First report of tinea corporis caused by Trichophyton quinckeanum in Iran and its antifungal susceptibility profile

Background and Purpose: Trichophyton quinckeanum, a known zoophilicdermatophyte responsible for favus form in rodents and camels, is occasionally reported to cause human infections.Case Report: This study aimed to report a case of tinea corporis caused by T. quinckeanum that experienced annular erythematous pruritic plaque with abundantpurulent secretions. In June 2021, a 15-year-old girl with an erythematous cup shape lesion on the right wrist bigger than 3 cm in diameter was examined for tinea corporis. Since March, 2016 her family has kept several camels at home. Direct examination of skin scraping and purulent exudates revealed branching septal hyaline hyphae and arthrospore. Morphological evaluation of the recovered isolate from the culture and sequencing of ITS1-5.8S rDNA-ITS2 region resulted in the identification of T. quinckeanum. Antifungal susceptibility testing showed that this isolate had low minimum inhibitory concentration (MIC) values for luliconazole,terbinafine, and tolnaftate, but high MICs to itraconazole, fluconazole, posaconazole, miconazole, isavuconazole, ketoconazole, clotrimazole, andgriseofulvin. However, the patient was successfully treated with oral terbinafine andtopical ketoconazole.Conclusion: It can be said that T. quinckeanum is often missed or misidentified due to its morphological similarity to T. mentagrophytes/T. interdigitale or other similar species. This dermatophyte species is first reported as the cause of tinea corporis in Iran. As expected, a few months after our study, T. quinckeanum was detected in other areas of Iran, in a few case

National, sub-national, and risk-attributed burden of thyroid cancer in Iran from 1990 to 2019

An updated exploration of the burden of thyroid cancer across a country is always required for making correct decisions. The objective of this study is to present the thyroid cancer burden and attributed burden to the high Body Mass Index (BMI) in Iran at national and sub-national levels from 1990 to 2019. The data was obtained from the GBD 2019 study estimates. To explain the pattern of changes in incidence from 1990 to 2019, decomposition analysis was conducted. Besides, the attribution of high BMI in the thyroid cancer DALYs and deaths were obtained. The age-standardized incidence rate of thyroid cancer was 1.57 (95% UI: 1.33–1.86) in 1990 and increased 131% (53–191) until 2019. The age-standardized prevalence rate of thyroid cancer was 30.19 (18.75–34.55) in 2019 which increased 164% (77–246) from 11.44 (9.38–13.85) in 1990. In 2019, the death rate, and Disability-adjusted life years of thyroid cancer was 0.49 (0.36–0.53), and 13.16 (8.93–14.62), respectively. These numbers also increased since 1990. The DALYs and deaths attributable to high BMI was 1.91 (0.95–3.11) and 0.07 (0.04–0.11), respectively. The thyroid cancer burden and high BMI attributed burden has increased from 1990 to 2019 in Iran. This study and similar studies’ results can be used for accurate resource allocation for efficient management and all potential risks’ modification for thyroid cancer with a cost-conscious view

Propranolol for infantile hemangioma: An evaluation of its efficacy and safety in Iranian infants

Background: Propranolol has been recently indicated to inhibit the rapid growth and involution of infantile hemangioma. In the present study, we investigated the efficacy and safety of propranolol in Iranian infants. Methods: A total of 30 infants with indications for medical intervention, such as large hemangiomas, wounds with or without secondary infection, or active trauma-induced bleeding, were selected. First, a total concentration of 1 mg/kg/day was orally administered to the infants; the dosage further increased (2-3 mg/kg/day) in case the infants experienced no adverse effects. Following weekly (one month after treatment) and monthly (up to six months) follow-ups, hemangioma activity score (HAS) was calculated to evaluate swelling, color of the lesion, and ulcer size. Results: In the present study, infants with the mean age of 5.33±3.50 years received therapy. Improvement was observed in the lesions of all patients, characterized by a significant decline in size, change in color, and reduction in ulcer size (

Hands and feet radiologic involvements in systemic sclerosis

Abstract Aim Systemic sclerosis (SSc) is a rare autoimmune disorder characterized by vascular and fibrosing involvement of the skin and internal organs. In this study, we determined the prevalence and characteristics of radiological hands and feet involvements in Iranian SSc patients to identify the associations between clinical features and radiologic findings. Methods 43 SSc patients (41 women and 2 men), with a median age of 44.8 years (ranges 26–70 years) and a mean disease duration of 11.8 years (ranges 2–28 years) were studied in this cross-sectional study. Results 42 patients had radiological changes both in their hands and feet. Only one patient had alteration just in hand. The most frequent changes that we found in hand were Juxta-articular Osteoporosis (93%), Acro-osteolysis (58.2%), and Joint Space Narrowing (55.8%). The prevalence of joint space narrowing or acro-osteolysis was higher in subjects with active skin involvement [modified Rodnan skin score (mRSS) > 14] [16/21 vs. 4/16 for patients with inactive skin involvement (mRSS < 14); p = 0.002]. The most frequent changes that we found in the foot were Juxta-articular Osteoporosis (93%), Acro-osteolysis (46.5%), Joint Space Narrowing (58.1%), and subluxation (44.2%). The presence of anti-ccp antibody was detected in 4 (9.3%), while positive rheumatoid factor was found in 13 (30.2%) of SSc patients. Conclusion This study corroborates that arthropathy is common in SSc patients. The introduction of the specific radiological involvements of SSc needs to be confirmed by further studies, in order to define the appropriate prognosis and treatment of patients

Effects of the COVID-19 pandemic on lifestyle among Iranian population: A multicenter cross-sectional study

Background: Quarantine, an unpleasant experience, was implemented in many countries to limit the spread of Coronavirus disease 2019 (COVID-19), which it could associated whit lifestyle changes. The present study aimed to determine the changes in Iranian's lifestyle during COVID-19 pandemic. Materials and Methods: In the present cross-sectional study, 2710 Iranian people completed an online researcher-made questionnaire asking lifestyle regarding COVID-19, which includes five sections about physical activity, stress and anxiety, nutrition habit, sleep disorders, and interpersonal relationship in addition to demographic data from January to February 2021, using the multistage cluster sampling method. Results: The participants' mean age was 33.78 +/- 11.50 years and 68.3% of them were female. Traveling, sightseeing, and family visits have been eliminated from 91%, 83.5%, and 77.5% of participants' lives, respectively. There were increase in stress level (P < 0.001), weight of the participants (P < 0.001), sleep problems (P < 0.001), and healthier foods (P < 0.001) but decrease in interpersonal communication (P < 0.001) and the amount of physical activity (P < 0.001). Conclusion: In summary, this study indicates some changes in lifestyle of Iranian people, including changes in some eating practices, physical activity, social communication, and sleeping habits during the pandemic. However, as the COVID-19 pandemic is ongoing, a comprehensive understanding of these behaviors and habits can help develop interventions to mitigate the negative lifestyle behaviors during COVID-19 pandemic

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p