METHODS: 45 rats at 6 weeks of age, were randomly assigned to nine groups with 5 rats in each group, both azoxymethane (AOM) and 5-Fluorouracil (5-FU) were given to rats according to the body weight. NDV virus strains (AF2240 and V4-UPM) doses were determined to rats according to CD50 resulted from MTT assay. After 8 doses of NDV strians and 5-FU, tissue sections preparations and histopathological study of rats' organs were done.
RESULTS: In this article morphological changes of rats' organs, especially in livers, after treatment with a colon carcinogen (azoxymethane) and Newcastle disease virus strains have been recorded. We observed liver damage caused by AOM evidenced by morphological changes and enzymatic elevation were protected by the oncolytic viruses sections. Also we found that combination treatment NDV with 5-FU had greater antitumor efficacy than treatment with NDV or 5-FU alone.
CONCLUSION: We noted morphological changes in liver and other rats' organs due to a chemical carcinogen and their protection by NDV AF2240 and NDV V4-UPM seems to be most protective.
RESULTS: Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).
CONCLUSIONS: Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.
METHODOLOGY: A mutlinational study was conducted from April-June 2020 involving researchers from 12 countries (Japan, Austria, U.S., Taiwan, India, Sudan, Indonesia, Malaysia, Philippines, Myanmar, Vietnam and Thailand). Steps in this research consisted of carrying out open-ended questionnaires, qualitative analyses in NVivo, and a multinational meeting to reflect, exchange, and validate results. Lastly, a commuinty response model was synthesized from multinational experiences.
RESULTS: Effective communication is key in promoting collective action for preventing virus transmission. Health literacy, habits and social norms in different populations are core components of public health interventions. To enable people to stay home while sustaining livelihoods, economic and social support are essential. Countries could benefit from previous pandemic experience in their community response. Whilst contact tracing and isolation are crucial intervention components, issues of privacy and human rights need to be considered.
CONCLUSIONS: Understanding community responses to containment policies will help in ending current and future pandemics in the world.