Lab-on-chip microsystems for ex vivo network of neurons studies: A review

ABSTRACT: Increasing population is suffering from neurological disorders nowadays, with no effective therapy available to treat them. Explicit knowledge of network of neurons (NoN) in the human brain is key to understanding the pathology of neurological diseases. Research in NoN developed slower than expected due to the complexity of the human brain and the ethical considerations for in vivo studies. However, advances in nanomaterials and micro-/nano-microfabrication have opened up the chances for a deeper understanding of NoN ex vivo, one step closer to in vivo studies. This review therefore summarizes the latest advances in lab-on-chip microsystems for ex vivo NoN studies by focusing on the advanced materials, techniques, and models for ex vivo NoN studies. The essential methods for constructing lab-on-chip models are microfluidics and microelectrode arrays. Through combination with functional biomaterials and biocompatible materials, the microfluidics and microelectrode arrays enable the development of various models for ex vivo NoN studies. This review also includes the state-of-the-art brain slide and organoid-on-chip models. The end of this review discusses the previous issues and future perspectives for NoN studies

Energy Solutions for Wearable Sensors: A Review

Wearable sensors have gained popularity over the years since they offer constant and real-time physiological information about the human body. Wearable sensors have been applied in a variety of ways in clinical settings to monitor health conditions. These technologies require energy sources to carry out their projected functionalities. In this paper, we review the main energy sources used to power wearable sensors. These energy sources include batteries, solar cells, biofuel cells, supercapacitors, thermoelectric generators, piezoelectric and triboelectric generators, and radio frequency (RF) energy harvesters. Additionally, we discuss wireless power transfer and some hybrids of the above technologies. The advantages and drawbacks of each technology are considered along with the system components and attributes that make these devices function effectively. The objective of this review is to inform researchers about the latest developments in this field and present future research opportunities

Machine Learning Techniques for Effective Pathogen Detection Based on Resonant Biosensors

We describe a machine learning (ML) approach to processing the signals collected from a COVID-19 optical-based detector. Multilayer perceptron (MLP) and support vector machine (SVM) were used to process both the raw data and the feature engineering data, and high performance for the qualitative detection of the SARS-CoV-2 virus with concentration down to 1 TCID50/mL was achieved. Valid detection experiments contained 486 negative and 108 positive samples, and control experiments, in which biosensors without antibody functionalization were used to detect SARS-CoV-2, contained 36 negative samples and 732 positive samples. The data distribution patterns of the valid and control detection dataset, based on T-distributed stochastic neighbor embedding (t-SNE), were used to study the distinguishability between positive and negative samples and explain the ML prediction performance. This work demonstrates that ML can be a generalized effective approach to process the signals and the datasets of biosensors dependent on resonant modes as biosensing mechanism

Blocking Superantigen-Mediated Diseases: Challenges and Future Trends

Superantigens are virulence factors secreted by microorganisms that can cause various immune diseases, such as overactivating the immune system, resulting in cytokine storms, rheumatoid arthritis, and multiple sclerosis. Some studies have demonstrated that superantigens do not require intracellular processing and instated bind as intact proteins to the antigen-binding groove of major histocompatibility complex II on antigen-presenting cells, resulting in the activation of T cells with different T-cell receptor Vβ and subsequent overstimulation. To combat superantigen-mediated diseases, researchers have employed different approaches, such as antibodies and simulated peptides. However, due to the complex nature of superantigens, these approaches have not been entirely successful in achieving optimal therapeutic outcomes. CD28 interacts with members of the B7 molecule family to activate T cells. Its mimicking peptide has been suggested as a potential candidate to block superantigens, but it can lead to reduced T-cell activity while increasing the host’s infection risk. Thus, this review focuses on the use of drug delivery methods to accurately target and block superantigens, while reducing the adverse effects associated with CD28 mimic peptides. We believe that this method has the potential to provide an effective and safe therapeutic strategy for superantigen-mediated diseases

Label-Free LSPR-Vertical Microcavity Biosensor for On-Site SARS-CoV-2 Detection

Cost-effective, rapid, and sensitive detection of SARS-CoV-2, in high-throughput, is crucial in controlling the COVID-19 epidemic. In this study, we proposed a vertical microcavity and localized surface plasmon resonance hybrid biosensor for SARS-CoV-2 detection in artificial saliva and assessed its efficacy. The proposed biosensor monitors the valley shifts in the reflectance spectrum, as induced by changes in the refractive index within the proximity of the sensor surface. A low-cost and fast method was developed to form nanoporous gold (NPG) with different surface morphologies on the vertical microcavity wafer, followed by immobilization with the SARS-CoV-2 antibody for capturing the virus. Modeling and simulation were conducted to optimize the microcavity structure and the NPG parameters. Simulation results revealed that NPG-deposited sensors performed better in resonance quality and in sensitivity compared to gold-deposited and pure microcavity sensors. The experiment confirmed the effect of NPG surface morphology on the biosensor sensitivity as demonstrated by simulation. Pre-clinical validation revealed that 40% porosity led to the highest sensitivity for SARS-CoV-2 pseudovirus at 319 copies/mL in artificial saliva. The proposed automatic biosensing system delivered the results of 100 samples within 30 min, demonstrating its potential for on-site coronavirus detection with sufficient sensitivity

Heterogeneous-Nucleation Biosensor for Long-Term Collection and Mask-Based Self-Detection of SARS-CoV-2

The effective control of infectious diseases, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, depends on the availability of rapid and accurate monitoring techniques. However, conventional SARS-CoV-2 detection technologies do not support continuous self-detection and may lead to cross-infection when utilized in medical institutions. In this study, we introduce a prototype of a mask biosensor designed for the long-term collection and self-detection of SARS-CoV-2. The biosensor utilizes the average resonance Rayleigh scattering intensity of Au nanocluster-aptamers. The inter-mask surface serves as a medium for the long-term collection and concentration enhancement of SARS-CoV-2, while the heterogeneous-nucleation nanoclusters (NCs) contribute to the exceptional stability of Au NCs for up to 48 h, facilitated by the adhesion of Ti NCs. Additionally, the biosensors based on Au NC-aptamers exhibited high sensitivity for up to 1 h. Moreover, through the implementation of a support vector machine classifier, a significant number of point signals can be collected and differentiated, leading to improved biosensor accuracy. These biosensors offer a complementary wearable device-based method for diagnosing SARS-CoV-2, with a limit of detection of 103 copies. Given their flexibility, the proposed biosensors possess tremendous potential for the continuous collection and sensitive self-detection of SARS-CoV-2 variants and other infectious pathogens

Biosensors for waterborne virus detection: Challenges and strategies

Waterborne viruses that can be harmful to human health pose significant challenges globally, affecting health care systems and the economy. Identifying these waterborne pathogens is essential for preventing diseases and protecting public health. However, handling complex samples such as human and wastewater can be challenging due to their dynamic and complex composition and the ultralow concentration of target analytes. This review presents a comprehensive overview of the latest breakthroughs in waterborne virus biosensors. It begins by highlighting several promising strategies that enhance the sensing performance of optical and electrochemical biosensors in human samples. These strategies include optimizing bioreceptor selection, transduction elements, signal amplification, and integrated sensing systems. Furthermore, the insights gained from biosensing waterborne viruses in human samples are applied to improve biosensing in wastewater, with a particular focus on sampling and sample pretreatment due to the dispersion characteristics of waterborne viruses in wastewater. This review suggests that implementing a comprehensive system that integrates the entire waterborne virus detection process with high-accuracy analysis could enhance virus monitoring. These findings provide valuable insights for improving the effectiveness of waterborne virus detection, which could have significant implications for public health and environmental management