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 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.

With the development of clinical related disciplines, the update and establishment of relevant standards/guidelines at home and abroad, GBZ 185-2006 Diagnostic Criteria for Occupational Medicamentose-like Dermatitis due to Trichloroethylene (hereinafter referred to as “GBZ 185-2006”) was unable to meet clinical needs. Therefore, the GBZ 185-2006 was revised based on the principles of evidence-based medicine, in accordance with relevant laws/regulations and relevant standards/guidelines in combination with review of research data on occupational medicamentose-like dermatitis due to trichloroethylene (OMDT) home and abroad, and the development of clinical practice and clinical related disciplines. The main modifications include: adding terms and definitions of OMDT, modifying the description of clinical manifestations of the diagnostic principles, adjusting the description of latency, deleting the diagnostic requirement of the incidence probability, adding the specific allergen patch test as the etiological diagnostic index, standardizing the application scope, operating procedure and precautions of the specific allergen patch test. In addition, the relevant content of “Basic Characteristics and Clinical Types of Skin Damage of Medicamentose-like Dermatitis due to Trichloroethylene” in Appendix A is improved, the treatment principles are revised, and the content of new progress in treatment, artificial liver application, are added. The revised GBZ 185-2024 Diagnostic Standard for Occupational Medicamentose-like Dermatitis due to Trichloroethylene is more scientific and practical, and can provide technical basis for the standardized diagnosis and treatment of OMDT in medical and health institutions.

The rehabilitation compliance of stroke patients is generally low.Evaluating the rehabilitation motivation of patients is helpful to promote the rehabilitation management of patients,enhance the rehabilitation enthusiasm and compliance of patients,and improve the rehabilitation outcome.This paper reviews the existing stroke patients rehabilitation motivation assessment tools,and expounds the main contents,application status,characteristics and limitations of stroke patients rehabilitation motivation assessment tools,in order to provide references for the appropriate selection of clinical assessment tools,the rehabilitation management of stroke patients and the development of domestic localized stroke rehabilitation motivation assessment tools.

Objective To investigate the correlation of serum calmodulin(CaM)level with the con-dition and prognosis of patients with severe traumatic brain injury(sTBI).Methods A total of 208 elderly sTBI patients admitted to the Geriatric Hospital and Puren Hospital Affiliated to Wuhan University of Science and Technology from January 2021 to June 2023 were enrolled,and after 3 months'follow-up,192 cases were finally included in this study.According to their progno-sis,they were divided into poor prognosis group(n=56)and good prognosis group(n=136).Their general data were collected,and serum CaM level was measured by ELISA.Pearson correla-tion analysis was used to study the correlation between the level and the disease condition.Multi-variate logistic regression model was employed to screen the prognosis related factors of sTBI pa-tients,and restricted cubic spline was applied to fit the relationship between serum CaM level and prognosis of sTBI patients.Results Midline shift,significantly lower CaM level and acute physi-ology and chronic health evaluation Ⅱ(APACHE Ⅱ)score,and obviously higher Glasgow Coma Scale(GCS)score and albumin level were observed in the good prognosis group than the poor prognosis group(P＜0.05,P＜0.01).Pearson correlation analysis showed that serum CaM level was negatively correlated with condition of sTBI(r=-0.804,P＜0.05).Multivariate logistic analysis indicated that APACHE Ⅱ score(OR=1.248,95％CI:1.076-1.447,P=0.003)and ser-um CaM level(OR=1.030,95％CI:1.017-1.044,P=0.001)were risk factors for prognosis,while,GCS score(OR=0.730,95％CI:0.536-0.994,P=0.045)and serum albumin level(OR=0.730,95％CI:0.649-0.822,P=0.001)were protective factors for poor prognosis in sTBI pa-tients.Restricted cubic spline revealed that there was a linear dose-response relationship between serum CaM level and prognosis of sTBI patients(X2=27.080,P＜0.01).Conclusion Serum CaM level is correlated with sTBI disease and prognosis.

Objective To evaluate the diaphragm movement index of mechanical ventilation weaning patients by ultrosonography, and to explore its value for weaning. Methods Forty patients undergoing invasive mechanical ventilation for at least 48 hours admitted to emergency intensive care unit (EICU) of Hunan Provincial People's Hospital from September 2017 to February 2018 were enrolled. Low level pressure support ventilation (PSV) was used for spontaneous breathing test (SBT), and bedside M-mode ultrasonography was used to assess the diaphragm movement index of the patient within 1 hour of SBT, including the excursion of the diaphragm, diaphragmatic-rapid shallow breathing index (D-RSBI). The rapid shallow breathing index (RSBI) was measured by ventilator. The patients who met the clinical weaning criteria were weaned. According to the success or failure of the weaning, the patients were divided into the successful weaning group and the failure weaning group. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of each indicator to the failure of the weaning. Results A total of 40 patients were enrolled in the final analysis, including 28 patients in the successful weaning group and 12 patients in the failure weaning group. The excursion of the diaphragm in the failure weaning group was significantly less than that in the successful weaning group (mm: 9.56±2.13 vs. 13.66±4.10, P < 0.01), and the D-RSBI and RSBI were significantly higher than those in the successful weaning group [D-RSBI (times·min-1·mm-1): 2.06±0.68 vs. 1.44±0.66, RSBI (times·min-1·L-1): 61.70±25.00 vs. 44.91±14.51, both P < 0.05]. The area under the ROC curve (AUC) of diaphragm displacement, D-RSBI, and RSBI was 0.830, 0.851 and 0.711, respectively, and the predicted value of diaphragm excursion and D-RSBI was higher. When the optimal critical value of diaphragmatic excursion was 11.15 mm, the sensitivity of predicting weaning failure was 83.3%, the specificity was 71.4%; when the optimal critical value of D-RSBI was 1.42 times·min-1·mm-1, the sensitivity of predicting the failure of weaning was 91.7%, and the specificity was 82.1%. Conclusion Diaphragm excursion and D-RSBI of the diaphragmatic ultrosonography index could accurately predict the failure of the weaning, which was superior to the traditional RSBI in guiding weaning.

Objective To investigate the distribution and changes of microorganisms on the skin of submariners under a chronically closed environment , and provide reference for targeted medical support .Methods One hundred and twenty-two samples were collected using swab.After culture and isolation, the microbes were identified by matrix-assisted laser desorption/ionization time of flight mass spectrometry ( MALDI-TOF-MS) .Results A total of 52 types of 229 bacteria and 2 types of fungi were isolated . Major opportunistic pathogens included Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter cloacae, while major dwelling bacteria included Micrococcus luteus, Oslo Mora bacteria, Acinetobacter, Staphylococcus epidermidis, and Serratia marcescens.Compared with the early period of the task, major opportunistic pathogens and dwelling bacteria were significantly increased in the middle and late period of the task .Conclusion The skin microbes of the submariners are investigated , targeted drugs need to be prepared for daily medical support and war trauma .

Objective To establish a homothermal and fast detecting method on pathogenic bacteria by combining recombinase-aid amplification (RAA) with molecular beacon.Methods The establishment of the methodology.Staphylococcus aureus specific primers were designed from the relative region of the staphylococcal protein A (SPA).Asymmetry amplification was optimized by adjusting the primer concentration ratios.The results of amplification and hybridization were visualized and analyzed by agarose gel electrophoresis and fluorescence detection.The sensitivity was identified by detecting dilute positive plasmids.And the specificity was determined using RAA method by detecting 72 pathogenic bacteria,including Staphylococcus aureus and other Staphylococcus spp.from the Department of Clinical Laboratory of Daping Hospital in December 2016.Besides,the Kappa analysis and the clinical diagnosis efficiency were investigated by analyzing 39 extra strains in the laboratory in December 2016.Results When the concentration ratio of restrictive and non-restrictive primer was 1:20,the yield efficiency of single-stranded DNA (ssDNA) reached the peak.And as for the hybridization efficiency,the asymmetry amplification was higher than symmetry amplification.Twenty copies/μl was proposed as the limits of detection by testing dilute plasmids.And the RAA hybridization method could distinguish Staphylococcus aureus with other Staphylococcus spp.Comparing with traditional detection methods with a Kappa index of 0.860,this method shows a good consistency.By analyzing the 111 bacteria,the sensitivity of the method is 92.5％ (37/40),the specificity is 97.2％ (69/71),the positive predictive value is 94.9％ (37/39),the negative predictive value is 95.8％ (69/72),the positive likelihood radio is 33.04,the negative likelihood radio is 0.077,the Youden index is 0.897 and the Kappa index is 0.902.Conclusion Through the combination of asymmetry recombinase-aid amplification optimization and molecular beacon probe,a new method of detecting bacteria DNA with RAA hybridization technique is established,providing the foundation for its clinical application.