Extracellular vesicles (EVs), including exosomes, with nanoscale sizes, biological origins, various functions, and unique lipid and protein compositions have been introduced as versatile tools for diagnostic and therapeutic medical applications. Numerous studies have reported the importance of the lipid composition of EVs and its influence on their mechanism of action. For example, changes in the lipidomic profile of EVs have been shown to influence the progression of various diseases, including ovarian malignancies and prostate cancer. In this review, we endeavored to examine differences in the lipid content of EV membranes derived from different cell types to characterize their capabilities as diagnostic tools and treatments for diseases like cancer and Alzheimer’s disease. We additionally discuss designing functionalized vesicles, whether synthetically by hybrid methods or by changing the lipid composition of natural EVs. Lastly, we provide an overview of current and potential biomedical applications and perspectives on the future of this growing field.

Alzheimer's disease (AD) is one of the main causes of dementia worldwide, whereby neuronal death or malfunction leads to cognitive impairment in the elderly population. AD is highly prevalent, with increased projections over the next few decades. Yet current diagnostic methods for AD occur only after the presentation of clinical symptoms. Evidence in the literature points to potential mechanisms of AD induction beginning before clinical symptoms start to present, such as the formation of amyloid beta (Aβ) extracellular plaques and neurofibrillary tangles (NFTs). Biomarkers of AD, including Aβ₄₀, Aβ₄₂, and tau protein, amongst others, show promise for early AD diagnosis. Additional progress is made in the application of biosensing modalities to measure and detect significant changes in these AD biomarkers within patient samples, such as cerebral spinal fluid (CSF) and blood, serum, or plasma. Herein, a comprehensive review of the emerging nano‐biomaterial approaches to develop biosensors for AD biomarkers’ detection is provided. Advances, challenges, and potential of electrochemical, optical, and colorimetric biosensors, focusing on nanoparticle‐based (metallic, magnetic, quantum dots) and nanostructure‐based biomaterials are discussed. Finally, the criteria for incorporating these emerging nano‐biomaterials in clinical settings are presented and assessed, as they hold great potential for enhancing early‐onset AD diagnostics.

Surface‐enhanced Raman spectroscopy (SERS) has emerged as a powerful tool for biomedical diagnosis, combining heightened sensitivity with molecular precision. The integration of artificial intelligence (AI) and machine learning (ML) has further elevated its capabilities, refining data interpretation, pattern prediction, and bolstering diagnostic accuracy. This review chronicles advancements in SERS diagnostics, emphasizing the collaboration between ML and innovative nanostructures, substrates, and nanoprobes for SERS enhancement. The breakthroughs are highlighted in SERS‐based point‐of‐care techniques and the nuanced detection of key biomarkers, from nucleic acids to proteins and metabolites. The article also addresses prevailing challenges, such as the need for standardized SERS methodologies and optimized platforms. Moreover, the potential of portable SERS systems is discussed for clinical deployment, as well as current efforts and challenges in clinical trials. In essence, this review positions the fusion of nanoengineering, AI, ML, and SERS as the frontier for next‐generation biomedical diagnostics.

Semiconductors have great potential as surface-enhanced Raman scattering (SERS) substrates due to their excellent physiochemical properties. However, they provide low signal enhancements relative to their plasmonic counterparts, which necessitates innovation in their synthesis and application. Substitutional atomic doping is proposed to improve SERS enhancement by controlling electronic properties, such as the band gap. In this work, zinc oxide (ZnO) nanoparticles were synthesized by co-precipitation and doped with magnesium (Mg) at concentrations ranging from 2–10%. Nanoparticle morphology and size were obtained by scanning electron microscopy (SEM). Elemental composition and chemical states were determined using X-ray photoelectron spectroscopy (XPS). Optical properties were obtained with a UV-vis spectrophotometer, while a Raman spectrometer was used to acquire Raman signal enhancements. Stability was assessed by UV-vis spectroscopy, while cytotoxicity was evaluated by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The results showed that the absorption edge of Mg-doped ZnO nanoparticles was red-shifted compared to pure ZnO nanoparticles. The band gap decreased (3.3–3.01 eV) with increasing Mg doping, while the highest Raman enhancement was observed at 2% doping. No significant cytotoxic effects were observed at low concentrations (3–12 μg/mL). Overall, this study provides evidence for the tunability of ZnO substrates and may serve as a platform for applications in molecular biosensing.

Extracellular vesicles (EVs) have emerged as promising tools in diagnostics and therapy for chronic diseases, including cancer and Alzheimer's. Small EVs, also called exosomes, are lipid‐bound particles (≈30–150 nm) that play a role in healthy and pathophysiological interactions, including intercellular communication, by transporting bioactive molecules, including proteins, lipids, and nucleic acids. Their ability to cross biological barriers, such as the blood‐brain barrier, makes them ideal candidates for targeted therapeutic interventions. In the context of chronic diseases, exosomes can be engineered to deliver active agents, including small molecules and siRNAs to specific target cells, providing a novel approach to precision medicine. Moreover, exosomes show great promise as repositories for diagnostic biomarkers. Their cargo can reflect the physiological and pathological status of the parent cells, making them valuable indicators of disease progression and response to treatment. This paper presents a comprehensive review of the application of exosomes in four chronic diseases: cancer, cardiovascular disease, neurodegenerative disease, and orthopedic disease, which significantly impact global public health due to their high prevalence and associated morbidity and mortality rates. Furthermore, the potential of exosomes as valuable tools for theranostics and disease management is highlighted. Finally, the challenges associated with exosomes and their demonstrated potential for advancing future nanomedicine applications are discussed.

Extracellular vesicles (EVs) are membrane‐bound vesicles secreted by various cell types into the extracellular space and play a role in intercellular communication. Their molecular cargo varies depending on the cell of origin and its functional state. As a result, EVs serve as representatives of their parent cells and reservoirs of disease biomarkers. Their presence in diverse bodily fluids has fueled interest in their potential for biomarker discovery and signaling research. Advances in mass spectrometry, high‐throughput sequencing, and bioinformatics have expanded the molecular characterization of EVs, while emerging tools, including artificial intelligence (AI), image‐based systems biology, and curated EV repositories, are driving exploration of disease‐associated molecular signatures. Omics technologies generate extensive, multidimensional datasets that can be analyzed using bioinformatics techniques in conjunction with traditional statistical methods. Systems‐based approaches, such as network analysis, computer modeling, and AI, are particularly effective for interpreting these complex datasets. However, their application in EV studies requires a solid understanding of EV‐specific biological principles and analytical tools to ensure accuracy. By leveraging these analytical strategies, systems biology aims to unravel the intricate organization of biological processes, providing insights into how EVs interact within cells and organisms, and how they can be utilized to advance disease diagnostics, monitor disease progression, and develop novel therapeutic strategies. This review aims to elucidate the state‐of‐the‐art in EV research, integrating multiomics, modeling, and disease‐specific insights. EV‐specific data repositories and the future of EVs in systems biology will also be highlighted.