Objective To explore the correlation between blood uric acid/HDL-C ratio(UHR)and peripheral neuropathy(DPN)in T2DM.Methods A total of 127 T2DM patients admitted to the Endocrinology Department of Wujin Traditional Chinese Medicine Hospital in Changzhou City from August 2022 to August 2023 were selected.They were divided into a simple T2DM group(n=62)and a combined DPN group(DPN,n=65)based on whether or not they had DPN.Compare two groups of general information,biochemical indicators,and UHR.Results Compared with the T2DM group,DPN group DM course of disease,HbA1c,FPG,FIns,HOMA-IR,TG,vibration sensation threshold(VPT),hypersensitive C-reactive protein(hs-CRP),blood uric acid(SUA),and UHR(P＜0.05),HDL-C,tibial nerve motor nerve conduction velocity(mNCV),and superficial peroneal nerve sensory nerve conduction velocity (sNCV)decreased(P＜0. 05). Spearman correlation analysis showed that UHR was positively DM duration of disease,HbA1c,FPG,HOMA?IR,TG,VPT,hs?CRP,and SUA(P＜0. 05),negatively correlated with mNCV,sNCV,and HDL?C(P＜0. 05). Logistic regression analysis showed that UHR,DM duration, hs?CRP,and HbA1c were the influencing factors of DPN. Conclusion Elevated UHR is a influencing factor for the occurrence of DPN in T2DM patients and has good predictive value for DPN.

Objective:To investigate the changes of abnormal spontaneous brain activity and whole-brain effector connectivity in patients with obsessive-compulsive disorder (OCD) by combining low frequency amplitude (ALFF) and Granger causality analysis (GCA), and explore their relations with clinical symptoms.Methods:Forty-nine patients with OCD admitted to Department of Psychiatry, Second Affiliated Hospital of Xinxiang Medical College from January 2020 to September 2023 were selected as OCD group; 50 healthy volunteers matched with gender, age and years of education were enrolled as healthy control (HC) group. Obsessive-compulsive symptoms and severities in the OCD group were assessed by Yale Brown obsessive-compulsive scale (Y-BOCS). All subjects underwent whole-brain resting-state functional magnetic resonance imaging scanning (rs-fMRI). ALFF differences between the 2 groups were compared. Brain regions with ALFF differences were used as seed points, and effector connectivity changes in seed points were compared with those in whole-brain by GCA. Correlations of ALFF and effector connectivity in brain regions with ALFF differences with total scores, obsession scores and compulsion scores of Y-BOCS were analyzed by partial correlation analysis.Results:(1) Compared with that in the HC group, ALFF was significantly enhanced in the right supplementary motor area, right hippocampus, left caudate nucleus, and right fusiform gyrus, and statistically attenuated in the left suboccipital gyrus in the OCD group ( P<0.05). (2) Compared with that in the HC group, effector connectivity from the right dorsolateral superior frontal gyrus to right supplementary motor area was significantly attenuated, and effector connectivity from the left superior occipital gyrus to right supplementary motor area was significantly enhanced in the OCD group ( P<0.05); compared with that in the HC group, effector connectivity from the right fusiform gyrus to right precentral gyrus was significantly attenuated, and effector connectivity from the right hippocampus to left mesial temporal gyrus was significantly enhanced in the OCD group ( P<0.05). (3) In OCD patients, altered ALFF in the left caudate nucleus was positively correlated with obsession scores ( r=0.357, P=0.027), and altered effector connectivity from the right dorsolateral superior frontal gyrus to right supplementary motor area was negatively correlated with obsession scores ( r=-0.312, P=0.029). Conclusion:Abnormalities in sensorimotor network function are closely related to clinical symptoms in patients with OCD.

Objective To analyze the correlation of copper death inducer ferredoxin 1(FDX1)and lipoic acid(LA)with the occurrence and severity of coronary atherosclerosis and explore their roles in coronary heart disease(CHD).Methods We analyzed the data of 226 patients undergoing coronary artery angiography(CAG)in our hospital between October,2021 and October,2022,including 47 patients with normal CAG findings(control group)and 179 patients with mild,moderate or severe coronary artery stenosis(CHD group).Serum FDX1 and LA levels were determined with ELISA for all the patients.We also examined pathological changes in the aorta of normal and ApoE-/-mice using HE staining and observed collagen fiber deposition with Sirius red staining.Immunohistochemistry was used to detect the expression and distribution of FDX1 and LA in the aorta,and RT-PCR was performed to detect the expressions of FDX1,LIAS and ACO2 mRNAs in the myocardial tissues.Results Compared with the control patients,CHD patients had significantly lower serum FDX1 and LA levels,which decreased progressively as coronary artery stenosis worsened(P<0.01)and as the number of involved coronary artery branches increased(P<0.05).Serum FDX1 and LA levels were positively correlated(r=0.451,P<0.01)and they both negatively correlated with the Gensini score(r=-0.241 and-0.273,respectively;P<0.01).Compared with normal mice,ApoE-/-mice showed significantly increased lipid levels(P<0.01)and atherosclerosis index,obvious thickening,lipid aggregation,and collagen fiber hyperplasia in the aorta,and significantly reduced expressions of FDX1,LA,LIAS,and ACO2(P<0.05).Conclusion Serum FDX1 and LA levels decrease with worsening of coronary artery lesions,and theirs expressions are correlated with coronary artery lesions induced by hyperlipidemia.

Objective To analyze the correlation of copper death inducer ferredoxin 1(FDX1)and lipoic acid(LA)with the occurrence and severity of coronary atherosclerosis and explore their roles in coronary heart disease(CHD).Methods We analyzed the data of 226 patients undergoing coronary artery angiography(CAG)in our hospital between October,2021 and October,2022,including 47 patients with normal CAG findings(control group)and 179 patients with mild,moderate or severe coronary artery stenosis(CHD group).Serum FDX1 and LA levels were determined with ELISA for all the patients.We also examined pathological changes in the aorta of normal and ApoE-/-mice using HE staining and observed collagen fiber deposition with Sirius red staining.Immunohistochemistry was used to detect the expression and distribution of FDX1 and LA in the aorta,and RT-PCR was performed to detect the expressions of FDX1,LIAS and ACO2 mRNAs in the myocardial tissues.Results Compared with the control patients,CHD patients had significantly lower serum FDX1 and LA levels,which decreased progressively as coronary artery stenosis worsened(P<0.01)and as the number of involved coronary artery branches increased(P<0.05).Serum FDX1 and LA levels were positively correlated(r=0.451,P<0.01)and they both negatively correlated with the Gensini score(r=-0.241 and-0.273,respectively;P<0.01).Compared with normal mice,ApoE-/-mice showed significantly increased lipid levels(P<0.01)and atherosclerosis index,obvious thickening,lipid aggregation,and collagen fiber hyperplasia in the aorta,and significantly reduced expressions of FDX1,LA,LIAS,and ACO2(P<0.05).Conclusion Serum FDX1 and LA levels decrease with worsening of coronary artery lesions,and theirs expressions are correlated with coronary artery lesions induced by hyperlipidemia.

Fatigue is one of the common symptoms in patients with liver cirrhosis, which has a serious impact on the quality of life of patients. This article reviews the influencing factors and intervention strategies of fatigue in patients with cirrhosis, aiming to provide reference for early recognition and intervention of fatigue in patients with cirrhosis.

Objective:To analyze brain topological property data through machine learning methods and explore changes in brain network topological properties in patients with schizophrenia.Methods:From January 2022 to August 2023, functional magnetic resonance imaging data of 60 patients with schizophrenia and 56 healthy controls were collected , and the data were preprocessed to construct brain functional networks and extract global and nodal topological properties. All subjects were divided into a training group and a testing group.The data of training group were fitted based on support vector machine, and the predictive performance was evaluated through cross-validation.The model was optimized by recursive feature elimination algorithm, then the indicators that contributed the most to predictive performance were extrated.The classification performance of the testing group was calculated based on the trained model with optimal predictive performance.SPSS 20.0 software was used for data analysis, the independent t-test and χ2 test were used for comparing the differences between the two groups. Results:The support vector machine achieved an accuracy of 75.00% in predicting the test group of schizophrenia patients based on all indicators. After removing redundant features and combining with the recursive feature elimination algorithm, the accuracy of the SVM model in predicting the test group increased to 90.00%. The nodal global efficiency(Ne)of the left superior temporal gyrus, right dorsal agranular insula, bilateral dorsal granular insula, bilateral caudal cingulate gyrus, and left lateral orbitofrontal cortex in the model contributed the most to classification.Compared to the control group, patients with schizophrenia had abnormal Ne values in these brain regions.Conclusion:There are multiple brain regions with abnormal Ne values in patients with schizophrenia, indicating that the abnormalities in information integration and transmission functions may be related to the imbalance in the dynamic equilibrium of the patients' brain networks.

Objective To observe the value of 3D Res2Net deep learning model for predicting volume doubling time(VDT)of solid pulmonary nodule.Methods Chest CT data of 734 patients with solid pulmonary nodules were retrospectively analyzed.The patients were divided into progressive group(n=218)and non-progressive group(n=516)according to whether lung nodule volume increased by ≥25％during follow-up or not,also assigned into training set(n=515)and validation set(n=219)at a ratio of 7∶3.Then a clinical model was constructed based on clinical factors being significantly different between groups,CT features model was constructed based on features of nodules on 2D CT images using convolutional neural network,and 3D Res2Net model was constructed based on Res2Net network using 3D CT images as input.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated.Taken actual VDT as gold standard,the efficacy of the above models for predicting solid pulmonary nodule'VDT≤400 days were evaluated.Results No significant difference of predicting efficacy for solid pulmonary nodule'VDT≤400 days was found among clinical model,CT feature model and 3D Res2Net model,the AUC of which was 0.689,0.698 and 0.734 in training set,0.692,0.714 and 0.721 in validation set,respectively.3D Res2Net model needed 5-7 s to predict VDT of solid pulmonary nodules,with an average time of(5.92±1.08)s.Conclusion 3D Res2Net model could be used to predict VDT of solid pulmonary nodules,which might obviously reduce manual interpreting time.

Objective:To explore the value of nodal integrated topological attributes (NITA) based on machine learning model in identifying schizophrenia.Methods:A total of 56 patients with first-onset schizophrenia admitted to Department of Psychiatry, Second Affiliated Hospital of Xinxiang Medical University from January 2022 to August 2023 and 56 healthy volunteers recruited from community were selected. Functional MRI data were collected, and brain functional networks were constructed after preprocessing. Global and nodal topological attributes were extracted using graph theory as training features. Participants were divided into training set (46 schizophrenia patients and 46 heathy volunteers) and testing set (10 schizophrenia patients and 10 heathy volunteers). Random Forest Classifier (RFC), Support Vector Machine (SVM), and Gradient Boosting Tree (XGBoost) models were fitted to global and nodal topological attributes in the training set to calculate the accuracy, recall rate, F1 value, and area under receiver operating characteristic curve (AUC) of each model. Generalization ability was analyzed based on the performance of testing set, and excellent topological attributes were screened out. Selected topological attributes were reduced to one-dimensional features through principal component analysis,and then fitted to the above models, and feature-adapted model was selected based on the performances of training and testing sets. Statistical analysis of the new dimensional features of each brain region of schizophrenia patients and heathy volunteers was performed. Combined with false discovery rate (FDR), new dimension features with significant differences were selected and fitted with the adapted model.Results:In the training set, machine learning models using node topological attributes achieved higher accuracy, recall rate, F1 scores, and AUC compared with those using global topological attributes. In the test set, the SVM model using node topological attributes showed stable generalizability (accuracy=75.00%, recall rate=100.00%, F1 score=0.80, AUC=0.92). The node topological attribute metrics were down-dimensionally named NITA. Based on validation results of SVM model using NITA in the training set (accuracy of 77.00%, recall of 72.00%, F1 value of 0.76, AUC of 0.86) and performance in the testing set (accuracy of 66.67%, recall of 83.33%, F1 value of 0.71, AUC of 0.61), SVM was selected as the adapted model. NITA in the right middle frontal gyrus ventrolateral area, left inferior frontal gyrus dorsal area, right precentral gyrus caudal ventrolateral area, left superior temporal gyrus rostral area, right fusiform gyrus lateroventral area, right inferior parietal lobule rostrodorsal area, left occipital polar cortex showed significant difference between patients and volunteers ( P<0.05, FDR-corrected). The optimal model (FDR-PCAN-SVM) obtained via NITA being trained on corresponding brain area reached an accuracy of 93.74%, recall rate of 98.00%, F1 value of 0.94, and AUC of 0.96 in the training set and accuracy of 83.33%, recall rate of 66.67%, F1 value of 0.80, and AUC of 0.92 in the testing set. Conclusion:NITA may serve as a potential image biomarker for schizophrenia identification; brain regions with abnormal NITA is key nodes in information exchange and integration within the brain networks in schizophrenia patients.

OBJECTIVE: To explore the effect of Xuebijing injection on inflammation in sepsis by regulating intestinal microbiota and its metabolites. METHODS: A total of 45 male Sprague-Dawley (SD) rats were randomly divided into Sham operation group (Sham group), cecal ligation and perforation (CLP) induced sepsis group (CLP group), and Xuebijing intervention group (XBJ group, 4 mL/kg Xuebijing injection was injected intraperitoneally at 1 hour after CLP), with 15 rats in each group. The survival of rats was observed at 24 hours after operation and sacrificed. Feces were collected for 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) analysis. RESULTS: At 24 hours after operation, all rats in the Sham group survived, the mortality of rats in the XBJ group was lower than that in the CLP group [47% (7/15) vs. 60% (9/15), P > 0.05]. Compared with the Sham group, the diversity of gut microbiota in the CLP group decreased, the dominant flora changed, and the abundance of inflammation-related flora increased. Xuebijing improved the changes in gut microbiota caused by sepsis, and α diversity showed an increasing trend (Ace index: 406.0±22.5 vs. 363.2±38.2, Chao1 index: 409.7±21.8 vs. 362.4±42.5, both P > 0.05). Restrictive constrained principal coordinate analysis (cPCoA) showed a high similarity in gut microbiota among the same group of rats. The CLP group was dominated by Bacteroidetes, while the Sham and XBJ groups were dominated by Firmicutes. In addition, compared with the CLP group, Xuebijing treatment increased the abundance of beneficial bacteria in septic rats, such as Verrucomicrobia, Akkermansia and Lactobacillus. LC-MS and orthogonal partial least squares discriminant analysis (OPLS-DA) showed that there were 12 main differential metabolites among the three groups, and there were certain correlations between these metabolites, which were related to amino acid and lipid metabolism. Correlation analysis showed a significant correlation between changes in metabolites and microbial communities. CONCLUSIONS Xuebijing can improve the survival rate of septic rats, regulate the composition of intestinal flora and related metabolites, which provides a new pathophysiological mechanism for Xuebijing in the treatment of sepsis.
Rats ; Male ; Animals ; Rats, Sprague-Dawley ; Gastrointestinal Microbiome ; RNA, Ribosomal, 16S ; Sepsis/metabolism* ; Inflammation

Objective:To investigate the efficacy and safety of sivelestat sodium in patients with sepsis.Methods:The clinical data of 141 adult patients with sepsis admitted to the intensive care unit (ICU) of the First Affiliated Hospital of Zhengzhou University from January 1, 2019 to January 1, 2022 were retrospectively analyzed. The patients were divided into the sivelestat sodium group ( n = 70) and the control group ( n = 71) according to whether they received sivelestat sodium or not. The efficacy indexes included oxygenation index, procalcitonin (PCT), C-reactive protein (CRP), white blood count (WBC), sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) before and after 7 days of treatment, as well as ventilator supporting time, the length of ICU stay, the length of hospital stay and ICU mortality. The safety indicators included platelet count (PLT) and liver and kidney function. Results:There were no significant differences in age, gender, underlying diseases, infection site, basic drugs, etiology, oxygenation index, biochemical indexes, SOFA and APACHE Ⅱ scores between the two groups. Compared with the control group, the oxygenation index in 7 days was significantly increased [mmHg (1 mmHg ≈ 0.133 kPa): 233.5 (181.0, 278.0) vs. 202.0 (153.0, 243.0), P < 0.01], the levels of PCT, CRP, alanine aminotransferase (ALT) and APACHE Ⅱ score were significantly decreased in the sivelestat sodium group [PCT (μg/L): 0.87 (0.41, 1.61) vs. 1.53 (0.56, 5.33), CRP (mg/L): 64.12 (19.61, 150.86) vs. 107.20 (50.30, 173.00), ALT (U/L): 25.0 (15.0, 43.0) vs. 31.0 (20.0, 65.0), APACHE Ⅱ: 14 (11, 18) vs. 16 (13, 21), all P < 0.05]. However, there were no significant differences in SOFA, WBC, serum creatinine (SCr), PLT, total bilirubin (TBil), aspartate aminotransferase (AST) in 7 days between the sivelestat sodium group and the control group [SOFA: 6.5 (5.0, 10.0) vs. 7.0 (5.0, 10.0), WBC (×10 9/L): 10.5 (8.2, 14.7) vs. 10.5 (7.2, 15.2), SCr (μmol/L): 76.0 (50.0, 124.1) vs. 84.0 (59.0, 129.0), PLT (×10 9/L): 127.5 (59.8, 212.3) vs. 121.0 (55.0, 211.0), TBil (μmol/L): 16.8 (10.0, 32.1) vs. 16.6 (8.4, 26.9), AST (U/L): 31.5 (22.0, 62.3) vs. 37.0 (24.0, 63.0), all P > 0.05]. The ventilator supporting time and the length of ICU stay in the sivelestat sodium group were significantly shorter than those in control group [ventilator supporting time (hours): 147.50 (86.83, 220.00) vs. 182.00 (100.00, 360.00), the length of ICU stay (days): 12.5 (9.0, 18.3) vs. 16.0 (11.0, 23.0), both P < 0.05]. However, there were no significant differences in the length of hospital stay and ICU mortality between the sivelestat sodium group and the control group [the length of hospital stay (days): 20.0 (11.0, 27.3) vs. 13.0 (11.0, 21.0), ICU mortality: 17.1% (12/70) vs. 14.1% (10/71), both P > 0.05]. Conclusions:Sivelestat sodium is safe and effective in patients with sepsis. It can improve the oxygenation index and APACHE Ⅱ score, reduce the levels of PCT and CRP, shorten ventilator supporting time and the length of ICU stay. No adverse reactions such as liver and kidney function injury and platelet abnormality are observed.