Detecting COVID-19 from Breath: A Game Changer for a Big Challenge

Recapitulating COVID-19 detection methods: RT-PCR, sniffer dogs and electronic nose

In December 2019, a number of subjects presenting with an unexplained pneumonia-like illness were suspected to have a link to a seafood market in Wuhan, China. Subsequently, this illness was identified as the 2019-novel coronavirus (2019-nCoV) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the World Committee on Virus Classification. Since its initial identification, the virus has rapidly sperad across the globe, posing an extraordinary challenge for the medical community. Currently, the Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) is considered the most reliable method for diagnosing SARS-CoV-2. This procedure involves collecting oro-pharyngeal or nasopharyngeal swabs from individuals. Nevertheless, for the early detection of low viral loads, a more sensitive technique, such as droplet digital PCR (ddPCR), has been suggested. Despite the high effectiveness of RT-PCR, there is increasing interest in utilizing highly trained dogs and electronic noses (eNoses) as alternative methods for screening asymptomatic individuals for SARS-CoV-2. These dogs and eNoses have demonstrated high sensitivity and can detect volatile organic compounds (VOCs), enabling them to distinguish between COVID-19 positive and negative individuals. This manuscript recapitulates the potential, advantages, and limitations of employing trained dogs and eNoses for the screening and control of SARS-CoV-2.

Real-world evaluation of a QCM-based biosensor for exhaled air

The biosensor, named "virusmeter" in this study, integrates quartz crystal microbalance technology with an immune-functionalized chip to distinguish between symptomatic patients with respiratory diseases and healthy individuals by analyzing exhaled air samples. Renowned for its compact design, rapidity, and noninvasive nature, this device yields results within a 5-min timeframe. Evaluated under controlled conditions with 54 hospitalized symptomatic COVID-19 patients and 128 control subjects, the biosensor demonstrated good overall sensitivity (98.15%, 95% CI 90.1-100.0) and specificity (96.87%, 95% CI 92.2-99.1). This proof-of-concept presents an innovative approach with significant potential for leveraging piezoelectric sensors to diagnose respiratory diseases.

Recent Advances in DNA Nanotechnology-Based Sensing Platforms for Rapid Virus Detection

Several major viral pandemics in history have significantly impacted the public health of human beings. The COVID-19 pandemic has further underscored the critical need for early detection and screening of infected individuals. However, current detection techniques are confronted with deficiencies in sensitivity and accuracy, restricting the capability of detecting trace amounts of viruses in human bodies and in the environments. The advent of DNA nanotechnology has opened up a feasible solution for rapid and sensitive virus determination. By harnessing the designability and addressability of DNA nanostructures, a range of rapid virus sensing platforms have been proposed. This review overviewed the recent progress, application, and prospect of DNA nanotechnology-based rapid virus detection platforms. Furthermore, the challenges and developmental prospects in this field were discussed.

Nanowire Array Breath Acetone Sensor for Diabetes Monitoring

Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes characterized by the accumulation of ketone bodies in the blood. Breath acetone, a ketone, directly correlates with blood ketones. Therefore, monitoring breath acetone can significantly enhance the safety and efficacy of diabetes care. In this work, the design and fabrication of an InP/Pt/chitosan nanowire array-based chemiresistive acetone sensor is reported. By incorporation of chitosan as a surface-functional layer and a Pt Schottky contact for efficient charge transfer processes and photovoltaic effect, self-powered, highly selective acetone sensing is achieved. The sensor has exhibited an ultra-wide acetone detection range from sub-ppb to >100 000 ppm level at room temperature, covering those in the exhaled breath from healthy individuals (300-800 ppb) to people at high risk of DKA (>75 ppm). The nanowire sensor has also been successfully integrated into a handheld breath testing prototype, the Ketowhistle, which can successfully detect different ranges of acetone concentrations in simulated breath samples. The Ketowhistle demonstrates the immediate potential for non-invasive ketone monitoring for people living with diabetes, in particular for DKA prevention.

Assessment of UV radiation effects on airborne mucormycetes and bacterial populations in a hospital environment

Infections, such as mucormycosis, often result from inhaling sporangiospore present in the environment. Surprisingly, the extent of airborne Mucormycetes sporangiospore concentrations remains inadequately explored. This study aimed to assess the influence of UV radiation on microbial populations and Mucormycetes spore levels within a hospital environment in northern Iran. A comprehensive dataset comprising 298 air samples collected from both indoor and outdoor settings was compiled. The culture was conducted using Blood Agar and Dichloran Rose Bengal Chloramphenicol (DRBC) culture media, with Chloramphenicol included for fungal agents and Blood Agar for bacterial. Before UV treatment, the average count of Mucormycetes ranged from 0 to 26.4 ± 25.28 CFU m-3, fungal agents from 2.24 ± 3.22 to 117.24 ± 27.6 CFU m-3, and bacterial agents from 29.03 ± 9.9 to 359.37 ± 68.50 CFU m-3. Following UV irradiation, the averages were as follows: Mucormycetes ranged from 0 to 7.85 ± 6.8 CFU m-3, fungal agents from 16.58 ± 4.79 to 154.98 ± 28.35 CFU m-3, and bacterial agents from 0.38 ± 0.65 to 43.92 ± 6.50 CFU m-3. This study, notably marks the pioneering use of UV light to mitigate Mucormycetes spore counts and bacterial agents in northeastern Iran, contributing to the advancement of environmental health and safety practices in hospital settings.

Flicker Noise in Resistive Gas Sensors-Measurement Setups and Applications for Enhanced Gas Sensing

We discuss the implementation challenges of gas sensing systems based on low-frequency noise measurements on chemoresistive sensors. Resistance fluctuations in various gas sensing materials, in a frequency range typically up to a few kHz, can enhance gas sensing by considering its intensity and the slope of power spectral density. The issues of low-frequency noise measurements in resistive gas sensors, specifically in two-dimensional materials exhibiting gas-sensing properties, are considered. We present measurement setups and noise-processing methods for gas detection. The chemoresistive sensors show various DC resistances requiring different flicker noise measurement approaches. Separate noise measurement setups are used for resistances up to a few hundred kΩ and for resistances with much higher values. Noise measurements in highly resistive materials (e.g., MoS2, WS2, and ZrS3) are prone to external interferences but can be modulated using temperature or light irradiation for enhanced sensing. Therefore, such materials are of considerable interest for gas sensing.

Wireless, battery-free, multifunctional integrated bioelectronics for respiratory pathogens monitoring and severity evaluation

The rapid diagnosis of respiratory virus infection through breath and blow remains challenging. Here we develop a wireless, battery-free, multifunctional pathogenic infection diagnosis system (PIDS) for diagnosing SARS-CoV-2 infection and symptom severity by blow and breath within 110 s and 350 s, respectively. The accuracies reach to 100% and 92% for evaluating the infection and symptom severity of 42 participants, respectively. PIDS realizes simultaneous gaseous sample collection, biomarker identification, abnormal physical signs recording and machine learning analysis. We transform PIDS into other miniaturized wearable or portable electronic platforms that may widen the diagnostic modes at home, outdoors and public places. Collectively, we demonstrate a general-purpose technology for rapidly diagnosing respiratory pathogenic infection by breath and blow, alleviating the technical bottleneck of saliva and nasopharyngeal secretions. PIDS may serve as a complementary diagnostic tool for other point-of-care techniques and guide the symptomatic treatment of viral infections.

The Application of Graphene Field-Effect Transistor Biosensors in COVID-19 Detection Technology: A Review

Coronavirus disease 2019 (COVID-19) is a disease caused by the infectious agent of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). The primary method of diagnosing SARS-CoV-2 is nucleic acid detection, but this method requires specialized equipment and is time consuming. Therefore, a sensitive, simple, rapid, and low-cost diagnostic test is needed. Graphene field-effect transistor (GFET) biosensors have become the most promising diagnostic technology for detecting SARS-CoV-2 due to their advantages of high sensitivity, fast-detection speed, label-free operation, and low detection limit. This review mainly focus on three types of GFET biosensors to detect SARS-CoV-2. GFET biosensors can quickly identify SARS-CoV-2 within ultra-low detection limits. Finally, we will outline the pros and cons of the diagnostic approaches as well as future directions.

Recent Advances in Gas Detection Methodologies with a Special Focus on Environmental Sensing and Health Monitoring Applications─A Critical Review

With the expansion of the Internet-of-Things (IoT), the use of gas sensors in the field of wearable technology, smart devices, and smart homes has increased manifold. These gas sensors have two key applications─one is the detection of gases present in the environment and the other is the detection of Volatile Organic Compounds (VOCs) that are found in the breath. In this review, we focus systematically on the advancements in the field of various spectroscopic methods such as mass spectrometry-based analysis and point-of-care approach to detect VOCs and gases for environmental monitoring and disease diagnosis. Additionally, we highlight the development of smart sensors that work on the principle of electrochemical detection and provide examples of the same through an extensive literature review. At the end of this review, we highlight various challenges and future perspectives.

Detection of SARS-CoV-2 RNA in exhaled breath and its potential for prevention measures

Background

To propose infection prevention measures it is essential to understand the dynamics of SARS-CoV-2 shedding, particularly in asymptomatic patients. This report compares the viral load progression in exhaled breath (EB) with the symptom severity. We aim to evaluate the adequacy of symptom assessment regarding the infectivity level of individuals.

Methods

We observed infected patients since their first positive test during hospitalization. EB samples were collected on days 1, 3, 5, 7, 10, 12 and 14 of hospitalization using a filter-based device. After extraction, viral loads were quantified with qRT-PCR. The infection trajectory was documented after symptom onset.

Case Presentation and Discussion

A 34-year old patient showed mild symptoms, e.g. fever, cough, headache, muscle pain and loss of taste and smell across trajectory of infection (Case 1). The viral loads emitted via exhaling were nearly constant and ranged from 8.6 x 103 and 4.1 x 104 RNA copies per hour. After the infection, the patient developed a pneumonia. The second case of a 65-year old patient depicted an asymptomatic infection trajectory for 14 days after the first diagnosis (Case 2). Nevertheless, the patient exhaled up to 2 x 105 SARS-CoV-2 virus copies hourly, approximately 10 fold higher than measured for Case 1.

Conclusion

Symptomatic and asymptomatic COVID-19 patients exhale distinctive amounts of SARS-CoV-2 not necessarily correlating with symptom severity. Particularly, asymptomatic patients might show higher EB viral shedding. Therefore, EB testing should be included in infection prevention measures as it has high potential to reveal the most infectious individuals regardless of their symptoms during infection.

Rapid Direct Detection of SARS-CoV-2 Aerosols in Exhaled Breath at the Point of Care

Airborne transmission via virus-laden aerosols is a dominant route for the transmission of respiratory diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Direct, non-invasive screening of respiratory virus aerosols in patients has been a long-standing technical challenge. Here, we introduce a point-of-care testing platform that directly detects SARS-CoV-2 aerosols in as little as two exhaled breaths of patients and provides results in under 60 s. It integrates a hand-held breath aerosol collector and a llama-derived, SARS-CoV-2 spike-protein specific nanobody bound to an ultrasensitive micro-immunoelectrode biosensor, which detects the oxidation of tyrosine amino acids present in SARS-CoV-2 viral particles. Laboratory and clinical trial results were within 20% of those obtained using standard testing methods. Importantly, the electrochemical biosensor directly detects the virus itself, as opposed to a surrogate or signature of the virus, and is sensitive to as little as 10 viral particles in a sample. Our platform holds the potential to be adapted for multiplexed detection of different respiratory viruses. It provides a rapid and non-invasive alternative to conventional viral diagnostics.

Real-time environmental surveillance of SARS-CoV-2 aerosols

Real-time surveillance of airborne SARS-CoV-2 virus is a technological gap that has eluded the scientific community since the beginning of the COVID-19 pandemic. Offline air sampling techniques for SARS-CoV-2 detection suffer from longer turnaround times and require skilled labor. Here, we present a proof-of-concept pathogen Air Quality (pAQ) monitor for real-time (5 min time resolution) direct detection of SARS-CoV-2 aerosols. The system synergistically integrates a high flow (~1000 lpm) wet cyclone air sampler and a nanobody-based ultrasensitive micro-immunoelectrode biosensor. The wet cyclone showed comparable or better virus sampling performance than commercially available samplers. Laboratory experiments demonstrate a device sensitivity of 77-83% and a limit of detection of 7-35 viral RNA copies/m³ of air. Our pAQ monitor is suited for point-of-need surveillance of SARS-CoV-2 variants in indoor environments and can be adapted for multiplexed detection of other respiratory pathogens of interest. Widespread adoption of such technology could assist public health officials with implementing rapid disease control measures.

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.

Polydopamine Nanoparticles-Based Three-Line Lateral Flow Immunoassay for COVID-19 Detection

Currently, the global trend of several hundred thousand new confirmed COVID-19 patients per day has not abated significantly. Serological antibody detection has become an important tool for the self-screening of people. While the most commonly used colorimetric lateral flow immunoassay (LFIA) methods for the detection of COVID-19 antibodies are limited by low sensitivity and a lack of quantification ability. This leads to poor accuracy in the screening of early COVID-19 patients. Therefore, it is necessary to develop an accurate and sensitive autonomous antibody detection technique that will effectively reduce the COVID-19 infection rate. Here, we developed a three-line LFIA immunoassay based on polydopamine (PDA) nanoparticles for COVID-19 IgG and IgM antibodies detection to determine the degree of infection. The PDA-based three-line LFIA has a detection limit of 1.51 and 2.34 ng/mL for IgM and IgG, respectively. This assay reveals a good linearity for both IgM and IgG antibodies detection and is also able to achieve quantitative detection by measuring the optical density of test lines. In comparison, the commercial AuNP-based LFIA showed worse quantification results than the developed PDA-based LFIA for low-concentration COVID-19 antibody samples, making it difficult to distinguish between negative and positive samples. Therefore, the developed PDA-based three-line LFIA platform has the accurate quantitative capability and high sensitivity, which could be a powerful tool for the large-scale self-screening of people.

Light-Based Therapy: Novel Approach to Treat COVID-19

The pandemic outbreak of Coronavirus disease 2019 (COVID-19) which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), is a new viral infection in all countries around the world. An increase in inflammatory cytokines, fever, dry cough, and pneumonia are the main symptoms of COVID-19. A shared of growing clinical evidence confirmed that cytokine storm correlates with COVID-19 severity which is also a crucial cause of death from COVID-19. The success of anti-inflammatory therapies in the recovery process of COVID-19 patients has been well established. Over the years, phototherapy (PhT) has been identified as a promising non-invasive treatment approach for inflammatory conditions. New evidence suggests that PhT as an anti-inflammatory therapy may be effective in treating acute respiratory distress syndrome (ARDS) and COVID-19. This review aims to a comprehensive overview of the direct and indirect effects of anti-inflammatory mechanisms of PhT in ARDS and COVID-19 patients.

Advancement in COVID-19 detection using nanomaterial-based biosensors

Coronavirus disease 2019 (COVID-19) pandemic has exemplified how viral growth and transmission are a significant threat to global biosecurity. The early detection and treatment of viral infections is the top priority to prevent fresh waves and control the pandemic. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been identified through several conventional molecular methodologies that are time-consuming and require high-skill labor, apparatus, and biochemical reagents but have a low detection accuracy. These bottlenecks hamper conventional methods from resolving the COVID-19 emergency. However, interdisciplinary advances in nanomaterials and biotechnology, such as nanomaterials-based biosensors, have opened new avenues for rapid and ultrasensitive detection of pathogens in the field of healthcare. Many updated nanomaterials-based biosensors, namely electrochemical, field-effect transistor, plasmonic, and colorimetric biosensors, employ nucleic acid and antigen-antibody interactions for SARS-CoV-2 detection in a highly efficient, reliable, sensitive, and rapid manner. This systematic review summarizes the mechanisms and characteristics of nanomaterials-based biosensors for SARS-CoV-2 detection. Moreover, continuing challenges and emerging trends in biosensor development are also discussed.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant

Importance

Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown.

Objective

To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent.

Design, Setting, and Participants

This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography.

Main Outcomes and Measures

Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness.

Results

Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy.

Conclusions and Relevance

The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.

Machine Learning and COVID-19: Lessons from SARS-CoV-2

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.

Wearable bioelectronic masks for wireless detection of respiratory infectious diseases by gaseous media

Respiratory infectious diseases (H1N1, H5N1, COVID-19, etc.) are pandemics that can continually spread in the air through micro-droplets or aerosols. However, the detection of samples in gaseous media is hampered by the requirement for trace amounts and low concentrations. Here, we develop a wearable bioelectronic mask device integrated with ion-gated transistors. Based on the sensitive gating effect of ion gels, our aptamer-functionalized transistors can measure trace-level liquid samples (0.3 μL) and even gaseous media samples at an ultra-low concentration (0.1 fg/mL). The ion-gated transistor with multi-channel analysis can respond to multiple targets simultaneously within as fast as 10 min, especially without sample pretreatment. Integrating a wireless internet of things system enables the wearable mask to achieve real-time and on-site detection of the surrounding air, providing an alert before infection. The wearable bioelectronic masks hold promise to serve as an early warning system to prevent outbreaks of respiratory infectious diseases.

A Sensor-Integrated Face Mask Using Au@SnO2 Nanoparticle Modified Fibers and Augmented Reality Technology

In this work, we develop a wireless sensor-integrated face mask using Au@SnO₂ nanoparticle-modified conductive fibers based on augmented reality (AR) technology. AR technology enables the overlay of real objects and environments with virtual 3D objects and allows virtual interactions with real objects to create desired meanings. With the help of the AR system, the size of the mask could be precisely estimated and then manufactured using 3D printing technology. The body temperature sensor and respiratory sensor were integrated into the mask so that vital parameters of the human body could be continuously monitored without removing the personal protective equipment. Furthermore, the outer part of the mask consists of conductive fabric modified with Au@SnO₂ core-shell nanoparticle additives, which enhanced the filtration efficiency of airborne aerosols. A significant improvement in the filtration efficiency of particulate matter 2.5 was observed after applying an external voltage to the conductive textiles. A smartwatch with a heart rate sensor was paired with the mask to display sensor data on the mask through wireless transmission. Therefore, this sensor-integrated mask system with AR technology provides the first line of defense to combat global threats from pathogens and air pollutants.

Accuracy of Volatile Organic Compound (VOC) Detection in Exhaled Breath Compared to Reverse-transcriptase Polymerase Chain Reaction (RT-PCR) for Diagnosis of COVID-19: An Evidence-based Case Report

Background: Coronavirus disease 2019 (COVID-19) is a contagious infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The World Health Organization (WHO) declared this infection a global pandemic in 2020. In addition, various methods have been developed to diagnose COVID-19 rapidly and accurately to reverse transcription-polymerase chain reaction (RT-PCR) as a gold standard method. One of these methods is the detection of volatile organic compounds (VOC) in exhaled breath. Objectives: The aim was to collect and investigate studies on the accuracy of VOC detection as a diagnostic method for COVID-19. Methods: A literature search was performed in five electronic databases, including PubMed, Cochrane Library, ProQuest, EBSCOhost, and Scopus, along with hand searching. The search was conducted in the titles and abstracts of articles using keywords and their equivalent terms, combined with the Boolean operators (OR and AND). The search results were then selected according to the inclusion and exclusion criteria and compatibility with the Population, Intervention, Control, and Outcomes (PICO) framework. Results: Based on the search results, two cross-sectional studies by Wintjens et al. and Ruszkiewicz et al. were selected, which were then critically appraised. Both studies showed good validity. Wintjens et al. reported 86% sensitivity and 54% specificity for their method, with a positive predictive value (PPV) and a negative predictive value (NPV) of 40% and 92%, respectively. Besides, Ruszkiewicz et al., who conducted a study in two different locations, reported 82.4% sensitivity and 75% specificity for their method in Edinburgh (UK), with PPV and NPV of 87.5% and 66.7%, respectively, while they reported 90% sensitivity and 80% specificity in Dortmund (Germany), with PPV and NPV of 45% and 97.8%, respectively. The accuracy of these three methods was 62%, 80%, and 82%, respectively. Conclusions: Detection of VOCs from exhaled breath can be a rapid, cost-effective, and simple method for diagnosing COVID-19. However, the accuracy of this method is still relatively low (62 - 82%) and inconsistent; therefore, it is only recommended for screening.

The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures

The spread of SARS-CoV-2, which causes the disease COVID-19, is difficult to control as some positive individuals, capable of transmitting the disease, can be asymptomatic. Thus, it remains critical to generate noninvasive, inexpensive COVID-19 screening systems. Two such methods include detection canines and analytical instrumentation, both of which detect volatile organic compounds associated with SARS-CoV-2. In this study, the performance of trained detection dogs is compared to a noninvasive headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) approach to identifying COVID-19 positive individuals. Five dogs were trained to detect the odor signature associated with COVID-19. They varied in performance, with the two highest-performing dogs averaging 88% sensitivity and 95% specificity over five double-blind tests. The three lowest-performing dogs averaged 46% sensitivity and 87% specificity. The optimized linear discriminant analysis (LDA) model, developed using HS-SPME-GC-MS, displayed a 100% true positive rate and a 100% true negative rate using leave-one-out cross-validation. However, the non-optimized LDA model displayed difficulty in categorizing animal hair-contaminated samples, while animal hair did not impact the dogs' performance. In conclusion, the HS-SPME-GC-MS approach for noninvasive COVID-19 detection more accurately discriminated between COVID-19 positive and COVID-19 negative samples; however, dogs performed better than the computational model when non-ideal samples were presented.

Angiotensin-Converting Enzyme 2-Based Biosensing Modalities and Devices for Coronavirus Detection

Rapid and cost-effective diagnostic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a critical and valuable weapon for the coronavirus disease 2019 (COVID-19) pandemic response. SARS-CoV-2 invasion is primarily mediated by human angiotensin-converting enzyme 2 (hACE2). Recent developments in ACE2-based SARS-CoV-2 detection modalities accentuate the potential of this natural host-virus interaction for developing point-of-care (POC) COVID-19 diagnostic systems. Although research on harnessing ACE2 for SARS-CoV-2 detection is in its infancy, some interesting biosensing devices have been developed, showing the commercial viability of this intriguing new approach. The exquisite performance of the reported ACE2-based COVID-19 biosensors provides opportunities for researchers to develop rapid detection tools suitable for virus detection at points of entry, workplaces, or congregate scenarios in order to effectively implement pandemic control and management plans. However, to be considered as an emerging approach, the rationale for ACE2-based biosensing needs to be critically and comprehensively surveyed and discussed. Herein, we review the recent status of ACE2-based detection methods, the signal transduction principles in ACE2 biosensors and the development trend in the future. We discuss the challenges to development of ACE2-biosensors and delineate prospects for their use, along with recommended solutions and suggestions.

Wearable respiratory sensors for COVID-19 monitoring

Since its outbreak in 2019, COVID-19 becomes a pandemic, severely burdening the public healthcare systems and causing an economic burden. Thus, societies around the world are prioritizing a return to normal. However, fighting the recession could rekindle the pandemic owing to the lightning-fast transmission rate of SARS-CoV-2. Furthermore, many of those who are infected remain asymptomatic for several days, leading to the increased possibility of unintended transmission of the virus. Thus, developing rigorous and universal testing technologies to continuously detect COVID-19 for entire populations remains a critical challenge that needs to be overcome. Wearable respiratory sensors can monitor biomechanical signals such as the abnormities in respiratory rate and cough frequency caused by COVID-19, as well as biochemical signals such as viral biomarkers from exhaled breaths. The point-of-care system enabled by advanced respiratory sensors is expected to promote better control of the pandemic by providing an accessible, continuous, widespread, noninvasive, and reliable solution for COVID-19 diagnosis, monitoring, and management.

Real-time COVID-19 detection via graphite oxide-based field-effect transistor biosensors decorated with Pt/Pd nanoparticles

Coronavirus 2019 (COVID-19) spreads an extremely infectious disease where there is no specific treatment. COVID-19 virus had a rapid and unexpected spread rate which resulted in critical difficulties for public health and unprecedented daily life disruption. Thus, accurate, rapid, and early diagnosis of COVID-19 virus is critical to maintain public health safety. A graphite oxide-based field-effect transistor (GO-FET) was fabricated and functionalized with COVID-19 antibody for the purpose of real-time detection of COVID-19 spike protein antigen. Thermal evaporation process was used to deposit the gold electrodes on the surface of the sensor substrate. Graphite oxide channel was placed between the gold electrodes. Bimetallic nanoparticles of platinum and palladium were generated via an ultra-high vacuum (UHV) compatible system by sputtering and inert-gas condensation technique. The biosensor graphite oxide channel was immobilized with specific antibodies against the COVID-19 spike protein to achieve selectivity and specificity. This technique uses the attractive semiconductor characteristics of the graphite oxide-based materials resulting in highly specific and sensitive detection of COVID-19 spike protein. The GO-FET biosensor was decorated with bimetallic nanoparticles of platinum and palladium to investigate the improvement in the sensor sensitivity. The in-house developed biosensor limit of detection (LOD) is 1 fg/mL of COVID-19 spike antigen in phosphate-buffered saline (PBS). Moreover, magnetic labelled SARS-CoV-2 spike antibody were studied to investigate any enhancement in the sensor performance. The results indicate the successful fabrication of a promising field effect transistor biosensor for COVID-19 diagnosis.

Exhaled breath SARS-CoV-2 shedding patterns across variants of concern

OBJECTIVES

We performed exhaled breath (EB) and nasopharyngeal (NP) quantitative polymerase chain reaction (qPCR) and NP rapid antigen testing (NP RAT) of SARS-CoV-2 infections with different variants.

METHODS

We included immuno-naïve alpha-infected (n = 11) and partly boosted omicron-infected patients (n = 8) as high-risk contacts. We compared peak NP and EB qPCR cycle time (ct) values between cohorts (Wilcoxon-Mann-Whitney test). Test positivity was compared for three infection phases using Cochran Q test.

RESULTS

Peak median NP ct was 11.5 (interquartile range [IQR] 10.1-12.1) for alpha and 12.2 (IQR 11.1-15.3) for omicron infections. Peak median EB ct was 25.2 (IQR 24.5-26.9) and 28.3 (IQR 26.4-30.8) for alpha and omicron infections, respectively. Distributions did not differ between cohorts for NP (P = 0.19) or EB (P = 0.09). SARS-CoV-2 shedding peaked on day 1 in EB (confidence interval [CI] 0.0 - 4.5) and day 3 in NP (CI 1.5 - 6.0). EB qPCR positivity equaled NP qPCR positivity on D0-D1 (P = 0.44) and D2-D6 (P = 1.0). It superseded NP RAT positivity on D0-D1 (P = 0.003) and D2-D6 (P = 0.008). It was inferior to both on D7-D10 (P < 0.001).

CONCLUSION

Peak EB and nasopharynx shedding were comparable across variants. EB qPCR positivity matched NP qPCR and superseded NP RAT in the first week of infection.

Optical Monitoring of Breathing Patterns and Tissue Oxygenation: A Potential Application in COVID-19 Screening and Monitoring

The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O₂Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.

Clinical studies of detecting COVID-19 from exhaled breath with electronic nose

The COVID-19 pandemic has attracted numerous research studies because of its impact on society and the economy. The pandemic has led to progress in the development of diagnostic methods, utilizing the polymerase chain reaction (PCR) as the gold standard for coronavirus SARS-CoV-2 detection. Numerous tests can be used at home within 15 min or so but of with lower accuracy than PCR. There is still a need for point-of-care tests available for mass daily screening of large crowds in airports, schools, and stadiums. The same problem exists with fast and continuous monitoring of patients during their medical treatment. The rapid methods can use exhaled breath analysis which is non-invasive and delivers the result quite fast. Electronic nose can detect a cocktail of volatile organic com-pounds (VOCs) induced by virus infection and disturbed metabolism in the human body. In our exploratory studies, we present the results of COVID-19 detection in a local hospital by applying the developed electronic setup utilising commercial VOC gas sensors. We consider the technical problems noticed during the reported studies and affecting the detection results. We believe that our studies help to advance the proposed technique to limit the spread of COVID-19 and similar viral infections.

Potential Use of Exhaled Breath Condensate for Diagnosis of SARS-CoV-2 Infections: A Systematic Review and Meta-Analysis

Background. Reverse-transcriptase polymerase chain reaction (RT-qPCR) assays performed on respiratory samples collected through nasal swabs still represent the gold standard for COVID-19 diagnosis. Alternative methods to this invasive and time-consuming options are still being inquired, including the collection of airways lining fluids through exhaled breath condensate (EBC). Materials and Methods. We performed a systematic review and meta-analysis in order to explore the reliability of EBC as a way to collect respiratory specimens for RT-qPCR for diagnosis of COVID-19. Results. A total of 4 studies (205 specimens), were ultimately collected, with a pooled sensitivity of 69.5% (95%CI 26.8–93.4), and a pooled specificity of 98.3% (95%CI 87.8–99.8), associated with high heterogeneity and scarce diagnostic agreement with the gold standard represented by nasal swabs (Cohen’s kappa = 0.585). Discussion. Even though non-invasive options for diagnosis of COVID-19 are still necessary, EBC-based RT-qPCR showed scarce diagnostic performances, ultimately impairing its implementation in real-world settings. However, as few studies have been carried out to date, and the studies included in the present review are characterized by low numbers and low sample power, further research are requested to fully characterize the actual reliability of EBC-based RT-qPCR in the diagnosis of COVID-19.

Visual diagnosis of COVID-19 disease based on serum metabolites using a paper-based electronic tongue

This study aims to use a paper-based sensor array for point-of-care detection of COVID-19 diseases. Various chemical compounds such as nanoparticles, organic dyes and metal ion complexes were employed as sensing elements in the array fabrication, capturing the metabolites of human serum samples. The viral infection caused the type and concentration of serum compositions to change, resulting in different color responses for the infected and control samples. For this purpose, 118 serum samples of COVID-19 patients and non-COVID controls both men and women with the age range of 14–88 years were collected. The serum samples were initially subjected to the sensor, followed by monitoring the variation in the color of sensing elements for 5 min using a scanner. By taking into consideration the statistical information, this method was capable of discriminating COVID-19 patients and control samples with 83.0% accuracy. The variation of age did not influence the colorimetric patterns. The desirable correlation was observed between the sensor responses and viral load values calculated by the PCR test, proposing a rapid and facile way to estimate the disease severity. Compared to other rapid detection methods, the developed assay is cost-effective and user-friendly, allowing for screening COVID-19 diseases reliably.

Metal-organic framework MIL-100(Fe) as a promising sensor for COVID-19 biomarkers detection

The development of fast and non-invasive techniques to detect SARS-CoV-2 virus at the early stage of the infection would be highly desirable to control the COVID-19 outbreak. Metal-organic frameworks (MOFs) are porous materials with uniform porous structures and tunable pore surfaces, which would be essential for the selective sensing of the specific COVID-19 biomarkers. However, the use of MOFs materials to detect COVID-19 biomarkers has not been demonstrated so far. In this work, for the first time, we employed the density functional theory calculations to investigate the specific interactions of MOFs and the targeted biomarkers, in which the interactions were confirmed by experiment. The five dominant COVID-19 biomarkers and common exhaled gases are comparatively studied by exposing them to MOFs, namely MIL-100(Al) and MIL-100(Fe). The adsorption mechanism, binding site, adsorption energy, recovery time, charge transfer, sensing response, and electronic structures are systematically investigated. We found that MIL-100(Fe) has a higher sensing performance than MIL-100(Al) in terms of sensitivity and selectivity. MIL-100(Fe) shows sensitive to COVID-19 biomarkers, namely 2-methylpent-2-enal and 2,4-octadiene with high sensing responses as 7.44 x 10⁵ and 9 x 10⁷ which are exceptionally higher than those of the common gases which are less than 6. The calculated recovery times of 0.19 and 1.84 x 10^-4 s are short enough to be a resuable sensor. An experimental study also showed that the MIL-100(Fe) provides a sensitivity toward 2-methylpent-2-enal. In conclusion, we suggest that MIL-100(Fe) could be used as a potential sensor for the exhaled breath analysis. We hope that our research can aid in the development of a biosensor for quick and easy COVID-19 biomarker detection in order to control the current pandemic.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

State-of-the-Art Smart and Intelligent Nanobiosensors for SARS-CoV-2 Diagnosis

The novel coronavirus appeared to be a milder infection initially, but the unexpected outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), commonly called COVID-19, was transmitted all over the world in late 2019 and caused a pandemic. Human health has been disastrously affected by SARS-CoV-2, which is still evolving and causing more serious concerns, leading to the innumerable loss of lives. Thus, this review provides an outline of SARS-CoV-2, of the traditional tools to diagnose SARS-CoV-2, and of the role of emerging nanomaterials with unique properties for fabricating biosensor devices to diagnose SARS-CoV-2. Smart and intelligent nanomaterial-enabled biosensors (nanobiosensors) have already proven their utility for the diagnosis of several viral infections, as various detection strategies based on nanobiosensor devices are already present, and several other methods are also being investigated by researchers for the determination of SARS-CoV-2 disease; however, considerably more is undetermined and yet to be explored. Hence, this review highlights the utility of various nanobiosensor devices for SARS-CoV-2 determination. Further, it also emphasizes the future outlook of nanobiosensing technologies for SARS-CoV-2 diagnosis.

Non-invasive detection of COVID-19 using a microfluidic-based colorimetric sensor array sensitive to urinary metabolites

A colorimetric sensor array designed on a paper substrate with a microfluidic structure has been developed. This array is capable of detecting COVID-19 disease by tracking metabolites of urine samples. In order to determine minor metabolic changes, various colorimetric receptors consisting of gold and silver nanoparticles, metalloporphyrins, metal ion complexes, and pH-sensitive indicators are used in the array structure. By injecting a small volume of the urine sample, the color pattern of the sensor changes after 7 min, which can be observed visually. The color changes of the receptors (recorded by a scanner) are subsequently calculated by image analysis software and displayed as a color difference map. This study has been performed on 130 volunteers, including 60 patients infected by COVID-19, 55 healthy controls, and 15 cured individuals. The resulting array provides a fingerprint response for each category due to the differences in the metabolic profile of the urine sample. The principal component analysis-discriminant analysis confirms that the assay sensitivity to the correctly detected patient, healthy, and cured participants is equal to 73.3%, 74.5%, and 66.6%, respectively. Apart from COVID-19, other diseases such as chronic kidney disease, liver disorder, and diabetes may be detectable by the proposed sensor. However, this performance of the sensor must be tested in the studies with a larger sample size. These results show the possible feasibility of the sensor as a suitable alternative to costly and time-consuming standard methods for rapid detection and control of viral and bacterial infectious diseases and metabolic disorders.

Molecular detection of SARS-COV-2 in exhaled breath at the point-of-need

The SARS-CoV-2 pandemic has highlighted the need for improved technologies to help control the spread of contagious pathogens. While rapid point-of-need testing plays a key role in strategies to rapidly identify and isolate infectious patients, current test approaches have significant shortcomings related to assay limitations and sample type. Direct quantification of viral shedding in exhaled particles may offer a better rapid testing approach, since SARS-CoV-2 is believed to spread mainly by aerosols. It assesses contagiousness directly, the sample is easy and comfortable to obtain, sampling can be standardized, and the limited sample volume lends itself to a fast and sensitive analysis. In view of these benefits, we developed and tested an approach where exhaled particles are efficiently sampled using inertial impaction in a micromachined silicon chip, followed by an RT-qPCR molecular assay to detect SARS-CoV-2 shedding. Our portable, silicon impactor allowed for the efficient capture (>85%) of respiratory particles down to 300 nm without the need for additional equipment. We demonstrate using both conventional off-chip and in-situ PCR directly on the silicon chip that sampling subjects’ breath in less than a minute yields sufficient viral RNA to detect infections as early as standard sampling methods. A longitudinal study revealed clear differences in the temporal dynamics of viral load for nasopharyngeal swab, saliva, breath, and antigen tests. Overall, after an infection, the breath-based test remains positive during the first week but is the first to consistently report a negative result, putatively signalling the end of contagiousness and further emphasizing the potential of this tool to help manage the spread of airborne respiratory infections.

Diagnostic Tools for Rapid Screening and Detection of SARS-CoV-2 Infection

The novel coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has severely impacted human health and the health management system globally. The ongoing pandemic has required the development of more effective diagnostic strategies for restricting deadly disease. For appropriate disease management, accurate and rapid screening and isolation of the affected population is an efficient means of containment and the decimation of the disease. Therefore, considerable efforts are being directed toward the development of rapid and robust diagnostic techniques for respiratory infections, including SARS-CoV-2. In this article, we have summarized the origin, transmission, and various diagnostic techniques utilized for the detection of the SARS-CoV-2 virus. These higher-end techniques can also detect the virus copy number in asymptomatic samples. Furthermore, emerging rapid, cost-effective, and point-of-care diagnostic devices capable of large-scale population screening for COVID-19 are discussed. Finally, some breakthrough developments based on spectroscopic diagnosis that could revolutionize the field of rapid diagnosis are discussed.

Smart Approach for the Design of Highly Selective Aptamer-Based Biosensors

Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This “smart” SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor’s response. This can be explained by considering the aptamers’ conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule.

Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection.

Detection of SARS-CoV-2 virus via dynamic light scattering using antibody-gold nanoparticle bioconjugates against viral spike protein

Mass testing for the diagnosis of COVID-19 has been hampered in many countries owing to the high cost of genetic material detection. This study reports on a low-cost immunoassay for detecting SARS-CoV-2 within 30 min using dynamic light scattering (DLS). The immunosensor comprises 50-nm gold nanoparticles (AuNPs) functionalized with antibodies against SARS-CoV-2 spike glycoprotein, whose bioconjugation was confirmed using transmission electron microscopy (TEM), UV-Vis spectroscopy, Fourier transform infrared spectroscopy (FTIR), and surface-enhanced Raman scattering spectroscopy (SERS). The specific binding of the bioconjugates to the spike protein led to an increase in bioconjugate size, with a limit of detection (LOD) 5.29 × 10³ TCID₅₀/mL (Tissue Culture Infectious Dose). The immunosensor was also proven to be selective upon interaction with influenza viruses once no increase in size was observed after DLS measurement. The strategy proposed here aimed to use antibodies conjugated to AuNPs as a generic platform that can be extended to other detection principles, enabling technologies for low-cost mass testing for COVID-19.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Nanotechnology Role Development for COVID-19 Pandemic Management

The global outbreak of coronavirus disease has sent an ominous message to the field of innovative and advanced technology research and development (COVID-19). To accomplish this, convectional technology and recent discoveries can be combined, or new research directions can be opened up using nanotechnology. Nanotechnology can be used to prevent, diagnose, and treat SARS-CoV-2 infection. As the pandemic spreads, a thorough examination of nanomaterials' role in pandemic response is highly desirable. According to this comprehensive review article, nanotechnology can be used to prevent, diagnose, and treat COVID-19. This research will be extremely useful during the COVID-19 outbreak in terms of developing rules for designing nanostructure materials to combat the outbreak.

Graphene-based temperature, humidity, and strain sensor: A review on progress, characterization, and potential applications during Covid-19 pandemic

Graphene's potential as material for wearable, highly sensitive and robust sensor in various fields of technology has been widely investigated until now in order to capitalize on its unique intrinsic physical and chemical properties. In the wake of Covid-19 pandemic, it has been noticed that there are various potentials roles that can be fulfilled by graphene-based temperature, humidity and strain sensor, whose roles has not been widely explored to date. This paper takes the liberty to mainly highlight the progress layout and characterization technique for graphene-based sensor while including a brief discussion on the possible strategy of sensing data analysis that can be employed to minimize and prevent the risk of Covid-19 infection within a living community. While majority of the reported sensor is still in the in-progress status, its highlighted role in this work may provide a brief idea on how the ongoing research in graphene-based sensor may lead to the future implementation of the device for routine healthcare check-up and diagnostic point-care during and post-pandemic era. On the other hand, the sensitivity and response time data against working temperature, humidity and strain range that are provided could serve as a reference for benchmarking purpose, which certainly would help enthusiast in the development of a graphene-based sensor with a better performance for the future.

An Experimental Apparatus for E-Nose Breath Analysis in Respiratory Failure Patients

Background: Non-invasive, bedside diagnostic tools are extremely important for tailo ring the management of respiratory failure patients. The use of electronic noses (ENs) for exhaled breath analysis has the potential to provide useful information for phenotyping different respiratory disorders and improving diagnosis, but their application in respiratory failure patients remains a challenge. We developed a novel measurement apparatus for analysing exhaled breath in such patients. Methods: The breath sampling apparatus uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen and prevent contamination of exhaled gas from ambient Volatile Organic Compounds (VOCs) It is designed to minimise the dead space and respiratory load imposed on patients. Breath odour fingerprints were assessed using a commercial EN with custom MOX sensors. We carried out a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls to gather data on tolerability and for a preliminary assessment of sensitivity and specificity. The most significant features for the discrimination between breath-odour fingerprints from respiratory failure patients and controls were identified using the Boruta algorithm and then implemented in the development of a support vector machine (SVM) classification model. Results: The novel sampling system was well-tolerated by all patients. The SVM differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve) and a sensitivity and specificity of 0.920 and 0.682, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p < 0.001 and 0.046, respectively). Conclusions: the developed system is suitable for the collection of exhaled breath samples from respiratory failure patients. Our preliminary results suggest that breath-odour fingerprints may be sensitive markers of lung disease severity and aetiology.

Post-COVID syndrome screening through breath analysis using electronic nose technology

There is an urgent need to have reliable technologies to diagnose post-coronavirus disease syndrome (PCS), as the number of people affected by COVID-19 and related complications is increasing worldwide. Considering the amount of risks associated with the two chronic lung diseases, asthma and chronic obstructive pulmonary disease (COPD), there is an immediate requirement for a screening method for PCS, which also produce symptoms similar to these conditions, especially since very often, many COVID-19 cases remain undetected because a good share of such patients is asymptomatic. Breath analysis techniques are getting attention since they are highly non-invasive methods for disease diagnosis, can be implemented easily for point-of-care applications even in primary health care centres. Electronic (E-) nose technology is coming up with better reliability, ease of operation, and affordability to all, and it can generate signatures of volatile organic compounds (VOCs) in exhaled breath as markers of diseases. The present report is an outcome of a pilot study using an E-nose device on breath samples of cohorts of PCS, asthma, and normal (control) subjects. Match/no-match and k-NN analysis tests have been carried out to confirm the diagnosis of PCS. The prediction model has given 100% sensitivity and specificity. Receiver operating characteristics (ROC) has been plotted for the prediction model, and the area under the curve (AUC) is obtained as 1. The E-nose technique is found to be working well for PCS diagnosis. Our study suggests that the breath analysis using E-nose can be used as a point-of-care diagnosis of PCS.

Trial registration

Breath samples were collected from the Kasturba Hospital, Manipal. Ethical clearance was obtained from the Institutional Ethics Committee, Kasturba Medical College, Manipal (IEC 60/2021, 13/01/2021) and Indian Council of Medical Research (ICMR) (CTRI/2021/02/031357, 06/02/2021) Government of India; trials were prospectively registered.

E.Co.Tech-electrochemical handheld breathalyzer COVID sensing technology

Breathomics is widely emerging as a strategy for non-invasive diagnosis of respiratory inflammation. In this study, we have evaluated the metabolic signals associated with Coronavirus (SARS COV-2), mainly the release of nitric oxide in breath. We have demonstrated the utility of a breath analyzer-based sensor platform for the detection of trace amounts of this target species. The sensor surface is modified with Room Temperature Ionic Liquid (RTIL) that allows faster diffusion of the target gas and can be used for gas sensing application. A low limit of detection (LOD) of 50 parts per billion has been achieved with a 95% confidence interval for detection of nitric oxide.. This inhouse designed sensor is incorporated into a breath analyzer system that displays enhanced sensitivity, specificity, linearity, and reproducibility for NO gas monitoring. The developed sensor platform can detect target concentrations of NO ranging from 50 to 250 ppb, using 1-Ethyl-3-methylimidazolium Tetrafluoroborate ([EMIM]BF₄) as RTIL and displays fast response time of 5 s, thereby allowing easy detection of the target gas species. The sensor successfully quantifies the diffusion current and charge modulations arising within the electrical double layer from the RTIL–NO interactions through DC-based chronoamperometry (CA). The subjects tested negative and positive are significantly different (p < 0.01). The prototype can potentially be used for human health monitoring and screening, especially during the pandemic due to its portability, small size, an embedded RTIL sensing element, integrability with a low-power microelectronic device, and an IoT interface.

Highly Sensitive Immunoresistive Sensor for Point-Of-Care Screening for COVID-19

Current point-of-care (POC) screening of Coronavirus disease 2019 (COVID-19) requires further improvements to achieve highly sensitive, rapid, and inexpensive detection. Here we describe an immunoresistive sensor on a polyethylene terephthalate (PET) film for simple, inexpensive, and highly sensitive COVID-19 screening. The sensor is composed of single-walled carbon nanotubes (SWCNTs) functionalized with monoclonal antibodies that bind to the spike protein of SARS-CoV-2. Silver electrodes are silkscreen-printed on SWCNTs to reduce contact resistance. We determine the SARS-CoV-2 status via the resistance ratio of control- and SARS-CoV-2 sensor electrodes. A combined measurement of two adjacent sensors enhances the sensitivity and specificity of the detection protocol. The lower limit of detection (LLD) of the SWCNT assay is 350 genome equivalents/mL. The developed SWCNT sensor shows 100% sensitivity and 90% specificity in clinical sample testing. Further, our device adds benefits of a small form factor, simple operation, low power requirement, and low assay cost. This highly sensitive film sensor will facilitate rapid COVID-19 screening and expedite the development of POC screening platforms.

Breathing new life into clinical testing and diagnostics: perspectives on volatile biomarkers from breath

Human breath offers several benefits for diagnostic applications, including simple, noninvasive collection. Breath is a rich source of clinically-relevant biological information; this includes a volatile fraction, where greater than 1,000 volatile organic compounds (VOCs) have been described so far, and breath aerosols that carry nucleic acids, proteins, signaling molecules, and pathogens. Many of these factors, especially VOCs, are delivered to the lung by the systemic circulation, and diffusion of candidate biomarkers from blood into breath allows systematic profiling of organismal health. Biomarkers on breath offer the capability to advance early detection and precision medicine in areas of global clinical need. Breath tests are noninvasive and can be performed at home or in a primary care setting, which makes them well-suited for the kind of public screening program that could dramatically improve the early detection of conditions such as lung cancer. Since measurements of VOCs on breath largely report on metabolic changes, this too aids in the early detection of a broader range of illnesses and can be used to detect metabolic shifts that could be targeted through precision medicine. Furthermore, the ability to perform frequent sampling has envisioned applications in monitoring treatment responses. Breath has been investigated in respiratory, liver, gut, and neurological diseases and in contexts as diverse as infectious diseases and cancer. Preclinical research studies using breath have been ongoing for some time, yet only a few breath-based diagnostics tests are currently available and in widespread clinical use. Most recently, tests assessing the gut microbiome using hydrogen and methane on breath, in addition to tests using urea to detect Helicobacter pylori infections have been released, yet there are many more applications of breath tests still to be realized. Here, we discuss the strengths of breath as a clinical sampling matrix and the technical challenges to be addressed in developing it for clinical use. Historically, a lack of standardized methodologies has delayed the discovery and validation of biomarker candidates, resulting in a proliferation of early-stage pilot studies. We will explore how advancements in breath collection and analysis are in the process of driving renewed progress in the field, particularly in the context of gastrointestinal and chronic liver disease. Finally, we will provide a forward-looking outlook for developing the next generation of clinically relevant breath tests and how they may emerge into clinical practice.

Peptidic Sulfhydryl for Interfacing Nanocrystals and Subsequent Sensing of SARS-CoV-2 Protease

There is a need for surveillance of COVID-19 to identify individuals infected with SARS-CoV-2 coronavirus. Although specific, nucleic acid testing has limitations in terms of point-of-care testing. One potential alternative is the nonstructural protease (nsp5, also known as Mpro/3CLpro) implicated in SARS-CoV-2 viral replication but not incorporated into virions. Here, we report a divalent substrate with a novel design, (Cys)2-(AA)x-(Asp)3, to interface gold colloids in the specific presence of Mpro leading to a rapid and colorimetric readout. Citrate- and tris(2-carboxyethyl)phosphine (TCEP)-AuNPs were identified as the best reporter out of the 17 ligated nanoparticles. Furthermore, we empirically determined the effects of varying cysteine valence and biological media on the sensor specificity and sensitivity. The divalent peptide was specific to Mpro, that is, there was no response when tested with other proteins or enzymes. Furthermore, the Mpro detection limits in Tris buffer and exhaled breath matrices are 12.2 and 18.9 nM, respectively, which are comparable to other reported methods (i.e., at low nanomolar concentrations) yet with a rapid and visual readout. These results from our work would provide informative rationales to design a practical and noninvasive alternative for COVID-19 diagnostic testing-the presence of viral proteases in biofluids is validated.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.