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PubMed2026

Retinoid dynamics in immune cells during age-related diseases.

Retinoids comprise vitamin A and its structurally related natural and synthetic derivatives. Retinoid dynamics involves multiple retinoid forms, carrier proteins, and enzymes that orchestrate the absorption, transport, storage and biotransformation of dietary vitamin A. Beyond their canonical metabolic functions, metabolites and proteins involved in retinoid metabolism also play distinct roles in signal transduction and transcriptome reprogramming, broadening the mechanisms that influence immune cell fate decisions. Age‑related changes in retinoid bioavailability and signaling intensity alter immune cell polarization and function, thereby contributing to the pathogenesis of chronic inflammation in neurodegenerative diseases, cardiovascular diseases, osteoarthritis, and other age-related diseases. In this review, we focus on age-related alterations in the retinoid metabolic pathway and their impact on inflammation and the progression of age-related diseases. This review highlights the pivotal role of retinoid metabolism in anti-ageing interventions and considers future directions and challenges in this field.

Humans↗

PubMed2026

Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology.

Computational pathology models that use digitized histopathology whole-slide images have the potential to become a cost-effective and scalable alternative to molecular assays for the prediction of genomic biomarkers, a key task in precision oncology. However, as new genomic biomarkers are discovered or quantified, large, labeled datasets must be prospectively collected to train new models. To address this challenge, we developed multimodal alignment for biomarker learning and generalization (MARBLE), a multimodal contrastive pretraining strategy that integrates structured biomarker knowledge into representation learning of histopathology images. MARBLE aligns histopathology-derived representations with representations of genomic biomarkers generated by a large language model (LLM) and a protein language model (PLM). This biologically informed alignment enables data-efficient generalization to novel, out-of-distribution biomarkers. Using the MSK-IMPACT cohort of over 40,000 patients across multiple biomarker panel versions, we design experiments grounded in real-world data to demonstrate the value of our proposed approach.

CP: computational biology↗

PubMed2026

Decoding bipotency: a transient regulatory state bridging totipotency and lineage commitment.

Early mammalian embryogenesis entails a coordinated transition from totipotency to the first lineage bifurcation, giving rise to embryonic lineages and the extra-embryonic trophectoderm. The mechanisms by which totipotency is resolved into lineage-primed states remain incompletely understood. Emerging evidence supports a non-binary model in which cells traverse a continuum of potency states, passing through a transient bipotent intermediate that retains both embryonic and extra-embryonic potential while exiting totipotency. Here, we synthesize recent advances in the mechanisms that establish, maintain, and resolve bipotency. We emphasize the coordinated roles of transposable elements, transcription factors, and signaling pathways in regulating this transition. We also highlight newly developed bipotent stem cell models and their implications in generating advanced embryo models in vitro. Notably, current insights are largely derived from mouse systems; given key differences between mouse and human early embryogenesis, extending these findings to human models remains a critical next step.

Animals↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

Algae-to-host horizontal gene transfer in Paramecium bursaria is associated with host adaptation during endosymbiosis.

Paramecium bursaria maintains a stable endosymbiosis with green algae, yet the evolutionary consequences of this association remain unclear. Here, we screened the host genome for algal-derived horizontally transferred genes (HTGs) using a lineage-aware workflow designed to detect horizontal gene transfer (HGT) between two defined lineages. We identified 16 candidate HTGs, including four putative newly transferred genes and 12 homologous transferred genes, most of which were functionally associated with redox homeostasis and metabolism. Five HTGs showed symbiosis-dependent expression. RNAi knockdown of GH32s and SATs reduced host proliferation, total cell area, and motility, while GH32s knockdown also reduced endosymbiont load. Duplication patterns suggest that most transfers may have occurred after the P. bursaria lineage diverged from the sampled Paramecium species but before its lineage-specific whole-genome duplication (WGD). The HTGs also showed host-associated shifts in GC content and gene length, while representative HTGs retained conserved domains and functional motifs. Together, our results support algae-to-host HGT in P. bursaria and suggest that some transferred genes may contribute to metabolic integration during endosymbiosis.

Gene Transfer, Horizontal↗

PubMed2026

Boosting kynurenic acid in kombucha via substrate selection: metagenomic and biochemical insights.

Kombucha is gaining global popularity for its health benefits. This study explored the use of chestnut honey, a rich source of kynurenic acid (KYNA), to produce kombucha enriched with this metabolite. Five variants were prepared using different green/black tea blends and carbon sources: white sugar or acacia honey (controls) versus chestnut honey. Samples were analyzed for tryptophan metabolites, physicochemical properties, and microbial diversity. Komagataeibacter and Enterobacter were predominant bacterial genera in SCOBY. Candida and Aspergillus were predominated in the single sample analyzed for fungi. During fermentation, tryptophan decreased, while kynurenine increased. KYNA levels remained largely stable during fermentation and were mainly influenced by the fermentation substrate. No melatonin pathway derivatives were detected. On day 7, chestnut honey yielded kombucha with 381.680-739.915 μmol/L KYNA and elevated myricetin. Overall, chestnut honey-based kombucha represents a system in which substrate composition appears to be the main factor influencing KYNA levels in the final beverage.

Kynurenic Acid↗

PubMed2026

Stroke risk following BNT162b2 vaccination: a systematic review and meta-analysis of self-controlled case series studies.

INTRODUCTION: Whether BNT162b2 (Pfizer-BioNTech) vaccination increases stroke risk remains a public health concern. This is the first meta-analysis to synthesize self-controlled case series (SCCS)-derived stroke risk estimates specifically for BNT162b2 vaccination. METHODS: PubMed and Embase were searched from inception through 9 May 2026, following PRISMA 2020 guidelines. Eight eligible SCCS studies were pooled using a random-effects model with restricted maximum likelihood (REML) estimation and the Knapp-Hartung adjustment. RESULTS: Eight studies across six countries encompassing several million vaccinated individuals were included. The pooled incidence rate ratio (IRR) was 0.967 (95% CI 0.892-1.049; I2 = 69.2%), indicating no statistically significant increase in stroke risk. Subgroup analyses showed no evidence of effect modification across continent, risk-window length, dose category, SCCS variant, or age group. CONCLUSIONS: These findings provide no evidence of increased short-term stroke risk following BNT162b2 vaccination at the population level. The observed heterogeneity appeared to be partly driven by methodological differences rather than true biological variation in vaccine effect.

Humans↗

PubMed2026

Epigenetic drift and LINE-1 activation in aging brain: Implications for neurodegenerative disease.

Brain aging and age-associated neurological diseases, such as Alzheimer's Disease (AD), Parkinson's Disease (PD), and Amyotrophic Lateral Sclerosis (ALS), are largely attributed to epigenetic drift which is characterized by the gradual accumulation of alterations in neural cell methylation patterns over time. These methylation changes are particularly evident in transposable element (TE)-derived sequences such as Long interspersed element-1 (LINE-1) which comprises approximately 17% of the human genome. During aging, LINE-1 elements gradually lose their methylation, as well as the regulatory safeguard mechanisms that usually keep them inactive. This repression loss can lead to LINE-1 reactivation, contributing to harmful effects including genomic instability, neuroinflammation, and more. Together these findings indicate that impaired epigenetic maintenance, especially in repetitive genome regions, plays a key role in biological aging of neurons and glial cells. In this narrative review, we discuss the methylation dynamics and regulatory mechanisms of LINE-1 retrotransposons, their activation processes during aging, and contribution to age-associated neurological diseases. We also highlight the potential of targeting LINE-1 methylation to restore methylation homeostasis, epigenetic stability and delay brain aging.

Humans↗

PubMed2026

Self-healing materials for food packaging: Design principles, activation mechanisms and implications for food safety.

Self-healing materials (SHMs), originally developed to restore mechanical integrity, have recently attracted growing interest in food packaging. By autonomously repairing physical damage, SHMs help preserve packaging integrity, barrier performance, food safety, and shelf-life during storage and transportation. This review summarizes recent advances in the design principles, activation mechanisms, material systems and food packaging applications of SHMs. Key healing strategies, including microencapsulation, dynamic covalent bond exchange, reversible non-covalent interactions and responsiveness to external stimuli such as temperature, pH, and humidity, are discussed. Representative material systems, including biopolymer-based films, hydrogels, nanocomposites, and stimuli-responsive polymers are evaluated with respect to their relevance to packaging animal-derived foods, fruits, and vegetables. Performance evaluation methods, sustainability implications, and food-contact safety concerns are addressed. Despite promising healing efficiency and mechanical resilience, challenges remain regarding production cost, food-grade safety, migration risks, trigger compatibility and stability under fluctuating environmental conditions. Future research should focus on scalable manufacturing, standardized evaluation protocols, repeated damage-healing safety assessment, regulatory compliance, and integration with intelligent packaging technologies.

Food Packaging↗

PubMed2026

Gastrointestinal digestion governs insect protein hydrolysis and predicted bioactive peptide release: Species-dependent implications for functional food applications.

This study investigates the digestion of insect proteins and the release of predicted bioactive peptides during human gastrointestinal digestion. Using the Infogest in vitro model, mealworm, cricket, and black soldier fly larvae (BSFL) proteins were digested and analyzed through discovery proteomics and bioinformatics to identify predicted bioactive peptides. Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) quantified insect proteins including predicted bioactive peptide precursor proteins, the precursors of predicted bioactive peptides. Results indicated that gastrointestinal digestion strongly influences peptide release, with the gastric phase exhibiting a richer predicted bioactive peptide profile than the small intestinal phase. Many predicted bioactive peptides were rapidly hydrolysed under small intestine conditions, which may lead to reduced stability or diminished activity in vivo, potentially explaining why certain peptides show strong bioactivity in vitro but limited effects in vivo. Additionally, predicted bioactive peptide release varied by insect species, influenced by genetic factors and peptide abundance. These findings highlight the importance of species selection and consideration of proteolytic digestion patterns in optimizing insect-derived bioactive peptides for functional foods and nutraceutical applications.

Animals↗

PubMed2026

Proteomic profiling reveals that DPP4 overexpression increases cell adhesion, inhibits cell migration, and restores androgen sensitivity in prostate cancer.

Dipeptidyl peptidase-4 (DPP4), a serine protease with both enzymatic and non-enzymatic roles, has emerged as a context-dependent modulator of tumor progression. In the present study, we investigated the expression and function of DPP4 in androgen-sensitive and castration-resistant prostate cancer (CRPC) models. Proteomic analysis of androgen-resistant prostate cells overexpressing DPP4 identified the involvement of the cellular adhesion molecules pathway. In prostate cells, lentiviral-mediated DPP4 overexpression restored androgen receptor signaling, inhibited epithelial-to-mesenchymal transition, and reduced cell migration, whereas DPP4 silencing produced the opposite effects. We demonstrate that DPP4 expression is down-regulated in CRPC cells and that treatment with capsaicin (CAP), a bioactive compound derived from red peppers, restores DPP4 expression. Moreover, DPP4 restoration by CAP suppresses prostate tumorigenesis in the TRAMP mice in vivo model of prostate cancer. Our results suggest that DPP4 could be a new target for CRPC.

Male↗

PubMed2026

An Assessment of Reliability Estimation Methods for Binomial Health Care Quality Measures.

We evaluated the performance of commonly used methods for estimating the reliability of binomial health care quality measures using simulated datasets spanning a range of performance score means and variances, numbers of entities, and patient sample sizes. For each simulation, reliability was estimated for all selected methods and compared with the known true reliability derived from the simulation parameters, with methods assessed on their accuracy and precision. Logistic regression with reliability estimated on the outcome scale demonstrated the highest accuracy and precision among all methods evaluated. The widely used Adams beta-binomial method performed poorly, although a modification recommended by Nieser and Harris substantially improved its performance. These approaches are applicable only to binomial measures. Among methods that can be applied to both binomial and continuous measures, permutation resampling of the Spearman rank correlation coefficient was the most accurate and precise, outperforming other commonly used approaches. Overall, for binomial quality measures, logistic regression on the outcome scale is the preferred method for reliability estimation, followed closely by the modified beta-binomial approach, while for non-binomial measures, permutation-based Spearman rank correlation appears to be the most suitable method.

Reproducibility of Results↗

PubMed2026

Assembly and Characterization of the First Complete Mitochondrial Genome of Tussilago farfara L.: Insights into Biological Functions and Phylogenetic Relationships within the Asteraceae Family.

Tussilago farfara L., a member of the Asteraceae family, is an economically valuable species due to its edible and medicinal properties. To elucidate the structural characteristics, genetic mechanisms, and evolutionary pathways of the organelle genomes of T. farfara, we sequenced, assembled, and annotated its mitochondrial genome for the first time. The complete mitochondrial genome of T. farfara spans 306,024 bp and contains 33 mitochondrial protein-coding genes (PCGs), 3 rRNAs, and 22 tRNAs. Analysis of the nucleotide substitution rate and genetic diversity revealed that most mitochondrial genome genes may have undergone purifying selection, indicating a slow evolutionary rate and a relatively conserved genomic structure. We further identified 13 fragments of chloroplast-derived DNA integrated into the mitochondrial genome, evidencing intracellular gene transfer. Collinearity analysis showed that Arctium lappa shares the most extensive mitochondrial homologous sequences and the highest sequence similarity with T. farfara. Phylogenetic analysis based on the mitochondrial genome helped to clarify the evolutionary and taxonomic position of T. farfara within the Asteraceae family. The mitochondrial genome sequence of T. farfara provides a valuable genomic resource for species identification and for evolutionary studies within the Asteraceae family.

Genome, Mitochondrial↗

PubMed2026

Targeting TP53 in triple-negative breast cancer: Molecular pathogenesis, therapeutic implications, and emerging pharmacological strategies.

Triple-negative breast cancer (TNBC) remains a highly aggressive and therapeutically challenging subtype, defined by the absence of oestrogen, progesterone, and HER2 expression. Tumour Protein 53 (TP53) mutations represent the most frequent genetic alteration, occurring in over 80% of cases and driving tumour initiation, progression, and therapeutic resistance. Mutant p53 proteins not only lose canonical tumour-suppressive functions but also often acquire gain-of-function (GOF) oncogenic properties that promote metastasis, genomic instability, and resistance to mechanisms like ferroptosis. This review examines the biological role of TP53 in TNBC pathogenesis and evaluates emerging pharmacological strategies aimed at targeting these vulnerabilities. Key approaches include the pharmacological reactivation of mutant p53 using small molecules such as APR-246, COTI-2, and the mutation-specific reactivator rezatapopt (PC14586), which has shown significant clinical tumour reduction in Y220C-mutant patients. Other strategies involve targeted protein degradation, the exploitation of synthetic lethal interactions (e.g., Chk1 or Aurora kinase B inhibition), and the use of natural products like cryptolepine or piperine derivatives. Recent clinical evidence further highlights the potential of combining epigenetic agents like decitabine with chemotherapy in TP53-mutant populations. Integrating TP53 mutation status into biomarker-driven treatment paradigms is a pivotal step toward achieving precision oncology and improving clinical outcomes for patients with TNBC.

Precision oncology↗

PubMed2026

Functional and Nutritional Potential of Chickpea Protein Hydrolysates: A Systematic Review and Plant-protein Network Analysis.

Chickpea is a protein-rich legume increasingly explored as a substrate for functional plant-based ingredients. Chickpea protein hydrolysates (CPHs) and chickpea-derived peptides (CPs), obtained through enzymatic hydrolysis or simulated gastrointestinal digestion, may provide technological and biological properties while supporting the valorization of chickpea fractions and by-products. This review integrates a network analysis of title-abstract terms from 5,728 unique Scopus and PubMed records on plant protein hydrolysates with a systematic review of 72 studies focused on CPH production, peptide characterization, bioactivity, and translational gaps. The evidence indicates that CPHs and CPs show promising antioxidant, antihypertensive, antidiabetic, anti-inflammatory, lipid-lowering, immunomodulatory, antimicrobial, and anticancer-related activities, mainly supported by biochemical assays, cell models, and animal studies. However, heterogeneous hydrolysis protocols, incomplete peptide characterization, inconsistent bioactivity methods, limited scale-up evidence, and the absence of human intervention trials restrict translation. Future studies should prioritize standardized protocols, mechanistic validation, bioavailability, sensory and regulatory assessment, food-matrix validation, and clinical trials.

Cicer↗

PubMed2026

Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.

BACKGROUND: Stromal tumour-infiltrating lymphocytes (sTILs) are prognostic in early-stage HER2-positive breast cancer, but their role in the context of dual HER2 blockade remains undefined. We evaluated manual, digital, and artificial intelligence (AI)-based sTIL quantification, together with AI-derived spatial metrics, for prognostic and treatment-benefit stratification using tumour samples from the phase 3 APHINITY trial. METHODS: In the APHINITY trial, 4805 patients were randomly assigned to receive chemotherapy plus trastuzumab with pertuzumab or chemotherapy plus trastuzumab with placebo. Median follow-up was 74·1 months (IQR 68·3-75·4). We analysed 4262 haematoxylin and eosin-stained images using manual assessment, an automated digital approach, AI-based lymphocyte quantification (AI percentage lymphocytes), and two AI-derived spatial features (AI-TIL and immune hotspot). Interobserver reproducibility was assessed in 262 randomly chosen tumour samples scored independently by five pathologists. Multivariable Cox models were used to assess associations between TIL levels and invasive disease-free survival (primary outcome in APHINITY), distant recurrence-free interval, and overall survival. The heterogeneity of pertuzumab benefit was evaluated using subgroup analyses, subpopulation treatment effect pattern plot analyses, and nested Cox models with treatment-by-biomarker interaction terms. FINDINGS: Manual scoring showed high interobserver reproducibility (intraclass correlation coefficient 0·84 [95% CI 0·79-0·88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11·6%) of 1035 node-positive tumours from immune-low (by manual scoring) to immune-high; this subgroup of patients showed greater separation of 5-year invasive disease-free survival curves between pertuzumab and placebo groups compared with patients whose tumours were concordantly classified as immune-low by both manual and AI-based approaches. Higher levels of TILs were associated with improved invasive disease-free survival for all sTIL measurement approaches and spatial measurements (hazard ratios [HRs] 0·41-0·93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0·36-0·48), but was not associated with higher values of spatial measures. The largest 6-year absolute improvements with pertuzumab were observed in patients with node-positive disease whose tumours scored in the highest level of immune infiltration of manual sTIL scoring (≥70·0%; mean absolute improvement 12·1 percentage points [SD 2·8]). In nested prognostic and predictive models, AI-based immune hotspot scores provided the most consistent additional information when combined with any sTIL measurement (all p<0·010). INTERPRETATION: Standardised manual sTIL scoring was reproducible, and digital and AI-based methods showed consistent prognostic stratification and potential for treatment-benefit stratification despite only modest correlation between platforms. AI spatial metrics provided complementary information beyond sTIL density and could support more scalable immune assessment. Future studies are needed to validate these approaches in independent cohorts and to clarify their clinical utility for stratifying contemporary HER2-directed therapies. FUNDING: None.

Humans↗