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.

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.

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.

With the rapid advancement of large language models, both general-purpose and ophthalmology-specific variants have shown substantial potential for ophthalmic image analysis. However, their task-specific effectiveness and applicable boundaries in clinical practice remain to be fully clarified. This review outlines the technical evolution of large models in ophthalmology, including masked visual modeling, vision-language contrastive learning, and subsequent stages of knowledge integration and multimodal reasoning. The performance of general-purpose versus ophthalmology-specific models across common clinical scenarios, including disease screening and initial triage, disease grading and lesion segmentation, and medical report generation with multimodal reasoning were further compared. Results show that general-purpose models are well-suited for multi-disease screening and primary-care triage, whereas ophthalmology-specific models excel in fine-grained grading, subtle lesions detection, and rare disease diagnosis. Moreover, instruction-tuned domain-specific models produce medical reports with greater clinical consistency. These findings provide practical guidance for selecting appropriate model types and technical pathways tailored to specific ophthalmic tasks. Furthermore, the review identifies the current challenges facing these models, including reliability, generalizability, evaluation frameworks, and reproducibility, as well as the resulting limitations in clinical application.

One of the significant hurdles in telemedicine, particularly in ophthalmology, is the absence of direct physical examination. This specialty depends extensively on specialized instruments that typically require proficient operators. Visual function tests are crucial for both outpatient and inpatient ophthalmic services, playing a vital role in screening, diagnosing, monitoring treatment effectiveness, and managing follow-ups for various eye conditions. The progress in mobile technology has paved the way for expanding these tests beyond traditional clinic settings, promoting the creation of patient-focused, straightforward, cost-effective, and efficient measurement tools. In light of the swift advancement of digital technologies, this article reviews the characteristics, and reliability of self-administered visual function tests tools, including visual acuity, refractive error assessment, visual field, contrast sensitivity, and color vision, along with other pertinent diagnostic tools that have been developed and validated for accuracy and repeatability through research, with a view to providing ophthalmologists and patients with scientific and practical references when selecting and using these tools, further promoting efficiency and efficacy of teleophthalmology.

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.

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.