More than 60% of the world’s rural population live in the Asia-Pacific region. Of these, more than 90% reside in low- and middle-income countries (LMICs). Asia-Pacific LMICs rural populations are more impoverished and have poorer access to medical care, placing them at greater risk of poor health outcomes. Understanding factors associated with doctors working in rural areas is imperative in identifying effective strategies to improve rural medical workforce supply in Asia-Pacific LMICs.

We performed a scoping review of peer-reviewed and grey literature from Asia-Pacific LMICs (1999 to 2019), searching major online databases and web-based resources. The literature was synthesized based on the World Health Organization Global Policy Recommendation categories for increasing access to rural health workers.

Seventy-one articles from 12 LMICs were included. Most were about educational factors (82%), followed by personal and professional support (57%), financial incentives (45%), regulatory (20%), and health s range of factors that can be targeted to strengthen strategies to increase rural medical workforce supply in Asia-Pacific LMICs. Multi-faceted approaches were evident, including selecting more students into medical school with a rural background, increasing public-funded universities, in combination with rural-focused education and rural scholarships, workplace and rural living support and ensuring an appropriately financed rural health system. The review identifies the need for more studies in a broader range of Asia-Pacific countries, which expand on all strategy areas, define rural clearly, use multivariate analyses, and test how various strategies relate to doctor’s career stages.

Increasing evidence suggested that microRNA and kinesin superfamily proteins play an essential role in ovarian cancer. The association between KIF4A and ovarian cancer (OC) was investigated in this study.

We performed bioinformatics analysis in the GEO database to screen out the differentially expressed miRNAs (DEmiRNAs) associated with ovarian cancer prognosis. Upstream targeting prediction for KIF4A was acquired by using the mirDIP database. The potential regulatory factor miR-29c-3p for KIF4A was obtained from the intersection of the above all miRNAs. The prognosis of KIF4A and target-miRNA in OC was obtained in the subsequent analysis. qRT-PCR and Western blot detected KIF4A expression level in IOSE80 (human normal ovarian epithelial cell line). In the meantime, the gene expression level was detected in A2780, HO-8910PM, COC1, and SKOV3 cell lines (human ovarian carcinoma cell line). MTT and colony formation assays were used to detect cell proliferation of SKOV3 cell line. The following assays detecteelopment of OC.

Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments.

This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol.

Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity.

Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. AP1903 nmr If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

Schistosomiasis is a chronic, debilitating infectious disease caused by members of the genus Schistosoma. Previous findings have suggested a relationship between infection with Schistosoma spp. and alterations in the liver and spleen of infected animals. Recent reports have shown the regulatory role of noncoding RNAs, such as long noncoding RNAs (lncRNAs), in different biological processes. However, little is known about the role of lncRNAs in the mouse liver and spleen during Schistosoma japonicum infection.

In this study, we identified and investigated lncRNAs using standard RNA sequencing (RNA-Seq). The biological functions of the altered expression of lncRNAs and their target genes were predicted using bioinformatics. Ten dysregulated lncRNAs were selected randomly and validated in reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) experiments.

Our study identified 29,845 and 33,788 lncRNAs from the liver and spleen, respectively, of which 212 were novel lncRNAs. We obsinfected liver and spleen compared to control liver and spleen; this suggested that lncRNAs may be involved in pathogenesis in the liver and spleen during S. japonicum infection.

The Seattle Angina Questionnaire (SAQ) is a widely-used patient-reported outcomes measure in patients with heart disease. This study assesses the validity and reliability of the SAQ in a Canadian cohort of individuals with stable angina.

Data are from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) registry, a population-based registry of patients who received cardiac catheterization in Alberta, Canada. The cohort consists of 4052 patients undergoing cardiac catheterization for stable angina and completed the SAQ within 2weeks. Exploratory factor analysis and confirmatory factor analysis (CFA) were used to assess the factorial structure of the SAQ. Internal and test-retest reliabilities of a new measure (i.e., SAQ-CAN) was measured using Cronbach α and intraclass correlation coefficient, respectively. CFA model fit was assessed using the root mean square error of approximation (RMSEA) and comparative fit index (CFI). Construct validity of the SAQ-CAN was assessed in relation to Hospital Anxiety and Depression Scales (HADS), Euro Quality of life 5 dimension (EQ5D), and original SAQ.