Activity

TP53 and PGAM1 genes play a key role in glycolysis which is an essential metabolic pathway of cancer cells for obtaining energy. The purpose of this work was to evaluate PGAM1 and TP53 mRNA expressions in canine mammary carcinomas (CMC) and to correlate them with animal data and tumour histological features. None of the nine samples analysed revealed PGAM1 DNA sequence variations. PGAM1 and TP53 RNA expressions from 21 CMC were analysed using a one-step reverse transcription-PCR kit and its platform system. Most CMC samples had low levels of PGAM1 mRNA (71.5%) and normal expression of TP53 mRNA (95.2%). Our results suggest a different feature of the Warburg effect on canine mammary cancer cells compared to human cells.

Web-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade.

This study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020.

Search terms from the following regions were used for the analysis India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. SKF96365 datasheet All statistical calculations were performed through exploratory data analysis and nonparametric Mann-Whitney U teststa are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups.

A detailed understanding of the public’s knowledge and perceptions of COVID-19 could inform governments’ public health actions in response to the pandemic.

The aim of this study was to determine the knowledge and perceptions of COVID-19 among adults in China and its variation among provinces and by sociodemographic characteristics.

Between May 8 and June 8, 2020, we conducted a cross-sectional online survey among adults in China who were registered with the private survey company KuRunData. We set a target sample size of 10,000 adults, aiming to sample 300-360 adults from each province in China. Participants were asked 25 questions that tested their knowledge about COVID-19, including measures to prevent infection, common symptoms, and recommended care-seeking behavior. We disaggregated responses by age; sex; education; province; household income; rural-urban residency; and whether or not a participant had a family member, friend, or acquaintance who they know to have been infected with SARS-CoV-2. All ern China. This study can inform the development of tailored public health policies and promotion campaigns by identifying knowledge areas for which misconceptions are comparatively common and provinces that have relatively low knowledge.

National governments worldwide have implemented nonpharmaceutical interventions to control the COVID-19 pandemic and mitigate its effects.

The aim of this study was to investigate the prediction of future daily national confirmed COVID-19 infection growth-the percentage change in total cumulative cases-across 14 days for 114 countries using nonpharmaceutical intervention metrics and cultural dimension metrics, which are indicative of specific national sociocultural norms.

We combined the Oxford COVID-19 Government Response Tracker data set, Hofstede cultural dimensions, and daily reported COVID-19 infection case numbers to train and evaluate five non-time series machine learning models in predicting confirmed infection growth. We used three validation methods-in-distribution, out-of-distribution, and country-based cross-validation-for the evaluation, each of which was applicable to a different use case of the models.

Our results demonstrate high R

values between the labels and predictions for the in-distribution method (0.959) and moderate R

values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and adaptive boosting (AdaBoost) regression. Although these models may be used to predict confirmed infection growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case.

This work provides new considerations in using machine learning techniques with nonpharmaceutical interventions and cultural dimensions as metrics to predict the national growth of confirmed COVID-19 infections.

This work provides new considerations in using machine learning techniques with nonpharmaceutical interventions and cultural dimensions as metrics to predict the national growth of confirmed COVID-19 infections.

Due to the influence of the COVID-19 pandemic, conventional face-to-face academic conferences have been restricted, and many of these conferences have moved onto the internet.

The aim of this study was to investigate the virtual conferences in the field of urology during the COVID-19 pandemic and provide suggestions for better organization of such conferences.

A cross-sectional survey was conducted from May 30 to June 15, 2020, in China. Our team designed a 23-item questionnaire to investigate the conferences attended by urologists during the COVID-19 pandemic. SPSS 22.0 (IBM Corporation) was applied to analyze the data collected.

A total of 330 Chinese urologists participated in our survey, and the response rate was 89.7% (330/368). Among the participants, 40.9% (135/330) were associate chief physicians. The proportion of participants who took part in conventional face-to-face academic conferences decreased from 92.7% (306/330) before the COVID-19 pandemic to 22.1% (73/330) during the pandemic (P<.