Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Objective:To investigate the feasibility of the bortezomib, lenalidomide, and dexamethasone (VRD) regimen combined with autologous hematopoietic stem cell transplantation (auto-HSCT) in patients with multiple myeloma (MM) and renal impairment, analyze treatment efficacy and renal responses stratified based on renal dysfunction severity, and explore the prognostic significance of early renal response and its affecting factors.Methods:This retrospective study, conducted at the First Affiliated Hospital of Soochow University, categorized 316 patients with newly diagnosed MM (NDMM) from August 2018 to October 2022 based on renal function for analysis of clinical characteristics, treatment response, and prognosis. Continuous variables were compared using t-tests or Mann-Whitney U tests, categorical variables utilizing Chi-square tests, survival outcomes employing Kaplan-Meier and Log-rank tests, and renal response predictors with logistic regression.Results:Patients were stratified based on baseline estimated glomerular filtration rate (eGFR) : normal [≥90 ml·min -1· (1.73 m 2) -1, n=160], mild [≥60 ml·min -1· (1.73 m 2) -1 to <90 ml·min -1· (1.73 m 2) -1, n=55], moderate [≥30 ml·min -1· (1.73 m 2) -1 to <60 ml·min -1· (1.73 m 2) -1, n=39], and severe impairment [<30 ml·min -1· (1.73 m 2) -1, n=62]. Moderate and severe renal impairment correlated with advanced International Staging System/Revised International Staging System classification, lower hemoglobin levels, frailty, and higher light-chain/IgD subtype prevalence ( P<0.05). Despite younger age ( P=0.001) and higher transplant rates ( P=0.041) in severe cases, overall response rates ( ORR: 93.7% ; ≥VGPR: 82.9% ) were comparable across groups ( P>0.05). Among 24 dialysis-dependent patients at diagnosis, 11 (45.8% ) achieved dialysis independence after induction [median: 3.0 (0.5–4.0) months], including 10 undergoing auto-HSCT. In 89 evaluable patients [baseline eGFR <50 ml·min -1· (1.73 m 2) -1], renal ORR (RORR) was 70.8% [rapid complete response: 31.5% ; rapid partial response: 11.2% ; rapid minimal response (RMR) : 28.1% ]. Renal response predicted better survival (overall survival: HR=0.36, 95% CI: 0.13–0.99, P=0.049). Moderate-to-severe renal impairment was associated with increased transplant-related adverse events and delayed engraftment ( P<0.05) ; however, auto-HSCT significantly improved outcomes after 33.5-month median follow-up (range: 2–65 months). Multivariate analysis identified 1q21+ ( OR=3.58, 95% CI: 1.17–11.02, P=0.026) and light-chain subtype ( OR=2.86, 95% CI: 1.08–7.69, P=0.036) as independent predictors of poor renal response. Conclusion:VRD regimen plus auto-HSCT demonstrates robust efficacy in NDMM, including patients with renal impairment, with a 70.8% RORR and manageable toxicity. Achieving ≥RMR correlates with superior prognosis, whereas 1q21+ and light-chain subtype independently predict inferior renal response.

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

Multiple myeloma (MM) is a hematological malignancy that poses significant treatment challenges due to its heterogeneity and propensity for relapse and progression. In the last two decades, the therapeutic landscape of MM has changed dramatically, but the disease remains largely incurable, with many patients facing treatment resistance. This review evaluates the current status of MM treatments, emphasizing the limitations of traditional therapies and the emerging role of immunotherapy in improving patient outcomes. It highlights the importance of achieving and maintaining minimal residual disease negativity and a balanced immune response as key treatment goals. Furthermore, it discusses the advancements in immunotherapies that are improving the prospects for patients, particularly those with relapsed or refractory disease. Innovative strategies, such as chimeric antigen receptor T-cell therapy, bispecific antibodies, and bispecific T cell engagers, have shown significant promise by targeting the malignant cells and the bone marrow microenvironment, which are essential for disease persistence and resistance to therapy. Future research should focus on refining MM treatment strategies, including the integration of immunotherapy into earlier treatment lines and the development of predictive biomarkers for personalized treatment approaches, ultimately enhancing patient outcomes.

Objective·To detect immunoglobulin(Ig)expression levels in newly diagnosed multiple myeloma(MM)patients before and after induction therapy,and to explore the clinical significance of Ig expression levels and their dynamic changes in relation to treatment efficacy,infection occurrence,and prognosis.Methods·Clinical data from 142 MM patients treated at the Department of Hematology,The First Affiliated Hospital of Soochow University between August 2018 and September 2020 were analyzed.Baseline Ig expression levels and post-induction changes following bortezomib-lenalidomide-dexamethasone(VRD)regimen were assessed.Immunoparesis was defined as uninvolved Igs below the laboratory lower limit of normal.Patients were stratified by immunoparesis severity(mild,moderate,severe,extremely severe).ANOVA,rank-sum tests,and x2 tests were used to analyze correlations with baseline characteristics.The relationship between the improvement in immunoparesis and the induction efficacy,infection occurrence,and prognosis was analyzed based on the dynamic changes in immunoparesis.Results·Normal Igs were severely reduced in newly diagnosed MM patients.Immunoparesis was present in 128 patients(90.1%),with severe or extremely severe immunoparesis accounting for 76.1%.Patients with extensive immunoparesis(all uninvolved Ig levels below the lower normal limit)were more likely to have severe immunoparesis(P<0.05).There were no statistically significant differences in age,gender,presence of severe renal insufficiency,and high-risk cytogenetics among MM patients with different degrees of immunoparesis(P>0.05),but there were statistically significant differences in MM staging(P=0.008)and typing(P=0.010).Most patients with severe immunoparesis were at stage Ⅱ/Ⅲ based on the Revised International Staging System(R-ISS)and were of the IgG type.At diagnosis,the levels of the involved Ig or light chain were negatively correlated with normal Ig levels(P<0.05).Improvement in immunoparesis after induction therapy was positively correlated with treatment response(P=0.006).The infection rate was high(26.8%),but no significant correlation was found between immunoparesis and infection occurrence(P>0.05).After induction therapy,patients showing improvement in immunoparesis had significantly longer progression-free survival(PFS)(median PFS:not reached vs 38 months,P=0.025),but no significant impact on overall survival(OS)was observed(P=0.450).Conclusion·Immunoparesis is common and severe in newly diagnosed MM patients,with severity correlating with disease stage and subtype.VRD therapy can partially reverse immunoparesis,and improvement is positively associated with treatment response and PFS benefit.Infection risk appears unrelated to immunoparesis severity and warrants comprehensive prevention strategies.Humoral immune deficiency may serve as a prognostic indicator in MM,but its impact on OS requires further investigation.

Objective·To detect immunoglobulin(Ig)expression levels in newly diagnosed multiple myeloma(MM)patients before and after induction therapy,and to explore the clinical significance of Ig expression levels and their dynamic changes in relation to treatment efficacy,infection occurrence,and prognosis.Methods·Clinical data from 142 MM patients treated at the Department of Hematology,The First Affiliated Hospital of Soochow University between August 2018 and September 2020 were analyzed.Baseline Ig expression levels and post-induction changes following bortezomib-lenalidomide-dexamethasone(VRD)regimen were assessed.Immunoparesis was defined as uninvolved Igs below the laboratory lower limit of normal.Patients were stratified by immunoparesis severity(mild,moderate,severe,extremely severe).ANOVA,rank-sum tests,and x2 tests were used to analyze correlations with baseline characteristics.The relationship between the improvement in immunoparesis and the induction efficacy,infection occurrence,and prognosis was analyzed based on the dynamic changes in immunoparesis.Results·Normal Igs were severely reduced in newly diagnosed MM patients.Immunoparesis was present in 128 patients(90.1%),with severe or extremely severe immunoparesis accounting for 76.1%.Patients with extensive immunoparesis(all uninvolved Ig levels below the lower normal limit)were more likely to have severe immunoparesis(P<0.05).There were no statistically significant differences in age,gender,presence of severe renal insufficiency,and high-risk cytogenetics among MM patients with different degrees of immunoparesis(P>0.05),but there were statistically significant differences in MM staging(P=0.008)and typing(P=0.010).Most patients with severe immunoparesis were at stage Ⅱ/Ⅲ based on the Revised International Staging System(R-ISS)and were of the IgG type.At diagnosis,the levels of the involved Ig or light chain were negatively correlated with normal Ig levels(P<0.05).Improvement in immunoparesis after induction therapy was positively correlated with treatment response(P=0.006).The infection rate was high(26.8%),but no significant correlation was found between immunoparesis and infection occurrence(P>0.05).After induction therapy,patients showing improvement in immunoparesis had significantly longer progression-free survival(PFS)(median PFS:not reached vs 38 months,P=0.025),but no significant impact on overall survival(OS)was observed(P=0.450).Conclusion·Immunoparesis is common and severe in newly diagnosed MM patients,with severity correlating with disease stage and subtype.VRD therapy can partially reverse immunoparesis,and improvement is positively associated with treatment response and PFS benefit.Infection risk appears unrelated to immunoparesis severity and warrants comprehensive prevention strategies.Humoral immune deficiency may serve as a prognostic indicator in MM,but its impact on OS requires further investigation.

Objective:To investigate the feasibility of the bortezomib, lenalidomide, and dexamethasone (VRD) regimen combined with autologous hematopoietic stem cell transplantation (auto-HSCT) in patients with multiple myeloma (MM) and renal impairment, analyze treatment efficacy and renal responses stratified based on renal dysfunction severity, and explore the prognostic significance of early renal response and its affecting factors.Methods:This retrospective study, conducted at the First Affiliated Hospital of Soochow University, categorized 316 patients with newly diagnosed MM (NDMM) from August 2018 to October 2022 based on renal function for analysis of clinical characteristics, treatment response, and prognosis. Continuous variables were compared using t-tests or Mann-Whitney U tests, categorical variables utilizing Chi-square tests, survival outcomes employing Kaplan-Meier and Log-rank tests, and renal response predictors with logistic regression.Results:Patients were stratified based on baseline estimated glomerular filtration rate (eGFR) : normal [≥90 ml·min -1· (1.73 m 2) -1, n=160], mild [≥60 ml·min -1· (1.73 m 2) -1 to <90 ml·min -1· (1.73 m 2) -1, n=55], moderate [≥30 ml·min -1· (1.73 m 2) -1 to <60 ml·min -1· (1.73 m 2) -1, n=39], and severe impairment [<30 ml·min -1· (1.73 m 2) -1, n=62]. Moderate and severe renal impairment correlated with advanced International Staging System/Revised International Staging System classification, lower hemoglobin levels, frailty, and higher light-chain/IgD subtype prevalence ( P<0.05). Despite younger age ( P=0.001) and higher transplant rates ( P=0.041) in severe cases, overall response rates ( ORR: 93.7% ; ≥VGPR: 82.9% ) were comparable across groups ( P>0.05). Among 24 dialysis-dependent patients at diagnosis, 11 (45.8% ) achieved dialysis independence after induction [median: 3.0 (0.5–4.0) months], including 10 undergoing auto-HSCT. In 89 evaluable patients [baseline eGFR <50 ml·min -1· (1.73 m 2) -1], renal ORR (RORR) was 70.8% [rapid complete response: 31.5% ; rapid partial response: 11.2% ; rapid minimal response (RMR) : 28.1% ]. Renal response predicted better survival (overall survival: HR=0.36, 95% CI: 0.13–0.99, P=0.049). Moderate-to-severe renal impairment was associated with increased transplant-related adverse events and delayed engraftment ( P<0.05) ; however, auto-HSCT significantly improved outcomes after 33.5-month median follow-up (range: 2–65 months). Multivariate analysis identified 1q21+ ( OR=3.58, 95% CI: 1.17–11.02, P=0.026) and light-chain subtype ( OR=2.86, 95% CI: 1.08–7.69, P=0.036) as independent predictors of poor renal response. Conclusion:VRD regimen plus auto-HSCT demonstrates robust efficacy in NDMM, including patients with renal impairment, with a 70.8% RORR and manageable toxicity. Achieving ≥RMR correlates with superior prognosis, whereas 1q21+ and light-chain subtype independently predict inferior renal response.

Objective To investigate the expression levels of selenoprotein genes in the patients with coronavirus disease 2019(COVID-19)and the possible regulatory mechanisms.Methods The dataset GSE177477 was obtained from the Gene Expression Omnibus,consisting of a symptomatic group(n=11),an asymptomatic group(n=18),and a healthy control group(n=18).The dataset was preprocessed to screen the differentially expressed genes(DEG)related to COVID-19,and gene ontology functional annotation and Kyoto encyclopedia of genes and genomes enrichment analysis were performed for the DEGs.The protein-protein interaction network of DEGs was established,and multivariate Logistic regression was employed to analyze the effects of selenoprotein genes on the presence/absence of symptoms in the patients with COVID-19.Results Compared with the healthy control,the symptomatic COVID-19 patients presented up-regulated expression of GPX1,GPX4,GPX6,DIO2,TXNRD1,SELENOF,SELENOK,SELENOS,SELENOT,and SELENOW and down-regulated ex-pression of TXNRD2 and SELENON(all P＜0.05).The asymptomatic patients showcased up-regulated expres-sion of GPX2,SELENOI,SELENOO,SELENOS,SELENOT,and SELENOW and down-regulated expression of SELP(all P＜0.05).The results of multivariate Logistic regression analysis showed that the abnormally high expression of GPX1(OR=0.067,95％CI=0.005-0.904,P=0.042)and SELENON(OR=56.663,95％CI=3.114-856.999,P=0.006)was the risk factor for symptomatic COVID-19,and the abnormally high expres-sion of SELP was a risk factor for asymptomatic COVID-19(OR=15.000,95％CI=2.537-88.701,P=0.003).Conclusions Selenoprotein genes with differential expression are involved in the regulation of COVID-19 development.The findings provide a new reference for the prevention and treatment of COVID-19.

Objective To investigate the effect of Galangin on pyroptosis of bone marrow derived macrophages(BMDMs).Methods BMDMs were cultured in vitro and divided into blank control group,model group and Galangin group with different concentrations.Lipopolysaccharide(LPS)and adenosine triphosphate(ATP)were used to construct the pyroptosis model.The effect of different concentrations of Galangin on the proliferation of BMDMs was detected by cell counting Kit-8(CCK-8).The level of cysteinyl aspartate specific proteinase-1 p10 subunit(caspase-1 p10),interleukin-1β(IL-1β)in supernatant and intracellular nucleotide NOD-like receptor protein 3(NLRP3)were detected by Western blotting.IL-1β in supernatant was detected by enzyme-linked immunosorbent assay(ELISA).The cell death was observed by propidium iodide(PI)staining.High-throughput sequencing was used to compare the gene expression in the model group and Galangin groups at 20 μmol/L.Results There was no statistically significant difference between the 5,10,20,40,60,80 μmol/L Galangin groups on the proliferation level of BMDMs(all P>0.05),indicating that no significant effect of Galangin at 5,10,20,40,60,80 μmol/L was observed on the proliferation of BMDMs.So we selected Galangin at 5,10,20 μmol/L and treatment for 1,2 and 4 hours as the effects of different concentrations and time on the pyroptosis of BMDMs.Compared with blank control group,the expression of caspase-1 p10 and mature IL-1β protein and IL-1β in supernatant in model group were significantly increased(all P<0.05).Compared with model group,the expression of caspase-1 p10 and mature IL-1β protein and IL-1β in supernatant of Galangin at 5,10 and 20 μmol/L were significantly decreased[IL-1β protein expression(gray value):0.155±0.006,0.113±0.006,0.111±0.007 vs.1.000±0.000,caspase-1 p10 protein expression(gray value):0.207±0.044,0.160±0.008,0.082±0.008 vs.1.000±0.000,IL-1β(μg/L):99.80±10.36,85.21±8.78,26.53±4.56 vs.494.10±35.47,all P<0.05].There was no significant difference between the different concentration groups(all P>0.05),but with the extension of treatment time of Galangin,the inhibitory effect was enhanced.The inhibitory effect of Galangin at 20 μmol/L for 4 hours was the most obvious[IL-1β protein expression(gray value):0.186±0.004 vs.1.000±0.000,caspase-1 p10 protein expression(gray value):0.247±0.009 vs.1.000±0.000,IL-1β(μg/L):173.80±10.56 vs.653.80±76.02,all P<0.05].Treatment with 20 μmol/L Galangin for 4 hours could reduce the number of pyroptotic cell deaths(number of view:23.00±3.61 vs.67.67±15.63,P<0.05)and inhibited the expression of NLRP3 protein(gray value:0.178±0.025 vs.0.406±0.066,P<0.05).High-throughput sequencing showed that,compared with the model group,Galangin down-regulated the genes of Nlrp3,Nod2,IL-1β and up-regulated genes of Skp2(also known as Fbxl1),Fbxl20,Fbxl4,Fbxo32 and Fbxw7.Conclusion Galangin inhibited pyroptosis mediated by NLRP3 inflammasome in macrophages.

Objective:To evaluate the epidemiological characteristics and explore the associated factors of breakthrough cases (BC) from Public Health Emergency Events (PHEEs) of varicella in Zhejiang Province from 2019 to 2022.Methods:Data on cases were obtained from the China Information System for Disease Control and Prevention and the PHEEs Reporting Information Database of Varicella in Zhejiang Province. History records were matched through the Zhejiang Provincial Immunization Information System. Descriptive analysis and multiple logistic regression model with a bidirectional stepwise selection method were performed to explore associated factors for BC during 2019-2022.Results:A total of 144 276 varicella cases were reported from 2019 to 2022, with the annual reported incidence of 47.35-82.80 cases per 100 000 population. Among these cases, 109 172 were non-breakthrough cases (NBC, accounting for 75.67%), 34 517 were BC (23.92%), and the rest 587 cases had unclear vaccination history on varicella (0.41%). A total of 214 PHEEs of varicella were reported, of which 99.07% occurred in school settings. The proportion of PHEEs that occurred in high school increased significantly as time went on ( χ2trend=5.742, P=0.017). Multiple logistic regression model which focused on "BC vs. NBC (as the reference)" indicated that the year of onset ( OR=1.585, 95% CI:1.343-1.878), the month of onset (taking January as the reference, OR=2.311-15.652), city (taking Hangzhou as the reference, Jiaxing OR=2.370, Jinhua OR=2.197, Lishui OR=0.134), age ( OR=0.887, 95% CI: 0.826-0.944), PHEEs setting (taking "primary school and below" as the reference, "high school and above" OR=0.516, 95% CI: 0.305-0.897), and the number of rashes ( OR=0.569, 95% CI: 0.458-0.703) were associated factors. Multiple logistic regression model which focused on "two-dose BC vs. one-dose BC (as the reference)" showed that the age of initial vaccination ( OR=0.045, 95% CI: 0.014-0.107), the time interval from onset to the last dose ( OR=0.037, 95% CI: 0.011-0.087) and the age of onset ( OR=20.724, 95% CI: 8.383-72.485) were associated factors. Conclusion:During 2019-2022, the reported high-risk group of varicella in Zhejiang Province has shifted to adolescents and young adults. Although vaccination could not completely prevent the onset of VZV, it could relieve clinical symptoms and delay the age of onset.