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Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced to address spatial heterogeneity in crime analysis. However, GWPR could not account for overdispersion, which is commonly observed in crime data. The geographically weighted negative binomial model (GWNBR) was adopted to address spatial heterogeneity and overdispersion simultaneously in crime analysis, based on a 3-year data set collected from ZG city, China, in this study. The count of residential burglaries was used as the dependent variable to calibrate the above models, and the results revealed that the GWPR and GWNBR models performed better than NB for reducing spatial dependency in the model residuals. GWNBR outperformed GWPR for incorporating overdispersion. Therefore, GWNBR was proven to be a promising tool for crime modeling.

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This study aimed to examine hTERC gene in different grades of cervical intraepithelial neoplasia (CIN) and cervical cancer, and the association between hTERC and high risk-human papillomavirus (HR-HPV) infection. Patients who underwent cervical cancer screening at the Second Affiliated Hospital of Kunming Medical University between October 2010 and December 2011 were enrolled. All patients underwent liquid-based cytology test and hybrid capture 2 (HC2) for HPV detection. hTERC was examined using fluorescence in situ hybridization (FISH). Cervical colposcopy biopsy was performed if any of the three results was positive. HC2, FISH, and pathology were compared. A total of 1200 women underwent screening, 150 patients underwent cervical biopsy: 32 in the normal group, 38 in the CIN1 group, 66 in the CIN2/3 group, and 14 in the invasive cervical cancer group. More patients had HR-HPV infection in the CIN2/3 group and ICC group compared with the CIN1 group. hTERC increased with increasing histological dysplasia. There was significant difference in hTERC positive rate between each of the three groups. More patients with hTERC gene amplification were observed in the positive HR-HPV group than in the HR-HPV negative group. In conclusion, hTERC is a potential marker for precancerous cervical cancer lesions. hTERC might be correlated with HR-HPV infection in cervical diseases.

Background The significance of HPV viral load in the detection of cervical lesions is still controversial. This study analyzed the correlation between the high-risk HPV viral load and different cervical lesion degrees. Methods This retrospective study included women positive for high-risk HPV DNA and screened for cervical lesions between 01/2015 and 06/2018. The high-risk HPV DNA load was measured by the second-generation Hybrid Capture technology and classified as low, moderate, and high. Colposcopy and biopsy were performed in all patients. The patients were grouped as normal, cervical intraepithelial neoplasia (CIN) grade 1, CIN grade 2, CIN grade 3, and cervical cancer. Multivariable logistic regression was performed to explore the association between high-risk HPV DNA load and cervical lesions. The odds ratios (ORs) represent the odds for increasing from low to high viral load. Results Finally, 265 patients were grouped as normal (n = 125), CIN 1 (n = 51), CIN 2 (n = 23), CIN 3 (n = 46), and cervical cancer (n = 20). Among them, 139 (52.5%) had a low viral load, 90 (34.0) had a moderate viral load, and 36 (13.4%) had a high viral load. Taking the normal control group as a reference, a high viral load was an independent factor for CIN 1 (OR = 3.568, 95% CI: 1.164–10.941, P = 0.026), CIN 2 (OR = 6.939, 95% CI: 1.793–26.852, P = 0.005), CIN 3 (OR = 7.052, 95% CI: 2.304–21.586, P = 0.001), and cervical cancer (OR = 8.266, 95% CI: 2.120–32.233, P = 0.002). Conclusions Among women who underwent cervical biopsy, higher high-risk HPV viral load in cervical lesions was associated with a higher risk of high-grade cervical lesions.