A Serum Metabolomic Study Reveals Changes in Metabolites During the Treatment of Lung Cancer-Bearing Mice with Anlotinib.

BACKGROUND: Anlotinib is a vascular endothelial growth factor receptor tyrosine kinase inhibitor recommended for the treatment of advanced lung cancer patients after at least two previous systemic chemotherapies. Currently, many patients with lung cancer do not respond well to anlotinib treatment. Therefore, the aim of this metabolomic study was to determine the internal mechanism of anlotinib action at the molecular level and to identify the potential biomarkers and pathways associated with the therapeutic effects of anlotinib. METHODS: A total of 20 male nude mice were randomly divided into 2 groups and treated with anlotinib or physiological saline. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was performed to analyze the serum samples and determine the differential metabolites and pathways between anlotinib and control groups. RESULTS: We observed significant differences between the anlotinib and control groups, and 13 endogenous differential metabolites and 5 potential metabolic pathways were identified. Glyoxylate and dicarboxylate metabolism, tryptophan metabolism, glycine, serine and threonine metabolism, phenylalanine metabolism and valine, leucine and isoleucine biosynthesis were the most important pathways regulated by anlotinib in vivo. Notably, these 5 differential pathways were highly associated with the TCA cycle, which is important in the proliferation and apoptosis of cancer cells. CONCLUSION: This serum metabolomic study revealed distinct metabolic profiles in lung cancer-bearing mice treated with anlotinib and identified differential metabolites and pathways between the anlotinib and control groups, which may provide new ideas for the clinical application of anlotinib.

Examining geographical disparities in the incubation period of the COVID-19 infected cases in Shenzhen and Hefei, China.

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.

Examining the incubation period distributions of COVID-19 on Chinese patients with different travel histories.

INTRODUCTION: Current studies estimated a general incubation period distribution of COVID-19 based on early-confirmed cases in Wuhan, and have not examined whether the incubation period distribution varies across population segments with different travel histories. We aimed to examine whether patients infected by community transmission had extended incubation periods than the early generation patients who had direct exposures to Wuhan. METHODOLOGY: Based on 4741 patient case reports from municipal centers of disease control by February 21, 2020, we calculated the incubation periods of 2555 patients with clear epidemiological survey information and illness development timeline. All patients were categorized into five groups by their travel histories. Incubation period distributions were modeled for each group by the method of the posterior Weibull distribution estimation. RESULTS: Adults aged 30 to 59 years had the most substantial proportion of confirmed cases in China. The incubation period distribution varied slightly across patient groups with different travel histories. Patients who regularly lived in Wuhan and left to other locations before January 23, 2020 had the shortest posterior median value of 7.57 days for the incubation period, while the incubation periods for persons affected by local community transmission had the largest posterior median of incubation periods, 9.31 days. CONCLUSIONS: The median incubation period for all patients infected outside Wuhan was 9 days, a bit of more extended than the early estimated 5-day incubation period that was based on patients in Wuhan. Our findings may imply the decreases of virulence of the COVID-19 virus along with intergenerational transmission.