Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To investigate the status of population dynamics and distribution changes of Aedes aegypti in Guangdong Province.Methods Continuous monitoring was conducted from May 2018 to July 2024 in Wushi Town and Qishui Town,Leizhou City,Zhanjiang City,Guangdong Province.Additionally,a survey of the distribution of Ae.aegypti along the Leizhou Peninsula coast was carried out.Results The density of Ae.aegypti in Zhanjiang showed a gradual decline from 2018 to 2024.The last detection of adult Ae.aegypti in Wushi Town was in September 2021,and the last larva was found in October 2023.No Ae.aegypti was detected in Qishui Town during surveys from 2021 to 2024.A survey of 18 coastal villages in the Leizhou Peninsula revealed no detections of Ae.aegypti.Conclusions This study provides a basis for understanding the distribution and population density fluctuations of Ae.aegypti,assessing its invasion risk,and scientifically conducting relevant prevention and control efforts.

Objective:To analyze the clinical characteristics and cytokines expression characteristics in infants with mild and severe respiratory syncytial virus (RSV) infection.Methods:From May 2023 to December 2023, plasma samples and clinical information were collected from 16 infants with RSV infection and 14 control infants. Cytek Aurora flow cytometry (Cytek, America) and Enzyme linked immunosorbent assay (ELISA) were used to detect the expression levels of 25 cytokines after mild and severe RSV infection.Results:Cough and nasal obstruction were the main clinical manifestations in infants with mild RSV infection, accompanied by polypnea, wheezing and other symptoms. The main symptoms of severe RSV infection were cough and rales, accompanied by fever and polypnea. In comparison with the control group, the expression levels of IL-2, IL-4, IL-5, IL-6, IL-9, IL-13, IL-22, TNF-α, IFN-α, IFN-β, MIP-1β, I-TAC, ENA-78, GROα, Eotaxin, and MCP-1 in the RSV infection group all exhibited an upregulation trend. Both IP-10 and MIP-3α demonstrated a downward trend in the RSV infection group; however, there was no statistically significant difference ( P>0.05). The levels of IL-10, IFN-γ, MIP-1α, and IL-8 in the RSV infection group were significantly higher than those in the control group, whereas the levels of MIG, TARC, and RANTES in the RSV infection group were significantly lower than those in the control group ( P<0.05). The levels of IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-13, IL-22, IFN-β, IFN-γ, TNF-α, IL-8, I-TAC, MIP-1β, Eotaxin, and MCP-1 in the mild RSV infection group were significantly higher than those in the severe RSV infection group ( P>0.05). Among these, the levels of MIG, RANTES, TARC, MIP-3α, and ENA-78 in the mild infection group were all lower than those in the severe infection group. The expressions of ENA-78 and MIP-1α in the severe infection group were significantly higher than those in the mild infection group and also higher than those in the control group. There was no significant difference in IP-10 and GROα between the mild and severe RSV infection groups ( P>0.05). Conclusions:The differences in clinical features and cytokines between infants with mild and severe RSV infection provide important data support for the prevention and treatment of RSV infection in infants.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

This study was aimed at developing an in vitro fluorescent substrate assay for the activity of leucyl aminopeptid-ase(LAP)from Echinococcus multilocularis and comparing it with the chemical chromogenic substrate enzyme activity assay.Through the establishment of reaction conditions for the fluorescent substrate-based in vitro enzyme activity assay,we com-pared the differences between the fluorescent substrate L-Leucine-7-amido-4-methylocoumarin(Leu-AMC)and the chemical chromogenic substrate L-Leucine-4-nitroanilide(Leu-pNA)through molecular docking,inhibition rates,and precision measures.Molecular docking revealed that the fluorescent substrate Leu-AMC had higher affinity for the protein than the chemical chromogenic substrate Leu-pNA.Through analysis of the effects of varying reaction conditions on fluorescence intensi-ty,we optimized the fluorescent substrate enzyme activity assay to demonstrate favorable performance at a reaction temperature of 37℃,a pH of 9.0,a protein concentration of 800 nmol/L,and a reaction duration of 60 minutes.Leu-AMC exhibited significant and distinct responses at a 5 μmol/L substrate concentration,under varying substrate conditions.The fluo-rescent substrate assay demonstrated more significant intergroup differences than the chemical chromogenic substrate assay when various inhibitors were added.This study established a fluorescence-based enzyme activity assay for leucyl aminopeptidase from Echinococcus multilocularis by using Leu-AMC as the substrate;this method demonstrated a more significant intergroup difference and sensitivity than the chemical chromogenic substrate assay.

This study was aimed at developing an in vitro fluorescent substrate assay for the activity of leucyl aminopeptid-ase(LAP)from Echinococcus multilocularis and comparing it with the chemical chromogenic substrate enzyme activity assay.Through the establishment of reaction conditions for the fluorescent substrate-based in vitro enzyme activity assay,we com-pared the differences between the fluorescent substrate L-Leucine-7-amido-4-methylocoumarin(Leu-AMC)and the chemical chromogenic substrate L-Leucine-4-nitroanilide(Leu-pNA)through molecular docking,inhibition rates,and precision measures.Molecular docking revealed that the fluorescent substrate Leu-AMC had higher affinity for the protein than the chemical chromogenic substrate Leu-pNA.Through analysis of the effects of varying reaction conditions on fluorescence intensi-ty,we optimized the fluorescent substrate enzyme activity assay to demonstrate favorable performance at a reaction temperature of 37℃,a pH of 9.0,a protein concentration of 800 nmol/L,and a reaction duration of 60 minutes.Leu-AMC exhibited significant and distinct responses at a 5 μmol/L substrate concentration,under varying substrate conditions.The fluo-rescent substrate assay demonstrated more significant intergroup differences than the chemical chromogenic substrate assay when various inhibitors were added.This study established a fluorescence-based enzyme activity assay for leucyl aminopeptidase from Echinococcus multilocularis by using Leu-AMC as the substrate;this method demonstrated a more significant intergroup difference and sensitivity than the chemical chromogenic substrate assay.

Objective:To analyze the clinical characteristics and cytokines expression characteristics in infants with mild and severe respiratory syncytial virus (RSV) infection.Methods:From May 2023 to December 2023, plasma samples and clinical information were collected from 16 infants with RSV infection and 14 control infants. Cytek Aurora flow cytometry (Cytek, America) and Enzyme linked immunosorbent assay (ELISA) were used to detect the expression levels of 25 cytokines after mild and severe RSV infection.Results:Cough and nasal obstruction were the main clinical manifestations in infants with mild RSV infection, accompanied by polypnea, wheezing and other symptoms. The main symptoms of severe RSV infection were cough and rales, accompanied by fever and polypnea. In comparison with the control group, the expression levels of IL-2, IL-4, IL-5, IL-6, IL-9, IL-13, IL-22, TNF-α, IFN-α, IFN-β, MIP-1β, I-TAC, ENA-78, GROα, Eotaxin, and MCP-1 in the RSV infection group all exhibited an upregulation trend. Both IP-10 and MIP-3α demonstrated a downward trend in the RSV infection group; however, there was no statistically significant difference ( P>0.05). The levels of IL-10, IFN-γ, MIP-1α, and IL-8 in the RSV infection group were significantly higher than those in the control group, whereas the levels of MIG, TARC, and RANTES in the RSV infection group were significantly lower than those in the control group ( P<0.05). The levels of IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-13, IL-22, IFN-β, IFN-γ, TNF-α, IL-8, I-TAC, MIP-1β, Eotaxin, and MCP-1 in the mild RSV infection group were significantly higher than those in the severe RSV infection group ( P>0.05). Among these, the levels of MIG, RANTES, TARC, MIP-3α, and ENA-78 in the mild infection group were all lower than those in the severe infection group. The expressions of ENA-78 and MIP-1α in the severe infection group were significantly higher than those in the mild infection group and also higher than those in the control group. There was no significant difference in IP-10 and GROα between the mild and severe RSV infection groups ( P>0.05). Conclusions:The differences in clinical features and cytokines between infants with mild and severe RSV infection provide important data support for the prevention and treatment of RSV infection in infants.