Antimicrobial Effects of Mass and Oral-B Mouthwashes on Streptococcus mutans and Candida albicans: An In Vitro Study

Objectives The present study aimed to compare the antimicrobial properties of Iranian Mass mouthwash and alcohol-free Oral-B mouthwash against Streptococcus mutans (S. mutans) and Candida albicans (C. albicans). Methods In this in vitro study, S. mutans and C. albicans were separately cultured on BHI agar plates. The agar well-diffusion method was used to compare the antimicrobial properties of Mass and Oral-B mouthwashes, and 0.2% chlorhexidine (CHX) as the positive control and saline as the negative control. The diameter of growth inhibition zones was then measured. The experiment was performed in triplicate. The minimum inhibitory concentration (MIC), and the minimum bactericidal concentration (MBC) of the two mouthwashes were determined for each microorganism using the broth micro-dilution method. Data were analyzed by the Kruskal-Wallis and Dunn's test (Benjamini-Hochberg). Results The mean diameter of the growth inhibition zone of S. mutans was 26.33 and 27.66 mm for Mass and Oral-B mouthwashes, respectively. These values were 18 mm and 17.66 mm, respectively for C. albicans.  There was no significant difference in the mean diameter of growth inhibition zones of the two mouthwashes against C. albicans (P=0.38) or S. mutans (P=0.23). The MIC of Mass and Oral-B mouthwash for S mutans was in 1/1024 dilution ratio and the MIC of Mass and Oral-B mouthwashes for C. albicans was in 1/512 and 1/256 dilution ratios, respectively. The MBC values were the same as the MIC values for both mouthwashes. Conclusion Mass mouthwash was as effective as Oral-B mouthwash against S. mutans and C. albicans

The risk of chronic kidney disease among women with polycystic ovary syndrome : A long‐term population‐based cohort study

acceptedVersio

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach

Purpose: To establish the early prediction models of radiation-induced oral mucositis (RIOM) based on baseline CT-based radiomic features (RFs), dosimetric data, and clinical features by machine learning models for head and neck cancer (HNC) patients. Methods: In this single-center prospective study, 49 HNCs treated with curative intensity modulated radiotherapy (IMRT) were enrolled. Baseline CT images (i.e., CT simulation), dosimetric, and clinical features were collected. RIOM was assessed using CTCAE v.5.0. RFs were extracted from manually-contoured oral mucosa structures. Minimum-redundancy-maximum-relevance (mRMR) method was applied to select the most informative radiomics, dosimetric, and clinical features. Then, binary prediction models were constructed for predicting acute RIOM based on the top mRMR-ranked radiomics, dosimetric, and clinical features alone or in combination, using random forest classifier algorithm. The predictive performance of models was assessed using the area under the receiver operating curve (AUC), accuracy, weighted-average based sensitivity, precision, and F1-measure. Results: Among extracted features, the top 10 RFs, the top 5 dose-volume features, and the top 5 clinical features were selected using mRMR method. The model exploiting the integrated features (10-radiomics + 5-dosimetric + 5-clinical) achieved the best prediction with AUC, accuracy, sensitivity, precision, and F1-measure values of 91.7 %, 90.0 %, 83.0 % 100.0 %, and 91.0 %, respectively. The model developed using baseline CT RFs alone provided the best performance compared to dose-volume features or clinical features alone, with an AUC of 87.0 %. Conclusion: Our results suggest that the integration of baseline CT radiomic features with dosimetric and clinical features showed promising potential to improve the performance of machine learning models in early prediction of RIOM. The ultimate goal is to personalize radiotherapy for HNC patients

Evaluation of the antimicrobial effect of chitosan and whey proteins isolate films containing free and nanoliposomal garlic essential oils against Listeria monocytegenes, E.coli O157:H7 and Staphylococcus aureus

Background and Aim: Active antimicrobial packaging is an innovative technique that can enhance safety and shelf life of foods. In this study antimicrobial activity of chitosan and whey protein isolate (WPI) films incorporated with free and nano-liposomal garlic essential oil was investigated against Listeria monocytogenes, Escherichia coli O157:H7 and Staphylococcus aureus. Materials and Methods: This study was done in 2015 and disk diffusion method was applied to determine antimicrobial effect of films. Films were cut into circular disks with 9 mm diameter and put on the inoculated BHI agar plates with tested microorganisms. Then plates incubated for 24 h at 37oC. The diameter of inhibition zone was measured by digital caliper. The statistical analysis was done by one-way analysis of variance (ANOVA) followed by Tukey's test. Results: The results of this study revealed pure chitosan and WPI films alone or incorporated with nano-liposomal garlic essential oil did not show any inhibitory effects on tested microorganisms. Incorporation of 2% or higher concentrations of garlic essential oil to the chitosan solution showed the antibacterial activity of films against all tested microorganisms, whereas when the WPI solution incorporated with 3% or higher concentrations of garlic essential oil the antibacterial activity films was seen against all tested microorganisms. Also the results revealed that S. aureus and L. monocytogenes were more sensitive to chitosan and WPI films incorporated with garlic essential oil. Conclusions: Our results declared that the films incorporated with garlic essential oil have the potential to be used as an active antimicrobial packaging

Menstrual Cycle Irregularity and Metabolic Disorders: A Population-Based Prospective Study

<div><p>The regularity of menstrual cycles is considered an indicator of women’s reproductive health. Previous studies with a cross-sectional design have documented the relationship between menstrual cycle irregularities, insulin-resistance and the future risks for metabolic disorders. Limited data documented by prospective studies can lead to premature conclusions regarding the relationship between menstrual cycle irregularities and other conditions influencing women’s health. The present study therefore, using a prospective design aimed to assess the risk of metabolic disorders in women with a history of irregular menstrual cycles, was based on the data gathered from the Tehran Lipid and Glucose study (TLGS) an ongoing prospective cohort study initiated in 1999. Participants of the current study were 2128 women, aged between 18–49 years, followed for 15 years. Based on their menstrual cycles, the women were divided into two groups: (i) women with regular menstrual cycles (n = 1749), and (ii) those with irregular menstrual cycles (n = 379). The proportional COX regression model was used to compare hazard ratios (HRs) between the groups for the proposed events, including diabetes mellitus (DM), pre-diabetes (pre-DM), hypertension (HTN), pre-hypertension (pre-HTN) and dyslipidemia. It was found that during a 15-year follow up, there were 123 cases of DM, 456 cases of pre-DM, 290 cases of HTN, 481 cases of pre-HTN, and 402 cases of dyslipidemia. Compared to those with regular cycles, women with irregular menstrual cycles were found to have an increased risk for DM2 (age adjusted Hazard Ratios (HRs), 2.01; 95% confidence intervals (CI:1.59–3.50), the increased risk for DM, associated with irregular cycles remained significant after the adjustment for Body Mass Index (BMI), fasting blood sugar (FBS), family history of diabetes, and parity (HRS, 1.73; 95% CI: 1.14–2.64). There was no significant difference in the increased risk for pre-DM between the groups (age adjusted HRs, 1.33, 95% CI: 1.05–1.69). However, after the adjustment of BMI, FBS and family history of pre-DM, compared to those with regular menstrual cycles, irregular menstrual cycles showed an increased risk for pre-DM (HRs, 1.33; 95% CI: 1.05–1.69). No statistically significant difference was found in the increasing risk for other proposed events between the groups demonstrating that menstrual cycle irregularities could be considered a marker of metabolic disorders and a predisposing factor of the increased risk for diabetes mellitus and pre-diabetes in women with irregular menstrual cycles.</p></div

The study samples in each proposed event.

<p>Abbreviations: DM: diabetes mellitus; HTN: hypertension; Pre-DM: pre-diabetes mellitus; Pre- HTN: pre hypertension; py: per year.</p

Kaplan-Meier curves for pre-diabetes mellitus in the groups.

<p>p value for log rank = 0.06.</p

Comparison of baseline characteristics of participants in the groups.

<p>Comparison of baseline characteristics of participants in the groups.</p

Multivariate adjusted hazard ratios (HRs) and 95% confidence interval for the association between the demographic and anthropometric factors and the proposed events in the groups.

<p>Multivariate adjusted hazard ratios (HRs) and 95% confidence interval for the association between the demographic and anthropometric factors and the proposed events in the groups.</p