Certain probiotics may prevent the development of antibiotic-associated diarrhoea (AAD) and Clostridium difficile-associated diarrhoea (CDAD), but their effectiveness depends on both strain and dose. There are few data on nutritional interventions to control AAD/CDAD in the spinal cord injury (SCI) population. The present study aimed to assess (1) the efficacy of consuming a commercially produced probiotic containing at least 6·5 £ 10 9 live Lactobacillus casei Shirota (LcS) in reducing the incidence of AAD/CDAD, and (2) whether undernutrition and proton pump inhibitors (PPI) are risk factors for AAD/CDAD. A total of 164 SCI patients (50·1 (SD 17·8) years) with a requirement for antibiotics (median 21 d, range 5-366) were randomly allocated to receive LcS (n 76) or no probiotic (n 82). LcS was given once daily for the duration of the antibiotic course and continued for 7 days thereafter. Nutritional risk was assessed by the Spinal Nutrition Screening Tool. The LcS group had a significantly lower incidence of AAD (17·1 v. 54·9 %, P,0·001). At baseline, 65 % of patients were at undernutrition risk. Undernutrition (64·1 v. 33·3 %, P,0·01) and the use of PPI (38·4 v. 12·1 %, P¼0·022) were found to be associated with AAD. However, no significant difference was observed in nutrient intake between the groups. The multivariate logistic regression analysis identified poor appetite (, 1/2 meals eaten) (OR 5·04, 95 % CI 1·28, 19·84) and no probiotic (OR 8·46, 95 % CI 3·22, 22·20) as the independent risk factors for AAD. The present study indicated that LcS could reduce the incidence of AAD in hospitalised SCI patients. A randomised, placebo-controlled study is needed to confirm this apparent therapeutic success in order to translate into improved clinical outcomes.

Surface treatment of the adherends prior to adhesive bonding plays an important role in the enhancing of strength and durability of bonded joints. In this work, an investigation on effect of adherend surface roughness on adhesive bond strength was performed. Single strap joints with different adherends (mild steel and aluminium) bonded with an epoxy resin (Araldite® 2015) were tested. The adherend surface was treated by mechanical abrasion process using an emery paper. Contact angle measurement and SEM analysis to understand the wettability and the failure mechanism of the joints were performed. It was found that an optimum surface roughness exists for a maximum bonding strength and the roughness range depends on the adherend material. The joint strength changes are associated not only simply by the increased bonding area, surface texture or mechanical interlocking, but also by the chemical characteristics of the surface and the chemical bond between them.

No abstract

No abstract

Background Diagnostic methods for Covid-19 have improved, both in speed and availability. Because of atypical and asymptomatic carriage of the virus and nosocomial spread within institutions, timely diagnosis remains a challenge. Machine learning models trained on blood test results have shown promise in identifying cases of Covid-19. Aims To train and validate a machine learning model capable of differentiating Covid-19 positive from negative patients using routine blood tests and assess the model’s accuracy against atypical and asymptomatic presentations. Design and Methods We conducted a retrospective analysis of medical admissions to our institution during March and April 2020. Participants were categorised into Covid-19 positive or negative groups based on clinical, radiological features or nasopharyngeal swab. A machine learning model was trained on laboratory parameters and validated for accuracy, sensitivity and specificity and externally validated at an unconnected establishment. Results An Ensemble Bagged Tree model was trained on data collected from 405 patients (212 Covid-19 positive) producing accuracy of 81.79% (95% confidence interval (CI) 77.53% to 85.55%), sensitivity of 85.85% (CI 80.42% to 90.24%) and specificity of 76.65% (CI 69.49% to 82.84%). Accuracy was preserved for atypical and asymptomatic subgroups. Using an external data set for 226 patients (141 Covid-19 positive) accuracy of 76.82% (CI 70.87% to 82.08%), sensitivity of 78.38% (CI 70.87% to 84.72%) and specificity of 74.12% (CI 63.48% to 83.01%) was achieved. Conclusion A machine learning model using routine laboratory parameters can detect atypical and asymptomatic presentations of Covid-19, and might be an adjunct to existing screening measures.