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PubMed2026

Opposing kinase signaling may underlie the inverse relationship between cancer and Alzheimer's disease.

Cancer and Alzheimer's disease (AD) are leading causes of mortality and exhibit an inverse relationship, where AD patients have reduced cancer risk and vice versa. However, the molecular basis of this relationship remains poorly understood. We reanalyzed published proteomic and phosphoproteomic datasets to investigate this relationship. Differentially abundant proteins were identified in lung adenocarcinoma and glioblastoma samples relative to controls and compared with proteins altered in AD brains, revealing 37 proteins with opposing abundance patterns. Protein-protein interaction and pathway analyses revealed enrichment in kinase signaling and phosphorylation pathways. Phosphoproteomic analysis identified 52 differentially phosphorylated sites with opposing patterns, while kinase-substrate enrichment analysis identified 44 kinases with opposing inferred activity profiles. Integration of kinase activity and phosphosite data identified 29 kinase-phosphosite pairs, including 4 prioritized pairs with opposing patterns relevant to both diseases. Across seven independent cancer cohorts, 17 of 20 statistically significant phosphosite-cohort comparisons (85%) were concordant with the discovery findings, supporting reproducibility of the prioritized phosphosites. Together, these findings highlight opposing kinase signaling as a prominent feature of the inverse relationship and suggest potential biomarkers and therapeutic targets. This study provides a novel systems-level framework for investigating inverse relationships, supported by an R Shiny application for data exploration (https://advscancer.shinyapps.io/advscancer/). SIGNIFICANCE: This study presents an integrated proteomic and phosphoproteomic framework for investigating the inverse relationship between cancer and Alzheimer's disease (AD). By integrating differential protein abundance, phosphosite phosphorylation, inferred kinase activity, and curated kinase-substrate relationships, we identified opposing signaling patterns and prioritized four kinase-phosphosite pairs. Independent evaluation across seven CPTAC cancer cohorts supported the reproducibility of the prioritized phosphosite patterns. These findings provide insight into molecular processes potentially associated with the inverse relationship between cancer and AD, identify candidate biomarkers and therapeutic targets, and demonstrate the value of systems-level, data-driven approaches for investigating shared and opposing disease processes.

Humans↗

PubMed2026

Computational metabolomics at scale: from open data to insight.

Metabolomics data are currently generated at scale thanks to the evolution of technologies that have led to marked improvements in the number of metabolites detected, spanning all chemical classes. These data are increasingly submitted to public repositories for data reuse, integration, and interpretation. Despite the availability of public resources and associated computational tools, the field still lacks a widely adopted, consistent data and analytics infrastructure capable of transforming this wealth of information into scientific insight. Indeed, the metabolomics field is just now scratching the surface of being able to harness the power of new computational technologies. In this review, we summarize discussions from the "Dagstuhl-Seminar 24181 Computational Metabolomics: Towards Molecules, Models, and their Meaning" with a focus on public data availability, open data standards, data and knowledge integration, and education. Our goal is to raise awareness and adoption of the latest open science resources while highlighting key areas needing further development.

Metabolomics↗

PubMed2026

Cost-Effectiveness of Electronic Patient-Reported Outcome Measure Interventions in Cancer: Systematic Review and Parameter Extraction for Economic Modeling.

BACKGROUND: Complex digital interventions that integrate electronic patient-reported outcome measures (ePROM) into clinical practice in cancer have the potential to improve quality of life, increase survival, and reduce health resource use and costs. Such systems can help patients with cancer self-manage chemotherapy symptoms, reduce clinicians' workloads through automated decision support, and resolve problems earlier. However, more research on the cost-effectiveness of ePROM monitoring is needed. OBJECTIVE: This paper comprises two complementary components: (1) a systematic literature review summarizing and evaluating the quantitative and qualitative evidence related to the cost-effectiveness of ePROM monitoring and (2) a health economic model parameter extraction. We also conducted supplementary targeted searches and scoping to provide context to our findings. METHODS: We searched Ovid (including MEDLINE and Embase), Scopus, and the International Health Technology Assessment Database for original English-language papers published on or before March 2025 using search strings that combined terms related to ePROMs, health economics, and cancer/oncology. We included papers reporting health economic-related outcomes for ePROM interventions designed for adult cancer populations and excluded screening tools and conference abstracts. RESULTS: We included 34 publications from 27 unique studies and identified and analyzed 26 ePROM-integrated interventions within these. Most (23/26) of the included interventions explicitly described some form of alert handling and automated decision support based on remote ePROM monitoring. Of the 34 publications, 5 presented full cost-effectiveness analysis results, of which 3 were highly uncertain and lacked clear differences in costs and health outcomes between ePROMs and standard care; conversely, 2 presented strong evidence of cost-effectiveness due to quality-of-life improvements, reduced hospitalizations, and potentially more autonomy in health-related travel (eg, ePROM-monitored patients can drive or walk to the hospital instead of using taxis or ambulances). A further 5 publications reported partial health economic results (eg, cost-consequence and budget impact), of which 1 detected no difference in strategies; in contrast, 4 reported lower health resource use and costs of ePROMs, mainly due to hospitalization reductions. Overall, 12 of the 27 studies included a qualitative component but mostly focused on user experience and design-related themes; only 2 of these addressed economic-specific themes (eg, changes in workflow and resource use due to ePROM implementation and integration), indicating some potential for time saving due to ePROM monitoring. CONCLUSIONS: Some ePROM-integrated interventions demonstrated cost-effectiveness in cancer care, but the evidence base remains limited. Where evidence does exist, cost-effectiveness appears driven by reduced hospitalization and improved quality of life. Qualitative research within the included studies rarely addressed economic questions. We provide a detailed parameter extraction for use in future economic modeling and recommend research priorities, including quantitative mapping of ePROM symptom data onto health resource use patterns, and qualitative work exploring how ePROM implementation affects clinical workloads and patient-perspective costs.

Humans↗

PubMed2026

Copper-Containing Surface Engineering for Soft-Tissue Biomedical Devices: Structure-Function Relationships and Ion Release-Driven Biological Performance, A Systematic Review.

Copper and copper-based materials have gained increasing attention for the functional modification of implantable medical devices intended for prolonged soft-tissue contact, including vascular stents, catheters, and intrauterine devices. Owing to their broad-spectrum antimicrobial activity, redox reactivity, and involvement in angiogenesis and cellular signaling, copper-based systems offer significant potential for multifunctional surface engineering. However, achieving a balance between antibacterial efficacy, corrosion behavior, controlled ion release, and cytocompatibility remains a critical challenge. This PRISMA-compliant systematic review analyzes copper-containing materials and surface modification strategies for soft-tissue biomedical applications. A structured search of Scopus, Web of Science, and PubMed (2015-2025) identified 65 eligible studies. The review encompasses bulk copper-containing alloys, electrochemical and chemical surface modification techniques, physical vapor deposition approaches, and advanced hybrid systems integrating copper with polymers, hydrogels, or metal-phenolic networks. Across the reviewed literature, antibacterial performance was strongly dependent on copper concentration, microstructural distribution, and spatiotemporal ion release profiles. Moderate, well-controlled copper incorporation frequently improved antibacterial efficacy while maintaining acceptable hemocompatibility and cytocompatibility, particularly in vascular and blood-contacting devices. In contrast, excessive copper loading often accelerated corrosion and induced adverse cellular responses. Emerging multifunctional architectures demonstrated improved regulation of biological interactions, enabling simultaneous antibacterial, antithrombotic, and proendothelial effects. Overall, copper-based surface technologies represent a versatile platform for soft-tissue implant modification. Future translational progress will require precise control of copper release kinetics and comprehensive long-term in vivo validation to ensure safety and sustained therapeutic performance. From the authors' perspective, the most promising future direction involves multifunctional copper-based hybrid coatings capable of dynamically regulating ion release, host tissue integration, and antibacterial performance simultaneously. Strategies integrating hierarchical architectures, stimulus-responsive release systems, and clinically scalable fabrication methods are expected to play a key role in translating copper-containing surfaces from experimental concepts toward commercially viable soft-tissue biomedical devices.

Copper↗

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

Open Dialogue versus treatment as usual for adults presenting in crisis to mental health services in England (the ODDESSI Trial): a multisite cluster-randomised trial.

BACKGROUND: Open Dialogue is a person-centred, transdiagnostic model of mental health care that emphasises continuity, therapeutic relationships, and collaboration with the service user's social network. Open Dialogue is a service-wide approach to care involving network meetings with the service user, members of their social network, and usually two practitioners who support the network throughout the duration of care. In this cluster-randomised trial, we aimed to evaluate the clinical effectiveness of Open Dialogue versus treatment as usual for adults presenting in crisis to community mental health services in England. METHODS: This multicentre, parallel two-arm, cluster-randomised, controlled superiority trial was conducted in mental health services in five National Health Service trusts in London and the South of England. Clusters were defined at the level of primary care practices within service catchment areas. Participants were adults aged 18 years or older presenting in crisis to mental health services and registered with a practice within trial clusters. Randomisation was done at the cluster level (1:1), stratified by catchment area, and balanced on average general practice (GP) list size and Index of Multiple Deprivation (2015). The chief investigator, senior statistician, and assessors of the primary outcome were masked in the study. Participants either received Open Dialogue or treatment as usual, which refers to the functional team model currently implemented throughout English mental health services. The primary outcome was time (days) to first relapse following initial recovery from the index crisis censored at the end of the 2-year follow-up period. Participant-reported secondary outcomes were EuroQol Visual Analogue Scale, Social Provisions Scale, Lubben Social Network Scale, Questionnaire about the Process of Recovery, and the Client Satisfaction Questionnaire, measured at five timepoints over 2 years, and clinical measures were extracted from electronic health records. People with relevant lived experience were involved in the design and execution of the study. Fidelity to the model of care in Open Dialogue and treatment as usual, and adherence to the delivery of Open Dialogue, were measured prior to each site starting participant recruitment, then every 6 months thereafter until the final participant follow-up in that site. The trial was retrospectively registered (ISRCTN52653325) and is complete. FINDINGS: 185 general practices associated with six mental health Trusts across England were identified for screening. 105 practices were excluded, and 80 were included in cluster formation, forming 32 clusters that were randomly assigned (16 to treatment as usual and 16 to the Open Dialogue intervention). One mental health trust (two clusters) withdrew, resulting in five mental health trusts (30 clusters) participating in the trial. Between June 25, 2019, and Dec 9, 2021, 494 participants (266 [54%] female gender, 221 [45%] male gender, 341 [69%] White British) with a mean age of 38·1 years (SD 13·4) provided consent for study inclusion (223 in the treatment as usual group and 271 in the Open Dialogue group). Of these, 174 (78%) in the treatment as usual group and 225 (83%) in the Open Dialogue group recovered and had data enabling relapse determination; there was no significant difference between groups on the primary outcome of time to relapse following initial recovery (marginal hazard ratio 0·95 [95% CI 0·67-1·32]). For secondary outcomes, Open Dialogue was associated with significantly lower probabilities of psychiatric inpatient admission and re-referral to crisis care or secondary mental health services, and with improvements in self-rated recovery, health-related quality of life, and satisfaction with services. There were no significant differences in social network quality or size. There were 386 serious adverse events (281 in the treatment as usual group and 105 in the Open Dialogue group); 376 (97%) were deemed to be unrelated to the intervention. INTERPRETATION: Open Dialogue did not reduce time to first relapse compared with treatment as usual, the primary outcome, but it reduced acute inpatient bed use, improved service user reported outcomes and experience, and there were no significant safety concerns. Further investigation is required to determine whether Open Dialogue can enhance the effectiveness and acceptability of crisis care and continuing care in community mental health services. FUNDING: National Institute for Health Research.

Humans↗

PubMed2026

Venetoclax added to dose-adjusted EPOCH-R for newly diagnosed double-hit lymphomas: phase 2 results from ALLIANCE A051701, an open-label, randomised, controlled, phase 2-3 trial.

BACKGROUND: High-grade B-cell lymphoma with rearrangements of MYC and BCL2 and/or BCL6, known as double-hit lymphoma, is a highly aggressive malignancy with poor outcomes after standard chemoimmunotherapy. We aimed to study whether the addition of the BCL2-inhibitor venetoclax to chemoimmunotherapy in patients with double-hit lymphoma resulted in superior efficacy compared with chemotherapy alone. METHODS: ALLIANCE A051701 is an open-label, randomised, controlled, phase 2-3 trial in separate cohorts of patients with double-hit lymphoma and patients with double-expressor lymphoma. In this analysis, we report phase 2 results from the double-hit lymphoma cohort. Patients aged 18-80 years with newly diagnosed double-hit lymphoma and Eastern Cooperative Oncology Group (ECOG) performance status 0-2 were recruited from 41 hospitals and outpatient clinics in the USA. Patients were randomly assigned (1:1) to receive DA-EPOCH-R (dose-adjusted etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin, and rituximab) either alone (DA-EPOCH-R group) or with venetoclax (DA-EPOCH-R plus venetoclax group) using permuted block randomisation schedule. All patients and investigators were aware of group assignment. DA-EPOCH-R was administered on a 21-day schedule for up to six total cycles. Venetoclax was given as 600 mg by mouth daily on days 4-8 of cycle 1 and on days 1-5 of cycles 2-6. The primary endpoint was progression-free survival in the modified intent-to-treat population inclusive of all eligible patients with centrally confirmed double-hit lymphoma. The safety analysis population consisted of all evaluable patients who received at least one dose of protocol treatment. This trial is registered with ClinicalTrials.gov (NCT03984448) and is closed to enrolment. FINDINGS: 36 patients were randomly assigned to the DA-EPOCH-R group and 37 to the DA-EPOCH-R plus venetoclax group between Oct 22, 2019, and Sept 18, 2020. Median age was 65 years (IQR 56-73) and baseline demographic factors were well balanced between groups, with 30 (45%) female and 36 (55%) male patients. Most patients (59 [89%]) were white, two (3%) were Asian, one (2%) was Black or African American, and four (6%) had unknown or unreported ethnicity. The majority of patients had MYC-BCL2 double-hit lymphoma (59 [89%] patients), advanced stage disease (57 [86%] patients), and high-intermediate/high-risk IPI score (42 [64%] patients). Median follow-up was 34·7 months (IQR 30·1-36·8). Median progression-free survival was 28·4 months (95% CI 5·2-not estimable) in the DA-EPOCH-R group (n=30) and 7·7 months (95% CI 4·7-NE) in the DA-EPOCH-R plus venetoclax group (n=36; hazard ratio [HR] 1·13, 95% CI 0·53-2·37; p=0·75). Deaths on treatment occurred in one (3%) patient in the DA-EPOCH-R group (due to dyspnoea; possibly related to treatment) and six (17%) patients in the DA-EPOCH-R plus venetoclax group (four due to sepsis [three at least possible related and one unrelated], two due to cardiac arrest [at least possibly related]), prompting early closure of the double-hit lymphoma cohort. The most common grade 3-4 non-haematological adverse event was febrile neutropenia, occurring in 15 (43%) of 35 patients in the DA-EPOCH-R plus venetoclax group and 11 (37%) of 30 patients in the DA-EPOCH-R group. The median overall survival has not been reached in either group. The 24-month overall survival estimates were 72% (95% CI 52-85) in the DA-EPOCH-R group compared with 52% (95% CI 33-68) in the DA-EPOCH-R plus venetoclax group (HR 2·49, 95% CI 1·03-6·04; p=0·038). INTERPRETATION: The addition of venetoclax to DA-EPOCH-R resulted in excess mortality, prompting early study closure. Robust accrual shows that prospective multicentre trials are feasible in double-hit lymphoma, and the outcomes in the DA-EPOCH-R group serve as a benchmark for future studies. FUNDING: National Cancer Institute of the National Institutes of Health.

Humans↗

PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗

PubMed2026

Comprehensive assessment of vasospastic angina using coronary computed tomography angiography: synergistic value of the presence of myocardial bridge, perivascular inflammation, and myocardial extracellular volume fraction.

AIMS: Coronary computed tomography angiography (CCTA) has evolved beyond anatomical assessment to include sophisticated tissue characterization. While an elevated perivascular fat attenuation index around the right coronary artery (FAI-RCA) is known to reflect coronary inflammation in vasospastic angina (VSA), recurrent vasospasms may also induce chronic subclinical myocardial injury and subsequent remodelling, potentially associated with an increased myocardial extracellular volume fraction (ECV). However, the diagnostic integration of ECV and FAI-RCA for identifying VSA in patients with angina with non-obstructive coronary arteries (ANOCA) remains to be elucidated. METHODS AND RESULTS: This study included consecutive ANOCA patients who underwent CCTA with a dedicated ECV protocol, followed by an invasive spasm provocation test. Comprehensive CCTA analysis quantified both FAI-RCA and the transmural ECV gradient (the difference between endocardial and epicardial ECV: ECVEndo - ECVEpi). Of the 100 patients analysed (mean age: 65.3 ± 11.8 years; 55% male), 27 were diagnosed with VSA. Multivariable logistic regression analysis identified transmural ECV gradient [odds ratio (OR): 1.12, 95% confidence interval (CI): 1.01-1.25], presence of myocardial bridging (MB) (OR: 3.49, 95% CI: 1.25-9.74), and high FAI-RCA (> -70.95 Hounsfield units [HU]) (OR: 5.79, 95% CI: 2.06-16.30) as significant independent predictors of VSA (all P < 0.05). Notably, the integration of transmural ECV gradient provided incremental diagnostic value beyond FAI-RCA and MB, as assessed by the Net Reclassification Improvement and Integrated Discrimination Improvement. CONCLUSION: A multi-parametric CCTA approach potentially identifies patients at high risk for VSA. The significant association of the transmural ECV gradient with VSA suggests that myocardial remodelling imaging provides a novel diagnostic window into the cumulative myocardial impact of vasospasm, independent of active adipose tissue inflammation and the presence of MB.

Humans↗

PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

PubMed2026

Peptide molecular lock-engineered nanobodies enable an oriented dual-modal immunoassay for reliable detection of Cronobacter sakazakii.

Conventional nanobody ELISAs for trace Cronobacter sakazakii in powdered infant formula suffer from random orientation and low signal output. We developed an oriented dual-modal immunoassay that combines site-specific biotinylation via a C-terminal AviTag and a peptide molecular lock, enabling controlled surface orientation while preserving nanobody structural integrity. This strategy was further integrated with phage-displayed nanobodies for multivalent amplification and both fluorescent and colorimetric readouts. The assay exhibited a broad linear range of 103-106 CFU/mL, with limits of detection (LODs) of 6.70 × 102 CFU/mL for fluorescence and 1.55 × 103 CFU/mL for colorimetry, showing improved sensitivity compared with the conventional passive adsorption-based Nb-ELISA evaluated in this study. XGBoost-based multimodal fusion improved quantitative accuracy, and SHAP analysis elucidated modality contributions. In spiked powdered infant formula samples, recoveries ranged from 92.1% to 118% with coefficients of variation below 5.98%, confirming acceptable matrix tolerance and analytical reliability.

Cronobacter sakazakii↗

PubMed2026

CAR-T Cell Therapy: Manufacturing Platforms and Clinical Consequences.

Chimeric antigen receptor (CAR) T-cell therapy has transformed hematological cancer care, yet variability in efficacy, durability, and safety cannot be explained solely by antigen selection or patient factors. We propose that manufacturing platforms are active biological determinants of outcome. Viral vectors, used in all licensed products, provide stable genomic integration and durable expression but are limited by cost, cargo capacity, and centralized production. Nonviral strategies, including transposons, CRISPR knock-ins, and messenger RNA delivery, enable faster, less-expensive manufacturing with larger payloads, while introducing distinct safety and persistence profiles. This review presents a three-layer mechanistic framework that reframes manufacturing as biology: integration biology determines genomic risk and transgene stability; clonal fitness shapes persistence, dominance, and exhaustion; and epigenomic imprinting, influenced by gene transfer method, cytokines, and culture stress, preconfigures functional trajectories. Clinical observations link platform choice to immune recovery, where prolonged B-cell aplasia and delayed T-cell reconstitution contribute to infection-related nonrelapse mortality, and hematopoietic reserve at apheresis emerges as a practical predictor. Finally, manufacturing is positioned as the key to democratizing cell therapy. Decentralized, nonviral production aligned with regulatory standards may enable equitable access and transition CAR-T therapy from innovation to sustainable global care.

Humans↗

PubMed2026

Multi-omics reveals that burdock seed aglycone alleviates renal fibrosis by restoring mitochondrial oxidative phosphorylation function.

Renal fibrosis (RF), a common pathological process driving chronic kidney disease (CKD) progression to end-stage renal failure, is closely associated with oxidative phosphorylation (OXPHOS). Arctigenin (ATG), the main active component of burdock seed, exhibits anti-inflammatory and anti-fibrotic activities, but its mechanisms in RF treatment remain unclear. Here, we performed integrated transcriptomic and proteomic analyses to identify key targets and pathways of ATG in a unilateral ureteral obstruction-induced rat RF model. Multi-omics enrichment analysis revealed that NDUFS8 and NDUFS2 were the core targets of ATG, with the OXPHOS pathway as the central intersecting pathway. Our results suggest that ATG exerts anti-renal fibrosis effects by targeting the OXPHOS pathway to inhibit excessive reactive oxygen species production and oxidative stress. SIGNIFICANCE: Chronic kidney disease (CKD) continues to impose an escalating global health and socioeconomic burden, while renal fibrosis (RF), as the convergent pathological endpoint of virtually all progressive nephropathies, remains the principal determinant of irreversible renal failure and adverse clinical outcomes. Despite extensive efforts to develop antifibrotic therapies, effective clinical interventions remain elusive, largely due to the complex and multifactorial nature of RF pathogenesis. In this study, we employed an integrated multi-omics framework encompassing transcriptomics, proteomics, and metabolomics to systematically decipher the antifibrotic mechanism of arctigenin (ATG), a bioactive natural compound derived from traditional Chinese medicine. Our findings identify mitochondrial oxidative phosphorylation as the pivotal regulatory axis underlying the renoprotective effects of ATG and further establish key catalytic subunits of mitochondrial complex I as its direct molecular targets. Mechanistically, ATG not only restores complex I activity and reprograms mitochondrial energy metabolism but also preserves the intracellular stability and localization of these subunits, thereby preventing their aberrant release-mediated inflammatory activation and disrupting the self-perpetuating cycle linking metabolic dysfunction, inflammation, and fibrosis progression. Beyond revealing a previously unrecognized dual mechanism integrating metabolic and inflammatory regulation, this study provides compelling evidence that mitochondrial dysfunction is not merely a secondary consequence of tissue injury but a fundamental driver of fibrotic remodeling. Importantly, our work highlights the translational potential of natural product-based mitochondrial interventions for CKD treatment and supports a broader conceptual shift toward metabolism-centered therapeutic strategies for chronic fibrotic diseases. Given the central role of mitochondrial dysfunction across multiple organs, these findings may also have far-reaching implications for the treatment of systemic fibrosis-related disorders beyond the kidney.

Animals↗

PubMed2026

Multiscale Modeling Primer: Focus on Chromatin and Epigenetics.

A central challenge in modern biology is to understand how molecular interactions produce cellular and organismal functions across vast spatiotemporal scales. Nowhere is this challenge more apparent than in the study of chromatin, where meters of DNA compact into a micron-sized nucleus. How this polymer folds is a dynamic process, regulated by epigenetic modifications-chemical changes to DNA and histones that involve only a handful of atoms. These small changes cooperate to produce emergent, higher-order structures that define cellular identity and function. To explain this system, we must integrate static, high-resolution snapshots from techniques like cryo-EM with dynamic, lower-resolution data from microscopy and genomics. Multiscale computational models are essential tools that bridge these experimental gaps and reveal the mechanisms of emergent behavior. However, the communication divide between experimental biologists and quantitative modelers often hampers progress. This primer addresses that gap. It first introduces the fundamental biology of chromatin and epigenetics at an introductory level for non-biologists audiences. We then survey the landscape of computational approaches, from atomistic to systems-level models, and connect them to the experimental data that inform and validate them at an introductory level for non-computationalists. We argue that the next frontier will require us to build integrative models that can predict how molecular perturbations mechanistically alter cellular phenotypes, which will open a new era of chromatin-targeted therapeutics.

Chromatin Dynamics↗

PubMed2026

Combining neuromelanin-sensitive MRI and quantitative susceptibility mapping for enhanced diagnosis and differentiation of parkinson's disease: A systematic review.

BACKGROUND: Loss of dopaminergic neurones and iron deposition in the substantia nigra pars compacta (SNpc) are two major pathological hallmarks of Parkinson's disease (PD). Such changes can be visualised by advanced techniques including neuromelanin-sensitive MRI (NM-MRI) and quantitative susceptibility mapping (QSM). This systematic review investigates the diagnostic performance and methodological development of the integrated use of NM-MRI and QSM in PD. METHODS: The systematic search was performed in four databases (Scopus, PubMed, ScienceDirect, and Web of Science) according to the PRISMA 2020 guidelines until July 2026. Bias was assessed using QUADAS-2 and certainty of evidence was assessed using GRADE. RESULTS: Seventeen studies with 2228 participants were included. Combined NM-MRI and QSM consistently showed reduced neuromelanin volume/contrast and increased iron deposition in the SNpc of PD patients compared to healthy controls. Multimodal integration yielded a significant improvement in diagnostic accuracy (AUC values 0.86-0.99), and was able to successfully differentiate PD. Recent methodological advances included simultaneous acquisition sequences (e.g. MTC-GRE, STAGE, setMag) and AI-driven automated segmentation, which led to significantly reduced scan times and improved reproducibility. CONCLUSION: The combination of NM-MRI and QSM has a synergistic effect and provides powerful complementary biomarkers for the diagnosis and differential diagnosis of PD.

Humans↗

PubMed2026

Identifying and Prioritizing Core Components of Relationship Education Programs: a Case Study of an Artificial Intelligence (AI) Assisted Systematic Review.

The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical aspect of this endeavor is determining the core components of prevention programs that drive positive outcomes. This article presents a case study utilizing artificial intelligence (AI)-assisted systematic review methods to identify key components of healthy marriage and relationship education programs. Given the growing body of research in this domain, AI tools offer a promising means to enhance the efficiency and accuracy of literature reviews. This study employed AI to screen, code, and validate research articles, demonstrating its effectiveness in expediting systematic reviews while maintaining high accuracy in inclusion screening. This case study involved a systematic review of 22,028 resources (identified from PsycINFO, Academic Search Ultimate, and Google) and a final data set of 268 relevant studies. AI screening was integral in effectively conducting multiple rounds of screening. However, findings also highlight challenges in AI-assisted qualitative data abstraction, underscoring the continued need for human expertise in complex coding tasks. The study contributes to the ongoing discourse on integrating AI into prevention science methodologies and offers insights for optimizing AI applications in systematic reviews.

Artificial Intelligence↗

PubMed2026

Molecular Determinants and Therapeutic Targeting of Stop Codon Readthrough in Eukaryotic Translation.

Accurate translation termination is essential for proteome integrity and in eukaryotes is primarily governed by the release factors eRF1 and eRF3, which ensure precise recognition of stop codons and efficient release of nascent polypeptides. However, proteome integrity is challenged by mutations that generate premature termination codons (PTCs), leading to truncated, nonfunctional proteins and degradation of the aberrant transcript via nonsense-mediated mRNA decay (NMD). Collectively, these events account for ∼1800 human genetic diseases. Translational readthrough, the process by which near-cognate tRNAs decode stop codons and allow ribosomes to continue elongation beyond the stop codon, represents a possibility to suppress PTCs and restore full-length protein synthesis. Initially discovered in viruses as a mechanism to expand coding capacity, readthrough is now recognized as a regulated feature of eukaryotic gene expression influenced by both cis-acting sequence elements and trans-acting factors. Recent evidence highlights the remarkable context dependence of readthrough, revealing variation across transcripts, tissues, and developmental stages. In this review, we examine the molecular determinants that define stop codon recognition and readthrough efficiency, with particular emphasis on nucleotide context. We further discuss the mechanisms and binding sites of small molecules that promote PTC readthrough, and summarize the clinical development landscape of readthrough-inducing compounds for the treatment of diseases caused by nonsense mutations.

Humans↗

PubMed2026

Endocrine-disrupting chemical-induced gene networks confer coronary heart disease risk revealed by causal inference and single-cell analyses.

BACKGROUND: Endocrine-disrupting chemicals (EDCs) are linked to coronary heart disease (CHD), but underlying mechanisms remain unclear. We aimed to identify EDC-related genes and evaluate their causal roles in CHD. METHODS: We curated EDC-related genes from a compound-gene interaction database and integrated them with CHD genome-wide association study (GWAS) summary statistics and tissue-specific expression quantitative trait loci (eQTL) data. Two-sample Mendelian randomization (MR) and Bayesian colocalization were applied to infer causality. Functional enrichment, single-cell RNA sequencing of human coronary arteries, and EDC-gene networks were further analyzed. RESULTS: After FDR correction, 39 genes were significantly associated with CHD risk via MR. Four genes-ZNF827, FCHO1, IPO9 (protective), and RPL13 (risk-increasing)-showed strong colocalization (PPH4 > 0.9). Pathway and single-cell analyses of coronary artery tissue indicated that vascular and immune pathways mediate these effects. An interaction network highlighted associations between specific EDCs and candidate genes implicated in CHD susceptibility. CONCLUSION: This integrative genomic study provides evidence that EDCs influence CHD susceptibility through distinct gene networks, revealing potential mechanisms and molecular targets for prevention and therapy.

Humans↗