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Sphonic: Development of a Mobile Application Using AI and AR for Learning Biomedical Concepts
(Springer Nature Switzerland, 2025)
Escobar-Castillejos, Daisy
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Báez Gómez Tagle, Enrique Ulises
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Chavarría-Reyes, Fernando Mauricio
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Sigüenza Noriega, Iñaki
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Cruz-Ledesma, Iván
This chapter covers the development of Sphonic, an iOS mobile application that combines artificial intelligence (AI) and augmented reality (AR) to improve biomedical education. Sphonic was designed to employ SwiftUI for its front end, while its back end is based on Django and hosted on Amazon Web Services. Sphonic intends to assist in explaining complex topics in chemistry and biology. Using Apple’s ARKit framework, Sphonic allows students to explore dynamic representations of DNA architecture, chemical bonding interactions, and biological processes to promote engagement and retention. On the other hand, the application features an OCR-based chemical equation solver, using Amazon Bedrock’s generative AI capabilities, that balances equations and offers comprehensive, step-by-step guidance, addressing conceptual deficiencies for learners. Additionally, Sphonic implements secure authentication protocols to protect user data and features a user-friendly interface that simplifies navigation. This chapter concludes by emphasizing the importance of AI and AR in modern education, demonstrating how these technologies could democratize access and understanding of scientific information and foster creativity in academic environments. ©The authors ©Srpinger.
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Development of a Mobile Application for Dermatological Diagnosis Using Image Recognition: The DermAware Case Study
(Springer Nature Switzerland, 2025)
Cedillo-Maldonado, Luis
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Sigüenza-Noriega, Iñaki
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Miranda-Mateos, Sara Rocio
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Reinoso-Fuentes, Lorenzo
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Pérez-Aguirre, Mauricio
This chapter discusses the development of DermAware, an iOS mobile application intended for assisting dermatological diagnosis through image recognition and machine learning. DermAware aims to serve as a supplementary resource for specialized medical professionals and patients, aiding the early identification and supervision of dermatological illnesses. The application was developed with SwiftUI for the front end and Django for the back end, providing scalability and secure data management. The application incorporates multiple components designed to enhance its usability. A real-time messaging module was designed to facilitate direct communication among users for prompt consultation scheduling. Health tracking functionalities, supported by Apple’s HealthKit, enable the collection and monitoring of patient data. The application integrates patient history management, allowing doctors to review previous assessments and track disease development conveniently. Ultimately, an authentication system guarantees data confidentiality and adherence to regulations. DermAware uses Apple’s CoreML framework alongside the ResNet50 convolutional neural network model to classify skin diseases. The system was trained using publicly accessible dermatological datasets, achieving an accuracy of 85% for detecting various skin conditions, including melanoma. The chapter finishes by addressing the technical challenges faced during development, evaluating potential enhancements, and discussing future developments in the field. These factors highlight the significance of AI-driven applications for improving medical diagnostics and healthcare accessibility. ©The authors ©Springer.
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Machine Learning Methods in Biomedical Field : Computer-Aided Diagnostics, Healthcare and Biology Applications
(Springer Nature Switzerland, 2026)
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Gomez-Coronel, Sandra L.
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Renza Torres, Diego
This book provides an in-depth exploration of machine learning techniques and their biomedical applications, particularly in developing intelligent computer-aided diagnostic systems, creating groundbreaking healthcare technologies, uncovering novel biological applications, and fostering sustainable health solutions. Integrating artificial intelligence, mathematical modeling, and emergent systems, this book highlights the profound impact of these advanced tools in not only enhancing problem-solving within the biomedical field but also in catalyzing the development of novel solutions. This book is a valuable resource for readers interested in understanding the revolutionary impact of novel machine learning methodologies on the biomedical landscape. Furthermore, it offers a blend of theory and practical applications for those interested in biomedical education and training, biology, medicine, and sustainable health development. ©The authors ©Springer.
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Orthosis Control Based on Electromyographic Signals and Machine Learning
(Springer Nature Switzerland, 2025)
Escobedo-Gordillo, Andrés
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Díaz, Fernando
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Villa, Jesús
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Sepúlveda, Miguel
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García-Casas, Sebastián
The human hand is indispensable for daily activities, and those who suffer from dysfunction due to strokes or accidents often require therapy to improve their condition. This study has developed a hand orthosis that uses surface Electromyographic (sEMG) signals and machine learning to address therapeutic needs and improve the quality of life for individuals with reduced motor skills in their hands and/or wrists. While current orthoses meet therapy requirements, they do not incorporate machine learning (ML) or sEMG sensors to optimize performance and accessibility. This chapter describes a remote-controlled, electro-mechanical orthosis that can replicate six basic movements of the human hand using three sEMG channels and ML. Our dataset of 14,400 samples, each labeled with a hand gesture, was generated by eight participants. The orthosis is comfortable and customizable for different users, as shown in prototype testing. The convolutional neural network (CNN) used achieves an accuracy of 90.38% with an inference time of 1.515 ms. Therefore, this orthosis system has significant potential for further development and practical application in patients who require such intervention. ©The authors ©Springer.
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Digital financial inclusion as a catalyst for innovation, economic growth, and sustainability: A bibliometric analysis (2014-2024)
(Pro-Metrics, 2025)
Salazar-Uribe, Mayra Yvette
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Salgado-García, Jorge Arturo
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Hernández-Lara, Ana Beatriz
Objective. This study aimed to conduct a bibliometric analysis of keywords to identify strategic topics in digital financial inclusion (DFI) and their relationship with sustainability and economic growth between 2014 and 2024. Design/Methodology/Approach. A bibliometric analysis was conducted on a sample of 1,234 academic articles indexed in Scopus using the Bibliometrix tool in R. Keyword co-occurrence was examined using multiple correspondence analysis and K-means clustering to reveal thematic structures. Results/Discussion. A total of six thematic clusters were identified: (1) threshold effect, (2) digital transformation, (3) central bank digital currencies (CBDCs), (4) sustainable development, (5) financial and digital literacy, and (6) fintech. These clusters demonstrated the evolution of DFI from its initial role as a technological enabler, such as fintech and blockchain, to its current impact on economic development, growth, and sustainability. This analysis proposed a conceptual model of DFI. In this model, digital literacy and fintech functioned as enablers. Meanwhile, CBDCs and blockchain technology served as structural tools. Digital financial inclusion was defined as a mechanism for inclusive economic development. Conclusion. The findings contributed to an understanding of how financial digitization is linked to sustainability strategies and long-term economic growth. ©The authors ©Iberoamerican Journal of Science Measurement and Communication ©Pro-Metrics
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WEFE nexus unveiled: a comprehensive review of monitoring and modelling methods in the water-energy-food-ecosystems nexus
(Purpose Led Publishing, 2025-10)
Sustainable resource management in the face of climate change is a pressing challenge for our society. This paper delves into the water-energy-food-ecosystems (WEFE) nexus, a scientific framework that supports the integrated assessment and management of the interconnected resources. Shifting from sectoral to cross-sectoral and transdisciplinary perspectives, the WEFE nexus addresses interdependencies and interactions among water, energy, food, ecosystems, and climate. This paper focuses on the extended nexus, incorporating ecosystems as a fourth pillar, underscoring the importance of considering ecosystems on an equal footing with water, energy, and food sectors. In addition, the paper emphasizes the significance of monitoring and modelling techniques, laying the foundations for understanding the nexus complexities and assessing uncertainty. The paper offers an overview of integrated nexus modelling, system analysis and socio-economic modelling, bridging the gap between nexus science and practice. It highlights the role of multifaceted stakeholder engagement methods, policy assessment, and institutional analysis in nexus models. Quantifying the nexus through indicators, and its alignment with the Sustainable Development Goals, EU Green Deal, and EU Blue Deal are also key focal points. Finally, the last part of the paper addresses challenges in existing nexus modelling attempts, advocates for the integration of transdisciplinary information, and presents lessons learned. The paper concludes with recommendations for the future of the WEFE nexus, emphasizing its potential in fostering transformative change toward sustainable resource management and inclusive policymaking.
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Creating Onomastic Social Network Maps of Books Using Their Indexes. Case Study: ‘Papyrus: The Invention of Books in the Ancient World’
(Wiley, 2025)
Repiso, Rafael
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Ortiz-Díaz, Erik M
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This paper proposes a methodology to create social networks using subject and onomastic indexes from books to facilitate content analysis in monographic works. Books have not been largely studied in this context because their contents have not been digitized as in comparison with scientific research articles. However, indexes have been proven as a useful tool to identify relations between terms or characters mentioned inside the texts. This technique is based in the co-presence among these elements and they further visualization, offering a new perspective for structural analysis within Social Sciences and Humanities books. The book ‘Papyrus: The Invention of Books in the Ancient World’ has been used as a study object to illustrate this technique and its possibilities. ©The authors ©Learned Publishing ©Wiley.
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Impact of a bioethics and humanities program on the educational training of nephrology residents
(Oxford University Press (OUP), 2025-09-24)
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Gómez Guerrero, Irma
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Garcia-Villalobos, Gloria
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Martin Alemañy, Geovana
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Aguiñaga-Chiñas, Nuria
Background: Modern medical training must integrate not only clinical skills but also ethical and humanistic competencies. In 2013, a structured program in bioethics and humanism was implemented as part of a nephrology residency curriculum. The objective of this study was to evaluate the impact of a 3-year humanism and bioethics program for nephrology residents that focused on improving clinical communication, reducing complaint and lawsuit numbers, increasing patient satisfaction, and supporting decision-making centered on quality of life. Methods: A longitudinal, ambispective cohort (2010–19), our 3-year curriculum delivered weekly 1-h sessions for 6 months/year to 45 residents and was facilitated by three faculty instructors across six core themes. To relate outcomes to the intervention, analyses were anchored to the 2013 launch and compared pre-program (2010–13) versus post-program (2014–19) rates of formal complaints, legal claims, patient satisfaction and maximum benefit discharges. Results: Formal complaints decreased from 47.8 to 26.0 per year [incidence rate ratio (IRR) 0.54, 95% confidence interval (CI) 0.44–0.67; P < .001; Holm <0.001]. Legal claims were reduced from 4.25 to 0.17 per year (IRR 0.039, 95% CI 0.005–0.295; P = .0016; Holm = 0.0016). Maximum benefit discharges increased from 4.25 to 76.5 per year (IRR 18.0, 95% CI 11.09–29.21; P < .001; Holm <0.001). For satisfaction, the ordinal logistic model showed an odds ratio (OR) of 3.53 (95% CI 1.96–6.38; P < .001; Holm = 0.0001), consistent with the dichotomous sensitivity analysis (≥4 vs ≤3) (OR 4.08, 95% CI 2.16–7.71; P < .000). Conclusions: The humanism and bioethics program was proven to be an effective and transformative educational tool that promoted ethical, empathetic and patient-centered nephrology practices. The positive impact of this program was evident in both clinical indicators and strengthened medical professionalism. ©The authors ©Oxford University Press (OUP) © Clinical Kidney Journal.
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Ligand-Based Virtual Screening Workflow for Antimalarial Repositioning from Known Drugs and Chemical Libraries
(Springer Nature Switzerland, 2025-10-11)
Machado-Tugores,Yanetsy
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Meneses-Marcel, Alfredo
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Cristina Aguilar, Ana
The present report outlines a workflow integrating various virtual screening methods to identify potential antimalarial compounds. To develop QSAR models, a dataset of 2,314 compounds was analyzed using linear discriminant analysis and the QuBiLs-MAS software. 37 individual models were generated and subsequently combined into a fusion-based multiclassifier system (MCS), which achieved predictive performances of 91.35% for the training set and 92.06% for the test set. The MCS was further evaluated through a virtual screening simulation involving 13,410 compounds from GlaxoSmithKline, yielding an extrapolation rate of 91.43%. Following this, several drug-likeness filters, the finalized MCS, and chemical diversity analyses were applied to select candidate compounds from three datasets for parasitological assays. Using the proposed in silico pipeline, a total of 6,811 drugs, 15,000 chemical compounds, and 1,120 biologically active molecules from the DrugBank, PrintScreen15, and Tocriscreen collections, respectively, were virtually screened. From these, 80 compounds were shortlisted as potential antimalarial candidates. Ultimately, 15 compounds were purchased and tested in vitro against two Plasmodium falciparum strains (3D7 and Dd2). Of these, five drugs (ziprasidone, isradipine, amcinonide, triflupromazine, and anisotropine) and four chemical compounds (NGB 2904, A23187, Otava-7019050991, and Otava-1677649) demonstrated antimalarial activity, with values μ. This approach represents a promising computational tool for the early stages of antimalarial drug discovery. ©The authors ©Springer.
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Challenges and Advances in Digital Processing of Fetal Phonocardiography Signal: A Review
(Springer Nature Switzerland, 2025)
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Gomez-Coronel, Sandra L.
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Renza, Diego
This chapter presents a state-of-the-art review of different investigations focused on Fetal Phonocardiography (fPCG). fPCG signals allow the identification of the fetus’s cardiac alterations during pregnancy through a noninvasive and secure approach. However, fPCG signals present some challenges, for example: very weak signal sources, high levels of noise, source mixing, and significant signal attenuation. This work provides a review of available fPCG datasets and the methods proposed for source separation, extraction, and filtering of fPCG signals, as well as the methods for estimating fetal heart rate (fHR) and detecting fetal Heart Sounds (fHS). Additionally, since it is sometimes necessary to transmit or store fPCG signals, the chapter also discusses signal compression approaches and applications involving fPCG signals. ©The authors ©Springer.

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