A total of 1,811 individuals from two villages located in the areas of China endemic for Schistosoma japonicum were analyzed by the Kato-Katz parasitologic examination, indirect hemagglutination assay (IHA), and enzyme-linked immunosorbent assay (ELISA). Statistical analysis of the results showed the kappa indices ranged from 0.106 to 0.234 between IHA and the stool examination and ranged from 0.037 to 0.134 between ELISA and the fecal examination. The sensitivity value of the IHA was 83.7% in Village A and 92.3% in Village B; the specificity value of the IHA was 55.8% in Village A and 67.3% in Village B. The sensitivity value of the ELISA was 88.4% in Village A and 96.2% in Village B; the specificity value of the ELISA was 38.4% in both Village A and Village B. A search for a good diagnostic test that can be applied in field situations in China should be given high priority.

Gastrointestinal stromal tumor (GIST) is a mesenchymal tumor of the gastrointestinal tract. Mutation of KIT and PDGFRA genes is implicated in the tumorigenesis. Approximately 10% of GISTs do not harbor mutation of these genes, and they are designated as "wild type" GIST. They are classified into succinate dehydrogenase (SDH)-deficient and non-SDH-deficient groups. SDH-deficient group includes Carney triad and Carney Stratakis syndrome. The patients are young women. Tumors occur in the antrum of the stomach, and tumor cells are epithelioid. Lymph node metastasis is frequent. The non-SDH-deficient group includes neurofibromatosis (NF) type 1 and GISTs with mutations of BRAF, KRAS, and PIK3CA and with the ETV6-NTRK3 fusion gene. GIST in NF occurs in the small intestine, and tumor cells are spindle shaped. GIST with BRAF mutation arises in the small intestine. Attention to the age, gender, family history and other neoplasms may raise the prediction of syndromic disease. Location of the tumor, morphology, and pleomorphism of the tumor cells are further informative. Lymphovascular invasion should be carefully evaluated. The determination of KIT expression is essential for the diagnosis. When wild type GIST is suspected, intensive genetic analysis is required. Further, a careful and long-time observation is recommended.

Schistosomiasis control in China is facing a new challenge due to the rebound of epidemics in many areas and the unsustainable effects of the chemotherapy-based control strategy. Identifying high-risk regions for schistosomiasis is an important first step for an effective and sustainable strategy. Direct surveillance of snail habitats to detect high-risk regions is costly and no longer a desirable approach, while indirect monitoring of acute schistosomiasis may be a satisfactory alternative. To identify high-risk regions for schistosomiasis, we jointly used multiplicative and additive models with the kernel smoothing technique as the main approach to estimate the relative risk (RR) and excess risk (ER) surfaces by analyzing surveillance data for acute schistosomiasis. The feasibility of detecting high-risk regions for schistosomiasis through nonparametric spatial analysis was explored and confirmed in this study, and two significant high-risk regions were identified. The results provide useful hints for improving the national surveillance network for acute schistosomiasis and possible approaches to utilizing surveillance data more efficiently. In addition, the commonly used epidemiological indices, RR and ER, are examined and emphasized from the spatial point of view, which will be helpful for exploring many other epidemiological indices.

Schistosomiasis control in China has, in general, been very successful during the past several decades. However, the rebounding of the epidemic situation in some areas in recent years raises concerns about a sustainable control strategy of which locating active transmission sites (ATS) is a necessary first step. This study presents a systematic approach for locating schistosomiasis ATS by combining the approaches of identifying high risk regions for schisotosmiasis and extracting snail habitats. Environmental, topographical, and human behavioural factors were included in the model. Four significant high-risk regions were detected and 6 ATS were located. We used the normalized difference water index (NDWI) combined with the normalized difference vegetation index (NDVI) to extract snail habitats, and the pointwise 'P-value surface' approach to test statistical significance of predicted disease risk. We found complicated non-linear relationships between predictors and schistosomiasis risk, which might result in serious biases if data were not properly treated. We also found that the associations were related to spatial scales, indicating that a well-designed series of studies were needed to relate the disease risk with predictors across various study scales. Our approach provides a useful tool, especially in the field of vector-borne or environment-related diseases.

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