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Antibacterial effects of Thymus vulgaris, Mentha pulegium, Crocus sativus and Salvia officinalis on pathogenic bacteria: A brief review study based on gram-positive and gram-negative bacteria
Behboud Jafari, Abolfazl Jafari-Sales, ‪homeira Khaneshpour‬‏, Salar Fatemi, Mehrdad Pashazadeh, Ali Esmail Al-Snafi, Afsoon Shariat,
Volume 8, Issue 3 (10-2020)
Abstract
Background and objective: In recent years, with the increase in resistance due to the indiscriminate use of synthetic antibiotics, it seems necessary to find alternative drugs that have both antibacterial properties and have the least side effects for humans. The purpose of this study is to review the antibacterial properties of some medicinal plants.
Material And Method In this review study, the content related to the antibacterial properties of Thymus vulgaris, Mentha pulegium, Crocus sativus, and Salvia officinalis were studied within Magiran, SID, PubMed, MEDLINE, Science Direct, Cochrane Library, Google Scholar, EMBASE, and Scopus databases from 1981 to 2019. Previously published specialized articles and systematic meta-analysis were used as a supplementary source for identifying relevant articles.  Finally, data from 46 articles were pooled and analyzed.
Result: Extracts and essential oils of Thymus vulgaris, Mentha pulegium, Crocus sativus, and Salvia officinalis had a good antibacterial properties against a variety of pathogenic bacteria and their infections.
Conclusion: According to the results of this study, the studied plants can be considered as a suitable option for treating infections caused by pathogenic bacteria and helping to Return the sensitivity of antibiotics in these bacteria, and this requires more comprehensive research on medicinal plants.

Enhanced missing value imputation and gaussian mixture model-based semi-supervised learning for predicting type 2 diabetes
Hediye Shariaty , Fatemeh Bagheri ,
Volume 12, Issue 4 (4-2024)
Abstract
Background: Diabetes is a prevalent condition with no definitive cure, often referred to as a” silent killer.” Diabetes is primarily categorized into three types: Type I, Type II, and gestational diabetes. In Type I diabetes, the body's immune system attacks and damages the insulin-producing cells. Conversely, Type II diabetes, which is more common than Type I, occurs when the body does not respond adequately to the insulin being produced, resulting in elevated blood sugar levels. Effectively treating pre-diabetes can prevent its progression to full-blown diabetes.
Methods: In the present research, a semi-supervised approach is proposed to predict diabetes. Improved missing value imputation (MVI) is achieved by utilizing Gaussian mixture model (GMM) clustering. The proposed classifier integrates GMM with a machine learning algorithm, specifically random forest (RF), thereby inducing a more robust predictive model via the fusion of clustering and classification techniques.
Results: The proposed method achieves an accuracy of 84%, a precision of 82.03%, a recall of 69.75%, and an F1-score of 75.12% base on experiments conducted on the PIMA Indian population.
Conclusion: Employing GMM to fill in missing values provides the advantage of replacing invalid data with the most similar records, thereby enhancing the quality of the dataset. The proposed classifier also exhibits strong predictive capabilities in identifying diabetes. By integrating this combined approach, this study offers an effective method for predicting diabetes, making a significant contribution to healthcare analytics as a whole.

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