Metabolic reprogramming in cancer significantly impacts immune responses within the tumor microenvironment, but its influence on cancer immunotherapy effectiveness remains uncertain. This study aims to elucidate the prognostic significance of metabolic genes in cancer immunotherapy through a comprehensive analytical approach. Utilizing data from the IMvigor210 trial (n = 348) and validated by retrospective datasets, we performed patient clustering using non-negative matrix factorization based on metabolismrelated genes. A metabiotic score was developed using a “DeepSurv” neural network to assess correlations with overall survival (OS), progression-free survival, and immunotherapy response. Validation of the metabolic score and key genes was achieved via comparative gene expression analysis using qPCR. Our analysis identified four distinct metabolic classes with significant variations in OS. Notably, the metabolism-inactive and hypoxia-low class demonstrated the most pronounced benefit in terms of OS. The metabolic score predicted immunotherapeutic benefits with high accuracy (AUC: 0.93 at 12 months). SETD3 emerged as a crucial gene, showing strong correlations with improved OS outcomes. This study underscores the importance of metabolic profiling in predicting cancer immunotherapy success. Specifically, patients classified as metabolism-inactive and hypoxia-low appear to derive substantial benefits. SETD3 is established as a promising prognostic marker, linking metabolic activity with patient outcomes, advocating for the integration of metabolic profiling into immunotherapy strategies to enhance treatment precision and efficacy.

The rising psychological issues among cancer patients call for timely treatment. Psychological issues such as anxiety, depression, and distress are particularly common among cancer patients and have a significant impact on their treatment and prognostic outcomes. Distraction has been proven to mitigate mental disorders as a strategy of intervention. Digital tools like social short video platforms offer cost-effective mental healthcare potential, but there is a lack of longitudinal studies demonstrating their intervention effectiveness. This study aimed to assess the impact of social short video platforms on the psychological well-being of cancer patients, focusing on anxiety, depression, and distress. We studied 455 digestive system cancer patients using mixed methods. The effect on psychological symptoms was evaluated via cross-sectional analysis of 392 patients and pre-post intervention analysis of 63 patients, employing the Hospital Anxiety and Depression Scale and Distress Thermometer. The findings showed lower anxiety, depression, and distress scores among regular users of social short video platforms. The intervention led to reduced anxiety and depression scores. As a prevalent app of social short video platforms, these platforms might be a safe and convenient nonpharmacological assisted tool for enhancing mental health care during cancer treatment.

Severe trauma is a critical aspect of medical practice, profoundly impacting patient care and outcomes. Over the past 20 years, advancements in trauma care concepts and the utilization of advanced technologies have led to substantial growth in severe trauma research, evidenced by a notable increase in research activity and subsequent publications. To understand the publication landscape in severe trauma, identify prevailing research trends, and highlight areas requiring further development for future insights, we conducted a bibliometric analysis. Our analysis indicates that there are 16,939 severe trauma-related publications from the past 20 years, with a continuous increase in publication volume, particularly showing a rapid growth trend from 2018 to 2021. The United States leads in both volume and citation frequency. Moreover, we synthesized data on productive countries/regions and research institutions, showcasing extensive collaboration across diverse geographic locations and institutional affiliations. Substantial progress has been achieved in severe trauma research, particularly in clinical diagnosis, treatment, epidemiology, prevention, and pathogenesis. However, there is still a gap in adopting cutting-edge interdisciplinary methodologies. This study provides a comprehensive overview of the current state of severe trauma research and suggests pathways for future advancement.

In the realm of malignant tumor treatment, particularly regarding hematologic malignancies, chimeric antigen receptor T-cell (CAR-T) immunotherapy has witnessed remarkable advancements in recent years. This approach involves genetically modifying and engineering a patient's T-cells ex vivo to express a specific CAR, known as CAR-T cells.When these modified cells are reintroduced into the patient, they can specifically recognize target antigens and exhibit highly efficient cytotoxicity against cells expressing these antigens, making them suitable for the treatment of malignant tumors. CD19, which is expressed on the surface of B lymphocytes at different stages of differentiation, has been identified as a suitable target for the treatment of most B-cell lymphomas. CAR-T cells targeting CD19 have demonstrated excellent specificity, cytotoxicity, and persistence in both in vitro and clinical trials, showing tremendous potential for application. However, identifying appropriate targets for CAR-T therapy in solid tumors remains a challenge, leading to limited advancements in this area. This review discusses the mechanisms, applications, limitations, and prospects of CAR-T therapy in hematologic malignancies and solid tumors, aiming to provide directions for future research in this field.

Diabetic nephropathy (DN) represents a prevalent chronic microvascular complication of diabetes mellitus (DM) and is a major cause of end-stage renal disease. The anfractuous surrounding of DN pathogenesis and the intricate nature of this metabolic disorder often pose challenges in both the diagnosis and treatment of DN compared to other kidney diseases. Hyperglycaemia in DM predispose vulnerable renal cells into microenvironmental disequilibrium and thereby results in innate immunocytes infiltration including neutrophils, macrophages, myeloid-derived suppressor cells, dendritic cells, and so forth. These immune cells play dual roles in kidney injury and closely correlated with the degree of proteinuria in DN patients. Additionally, innate immune signaling cascades, initiated by altered metabolic and hemodynamic in diabetic context, are crucial in instigating and perpetuating renal inflammation, which detrimentally contribute to DN pathogenesis. As such, antiinflammatory therapies, particularly those targeting innate immunity, hold renoprotective promise in DN. In this article, we reviewed the origin and feature of the above four prominent kidney innate immune cells, analyze their pathogenic role in DN, and discuss potential targeted-therapeutic strategies, aiming to enhance the current understanding of renal innate immunity and hence help to discover promising therapeutic approaches for DN.

The eye serves as a unique window into systemic health, offering clinicians a valuable opportunity for early detection and targeted treatment. Against this backdrop, advancements in artificial intelligence (AI) and ophthalmic imaging are converging to pave the way for more precise and predictive diagnostics. This review aims to elucidate the transformative role of AI in utilizing ophthalmic imaging for the detection and prediction of systemic diseases. We begin by introducing the advantages of the eye as a valuable tool for detecting systemic diseases. We also provide an overview of various ophthalmic imaging techniques that have proven useful in predicting systemic ailments. Then, we summarize two research patterns for analyzing ocular data, followed by the introduction of current AI applications using ophthalmic images that significantly increase diagnostic precision. Despite the promise, challenges such as data heterogeneity and model interpretability persist, which are also covered in this review. We conclude by discussing future directions and the immense potential these AI-enabled approaches hold for revolutionizing healthcare. As AI technologies advance, their potential integration with ophthalmic imaging offers promising avenues for improving the diagnosis, prediction, and management of various systemic diseases, thereby contributing to the evolving landscape of integrated healthcare.

Chronic obstructive pulmonary disease (COPD) stands as a global health crisis, responsible for substantial morbidity and mortality on a worldwide scale. Its insidious nature underscores the importance of early detection and accurate diagnosis. While spirometry has been the cornerstone for COPD diagnosis, the role of computed tomography (CT) imaging has evolved, offering a valuable avenue for early detection and subtype classification. Recently, the advent of artificial intelligence (AI) has brought forth the potential to revolutionize the accuracy and efficiency of COPD diagnosis, with a specific focus on CT images. This intersection of healthcare and technology signifies a paradigm shift in the way we approach COPD management. The transformative capacity of AI positions it as a vital instrument for early detection and precise subtype classification of COPD. Moreover, the synergistic relationship between medical imaging and AI paves the way for more precise and efficient disease management. Therefore, in this perspective, we tend to offer a comprehensive exploration of the latest breakthroughs in the field of CT-based AI in COPD diagnosis, aiming to demonstrate the promise and potential of AI in refining the accuracy of COPD classification and to illuminate the evolving landscape of AI’s impact on COPD management.