Immunoglobulin A vasculitis (IgAV), also known as Henoch-Schönlein purpura, has complex etiology and pathogenesis which have not been fully clarified. The latest research shows that SARS-CoV-2 and related vaccines, human papilloma vaccine, and certain biological agents can also induce IgAV. Most studies believe that the formation of galactose-deficient IgA1 (Gd-IgA1) and Gd-IgA1-containing immune complex plays a crucial role in the pathogenesis of IgAV. It is hypothesized that the pathogenesis of IgAV is associated with the binding of IgA1 to anti-endothelial cell antibodies. In addition, genetics also constitutes a major focus of IgAV research. This article reviews the new advances in the etiology of IgAV and summarizes the role of Gd-IgA1, Gd-IgA1-containing immune complex, anti-endothelial antibody, IgA1 conjugates, T lymphocyte immunity, and genetic factors in the pathogenesis of IgAV.
Humans ; IgA Vasculitis ; Antigen-Antibody Complex ; Immunoglobulin A/genetics*

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed. Results: Four hundred sixty four relevant papers were retrieved from the PubMed database. The number of papers published showed an annual increase, in line with the growing trend of the index. Most papers were published in the journal of Environmental Health Perspectives. Results from the Co-word cluster analysis identified five clusters: cluster#0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution; cluster#1 referred to land use regression modeling and exposure assessment; cluster#2 was related to the epidemiology on traffic exposure; cluster#3 dealt with the exposure to ultrafine particles and related health effects; cluster#4 described the exposure to black carbon and related health effects. Data from Timeline mapping indicated that cluster#0 and#1 were the main research areas while cluster#3 and#4 were the up-coming hot areas of research. Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling. Conclusion: In order to better assess the health-related risks of ambient air pollution, and to best inform preventative public health intervention policies, application of LUR models to environmental epidemiology studies in China should be encouraged.
Air Pollutants/analysis* ; Air Pollution ; Bibliometrics ; China ; Environment ; Environmental Monitoring/methods* ; Humans ; Models, Theoretical ; Periodicals as Topic ; Regression Analysis ; Research

Objective: To investigate the clinicopathological features of glomuvenous malformation (GVM). Methods: Thirty-one cases of GVM diagnosed at the Henan Provincial People's Hospital from January 2011 to December 2021 were collected. Their clinical and pathological features were analyzed. The expression of relevant markers was examined using immunohistochemistry. The patients were also followed up. Results: There were 16 males and 15 females in this study, with an average age of 11 years (range, 1-52 years). The locations of the disease included 13 cases in the limbs (8 cases in the upper limbs, 5 cases in the lower limbs), 9 cases in the trunks, and 9 cases in the foot (toes or subungual area). Twenty-seven of the cases were solitary and 4 were multifocal. The lesions were characterized by blue-purple papules or plaques on the skin surface, which grew slowly. The lumps became larger and appeared to be conspicuous. Microscopically, GVM mainly involved the dermis and subcutaneous tissue, with an overall ill-defined border. There were scattered or clustered irregular dilated vein-like lumens, with thin walls and various sizes. A single or multiple layers of relatively uniform cubic/glomus cells were present at the abnormal wall, with scattered small nests of the glomus cells. The endothelial cells in the wall of abnormal lumen were flat or absent. Immunohistochemistry showed that glomus cells strongly expressed SMA, h-caldesmon, and collagen IV. Malformed vascular endothelial cells expressed CD31, CD34 and ERG. No postoperative recurrence was found in the 12 cases. Conclusions: GVM is an uncommon type of simple venous malformation in the superficial soft tissue and different from the classical glomus tumor. Morphologically, one or more layers of glomus cells grow around the dilated venous malformation-like lumen, which can be combined with common venous malformations.
Male ; Female ; Humans ; Child ; Glomus Tumor/surgery* ; Endothelial Cells/pathology* ; Paraganglioma, Extra-Adrenal/pathology* ; Immunohistochemistry

Objective: To analyze the epidemiological and temporal-spatial distribution characteristics of hemorrhagic fever with renal syndrome (HFRS) in Shandong province during 2010-2016 and provide references for developing prevention and control measures. Methods: Based on the data of Infectious Disease Reporting Information System in China, the incidence and temporal-spatial distribution of HFRS in Shandong from 2010 to 2016 were analyzed by spatial autocorrelation and space-time scan statistics. Results: A total of 9 114 HFRS cases were reported in Shandong during this period. The cases were mainly distributed in age group 30-70 years, and the male to female ratio of the cases was 2.63 ∶ 1. Most cases were farmers. The higher incidence rate was reported in southeastern Shandong, while the lower incidence rate was reported in northwestern Shandong. Among the epidemic periods, the highest incidence rate was 1.87/100 000 in 2013. The results of spatial autocorrelation and space-time scanning indicated that the high-high clusters of HFRS were concentrated in southeastern Shandong and then spread to central Shandong. The cluster mainly occurred from the end of 2011 to the first half of 2015. Both the incidence rate and the cluster decreased in 2016. Conclusions: The epidemic and cluster of HFRS still existed in Shandong from 2010 to 2016. The key areas for the prevention and control of HFRS were in southeastern and central Shandong.
Adolescent ; Adult ; Aged ; China/epidemiology* ; Epidemics ; Female ; Hantaan virus ; Hemorrhagic Fever with Renal Syndrome/virology* ; Humans ; Incidence ; Male ; Seasons ; Spatio-Temporal Analysis ; Young Adult

Objective: To understand the disease progression and human leukocyte antigen (HLA) gene polymorphism of HIV-infected persons without disease progress for long term, also known as long-term non-progressors (LTNPs), in Henan province. Methods: A retrospective study was conducted in 48 LTNPs with complete detection and follow-up information during 2011-2016 in Henan. Changes of CD(4)(+)T cells counts (CD(4)) and viral load (VL) during follow-up period were discussed. Polymerase chain reaction-sequence-specific oligonucleotide probe (PCR-SSOP) was used for the analyses of HLA-A, HLA-B and HLA-DRB1 alleles between LTNPs and healthy controls. Results: From 2011 to 2016, forty-eight LTNPs showed a decrease of the quartile (P(25)-P(75)) of CD(4) from 601.00 (488.50-708.72)/μl to 494.00 (367.00-672.00)/μl, and the difference was significant (P<0.05). The increase of the quartile (P(25)-P(75)) of log(10)VL from 3.40 (2.87-3.97) to 3.48 (2.60-4.37), but the difference was not significant (P>0.05). HLA polymorphism analysis revealed that HLA-B*13:02 and HLA-B*40:06 were more common in LTNPs (P<0.05), while HLA-B*46:01 and HLA-DRB1*09:01 were more common in healthy controls (P<0.05). Conclusions: The CD(4) of LTNPs in Henan showed a downward trend year by year. HLA-B*13:02 and B*40:06 might be associated with delayed disease progression for HIV infected persons in Henan.
Adult ; Alleles ; Asian People/genetics* ; China ; Disease Progression ; Female ; HIV ; HIV Infections/virology* ; HIV-1/immunology* ; HLA-B Antigens/genetics* ; Humans ; Middle Aged ; Polymorphism, Genetic ; Retrospective Studies ; Viral Load