OBJECTIVE To investigate the apoptosis-inducing effect of esculetin （Esc） on acute myeloid leukemia （AML） HL-60 cells by regulating the protein kinase B （AKT）/S-phase kinase-associated protein 2 （SKP2）/MutT homolog 1 （MTH1） pathway. METHODS AML HL-60 cells were randomly divided into control group （routine culture）， Esc low-concentration group （L-Esc group， 25 μmol/L Esc）， Esc medium-concentration group （M-Esc group， 50 μmol/L Esc）， Esc high-concentration group （H-Esc group， 100 μmol/L Esc）， and high-concentration of Esc+ SC79 （AKT agonist） group （100 μmol/L Esc+5 μmol/L SC79）. Cell proliferation in each group was detected by MTT assay and colony formation assay. The level of reactive oxygen species （ROS） in cells was measured by using the CM-H2DCFDA fluorescent probe. Cell apoptosis was analyzed by flow cytometry. Western blot assay was performed to detect the expression levels of apoptosis-related proteins [B-cell lymphoma 2 （Bcl-2）， Bcl-2-associated X protein （Bax）， cleaved caspase-3]， AKT/SKP2/MTH1 pathway-related proteins （p-AKT， AKT， SKP2， MTH1）， along with the upstream and downstream proteins of AKT phosphatidylinositol 3-kinase （PI3K）， cyclin-dependent kinase inhibitor 1 （P21） and cyclin-dependent kinase inhibitor 1B （P27）. RESULTS Compared with control group， the cell viability， colony number， and the phosphorylation levels of AKT and PI3K proteins as well as protein expressions of SKP2， MTH1 and Bcl-2 were significantly decreased （P＜0.05）， while ROS level， apoptosis rate， and the expression levels of Bax， cleaved caspase-3， P21 and P27 proteins were significantly increased （P＜0.05）. Moreover， the effects of Esc exhibited concentration-dependence （P＜0.05）. Compared with H-Esc group， above indexes of high-concentration of Esc+ SC79 group were reversed significantly （P＜0.05）. CONCLUSIONS Esc may promote massive ROS production and induce activation of apoptosis in HL-60 cells by inhibiting the AKT/SKP2/MTH1 pathway， thus inhibiting the proliferation of HL-60 cells.

With the rapid advancement of artificial intelligence (AI) technology, particularly breakthroughs in large models, AI applications in healthcare are becoming increasingly widespread.Ophthalmology, as a critical branch of medicine, has emerged as a clinical specialty with comprehensive AI research and applications, especially in the AI-driven analysis of ophthalmic imaging.Currently, AI in ophthalmology is undergoing profound transformation driven by large model technology.AI large models, with their robust data comprehension and multimodal interaction capabilities, offer new possibilities for the diagnosis, treatment, and health education of ophthalmic diseases, powerfully propelling the evolution of current AI paradigms in ophthalmology.This article explores the novel opportunities brought by the extensive application of AI large models in ophthalmic research and practice, and provides recommendations for ophthalmologists and insights for industry development.

Objective To study the influencing factors of intradialytic hypotension(IDH)in diabetic maintenance hemodialysis(MHD)patients,and to provide references for clinical prevention of IDH quality control.Methods A total of 200 diabetic patients from four hemodialysis centers in Beijing from March 2022 to September 2022 were collected as the research objects.According to the definition of IDH[systolic blood pressure during hemodialysis≤90 mmHg(1 mmHg=0.133 kPa)or systolic blood pressure reduction during dialysis≥30 mmHg],the patients were divided into IDH group(frequency of hypotension events during dialysis≥30%during 7 months of follow-up)and non-IDH group.Univariate analysis and multivariate Logistic regression were used to analyze the influencing factors of IDH.receive operating characteristic curve(ROC)curve analysis was used to evaluate the predictive value of each influencing factor for IDH.Results Univariate analysis showed that compared with non-IDH group,IDH group had higher systolic blood pressure,higher blood glucose and lower serum albumin before dialysis(P<0.05).There were more patients with orthostatic hypotension in the IDH group than in the non-IDH group(P<0.05).Multivariate Logistic regression analysis showed that pre-dialysis systolic blood pressure,orthostatic hypotension and serum albumin were the influencing factors of IDH(P<0.05).ROC curve was used to evaluate the diagnostic accuracy of pre-hemodialysis systolic blood pressure for IDH.The area under the ROC curve was 0.787(95%CI:0.720-0.854,P<0.001),the threshold of IDH predicted by the Jorden index was 153 mmHg,the sensitivity was 75.5%,and the specificity was 75.4%.Conclusion Pre-hemodialysis systolic blood pressure,blood albumin and postural hypotension are independent factors of IDH in diabetic patients.In order to predict the occurrence of IDH,the pre-hemodialysis systolic blood pressure threshold was 153 mmHg.

Objective To meet the clinical need for dynamic monitoring on lactate metabolism in septic shock pa-tients,a time-series prediction model based on a long short-term memory(LSTM)network was developed to predict 24-hour lactate clearance rate at admission.Methods A multi-stage retrospective cohort design was adopted to en-roll septic shock patients admitted to the department of critical care medicine of a hospital from January 2018 to Sep-tember 2024.By conducting univariate analysis and LASSO combined feature screening,predictive factors were extracted from multidimensional clinical data.An end-to-end LSTM framework(two-layer 64/32 units,dropout rate=0.3)was constructed.A sliding window strategy(six-hour step size)was adopted for dynamic prediction and compared with traditional logistic model in terms of three dimensions:calibration(Brier score),discrimination(area under the curve[AUC]of time-dependent receiver operating characteristic[ROC]),and clinical practicality(deci-sion curve analysis).Consistency between model prediction result and actual lactate clearance rate was analyzed,and the accuracy of prediction was evaluated.Results A total of 112 septic shock patients were enrolled in the analysis,including 65 males and 47 females,with an average age of(67.35±7.28)years.65 patients were assigned in the lactate good clearance rate group(lactate good clearance rate≥10％)and 47 in the lactate poor clearance rate group(lactate good clearance rate＜10％);78 patients were in the training set and 34 in the validation set.Time-depen-dent AUC analysis revealed that the predictive performance of the LSTM model in the time windows of 6,12,and 24 hours were 0.89(0.85-0.93),0.91(0.88-0.95),and 0.92(0.89-0.96),respectively,superior to the logistic regression model(ΔAUC=0.085,P＜0.01).The core predictive factors included APACHE Ⅱ score(OR=1.38),lactate level at admission(OR=1.65),vasoactive drug dosage(OR=1.42),and 6-hour fluid resuscitation dosage(OR=1.35).The Pearson correlation coefficient between the predicted value of the model and the actual 24-hour lactate clearance rate was 0.83(P＜0.001),with an average absolute error of 8.2％.Decision curve analysis confirmed that when the threshold probability was 15％-60％,the LSTM model could increase clinical net benefits by 27.3％.The validation of each subgroup showed that the model maintained the optimal predictive performance(AUC=0.87)in the lung infection subgroup(n=16).Conclusion The LSTM-based dynamic prediction model for predicting 24-hour lactate clearance rate through integrating early admission indicators demonstrates excellent pre-dictive performance and clinical application value,which can provide important reference for individualized treatment decisions in septic shock patients.

Objective:To explore the effects of different titanium(Ti)surfaces on mitochondria metabolism and polarization of mac-rophages.Methods:The anodic oxidation method prepared nanotubes with distinct morphologies(NT5 and NT20 groups)on smooth titanium surfaces(P group)were characterized by scanning electron microscopy(SEM).After 24 hours of culturing RAW264.7 cells on the samples,SEM was employed to observe the morphology of the cells of all groups.Levels of mitochondria ATP and ROS,as well as the activity of hexokinase(HK),were measured by fluorescence probes and luciferase assays.The expression of iNOS,Arg-1,TNF-α and CD206 mRNA was evaluated by RT-qPCR.Changes in mitochondria metabolism and macrophage polarization were studied following inhibition of cellular glycolysis by using 2-deoxy-D-glucose(2-DG)in groups P*and NT20*.Results:Compared with the P group,the NT5 group showed no significant changes in mitochondria ATP,ROS levels and HK activity,but an upregula-tion in M2-associated gene expression was observed.In the NT20 group,mitochondria ATP and ROS levels were increased,HK ac-tivity was elevated,and M1-associated gene expression was upregulated.After glycolysis inhibition,there was no significant differ-ence in mitochondria ATP,ROS levels,HK activity and polarization-related gene expression levels between the P*and NT20*groups compared with the P group.Conclusion:Large-diameter nanotubes may promote macrophage M1 polarization by regulating the metabolic reprogramming that occurs in macrophage mitochondria.

Young management talents are the driving force behind the refined management of hospitals and the future and hope of high-quality development.In the context of the new era,high-quality development has become the main theme of reform and development in public hospitals,which puts forward new and higher requirements for the cultivation of young management tal-ents in hospitals.Based on literature research,this article systematically summarizes and explores the concept and characteristics of young management talents in hospitals.Taking Jiangsu Provincial People's Hospital as an example,it actively supports and serves the growth and development of young management talents through specific practices such as optimizing organizational man-agement,improving training systems,strengthening incentive mechanisms,and smoothing development channels.These practices have achieved good results and aim to provide references for the cultivation and development of young management talents in pub-lic hospitals in China.

With the rapid advancement of artificial intelligence (AI) technology, particularly breakthroughs in large models, AI applications in healthcare are becoming increasingly widespread.Ophthalmology, as a critical branch of medicine, has emerged as a clinical specialty with comprehensive AI research and applications, especially in the AI-driven analysis of ophthalmic imaging.Currently, AI in ophthalmology is undergoing profound transformation driven by large model technology.AI large models, with their robust data comprehension and multimodal interaction capabilities, offer new possibilities for the diagnosis, treatment, and health education of ophthalmic diseases, powerfully propelling the evolution of current AI paradigms in ophthalmology.This article explores the novel opportunities brought by the extensive application of AI large models in ophthalmic research and practice, and provides recommendations for ophthalmologists and insights for industry development.

Objective To meet the clinical need for dynamic monitoring on lactate metabolism in septic shock pa-tients,a time-series prediction model based on a long short-term memory(LSTM)network was developed to predict 24-hour lactate clearance rate at admission.Methods A multi-stage retrospective cohort design was adopted to en-roll septic shock patients admitted to the department of critical care medicine of a hospital from January 2018 to Sep-tember 2024.By conducting univariate analysis and LASSO combined feature screening,predictive factors were extracted from multidimensional clinical data.An end-to-end LSTM framework(two-layer 64/32 units,dropout rate=0.3)was constructed.A sliding window strategy(six-hour step size)was adopted for dynamic prediction and compared with traditional logistic model in terms of three dimensions:calibration(Brier score),discrimination(area under the curve[AUC]of time-dependent receiver operating characteristic[ROC]),and clinical practicality(deci-sion curve analysis).Consistency between model prediction result and actual lactate clearance rate was analyzed,and the accuracy of prediction was evaluated.Results A total of 112 septic shock patients were enrolled in the analysis,including 65 males and 47 females,with an average age of(67.35±7.28)years.65 patients were assigned in the lactate good clearance rate group(lactate good clearance rate≥10％)and 47 in the lactate poor clearance rate group(lactate good clearance rate＜10％);78 patients were in the training set and 34 in the validation set.Time-depen-dent AUC analysis revealed that the predictive performance of the LSTM model in the time windows of 6,12,and 24 hours were 0.89(0.85-0.93),0.91(0.88-0.95),and 0.92(0.89-0.96),respectively,superior to the logistic regression model(ΔAUC=0.085,P＜0.01).The core predictive factors included APACHE Ⅱ score(OR=1.38),lactate level at admission(OR=1.65),vasoactive drug dosage(OR=1.42),and 6-hour fluid resuscitation dosage(OR=1.35).The Pearson correlation coefficient between the predicted value of the model and the actual 24-hour lactate clearance rate was 0.83(P＜0.001),with an average absolute error of 8.2％.Decision curve analysis confirmed that when the threshold probability was 15％-60％,the LSTM model could increase clinical net benefits by 27.3％.The validation of each subgroup showed that the model maintained the optimal predictive performance(AUC=0.87)in the lung infection subgroup(n=16).Conclusion The LSTM-based dynamic prediction model for predicting 24-hour lactate clearance rate through integrating early admission indicators demonstrates excellent pre-dictive performance and clinical application value,which can provide important reference for individualized treatment decisions in septic shock patients.

ObjectiveTo analyze the detected results of CD4⁺T lymphocytes and viral load in newly identified human immunodeficiency virus (HIV) infected patients in Huangpu District of Shanghai in 2023, to explore the correlation between them, so as to provide a scientific basis for the development of targeted prevention and control measures and antiviral treatment programs. MethodsThe data of CD4 cell count, viral load and demographic characteristics of the newly infected patients living with HIV in Huangpu District, Shanghai in 2023 were collected and analyzed by using descriptive epidemiological method. ResultsThe mean CD4 cell count of the 67 newly identified HIV infected patients in Huangpu District was (301.22±235.19) cells·µL^-1, with a mean viral load of (5.15±1.28) ×10⁵ copies·mL^-1.There were statistically significant differences in CD4 cell count and viral load among different age groups (P<0.05), but there were no statistically significant differences by gender and marital status (both P>0.05). The CD4 cell count and CD4/CD8 ratio both were negatively correlated with the lg value of viral load (r=-0.290, -0.378; P=0.027, 0.002). ConclusionThe CD4 cell counts of the newly identified HIV infected patients in Huangpu District in 2023 were generally low, the proportion of patients with high viral load was high, but the risk for elderly infected with HIV was high. The elderly have gradually become the key population for AIDS prevention and control in Huangpu District. It is recommended to expand HIV screening in the elderly to reduce the risk of HIV transmission and increase the rate of early detection and treatment.

Exosomes are ubiquitous in all types of body fluids, exhibiting a high degree of abundance and diversity. Given their distinctive structure and function, exosomes are involved in a range of life activities, including intercellular communication, material transport, and immune regulation. An increasing number of studies have identified exosomes as a source of diagnostic markers for diabetic retinopathy. Furthermore, exosomes represent a novel avenue for therapeutic intervention, with promising clinical applications. This paper examines the diagnostic and therapeutic mechanisms of exosomes in diabetic retinopathy, reviews the advancements in exosomes-based diagnostics and therapeutics for diabetic retinopathy, and aims to enhance the precision and efficiency of clinical diagnosis and treatment of diabetic retinopathy.