Detection of Avian Influenza Virus from Cloacal Swabs Using a Disposable Well Gate FET Sensor

Advancing diagnostic efficacy using a computer vision-assisted lateral flow assay for influenza and SARS-CoV-2 detection

Lateral flow assays (LFAs) have emerged as indispensable tools for point-of-care testing during the pandemic era. However, the interpretation of results through unassisted visual inspection by untrained individuals poses inherent limitations. In our study, we propose a novel approach that combines computer vision (CV) and lightweight machine learning (ML) to overcome these limitations and significantly enhance the performance of LFAs. By incorporating CV-assisted analysis into the LFA assay, we achieved a remarkable three-fold improvement in analytical sensitivity for detecting Influenza A and for SARS-CoV-2 detection. The obtained R2 values reached approximately 0.95, respectively, demonstrating the effectiveness of our approach. Moreover, the integration of CV techniques with LFAs resulted in a substantial amplification of the colorimetric signal specifically for COVID-19 positive patient samples. Our proposed approach, which incorporates a simple machine learning algorithm, provides substantial enhancements in assay sensitivity, improving diagnostic efficacy and accessibility of point-of-care testing without requiring significant additional resources. Moreover, the simplicity of the machine learning algorithm enables its standalone use on a mobile phone, further enhancing its practicality for point-of-care testing.

Aptamer-based biosensors for virus protein detection

Virus threatens life health seriously. The accurate early diagnosis of the virus is vital for clinical control and treatment of virus infection. Aptamers are small single-stranded oligonucleotides (DNAs or RNAs). In this review, we summarized aptasensors for virus detection in recent years according to the classification of the viral target protein, and illustrated common detection mechanisms in the aptasensors (colorimetry, fluorescence assay, surface plasmon resonance (SPR), surface-enhanced raman spectroscopy (SERS), electrochemical detection, and field-effect transistor (FET)). Furthermore, aptamers against different target proteins of viruses were summarized. The relationships between the different biomarkers of the viruses and the detection methods, and their performances were revealed. In addition, the challenges and future directions of aptasensors were discussed. This review will provide valuable references for constructing on-site aptasensors for detecting viruses, especially the SARS-CoV-2.

Biological and technical challenges for implementation of yeast-based biosensors

Biosensors are low-cost and low-maintenance alternatives to conventional analytical techniques for biomedical, industrial and environmental applications. Biosensors based on whole microorganisms can be genetically engineered to attain high sensitivity and specificity for the detection of selected analytes. While bacteria-based biosensors have been extensively reported, there is a recent interest in yeast-based biosensors, combining the microbial with the eukaryotic advantages, including possession of specific receptors, stability and high robustness. Here, we describe recently reported yeast-based biosensors highlighting their biological and technical features together with their status of development, that is, laboratory or prototype. Notably, most yeast-based biosensors are still in the early developmental stage, with only a few prototypes tested for real applications. Open challenges, including systematic use of advanced molecular and biotechnological tools, bioprospecting, and implementation of yeast-based biosensors in electrochemical setup, are discussed to find possible solutions for overcoming bottlenecks and promote real-world application of yeast-based biosensors.

Securing Bio-Cyber Interface for the Internet of Bio-Nano Things using Particle Swarm Optimization and Artificial Neural Networks based parameter profiling

Internet of bio-nano things (IoBNT) is a novel communication paradigm where tiny, biocompatible and non-intrusive devices collect and sense biological signals from the environment and send them to data centers for processing through the internet. The concept of the IoBNT has stemmed from the combination of synthetic biology and nanotechnology tools which enable the fabrication of biological computing devices called Bio-nano things. Bio-nano things are nanoscale (1-100 nm) devices that are ideal for in vivo applications, where non-intrusive devices can reach hard-to-access areas of the human body (such as deep inside the tissue) to collect biological information. Bio-nano things work collaboratively in the form of a network called nanonetwork. The interconnection of the biological world and the cyber world of the Internet is made possible by a powerful hybrid device called Bio Cyber Interface. Bio Cyber Interface translates biochemical signals from in-body nanonetworks into electromagnetic signals and vice versa. Bio Cyber Interface can be designed using several technologies. In this paper, we have selected bio field-effect transistor (BioFET) technology, due to its characteristics of being fast, low-cost, and simple The main concern in this work is the security of IoBNT, which must be the preliminary requirement, especially for healthcare applications of IoBNT. Once the human body is accessible through the Internet, there is always a chance that it will be done with malicious intent. To address the issue of security in IoBNT, we propose a framework that utilizes Particle Swarm Optimization (PSO) algorithm to optimize Artificial Neural Networks (ANN) and to detect anomalous activities in the IoBNT transmission. Our proposed PSO-based ANN model was tested for the simulated dataset of BioFET based Bio Cyber Interface communication features. The results show an improved accuracy of 98.9% when compared with Adam based optimization function.

An NIR dual-emitting/absorbing inorganic compact pair: A self-calibrating LRET system for homogeneous virus detection

Many conventional optical biosensing systems use a single responsive signal in the visible light region. This limits their practical applications, as the signal can be readily perturbed by various external environmental factors. Herein, a near-infrared (NIR)-based self-calibrating luminescence resonance energy transfer (LRET) system was developed for background-free detection of analytes in homogeneous sandwich-immunoassays. The inorganic LRET pair was comprised of NIR dual-emitting lanthanide-doped nanoparticles (LnNPs) as donors and NIR-absorbing LnNPs as acceptors, which showed a narrow absorption peak (800 nm) and long-term stability, enabling stable LRET with a built-in self-calibrating signal. Screened single-chain variable fragments (scFvs) were used as target avian influenza virus (AIV)-binding antibodies to increase the LRET efficiency in sandwich-immunoassays. The compact sensor platform successfully detected AIV nucleoproteins with a 0.38 pM limit of detection in buffer solution and 64 clinical samples. Hence, inorganic LnNP pairs may be effective for self-calibrating LRET systems in the background-free NIR region.

A review of BioFET's basic principles and materials for biomedical applications

Interest in biomolecular sensors for diagnosis of early diseases and prognosis of the diseases is increasing day by day. Among them, FET-based sensors are very useful in that of their versatile operating characteristics using various materials. Herein, after addressing the basic principles of BioFET, we conduct an overall review of BioFET on two of the main structural elements: transducing materials and probes. Transducing materials were classified into graphene, carbon nanotube, silicon, MOF, etc., and probes were classified into antibodies, enzymes, aptamers, etc.. The important elements in designing BioFETs, such as electrical properties of each material, Debye length, and fabrication process are introduced along with their respective structures and materials. After the review of each of these structures and characteristics, examples are discussed along with sensitivity, selectivity, and limit of detection. In addition to the operating aspects of the senser, novel processes, treatments, and materials that can be considered for various purposes are also introduced. Based on the understanding, an overview of diverse examples is given by dividing the applications of BioFET into three main types: antigen sensing, biomarker sensing, and drug effect monitoring. Focusing on these general reviews, we conclude how the future direction of development will move forward and what the main challenge is.

Recent Advances of Field-Effect Transistor Technology for Infectious Diseases

Field-effect transistor (FET) biosensors have been intensively researched toward label-free biomolecule sensing for different disease screening applications. High sensitivity, incredible miniaturization capability, promising extremely low minimum limit of detection (LoD) at the molecular level, integration with complementary metal oxide semiconductor (CMOS) technology and last but not least label-free operation were amongst the predominant motives for highlighting these sensors in the biosensor community. Although there are various diseases targeted by FET sensors for detection, infectious diseases are still the most demanding sector that needs higher precision in detection and integration for the realization of the diagnosis at the point of care (PoC). The COVID-19 pandemic, nevertheless, was an example of the escalated situation in terms of worldwide desperate need for fast, specific and reliable home test PoC devices for the timely screening of huge numbers of people to restrict the disease from further spread. This need spawned a wave of innovative approaches for early detection of COVID-19 antibodies in human swab or blood amongst which the FET biosensing gained much more attention due to their extraordinary LoD down to femtomolar (fM) with the comparatively faster response time. As the FET sensors are promising novel PoC devices with application in early diagnosis of various diseases and especially infectious diseases, in this research, we have reviewed the recent progress on developing FET sensors for infectious diseases diagnosis accompanied with a thorough discussion on the structure of Chem/BioFET sensors and the readout circuitry for output signal processing. This approach would help engineers and biologists to gain enough knowledge to initiate their design for accelerated innovations in response to the need for more efficient management of infectious diseases like COVID-19.

Electrical Cartridge Sensor Enables Reliable and Direct Identification of MicroRNAs in Urine of Patients

Urinary miRNAs are biomarkers that demonstrate considerable promise for the noninvasive diagnosis and prognosis of diseases. However, because of background noise resulting from complex physiological features of urine, instability of miRNAs, and their low concentration, accurate monitoring of miRNAs in urine is challenging. To address these limitations, we developed a urine-based disposable and switchable electrical sensor that enables reliable and direct identification of miRNAs in patient urine. The proposed sensing platform combining disposable sensor chips composed of a reduced graphene oxide nanosheet and peptide nucleic acid facilitates the label-free detection of urinary miRNAs with high specificity and sensitivity. Using real-time detection of miRNAs in patient urine without pretreatment or signal amplification, this sensor allows rapid, direct detection of target miRNAs in a broad dynamic range with a detection limit down to 10 fM in human urine specimens within 20 min and enables simultaneous quantification of multiple miRNAs. As confirmed using a blind comparison with the results of pathological examination of patients with prostate cancer, the sensor offers the potential to improve the accuracy of early diagnosis before a biopsy is taken. This study holds the usefulness of the practical sensor for the clinical diagnosis of urological diseases.

Aptamer-Based Field-Effect Transistor for Detection of Avian Influenza Virus in Chicken Serum

Early diagnosis of the highly pathogenic H5N1 avian influenza virus (AIV) is significant for preventing and controlling a global pandemic. However, there is no existing electrical biosensor for detecting biomarkers for AIV in clinically relevant samples such as chicken serum. Herein, we report the first use of an aptamer-functionalized field-effect transistor (FET) as a label-free sensor for AIV detection in chicken serum. A DNA aptamer is employed as a sensitive and selective receptor for hemagglutinin (HA) protein, which is a biomarker for AIVs. This aptamer is immobilized on a gold microelectrode that is connected to the gate of a reusable FET transducer. The specific binding of the target protein results in a change in the surface potential, which generates a signal response of the FET transducer. We hypothesize that a conformational change in the DNA aptamer upon specific binding of HA protein may alter the surface potential. The signal of the aptamer-based FET biosensor increased linearly with the increase in the logarithm of HA protein concentration in a dynamic range of 10 pM to 10 nM with a detection limit of 5.9 pM. The selectivity of the biosensor for HA protein was confirmed by employing relevant interfering proteins. The proposed biosensor was successfully applied to the selective detection of HA protein in a chicken serum sample. Owing to its simple and low-cost architecture, portability, and sensitivity, the aptamer-based FET biosensor has potential as a point-of-care diagnosis of H5N1 AIVs in clinical samples.

MicroRNA expression profiles from HEK293 cells expressing H5N1 avian influenza virus non-structural protein 1

H5N1 avian influenza poses a serious threat to the poultry industry and human health. Non-structural protein 1 (NS1) plays an important role in the replication and pathogenesis of avian influenza virus (AIV). However, the function of the NS1 gene is still unclear. In this study, illumina genome analyzer iix screening was used to identify the differentially expressed microRNAs (miRNAs) in HEK293 cells expressing H5N1 AIV NS1. There were 13 differentially expressed miRNAs (hsa-miR-17-5p, hsa-miR-221-3p, hsa-miR-22-3p, hsa-miR-31-5p, hsa-miR-20a-5p, hsa-miR-222-3p, hsa-miR-24-3p, hsa-miR-3613-3p, hsa-miR-3178, hsa-miR-4505, hsa-miR-345-3p, hsa-miR-3648, and hsa-miR-455-3p) ( P < 0.01). The qRT-PCR validation results demonstrated that hsa-miR-221-3p, hsa-miR-22-3p, hsa-miR-20a-5p, and hsa-miR-3613-3p were upregulated, while hsa-miR-3178 and hsa-miR-4505 were down-regulated. The softwares targetscan and miranda were further used to predict their target genes, and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results showed that 20 GO terms and 20 KEGG pathways were significantly enriched. Our findings are the first to report expression profiling of miRNA and their functions in H5N1 AIV NS1-expressing HEK293 cells, and pave the way to further elucidating the accurate interaction mechanism between NS1 and virus replication, thus providing brand new insight into the prophylaxis and treatment of H5N1 AIV.

Functional Carbon Quantum Dots for Highly Sensitive Graphene Transistors for Cu2+ Ion Detection

Cu²⁺ ions play essential roles in various biological events that occur in the human body. It is important to establish an efficient and reliable detection of Cu²⁺ ions for people's health. The solution-gated graphene transistors (SGGTs) have been extensively investigated as a promising platform for chemical and biological sensing applications. Herein, highly sensitive and highly selective sensor for Cu²⁺ ion detection is successfully constructed based on SGGTs with gate electrodes modified by functional carbon quantum dots (CQDs). The sensing mechanism of the sensor is that the coordination of CQDs and Cu²⁺ ions induces the capacitance change of the electrical double layer (EDL) near the gate electrode and then results in the change of channel current. Compared to other metal ions, Cu²⁺ ions have an excellent binding nature with CQDs that make it an ultrahigh selective sensor. The CQD-modified sensor achieves excellent Cu²⁺ ion detection with a minimal level of concentration (1 × 10^-14 M), which is several orders of magnitude lower than the values obtained from other conventional detection methods. Interestingly, the device also displays a quick response time on the order of seconds. Due to the functionalized nature of CQDs, SGGTs with CQD-modified gate show good prospects to achieve multifunctional sensing platform in biochemical detections.

Nanoscale FET-Based Transduction toward Sensitive Extended-Gate Biosensors

Owing to their simple and low-cost architecture, extended-gate biosensors based on the combination of a disposable sensing part and a reusable transducer have been widely utilized for the label-free electrical detection of chemical and biological species. Previous studies have demonstrated that sensitive and selective detection of ions and biomolecules can be achieved by controlled modification of the sensing part with an ion-selective membrane and receptors of interest. However, no systematic studies have been performed on the impact of the transducer on sensing performance. In this paper, we introduce the concept of a nanoscale field-effect transistor (FET) as a reusable and sensitive transducer for extended-gate biosensors. The capacitive effect from the external sensing part can degrade the sensing performance, but the nanoscale FET can reduce this effect. The nanoscale FET with a gate-all-around (GAA) structure exhibits a higher pH sensitivity than a commercially available FET, which is widely used in conventional extended-gate biosensors. A sensitivity reduction is observed for the commercial FET, whereas the pH sensitivity is insensitive to the area of the sensing region in the nanoscale FET, thus allowing the scaling of the detection area. Our analysis based on a capacitive model suggests that the high pH sensitivity in the compact sensing area originates from the small input capacitance of the nanoscale FET transducer. Moreover, a decrease in the nanowire width of the GAA FET leads to an improvement in the pH sensitivity. The extended-gate approach with the nanoscale FET-based transduction can pave the way for a highly sensitive analysis of chemical and biological species with a small sample volume.

A Novel Blood-Based Colorectal Cancer Diagnostic Technology Using Electrical Detection of Colon Cancer Secreted Protein-2

Colorectal cancer (CRC) is the second-leading cause of cancer-related mortality worldwide, which may be effectively reduced by early screening. Colon cancer secreted protein-2 (CCSP-2) is a promising blood marker for CRC. An electric-field effect colorectal sensor (E-FECS), an ion-sensitive field-effect transistor under dual gate operation with nanostructure is developed, to quantify CCSP-2 directly from patient blood samples. The sensing performance of the E-FECS is verified in 7 controls and 7 CRC samples, and it is clinically validated on 30 controls, 30 advanced adenomas, and 81 CRC cases. The concentration of CCSP-2 is significantly higher in plasma samples from CRC and advanced adenoma compared with controls (both P < 0.001). Sensitivity and specificity for CRC versus controls are 44.4% and 86.7%, respectively (AUC of 0.67), and 43.3% and 86.7%, respectively, for advanced adenomas (AUC of 0.67). CCSP-2 detects a greater number of CRC cases than carcinoembryonic antigen does (45.6% vs 24.1%), and the combination of the two markers detects an even greater number of cases (53.2%). The E-FECS system successfully detects CCSP-2 in a wide range of samples including early stage cancers and advanced adenoma. CCSP-2 has potential for use as a blood-based biomarker for CRC.

Fully Packaged Portable Thin Film Biosensor for the Direct Detection of Highly Pathogenic Viruses from On-Site Samples

The thin film transistor (TFT) is a promising biosensor system with great sensitivity, label-free detection, and a quick response time. However, even though the TFT sensor has such advantageous characteristics, the disadvantages hamper the TFT sensor's application in the clinical field. The TFT is susceptible to light, noise, vibration, and limited usage, and this significantly limits its on-site potential as a practical biosensor. Herein, we developed a fully packaged, portable TFT electrochemical biosensor into a chip form, providing both portability through minimizing the laboratory equipment size and multiple safe usages by protecting the semiconductor sensor. Additionally, a safe environment that serves as a miniature probe station minimizes the previously mentioned disadvantages, while providing the means to properly link the TFT biosensor with a portable analyzer. The biosensor was taken into a biosafety level 3 (BSL-3) laboratory setting to analyze highly pathogenic avian influenza virus (HPAIV) samples. This virus quickly accumulates within a host, and therefore, early stage detection is critical to deterring the further spread of the deadly disease to other areas. However, current on-site methods have poor limits of detection (10⁵-10⁶ EID₅₀/mL), and because the virus has low concentration in its early stages, it cannot be detected easily. We have compared the sample measurements from our device with virus concentration data obtained from a RT-PCR (virus range: 10⁰-10⁴ EID₅₀/mL) and have identified an increasing voltage signal which corresponds to increasing virus concentration.

Recent Advances in AIV Biosensors Composed of Nanobio Hybrid Material

Since the beginning of the 2000s, globalization has accelerated because of the development of transportation systems that allow for human and material exchanges throughout the world. However, this globalization has brought with it the rise of various pathogenic viral agents, such as Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), Zika virus, and Dengue virus. In particular, avian influenza virus (AIV) is highly infectious and causes economic, health, ethnical, and social problems to human beings, which has necessitated the development of an ultrasensitive and selective rapid-detection system of AIV. To prevent the damage associated with the spread of AIV, early detection and adequate treatment of AIV is key. There are traditional techniques that have been used to detect AIV in chickens, ducks, humans, and other living organisms. However, the development of a technique that allows for the more rapid diagnosis of AIV is still necessary. To achieve this goal, the present article reviews the use of an AIV biosensor employing nanobio hybrid materials to enhance the sensitivity and selectivity of the technique while also reducing the detection time and high-throughput process time. This review mainly focused on four techniques: the electrochemical detection system, electrical detection method, optical detection methods based on localized surface plasmon resonance, and fluorescence.

Detecting and Predicting Emerging Disease in Poultry With the Implementation of New Technologies and Big Data: A Focus on Avian Influenza Virus

Future demands for food will place agricultural systems under pressure to increase production. Poultry is accepted as a good source of protein and the poultry industry will be forced to intensify production in many countries, leading to greater numbers of farms that house birds at elevated densities. Increasing farmed poultry can facilitate enhanced transmission of infectious pathogens among birds, such as avian influenza virus among others, which have the potential to induce widespread mortality in poultry and cause considerable economic losses. Additionally, the capability of some emerging poultry pathogens to cause zoonotic human infection will be increased as greater numbers of poultry operations could increase human contact with poultry pathogens. In order to combat the increased risk of spread of infectious disease in poultry due to intensified systems of production, rapid detection and diagnosis is paramount. In this review, multiple technologies that can facilitate accurate and rapid detection and diagnosis of poultry diseases are highlighted from the literature, with a focus on technologies developed specifically for avian influenza virus diagnosis. Rapid detection and diagnostic technologies allow for responses to be made sooner when disease is detected, decreasing further bird transmission and associated costs. Additionally, systems of rapid disease detection produce data that can be utilized in decision support systems that can predict when and where disease is likely to emerge in poultry. Other sources of data can be included in predictive models, and in this review two highly relevant sources, internet based-data and environmental data, are discussed. Additionally, big data and big data analytics, which will be required in order to integrate voluminous and variable data into predictive models that function in near real-time are also highlighted. Implementing new technologies in the commercial setting will be faced with many challenges, as will designing and operating predictive models for poultry disease emergence. The associated challenges are summarized in this review. Intensified systems of poultry production will require new technologies for detection and diagnosis of infectious disease. This review sets out to summarize them, while providing advantages and limitations of different types of technologies being researched.

Field-Effect Biosensors for On-Site Detection: Recent Advances and Promising Targets

There is an explosive interest in the immediate and cost-effective analysis of field-collected biological samples, as many advanced biodetection tools are highly sensitive, yet immobile. On-site biosensors are portable and convenient sensors that provide detection results at the point of care. They are designed to secure precision in highly ionic and heterogeneous solutions with minimal hardware. Among various methods that are capable of such analysis, field-effect biosensors are promising candidates due to their unique sensitivity, manufacturing scalability, and integrability with computational circuitry. Recent developments in nanotechnological surface modification show promising results in sensing from blood, serum, and urine. This report gives a particular emphasis on the on-site efficacy of recently published field-effect biosensors, specifically, detection limits in physiological solutions, response times, and scalability. The survey of the properties and existing detection methods of four promising biotargets, exosomes, bacteria, viruses, and metabolites, aims at providing a roadmap for future field-effect and other on-site biosensors.