Molecular identification of genus Pipistrellus (Mammalia: Chiroptera) from Fata region, Pakistan

A total of 10 specimens were captured from selected sites of Bajaur Agency FATA, Pakistan using mist nets. The captured specimens were morphologically identified and various morphometric measurements were taken. The head and Body length (HB) of Pipistrellus coromondra and Pipistrellus kuhlii lepidus (n=10) was 43±0.11 mm and 45±1.1 respectively. Morphologically identified Pipistrellus kuhlii confirmed as Pipistrellus kuhlii lepidus based on 16S rRNA sequences. The DNA sequences were submitted to GenBank and accession numbers were obtained (MN 719478 and MT430902). The available 16S rRNA gene sequences of Pipistrellus coromondra and Pipistrellus kuhlii lepidus were retrieved from NCBI and incorporated in N-J tree analysis. Overall, the interspecific genetic variations among Pipistrellus coromondra and Pipistrellus kuhlii lepidus were 8% and 1% respectively. In our recommendation, a comprehensive molecular identification of bats is need of hour to report more cryptic and new species from Pakistan.

Development of a decision flowchart to identify the patients need high-dose vancomycin in early phase of treatment

Background The standard dose of vancomycin (VCM, 2 g/day) sometimes fails to achieve therapeutic concentration in patients with normal renal function. In this study, we aimed to identify factors to predict patients who require high-dose vancomycin (> 2 g/day) to achieve a therapeutic concentration and to develop a decision flowchart to select these patients prior to VCM administration. Methods Patients who had an estimated creatinine clearance using the Cockcroft–Gault equation (eCCr) of ≥50 mL/min and received intravenous VCM were divided into 2 cohorts: an estimation set (n = 146, from April to September 2016) and a validation set (n = 126, from October 2016 to March 2017). In each set, patients requiring ≤2 g/day of VCM to maintain the therapeutic trough concentration (10–20 μg/mL) were defined as standard-dose patients, while those who needed > 2 g/day were defined as high-dose patients. Univariate and multivariate logistic regression analysis was performed to identify the predictive factors for high-dose patients and decision tree analysis was performed to develop decision flowchart to identify high-dose patients. Results Among the covariates analyzed, age and eCCr were identified as independent predictors for high-dose patients. Further, the decision tree analysis revealed that eCCr (cut off value = 81.3 mL/min) is the top predictive factor and is followed by age (cut off value = 58 years). Based on these findings, a decision flowchart was constructed, in which patients with eCCr ≥81.3 mL/min and age < 58 years were designated as high-dose patients and other patients were designated as standard-dose patients. Subsequently, we applied this decision flowchart to the validation set and obtained good predictive performance (positive and negative predictive values are 77.6 and 84.4%, respectively). Conclusion These results suggest that the decision flowchart constructed in this study provides an important contribution for avoiding underdosing of VCM in patients with eCCr of ≥50 mL/min.

Ten years of NIHR research training: who got an award? A retrospective cohort study

ObjectiveIn 2017, the National Institute for Health Research (NIHR) academy produced a strategic review of training, which reported the variation in application characteristics associated with success rates. It was noted that variation in applicant characteristic was not independent of one another. Therefore, the aim of this secondary analysis was to investigate the inter-relationships in order to identify factors (or groups of factors) most associated with application numbers and success rates.DesignRetrospective data were gathered from 4388 applications to NIHR Academy between 2007 and 2016. Multinominal logistic regression models quantified the likelihood of success depending on changes in the explanatory factors; relative risk ratios with 95% CIs. A classification tree analysis was built using exhaustive χ2 automatic interaction detection to better understand the effect of interactions between explanatory variables on application success rates.Results936 (21.3%) applications were awarded. Applications from males and females were equally likely to be successful (p=0.71). There was an overall reduction in numbers of applications from females as award seniority increased from predoctoral to professorship. Applications from institutions with a medical school had a 2.6-fold increase in likelihood of success (p<0.001). Classification tree analysis revealed key predictors of application success: award level, type of programme, previous NIHR award experience and applying form a medical school.ConclusionSuccess rates did not differ according to gender, and doctors were not more likely to be successful than applications from other professions. Taken together, these findings suggest an essential fairness in how the quality of a submitted application is assessed, but they also raise questions about variation in the opportunity to submit a high-quality application. The companion qualitative study (Burkshaw et al. (2021) BMJ Open) provides valuable insight into potential candidate mechanisms and discusses how research capacity development initiatives might be targeted in the future.

Análise da confiabilidade humana em um laboratório de análises clínicas utilizando FMEA e FTA

A análise de confiabilidade humana surge como suporte para quantificar e qualificar as falhas relacionadas às pessoas na execução de processos. Este trabalho apresenta um estudo de caso com o objetivo analisar a confiabilidade humana nos processos de atendimento de clientes e coleta de material biológico em um laboratório de análises clínicas. A pesquisa constituiu-se das seguintes etapas: revisão da literatura sobre os conceitos de falhas humanas e métodos para análise dessas falhas; mapeamento dos processos a fim de caracterizar falhas, levantamento do quantitativo de falhas por tipo e processo; análise do risco das falhas por meio da aplicação do método FMEA (Failure Mode and Effect Analysis); análise das causas das falhas, com o suporte de FTA (Fault Tree Analysis); e conclusões acerca das análises realizadas durante a pesquisa. Os levantamentos e análises realizados permitiram categorizar as falhas entre erros e transgressões e priorizar ações para preveni-las. Constatou-se que a ocorrência dessas falhas pode ser reduzida, se a qualificação profissional dos funcionários for consolidada.

Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model

Objective This study was performed to identify predictive factors for bacteremia among patients with pyelonephritis using a chi-square automatic interaction detector (CHAID) decision tree analysis model. Methods This retrospective cross-sectional survey was performed at Juntendo University Nerima Hospital, Tokyo, Japan and included all patients with pyelonephritis from whom blood cultures were taken. At the time of blood culture sample collection, clinical information was extracted from the patients’ medical charts, including vital signs, symptoms, laboratory data, and culture results. Factors potentially predictive of bacteremia among patients with pyelonephritis were analyzed using Student’s t-test or the chi-square test and the CHAID decision tree analysis model. Results In total, 198 patients (60 (30.3%) men, 138 (69.7%) women; mean age, 74.69 ± 15.27 years) were included in this study, of whom 92 (46.4%) had positive blood culture results. The CHAID decision tree analysis revealed that patients with a white blood cell count of >21,000/μL had a very high risk (89.5%) of developing bacteremia. Patients with a white blood cell count of ≤21,000/μL plus chills plus an aspartate aminotransferase concentration of >19 IU/L constituted the high-risk group (69.0%). Conclusion The present results are extremely useful for predicting the results of bacteremia among patients with pyelonephritis.