Two were OOT over the full dose range one elected not to remeasure; the other also had positional discrepancy greater than 1 mm and repeat measurement with a new plan was in tolerance. The original OOT was attributed to inappropriate MLC constraints. All centres delivered planned target-cord dose gradient within 1 mm. CONCLUSION Credentialing measurements for vertebral SABR in a multi-centre trial showed although the majority of centres delivered accurate vertebral SABR, there is high value in independent audit measurements. One centre with inappropriate MLC settings was detected, which may have resulted in delivery of clinically unacceptable vertebral SABR plans. OBJECTIVE Predicting the risk of falls in advance can benefit the quality of care and potentially reduce mortality and morbidity in the older population. The aim of this study was to construct and validate an electronic health record-based fall risk predictive tool to identify elders at a higher risk of falls. METHODS The one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost, and tested on an independent validation cohort. The data were collected from electronic health records (EHR) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age). RESULTS This model attained a validated C-statistic of 0.807, where 50 % of the identified high-risk true positives were confirmed to fall during the first 94 days of next year. The model also captured in advance 58.01 % and 54.93 % of falls that happened within the first 30 and 30-60 days of next year. The identified high-risk patients of fall showed conditions of severe disease comorbidities, an enrichment of fall-increasing cardiovascular and mental medication prescriptions and increased historical clinical utilization, revealing the complexity of the underlying fall etiology. The XGBoost algorithm captured 157 impactful predictors into the final predictive model, where cognitive disorders, abnormalities of gait and balance, Parkinson’s disease, fall history and osteoporosis were identified as the top-5 strongest predictors of the future fall event. CONCLUSIONS By using the EHR data, this risk assessment tool attained an improved discriminative ability and can be immediately deployed in the health system to provide automatic early warnings to older adults with increased fall risk and identify their personalized risk factors to facilitate customized fall interventions. Influenza B virus has two distinct lineages (Victoria and Yamagata) and are associated with seasonal influenza epidemics that cause respiratory illness. selleck chemicals Influenza B hemagglutinin (HA) is a major surface glycoprotein with the receptor-binding site (RBS) primarily involved in viral pathogenesis. Generally, influenza B exclusively infects the human population which would insinuate that the structural variability of the influenza B HA RBS rarely changes. However, to our knowledge, the potential impact of variations in the influenza B HA RBS structural variability was not fully elucidated. Throughout this study, we generated models from the transitioning (evolving viral lineage) 1998-2018 influenza B/Yamagata HA, verified the quality of each HA model, performed HA RBS structural variability measurements, superimposed varying HA models for comparison, and designed a phylogenetic tree network for further analyses. We found that measurements of the transitioning HA RBS structural variability were generally maintained and, similarly, measurements of the altered (years that differed from the evolving viral lineage, specifically 2003, 2007, 2017) HA RBS structural variability differed from the transitioning HA RBS. Moreover, we observed that the altered HA RBS structural variability favored the formation of a putative Y202-H191 hydrogen bond which we postulate may increase structural stability, thereby, allowing for a winter infection of the virus. Furthermore, we established that changes in HA RBS structural variability does not influence viral evolution, but putatively seasonal infection. To define the skin temperature at which diseased nerves are better differentiated from the healthy. Motor and sensory conduction of median and ulnar nerve were evaluated in 52 patients with carpal tunnel syndrome (CTS) and 52 matched healthy controls at environmental skin temperature (mean 32-33 °C), after warming by an average of 2 °C and cooling to approximately 6 °C below baseline. In the hot condition, group comparisons for the median nerve showed a similar rate of distal motor latency (DML) reduction and sensory conduction velocity (SCV) increase in CTS and controls. With cold, the rate of change was smaller for the patients DML mean increase was 5% /°C (7% for controls) and SCV mean decrease was 2.5%/°C (3.2% for controls). Individual patients’ analysis revealed fewer abnormal median DML and SCV values at hot or at cold, compared to environmental temperature. It is concluded that conduction adjustments for low hand temperatures based on healthy measurements resulted in overcorrection and therefore underdiagnosis of CTS. Alternatively, at excessive hand warming the convergence of patient and healthy measurements also lead to underdiagnosis. Maintenance of skin temperature at 32-33 °C, corresponding to normal body temperature, is the optimum approach and should always be employed in clinical practice. BACKGROUND AND AIMS Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based classifiers for automated detection and classification of diabetes. METHODS The diabetes dataset have taken from Bangladesh demographic and health survey, 2011 data having 1569 respondents are 127 diabetes. Two statistical tests as independent t for continuous and chi-square for categorical variables are used to determine the risk factors of diabetes. Six ML-based classifiers as support vector machine, random forest, linear discriminant analysis, logistic regression, k-nearest neighborhood, bagged classification and regression tree (Bagged CART) have been adopted to predict and classify of diabetes. RESULTS Our findings show that 11 factors out of 15 factors are significantly associated with diabetes. Bagged CART provides the highest accuracy and area under the curve of 94.