A Survey of Students’ Attitudes to Big Data Analysis in Iranian Universities

Today, with the emergence of new technologies and massive data, big data analysis has attracted the attention of researchers, industries and universities on a global scale. The present research aims to explore students’ attitude to big data analysis in different fields of study. The present cross-sectional study was conducted with students at different universities and fields of study in Iran. A questionnaire was developed. This questionnaire explored students’ attitude toward big data analysis. To this aim, 359 university students participated in the research. The data were analyzed using descriptive and inferential statistics. The age of the students ranged between 25 and 34 years. 55.2% were female and 54% were economically active. 40.9% had a work experience of less than a year. The academic degree of the majority of participants was master’s degree. 93.9% of the participants believed that big data analysis was essential for the country. 43.2% maintained that big data mostly belonged to the communication industry. 28.1% perceived MATLAB useful software for analysis. 40.9% were familiar with the benefits of analysis. Engage in economic activities, less than 1 year of experience and studies for a Master’s degree showed to be significantly correlated with familiarity with the benefits of big data (p≤0.01). Such issues as high costs, managers’ unfamiliarity and lack of expertise and complexity were raised by the respondents. Considering the undeniable benefits of big data analysis, it seems essential to familiarize university students with these analyses through particular training courses, conferences and so on

Influence of Near Vision Tasks on Intraocular Pressure in Normal Subjects and Glaucoma Patients

Purpose: To investigate the effect of static accommodative tasks on intraocular pressure (IOP) of glaucomatous and normal eyes. Methods: Four groups of subjects categorized as primary open-angle glaucoma (POAG), primary angle-closure suspects (PACS), normal age-matched controls, and normal young adults (NYA; age <40 years) were enrolled. The baseline IOPs were measured after the subjects were looking at a distant target for 15 min. Static accommodation was obtained by execution of near vision tasks (reading at 33 cm in daylight [300 lux] for 60 min). IOPs were measured at 15, 30, 45, and 60 min intervals while accommodating and then measured again after 15 min of relaxing accommodation while looking at a distant target. Results: One-hundred and eighteen eyes of 98 subjects were recruited. The study groups consisted of the following categories: 25 POAG (46 eyes), 24 PACS (47 eyes), 25 matched controls (50 eyes), and 24 NYA (48 eyes). Within all groups, the mean IOP decreased throughout the accommodation period at all time points. Maximum IOP reduction after accommodation was detected at the 30-min time among the POAG subjects, at the 45-min time in the PACS and matched control groups, and at 15 min after the relaxation of accommodation in the NYA group. IOP reduction levels showed no statistically significant difference among POAG, PACS, and the normal matched groups in their response to accommodation. However, NYA had significantly lower IOP and greater IOP reduction after the resting period (relaxation of accommodation). Conclusion: Static accommodative tasks can significantly reduce IOP in normal, POAG, and PACS individuals. Encouraging glaucoma patients to practice periodical near vision tasks could be viewed as an adjunctive measure for glaucoma management

Decision fusion in healthcare and medicine : a narrative review

Objective: To provide an overview of the decision fusion (DF) technique and describe the applications of the technique in healthcare and medicine at prevention, diagnosis, treatment and administrative levels. Background: The rapid development of technology over the past 20 years has led to an explosion in data growth in various industries, like healthcare. Big data analysis within the healthcare systems is essential for arriving to a value-based decision over a period of time. Diversity and uncertainty in big data analytics have made it impossible to analyze data by using conventional data mining techniques and thus alternative solutions are required. DF is a form of data fusion techniques that could increase the accuracy of diagnosis and facilitate interpretation, summarization and sharing of information. Methods: We conducted a review of articles published between January 1980 and December 2020 from various databases such as Google Scholar, IEEE, PubMed, Science Direct, Scopus and web of science using the keywords decision fusion (DF), information fusion, healthcare, medicine and big data. A total of 141 articles were included in this narrative review. Conclusions: Given the importance of big data analysis in reducing costs and improving the quality of healthcare; along with the potential role of DF in big data analysis, it is recommended to know the full potential of this technique including the advantages, challenges and applications of the technique before its use. Future studies should focus on describing the methodology and types of data used for its applications within the healthcare sector

A survey on the knowledge of patients’ companions about the concept and application of hospital triage

Introduction: Given the importance of the subject and the knowledge gap, we decided to perform this survey. Methods: In this descriptive cross-sectional study, the knowledge levels of patients’ companions about the concept and application of hospital triage were evaluated in 202 subjects. The research population included patient companions referred to the emergency department (ED). The exclusion criterion was the failure to complete the questionnaire. Convenience sampling was done. Questionnaires were delivered to the companions of the patients. In this study, the data gathering tool was a questionnaire consisting of two parts. The final questionnaire included 13 questions, 10 and 3 of which were closed (multiple choice) and open questions, respectively. Finally, the age, level of education, and gender were also asked from participants. The level of patient triage was also determined in this research. Results: 202 questionnaires were completed by companions of patients admitted to the ED. The mean age of those who completed the questionnaire was 39.95±17.92 years, and 192 individuals expressed their gender, of which 123 (64.1%) and 69 (35.9%) persons were male and female, respectively. The level of education was also reported in 180 people. Moreover, diploma and postgraduate diplomas, bachelor’s degrees, and postgraduate or doctoral degrees were observed in 59 (32.77%), 90 (50%), and 31 (17.22%) patients’ companions respectively. Conclusion: According to the results, it seems that the knowledge about the concept of triage among ordinary people of the society is lower than the desired level

Keratoconus Diagnosis: From Fundamentals to Artificial Intelligence: A Systematic Narrative Review

The remarkable recent advances in managing keratoconus, the most common corneal ectasia, encouraged researchers to conduct further studies on the disease. Despite the abundance of information about keratoconus, debates persist regarding the detection of mild cases. Early detection plays a crucial role in facilitating less invasive treatments. This review encompasses corneal data ranging from the basic sciences to the application of artificial intelligence in keratoconus patients. Diagnostic systems utilize automated decision trees, support vector machines, and various types of neural networks, incorporating input from various corneal imaging equipment. Although the integration of artificial intelligence techniques into corneal imaging devices may take time, their popularity in clinical practice is increasing. Most of the studies reviewed herein demonstrate a high discriminatory power between normal and keratoconus cases, with a relatively lower discriminatory power for subclinical keratoconus

Psychological Impact of COVID- 19 on the Ophthalmologists in Iran

Purpose: To identify the psychological impact of coronavirus disease on ophthalmologists practicing in Iran between August and December 2020. Methods: In this cross-sectional online survey, a standard Patient Health Questionnaire- 9 (PHQ- 9) was completed by 228 ophthalmologists who were practicing in Iran. The PHQ- 9 questionnaire was revised by adding two additional questions specifically applicable for the assessment of the psychological impact of coronavirus disease on the Iranian ophthalmologists. An organized classification regarding the assessment of different depression severities identified as no (0–4), mild (5–9), moderate (10–14), or severe (15–21) was then considered for data analysis. Results: The mean age of our participants was 49.0 ± 15.61 years and the majority of them (67.1%) were male. Depression was discovered in 73.68% (n = 168) with different severities ranging from mild (n = 61, 26.75%), moderate (n = 63, 27.63%), and severe (n = 44, 19.3%). It was found that participants with depression were older as compared to those without depression (P = 0.038). Higher percentages of severe depression were noticed in the high-risk regions contaminated with coronavirus as compared to the other low-risk regions (P = 0.003). Based on multivariable models, we determined that ophthalmologists who were somewhat concerned about their training/ profession (OR: 0.240; 95% CI: 0.086–0.672; P = 0.007) and those with no concerns about their income had lower association with depression (OR: 0.065; 95% CI: 0.005–0.91; P = 0.042). Conclusion: High prevalence of depression was observed among older-aged Iranian ophthalmologists living in high-risk contaminated regions who possessed serious concerns with respect to their training/profession and income. It is recommended that the health policymakers of Iran pay more attention to the ophthalmologists who experience the aforementioned factors

Transient Expression of cor Gene in Papaver somniferum

Introduction: Papaver somniferum is the commercial source of morphine and codeine. The isolation of effective genes involved in the morphine biosynthesis of P. somniferum is very important in the production of specific metabolites achieved using metabolic engineering techniques. In this pathway, the key enzyme COR is involved in the conversion of codeinone to codeine and morphinone to morphine. Methods: the gene encoding of this enzyme was isolated using primers designed on the base of gene sequence available on (NCBI) for P. somniferum. This gene correct size around (960 bp) was first subcloned into pTZ57RIT vector then cloned into expression vectors (pBI121) between BamHI and SacI sites to allow the expression of cor gene driven by the cauliflower mosaic virus 35S promoter. The result was confirmed through different molecular methods e.g. PCR and enzyme digestion by BamHI and SacI. The recombinant plasmid was transformed into the E. coli strain DH5α using a freeze-thaw method. Having selected positive colones on selection medium, plasmid was extracted by miniprep method and recombinant plasmids were selected based on PCR and digestion. The construct was then mobilized in Agrobacterium tumefaciens C58/pGV3850 (KmR RifR). After gene transformation to P. somniferum plants, the agroinfiltration method was also used for transient expression of COR enzyme. Results: evaluation results showed that morphine and codeine were detectable in the leaves of transgenic plants containing cor transgene and there was significant difference in the final production. After completing this experiment for three times, results showed that in 11 sets from 15 sets of leaves experiment tested, main alkaloids (codeine, morphine, papaverin, noscapine and thebaine) were detectable. Conclusion: Whereas no signal was detected in non-infiltrated control leaves or in leaves infiltrated with non-recombinant bacteria for morphine and codeine, others such as thebaine and papaverine were detectable