Exploring the usage of learning resources by medical students in the basic science stage and their effect on academic performance.

BACKGROUND: The United States Medical Licensing Examination (USMLE) step 1 is one of the two examinations written after completion of the first two years (basic science stage) of medical school to be eligible to apply for residency training in the USA. A huge number and types of study materials are available to prepare for the exam which might confuse students choosing a resource. We investigated learning resources being used by the third and fifth-semester medical students and their association with academic performance. We also compared learning resources and exam scores of high-performing and low-performing students. METHODS: Data collection was done using structured (quantitative study) and semi-structured (qualitative study) questionnaires during a face-to-face interview. This article is about the quantitative part which was designed as a correlational study. Single factor one-way analysis of variance (ANOVA), Pearson correlation coefficient test, T-test, and Fisher's exact test were used to analyze the data. RESULTS: About half of all students used three or more commercial resources dealing with the same content. A weak negative correlation was observed between the number of commercial resources and the exam scores, especially when the number of these resources was three or more (r = -0.26). The mean exam score of textbook users was statistically significantly higher than the mean score of textbook non-users (p = 0.01). The usage of textbooks was statistically significantly higher in the cohort of top performers in comparison to the rest of the students (p = 0.006). In addition to less usage of textbooks, the mean number of review books was higher in the group of weakest students (2.84 versus 3.7; p = 0.75). CONCLUSIONS: Most students did not use professional textbooks and about half used too many commercial review resources. While the former fact was significantly associated with poor academic performance, the later fact had weak negative correlation with exam score. Pedagogical interventions are urgently needed to make the right type of learning resources available by making professional textbooks more USMLE-oriented and helping the students choose the best and right number of resources for optimum academic performance. By fulfilling the observed needs of the students in this way, they might feel empowered because of self-determination which will motivate studies.

DermAI 1.0: A Robust, Generalized, and Novel Attention-Enabled Ensemble-Based Transfer Learning Paradigm for Multiclass Classification of Skin Lesion Images.

Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, a powerful, novel, and generalized method for extracting features for the classification of skin lesions. This technique holds significant promise in enhancing diagnostic accuracy by using seven pre-trained TL models for classification. Six ensemble-based DL (EBDL) models were created using stacking, softmax voting, and weighted average techniques. Furthermore, we investigated the attention mechanism as an effective paradigm and created seven attention-enabled transfer learning (aeTL) models before branching out to construct three attention-enabled ensemble-based DL (aeEBDL) models to create a reliable, adaptive, and generalized paradigm. The mean accuracy of the TL models is 95.30%, and the use of an ensemble-based paradigm increased it by 4.22%, to 99.52%. The aeTL models' performance was superior to the TL models in accuracy by 3.01%, and aeEBDL models outperformed aeTL models by 1.29%. Statistical tests show significant p-value and Kappa coefficient along with a 99.6% reliability index for the aeEBDL models. The approach is highly effective and generalized for the classification of skin lesions.

Morphological Study of Hashimoto Thyroiditis in Fine Needle Aspiration Cytology Specimens.

Background: Hashimoto thyroiditis is an autoimmune disease which is diagnosed based on well-defined clinical and cytological criteria. Purpose: The objective of this research is to study cytomorphological features in patients of Hashimoto thyroiditis and compare the findings with other studies. Literature on morphology of multinucleated giant cells was found to be lacking, and this study has focused on the number and morphology of these cells in this study. Material and Methods: FNAC was done in patients who met the clinical diagnostic criteria of Hashimoto thyroiditis formulated by "Japan Thyroid Association" and smears were analyzed by light microscopy. Data analysis was done by XLSTAT in Microsoft Excel 2010. The Wilcoxon Signed Rank Test was done to analyze the data on multinucleated giant cells. The null hypothesis was that the median of the population of differences between the paired data of small and large giant cells is zero. Results: A total of 26 patients were included in a period of one year. Contrary to observations in other studies, multinucleated giant cells were found in most participants. The Wilcoxon Signed Rank Test proved that small multinucleated giant cells were significantly more common than large multinucleated giant cells in Hashimoto thyroiditis; P value (two-tailed) being <0.0001 at significance alpha of 0.05. This study has also revealed that a few patients with Hashimoto thyroiditis can have large and very large multinucleated giant cells in a small number. Data on other cytomorphological features were no different than in other studies. Conclusion: The presence of multinucleated giant cells in 92.3% of patients in this study is far higher than in other studies which can have important diagnostic implications. Few large multinucleated giant cells can be present in a small number in a few patients as in Hashimoto thyroiditis.

Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.

BACKGROUND AND MOTIVATION: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY: The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

Deep Learning Approaches for Automatic Localization in Medical Images.

Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study.

Spatial and temporal variation of the ambient noise environment of the Sikkim Himalaya.

Ambient noise characteristics are perused to assess the station performance of 27 newly constructed broadband seismic stations across Sikkim Himalaya and adjoining Himalayan foreland basin, installed to study the seismogenesis and subsurface structure of the region. Power spectral densities obtained at each station, compared against the global noise limits, reveal that observed vertical component noise levels are within the defined global limits. However, the horizontal components marginally overshoot the limits due to the tilt effect. Ambient noise conditions significantly vary with different installation techniques, analysis revealing that seismic sensors buried directly in the ground have reduced long-period noise in comparison to pier installations. Tectonic settings and anthropogenic activities are also noted to cause a significant rise across short-period and microseism noise spectrum, varying spatially and temporally across the region. Day-time records higher cultural noise than night-time, while the microseism noise dominates during the monsoonal season. An assessment of the effect of the nationwide lockdown imposed due to COVID-19 pandemic revealed a significant decrease in the short-period noise levels at stations installed across the foreland basin marked with higher anthropogenic activity. Our study summarizes the overall ambient noise patterns, validating the stability and performance of the seismic stations across the Sikkim Himalayas.

Emerging Pharmaceutical Treatments of Novel COVID-19: A Review.

As a new decade began, COVID-19 quickly gained importance as it became the cause of the current global pandemic. Research has been focusing on studying the structure of SARS-CoV-2 and investigates possible pharmaceutical approaches. With the number of cases increasing every day, globally, multiple drugs are being researched as possible candidates. Although multiple drugs show promise in the treatment of COVID-19 via either inhibiting viral replication or preventing fusion of the virus to the ACE2 receptors, further investigation is still warranted and necessary before the admission of any type of pharmaceutical agent. Furthermore, several supplements have also been documented in being utilized as treatment of COVID-19. The exact mechanism and efficacy of current candidate drugs are still being explored through clinical trials. Despite the advancements in current research with emerging treatments, social distancing and engaging in preventative measures remains crucial to attempt to prevent the occurrence of more cases and deaths, worldwide. This review explores various drugs and their mechanism of action which are either currently being used in clinical trials or may be used in the future for the treatment of COVID-19.

Role of Zinc in Mucosal Health and Disease: A Review of Physiological, Biochemical, and Molecular Processes.

Zinc is an essential trace element of all highly proliferating cells in the human body. It is essential to the development and growth of all organisms. Zinc plays a critical role in modulating resistance to infectious agents and reduces the duration, severity, and risk of diarrheal disease via improved regeneration of intestinal epithelium, improved absorption of water and electrolytes, increased levels of brush border enzymes, and, possibly, an enhancement in the immune response allowing better clearance of pathogens. On the cellular level, zinc finger motifs play various roles including diverse functions that involve specific gene expression for ion channels throughout the body. It maintains the function and the structure of the membrane barrier by contributing to host defense, which is particularly crucial in the intestines due to the continuous exposure to noxious agents and pathogens. Zinc deficiency is characterized by impaired immune function, loss of appetite, and growth retardation. More severe cases cause diarrhea, delayed sexual maturation, hair loss, eye and skin lesions, impotence and hypogonadism in males, as well as weight loss, taste abnormalities, delayed healing of wounds, and mental lethargy. The objective of this study is a critical review of the molecular and genetic regulation of zinc in various cellular processes and organs, the association between zinc and diarrheal disease, the recommended dietary zinc intake, and the effects of zinc deficiency on the human body.

Comparing Standard Setting Methods for Objective Structured Clinical Examinations in a Caribbean Medical School.

BACKGROUND: OSCE are widely used for assessing clinical skills training in medical schools. Use of traditional pass fail cut off yields wide variations in the results of different cohorts of students. This has led to a growing emphasis on the application of standard setting procedures in OSCEs. PURPOSE/AIM: The purpose of the study was comparing the utility, feasibility and appropriateness of 4 different standard setting methods with OSCEs at XUSOM. METHODS: A 15-station OSCE was administered to 173 students over 6 months. Five stations were conducted for each organ system (Respiratory, Gastrointestinal and Cardiovascular). Students were assessed for their clinical skills in 15 stations. Four different standard setting methods were applied and compared with a control (Traditional method) to establish cut off scores for pass/fail decisions. RESULTS: OSCE checklist scores revealed a Cronbach's alpha of 0.711, demonstrating acceptable level of internal consistency. About 13 of 15 OSCE stations performed well with "Alpha if deleted values" lower that 0.711 emphasizing the reliability of OSCE stations. The traditional standard setting method (cut off score of 70) resulted in highest failure rate. The Modified Angoff Method and Relative methods yielded the lowest failure rates, which were typically less than 10% for each system. Failure rates for the Borderline methods ranged from 28% to 57% across systems. CONCLUSIONS: In our study, Modified Angoff method and Borderline regression method have shown to be consistently reliable and practically suitable to provide acceptable cut-off score across different organ system. Therefore, an average of Modified Angoff Method and Borderline Regression Method appeared to provide an acceptable cutoff score in OSCE. Further studies, in high-stake clinical examinations, utilizing larger number of judges and OSCE stations are recommended to reinforce the validity of combining multiple methods for standard setting.

Introduction to medication errors and the error prevention initiatives in a teaching hospital in Western Nepal.

Pharmacotherapy is a complex process and involves interaction of the patient and the healthcare professionals at various levels. Prevention of medication errors is important however, errors may occur even in a carefully monitored healthcare setup. The out comes of the errors may range from mild inconvenience to the patient to even fatal toxic reactions. There are several predisposing factors for the occurrence of errors starting from improper drug selection to errors in administration technique by the healthcare providers' and patients. Several methods can be employed to detect the occurrence of errors. At the Manipal Teaching Hospital, Pokhara, Nepal, the Department of Hospital and Clinical Pharmacy has taken the initiative in identifying the error prone situations and has taken remedial measures including educational and managerial interventions to minimize the occurrence of errors.

Teratogenic effects of 5-fluorouracil on rat brain.

Nine female pregnant rats were treated with single dose (30mg/kg) of 5-fluorouracil on 12th day of gestation. Gross defects in developing brain of rat fetuses were observed. 5-Fluorouracil is an antineoplastic drug, which has not been adequately studied. 5-FU induced about 5% mortality with significant reduction in body weight and various dimensions of the developing brain (p<0.001). Macroscopic findings of the developing brain revealed microcephaly, regression or absence of olfactory lobe and obliteration of the various fissures on the dorsal and ventral surfaces of the brain. Neuroembryopathic effects of 5-FU is more marked when given in late phase of gestation. So, it is advisable that the drug should be avoided during this period of pregnancy.