Search PubMedSearch

SEARCH · Search PubMed

Results for “Cross-lagged panel network analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

1,713 records · Page 21Linked to original sources

Health and Physical Activity Outcomes in Age-Friendly Cities and Communities: A Systematic Review of Emerging Evidence and a Future Research Agenda.

OBJECTIVES: The World Health Organization's (WHO) Global Network of Age-Friendly Cities and Communities (AFCCs) promotes the development of urban environments, policies and services that support the health and participation of older adults. This systematic review examined contemporary evidence concerning associations between WHO AFCC conditions and directly measured health and physical activity outcomes among older residents. METHODS: The registered review adhered to the PRISMA protocol for systematic reviews and meta-analyses and applied the Downs and Black quality criteria for randomised and non-randomised research. RESULTS: Structured Boolean searches of five research repositories identified 17 peer-reviewed studies published between 2017 and 2025 based upon original research conducted in WHO AFCC signatory cities. Although most studies reported positive associations between age-friendly features and domains, such as accessible transport, walkable environments, outdoor infrastructure and self-rated health or physical activity, the strength of evidence was limited by methodological inconsistency, variable study quality and reliance on self-reports. Barriers to evaluation included limited use of longitudinal or quasi-experimental designs, heterogeneous outcome measures, subjective response data and the challenge of establishing appropriate comparison conditions in complex municipal settings. CONCLUSIONS: Strengthening evaluation frameworks for AFCC initiatives is essential for evidence-based urban health policy and governance in rapidly ageing societies. A research agenda is proposed to strengthen AFCC evaluation through standardised measurement, community-based and mixed-methods research, and a greater commitment to co-designed assessment frameworks.

Humans

Hierarchical modeling of tumor subtypes in cell lines using large-scale genomic datasets.

Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.

cancer cell lines

Non-tobacco nicotine dependence and postoperative complications after total ankle arthroplasty: A propensity-matched cohort study.

BACKGROUND: The clinical impact of non-tobacco nicotine dependence (NTND) is poorly defined. This study evaluated the association between NTND and complications following total ankle arthroplasty (TAA). METHODS: A retrospective cohort study using the TriNetX Research Network was performed. Adults undergoing primary TAA (Current Procedural Terminology [CPT] 27702) between 2010 and 2025 were included. Patients were categorized into NTND (International Classification of Diseases, Tenth Revision [ICD-10]: F17, excluding tobacco-specific codes) and nonsmoker cohorts. Propensity score matching (1:1) yielded 939 NTND patients and 939 controls. Ninety-day medical and wound complications and ≥ 2-year mechanical outcomes were assessed. RESULTS: NTND patients had higher rates of 90-day readmission (11.7% vs 6.5%; OR 1.9), wound disruption (4.3% vs 2.6%; OR 1.7), surgical site infection (3.1% vs 1.6%; OR 2.0), and sepsis (2.4% vs 1.1%; OR 2.3). At a minimum 2-year follow-up, NTND was not associated with increased risk of mechanical complications. CONCLUSION: These findings challenge the assumption that smokeless nicotine products are benign in the perioperative setting and support incorporating NTND screening and cessation counseling into preoperative optimization protocols. Future prospective studies are warranted to further characterize the dose-dependent effects of non-tobacco nicotine exposure and to evaluate the impact of perioperative cessation strategies on outcomes following TAA. LEVEL OF EVIDENCE: IV.

Humans

Genomic and Molecular Interaction Analysis of NodD1 in a Novel Bradyrhizobium yuanmingense sp. B64 Isolate for Nodulation and Symbiosis of Legume Plants.

Rhizobial bacteria are known for their ability to fix nitrogen for leguminous plants and their essential function for sustainable agriculture. This study characterizes the taxonomic status and functional potential of the Bradyrhizobium B64 isolate using integrated genomic and molecular approaches. The whole genome of the B64 isolate was sequenced via Illumina paired-end technology. Species delimitation was performed using average nucleotide identity (ANI) and digital DNA-DNA Hybridization (dDDH). The NodD1 protein structure was modeled using AlphaFold3 and validated by Ramachandran plot analysis. Molecular docking was then conducted to evaluate interactions between NodD1 and four signaling flavonoids: Apigenin, Daidzein, Genistein, and Naringenin. Genomic analysis revealed a maximum ANI of 94.4% and dDDH values between 51.4 and 62.4%. Since these values fall below the standard prokaryotic thresholds (ANI&#x2009;<&#x2009;95%; dDDH&#x2009;<&#x2009;70%), the B64 isolate is identified as a novel species. Physiological assays confirmed nitrogen fixation (1.97 ppm), IAA production (3.67 ppm), and phosphate solubilization (26.10 ppm). Structural validation showed 100% of NodD1 residues in allowed regions, ensuring high model reliability. Docking simulations demonstrated strong binding affinities across all flavonoids, with binding free energies ranging from -&#x2009;8.8 to -&#x2009;9.0&#xa0;kcal/mol. Daidzein exhibited the highest thermodynamic stability (-&#x2009;9.0&#xa0;kcal/mol), whereas apigenin showed the most extensive residue interaction network. The B64 isolate is a novel Bradyrhizobium species with a high symbiotic capacity. The stable NodD1-flavonoid interactions provide a molecular basis for efficient nodulation, positioning B64 as a promising candidate for developing lipo-chitooligosaccharide (LCO)-based biofertilizers.

Bradyrhizobium

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

Humans

Efficacy of pharmacological and microbiota-based therapies in preclinical models of autism spectrum disorder: a systematic review.

BACKGROUND: Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental condition in which pharmacological and microbiota-targeted interventions are emerging as promising therapeutic avenues. Animal models are the main tool to investigate etiology, molecular mechanisms and screening for pharmacological therapies. Methodological differences, outcome measure variability, incomplete reporting, biological confounders, and overgeneralization of the results made evaluating innovative pharmacological agents challenging. These limitations in the field highlight a need for systematic and standardized research to reliably assess and translate pharmacological interventions from ASD animal models to human clinical relevance. SUBJECTS: This systematic review synthesized efficacy evidence for pharmacological and microbiota-based therapies across established ASD animal models. RESULTS: We identified 52 recent (2010-2025) studies that reported key ASD behavioral outcomes after pharmacological or microbiota-focused treatments. Interventions were grouped into therapeutic classes - including oxytocinergic agents, E/I balance therapeutic targets, metabolic drugs, cannabinoids, purine-based interventions and emerging targets - alongside microbiota-directed strategies such as probiotics, prebiotics, and fecal microbiota transplantation. By integrating effect directions and robustness across models, we identified most potential drug candidates, evaluated the efficacy of novel strategies, and recognized critical translational gaps. The reviewed studies demonstrate that ASD-like behavioral deficits in preclinical models can be modulated through interventions targeting diverse biological systems, including neurotransmission, neuroinflammation, metabolism, and the gut-brain axis. CONCLUSIONS: These findings support the multifactorial nature of ASD pathophysiology which arises from a network of interacting systemic processes rather than a single molecular defect. It could explain the limited success of traditionally narrowly targeted interventions and suggest a paradigm shift into a more systemic approach.

Animals

RPLP0 drives diffuse large B-cell lymphoma cell proliferation through reactive oxygen species-dependent AKT/mTOR activation and inhibition of stress-induced autophagy.

Diffuse large B-cell lymphoma (DLBCL) is a common, aggressive subtype of non-Hodgkin lymphoma with poor outcomes. Identifying the primary molecular causes of DLBCL remains key. The present study examined the function of ribosomal protein lateral stalk subunit P0 (RPLP0) in DLBCL pathogenesis. The Cancer Genome Atlas-DLBCL and GSE12453 datasets overlapping differentially expressed genes were identified. Hub genes were identified via protein-protein interaction network analysis. DLBCL cells were subjected to functional tests following RPLP0 overexpression or knockdown. Reverse transcription-quantitative PCR, western blotting, flow cytometry, transmission electron microscopy, colony formation assay and biochemical analysis were among the tests performed. N-acetylcysteine (NAC), rapamycin (RAPA) and 3-MA were among the medication therapies. In the DLBCL datasets, six ribosome-associated genes were differentially expressed. RPLP0 knockdown inhibited the proliferation of DLBCL cells and caused G2-phase arrest, without impacting apoptosis. Thioredoxin, heat shock protein family A member 1A and heat shock protein family B member 1 expression was downregulated by RPLP0 knockdown, which also increased the NAD+/NADH ratio, promoted reactive oxygen species (ROS) accumulation and caused mitochondrial membrane potential depolarization. Meanwhile, 3-MA reversed the effects of RPLP0 knockdown, which encouraged LC3-II accumulation, autophagy-related gene 5 (ATG5) overexpression and an increase in autophagic vesicles. Autophagy-related indicators were decreased, and AKT/mTOR phosphorylation was increased by RPLP0 overexpression, which RAPA inhibited. NAC therapy preserved the viability of RPLP0-silenced cells, restored p-AKT/p-mTOR levels and restored normal LC3 and ATG5 expression. These findings suggest that RPLP0 regulates stress-induced autophagy through ROS-dependent AKT/mTOR signaling and may represent a potential therapeutic target for DLBCL.

AKT/mTOR signaling pathway

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics

Unacknowledged Burdens and Clinical Assets of BIPOC Genetic Counseling Students: Qualitative Evidence to Inform Supervision.

As the genetic counseling profession works to diversify its predominantly white workforce, understanding the experiences of Black, Indigenous, and People of Color (BIPOC) students is central to equity efforts. While BIPOC students bring invaluable cultural and linguistic diversity that improves patient care, they often navigate clinical training environments that lack diversity and psychological safety. This article draws on data from a longitudinal constructivist qualitative study to examine how racial and ethnic concordance (or lack thereof) with patients and clinical supervisors influenced the clinical training, professional development, and well-being of BIPOC genetic counseling students. Semi-structured interviews were conducted with 25 BIPOC genetic counseling students in the United States and Canada. Interviews were recorded using Zoom.us, transcribed using Rev.com, and analyzed in NVivo using reflexive thematic analysis. The analysis led to the construction of three themes: (1)Shared identity with patients is a clinical advantage: Participants leveraged their cultural and linguistic intuition to establish trust and rapport with patients; (2) Identity navigation involves cognitive and emotional labor: Participants shouldered an unacknowledged burden in managing stereotype threat, overcoming feelings of exclusion, and educating supervisors; and (3) Racial/ethnic identity shapes supervisory dynamics: Participants described BIPOC supervisors as providing identity-affirming support, while some white supervisors avoided discussions about identity or committed microaggressions. These results suggest that BIPOC genetic counseling students have clinical assets rooted in biculturalism, yet carry a burden that often goes unacknowledged of managing power imbalances and pressure to assimilate in predominantly white clinical supervision spaces. To promote equitable training, programs should implement supervisor training on culturally responsive identity broaching, establish independent, transparent mechanisms for students to report biases they encounter in clinic, and expand mentorship networks to provide additional support.

Humans

The Addition of Concurrent Immune Checkpoint Inhibitors for Chemoradiotherapy With Consolidative Immune Checkpoint Inhibitors in Unresectable Cancers: A Systematic Review and Meta-Analysis.

Following the success of chemoradiotherapy (CRT) combined with consolidative immune checkpoint inhibitors (ICIs) in locally advanced tumors, over 30 ongoing randomized controlled trials (RCTs) are investigating the potential benefits of adding concurrent ICIs. To investigate the differences in efficacy and safety between adding and not adding concurrent ICIs to CRT followed by consolidative ICIs, a literature search was conducted in PubMed, Embase, and the Cochrane Library, incorporating RCTs comparing CRT combined with consolidative ICIs versus CRT alone, or CRT with both concurrent and consolidative ICIs versus CRT alone. The primary outcomes were overall survival (OS) and progression-free survival (PFS). To reduce potential bias, an additional mirror-design analysis was performed through network meta-analysis. A total of 13 RCTs comprising 6868 patients and 14 cohort studies comprising 4724 patients were included. While patients treated with CRT and consolidative ICIs demonstrated significantly superior OS and PFS to patients treated with CRT alone in RCTs (HR of OS, 0.743, 95% CI, 0.654-0.843; HR of PFS, 0.674, 95% CI, 0.577-0.786), CRT and concurrent-plus-consolidative ICIs did not improve OS and PFS compared with CRT alone (HR of OS, 0.942, 95% CI, 0.782-1.134; HR of PFS, 0.880, 95% CI, 0.752-1.030). Significant differences were detected in OS (p&#x2009;=&#x2009;0.038) and PFS (p&#x2009;=&#x2009;0.017) between CRT combined with consolidative ICIs treatment versus CRT combined with concurrent and consolidative ICIs treatment from RCTs. In conclusion, adding concurrent ICIs may dampen the survival benefits of CRT combined with consolidative ICIs. This evidence informs future RCT design strategies.

Humans

Comparative in silico analysis of Apis mellifera immune responses to Varroa destructor and Tropilaelaps mercedesae: Common and mite-specific molecular signatures.

Parasitic mites Varroa destructor and Tropilaelaps mercedesae represent major threats to global honey bee (Apis mellifera) health and productivity, yet comparative molecular insights into host responses remain limited. To address this, we systematically compiled published studies (2015-2025) reporting genes associated with honey bee interactions with V. destructor (11 studies, 87 genes), T. mercedesae (4 studies, 35 genes), and hygienic behavior (6 studies, 44 genes). Gene identifiers were harmonized to the Amel_HAv3.1 genome assembly, yielding three non-redundant sets: 64 Varroa-associated, 34 Tropilaelaps-associated, and 44 hygienic behavior-associated genes. Venn analysis identified 10 overlapping genes (including A0A088A8D5, A0A088ADL8, ABAE_APIME, Def1, Def2, Gapdh, HYTA_APIME, Imd, LOC726783, and Vg), suggesting conserved defense mechanisms, while 41 and 24 genes were uniquely associated with Varroa and Tropilaelaps, respectively. Enrichment analyses revealed Varroa-responsive genes were enriched in immune processes, chitin catabolism, and signaling pathways (Toll/Imd, MAPK, Wnt). Tropilaelaps-associated genes were enriched for antibacterial defense and stress response, with Toll/Imd signaling as the sole significantly enriched pathway. Overlapping genes reinforced core innate immunity activation. Protein-protein interaction network centrality analysis identified key hub genes: Def1, HYTA_APIME, ABAE_APIME, PPO, Imd, PGRP-LC, Vg for Varroa; and ACPH1_APIME, MRJP1, Vg, LOC726783 for Tropilaelaps. Results demonstrate that, despite differences in mite biology, honey bees show a conserved immune response against both parasites, centered on antibacterial defense, humoral immunity, and activation of the Toll/Imd pathway. Although limited by the in-silico nature and research asymmetries reflecting Tropilaelaps' emergence, this curated resource establishes a comprehensive framework for elucidating shared and distinct molecular defense mechanisms. Ultimately, this approach prioritizes diagnostic markers and candidate genes for functional validation and breeding strategies to enhance colony resilience against mite&#x2011;driven disease globally.

Animals

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

Redox Rewiring in Nicotine-Driven Gastric Carcinogenesis: Uncovering ROS-Dependent Oncogenic Circuits.

SIGNIFICANCE: Nicotine from tobacco products, secondhand smoke, and emerging delivery systems remains a major but underappreciated driver of gastric carcinogenesis (GC). Although reactive oxygen species (ROS) have long been implicated in tumor biology, current models incompletely explain how chronic nicotine selectively reprograms gastric epithelial signaling. This review advances the concept of redox rewiring, whereby nicotine establishes a persistent oxidative state that orchestrates multiple oncogenic programs via spatially compartmentalized NOX signaling. RECENT ADVANCES: We synthesize evidence for a unified model wherein nicotine activates nAChR/&#x3b2;-AR signaling, Ca2+ influx, PKC, and compartmentalized NOX-derived ROS to generate distinct oncogenic outputs. Beyond the established NOX/ROS/NF-&#x3ba;B/MAPK-driven IL-8 and MMP-9 axes, we integrate emerging evidence into three interconnected modules governing EMT/metastasis (ABL1/STAT3/COX-2/periostin), survival/chemoresistance (ERK/GLI1/Bcl-2), and invasion/immune evasion (miR-21/PDCD4). Collectively, these circuits suggest that ROS function not merely as damaging byproducts but as spatially organized signaling mediators dictating tumor behavior. CRITICAL ISSUES: A major challenge is distinguishing established mechanisms from incompletely validated models. The three proposed axes are testable hypotheses requiring experimental validation. Most data derive from in vitro studies with nonphysiologic nicotine concentrations, and artifacts from nonspecific ROS probes are common. Compensatory pathway activation and multi-target effects of natural products remain underexplored. FUTURE DIRECTIONS: We outline a precision-redox oncology roadmap linking pathway-specific biomarkers, mechanistically matched natural products, and biomarker-enriched trials. Priorities include genetic validation of the three axes, time-resolved ROS imaging, and pulsed natural product regimens. By reframing nicotine-driven GC as adaptive redox network remodeling, this review provides a framework for prevention, stratification, and next-generation therapy. Antioxid. Redox Signal. 00, 000-000.

gastric cancer

An oxidative stress - and immunotherapy-related six-gene signature defines immune subtypes and predicts prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Oxidative stress and the tumor immune microenvironment jointly shape hepatocellular carcinoma (HCC) progression and response to immunotherapy, yet integrated biomarkers linking these processes are lacking. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to identify oxidative stress- and immunotherapyrelated differentially expressed genes (OSIRDEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression were used to construct a prognostic signature. Consensus clustering, TIDE, CIBERSORT and ssGSEA characterized immune phenotypes. Somatic mutation, copy-number and drug-response data were integrated to assess genomic alterations and drug sensitivity. Expression of model genes was validated by qRT-PCR and western blotting in HCC cell lines. RESULTS: We identified 24 OSIRDEGs enriched in cell-cycle and mitotic pathways. WGCNA intersection yielded 18 module genes, from which a six-gene signature (BUB1B, CDKN2A, CENPE, HMMR, PTTG1, SPP1) was derived. The signature robustly stratified patients into high- and low-risk groups with significantly different progression-free and disease-free survival in both TCGA-LIHC and GSE14520. Based on signature expression, two molecular subtypes were defined, exhibiting distinct survival, immune landscapes and predicted immunotherapy responsiveness. Model genes harbored recurrent alterations and showed significant correlations with anticancer agents. All six genes were upregulated at mRNA and protein levels in metastatic HCC cell lines versus normal hepatocytes. CONCLUSIONS: We systematically explored the landscape of OSIRDEGs in HCC, and proposed a validated six-gene signature that refines prognostic stratification, delineates immunerelevant HCC subtypes and highlights candidate biomarkers for therapeutic selection and mechanistic investigation.

Humans

Percutaneous left ventricular assist device in cardiogenic shock associated with and without acute myocardial infarction: a real-world retrospective cohort study.

BACKGROUND: Percutaneous left ventricular assist devices (pLVAD, such as Impella), are increasingly used for cardiogenic shock (CS). Outcomes may differ between acute myocardial infarction-related CS (AMI-CS) and non-AMI CS due to differing pathophysiology and trajectories. METHODS: Using the USA TriNetX Network (2016-2024), we identified adults with CS treated withpLVAD. AMI-CS was defined by MI within seven days of implantation; non-AMI CS included all patients with CS not attributable to acute MI, representing heterogeneous etiologies such as decompensated cardiomyopathy, myocarditis, valvular failure, pulmonary vascular causes, and arrhythmic shock. Patients with recent coronary artery bypass graft (CABG) were excluded. Propensity matching produced two balanced cohorts (n&#x2009;=&#x2009;2,026 each). RESULTS: Among 6,873 AMI-CS and 4,521 non-AMI CS patients, matched groups were similar (mean age 63&#x2009;years, 26% female). AMI-CS had higher mortality at 30&#x2009;days (hazard ratio [HR] 1.19, p&#x2009;=&#x2009;0.002), 90&#x2009;days (HR 1.13, p&#x2009;=&#x2009;0.02), and 180&#x2009;days (HR 1.14, p&#x2009;=&#x2009;0.007). Heart failure (HF) exacerbations (HR 1.21, p&#x2009;<&#x2009;0.001) and pulmonary edema (HR 1.23, p&#x2009;=&#x2009;0.005) were also more common in AMI-CS. Stroke, ventricular arrhythmias, cardiac arrest, acute kidney injury, major bleeding, vascular complications, and hemodialysis were comparable. CONCLUSION: AMI-CS patients supported with pLVAD experienced higher mortality and greater HF-related morbidity than non-AMI CS.

Humans

Linking women leaving jail to medications for opioid use disorder: Costs to implement pre-release telehealth and peer navigation services.

AIMS: Telehealth and peer navigation are feasible strategies for connecting women in the criminal-legal system with medications for opioid use disorder (MOUD), yet implementation costs are not well understood. This study conducted a microcosting analysis of two interventions for women leaving jail in Kentucky: pre-release, PreTreatment Telehealth with a MOUD provider (TH-Only) and PreTreatment Telehealth combined with peer navigation (TH+PN) through the Justice Community Opioid Innovation Network (JCOIN). METHODS: From the provider perspective, we estimated total start-up costs, total intervention costs, and average cost per participant. Women participating in the clinical trial were randomly assigned to TH-Only (n=299) or TH+PN (n=301). Start-up costs were incurred primarily in 2019 - 2020; intervention costs represent expenses in 2021 - 2023. Cost data were collected from study and agency financial records and interviews with research staff and analyzed using Microsoft Excel (version 16.90.2). RESULTS: Start-up costs were $36,320, comprising planning, meetings, travel, and supplies. The total cost of TH-Only was $60,767, representing 259 telehealth sessions with an average duration of 47 minutes. Total cost of TH+PN was $472,148 based on 270 telehealth sessions (48 minutes), 268 peer navigation (PN) sessions (30 minutes), and 12 weeks of PN support post-release per participant. Average cost per TH-Only participant was $235 and per TH+PN participant was $1,760. CONCLUSIONS: Telehealth may be a relatively low-cost approach for jails lacking on-site MOUD services. Although more costly, combining telehealth with PN may add value by supporting service continuity and facilitating linkage to treatment during the jail to community transition.

Humans

A system-level metastable model of cancer evolution: integrating replication stress, cell cycle deregulation and chromosomal instability.

INTRODUCTION: Cancer cell proliferation occurs within the context of persistent genomic instability. In this review, we propose the RS-CCD-CIN axis as a systems-level framework in which replication stress (RS), cell cycle deregulation (CCD) and chromosomal instability (CIN) form an interdependent triad that shapes tumour evolution. This axis represents a constrained metastable state in which genomic instability is tolerated and buffered. The objective of this review is to synthesize the current understanding of how the RS-CCD-CIN axis contributes to tumour heterogeneity, adaptability and therapy response. DISCUSSION: Evidence indicates that RS, CCD and CIN operate as a dynamic, interconnected network rather than as independent processes. Replication stress induces DNA damage and mutagenesis, while partial checkpoint disruption permits cells with unresolved lesions to proliferate. Chromosomal instability generates both structural and numerical alterations, contributing to intratumoural heterogeneity. Together, these processes facilitate adaptation to environmental and therapeutic pressures. Extrachromosomal DNA, micronuclei formation and cytosolic DNA signalling, including the cGAS-STING pathway, connect genomic instability to adaptive responses and immune modulation. Single-cell and spatial profiling reveal temporal and spatial variability in RS, CCD and CIN states, highlighting the limitations of static biomarkers. Therapeutically, targeting individual components often yields limited durability, whereas approaches that simultaneously perturb multiple aspects of the RS-CCD-CIN axis may improve clinical outcomes. CONCLUSIONS: This review highlights the RS-CCD-CIN axis as a fragile and metastable architecture that supports cancer evolution, while also being susceptible to collapse. A deeper understanding of this interconnected framework may inform the development of therapeutic strategies and enhance the management of resistance.

Humans

Strategies for mosaic variant calling in brain disorders.

The human brain is a genomic mosaic, where postzygotic mutations arising from embryogenesis to senescence drive diverse neurodevelopmental and neurodegenerative diseases. Because of numerous sequencing artifacts at ultralow variant allele frequencies (VAFs), detecting these variants remains a significant analytical challenge. This review focuses on single-nucleotide variants and small indels, summarizing current strategies for aligning sampling methods, including bulk, laser capture microdissection, and single-cell genomics, with the expected clonal architecture of the brain. It emphasizes that mosaic detection sensitivity is fundamentally constrained by sequencing depth, since even the most advanced algorithms cannot identify variants not physically represented in the sequencing library. The review further recommends the selection of variant calling algorithms based on validated VAF detection performance, matching tools like MuTect2 and MosaicForecast to their optimal performance ranges. Furthermore, we discuss how multitissue sampling, as emphasized by the SMaHT project, addresses the matched-control dilemma and supports accurate variant classification via cross-tissue VAF gradients. Integrating these established pipelines with multiomics modalities, including transcriptomic and epigenetic data, could advance the field toward a functional understanding of how the somatic genome impacts human brain health and disease.

Humans