Cell Reports Medicine最新文献

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex disorder with undefined mechanisms, no diagnostic tools and treatments. To investigate concurrent system dysfunctions, we recruited age- and sex-matched ME/CFS patients and healthy controls for a multimodal analysis of energy metabolism, immune profiles, and plasma proteomics. Immune cells from ME/CFS patients show elevated adenosine monophosphate (AMP) and adenosine diphosphate (ADP) with a reduced ATP/ADP ratio, indicating decreased ATP generation and cellular energy stress. Immune profiling reveals skewing toward less mature effector subsets of CD4⁺, CD8⁺, and γδ T cells, with reduced CD1c⁺CD141^- conventional DC type 2 and CD56^lowCD16⁺ terminal natural killer cells. Elevated levels of plasma proteins associated with thrombus formation and vascular reactivity may contribute to the endothelial dysfunction observed in ME/CFS patients. Classification and regression tree modeling identifies variables with strong predictive potential for ME/CFS. Together, this study provides insights into the somatic symptoms and underlying biology of ME/CFS.

Understanding the biology and clinical relevance of disease heterogeneity in hepatocellular carcinoma (HCC) is important for guiding therapeutic strategies. Through multi-omics and in situ analyses in three independent cohorts of patients with advanced HCC including GO30140 phase 1b and IMbrave150 phase 3 trials, we identified three robust molecular subtypes of HCC, i.e., cholangiocyte-like, progenitor-like, and hepatocyte-like, based on their association with different liver epithelial cell lineages. These subtypes showed distinct tumor cell-intrinsic and extrinsic features, including different immune contextures, and importantly an association with clinical response to atezolizumab plus bevacizumab combination immunotherapy. In a humanized HCC xenograft mouse model recapitulating the GPC3-high progenitor-like subtype, a GPC3/CD3 bispecific antibody elicited strong antitumor activity mediated by intratumoral recruitment and activation of T cells. Our study provides biological insights into HCC heterogeneity and potential strategies for targeting subtype-specific vulnerabilities.

Large language models (LLMs) are increasingly explored for clinical applications, but their ability to generate management recommendations for lung cancer screening remains uncertain. In this cross-sectional, multi-center study, 148 anonymized low-dose computed tomography (CT) reports from three healthcare institutions are used to assess the readability, accuracy, and consistency of four widely adopted models (GPT-3.5, GPT-4, Claude 3 Sonnet, and Claude 3 Opus). Among them, Claude 3 Opus produces the most readable recommendations, while GPT-4 achieves the highest clinical accuracy. Importantly, performance dose not differ significantly across institutions, underscoring the robustness of these models to variations in reporting templates and their utility in diverse healthcare settings. In an exploratory analysis, two state-of-the-art models, proprietary GPT-4o and its open-source counterpart DeepSeek-R1, show comparable performance to GPT-4, outperforming GPT-3.5. These findings highlight the potential role of LLMs to enhance clinical decision support in lung cancer screening across diverse healthcare settings.

Accelerated intermittent and continuous theta burst stimulation (a-iTBS and a-cTBS) show strong efficacy for treatment-resistant depression (TRD), yet their neural mechanisms remain unclear. This study uses concurrent transcranial magnetic stimulation (TMS) and electroencephalography (TMS-EEG) to examine these mechanisms in 40 TRD patients and 40 healthy controls (HCs). TRD individuals demonstrate abnormal local cortical excitability at baseline, characterized by left hypoactivity and right disinhibition. A-iTBS increases left excitability, and a-cTBS increases right inhibition, and both normalize it to the level of HCs. Network analyses reveal that a-iTBS improves current propagation to the left inferior parietal lobule (IPL), correlating with a better antidepressant effect. Contrastingly, a-cTBS induces a widespread inhibition as indicated by current propagation over parietal cortices, with the left IPL being most prominent, and this also correlates with a better antidepressant effect. These findings outline the frontoparietal circuitry in TMS antidepressant effects and provide insights for optimizing treatment efficacy. This study was registered at the Chinese Clinical Trial Registry (ChiCTR2200055320).

Venetoclax-azacitidine (VEN/AZA) has transformed acute myeloid leukemia (AML) therapy, yet reliable predictors of response remain lacking. We employ a multidisciplinary strategy combining ex vivo drug sensitivity testing, transcriptomic profiling, functional assays, and clinical data to identify determinants of VEN/AZA response. Core genes consistently associated with responsiveness are validated through CRISPR-Cas9 screening, with silencing of BCL2L1 and PINK1 preferentially enhancing drug sensitivity. Building on these insights, we develop and validate an eight-gene random forest model (RF8) that achieves high accuracy across four independent cohorts (n = 498). RF8 distills the downstream effects of genetic alterations to assist in predicting treatment response and outperforms existing genetic mutation-based signatures. Moreover, RF8 scores show a nearly monotonic relationship with clinical response probabilities and survival outcomes, enabling precise patient stratification. These findings demonstrate the feasibility of integrating transcriptomic and drug-response data to guide VEN/AZA therapy, representing an advance toward personalized therapeutic strategies.

Women's reproductive health plays a pivotal role in both longevity and the aging process. We conduct Mendelian randomization (MR) and observational analyses to investigate these relationships. Univariate MR analyses reveal that older age at first birth, later menarche, higher estradiol, and sex hormone-binding globulin (SHBG) increase longevity, while pre-eclampsia liability decreases longevity. Older ages at first birth and at first sexual intercourse are associated with lower DNAmGrimAgeAccel, but these associations disappear after mutual adjustment. Mediation analyses identify cardiometabolic diseases, lung diseases, and mental disorders as key mediators. In corroborating the MR results, observational analyses show that early reproductive behaviors, such as age at first sex, are associated with accelerated biological aging. Additionally, we observe significant non-linear associations between hormone levels, age at menopause, and aging outcomes. This study highlights the impact of reproductive health on aging and suggests potential strategies for promoting healthy aging in women.

Synaptic loss is a hallmark of Alzheimer's disease (AD) but lacks robust blood-based biomarkers. We investigate growth-associated protein 43 (GAP-43), previously identified as a synaptic candidate in the cerebrospinal fluid (CSF). Postmortem proteomic profiling of brain-derived extracellular vesicles (n = 21) highlights GAP-43 as a central hub within synaptic protein networks co-depleted in AD and closely linked with proteins enriched in immune-, metabolic-, and synaptic-related modules. In two well-characterized Chinese AD cohorts (n = 785), we measure plasma GAP-43, including subgroups with CSF biomarkers (n = 72), SV2A-PET (positron emission tomography) (n = 85), tau-PET (n = 280), and magnetic resonance imaging (MRI) (n = 595). Plasma GAP-43 correlates with CSF GAP-43, neurofilament light, and both baseline and longitudinal synaptic PET. Elevated plasma GAP-43 is associated with greater tau aggregation, faster brain atrophy, and accelerated cognitive decline, particularly among cognitively unimpaired individuals. These findings support plasma GAP-43 as a promising biomarker of early synaptic degeneration and a potential tool for identifying individuals at risk of AD progression.

The use of adeno-associated virus (AAV) as a gene therapy vector is significantly limited by pre-existing immunity. The high seroprevalence and broad antigenic cross-reactivity of primate-derived AAVs restrict patient eligibility and preclude therapeutic redosing. Here, we harness the phylogenetic diversity of non-mammalian dependoparvoviruses to engineer serologically distinct AAV capsids for immune evasion. A barcoded screen of divergent Dependoparvovirus isolates identifies AAV.div3A, a chimeric capsid with robust transduction, zero antigenic cross-reactivity, and undetectable seroprevalence. Derived from a phylogenetically distant Muscovy duck isolate, AAV.div3A fully evades neutralization in mice, even after passive immunization with NAb+ human serum or following initial vector dosing. Further engineering yields AAV.div3A-M1, a myotropic, liver-detargeted capsid with enhanced cardiac and diaphragm transduction. In a Pompe disease model, redosing with AAV.div3A or div3A-M1 significantly increases therapeutic GAA levels. Overall, our work leverages untapped dependoparvoviral diversity to overcome pre-existing and vector-induced immunity, enabling expansion of patient eligibility and effective redosing.

Neoadjuvant immunochemotherapy (nICT) has significantly improved the treatment of locally advanced esophageal cancer (EC), yet accurately identifying patients' response remains a major challenge. In this study, we introduce eSPARK, a multimodal framework designed to integrate routinely available clinical data for informed decision-making in nICT treatment for EC. The model is developed using 344 patients from three independent regions, each with pre-treatment-paired computed tomography (CT) imaging and pathological slides, and postoperative pathological complete response (pCR) outcomes. By incorporating cytological semantic information, eSPARK demonstrates superior generalizability, outperforming single-modality models and achieving robust predictive accuracy across multicenter datasets. Additionally, a multi-scale interpretability module identifies several biomarkers, including the neutrophil-to-lymphocyte ratio (NLR) in the tumor microenvironment, associated with nICT response. Our findings underscore the potential of eSPARK as a powerful tool for personalized therapeutic decision-making in locally advanced EC and its broader implications for advancing precision oncology through multidisciplinary data integration.

Physicians offer invaluable clinical insights, but their involvement in medical AI research is hindered by limited technical expertise. We conduct a superiority, open-label, randomized controlled trial involving 64 junior ophthalmologists to undertake a 2-week project on "automated cataract identification" under minimal engineering assistance, with (intervention, n = 32) or without (control, n = 32) ChatGPT-3.5. The overall project completion rate is higher in intervention group than controls (87.5% vs. 25.0%; difference 62.5%, p = 9.42e-7), and the unassisted completion rate likewise (68.7% vs. 3.1%; difference 65.6%, p = 5.70e-8). The intervention group demonstrates better project planning and faster completion times (p < 0.01). After a 2-week washout, 41.2% of successful intervention participants complete a new project without the support of large language models (LLMs). A survey shows that 42.6% of participants fear regurgitating information without understanding and 40.4% worry about fostering lazy thinking, indicating potential dependency. Therefore, LLMs can help physicians overcome technical barriers, although long-term risks require further study. Trial registration: This study was registered at ClinicalTrials.gov (NCT06015178).