Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis.

Los avances acelerados en tecnologías de la información combinados con los métodos de industrialización han estimulado el progreso en el desarrollo de nuevas generaciones en tecnologías de manufactura que hoy en día se vive el auge de la cuarta revolución industrial. La industria 4.0 demanda la aplicación de tecnologías emergentes que se originan de diferentes disciplinas incluyendo los sistemas físico-cibernéticos, el internet de las cosas, cómputo en la nube, integración industrial, arquitecturas orientadas a servicios, administración de procesos de negocios, integración de la información industrial, entre otros. La falta de herramientas sigue siendo un obstáculo para explotar el gran potencial de la industria 4.0, la cual presenta grandes retos para las empresas donde se requiere implementar métodos y sistemas formales para entrar a la industria 4.0. En esta investigación nos concentramos en los retos para la integración del Internet de las Cosas como una oportunidad para la manufactura sustentable en la Industria 4.0 y con ello generar nuevos servicios cualitativos los cuales representan una evolución del internet.

Los sistemas de espectrofotometría requeridos comúnmente tanto en áreas de investigación como en industrias químicas y de materiales resultan ser equipos costosos para laboratorio que en muchas ocasiones se vuelve inviable la adquisición de estos, además de que en varias de las aplicaciones también se requiere portabilidad para realizar mediciones en campo y que los mismos pudieran ser de amplio espectro. En este trabajo se llevó a cabo una revisión de diversos proyectos de desarrollo de dispositivos de espectrofotometría que van desde prototipos académicos hasta productos industriales utilizando distintas estrategias de implementación con el fin de conocer la factibilidad de crear dispositivos portátiles y de amplio espectro o productos específicos para un sector en particular, pero con la intención de lograr una alta precisión y funcionalidad. La investigación permitió concluir con una aportación novedosa para el desarrollo de prototipos de espectrofotometría de gran alcance para industrias o sectores específicos y de bajo costo.

Inflammation is a condition that affects a large percentage of the population, rendering the search for alternative treatments urgent. Medicinal plant such as Brugmansia arborea has been employed in traditional medicine, it is used to alleviate menstrual pain, acne, promote wound healing, and ease joint pain, headaches, and other ailments. Some alkaloids (mainly atropine, scopolamine, and nor-hyoscyamine) have been described. In this paper, the in-vitro cultivation from leaf explants of B. arborea through indirect organogenesis is described. From wild plants (BA) and shoots (BAB) their extracts were prepared, and were analyzed by thin-layer chromatography and gas chromatography-mass spectrometry, identifying 18 main metabolites. Antioxidant activity was assessed with EC₅₀ = 199.52 and 31.63 mg/mL for BA and BAB, respectively. Boths extracts showed a DL₅₀ > 2 g/kg and is classified as non-toxic. In Acetylated and Hydrolyzed extracts, acetyl-rutin and kaempferol, respectively were identified. Anti-inflammatory activity was assayed employing carrageenan model. The ED₅₀ values were < 158.48 mg/kg by BA, BAB, Ac-BA, and H-BA, being more active the hydrolyzed extract (ED₅₀ = 63.09 and 64.56 mg/kg) for females and male mice. In topical TPA model, the ED₅₀ value were < 0.47 mg/ear for BA, BAB, Ac-BA, and H-BA. Thermographic analysis is a non-invasive, cheap, effective and reproducible method to evaluate the anti-inflammatory effect of substances, since it quantifies the increase in temperature in an inflammatory process, so this technique constitutes an alternative in the search for this type of agent.