OBJECTIVE:To evaluate the efficacy and safety of different immunosorbent columns in the treatment of rheumatoid arthritis through a network meta-analysis,and provide evidence-based basis for clinical diagnosis and treatment.METHODS:By computer,the databases of VIP,WanFang,CNKI,PubMed,CBM,CochraneLibrary,and Web of Science were searched for published cohort studies of immunosorbent column for the treatment of rheumatoid arthritis,with a time limit until August 2024.The quality of the included randomized controlled trials was assessed using the Cochrane5.4 manual.The quality of retrospective cohort studies were evaluated via the Newcastle-Ottawa Scale(NOS).Bayesian network meta-analysis was performed using R4.1.1 software.RESULTS:A total of 13 studies were included,with a total sample size of 891 cases,and 4 immunosorbent columns were included.The results of the network meta-analysis showed that the top three orders that reduce C-reactive protein level:HA280 adsorption column+conventional Western medicine＞PH-350 adsorption column+conventional Western medicine＞A protein adsorption column;the top three orders that reduce erythrocyte sedimentation rates:leukocyte adsorption column＞HA280 adsorption column+conventional Western medicine＞PH-350 adsorption column+conventional western medicine;the top three orders that reduce swollen joint count:leukocyte adsorption column＞A protein adsorption column+conventional western medicine＞PH-350 type adsorption column+conventional Western medicine;the top three orders that reduce tenderness joint counts:leukocyte adsorption column＞A protein adsorption column+conventional western medicine＞PH-350 adsorption column+conventional Western medicine;the top three orders that reduce patients' disease activity evaluation:PH-350 adsorption column+conventional western medicine＞leukocyte adsorption column＞A protein adsorption column;the top three orders that reduce visual analogue scale scores:PH-350 adsorption column+conventional Western medicine＞A protein adsorption column＞leukocyte adsorption column;the top three orders that reduce physician's disease activity assessment:PH-350 adsorption column+conventional Western medicine＞leukocyte adsorption column＞conventional Western medicine.CONCLUSION:Based on the 13 articles,in terms of reducing C-reactive protein level,HA280 adsorption column and conventional Western medicine are the preferred choice.In terms of reducing erythrocyte sedimentation rate,swollen joint count,and tender joint count,leukocyte adsorption column is the preferred choice.In terms of reducing patient's disease activity evaluation,physician's disease activity evaluation and visual analogue scale scores,PH-350 adsorption column and conventional Western medicine are the first choice.Different immunosorbent columns can be reasonably and accurately selected according to the patient's specific conditions.

OBJECTIVE:To evaluate the efficacy and safety of different immunosorbent columns in the treatment of rheumatoid arthritis through a network meta-analysis,and provide evidence-based basis for clinical diagnosis and treatment.METHODS:By computer,the databases of VIP,WanFang,CNKI,PubMed,CBM,CochraneLibrary,and Web of Science were searched for published cohort studies of immunosorbent column for the treatment of rheumatoid arthritis,with a time limit until August 2024.The quality of the included randomized controlled trials was assessed using the Cochrane5.4 manual.The quality of retrospective cohort studies were evaluated via the Newcastle-Ottawa Scale(NOS).Bayesian network meta-analysis was performed using R4.1.1 software.RESULTS:A total of 13 studies were included,with a total sample size of 891 cases,and 4 immunosorbent columns were included.The results of the network meta-analysis showed that the top three orders that reduce C-reactive protein level:HA280 adsorption column+conventional Western medicine＞PH-350 adsorption column+conventional Western medicine＞A protein adsorption column;the top three orders that reduce erythrocyte sedimentation rates:leukocyte adsorption column＞HA280 adsorption column+conventional Western medicine＞PH-350 adsorption column+conventional western medicine;the top three orders that reduce swollen joint count:leukocyte adsorption column＞A protein adsorption column+conventional western medicine＞PH-350 type adsorption column+conventional Western medicine;the top three orders that reduce tenderness joint counts:leukocyte adsorption column＞A protein adsorption column+conventional western medicine＞PH-350 adsorption column+conventional Western medicine;the top three orders that reduce patients' disease activity evaluation:PH-350 adsorption column+conventional western medicine＞leukocyte adsorption column＞A protein adsorption column;the top three orders that reduce visual analogue scale scores:PH-350 adsorption column+conventional Western medicine＞A protein adsorption column＞leukocyte adsorption column;the top three orders that reduce physician's disease activity assessment:PH-350 adsorption column+conventional Western medicine＞leukocyte adsorption column＞conventional Western medicine.CONCLUSION:Based on the 13 articles,in terms of reducing C-reactive protein level,HA280 adsorption column and conventional Western medicine are the preferred choice.In terms of reducing erythrocyte sedimentation rate,swollen joint count,and tender joint count,leukocyte adsorption column is the preferred choice.In terms of reducing patient's disease activity evaluation,physician's disease activity evaluation and visual analogue scale scores,PH-350 adsorption column and conventional Western medicine are the first choice.Different immunosorbent columns can be reasonably and accurately selected according to the patient's specific conditions.

Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

OBJECTIVES: To analyze the multimorbidity patterns and core diseases among hospitalized patients in different age groups and to explore the impacts of multimorbidity patterns on hospitalization costs. METHODS: Electronic medical records of adult inpatients (aged ≥18 years) from Ningbo Medical Center Lihuili Hospital between January 1, 2018, and June 30, 2023 were collected. The multimorbidity status involving 53 specific diseases was analyzed across different age groups. Association rule mining was used to identify common multimorbidity patterns. Complex network analysis was used to identify core diseases within the multimorbidity networks. Generalized estimating equations (GEE) were used to analyze the impact of different multimorbidity patterns on hospitalization costs. RESULTS: The prevalence of multimorbidity among the 359 402 adult inpatients was 38.51%, with higher rates observed in males (43.60%) and elderly patients (58.29%). Association rule mining identified 15 common multimorbidity patterns, which exhibited differences across age groups. The most prevalent multimorbidity pattern overall was "diabetes→hypertension" (support=7.04%, confidence=62.17%, lift=2.17). In the young adult group, the most prevalent pattern was "dyslipidemia→chronic liver disease" (support=1.19%, confidence=53.17%, lift=6.04). In the middle-aged group, it was "diabetes→hypertension" (support=4.84%, confidence=50.28%, lift=2.15). In the elderly group, it was "coronary heart disease, diabetes→hypertension" (support=2.38%, confidence=77.43%, lift=1.63). Complex network analysis revealed that the core diseases within multimorbidity networks differed across age groups. The core disease identified in the young adult group was chronic liver disease (degree centrality=50, betweenness centrality=0.055, closeness centrality=0.963). Core diseases in the middle-aged group included hypertension, chronic liver disease, and diabetes (all with degree centrality=52, betweenness centrality=0.022, closeness centrality=1.000). Core diseases in the elderly group comprised hypertension, diabetes, malignant tumors, chronic liver disease, thyroid disease, anemia, and arrhythmia (all with degree centrality=52, betweenness centrality=0.009, closeness centrality=1.000). Generalized estimating equations analysis indicated that, most multimorbidity patterns were significantly associated with increased hospitalization costs. However, the magnitude of cost increase varied across different multimorbidity patterns. Specifically, hospitalization costs for patients with patterns such as "heart failure→hypertension", "stroke→hypertension", "malignant tumor, diabetes→hypertension", "stroke, diabetes→hypertension", and "diabetes, heart failure→hypertension" were more than double those of patients without any target diseases. CONCLUSIONS Multimorbidity patterns and core diseases among hospitalized patients differ significantly across age groups, and different patterns exert varying impacts on hospitalization costs. These findings underscore the necessity for age-stratified and multimorbidity pattern specific management strategies.
Humans ; Multimorbidity ; Male ; Hospitalization/economics* ; Female ; Aged ; Middle Aged ; Adult ; Age Factors ; Young Adult ; Adolescent ; Diabetes Mellitus/epidemiology* ; Electronic Health Records ; Aged, 80 and over ; Hospital Costs ; China/epidemiology* ; Hypertension/economics* ; Liver Diseases/epidemiology*

Objective:To understand the incidence trend of brucellosis over time in Shandong Province from 2004 to 2023, and provide evidence for the prevention and control of brucellosis.Methods:The incidence data of brucellosis in Shandong from 2004 to 2023 were collected from China Disease Prevention and Control Information System. The annual change percentage (APC) and annual average change percentage (AAPC) of the incidence rate were calculated by using Joinpoint regression model. A age-period-cohort model was used to analyze changes in brucellosis incidence with age, period, and birth cohort.Results:The average annual incidence of brucellosis was 1.76/100 000 in Shandong from 2004 to 2023. The Joinpoint regression analysis results showed that the reported incidence of brucellosis increased by an average of 92.0% and 18.9% each year from 2004 to 2010 and from 2010 to 2014, respectively, and decreased by an average of 0.2% each year from 2014 to 2023. The results of APC model showed that the incidence of brucellosis increased first and then decreased with age ( χ2=176.92, P<0.001), and incidence of brucellosis showed slow increase and rapid increase first, then decrease ( χ2=2 921.03, P<0.001) over time. The risk for brucellosis reached peak in 2016 ( RR=5.29, 95% CI: 4.96-5.65) and became the lowest in 2006 ( RR=0.24, 95% CI: 0.21-0.28). The incidence increased in later birth cohort ( χ2=348.88, P<0.001), the AAPCs of all the age groups were between 15.0% and 40.0%, and the older the age, the greater the risk ( χ2=348.77, P<0.001). Conclusions:From 2004 to 2023, the reported incidence of brucellosis in Shandong showed a significant age-period-cohort effect, which increased first and then decreased, first increased and then decreased with age, increased slowly and rapidly first, then decreased over time, and increased in later birth cohort. It is necessary to conduct targeted prevention and control, health education to reduce the risk for brucellosis.

Accurate prediction of drug responses in cancer cell lines(CCLs)and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine.Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response(CDR)prediction,chal-lenges remain regarding the generalization of new drugs that are unseen in the training set.Herein,we propose a multimodal fusion deep learning(DL)model called drug-target and single-cell language based CDR(DTLCDR)to predict preclinical and clinical CDRs.The model integrates chemical descriptors,mo-lecular graph representations,predicted protein target profiles of drugs,and cell line expression profiles with general knowledge from single cells.Among these features,a well-trained drug-target interaction(DTI)prediction model is used to generate target profiles of drugs,and a pretrained single-cell language model is integrated to provide general genomic knowledge.Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods.Further ablation studies verified the effectiveness of each component of our model,highlighting the significant contribution of target information to generalizability.Subsequently,the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments,demonstrating its potential for real-world applications.Moreover,DTLCDR was transferred to the clinical datasets,demonstrating satisfactory performance in the clinical data,regardless of whether the drugs were included in the cell line dataset.Overall,our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

BACKGROUND: Macrophage polarization anomalies and dysfunction play a crucial role in the pathogenesis of immune thrombocytopenia (ITP). Itaconate is a Krebs cycle-derived immunometabolite synthesized by myeloid cells to modulate cellular metabolism and inflammatory responses. This study aimed to evaluate the immunoregulatory effects of an itaconate derivative on macrophages in patients with ITP. METHODS: Peripheral blood-derived macrophages from patients with ITP and healthy controls were treated with 4-octyl itaconate (4-OI), a derivative of itaconate that can penetrate the cell membrane. Macrophage polarization, antigen-presenting functions, and phagocytic capability were measured via flow cytometry and enzyme-linked immunosorbent assay (ELISA). Macrophage glycolysis in patients with ITP and the metabolic regulatory effect of 4-OI were detected using a Seahorse XFe96 Analyzer. An active murine model of ITP was used to evaluate the therapeutic effects of 4-OI in vivo . RESULTS: 4-OI reduced the levels of CD80 and CD86 in M1 macrophages and suppressed the release of tumor necrosis factor-α (TNF-α) and interleukin (IL)-6 pro-inflammatory cytokines, suggesting that 4-OI could hinder the polarization of macrophages toward an M1 phenotype. We found that 4-OI pretreated M1 macrophages reduced the proliferation of CD4 + T cells and promoted the differentiation of regulatory T cells. In addition, after 4-OI treatment, the phagocytic capacity of M1 macrophages toward antibody-coated platelets decreased significantly in patients with ITP. In addition, the glycolytic function of M1 macrophages was elevated in individuals with ITP compared to those in healthy controls. 4-OI treatment downregulated glycolysis in M1 macrophages. The glycolysis inhibitor 2-deoxy-d-glucose (2-DG) also inhibited the polarization of M1 macrophages and restored their functions. In vivo , 4-OI treatment significantly increased platelet counts in the active ITP murine model. CONCLUSIONS Itaconate derivative 4-OI inhibited M1 macrophage polarization and restored impaired functions through metabolic reprogramming. This study provides a novel therapeutic option for ITP.
Macrophages/metabolism* ; Humans ; Animals ; Succinates/pharmacology* ; Mice ; Male ; Female ; Adult ; Middle Aged ; Flow Cytometry ; Tumor Necrosis Factor-alpha/metabolism* ; Enzyme-Linked Immunosorbent Assay ; Purpura, Thrombocytopenic, Idiopathic/metabolism* ; Glycolysis/drug effects* ; Metabolic Reprogramming

Objective:To understand the incidence trend of brucellosis over time in Shandong Province from 2004 to 2023, and provide evidence for the prevention and control of brucellosis.Methods:The incidence data of brucellosis in Shandong from 2004 to 2023 were collected from China Disease Prevention and Control Information System. The annual change percentage (APC) and annual average change percentage (AAPC) of the incidence rate were calculated by using Joinpoint regression model. A age-period-cohort model was used to analyze changes in brucellosis incidence with age, period, and birth cohort.Results:The average annual incidence of brucellosis was 1.76/100 000 in Shandong from 2004 to 2023. The Joinpoint regression analysis results showed that the reported incidence of brucellosis increased by an average of 92.0% and 18.9% each year from 2004 to 2010 and from 2010 to 2014, respectively, and decreased by an average of 0.2% each year from 2014 to 2023. The results of APC model showed that the incidence of brucellosis increased first and then decreased with age ( χ2=176.92, P<0.001), and incidence of brucellosis showed slow increase and rapid increase first, then decrease ( χ2=2 921.03, P<0.001) over time. The risk for brucellosis reached peak in 2016 ( RR=5.29, 95% CI: 4.96-5.65) and became the lowest in 2006 ( RR=0.24, 95% CI: 0.21-0.28). The incidence increased in later birth cohort ( χ2=348.88, P<0.001), the AAPCs of all the age groups were between 15.0% and 40.0%, and the older the age, the greater the risk ( χ2=348.77, P<0.001). Conclusions:From 2004 to 2023, the reported incidence of brucellosis in Shandong showed a significant age-period-cohort effect, which increased first and then decreased, first increased and then decreased with age, increased slowly and rapidly first, then decreased over time, and increased in later birth cohort. It is necessary to conduct targeted prevention and control, health education to reduce the risk for brucellosis.

The article presents the diagnosis and treatment of an imported case with severe Plasmodium falciparum malaria, and the effect of plasma exchange combined with continuous renal replacement therapy. Severe P. falciparum malaria is characterized by complex clinical symptoms and multiple complications, and plasma exchange combined with continuous renal replacement therapy has a satisfactory therapeutic efficacy for severe P. falciparum malaria.

Objective:To estimate the longitudinal association between serum lipid biomarkers and the development of cardiometabolic multimorbidity (CMM) in middle-aged and old adults (≥45) in China, while examining effect differences among degree of dyslipidemia aggregation and various dyslipidemia combination patterns.Methods:Based on data from the China Health and Retirement Longitudinal Study (2011-2018), logistic regression analysis was used to evaluate the associations of TC, LDL-C, HDL-C, TG (4 forms of dyslipidemias), degree and pattern of dyslipidemia combination with CMM. We also used restricted cubic splines to show the dose-response associations between 4 lipid biomarkers and CMM development.Results:Of the 6 522 participants included, 590 (9.05%) developed CMM. After adjusting for covariates, all 4 forms of dyslipidemias were positively associated with CMM development (high TC: OR=1.33, 95% CI: 1.03-1.71; high LDL-C: OR=1.35, 95% CI: 1.05-1.75; low HDL-C: OR=1.45, 95% CI: 1.19-1.77; high TG: OR=1.50, 95% CI: 1.20-1.88). The U-shaped dose-response relationship between LDL-C and CMM development was observed ( P for non-linear =0.022). The odds of CMM increased with the increase of dyslipidemias forms, which was highest in those with ≥3 forms of dyslipidemias ( OR=2.02, 95% CI: 1.33-3.06). In various dyslipidemia form combinations, the possibility of CMM development was highest in those with high TC, high LDL-C and low HDL-C ( OR=3.54, 95% CI: 1.40-8.67). High TC and high LDL-C were significantly associated with CMM development in people without cardiometabolic diseases. Low HDL-C was positively associated with diabetes and CMM development in participants without cardiometabolic diseases, cardiovascular disease (CVD) followed by diabetes, and diabetes followed by CVD. High TG was positively associated with diabetes and CMM in participants without cardiometabolic diseases, and diabetes followed by CVD. Conclusions:A total of 4 forms of dyslipidemia were all independently associated with CMM development in middle-aged and old adults in China. The dose-response relationship between LDL-C level and CMM development was U-shaped. The aggregation of 4 forms of dyslipidemia were associated with the development of CMM. Low HDL-C and high TG were significantly associated with multiple patterns of cardiometabolic diseases development.