Gabriela del Carmen López-Armas

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a major global health challenge due to its high prevalence and association with chronic complications, highlighting the need for reliable predictive tools to support clinical decision-making. Methods: This study proposes a two-stage hierarchical prediction system based on a Random Forest (RF) classifier. In Stage 1, the model performs multiclass classification into healthy (H), T2DM without complications (D), and T2DM with complications (C). In Stage 2, patients classified as C are further stratified into microvascular or macrovascular complications. The dataset included 31 biochemical, molecular, inflammatory, and oxidative stress variables from Mexican and Spanish cohorts. Feature selection was performed using Pearson correlation, and feature relevance was further assessed using RF importance measures. Model training used stratified cross-validation, with additional evaluation on a hold-out set to approximate real-world performance. Results: The optimized RF achieved an accuracy of 92% and a macro F1-score of 0.92, outperforming baseline models, with an AUC-ROC of 0.89 for complication prediction. Key predictive features included IL-18, miR-126, duration of T2DM, HbA1c, and IL-10. Conclusions: The novelty of this study lies in integrating heterogeneous biomarkers within a hierarchical predictive framework, rather than in the machine learning algorithm itself. This multimodal approach, combined with interpretable machine learning techniques, is designed to deliver clinically meaningful insights for patient stratification and personalized management in T2DM.

Breast density has a significant impact on how clearly masses appear in mammography. It can also introduce bias in automatic localization systems when density distributions are uneven. Although advances in deep learning-based detection methods have been made, most studies report overall performance without explicitly accounting for variability associated with breast density. Breast cancer diagnosis from mammography is strongly influenced by dataset composition, annotation variability, and breast density distribution, factors that are rarely controlled in current AI evaluations. We introduce Mass-Bench, a clinically balanced and harmonized multi-dataset benchmark that integrates CBIS-DDSM, INBREAST, VINDr-Mammo, and DMID under a unified canonical schema, with standardized ACR density and BI-RADS encoding. Using a leakage-controlled and distribution-aware evaluation protocol, density-stratified mass detection and lesion-centered regions of interest (ROIs) classification were assessed across datasets. YOLO-based detection models achieved peak area under the curve (AUC) values up to 0.943; however, performance systematically degraded with increasing ACR density, revealing limitations that are often masked in imbalanced evaluations. By enforcing clinically representative density distributions, Mass-Bench provides a more reliable estimation of localization performance, which directly impacts downstream clinical tasks. In this context, binary ACR classification achieved F1-scores up to 0.976, while binary BI-RADS discrimination reached accuracies up to 0.93. However, multi-class classification remained more challenging, showing increased sensitivity to dataset heterogeneity and contextual information. These findings demonstrate that conventional evaluations may overestimate robustness, particularly in dense breast categories, and highlight the importance of density-aware benchmarking for developing reliable and clinically applicable AI systems in mammography.

Current approaches to dynamic sign language recognition commonly rely on dense landmark representations, which impose high computational cost and hinder real-time deployment on resource-constrained devices. To address this limitation, this work proposes a computationally efficient framework for real-time dynamic Mexican Sign Language (MSL) recognition based on a multimodal minimal angular-geometry representation. Instead of processing complete landmark sets (e.g., MediaPipe Holistic with up to 468 keypoints), the proposed method encodes the relational geometry of the hands, face, and upper body into a compact set of 28 invariant internal angular descriptors. This representation substantially reduces feature dimensionality and computational complexity while preserving linguistically relevant manual and non-manual information required for grammatical and semantic discrimination in MSL. A real-time end-to-end pipeline is developed, comprising multimodal landmark extraction, angular feature computation, and temporal modeling using a Bidirectional Long Short-Term Memory (BiLSTM) network. The system is evaluated on a custom dataset of dynamic MSL gestures acquired under controlled real-time conditions. Experimental results demonstrate that the proposed approach achieves 99% accuracy and 99% macro F1-score, matching state-of-the-art performance while using fewer features dramatically. The compactness, interpretability, and efficiency of the minimal angular descriptor make the proposed system suitable for real-time deployment on low-cost devices, contributing toward more accessible and inclusive sign language recognition technologies.

An anthropomorphic robot is a mechanical device designed to perform human-like tasks, such as manipulating objects, and has been one of the significant contributions in robotics over the past 60 years. This paper presents an advanced system for controlling a robotic arm using user hand gestures and movements. It eliminates the need for traditional sensors or physical controls by implementing an intuitive approach based on MediaPipe and computer vision. The system recognizes the user’s hand movements. It translates them into commands that are sent to a microcontroller, which operates a robotic hand equipped with six servomotors: five for the fingers and one for the wrist, which stands out for its orthonormal design that avoids occlusion problems in turns of up to 180°, guaranteeing precise wrist control. Unlike conventional systems, this approach uses only a 2D camera to capture movements, simplifying design and reducing costs. The proposed system allows replicating the user’s activity with high precision, expanding the possibilities of human-robot interaction. Notably, the system has been able to replicate the user’s hand gestures with an accuracy of up to 95%.

To assay new circulating markers related to macrovascular complications (MVC) in type 2 diabetes mellitus (T2DM), we carried out a descriptive cross-sectional study. We recruited 30 controls (CG), 34 patients with T2DM (DG), and 28 patients with T2DM and vascular complications (DG+C); among them, 22 presented MVC. Peripheral blood was used to determine redox status (superoxide dismutase, SOD; catalase, CAT; glutathione reductase, GRd; glutathione peroxidase, GPx; glucose-6-phosphate dehydrogenase, G6PD) and markers of oxidative damage (advanced oxidation protein products, AOPP; lipid peroxidation, LPO), nitrite levels in plasma (NOx). Inflammatory markers (IL-1β, IL-6, IL-10, IL-18, MCP-1, TNF-α) and the relative expression of c-miRNAs were analyzed. The real-time PCR results showed that the expressions of miR-155-5p, miR-21-5p, miR-146a-3p, and miR-210-3p were significantly higher in the DG group compared to the CG. The DG+C group presented statistically relevant differences with CG for four miRs: the increased expression of miR-484-5p, miR-21-5p, and miR-210-3p, and decreased expression of miR-126a-3p. Moreover, miR-126a-3p was significantly less expressed in DG+C compared to DG. The application of binary logistic regression analysis and construction of receiving operator characteristic curves (ROC) revealed two models with high predictive values for vascular complications presence: (1) HbAc1, creatinine, total cholesterol (TC), LPO, GPx, SOD, miR-126, miR-484 (Exp(B) = 0.926, chi² = 34.093, p < 0.001; AUC = 0.913). (2) HbAc1, creatinine, TC, IL-6, LPO, miR-126, miR-484 (Exp(B) = 0.958, Chi² = 33.863, p < 0.001; AUC = 0.938). Moreover, our data demonstrated that gender, TC, GPx, CAT, and miR-484 were associated with MVC and exhibited higher predictive values (Exp(B) = 0.528, p = 0.024, Chi² = 28.214, AUC = 0.904) than classical variables (Exp(B) 0.462, p = 0.007, Chi² = 18.814, AUC = 0.850). miR-126, miR-484, IL-6, SOD, CAT, and GPx participate in vascular damage development in the studied diabetic population and should be considered for future studies.

To evaluate new circulating markers related to macrovascular complications (MVC) in type 2 diabetes mellitus (T2DM), we recruited 30 controls (CG), 34 patients with T2DM (DG), and 28 patients with T2DM and vascular complications (DG+C), among them, 22 presented MVC. Pe-ripheral blood was used to determine redox status (superoxide dismutase, SOD; catalase, CAT; glutathione reductase, GRd; glutathione peroxidase, GPx; glucose-6-phosphate dehydrogenase, G6PD) and markers of oxidative damage (advanced oxidation protein products, AOPP; lipid pe-roxidation, LPO). Inflammatory markers (IL-1β, IL-6, IL-10, IL-18, MCP-1, TNF-α) and the relative expression of c-miRNAs were analyzed. The real-time PCR results showed that the expressions of miR-155-5p, miR-21-5p, miR-146a-3p, and miR-210-3p were significantly higher in the DG group compared to CG. DG+C group presented statistically relevant differences with CG for four miRs: the increased expression of miR-484-5p, miR-21-5p, and miR-210-3p, and decreased for miR-126a-3p. Moreover, miR-126a-3p was significantly less expressed in DG+C compared to DG. The application of binary logistic regression analysis and construction of receiving operator characteristic curves (ROC) revealed two models with high predictive value for vascular complications presence: 1) HbAc1, creatinine, total cholesterol (TC), LPO, GPx, SOD, miR-126, miR-484. 2) HbAc1, creatinine, TC, IL-6, LPO, miR-126, miR-484. Moreover, our data demonstrated that gender, TC, GPx, CAT, and miR-484 were associated with MVC and exhibited higher predictive values than classical variables. miR-126, miR-484, IL-6, SOD, CAT, and GPx participate in vascular damage development in the studied diabetic population and should be considered for future studies.

Effective communication is crucial in daily life, and for people with hearing disabilities, sign language is no exception, serving as their primary means of interaction. Various technologies, such as cochlear implants and mobile sign language translation applications, have been explored to enhance communication and improve the quality of life of the deaf community. This article presents a new, innovative method that uses real-time machine learning (ML) to accurately identify Mexican sign language (MSL) and is adaptable to any sign language. Our method is based on analyzing six features that represent the angles between the distal phalanges and the palm, thus eliminating the need for complex image processing. Our ML approach achieves accurate sign language identification in real-time, with an accuracy and F1 score of 99%. These results demonstrate that a simple approach can effectively identify sign language. This advance is significant, as it offers an effective and accessible solution to improve communication for people with hearing impairments. Furthermore, the proposed method has the potential to be implemented in mobile applications and other devices to provide practical support to the deaf community.

Breast cancer (BC) affects millions of women worldwide, causing over 500,000 deaths annually. It is the leading cause of cancer mortality in women, with 70% of deaths occurring in developing countries. Elastography, which evaluates tissue stiffness, is a promising real-time minimally invasive technique for BC diagnosis. This study assessed strain elastography (SE) and the fat-to-lesion (F/L) index for BC diagnosis. This prospective study included 216 women who underwent SE, ultrasound, mammography, and breast biopsy (108 malignant, 108 benign). Three expert radiologists performed imaging and biopsies. Mean F/L index was 3.70 ± 2.57 for benign biopsies and 18.10 ± 17.01 for malignant. We developed two predictive models: a logistic regression model with AUC 0.893, 79.63% sensitivity, 87.62% specificity, 86.9% positive predictive value (+PV), and 80.7% negative predictive value (−PV); and a neural network with AUC 0.902, 80.56% sensitivity, 88.57% specificity, 87.9% +PV, and 81.6% −PV. The optimal Youden F/L index cutoff was >5.76, with 84.26% sensitivity and specificity. The F/L index positively correlated with BI-RADS (Spearman’s r = 0.073, p < 0.001) and differed among molecular subtypes (Kruskal-Wallis, p = 0.002). SE complements mammography for BC diagnosis. With adequate predictive capacity, SE is fast, minimally invasive, and useful when mammography is contraindicated.

Breast cancer (BC) affects millions of women; it is the leading cause of death by neoplasia in wom-en worldwide, with at least 500 thousand deaths each year, 70% of which occur in developing countries. Elastography evaluates tissue stiffness, making this a promising non-invasive real-time technique. BI-RADS is recognized as a standard and helpful technique for establishing BC risk. This study aimed to determine, by strain elastography (SE), the fat-to-lesion index (F/L index), sen-sitivity, specificity, and optimal cut-off to diagnose BC. This prospective study included 216 wom-en who underwent SE, ultrasound, mammography, and breast biopsy; according to mammogra-phy, we recruited BI-RADS category 2 (n= 5), category 3 (n=29), category 4 (n= 94), category 5 (n= 71) and category 6 (n=17); after biopsy, 108 women were classified as negative, and 108 were pos-itive for BC. Three expert BC radiologists performed all imaging interpretations and biopsies. Our results show mean values of the F/L index for negative biopsies of 3.70±2.57 and positive biopsies of 18.10±17.01; we developed two models for the prediction of BC, a logistic regression model with an AUC of 0.893 and a neural network with an AUC of 0.902. The Youden index yielded a F/L index cut-off >5.76, with a sensitivity and specificity of 84.26%. Positive Spearman's Rho cor-relation coefficient was observed between the F/L index and BI-RADS, r=.073 (p< 0.001). ANOVA test showed a difference between F/L index vs. molecular subtypes (p=0.002). SE is helpful as a di-agnostic complement to mammography, or in cases where the latter is contraindicated, elas-tography has an adequate predictive capacity and is a fast and minimally invasive procedure.