Search

This study conducted experimental and machine learning (ML) modeling approaches to investigate the impact of using recycled glass powder in cement mortar in an acidic environment. Mortar samples were prepared by partially replacing cement and sand with glass powder at various percentages (from 0% to 15%, in 2.5% increments), which were immersed in a 5% sulphuric acid solution. Compressive strength (CS) tests were conducted before and after the acid attack for each mix. To create ML-based prediction models, such as bagging regressor and random forest, for the CS prediction following the acid attack, the dataset produced through testing methods was utilized. The test results indicated that the CS loss of the cement mortar might be reduced by utilizing glass powder. For maximum resistance to acidic conditions, the optimum proportion of glass powder was noted to be 10% as cement, which restricted the CS loss to 5.54%, and 15% as a sand replacement, which restricted the CS loss to 4.48%, compared to the same mix poured in plain water. The built ML models also agreed well with the test findings and could be utilized to calculate the CS of cementitious composites incorporating glass powder after the acid attack. On the basis of the R2 value (random forest: 0.97 and bagging regressor: 0.96), the variance between tests and forecasted results, and errors assessment, it was found that the performance of both the bagging regressor and random forest models was similarly accurate., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Alfaiad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches' results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Amin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Early revascularization and treatment is key to improving clinical outcomes and reducing mortality in acute myocardial infarction (AMI). In low- and middle-income countries such as Bangladesh, timely management of AMI is challenging, with pre-hospital delays playing a significant role. This study was designed to investigate pre-hospital delay and its associated factors among patients presenting with AMI in the capital city of Dhaka., Methods: This retrospective cohort study was conducted on 333 patients presenting with AMI over a 3-month period at two of the largest primary reperfusion-capable tertiary cardiac care centres in Dhaka. Of the total patients, 239(71.8%) were admitted in the National Institute of Cardiovascular Diseases, Dhaka and 94(28.2%) at Ibrahim Cardiac Hospital & Research Institute, Dhaka Data were collected from patients by semi-structured interview and hospital medical records. Pre-hospital delay (median and inter-quartile range) was calculated. Statistical significance was determined by Chi-square test. Multivariate logistic regression analysis was done to determine the independent predictors of pre-hospital delay., Results: The mean age of the respondents was 53.8±11.2 years. Two-thirds (67.6%) of the respondents were males. Median total pre-hospital delay was 11.5 (IQR-18.3) hours with median decision time from symptom onset to seeking medical care being 3.0 (IQR: 11.0) hours. Nearly half (48.9%) of patients presented to the hospital more than 12 hours after symptom onset. On multivariate logistic regression analysis, AMI patients with absence of typical chest pain [OR 5.21; (95% CI: 2.5-9.9)], diabetes [OR: 1.7 (95% CI: 1.0-2.9)], residing/staying > 30 km away from nearest hospital at the time of onset [OR: 4.3(95% CI = 2.3-7.2)] and belonged to lower and middle class [OR: 1.9(95% CI = 1.0-3.5)] were significantly associated with pre-hospital delays., Conclusion: Acute myocardial infarction (AMI) patients with atypical chest pain, diabetes, staying far away from nearest hospital and belonged to lower and middle socioeconomic strata were significantly associated with pre-hospital delays. The findings could have immense implications for improvements about timely reaching of AMI patients to the hospital within the context of their sociodemographic status and geographic barriers of the city., Competing Interests: The authors have declared that no competing interests exist.

Objective: Evidence-based characterization of the diagnostic and prognostic value of the hematological and immunological markers related to the epidemic of Coronavirus Disease 2019 (COVID-19) is critical to understand the clinical course of the infection and to assess in development and validation of biomarkers., Methods: Based on systematic search in Web of Science, PubMed, Scopus, and Science Direct up to April 22, 2020, a total of 52 eligible articles with 6,320 laboratory-confirmed COVID-19 cohorts were included. Pairwise comparison between severe versus mild disease, Intensive Care Unit (ICU) versus general ward admission and expired versus survivors were performed for 36 laboratory parameters. The pooled standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated using the DerSimonian Laird method/random effects model and converted to the Odds ratio (OR). The decision tree algorithm was employed to identify the key risk factor(s) attributed to severe COVID-19 disease., Results: Cohorts with elevated levels of white blood cells (WBCs) (OR = 1.75), neutrophil count (OR = 2.62), D-dimer (OR = 3.97), prolonged prothrombin time (PT) (OR = 1.82), fibrinogen (OR = 3.14), erythrocyte sedimentation rate (OR = 1.60), procalcitonin (OR = 4.76), IL-6 (OR = 2.10), and IL-10 (OR = 4.93) had higher odds of progression to severe phenotype. Decision tree model (sensitivity = 100%, specificity = 81%) showed the high performance of neutrophil count at a cut-off value of more than 3.74x109/L for identifying patients at high risk of severe COVID-19. Likewise, ICU admission was associated with higher levels of WBCs (OR = 5.21), neutrophils (OR = 6.25), D-dimer (OR = 4.19), and prolonged PT (OR = 2.18). Patients with high IL-6 (OR = 13.87), CRP (OR = 7.09), D-dimer (OR = 6.36), and neutrophils (OR = 6.25) had the highest likelihood of mortality., Conclusions: Several hematological and immunological markers, in particular neutrophilic count, could be helpful to be included within the routine panel for COVID-19 infection evaluation to ensure risk stratification and effective management., Competing Interests: The authors have declared that no competing interests exist.

Klebsiella pneumoniae (KP) and Pseudomonas aeruginosa (PA) are important human pathogens that are associated with a range of infection types, including wound and disseminated infections. Treatment has been complicated by rising rates of antimicrobial resistance. Immunoprophylactic strategies are not constrained by antimicrobial resistance mechanisms. Vaccines against these organisms would be important public health tools, yet they are not available. KP surface O polysaccharides (OPS) are protective antigens in animal models of infection. Similarly, PA flagellin (Fla), the major subunit of the flagellar filament, is required for virulence and is a target of protective antibodies in animal models. We report herein the development of a combined KP and PA glycoconjugate vaccine comprised of the four most common KP OPS types associated with human infections (O1, O2, O3, O5), chemically linked to the two Fla types of PA (FlaA, FlaB). Conjugation of KP OPS to PA Fla enhanced anti-polysaccharide immune responses and produced a formulation that generated antibody titers to the four KP OPS types and both PA Fla antigens in rabbits. Passive transfer of vaccine-induced rabbit antisera reduced the bacterial burden and protected mice against fatal intravenous KP infection. Mice passively transferred with conjugate-induced antisera were also protected against PA infection after thermal injury with a FlaB-expressing isolate, but not a FlaA isolate. Taken together, these promising preclinical results provide important proof-of-concept for a broad spectrum human vaccine to prevent KP and PA infections., Competing Interests: RS, SMT and ASC are co-inventors on a granted patent (U.S. Patent No. 9,988,426) and patent applications (U.S. Patent Application No. 15/842,068, European Patent Application No. 15 841 265.0) describing Klebsiella reagent strains and conjugates of Klebsiella OPS with Pseudomonas flagellin proteins. AL and MOS are employees of Fina Biosolutions. AL is also a patent holder for the aminooxy method used for conjugation (WO2005072778 A2). This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Structural hollow sections are gaining worldwide importance due to their structural and architectural advantages over open steel sections. The only obstacle to their use is their connection with other structural members. To overcome the obstacle of tightening the bolt from one side has given birth to the concept of blind bolts. Blind bolts, being the practical solution to the connection hindrance for the use of hollow and concrete filled hollow sections play a vital role. Flowdrill, the Huck High Strength Blind Bolt and the Lindapter Hollobolt are the well-known commercially available blind bolts. Although the development of blind bolts has largely resolved this issue, the use of structural hollow sections remains limited to shear resistance. Therefore, a new modified version of the blind bolt, known as the "Extended Hollo-Bolt" (EHB) due to its enhanced capacity for bonding with concrete, can overcome the issue of low moment resistance capacity associated with blind-bolted connections. The load transfer mechanism of this recently developed blind bolt remains unclear, however. This study uses a parametric approach to characterising the EHB, using diameter as the variable parameter. Stiffness and load-carrying capacity were evaluated at two different bolt sizes. To investigate the load transfer mechanism, a component-based study of the bond and anchorage characteristics was performed by breaking down the EHB into its components. The results of the study provide insight into the load transfer mechanism of the blind bolt in question. The proposed component-based model was validated by a spring model, through which the stiffness of the EHB was compared to that of its components combined. The combined stiffness of the components was found to be roughly equivalent to that of the EHB as a whole, validating the use of this component-based approach.

Efficiency of solar based disinfection of Pseudomonas aeruginosa (P. aeruginosa) in rooftop harvested rainwater was evaluated aiming the potability of rainwater. The rainwater samples were exposed to direct sunlight for about 8-9 hours and the effects of water temperature (°C), sunlight irradiance (W/m2), different rear surfaces of polyethylene terephthalate bottles, variable microbial concentrations, pH and turbidity were observed on P. aeruginosa inactivation at different weathers. In simple solar disinfection (SODIS), the complete inactivation of P. aeruginosa was obtained only under sunny weather conditions (>50°C and >700 W/m2) with absorptive rear surface. Solar collector disinfection (SOCODIS) system, used to improve the efficiency of simple SODIS under mild and weak weather, completely inactivated the P. aeruginosa by enhancing the disinfection efficiency of about 20% only at mild weather. Both SODIS and SOCODIS systems, however, were found inefficient at weak weather. Different initial concentrations of P. aeruginosa and/or Escherichia coli had little effects on the disinfection efficiency except for the SODIS with highest initial concentrations. The inactivation of P. aeruginosa increased by about 10-15% by lowering the initial pH values from 10 to 3. A high initial turbidity, adjusted by adding kaolin, adversely affected the efficiency of both systems and a decrease, about 15-25%; in inactivation of P. aeruginosa was observed. The kinetics of this study was investigated by Geeraerd Model for highlighting the best disinfection system based on reaction rate constant. The unique detailed investigation of P. aeruginosa disinfection with sunlight based disinfection systems under different weather conditions and variable parameters will help researchers to understand and further improve the newly invented SOCODIS system.