Air pollution, greenspace exposure and risk of Parkinson's disease: a prospective study of 441,462 participants.

BACKGROUND: The current understandings of the relationship between air pollution (AP), greenspace exposure and Parkinson's Disease (PD) remain inconclusive. METHODS: We engaged 441,462 participants from the UK Biobank who were not diagnosed with PD. Utilizing Cox proportional hazard regression model, relationships between AP [nitrogen dioxide (NO2), and nitrogen oxides (NOX), particulate matter < 2.5 µm in aerodynamic diameter(PM2.5), coarse particulate matter between 2.5 µm and 10 µm in aerodynamic diameter(PM2.5-10), particulate matter < 10 µm in aerodynamic diameter(PM10)], greenspace exposure, and PD risk were determined independently. Our analyses comprised three models, adjusted for covariates, and affirmed through six sensitivity analyses to bolster the robustness of our findings. Moreover, mediation analysis was deployed to discern the mediating effect of AP between greenspaces and PD. RESULTS: During a median follow-up of 12.23 years (5,574,293 person-years), there were 3,293 PD events. Each interquartile (IQR) increment in NO2 and PM10 concentrations were associated with 10% and 8% increase in PD onset risk, while the increases in NOX, PM2.5 and PM2.5-10 were not associated with PD risk. Additionally, greenspace may safeguard by reducing NO2 and PM10 levels, with the effect mediated by NO2 and PM10 in greenspace-PD relationship. CONCLUSION: Our findings indicate that an IQR increase in ambient NO2 and PM10 concentrations was associated with risk of PD development, while other pollutants (NOX, PM2.5 and PM2.5-10) were not associated with PD risk. Firstly, we find that augmented exposure to greenspace was associated with the lower PD risk by reducing NO2 and PM10 levels.

Clinico-pathological features of diabetic and non-diabetic renal diseases in type 2 diabetic patients: a retrospective study from a 10-year experience in a single center.

AIM: To compare clinical and pathological characteristics as well as prognosis between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) so as to explore potential diagnostic criteria of DN and provide some guidance for the treatment of type 2 diabetes mellitus (T2DM) patients with kidney involvement. METHODS: T2DM patients with renal impairment who underwent kidney biopsy were included in this study, who were classified into 3 groups (DN, NDRD, DN with NDRD) based on their renal pathological diagnosis. Baseline clinical characteristics as well as follow-up data were collected and analyzed among 3 groups. Logistic regression was performed to determine the best predictors for DN diagnosis. Additional 34 MN patients without diabetes were enrolled by propensity score matching method to compare serum PLA2R antibody titer and kidney outcomes between diabetic MN patients and MN alone. RESULTS: Among 365 patients with type 2 diabetes who underwent kidney biopsy, 179 (49.0%) patients were diagnosed with NDRD alone and 37 (10.1%) patients with NDRD combined DN. Risk factors for DN development in T2DM patients were longer time since diabetes diagnosis, higher level of serum creatinine, absence of hematuria and presence of diabetic retinopathy by multivariate analysis. Lower rate of proteinuria remission and higher risk of renal progression were observed in DN group compared with NDRD group. Membranous nephropathy was the most common NDRD in diabetic patients. There was no difference in serum PLA2R antibody positiveness or titer between MN patients with or without T2DM. There was lower remission rate but similar renal progression in diabetic MN when age, gender, baseline eGFR, albuminuria and IFTA score were adjusted. CONCLUSIONS: Non-diabetic renal disease is not uncommon in T2DM patients with renal impairment, which has better prognosis with proper treatment. Coexisting diabetic status does not exert negative impact on renal progression in MN patients, and immunosuppressive agents should be administered when necessary.

A multi-target protective effect of Danggui-Shaoyao-San on the vascular endothelium of atherosclerotic mice.

BACKGROUND: Atherosclerosis (AS) is a chronic disease characterized by abnormal blood lipid metabolism, inflammation and vascular endothelial injury. Vascular endothelial injury is the initial stage during the occurrence of AS. However, the function and mechanism of anti-AS are not well characterized. Danggui-Shaoyao-San (DGSY) is a classic Traditional Chinese Medicine (TCM) prescription for the treatment of gynecological diseases, and has been widely used in the treatment of AS in recent years. METHODS: ApoE-/- atherosclerosis male mice were established by feeding with high-fat diet, and then randomly divided into three groups: Atherosclerosis group (AS), Danggui-Shaoyao-San group (DGSY), and Atorvastatin calcium group (X). The mice were administered with the drugs for 16 weeks. Pathological changes in aortic vessels were examined by staining with Oil red O, Masson and hematoxylin-eosin. In addition, blood lipids were analyzed. The level of IL-6 and IL-8 in aortic vessels were detected by ELISA and the expression of ICAM-1 and VCAM-1 in the aortic vascular endothelium were measured by Immunohistochemical. The mRNA expression of interα5ß1/c-Abl/YAP in the aortic vessels were measured by Real-time quantitative PCR and location of expression was assessed by immunofluorescence. RESULTS: DGSY can significantly reduce the content of TC,TG and LDL-C and increase the level of HDL-C in the serum, reduce the plaque area and inhibit the concentration of IL-6 and IL-8, down-regulate the expression of IVAM-1,VCAM-1 and interα5ß1/ c-Abl/YAP in the aortic vessels. CONCLUSIONS: Collectively, DGSY can alleviate vascular endothelium damage and delay the occurrence of AS, and the underlying mechanism may be related to the multi-target protective of DGSY.

Analysis of glomerular PLA2R efficacy in evaluating the prognosis of idiopathic membranous nephropathy in the background of different serum anti-PLA2R levels.

OBJECTIVE: To verify glomerular PLA2R antigen and serum PLA2R antibody expression in membranous nephropathy as well as to explore glomerular PLA2R efficacy in evaluating the prognosis of idiopathic membranous nephropathy (IMN) in the background of different serum anti-PLA2R levels. METHODS: We retrospectively analyzed 155 patients who were diagnosed with IMN by kidney biopsy. Patients were divided into six groups according to their serum PLA2R antibody or glomerular PLA2R antigen positiveness and the level of serum anti-PLA2R titer. Both clinical features and pathological characteristics were recorded, and the remission time was compared among groups. Correlation between clinical figures and the anti-PLA2R titer or semi-quantity of glomerular PLA2R antigen was detected. RESULTS: A positive correlation between time to partial remission and serum anti-PLA2R titer was found. Among patients with serum anti-PLA2R titer <150 RU/ml, there were shorter remission time in negative glomerular PLA2R antigen group compared with positive glomerular PLA2R antigen, and a positive correlation between time to complete remission and semi-quantity of glomerular PLA2R antigen was found. CONCLUSION: Both glomerular PLA2R antigen and serum anti-PLA2R play a role in disease presentation and prognosis in primary membranous nephropathy. Glomerular PLA2R antigen has a major role on disease prognosis when serum anti-PLA2R titer is less than 150RU/ml, while serum anti-PLA2R has predominant role in IMN prognosis when serum anti-PLA2R titer is above 150RU/ml.

Natural Language Processing Algorithms for Normalizing Expressions of Synonymous Symptoms in Traditional Chinese Medicine.

BACKGROUND: The modernization of traditional Chinese medicine (TCM) demands systematic data mining using medical records. However, this process is hindered by the fact that many TCM symptoms have the same meaning but different literal expressions (i.e., TCM synonymous symptoms). This problem can be solved by using natural language processing algorithms to construct a high-quality TCM symptom normalization model for normalizing TCM synonymous symptoms to unified literal expressions. METHODS: Four types of TCM symptom normalization models, based on natural language processing, were constructed to find a high-quality one: (1) a text sequence generation model based on a bidirectional long short-term memory (Bi-LSTM) neural network with an encoder-decoder structure; (2) a text classification model based on a Bi-LSTM neural network and sigmoid function; (3) a text sequence generation model based on bidirectional encoder representation from transformers (BERT) with sequence-to-sequence training method of unified language model (BERT-UniLM); (4) a text classification model based on BERT and sigmoid function (BERT-Classification). The performance of the models was compared using four metrics: accuracy, recall, precision, and F1-score. RESULTS: The BERT-Classification model outperformed the models based on Bi-LSTM and BERT-UniLM with respect to the four metrics. CONCLUSIONS: The BERT-Classification model has superior performance in normalizing expressions of TCM synonymous symptoms.