BackgroundAccurate optical characterisation and removal of small adenomas (<10 mm) at colonoscopy would allow hyperplastic polyps to be left in situ and surveillance intervals to be determined without the need for histopathology. Although accurate in specialist practice the performance of narrow band imaging (NBI), colonoscopy in routine clinical practice is poorly understood.MethodsNBI-assisted optical diagnosis was compared with reference standard histopathological findings in a prospective, blinded study, which recruited adults undergoing routine colonoscopy in six general hospitals in the UK. Participating colonoscopists (N=28) were trained using the NBI International Colorectal Endoscopic (NICE) classification (relating to colour, vessel structure and surface pattern). By comparing the optical and histological findings in patients with only small polyps, test sensitivity was determined at the patient level using two thresholds: presence of adenoma and need for surveillance. Accuracy of identifying adenomatous polyps <10 mm was compared at the polyp level using hierarchical models, allowing determinants of accuracy to be explored.FindingsOf 1688 patients recruited, 722 (42.8%) had polyps <10 mm with 567 (78.5%) having only polyps <10 mm. Test sensitivity (presence of adenoma, N=499 patients) by NBI optical diagnosis was 83.4% (95% CI 79.6% to 86.9%), significantly less than the 95% sensitivity (p<0.001) this study was powered to detect. Test sensitivity (need for surveillance) was 73.0% (95% CI 66.5% to 79.9%). Analysed at the polyp level, test sensitivity (presence of adenoma, N=1620 polyps) was 76.1% (95% CI 72.8% to 79.1%). In fully adjusted analyses, test sensitivity was 99.4% (95% CI 98.2% to 99.8%) if two or more NICE adenoma characteristics were identified. Neither colonoscopist expertise, confidence in diagnosis nor use of high definition colonoscopy independently improved test accuracy.InterpretationThis large multicentre study demonstrates that NBI optical diagnosis cannot currently be recommended for application in routine clinical practice. Further work is required to evaluate whether variation in test accuracy is related to polyp characteristics or colonoscopist training.Trial registration numberThe study was registered with clinicaltrials.gov (NCT01603927).

BackgroundCare home residents have venous thromboembolism (VTE) risk profiles similar to medical inpatients; however, the epidemiology of VTE in care homes is unclear.AimTo determine the incidence of VTE in care homes.Design and settingObservational cohort study of 45 care homes in Birmingham and Oxford, UK.MethodA consecutive sample of care home residents was enrolled and followed up for 12 months. Data were collected via case note reviews of care home and GP records; mortality information was supplemented with Health and Social Care Information Centre (now called NHS Digital) cause of death data. All potential VTE events were adjudicated by an independent committee according to three measures of diagnostic certainty: definite VTE (radiological evidence), probable VTE (high clinical indication but no radiological evidence), or possible VTE (VTE cannot be ruled out). (Study registration number: ISTCTN80889792.)ResultsThere were 1011 participants enrolled, and the mean follow-up period was 312 days (standard deviation 98 days). The incidence rate was 0.71 per 100 person years of observation (95% confidence interval [CI] = 0.26 to 1.54) for definite VTE, 0.83 per 100 person years (95% CI = 0.33 to 1.70) for definite and probable VTE, and 2.48 per 100 person years (95% CI = 1.53 to 3.79) for definite, probable, and possible VTE.ConclusionThe incidence of VTE in care homes in this study (0.71–2.48 per 100 person years) is substantial compared with that in the community (0.117 per 100 person years) and in people aged ≥70 years (0.44 per 100 person years). Further research regarding risk stratification and VTE prophylaxis in this population is needed.

COVID-19 is an infectious disease discovered after the outbreak began in Wuhan, China, in December 2019. COVID-19 is still becoming an increasing global threat to public health. The virus has been escalated to many countries across the globe. This paper analyzed and compared the performance of three different supervised machine learning techniques; Linear Discriminant Analysis (LDA), Random Forest (RF), and Support Vector Machine (SVM) on COVID-19 dataset. The best level of accuracy between these three algorithms was determined by comparison of some metrics for assessing predictive performance such as accuracy, sensitivity, specificity, F-score, Kappa index, and ROC. From the analysis results, RF was found to be the best algorithm with 100% prediction accuracy in comparison with LDA and SVM with 95.2% and 90.9% respectively. Our analysis shows that out of these three classification models RF predicts COVID-19 patient's survival outcome with the highest accuracy. Chi-square test reveals that all the seven features except sex were significantly correlated with the COVID-19 patient's outcome (P-value < 0.005). Therefore, RF was recommended for COVID-19 patient outcome prediction that will help in early identification of possible sensitive cases for quick provision of quality health care, support and supervision.

The most crucial mechanical property of concrete is compression strength (CS).Insufficient compressive strength can therefore result in severe failure and is very difficult to fix.Therefore, predicting concrete strength accurately and early is a key challenge for researchers and concrete designers. High-Strength Concrete (HSC) is an extremely complicated material, making it challenging to simulate its behaviour. The CS of HSC was predicted in this research using an Adaptive Neuro-fuzzy Inference system (ANFIS), Backpropagation neural networks (BPNN), Gaussian Process Regression (GPR), and NARX neural network (NARX) In the initial case, whereas in the second case, an ensemble model of k-Nearest Neighbor (k-NN) was proposed due to the poor performance of model combination M1 & M2 in ANFIS, BPNN, NARX and M1 in GPR. The output variable is the 28-day CS (MP) and the input variables are cement (Ce) Kg/m 3 , water (W) Kg/m 3 , superplasticizer (S) Kg/m 3 , coarse aggregate (CA) Kg/m 3 , and Fine aggregate (FA) Kg/m 3 . The outcomes depict that the suggested approach is predictively consistent for forecasting the CS of HSC, to sum up. The MATLAB 2019a toolkit was employed to generate the MLs learning models (ANFIS, BPNN, GPR, and NARX), whereas E-Views 11.0 was used for pre-and post-processing of the data, respectively. The model for BPNN and NARX modelling was trained and validated using MATLAB code. The outcome depicts that, the Combination M3 partakes the preeminent performance evaluation criterion when associated to the other models, where ANFIS-M3 prediction outperforms all other models with NSE, R 2 , R = 1, and MAPE = 0.261 & 0.006 in both the calibration and verification phases, correspondingly, in the first case, In contrast, the ensemble of BPNN and GPR surpasses all other models in the second scenario, with NSE, R2, R = 1, and MAPE = 0.000, in both calibration and verification phases Comparisons of 3 total performance showed that the proposed models can be a valuable tool for predicting the CS of 47 HSC.

Background: There is a growing interest in the attempts, made to understand what the asthma risk factors are and their frequency in triggering asthma attacks. Methods: Retrospective study