Introduction: As of early December 2019, COVID-19, a disease induced by SARS-COV-2, has started spreading, originated in Wuhan, China, and now on, have infected more than 2 million individuals throughout the world. Purpose: This study aimed to nowcast the COVID-19 outbreak throughout Iran and to forecast the trends of the disease spreading in the upcoming month. Methods: The cumulative incidence and fatality data were extracted from official reports of the National Ministry of Health and Medical Educations of Iran. To formulate the outbreak dynamics, six phenomenological models, as well as a modified mechanistic Susciptible-Exposed-Infectious-Recovered (SEIR) model, were implemented. The models were calibrated with the integrated data, and trends of the epidemic in Iran was then forecasted for the next month. Results: The final outbreak size calculated by the best fitted phenomenological models was estimated to be in the range of 68,486 to 118,923 cases; however, the calibrated SEIR model estimated that the outbreak would rage again, starting from April 26. Moreover, projected by the mechanistic model, approximately half of the infections have undergone undetected. Conclusion: Although the advanced phenomenological models perfectly fitted the data, they are incapable of applying behavioral aspects of the outbreak and hence, are not reliable enough for authorities’ decision adoptions. In contrast, the mechanistic SEIR model alarms that the COVID-19 outbreak in Iran may peak for the second time, consequent to lifting the control measures. This implies that the government may implement a more granular decision making to control the outbreak.

Objective: The aim of this study was to propose first established diagnostic reference levels (DRLs) in computed tomography (CT) for adults, based on volume-averaged CTDI and dose length product (DLP) metrics in Yazd Province. Materials and Methods: Six multislice CT scanners located at diverse areas of Yazd Province and seven common procedures were selected for the present study. For each procedure, at least twenty patients 18 years and older were sampled at each institution. For each patient, dose report data and scan parameters as well as patient's information were abstracted from picture archiving and communication system. Results: Proposed DRLs in terms of computed tomography dose index (mGy) and DLP (mGy.cm) were as follows: brain (42, 527), sinus (25, 220), neck (14, 264), abdomen-pelvis (11, 295), routine chest (8, 247), CT pulmonary angiogram (32, 261) and chest HRCT (11, 455), respectively, slightly lower compared to other investigations. Conclusion: The proposed DRLs in this study should be considered as the local DRLs for the seven most common adult CT examinations in Yazd province so as to optimize the patient dose while maintaining acceptable image quality for the clinical task.

Osteoarthritis (OA) is a debilitating joint disease in which early microstructural and compositional changes in articular cartilage (AC) are challenging to detect with current diagnostic tools. We hypothesized that multispectral imaging (MSI) could capture optical signatures that predict key AC properties. To test this, we developed and evaluated an MSI-based approach for estimating tissue thickness, proteoglycan (PG) content, collagen fiber orientation, and cell morphological properties (area and circularity). Reflectance images of bovine patellar AC were acquired using a custom-built MSI system operating at six wavelengths (550–970 nm), and machine learning models were trained to predict the targeted markers. The models yielded reliable estimates for AC thickness, whereas for PG content, collagen fiber orientation, and cell circularity, they achieved moderate accuracy. This work suggests that MSI holds promise as a label-free tool for characterizing AC and detecting early degenerative changes.