Objective To analyze the unexamined items and situations in occupational health examinations of radiation workers, and provide a reference for the revision of occupational health examination standards for radiation workers. Methods A total of 29 630 radiation workers who underwent occupational health examinations at The Third People’s Hospital of Henan Province in 2023 were selected, and the non-examination rates were statistically analyzed according to occupation, gender, and age. Results The overall non-examination rate of non-medical radiation workers was significantly lower than that of the medical radiation workers (P<0.05). The non-examination rate of chest X-rays among medical radiation workers was significantly higher than that of non-medical radiation workers (P<0.05), while no significant differences were found in other items (P>0.05). Gender-stratified analysis showed that the non-examination rate of routine urine tests was higher in females than in males in both medical and non-medical radiation workers (P<0.05). Age-stratified analysis revealed no significant differences in non-examination rates among different age groups in non-medical radiation workers (P>0.05), whereas the chest X-ray non-examination rate was relatively high in medical radiation workers under 30 years old (P<0.05). Conclusion Significant differences were observed in the non-examination rates of occupational health examinations among radiation workers based on occupation, gender, and age. The overall non-examination rate was relatively low in non-medical radiation workers.

Objective To investigate the risk factors and intervention measures for surgical site infec-tion following posterolateral approach lumbar fusion surgery.Methods A total of 1 078 patients who under-went posterolateral approach lumbar fusion surgery in the department of spine surgery from January 1,2022 to December 31,2023 were included.Patient related information was collected through the real-time nosocomi-al infection monitoring system,while medical visit information was obtained via the outpatient electronic med-ical record system.Multivariate logistic regression analysis was performed to identify risk factors for surgical site infection.Results Among the 1 078 patients,34 cases(3.15%)developed surgical site infections,while 1 044 cases did not.Multivariate logistic regression analysis revealed that age,smoking,hypertension,diabetes,concurrent hospital stay,operative time,duration of postoperative antimicrobial use after initial surgery,and total antimicrobial use duration were significant risk factors for surgical site infection(P＜0.05).Among the 34 infected patients,the duration of antimicrobial use varied significantly across different infection sites(P＜0.05),with the longest duration observed in patients with deep space infections.Conclusion Targeted surveil-lance of surgical site infections should be reinforced based on these risk factors.Perioperative infection control measures must be strictly implemented to improve the scientific,precise,and standardized management of sur-gical-related nosocomial infections.

Objective To explore the value of analysis on the incidence of healthcare-associated infection(HAI)based on disease diagnosis-related grouping(DRG),case mix index(CMI),and relative weight(RW).Methods All discharged cases,DRG and HAI status in a tertiary first-class general hospital from January 1 to December 31,2023 were analyzed retrospectively.Incidences of HAI in different departments were adjusted and compared by CMI.Incidences of HAI in different DRG groups were adjusted by RW.Results Among the 47 695 cases included in the analysis,757 were HAI cases,including 225 DRG groups.The department of critical care medicine had the highest incidence of HAI(11.98％).After CMI adjustment,departments with higher incidence of HAI were main-ly the department of respiratory and critical care medicine(3.96％),department of critical care medicine(3.04％),and department of neurology(2.85％),et al.DRG groups with the top five high incidence of HAI were AH11(tracheotomy and with ventilator support ≥96 hours or extracorporeal membrane oxygenation[ECMO],accompa-nied by major complications and comorbidity[MCC],50.00％),BC29(ventricular shunt and revision surgery,31.43％),BB21(craniotomy other than trauma,accompanied by MCC,27.56％),BB11(craniotomy of brain trauma,accompanied by MCC,26.32％),and GB1A(major surgery of esophagus,stomach,and duodenum,accompanied by major or moderate complications and comorbidity,16.00％).After RW adjustment,the DRG groups with the top five high incidence of HAI were ES21(respiratory system infection/inflammation,accompanied by MCC,5.89％),BR21(cerebral ischemic disease,accompanied by MCC,5.17％),FR11(heart failure,shock,accompanied by MCC,4.80％),BC29(4.57％)and AH11(3.57％).Conclusion Analyzing the incidence of HAI based on CMI and RW can help to identify key departments and disease groups for infection prevention and control,and provide reference for precise prevention and control of HAI in the new era.

Objective:To construct a biosafety evaluation index system for major emergency public health events in medical institutions.Methods:Based on previous laboratory biosafety evaluation work, relevant regulations and standards on biosafety in China were collected through literature research and expert consultations. Candidate indicators for constructing the biosafety evaluation system for major emergency public health events in medical institutions were selected, and a framework was established. Two rounds of expert questionnaires were conducted to determine the content of the index system based on experts′ evaluation, and each indicator′s relevance and importance were scored. Finally, two rounds of Delphi consultations were carried out, and the Analytic Hierarchy Process (AHP) was applied to calculate the weights of indicators.Results:The response rates for the total four rounds of questionnaire surveys were all 100%. The first two rounds focused on determining the framework, while the latter two focused on determining the weights for each indicator. The authority coefficients of the expert consultations for the two rounds of weights were 0.65 and 0.70, respectively, indicating the reliability of the research results. In the final round of survey, the Kendall′s coefficients of concordance at each level were all greater than 0.1. Through statistical testing, the P-values were all less than 0.05, indicating good coordination of expert opinions. Ultimately, we established an operational biosafety evaluation system for major emergency public health events in medical institutions, consisting of 4 primary indicators, 26 secondary indicators, and 119 tertiary indicators, with additional deduction items, bonus items, unacceptable items, and monitoring indicators.Conclusions:Based on scientific theory, a biosafety evaluation system for major emergency public health events in medical institutions was constructed, achieving the integration of routine and emergency measures. This system can be used for self-assessment of laboratory biosafety during emergency public health events, addressing the lack of unified standards in biosafety evaluation. Through regular self-assessment, it can enhance the level of biosafety management in medical institution laboratories, to realize the value of application and dissemination.

With the rise of digital healthcare in recent years, digital tools, as a new type of health management tool, are expected to become a feasible tool for rehabilitation exercise in stroke patients. The aim of this article is to review the current status of the application of digital tools in self-management of stroke recovery. In addition, the concept, function and application effect of digital tools are introduced, and the existing problems and future research directions are pointed out, in order to provide reference for the self-management of stroke patients in China.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective To explore the value of analysis on the incidence of healthcare-associated infection(HAI)based on disease diagnosis-related grouping(DRG),case mix index(CMI),and relative weight(RW).Methods All discharged cases,DRG and HAI status in a tertiary first-class general hospital from January 1 to December 31,2023 were analyzed retrospectively.Incidences of HAI in different departments were adjusted and compared by CMI.Incidences of HAI in different DRG groups were adjusted by RW.Results Among the 47 695 cases included in the analysis,757 were HAI cases,including 225 DRG groups.The department of critical care medicine had the highest incidence of HAI(11.98％).After CMI adjustment,departments with higher incidence of HAI were main-ly the department of respiratory and critical care medicine(3.96％),department of critical care medicine(3.04％),and department of neurology(2.85％),et al.DRG groups with the top five high incidence of HAI were AH11(tracheotomy and with ventilator support ≥96 hours or extracorporeal membrane oxygenation[ECMO],accompa-nied by major complications and comorbidity[MCC],50.00％),BC29(ventricular shunt and revision surgery,31.43％),BB21(craniotomy other than trauma,accompanied by MCC,27.56％),BB11(craniotomy of brain trauma,accompanied by MCC,26.32％),and GB1A(major surgery of esophagus,stomach,and duodenum,accompanied by major or moderate complications and comorbidity,16.00％).After RW adjustment,the DRG groups with the top five high incidence of HAI were ES21(respiratory system infection/inflammation,accompanied by MCC,5.89％),BR21(cerebral ischemic disease,accompanied by MCC,5.17％),FR11(heart failure,shock,accompanied by MCC,4.80％),BC29(4.57％)and AH11(3.57％).Conclusion Analyzing the incidence of HAI based on CMI and RW can help to identify key departments and disease groups for infection prevention and control,and provide reference for precise prevention and control of HAI in the new era.

Objective To construct a risk prediction model for post-stroke depression based on the neural network algorithm.Methods A prospective study was conducted on 260 stroke patients admitted in our center from March 2021 to March 2024.They were randomly divided into a train-ing set(80％,208 cases)and a verification set(20％,52 cases).According to the occurrence of post-stroke depression within 1 month after stroke,the training set was assigned into post-stroke depression group(62 cases)and non-post-stroke depression group(146 cases).The predictive fac-tors for post-stroke depression occurrence were screened through the training set,and the risk prediction models for post-stroke depression occurrence were constructed with multivariate logis-tic and neural network algorithms in the training set.The prediction efficiency of the two predic-tion models was compared and verified in the verification set.Results Within 1 month after stroke,76 cases(29.23％)experienced post-stroke depression(62 cases in training set and 14 in the validation set).Based on the data in the training set,the levels of CRP,FIB,IL-6,IL-lβ,TNF-αand IL-18,and neutrophil and lymphocyte ratio(NLR)were significant higher in the post-stroke depression group than the non-post-stroke depression group(P＜0.01).Multivariate logistic re-gression analysis showed that CRP(OR=1.494,95％CI:1.239-1.802),FIB(OR=1.924,95％CI:1.191-3.109),IL-6(OR=1.128,95％CI:1.001-1.272),TNF-α(OR=1.051,95％CI:1.010-1.093),IL-1β(OR=1.096,95％CI:1.006-1.194),IL-18(OR=1.019,95％CI:1.002-1.036),and NLR(OR=1.873,95％CI:1.027-3.418)were risk factors for post-stroke depression(P＜0.05,P＜0.01).ROC curve analysis indicated that the AUC value of the predictive model of the neural network algorithm was higher than that of the model of multivariate logistic regression(0.931 vs 0.855,Z=3.448,P＜0.05).Based on the validation set,the former model also had bet-ter accuracy than the latter one(92.31％vs 75.00％,P＜0.05).Conclusion Circulating inflam-matory factors CRP,FIB,IL-6,IL-1β,TNF-α and IL-18,and NLR are related to the risk of post-stroke depression.The prediction model based on above factors combined with neural network al-gorithm can more effectively predict the risk of post-stroke depression.