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.

Plasmonic Gold Nanoisland Film for Bacterial Theranostics.

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

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with a long latency in the "prodromal" stage, for which there are currently no effective treatments. It is generally accepted that accurate diagnosis and subsequent treatment in the early stages of AD may lead to better therapeutic outcomes. Detection of body fluid biomarkers is an effective strategy for early AD diagnosis. Among them, amyloid β(1–42) (Aβ42), as one of the most important biomarkers of AD, plays a key role in the progression of AD. Currently, electrochemical biosensors offer many advantages in monitoring Aβ42 because they are fast, portable, and capable of on-site analysis. This article provides an overview of the dynamics of Aβ42 levels in biological fluids as AD progresses, followed by a detailed description of the design and analytical performance of various electrochemical sensors. Finally, the challenges of electrochemical sensors for AD diagnosis are discussed, and prospects and trends are outlined. 

Recent advances in electrochemical biosensors for the detection of Aβ42, a biomarker for Alzheimer disease diagnosis

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder with an irreversible, progressive, and untreatable nature that causes progressive deterioration, loss of neurons, and cognitive and behavioral decline. An exponential growth of AD is expected in the forthcoming decades. Studies show that amyloid β (Aβ) and Tau proteins are two key biomarkers of AD. Abnormality in Aβ and Tau levels in biofluids - especially cerebrospinal fluid (CSF) - is directly related to AD incidence and progress. Hence, developing sensitive, affordable, and reliable methods for screening and evaluating these biomarker levels is helpful in early diagnosis, tracking, and treatment of AD. This article scrutinizes the possible effect of Alzheimer's condition on CSF. Following a comprehensive summary of biosensors, the latest developed biosensors are discussed for the detection of Aβ and Tau in CSF samples. 

Cerebrospinal fluid: A specific biofluid for the biosensing of Alzheimer's diseases biomarkers

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.

https://www.mdpi.com/1424-8220/23/9/4184/pdf?version=1682150140

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

The development of minimally invasive and easy-to-use sensor devices is of current interest for ultrasensitive detection and signal recognition of Alzheimer's disease (AD) biomarkers. Over the years, tremendous effort has been made on diagnostic platforms specifically targeting neurological markers for AD in order to replace the conventional, laborious, and invasive sampling-based approaches. However, the sophistication of analytical outcomes, marker inaccessibility, and material validity strongly limit the current strategies towards effectively predicting AD. Recently, with the promising progress in biosensor technology, the realization of a clinically applicable sensing platform has become a potential option to enable early diagnosis of AD and other neurodegenerative diseases. In this review, various types of biosensors, which include electrochemical, fluorescent, plasmonic, photoelectrochemical, and field-effect transistor (FET)-based sensor configurations, with better clinical applicability and analytical performance towards AD are highlighted. Moreover, the feasibility of these sensors to achieve point-of-care (POC) diagnosis is also discussed. Furthermore, by grafting nanoscale materials into biosensor architecture, the remarkable enhancement in durability, functionality, and analytical outcome of sensor devices is presented. Finally, future perspectives on further translational and commercialization pathways of clinically driven biosensor devices for AD are discussed and summarized.

/pdf/clinically-oriented-alzheimer-s-biosensors-expanding-the-2t788tifxk.pdf

Yi June Huang

Papers

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