The healthcare industry collects huge amounts of healthcare data which, unfortunately, are not "mined" to

Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pretrained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification. INDEX TERMS Deep learning, magnetic resonance imaging, brain tumor classification, pre-trained model, dataset.

Objectives: To determine the need of contemporary immersive approaches (Virtual Reality) in teaching and training at medical sector. The main objective of this study was to explore the effects of text, video and immersive technologies learning methodologies for participants’ learning in public and private medical colleges and universities of Pakistan. Methods: In this quantitative research 87 medical students of 4th year from three public and five private medical colleges and universities participated. A laparoscopy operation was selected in consultation with senior medical consultants for this experiment. The experimental material was arranged in virtual reality, video and text based learning. At completion of each of which, participants completed a questionnaire about learning motivation and learning competency through the different mediums. Results: Statistical t-test was selected for the analysis of this study. By comparing the mean values of virtual reality, video, and text based learning methodologies in medical academics; result of virtual reality is at top of others. All performed model are statistically significant (P=0.000) and results can be applied at all population. Conclusion: Through this research, we contribute to medical students learning methodologies. In medical studies, both theoretical and practical expertise has a vital role, while repetition of hands-on practice can improve young doctors’ professional competency. Virtual reality was found best for medical students in both learning motivation and learning competency. Medical students and educationist may select virtual reality as new learning methodology for curriculum learning. doi: https://doi.org/10.12669/pjms.35.3.44 How to cite this:Sattar MU, Palaniappan S, Lokman A, Hassan A, Shah N, Riaz Z. Effects of Virtual Reality training on medical students’ learning motivation and competency. Pak J Med Sci. 2019;35(3):---------. doi: https://doi.org/10.12669/pjms.35.3.44 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

An exploratory study to compare the effects of immersive virtual reality based training on the learning motivation of final year medical students as compared to video and text-based learning. Different modes of delivery of a training simulation of laparoscopy operation were presented to students and learning motivation corresponding to which were evaluated using the Intrinsic Motivation Inventory. The study was conducted from September 2018 to May 2019. Undergraduate medical students from 8 medical colleges and universities across Punjab, Pakistan participated in this study. A total of 87 students with a mean age of 22.5 ± 4 years were recruited for the study. Of these, 57.4% (n = 50) were males and 42.6% (n = 37) were females. Paired sampled t-test was chosen for the statistical investigation for the study. The tests were conducted by comparing means of text, video, and virtual reality learning methodologies in medical students. All executed statistical models are having significance value P=.000. Therefore, results are generalizable and can be implemented across the population. Medical student motivation was observed to be the greatest in Virtual Reality settings as compared to video-based and text-based learning settings. Both theoretical and practical studies have importance in medical studies, whereas practical hand-on-practice can enhance medical students’ professional proficiency. Virtual reality was at the top in User experience, perceived competence, usefulness, and motivation for final year medical students. It can play a signficant role in contemporary teaching and learning methodology with medical educationist and students can get benefit from this technology.

Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio.

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Although the idea of bacteria causing different types of cancer has exploded about century ago, the potential mechanisms of carcinogenesis is still not well established. Many reports showed the involvement of M. hominis in the development of prostate cancer, however, mechanistic approach for growth and development of prostate cancer has been poorly understood. In the current study, we predicted M. hominis proteins targeting in the mitochondria and cytoplasm of host cells and their implication in prostate cancer. A total of 77 and 320 proteins from M. hominis proteome were predicted to target in the mitochondria and cytoplasm of host cells respectively. In particular, various targeted proteins may interfere with normal growth behaviour of host cells, thereby altering the decision of programmed cell death. Furthermore, we investigated possible mechanisms of the mitochondrial and cytoplasmic targeted proteins of M. hominis in etiology of prostate cancer by screening the whole proteome.

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