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بررسی فراوانی تظاهرات گوارشی و کبدی ناشی از کووید- ۱۹ در کودکان مبتلا، بستری در بیمارستان افضلی پور کرمان
Get PDFImproving Medical Students’ Essay Writing through Direct Focused Corrective Feedback, Revising Errors, and Group Discussions
Get PDFBackground: Writing in English has always been emphasized in educational programs. Objectives: This study aimed at investigating the effects of direct focused written feedback followed by amendments and group discussions on improving students’ English writing in different fields of medical sciences. Methods: The present research employed a quasi-experimental design. The participants were 168 Iranian undergraduate students from seven entire classes (taught by the main researcher), studying at Ahvaz Jundishapur University of medical sciences in 2019-2020. The writing tasks were the topics suggested at the end of each unit of the Inside Reading ("Intro" and "One") series. The length required for each topic was a paragraph with a hundred words at most. After writing each essay, the researcher spotted grammatical errors, recorded their types and frequencies, and gave direct feedback. The students received the corrected essays, and through group discussions and based on extra explanations provided by the researcher, the students became totally informed of their errors and were asked to apply this knowledge on their succeeding works. Results: Wrong tenses ( 30.47%), incorrect articles (23.48%), word order (17.48%), singular/plural nouns (11.59%), prepositions (10.90%), and subject-verb agreement (6.08%) were found to be the most common errors, respectively. Conclusion: Comparing the number of errors in the first essay with the errors spotted in the second and third essays showed that the corrective feedback was effective in improving the medical students’ essay writing. Keywords: Writing, Feedback, Medical Educatio
Self-Directed Learning Outcomes and Facilitators in Virtual Training of Graduate Students of Medical Education
Get PDFBackground: Although virtual training has been considered an educational emergency during the coronavirus crisis, it is still discussed in universities as a capacity. Student learning is the concern of all professors. Self-direction is an efficiency indicator in electronic learning (e-learning) widely used in effective educational systems. Objectives: The present study aimed to determine self-directed learning outcomes and facilitators in virtual course students of medical education. Methods: The statistical population of this descriptive-analytical cross-sectional study included the graduate of virtual medical education in the universities of medical sciences in Tehran, Iran, in the academic year 2019. The research instrument was the Persian version of the Self-Directed Learning Readiness Scale. Data analysis was performed using SPSS software (version 16), the indicators of descriptive statistics (e.g., mean, frequency, percentage, and standard deviation), linear regression, and Pearson correlation coefficient. Results: Out of 201 individuals, 46 (22.9%) and 155 (77.1%) students were male and female, respectively. The mean age of the students was 39.93±8.25 years. The mean values of the scores of self-directed learning outcomes and facilitators were 71.8±9.4 (out of 95) and 70.4±10.6 (out of 125), respectively. There was a direct and significant relationship between self-directed learning outcomes and facilitators (P<0.001); accordingly, with the increase of the score of facilitators, the score of self-directed learning also increased. Additionally, the variables of outcomes and facilitators had significant relationships with academic achievement (P<0.001). Conclusion: According to the study results, by increasing self-directed learning facilitators, the outcomes of this type of learning, especially students’ academic achievement, increased. Therefore, it is recommended to increase students’ participation and consolidate self-directed activities. Furthermore, by the application of technology and program appropriate to the subject of the course, professors in virtual training should strengthen and nurture students’ self-direction skills and guide them to do various assignments and activities related to their lesson objectives. Keywords: Facilitators, Self-directed Learning, Students, Medical Education, Academic Achievemen
Future Studies: Dimensions and Components in the Educational System of a University of Medical Sciences
Get PDFBackground: One way to make change in the field of education is through future studies. Considering the role of future studies in building a better future for the country’s medical education and health system, the establishment of the required bases in medical education is necessary. Objectives: The present study aimed to investigate the dimensions and components of future studies in the educational system of a university of medical sciences. Methods: In this grounded-theory study, the statistical population included expert and knowledgeable faculty members with an experience of delivering services as directors, principals, and deputies of the faculties of Babol University of Medical Sciences, Mazandaran, Iran. In-depth and exploratory individual interviews were held through a questionnaire containing six standard items within October and December 2018.The interview process was completed based on the data saturation law, and the required conclusion was drawn with 10 samples. Results: A questionnaire with 50 components in three main dimensions, including infrastructure (25 components), management and faculty members (9 components), and outputs (16 components), was designed. Moreover, the validity and reliability of the questionnaire were confirmed. Conclusion: It is time to build necessary capacities for future studies in the universities of medical sciences and provide the possibility for extensive participation and support of researchers and faculty members in future studies programs in the field of medical education and health research in Iran. The dimensions and components obtained from this study can be helpful in this regard. Keywords: Future Studies, Dimensions, Components, Universities, Education, Medical, Scienc
The potential therapeutic role of PTR1 gene in non-healing anthroponotic cutaneous leishmaniasis due to Leishmania tropica
No full textBackground: Drug resistance is a common phenomenon frequently observed in countries where leishmaniasis is endemic. Due to the production of the pteridine reductase enzyme (PTR1), drugs lose their efficacy, and consequently, the patient becomes unresponsive to treatment. This study aimed to compare the in vitro effect of meglumine antimoniate (MA) on non- healing Leishmania tropica isolates and on MA transfected non-healing one to PTR1. Methods: Two non-healing and one healing isolates of L. tropica were collected from patients who received two courses or one cycle of intralesional MA along with biweekly liquid nitrogen cryotherapy or systemic treatment alone, respectively. After confirmation of L. tropica isolates by polymerase chain reaction (PCR), the recombinant plasmid pcDNA-rPTR (antisense) was transfected via electroporation and cultured on M199. Isolates in form of promastigotes were treated with different concentrations of MA and read using an enzyme-linked immunosorbent assay (ELISA) reader and the half inhibitory concentration (IC50) value was calculated. The amastigotes were grown in mouse macrophages and were similarly treated with various concentrations of MA. The culture glass slides were stained, and the mean number of intramacrophage amastigotes and infected macrophages were assessed in triplicate for both stages. Results: All three transfected isolates displayed a reduction in optical density compared with the promastigotes in respective isolates, although there was no significant difference between non-healing and healing isolates. In contrast, in the clinical form (amastigotes), there was a significant difference between non-healing and healing isolates (p < 0.05). Conclusion: The results indicated that the PTR1 gene reduced the efficacy of the drug, and its inhibition by antisense and could improve the treatment of non-healing cases. These findings have future implications in the prophylactic and therapeutic modality of non- healing Leishmania isolates to drug. © 2020 The Authors. Journal of Clinical Laboratory Analysis Published by Wiley Periodicals, Inc
Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network
No full textObjectives: The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. Methods: In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). Results: The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. Conclusions: The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction. Key Points: � Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. � Deep learning�based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89. � Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations. © 2020, The Author(s)
