Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

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.

The molecular mechanisms underlying the develop-ment and metastasis of prostate cancer remain elusive.This comprehensive review delves into the intricate role of cofilin,an actin-binding protein,in the pathogenesis and progression of prostate cancer.Cofilin is a significant protein in cytoskeletal dynamics,and any dysregulation may result in the morphological changes in normal cells and the invasion and metastasis of tumor cells.Research has revealed that the activity of cofilin is regula-ted by various mechanisms,including phosphorylation/dephos-phorylation and interactions with other molecules.Moreover,this review discusses promising therapeutic interventions,such as co-filin inhibitors and gene therapy,which have demonstrated effica-cy in preclinical models.The challenge of clinically preventing the transition to castration-resistant prostate cancer and tumor metastasis is widely recognized,necessitating the development of precise drug treatments and biomarker identification.As a key regulatory protein,cofilin provides a more comprehensive refer-ence for the prevention and treatment of prostate diseases.

Objective To compare the effect of different intensity of neuromuscular training(NMT)on muscle strength and knee joint function of patients after anterior cruciate ligament reconstruction(ACLR).Methods From January,2023 to January,2024,60 ACLR patients in Changhai Hospital were selected,and they received the same intensity of NMT from one to eight weeks after surgery.Eight weeks after surgery,they were randomly divided into low intensity group(n=30)and high intensity group(n=30),and then they received different inten-sities of NMT from nine to 16 weeks after surgery,each training session lasted one hour,with three sessions per week,totaly 48 sessions.The Lysholm score,knee flexor and extensor muscle strength and muscle endurance-were compared at eight weeks and 16 weeks after surgery.Results After group training,the Lysholm score significantly increased in both groups(|t|>13.739,P<0.001),and was higher in the high intensity group than in the low intensity group(t=-2.574,P<0.05);in the high intensity group,the relative peak torque and endurance of the extensor and flexor muscles improved at angular velocities of 60°/s,120°/s and 180 °/s(|t|>2.320,P<0.05);in the low intensity group,the flexor peak torque improved at all the three angular velocities(t>2.177,P<0.05),the extensor peak torque improved at angular velocities of 60°/s and 180°/s(|t|>1.715,P<0.05),and the extensor endurance improved at angular velocity of 60°/s(t=-2.293,P<0.05).However,there was no significant difference in the relative peak torque and endurance of the extensor and flexor muscles at all the three angular velocities(P>0.05).Conclusion Both high and low intensity NMT could improve the muscle strength,muscle endurance and knee joint func-tion.Maybe,high intensity is superior to low intensity.Further verification is still needed.

[Purpose]To investigate the choice of medical institutions for the first-treatment of can-cer patients after diagnosis and the factors influencing it,so as to provide a reference basis for the rational allocation of health resources and the promotion of hierarchical diagnosis and treatment of cancer.[Methods]A total of 1 140 common cancer patients from 1 provincial,3 municipal and 6 county-level medical institutions in Shandong Province were included in the study,and a ques-tionnaire survey was conducted on their basic personal information and the selection of medical institutions.Chi-square test and Fisher exact test were used to compare different groups,and mul-ti-classification unordered Logistic regression were used to explore the factors influencing the se-lection of cancer patients.[Results]1 077 valid questionnaires were collected.The selection rates of first-treatment institutions for cancer patients were county hospitals(29.90％),city hospitals(35.28％),and provincial hospitals(34.82％),and the high level of medical care was the main reason for the selection of first-treatment institutions for cancer patients.The results of multi-clas-sification unordered Logistic regression showed that when choosing municipal and county medical institutions as the first treatment institution compared to choosing a county medical institution for the first treatment as a reference,compared to patients with stage Ⅰ tumors,patients with tumor stages at stage Ⅲ(OR=1.789,95％CI:1.014～3.158)and stage Ⅳ(OR=2.005,95％CI:1.179～3.409)were more likely to choose municipal medical institutions;and patients with annual house-hold income of 10 000～＜50 000 CNY(OR=0.625,95％CI:0.414～0.943)were less likely to choose mu-nicipal medical institutions compared to those with an annual household income of＜10 000 CNY.When choosing provincial and county medical institutions as the first treatment institution com-pared to patients in stage Ⅲ(OR=2.885,95％C1:1.549～5.372)and stage Ⅳ(OR=3.104,95％CI:1.724～5.586)compared to patients with stage Ⅰ tumors when choosing a county medical institu-tion for the first treatment was used as a reference;married(OR=2.248,95％CI:1.127～4.484)patients were more likely to choose provincial healthcare organizations than unmarried/divorced/widowed patients;patients with a family history of cancer compared to patients without a family history of cancer(OR=1.650,95％CI:1.115～2.441)were more inclined to choose provincial healthcare institutions;compared with patients＜45 years old,patients over 60 years old(OR=0.483,95％CI:0.242～0.962)were less inclined to choose provincial hospitals;and compared with patients who were not aware of knowledge related to cancer,those who were aware of knowledge related to cancer(OR=0.613,95％CI:0.441～0.852)patients were less inclined to choose provin-cial hospitals.[Conclusion]Vigorously carrying out publicity on cancer prevention and treatment knowledge,improving the cancer prevention and treatment literacy of cancer patients.strengthen-ing vertical cooperation among medical institutions at all levels,improving the provincial-munici-pal-county cancer prevention and treatment network,and promoting the sinking of high-quality medical resources are important ways to improve the cancer prevention and treatment capacity of counties and promote the work of hierarchical medical system.

Objective To evaluate the impact of Jixiong Jiedu decoction on the efficacy of diabetic kidney disease in mice and its influence on intestinal flora and trimethylamine oxide(TMAO)levels.Methods Twelve 7-week-old male db/db mice were randomly assigned to the model group or Jixiong Jiedu decoction group(6 mice per group),while 6 male db/m mice were designated as the control group.Following 8 weeks of continuous gavage,we monitored the body weight and blood glucose levels of the mice at weeks 0,4,and 8.Additionally,we assessed urinary microalbumin,kidney injury molecule-1(KIM-1),creatinine(Scr),and urea nitrogen(BUN)levels in urine.Renal pathology was evaluated using HE and PAS staining.Furthermore,fecal samples underwent 16s RNA sequencing,and the serum TMAO levels were determined.Results Compared with the control group,the blood glucose,body weight,8-hour urinary microalbumin,KIM-1 and Scr in the model group were significantly increased,and the renal pathology showed that glomerular segmental mesangial matrix increased,glomerular volume hypertrophy and renal tubular epithelial cell swelling.The abundance of Lactobacillaceae and Lactobacillus in the model group was significantly increased(P<0.01).The abundance of Lachnospiraceae,Helicobacter and Oscillospira decreased significantly(P<0.01),the abundance of each bacterial group changed,and the serum TMAO content increased significantly.Compared with the model group,the 8h urinary microalbumin,KIM-1(P<0.01)and Scr(P<0.05)in the Jixiong Jiedu decoction group were significantly decreased,and there was no significant difference in BUN(P>0.05),and the renal pathological damage was significantly improved.The abundance of Lactobacillaceae and Lactobacillus in intestinal flora decreased significantly(P<0.01),while the abundance of Lachnospiraceae and Oscillospira increased significantly(P<0.01,P<0.05).The structure of gut microbiota,the abundance of dominant and non-dominant bacteria were positively adjusted,and the serum TMAO content was significantly decreased(P<0.01).Conclusion Jixiong Jiedu decoction effectively ameliorates intestinal flora disorders in db/db mice and regulates serum TMAO levels,thereby exerting a nephroprotective effect.

Objective To develop a novel type of transparent simulation manikin as a surgical training model to meet the surgical treatment demand on the medical train.Methods A transparent manikin was developed with the steps of basic data collection,motherboard design and manufacture and module production and assembly.Firstly,basic data collection was carried out with reference to standardized human anatomy and parameters.Secondly,some software such as UG NX7.5 was used to construct the motherboard of the manikin.Finally,module production and assembly were performed with the materials of acrylic,transparent rubber,silicone and hydrogel and the technology of silicone infusion.Results The transparent manikin developed had its anatomy structure close to that of the real body and high visuality for its internal and external components,which simulated a variety of war wounds and thus could be integrated with the surgical training scenarios on the medical train effectively.Conclusion The transparent manikin developed is characterized by high visuality,modularity and blood flow,and meets the demands for surgical training on the medical train.[Chinese Medical Equipment Journal,2025,46(6):111-115]

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

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.