Conference Agenda

Abstract – This study examines the issue of missing data in biomedical research and evaluates the effectiveness of different imputation strategies. Multiple imputation is highlighted as a robust statistical method for improving the validity of analyses, compared to traditional approaches such as eliminating incomplete cases or imputing missing values with the mean, which can introduce bias and reduce statistical accuracy. Simulations and comparative analyses were conducted on biomedical databases to assess the impact of various imputation methods on the preservation of variability and the accuracy of predictive models. The results indicate that K-Nearest Neighbors (KNN) imputation better preserves the original data structure compared to mean imputation, which tends to reduce value dispersion. Additionally, challenges such as the correct specification of imputation models and the integration of machine learning algorithms in these processes are examined. Finally, recommendations are provided to enhance the implementation of multiple imputation in clinical and epidemiological studies, ensuring more accurate and reliable management of missing data in biomedical research.

Courses with a mathematical component are among the primary reasons why engineering students often decide to change their major. This study explores the correlation between brain activity and academic performance in Physics courses, focusing on the differences between students who passed and those who repeated the course. Using EEG to measure brainwave patterns (Beta and Gamma waves), alongside questionnaires to assess emotional states, sleep habits, and dietary factors, the research analyzes how these variables impact concentration and anxiety levels. The results reveal that students who repeated the course exhibited higher Gamma wave amplitude and lower Beta waves, suggesting different cognitive profiles. Additionally, sleep and eating habits were found to correlate with concentration and anxiety, influencing academic performance. The study provides a novel, replicable methodology for identifying neurocognitive profiles that can help personalize educational strategies to improve student outcomes.

Breast cancer is the most common malignancy and one of the leading causes of death in women worldwide. Breast cells grow uncontrollably, forming tumors that can spread through the blood or lymphatic system. The treatment of breast cancer includes surgery, radiotherapy and the use of drugs such as doxorubicin, which aim to reduce the size of the tumor and improve the patient's condition. However, their efficacy is limited due to factors such as cellular resistance and efflux transporter activity. The aim of the work was to analyze the expression of NF-kB, Bcl-2 and Bax in MCF-7 and MDA-MB-231 breast cancer cell lines treated with doxorubicin (DOX). IC50 analysis of DOX was performed by MTT assay, gene quantification was by qPCR and In silico analysis of TLR4 and DOX was performed. MTT results showed that the IC50 for MCF-7 and MDA-MB-231 were 0.1µM and 0.3µM respectively. The qPCR results showed that the overexpression of NF-κB was 1.825±0.054 and 10.85±1.000 in MDA-MB-231 and MCF-7 cell lines respectively. For Bax, the expression level was 1.827±0.1036 and 0.6869±0.092 in MDA-MB-231 and MCF-7 cell line consecutively. Furthermore, In silico analysis showed that DOX docks into the extracellular domain cavity of TLR4 through interactions with Lys52 and Ile65A residues.

Medicare fraud detection has become more challenging and urgent over time. It has become urgent because, as some sources suggest, US spending on the Medicare program reached a trillion dollars in 2023, while other reports indicate that the US has lost more than a billion dollars due to Medicare fraud in 2024. At the same time, it has become more challenging because data becomes larger yearly and suffers significant imbalance. Bauder and Khoshgoftaar [1] provide an excellent introductory paper to the problem, explicitly covering the imbalance issue. However, many more problems affect the task of fraud detection, such as data quality, variety of models' configuration, and proper validation of results. As a survey among multiple research studies in this area, this paper has encountered recurring findings and challenges relevant to future work, and it offers suggestions to approach the problem. For example, there are inherent properties in the data collection method used by most studies that provide unrealistic validation results. However, there are sampling methods that prove reliable in getting robust machine learning models regardless of the learners used. The existing studies also indicate the need for a more extensive survey with appropriate comparison methods, as existing works that usually suggest some models perform better (primarily due to tuning hyperparameters) ignore adjusting the competing models with configurations other studies have found important. In addition to other suggestions, this paper highlights the importance of industry collaboration in getting more realistic results and practical models.

The limited access to healthcare services in La Mosquitia, Honduras, presents a significant challenge for nutritional care. This study presents the development of a telemedicine prototype system focused on nutrition to overcome these barriers. Using Information and Communication Technologies (ICT), the system enables remote consultations with specialists, reducing geographical limitations and improving access to diagnoses and treatments. A mixed-methods approach was used, combining expert interviews and data analysis from the region. The results showed a high prevalence of food insecurity and malnutrition, as well as chronic diseases such as diabetes and hypertension. Challenges were identified, including healthcare personnel's resistance to adopting digital platforms and dependence on government support, which affects the sustainability of initiatives. The prototype was developed on WordPress, integrating tools for appointment scheduling and video calls. Internal tests and user trials indicated a positive response, highlighting ease of use and the integration of tools for remote consultations. This study contributes to evaluating the feasibility of a telemedicine-based nutritional care model in rural areas and establishes the minimum infrastructure requirements to ensure its operability.

As a result of the COVID 19 pandemic, universities have chosen to adapt their teaching models to virtual and hybrid environments, which means that students and teachers carry out their activities from home through a computer, cell phone or electronic tablet. In view of this, the study of the ergonomic risks that exist when working in these modalities becomes relevant. An online survey was carried out, obtaining 159 university students who received part of their training in teleteaching. As part of the results, an increase in visual, lumbar and auditory conditions is observed, where 75% of the sample studied presents some risk symptoms of a muscle disease