Mycobacterium tuberculosis has infected more than two billion individuals worldwide, of whom 5%–10% have clinically active disease and 90%–95% remain in the latent stage with a reservoir of viable bacteria in the macrophages for extended periods of time. The tubercle bacilli at this stage are usually called dormant, non-viable, and/or non-culturable microorganisms. The patients with latent bacilli will not have clinical pictures and are not infectious. The infections in about 2%–23% of the patients with latent status become reactivated for various reasons such as cancer, human immunodeficiency virus infection, diabetes, and/or aging. Many studies have examined the mechanisms involved in the latent state of Mycobacterium and showed that latency modified the expression of many genes. Therefore, several mechanisms will change in this bacterium. Hence, this study aimed to briefly examine the genes involved in the latent state as well as the changes that are caused by Mycobacterium tuberculosis. The study also evaluated the relationship between the functions of these genes.

Objectives: This study aimed to develop a model to predict fasting blood glucose status using machine learning and data mining, since the early diagnosis and treatment of diabetes can improve outcomes and quality of life. Methods: This crosssectional study analyzed data from 3376 adults over 30 years old at 16 comprehensive health service centers in Tehran, Iran who participated in a diabetes screening program. The dataset was balanced using random sampling and the synthetic minority over-sampling technique (SMOTE). The dataset was split into training set (80%) and test set (20%). Shapley values were calculated to select the most important features. Noise analysis was performed by adding Gaussian noise to the numerical features to evaluate the robustness of feature importance. Five different machine learning algorithms, including CatBoost, random forest, XGBoost, logistic regression, and an artificial neural network, were used to model the dataset. Accuracy, sensitivity, specificity, accuracy, the F1-score, and the area under the curve were used to evaluate the model. Results: Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the most important factors for predicting fasting blood glucose status. Though the models achieved similar predictive ability, the CatBoost model performed slightly better overall with 0.737 area under the curve (AUC). Conclusions A gradient boosted decision tree model accurately identified the most important risk factors related to diabetes. Age, waist-to-hip ratio, body mass index, and systolic blood pressure were the most important risk factors for diabetes, respectively. This model can support planning for diabetes management and prevention.

Objectives: The rapid spread of coronavirus disease 2019 (COVID-19) posed significant challenges to healthcare systems, prompting the widespread adoption of telehealth to provide medical services while minimizing the risk of virus transmission. This study aimed to assess the satisfaction rates of both patients and physicians with telehealth during the COVID-19 pandemic. Methods: Searches were conducted in the Web of Science, PubMed, and Scopus databases from January 1, 2020, to January 1, 2023. We included studies that utilized telehealth during the COVID-19 pandemic and reported satisfaction data for both patients and physicians. Data extraction was performed using a form designed by the researchers. A meta-analysis was carried out using random-effects models with the OpenMeta-Analyst software. A subgroup analysis was conducted based on the type of telehealth services used: telephone, video, and a combination of both. Results: From an initial pool of 1,454 articles, 62 met the inclusion criteria for this study. The most commonly used methods were video and telephone calls. The overall satisfaction rate with telehealth during the COVID-19 pandemic was 81%. Satisfaction rates were higher among patients at 83%, compared to 74% among physicians. Specifically, telephone consultations had a satisfaction rate of 77%, video consultations 86%, and a mix of both methods yielded a 77% satisfaction rate. Conclusions Overall, satisfaction with telehealth during the COVID-19 pandemic was considered satisfactory, with both patients and physicians reporting high levels of satisfaction. Telehealth has proven to be an effective alternative for delivering healthcare services during pandemics.

Objectives: The rapid spread of coronavirus disease 2019 (COVID-19) posed significant challenges to healthcare systems, prompting the widespread adoption of telehealth to provide medical services while minimizing the risk of virus transmission. This study aimed to assess the satisfaction rates of both patients and physicians with telehealth during the COVID-19 pandemic. Methods: Searches were conducted in the Web of Science, PubMed, and Scopus databases from January 1, 2020, to January 1, 2023. We included studies that utilized telehealth during the COVID-19 pandemic and reported satisfaction data for both patients and physicians. Data extraction was performed using a form designed by the researchers. A meta-analysis was carried out using random-effects models with the OpenMeta-Analyst software. A subgroup analysis was conducted based on the type of telehealth services used: telephone, video, and a combination of both. Results: From an initial pool of 1,454 articles, 62 met the inclusion criteria for this study. The most commonly used methods were video and telephone calls. The overall satisfaction rate with telehealth during the COVID-19 pandemic was 81%. Satisfaction rates were higher among patients at 83%, compared to 74% among physicians. Specifically, telephone consultations had a satisfaction rate of 77%, video consultations 86%, and a mix of both methods yielded a 77% satisfaction rate. Conclusions Overall, satisfaction with telehealth during the COVID-19 pandemic was considered satisfactory, with both patients and physicians reporting high levels of satisfaction. Telehealth has proven to be an effective alternative for delivering healthcare services during pandemics.

Objectives: The rapid spread of coronavirus disease 2019 (COVID-19) posed significant challenges to healthcare systems, prompting the widespread adoption of telehealth to provide medical services while minimizing the risk of virus transmission. This study aimed to assess the satisfaction rates of both patients and physicians with telehealth during the COVID-19 pandemic. Methods: Searches were conducted in the Web of Science, PubMed, and Scopus databases from January 1, 2020, to January 1, 2023. We included studies that utilized telehealth during the COVID-19 pandemic and reported satisfaction data for both patients and physicians. Data extraction was performed using a form designed by the researchers. A meta-analysis was carried out using random-effects models with the OpenMeta-Analyst software. A subgroup analysis was conducted based on the type of telehealth services used: telephone, video, and a combination of both. Results: From an initial pool of 1,454 articles, 62 met the inclusion criteria for this study. The most commonly used methods were video and telephone calls. The overall satisfaction rate with telehealth during the COVID-19 pandemic was 81%. Satisfaction rates were higher among patients at 83%, compared to 74% among physicians. Specifically, telephone consultations had a satisfaction rate of 77%, video consultations 86%, and a mix of both methods yielded a 77% satisfaction rate. Conclusions Overall, satisfaction with telehealth during the COVID-19 pandemic was considered satisfactory, with both patients and physicians reporting high levels of satisfaction. Telehealth has proven to be an effective alternative for delivering healthcare services during pandemics.

Objectives: The rapid spread of coronavirus disease 2019 (COVID-19) posed significant challenges to healthcare systems, prompting the widespread adoption of telehealth to provide medical services while minimizing the risk of virus transmission. This study aimed to assess the satisfaction rates of both patients and physicians with telehealth during the COVID-19 pandemic. Methods: Searches were conducted in the Web of Science, PubMed, and Scopus databases from January 1, 2020, to January 1, 2023. We included studies that utilized telehealth during the COVID-19 pandemic and reported satisfaction data for both patients and physicians. Data extraction was performed using a form designed by the researchers. A meta-analysis was carried out using random-effects models with the OpenMeta-Analyst software. A subgroup analysis was conducted based on the type of telehealth services used: telephone, video, and a combination of both. Results: From an initial pool of 1,454 articles, 62 met the inclusion criteria for this study. The most commonly used methods were video and telephone calls. The overall satisfaction rate with telehealth during the COVID-19 pandemic was 81%. Satisfaction rates were higher among patients at 83%, compared to 74% among physicians. Specifically, telephone consultations had a satisfaction rate of 77%, video consultations 86%, and a mix of both methods yielded a 77% satisfaction rate. Conclusions Overall, satisfaction with telehealth during the COVID-19 pandemic was considered satisfactory, with both patients and physicians reporting high levels of satisfaction. Telehealth has proven to be an effective alternative for delivering healthcare services during pandemics.

Objective: To investigate the prevalence of non-tuberculosis mycobacteria (NTM) among the samples deposited from the National Tuberculosis Reference Laboratory of Iran between 2011 and 2018. Methods: The study evaluated the prevalence of NTM among specimens from patients with pulmonary tuberculosis symptoms (n=15 771) deposited at the National Tuberculosis Reference Laboratory of Iran from 2011 to 2018. Detection of Mycobacterium (M.) tuberculosis was based on presence of a 190-bp amplicon from IS6110 insertion sequence using Tb1 and Tb2 primers, and amplicon-negative specimens were tested for NTM and M. tuberculosis (refractory to IS6110 amplification) using restriction fragment length polymorphism PCR of hsp65 amplicon fragment. Results: A total of 7 307 (46.33%) M. tuberculosis and 658 (4.17%) NTM specimens were found, the latter mainly comprising M. abscessus (10.18%), M. avium (2.28%), M. chelonae (8.97%), M. intracellulare (10.49%), M. kansasii (4.71%), and M. simiae (56.08%). Conclusions: As treatment for NTM differs from that for M. tuberculosis, accurate detection of Mycobacterium sp. is of public health significance.