Spatial epidemiological analysis of severe hand, foot and mouth disease in Guangxi, 2014-2018 / 中国热带医学

@#Abstract: Objective　To explore the spatial epidemiological characteristics of severe cases hand, foot and mouth disease (HFMD) in Guangxi, China, from 2014 to 2018, and to provide a basis for identifying the high-risk regions as well as the prevention and control of severe cases of HFMD in Guangxi. Methods　Spatial-temporal scanning analysis, global and local spatial autocorrelation analysis were used to analyze the spatial clustering of HFMD. The trend surface analysis was used to evaluate the spatial distribution trend of HFMD. Results　From 2014 to 2018, the incidence and severe case fatality rates of HFMD were 3.89/100 000 and 4.23%, respectively. Monte Carlo scanning analysis showed that the first cluster region was Cenxi City, the second cluster was mainly concentrated in northwest of Guangxi, and the aggregation time was mainly concentrated in April to May and August to October. The global spatial autocorrelation analysis showed that the severe HFMD was significant clustering distribution, and the Moran's I coefficients of the sever cases, severe morbidity and severe case fatality rate were 0.088, 0.118, 0.197, respectively (P<0.05). Local spatial autocorrelation analysis showed that hotspots of severe HFMD cases were concentrated in the southern Guangxi, mainly in Lingshan County. Anselin local Moran's I clustering and outlier analysis indicated that 5 high-high (H-H) clustering regions for fatality were Lingshan, Pubei, Zhongshan, Zhaoping and Pinggui County. There were 6 high-high (H-H) clustering regions for severe incidence rate, namely Lingshan, Qinnan, Lingyun, Youjiang, Bama Yao Autonomous and Pinggui County, and 1 high-low (H-L) clustering region, Cenxi County. The trend surface analysis showed that the overall number of severe cases of death decreased from east or west to the middle, and increased from north to middle, and then decreased to south. Conclusions　Severe HFMD cases in Guangxi have obvious spatial-temporal clustering, and the hop spots are mainly concentrated in southern Guangxi. The prevention and control of HFMD in areas with high incidence of severe cases should be strengthened to reduce the burden of HFMD cases.

Identification of human serum protein targets of Qianggu Decoction () in primary type I osteoporosis based on tandem mass tag labeling and liquid chromatography-tandem mass spectrometry technology.

OBJECTIVE: To investigate the serum protein targets of Qianggu Decoction (, QGD) on treating osteoporosis by the proteomics analysis using tandem mass tag (TMT) and liquid chromatographytandem mass spectrometry (LC-MS/MS). METHODS: Twenty serum protein samples were recruited (10 patients with primary type I osteoporosis before and after QGD treatment) and the high abundance ratios protein was removed, two serum samples were extracted and labeled with TMT reagent. Then, mass spectrometric detection, identification of differentially expressed proteins and bioinformatics analysis of differentially expressed proteins were carried out. RESULTS: A total of 60 proteins were identified, within a 99% confidence interval, to be differentially regulated of which, 34 proteins were up-regulated and 26 proteins were down-regulated. Differentially expressed proteins analyzed by Gene Ontology (GO) annotation mainly get involved in 12 different biological processes, 7 types of cellular components, and 6 kinds of molecular functions. Angiotensinogen (AGT), stromelysin-1 (MMP3), heparanase (HPSE) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were screened as candidate protein targets of QGD treatment, which were related to metabolic mechanism of bone remodeling and/or bone collagen of osteoporosis. By the utilization of the protein-protein interaction network analysis tool named STRING10.0, it showed that AGT, MMP3, HPSE and GAPDH were located in the key node of the protein-protein interactions network. Furthermore, AGT, MMP3, HPSE and GAPDH were found to be directly related to BMP, MAPK, Wnt, SMAD and tumor necrosis factor ligand superfamily member 11 (TNFSF11) families. CONCLUSIONS: The proteomics analysis by using TMT combined with LC-MS/MS was a feasible method for screening the potential therapeutic targets associated with QGD treatment. It suggests that AGT, MMP3, HPSE and GAPDH may be candidate protein targets of QGD treatment which can be used as therapeutic effect monitor and early diagnosis of primary type I osteoporosis.

Weak cation exchange magnetic beads coupled with matrix-assisted laser desorption ionization-time of flight-mass spectrometry in screening serum protein markers in osteopenia.

The present study aimed at investigating the weak cation magnetic separation technology and matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) in screening serum protein markers of osteopenia from ten postmenopausal women and ten postmenopausal women without osteopenia as control group, to find a new method for screening biomarkers and establishing a diagnostic model for primary type I osteoporosis. Serum samples were collected from postmenopausal women with osteopenia and postmenopausal women with normal bone mass. Proteins were extracted from serum samples by weak cation exchange magnetic beads technology, and mass spectra acquisition was done by MALDI-TOF-MS. The visualization and comparison of data sets, statistical peak evaluation, model recognition, and discovery of biomarker candidates were handled by the proteinchip data analysis system software(ZJU-PDAS). The diagnostic models were established using genetic arithmetic based support vector machine (SVM). The SVM result with the highest Youden Index was selected as the model. Combinatorial Peaks having the highest accuracy in distinguishing different samples were selected as potential biomarker. From the two group serum samples, a total of 133 differential features were selected. Ten features with significant intensity differences were screened. In the pair-wise comparisons, processing of MALDI-TOF spectra resulted in the identification of ten differential features between postmenopausal women with osteopenia and postmenopausal women with normal bone mass. The difference of features by Youden index showed that the highest features had a mass to charge ratio of 1699 and 3038 Da. A diagnosis model was established with these two peaks as the candidate marker, and the specificity of the model is 100 %, the sensitivity was 90 % by leave-one-out cross validation test. The two groups of specimens in SVM results on the scatter plot could be clearly distinguished. The peak with m/z 3038 in the SVM model was suggested as Secretin by TagIdent tool. To provide further validation, the secretin levels in serum were analyzed using enzyme-linked immunosorbent assays that is a competitive inhibition enzyme immunoassay technique for the in vitro quantitative measurement of secretin in human serum.