MedComm最新文献

Immune exhaustion is a state of sustained lymphocyte dysfunction that occurs following chronic antigenic stimulation and constitutes a shared hallmark of chronic infection and cancer. Beyond being a passive consequence of persistent antigen exposure, it actively drives tumor progression by fostering immunosuppressive microenvironments. Pathogens that evade immune detection to establish chronic infection can directly induce immune exhaustion through sustained inflammatory signaling, thereby crippling cytotoxic T cell-mediated tumor surveillance. This impairment facilitates both de novo tumorigenesis and the aggressive evolution of pre-existing malignancies. This comprehensive review delineates the mechanisms and characteristics of immune exhaustion within the contexts of chronic infection and cancer, as well as its impact on disease progression. Furthermore, we propose a chronic infection-exhaustion-tumor axis and analyze this pathway with reference to specific pathogens. Finally, we provide a critical appraisal of current strategies designed to reverse immune exhaustion and discuss their therapeutic potential and limitations within three defined contexts: chronic infection, cancer, and the interplay between chronic infection and tumor development. By integrating insights from virology and immuno-oncology, this work proposes therapeutic strategies to disrupt the infection-exhaustion-tumor axis, offering a roadmap for precision oncology.

Cerebral venous outflow insufficiency (CVOI) is a recently recognized cerebrovascular condition characterized by impaired venous drainage from the brain to the extracranial system. However, its clinical phenotypes and classification criteria remain poorly defined. In this single-center cross-sectional study, we analyzed 245 patients with CVOI using contrast-enhanced CT or MR venography to identify clinical features and propose a novel anatomical classification. We identified 10 major symptoms of cerebral venous congestion, with tinnitus cerebri, neck discomfort, and tinnitus being the most common. A new classification system was proposed based on lesion location and bilateral jugular foramen narrowing rate, categorizing CVOI into intracranial (CV), extracranial (JV), and tandem (CJV) types, each further stratified into four/five subtypes. Receiver operating characteristic (ROC) curve analysis showed that narrowing thresholds of 0.20 and 0.40 offered excellent discriminatory performance for subtype differentiation, with an area under the curve (AUC) approaching 1.0. Notably, tandem-type CVOI (CJV) was the most prevalent (56.7%) and exhibited distinct symptom patterns and pathogenesis. These findings provide a practical framework for diagnosing and stratifying CVOI and may inform individualized treatment strategies.

Bipolar disorder (BD) research confronts challenges: blood-based biomarkers offer limited insights into neurobiology, while cerebrospinal fluid (CSF) collection is clinically unusual. Linking genetic susceptibility to pathophysiology remains crucial for biologically informed risk stratification. We integrated cohort data and genome-wide association study (GWAS) summary statistics: the largest BD meta-analysis, CSF multi-omics profiles including 3107 proteomic and 2602 metabolomic participants, and a validation cohort of 247,834 UK Biobank participants. Unsupervised clustering revealed four single-nucleotide variant (SNV) clusters: metabolic-imbalance, metabolic-active, human leukocyte antigen (HLA)+immune, and HLA-immune. These clusters exhibited distinct clinical features, with the metabolic-imbalance cluster showing multi-directional associations with 21 psychiatric traits, while the HLA-immune cluster was associated with emotional instability in BD patients (odds ratio [OR] = 1.14, p = 0.027). The optimized multimodal cluster-specific polygenic risk scores (PRS) model significantly outperformed clinical-only prediction factors (C-index = 0.77), with the metabolic-imbalance PRS contributing a 22.6% incremental predictive value (hazard ratio [HR] = 1.23, 95% CI: 1.04-1.45, p = 0.016). Risk reclassification showed an 84% reduction in false-negative rates in the low-risk subgroup, identifying a high-risk layer with a 17.6-fold increased BD incidence. Altogether, genetically informed substitutes for CSF biomarkers emerged as a scalable tool for risk prediction, overcoming the barriers of CSF collection while capturing neurobiological heterogeneity.

Adenosine-to-inosine (A-to-I) RNA editing, predominantly catalyzed by the enzyme adenosine deaminase acting on RNA 1 (ADAR1), has attracted interest due to its essential functions in regulating immune response and cancer progression. This research investigates ADAR1 inhibition as a promising strategy aimed at improving immunotherapy efficacy in lung adenocarcinoma (LUAD) and explores the underlying mechanisms. Findings from murine models demonstrate that ADAR1 suppression within tumors notably improves the immune microenvironment, marked by increased PD-L1 expression and enhanced CD8⁺ T-cell infiltration, as well as elevated levels of CXCL9, CXCL10, and CXCL11. These changes promote antitumor T-cell immune responses and amplify the effects of immunotherapy. Mechanistic investigations further reveal that deficiency in ADAR1 leads to an increase in double-stranded RNA (dsRNA), which serves as a substrate for A-to-I editing. This activates downstream signaling via dsRNA receptors, including RIG-I and MAVS, thereby inducing the IFN-β pathway. Significantly, IFN-β contributes to the ADAR1-dependent modulation of the tumor immune microenvironment and carcinogenesis in LUAD. Clinical validation in LUAD patients further confirms that reduced ADAR1 expression is associated with improved immunotherapy responses. These findings suggest inhibiting ADAR1-mediated A-to-I RNA editing is a promising approach to enhance the efficacy of immunotherapy in LUAD.

Efficient detection of breathing impairment is critical for treatment and prognosis in neuromuscular disorders. However, standard pulmonary function tests often yield ambiguous results. This prospective study evaluates whether advanced real-time MRI (RT-MRI) combined with deep learning-based image segmentation provides sensitive outcome measures for respiratory dysfunction in late-onset Pompe disease (LOPD), a model disease for diaphragmatic weakness. Eleven Pompe patients (mean age 52.2 years; 55% female) and 11 controls (mean age 50.9 years; 55% female) were included. RT-MRI with a temporal resolution of 50 ms, combined with U-Net-supported lung segmentation, revealed significantly reduced diaphragmatic motion in Pompe patients compared to controls and unmasked paradoxical diaphragmatic motion in Pompe patients (7 of 11). Reduced diaphragmatic sniff velocity and pathological diaphragmatic/thoracic synchronicity were detected in Pompe patients with still normal results in standard pulmonary function tests. Fatty involution of the diaphragm as quantified by fast T1 mapping correlated significantly with functional parameters from RT-MRI and pulmonary function tests. RT-MRI combined with deep learning-based lung segmentation offers novel biomarkers for early detection of respiratory muscle weakness. This new technique provides useful outcome measures for clinical care as well as treatment studies in patients with neuromuscular breathing impairment. The technique can also be used to characterize physiologic breathing patterns in healthy individuals.

Cancer is a global health challenge. The initiation and progression of cancer are correlated with dynamic dysregulation of RNA regulatory networks. This review systematically explains how contending RNAs (including mRNA, miRNA, lncRNA, circRNA, etc.) remold gene expression programs across multiple dimensions. They do this primarily through the competing endogenous RNA sponge effect, RNA-protein complex assembly, RNA editing (A-to-I editing, m6A modification, etc.), tumorigenesis, heterogeneous evolution, and therapeutic resistance. RNA regulatory networks do not only help one to decode cancer biology but because they are dynamic in nature, they are now also being looked at as good precision targets for diagnosis and treatment. This article integrates recent findings on the emerging functions of RNA networks in tumor metabolic reprogramming, tumor immune microenvironment shaping, and cancer stem cell property maintenance, while highlighting their clinical application prospects as liquid biopsy biomarkers. Our therapies focus on assessing the potential and clinical translation bottlenecks of novel RNA-targeted interventions, including antisense oligonucleotides, RNA aptamers, and the CRISPR-Cas13 system. A dynamic adjustability made the RNA-targeted therapies promising intervention nodes in precision medicine even if most of them are still in a preclinical state.

The LKB1-AMPK signaling pathway is a master regulator of cellular energy homeostasis and a central hub in stress adaptation. As a conserved metabolic sensor, this pathway coordinates glucose, lipid, and protein metabolism, thereby sustaining physiological function across diverse tissues. Beyond its canonical role in energy balance, growing evidence highlights its dysregulation in multiple pathological conditions. Despite extensive mechanistic studies, the disease-specific regulation and translational potential of the LKB1-AMPK pathway remain incompletely understood. This review systematically studies the molecular basis and regulatory mechanisms of LKB1-AMPK signaling in cardiovascular diseases-including atrial fibrillation, ventricular fibrillation, myocardial infarction, cardiac hypertrophy, heart failure, and atherosclerosis-where impaired pathway activity underlies energy deficits, fibrosis, oxidative stress, and arrhythmogenesis. We further explore its involvement in metabolic disorders such as diabetes and diabetic nephropathy, in neurodegenerative diseases like Alzheimer's and Parkinson's disease, and in oncology, where LKB1 mutations drive tumorigenesis and alter therapeutic responses. Emerging strategies, including metformin, novel AMPK activators, and LKB1-based gene therapies, are highlighted as promising yet challenged by tissue specificity, off-target effects, and genetic variation. By integrating insights from cardiovascular, metabolic, neurological, and oncological research, this review underscores the pathway's potential as both a biomarker source and therapeutic target, providing a foundation for precision medicine in complex diseases.

Heart failure with preserved ejection fraction (HFpEF) is a highly heterogeneous syndrome that poses challenges for therapeutic development and contributes to suboptimal patient outcomes. The phenotypic classification of patients with HFpEF to guide etiology-specific therapeutic strategies represents a rational approach to address the current dilemma. However, the clinical outcomes of HFpEF under different etiological classifications remain poorly understood. Here, we assessed the clinical outcomes of HFpEF patients across different etiological phenotypes, based on a novel classification system comprising five categories: vascular-related, cardiomyopathy-related, right heart/pulmonary-related, valvular/rhythm-related, and extracardiac disease-related HFpEF. Data from the Chinese Cardiovascular Association Database-Heart Failure Center Registry (2017–2021) were analyzed, including 51,466 hospitalized HFpEF patients with 1-year follow-up. Significant differences in baseline characteristics and clinical outcomes were observed among phenotypes. Patients with right heart/pulmonary-related, valvular/rhythm-related, and extracardiac disease-related HFpEF showed a higher incidence of adverse outcomes. Specifically, the right heart/pulmonary-related and valvular/rhythm-related phenotypes were associated with increased heart failure rehospitalization, while extracardiac disease-related HFpEF was linked to higher cardiovascular mortality. Prognostic risk factors also varied across phenotypes. In conclusion, 1-year outcomes exhibit significant variations across HFpEF phenotypic subgroups. Future studies should explore whether phenotype-specific personalized treatment strategies can improve clinical outcomes, especially in high-risk phenotypes.

Electrical stimulation is a common technique in neuroscience and clinical therapies, with stimulation frequency being a critical factor in its efficacy. However, the cellular mechanisms by which different frequencies of pulsed electrical stimulation modulate neuronal activity remain poorly understood. In this study, we explore the effects of 60 Hz (low frequency [LF]) and 160 Hz (high frequency [HF]) pulsed electrical stimulation on excitatory and inhibitory neurons in the primary somatosensory cortex (S1) of mice using two-photon Ca²⁺ imaging. Our results show that HF stimulation significantly increased Ca²⁺ activity in excitatory neurons in layer 2/3, both during and after stimulation, while LF stimulation enhanced neuronal activity only post-stimulation. In layer 5 excitatory neurons, HF stimulation increased neuronal activity only after stimulation cessation, whereas LF stimulation transiently suppressed activity during stimulation. Both LF and HF stimulation enhanced activity in inhibitory neurons in layer 2/3 during stimulation. In summary, our study reveals that electrical stimulation activates both excitatory and inhibitory neurons, with its primary mechanism of action being the modulation of neuronal rhythm rather than the amplitude of their activity. These findings shed light on stimulation mechanisms, supporting its therapeutic potential for neuropsychiatric disorders targeting neuronal rhythmicity.