The technological improvement in the field of physics, chemistry, electronics, nanotechnology, biology, and molecular biology has contributed to the development of various electrochemical biosensors with a broad range of applications in healthcare settings, food control and monitoring, and environmental monitoring. In the past, conventional biosensors that have employed bioreceptors, such as enzymes, antibodies, Nucleic Acid (NA), etc., and used different transduction methods such as optical, thermal, electrochemical, electrical and magnetic detection, have been developed. Yet, with all the progresses made so far, these biosensors are clouded with many challenges, such as interference with undesirable compound, low sensitivity, specificity, selectivity, and longer processing time. In order to address these challenges, there is high need for developing novel, fast, highly sensitive biosensors with high accuracy and specificity. Scientists explore these gaps by incorporating nanoparticles (NPs) and nanocomposites (NCs) to enhance the desired properties. Graphene nanostructures have emerged as one of the ideal materials for biosensing technology due to their excellent dispersity, ease of functionalization, physiochemical properties, optical properties, good electrical conductivity, etc. The Integration of the Internet of Medical Things (IoMT) in the development of biosensors has the potential to improve diagnosis and treatment of diseases through early diagnosis and on time monitoring. The outcome of this comprehensive review will be useful to understand the significant role of graphene-based electrochemical biosensor integrated with Artificial Intelligence AI and IoMT for clinical diagnostics. The review is further extended to cover open research issues and future aspects of biosensing technology for diagnosis and management of clinical diseases and performance evaluation based on Linear Range (LR) and Limit of Detection (LOD) within the ranges of Micromolar µM (10⁻⁶), Nanomolar nM (10⁻⁹), Picomolar pM (10⁻¹²), femtomolar fM (10⁻¹⁵), and attomolar aM (10⁻¹⁸).

Phenolic compounds contain classes of flavonoids and non-flavonoids, which occur naturally as secondary metabolites in plants. These compounds, when consumed in food substances, improve human health because of their antioxidant properties against oxidative damage diseases. In this study, an electrochemical sensor was developed using a carbon paste electrode (CPE) modified with Fe₃O₄ nanoparticles (MCPE) for the electrosensitive determination of sinapic acid, syringic acid, and rutin. The characterization techniques adapted for CPE, MCPE electrodes, and the solution interface were cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). Scan rate and pH were the parameters subjected to optimization studies for the determination of phenolic compounds. The incorporation of Fe₃O₄ nanoparticles to the CPE as a sensor showed excellent sensitivity, selectivity, repeatability, reproducibility, stability, and low preparation cost. The limits of detection (LOD) obtained were 2.2 × 10⁻⁷ M for sinapic acid, 2.6 × 10⁻⁷ M for syringic acid, and 0.8 × 10⁻⁷ M for rutin, respectively. The fabricated electrochemical sensor was applied to determine phenolic compounds in real samples of red and white wine.

Purpose: Parkinson's disease (PD), which is the second most common neurodegenerative disease following Alzheimer’s disease, can be diagnosed clinically when about 70% of the dopaminergic neurons are lost and symptoms are noticed. Neuroimaging methods such as single photon emission computed tomography have become useful tools in vivo to assess dopamine transporters (DATs) in the striatal region. However, inter- and intra-reader variability of construing the images might result in misdiagnosis. To overcome the challenges posed by classification of the disease, image preparation techniques and a back propagation neural network (BPNN) have been proposed. The aim of this study is to show that the proposed method can be used for the classification of PD with high accuracy. Methods: In this study, we used basic image preparation techniques and a BPNN on DAT imaging datasets from the Parkinson’s Progression Markers Initiative. 1,334 PD and 212 normal control (NC) subjects were included. In the image preparation phase, adaptive histogram equalization was applied to the cropped images, followed by image binarization. Then, the mass-difference method was applied to separate the regions of interest with similar values. Finally, the binarized images were subtracted from the original images, and the average pixel per node approach was applied to the images to minimize the inputs. In the BPNN phase, 400 input neurons and 2 output neurons were used. The dataset was divided into three sets: training, validation, and test. The BPNN was trained several times in order to obtain the optimum values. Results: The use of 40 hidden neurons, a learning rate of 0.00079, and a momentum factor of 0.90 produced superior results and were applied in the final BPNN architecture. The tolerance value used was 0.80. Uniquely, we found the sensitivity, specificity, and accuracy for PD vs. NC classification to be 99.7%, 99.2%, 99.6%, respectively. To the best of our knowledge, this is the highest accuracy value achieved in the existing literature. Our method increases computational speed together with improved performance. Conclusion: We have shown that effective image processing methods and the use of BPNN can successfully be applied to PD datasets to accurately determine any abnormalities in DATs. Using the shallow neural network, this procedure requires less processing time compared to other methods, and its accuracy, sensitivity, and specificity are reliable. However, further studies are needed to establish a prediction method for the preclinical and prodromal stages of the disease.

Breast cancer (BC) is one of the leading causes of death globally, resulting in 2.26 million cases worldwide. Treatments to downregulate these tumor cells include surgery, immunotherapy, and chemotherapy. However, habituation is expressed by the tumor cells after using the latter. Different plants have been used to treat diseases ranging from bacterial infection to cancer. Natural health products confirmed to be potent and still undergoing clinical trials include the dandelion plant, also known as Taraxacum officinale. This study evaluated Dandelion root extract's (DRE) antioxidant, antimicrobial, and cytotoxic effects. The Gas Chromatography-Mass Spectrometry (GC–MS) result revealed the presence of bioactive compounds such as P-hydroxybenzoic, chlorogenic acid, vanillic acid, caffeic acid, lupeol, leutolin, apigenin, coumestrol and much more that are present in the DRE extract. Antioxidant activity using a diphenylpicryl hydrazine (DPPH) scavenging assay was employed, and the result suggested a stable radical IC₅₀ value of 2.860 ± 0.001 mg/mL. Trypan blue exclusion assay was used to determine the cytotoxic effect on MDA-MB-231 cells. Antimicrobial activity was assessed by disc diffusion assay, and the average diameter values obtained were 25 mm for gentamicin. No antimicrobial activity was observed for the extracts at different concentrations. DRE showed no inhibitory effect against two gram-negative and gram-positive bacteria pathogens. Our study indicates that DRE has proven novel in killing breast cancer cells, has a mildly vigorous antioxidant activity to scavenge free radicals, and has no inhibitory effect against some bacteria.

Introduction and Background: The world has witnessed several outbreaks, emergence and re-emergence of infectious diseases throughout the 21st century as a result of climate change, urbanization and migration. Several infectious diseases caused by pathogens such as SARS-CoV-2, Ebola, Zika, Dengue, Marburg viruses, Mycobacterium tuberculosis, etc. have caused a devastating impact on lives and livelihoods around the world. To counter these diseases, medical experts rely on conventional techniques, which include microscopy and serological testing. However, these conventional methods are hindered by several trade-offs, including high cost, longer processing times, low sensitivity, and a likelihood of false positive results. Biomedical sensors have gained momentum in clinical diagnostics due to their low cost, portability, and sensitivity, among other advantages. To improve their performance, scientists have incorporated nanomaterials. Other techniques used to enhance the performance of nanobiosensors include multiplex testing, point-of-care testing (POCT), and smart sensing. Methodology: Thus, in this review, we present a comprehensive overview of the state-of-the-art nanobiosensors for detecting infectious diseases. The review covers key topics which centred around the application of nanotechnology in biosensing, multiplex testing, POCT and smart nano-enhanced biosensors. Findings: The findings of this review highlighted the advantages of biosensors over conventional approaches, with a limit of detection ranging from nanomolar to attomolar concentrations and a time response ranging from 1 to 3 hours. Conclusion: Despite the prospect of nanobiosensors, several limitations such as complexity, very extensive, longer processing time, etc. Moreover, the integration of smart technologies in nanobiosensors can offer several benefits, which include high accuracy, faster detection and prediction.

The green synthesis of metal nanoparticles using plant extracts offers an environmentally friendly approach that reduces energy consumption and avoids hazardous chemicals, thus increasing various sustainable applications. This study investigates the green synthesis of copper oxide nanoparticles (CuO nanoparticles) using extracts from pomegranate ( Punica granatum ) leaves and their application in enhancing calcareous alkali-activated materials (AAMs). Characterization techniques, including Ultraviolet-Visible spectroscopy (UV-Vis), Fourier-Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Dynamic Light scattering (DLS), and Transmission Electron Microscopy (TEM), were employed. CuO nanoparticles were incorporated into cold-pressed AAMs at varying concentrations (0.25%, 0.5%, and 1% by weight). Mechanical and physical properties such as compressive strength, porosity, and water absorption were evaluated, followed by microstructural analysis using XRD, FTIR, Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray Spectroscopy (EDX). The synthesized CuO nanoparticles exhibited high crystallinity with minor amorphous content and a narrow size distribution, with an average particle size of 78 nm. The incorporation of 0.5% CuO nanoparticles yielded optimal results, increasing compressive strength (UCS) by 37.3% and reducing porosity and water absorption by 0.75% and 0.4%, respectively, compared to the control. Microstructural analysis indicated that CuO nanoparticles enhance matrix compactness and promote the formation of alkali-activated binder phases. Overall, the findings demonstrate that Punica granatum leaf extract can be effectively used for the green synthesis of semi-spherical CuO nanoparticles (<100 nm) with good stability. Their incorporation into calcareous AAMs significantly improves matrix densification and mechanical performance.