Objective·To establish a nomogram for the differential diagnosis of early systemic lupus erythematosus(SLE)and other autoimmune diseases based on laboratory indications,and to evaluate its efficacy.Methods·A total of 535 SLE patients admitted to the First Hospital of Lanzhou University from January 2017 to December 2021 were selected as SLE group,and 535 patients with other autoimmune diseases during the same period were selected as control group.Basic information and laboratory test indicators of the SLE group and control group were collected and compared.The SLE group and control group were randomly assigned to the training set and the validation set at a ratio of 7∶3,respectively.LASSO regression method and multivariate Logistic regression were used to select the main risk factors of SLE.The nomogram for differential diagnosis of early SLE(SLE nomogram)was established according to the selected main risk factors.Bootstrap method was used to conduct internal repeated sampling for 1 000 times to calibrate the nomogram.The receiver operator characteristic curve(ROC curve)and decision curve analysis(DCA)were performed to evaluate the differential diagnosis ability and the value in clinical application of SLE nomogram,respectively.The"DynNom"package of R language was used to convert the nomogram into an electronic calculator,and its consistency with SLE nomogram was verified by data from 3 groups of patients.Results·LASSO regression and multivariate Logistic regression identified six major risk factors for SLE,including antinuclear antibody(ANA),anti-double-stranded DNA(anti-dsDNA)antibody,anti-ribonucleoprotein antibody/anti-Simth antibody(anti-nRNP/Sm),anti-ribosomal P protein(anti-P)antibody,anti-nucleosome antibody(ANuA)and urinary protein(PRO),which were used to construct the SLE nomogram.The calibration curve of the SLE nomogram had standard errors of 0.009 and 0.015 in the training set and validation set,respectively,and its area under the curve(AUC)was 0.889 and 0.869,respectively.The results of DCA showed that when the risk threshold of SLE nomogram was 0.15?0.95,the model achieved more net benefit.The prediction results of the electronic calculator showed that when ANA(titer 1∶100)was positive in SLE patient No.1,the prevalence was 0.166;when both ANA(titer 1∶100)and ANuA(titer 1∶100)were positive in patient No.2,the prevalence was 0.676;when all of PRO,ANA(titer 1∶100),ANuA(titer 1∶100)and anti-P antibody(titer 1∶100)were positive in patient No.3,the prevalence was 0.990,which was consistent with the differential diagnosis results of the SLE nomogram.Conclusion·The established SLE nomogram based on ANA,anti-dsDNA antibody,anti-nRNP/Sm,anti-P antibody,ANuA and PRO and its conversion into an electronic calculator can effectively distinguish early SLE from other autoimmune diseases,and have important clinical application value.

Objective To predict the risk of in-hospital death in patients with chronic heart failure(CHF)complicated by lung infections using interpretable machine learning.Methods The clinical data of 1415 patients diagnosed with CHF complicated by lung infections were obtained from the MIMIC-IV database.According to the pathogen type,the patients were categorized into bacterial pneumonia and non-bacterial pneumonia groups,and their risks of in-hospital death were compared using Kaplan-Meier survival curves.Univariate analysis and LASSO regression were used to select the features for constructing LR,AdaBoost,XGBoost,and LightGBM models,and their performance was compared in terms of accuracy,precision,F1 value,and AUC.External validation of the models was performed using the data from eICU-CRD database.SHAP algorithm was applied for interpretive analysis of XGBoost model.Results Among the 4 constructed models,the XGBoost model showed the highest accuracy and F1 value for predicting the risk of in-hospital death in CHF patients with lung infections in the training set.In the external test set,the XGBoost model had an AUC of 0.691(95%CI:0.654-0.720)in bacterial pneumonia group and an AUC of 0.725(95%CI:0.577-0.782)in non-bacterial pneumonia group,and showed better predictive ability and stability than the other models.Conclusion The overall performance of the XGBoost model is superior to the other 3 models for predicting the risk of in-hospital death in CHF patients with lung infections.The SHAP algorithm provides a clear interpretation of the model to facilitate decision-making in clinical settings.

Objective To predict the risk of in-hospital death in patients with chronic heart failure(CHF)complicated by lung infections using interpretable machine learning.Methods The clinical data of 1415 patients diagnosed with CHF complicated by lung infections were obtained from the MIMIC-IV database.According to the pathogen type,the patients were categorized into bacterial pneumonia and non-bacterial pneumonia groups,and their risks of in-hospital death were compared using Kaplan-Meier survival curves.Univariate analysis and LASSO regression were used to select the features for constructing LR,AdaBoost,XGBoost,and LightGBM models,and their performance was compared in terms of accuracy,precision,F1 value,and AUC.External validation of the models was performed using the data from eICU-CRD database.SHAP algorithm was applied for interpretive analysis of XGBoost model.Results Among the 4 constructed models,the XGBoost model showed the highest accuracy and F1 value for predicting the risk of in-hospital death in CHF patients with lung infections in the training set.In the external test set,the XGBoost model had an AUC of 0.691(95%CI:0.654-0.720)in bacterial pneumonia group and an AUC of 0.725(95%CI:0.577-0.782)in non-bacterial pneumonia group,and showed better predictive ability and stability than the other models.Conclusion The overall performance of the XGBoost model is superior to the other 3 models for predicting the risk of in-hospital death in CHF patients with lung infections.The SHAP algorithm provides a clear interpretation of the model to facilitate decision-making in clinical settings.

Objective:To evaluate the efficacy and safety of rituximab in pediatric myasthenia gravis (MG).Methods:Case series study. The clinical manifestations, laboratory tests, treatment plans and prognosis of 27 pediatric MG patients treated with rituximab from June 2013 to June 2023 at Children′s Medical Center of Peking University First Hospital were retrospectively collected.Results:There were 5 males and 22 females in 27 MG children. The onset age was 2.1 (1.6, 4.8) years, ranging from 8 months to 11 years. The clinical classification included 20 children (74%) of ocular MG and 7 children (26%) of generalized MG. Seventeen children (63%) had positive MG-related pathogenic antibodies, including 17 children of anti-AchR antibody and 1 of them also had anti-MuSK antibody. Rituximab was used as first-line immunosuppressant in 13 children, second-line immunosuppressant in 13 children and third-line immunosuppressant in 1 child. Immunosuppressants used before rituximab including 8 children of cyclosporine, 3 children of tacrolimus, 1 child of azathioprine, 1 child of mycophenolate mofetil and 1 child of cyclosporine combined with azathioprine. Rituximab was used for at least half a year with a follow-up period of more than 12 months. At the last follow-up after rituximab treatment, all children achieved improved or above, 14 children (52%) achieved complete stable remission, 7 children (26%) achieved pharmacologic remission, 1 child (4%) achieved minimal manifestations, and 5 children (18%) improved. After rituximab treatment, 27 children all could reduce the immunomodulation therapy and shorten the course of glucocorticoid therapy, and 22 children (81%) had stopped the glucocorticoid therapy. Among the 14 children with poor efficacy of other immunosuppressants, rituximab had complete stable remission of 7 children. The most common adverse reaction was respiratory infection (4 children (15%)). Only 2 children had allergic reaction to rituximab and got better after symptomatic treatment.Conclusions:Rituximab has good efficacy and tolerance in pediatric MG. Early application of rituximab can improve the prognosis and shorten the course of glucocorticoid treatment.

Objective To explore the intestinal barrier regulation effect of Butuyajie recipe on antibiotic-associated diarrhea rats.Methods 60 SD male rats were randomly divided into blank group,model group,positive drug group(1 g·kg-1),and high-dose,medium-dose and low-dose groups of Butuyajie recipe(40.5,20.25,10.125 g·kg-1).The model was replicated by intragastric administration of lincomycin hydrochloride(5 g·kg-1)for 7 consecutive days.After successful modeling,the materials were obtained after drug intervention for 7 days.Intestinal pathological morphology was observed by HE staining.ELISA kit to detect DAO,MPO,LPS.Take each organ tissue to detect bacterial translocation.Feces were collected for 16S rRNA gene high-throughput sequencing analysis.Results Compared with the normal group,the serum levels of DAO,MPO and LPS in the model group were significantly increased(P<0.001,P<0.01),and the sIgA content in the intestinal mucosa was significantly decreased(P<0.001).Promote intestinal bacterial translocation(P<0.001,P<0.01).The diversity of intestinal flora was significantly reduced,and the levels of intestinal microflora and genera were significantly changed.Butuyajie recipe can reduce the content of DAO,MPO and LPS(P<0.001,P<0.01,P<0.05),significantly increase the content of sIgA(P<0.01,P<0.05),and effectively inhibit the translocation of intestinal bacteria(P<0.001,P<0.01,P<0.05).At the same time,it corrects the intestinal microecological structure by increasing Firmicutes,inhibiting the proportion of Bacteroidetes and Proteobacteria,and regulating Lactobacillus,Sphingomonas,Pseudomonas,and Enterobacter.Conclusion Butuyajie recipe can reduce the permeability of intestinal mucosa,reduce the translocation of intestinal flora,protect the intestinal immune barrier,regulate the diversity of intestinal flora structure,improve the intestinal microecological disorder,and can effectively treat antibiotic-associated diarrhea.

Objective In order to explore the preventive effect and potential mechanism of Dai medicine Shenyeshan leech on chronic renal failure(CRF).The rat model of chronic renal failure was induced and replicated by adenine,and the pharmacodynamics and mechanism of CRF prevention and treatment were discussed.Methods SD rats were randomly divided into normal group,model group,Yougui pill group,high-dose alcohol extract group,middle-dose group,low-dose group.Prophylactic administration was performed 5 days before modeling,and starting from the 6th day,rats in the model group and each administration group were given 1.0%adenine by gavage in the morning,and drug treatment was given in the afternoon for 15 days.24 hours after the last administration,urine specific gravity(SG)was determined by refraction method;urine creatinine(Ucr)and 24-hour proteinuria(PRO)were determined by biochemical analyzer;red blood cell count(RBC),platelet number(Plt)were determined by cell analyzer,hemoglobin(HGB),mean platelet volume(Mpv),mean corpuscular volume(Mcv).Enzyme-linked immunosorbent assay kits were used to determine blood urea nitrogen(BUN),serum creatinine(Scr)content and urinary α1-microglobulin(α1-MG),kidney injury molecule-1(KIM-1)expression.Hematoxylin-eosin staining was used to observe the pathological morphology of rat kidneys,and immunohistochemical staining was used to analyze the expression of aquaporin 2(AQP2),transforming growth factor-β1(TGF-β1),and hypoxia-inducible factor(HIF-1α)-related proteins.Results Compared with the model group,the rats were significantly improved in various symptoms such as lethargy,sluggish reaction,yellow and rough coat with falling off,and the pathological morphology of the kidneys after administration.Blood routine indexes RBC,HGB,Mpv,Mcv levels increased,Plt level decreased.The 24-hour total urine volume decreased significantly(P<0.05),and the SG in the low-dose group increased significantly(P<0.05).When URO=3.2 μmol·L-1,urinary PRO could be recovered to negative after drug treatment.The levels of renal function injury indexes BUN,α1-MG and KIM-1 were significantly decreased(P<0.05,P<0.01,P<0.001);the renal index and Ccr levels of the rats in the administration group were significantly improved(P<0.05,P<0.01).At the same time,the related AQP2,TGF-β1 and HIF-1α protein expressions were improved.To sum up,it can be seen that the leech of the kidney leaf mountain can improve the renal histopathology and various indicators of chronic renal failure rats,reduce the degree of renal injury and renal fibrosis,and has a positive effect on the prevention and treatment of CRF in rats.

Objective To explore the effect and possible mechanism of petroleum ether extract from Gastrodia elata on A amyloid β-protein deposition in Caenorhabditis elegans(C.elegans).Methods C.elegans was used as the model organism,and the experiment was divided into blank group(Control group),GEPEE 0.5 mg·mL-1 group and GEPEE 1 mg·mL-1 group.The effects of GEPEE on paralysis,life span,oxidative stress,heat stress,reactive oxygen species(ROS)level and Aβ aggregation of C.elegans were investigated,qRT-PCR was used to detect the changes of gene expression related to insulin/IGF-1 signaling pathway(IIS)in C.elegans.The main components were analyzed by high performance liquid chromatography(HPLC).Results Compared with Control group,GEPEE could significantly improve the paralysis phenotype of C.elegans(P<0.01),prolong the lifespan of C.elegans(P<0.01),enhance the motility of C.elegans(P<0.01),and increased the resistance to external oxidative stress(P<0.01),the stress ability of high temperature(P<0.01),improved the deposition of Aβ in vivo(P<0.01),decreased the ROS content in C.elegans(P<0.01),decreased the expression levels of Aβ and DAF-2(P<0.01),increased the expression levels of DAF-16 and its target genes SOD-3,GSH-Px,HSF-1 and its target gene HSP-16.2,SKN-1 and its target gene GST-4(P<0.01).Its main components were p-hydroxybenzyl alcohol and p-ethoxylbenzyl alcohol by HPLC.This study showed that GEPEE can reduce Aβ-induced toxicity in CL4176 C.elegans by reducing ROS level in vivo,increasing antioxidant level and regulating IIS pathway.Conclusion GEPEE can inhibit the toxicity of Aβ protein,and its mechanism is related to the regulation of IIS signaling pathway.

CaMKII is essential for long-term potentiation (LTP), a process in which synaptic strength is increased following the acquisition of information. Among the four CaMKII isoforms, γCaMKII is the one that mediates the LTP of excitatory synapses onto inhibitory interneurons (LTP_E→I). However, the molecular mechanism underlying how γCaMKII mediates LTP_E→I remains unclear. Here, we show that γCaMKII is highly enriched in cultured hippocampal inhibitory interneurons and opts to be activated by higher stimulating frequencies in the 10-30 Hz range. Following stimulation, γCaMKII is translocated to the synapse and becomes co-localized with the postsynaptic protein PSD-95. Knocking down γCaMKII prevents the chemical LTP-induced phosphorylation and trafficking of AMPA receptors (AMPARs) in putative inhibitory interneurons, which are restored by overexpression of γCaMKII but not its kinase-dead form. Taken together, these data suggest that γCaMKII decodes NMDAR-mediated signaling and in turn regulates AMPARs for expressing LTP in inhibitory interneurons.
Calcium-Calmodulin-Dependent Protein Kinase Type 2/metabolism* ; Hippocampus/metabolism* ; Interneurons/physiology* ; Long-Term Potentiation/physiology* ; N-Methylaspartate/metabolism* ; Receptors, AMPA/physiology* ; Receptors, N-Methyl-D-Aspartate/metabolism* ; Synapses/physiology*

Objective:To investigate the distribution, drug resistance and clinical significance of bacteria and fungi in pancreatic juice of patients with severe acute pancreatitis (SAP).Methods:Data of patients with severe acute pancreatitis receiving ERCP treatment and pancreatic juice bacterial culture at Department of Hepatobiliary Surgery, General Hospital of Ningxia Medical University from Jan 2019 to Jun 2020 were retrospectively analyzed.Results:A total of 97 patients were included. Pathogens were isolated from 46 (47.42%) pancreatic juice samples, with 71 strains including 43 (60.56%) gram negative bacteria, 26 (36.62%) gram positive bacteria, and 2 (2.82%) fungi. The C-reactive protein (CRP), D-dimer and Balthazar CT Score in the culture positive group were higher than those in the culture negative group ( P < 0.05). The incidence of complications and pancreatic infection in the culture positive group was also significantly higher ( P < 0.05). Conclusions:The positive rate of pancreatic juice bacterial culture in the early stage of severe acute pancreatitis is high, in which Gram-negative bacteria are most common, followed by Gram-positive bacteria and fungi. The presence of pathogens in pancreatic juice predicts ensuing infections.

Automatic classification of heart sounds plays an important role in the early diagnosis of congenital heart disease. A kind of heart sound classification algorithms based on sub-band envelope feature and convolution neural network was proposed in this paper, which did not need to segment the heart sounds according to cardiac cycle accurately. Firstly, the heart sound signal was divided into some frames. Then, the frame level heart sound signal was filtered with Gammatone filter bank to obtain the sub-band signals. Next, the sub-band envelope was extracted by Hilbert transform. After that, the sub-band envelope was stacked into a feature map. Finally, type Ⅰ and type Ⅱ convolution neural network were selected as classifier. The result shown that the sub-band envelope feature was better in type Ⅰ than type Ⅱ. The algorithm is tested with 1 000 heart sound samples. The test results show that the overall performance of the algorithm proposed in this paper is significantly improved compared with other similar algorithms, which provides a new method for automatic classification of congenital heart disease, and speeds up the process of automatic classification of heart sounds applied to the actual screening.
Algorithms ; Heart ; Heart Defects, Congenital/diagnosis* ; Heart Sounds ; Humans ; Neural Networks, Computer ; Signal Processing, Computer-Assisted