A novel prediction approach driven by graph representation learning for heavy metal concentrations.

The potential risk of heavy metals (HMs) to public health is an issue of great concern. Early prediction is an effective means to reduce the accumulation of HMs. The current prediction methods rarely take internal correlations between environmental factors into consideration, which negatively affects the accuracy of the prediction model and the interpretability of intrinsic mechanisms. Graph representation learning (GraRL) can simultaneously learn the attribute relationships between environmental factors and graph structural information. Herein, we developed the GraRL-HM method to predict the HM concentrations in soil-rice systems. The method consists of two modules, which are PeTPG and GCN-HM. In PeTPG, a graphic structure was generated using graph representation and communitization technology to explore the correlations and transmission paths of different environmental factors. Subsequently, the GCN-HM model based on the graph convolutional neural network (GCN) was used to predict the HM concentrations. The GraRL-HM method was validated by 2295 sets of data covering 21 environmental factors. The results indicated that the PeTPG model simplified correlation paths between factor nodes from 396 to 184, reducing by 53.5 % graph scale by eliminating the invalid paths. The concise and efficient graph structure enhanced the learning efficiency and representation accuracy of downstream prediction models. The GCN-HM model was superior to the four benchmark models in predicting the HM concentration in the crop, improving R2 by 36.1 %. This study develops a novel approach to improve the prediction accuracy of pollutant accumulation and provides valuable insights into intelligent regulation and planting guidance for heavy metal pollution control.

Circulating Immune Landscape Profiling in Psoriasis Vulgaris and Psoriatic Arthritis by Mass Cytometry.

Background: Psoriasis, a systemic disorder mediated by the immune system, can appear on the skin, joints, or both. Individuals with cutaneous psoriasis (PsC) have an elevated risk of developing psoriatic arthritis (PsA) during their lifetime. Despite this known association, the cellular and molecular mechanisms underlying this progression remain unclear. Methods: We performed high-dimensional, in-depth immunophenotyping of peripheral blood mononuclear cells (PBMCs) in patients with PsA and psoriasis vulgaris (PsV) by mass cytometry. Blood samples were collected before and after therapy for a longitudinal study. Then three sets of comparisons were made here: active PsA vs. active PsV, untreated PsV vs. treated PsV, and untreated PsA vs. treated PsA. Results: Marked differences were observed in multiple lymphocyte subsets of PsA related to PsV, with expansion of CD4+ T cells, CD16- NK cells, and B cells. Notably, two critical markers, CD28 and CD127, specifically differentiated PsA from PsV. The expression levels of CD28 and CD127 on both Naïve T cells (TN) and central memory CD4+ T cells (TCM) were considerably higher in PsA than PsV. Meanwhile, after treatment, patients with PsV had higher levels of CD28hi CD127hi CD4+ TCM cells, CD28hi CD127hi CD4+ TN cells, and CD16- NK cells. Conclusion: In the circulation of PsA patients, the TN and CD4+ TCM are characterized with more abundant CD28 and CD127, which effectively distinguished PsA from PsV. This may indicate that individuals undergoing PsV could be stratified at high risk of developing PsA based on the circulating levels of CD28 and CD127 on specific cell subsets.

Temporal-spatial distribution characteristics of leprosy: A new challenge for leprosy prevention and control in Zhejiang, China.

BACKGROUND: After the elimination of leprosy in 1995, there were 10-30 newly detected leprosy cases every year in Zhejiang Province, and the epidemiological characteristics of the newly detected leprosy cases have changed. While most of the newly detected cases came from other provinces in China, not Zhejiang, it brought a new challenge for leprosy prevention and control in post- elimination era in Zhejiang, China. This study was aimed to understand the temporal-spatial distribution characteristics of newly detected leprosy cases, and provide the scientific rationales for the development of leprosy control strategy. METHODS: Data on the demographic of Zhejiang Province from 2011 to 2019 were obtained from the China Information System for Disease Control and Prevention, and the epidemiological data on leprosy cases newly detected in Zhejiang Province from 2011 to 2019 were obtained from the LEPROSY MANAGEMANT INFORMATION SYSTEM IN CHINA (LEPMIS), and temporal-spatial distributions were described. The geographic information system software-ArcGIS 10.4 was used to draw the statistical maps, and Geoda 1.14.0 was used for local spatial autocorrelation analysis (local Getis coefficient method). Ridley-Jopling classification was used to classify the clinical types into I, TT, BT, BB, BL or LL. Two-group classification system developed by the World Health Organization (WHO) was used and cases were classified into multibacillary (MB) type or paucibacillary (PB) type. RESULTS: A total of 167 leprosy cases were reported in Zhejiang Province during 2011-2019, including 107 cases in males and 60 in females. The mean age at diagnosis was 37.99±14.81 years, and 94.01% of the cases were detected through the examination at skin-clinics. The number of workers, MB cases, G2D cases were 81 (48.50%), 159 (94.01%), 24 (14.37%) respectively, and the rate of early detection increased from 45.16% in 2011 to 90.91% in 2019. Leprosy cases were reported in all the prefectures of Zhejiang except Zhoushan City. The cases in local population accounted for 23.35% (39 cases), and the cases in floating population (especially coming from high epidemic provinces in China) accounted for 76.65% (128 cases). The annual number of newly detected cases showed a decreasing trend, from 31 cases in 2011 to 11 in 2019. Time of the floating population living in Zhejiang Province ranged from several months to more than 10 years. The annual proportion of new cases with G2D declined from 22.58% in 2011 to 9.09% in 2019. The results of local indicators of autocorrelation (LISA) analysis showed that the high-high areas were mainly concentrated in the middle and northeast of Zhejiang Province, while the low-low areas were in the east and southwest. CONCLUSION: A few scattered cases still can be seen in post-elimination era, and there was a spatial clustering of the newly detected leprosy cases in Zhejiang Province. Most of the cases in Zhejiang Province were from other high epidemic provinces in China, which brought a new challenge for leprosy control and prevention in post- elimination era in Zhejiang, and it is also necessary to strengthen the early detection and standard management of the leprosy cases in floating population in Zhejiang.

Mycobacterium marseillense Infection in Human Skin, China, 2018.

We describe a case of facial skin infection and sinusitis caused by Mycobacterium marseillense in an immunocompetent woman in China in 2018. The infection was cleared with clarithromycin, moxifloxacin, and amikacin. Antimicrobial drug treatments could not be predicted by genetic analyses; further genetic characterization would be required to do so.