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OBJECTIVE@#To predict the trends for fine-scale spread of Oncomelania hupensis based on supervised machine learning models in Shanghai Municipality, so as to provide insights into precision O. hupensis snail control.@*METHODS@#Based on 2016 O. hupensis snail survey data in Shanghai Municipality and climatic, geographical, vegetation and socioeconomic data relating to O. hupensis snail distribution, seven supervised machine learning models were created to predict the risk of snail spread in Shanghai, including decision tree, random forest, generalized boosted model, support vector machine, naive Bayes, k-nearest neighbor and C5.0. The performance of seven models for predicting snail spread was evaluated with the area under the receiver operating characteristic curve (AUC), F1-score and accuracy, and optimal models were selected to identify the environmental variables affecting snail spread and predict the areas at risk of snail spread in Shanghai Municipality.@*RESULTS@#Seven supervised machine learning models were successfully created to predict the risk of snail spread in Shanghai Municipality, and random forest (AUC = 0.901, F1-score = 0.840, ACC = 0.797) and generalized boosted model (AUC= 0.889, F1-score = 0.869, ACC = 0.835) showed higher predictive performance than other models. Random forest analysis showed that the three most important climatic variables contributing to snail spread in Shanghai included aridity (11.87%), ≥ 0 °C annual accumulated temperature (10.19%), moisture index (10.18%) and average annual precipitation (9.86%), the two most important vegetation variables included the vegetation index of the first quarter (8.30%) and vegetation index of the second quarter (7.69%). Snails were more likely to spread at aridity of < 0.87, ≥ 0 °C annual accumulated temperature of 5 550 to 5 675 °C, moisture index of > 39% and average annual precipitation of > 1 180 mm, and with the vegetation index of the first quarter of > 0.4 and the vegetation index of the first quarter of > 0.6. According to the water resource developments and township administrative maps, the areas at risk of snail spread were mainly predicted in 10 townships/subdistricts, covering the Xipian, Dongpian and Tainan sections of southern Shanghai.@*CONCLUSIONS@#Supervised machine learning models are effective to predict the risk of fine-scale O. hupensis snail spread and identify the environmental determinants relating to snail spread. The areas at risk of O. hupensis snail spread are mainly located in southwestern Songjiang District, northwestern Jinshan District and southeastern Qingpu District of Shanghai Municipality.
Subject(s)
Animals , Bayes Theorem , China/epidemiology , Ecosystem , Gastropoda , Supervised Machine LearningABSTRACT
Objective To understand the status of chronic filariasis patients in Jiangxi Province in 2018, so as to provide insights into the follow-up care of the patients. Methods In 2018, a case follow-up study was conducted in all registered patients with chronic filariasis in previously endemic areas of Jiangxi Province, and a clue investigation was done for identifying the missing patients. In addition, the data of caring sites for chronic filarisis patients were collected and analyzed in the province. Results A total of 802 chronic filariasis patients were identified in 56 counties (districts) of Jiangxi Province in 2018. The patients had a male/female ratio of 1∶1, and 85.41% had ages of over 70 years. There were 58.60%, 93.89%, 17.21% and 3.62% of chronic filariasis patients with lymphangitis, lymphedema/elephantiasis, chyluria and hydrocele, respectively. A total of 273 caring sites were assigned in 56 counties (districts) of Jiangxi Province, and 306 caring activities were carried out in 2018. Conclusion The number of chronic filariasis patients has significantly decreased in Jiangxi Province; however, the care remains to be intensified for chronic filariasis patients.
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Objective To explore the spatial-temporal distribution of malaria in Jiangxi Province from 1950 to 2017, so as to provide scientific evidence for developing the malaria elimination strategy. Methods The epidemic situation of malaria, demographic data, historical species of malaria parasites and transmission vectors were collected from each county of Jiangxi Province from 1950 to 2017 to create a geographic information system database of malaria in Jiangxi Province. The software ArcGIS 10.3 was used to analyze the incidence of malaria and display the spatial-temporal distribution of malaria in Jiangxi Province, so as to explore the spatial-temporal patterns of malaria in the province. Results From 1950 to 2017, the prevalence of malaria was classified into 3 stages in Jiangxi Province, including the peak period (from 1950 to 1975), the continuous decline period (from 1976 to 1997), and the low-level fluctuation period (from 1998 to 2017). During the period from 1950 through 2017, the incidence of malaria declined, the epidemic area of malaria shrank, and the intensity of malaria transmission gradually reduced to no local infections in Jiangxi Province. The spatial distribution of epidemic areas of malaria shifted from southern mountainous areas to northern plain areas, and finally aggregated, retained and disappeared in plain areas. The species of malaria parasites shifted from a co-endemic area for Plasmodium vivax, P. falciparum and P. malariae to a single endemic area for P. vivax, and finally a co-endemic area for imported P. vivax, P. falciparum, P. malariae and P. ovale. The transmission vectors shifted from multiple vectors of Anopheles sinensis, An. minimus, An. anthropophagus and others to a single vector of An. sinensis. Conclusions There are no local malaria cases for successive 6 years since 2012, and the transmission of malaria has been interrupted in Jiangxi Province, in which the criteria for malaria elimination have been achieved. However, the risk of malaria transmission secondary to imported malaria will emerge in Jiangxi Province for a long period of time.
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Objective To explore the spatial-temporal distribution of malaria in Jiangxi Province from 1950 to 2017, so as to provide scientific evidence for developing the malaria elimination strategy. Methods The epidemic situation of malaria, demographic data, historical species of malaria parasites and transmission vectors were collected from each county of Jiangxi Province from 1950 to 2017 to create a geographic information system database of malaria in Jiangxi Province. The software ArcGIS 10.3 was used to analyze the incidence of malaria and display the spatial-temporal distribution of malaria in Jiangxi Province, so as to explore the spatial-temporal patterns of malaria in the province. Results From 1950 to 2017, the prevalence of malaria was classified into 3 stages in Jiangxi Province, including the peak period (from 1950 to 1975), the continuous decline period (from 1976 to 1997), and the low-level fluctuation period (from 1998 to 2017). During the period from 1950 through 2017, the incidence of malaria declined, the epidemic area of malaria shrank, and the intensity of malaria transmission gradually reduced to no local infections in Jiangxi Province. The spatial distribution of epidemic areas of malaria shifted from southern mountainous areas to northern plain areas, and finally aggregated, retained and disappeared in plain areas. The species of malaria parasites shifted from a co-endemic area for Plasmodium vivax, P. falciparum and P. malariae to a single endemic area for P. vivax, and finally a co-endemic area for imported P. vivax, P. falciparum, P. malariae and P. ovale. The transmission vectors shifted from multiple vectors of Anopheles sinensis, An. minimus, An. anthropophagus and others to a single vector of An. sinensis. Conclusions There are no local malaria cases for successive 6 years since 2012, and the transmission of malaria has been interrupted in Jiangxi Province, in which the criteria for malaria elimination have been achieved. However, the risk of malaria transmission secondary to imported malaria will emerge in Jiangxi Province for a long period of time.
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Objective To study the adjuvant effects of chitosan on Helicobacter pylori (H.pylori) vaccine.Methods A total of 20 adult mice were randomly divided into four groups.Mice in groups Ⅰ,Ⅱ,Ⅲ, and Ⅳ were orally immunized with PBS,H.pylori antigen alone,H.pylori antigen plus chitosan solution or H. pylori antigen plus chitosan particles,respectively.An ELISA was used to detect anti-H.pylori IgA in saliva and gastric mucosa ,interlukin(IL)-2,IL-4,IL-10 levels in gastric mucosa.Immunohistochemical method was used to detect secretory IgA in gastric mucosa.Results ①The levels of special anti-H.pylori IgA in saliva and gastric mucosa in the groups with chitosan as adjuvant were significantly higher than those in the group without adjuvants and control groups(P
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AIM: To study the immunological protection of H. pylori vaccine with chitosa as adjuvant. METHODS: One-grade female BALB/c mice were randomly divided into nine groups and immunized by ①PBS alone; ②chitosan solution alone; ③chitosan particles alone; ④H. pylori antigen alone; ⑤H. pylori antigen plus chitosan solution; ⑥H. pylori antigen plus chitosan particles; ⑦H. pylori antigen plus CT; ⑧H. pylori antigen plus chitosan solution and CT; ⑨H. pylori antigen plus chitosan particles and CT. At 4 weeks after the last immunization, these mice were challenged by alive H. pylori(1?1012CFU/L) twice at two-day intervals. At 4 weeks after the last challenge, these mice were all killed and gastric mucosa were embedded in paraffin, sectioned and assayed with Giemsa staining. The other gastric mucosa were used to quantitatively culture with H. pylori. ELISA was used to detect H.pylori IgA in saliva and gastric mucosa and anti-H.pylori IgG, IgG1, IgG2a in serum, and immunohistochemical method was used to examine sIgA in gastric mucosa. RESULTS: ①In the groups with chitosan as adjuvant, 60% mice achieved immunological protection, which was according to that with CT as adjuvant (58.33%), and was significantly higher than H. pylori antigen alone and other groups without H. pylori antigen(P0.05)and were significantly higher than those in non-adjuvant groups, while those in the groups with chitosan plus CT were significantly higher than those in the group with CT as an adjuvant(P