve to highlight the need for additional robust research, conducted with an intersectional lens, to explore the needs of sexual and gender minority survivors of sexual violence.

Humans↗

PubMed2026

From premature adrenarche to adult metabolic risk and hyperandrogenism: a systematic review and meta-analysis.

CONTEXT: Idiopathic premature adrenarche (IPA) has been associated with a higher risk of metabolic and reproductive dysfunction, but long-term/adult outcomes remain incompletely known. OBJECTIVE: To assess the relationship between IPA and metabolic syndrome, as well as polycystic ovarian syndrome, in premenarcheal adolescent and adult women. METHODS: We conducted a systematic review and meta-analysis of observational studies reporting outcomes in females with IPA after menarche. Databases were searched through February 2025. Primary outcomes included body mass index (BMI), insulin resistance markers, and clinical and biochemical markers of hyperandrogenism. Data were pooled using random-effects models. The GRADE approach was applied to assess the certainty of evidence. RESULTS: A total of 21 studies comprising 635 females with IPA and 307 age-matched controls were included. Compared to controls, IPA individuals showed significantly higher BMI (mean difference: 1.4; CI: 1.0-1.9), fasting insulin, and homeostasis model assessment of insulin resistance, indicating persistent insulin resistance. Markers of hyperandrogenism, including Ferriman-Gallwey score, dehydroepiandrosterone sulfate, and Free androgenic index, were also elevated. Secondary analyses revealed higher triglycerides, lower high-density lipoprotein, increased leptin, and greater carotid intima-media thickness, supporting an early pattern of cardiometabolic risk. GRADE assessment rated most outcomes as low certainty. CONCLUSION: Women with a history of IPA are at increased risk of long-term insulin resistance and hyperandrogenism, with early signs of adverse cardiometabolic profiles. These findings support the need for long-term monitoring in this population.

Humans↗

PubMed2026

The composition of the periostracum in the razor clam Sinonovacula constricta and the mantle's response to sulfide.

The razor clam Sinonovacula constricta inhabits sulfide-rich intertidal sediments and exhibits remarkable tolerance to this toxicant, yet the role of its periostracum in sulfide adaptation remains poorly understood. In this study, we investigated the composition and structure of the periostracum proteins, and the response of the mantle to sulfide stress. Scanning electron microscopy and energy-dispersive X-ray spectroscopy revealed that the periostracum is approximately 10 μm thick and contains 1.43 wt% sulfur, and proteomic analysis further confirmed the presence of organic sulfur (Cys/Met-rich proteins), suggesting its involvement in sulfur deposition. Using LC-MS/MS, we identified 77 high-confidence proteins from the periostracum, which were classified into six functional categories: enzymes, framework proteins, immune-related proteins, calcium ion-related proteins, other proteins, and proteins with unknown functions. Phylogenetic analyses of representative proteins revealed bivalve-specific evolutionary patterns, with several proteins exclusively present in Bivalvia, such as Unknown protein 2 and 7, which possess signal peptides and low-complexity domains. For the sulfide exposure experiment, razor clams were subjected to three Na2S concentrations (0, 10, and 100 μM). qPCR analysis showed that, compared with the control group, Chitin-binding protein 3 and Tyrosinase were significantly upregulated in the mantle, peaking in the 100 μM group at 48 h (5677.84-fold and 157.20-fold, respectively), whereas Collagen and Cadherin 3 were generally suppressed. This study represents one of the most comprehensive proteomic profiles of the razor clam periostracum and highlights the mantle's potential role in sulfide tolerance, offering insights for sulfur-tolerant aquaculture breeding and bioremediation applications.

Animals↗

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

Pharmacological and non-pharmacological modulation of striatal dopamine release: a meta-analysis of [11C]raclopride PET studies.

The dopaminergic system has long been a central focus of functional neuroimaging. Positron emission tomography (PET) with the D2/D3 receptor radioligand [11C]raclopride remains the most widely used method for indirectly quantifying striatal dopamine release in vivo. However, no previous meta-analysis has studied the relative magnitude and regional distribution of dopamine release across different interventions or cognitive interventions overall. To address this gap, in this meta-analysis of 92 [11C]raclopride PET studies (n = 1640), we compared the magnitude and regional distribution of dopamine release induced by amphetamine, methylphenidate, ketamine, alcohol, and cognitive challenges with and without reward. Amphetamine induced approximately four-fold greater dopamine release than cognitive challenges (10.9 vs. 2.7%, p < 0.001), and approximately twice that of alcohol (4.8%, p < 0.001), with effects comparable to methylphenidate (11.5%) and slightly greater than ketamine (9.8%). Psychostimulant-induced increase in synaptic dopamine was greater in putamen and ventral striatum than in caudate, whereas alcohol preferentially engaged ventral striatum. Dopamine release did not differ between rewarded and non-rewarded cognitive tasks in the ventral striatum (p > 0.14) or overall striatum (p > 0.10). Methylphenidate-induced increases in synaptic dopamine appeared to attenuate with advancing age, whereas cognitive challenges were associated with greater dopamine release in older individuals. These findings demonstrate that individual pharmacological and cognitive interventions differ markedly in both magnitude and regional pattern of dopamine release. They also suggest that [¹¹C]raclopride PET may have limited sensitivity for distinguishing reward-related from non-reward-related dopamine release. These findings have implications for the design and interpretation of future neuroimaging studies of dopaminergic function in health and disease.

Journal Article↗

PubMed2026

Joint association of sedentary behaviour and physical activity with cardiovascular disease: a systematic review and meta-analysis.

This systematic review and meta-analysis of cohort studies aimed to synthesize existing evidence on the joint association of physical activity (PA) and sedentary behaviour (SB) with cardiovascular disease (CVD) risk among adults. We searched PubMed, EMBASE, and Cochrane for English studies published between January 2010 and February 2025 that examined the joint association of PA and SB (fatal and non-fatal) CVD among adults and pooled their results through meta-analyses using study-level data. Using findings from 17 studies, the pooled effect size for the lowest PA + highest SB group was 1.78 (95% CI: 1.59-2.00), suggesting increased risk for CVD compared with the reference group (i.e. highest PA + lowest SB). Compared with the same reference group, we found an increased risk for CVD in the lowest PA + lowest SB (HR = 1.25, 95% CI: 1.12-1.40) and the highest PA + highest SB (HR = 1.16, 95% CI: 1.06-1.27) groups. Subgroup analyses according to domains or types of PA and SB exposure, outcome measures, and exposure measurement method revealed a similar pattern. In conclusion, individuals with the lowest PA combined with the highest SB may experience an increased risk for CVD events compared with those with the highest PA and lowest SB. There was also an increased risk for individuals with a combination of either low PA + low SB and high PA + high SB, albeit to a lesser extent. Although substantial study-level heterogeneity exists, the results highlight the potential value of considering both behaviours jointly in relation to CVD risk.

Humans↗

PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗

PubMed2026

Symptom Networks and Core Symptoms in Patients with Solid Tumors Undergoing Chemotherapy: A Systematic Review.

OBJECTIVES: To summarize symptom network characteristics in patients with solid tumors undergoing chemotherapy and synthesize evidence on core symptoms, bridge symptoms, and temporal associations. METHODS: We systematically searched eight databases through October 2025 to identify studies that applied symptom network analysis to adults with solid tumors receiving chemotherapy. Eligible studies assessed symptoms using cross-sectional, longitudinal, or interventional designs. Two reviewers independently screened articles and extracted data on study characteristics, symptom assessment, and network outcomes. Methodological quality was assessed using the National Institutes of Health Study Quality Assessment Tool. RESULTS: Twenty-seven studies involving 13,452 participants were included, yielding 79 symptom networks. Fatigue was the most frequently identified core symptom (10/20, 50%), whereas sadness, lack of appetite, and nausea each occurred in 10% of studies, with variation across cancer types, treatment phases, and latent classes. Bridge symptoms included disturbed sleep, lack of appetite, and dry mouth (2/7, 28.6%). Studies evaluating temporal associations found that symptoms such as sadness, dyspnea, somnolence, and dry mouth predicted subsequent changes in appetite, distress, nausea, and other outcomes. Strength metrics showed acceptable stability (correlation stability coefficients: 0.28-0.83). CONCLUSIONS: Fatigue was frequently identified as a central symptom across studies, largely reflecting evidence from breast cancer studies. Core symptoms varied across cancer types, treatment phases, and latent classes, suggesting heterogeneity. IMPLICATIONS FOR NURSING PRACTICE: These findings highlight the importance of considering relationships among symptoms in clinical care. Focusing on key symptoms such as fatigue, while tailoring management strategies to cancer-specific symptom patterns, may support more effective symptom management.

Humans↗

PubMed2026

Hypothalamic-Pituitary Axis Involvement in Primary Central Nervous System Lymphoma.

CONTEXT: Primary central nervous system lymphoma (PCNSL) is a rare malignancy that may involve the hypothalamic-pituitary axis (HPA), leading to underrecognized but clinically significant endocrine dysfunction. OBJECTIVE: This work aims to characterize the spectrum and patterns of HPA-related endocrine disturbances in patients with PCNSL. DATA SOURCES: A systematic search was conducted in PubMed, EMBASE, Scopus, and Web of Science, supplemented by gray literature. The search concluded in February 2025. STUDY SELECTION: We included studies reporting adult PCNSL cases with documented dysfunction of at least one hormonal axis. Exclusion criteria were preexisting hypopituitarism or lack of endocrine data. DATA EXTRACTION: Data on demographics, tumor localization, hormonal axes affected, radiological findings, treatment, and outcomes were extracted. Risk of bias was assessed using JBI tools. RESULTS: Ninety-nine cases met the inclusion criteria. Diffuse large B-cell lymphoma accounted for 84% of cases. Endocrine dysfunction included isolated adenohypophyseal involvement (46%), neurohypophyseal (8%), and combined (45%). The most affected pituitary axes were the gonadal and thyroid axes, with 89.7% and 89.2% involvement, respectively. Hypothalamic tumors were strongly associated with combined dysfunction (odds ratio = 9.47; 95% CI, 3.76-23.86; P < .001). Persistent endocrinopathy was more frequent in progressive disease. No direct association was found between endocrine dysfunction and mortality. CONCLUSION: HPA dysfunction in PCNSL is frequent and often underdiagnosed. Hypothalamic involvement is associated broader hormonal impairment. Routine hormonal screening and multidisciplinary management should be standard in PCNSL care to minimize complications and improve outcomes.

Humans↗

PubMed2026

Smoking Cue Reactivity in Relation to Uncertain-Threat and Reward-Anticipation Networks: A Coordinate-Based fMRI Meta-Analysis.

BACKGROUND: Smoking is a concerning medical and social problem, yet how the brain links stress to continued smoking is still not well understood. This coordinate-based meta-analysis identified convergent activations for smoking cues, uncertain threat, and reward anticipation, and examined co-activation patterns to clarify whether smoking-cue activity in smokers relates to threat and reward activity in non-addicted controls. METHODS: We conducted a coordinate-based activation likelihood estimation (ALE) meta-analysis of 102 fMRI studies (N = 3,068), including 30 studies on smoking cue reactivity (n = 945), 48 on reward anticipation (n = 1,413), and 24 on uncertain threat processing (n = 1,621). We performed single, conjunction, and contrast analyses, followed by meta-analytic connectivity modeling (MACM) of key regions. RESULTS: Single analysis revealed smoking engaged bilateral ACC (-1.1, 46.2, -1.1), uncertain threat engaged bilateral insula (left = -33.6, 22, 4.3, right = 41.3, 22, 1), reward anticipation activated thalamus (0.9, 1.3, -3.1) and medial frontal gyrus (2.7, 6.6, 52.1). Conjunction and contrast analyses showed shared or unique activation for each task in its respective regions. MACM showed ACC co-activation with thalamus and medial frontal gyrus, while insula co-activated with ACC and inferior frontal gyrus. CONCLUSIONS: Each process converged in a separate region, with no overlap between the smoking-cue map and either the threat or reward map. The ACC nonetheless co-activated with reward-related regions and shared network membership with the threat-related insula. On this basis we hypothesize a shift in motivation from stress-driven reward toward cue-driven craving, to be tested within subjects, and identify candidate neuromodulation targets for preventing stress-precipitated relapse.

fMRI↗

PubMed2026

Depth-dependent microbial succession and interspecies hydrogen transfer drive pit mud maturation in Chinese strong-flavor baijiu fermentation.

Microbial communities in fermentation pit mud play a key role in determining the quality of Chinese strong-flavor baijiu (CSFB). However, the ecological processes underlying pit mud maturation across spatial and temporal scales remain unclear. In this study, amplicon sequencing and metagenomic analyses were employed to investigate the taxonomic succession, community assembly, and metabolic functions of bacterial and archaeal communities during the transition from fresh pit mud (FPM) to new pit mud (NPM) and old pit mud (OPM). A pronounced depth-dependent succession pattern was observed, with 4 cm representing a critical ecological boundary separating distinct community structures and maturation trajectories. During surface-layer maturation, community assembly shifted from stochastic to deterministic processes, accompanied by homogeneous selection and increasing network complexity. In contrast, stochastic processes remained dominant throughout deep-layer maturation. Metagenomic analyses revealed a functional transition from lactate and acetate production, primarily associated with Lactobacillus in FPM and NPM, to butyrate and caproate production associated with Clostridium and Caproiciproducens in OPM. This functional transition was accompanied by enhanced amino acid metabolism, which was associated with the enrichment of Proteiniphilum and Aminobacterium. Notably, methanogen-mediated interspecies hydrogen transfer (IHT) emerged as a key ecological feature during pit mud maturation. In OPM, IHT networks primarily involving Methanobacterium and Methanosarcina linked methanogenesis with reverse β-oxidation through diverse hydrogen-transfer pathways, reinforcing metabolic interactions underlying caproate production. These findings provide new insights into the ecological mechanisms underlying pit mud maturation and offer a theoretical basis for the directed cultivation of high-quality pit mud in CSFB production.

Hydrogen↗

PubMed2026

Self-selected goals outperform assigned goals in reducing mobile phone usage: Evidence from a randomized controlled trial.

Excessive smartphone use is increasingly recognized as a public-health concern, yet scalable approaches to help individuals regulate daily use remain limited. We examine whether allowing individuals to self-select reduction goals improves behavioral and psychological outcomes when incentives and average goal levels are held constant across conditions. In a twelve-week randomized controlled trial, (N = 149; over 9000 person-day observations), participants were assigned to (i) a self-selected condition (choosing a 10%, 20%, or 30% reduction in daily phone use), (ii) an assigned condition (assigned a 14% reduction goal), or (iii) a no-goal control condition. Participants who selected their own goals reduced phone use by 26 min more per day (73% larger reduction) and achieved their goals 11 percentage points more often than those assigned goals, despite identical incentives and average goal levels. Reductions in phone use and higher goal achievement were associated with improvements in perceived addiction, depressive, and anxiety symptoms. These psychological outcomes were secondary endpoints. Although the between-group estimates generally followed the same directional pattern as the behavioral outcomes, the sample size for these analyses was limited and the between-group differences were not statistically significant. These findings should therefore be interpreted with caution. Overall, the results provide causal field evidence that self-selection under this goal-setting design can improve behavioral outcomes. Allowing individuals to choose their own goals may strengthen engagement and support healthier digital behavior. Incorporating opportunities for goal-selection may represent a simple addition to digital-health and public-health interventions aimed at helping individuals moderate smartphone use and improve well-being.

Humans↗

PubMed2026

Human iPSC-EV-loaded nanofiber stent coatings accelerate vascular repair by enhancing EGFR/HIF-1α signaling and suppressing ROCK1-mediated remodeling.

Arterial disease management is shifting from antiproliferative drug-eluting stents toward approaches that restore endothelial function and modulate smooth muscle cell (SMC) behavior. Stem cell-derived extracellular vesicles (EVs) carry miRNAs that promote endothelial proliferation and migration while restraining aberrant SMC growth and inflammation. Here, human induced pluripotent stem cell (iPSC)-derived EVs were collected by ultracentrifugation and incorporated into 50:50 poly (lactic-co-glycolic acid) (PLGA 503) core-shell nanofibrous membranes, which were fabricated as stent coatings for sustained release to overcome rapid clearance and poor tissue retention. EVs derived from three independent iPSC lines all enhanced tube formation in human umbilical vein endothelial cells (HUVECs) under hypoxic and serum-starved conditions and revealed a trend toward reduced platelet-derived growth factor-BB (PDGF-BB)-induced smooth muscle cell (SMC) migration. The fabricated core-shell nanofibers enabled sustained EV release, maintaining therapeutic efficacy for 28 days. Small RNA sequencing (NGS) analysis demonstrated that EVs from these independent iPSC lines shared miR-148a-3p and members of the miR-92 family, which collectively accounted for more than 75% of the reads within the 25 top-expressed miRNA set. In vitro, iPSC-EVs enhanced HUVEC proliferation and survival signaling by downregulating the negative regulators ERRFI1 and VHL, which are specific targets of miR-148a-3p and the miR-92 family, thereby activating the EGFR and HIF-1α axes and driving downstream ERK1/2 and VEGF expression under hypoxic and serum starvation stress conditions. Concurrently, iPSC-EVs prevented PDGF-BB-induced SMC phenotypic switching by downregulating ROCK1, a target of miR-148a-3p, thereby inhibiting downstream AKT and ERK signaling and preserving contractile markers while suppressing the synthetic phenotype. In vivo, the iPSC-EV-functionalized scaffolds significantly accelerated re-endothelialization and inhibited neointimal hyperplasia, evidenced by the upregulation of angiogenic factors (VEGF, CD31) and the concurrent suppression of pathological remodeling markers (α-SMA, MMPs) and inflammatory cytokines (IL-6, TGF-β1). Therefore, iPSC-EVs enriched with specific miRNAs and delivered via PLGA 503 core-shell nanofibers promote endothelial repair while suppressing SMC overgrowth, providing a promising strategy for vascular healing.

Core-shell nanofibers↗