[Objective] To systematically evaluate the therapeutic effect of washed red blood cells and white suspended red blood cells on patients with autoimmune hemolytic anemia, and to provide reference for their clinical treatment. [Methods] CNKI, Wanfang, VIP, PubMed, Embase, Cochrane Library and other databases from the establishment of the database to August 2024 were searched, including the randomized controlled trials of washed red blood cells and white suspended red blood cells in the treatment of autoimmune hemolytic anemia that met the requirements. After literature screening, data extraction and quality evaluation, meta-analysis was performed using Review manager 5.3 software and Stata 15.1 software to analyze the therapeutic effect of blood transfusion in the primary outcome, hematological indicators (Hb, Ret, RBC, and TBIL) of the two groups after blood transfusion and the occurrence of adverse blood transfusion reactions. [Results] After screening, 10 literatures meeting the criteria were retrieved, and a total of 753 patients with autoimmune hemolytic anemia were treated with washed red blood cell infusion in the observation group and white suspended red blood cell infusion in the control group. Meta-analysis suggested that there was no significant difference in the therapeutic effect of transfusion between patients who received washed red cells and those received white suspended red cells[SMD=1.16, 95%CI (0.87, 1.54), P>0.05]. The hematological indexes of the two groups after transfusion (Hb [SMD=0.04, 95%CI (-0.14, 0.22), P>0.05]、Ret[SMD=-0.15, 95%CI (-0.34, 0.03), P>0.05]、RBC[SMD=0.08, 95%CI (-0.10, 0.26), P>0.05] and TBIL [SMD=-0.02, 95%CI (-0.18, 0.15), P>0.05]) and the incidence of transfusion adverse reactions[SMD=0.8, 95%CI (0.47, 1.39), P>0.05] were not significantly different. [Conclusion] Based on the current study, the efficacy and safety of infusion of washed red blood cells and white suspended red blood cells are comparable in patients with autoimmune hemolytic anemia. However, considering the simple preparation process of washed red blood cells and the low price, infusion of washed red blood cells is recommended for patients with autoimmune hemolytic anemia.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: To construct a scientific and perfect evaluation index system of infectious disease prevention and control ability in colleges and universities, so as to provide reference tools for colleges and universities to effectively respond to infectious disease. Methods: The initial framework of the evaluation index system of infectious disease prevention and control ability in colleges and universities was constructed by using literature analysis method. Experts familiar with infectious disease prevention and control or school health work were selected to conduct two rounds( n =16，18) of Delphi expert consultation for determining the evaluation index system. Analytical hierarchy process was used to calculate the index weights and combined weights. About 198 prevention and control personnel were conveniently selected from 3 universities in Inner Mongolia Autonomous Region to comprehensively evaluate the evaluation indicators by using fuzzy comprehensive evaluation method. Results: After two rounds of Delphi consultation questionnaire, the effective recovery rates were 80.0% and 90.0%, the expert authority levels were 0.89 and 0.86, the expert harmony coefficients for Kendall W were 0.166 and 0.310, and the variation coefficient of each index was <0.25. Finally, the evaluation index system of infectious disease prevention and control ability of colleges and universities included 4 first level indicators, 14 second level indicators and 75 third level indicators. The weights of prevention and monitoring and early warning, organizational system guarantee, emergency management, rehabilitation and summary were 0.176, 0.476, 0.268 and 0.080, respectively. The top 3 weights of the secondary indexes were 0.623 for infectious disease surveillance and early warning, 0.595 for loss assessment and 0.370 for emergency response. The score of fuzzy comprehensive evaluation of the index system of infectious disease prevention and control ability in colleges and universities was 79.148, suggesting a high level. Conclusion The established evaluation index system of infectious disease prevention and control ability in colleges and universities is scientific and reasonable, which is conducive to provide tool reference for the evaluation of infectious disease prevention and control ability in colleges and universities.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE To investigate the effect and mechanism of Jingangteng capsules in the treatment of non-alcoholic fatty liver disease （NAFLD）. METHODS Thirty-two SD rats were randomly divided into normal group and modeling group. The modeling group was fed a high-fat diet to establish a NAFLD model. The successfully modeled rats were then randomly divided into model group， atorvastatin group[positive control， 2 mg/（kg·d）]， and Jingangteng capsules low- and high-dose groups [0.63 and 2.52 mg/（kg·d）]， with 6 rats in each group. The pathological changes of the liver were observed by hematoxylin-eosin staining and oil red O staining. Enzyme-linked immunosorbent assay was performed to determine the serum levels of triglycerides （TG）， total cholesterol （TC）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， alanine transaminase （ALT）， aspartate transaminase （AST）， tumour necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， IL-6， IL-18. 16S rDNA amplicon sequencing and metabolomics techniques were applied to explore the effects of Jingangteng capsules on gut microbiota and metabolisms in NAFLD rats. Based on the E-mail：591146765@qq.com metabolomics results， Western blot analysis was performed to detect proteins related to the nuclear factor kappa-B （NF-κB）/NOD-like receptor family protein 3 （NLRP3） signaling pathway in the livers of NAFLD rats. RESULTS The experimental results showed that Jingangteng capsules could significantly reduce the serum levels of TG， TC， LDL-C， AST， ALT， TNF-α， IL-1β， IL-6， IL-18， while increased the level of HDL-C， and alleviated the hepatic cellular steatosis and inflammatory infiltration in NAFLD rats. They could regulate the gut microbiota disorders in NAFLD rats， significantly increased the relative abundance of Romboutsia and Oscillospira， and significantly decreased the relative abundance of Blautia （P＜0.05）. They also regulated metabolic disorders primarily by affecting secondary bile acid biosynthesis， fatty acid degradation， O-antigen nucleotide sugar biosynthesis， etc. Results of Western blot assay showed that they significantly reduced the phosphorylation levels of NF-κB p65 and NF-κB inhibitor α， and the protein expression levels of NLRP3， caspase-1 and ASC （P＜0.05 or P＜0.01）. CONCLUSIONS Jingangteng capsules could improve inflammation， lipid accumulation and liver injury in NAFLD rats， regulate the disorders of gut microbiota and metabolisms， and inhibit NF-κB/NLRP3 signaling pathway. Their therapeutic effects against NAFLD are mediated through the inhibition of the NF-κB/NLRP3 signaling pathway.

ObjectiveTo explore the clinical characteristics and risk factors for 30-day mortality in hospitalized patients with bloodstream infections (BSI) caused by carbapenem-resistant Klebsiella pneumoniae (CRKP). MethodsData were obtained retrospectively from the electronic medical records of inpatients at a tertiary A-grade hospital in Shanghai from January 2016 to December 2023. The collected variables included age, gender, department, surgical treatment, empirical antibiotic therapy, Pitt Bacteremia score (PBS), Charlson comorbidity index (CCI), INCREMENT-CPE score (ICS), length of hospital stay, the time from CRKP-BSI to discharge and, etc. The follow-up period ended upon discharge, with the follow-up outcomes defined as in-hospital mortality or discharge. The endpoint was defined as death within 30 days (including day 30) caused by CRKP-BSI or infection-related complications. Patients who survived within 30 days after CRKP-BSI were classified into the survival group, while those who died within 30 days were classified into the death group. Independent risk factors for 30-day mortality in patients with CRKP-BSI were analyzed using univariate and multivariate Cox regression analysis. ResultsA total of 71 hospitalized patients with CRKP-BSI, comprising 51 males and 20 females, with an average age of (65.12±18.25) years, were included during the study period. The M (P₂₅, P₇₅) of hospital stay were 37.00 (24.00, 56.00) days, and M (P₂₅, P₇₅) of the duration from CRKP-BSI to discharge or death were 18.00 (7.00, 35.00) days. There were 20 deaths (28.17%) in the death group and 51 survivors (71.83%) in the survival group. The results of multivariate Cox regression analysis showed that the ICS as an independent risk factor for 30-day mortality in CRKP-BSI patients (HR=1.379, 95%CI: 1.137‒1.671, P=0.001). Each 1-point increase in the ICS was associated with a 37.9% increase in the risk of mortality. ConclusionThe ICS is found to be a risk factor for 30-day mortality in patients with CRKP-BSI, which may facilitate the prediction for the risk of 30-day mortality and thereby support clinical decision-making for patients with CRKP-BSI.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.