Revista Mexicana de Ingenieria Biomedica

Evaluation of PLA and PETG filaments for the addition of hydroxyapatite in 3D-printed dental models

Tue, 16 Jun 2026 00:00:00 +0000

Additive manufacturing has established itself as a key technology in dentistry, enabling the manufacture of customized devices with precision and in reduced times. Among the most widely used filaments are polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG), both with different properties that influence clinical performance. This study presents a comparative evaluation of PLA and PETG filaments in the generation of hydroxyapatite on 3D-printed pieces through hydrothermal treatment in simulated body fluid (SBF) solution. Dental models were printed with PLA and PETG filaments under controlled conditions and immersed in SBF for 7, 14, and 21 days. Modified pieces were characterized by infrared spectroscopy (IR-TF), X-ray diffraction (XRD), and scanning electron microscopy (SEM) to evaluate surface modifications and mineral formation. The results showed that PLA, due to its greater porosity and roughness, favored early hydroxyapatite nucleation, presenting a stable layer at 21 days. PETG showed slow nucleation, but at 21 days it showed characteristic hydroxyapatite agglomerates. Cytotoxicity tests with 3T3 fibroblasts confirmed that both materials maintained cell viability above 70%.

Predictive models of anthropometric parameters for primary screening of sarcopenia based on Machine Learning

Mon, 29 Jun 2026 00:00:00 +0000

This work reports a free-access primary screening system for detecting sarcopenia risk in older Mexican adults, using machine learning and anthropometric variables obtained through accessible instruments such as measuring tapes. An observational, retrospective, and analytical study was conducted based on records from beneficiaries of the Mexican Social Security Institute from the year 2019, with a sample of 1,678 participants. The models, developed using data from individuals without comorbidities, followed a structured machine learning workflow that included data preprocessing, variable transformation and clustering, and supervised classification using decision-tree-based models. The optimal variable combinations for men and women achieved F1-scores above 0.94, accurately classifying the risk levels of sarcopenia and severe sarcopenia. The current models need to be expanded to include individuals with comorbidities such as type 2 diabetes, hypertension, and arthritis, which have been associated with greater muscle mass loss. This proposal does not replace clinical diagnostic testing but serves as a complementary tool to rule out low-risk individuals and prioritize specialized evaluation for those who may be affected by sarcopenia.

Point-Based Infrared Thermography and Two-Phase Machine Learning for Non-Invasive Periodontal Disease Classification

Sun, 05 Jul 2026 00:00:00 +0000

Periodontal diseases such as gingivitis and periodontitis are common oral health conditions that require timely and accurate detection. This study presents a non-invasive diagnostic support system that integrates infrared thermography with clinical data to classify periodontal health status. A cross-sectional study was conducted on 91 individuals, categorized as healthy, with gingivitis, or with periodontitis. Thermographic images from three facial perspectives were analyzed to extract gingival temperature features, which were combined with clinical parameters including plaque index, age, sex, smoking status, and the presence of systemic conditions. Several machine learning models were evaluated using ten-fold cross-validation, both with and without dimensionality reduction. A two-step classification approach yielded the best results: logistic regression was used to identify periodontitis, followed by XGBoost to differentiate between healthy and gingivitis cases. The combined model achieved an accuracy of 94.51% and an F1-score of 94.49%, while models based solely on thermographic data reached an accuracy of 75.82%. These findings support the feasibility of using localized infrared thermographic measurements and artificial intelligence to improve the classification of periodontal disease. The proposed method offers a promising non-invasive solution to enhance diagnostic accuracy and inform personalized dental care.

Evaluation of Asset Management Maturity in a Public Hospital: A Context of Limited Resources

Sun, 05 Jul 2026 00:00:00 +0000

This study aimed to evaluate the asset management maturity of a public hospital through the application of the Asset Management Capability Maturity Model (AMCaMM), aligned with ISO 55000 standards. The objective was to identify critical gaps and propose strategic actions to enhance operational sustainability in resource-limited settings.

A cross-sectional study with a descriptive-exploratory approach was conducted, assessing ten key AMCaMM areas through a validated 30-item questionnaire and semi-structured interviews with 24 participants (12 administrators and 12 technicians). The analysis combined descriptive statistics and inductive thematic analysis, supported by Excel® and qualitative coding techniques.

The hospital achieved an overall maturity score of 2.73 out of 5, corresponding to a “developing stage.” The most critical gaps were found in Operational Planning (2.25) and Corrective/Preventive Actions (1.75), while the highest score was recorded in Management and Commitment (3.25). Key deficiencies included the absence of structured policies, limited technical training, and a lack of technologies such as IoT for real-time monitoring.

The AMCaMM proved to be a robust tool for diagnosing asset management performance. These findings can guide similar institutions in resource-constrained environments toward more proactive and efficient asset management practices.