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 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 analyze the strengths, weaknesses, opportunities and threats of the construction on intelligent vaccination clinics in Zhejiang Province, so as to provide countermeasures for promoting the construction of intelligent vaccination clinics in Zhejiang Province. Methods: By reviewing the annual reports of Zhejiang immunization planning, survey data from Zhejiang Centers for Disease Control and Prevention and Immunization Intelligent Service System, data of human resources of immunization planning, vaccine procurement, construction progress of intelligent vaccination clinics and vaccination were collected. The relevant literature was searched to gather information on the construction standards and norms of intelligent vaccination clinics. The analysis of the strengths, weaknesses, opportunities and threats (SWOT) of the construction of intelligent vaccination clinics was conducted, and corresponding countermeasures and suggestions were proposed. Results: The National Immunization Program reported vaccine rate in Zhejiang Province is more than 99%, and standardized vaccination clinics have been popularized throughout the province. The vaccination staff are professional, and a province-wide intelligent immunization service information system has been established, providing the resources and conditions for the construction of intelligent vaccination clinics. However, there are problems such as low data quality and matching efficiency in vaccination, insufficient data interoperability and sharing, unbalanced regional capabilities in intelligent transformation, and uneven distribution of talent and resources. It is crucial to seize the opportunities presented by the development of big data and artificial intelligence, rely on the regional development of the Internet and health industry, seize the opportunity of rapid growth in demand for intelligent vaccination services and high public acceptance, accelerate the construction of intelligent vaccination clinics, and establish intelligent vaccination service standards as soon as possible. Conclusion We should seize the opportunities presented by the digital reform and development, fully utilize the existing vaccination resources and strengths, address the shortcomings, and accelerate the construction of intelligent vaccination clinics in Zhejiang Province.

Objective:To explore the role of corticotrophin releasing factor receptor 2 (CRFR2) in regulating pain sensitization and anxiety and its mechanism in chronic migraine mice.Methods:Forty-eight C57BL/6J mice were randomly divided into control group, model group, NBI35965 group and K41498 group ( n=12); chronic migraine models in the later 3 groups were established by intraperitoneally administrating 10 mg/kg nitroglycerin on the 1 st, 3 rd, 5 th, 7 th and 9 th d; mice in the NBI35965 group and K41498 group were injected with 100 nL NBI35965 or K41498 solution into the bilateral trigeminal nucleus caudalis on the 2 nd, 4 th, 6 th and 8 th d, and mice in the control group were injected with same volume of normal saline. Von frey fiber was used to detect the orbitofrontal mechanical pain threshold 2 h after intraperitoneal injection on the 1 st, 3 rd, 5 th, 7 th and 9 th d, and at 11 a.m. on the 10 th d. Elevated plus maze was used to detect the anxiety-like behaviors at 11 a.m. on the 11 th d. Western blotting was performed to detect the protein expressions of corticotrophin releasing factor (CRF), corticotrophin releasing factor receptor 1 (CRFR1), CRFR2 in the trigeminal nucleus caudalis. Real-time quantitative PCR (RT-qPCR) was used to detect the CRFR1 and CRFR2 mRNA expressions in the trigeminal nucleus caudalis. Immunofluorescent staining was used to detect the protein expressions of calcitonin gene-related peptide (CGRP), immediate-early gene c-fos, glial fibrillary acidic protein (GFAP) and ionized calcium-binding adapter molecule 1 (Iba-1) in the trigeminal nucleus caudalis. Results:Compared with the control group, the model group, NBI35965 group and K41498 group had significantly decreased orbitofrontal mechanical pain thresholds 3, 5, 7, 9, and 10 d after intraperitoneal injection ( P<0.05); compared with model group, the K41498 group had significantly increased orbitofrontal mechanical pain thresholds 7, 9, and 10 d after intraperitoneal injection ( P<0.05). Compared with control group, the model group, NBI35965 group and K41498 group had significantly decreased entries and shorter time in opened arms ( P<0.05); compared with the model group, the K41498 group had significantly increased entries and shorter time in opened arms ( P<0.05). Compared with the control group, the model group, NBI35965 group and K41498 group had significantly higher CRF and CRFR2 protein expressions in the trigeminal nucleus caudalis ( P<0.05); compared with the model group, the K41498 group had statistically lower CRF protein expression in the trigeminal nucleus caudalis ( P<0.05). Compared with the control group, the model group, NBI35965 group and K41498 group had significantly higher CRFR2 mRNA expression in the trigeminal nucleus caudalis ( P<0.05). Compard with the control group, the model group, NBI35965 group and K41498 group had significantly increased CGRP, c-fos, Iba-1 and GFAP protein expressions in the trigeminal nucleus caudalis ( P<0.05); compared with the model group, the K41498 group had significantly decreased CGRP and c-fos protein expressions in the trigeminal nucleus caudalis ( P<0.05). Conclusion:CRFR2 can alter the orbitofrontal pain sensitization and anxiety-like behaviors in chronic migraine mice by regulating neuronal activation and CGRP release in the trigeminal nucleus caudalis.

Objective To analyze the influencing factors of survival of patients with airway stenosis requiring clinical interventions after lung transplantation. Methods Clinical data of 66 patients with airway stenosis requiring clinical interventions after lung transplantation were retrospectively analyzed. Univariate and multivariate Cox’s regression models were adopted to analyze the influencing factors of survival of all patients with airway stenosis and those with early airway stenosis. Kaplan-Meier method was used to calculate the overall survival and delineate the survival curve. Results For 66 patients with airway stenosis, the median airway stenosis-free time was 72 (52,102) d, 27% (18/66) for central airway stenosis and 73% (48/66) for distal airway stenosis. Postoperative mechanical ventilation time [hazard ratio (HR) 1.037, 95% confidence interval (CI) 1.005-1.070, P=0.024] and type of surgery (HR 0.400, 95%CI 0.177-0.903, P=0.027) were correlated with the survival of patients with airway stenosis after lung transplantation. The longer the postoperative mechanical ventilation time, the higher the risk of mortality of the recipients. The overall survival of airway stenosis recipients undergoing bilateral lung transplantation was better than that of their counterparts after single lung transplantation. Subgroup analysis showed that grade 3 primary graft dysfunction (PGD) (HR 4.577, 95%CI 1.439-14.555, P=0.010) and immunosuppressive drugs (HR 0.079, 95%CI 0.022-0.287, P<0.001) were associated with the survival of patients with early airway stenosis after lung transplantation. The overall survival of patients with early airway stenosis after lung transplantation without grade 3 PGD was better compared with that of those with grade 3 PGD. The overall survival of patients with early airway stenosis after lung transplantation treated with tacrolimus was superior to that of their counterparts treated with cyclosporine. Conclusions Long postoperative mechanical ventilation time, single lung transplantation, grade 3 PGD and use of cyclosporine may affect the survival of patients with airway stenosis after lung transplantation.

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.

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.

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 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.