Health Care Science最新文献

We report the case of a newly diagnosed overweight patient with type 2 diabetes mellitus who achieved diabetes remission using continuous glucose monitoring (CGM), which promotes patient engagement in self-management. Key measures included selection of a patient newly diagnosed with type 2 diabetes mellitus, use of CGM, analysis of glucose trends and contributing factors, and patient guidance in maintaining a health diary and developing personalized self-management strategies (e.g., dietary modification and exercise regimens). Following 6-month CGM-guided self-management, the patient's glycated hemoglobin level decreased from 7.3% at baseline to 6.0% and his body weight decreased from 83 to 77 kg, a 7% reduction; thus, the patient met the criteria for diabetes remission.

Climate change poses a significant threat to global health. It exacerbates existing health challenges and generates new ones. Therefore, innovative solutions to mitigate and adapt to its adverse effects are urgently required. This article explores the potential of digital health technologies to address the challenge posed by climate change-related health issues. It discusses their dual functionality of diminishing the carbon footprint of healthcare services and increasing understanding and governance of climate-sensitive diseases. Notably, with advanced technologies such as Generative medical AI (GMAI) presenting environmental concerns like substantial energy consumption during data processing and the generation of electronic waste, it is essential to underscore the significance of their responsible development and implementation of these technologies. This will ensure that the benefits of digital health technologies can be maximized while minimizing their ecological drawbacks. This study, therefore propose, a framework for leveraging digital health technologies to support climate change adaptation, including disease surveillance, telemedicine, patient support systems, and public awareness campaigns.

Extracellular vesicles (EVs) have emerged as a promising technology for diagnostic and therapeutic applications in clinical settings over the past decade. However, their advancement is hindered by complex technological and regulatory challenges. This review outlines key considerations in the manufacturing process, quality management, and nonclinical evaluation relevant to EV-based drug development. Furthermore, we summarize and compare technical regulatory requirements across major countries to help clarify the regulatory principles governing EV products. Our analysis reveals an ongoing international debate regarding the regulatory review of EVs. Nevertheless, adopting a risk-based classification framework that categorizes EV products as advanced therapeutic drugs is a rational approach. Critical challenges include the development of standardized production protocols, a clearer understanding of therapeutic mechanisms, and resolving complex regulatory issues.

This study proposes a virtual healthcare assistant framework designed to provide support in multiple languages for efficient and accurate healthcare assistance. The system employs a transformer model to process sophisticated, multilingual user inputs and gain improved contextual understanding compared to conventional models, including long short-term memory (LSTM) models. In contrast to LSTMs, which sequence processes information and may experience challenges with long-range dependencies, transformers utilize self-attention to learn relationships among every aspect of the input in parallel. This enables them to execute more accurately in various languages and contexts, making them well-suited for applications such as translation, summarization, and conversational Comparative evaluations revealed the superiority of the transformer model (accuracy rate: 85%) compared with that of the LSTM model (accuracy rate: 65%). The experiments revealed several advantages of the transformer architecture over the LSTM model, such as more effective self-attention, the ability for models to work in parallel with each other, and contextual understanding for better multilingual compatibility. Additionally, our prediction model exhibited effectiveness for disease diagnosis, with accuracy of 85% or greater in identifying the relationship between symptoms and diseases among different demographics. The system provides translation support from English to other languages, with conversion to French (Bilingual Evaluation Understudy score: 0.7), followed by English to Hindi (0.6). The lowest Bilingual Evaluation Understudy score was found for English to Telugu (0.39). This virtual assistant can also perform symptom analysis and disease prediction, with output given in the preferred language of the user.