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Background/Objectives: To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Methods: Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. Results: The model demonstrated an overall precision of 0.89 on the testing set, with an accuracy of 0.88, a recall of 0.88, and an F1-score of 0.885. It presented better results for multiple categories, particularly in the panesophageal pressurization category, with precision = 0.99 and recall = 0.99, yielding a balanced F1-score of 0.99. Conclusions: This study demonstrates the potential of artificial intelligence, particularly Gemini, in aiding the creation of robust deep learning models for medical image analysis, solving not just simple binary classification problems but more complex, multi-class image classification tasks. [ABSTRACT FROM AUTHOR]

In the increasingly researched field of gamification, that is the application of the game design elements in no game contexts, one of the most used techniques is the use of digital badges. Through this paper, we conducted a literature review on the use and effectiveness of badges. We include other literature reviews, empirical case studies, and application reports. This analysis shows the widespread use, the different types and specialized nature of digital badges throughout the private and public sectors. [ABSTRACT FROM AUTHOR]

A large part of the job of a university teacher in computer science is to verify student exercises. The task of verifying correctness, coding style, conventions, and grading is laborious and if done correctly leaves little time for questions. Automating aspects of this work helps all parties involved by freeing time up for questions. The solution detailed in the paper automates a number of these tasks and provides near instant feedback for the students, together with a platform to gamify student learning. [ABSTRACT FROM AUTHOR]

Bilateral thalamic glioma is extremely rare and the incidence cannot be adequately expressed. We present the case of a 72 years old male suffering from the rapid deterioration of cognitive function to moderately severe dementia in a short period of time. Magnetic resonance studies demonstrated a bilateral thalamic glioma with a minimal focal gadolinium uptake in the left thalamus. Biopsy was performed and pathology report was of anaplastic astrocytoma, WHO grade III. Radiotherapy was proposed but was rejected by the patient's relatives. The patient deceased 57 days later. We performed an extensive review of the literature and by updating the previous described series we can state that to the best of our knowledge this is the 60th case described in the literature and the second eldest patient presented. Patients suffering from this disease present a poor prognosis, the longest survival described being of 3 years in patients diagnosed with grade II bilateral thalamic glioma. Adjuvant therapy in form of radiotherapy to the thalami is most commonly used but the benefits are unclear. The natural progression of WHO grade III bilateral thalamic glioma left untreated, as can be seen from our case, has an even poorer outcome.