Objective To analyze the risk factors for chronic mountain sickness(CMS)in young male migrants living in high-altitude areas and to construct a diagnostic model and evaluate its diagnostic efficacy.Methods From June 10 to December 29,2023,a cross-sectional study was conducted on young male migrants subjected with convenience sampling who had been living in high-altitude areas(4 500～5 000 m)for 6 months or longer.Their demographic data were collected and blood samples were collected for laboratory test.According to the Qinghai Score for Chronic Mountain Sickness,they were divided into CMS group and non-CMS group.Then the participants were randomly divided into a training set and a test set in a ratio of 8∶2.Independent risk factors for CMS occurrence were screened out,through random forest variable importance ranking,univariate and multivariable logistic regression analysis,and a diagnostic model was constructed based on these factors.Receiver operating characteristic(ROC)curve analysis,calibration curve analysis,clinical decision curve analysis,and influence curve analysis were used to comprehensively evaluate the diagnostic performance of the model.Results According to the inclusion and exclusion criteria,308 out of 376 participants were finally subjected,and 17.53%of them were diagnosed with CMS.The major clinical symptoms of the CMS patients were dyspnea or palpitations(79.63%)and sleep disorders(85.19%).Further analysis revealed that creatine kinase-MB/creatine kinase(CK-MB/CK,OR=2.17,95%CI:1.43～3.28),high-altitude residence time(OR=2.44,95%CI:1.08～5.54),and body mass index(BMI,OR=1.62,95%CI:1.05～2.50)were 3 major independent risk factors for CMS.The area under the curve(AUC)value of the CMS diagnostic model in the training set and test set was 0.821(95%CI:0.756～0.886)and 0.821(95%CI:0.700～0.944),the specificity was 66.30%and 73.90%,the sensitivity was 89.50%and 81.20%,respectively,indicating good discrimination ability.Hosmer-Lemeshow goodness-of-fit test showed consistency between predicted results and actual observations(χ2=10.029,P=0.263;χ2=4.477,P=0.812).Clinical decision curve analysis demonstrated that within the threshold probability range from 0.1 to 0.7,the net benefit of the model exceeded both full intervention and no intervention strategies.The influence curve analysis showed high consistency between the model predictions and actual incidence when the threshold probability exceeded 0.4.These two analyses together confirmed the clinical application value of the model.Conclusion CK-MB/CK,high-altitude residence time and BMI are independent risk factors for CMS,and their diagnostic model helps identify potential individuals at risk for CMS.Early intervention can prevent the harm of CMS to the health of young men migrating to high-altitude areas.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective:To understand the coverage and information consistency rate of National Immunization Program (NIP) vaccines among children born during 2020-2021 in Zhejiang Province, Chongqing City, and Shanxi Province (3 provinces) of China .Methods:A simple random sampling method was used to randomly select 3 counties (districts) from each of the 3 provinces, 5 townships from each county (district), and 5 villages from each township. Vaccination information for seven NIP vaccines was collected for children born between 2020 and 2021 in each village. The vaccination coverage, timely coverage, and consistency rates between the survey data and the Immunization Planning Information System data were analyzed.Results:A total of 1 117 children were investigated. The vaccination coverage for each dose of NIP vaccine ranged from 99.10% to 100.00%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 99.19% to 100.00%, 98.92% to 100.00%, and 99.20% to 100.00%, respectively. The timely coverage of each dose of NIP vaccine ranged from 89.79% to 99.82%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 94.09% to 99.73%, 89.52% to 99.73%, and 78.55% to 100.00%, respectively. The consistency rate of information on each dose of NIP vaccine ranged from 94.36% to 99.91%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 97.85% to 99.73%, 98.92% to 100.00%, and 86.06% to 100.00%, respectively.Conclusions:Coverage of NIP vaccines was generally high among children born during 2020-2021 in the 3 provinces of China, but there were regional differences in the timely coverage of some vaccine doses and the vaccination information consistency rate. It is necessary to strengthen the timely vaccination of children's vaccine booster doses and optimize the management of vaccination services.

Objective:To evaluate the cost-effectiveness of Artificial Intelligence(AI)-assisted physician image interpretation in the screening of early-stage lung cancer(stage Ⅰ)from the perspective of the healthcare system,so as to provide evidence for screening strategy optimization.Methods:Based on community populations,a decision tree model was constructed to simulate the cost-effectiveness of two screening strategies:Al-assisted physician image interpretation and independent physician image interpretation,and the Incremental Cost-Effectiveness Ratio(ICER)was calculated.Results:In the basic analysis,the per capita costs of the AI-assisted group and the physician group were 1 483 yuan and 1 489 yuan,respectively,and the effectiveness was 17.02 Quality-Adjusted Life Years(QALYs)and 16.99 QALYs,respectively.Compared with the physician group,the AI-assisted group saved 6 yuan per case and obtained an additional 0.03 QALY per case.The ICER was negative,indicating that the AI-assisted group had an absolute advantage.Threshold analysis showed that when the inspection cost of Al-assisted physician image interpretation increased to 428 yuan per case,the average cost per case was the same between these two groups,and the ICER was 0.When the inspection cost of Al-assisted physician image interpretation was above 428 yuan,the ICER was positive,still demonstrating economic efficiency.Conclusion:AI-assisted image interpretation is cost-effective in the screening of early-stage lung cancer and can facilitate the"early detection,early diagnosis,and early treatment"of lung cancer based on improving the efficiency and accuracy of screening,so as to provide scientific support for health system resource optimization.

Background: Automated insulin delivery (AID) systems studies are upsurging, half of which were published in the last 5 years. We aimed to evaluate the efficacy and safety of AID systems in patients with type 1 diabetes mellitus (T1DM). Methods: We searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov until August 31, 2023. Randomized clinical trials that compared AID systems with other insulin-based treatments in patients with T1DM were considered eligible. Studies characteristics and glycemic metrics was extracted by three researchers independently. Results: Sixty-five trials (3,623 patients) were included. The percentage of time in range (TIR) was 11.74% (95% confidence interval [CI], 9.37 to 14.12; P<0.001) higher with AID systems compared with control treatments. Patients on AID systems had more pronounced improvement of time below range when diabetes duration was more than 20 years (–1.80% vs. –0.86%, P=0.031) and baseline glycosylated hemoglobin lower than 7.5% (–1.93% vs. –0.87%, P=0.033). Dual-hormone full closed-loop systems revealed a greater improvement in TIR compared with hybrid closed-loop systems (–19.64% vs. –10.87%). Notably, glycemia risk index (GRI) (–3.74; 95% CI, –6.34 to –1.14; P<0.01) was also improved with AID therapy. Conclusion AID systems showed significant advantages compared to other insulin-based treatments in improving glucose control represented by TIR and GRI in patients with T1DM, with more favorable effect in euglycemia by dual-hormone full closedloop systems as well as less hypoglycemia for patients who are within target for glycemic control and have longer diabetes duration.

Objective:To analyze differences in the expression of routine laboratory parameters and cerebrospinal fluid (CSF) examination indicators between patients with non-neurosyphilis (syphilis without nervous system involvement) and those with neurosyphilis, to screen for key predictive factors, and to construct a predictive model for neurosyphilis.Methods:A retrospective analysis was conducted on the clinical data from patients with syphilis at the Fifth People's Hospital of Suzhou from 2019 to 2024. Patients with neurosyphilis and non-neurosyphilis who were hospitalized from November 2019 to June 2022 were included in the model cohort, and those hospitalized from January 2024 to October 2024 were included in the validation cohort. The patients' basic information and laboratory test indicators (including routine blood tests, CSF biochemical analysis, and syphilitic antibody tests) were collected. Statistical analysis was performed using the GraphPad software. The receiver operating characteristic (ROC) curve and the binary logistic regression method were used to analyze the predictive performance of key indicators in patients from the model cohort with SPSS software, and a predictive model for neurosyphilis was constructed. The performance of the neurosyphilis predictive model for neurosyphilis was validated based on relevant indicators from the validation cohort.Results:The model cohort included 99 patients with non-neurosyphilis (including 49 males and 50 females), and they were aged between 19 and 85 years, with an average age of 47 years; 69 patients with neurosyphilis were also included in the model cohort, including 58 males and 11 females, and they were aged between 26 and 73 years, with an average age of 51 years. The neurosyphilis group showed a significant increase in the median levels of CSF adenosine deaminase (1 U/L) and microprotein (711 mg/L), white blood cell counts (0.009 × 10?/L), as well as in the proportion of positive Pandy tests (35/69, 50.7%) compared with the non-neurosyphilis group (0 U/L, 309 mg/L, 0.002 × 10?/L, 2 /99 [2.0%], respectively, all P < 0.001). Based on the ROC curve analysis, the CSF microprotein and white blood cell count had relatively high discriminative ability (area under the ROC curve [AUC] > 0.85), while adenosine deaminase and the Pandy test showed moderate discriminative ability (0.7 < AUC < 0.85). According to the above four indicators, the logistic regression analysis showed that CSF microprotein combined with CSF white blood cell counts could construct the best predictive model for neurosyphilis, with a prediction accuracy rate of 0.980, a sensitivity of 98.5%, and a specificity of 89.9%. The prediction formula was logit (p) = -9.926 + 0.015 × microprotein + 362.33 × CSF white blood cell count, with a cutoff value of ≥ -0.867. The validation cohort enrolled 72 patients with non-neurosyphilis and 51 with neurosyphilis, and there were significant differences in CSF microprotein levels and white blood cell counts between the two groups (both P < 0.001). In the validation cohort, the predictive model demonstrated an accuracy of 86.2%, with a sensitivity of 83.6% and a specificity of 91.1% for predicting neurosyphilis. Conclusion:The predictive model for neurosyphilis constructed by combining CSF microprotein and CSF white blood cell count may contribute to the early differential diagnosis of neurosyphilis.

Objective:To analyze differences in the expression of routine laboratory parameters and cerebrospinal fluid (CSF) examination indicators between patients with non-neurosyphilis (syphilis without nervous system involvement) and those with neurosyphilis, to screen for key predictive factors, and to construct a predictive model for neurosyphilis.Methods:A retrospective analysis was conducted on the clinical data from patients with syphilis at the Fifth People's Hospital of Suzhou from 2019 to 2024. Patients with neurosyphilis and non-neurosyphilis who were hospitalized from November 2019 to June 2022 were included in the model cohort, and those hospitalized from January 2024 to October 2024 were included in the validation cohort. The patients' basic information and laboratory test indicators (including routine blood tests, CSF biochemical analysis, and syphilitic antibody tests) were collected. Statistical analysis was performed using the GraphPad software. The receiver operating characteristic (ROC) curve and the binary logistic regression method were used to analyze the predictive performance of key indicators in patients from the model cohort with SPSS software, and a predictive model for neurosyphilis was constructed. The performance of the neurosyphilis predictive model for neurosyphilis was validated based on relevant indicators from the validation cohort.Results:The model cohort included 99 patients with non-neurosyphilis (including 49 males and 50 females), and they were aged between 19 and 85 years, with an average age of 47 years; 69 patients with neurosyphilis were also included in the model cohort, including 58 males and 11 females, and they were aged between 26 and 73 years, with an average age of 51 years. The neurosyphilis group showed a significant increase in the median levels of CSF adenosine deaminase (1 U/L) and microprotein (711 mg/L), white blood cell counts (0.009 × 10?/L), as well as in the proportion of positive Pandy tests (35/69, 50.7%) compared with the non-neurosyphilis group (0 U/L, 309 mg/L, 0.002 × 10?/L, 2 /99 [2.0%], respectively, all P < 0.001). Based on the ROC curve analysis, the CSF microprotein and white blood cell count had relatively high discriminative ability (area under the ROC curve [AUC] > 0.85), while adenosine deaminase and the Pandy test showed moderate discriminative ability (0.7 < AUC < 0.85). According to the above four indicators, the logistic regression analysis showed that CSF microprotein combined with CSF white blood cell counts could construct the best predictive model for neurosyphilis, with a prediction accuracy rate of 0.980, a sensitivity of 98.5%, and a specificity of 89.9%. The prediction formula was logit (p) = -9.926 + 0.015 × microprotein + 362.33 × CSF white blood cell count, with a cutoff value of ≥ -0.867. The validation cohort enrolled 72 patients with non-neurosyphilis and 51 with neurosyphilis, and there were significant differences in CSF microprotein levels and white blood cell counts between the two groups (both P < 0.001). In the validation cohort, the predictive model demonstrated an accuracy of 86.2%, with a sensitivity of 83.6% and a specificity of 91.1% for predicting neurosyphilis. Conclusion:The predictive model for neurosyphilis constructed by combining CSF microprotein and CSF white blood cell count may contribute to the early differential diagnosis of neurosyphilis.

Objective:To understand the coverage and information consistency rate of National Immunization Program (NIP) vaccines among children born during 2020-2021 in Zhejiang Province, Chongqing City, and Shanxi Province (3 provinces) of China .Methods:A simple random sampling method was used to randomly select 3 counties (districts) from each of the 3 provinces, 5 townships from each county (district), and 5 villages from each township. Vaccination information for seven NIP vaccines was collected for children born between 2020 and 2021 in each village. The vaccination coverage, timely coverage, and consistency rates between the survey data and the Immunization Planning Information System data were analyzed.Results:A total of 1 117 children were investigated. The vaccination coverage for each dose of NIP vaccine ranged from 99.10% to 100.00%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 99.19% to 100.00%, 98.92% to 100.00%, and 99.20% to 100.00%, respectively. The timely coverage of each dose of NIP vaccine ranged from 89.79% to 99.82%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 94.09% to 99.73%, 89.52% to 99.73%, and 78.55% to 100.00%, respectively. The consistency rate of information on each dose of NIP vaccine ranged from 94.36% to 99.91%, with those in Zhejiang Province, Chongqing City, and Shanxi Province ranging from 97.85% to 99.73%, 98.92% to 100.00%, and 86.06% to 100.00%, respectively.Conclusions:Coverage of NIP vaccines was generally high among children born during 2020-2021 in the 3 provinces of China, but there were regional differences in the timely coverage of some vaccine doses and the vaccination information consistency rate. It is necessary to strengthen the timely vaccination of children's vaccine booster doses and optimize the management of vaccination services.

Loss-of-function variants in CSDE1 have been strongly linked to neuropsychiatric disorders, yet the precise role of CSDE1 in neurogenesis remains elusive. In this study, we demonstrate that knockout of Csde1 during cortical development in mice results in impaired neural progenitor proliferation, leading to abnormal cortical lamination and embryonic lethality. Transcriptomic analysis revealed that Csde1 upregulates the transcription of genes involved in the cell cycle network. Applying a dual thymidine-labelling approach, we further revealed prolonged cell cycle durations of neuronal progenitors in Csde1-knockout mice, with a notable extension of the G1 phase. Intersection with CLIP-seq data demonstrated that Csde1 binds to the 3' untranslated region (UTR) of mRNA transcripts encoding cell cycle genes. Particularly, we uncovered that Csde1 directly binds to the 3' UTR of mRNA transcripts encoding Cdk6, a pivotal gene in regulating the transition from the G1 to S phases of the cell cycle, thereby maintaining its stability. Collectively, this study elucidates Csde1 as a novel regulator of Cdk6, sheds new light on its critical roles in orchestrating brain development, and underscores how mutations in Csde1 may contribute to the pathogenesis of neuropsychiatric disorders.
Animals ; Neurogenesis/genetics* ; Cell Cycle/genetics* ; Mice, Knockout ; Mice ; Neural Stem Cells/metabolism* ; DNA-Binding Proteins/metabolism* ; Cyclin-Dependent Kinase 6/genetics* ; Cell Proliferation ; 3' Untranslated Regions ; Cerebral Cortex/embryology* ; RNA-Binding Proteins ; Mice, Inbred C57BL

Background: Automated insulin delivery (AID) systems studies are upsurging, half of which were published in the last 5 years. We aimed to evaluate the efficacy and safety of AID systems in patients with type 1 diabetes mellitus (T1DM). Methods: We searched PubMed, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov until August 31, 2023. Randomized clinical trials that compared AID systems with other insulin-based treatments in patients with T1DM were considered eligible. Studies characteristics and glycemic metrics was extracted by three researchers independently. Results: Sixty-five trials (3,623 patients) were included. The percentage of time in range (TIR) was 11.74% (95% confidence interval [CI], 9.37 to 14.12; P<0.001) higher with AID systems compared with control treatments. Patients on AID systems had more pronounced improvement of time below range when diabetes duration was more than 20 years (–1.80% vs. –0.86%, P=0.031) and baseline glycosylated hemoglobin lower than 7.5% (–1.93% vs. –0.87%, P=0.033). Dual-hormone full closed-loop systems revealed a greater improvement in TIR compared with hybrid closed-loop systems (–19.64% vs. –10.87%). Notably, glycemia risk index (GRI) (–3.74; 95% CI, –6.34 to –1.14; P<0.01) was also improved with AID therapy. Conclusion AID systems showed significant advantages compared to other insulin-based treatments in improving glucose control represented by TIR and GRI in patients with T1DM, with more favorable effect in euglycemia by dual-hormone full closedloop systems as well as less hypoglycemia for patients who are within target for glycemic control and have longer diabetes duration.