Objectives To investigate potential oxalate‐degrading bacteria, including Oxalobacter formigenes, Lactobacillus (Lac) and Bifidobacterium (Bif) genera, and Oxalyl‐CoA decarboxylase (oxc) encoding Lac (LX) and Bif (BX) species in participants with recurrent calcium kidney stones, and their correlation with 24‐h urine oxalate. Participants and Methods Stool and 24‐h urine samples were collected from 58 patients with urolithiasis (29 cases with and 29 without hyperoxaluria) and 29 healthy controls. Absolute quantitation and relative abundance of the bacteria were measured by real‐time PCR. The relationship between the investigated bacteria and 24‐h urine oxalate were assessed statistically. Results The count per gram of stool and relative abundance of O. formigenes, Lac, Bif, LX and BX and the number of participants carrying O. formigenes, LX and BX bacteria were not significantly different between the groups; however, the relative abundance of O. formigenes in the kidney stone group was lower than in healthy controls (P = 0.035). More healthy controls were O. formigenes‐positive compared with participants in the kidney stone group (P = 0.052). The results of the linear regression model, including all study participants, showed that the presence of O. formigenes could decrease 24‐h urine oxalate (β = −8.4, P = 0.047). Neither Lac and Bif genera nor LX and BX species were correlated with calcium stones or urine oxalate. Conclusion These results emphasize the role of O. formigenes in kidney stone formation and its role in hyperoxaluria, which may be independent of kidney stone disease. Moreover, our results suggest that, although some Lac and Bif strains have oxalate‐degrading potential, they may not be among the major oxalate‐degrading bacteria of the gut microbiome.

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Background: In the face of the COVID-19, as a public health emergency, the restaurant industry is struggling to organize itself. The aim of this study is to determine the knowledge, attitude, and practice and also the perceptions of restaurants' customers and managers toward COVID-19 prevention.Methods: This cross-sectional study was conducted using the mixed-method approach. Two online questionnaires were undertaken through WhatsApp Messenger among the 210 customers and 50 managers of restaurants. Multivariate linear regression analysis was conducted to identify the predictors of knowledge, attitude, and practice toward COVID-19 prevention. Then semi-structured, in-depth phone interviews were conducted with 45 subjects to identify their perceptions about the restaurant industry during the COVID-19 pandemic.Results: The majority of customers had moderate knowledge (72.4%), positive attitude (90.5%), and desirable practice (38.6%); whereas the majority of managers had sufficient knowledge (50%), negative attitude (82%), and acceptable practice (58%) toward the prevention of COVID-19 in restaurants. Multiple linear regression analysis showed with increasing each 10 years in the age of the customers, the practice score significantly decreased (Beta = −0.155, p < 0.05). Moreover, qualitative results revealed three categories (1. restaurant industry, 2. social media, and 3. government) in 9 themes with 32 sub-themes which were explored based on the perception of the participants toward COVID-19 prevention in restaurants.Conclusion: The majority of restaurant customers and managers have sufficient knowledge and acceptable practice, but a positive attitude among customers and a negative attitude among managers about the prevention of COVID-19 were shown. There is an urgent need to understand public awareness about preventing COVID-19 in restaurants at these critical moments. The results seek to provide strategies for the policymakers and restaurant industry to plan the specific educational intervention about how to manage future crises and public health improvement.

Background:Identification of a normal range for biomarkers, based on pregnancy outcomes (caused by their high or low values) is of special importance in clinical studies. As some pregnancy outcomes can happen in both high and low levels of biomarkers, the receiver-operating characteristic (ROC) curve is unsuitable for identifying these levels separately; rather, a statistical method is preferable which identifies both levels simultaneously.Objectives:To this effect, our research introduces a generalization of ROC curve (by using a number of related consequences) to identify a normal range for the biomarker. Practically, the study intends to identify a normal range of hemoglobin in the first trimester of pregnancy to prevent adverse outcomes that can be caused by high and low levels of hemoglobin.Patients and Methods:The current article introduces an ROC generalization curve to determine a normal range for biomarkers based on a number of pregnancy outcomes, which may occur in high and low levels of biomarkers. Simulated data were also used to compare the current method with the ROC curve method. Our data collected from a cohort study carried out on 600 pregnant women referring to Milad Hospital in Tehran, Iran in 2010. The data comprised hemoglobin level in the first trimester of pregnancy as well as pregnancy outcomes such as preterm delivery, low birth weight, preeclampsia, and gestational diabetes. We calculated an estimation of the normal range of hemoglobin for the study population. Statistical analysis was carried out by R software, version 3.0.2.Results:Results from the simulation study indicated that, the new method was better than the methods which used two ROC curves separately with regard to sensitivity and specificity. In this method, the level of normal hemoglobin in the first trimester ranged from 10 to 12.4 with sensitivity and specificity levels of 76.2% and 48% respectively, which is higher than previous studies.Conclusions:With regard to the normal range of biomarkers, our method yielded greater sensitivity and specificity levels than methods using the ROC curve, which separately analyzes the data, particularly in occasions with common consequences in high and low levels of the biomarker.