2.Spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020.
Y ZHOU ; L TANG ; Y TONG ; J HUANG ; J WANG ; Y ZHANG ; H JIANG ; N XU ; Y GONG ; J YIN ; Q JIANG ; J ZHOU ; Y ZHOU
Chinese Journal of Schistosomiasis Control 2023;35(5):444-450
OBJECTIVE:
To investigate the spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody, and to examine the correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, so as to provide insights into advanced schistosomiais control in the province.
METHODS:
The epidemiological data of schistosomiasis in Hunan Province in 2020 were collected, including number of permanent residents in survey villages, number of advanced schistosomiasis patients, number of residents receiving serological tests and number of residents seropositive for anti-Schistosoma antibody, and the prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were descriptively analyzed. Village-based spatial distribution characteristics of prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were identified in Hunan Province in 2020, and the correlation between the revalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody was examined using Spearman correlation analysis.
RESULTS:
The prevalence of advanced schistosomiasis was 0 to 2.72% and the seroprevalence of anti-Schistosoma antibody was 0 to 20.25% in 1 153 schistosomiasis-endemic villages in Hunan Province in 2020. Spatial clusters were identified in both the prevalence of advanced schistosomiasis (global Moran's I = 0.416, P < 0.01) and the seroprevalence of anti-Schistosoma antibody (global Moran's I = 0.711, P < 0.01) in Hunan Province. Local spatial autocorrelation analysis identified 98 schistosomiasis-endemic villages with high-high clusters of the prevalence of advanced schistosomiasis, 134 endemic villages with high-high clusters of the seroprevalence of anti-Schistosoma antibody and 36 endemic villages with high-high clusters of both the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province. In addition, spearman correlation analysis showed a positive correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody (rs = 0.235, P < 0.05).
CONCLUSIONS
There were spatial clusters of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, which were predominantly located in areas neighboring the Dongting Lake. These clusters should be given a high priority in the schistosomiasis control programs.
Animals
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Humans
;
Prevalence
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Seroepidemiologic Studies
;
Schistosomiasis/epidemiology*
;
Schistosoma
;
Spatial Analysis
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Antibodies, Helminth
;
China/epidemiology*
4.The application of the non-woven fabric and filter paper "sandwich" fixation method in preventing the separation of the mucosal layer and muscular layer in mouse colon histopathological sections.
L SHEN ; Y T LI ; M Y XU ; G Y LIU ; X W ZHANG ; Y CHENG ; G Q ZHU ; M ZHANG ; L WANG ; X F ZHANG ; L G ZUO ; Z J GENG ; J LI ; Y Y WANG ; X SONG
Chinese Journal of Pathology 2023;52(10):1040-1043
6.Pulmonary anaplastic lymphoma kinase positive histiocytosis: report of a case.
W M XU ; Z R GAO ; X LI ; Y JIANG ; Q FENG ; L W RUAN ; Y Y WANG
Chinese Journal of Pathology 2023;52(11):1168-1170
8.Application and evaluation of artificial intelligence TPS-assisted cytologic screening system in urine exfoliative cytology.
L ZHU ; M L JIN ; S R HE ; H M XU ; J W HUANG ; L F KONG ; D H LI ; J X HU ; X Y WANG ; Y W JIN ; H HE ; X Y WANG ; Y Y SONG ; X Q WANG ; Z M YANG ; A X HU
Chinese Journal of Pathology 2023;52(12):1223-1229
Objective: To explore the application of manual screening collaborated with the Artificial Intelligence TPS-Assisted Cytologic Screening System in urinary exfoliative cytology and its clinical values. Methods: A total of 3 033 urine exfoliated cytology samples were collected at the Henan People's Hospital, Capital Medical University, Beijing, China. Liquid-based thin-layer cytology was prepared. The slides were manually read under the microscope and digitally presented using a scanner. The intelligent identification and analysis were carried out using an artificial intelligence TPS assisted screening system. The Paris Report Classification System of Urinary Exfoliated Cytology 2022 was used as the evaluation standard. Atypical urothelial cells and even higher grade lesions were considered as positive when evaluating the recognition sensitivity, specificity, and diagnostic accuracy of artificial intelligence-assisted screening systems and human-machine collaborative cytologic screening methods in urine exfoliative cytology. Among the collected cases, there were also 1 100 pathological tissue controls. Results: The accuracy, sensitivity and specificity of the AI-assisted cytologic screening system were 77.18%, 90.79% and 69.49%; those of human-machine coordination method were 92.89%, 99.63% and 89.09%, respectively. Compared with the histopathological results, the accuracy, sensitivity and specificity of manual reading were 79.82%, 74.20% and 95.80%, respectively, while those of AI-assisted cytologic screening system were 93.45%, 93.73% and 92.66%, respectively. The accuracy, sensitivity and specificity of human-machine coordination method were 95.36%, 95.21% and 95.80%, respectively. Both cytological and histological controls showed that human-machine coordination review method had higher diagnostic accuracy and sensitivity, and lower false negative rates. Conclusions: The artificial intelligence TPS assisted cytologic screening system has achieved acceptable accuracy in urine exfoliation cytologic screening. The combination of manual screening and artificial intelligence TPS assisted screening system can effectively improve the sensitivity and accuracy of cytologic screening and reduce the risk of misdiagnosis.
Humans
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Artificial Intelligence
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Urothelium/pathology*
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Cytodiagnosis
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Epithelial Cells/pathology*
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Sensitivity and Specificity
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Urologic Neoplasms/urine*
9.Development and validation of a prognostic prediction model for patients with stage Ⅰ to Ⅲ colon cancer incorporating high-risk pathological features.
K X LI ; Q B WU ; F Q ZHAO ; J L ZHANG ; S L LUO ; S D HU ; B WU ; H L LI ; G L LIN ; H Z QIU ; J Y LU ; L XU ; Z WANG ; X H DU ; L KANG ; X WANG ; Z Q WANG ; Q LIU ; Y XIAO
Chinese Journal of Surgery 2023;61(9):753-759
Objective: To examine a predictive model that incorporating high risk pathological factors for the prognosis of stage Ⅰ to Ⅲ colon cancer. Methods: This study retrospectively collected clinicopathological information and survival outcomes of stage Ⅰ~Ⅲ colon cancer patients who underwent curative surgery in 7 tertiary hospitals in China from January 1, 2016 to December 31, 2017. A total of 1 650 patients were enrolled, aged (M(IQR)) 62 (18) years (range: 14 to 100). There were 963 males and 687 females. The median follow-up period was 51 months. The Cox proportional hazardous regression model was utilized to select high-risk pathological factors, establish the nomogram and scoring system. The Bootstrap resampling method was utilized for internal validation of the model, the concordance index (C-index) was used to assess discrimination and calibration curves were presented to assess model calibration. The Kaplan-Meier method was used to plot survival curves after risk grouping, and Cox regression was used to compare disease-free survival between subgroups. Results: Age (HR=1.020, 95%CI: 1.008 to 1.033, P=0.001), T stage (T3:HR=1.995,95%CI:1.062 to 3.750,P=0.032;T4:HR=4.196, 95%CI: 2.188 to 8.045, P<0.01), N stage (N1: HR=1.834, 95%CI: 1.307 to 2.574, P<0.01; N2: HR=3.970, 95%CI: 2.724 to 5.787, P<0.01) and number of lymph nodes examined (≥36: HR=0.438, 95%CI: 0.242 to 0.790, P=0.006) were independently associated with disease-free survival. The C-index of the scoring model (model 1) based on age, T stage, N stage, and dichotomous variables of the lymph nodes examined (<12 and ≥12) was 0.723, and the C-index of the scoring model (model 2) based on age, T stage, N stage, and multi-categorical variables of the lymph nodes examined (<12, 12 to <24, 24 to <36, and ≥36) was 0.726. A scoring system was established based on age, T stage, N stage, and multi-categorical variables of lymph nodes examined, the 3-year DFS of the low-risk (≤1), middle-risk (2 to 4) and high-risk (≥5) group were 96.3% (n=711), 89.0% (n=626) and 71.4% (n=313), respectively. Statistically significant difference was observed among groups (P<0.01). Conclusions: The number of lymph nodes examined was an independent prognostic factor for disease-free survival after curative surgery in patients with stage Ⅰ to Ⅲ colon cancer. Incorporating the number of lymph nodes examined as a multi-categorical variable into the T and N staging system could improve prognostic predictive validity.
Male
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Female
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Humans
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Prognosis
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Neoplasm Staging
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Retrospective Studies
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Nomograms
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Lymph Nodes/pathology*
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Risk Factors
;
Colonic Neoplasms/surgery*

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