During the progression of ischemic stroke,cells experience oxidative stress and intracellular energy depletion,triggering the dynamic assembly and disassembly of stress granules.Stress granules may have a dual role in ischemic stroke.In the ultra-early reperfusion period(6-36 h),under acute stress,the stress granules may inhibit neuronal apoptosis and exert neuroprotective effects through reversible liquid-liquid phase separation.In contrast,in the acute reperfusion period(36 h to 2 weeks),under prolonged stress,pathological stress granules may accumulate through liquid-solid phase transition,leading to neuronal dysfunction and inducing ischemic stroke sequelae such as motor and cognitive impairments.This article reviewed the mechanisms of stress granules dynamic phase transitions during ischemic stroke and their effects on neurons,aiming to provide references and insights for future stress granule-targeted interventions for ischemic stroke and its sequelae.

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.

Medical ultrasound technology is widely used for diagnosis and therapy in clinical practice.Ultrasound probes,which are directly contact with patients,pose a potential risk of pathogen transmission.This expert consen-sus was developed by a multidisciplinary team based on international guidelines,standards in China,and the results of a national survey,aiming to reduce the risk of healthcare-associated infection through standardizing reprocessing of medical ultrasound probes,and formulating consensus recommendations with the Delphi method.The consensus clarifies the reprocessing principles for three types of ultrasound probes of different infection risks:external-use ul-trasound probes,interventional percutaneous ultrasound probes,and internal-use ultrasound probes,puts forward systematic suggestions on the reprocessing standards and disinfection levels of ultrasound probe isolation covers and coupling agents,the reprocessing procedures and methods of ultrasound probes,as well as architectural layout and management of reprocessing,so as to provide a scientific prevention and control framework for ensuring ultrasound diagnosis and therapy safety.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

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 This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Aim To investigate the effects of Mdivi-1 on A1 astrocyte activation and its associated signaling molecules.Methods CTX-TNA2 astrocytes were di-vided into the control,ACM,and low-,medium-,and high-dose Mdivi-1 groups based on concentration screening via CCK-8 assay.ACM,a DMEM high-glu-cose medium containing preset concentrations of IL-1α,TNF-α,and C1q,was used to induce A1 activa-tion.The ACM group was stimulated with ACM for 24 hours.Mdivi-1 groups were pretreated with correspond-ing concentrations of Mdivi-1 for 2 hours,followed by ACM stimulation for 24 hours.Real-time quantitative PCR and Western blot were employed to assess mRNA levels and protein expression of IL-1β,C3,and iNOS in all groups.Immunofluorescence and Western blot were used to detect the expression of signaling molecules NLRP3,caspase-1,and ASC.DHE labeling was used to assess and flow cytometry was used to examine reac-tive oxygen species(ROS)levels.Results The CCK-8 assay identified 5,10,and 25 μmol·L-1as ap-propriate concentrations for Mdivi-1 intervention in CTX-TNA2 cells.Real-time quantitative PCR and Western blot results indicated that,compared to the control group,IL-1 β,C3,and iNOS mRNA levels and protein expression were significantly elevated in the ACM group(P＜0.05).In contrast,these levels were significantly reduced in the 10 and 25 μmnol·L-1 Mdi-vi-1 groups compared to the ACM group(P＜0.05).Immunofluorescence and Western blot results confirmed that ACM stimulation significantly activated the NLRP3 inflammasome in astrocytes,while Mdivi-1 intervention effectively reversed the ACM-induced upregulation of NLRP3,caspase-1,and ASC.DHE staining results demonstrated that 5,10,and 25 μmol·L-1Mdivi-1 in-terventions partially reversed the ACM-induced in-crease in ROS levels in a dose-dependent manner.Conclusion Mdivi-1 effectively inhibits A1 astrocyte activation,potentially through modulation of ROS and the NLRP3 inflammasomes.

Objective:To explore behavioral and electrophysiological differences in risky decision-making between depressed patients with and without suicidal ideation.Methods:A total of 61 patients with first-episode untreated depression were enrolled in the depression clinic of Nanjing Brain Hospital from September 2023 to January 2024, which were divided into the suicidal ideation group( n=32) and the non-suicidal ideation group ( n=29).At the same time, healthy controls matched with sex, age and years of education were recruited from the community( n=36).The event-related potentials (ERP) of the participants were detected, and the amplitude and latency of feedback related negative waves (FRN) and P300 during the feedback phase under Iowa gambling task (IGT) were recorded. Statistical analysis was performed using SPSS 26.0 software.The inter-and intra-group differences of ERP indexes were compared using two-way ANOVA, and Spearman correlation analysis was conducted to examine the relationship between ERP indexes and scores of the Beck scale for suicidal ideation. Results:(1)Compared with healthy controls, depressed patients with and without suicidal ideation had both lower net scores in IGT (both P<0.05).(2)When comparing the mean FRN amplitude under different feedback types among the three groups, the main effect of feedback type ( F=8.799, P=0.004), the main effect of group ( F=6.396, P=0.002) and the interaction effect ( F=4.200, P=0.018)were all significant. Under gain feedback conditions, the mean FRN amplitude was lower in both depressed groups compared with healthy controls (both P<0.05). (3)The comparison of the mean P300 amplitude under different feedback types among the three groups showed that the main effect of group ( F=15.719, P<0.001) and the main effect of feedback type ( F=15.949, P=0.001) were both significant, while the interaction effect between group and feedback type was not significant ( F=1.573, P=0.213). The group with suicidal ideation ((0.85±0.21) μV) had a smaller amplitude than both the non-suicidal ideation group ((1.61±0.22) μV) and healthy controls ((2.46±0.20) μV) (both P<0.05). (4)In depressed patients, P300 mean amplitude under both loss and gain feedback conditions were both negatively correlated with suicidal ideation (loss: r=-0.435, P=0.001; gain: r=-0.318, P=0.013). Conclusion:Depressed patients with and without suicidal ideation both exhibit impaired risk decision-making. The decrease of P300 mean amplitude is more significant in depressed patients with suicidal ideation than those without suicidal ideation.P300 mean amplitude may serve as an electrophysiological marker to differentiate depressed patients with suicidal ideation and those without suicidal ideation.

Objective:To analyze the characteristics of electroencephalogram microstate parameters in first-episode drug-naive patients with major depressive disorder (MDD), so as to provide electrophysiological evidence for the pathogenesis and early diagnosis of MDD.Methods:Eighty-four first-episode, drug-naive outpatients diagnosed with MDD(MDD group) and 82 healthy controls(healthy group) participated in this study. Resting-state EEG data (5-6 min, with eyes closed) were recorded for all participants. Data preprocessing and microstate analysis were performed using MATLAB and EEGLAB software. Temporal parameters of resting-state brain network microstates were compared using SPSS 26.0.Results:This study identified four typical microstates: Class A microstate(auditory network), Class B microstate(visual network), Class C microstate(salient network), and Class D microstate(attention and control network). The coverage rate (0.16±0.06, 0.21±0.06), duration (67.72±7.07, 72.28±8.59), and incidence rate (2.38±0.68, 2.82±0.67) of microstate A in MDD group were significantly lower than those in healthy group ( F=22.115, 13.368, 18.779, all P<0.001), while the above indexes of microstate B in MDD group were significantly higher than those in healthy group(coverage rate: 0.24±0.07 vs 0.18±0.06, duration: 76.35±11.28 vs 69.46±8.52, incidence rat: 3.16±0.52 vs 2.52±0.57) ( F=41.287, 18.999, 52.245, all P<0.001). Additionally, the microstate D in MDD group showed significantly lower coverage rate(0.33±0.08, 0.36±0.08) and duration (89.66±15.38, 95.46±16.79)compared with healthy group( F=3.932, 4.215, both P<0.05). Notably, significant differences were observed in the transition probabilities between the following microstates: A→B, A→D, B→A, C→A, C→B, D→A and D→B (all P<0.05). Conclusion:First-episode drug-naive depressive patients are characterized by alterations in microstate A, microstate B, and microstate D, which may be the potential pathogenesis of MDD and may serve as electrophysiological indicators for early diagnosis of MDD.

Objective:To explore behavioral and electrophysiological differences in risky decision-making between depressed patients with and without suicidal ideation.Methods:A total of 61 patients with first-episode untreated depression were enrolled in the depression clinic of Nanjing Brain Hospital from September 2023 to January 2024, which were divided into the suicidal ideation group( n=32) and the non-suicidal ideation group ( n=29).At the same time, healthy controls matched with sex, age and years of education were recruited from the community( n=36).The event-related potentials (ERP) of the participants were detected, and the amplitude and latency of feedback related negative waves (FRN) and P300 during the feedback phase under Iowa gambling task (IGT) were recorded. Statistical analysis was performed using SPSS 26.0 software.The inter-and intra-group differences of ERP indexes were compared using two-way ANOVA, and Spearman correlation analysis was conducted to examine the relationship between ERP indexes and scores of the Beck scale for suicidal ideation. Results:(1)Compared with healthy controls, depressed patients with and without suicidal ideation had both lower net scores in IGT (both P<0.05).(2)When comparing the mean FRN amplitude under different feedback types among the three groups, the main effect of feedback type ( F=8.799, P=0.004), the main effect of group ( F=6.396, P=0.002) and the interaction effect ( F=4.200, P=0.018)were all significant. Under gain feedback conditions, the mean FRN amplitude was lower in both depressed groups compared with healthy controls (both P<0.05). (3)The comparison of the mean P300 amplitude under different feedback types among the three groups showed that the main effect of group ( F=15.719, P<0.001) and the main effect of feedback type ( F=15.949, P=0.001) were both significant, while the interaction effect between group and feedback type was not significant ( F=1.573, P=0.213). The group with suicidal ideation ((0.85±0.21) μV) had a smaller amplitude than both the non-suicidal ideation group ((1.61±0.22) μV) and healthy controls ((2.46±0.20) μV) (both P<0.05). (4)In depressed patients, P300 mean amplitude under both loss and gain feedback conditions were both negatively correlated with suicidal ideation (loss: r=-0.435, P=0.001; gain: r=-0.318, P=0.013). Conclusion:Depressed patients with and without suicidal ideation both exhibit impaired risk decision-making. The decrease of P300 mean amplitude is more significant in depressed patients with suicidal ideation than those without suicidal ideation.P300 mean amplitude may serve as an electrophysiological marker to differentiate depressed patients with suicidal ideation and those without suicidal ideation.