Nanoceria (cerium oxide nanoparticles: CeO₂-NPs) has received significant attention due to its biocompatibility, good conductivity, and the ability to transfer oxygen.

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).

Thermal air oxidation enhances the adsorption capacity of steam-activated carbon coconut shell (SACCS) on MB removal and recovers the adsorption ability of the spent SACCS treated at 350 °C.

The presence of pork content in food products can be a serious issue over an adulteration of food, particularly for consumers who pay attention to halal certification or other religious beliefs concerning the consumption of pork. Due to this fact, finding a method for the detection of pork content in food products that is sensitive, fast, and accurate is of interest. In this study, an electrochemical DNA biosensor with gold nanoparticle–DNA probe bioconjugates was developed to detect Sus scrofa mitochondrial DNA (mtDNA) using a gold-modified screen-printed carbon electrode (SPCE-Gold). The bioconjugates were formed by attaching the DNA probe with gold nanoparticles (AuNPs). Detection was carried out based on the optimum conditions resulting from the Box–Behnken experimental design that were 40 μL of 153 μg/mL bioconjugates; bioconjugate immobilization time, 20 min; and DNA hybridization time, 60 min. The hybridization of target synthetic DNA to the DNA probe was characterized by voltammetry based on the methylene blue indicator signal at a potential of about − 0.35 V. The results showed that the detection limit was 0.58 μg/mL and the recovery was 101.74%. mtDNA samples from various raw and processed meat were isolated and cut with restriction enzyme Sal1. Hybridization of mtDNA samples to the gold nanoparticle–DNA probe bioconjugates anchored to the SPCE-Gold surface was characterized based on the current response generated by methylene blue as an indicator. The result shows that there is an increasing trend in the response of raw and processed meat samples containing pork DNA. The development of an electrochemical DNA biosensor, based on gold nanoparticle–DNA probe bioconjugates, has not been reported previously. This biosensor is selective towards 10% of the pork DNA content in the mixture. Therefore, this biosensor method can be used as an alternative method to distinguish samples containing pork, according to the detection limit.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus identified as the cause of the Coronavirus Disease 2019 (COVID-19) outbreak. The gold standard for detecting this virus is polymerase chain reaction (PCR). The electrochemical biosensor method can be an alternative method for detecting several biomolecules, such as viruses, because it is proven to have several advantages, including portability, good sensitivity, high specificity, fast response, and ease of use. This study aims to optimize an electro­chemical aptasensor using an AuNP-modified screen-printed carbon electrode (SPCE) with an aptamer to detect RBD protein S SARS-CoV-2. Aptasensors with the streptavidin-biotin system were immobilized on the SPCE/AuNP surface via covalent bonding with linkers to 3-mercaptopropionic acid (MPA) and electrochemically characterized by the [Fe(CN)6]3-/4- redox system using differential pulse voltammetry. The results showed that the immobi­lized aptamer on the SPCE/AuNP electrode surface experienced a decrease in current from 11.388 to 4.623 µA. The experimental conditions were optimized using the Box-Behnken experimental design for the three factors that affect the current response. The results of the optimization of the three parameters are the concentration of aptamer, incubation time of aptamer, and incubation time of RBD protein S SARS-CoV-2, each of which is 0.5 µg/mL, 40 minutes, and 60 minutes, respectively. The RBD protein S SARS-CoV-2 with various concentrations was tested on an electrochemical aptasensor to determine the de­tection limit and quantification limit, and the respective results were 2.63 and 7.97 ng/mL. The electrochemical aptasensor that has been developed in this study can be applied to detect RBD protein S SARS-CoV-2 as a COVID-19 biomarker in a simple, practical, and sensitive way.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen–antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.