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 explore the association of cerebral venous outflow assessed by CT angiography (CTA) with first pass effect (FPE) in patients with acute anterior circulation large vessel occlusion accepted mechanical thrombectomy (MT).Methods:A retrospective analysis was performed; patients with acute anterior circulation large vessel occlusion accepted MT and CTA in Department of Neurology, Affiliated Hospital of Xuzhou Medical University from July 2018 to June 2021 were consecutively enrolled. Cerebral venous outflow in baseline CTA was evaluated using Cortical Vein Opacification Score (COVES). Patients were categorized into either FPE or non-FPE groups based on recanalization of occluded vessels after initial MT. General information, clinical features, radiological data, and surgery-related data between the 2 groups of patients were collected and compared. Significant variables ( P<0.1) from univariate analysis were included into a multivariable Logistic regression model to explore the relation between COVES and FPE. Predictive value of COVES in FPE was assessed using receiver operating characteristic (ROC) curve. Results:Out of the 143 patients enrolled in this study, 52 were into the FPE group and 91 were into the non-FPE group. Compared with the non-FPE group, the FPE group had higher COVES scores, higher proportion of patients with good cerebral venous drainage (COVES≥3), smaller core infarct volume, and shorter time from femoral artery puncture to vessel recanalization, with significant differences ( P<0.05). Multivariable Logistic regression analysis revealed that COVES was still corelated with FPE after adjusting covariates such as baseline NIHSS scores, core infarct volume, and time from femoral artery puncture to vessel recanalization ( OR=0.730, 95% CI: 0.567-0.940, P=0.015). ROC curve demonstrated that the combined model of COVES with aforementioned factors (COVES scores+baseline NIHSS scores+core infarct volume+time from femoral artery puncture to vessel recanalization) had an area under the curve of 0.757 (95% CI: 0.672-0.841, P<0.001), with sensitivity of 61.5% and specificity of 78.0%. Conclusion:Favorable cerebral venous drainage is an independent predictor for successful FPE in patients with acute anterior circulation large vessel occlusion accepted MT.

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 investigate the value of a nomogram model based on contrast-enhanced CT radiomics in predicting the histological type of thymoma.Methods A total of 154 patients(101 in low-risk group and 53 in high-risk group)with thymoma confirmed by pathology were retrospectively selected.The cases were randomly divided into training set(n=107)and validation set(n=47)at a ratio of 7∶3.The three-dimensional volume of interest(VOI)of the whole lesion on the image from the arterial phase of contrast-enhanced CT was manually delineated,and the radiomics features were extracted.Based on the selected radiomics features,the radiomics model was constructed and the model Radiomics score(Radscore)was calculated.Clinical risk factors were screened to construct a clinical model,and a nomogram model was constructed by fusing Radscore and clinical risk factors.The receiver operating characteristic(ROC)curve,area under the curve(AUC),accuracy,sensitivity and specificity were compared to analyze the predictive efficacy and difference of different models for high-risk and low-risk thymoma.The decision curve and calibration curve were drawn to evaluate the clinical value and fitting performance of the nomogram model.Results Eleven radiomics features were selected to construct the radiomics model,and five clinical risk factors[myasthenia gravis(MG),morphology,border,surrounding tissue invasion and CT value in arterial phase]were used to construct the clinical model.In the training set,the AUC of the nomogram model(0.88)was higher than that of the radiomics model(0.80)and the clinical model(0.79),and the difference was statistically significant(Z=2.233,2.713,P=0.026,0.007,respectively).In the validation set,the AUC of the nomogram model was higher than that of the radiomics and clinical models,but the difference was not statistically significant.The calibration curve showed that the nomogram model had good fitting performance,and the decision curve showed that the nomogram model had high clinical benefit.Conclusion The nomogram model based on contrast-enhanced CT can effectively predict high-risk and low-risk thymoma,which is helpful to guide clinicians to make relevant decisions.

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 investigate the expression levels of plasma circular RNA PTP4A2(circPTP4A2)and circTLK2 in elderly patients with acute ischemic stroke(AIS)and their predic-tive value for neurological functional outcomes.Methods A total of 122 elderly AIS patients admitted to our department from May 2021 to December 2022 were prospectively recruited,and according to their modified Rankin Scale(mRS)score at 3 months after stroke onset,they were divided into a good outcome group(mRS score≤2,81 cases)and a poor outcome group(mRS score:3-6,41 cases).Their baseline data,and plasma circPTP4A2 and circTLK2 levels were compared between the two groups.Multivariate logistic regression analysis was employed to iden-tify prognostic factors for poor outcomes in the elderly AIS patients.ROC curve analysis was ap-plied to evaluate the prognostic value of circPTP4A2 and circTLK2 for adverse outcomes in the patients.Pearson correlation analysis was performed to assess the relationship of plasma levels of circPTP4A2 and circTLK2 with NIHSS score,as well as mRS score.Results The plasma expres-sion levels of circPTP4A2 and circTLK2 were significantly higher in the poor outcome group than the good outcome group[2.08(0.87,2.77)vs 0.93(0.63,1.20),1.71(0.92,2.80)vs 0.75(0.49,1.09),P＜0.01].Multifactor logistic regression analysis showed that plasma circPTP4A2 and circTLK2 were independent predictive factors for poor functional outcomes in elderly AIS patients(P＜0.01,P＜0.05).ROC curve analysis demonstrated that the AUC value of combined circPTP4A2 and circTLK2 in predicting poor outcome in elderly AIS patients was 0.787(95％CI:0.691-0.883).Pearson correlation analysis revealed that the expression levels of circPTP4A2 and cir-cTLK2 in elderly AIS patients were mildly positively correlated with baseline NIHSS scores(r=0.463,r=0.456;P＜0.01)and moderately positively correlated with mRS scores at 3 months after stroke onset(r=0.682,r=0.604;P＜0.01).Conclusion Plasma circPTP4A2 and circTLK2 may be potential biomarkers for predicting neurological functional outcomes in elderly AIS patients.

Objective:To investigate the effects of mixed probiotics on food allergy and the underlying mechanism.Methods:BALB/c mice on the 15 th day of pregnancy were randomly (random number table method) classified into the control group, food allergy model group and mixed probiotics group.Mice in the food allergy model and mixed pro-biotics group were subjected to ovalbumin (OVA) sensitization after birth, and those in the mixed probiotics group were then given probiotic solution by gavage from day 21 to day 35.Mice in control group were similarly given 9 g/L saline.Twenty-four hours after the last OVA sensitization, intestinal histopathological sections were prepared to observe intestinal pathological changes.Blood smears were prepared to detect eosinophil count.In addition, serum samples were collected to measure cytokine levels and OVA specific antibodies.The number of dendritic cells (DCs) and regulatory T cells (Tregs) in mouse mesenteric lymph nodes was calculated.Differences among 3 groups were compared by the One- Way ANOVA or Kruskal- Wallis H test. Results:Compared with those of food allergy model group, diarrhea score, the ratio of eosinophils and serum levels of interleukin(IL)-4, IL-5, IL-13, mast cell protease 1 (MCPT-1), and OVA specific antibodies IgE and IgG were significantly lower in mixed probiotics group[(2.00±0.71) points vs.(3.22±0.97) points, (2.28±1.61)% vs.(10.99±2.26)%, (413.68±22.81) ng/L vs.(708.78±27.66) ng/L, (36.64±3.74) ng/L vs.(46.05±4.95) ng/L, (201.37±65.61) ng/L vs.(495.22±96.66) ng/L, (31 924.15±1 177.77) ng/L vs.(36 175.77±618.29) ng/L, (9.10±8.08) ng/L vs.(19.69±0.84) ng/L, (30.50±8.81) ng/L vs.(190.32±6.40) ng/L], while IL-10 level was significantly higher[(164.12±3.88) ng/L vs.(123.90±7.31) ng/L] ( t=3.37, 8.72, 16.07, 3.90, 7.40, 7.95, 3.91, 44.00 and 7.76, respectively, all P<0.01). Compared with those of food allergy model group, programmed cell death ligand 1 (PD-L1) level on the surface of CD 103+ DCs and CD 103+ CD 80-CD 40-DCs, the proportion of Tregs in CD4 + T cells, and the level of programmed cell death 1 (PD-1) on the surface of Tregs were significantly higher in mixed probiotics group[(75.59±0.45)% vs.(45.60±4.73)%, (67.56±1.87)% vs.(37.12±6.07)%, (8.24±0.69)% vs.(6.20±0.66)%, (11.25±3.12)% vs.(4.08±2.33)%]( t=7.88, 4.48, 3.63 and 3.71, all P<0.01). Conclusions:Mixed probiotics can alleviate the symptoms of food allergy and inflammatory response of young rats through mediating Tregs via the PD-1/PD-L1 pathway.

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.

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 study the effect of air inlet modes on the temperature variation, fluctuation, uniformity and coefficient of variation(CV), so as to evaluate the stability and uniformity of the temperature field in refrigerated warehouse for blood. 【Methods】 The temperature changes of blood refrigerated warehouse under different modes of air inlet during compressor operation were analyzed. The stability of the temperature field in the storehouse was evaluated by the changes, fluctuation, uniformity, CV and deviation of temperature at each testing point. 【Results】 The average value of temperature in the storehouse, adopting air inlet via straight blow, was (4.98±0.92)℃, while that of air inlet via micro-hole mode was(4.15±0.25)℃, with significant differences between each other(P<0.05). As to the CV of temperature, air inlet via straight blow was significantly different from that via micro hole(P<0.01). The fluctuation, uniformity and deviation of temperature created by straight blow and micro hole were 1.85±1.11 vs 0.49±0.38, 1.00±0.68 vs 0.47±0.37, and 0.61±0.45 vs 0.27±0.21, respectively, with significant differences between each other(P<0.01). 【Conclusion】 Compared with straight blow, the mean temperature created by micro hole was closer to the median value (4℃) of the temperature range, i. e.(4±2)℃, during blood storage. Otherwise, micro hole demonstrated a smaller CV of temperature, and superior performance in fluctuation, uniformity and deviation of the temperature at the testing points, which was conducive to ensure the stability of storehouse temperature field.