Clinically oriented Alzheimer's biosensors: expanding the horizons towards point-of-care diagnostics and beyond

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Metal-organic frameworks: A promising option for the diagnosis and treatment of Alzheimer's disease

Beta-amyloid (Aβ) peptide is one of the main characteristic biomarkers of Alzheimer's disease (AD). Previous clinical investigations have proposed that unusual concentrations of this biomarker in cerebrospinal fluid, blood, and brain tissue are closely associated with the AD progression. Therefore, the critical point of early diagnosis, prevention, and treatment of AD is to monitor the levels of Aβ. In view of the potential of metal-organic frameworks (MOFs) for diagnosing and treating the AD, much attention has been focused in recent years. This review discusses the latest advances in the applications of MOFs for the early diagnosis of AD via fluorescence and electrochemiluminescence (ECL) detection of AD biomarkers, fluorescence detection of the main metal ions in the brain (Zn²⁺, Cu²⁺, Mn²⁺, Fe³⁺, and Al³⁺) in addition to magnetic resonance imaging (MRI) of the Aβ plaques. The current challenges and future strategies for translating the in vitro applications of MOFs into in vivo diagnosis of the AD are discussed.

Plasmonic Gold Nanoisland Film for Bacterial Theranostics

Plasmonic nanomaterials have been intensively explored for applications in biomedical detection and therapy for human sustainability. Herein, plasmonic gold nanoisland (NI) film (AuNIF) was fabricated onto a glass substrate by a facile seed-mediated growth approach. The structure of the tortuous gold NIs of the AuNIF was demonstrated by scanning electron microscopy and energy-dispersive X-ray spectroscopy. Based on the ultraviolet-visible spectrum, the AuNIF revealed plasmonic absorption with maximum intensity at 624 nm. With the change to the surface topography created by the NIs, the capture efficiency of Escherichia coli (E. coli) by the AuNIF was significantly increased compared to that of the glass substrate. The AuNIF was applied as a surface-enhanced Raman scattering (SERS) substrate to enhance the Raman signal of E. coli. Moreover, the plasmonic AuNIF exhibited a superior photothermal effect under irradiation with simulated AM1.5 sunlight. For photothermal therapy, the AuNIF also displayed outstanding efficiency in the photothermal killing of E. coli. Using a combination of SERS detection and photothermal therapy, the AuNIF could be a promising platform for bacterial theranostics.