Progress in molecular biology and translational science最新文献

Non-coding RNAs (ncRNAs) play a crucial role in various cellular processes, challenging the earlier notion that non-coding regions of the genome are merely "junk DNA." Advances in molecular biology and sequencing technologies have revealed that these regions contain essential genetic elements, including ncRNAs, which influence gene regulation and other biological functions. As the significance of ncRNAs in human health and disease becomes increasingly clear, researchers have developed methods to identify, annotate, and classify these molecules into distinct types. Additionally, a growing interest has emerged in studying genetic variations within ncRNA genes and their potential implications in disease development. Understanding these variations can provide insights into how ncRNAs contribute to gene regulation and cellular function. This article explores the current landscape of ncRNA research, highlighting the latest techniques for detecting and identifying different classes of ncRNAs. Furthermore, it discusses computational tools and databases available for annotating ncRNAs and analyzing their functions across various biological processes. By integrating experimental and computational approaches, researchers can gain a deeper understanding of ncRNA roles, paving the way for potential therapeutic applications.

Wearable biosensors enable continuous, non-invasive monitoring of cardiovascular parameters, including heart rate, heart rate variability, blood pressure, and thoracic fluid status. Utilizing technologies such as photoplethysmography, electrocardiography and piezoelectric sensing, these devices capture essential haemodynamic and arrhythmias data, facilitating early diagnosis and intervention. Advanced indices, such as pulse wave velocity and heart sounds, provide insights into arterial stiffness and valvular function, enhancing clinical assessment. Artificial intelligence and machine learning further refine data interpretation, generating predictive insights for personalized health. By integrating multiple sensors, these devices provide a comprehensive, individualized assessment, advancing personalized health and real-time disease management while empowering individuals with greater control over their health and enhancing data collection for improved diagnostics and treatment.

Wearable biosensors have emerged as transformative instruments for continuous, non-invasive health monitoring, providing real-time analysis of biomarkers in biofluids such as sweat, interstitial fluid, and saliva. This chapter offers a comprehensive overview of the pivotal role of biomaterials in the design and functionality of wearable biosensors. It examines the selection criteria for biocompatible materials, emphasizing properties such as flexibility, stretchability, conductivity, and long-term stability. The discussion categorizes advanced materials, including hydrogels, polyurethanes, carbon-based nanomaterials, metallic nanoparticles, and microneedles, and evaluates their applications in biosensing platforms for glucose, pH, and metal ion detection. Through case studies and figure-integrated explanations, the chapter highlights innovations such as smart hydrogel contact lenses, self-powered alcohol biosensors, and closed-loop microneedle patches for autonomous insulin delivery. It further explores key challenges, including biofluid variability, sensor biocompatibility, and the correlation of biofluid biomarkers with blood concentrations. Finally, the chapter underscores future directions involving AI integration, federated learning, and next-generation biomaterials like biodegradable polymers and stretchable composites. By bridging materials science with digital health technologies, wearable biosensors are poised to revolutionize personalized medicine, enabling early diagnosis, disease prevention, and optimized therapeutic interventions.

Wearable biosensors have emerged as game changers in healthcare, particularly for cancer detection and monitoring. Continuously sensing physiological and biochemical markers improves cancer diagnosis and treatment significantly. Conventional diagnostic methods, such as biopsies and imaging, are invasive, expensive, and logistically challenging, limiting their frequency and accessibility. Over the past decade, advances in microfluidics and surface engineering have expanded the capabilities of wearable biosensors. Readily accessible body fluids, such as sweat, saliva, tears, and interstitial fluid (ISF), are now recognized as valuable, non-invasive sources of tumor biomarkers. These fluids provide critical insights into tumor progression and therapy response, offering a patient-friendly alternative to traditional diagnostics. The integration of cutting-edge materials, advanced sensing technologies, and microfluidics has dramatically enhanced the sensitivity and specificity of wearable biosensors. This progress paves the way for personalized and preventive healthcare, improving patient convenience and reducing clinical visits and invasive procedures. This chapter explores the fundamental design principles, practical applications, and existing challenges of wearable biosensors. By addressing these issues, wearable biosensors can play a transformative role in early cancer detection and personalized treatment, ultimately improving patient outcomes.

Wearable technology has a promising potential to transform users' lives by continuously collecting data and providing convenient services on demand. Yet, there is also a large potential to breach users' privacy compromising the confidentiality of sensitive data. The lack of privacy regulations is caused by a limited understanding of how to control data collection, access and sharing. Also, the lack of privacy regulations for wearables hinders effective controls for data sharing. This issue is further complicated due to the lack of policies governing the use of wearables in public places, the novelty of such devices and their services, as well as the challenges to identify the threats to data misuse. In this chapter, we examine the landscape of wearable technology focusing on user privacy and data protection. We describe the implications of data disclosure, discussing risks and benefits involved. We provide scenarios where these issues arise and discuss technical solutions including data anonymization and access controls. We review solutions designed to protect users' privacy rights, evaluating their effectiveness and limitations. Lastly, we present solutions created to protect users' privacy. The chapter concludes by outlining a roadmap for future research and development in wearable privacy. We highlight open questions for next-generation devices and provide practical recommendations for end users to safeguard their privacy. Key areas for future research include enhancing user consent mechanisms and improving transparency in data practices. By addressing these gaps, we aim to better protect user privacy in an increasingly interconnected world.

Biosensing technologies, including wearables, ingestibles, and implantables, are transforming modern healthcare by enabling real-time monitoring and precise personalized treatment. As these technologies advance, ensuring their safety, efficacy, and ethical deployment remains paramount. This chapter explores the regulatory framework that governs biosensing devices in the United States, detailing premarket approval pathways, postmarket surveillance, and emerging regulatory incentives that balance innovation with patient protection. The discussion extends beyond compliance to address critical ethical considerations, including data privacy, informed consent, patient data ownership, and equitable access. By examining case studies of successful and failed biosensing devices, this chapter provides a comprehensive analysis of the evolving landscape, offering guidance for manufacturers, policymakers, and healthcare providers. A proactive approach to regulation and ethics will be essential to ensuring biosensing technologies enhance healthcare outcomes while maintaining public trust.

Artificial intelligence (AI) has emerged as a powerful tool in molecular biology, significantly advancing the study of long non-coding RNAs (lncRNAs). This chapter examines the application of AI techniques, including machine learning (ML) and deep learning (DL), in predicting lncRNA functions, identifying disease associations, and annotating protein interactions. The discussion covers key methodologies such as supervised and unsupervised ML algorithms, recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer-based models. A detailed description of a deep learning pipeline for functional annotation of lncRNA-binding proteins (lncRBPs) is provided, highlighting challenges in dataset preparation, model design, and usability. Integrating experimental validation with computational predictions is emphasized as a pathway to bridge AI-driven insights with biological understanding.

Millions of people are affected worldwide by autoimmune disorders, including rheumatoid arthritis, lupus, multiple sclerosis and others, whose immune attacks also do not occur in a straightforward or stable manner. Dynamic changes of disease progression and treatment efficacy are not regularly monitored or diagnosed using conventional diagnostic and monitoring techniques. Wearable biosensors (WBS) provide a transformational solution for continuous, minimally invasive biomarker monitoring including cytokines, autoantibodies and acute phase proteins. This paper reviews the pathophysiology underlying WBS in autoimmune disease management, the relevant biomarkers, and the recent advances in biosensor technology. Sensitivity, specificity, stability and biocompatibility issues have also been addressed. Towards the future, our vision includes future prospects in big data analytics and artificial intelligence for predictive modeling, material science advancements for improving sensor performance, noninvasive monitoring technologies, and omics integration for a complete health status of the patient. These innovations have the ability to affect patients suffering from autoimmune disease in a personalized, data driven, and real time manner.

Non-coding RNAs (ncRNAs) regulate gene expression by acting as transcriptional modulators, either via binding to specific stretches of DNA sequences/proteins, or by interacting with transcription factors. Previous genome scans often dismissed ncRNAs as transcriptional noise; recent studies have established them as critical regulators in cellular function, epigenetic alterations, and RNA stability, far exceeding beyond their originally perceived passive roles. In this chapter, we explore the regulatory functions of ncRNAs, the complex interplay between coding and non-coding gene networks, and review available in silico tools/methods for ncRNA identification, annotation and analysis. Moreover, we highlight key studies and case examples demonstrating how ncRNAs contribute to disease pathophysiology particularly focusing on cancer, and neurological disorders illustrating their functional and clinical significance.

Daptomycin is a cyclic lipodepsipeptide antibiotic that is a mainstay for the treatment of serious infections caused by Gram-positive bacteria, including methicillin-resistant Streptococcus aureus and vancomycin resistant enterococci. It is one of the so-called last-resort antibiotics that are used to tackle life-threatening infections that do not respond to first-line treatments. However, resistance to daptomycin is eroding its clinical efficacy motivating the design and/or discovery of analogues that overcome resistance. The strategy of antibiotic analogue synthesis has been used to overcome bacterial resistance to many classes of antibiotics such as the β-lactams. Pursuing this strategy with daptomycin requires a detailed understanding of daptomycin's action mechanism and synthesis. Here, we discuss the action mechanism of daptomycin in a holistic manner and expand this discussion to rationalize conferred modes of resistance. Synthetic efforts, both chemical and biological, are discussed in detail and the structure-activity relationship emanating from these works is distilled into a usable model that can guide the design of new daptomycin analogues.