[Abstract] Objective: To investigate the molecular mechanism of miR-143-3p regulating the proliferation, migration and invasion of colon cancer RKO cells via targeting enhancer of zeste homolog 2 (EZH2). Methods: A total of 40 pairs of colon cancer tissues and corresponding para-cancerous tissues resected in the First Affiliated Hospital of Kunming Medical University from March 2015 to July 2017 were collected for this study. In addition, colon cancer cell lines (COLO320, RKO and CL-11) and normal intestinal mucosa NCM460 cells were also collected. qPCR was applied to detect the expression level of miR-143-3p in colon cancer tissues and cell lines. miR-143-3p mimics, miR-143-3p inhibitor, EZH2 siRNA and negative control plasmids were transfected into RKO cells, respectively. The effect of miR-143-3p/EZH2 axis on the proliferation, migration and invasion of RKO cells were detected by CCK-8 and Transwell assay, respectively. Western blotting was used to detect the expression level of EZH2 protein in RKO cells. The targeting relationship between miR-143-3p and EZH2 was verified by Dual luciferase reporter gene assay. Results: The expression level of miR-143-3p was downregulated in colon cancer tissues and cell lines (all P<0.01). Overexpression of miR-143-3p significantly inhibited the proliferation, migration and invasion of RKO cells (all P<0.01). Dual luciferase reporter gene assay confirmed that EZH2 was a target gene of miR-143-3p. Simultaneous knockdown of miR-143-3p and EZH2 attenuated the inhibition of EZH2 knockdown on the proliferation, migration and invasion of RKO cells. Conclusion: miR-143-3p suppresses the proliferation, migration and invasion of colon cancer cells via targetedly down-regulating EZH2.

BACKGROUND: Current studies on the COVID-19 depicted a general incubation period distribution and did not examine whether the incubation period distribution varies across patients living in different geographical locations with varying environmental attributes. Profiling the incubation distributions geographically help to determine the appropriate quarantine duration for different regions. METHODS: This retrospective study mainly applied big data analytics and methodology, using the publicly accessible clinical report for patients (n = 543) confirmed as infected in Shenzhen and Hefei, China. Based on 217 patients on whom the incubation period could be identified by the epidemiological method. Statistical and econometric methods were employed to investigate how the incubation distributions varied between infected cases reported in Shenzhen and Hefei. RESULTS: The median incubation period of the COVID-19 for all the 217 infected patients was 8 days (95% CI 7 to 9), while median values were 9 days in Shenzhen and 4 days in Hefei. The incubation period probably has an inverse U-shaped association with the meteorological temperature. The warmer condition in the winter of Shenzhen, average environmental temperature between 10 °C to 15 °C, may decrease viral virulence and result in more extended incubation periods. CONCLUSION Case studies of the COVID-19 outbreak in Shenzhen and Hefei indicated that the incubation period of COVID-19 had exhibited evident geographical disparities, although the pathological causality between meteorological conditions and incubation period deserves further investigation. Methodologies based on big data released by local public health authorities are applicable for identifying incubation period and relevant epidemiological research.
Adolescent ; Adult ; Aged ; COVID-19/prevention & control* ; Child ; China/epidemiology* ; Female ; Geography ; Humans ; Infectious Disease Incubation Period ; Male ; Middle Aged ; Quarantine ; Retrospective Studies ; SARS-CoV-2 ; Young Adult

Objective:To develop an air particulate protective mask filter test device that can simulate the dynamics process of human breathing.Methods:The new device used two air pumps working alternately to simulate the dynamics process of human breathing. On March 4th to 17th, 2017, the new device and the traditional one-way airflow mask filtration test device were used to measure the internal and external particle levels of 39 masks of 13 models of 6 brands, and then the filtration efficiency of the mask was calculated and the test results were compared.Results:For the mask without breathing valve, there was no statistically significant difference between the filter efficiency test results of the new device and the traditional unidirectional airflow filter performance test device ( P>0.05) . For masks with breathing valves, the new device detected that three of them had lower filtration efficiency (99.50% vs 98.63%, P<0.01) . After sealing the mask breathing valve with glue, the filtering efficiency of the mask with a breathing valve detected by the new device significantly improved (98.63% vs 99.50%, P<0.01) . Conclusion:This new device can simulate the dynamic process of human exhalation and inhalation, and measure the filtration efficiency of the mask. For masks with breathing valves, the new device makes it easier to detect the decrease in the filtering efficiency of the mask caused by the breathing valve.

Objective:To develop an air particulate protective mask filter test device that can simulate the dynamics process of human breathing.Methods:The new device used two air pumps working alternately to simulate the dynamics process of human breathing. On March 4th to 17th, 2017, the new device and the traditional one-way airflow mask filtration test device were used to measure the internal and external particle levels of 39 masks of 13 models of 6 brands, and then the filtration efficiency of the mask was calculated and the test results were compared.Results:For the mask without breathing valve, there was no statistically significant difference between the filter efficiency test results of the new device and the traditional unidirectional airflow filter performance test device ( P>0.05) . For masks with breathing valves, the new device detected that three of them had lower filtration efficiency (99.50% vs 98.63%, P<0.01) . After sealing the mask breathing valve with glue, the filtering efficiency of the mask with a breathing valve detected by the new device significantly improved (98.63% vs 99.50%, P<0.01) . Conclusion:This new device can simulate the dynamic process of human exhalation and inhalation, and measure the filtration efficiency of the mask. For masks with breathing valves, the new device makes it easier to detect the decrease in the filtering efficiency of the mask caused by the breathing valve.

To compare the performance of generalized additive model (GAM) and long short-term memory recurrent neural network (LSTM-RNN) on the prediction of daily admissions of respiratory diseases with comorbid diabetes. Daily data on air pollutants, meteorological factors and hospital admissions for respiratory diseases from Jan 1st, 2014 to Dec 31st, 2019 in Beijing were collected. LSTM-RNN was used to predict the daily admissions of respiratory diseases with comorbid diabetes, and the results were compared with those of GAM. The evaluation indexes were calculated by five-fold cross validation. Compared with the GAM, the prediction errors of LSTM-RNN were significantly lower [root mean squared error (RMSE): 21.21±3.30 vs. 46.13±7.60, <0.01; mean absolute error (MAE): 14.64±1.99 vs. 36.08±6.20, <0.01], and the value was significantly higher (0.79±0.06 vs. 0.57±0.12, <0.01). In gender stratification, RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting female admission (all <0.05), but there were no significant difference in predicting male admission between two models (all >0.05). In seasonal stratification, RMSE and MAE of LSTM-RNN were lower than those of GAM in predicting warm season admission (all <0.05), but there was no significant difference in value (>0.05). There were no significant difference in RMSE, MAE and between the two models in predicting cold season admission (all >0.05). In the stratification of functional areas, the RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting core area admission (all <0.05). has lower prediction errors and better fitting than the GAM, which can provide scientific basis for precise allocation of medical resources in polluted weather in advance.
Beijing/epidemiology* ; Diabetes Mellitus/epidemiology* ; Female ; Hospitalization ; Humans ; Male ; Memory, Short-Term ; Neural Networks, Computer