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PubMed2026

Combined Effects of Nicorandil and Enhanced External Counterpulsation on Coronary Microcirculation and Exercise Capacity in Patients With Coronary Slow Flow Phenomenon: A Randomized, Controlled, 3-Arm Trial.

PURPOSE: To evaluate the combined efficacy and safety of combined nicorandil and enhanced external counterpulsation (EECP) therapy compared with respective monotherapies in patients with coronary slow flow phenomenon (CSFP). METHODS: In this prospective, randomized, 3-arm clinical trial, 309 patients with angiographically defined CSFP based on corrected TIMI frame count were assigned (1:1:1) to the Nicorandil group (N group, n = 103), the EECP group (E group, n = 103), or the Combined therapy group (N+E group, n = 103). The trial was prospectively registered at ClinicalTrials.gov (NCT07534410). IMR and CFR were measured to characterize coronary microvascular physiological status and treatment response. The primary endpoint was corrected TFC at 6 months. Key secondary endpoints included invasive physiological indices (IMR and CFR), Seattle Angina Questionnaire scores, 6-minute walk test (6MWT) distance, peak oxygen uptake via cardiopulmonary exercise testing, and the 12-month rate of re-hospitalization due to recurrent angina. FINDINGS: At 6 months, the N+E group demonstrated superior improvement in coronary hemodynamics compared to the N and E monotherapy groups, with significantly lower TFC (30.4 ± 3.5 vs 38.2 ± 3.8 and 37.5 ± 4.0, respectively; P < 0.001) and IMR (21.2 ± 2.8 vs 28.4 ± 3.2 and 27.6 ± 3.5, respectively; P < 0.001). Clinical symptoms and functional capacity showed the most substantial gains in the N+E group, with significantly higher Seattle Angina Questionnaire angina frequency scores (87.5 ± 8.8) and 6MWT distances (506.8 ± 41.8 m) compared to monotherapy groups (all P < 0.001). Furthermore, peak oxygen uptake in the N+E group increased to 23.5 ± 2.6 mL/kg/min, significantly outperforming the N and E groups (P < 0.001). During the 12-month follow-up, the observed rate of re-hospitalization due to recurrent angina was lower in the N+E group (5.8%) than in the N group (17.5%, P = 0.017), although this clinical outcome should be interpreted cautiously because the trial was powered primarily for physiological endpoints. No significant differences were observed in the incidence of adverse reactions among the 3 groups (P = 0.954). IMPLICATIONS: For patients with CSFP, the combination of Nicorandil and EECP improved coronary microvascular function, anginal symptoms, and objective exercise tolerance more effectively than either active monotherapy. The lower observed rate of angina-related re-hospitalization suggests a potential clinical benefit, but this finding should be considered exploratory and requires confirmation in trials adequately powered for clinical outcomes.

Humans↗

PubMed2026

Role of bioprocessing in modifying cardiometabolic outcomes of an oat-based dairy alternative in a randomised controlled clinical intervention.

BACKGROUND & AIMS: A healthy diet rich in fibre-containing foods such as oats supports cardiometabolic health. Bioprocessing methods, including fermentation and enzymatic treatment, may further enhance the health benefits of oat-based foods by altering their physicochemical properties. The aims of this study were to investigate the effects of consuming fermented and non-fermented oat-based products enriched with fibre and protein on cardiometabolic outcomes gastrointestinal symptoms, and to consider how assessed physicochemical and nutritional differences between the products might relate to any observed effects. METHODS: In a 12-week randomised crossover trial, 56 adults with mild metabolic deterioration consumed fermented (gurt) and non-fermented (porridge) oat-based products enriched with fibre and protein as part of their habitual diet for three weeks each. The study products were specifically developed and prepared for this study using identical ingredients. Primary cardiometabolic factors and gastrointestinal symptoms (GSRS) were measured at four time points, while secondary outcomes were assessed at baseline and after both product periods. Physicochemical and nutritional characterization of the study products included cereal β-glucan (BG) and protein molecular weight distribution, starch and sugar analysis, microscopy, acidity, and viscosity. RESULTS: During the gurt consumption, non-high-density lipoprotein (non-HDL) and low-density lipoprotein (LDL) cholesterol concentrations decreased (-0.15 ± 0.51 mmol/L, p = 0.028; and -0.12 ± 0.46 mmol/L, p = 0.047, respectively), with a minimal impact on blood pressure and GSRS scores. Additionally, ferritin was lower after the gurt compared with baseline (-4.00 [-16.50, 6.25] μg/L, p = 0.015). Similarly, ferritin levels were lower after the porridge period (-7.50 [-20.50, 4.25] μg/L), accompanied with a modest decrease in blood pressure and HbA1c. These effects, however, did not substantially differ between the product periods. Insulin showed a significant sequence effect (psequence∗time <0.05) and was analysed in sequence groups. Insulin levels significantly decreased during the gurt consumption in the group that started with the porridge (-2.22 ± 6.16 mU/L, p = 0.015). Fermentation and enzymatic treatment induced significant changes in BG MW, starch, and composition in the gurt, which may alongside with increased fibre intake during the intervention explain the observed results. CONCLUSION: Consuming a fermented, oat-based gurt as part of habitual diet may improve cholesterol metabolism, likely due to increased oat fibre intake rather than fermentation as such. Moreover, greater intake of oat-based products, regardless of processing, can reduce ferritin concentrations and marginally improve other cardiometabolic factors. The study was registered in ClinicalTrials.gov as NCT06393114.

Humans↗

PubMed2026

Preoperative Carbohydrate Supplementation Reduces Thirst and Improves Patient Satisfaction Before Elective Cesarean Delivery: A Randomized Controlled Trial.

BACKGROUND & AIMS: Prolonged preoperative fasting is a major source of patient discomfort, particularly thirst, before elective cesarean delivery. This study aimed to evaluate whether preoperative carbohydrate (CHO) supplementation could alleviate these discomforts and improve patient satisfaction without compromising safety. METHODS: In this single-center randomized controlled trial, 262 women scheduled for elective cesarean delivery under neuraxial anesthesia were randomly allocated to either the CHO group (Group CHO, n = 131), which received 355 mL of an oral carbohydrate solution on the night before and the morning of surgery, or the control group (Group C, n = 131), which followed conventional fasting. The primary outcome was the thirst Numeric Rating Scale (NRS, 0-10) score measured immediately before surgery. Secondary outcomes included hunger NRS, satisfaction NRS, and maternal and neonatal safety parameters. RESULTS: Baseline characteristics were comparable between groups. Despite a longer preoperative fasting duration in Group CHO (9.25 ± 1.05 vs. 8.74 ± 0.97 h, P < 0.001), this group exhibited significantly lower thirst NRS scores (1.69 ± 0.90 vs. 4.02 ± 0.99, P < 0.001) and hunger NRS scores (1.25 ± 0.94 vs. 2.09 ± 0.82, P < 0.001), as well as higher satisfaction NRS scores (7.70 ± 0.69 vs. 5.69 ± 1.17, P < 0.001). Subgroup analyses confirmed consistent benefits of CHO supplementation across most patient characteristics. Further analyses suggested that the maximum effect on thirst reduction occurred at approximately 9.2 h of solid fasting; however, the interaction between fasting duration and treatment group was not statistically significant (P = 0.187). CONCLUSION: Preoperative carbohydrate supplementation effectively reduces thirst and hunger and improves patient satisfaction before elective cesarean delivery without increasing maternal or neonatal risk. The beneficial effects were consistent across varying fasting durations, with exploratory spline analyses suggested a potential peak effect around 9.2 h, though this was not statistically significant and should be interpreted cautiously. These findings support the incorporation of carbohydrate loading into enhanced recovery protocols. TRIAL REGISTRATION: China Clinical Trial Registry ChiCTR2500097956.

Humans↗

PubMed2026

An expanded breakfast buffet increases daily energy and protein intakes in hospitalised patients: A prospective crossover quality improvement study.

BACKGROUND & AIMS: Inadequate dietary intake remains common during hospitalisation. Ordinary hospital meals are central to nutritional intake, but their contribution depends on what is offered and what patients are able and willing to eat. We evaluated whether a preference-informed, limited expansion of the hospital breakfast buffet could increase total daily energy and protein intakes. METHODS: This prospectively structured, ward-based crossover quality-improvement study was conducted in seven inpatient wards at a tertiary university hospital. Each ward was observed for four consecutive days and randomly allocated to begin with standard or expanded breakfast, after which conditions alternated daily. The expanded buffet consisted of standard breakfast supplemented with familiar energy- and protein-rich foods selected from previous patient-choice data. Twenty-four-hour intake was registered using component-level weighed food records during the day and nursing registration overnight. Primary outcomes were total daily energy and protein intakes. Linear mixed-effects models adjusted for observation day and ward-level starting sequence and accounted for repeated patient observations and ward-level clustering. Analyses used data from patients who consumed breakfast and contributed analysable observations under both breakfast conditions. RESULTS: The primary crossover population included 71 patients contributing 188 analysable patient-days. Compared with standard breakfast, the expanded breakfast increased total daily energy intake by +198 kcal/day (95% CI 44 to 352) and protein intake by +6.8 g/day (95% CI 1.0 to 12.5), without a statistically significant increase in total food weight. Daily energy and protein adequacy increased by +8.7 and + 6.8 percentage points, respectively. The increase was driven mainly by breakfast intake, with no measurable reduction in non-breakfast intake. CONCLUSIONS: A limited expansion of the ordinary hospital breakfast buffet increased total daily energy and protein intakes in the primary crossover population of hospitalised adults who consumed breakfast. This increase occurred without a statistically significant increase in total food weight or a measurable reduction in non-breakfast intake. Small, preference-informed additions of familiar energy- and protein-rich foods at breakfast may improve daily intake by increasing the nutrient yield of foods patients are able or willing to eat.

Humans↗

PubMed2026

Reduced Length of ADT and ARTA With XRT in High-Risk Prostate Cancer (RELAX): A Randomised Controlled Trial.

AIM: To assess the efficacy of a short, intensified regimen of androgen deprivation therapy (ADT) and androgen receptor-targeted agent (ARTA) with curative-intent external beam radiotherapy (XRT) for high-risk prostate cancer (HRPCa) staged with prostate specific membrane antigen (PSMA) imaging. MATERIALS AND METHODS: The RELAX (Reduced Length of ADT and ARTA with XRT in high-risk prostate cancer) trial is a multicentre, phase II randomised non-inferiority trial comparing 9 months of ADT and 6 months of ARTA against the standard 24-month ADT, with definitive dose-escalated hypofractionated pelvic radiotherapy for PSMA-staged non-metastatic HRPCa. The primary endpoint is 5-year disease-free survival (DFS), defined from randomisation to first biochemical or clinico-radiological recurrence or any-cause death. Secondary endpoints include biochemical failure-free survival, metastasis-free survival, overall survival, testosterone recovery, treatment-related adverse effects, and patient-reported outcomes. Participants are monitored with serial serum PSA and testosterone levels, CTCAE and PRO-CTCAE assessments, and PSMA-PETCT at recurrence. The non-inferiority margin is set at 10% absolute difference from an expected 5-year DFS of 85% in the standard arm, with intention-to-treat analysis. The trial is ethics board approved and funded by institutional intramural grant, and registered on clinicaltrials.gov (NCT06818682). RESULTS: The planned accrual of 206 participants commenced in February 2025, with nearly a third of enrolment completed till date. CONCLUSION: The RELAX trial incorporates contemporary standards of PSMA-based staging, dose-escalated hypofractionated radiotherapy, and ARTA for HRPCa. The results are expected to inform the efficacy of a shorter, intensified androgen suppression strategy against the prevailing standard of long-term ADT for these patients.

Humans↗

PubMed2026

Maternal vitamin B12 deprivation exacerbates offspring obesity by reducing early-life colonization with Bifidobacterium pseudolongum.

Vitamin B12 deficiency during pregnancy and lactation is common, yet its mechanistic impact on reproductive outcomes and offspring health remains poorly understood. Here, we show that maternal dietary vitamin B12 deprivation not only impairs maternal glucose metabolism and reproductive outcomes but also exacerbates high-fat-diet-induced obesity in offspring. These effects are mediated by gut microbiota and associated with a marked reduction of Bifidobacterium pseudolongum (B. pseudolongum) in both dams and their offspring. Maternal vitamin B12 deprivation limits early-life acquisition of B. pseudolongum in offspring during lactation, subsequently intensifying obesity and metabolic dysregulation. Early-life restoration of B. pseudolongum or its key metabolite, acetate, effectively ameliorates this aggravated obesity. Mechanistically, acetate acts through the Ffar2 receptor to upregulate Ehhadh expression. Together, these data establish that perinatal nutrition imprints long-term metabolic phenotypes in offspring via early-life acquisition of the gut microbiota, with a critical window during lactation.

Animals↗

PubMed2026

A contextual activity score (CAS) for inferring ADAR-associated transcriptional activity across RNA-seq, single-cell, and spatial transcriptomics.

BACKGROUND AND OBJECTIVE: Adenosine-to-inosine RNA editing, catalyzed by Adenosine Deaminases Acting on RNA (ADARs), is a widespread modification involved in neural function, immune regulation, and cancer. The Alu Editing Index (AEI) is the standard metric to estimate ADAR activity but requires raw sequencing reads and is poorly suited for single-cell and spatial transcriptomic data. This study aimed to develop an alternative framework for inferring ADAR-associated transcriptional activity from gene expression data across diverse transcriptomic technologies. METHODS: We developed the Contextual Activity Score (CAS), a framework based on transcriptional signatures from ADAR perturbation experiments. Context-specific signatures were generated for human neurons, mouse neurons, and cancer models to infer ADAR1 and ADAR2 activity. CAS was computed from normalized gene expression matrices using regulon-based enrichment analysis. Performance was evaluated by comparing with the Alu Editing Index across bulk RNA sequencing datasets, simulated sequencing depths, and library preparation protocols. RESULTS: CAS showed strong concordance with the Alu Editing Index across multiple datasets, while remaining robust to reduced sequencing depth and different library protocols. Unlike the Alu Editing Index, CAS can be applied to single-cell and spatial transcriptomic data and enables the independent assessment of ADAR2 activity. In cancer and neuronal contexts, CAS captured biologically meaningful variations in ADAR-associated transcriptional activity at sample, cell-type, and spatial levels. CONCLUSION: CAS provides a scalable approach applicable across multiple RNA-seq protocols for estimating ADAR-associated transcriptional activity using gene expression data. This method, implemented in an open-source R package for broad adoption, expands the ability to study ADAR-associated transcriptional activity across transcriptomic modalities where direct editing quantification is challenging, such as single-cell and spatial transcriptomics.

Adenosine Deaminase↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗

PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗

PubMed2026

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

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

Humans↗

PubMed2026

Emerging Principles in Spatial Functional Genomics.

Spatial transcriptomic and proteomic atlases have enabled mapping of gene programs within intact tissues, but these measurements remain largely descriptive and do not define the mechanisms controlling tissue biology. Pooled CRISPR screening provides scalable causal interrogation of gene function but remains largely confined to dissociated systems that lack spatial context. In vivo spatial functional genomics (SFG) bridges these approaches by integrating genetic perturbations with in situ transcriptomic and proteomic readouts to measure gene function within intact tissue ecosystems. By preserving spatial organization, SFG enables interpretation of perturbations through effects on cell-cell interactions, diffusible signals, multicellular niches, and tissue architecture. Here, we outline key design axes of SFG: perturbation strategy, barcoding strategy, and phenotypic readout. We discuss computational challenges, including spatial autocorrelation, neighborhood dependence, and context-aware null modeling, and highlight how SFG reveals non-cell-autonomous, architecture-dependent mechanisms of gene function, advancing toward predictive models of tissue organization and gene function.

Genomics↗

PubMed2026

Mapping antibody sequences and effector functions across spatial niches.

Antibodies are fundamental to human health but can also drive pathology. Each antibody has a molecular specificity, encoded by their clonally heritable B cell receptor (BCR). Recent advances in spatial transcriptomics coupled with repertoire sequencing have enabled capturing antibody-secreting cells (ASCs) and their clonal BCR within their tissue microenvironment. However, our understanding of antibody production niches remains limited. Furthermore, where antibodies are produced can be distinct from where antibodies exert their effector function. Here, we propose a conceptual spatial framework to distinguish between 'antibody production niches', defined by the ASC, BCR, and niche composition, versus 'antibody functional niches', composed of the antibody, antigen, and effector landscape. We then examine the possibilities and challenges to map and link antibody-encoding sequences and antibody effector functions using current and emerging technologies. Combined, we argue that integrating spatial sequence data with the antibody functional context is essential to decode the architecture of antibody-mediated immunity.

Humans↗

PubMed2026

GATA2 deficiency: enhancer deregulation, immune surveillance failure, and clonal evolution.

Germline mutations in GATA2 cause a syndromic inborn error of immunity characterized by cytopenia, infections, immune dysregulation, and a marked predisposition to myelodysplastic syndrome and acute myeloid leukemia. Initially defined by the DCML phenotype-dendritic cell, monocyte, B- and NK-cell deficiency-GATA2 deficiency is now recognized as a disorder of global immune-hematopoietic homeostasis. Recent multi-omics and experimental models reveal enhancer-driven inflammatory rewiring, IRF8-dependent lineage imbalance, and premature hematopoietic aging. In parallel, adaptive immune defects, including impaired B- and T-cell development and function, contribute to defective immune surveillance. These alterations not only explain susceptibility to infection but also shape clonal evolution and malignant transformation. Clinically, improved risk stratification and transplant outcomes underscore the importance of early recognition and monitoring of immune dysfunction. GATA2 deficiency thus represents a paradigm linking immune dysregulation, inflammatory stress, and cancer predisposition.

Humans↗

PubMed2026

Human endogenous retroviruses leading to autoimmune diseases.

Human endogenous retroviruses (HERVs) comprise approximately 8% of the human genome and were long regarded as inert remnants of ancestral retroviral infections. Increasing evidence indicates that HERVs are active genomic elements capable of influencing transcriptional programs, modulating immune responses, and contributing to disease pathogenesis. Under physiological conditions, HERV expression is tightly controlled by epigenetic mechanisms; however, infections, chronic inflammation, aging, and diverse environmental stimuli can promote HERV reactivation. HERV-derived RNAs and proteins engage innate immune sensors and trigger antiviral-like responses through mechanisms of viral mimicry, leading to activation of type I interferon and other inflammatory pathways. HERV dysregulation has been associated with disease-relevant immune pathways. This review summarizes recent advances linking HERVs to autoimmune disease pathogenesis and discusses their potential translational relevance as biomarkers and therapeutic targets.

Humans↗

PubMed2026

Preparation and study of non-thrombotic and biostable sulfobetaine-modified small-diameter polyurethane vascular grafts.

A novel sulfobetaine-modified polysiloxane-polycarbonate polyurethane (ZSiPCU) was synthesized. In vitro characterizations revealed that polysiloxane surface enrichment endowed the material with excellent biostability. Importantly, sulfobetaine zwitterions formed a robust hydration layer, effectively suppressing protein adsorption and platelet adhesion to ensure outstanding hemocompatibility. Furthermore, the material supported the adhesion and proliferation of vascular endothelial cells, confirming its cytocompatibility, while its elastomeric matrix provided rapid mechanical self-sealing capabilities. Electrospun ZSiPCU grafts were evaluated in a 3-month rat abdominal aorta model, maintaining high patency rates and facilitating in situ luminal endothelialization and smooth muscle cell remodeling. Additionally, superior puncture resistance of the grafts was demonstrated by puncture tests, with complete hemostasis achieved within 2 mins through mechanical self-sealing.

Polyurethanes↗

PubMed2026

Proteomic insights into the immunomodulatory effects of Ca/Sr co-doped sol-gel coatings for titanium implants.

Ionic functionalization of biomaterial coatings has emerged as a powerful strategy to regulate early host responses at the implant interface. However, how combined Ca/Sr incorporation governs the adsorbed proteome and downstream immune signaling remains poorly understood. This study analyses, employing in vitro tests and proteomics, the effect of adding Sr and Ca to Si-based coatings designed to bioactivate Ti implants. Hybrid Si-based coatings were synthesized by the sol-gel route with a fixed Ca content (0.5 wt%) and increasing Sr contents (0.5, 1.0, 1.5 wt%), and their physicochemical properties, ion release kinetics, and hydrolytic stability were characterized. The coatings remained highly crosslinked despite Ca/Sr incorporation, whereas the highest Sr content increased hydrolytic degradation to around 70% after 56 days. Proteomic analysis identified 183 adsorbed proteins, of which 56 were differentially adsorbed on Ca/Sr-coatings, mainly associated with immune and coagulation pathways. In vitro, RAW 264.7 showed increased gene expression of TNF-α and TGF-β; with an enhanced TNF-α secretion by the addition of Ca and Sr. In parallel, MC3T3-E1 indicated that Ca/Sr-coatings were not cytotoxic and did not impair cell proliferation. However, ALP activity was reduced in the co-doped groups, indicating that the immunomodulatory effects induced by Ca/Sr incorporation were not accompanied by enhanced early osteogenic differentiation. The Ca/Sr combination induced alterations in the adsorption of immune-related proteins, which correlated with the in vitro findings. The deeper insight into how Ca/Sr mixtures modulate protein adsorption on biomaterial surfaces may be key to understanding the immunomodulatory capacity of these bioactive cations.

Animals↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗