Real-time Recognition of Mycobacterium tuberculosis and Lipoarabinomannan using the Quartz Crystal Microbalance

An insight to the recent advancements in detection of Mycobacterium tuberculosis using biosensors: A systematic review

Since ancient times, Tuberculosis (TB) has been a severe invasive illness that has been prevalent for thousands of years and is also known as "consumption" or phthisis. TB is the most common chronic lung bacterial illness in the world, killing over 2 million people each year, caused by Mycobacterium tuberculosis (MTB). As per the reports of WHO, in spite of technology advancements, the average rate of decline in global TB infections from 2000-2018 was only 1.6% per year, and the worldwide reduction in TB deaths was only 11%. In addition, COVID-19 pandemic has reversed years of global progress in tackling TB with fewer diagnosed cases. The majority of undiagnosed patients of TB are found in low- and middle-income countries where the GeneXpert MTB/RIF assay and sputum smear microscopy have been approved by the WHO as reference procedures for quickly detecting TB. Biosensors, like other cutting-edge technologies, have piqued researchers' interest since they offer a quick and accurate way to identify MTB. Modern integrated technologies allow for the rapid, low-cost, and highly precise detection of analytes in extremely little amounts of sample by biosensors. Here in this review, we outlined the severity of tuberculosis (TB) and the most recent developments in the biosensors sector, as well as their various kinds and benefits for TB detection. The review also emphasizes how widespread TB is and how it needs accurate diagnosis and effective treatment.

An insight into biosensing platforms used for the diagnosis of various lung diseases: A review

Many of the infectious diseases are ubiquitous in nature and pose a threat to global and public health. The original cause for such type of serious maladies can be summarized as the scarcity of appropriate analysis and treatment methods. Pulmonary diseases are considered one of the life-threatening lung diseases that affect millions of people globally. It consists of several types, namely, asthma, lung cancer, tuberculosis, chronic obstructive pulmonary disease, and several respiratory-related infections. This is due to the limited access to well-equipped healthcare facilities for early disease diagnosis. This needs the availability of processes and technologies that can help to stop this harmful disease-diagnosing practice. Various approaches for diagnosing various lung diseases have been developed over time, namely, autopsy, chest X-rays, low-dose CT scans, and so forth. The need of the hour is to develop a rapid, simple, portable, and low-cost method for the diagnosis of pulmonary diseases. So nowadays, biosensors have been becoming one of the highest priority research areas as a potentially useful tool for the early diagnosis and detection of many pulmonary lung diseases. In this review article, various types of biosensors and their applications in the diagnosis of lung-related disorders are expansively explained.

Evolution of tuberculosis diagnostics: From molecular strategies to nanodiagnostics

Tuberculosis has remained a global concern for public health affecting the lives of people for ages. Approximately 10 million people are affected by the disease and 1.5 million succumb to the disease worldwide annually. The COVID-19 pandemic has highlighted the role of early diagnosis to win the battle against such infectious diseases. Thus, advancement in the diagnostic approaches to provide early detection forms the foundation to eradicate and manage contagious diseases like tuberculosis. The conventional diagnostic strategies include microscopic examination, chest X-ray and tuberculin skin test. The limitations associated with sensitivity and specificity of these tests demands for exploring new techniques like probe-based assays, CRISPR-Cas and microRNA detection. The aim of the current review is to envisage the correlation between both the conventional and the newer approaches to enhance the specificity and sensitivity. A significant emphasis has been placed upon nanodiagnostic approaches manipulating quantum dots, magnetic nanoparticles, and biosensors for accurate diagnosis of latent, active and drug-resistant TB. Additionally, we would like to ponder upon a reliable method that is cost-effective, reproducible, require minimal infrastructure and provide point-of-care to the patients.

Spotlight on mycobacterial lipid exploitation using nanotechnology for diagnosis, vaccines, and treatments

Tuberculosis (TB), historically the most significant cause of human morbidity and mortality, has returned as the top infectious disease worldwide, under circumstances worsened by the COVID-19 pandemic's devastating effects on public health. Although Mycobacterium tuberculosis, the causal agent, has been known of for more than a century, the development of tools to control it has been largely neglected. With the advancement of nanotechnology, the possibility of engineering tools at the nanoscale creates unique opportunities to exploit any molecular type. However, little attention has been paid to one of the major attributes of the pathogen, represented by the atypical coat and its abundant lipids. In this review, an overview of the lipids encountered in M. tuberculosis and interest in exploiting them for the development of TB control tools are presented. Then, the amalgamation of nanotechnology with mycobacterial lipids from both reported and future works are discussed.

ANN-Based Discernment of Septic and Inflammatory Synovial Fluid: A Novel Method Using Viscosity Data from a QCR Sensor

The synovial fluid (SF) analysis involves a series of chemical and physical studies that allow opportune diagnosing of septic, inflammatory, non-inflammatory, and other pathologies in joints. Among the variety of analyses to be performed on the synovial fluid, the study of viscosity can help distinguish between these conditions, since this property is affected in pathological cases. The problem with viscosity measurement is that it usually requires a large sample volume, or the necessary instrumentation is bulky and expensive. This study compares the viscosity of normal synovial fluid samples with samples with infectious and inflammatory pathologies and classifies them using an ANN (Artificial Neural Network). For this purpose, a low-cost, portable QCR-based sensor (10 MHz) was used to measure the viscous responses of the samples by obtaining three parameters: Δf, ΔΓ (parameters associated with the viscoelastic properties of the fluid), and viscosity calculation. These values were used to train the algorithm. Different versions of the ANN were compared, along with other models, such as SVM and random forest. Thirty-three samples of SF were analyzed. Our study suggests that the viscosity characterized by our sensor can help distinguish infectious synovial fluid, and that implementation of ANN improves the accuracy of synovial fluid classification.

Biosensors for the detection of Mycobacterium tuberculosis: a comprehensive overview

Tuberculosis (TB) infection is one of the leading causes of death in the world. According to WHO reports 2019, the average rate of decrease in global TB incidences was only 1.6% per year from 2000 to 2018, besides that the global decline in TB deaths was just 11%. Therefore, the dire need for early detection of the pathogen for the successful diagnosis of TB seems justified. Mycobacterium tuberculosis secretory proteins have gained more attention as TB biomarkers, for the early diagnosis and treatment of TB. Here in this review, we elaborate on the recent advancements made in the field of piezoelectric, magnetic, optical, and electrochemical biosensors, in addition to listing their merits and setbacks. Additionally, this review also discusses the construction of biosensors through modern integrated technologies, such as combinations of analytical chemistry, molecular biology, and nanotechnology. Integrated technologies enhance the detection for perceiving highly selective, specific, and sensitive signals to detect M. tuberculosis. Furthermore, this review highlights the recent challenges and scope of improvement in numerous biosensors developed for rapid, specific, selective, and sensitive detection of tuberculosis to reduce the TB burden and successful treatment.

Quartz crystal microbalance-based biosensors as rapid diagnostic devices for infectious diseases

Infectious diseases are the ever-present threats to public health and the global economy. Accurate and timely diagnosis is crucial to impede the progression of a disease and break the chain of transmission. Conventional diagnostic techniques are typically time-consuming and costly, making them inefficient for early diagnosis of infections and inconvenient for use at the point of care. Developments of sensitive, rapid, and affordable diagnostic methods are necessary to improve the clinical management of infectious diseases. Quartz crystal microbalance (QCM) systems have emerged as a robust biosensing platform due to their label-free mechanism, which allows the detection and quantification of a wide range of biomolecules. The high sensitivity and short detection time offered by QCM-based biosensors are attractive for the early detection of infections and the routine monitoring of disease progression. Herein, the strategies employed in QCM-based biosensors for the detection of infectious diseases are extensively reviewed, with a focus on prevalent diseases for which improved diagnostic techniques are in high demand. The challenges to the clinical application of QCM-based biosensors are highlighted, along with an outline of the future scope of research in QCM-based diagnostics.

Sandwich antibody-based biosensor system for identification of Mycobacterium tuberculosis complex and nontuberculous mycobacteria

Mycobacterial infection, leading to pulmonary disease, remains a world health problem. Clinical symptoms of pulmonary disease caused by Mycobacterium tuberculosis complex (MTBC) and nontuberculous mycobacteria (NTM) are very similar. A rapid method for the differentiation of MTBC and NTM infection is essential for appropriate therapy. In this study, we aim to establish an antibody-based biosensor system for the identification of MTBC and NTM infection. Monoclonal antibodies (mAbs) specific for Ag85B proteins of mycobacteria were generated and characterized. The generated anti-Ag85B mAb clones AM85B-5 and AM85B-8 reacted to Ag85B of Mycobacterium spp.; in contrast, clone AM85B-9 specifically reacted to Ag85B of MTBC. By employing the produced mAbs, single and sandwich antibody-based biosensors using bio-layer interferometry were established for determination of Ag85B proteins. The sandwich antibody-based biosensor system was demonstrated to be suitable for detection of Ag85B protein and identification of MTBC and NTM. Using anti-Ag85B mAbs AM85B-8 and AM85B-9 as immobilized antibodies on sensor chips and using mAb AM85B-5 as secondary antibody, the established sandwich antibody-based biosensor could discriminate MTBC and NTM. The developed biosensor system can be used for culture confirmation of mycobacteria and speciation to MTBC and NTM.

CFD Modeling of Chamber Filling in a Micro-Biosensor for Protein Detection

Tuberculosis (TB) remains one of the main causes of human death around the globe. The mortality rate for patients infected with active TB goes beyond 50% when not diagnosed. Rapid and accurate diagnostics coupled with further prompt treatment of the disease is the cornerstone for controlling TB outbreaks. To reduce this burden, the existing gap between detection and treatment must be addressed, and dedicated diagnostic tools such as biosensors should be developed. A biosensor is a sensing micro-device that consists of a biological sensing element and a transducer part to produce signals in proportion to quantitative information about the binding event. The micro-biosensor cell considered in this investigation is designed to operate based on aptamers as recognition elements against Mycobacterium tuberculosis secreted protein MPT64, combined in a microfluidic-chamber with inlet and outlet connections. The microfluidic cell is a miniaturized platform with valuable advantages such as low cost of analysis with low reagent consumption, reduced sample volume, and shortened processing time with enhanced analytical capability. The main purpose of this study is to assess the flooding characteristics of the encapsulated microfluidic cell of an existing micro-biosensor using Computational Fluid Dynamics (CFD) techniques. The main challenge in the design of the microfluidic cell lies in the extraction of entrained air bubbles, which may remain after the filling process is completed, dramatically affecting the performance of the sensing element. In this work, a CFD model was developed on the platform ANSYS-CFX using the finite volume method to discretize the domain and solving the Navier–Stokes equations for both air and water in a Eulerian framework. Second-order space discretization scheme and second-order Euler Backward time discretization were used in the numerical treatment of the equations. For a given inlet–outlet diameter and dimensions of an in-house built cell chamber, different inlet liquid flow rates were explored to determine an appropriate flow condition to guarantee an effective venting of the air while filling the chamber. The numerical model depicted free surface waves as promoters of air entrainment that ultimately may explain the significant amount of air content in the chamber observed in preliminary tests after the filling process is completed. Results demonstrated that for the present design, against the intuition, the chamber must be filled with liquid at a modest flow rate to minimize free surface waviness during the flooding stage of the chamber.

Quartz-Crystal Microbalance (QCM) for Public Health: An Overview of Its Applications

Nanobiotechnologies, from the convergence of nanotechnology and molecular biology and postgenomics medicine, play a major role in the field of public health. This overview summarizes the potentiality of piezoelectric sensors, and in particular, of quartz-crystal microbalance (QCM), a physical nanogram-sensitive device. QCM enables the rapid, real time, on-site detection of pathogens with an enormous burden in public health, such as influenza and other respiratory viruses, hepatitis B virus (HBV), and drug-resistant bacteria, among others. Further, it allows to detect food allergens, food-borne pathogens, such as Escherichia coli and Salmonella typhimurium, and food chemical contaminants, as well as water-borne microorganisms and environmental contaminants. Moreover, QCM holds promises in early cancer detection and screening of new antiblastic drugs. Applications for monitoring biohazards, for assuring homeland security, and preventing bioterrorism are also discussed.

Evaluating the Equilibrium Association Constant between ArtinM Lectin and Myeloid Leukemia Cells by Impedimetric and Piezoelectric Label Free Approaches

Label-free methods for evaluating lectin-cell binding have been developed to determine the lectin-carbohydrate interactions in the context of cell-surface oligosaccharides. In the present study, mass loading and electrochemical transducer signals were compared to characterize the interaction between lectin and cellular membranes by measuring the equilibrium association constant, Ka , between ArtinM lectin and the carbohydrate sites of NB4 leukemia cells. By functionalizing sensor interfaces with ArtinM, it was possible to determine Ka over a range of leukemia cell concentrations to construct analytical curves from impedimetric and/or mass-associated frequency shifts with analytical signals following a Langmuir pattern. Using the Langmuir isotherm-binding model, the Ka obtained were (8.9 ± 1.0) × 10(-5) mL/cell and (1.05 ± 0.09) × 10(-6) mL/cell with the electrochemical impedance spectroscopy (EIS) and quartz crystal microbalance (QCM) methods, respectively. The observed differences were attributed to the intrinsic characteristic sensitivity of each method in following Langmuir isotherm premises.

Advances and challenges in biosensor-based diagnosis of infectious diseases

Rapid diagnosis of infectious diseases and timely initiation of appropriate treatment are critical determinants that promote optimal clinical outcomes and general public health. Conventional in vitro diagnostics for infectious diseases are time-consuming and require centralized laboratories, experienced personnel and bulky equipment. Recent advances in biosensor technologies have potential to deliver point-of-care diagnostics that match or surpass conventional standards in regards to time, accuracy and cost. Broadly classified as either label-free or labeled, modern biosensors exploit micro- and nanofabrication technologies and diverse sensing strategies including optical, electrical and mechanical transducers. Despite clinical need, translation of biosensors from research laboratories to clinical applications has remained limited to a few notable examples, such as the glucose sensor. Challenges to be overcome include sample preparation, matrix effects and system integration. We review the advances of biosensors for infectious disease diagnostics and discuss the critical challenges that need to be overcome in order to implement integrated diagnostic biosensors in real world settings.