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.

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.

ObjectiveTo investigate the value of preoperative dual-layer spectral detector CT （DLCT） parameters in predicting lymph node metastasis （LNM） of non-functional pancreatic neuroendocrine neoplasms （NF-pNENs）. MethodsA retrospective study was conducted on patients with pathologically confirmed NF-pNENs who underwent preoperative contrast-enhanced DLCT examination between September 2019 and July 2024. Regions of interest （ROIs） were manually delineated on three representative slices of the solid component of the lesion， and the mean values were recorded. Conventional CT parameters， monochromatic energy images （40 keV， 70 keV， 100 keV）， iodine concentration maps， and effective atomic number （Z_eff） maps in the pancreatic parenchymal phase and portal venous phase were acquired. A total of 34 parameters， including clinical characteristics， imaging features， conventional CT and DLCT quantitative parameters， were analyzed. Intergroup differences were compared between the LNM and non-LNM groups. Independent predictive factors were screened using logistic regression and least absolute shrinkage and selection operator （LASSO） regression. Receiver operating characteristic （ROC） curves were generated for each predictive model， and the area under the curve （AUC） was calculated to evaluate the predictive efficacy. ResultsA total of 82 patients with NF-pNENs were enrolled， including 37 males and 45 females， with a mean age of 49.7±14.7 years. Thirty patients were performed with LNM and 52 were with non-LNM. After univariate logistic regression， LASSO regression and multivariate stepwise logistic regression analyses， body mass index （BMI）， organ invasion， normalized CT value in the portal venous phase （nCTv） and normalized iodine concentration in the portal venous phase （nICv） were identified as independent predictive factors. The AUCs of the clinical-radiological qualitative model （BMI+organ invasion）， conventional CT model （nCTv）， DLCT model （nICv） and comprehensive model （organ invasion+nICv） were 0.815 （0.719， 0.910）， 0.750 （0.642， 0.858）， 0.829 （0.733， 0.925） and 0.865 （0.879， 0.953）， respectively. The comprehensive model combining clinical-radiological and DLCT characteristics exhibited superior predictive performance. ConclusionDLCT parameters are more effective than conventional CT parameters in predicting LNM in NF-pNENs， enabling non-invasive preoperative prediction and assisting clinical decision-making.

ObjectiveTo investigate the incidence of postherpetic neuralgia （PHN） and its associated risk factors in patients aged ≥50 years with herpes zoster （HZ） who received standard antiviral therapy at the dermatology outpatient clinic of our center. MethodsHZ patients were identified by searching the medical record system using "herpes zoster" as the keyword， and those diagnosed between July 2020 and July 2021. Patients meeting the inclusion criteria were followed up via telephone to retrospectively assess the occurrence of PHN. Patients were divided into a PHN group and a non-PHN group based on the presence of PHN. General patient information （gender， age， and underlying diseases）， prodromal pain intensity， and lesion characteristics （area， location， and acute-phase pain intensity） as well as treatment modalities （pharmacotherapy， physical therapy， and combined analgesic therapy） were compared between the two groups. Intergroup comparisons and multifactorial analyses were performed to identify factors associated with the development of PHN in HZ patients. Pain intensity was assessed using the Numerical Rating Scale （NRS） for PHN pain， acute-phase HZ pain， and prodromal pain. Intergroup comparisons were conducted using the chi-square test， Mann-Whitney U test， and t-test. Binary logistic regression was used for multivariate analysis to identify independent risk factors. ResultsA total of 238 HZ patients aged ≥50 years who received standard antiviral therapy were ultimately included， of whom 76 （31.9%） developed PHN. Unifactorial analysis showed that age， lesion area， acute-phase pain intensity， and the use of gabapentin or combined analgesic therapy significantly influenced the occurrence of PHN after HZ （P<0.05）. Statistically significant indicators （P<0.05） from the unifactorial analysis and the different analgesic regimens adopted by patients were included in the multifactorial analysis. Multifactorial analysis revealed that lesion area ≥1% （OR=2.501； 95%CI=1.408-4.443； P=0.002<0.05） and acute-phase pain score （OR=1.160； 95%CI=1.028-1.309； P=0.016<0.05） were independent risk factors for PHN. Age and the use of gabapentin or combined analgesic therapy showed no statistically significant association with the occurrence of PHN （P>0.05）. ConclusionsIn HZ patients aged ≥50 years receiving standard antiviral therapy， the incidence of PHN is 31.9%. A lesion area ≥1% and higher acute-phase pain NRS scores are identified as independent risk factors for the development of PHN in these patients.

ObjectiveTo screen and identify the key proteins of Monkeypox Virus （MPXV） that regulate the NOD-like receptor family pyrin domain containing 3 （NLRP3） inflammasome. MethodsUsing the genomic sequence of the domestic epidemic strain of MPXV as a template to construct an expression plasmid library containing 82 viral genes. To screen for viral genes that significantly inhibit the production of interleukin-1β （IL-1β）， the expression plasmids encoding NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， cysteine protease-1（Caspase-1）， and pro-IL-1β-Luc reporter gene were co-transfected into HEK293T cells， along with the viral gene plasmids. The signal of the NLRP3 inflammasome was monitored by measuring luciferase activity in the cell supernatant. The inhibition effects were further confirmed by Western Blot （WB）， and the interactions between the viral proteins and the components of the NLRP3 inflammasome were determined by Co-immunoprecipitation （Co-IP）. Additionally， site-directed mutagenesis was performed to identify the specific interaction motifs. ResultsAn NLRP3 inflammasome signal screening system were successfully established and optimized. Screening the MPXV plasmids using this system revealed that B10R most significantly inhibits IL-1β maturation in a dose-dependent manner （P＜0.000 1）. Further characterization demonstrated that B10R interacts directly with pro-IL-1β. Notably， the C-terminal motif mutant of B10R （B10Rmut-C， T96A， Y97A， I98A） lost its ability to interact with pro-IL-1β. ConclusionsThe MPXV B10R protein interacts with pro-IL-1β， thereby inhibiting the IL-1β maturation. The C-terminal motif （T96， Y97， I98） of the B10R protein is crucial for function. This study provides important clues and a foundation for further elucidating the molecular mechanisms by which MPXV regulates the NLRP3 inflammasome.

ObjectiveThis study aimed to investigate the therapaeutic effects of rabies virus glycoprotein （RVG）-modified mesenchymal stem cell-derived exosomes （MSC-EXO） in a mouse model of obsessive-compulsive disorder （OCD）， and to explore the underlying mechanisms. MethodsPrimary mouse adipose-derived MSCs were isolated and characterized by adipogenic and osteogenic differentiation assays and flow cytometry. RVG-overexpressing MSCs were generated， and the derived exosomes were characterized by nanoparticle size analysis， Western blotting， and transmission electron microscopy. An OCD mouse model was established using quinpirole， and the mice were divided into five groups： control， model， clomipramine（CMI）， MSC-EXO， and RVG-MSC-EXO. Compulsive-like behaviors and cognitive function were evaluated using the compulsive checking test， open field test， marble-burying tes， and Morris water maze. The levels of inflammatory cytokines in the mouse prefrontal cortex were measured by qPCR and ELISA. Microglial polarization in the prefrontal cortex was assessed by immunofluorescence staining. The brain-targeting ability of RVG-MSC-EXO was verified using an in vivo imaging system （IVIS）. ResultsRVG-modified MSC-derived exosomes exhibited significant brain-targeting ability in mice. Exosome treatment significantly reduced compulsive checking and marble-burying behaviors in mice. Exosome treatment decreased pro-inflammatory cytokines and increased anti-inflammatory cytokines. Exosome treatment promoted a shift in microglial polarization from the M1 phenotype to M2 phenotype. ConclusionRVG-modified mesenchymal stem cell-derived exosomes can target the mouse brain， alleviate neuroinflammation by promoting microglial polarization towards the M2 phenotype， and ultimately reduce compulsive-like behaviors in OCD mice.

ObjectiveTo investigate whether photoreceptor-specific knockout of lanosterol synthase （Lss） induces age-related retinal degeneration and to elucidate the underlying molecular mechanisms. MethodsPhotoreceptor-specific Lss knockout mice （Lss^flox/flox； Rho-Cre， CKO） models were established. Retinal structure and function were assessed at 1， 4， and 8 months of age using fundus photography， optical coherence tomography （OCT）， scotopic electroretinogram （ERG） and hematoxylin and eosin （HE） staining. The mouse photoreceptor cell line 661W served as an in vitro model. Lss was knocked down via siRNA， followed by analyses including Seahorse XF mitochondrial stress test， JC-1， MitoSOX Red and DCFH-DA probes to evaluate mitochondrial function and oxidative stress， glycolytic rate assay for glycolysis ability， RT-qPCR for inflammatory factors， γH2AX immunofluorescence for double-strand DNA damage detection， and transcriptome sequencing to identify differentially expressed genes and enriched pathways. ResultsCompared with controls， 1-month-old CKO mice showed no abnormalities. Mild fundus lesions appeared at 4 months. By 8 months， CKO mice exhibited significant thinning of the outer nuclear layer， disorganization of the inner/outer segment structure， and markedly reduced amplitudes of the dark-adapted ERG a- and b-waves （P<0.05）. Transcriptome analysis revealed 1 842 differentially expressed genes （826 up-regulated， 1 016 down-regulated）， with Lss being the most significantly downregulated gene； these genes significantly enriched in sterol biosynthesis， oxidative phosphorylation， aerobic electron transport chain， and ATP synthesis （P<0.05）. In 661W cells， knockdown of Lss significantly reduced mitochondrial basal respiration （P=0.006）， ATP production （P=0.015）， maximal respiration （P<0.001）， and spare respiratory capacity （P<0.001）， while basal glycolysis （P=0.005） and compensatory glycolysis （P=0.028） were increased. These changes were accompanied by mitochondrial membrane potential depolarization （P<0.001）， elevated mitochondrial reactive oxygen species （ROS） levels （P<0.001）， and increased total ROS levels （P=0.013）. Additionally， the mRNA expression of inflammatory factors， including Ccl2 （P=0.004）， Ccl5 （P=0.012）， Cxcl1 （P<0.001）， Cxcl5 （P<0.000 1）， Tgfb （P=0.008）， Hgf （P<0.000 1） and Mmp3 （P=0.003） was significantly upregulated. Moreover， γH2AX fluorescence intensity was increased （P=0.002）. ConclusionsPhotoreceptor-specific Lss deficiency induces mitochondrial dysfunction， oxidative stress and cellular inflammation， ultimately leading to age-dependent retinal degeneration. This study provides novel mechanistic insights and potential therapeutic targets for hereditary and age-related retinal degenerative diseases.