Timing of surgery following SARS-CoV-2 infection: an international prospective cohort study.

Peri-operative SARS-CoV-2 infection increases postoperative mortality. The aim of this study was to determine the optimal duration of planned delay before surgery in patients who have had SARS-CoV-2 infection. This international, multicentre, prospective cohort study included patients undergoing elective or emergency surgery during October 2020. Surgical patients with pre-operative SARS-CoV-2 infection were compared with those without previous SARS-CoV-2 infection. The primary outcome measure was 30-day postoperative mortality. Logistic regression models were used to calculate adjusted 30-day mortality rates stratified by time from diagnosis of SARS-CoV-2 infection to surgery. Among 140,231 patients (116 countries), 3127 patients (2.2%) had a pre-operative SARS-CoV-2 diagnosis. Adjusted 30-day mortality in patients without SARS-CoV-2 infection was 1.5% (95%CI 1.4-1.5). In patients with a pre-operative SARS-CoV-2 diagnosis, mortality was increased in patients having surgery within 0-2 weeks, 3-4 weeks and 5-6 weeks of the diagnosis (odds ratio (95%CI) 4.1 (3.3-4.8), 3.9 (2.6-5.1) and 3.6 (2.0-5.2), respectively). Surgery performed ≥ 7 weeks after SARS-CoV-2 diagnosis was associated with a similar mortality risk to baseline (odds ratio (95%CI) 1.5 (0.9-2.1)). After a ≥ 7 week delay in undertaking surgery following SARS-CoV-2 infection, patients with ongoing symptoms had a higher mortality than patients whose symptoms had resolved or who had been asymptomatic (6.0% (95%CI 3.2-8.7) vs. 2.4% (95%CI 1.4-3.4) vs. 1.3% (95%CI 0.6-2.0), respectively). Where possible, surgery should be delayed for at least 7 weeks following SARS-CoV-2 infection. Patients with ongoing symptoms ≥ 7 weeks from diagnosis may benefit from further delay

Food Security Status of Elders and Its Related Factors in Arak in 2012

Abstract Introduction: Today, increased life expectancy, reduced mortality rates, and improved health conditions caused to an increase in number of elderly people, as one of the vulnerable groups in the society. On the other hand, food security is one of the necessary prerequisites for the health of the elderly people. Therefore, the present study intended to investigate the food security status of the elderly people as well as its related factors. Methods: This descriptive cross-sectional study was conducted in 2012 consisting of 300 elderly people of Arak city. The study data were collected through FaCPS-FSSM food security questionnaire validated for the elderly via conducting interviews. In order to analyze the study data, SPSS software was utilized applying Chi square test, independent t&ndash;test, Pearson's correlation and regression. Results: The results of the present study revealed that 39.3% of the elderly people had a full food security, 29 % reported food insecurity without hunger, 20.7% showed moderate food insecurity and 11% had severe food insecurity. Moreover, a significant relationship was detected between education level, occupation, marital status, body mass index, disease and household dimension with the food security (p <%5). Conclusions: Considering that close to 60.7% of the studied elderly revealed some degree of food insecurity as well as the various factors significantly associated with this problem in the present study, this problem in this level of vulnerable population demonds to be attended more than ever

A Survey on Lead and Cadmium Content in Bread Produced in Yazd

Introduction: Due to such complications of absorbing lead and cadmium heavy metals as kidney and liver dysfunction, vascular and heart diseases, anemia, digestive complications, nervous and skeletal problems and due to importance of bread as one of the most important food diets in Iran, especially in Yazd, the amount of lead and cadmium was evaluated in a variety of breads in Yazd. Methods: This descriptive cross-sectional study was carried out in 2013. Out of 69 bakeries, random probability proportionate sampling was applied in order to measure the heavy metals (lead and cadmium content) in samples by ash and atomic absorption equipped with grafiti furnace(ETAAS) with correction of background time. The study data were analyzed using SPSS (v.17) considering p-value of less than 0.05 as significant. Results: The average amounts of lead and cadmium were 99.05 and 7.49 mg/kg respectively. The amount of lead in Sangak bread was higher than that of other types of breads, whereas lead amounts of fantasy bread was reported less than those of other breads. Cadmium content demonstrated no significant differences among breads. Lead amount was higher in direct heat breads. Whereas, cadmium amount showed no significant differences between direct and indirect heat breads. It is worth mentioning that lead and cadmium content were reported lower than allowable levels in all samples. Conclusions: As the study results revealed and considering per capita consumption of bread in Iran (about 160 kg), it seems that weekly intake of lead and cadmium in Yazd is at an acceptable level, though possible risk of heavy metals(lead and cadmium) need to decrease in order to prevent the probable risks of lead and cadmium heavy metals

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients: COVID-19 prognostic modeling using CT radiomics and machine learning

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95: 0.81�0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95: 0.81�0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients. © 2022 The Author

Niosomes

The chapter spans the chemistries, which are harnessed to create niosomes, the concepts upon which their application rests and model examples of the exploitation of this new knowledge to bring healthcare benefits