Objective: Forecasting the seasonality and trend of pulmonary tuberculosis is important for the rational allocation of health resources; however, this foresting is often hampered by inappropriate prediction methods. In this study, we performed validation research by comparing the accuracy of the autoregressive integrated moving average (ARIMA) model and the back-propagation neural network (BPNN) model in a southeastern province of China. Methods: We applied the data from 462,214 notified pulmonary tuberculosis cases registered from January 2005 to December 2015 in Jiangsu Province to modulate and construct the ARIMA and BPNN models. Cases registered in 2016 were used to assess the prediction accuracy of the models. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to evaluate the model fitting and forecasting effect. Results: During 2005–2015, the annual pulmonary tuberculosis notification rate in Jiangsu Province was 56.35/100,000, ranging from 40.85/100,000 to 79.36/100,000. Through screening and comparison, the ARIMA (0, 1, 2) (0, 1, 1)₁₂ and BPNN (3-9-1) were defined as the optimal fitting models. In the fitting dataset, the RMSE, MAPE, MAE and MER were 0.3901, 6.0498, 0.2740 and 0.0608, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)₁₂ model, 0.3236, 6.0113, 0.2508 and 0.0587, respectively, for the BPNN model. In the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.1758, 4.6041, 0.1368 and 0.0444, respectively, for the ARIMA (0, 1, 2) (0, 1, 1)₁₂ model, and 0.1382, 3.2172, 0.1018 and 0.0330, respectively, for the BPNN model. Conclusion: Both the ARIMA and BPNN models can be used to predict the seasonality and trend of pulmonary tuberculosis in the Chinese population, but the BPNN model shows better performance. Applying statistical techniques by considering local characteristics may enable more accurate mathematical modeling.

To explore the long-term effect of exposure to ambient air pollution on the risk of active tuberculosis (TB). We constructed a distributed-lag nonlinear model (DLNM) to evaluate the relatively long-term influence of outdoor PM2.5, PM10, SO2 and NO2 exposure on active TB risk in the city of Lianyungang in Jiangsu Province, China. There were 7,282 TB cases reported in the study area during 2014-2017, with annual median (interquartile range) concentrations of PM2.5, PM10, SO2 and NO2 at 45.86 (34.57-64.14) μg/m3, 85.43 (62.86-116.14) μg/m3, 22.00 (15.71-30.86) μg/m3 and 30.00 (23.29-38.57) μg/m3, respectively. The single-pollutant model showed that for each 10 μg/m3 increase in concentration, the cumulative relative risk of TB was 1.12 (lag 0-24 weeks, 95% CI: 1.03-1.22) for PM2.5 with reference to 35 μg/m3; 1.11 (lag 0-21 weeks, 95% CI: 1.06-1.17) for PM10 with reference to 70 μg/m3; 1.37 (lag 0-20 weeks, 95% CI: 1.16-1.62) for SO2 with reference to 60 μg/m3; and 1.29 (lag 0-22 weeks, 95% CI: 1.11-1.49) for NO2 with reference to 40 μg/m3. In the multipollutant model considering both PM10 and NO2, the association remained significant. Our results revealed a potential association between outdoor exposure to PM2.5, PM10, SO2, and NO2 and active TB. Considering that people from developing countries continue to be exposed to both severe outdoor air pollution and high rates of latent TB infection, the association between worsening air pollution and active TB deserves further attention.

A variety of abnormalities in vitamin D metabolism have been reported in patients with active tuberculosis. However, intervention trials have produced inconsistent results. We hypothesized that genetic and epigenetic changes in the key genes of the vitamin D metabolic pathway may partly explain the differences between studies. We performed a case-control study followed by a prospective cohort study. We recruited 122 patients with pulmonary tuberculosis and 118 healthy controls. The serum 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D levels were measured. The methylation of the promoter regions of key genes in the vitamin D metabolic pathway (CYP24A1, CYP27A1, CYP27B1, CYP2R1, and VDR) was detected using the Illumina MiSeq platform. The specific methylation profiles were examined as epigenetic biomarkers. The sensitivity, specificity, and receiver operating characteristic (ROC) curves were used to estimate the predictive value of the biomarkers. The baseline serum 25-hydroxyvitamin D and 1,25-dihydroxyvitamin D concentrations in the cases were significantly lower than those in the controls (51.60 ± 27.25 nmol/L vs. 117.50 ± 75.50 nmol/L, Z = − 8.515, P < 0.001; 82.63 ± 51.43 pmol/L vs. 94.02 ± 49.26 pmol/L, Z = − 2.165, P = 0.03). We sequenced 310 CpG sites in five candidate genes. After Bonferroni correction, there were 55 differentially methylated CpG sites between cases and controls; 41.5% were in the CYP27B1 gene, 31.7% were in the CYP24A1 gene, 14.7% were in the VDR gene, and 12.3% were in the CYP27A1 gene. When we designated the CpG sites that remained significant after the Bonferroni correction as the biomarkers, the area under the curve (AUC) for the cumulative methylation was 0.810 (95% CI 0.754–0.866). There was an interaction between CYP27A1 methylation level and 1,25-dihydroxyvitamin D concentration associated with the risk of TB (ORinteraction = 4.11, 95% CI 1.26–13.36, P = 0.019). The serum 1,25-dihydroxyvitamin D concentration at the end of the intensive treatment stage was related to a patient’s prognosis (P = 0.008). There were 23 CpG sites that were individually related to the treatment outcomes, but the relationships were not significant after the Bonferroni correction. Both serum vitamin D concentrations and the methylation levels of key genes in the vitamin D metabolic pathway are related to the risk and prognosis of tuberculosis.

Objective: To investigate suitable forecasting models for tuberculosis (TB) in a Chinese population by comparing the predictive value of the autoregressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) hybrid model. Methods: We used the monthly incidence rate of TB in Lianyungang city from January 2007 through June 2016 to construct a fitting model, and we used the incidence rate from July 2016 to December 2016 to evaluate the forecasting accuracy. The root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and mean error rate (MER) were used to assess the performance of these models in fitting and forecasting the incidence of TB. Results: The ARIMA (10, 1, 0) (0, 1, 1)₁₂ model was selected from plausible ARIMA models, and the optimal spread value of the ARIMA-GRNN hybrid model was 0.23. For the fitting dataset, the RMSE, MAPE, MAE and MER were 0.5594, 11.5000, 0.4202 and 0.1132, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)₁₂ model, and 0.5259, 11.2181, 0.3992 and 0.1075, respectively, for the ARIMA-GRNN hybrid model. For the forecasting dataset, the RMSE, MAPE, MAE and MER were 0.2805, 8.8797, 0.2261 and 0.0851, respectively, for the ARIMA (10, 1, 0) (0, 1, 1)₁₂ model, and 0.2553, 5.7222, 0.1519 and 0.0571, respectively, for the ARIMA-GRNN hybrid model. Conclusions: The ARIMA-GRNN hybrid model was shown to be superior to the single ARIMA model in predicting the short-term TB incidence in the Chinese population, especially in fitting and forecasting the peak and trough incidence.

Background: As a new class of endogenous ncRNAs, circRNAs have been recently verified to be involved in the carcinogenesis and progression of human cancers. In the current study, we attempted to explore the potential function of a candidate circRNA (hsa_circ_0000337) in esophageal squamous cell carcinoma (ESCC). Patients and methods: The altered expression of hsa_circ_0000337 was validated in clinical samples from 48 patients with ESCC. The human esophageal carcinoma cell lines KYSE-150 and TE-1, and the normal human esophageal epithelial cell line (HET-1A) were applied for functional analysis of hsa_circ_0000337. Cell proliferation was measured using the Cell Counting Kit-8 assay and the colony formation assay. Cell invasion and migration were detected by Transwell and wound healing assays, respectively. We further performed bioinformatic analysis and luciferase reporter assays to explore the role of hsa_circ_0000337 as a miRNA sponge. Results: hsa_circ_0000337 was significantly upregulated in ESCC tissues compared to adjacent normal-appearing tissues (P<0.0001). In our in vitro experiment, the expression of hsa_circ_0000337 was higher in TE-1 compared to the normal human esophageal epithelial cell line HET-1A (P0.05). Knockdown of hsa_circ_0000337 significantly inhibited cell proliferation, migration, and invasion in TE-1 and KYSE-150 cell lines. Bioinformatics predicted and luciferase reporter assay verified that hsa_circ_0000337 could bind to miR-670-5p, a ncRNA involved in carcinogenesis. It is estimated that 21 genes are regulated by miR-670-5p. Conclusion: hsa_circ_0000337 was found to be an upregulated circRNA that is related to ESCC and promotes the progression of disease by regulating cell proliferation, migration, and invasion. These findings suggest that this circRNA could be a promising diagnostic biomarker and potential therapeutic target.

Introduction: Circular RNAs (circRNAs) function as efficient microRNA sponges with gene-regulatory potential and are promising cancer biomarkers. In this study, we used the Arraystar Human circRNA Array to construct a genome-wide circRNA profile of esophageal squamous cell cancer (ESCC) and breast cancer (BC). Patients and methods: Expression levels between cancer lesions and adjacent normal-appearing tissues were compared. We observed 469 upregulated circRNAs and 275 downregulated circRNAs in ESCC. Hsa_circRNA_103670 was upregulated 20.3-fold, while hsa_circRNA_030162 was downregulated 12.1-fold. For BC, 715 circRNAs were upregulated, and 440 circRNAs were downregulated. Hsa_circRNA_005230 was upregulated 12.2-fold, while hsa_circRNA_406225 was downregulated 12.4-fold. Results: When we set the criteria as fold change in expression ≥2 between cancer and adjacent normal-appearing tissue with a P-value <0.01, there were 22 common circRNAs (11 upregulated and 11 downregulated) in relation to both ESCC and BC. Gene ontology and the Kyoto encyclopedia of genes and genomes analyses showed that these circRNAs were involved in the tumorigenesis of human cancers. Conclusion: Our study revealed that circRNAs are promising candidates as valuable biomarkers for ESCC and BC, although relevant research is still in its infancy and the functional role of specific circRNAs in tumorigenesis is just starting to be elucidated.