Conference Agenda

Breast cancer remains a major public health problem, especially the triple-negative molecular subtype, due to its limited response to chemotherapy. Therefore, the objective of this study was to evaluate the cellular viability and migratory capacity of triple-negative breast cancer cells treated with doxorubicin, a drug used in neoadjuvant chemotherapy. For this purpose, the MDA-MB-231 cell line was used, cultured in DMEM medium supplemented with 10% fetal bovine serum and 1% antibiotic. Response to treatment was evaluated using MTT, clonogenic, and wound healing assays. The results showed an IC₅₀ value of 0.3 µM for doxorubicin in the MDA-MB-231 cell line. It was also observed that, despite treatment, the cells retained partial migratory capacity. Taken together, these findings indicate that doxorubicin reduces cell viability but does not completely inhibit the migration of MDA-MB-231 cells, which could contribute to doxorubicin favoring the survival of this triple-negative molecular phenotype.

The present study has analyzed the waiting times in the Pharmacy area of the Honduran Institute of Social Security (IHSS), with the objective of evaluating the factors that may affect the long wait tolerated by beneficiaries. To understand this problem, a mixed approach has been applied that combined the collection of quantitative data - such as times between arrivals, waiting times and service times - together with qualitative observations on the patient’s journey and how the staff operates. The dependent variable has been defined as the average waiting time, average time in the system and number of patients served. As independent variables, the type of patient, time between arrivals, service time, time between service and inactivity, and the number of windows. Data collection has been carried out for three weeks at the times of greatest demand, using instruments such as Timestamper Log, Google Forms and Excel. The results obtained from the simulation model showed that older adult patients have a longer average waiting time, the main influencing factor being the inactivity of the windows during certain sections of the day. Through the analysis of different operational changes, it was found that there is no single solution capable of simultaneously reducing waiting times for all types of patients; that is, some scenarios generate benefits for certain groups, while negatively impacting others.

Sexual harassment represents a complex social problem, with women’s responses often categorized as acute stress when their safety in public spaces is compromised. Biomarkers such as heart and respiratory rate are directly related to autonomic nervous system activity under stress conditions. The present study proposes the development of a prototype warning system designed to automatically detect and alert about possible sexual harassment by identifying stress responses in women through real time heart and respiratory rate monitoring derived from ECG readings. Signal acquisition was performed using an ECG sensor with a microcontroller platform, while respiratory rate was obtained via ECG-Derived Respiration (EDR) analysis. Due to complexity of the issue, controlled simulations of street harassment were conducted using virtual reality headsets to induce responses in participants. When predetermined biomarkers thresholds were exceeded, an automated Telegram alert was sent to a trusted contact with the individual's readings. A pilot study with 12 female volunteers showed substantial increases in heart and respiratory rate during simulated harassment scenarios. The prototype validated the acquisition, processing and remote notification pipeline, providing proof of concept for real-time stress detection. These findings underscore the potential of biomedical signal processing as a tool for preventing gender-based violence. Future research will increase sample size, refine threshold calibration, and explore integration with wearable devices to enhance usability and scalability.

The accurate estimation of causal effects is fundamental for evidence-based health policy decisions. Traditional correlational approaches often produce biased estimates due to uncontrolled confounding, leading to potentially misleading policy recommendations. This study proposes and evaluates a causal inference framework based on Directed Acyclic Graphs (DAGs) for assessing health intervention effects in observational settings. Using simulated data from a nutrition intervention program (n=1,500), we systematically compared DAG-based causal estimation methods against conventional correlational approaches. Our simulations incorporated realistic confounding structures including socioeconomic status, parental education, and baseline health metrics. Results demonstrate that naive correlational analysis overestimated the intervention effect by 25.6% (10.05 vs. 8.00 points), while DAG-based backdoor adjustment methods yielded estimates within 5.4% of the true effect (8.43, 95% CI: 7.88-8.95). Inverse probability weighting produced intermediate results (9.83 points). Sensitivity analyses revealed that causal estimates remained robust across varying degrees of unmeasured confounding, whereas correlational estimates deteriorated rapidly. These findings suggest that DAG-based frameworks provide more reliable effect estimates for health policy evaluation, particularly when randomized trials are infeasible. The methodology presented offers practical guidance for public health researchers and policymakers seeking to make evidence-informed decisions from observational data. Implementation of these causal inference tools could substantially improve the validity of policy impact assessments in resource-limited settings where experimental designs are often impractical.