ObjectiveTo establish a regional risk assessment system for hospital-acquired infections in neurosurgery department of general hospital, and to evaluate its prevention and control effectiveness. MethodsFailure mode and effect analysis (FMEA) was used to identify the core risk factors for infections in neurosurgery department. The risk priority number (RPN) of each risk factor was calculated to determine the priority intervention targets. Targeted interventions were developed and continuously refined through the plan-do-check-act (PDCA) cycles. Data from January to June 2023 (control group) and July to December 2023 (intervention group) were collected to compare the differences in environmental hygiene monitoring qualification rate, incidence rate of hospital-acquired infections among inpatients, and detection rate of bacterial antimicrobial resistance. ResultsHigh-risk factors for hospital-acquired infections in neurosurgery department included patient-related risk factors, inadequate implementation of isolation measures for special infections, and poor compliance with surgical site infection (SSI) prevention protocols. After intervention, the environmental hygiene qualification rate significantly increased from 81.55% to 100.00% (χ²=120.49, P<0.001). The overall hospital-acquired infection rate among inpatients decreased from 2.62% to 2.45%, the infection rate of per case declined from 3.12% to 2.84%, and the detection rate of multidrug-resistant organism infections reduced from 43.72% to 36.79%. Additionally, antimicrobial utilization rate decreased from 48.75% to 42.53% (χ²=34.09, P<0.001). ConclusionThe FMEA-based risk assessment system can effectively identify critical infection risks in neurosurgery department, and targeted interventions can significantly improve infection prevention and control performance.

ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

Objective To investigate the relationship between serum levels of interferon-inducible protein 10(IP-10)and S100 calcium-binding protein A8(S100A8)and the prognosis of patients with severe pneumo-nia(SP).Methods A total of 315 SP patients admitted to the hospital from January 2021 to December 2023 were selected as the SP group,and 315 non-SP patients admitted to the hospital during the same period were selected as the non-SP group.According to the prognosis of SP patients 28 d after admission,they were divided into good prognosis group(199 cases)and poor prognosis group(116 cases).Enzyme-linked immunosorbent assay was used to detect serum IP-10 and S100A8 levels in SP patients.Pearson correlation analysis was used to analyze the correlation between IP-10,S100A8 levels and infection indicators[high-sensitivity C-reactive protein(hs-CRP),procalcitonin(PCT),interleukin-6(IL-6)].Multivariate Logistic regression was used to analyze the influencing factors of poor prognosis in SP patients.Receiver operating characteristic(ROC)curve was used to analyze the value of hs-CRP,PCT,IP-10 and S100A8 levels in predicting the poor prognosis of SP patients.Results The serum levels of IP-10,S100A8,hs-CRP,PCT and IL-6 in SP group were higher than those in non-SP group(P＜0.05).Paerson correlation analysis showed that serum IP-10 level in SP group was positively correlated with hs-CRP,PCT and IL-6(r=0.744,0.757,0.740,P＜0.05).S100A8 level was posi-tively correlated with hs-CRP,PCT and IL-6(r=0.769,0.718,0.744,P＜0.05).There were significant differences in oxygenation index,acute physiology and chronic health evaluation Ⅱ score,clinical pulmonary in-fection score and levels of hs-CRP,PCT,IP-10 and S100A8 between the poor prognosis group and the good prognosis group(P＜0.05).Multivariate Logistic regression analysis showed that hs-CRP,PCT,IP-10 and S100A8 were risk factors for poor prognosis in SP patients,and oxygenation index was a protective factor(P＜0.05).The ROC curve showed that the area under curve(AUC)of the combination of hs-CRP,PCT,IP-10 and S100A8 in predicting poor prognosis of SP patients was 0.965,which was greater than the AUC of the four alone(Zhs-CRP=4.009,ZPCT=4.022,ZIP-10=3.696,ZS100A8=3.309,P＜0.05).Conclusion The serum lev-els of IP-10 and S100A8 are up-regulated in SP patients.The combination of IP-10 and S100A8 has a high pre-dictive value for poor prognosis in SP patients.

Objective:To evaluate the effectiveness of anti-G straining maneuver (AGSM) in pilots by surface electromyography (sEMG), and to explore the relationships between characteristics of sEMG and anti-G endurance in pilots.Methods:Thirty-eight male high-performance fighter pilots who completed the human centrifuge test at the Air Force Medical Center were selected. Among them, 25 completed the 8.0 G for 10 s anti-G endurance test and 13 completed the 9.0 G for 10 s anti-G endurance test. The sEMG tester was used to keep track of the changes of sEMG in rectus abdominis, rectus femoris, anterior tibial and gastrocnemius muscles while pilots were engaged in AGSM. The anti-G endurance was evaluated according to the changes of visual fields and consciousness. The pilots were divided into 3 groups: the good vision and consciousness group, peripheral visual field narrowing group and endurance endpoint group. The differences in the integral electromyogram (iEMG), mean power frequency (MPF) and muscle input rates between the 3 groups were investigated.Results:A total of 25 pilots completed the 8.0 G for 10 s anti-G endurance test. Among them, 8 (32.0%) were in the good vision and consciousness group, 13 (52.0%) in the peripheral visual field narrowing group and 4 (16.0%) reached the endurance endpoint. Among the 13 pilots who completed the 9.0 G for 10 s anti-G endurance test, 3 (23.1%) were in the good vision and consciousness group, 6 (46.1%) in the peripheral visual field narrowing group, and 4 (30.8%) in the endurance endpoint group. The results of sEMG showed that the iEMG values of the anterior tibialis muscle in pilots under the 9.0 G for 10 s load were significantly different across endurance groups ( H=7.54, P=0.023), and that the iEMG values of the tibialis anterior muscle in the good vision and consciousness group were higher than those in the endurance endpoint group ( P=0.036). The negative slopes of MPF for the rectus abdominis, rectus femoris, anterior tibialis, and gastrocnemius muscles were higher in the good vision and consciousness group than in the other 2 groups, but the differences were not statistically significant ( P>0.05). During the 8.0 G for 10 s anti-G endurance test, there were significant differences in lower limb muscle contribution rates between the 3 groups ( F=4.19, P=0.029). The endurance endpoint group exhibited a lower contribution rate than the good vision and consciousness group ( P=0.025). During the 9.0 G for 10 s anti-G endurance test, there were significant differences in tibialis anterior muscle contribution rates between the 3 groups ( F=4.16, P=0.049). The endurance endpoint group demonstrated a lower contribution rate than the good vision and consciousness group ( P=0.049). Conclusions:The full and balanced activation of abdominal muscles and lower limb muscles, especially the effective mobilization of calf muscles, plays a pivotal role in improving pilots′ AGSM efficiency in high G environments.

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.

The human gravity line(GL)is a virtual vertical line that passes through the centre of gravity(COG)of the human body and holds significant importance in assessing human biomechanics.Due to the inability to directly determine GL through imaging method,its application in past research and clinical practice has been somewhat limited.However,with the advancement of technology,the measurement and application of GL have made remarkable progress,becoming an essential supplement to traditional radiographic measurements and demonstrating an increasingly broad application prospect in the field of spinal health.This review summarizes the GL's anatomical basis,measurement method,and its relationship with other commonly used vertical lines.Furthermore,the current status of GL's clinical application in the field of spinal health is also summmrzied,and its potential role in the diagnosis and treatment of spinal diseases is discussed.It is hoped that the findings will offer new perspectives for the diagnosis and treatment of spinal diseases,and promote further research and application of GL in the field of spinal medicine.

Objective:To explore the relationship between general demographic characteristics,inflammatory indicators,nutritional indicators,pathological data and lymph node metastasis in endometrial cancer(EC)pa-tients,and to construct and validate a model for preoperative prediction of lymph node status in endometrial canc-er patients.Methods:The preoperative clinical data of 473 patients with EC who underwent surgical treatment in the Zhu Jiang Hospital of Southern Medical University from January 2010 to April 2024 were retrospectively ana-lyzed.The independent risk factors of lymph node metastasis of endometrial cancer were screened by univariate and multivariate Logistic regression analyses,and the nomogram prediction model was constructed by R soft-ware.The performance of the model was evaluated by the receiver operating characteristic(ROC)curve,calibra-tion curve and clinical decision curve.Results:Menopausal status,high grade biopsy pathology,CA125 ≥24.47U/ml,systemic immune inflammatory index(SII)≥710.91,and prognostic nutritional index(PNI)＜52.90 were in-dependent risk factors for lymph node metastasis in endometrial cancer(OR＞1,P＜0.05).The nomogram model constructed based on these five factors had an AUC of 0.853 in the training set and 0.871 in the test set.The cali-bration curve fitted well,and the clinical decision curve shows a positive benefit.Conclusions:The endometrial cancer lymph node metastasis prediction model constructed based on menopausal status,biopsy pathology,CA125,SII,and PNI has good accuracy and fit,with certain clinical application value.

Objective To investigate the influencing factors of postoperative complications with Clavien-Dindo grade ≥ Ⅲ in elderly patients with esophageal cancer and construct a line graph prediction model.Methods 376 esophageal cancer patients who underwent radical esophagectomy in our hospital from January 2021 to December 2022 were selected as the study subjects.According to the Claven Mindo surgical complication grading system,the groups were divided into two groups:Clavien Mindo grading Grade Ⅲ group(93 cases)and Clavien Mindo grading＜Grade Ⅲ group(283 cases).Univariate analysis and multifactor Logistic regression were used to analyze the influencing factors of postoperative complications of esophageal cancer with Clavien-Dindo grade ≥ Ⅲ,and establish a risk prediction column chart model using R 3.5.1 software;The discrimination and calibration of the area verification model under the ROC curve;Use calibration curves and Hosmer Lemeshoe goodness of fit tests to evaluate the fit of the column chart model.Results 376 patients with esophageal cancer,93 patients with Clavien-Dindo grade ≥ Ⅲ had complications,accounting for 24.73％.The results of univariate analysis showed that chronic respiratory disease,intraoperative blood loss,surgical time,ASA grading,prognostic nutritional index,and Charlson comorbidities index were significantly correlated with postoperative complications in elderly esophageal cancer patients with Clavien Dundo grading ≥ grade Ⅲ(P＜0.05).The results of the logistic multiple regression model showed that intraoperative bleeding volume,surgical time,ASA grading,prognostic nutritional index,and Charlson Comorbidity Index were independent risk factors for postoperative complications in elderly esophageal cancer patients with Clavien Dundo grading≥grade Ⅲ(all P＜0.05),Based on five variables,a nomogram prediction model for postoperative complications in elderly patients with esophageal cancer with Clavien-Dindo grade ≥ Ⅲ was constructed.ROC curve analysis verified that the area under ROC curve was 0.835(0.794～0.876),indicating a good differentiation of the model.The calibration curve is drawn.The slope of the calibration curve is close to 1,and the predicted probability is basically consistent with the actual probability.Hosmer-Lemeshow goodness of fit test,x2=5.737,df=8,P=0.676.The analysis results of decision curve show that the threshold probability is less than 0.8 and the net benefit rate is greater than 0.Conclusion The prediction model based on 5 independent risks can reliably predict the probability of postoperative complications with Clavien-Dindo grade ≥ Ⅲ,which is helpful to provide targeted preventive measures for high-risk patients and reduce the occurrence of complications with Clavien-Dindo grade≥Ⅲ.

Objective:To evaluate the effectiveness of anti-G straining maneuver (AGSM) in pilots by surface electromyography (sEMG), and to explore the relationships between characteristics of sEMG and anti-G endurance in pilots.Methods:Thirty-eight male high-performance fighter pilots who completed the human centrifuge test at the Air Force Medical Center were selected. Among them, 25 completed the 8.0 G for 10 s anti-G endurance test and 13 completed the 9.0 G for 10 s anti-G endurance test. The sEMG tester was used to keep track of the changes of sEMG in rectus abdominis, rectus femoris, anterior tibial and gastrocnemius muscles while pilots were engaged in AGSM. The anti-G endurance was evaluated according to the changes of visual fields and consciousness. The pilots were divided into 3 groups: the good vision and consciousness group, peripheral visual field narrowing group and endurance endpoint group. The differences in the integral electromyogram (iEMG), mean power frequency (MPF) and muscle input rates between the 3 groups were investigated.Results:A total of 25 pilots completed the 8.0 G for 10 s anti-G endurance test. Among them, 8 (32.0%) were in the good vision and consciousness group, 13 (52.0%) in the peripheral visual field narrowing group and 4 (16.0%) reached the endurance endpoint. Among the 13 pilots who completed the 9.0 G for 10 s anti-G endurance test, 3 (23.1%) were in the good vision and consciousness group, 6 (46.1%) in the peripheral visual field narrowing group, and 4 (30.8%) in the endurance endpoint group. The results of sEMG showed that the iEMG values of the anterior tibialis muscle in pilots under the 9.0 G for 10 s load were significantly different across endurance groups ( H=7.54, P=0.023), and that the iEMG values of the tibialis anterior muscle in the good vision and consciousness group were higher than those in the endurance endpoint group ( P=0.036). The negative slopes of MPF for the rectus abdominis, rectus femoris, anterior tibialis, and gastrocnemius muscles were higher in the good vision and consciousness group than in the other 2 groups, but the differences were not statistically significant ( P>0.05). During the 8.0 G for 10 s anti-G endurance test, there were significant differences in lower limb muscle contribution rates between the 3 groups ( F=4.19, P=0.029). The endurance endpoint group exhibited a lower contribution rate than the good vision and consciousness group ( P=0.025). During the 9.0 G for 10 s anti-G endurance test, there were significant differences in tibialis anterior muscle contribution rates between the 3 groups ( F=4.16, P=0.049). The endurance endpoint group demonstrated a lower contribution rate than the good vision and consciousness group ( P=0.049). Conclusions:The full and balanced activation of abdominal muscles and lower limb muscles, especially the effective mobilization of calf muscles, plays a pivotal role in improving pilots′ AGSM efficiency in high G environments.

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.