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Nursing education institutions are required to select and train applicants who have appropriate characteristics for delivering effective healthcare. Unlike other healthcare professions and despite the need to attract and select a competent workforce, there has been no comprehensive analysis of the selection criteria and methods used to recruit nursing students. As there is relatively limited prior research available, we conducted a scoping review to explore and synthesise the existing evidence regarding admission criteria and selection methods of nursing students and for the purpose of identifying an agenda for future research in this field.

Our scoping review follows the Arksey and O'Malley five-step proposition including identifying the research question and relevant studies, study selection, tabulation of data, and summarizing and reporting the results. Seven databases (PubMed, CINAHL, Scopus, ERIC, SID, Irandoc and PsycINFO) were searched systematically using relevant keywords. Articles on admission otion policymakers and institutions in the design of their selection practices. Future research should concentrate on the evaluation and improvement methods of student selection including content and predictive validity analysis of multiple mini interview and standardized tests, development of cost-effective selection methods and job analysis studies to identify specific non-cognitive characteristics for nursing.

This is the first scoping review of literature regarding nursing education selection and recruitment. Results can be used to inform nursing education policymakers and institutions in the design of their selection practices. Future research should concentrate on the evaluation and improvement methods of student selection including content and predictive validity analysis of multiple mini interview and standardized tests, development of cost-effective selection methods and job analysis studies to identify specific non-cognitive characteristics for nursing.

Dispensing errors, known to result in significant patient harm, are preventable if their nature is known and recognized. However, there is a scarcity of such data on dispensing errors particularly in resource poor settings, where healthcare is provided free-of-charge. Therefore, the purpose of this study was to determine the types, and prevalence of dispensing errors in a selected group of hospitals in Sri Lanka.

A prospective, cross sectional, multi-center study on dispensing errors was conducted, in a single tertiary care, and two secondary care hospitals, in a cohort of 420 patients attending medical, surgical, diabetic and pediatric clinics. The patients were selected according to the population size, through consecutive sampling. The prescription audit was conducted in terms of dispensing errors which were categorized as i) content, ii) labelling, iii) documentation, iv) concomitant, and v) other errors based on in-house developed definitions.

A total of 420 prescriptions (1849 medicines) were analited resources and provide free healthcare to all citizenry. Over one half of the errors were labeling errors with minimal content errors. Awareness on common types of dispensing errors and emphasis on detecting them could improve medication safety in Sri Lankan hospitals.

Dispensing errors are frequent in Sri Lankan hospitals which operate with limited resources and provide free healthcare to all citizenry. Tofacitinib chemical structure Over one half of the errors were labeling errors with minimal content errors. Awareness on common types of dispensing errors and emphasis on detecting them could improve medication safety in Sri Lankan hospitals.

Differences in the expression of regulatory T cells (Tregs) have been suggested to explain why some smokers develop COPD and some do not. Upregulation of Tregs in response to smoking would restrain airway inflammation and thus the development of COPD; while the absense of such upregulation would over time lead to chronic inflammation and COPD. We hypothesized that-among COPD patients-the same mechanism would affect rate of decline in lung function; specifically, that a decreased expression of Tregs would be associated with a more rapid decline in FEV

.

Bronchoscopy with BAL was performed in 52 subjects recruited from the longitudinal OLIN COPD study; 12 with COPD and a rapid decline in lung function (loss of FEV

 ≥ 60ml/year), 10 with COPD and a non-rapid decline in lung function (loss of FEV

 ≤ 30ml/year), 15 current and ex-smokers and 15 non-smokers with normal lung function. BAL lymphocyte subsets were determined using flow cytometry.

The proportions of Tregs with regulatory function (FoxP3

/CD4

ntifier NCT02729220.

The proportion of infections among young children that are antimicrobial-resistant is increasing across the globe. Newborns may be colonized with enteric antimicrobial-resistant pathogens early in life, which is a risk factor for infection-related morbidity and mortality. Breastfeeding is actively promoted worldwide for its beneficial impacts on newborn health and gut health. However, the role of breastfeeding and human milk components in mitigating young children's carriage of antimicrobial-resistant pathogens and antibiotic resistance genes has not been comprehensively explored.

Here, we review how the act of breastfeeding, early breastfeeding, and/or human milk components, such as the milk microbiota, secretory IgA, human milk oligosaccharides, antimicrobial peptides, and microRNA -bearing extracellular vesicles, could play a role in preventing the establishment of antimicrobial-resistant pathogens in young children's developing gut microbiomes. We describe findings from recent human studies that suppotimicrobial resistance, particularly in low- and middle-income settings.

The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI).

A retrospective analysis was conducted for all adult patients who sustained TBI and were hospitalized at the trauma center from January 2014 to February 2019 with an abbreviated injury severity score for head region (HAIS) ≥ 3. We used the demographic characteristics, injuries and CT findings as predictors. Logistic regression (LR) and Artificial neural networks (ANN) were used to predict the in-hospital mortality. Accuracy, area under the receiver operating characteristics curve (AUROC), precision, negative predictive value (NPV), sensitivity, specificity and F-score were used to compare the models` performance.

Across the study duration; 785 patients met the inclusion criteria (581 survived and 204 deceased). The two models (LR and ANN) achieved good performance with anaccuracy over 80% and AUROC over 87%. However, when taking the other performance measures into account, LR achieved higher overall performance than the ANN with anaccuracy and AUROC of 87% and 90.