The Effect of Topical Agents on the Tensile Strength of Absorbable and Non-absorbable Suture Materials-An in Vitro Study.

INTRODUCTION: Topical agents are sometimes applied to surgical wounds after closure; these may include antiseptics or antibiotics. Minimal research has been undertaken to investigate the effect of topical regimens on the tensile strength of suture materials. AIM: To investigate the effect of four commonly used wound care regimens on the tensile strength of suture materials. METHODS: The failure load of 9 different suture materials was tested using the Instron Electroplus E3000 tensile testing machine (Instron Corporation, Norwood, Massachusetts). Tensile strength was represented as the failure load, measured in Newtons (N), and defined as the maximal load that could be applied across the suture prior to failure. Each suture was tested dry and after immersion in one of 4 products for 7 days and tested on day 7. The immersion agents tested were: sodium chloride 0.9%, MicroSafe® (Sonoma Pharmaceuticals, Petaluma, CA), Aqueous Povidone-iodine 10% solution (Betadine-Mundipharma), and Fucidin ointment. RESULTS: Sodium chloride 0.9%, MicroSafe®, Aqueous Povidone-iodine 10%, and Fucidin seem to increase the failure load of most absorbable and non-absorbable sutures. However, the failure load of Polyglactin 910 suture (Surgilactin, coated, violet-Ethicon) is reduced by long-term exposure to either sodium chloride 0.9% or MicroSafe®, while the failure load of the Polydioxanone suture (PDS Plus-Ethicon) is reduced by long-term exposure to MicroSafe® only. CONCLUSION: In our experiment, the commonly used wound care products have been shown to alter the tensile strength of suture materials. Further human studies are required to ascertain the clinical validity and applicability of our findings.

Exploring the impact of air pollution on COVID-19 admitted cases: Evidence from vector error correction model (VECM) approach in explaining the relationship between air pollutants towards COVID-19 cases in Kuwait.

In urban areas, air pollution is one of the most serious global environmental issues. Using time-series approaches, this study looked into the validity of the relationship between air pollution and COVID-19 hospitalization. This time series research was carried out in the state of Kuwait; stationarity test, cointegration test, Granger causality and stability test, and test on multivariate time-series using the Vector Error Correction Model (VECM) technique. The findings reveal that the concentration rate of air pollutants ( O 3 , SO 2 , NO 2 , CO , and PM 10 ) has an effect on COVID-19 admitted cases via Granger-cause. The Granger causation test shows that the concentration rate of air pollutants ( O 3 , PM 10 , NO 2 , temperature and wind speed) influences and predicts the COVID-19 admitted cases. The findings suggest that sulfur dioxide ( SO 2 ), NO 2 , temperature, and wind speed induce an increase in COVID-19 admitted cases in the short term according to VECM analysis. The evidence of a positive long-run association between COVID-19 admitted cases and environmental air pollution might be shown in the cointegration test and the VECM. There is an affirmation that the usage of air pollutants ( O 3 , SO 2 , NO 2 , CO , and PM 10 ) has a significant impact on COVID-19-admitted cases' prediction and its explained about 24% of increasing COVID-19 admitted cases in Kuwait.

Correction to: Exploring the impact of air pollution on COVID-19 admitted cases: Evidence from vector error correction model (VECM) approach in explaining the relationship between air pollutants towards COVID-19 cases in Kuwait.

[This corrects the article DOI: 10.1007/s42081-022-00165-z.].

Handling missing data in a rheumatoid arthritis registry using random forest approach.

Missing data in clinical epidemiological research violate the intention-to-treat principle, reduce the power of statistical analysis, and can introduce bias if the cause of missing data is related to a patient's response to treatment. Multiple imputation provides a solution to predict the values of missing data. The main objective of this study is to estimate and impute missing values in patient records. The data from the Kuwait Registry for Rheumatic Diseases was used to deal with missing values among patient records. A number of methods were implemented to deal with missing data; however, choosing the best imputation method was judged by the lowest root mean square error (RMSE). Among 1735 rheumatoid arthritis patients, we found missing values vary from 5% to 65.5% of the total observations. The results show that sequential random forest method can estimate these missing values with a high level of accuracy. The RMSE varied between 2.5 and 5.0. missForest had the lowest imputation error for both continuous and categorical variables under each missing data rate (10%, 20%, and 30%) and had the smallest prediction error difference when the models used the imputed laboratory values.

In-hospital mortality in SARS-CoV-2 stratified by hemoglobin levels: A retrospective study.

This study is to estimate in-hospital mortality in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients stratified by hemoglobin (Hb) level. Patients were stratified according to hemoglobin level into two groups, that is, Hb <100 g/L and Hb >100 g/L. A total of 6931 patients were included. Of these, 6377 (92%) patients had hemoglobin levels >100 g/L. The mean age was 44 ± 17 years, and 66% of the patients were males. The median length of overall hospital stay was 13 days [2; 31]. The remaining 554 (8%) patients had a hemoglobin level <100 g/L. Overall mortality was 176 patients (2.54%) but was significantly higher in the group with hemoglobin levels <100 g/L (124, 22.4%) than in the group with hemoglobin levels >100 g/L (52, 0.82%). Risk factors associated with increased mortality were determined by multivariate analysis. The Kaplan-Meier survival analysis showed hemoglobin as a predictor of mortality. Cox proportional hazards regression coefficients for hemoglobin for the HB ≤ 100 category of hemoglobin were significant, B = 2.79, SE = 0.17, and HR = 16.34, p < 0.001. Multivariate logistic regression showed Hb < 100 g/L had a higher cumulative all-cause in-hospital mortality (22.4% vs. 0.8%; adjusted odds ratio [aOR], 0.33; 95% [CI]: [0.20-0.55]; p < 0.001). In this study, hemoglobin levels <100 g/L were found to be an independent predictor of in-hospital mortality.