Mcculloughbloom1173
The gram-negative Coxiella burnetii bacterium is the pathogen that causes Q fever. The bacterium is transmitted to animals via ticks, and manure, air, dead infected animals, etc. and can cause infection in domestic animals, wild animals, and humans. Xinjiang, the provincial-level administrative region with the largest land area in China, has many endemic tick species. The infection rate of C. burnetii in ticks in Xinjiang border areas has not been studied in detail.
For the current study, 1507 ticks were collected from livestock at 22 sampling sites in ten border regions of the Xinjiang Uygur Autonomous region from 2018 to 2019. C. burnetii was detected in 205/348 (58.91%) Dermacentor nuttalli; in 110/146 (75.34%) D. pavlovskyi; in 66/80 (82.50%) D. silvarum; in 15/32 (46.90%) D. niveus; in 28/132 (21.21%) Hyalomma rufipes; in 24/25 (96.00%) H. anatolicum; in 219/312 (70.19%) H. asiaticum; in 252/338 (74.56%) Rhipicephalus sanguineus; and in 54/92 (58.70%) Haemaphysalis punctata. Among these samples, C. b rate of C. burnetii detected in the ticks found in domestic animals may indicate a high likelihood of Q fever infection in both domestic animals and humans.
Knowledge discovery from breast cancer treatment records has promoted downstream clinical studies such as careflow mining and therapy analysis. However, the clinical treatment text from electronic health data might be recorded by different doctors under their hospital guidelines, making the final data rich in author- and domain-specific idiosyncrasies. Therefore, breast cancer treatment entity normalization becomes an essential task for the above downstream clinical studies. The latest studies have demonstrated the superiority of deep learning methods in named entity normalization tasks. Fundamentally, most existing approaches adopt pipeline implementations that treat it as an independent process after named entity recognition, which can propagate errors to later tasks. In addition, despite its importance in clinical and translational research, few studies directly deal with the normalization task in Chinese clinical text due to the complexity of composition forms.
To address these issues, we propose PASCnd efficiency of the presented pseudo cascade learning framework were validated for breast cancer treatment normalization in clinical text. We believe the predominant performance lies in its ability to extract valuable information from unstructured text data, which will significantly contribute to downstream tasks, such as treatment recommendations, breast cancer staging and careflow mining.
The effectiveness and efficiency of the presented pseudo cascade learning framework were validated for breast cancer treatment normalization in clinical text. We believe the predominant performance lies in its ability to extract valuable information from unstructured text data, which will significantly contribute to downstream tasks, such as treatment recommendations, breast cancer staging and careflow mining.
The closure of educational activities in the Kingdom of Saudi Arabia due to the ongoing COVID-19 pandemic resulted in an unplanned shift from traditional learning to a setup that exclusively involves digital teaching and learning. Within this context, the present study aimed to explore undergraduate medical students' perceptions regarding the effectiveness of synchronized online learning at Unaizah College of Medicine and Medical Sciences, Qassim University, Saudi Arabia.
A qualitative study was conducted using virtual focus group discussions synchronously with the help of a discussion guide consisting of seven open-ended questions. Overall, 60 medical students were recruited using a maximum variation sampling technique; these students then participated in eight focus group discussions. All interviews were recorded, transcribed verbatim, and analyzed for thematic contents using the standard (Mayring, Kiger. M. E. and Braun.V) content analysis framework.
A thematic content analysis yielded four core themn. The principles of the online learning model and learning outcomes should be rigorously and regularly evaluated to monitor its effectiveness.
To investigate the effectiveness of technology-enhanced teaching and assessment methods of undergraduate preclinical skills in comparison to conventional methods.
A comprehensive search strategy was implemented using both manual and electronic search methods, including PubMed, Wiley, ScienceDirect, SCOPUS, and the Cochrane Central Register of Controlled Trials. The search and selection of articles that met the inclusion criteria were carried out in duplicates. A Cochrane data extraction form for RCTs was used to extract the relevant information from all included articles. A2ti1 Risk of bias of all included articles was assessed independently by two authors using the Cochrane risk of bias tool.
A total of 19 randomized controlled clinical trials met the inclusion criteria and were included in this review. The majority of the studies included in this review have a high risk of bias mainly due to incomplete data, lack of blinding of the examiners, and due to other biases, such as small sample sizes, not accounti the conflicting outcomes reported in the 19 studies included in this systematic review and their high risk of bias, better quality studies are required to find a definitive answer to the research question of this systematic review.
Falls in community-dwelling older people have been recognised as a significant public health issue in China given the rapidly growing aged population. Although there are several reviews documenting falls prevention programs for community-dwelling older adults, no systematic reviews of the scope and quality of falls prevention interventions in Mainland China exist. Therefore, the aim of this study was to systematically review falls prevention interventions for community-dwelling older people living in Mainland China.
We systematically reviewed literature from Chinese and English databases. All types of randomised controlled trials (RCTs) and quasi-experimental studies published from 1st January 1990 to 30th September 2019 were included. Observational studies and studies in care facilities and hospitals were excluded. Narrative synthesis was performed to summarise the key features of all included studies. Quality assessment was conducted using the Cochrane Risk of Bias Tool and ROBINS-I tool for randomised and non-randomised studies respectively.
