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Treating ankle fractures in athletes, commonly resulting from training injuries, remains a significant challenge. Current approaches to managing both non-surgical and postoperative foot and ankle disorders have focused on integrating sensory systems into orthotic devices. Recent analyses have identified several gaps in rehabilitation strategies, especially regarding gait pattern reformation during recovery. This work aims to enhance rehabilitation effectiveness for patients with ankle injuries by controlling load distribution and monitoring joint flexion/extension angles, as well as the reactive forces during therapeutic exercises and walking. We developed an exoskeleton device model using SolidWorks 2024 software, based on data from two patients: one healthy and one with an ankle fracture. Pressure measurements in the posterior limb region were taken using the F-Socket system and a custom electromechanical sensor designed by the authors. The collected data were analyzed using the butterfly parameterization method. This research led to the development of an adaptive exoskeleton device that provided pressure distribution data, gait cycle graphs, and a diagram correlating foot angles with the duration of exoskeleton use. The device demonstrated improvement in the patients’ conditions, facilitating a more normalized gait pattern. A reduction in the load applied to the ankle joint was also observed, with the butterfly parameter confirming the device’s correct operation. Full article

(This article belongs to the Section Sensors and Robotics)

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16 pages, 924 KiB

Open AccessArticle

Age- and Gender-Based Tongue Volume Variations on Asymptomatic Patients: A Simplified Approach to Form Baseline Data for Obstructive Sleep Apnea

by Betül Tiryaki Baştuğ

Diagnostics 2025, 15(3), 322; https://doi.org/10.3390/diagnostics15030322 (registering DOI) - 30 Jan 2025

Abstract

Background: Tongue anatomy plays a critical role in airway-related disorders such as obstructive sleep apnea (OSA). Understanding variations in tongue volume across age and gender is essential for refining diagnostic and therapeutic strategies. This study aims to establish baseline data for tongue volume using a simplified geometric approach, addressing the gap in large-scale anatomical assessments, specifically in asymptomatic patients without clinical indications of OSA. Materials and Methods: This retrospective cross-sectional study included 120 asymptomatic patients aged 18–75 years, stratified into three age groups (18–40, 41–60, 61+). Tongue volume was estimated using anterior–posterior length, width, and height measurements from neck CT scans, applying a geometric approximation formula. Statistical analysis, including ANOVA and post hoc tests, was used to evaluate differences across age groups and between genders. Regression analysis examined the influence of age and gender on tongue volume. Results: Tongue volume showed a significant decline with advancing age (p < 0.05), with the 61+ age group exhibiting the smallest volumes. Gender differences were pronounced, with males consistently having larger volumes than females (p < 0.05). Post hoc analyses confirmed significant differences between age groups, and regression analysis indicated that gender was a stronger predictor of tongue volume than age. Conclusions: This study highlights the impact of age and gender on tongue volume, emphasizing the need for demographic-specific approaches in the evaluation and management of airway-related conditions. The simplified measurement method offers a practical solution for large-scale studies, providing baseline data for future research and clinical applications. These findings pave the way for personalized diagnostic thresholds and therapeutic strategies in conditions like OSA. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Oral and Maxillofacial Disease: 2nd Edition)

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23 pages, 614 KiB

Open AccessArticle

Robust Distribution-Aware Ensemble Learning for Multi-Sensor Systems

by Payman Goodarzi, Julian Schauer and Andreas Schütze

Sensors 2025, 25(3), 831; https://doi.org/10.3390/s25030831 (registering DOI) - 30 Jan 2025

Abstract

Detecting distribution and domain shifts is critical in decision-sensitive applications, such as industrial monitoring systems. This paper introduces a novel, robust multi-sensor ensemble framework that integrates principles of automated machine learning (AutoML) to address the challenges of domain shifts and variability in sensor data. By leveraging diverse model architectures, hyperparameters (HPs), and decision aggregation strategies, the proposed framework enhances adaptability to unnoticed distribution shifts. The method effectively handles tasks with various data properties, such as the number of sensors, data length, and information domains. Additionally, the integration of HP optimization and model selection significantly reduces the training cost of ensemble models. Extensive evaluations on five publicly available datasets demonstrate the effectiveness of the proposed framework in both targeted supervised tasks and unsupervised distribution shift detection. The proposed method significantly improves common evaluation metrics compared to single-model baselines. Across the selected datasets, the framework achieves near-perfect test accuracy for classification tasks, leveraging the AutoML approach. Additionally, it effectively identifies distribution shifts in the same scenarios, with an average AUROC of 90% and an FPR95 of 20%. This study represents a practical step toward a distribution-aware front-end approach for addressing challenges in industrial applications under real-world scenarios using AutoML, highlighting the novelty of the method. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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23 pages, 4551 KiB

Open AccessArticle

The Influence of Basil and Cinnamon Essential Oils on Bioactive Sponge Composites of Collagen Reinforced with Hydroxyapatite

by Alina Robu, Madalina Georgiana Albu Kaya, Aurora Antoniac, Durmuș Alpaslan Kaya, Alina Elena Coman, Maria-Minodora Marin, Robert Ciocoiu, Rodica Roxana Constantinescu and Iulian Antoniac

Materials 2025, 18(3), 626; https://doi.org/10.3390/ma18030626 (registering DOI) - 30 Jan 2025

Abstract

The increasing prevalence of acute traumas, surgical wounds, and chronic skin wounds poses significant therapeutic challenges for wound treatment. One of the main concerns in wound care is the danger of infection, which is a significant barrier to healing and a cause of higher morbidity and mortality rates. The emergence of drug-resistant bacterial species is becoming more frequent every day. Antimicrobial dressings have become a viable strategy for wound healing and hospital expense savings. Several factors, such as the wound’s localization and state, microbial load, and cost, must be considered when choosing an appropriate antimicrobial dressing. One of the key goals of wound care is infection avoidance. This study addresses the therapeutic challenges of acute traumas, surgical wounds, and chronic skin wounds, focusing on infection prevention and combating drug-resistant bacterial strains. The research explores the development of novel composite wound dressings incorporating hydroxyapatite, known for its osteoconductive properties, and essential oils from basil and cinnamon, recognized for their antimicrobial activity. The study evaluates the impact of these additives on key properties such as surface morphology, water absorption, enzymatic degradation, and mechanical performance. Antimicrobial tests showed that two experimental samples (A1S and A1BS) exhibited significant activity against Escherichia coli but not on Staphylococcus aureus. The results highlight the dressings’ enhanced antimicrobial properties, mechanical strength, and controlled degradation, making them promising candidates for advanced wound healing. Tailored applications were identified, with each dressing composition offering unique benefits for specific wound-healing scenarios based on the balance between flexibility, structural support, and bioactivity. Full article

(This article belongs to the Special Issue Novel Green Nanotechnologies Applied in Environmental Protection and Health—2nd Edition)

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23 pages, 3658 KiB

Open AccessArticle

A Supply and Demand Framework for Bitcoin Price Forecasting

by Murray A. Rudd and Dennis Porter

J. Risk Financial Manag. 2025, 18(2), 66; https://doi.org/10.3390/jrfm18020066 (registering DOI) - 30 Jan 2025

Abstract

We develop a flexible supply and demand equilibrium framework that can be used to develop pricing models to forecast Bitcoin’s price trajectory based on its fixed, inelastic supply and evolving demand dynamics. This approach integrates Bitcoin’s unique monetary attributes with demand drivers such as institutional adoption and long-term holding patterns. Using the April 2024 halving as a baseline, we explore model scenarios with varying assumptions about growth in adoption and supply-side constraints, calibrated to real-world data. Our findings indicate that institutional and sovereign accumulation can significantly influence price trajectories, with increasing demand intensifying the impact of Bitcoin’s constrained liquidity. Forecasts suggest that modest withdrawals from liquid supply to strategic reserves could lead to substantial price appreciation over the medium term, while higher withdrawal levels may induce volatility due to supply scarcity. These results highlight Bitcoin’s potential as a long-term investment and underline the importance of integrating economic fundamentals into forward-looking portfolio strategies. Our framework provides flexibility for testing different market scenarios, demand curve functional forms, and parameterizations, offering a tool for investors and policymakers considering Bitcoin’s role as a strategic asset. By advancing a fundamentals-based approach, this study contributes to the broader understanding of how Bitcoin’s supply–demand dynamics influence market behavior. Full article

(This article belongs to the Special Issue Blockchain Technologies and Cryptocurrencies)

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33 pages, 9955 KiB

Open AccessArticle

Thermal Performance Investigation in Historical Urban Neighborhoods Using ENVI-Met Simulation Software

by Stergios Koutsanitis, Maria Sinou, Zoe Kanetaki, Evgenia Tousi and George Varelidis

Land 2025, 14(2), 284; https://doi.org/10.3390/land14020284 (registering DOI) - 30 Jan 2025

Abstract

Urban heritage areas are characterized by unique architectural and cultural elements, often coupled with specific challenges such as vulnerability to climate change and urban heat islands (UHIs). Investigating thermal performance at the neighborhood scale is crucial for preserving these areas while enhancing thermal comfort and sustainability. The aim of this research is to prove that the application of passive cooling techniques and urban green spaces can reduce the urban temperature and upgrade the conditions of thermal comfort, even in densely populated areas with small urban void spaces. ENVI-Met, a microclimate modeling software for evaluating the thermal performance of heritage urban neighborhoods, is applied in order to assess current thermal conditions, identify hotspots, perform simulations, and propose mitigation strategies to improve thermal comfort while preserving the architectural and cultural integrity of these areas. The test bed of this study is a historical urban area in central Athens, “Academia Platonos”. The methodology is mainly based on the design of different parametric scenarios for the study area, by integrating specific parameters that characterize the area of Academia Platonos (elevation distribution, materials, vegetation, etc.) and the microclimatic simulations of the area, designed in the digital environment of ENVI-Met. Five scenarios are implemented and studied in the study area, four of which are based on the existing situation of the study area, either by changing the construction materials of the built environment (passive cooling through cool material techniques) or by enhancing the area with vegetation. One of the most important findings of this study is that the use of plants with a high foliage density is more effective in reducing air temperature than the selection of species with sparse foliage. Full article

(This article belongs to the Section Urban Contexts and Urban-Rural Interactions)

16 pages, 3560 KiB

Open AccessArticle

Using Human-Centered Design in Community-Based Public Health Research: Insights from the ECHO Study on COVID-19 Vaccine Hesitancy in Montreal, Canada

by Krystelle Marie Abalovi, Geneviève Fortin, Maryam Parvez, Joyeuse Senga, Joe Abou-Malhab, Cat Tuong-Nguyen, Caroline Quach, Ashley Vandermorris, Kate Zinzser and Britt McKinnon

Int. J. Environ. Res. Public Health 2025, 22(2), 198; https://doi.org/10.3390/ijerph22020198 (registering DOI) - 30 Jan 2025

Abstract

(1) Background: This study used human-centered design (HCD) within a community-based research project to collaboratively develop local strategies aimed at enhancing COVID-19 vaccine confidence among children and youth. (2) Methods: HCD projects were carried out between December 2021 and August 2022 by four community-based design (CBD) teams in Montreal, Canada. The CBD teams were composed of parent and youth community members, public health and social science researchers, and HCD specialists. Process evaluation data, collected from the CBD team members through focus group discussions and written questionnaires, were used to reflect on the use of HCD in this project. (3) Results: The CBD teams designed and implemented projects addressing factors they identified as contributing to COVID-19 vaccine hesitancy for children and youth in their communities, including misinformation, lack of trust, social inequities, and resistance to pandemic-related restrictions. The CBD team members appreciated many aspects of the HCD approach, especially the values it stands for, such as empathy, co-creation, and collaboration. HCD and public health specialists described some tension between the different disciplinary approaches. (4) Conclusions: HCD holds promise for addressing complex public health issues, though further exploration of strategies for integrating HCD within established models of community-based public health research is needed. Full article

(This article belongs to the Special Issue Community Interventions in Health Disparities)

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30 pages, 1789 KiB

Open AccessReview

Retinal Pigment Epithelium Under Oxidative Stress: Chaperoning Autophagy and Beyond

by Yuliya Markitantova and Vladimir Simirskii

Int. J. Mol. Sci. 2025, 26(3), 1193; https://doi.org/10.3390/ijms26031193 (registering DOI) - 30 Jan 2025

Abstract

The structural and functional integrity of the retinal pigment epithelium (RPE) plays a key role in the normal functioning of the visual system. RPE cells are characterized by an efficient system of photoreceptor outer segment phagocytosis, high metabolic activity, and risk of oxidative damage. RPE dysfunction is a common pathological feature in various retinal diseases. Dysregulation of RPE cell proteostasis and redox homeostasis is accompanied by increased reactive oxygen species generation during the impairment of phagocytosis, lysosomal and mitochondrial failure, and an accumulation of waste lipidic and protein aggregates. They are the inducers of RPE dysfunction and can trigger specific pathways of cell death. Autophagy serves as important mechanism in the endogenous defense system, controlling RPE homeostasis and survival under normal conditions and cellular responses under stress conditions through the degradation of intracellular components. Impairment of the autophagy process itself can result in cell death. In this review, we summarize the classical types of oxidative stress-induced autophagy in the RPE with an emphasis on autophagy mediated by molecular chaperones. Heat shock proteins, which represent hubs connecting the life supporting pathways of RPE cells, play a special role in these mechanisms. Regulation of oxidative stress-counteracting autophagy is an essential strategy for protecting the RPE against pathological damage when preventing retinal degenerative disease progression. Full article

(This article belongs to the Special Issue Redox Homeostasis and Oxidative Stress in Human Metabolism and Disease)

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30 pages, 664 KiB

Open AccessArticle

The Need for a Quality Strategy in Metal Additive Manufacturing Technology

by Cindy Sithole, Athena Jalalian, Sipke Hoekstra and Ian Gibson

J. Manuf. Mater. Process. 2025, 9(2), 43; https://doi.org/10.3390/jmmp9020043 (registering DOI) - 30 Jan 2025

Abstract

This study investigates the need for a quality strategy in metal additive manufacturing (AM) technology effective for batch production. We carried out two surveys aimed at gathering data from industry experts to understand the challenges and the requirements of quality strategies in metal AM. Survey 1 investigated general quality aspects in metal technology, revealing key areas where quality strategies are required, such as process control, material consistency, and post-processing. It also highlighted challenges that directly impact part quality, including process variability, material inconsistencies, and surface finish issues. Survey 2, focused on batch production, showed that 75% of participants strongly agreed with the need for a specific quality strategy to address challenges specific to metal AM. The respondents highlighted critical processes requiring quality strategies, including powder quality, process optimisation, and defect detection, while identifying ongoing issues with process variability and material inconsistency. Both surveys indicate a need for a standardised and effective quality strategy to enhance production consistency, efficiency, and regulatory compliance. Regardless of a limited sample size of 59 respondents, these results emphasise the need for improved quality strategies in metal AM to reduce defects effectively, meet customer expectations, and ensure scalable production. This study provides insights into the strategic development of quality strategies significant for advancing metal AM technology. Full article

17 pages, 7119 KiB

Open AccessArticle

Cetuximab-Immunoliposomes Loaded with TGF-β1 siRNA for the Targeting Therapy of NSCLC: Design, and In Vitro and In Vivo Evaluation

by Yanan Shi, Houqian Zhang, Hao Chen, Jianwei Guo, Ranran Yuan, Yu Tian, Quanlin Xin, Zhen Mu, Yuping Tao, Yongchao Chu, Aiping Wang, Zhiwen Zhang, Jingwei Tian and Hongbo Wang

Int. J. Mol. Sci. 2025, 26(3), 1196; https://doi.org/10.3390/ijms26031196 (registering DOI) - 30 Jan 2025

Abstract

Transforming growth factor-β₁ (TGF-β₁) promotes the growth and metastasis of lung cancer cells. Therefore, TGF-β₁ siRNA (siTGF-β₁) gene therapy was introduced to inhibit the expression of TGF-β₁ at the nucleic acid level to avert tumor growth and metastasis. However, the delivery of naked siRNA is typically restricted by a short half-life in vivo, difficulties in delivery in vivo, and safety issues. Using siTGF-β₁ as a model drug, we established an actively targeted immunoliposome delivery system to investigate the role of siTGF-β₁ in non-small-cell lung cancer (NSCLC). The results showed that the constructed immune liposomes were in a position to deliver siTGF-β₁ to tumor cells, thus achieving a series of effects such as improving the poor stability and short half-life of naked siRNA. RNA interference of siTGF-β₁ reduced the cell viability, growth, and migration potential of human non-small cell lung cancer cells (A549). Moreover, in an A549 tumor-bearing nude mouse model, siTGF-β₁ transfection markedly reduced tumor growth and tumor volume. Inhibiting TGF-β₁ diminished cancer cell viability and migration and promoted apoptosis in NSCLC, as confirmed by the findings of this study. Therefore, targeting siTGF-β₁ with immunoliposomes may be a new therapeutic strategy for treating non-small-cell lung cancer. Full article

(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)

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25 pages, 14810 KiB

Open AccessArticle

Spatiotemporal Coupling of New-Type Urbanization and Ecosystem Services in the Huaihe River Basin, China: Heterogeneity and Regulatory Strategy

by Muyi Huang, Qin Guo, Guozhao Zhang, Yuru Tang and Xue Wu

Land 2025, 14(2), 286; https://doi.org/10.3390/land14020286 (registering DOI) - 30 Jan 2025

Abstract

Strengthening the exploration of synergistic promotion mechanisms between ecosystem services (ESs) and new urbanization is of great significance for watershed development. In this work, we revealed the evolution mechanism of coupling coordination development degree (CCD) between ESs and new urbanization and its driving factors in the Huaihe River Basin (HRB) from 1980 to 2020 using a combination of the CCD model, Exploratory Spatial Data Analysis (ESDA) method, and GeoDetector model. Additionally, we employed the PLUS model to investigate multi-scenario simulations. The results demonstrate that ESs showed a decline initially, followed by an increase, while the urbanization index showed consistent annual growth over the four decades. Furthermore, the CCD between the ESs and urbanization showed a yearly optimization trend. The CCD demonstrated notable spatial clustering characteristics, with factors such as precipitation, distance from water body, elevation, and per area GDP emerged as the primary drivers. Under scenarios of ecological protection, comprehensive development, and natural protection, the value of ESs from 2020 to 2050 maintained an upward trend; however, it fell with the decrease under the scenario of cropland protection. These research findings offer valuable decision-making support for the differentiated regulation of ecosystem functions and promotion of high-quality urbanization development in the HRB. Full article

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32 pages, 17900 KiB

Open AccessArticle

Generalization Enhancement Strategies to Enable Cross-Year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples

by Sam Khallaghi, Rahebeh Abedi, Hanan Abou Ali, Hamed Alemohammad, Mary Dziedzorm Asipunu, Ismail Alatise, Nguyen Ha, Boka Luo, Cat Mai, Lei Song, Amos Olertey Wussah, Sitian Xiong, Yao-Ting Yao, Qi Zhang and Lyndon D. Estes

Remote Sens. 2025, 17(3), 474; https://doi.org/10.3390/rs17030474 (registering DOI) - 30 Jan 2025

Abstract

Mapping agricultural fields using high-resolution satellite imagery and deep learning (DL) models has advanced significantly, even in regions with small, irregularly shaped fields. However, effective DL models often require large, expensive labeled datasets, which are typically limited to specific years or regions. This restricts the ability to create annual maps needed for agricultural monitoring, as changes in farming practices and environmental conditions cause domain shifts between years and locations. To address this, we focused on improving model generalization without relying on yearly labels through a holistic approach that integrates several techniques, including an area-based loss function, Tversky-focal loss (TFL), data augmentation, and the use of regularization techniques like dropout. Photometric augmentations helped encode invariance to brightness changes but also increased the incidence of false positives. The best results were achieved by combining photometric augmentation, TFL, and Monte Carlo dropout, although dropout alone led to more false negatives. Input normalization also played a key role, with the best results obtained when normalization statistics were calculated locally (per chip) across all bands. Our U-Net-based workflow successfully generated multi-year crop maps over large areas, outperforming the base model without photometric augmentation or MC-dropout by 17 IoU points. Full article

(This article belongs to the Special Issue Remote Sensing and Associated Artificial Intelligence in Agricultural Applications Second Edition)

18 pages, 2114 KiB

Open AccessArticle

Microplate Reader–TLC–HPLC–UPLC-MS: A Rapid Screening Strategy for Isoliquiritigenin-Transforming Bacteria

by Chuanhong Nie, Ruiqi Liu, Songhao Yang, Panpan Li and Jing Zhang

Sensors 2025, 25(3), 827; https://doi.org/10.3390/s25030827 (registering DOI) - 30 Jan 2025

Abstract

This article primarily develops a new technology for the rapid large-scale screening of isoliquiritigenin-transforming strains based on the MTHM (microplate reader–TLC–HPLC–UPLC-MS) method. ISO is a chalcone compound with potential pharmacological activity, and its rich substitution sites on the benzene ring provide a solid foundation for structural modification and drug development. This study screened approximately 1500 strains and employed a microplate reader, thin-layer chromatography, high-performance liquid chromatography, and mass spectrometry to verify the transformation products, identifying 15 strains with significant transformation capabilities. This study demonstrates that the optimized MTHM method is efficient and reliable, capable of rapidly detecting subtle structural changes in flavonoids before and after microbial transformation. During the transformation process, bioactive flavonoid compounds, such as amentoflavone and 5′-methoxyflavonoid, were discovered. Additionally, the experiments revealed that Czapek medium, modified Martin medium, and LB medium exhibited high efficiency in screening transforming strains. This research provides new technical approaches for ISO structural optimization and drug development while highlighting the important application potential of microbial transformation in natural product development. Future studies could further explore the metabolic potential of these strains, optimize transformation conditions, and promote the application of ISO in the medical field. Full article

(This article belongs to the Section Chemical Sensors)

28 pages, 4793 KiB

Open AccessArticle

Deep Learning-Based Land Cover Extraction from Very-High-Resolution Satellite Imagery for Assisting Large-Scale Topographic Map Production

by Yofri Furqani Hakim and Fuan Tsai

Remote Sens. 2025, 17(3), 473; https://doi.org/10.3390/rs17030473 (registering DOI) - 30 Jan 2025

Abstract

The demand for large-scale topographic maps in Indonesia has significantly increased due to the implementation of several government initiatives that necessitate the utilization of spatial data in development planning. Currently, the national production capacity for large-scale topographic maps in Indonesia is 13,000 km²/year using stereo-plotting/mono-plotting methods from photogrammetric data, Lidar, high-resolution satellite imagery, or a combination of the three. In order to provide the necessary data to the respective applications in a timely manner, one strategy is to only generate critical layers of the maps. One of the topographic map layers that is often needed is land cover. This research focuses on providing land cover to support the accelerated provision of topographic maps. The data used are very-high-resolution satellite images. The method used is a deep learning approach to classify very-high-resolution satellite images into land cover data. The implementation of the deep learning approach can advance the production of topographic maps, particularly in the provision of land cover data. This significantly enhances the efficiency and effectiveness of producing large-scale topographic maps, hence increasing productivity. The quality assessment of this study demonstrates that the AI-assisted method is capable of accurately classifying land cover data from very-high-resolution images, as indicated by the Kappa values of 0.81 and overall accuracy of 86%, respectively. Full article

(This article belongs to the Special Issue Advances in Deep Learning Approaches in Remote Sensing)

15 pages, 36491 KiB

Open AccessArticle

Impact of the 2024 Noto Peninsula Earthquake on Nutritional Status in Residents of an Integrated Medical and Long-Term Care Facility: A Descriptive Study

by Yoji Kokura

Nutrients 2025, 17(3), 506; https://doi.org/10.3390/nu17030506 (registering DOI) - 30 Jan 2025

Abstract

Background/Objectives: The dietary changes experienced by residents in long-term care facilities (LTCFs) following an earthquake are poorly understood. This study aimed to examine variations in nutritional status among residents of an Integrated Facility for Medical and Long-term Care (IFMLC), a particular type of Japanese LTCF, after the 2024 Noto Peninsula Earthquake. Methods: This descriptive study was conducted at the single IFMLC. A total of 115 residents living at the facility on 1 January 2024, at the time of the earthquake, were recruited for the study. The focus was the body weight and skeletal muscle mass changes observed before and after the earthquake. The observation period lasted for three months following the earthquake. Results: Water outage persisted for over a month, making dishwashing impossible and leading to an extended reliance on disposable dishes with limited capacity. This situation consequently reduced the variety and volume of meal options and overall energy intake meals. Residents’ body weight significantly decreased 3 months after the earthquake, and the prevalence of weight loss and skeletal muscle mass loss was particularly high in residents with normal swallowing function. To address nutritional deficiencies post-earthquake, the registered dietitian enhanced energy sufficiency through food fortification, oral nutritional supplements, and pre-prepared ready-to-hang liquid formulas. Conclusions: To prevent further weight and skeletal muscle mass reduction among IFMLC residents, providing ample water, and a disaster manual that can be used even with limited resources is essential. Furthermore, preparing for disasters by stockpiling foods and implementing strategies to enhance energy sufficiency is crucial. Full article

(This article belongs to the Special Issue Nutritional Approaches and Novel Dietary Strategies to Manage Sarcopenia)

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