Citations

Peroxisome-proliferator–activated receptor delta (PPARδ) regulates metabolic, mitochondrial, and inflammatory pathways implicated in neurodegeneration, making it an attractive therapeutic target for amyotrophic lateral sclerosis (ALS). In this study, we evaluated two PPARδ agonists, KD3010 and T3D-959, in two established ALS/FTD mouse models: an AAV-mediated C9orf72 G4C2-repeat expansion model (C9-149R) and the TDP-43Q331K transgenic model. Drug treatment was initiated prior to the emergence of key disease features and continued for 9–10 months. Comprehensive behavioral, neuropathological, and biomarker analyses revealed marked differences between the two models. C9-149R mice exhibited reduced body weight and subtle behavioral alterations without robust motor deficits, whereas TDP-43Q331K mice developed pronounced, progressive motor and cognitive impairments accompanied by a ~7-fold elevation in plasma neurofilament light chain (NfL). Despite effective target engagement—particularly for T3D-959—neither PPARδ agonist improved motor performance, cognitive behavior, neuroanatomical measures, plasma NfL levels, or disease-associated molecular phenotypes in either model. Prolonged KD3010 treatment resulted in loss of target engagement, consistent with drug tolerance, while T3D-959 sustained PPARδ activation without therapeutic benefit. Together, these findings demonstrate that PPARδ agonism is insufficient to modify disease progression in these ALS/FTD mouse models and underscore the importance of publishing well-powered negative preclinical studies to refine therapeutic strategies for ALS.

Abstract Motivation Illumina DNA methylation arrays have evolved rapidly, expanding genomic coverage while introducing backward incompatibilities by removing many CpG sites present in earlier versions. These changes result in systematic missing values when integrating data across array generations and substantially limiting the reuse of legacy datasets. Results We developed a two-stage framework for imputing missing DNA methylation values. The procedure first imputes randomly missing values using standard imputation techniques and then addresses systematic missingness using multi-output machine learning models, including support vector regression, nearest-neighbor methods, random forest models, and deep neural networks. When evaluated on real datasets with up to fifty percent induced missingness, the proposed framework consistently outperformed conventional imputation approaches. It also accurately imputes the missing CpG sites between methylation arrays and reduced representation bisulfite sequencing data, enabling robust cross-platform data integration. Analyses of large brain tumor methylation datasets demonstrate that the method restores array-specific methylation patterns while preserving biological complexity. Importantly, imputing missing methylation sites significantly improves the performance of epigenetic age prediction models. Availability and implementation This tool is implemented in the Python package “ultra-impute,” freely available at https://github.com/liguowang/ultra-impute. A code snippet demonstrating the usage of the ultra-impute package is provided in a Jupyter Notebook (https://github.com/liguowang/ultra-impute/blob/master/doc/Tutorial.ipynb).

Abstract Patients at risk for progression from myeloid precursor states (idiopathic cytopenia of undetermined significance [ICUS] and clonal cytopenia of undetermined significance [CCUS]) to myelodysplastic syndrome (MDS) are not well defined. Epidemiologic risk factors, clonal changes for MDS development, and evolution from one MDS state to another were available in the MDS Natural History Study (NHS), a prospective cohort enrolled at diverse US sites. Extensive clinical data and biospecimens were collected at baseline and follow-up. A total of 1177 (56%) individuals with MDS or MDS-related precursor conditions (ICUS/idiopathic dysplasia of undetermined significance [IDUS], CCUS, and MDS/myeloproliferative neoplasm overlap) were enrolled and diagnosed in the MDS NHS. The median age was 74 years, with 89% aged ≥60 years, and most were male patients. Median follow-up time was 1.6 years and was similar among ICUS/IDUS, CCUS, and MDS. During follow-up period, 68% of participants terminated the study, 35% died, and 38% had disease progression. Using a refined clinical definition of progression, the greatest proportion of progression was observed among higher-risk-MDS (116 [73%]), followed by lower-risk-MDS (170 [52%]); fewer progression events were observed among participants with CCUS (65 [22%]) and ICUS/IDUS (46 [17%]). Predictors of progression are delineated, including female sex, higher quantity of mutations, the presence of TP53 mutation, and poor/very poor cytogenetic score. Based on these prospective data, guidelines for clinical management including monitoring and surveillance are outlined. The MDS NHS provides real-world data to illustrate how clinical and genotypic differences inform the classification, disease course, and approach to therapy, with informed monitoring guidelines for patients. This trial was registered at www.ClinicalTrials.gov as NCT02775383.

Context: Traditional Chinese medicine (TCM) techniques, grounded in TCM theory, offer flexible and personalized therapeutic strategies. Increasing evidence indicates that gut microbiota dysbiosis is closely associated with the onset and progression of various diseases, highlighting a complex mutual relationship. Investigating the interaction between TCM techniques and gut microbiota may provide both a theoretical and practical foundation for clinical application. However, comprehensive systematic analyses remain limited. Objective: This study aimed to identify core themes and emerging trends in research on the interactions between TCM techniques and gut microbiota. In addition, it sought to explore underlying correlations using data mining approaches to provide insights supporting clinical applications and mechanistic studies. Materials and Methods: A systematic literature review was conducted using the Web of Science Core Collection and China National Knowledge Infrastructure for bibliometric analysis. Co-occurrence analysis was performed using CiteSpace software to identify research characteristics and development trends. Key data were extracted and visualized through an interactive network diagram generated with Cytoscape. Results: Current research predominantly focuses on acupoint application, moxibustion, and TCM enema, all of which exhibit significant effects in promoting beneficial microbiota and suppressing pathogenic strains. The regulatory impact of these TCM techniques on gut microbiota is closely associated with inflammatory response pathways and immunity. Conclusions: TCM techniques are extensively applied in disease management. Among them, moxibustion, acupoint application, TCM enema, and Tuina remain prominent and evolving areas of interest. Further empirical research is warranted to elucidate the biological mechanism underpinning these effects within the guidance of TCM pattern theory.

Convergent framework for future POC cancer diagnostics aligning clinical needs (why), enabling technologies (how), and translational bridges. It integrates lab-on-chip biosensing and analytics for decentralized, early, longitudinal care.

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

NF-κB activating protein (NKAP) plays important roles in various cancers, including breast cancer. However, its expression and prognosis value in breast cancer remains uncertain. Gene expression profiling interactive analysis, Human protein atlas database, and University of Alabama at Birmingham Cancer data analysis portal database were used to predict the expression and prognostic value of NKAP in breast cancer. Immunohistochemistry, quantitative real time polymerase chain reaction (qRT-PCR) and western blot were performed to detect NKAP expression. The effects of NKAP on cell proliferation, migration and drug sensitivity were investigated in MDA-MB-231 and SK-BR-3 cells. NKAP protein expression differed in breast cancer tissues and paraneoplastic tissues based on the cancer genome atlas data. The high NKAP expression was significantly correlated with a poor prognosis in breast cancer patients. The results of immunohistochemistry, western blot assay, and qRT-PCR proved that NKAP was highly expressed in breast cancer tissues compared with paraneoplastic tissues. In addition, qRT-PCR results showed that high expression of NKAP was significantly correlated with the larger tumor size and higher TNM stage. Moreover, knockdown of NKAP significantly inhibited the proliferation, migration, and enhanced drug sensitivity of MDA-MB-231 and SK-BR-3 cells. NKAP is highly expressed in breast cancer tissues, and its high expression is closely associated with poor prognosis. NKAP also promotes proliferation, migration, and inhibits drug sensitivity of breast cancer cells.

Abstract Isolated/idiopathic rapid-eye-movement (REM)-Sleep Behavior Disorder (iRBD) is characterized by dream enactment behaviors associated with loss of REM atonia. iRBD is in most cases a prodromal synucleinopathy, and emerging evidence suggests associations between RBD and other neurological and psychiatric conditions. In this study, we performed pathway-based polygenic risk score (PRS) and rare variant burden analyses to examine these potential associations. Pathway-specific PRS were constructed from genome-wide association study summary statistics of five neurodegenerative and seven psychiatric traits across 10 biologically relevant pathway categories, including a total of 279 pathways, in 1,573 iRBD cases and 16,022 controls from the International RBD Study Group and UK Biobank. Rare variant burden tests were performed in 1,264 iRBD cases and 2,581 controls. We identified multiple potential pathways indicating shared polygenic risk between RBD and both neurodegenerative and psychiatric disorders. Lewy body diseases and post-traumatic stress disorder had the most shared polygenic risk pathways in neurological and psychiatric disorders, respectively. Two pathways, the serotonin transport pathway and the chaperone-mediated autophagy pathway, showed the strongest association with iRBD, and gene-based rare variants analyses revealed five genes associated with iRBD: GBA1 , PLEKHM1 , LRP2 , P2RX1 , and HAP1. Subsequent analysis of these genes in Parkinson’s disease and dementia with Lewy bodies replicated several associations. Together, these findings provide novel insights into the shared genetic architecture underlying iRBD, neurodegenerative disorders, and psychiatric traits, with implications for early identification and mechanistic understanding.

We provide the first longitudinal and most comprehensive analysis of the intrarenal microvasculature in a slowly progressive model of autosomal dominant polycystic kidney disease (ADPKD) and integrate the findings with studies in a young cohort of individuals with ADPKD. We identified vasculature-related pathways that could be targeted for therapeutic interventions and contribute promising, noninvasive biomarkers in patients with ADPKD. Alterations of the intrarenal microcirculation may affect drug delivery; a better understanding of its longitudinal changes may aid in treatment management.

Abstract In a real-world analysis of brexucabtagene autoleucel recipients with relapsed/refractory B-cell acute lymphoblastic leukemia (N = 278), lack of response to prior blinatumomab correlates with significantly worse following chimeric antigen receptor T-cell therapy outcomes.