BACKGROUND: To provide a comprehensive analysis of the landscape of artificial intelligence (AI) applications in cardiac arrest (CA). METHODS: Comprehensive searches were conducted in PubMed, the Cochrane Library, Web of Science, and EMBASE from database inception through 10 June 2025. Studies that applied AI in both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) populations across the following domains were included: prediction of cardiac arrest occurrence, prognostication of CA outcomes, applications of large language models (LLMs), and evaluation of cardiopulmonary resuscitation (CPR) and other AI-driven interventions related to CA. RESULTS: The scoping review included 114 studies, encompassing data from 9,574,462 patients in total. AI was most commonly applied to the prediction of CA (overall, n=40; IHCA, n=30; OHCA, n=4; and both, n=6), CPR-related decision support during CA (n=16), and post-arrest prognosis and rehabilitation outcomes (overall, n=38; OHCA, n=21; IHCA, n=3; and both, n=14). Additional application areas included LLM-based applications (n=8), emergency call handling (n=4), wearable device-based detection (n=3), heart rhythm identification (n=2), education (n=2), and extracorporeal cardiopulmonary resuscitation (ECPR) candidate identification (n=1). Across all application scenarios, the highest area under the receiver operating characteristic curve (AUROC) value for pre-arrest CA prediction in IHCA patients was 0.998 using a multilayer perceptron (MLP) model, whereas the optimal AUROC for pre-arrest CA prediction in OHCA patients was 0.950 using extreme gradient boosting (XGBoost) or random forest (RF) models. For CPR-related decision support during CA, the highest AUROC achieved was 0.990 with a convolutional neural network (CNN) model. In prognostic prediction, the optimal AUROC for IHCA patients was 0.960 using XGBoost, while for OHCA patients it reached 0.976 using an MLP model. CONCLUSION: This review shows that AI is most commonly used for the prediction of CA and CPR-related support, as well as post-arrest and rehabilitation outcomes. Future research directions include drug discovery, post-resuscitation management, neurorehabilitation, and clinical trial innovation. Further studies should prioritize multicenter clinical trials to evaluate AI models in real-world settings and validate their effectiveness across diverse patient populations. Overall, AI has significant potential to improve clinical practice, and its role in CA application is increasingly important.

High myopia has become a major public health concern worldwide, particularly in China and Southeast Asia. It is associated not only with a variety of fundus diseases but also with earlier onset and greater severity of cataracts, resulting in significant visual impairment. Phacoemulsification is currently the main surgical treatment for cataracts. However, intraoperative fluctuations in perfusion pressure and exposure to ultrasonic energy may affect the vitreoretinal structures, potentially accelerating the progression of fundus pathology after surgery in highly myopic eyes. This article summarizes current evidence on the progression of posterior scleral staphyloma, myopic maculopathy, and retinal detachment following phacoemulsification in highly myopic eyes.

OBJECTIVE To establish a scientific， systematic， multi-dimensional performance management system for pharmacy practice， so as to improve the efficiency and quality of pharmacy practice performance management in public hospitals. METHODS Based on the four dimensions of the balanced scorecard theory， finance， customer， internal process， learning and growth， reference indicators for pharmacy practice performance management were summarized. The Delphi method was used to screen indicators， and the analytic hierarchy process was applied to determine the weights of indicators. A pharmacy practice performance management system was then constructed. Based on this system， action plans were formulated and implemented. The effectiveness was evaluated from two aspects： customer reviews and changes in pharmacy practice outcomes. RESULTS A total of 28 reference indicators were summarized， and a performance management system for pharmacy practice was constructed， consisting of 4 primary indicators， 9 secondary indicators， and 20 tertiary indicators. Compared with action plans implementation before， the satisfaction of clinical departments was significantly improved， and 11 pharmacy practice performance management indicators were optimized after implementation. CONCLUSIONS A scientific and systematic performance management system for pharmacy practice has been successfully established， which can provide a reference for the innovation of hospital pharmacy practice management and the high quality development of pharmacy practice.

The clinical diagnosis and treatment of kidney transplantation involve a large amount of high-dimensional, nonlinear and multimodal complex data. Traditional statistical methods make it difficult to fully identify the underlying patterns. However, artificial intelligence (AI) technology has stronger capabilities in data integration and analysis, and is particularly adept at handling complex data. This article reviews the application of AI in the field of kidney transplantation, covering the role of deep learning models in the quantitative assessment of donor kidney quality in the pre-transplantation stage, the value of virtual biopsy systems and the optimization strategies for donor-recipient matching, as well as the dynamic prediction of graft survival rate by the iBox system in the post-transplantation stage, the advantages of AI models in non-invasive monitoring of rejection and the application results of individualized dose prediction of immunosuppressive drugs. Although the clinical application of medical AI is still limited by insufficient model interpretability, questionable generalization ability, and ethical and legal risks, technologies such as federated learning and digital twins are expected to solve the problems of data privacy and simulation prediction, and further promote the precise development of kidney transplantation.

Fungal keratitis represents a significant cause of blindness, with current therapeutic approaches yielding limited success. The disease's onset and progression are primarily driven by fungal virulence factors and the host's immune response. The innate immune system is the first to respond, with neutrophils playing a pivotal role in the antifungal defense. Although neutrophils are critical for pathogen clearance, their excessive or abnormal activation can lead to tissue damage, exacerbating the disease. Thus, elucidating the mechanisms underlying neutrophil activity in fungal keratitis is crucial for refining treatment strategies. This article aims to systematically review the principal antimicrobial mechanisms employed by neutrophils, including phagocytosis, degranulation, and the formation of neutrophil extracellular traps(NETs). Furthermore, it explores the crosstalk between neutrophils and macrophages, alongside their collective impact and underlying mechanisms in the context of fungal keratitis. Exploration of the mechanisms of fungal keratitis facilitates precise intervention and enhances the efficacy of treatment.

Tumor drug resistance is an important problem in the failure of chemotherapy and targeted drug therapy, which is a complex process involving chromatin remodeling. SWI/SNF is one of the most studied ATP-dependent chromatin remodeling complexes in tumorigenesis, which plays an important role in the coordination of chromatin structural stability, gene expression, and post-translation modification. However, its mechanism in tumor drug resistance has not been systematically combed. SWI/SNF can be divided into 3 types according to its subunit composition: BAF, PBAF, and ncBAF. These 3 subtypes all contain two mutually exclusive ATPase catalytic subunits (SMARCA2 or SMARCA4), core subunits (SMARCC1 and SMARCD1), and regulatory subunits (ARID1A, PBRM1, and ACTB, etc.), which can control gene expression by regulating chromatin structure. The change of SWI/SNF complex subunits is one of the important factors of tumor drug resistance and progress. SMARCA4 and ARID1A are the most widely studied subunits in tumor drug resistance. Low expression of SMARCA4 can lead to the deletion of the transcription inhibitor of the BCL2L1 gene in mantle cell lymphoma, which will result in transcription up-regulation and significant resistance to the combination therapy of ibrutinib and venetoclax. Low expression of SMARCA4 and high expression of SMARCA2 can activate the FGFR1-pERK1/2 signaling pathway in ovarian high-grade serous carcinoma cells, which induces the overexpression of anti-apoptosis gene BCL2 and results in carboplatin resistance. SMARCA4 deletion can up-regulate epithelial-mesenchymal transition (EMT) by activating YAP1 gene expression in triple-negative breast cancer. It can also reduce the expression of Ca²⁺ channel IP3R3 in ovarian and lung cancer, resulting in the transfer of Ca²⁺ needed to induce apoptosis from endoplasmic reticulum to mitochondria damage. Thus, these two tumors are resistant to cisplatin. It has been found that verteporfin can overcome the drug resistance induced by SMARCA4 deletion. However, this inhibitor has not been applied in clinical practice. Therefore, it is a promising research direction to develop SWI/SNF ATPase targeted drugs with high oral bioavailability to treat patients with tumor resistance induced by low expression or deletion of SMARCA4. ARID1A deletion can activate the expression of ANXA1 protein in HER2+ breast cancer cells or down-regulate the expression of progesterone receptor B protein in endometrial cancer cells. The drug resistance of these two tumor cells to trastuzumab or progesterone is induced by activating AKT pathway. ARID1A deletion in ovarian cancer can increase the expression of MRP2 protein and make it resistant to carboplatin and paclitaxel. ARID1A deletion also can up-regulate the phosphorylation levels of EGFR, ErbB2, and RAF1 oncogene proteins.The ErbB and VEGF pathway are activated and EMT is increased. As a result, lung adenocarcinoma is resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Although great progress has been made in the research on the mechanism of SWI/SNF complex inducing tumor drug resistance, most of the research is still at the protein level. It is necessary to comprehensively and deeply explore the detailed mechanism of drug resistance from gene, transcription, protein, and metabolite levels by using multi-omics techniques, which can provide sufficient theoretical basis for the diagnosis and treatment of poor tumor prognosis caused by mutation or abnormal expression of SWI/SNF subunits in clinical practice.

Purpose: Neuroendocrine prostate cancer (NEPC) represents a particularly aggressive subtype of prostate cancer with a challenging prognosis. The purpose of this investigation is to craft and confirm the reliability of nomograms that can accurately forecast the 1-, 3-, and 5-year overall survival (OS) and cancer-specific survival (CSS) rates for individuals afflicted with NEPC. Materials and Methods: Data pertaining to patients diagnosed with NEPC within the timeframe of 2010 to 2020 was meticulously gathered and examined from the Surveillance, Epidemiology, and End Results Program (SEER). To predict OS and CSS, we devised and authenticated two distinct nomograms, utilizing predictive variables pinpointed through both univariate and multivariate Cox regression analyses. Results: The study encompassed 393 of NEPC patients, who were systematically divided into training and validation cohorts at a 2:1 ratio. Key prognostic factors were isolated, verified, and integrated into the respective nomograms for OS and CSS. The performance metrics, denoted by C-indices, stood at 0.730, 0.735 for the training set, and 0.784, 0.756 for the validation set. The precision and clinical relevance of the nomograms were further corroborated by the analysis of receiver operating characteristic curves, calibration plots, and decision curve analyses. Conclusions The constructed nomograms have demonstrated impressive efficacy in forecasting the 1-, 3-, and 5-year OS and rates for patients with NEPC. Implementing these predictive tools in clinical settings is anticipated to considerably enhance the care and treatment planning for individuals diagnosed with this aggressive form of prostate cancer, thus providing tailored and more precise prognostic assessments.