Hollow-fiber bioreactors (HFBs) have emerged as promising platforms for tissue engineering because their capillary-like architecture can improve perfusion, molecular exchange, and three-dimensional cell organization. Their high surface-area-to-volume ratio and compartmentalized design help reduce the diffusion limitations commonly observed in static cultures, particularly in large or metabolically active tissue constructs. This review discusses recent advances in hollow-fiber-based systems for bone, liver, and pancreatic tissue engineering from experimental, computational, and materials-engineering perspectives. Particular attention is given to membrane composition, scaffold architecture, perfusion strategy, cell source, and organ-specific functional requirements. The reviewed studies show that hollow-fiber systems support different biological functions depending on the target tissue. In bone tissue engineering, hollow and hollow-channel scaffolds mainly contribute to vascular-like transport, osteogenic differentiation, and structurally guided tissue formation. In bioartificial liver systems, stable semipermeable membranes support compartmentalized hepatocyte culture, controlled solute exchange, and partial metabolic function. In bioartificial pancreas systems, hollow-fiber and encapsulation-based membranes are primarily designed to balance immunoprotection with rapid glucose sensing and insulin release. Across these applications, material selection is a critical determinant of performance, as biodegradable, bioactive, or mechanically stable polymers are required depending on the intended organ-specific function. Hollow-fiber-based platforms offer a versatile framework for engineering complex tissue constructs, but their clinical translation remains limited by challenges in scale-up, long-term cell functionality, oxygen supply, immune compatibility, and standardized evaluation. Future progress will require integrated optimization of membrane properties, dynamic perfusion, biomaterial design, and multicellular culture systems to improve the translational potential of hollow-fiber technologies in regenerative medicine.
Artificial intelligence (AI) has rapidly become the focal point of global governmental attention and investment. Nations are launching AI for science strategies on a scale comparable to historic endeavors such as Apollo and the Manhattan Project. These coordinated programs carry profound promise for people living with cancer, for those at risk of disease and for transformative public benefit. Central to this transformation is the rise of sovereign AI supercomputers which are fundamentally reshaping biomedical research. These publicly owned systems provide secure, large-scale computational capacity, enabling integration of complex health data and rapid analysis that was previously constrained. This review examines the geographic distribution, technical capabilities, and biomedical applications of these infrastructures. Key computational workloads that now benefit significantly from AI implementations include cancer imaging and diagnosis, personalized treatments, whole-genome and single-cell level analysis, and computational drug discovery. This approach has supercharged our efforts at the United Kingdom's Cancer Vaccine AI & Supercomputing Project, our flagship national initiative to create new AI foundation models to accelerate the development of tools to establish immunity from cancer. In addition, this review evaluates governance models that safeguard patient privacy and intellectual property as well as measures that promote international collaboration while preserving compliance with regional regulations and make safer, more precise and effective treatments for public benefit. Substantial challenges exist, however, including inequitable resource availability, heterogeneous data standards and regulatory frameworks, and unbalanced computational expertise impeding the effective use of sovereign compute. Global collaborations are key to providing equitable access to advanced analytics, shortening the path from bench to bedside, and developing critical innovative tools for people affected by cancer.
Immune-cold tumours such as prostate cancer often resist immune checkpoint therapies (ICT) due to impaired antigen presentation via major histocompatibility complex class I (MHC-I). While MHC-I downregulation is a common immune evasion mechanism, no approved therapies selectively restore MHC-I expression in tumours. We developed a programmable RNA engineering platform, termed the 3'UTR CRISPR/dCas13 Engineering System (3'UTRCES), to precisely manipulate mRNA alternative polyadenylation (APA) in vivo. We identified tumour-specific 3'UTR shortening of the E3 ligase adaptor SPSB1 as a driver of MHC-I degradation via SPSB1-mediated ubiquitination, without affecting PD-L1. Lipid nanoparticle (LNP)-delivered 3'UTRCES reversed SPSB1 3'UTR shortening, restored MHC-I expression and sensitized tumours to ICT in syngeneic mice. These effects were elicited by MHC-I-dependent increases in CD8 T cell infiltration and antitumour cytotoxic activity. Our findings reveal APA-driven MHC-I suppression as a previously unrecognized mechanism of immune escape and establish LNP-3'UTRCES as a versatile platform for post-transcriptional RNA engineering in cancer.
Spatial transcriptomics (ST) enables a high-resolution interrogation of molecular characteristics within specific spatial contexts and tissue morphology. Despite its potential, visualization of ST data is a challenging task due to the complexities in handling, sharing, and visualizing large image datasets together with molecular information. We introduce ScopeViewer, a browser-based software designed to overcome these challenges. ScopeViewer offers the following functionalities: (1) It visualizes large image data and associated annotations at various zoom levels, allowing for intricate exploration of the data; (2) It enables dual interactive viewing of the original images along with their annotations, providing a comprehensive understanding of the context; (3) It displays spatial molecular features with optimized bandwidth, ensuring a smooth user experience; and (4) It bolsters data security by circumventing data transfers. ScopeViewer offers the research community a convenient, powerful, and secure software for high-resolution images including pathology images and spatial transcriptomics. It serves as an open-source platform for imaging-based research. Future enhancements and new features will be shared on GitHub by the creators and are open for contributions from other researchers. ScopeViewer is freely available on the website at: https://cdc.biohpc.swmed.edu/scopeviewer.
Computational prediction of drug-target interaction (DTI) is critical for drug discovery and precision medicine. Herein, we constructed a biologically enriched heterogeneous knowledge graph (KG) integrating clustered mutations, synthetic lethal interactions, drug structures, protein sequences, and functional annotations. This multi-dimensional framework was designed to enable the identification of actionable diagnostic signatures and precision therapeutic strategies by leveraging multi-layered biological network factors. Entities within the KG were embedded into low-dimensional vectors using various graph embedding techniques (TransE, RotatE, DistMult, Node2vec and R-GCN). These multimodal embeddings served as input for deep learning models (DNN, NFM, AutoInt), with standardization and PCA-based dimensionality reduction applied. Under a challenging protein cold-start scenario, the CME-KGDTI model demonstrated a better performance. These results highlight the multi-source biological information in enhancing positive sample identification and overall model generalization. Additionally, the CME-KGDTI platform (https://www.tmliang.cn/cmekgdti/#/home) was developed, integrating resources for clustered mutation identification, cancer-specific SL-based genetic networks, DTI prediction, and multi-omics analysis, enabling users to comprehensively explore mutation detection, target prioritization, and mechanistic insights. By incorporating biological features, the CME-KGDTI model exhibits high accuracy and robust generalization, highlighting its essential complementary role in drug target discovery. The developed CME-KGDTI platform will serve as a flexible, interactive, and implementable technical support platform, contributing to the advancement of precision oncology research.
The goal of this investigation is to thoroughly analyze and validate the proposed model by using the back-propagation Levenberg-Marquardt (BLM) methodology as an effective training strategy. The efficiency and precision of the suggested methodology have been verified using the generated mean square error (MSE), error histograms (EH), suggested solutions, and regression plots. The impact of temperature and solutal gradients on oxytactic microbes in the bioconvection flow of hybrid nanofluid is briefly analyzed in this paper using the Xue model. This model is useful in biomedical engineering, environmental research, and advanced thermal systems, where microorganism-nanofluid interactions are significant. It aids in the design of microfluidic devices and bioreactors by anticipating the impacts of oxytactic microbes on mass and heat transport under thermal radiation and cross-diffusion (Soret-Dufour) conditions. In nanomedicine, the approach enables regulated medication delivery and tailored therapy via hybrid nanofluid. It is also useful in renewable energy systems, such as bio-inspired cooling devices and solar thermal collectors, where better heat transfer is needed. Furthermore, the machine-learning framework enables more rapid and accurate predictions of complicated bioconvection events in industrial and biological systems.
The eye is a recognized source of biomarkers for cardiovascular and neurodegenerative disease risk. Here we characterize the breadth of these associations and identify biological axes that may mediate them. Using UK Biobank data, we developed a multi-omic analysis pipeline integrating physiological, radiomic, metabolomic and genomic information. We trained retinal adversarial autoencoders to represent optical coherence tomography images and color fundus photographs as 256-dimensional embeddings. Retinal adversarial autoencoder-derived embeddings were associated with a range of cardiovascular and neurodegenerative diseases, including ischemic heart disease, cerebrovascular disease, Parkinson's disease and dementia. Examining associations across diverse omics datasets, we provide evidence linking ophthalmic imaging features to neurological and cardiovascular anatomy and function, lipid metabolism and gene sets associated with neurodegenerative pathology. Collectively, our findings show that ophthalmic features reflect complex, multisystem biological processes and reinforce the role of the eye as a composite indicator of systemic health.
Dry eye is a common ocular surface disorder resulting from tear deficiency or excess tear evaporation. This protocol will be implemented for a study designed to determine the effectiveness of a mobile self-care application on objective clinical test results and subjective symptoms in patients with dry eye disease. The first stage will include the development of a mobile self-care application using the Python framework of Flask technology and SQLite database in the backend, and the Flutter framework of Dart for the bot technology in the front end. The second stage will be conducted using a two-arm blinded randomized clinical trial. The sample size calculation initially targeted 104 patients with dry eye disease, intended for division into intervention and control groups via an online random number generator. However, unforeseen challenges, including COVID-19 disruptions, led to randomization of 181 patients (91 intervention, 90 control).This resulted in 104 completers after a 42.5% dropout rate. Patients in both groups will receive usual medical care, but those in the intervention group will also use a mobile-based application for a period of 12 weeks. Study outcomes including the Tear Break-Up Time clinical test as the primary outcome, and the validated Persian version of the Ocular Surface Disease Index questionnaire as a secondary outcome measure in patients with dry eye disease, will be assessed at 6- and 12-weeks post-baseline. It is expected that delivering customized training via a mobile application during clinical visits would promote improvement and reduction of disease severity, as well as strengthen the physician-patient relationship. This study was developed by an interdisciplinary research team in accordance with current dry eye disease guidelines and taking into account the medical history and classification of dry eye disease. Considering the lack of financial resources for traditional self-care methods and the development of technologies based on mobile health, it seems that one can hope for the acceptance of such systems in the patients with dry eyes. This protocol is registered in the Iranian registration of clinical trial (IRCT) with the code IRCT20200721048162N1. Registered 31 August 2020, URL: https://irct.behdasht.gov.ir/trial/49779.
Digital models and digital twins of human circulatory transport could transform the way cardiovascular and haematological diseases are understood, monitored and treated. Digital twins are dynamic virtual representations of physical systems that continuously assimilate real-world data to simulate and predict system behaviour. However, translating digital twins into clinical practice remains challenging owing to the complexity of human physiology and the need for continuous bidirectional coupling between virtual models and their physical counterparts. Advances in medical-grade sensors, wearable devices, microfluidics, artificial intelligence and high-performance computing are accelerating the evolution of digital models into clinically meaningful digital twins. In this Review, we examine how digital twins can model the human circulatory system across scales, from macroscopic blood flow to molecular and cellular transport. We outline the essential components of a circulatory-transport digital twin, describe the pathophysiological conditions that can be digitally represented, and discuss approaches for acquiring and integrating physiological data, computational modelling strategies and model-based inference. We further survey applications of digital models and digital twins across various types of model inferences, from mechanistic insights to clinical decisions such as disease diagnosis, risk stratification, surgical planning and treatment planning. Finally, we identify key challenges and opportunities for next-generation circulatory digital twins capable of real-time monitoring, predictive simulation and closed-loop therapeutic control.
Rheumatoid arthritis (RA) is a chronic inflammatory systemic disease, and the use of biological agents in the treatment of RA in recent years has significantly improved RA disease activities and clinical outcomes. However, the greatly increased medical costs due to the high costs of biologics are a major concern. We aimed to investigate healthcare utilization and costs in patients with RA pre- to post-initiation of biologics or tofacitinib. We conducted a nationwide, population-based study from 1996 to 2017 using Taiwan's National Health Insurance Research Database (NHIRD). In total, 57,084 newly diagnosed RA patients aged ≥ 20 years were identified, of whom 10,566 patients using biologics or tofacitinib were selected and included in the final analysis. The dose adjustments of anti-rheumatic drugs and healthcare utilization and costs among RA patients 3 months before and 6 months after use of biologics were compared. Additionally, a sensitivity analysis evaluating healthcare utilization and costs over a 12-month period pre- to post-initiation of biologics or tofacitinib was conducted. RA patients had more frequent all-cause and RA-related outpatient department (OPD) visits after receiving biologics or tofacitinib, but fewer RA-related emergency room (ER) visits (0.00 ± 0.04 times/month, p = 0.005). There were fewer OPD visits and lower OPD healthcare costs in RA patients using tocilizumab (OPD visits: β - 0.20, p = 0.013; OPD costs: β - 16,366.92, p < 0.001) and abatacept (OPD visits: β - 0.41, p < 0.001; OPD costs: β - 4436.24, p < 0.001), compared with etanercept users. Moreover, significant dose reductions of concomitant anti-rheumatic drugs were observed in RA patients after biologics or tofacitinib, including corticosteroid, leflunomide, hydroxychloroquine, and cyclosporin. Between 10.8 and 47.0% of RA patients experienced a reduction in the dose of anti-rheumatic drugs. This nationwide, population-based study revealed that the dose of concomitant anti-rheumatic drugs and RA-related ER visits significantly reduced after initiating biologics or tofacitinib. Compared with etanercept users, patients treated with tocilizumab or abatacept had significantly lower outpatient care-related visit numbers and costs. Key Points • This nationwide population-based study investigated healthcare utilization and costs in RA patients pre- to post-initiation of biologics or tofacitinib. • RA-related emergency room visits and doses of concomitant anti-rheumatic drugs significantly declined after starting biologic or tofacitinib therapy, suggesting improved disease control. • Tocilizumab and abatacept use were associated with fewer outpatient visits and lower costs than etanercept, offering more resource-efficient options for certain patients. • Older age, male sex, and higher comorbidity burden predicted more dose reduction of conventional anti-rheumatic medications, supporting individualized treatment planning and de escalation strategies in clinical practice.
Integrated microfluidic biosensors have rapidly evolved into powerful platforms to meet the increasing demand for ultrasensitive and high-throughput quantitative analysis. By seamlessly combining sample handling through microfluidics with real-time detection via biosensors, these systems provide unmatched benefits in sensitivity, speed, portability, and immediate monitoring, thereby transforming diagnostics in human and animal health, environmental sensing, and point-of-care testing. In this review, we provide a comprehensive overview of integrated microfluidics with biosensors, highlighting the synergistic interplay between these two complementary fields and their various biomedical applications. We begin by examining different microfluidic technologies, including 3D dynamic cell culture systems, inertial microfluidic separation, acoustofluidics, dielectrophoresis, optofluidics, and immunoassays. Next, we discuss integrated microfluidic systems that incorporate various biosensor technologies, including electrochemical, electrophysiological, plasmonic, Raman, and quantum sensors. These are designed to detect and analyze DNA, RNA, proteins, exosomes, cells, and small organisms, covering a size range from nanometers to millimeters. Additionally, we discuss the wide range of applications for integrated microfluidic biosensors and examine significant challenges and future opportunities that will influence their ongoing development and practical use. Finally, we highlight successful commercial products developed with integrated microfluidic technologies.
Preclinical research in traumatic brain injury (TBI) continues to significantly increase knowledge and yield a large number of peer-reviewed studies, but translation of these results to the clinical setting has been minimal. Rigor and transparency factors such as concealment of group allocation (e.g., "blinding") or ensuring that reagents are identifiable are critical in ensuring that scientific studies are replicable and translatable. Yet, nearly all efforts aimed at measuring these factors have concluded that reporting practices are problematic and incomplete. One way to improve transparency of reporting practices is to require that authors address a set of transparency-related items in some way, such as a checklist or an article section. Recently, Journal of Neurotrauma, a leading publisher of preclinical TBI research, instituted a required rigor-related section, which is explained to authors via a set of transparency, rigor, and reproducibility (TRR) instructions (one example for each article type). These documents include specific transparency sections explaining blinding, power calculations, protocols, code, and data deposition. Experimental Neurology is a journal that is similar in size, impact, and topic, but the journal does not have explicit instructions to authors about transparency items. The purpose of this study was to assess the degree to which transparency reporting items were included in published articles comparing reporting practices in the Journal of Neurotrauma and Experimental Neurology. We used a commercial software, SciScore, which is an AI tool tuned to detect rigor/transparency sentences in published articles and count the number found (roughly dividing by the number expected) to obtain a score. Overall, SciScore found that in six of eight items that were explicitly asked for, such as power calculations, investigator blinding, inclusion criteria, attrition, and data, there were significant differences (more than 10%) compared to Experimental Neurology. However, in Journal of Neurotrauma articles with the extra rigor section, three of four rigor items that were not explicitly asked for in the template rigor documents, such as subject demographics or transparent antibody reporting, were not different from Experimental Neurology. One item, reporting of the sex of subjects, was significantly better in Experimental Neurology. This shows that the Journal of Neurotrauma's required rigor section is effective in improving reporting, but it would be far better if sex as a biological variable and transparent reporting of reagents (items present on major checklists, including NIH rigor criteria) would be included.
Acute myeloid leukemia (AML) is a genetically and phenotypically heterogeneous hematological malignancy. Here, to better define this clinically taxing and translationally challenging malignancy, we applied a multiomics approach, consisting of 13 modalities to analyze 173 treatment-naive individuals with AML. By integrating these 'omes', we identified distinct AML subtypes, genotype-phenotype associations, biomarkers and pathobiological mechanisms. Across the spectrum of primitive and committed AML, we found extensive metabolomic and lipidomic reprogramming driven by divergent MYC and mTOR activity. We linked metabolic changes to striking hyperacetylation of mitochondrial proteins in CEBPA-mutant AML. Protein-centric subtyping revealed a distinct NPM1-mutant subset characterized by outlier expression of FOXC1 and HOXB8/9. To nominate therapeutic targets across subtypes, we developed a multiomic machine-learning approach and validated MTA1 as a contributor to panobinostat resistance. Altogether our findings underscore the complex nature of AML and provide a clinically and translationally informed unified view that reveals coalescent phenotypes across multiomic layers.
Population-scale radiation exposure assessment during radiological emergencies is hindered by the slow and costly nature of current methods, creating a need for rapid, affordable screening tools. Radiation biodosimetry using peripheral blood counts is a promising approach, but estimating low-dose exposures and exposure at extended time points remains challenging, especially when accounting for inter-individual differences in radiation sensitivity. We analyze complete blood count (CBC) profiles from a retrospective cohort of 1151 male and female BALB/cJ and C57BL/6 J mice exposed to total-body X-ray radiation at doses ranging from 0.05 to 4 Gy. CBCs are collected 1 to 150 days post exposure. We develop a predictive model of radiation exposure using a sparse representation learning strategy to identify the most informative CBC parameters. Model performance is evaluated through exhaustive cross-validation and validated in a double-blind prospective cohort of 431 animals. To evaluate robustness in a genetically diverse population, we further test the model on CBC data from a Collaborative Cross (CC) cohort of 1720 animals representing 35 CC strains, 24 h and 28 days after sham or 1 Gy total-body X-ray exposure. Exhaustive cross-validation shows good performance of the Sparse CBC model, with AUC, accuracy and sensitivity exceeding 80%. Similar performance is observed in the prospective cohort. In the CC cohort, performance is modest. Importantly, model performance varies across CC strains, suggesting that host genetic background significantly influences predictive accuracy. Our findings demonstrate that the Sparse CBC model effectively leverages CBC data to estimate radiation exposure across multiple mouse cohorts, including genetically diverse CC populations. While CBC-based predictions provide a complementary tool for exposure assessment, model performance varies with genetic backgrounds. During radioactive exposure emergencies, rapidly identifying who has been exposed to radiation is essential for providing timely medical care. Current methods are often expensive, and unscalable. Routine complete blood count (CBC) tests offer a potential alternative because radiation alters blood cell numbers and composition. In this study, we analyzed CBC data from more than 1100 mice over periods up to 150 days after radiation exposure. Using these data, we developed a computer program that identifies the most informative blood cell measurements for detecting radiation exposure. The program performed well across multiple, but performance varied across genetically diverse mouse strains, highlighting the influence of genetic background.
Periodontitis is a prevalent chronic inflammatory disease characterized by progressive destruction of periodontal supporting tissues, yet its precise molecular mechanisms remain incompletely understood. This study aimed to identify key pathogenic genes and elucidate the underlying molecular mechanisms of periodontitis through integrated bioinformatics analysis. Periodontitis-related gene expression datasets were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package and visualized via volcano plots. Gene set enrichment analysis (GSEA) was performed to characterize enriched biological pathways. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized in Cytoscape. Functional enrichment analysis was conducted using the ClueGO plugin, incorporating Reactome, Gene Ontology (GO), and KEGG annotations. Hub genes were identified using the cytoHubba plugin with five topological algorithms (Degree, MNC, MCC, Closeness, and EPC), and key genes were determined through Venn diagram analysis. Individual GSEA was subsequently performed for each key gene. Finally, a competing endogenous RNA (ceRNA) regulatory network was constructed based on the lncRNA-miRNA-mRNA axis. A total of 7 key genes were identified: IL1B, CXCR4, FCGR3B, SELL, CD19, CXCL8, and CD38. Functional enrichment analyses revealed significant involvement of cytokine-cytokine receptor interaction, hematopoietic cell lineage, collagen degradation, and chemokine signaling pathways. GSEA of individual key genes further confirmed the central roles of immune and inflammatory pathways in periodontitis. The ceRNA network revealed regulatory interactions among lncRNAs, miRNAs, and key genes, particularly centered on IL1B, SELL, and FCGR3B. This integrated bioinformatics study systematically identified key genes and regulatory networks in periodontitis, offering promising candidates for future therapeutic development.
Left atrial (LA) myopathy is a key driver of atrial fibrillation (AF) development and progression. Late gadolinium enhancement (LGE) cardiovascular magnetic resonance enables non-invasive quantification of LA fibrosis, a hallmark of atrial myopathy. However, conventional LGE sequences lack sufficient spatial resolution to accurately depict the thin atrial wall, and reference data in healthy cohorts are scarce. This study aimed to evaluate a high-resolution isotropic 3D LGE Dixon sequence for assessing LA fibrosis in healthy controls and AF patients. In this prospective study, 40 ablation-naïve AF patients (21 paroxysmal, 19 persistent) and 20 healthy controls underwent isotropic (1.3 mm3) 3D whole-heart LGE imaging. Segmentation was successfully performed using CemrgApp in all participants. A setup-specific threshold for fibrosis detection was defined as an image-intensity ratio (IIR) > 1.34 (mean + 2SD of healthy controls) and validated against pre-procedural electroanatomical mapping (EAM) and follow-up imaging at six months post ablation. At baseline, total LA enhancement was higher in persistent than paroxysmal AF (3.65% [1.84-7.16] vs. 1.16% [0.43-2.27]; P = 0.044) and controls (1.25% [0.65-1.75]; P = 0.041). No significant correlation was observed between total LGE-derived fibrosis and bipolar low-voltage area (ρ = -0.03, P = 0.87), though point-by-point analysis showed a weak negative correlation (ρ = -0.05, P < 0.001). In patients with sinus rhythm at follow-up, total fibrosis increased from 1.68% [0.64-6.51] to 6.30% [2.53-12.28]; P < 0.001, driven by peri-ablational scar formation, with no change in remote myocardium. Intra-reader correlation for LA-LGE was excellent: ICC 0.99 (95% CI 0.95-0.99). High resolution isotropic 3D LA-LGE enables robust detection of ablation-induced scarring and biologically plausible fibrosis differences between AF stages. However, its correlation with bipolar voltage mapping remains limited, suggesting that LGE and EAM provide complementary information on atrial myopathy.
Protein-protein interactions (PPIs) are fundamental to nearly all biological processes, yet their experimental characterization remains costly and time-consuming. While computational methods, particularly those using protein language models (pLMs), offer higher-throughput solutions, they often report unexpectedly high performance on multi-species datasets. Here, we introduce the accidental taxonomist hypothesis, proposing that neural networks can exploit the phylogenetic distances across labels in protein datasets rather than genuine interaction features. We show that in standard multi-species PPI datasets, positive pairs typically share a taxonomic origin, while randomly sampled negatives do not. We then demonstrate that pLM embeddings can be used to accurately distinguish whether two proteins share a taxonomic origin, allowing models to "cheat" by learning phylogeny instead of genuine PPI features. By employing a strategic sampling strategy that restricts negative examples to protein pairs from the same species, we reveal a marked drop in model performance, confirming our hypothesis. Compellingly, these strategically trained models still outperform single-species models, suggesting that multi-species data can improve performance if carefully curated. These findings suggest that accidental taxonomist behavior is a particularly influential confounder for PPI, and it is also broadly applicable to any supervised-learning protein dataset.
We introduce a framework for analysing topological tipping in time evolutionary point clouds by extending the recently proposed topological optimal transport (TpOT) distance. While TpOT unifies geometrical, homological and higher-order relations into one metric, its global scalar distance can obscure transient, localized structural reorganizations during dynamic phase transitions. To overcome this limitation, we present a hierarchical dynamic evaluation framework driven by a novel topological and hypergraph reconstruction strategy. Instead of directly interpolating abstract network parameters, our method interpolates the underlying spatial geometry and rigorously re-computes the valid topological structures, ensuring physical fidelity. Along this geodesic, we introduce a set of multi-scale indicators: macroscopic metrics (topological distortion and persistence entropy) to capture global shifts, and a novel mesoscopic dual-perspective hypergraph entropy (node-perspective and edge-perspective) to detect highly sensitive, asynchronous local rewirings. We further propagate the cycle-level entropy change onto individual vertices to form a point-level topological field. Extensive evaluations of physical dynamical systems (Rayleigh-van der Pol limit cycles, double-well cluster fusion), high-dimensional biological aggregation (D'Orsogna model) and longitudinal stroke fMRI data demonstrate the utility of combining transport-based alignment with multi-scale entropy diagnostics for dynamic topological analysis. This article is part of the theme issue 'Critical transitions and intelligent control in complex systems'.
Sex-related differences have been consistently reported in the epidemiology of acute hypoxemic respiratory failure (AHRF) and COVID-19. However, whether computed tomography (CT)-derived measures of lung injury differ between sexes and contribute to outcome disparities remains unclear. In this large multicenter retrospective cohort study, we analyzed 850 spontaneously breathing patients with COVID-19-related AHRF who underwent early chest CT at hospital admission. Quantitative CT analysis provided measures of lung density, volume, mass, and superimposed pressure (SP), a CT-derived estimate of gravitational stress. Sex-stratified analyses compared morphological, physiological, and outcome variables. Multivariable logistic regression models identified independent predictors of mortality. Among 850 patients (35% women), men exhibited larger lung volume (2.91 vs. 2.28 L, p < 0.001), greater lung mass (1.14 vs. 0.93 kg, p < 0.001), and higher SP (5.79 vs. 5.21 cmH₂O, p < 0.001) despite similar fractions of ground-glass opacities and consolidation. In the multivariable model, older age (OR 1.08, 95% CI 1.06-1.11; p < 0.001), lower PaO2/FiO2 (OR 0.99, 95% CI 0.98-0.99; p < 0.001), higher SOFA score (OR 2.67, 95% CI 1.43-4.98; p = 0.002 for SOFA ≥ 2), higher global SP (OR 1.18, 95% CI 1.05-1.34; p = 0.005), and male sex (OR 1.76, 95% CI 1.06-2.92; p = 0.028) were independently associated with an increased risk of mortality. In the mediation analysis, the effect of global SP on mortality does not appear to be mediated by male sex (coefficient 0.00). Male patients with COVID-19-related AHRF exhibited higher global SP than females, reflecting greater gravitational lung load and mechanical disadvantage. Both global SP and male sex were independently associated with mortality, with no evidence of mediation of male sex on mortality. These finding suggest that, beyond anatomical and mechanical differences, biological and hormonal factors likely contribute to the increased disease severity observed in men.
Disease phenotype onset is critical for timely and accurate diagnosis and clinical decision-making, yet it remains poorly characterized in the literature. Estimating phenotype onset using electronic health record (EHR) data holds promise but remains challenging. Researchers often resort to EHR documentation timestamps as proxies for phenotype onset, which can be inaccurate. Conventional natural language processing (NLP) approaches suffer from limited scalability and generalizability, and struggle to interpret implicit or vague temporal expressions. To address these gaps, we introduce TimeX, a novel open-source pipeline that leverages Llama-3.1, using instruction-based prompting for extracting phenotype onset from clinical narratives. TimeX employs a modular workflow comprising family history filtering, phenotype extraction, negation handling, and temporal information extraction to estimate phenotype onset. It yielded an average accuracy of 81.24% in timestamp extraction using 102 manually annotated clinical notes from the Columbia University Irving Medical Center, substantially outperforming all five baselines by at least 14.86%. A case study of four rare disease cohorts revealed that narrative-derived phenotype onset is more precise than that based on documentation timestamps. TimeX supports accurate and scalable phenotype onset extraction, with the potential to enable more precise disease trajectory characterization and timely disease diagnosis.