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.
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.
Type 2 diabetes (T2D) is considered as a risk factor of triple-negative breast cancer (TNBC). So, there is a significant chance of their co-existence. The management of TNBC with T2D becomes more complex than without T2D due to the conflict of therapies, since some T2D drugs may have bad impact on TNBC and vice-versa. Beside this, drug-drug interaction due to the polypharmacy of multiple drugs, during a one-drug one-disease strategy, may create toxicity or side effect to the patients who are suffering from both diseases simultaneously. Therefore, it is required to explore effective unique drugs as the same treatment for both diseases. This study attempted to contribute in this issue. At first, we identified 36 shared differentially expressed genes (sDEGs) that can separate both TNBC and T2D patients from the control group through integrated transcriptomics analysis. Then top-ranked four sDEGs (S100A9, CIRBP, USP10, and PSMD1) were detected as the overlapping dysregulated shared key-genes (sKGs) through the protein-protein interaction (PPI) network analysis and filtering with a machine learning (ML) approach. The gene regulatory network analysis revealed three key transcriptional (TFs proteins) and post-transcriptional (micro-RNAs) factors of sKGs. The enrichment analysis of sKGs with the GO-terms and KEGG-pathways revealed some crucial molecular functions, biological processes, cellular components, and pathways as the key pathogenetic mechanisms for the development and progression of both diseases. Finally, we recommended sKGs-guided two repurposable common candidate drugs (tepotinib and ursodiol) for both diseases by molecular docking, ADME/T analysis and MD simulation studies. Thus, the results of this study could provide useful insights for researchers and medical professionals for improving the diagnostic and therapeutic strategies for the treatment TNBC with T2D as comorbidity.
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.
Silk from Bombyx mori is a premier biomaterial. Yet, comprehensive structural and immunoinformatic characterization of its protein components-fibroin subunits (FibH, FibL, P25) and five sericin isoforms-remains incomplete, hindering rational design of biocompatible medical devices. We integrated AlphaFold3 structural prediction with multi-algorithm immunoinformatic profiling (VaxiJen, NetMHCpan, BepiPred, AllerTOP, ToxinPred) to establish structure-immunogenicity relationships across the silk proteome. Structural modeling revealed that FibL and P25 adopt well-defined architectures (pTM = 0.85), whereas FibH exhibited low confidence (ipTM/pTM = 0.28), reflecting its intrinsically disordered pre-assembly state. Sericins displayed predominantly disordered conformations that undergo partial ordering upon complexing with FibL-P25-Cu2+, supporting a disorder-to-order templating mechanism for fiber assembly. Immunoinformatic analysis revealed striking antigenic heterogeneity: P25 emerged as uniquely hypoimmunogenic, with subthreshold antigenicity (VaxiJen: 0.395), a single strong MHC-I binder for a single HLA allele, and minimal MHC-II reactivity. Conversely, FibL and FibH showed the highest potential for inducing CD8+ and CD4+ cell responses among the fibroin subunits, respectively. Ser-4 and Ser-1 exhibited a broad MHC coverage, presenting ≥ 10 strong binders in 81% and 30% of MHC-I alleles, respectively, as well as ≥ 4 high-priority peptides across all 27 and 6 tested MHC-II alleles, respectively. Allergenicity prediction classified FibH, FibL, Ser-2, and Ser-5 as probable allergens. All proteins were non-toxic. These findings challenge the paradigm that sericins exclusively drive silk immunogenicity, revealing instead an HLA-dependent risk profile dominated by FibL, FibH, Ser-4, and Ser-1. This computational framework provides a rational foundation for engineering hypoimmunogenic silk variants through P25 enrichment, epitope deletion, or HLA-matched biomaterial selection.
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.
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.
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.
The clinical management of neurological disorders remains a major challenge worldwide, constrained by fundamental limitations in both diagnosis and therapy. Electroencephalography (EEG), the cornerstone of neurological assessment, is limited by low spatial resolution and inconsistent signal quality. Therapeutically, the blood-brain barrier (BBB) restricts drug delivery to the brain, resulting in subtherapeutic intracerebral concentrations. These convergent diagnostic and delivery bottlenecks underscore an urgent imperative for innovative materials and technologies. Hydrogels, characterized by biomimetic three-dimensional (3D) architectures, have emerged as a versatile material platform to bridge this gap. From a diagnostic perspective, hydrogels-based electrodes exhibit exceptional biocompatibility and low interfacial impedance, enabling high-fidelity EEG acquisition while minimizing insult to sensitive neural and skin tissues. From a therapeutic perspective, their 3D architecture provides versatile scaffolds for therapeutic agents, supporting high loading efficiency and programmable release profiles for neurological interventions. In this review, we first outline the physicochemical properties and fabrication techniques of hydrogels. We then discuss their applications, with particular emphasis on neural bio-electrodes, brain-computer interfaces (BCIs), drug delivery, and neuro-bioengineering. Finally, we examine the challenges impeding the clinical translation of hydrogels and outline prospective mitigation strategies. The integration of these functionalities is anticipated to advance closed-loop therapeutic systems for the precise management of complex neurological disorders.
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.
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.
Histologically stained tissue sections are considered the gold standard for studying microscopic anatomy and diagnosing disease in clinical practice. However, the processes of sectioning and staining are laborious, and the overall method relies on two-dimensional analysis. In contrast, X-ray-based virtual histology offers the advantage of virtual sectioning while retaining the full three-dimensional (3D) volumetric representation of the tissue. Nevertheless, its greyscale nature limits its specificity compared to conventional histological stains and creates an additional barrier for pathologists, whose training is primarily based on colour-stained histology. In this work, we present a histology-guided enhancement platform that can integrate the 3D information provided by synchrotron radiation phase-contrast microCT (PCµCT) with the rich visual features characteristic of histological stains. We introduce a multistage PCµCT-histology co-registration method combined with a virtual staining deep neural network and demonstrate successful virtual histological staining of PCµCT human and mouse lung tissue that closely resembles standard histology. We evaluate our strategy on multiple histological stains and apply it to identify 3D collagen-based remodelling of pulmonary arteries in patients with pulmonary hypertension. Overall, we expect our work to facilitate the integration of PCµCT as a clinical tool for 3D analysis of biological tissues and support non-destructive 3D pathology for disease biomarker exploration.
Psychological traits reflecting neuroticism, depressive symptoms, loneliness, and purpose in life are risk factors of AD dementia; however, the underlying biological mechanisms remain largely unknown. Using multi-omic data from the dorsolateral prefrontal cortex of 822 decedents in the Religious Orders Study and Rush Memory and Aging Project, we utilized a previously derived multi-omic brain molecular pseudotime representing molecular distance from no cognitive impairment (NCI) to AD dementia, and three distinct multi-omic brain molecular subtypes of AD dementia. We first confirmed generalizability of pseudotime and subtypes in two independent samples. We then annotated the subtypes, and explored whether they differed by neuropathologic burden, brain morphology or genetic risk, and found that while these indices differentiated all subtypes from NCI they did not differentiate amongst them. Finally, we tested for differential associations between the psychological traits and the subtypes, adjusting first for age, sex, education, and time to death, and then additionally for 9 common AD and Related Dementias pathologies. We found that in fully adjusted models, neuroticism, loneliness and purpose in life remained differentially associated with some AD subtypes relative to NCI. Our results are consistent with a two-stage model in which (i) upstream genetic risk influences overall disease liability, while (ii) intermediary psychological predispositions align more directly with subtype differentiation capturing AD-related heterogeneity not explained by neuropathology or brain atrophy. These results indicate that psychological risk factors may be associated with AD dementia via multi-omic molecular pathways, predominantly informed by metabolomic dysregulation, capturing heterogeneity not explained by neuropathology.
Vaccine adjuvants enhance immune responses by boosting vaccine efficacy, reducing required doses, and improving long-term immunity. The Vaxjo database is a web-based resource that stores information on vaccine adjuvants, including their names, storage conditions, structures, preparation methods, components, functions, safety, and references. The original version of Vaxjo, released in 2012 with 103 vaccine adjuvants, has been expanded and modernized as Vaxjo 2.0, a significantly enhanced version. In Vaxjo 2.0, newly identified adjuvants from biomedical literature retrieved through PubMed searches, as well as from the Vaccine Adjuvant Compendium (VAC) and AdjuvareDB, were added. To accelerate data collection and curation, we developed a vaccine adjuvant large language model (Vaxjo-LLM) system that automatically identifies and annotates new vaccine adjuvants and characterizes their mechanisms. The LLM-mined results were manually evaluated, annotated, and selectively included in the database to ensure quality. Overall, Vaxjo 2.0 includes 448 vaccine adjuvants, organized into 16 distinct categories (e.g., mineral salt, emulsion, cytokine, peptide, and toll-like receptor (TLR) agonist vaccine adjuvants), all of which are represented in the Vaccine Ontology (VO) to streamline information storage and exchange. From 817 PubMed abstracts, Vaxjo-LLM identified mechanisms for 323 unique vaccine adjuvants across 16 mechanism families, including T cell activation/polarization, dendritic cell activation, TLR signaling, inflammasome activation, cytokine signaling, B cell/antibody production, and pattern recognition receptor (PRR) sensing. The mined information was subsequently manually reviewed to ensure consistency and accuracy. Based on this analysis, the mechanism profiles and clustering of the top 20 adjuvants were generated, revealing shared and distinct mechanistic signatures. For deeper mechanistic understanding, Vaxjo 2.0 further classified adjuvants based on PRR families, including TLRs, C-type lectin receptors, NOD-like receptors, and RIG-I-like receptors. Vaccine adjuvants were also categorized based on host immune response profiles, such as Th1/Th2/Th17-biased, Th1/Th2-mixed, and Treg-biased immune profiles. The newly designed Vaxjo 2.0 web interface (https://violinet.org/vaxjo) provides an openly available and user-friendly platform for querying, visualizing, and analyzing vaccine adjuvant data.
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.