Automated in Vivo Nanosensing of Breath-Borne Protein Biomarkers

Recent progress in online detection methods of bioaerosols

The aerosol transmission of coronavirus disease in 2019, along with the spread of other respiratory diseases, caused significant loss of life and property; it impressed upon us the importance of real-time bioaerosol detection. The complexity, diversity, and large spatiotemporal variability of bioaerosols and their external/internal mixing with abiotic components pose challenges for effective online bioaerosol monitoring. Traditional methods focus on directly capturing bioaerosols before subsequent time-consuming laboratory analysis such as culture-based methods, preventing the high-resolution time-based characteristics necessary for an online approach. Through a comprehensive literature assessment, this review highlights and discusses the most commonly used real-time bioaerosol monitoring techniques and the associated commercially available monitors. Methods applied in online bioaerosol monitoring, including adenosine triphosphate bioluminescence, laser/light-induced fluorescence spectroscopy, Raman spectroscopy, and bioaerosol mass spectrometry are summarized. The working principles, characteristics, sensitivities, and efficiencies of these real-time detection methods are compared to understand their responses to known particle types and to contrast their differences. Approaches developed to analyze the substantial data sets obtained by these instruments and to overcome the limitations of current real-time bioaerosol monitoring technologies are also introduced. Finally, an outlook is proposed for future instrumentation indicating a need for highly revolutionized bioaerosol detection technologies.

Exhaled VOC Biomarkers from Rats Injected with PMs from Thirty-One Major Cities in China

Particulate matter (PMs) of different origins can cause diverse health effects. Here, a homemade box was used to facilitate real-time measurements of breath-borne volatile organic compounds (VOCs) by gas chromatography-ion mobility spectrometry. We have tracked exhaled VOC changes in 228 Wistar rats that were injected with water-soluble PM suspension filtrates (after 0.45 μm) from 31 China cities for 1 h to up to 1-6 days during the experiments. Rats exposed to the filtrates exhibited significant changes in breath-borne VOCs within hours, featuring dynamic fluctuations in the levels of acetone, butan-2-one, heptan-2-one-M, acetic acid-M, and ethanol. Subsequently, on the fifth to sixth day after the injection, there was a notable increase in the proportion of aldehydes (including hexanal-M, hexanal-D, pentanal, heptanal-M, and (E)-2-hexenal). The 10 dynamic VOC fingerprint patterns mentioned earlier showcased the capability to indirectly differentiate urban PM toxicity and categorize the 31 cities into four distinct groups based on their health effects. This study provides valuable insights into the mechanisms of exhaled VOCs and underscores their critical role as biomarkers for differentiating the toxicity of different PMs and detecting the early signs of potential diseases. The results from this work also provide a scientific basis for city-specific air pollution control and policy development.

Biomolecular sensors for advanced physiological monitoring

Body-based biomolecular sensing systems, including wearable, implantable and consumable sensors allow comprehensive health-related monitoring. Glucose sensors have long dominated wearable bioanalysis applications owing to their robust continuous detection of glucose, which has not yet been achieved for other biomarkers. However, access to diverse biological fluids and the development of reagentless sensing approaches may enable the design of body-based sensing systems for various analytes. Importantly, enhancing the selectivity and sensitivity of biomolecular sensors is essential for biomarker detection in complex physiological conditions. In this Review, we discuss approaches for the signal amplification of biomolecular sensors, including techniques to overcome Debye and mass transport limitations, and selectivity improvement, such as the integration of artificial affinity recognition elements. We highlight reagentless sensing approaches that can enable sequential real-time measurements, for example, the implementation of thin-film transistors in wearable devices. In addition to sensor construction, careful consideration of physical, psychological and security concerns related to body-based sensor integration is required to ensure that the transition from the laboratory to the human body is as seamless as possible.

On-site bioaerosol sampling and detection in microfluidic platforms

As the recent coronavirus disease (COVID-19) pandemic and several severe illnesses such as Middle East respiratory syndrome coronavirus (MERS-CoV), Influenza A virus (IAV) flu, and severe acute respiratory syndrome (SARS) have been found to be airborne, the importance of monitoring bioaerosols for the control and prevention of airborne epidemic diseases outbreaks is increasing. However, current aerosol collection and detection technologies may be limited to on-field use for real-time monitoring because of the relatively low concentrations of targeted bioaerosols in air samples. Microfluidic devices have been used as lab-on-a-chip platforms and exhibit outstanding capabilities in airborne particulate collection, sample processing, and target molecule analysis, thereby highlighting their potential for on-site bioaerosol monitoring. This review discusses the measurement of airborne microorganisms from air samples, including sources and transmission of bioaerosols, sampling strategies, and analytical methodologies. Recent advancements in microfluidic platforms have focused on bioaerosol sample preparation strategies, such as sorting, concentrating, and extracting, as well as rapid and field-deployable detection methods for analytes on microfluidic chips. Furthermore, we discuss an integrated platform for on-site bioaerosol analyses. We believe that our review significantly contributes to the literature as it assists in bridging the knowledge gaps in bioaerosol monitoring using microfluidic platforms.

Designable microfluidic ladder networks from backstepping microflow analysis for mass production of monodisperse microdroplets

Controllable mass production of monodisperse droplets plays a key role in numerous fields ranging from scientific research to industrial application. Microfluidic ladder networks show great potential in mass production of monodisperse droplets, but their design with uniform microflow distribution remains challenging due to the lack of a rational design strategy. Here an effective design strategy based on backstepping microflow analysis (BMA) is proposed for the rational development of microfluidic ladder networks for mass production of controllable monodisperse microdroplets. The performance of our BMA rule for rational microfluidic ladder network design is demonstrated by using an existing analogism-derived rule that is widely used for the design of microfluidic ladder networks as the control group. The microfluidic ladder network designed by the BMA rule shows a more uniform flow distribution in each branch microchannel than that designed by the existing rule, as confirmed by single-phase flow simulation. Meanwhile, the microfluidic ladder network designed by the BMA rule allows mass production of droplets with higher size monodispersity in a wider window of flow rates and mass production of polymeric microspheres from such highly monodisperse droplet templates. The proposed BMA rule provides new insights into the microflow distribution behaviors in microfluidic ladder networks based on backstepping microflow analysis and provides a rational guideline for the efficient development of microfluidic ladder networks with uniform flow distribution for mass production of highly monodisperse droplets. Moreover, the BMA method provides a general analysis strategy for microfluidic networks with parallel multiple microchannels for rational scale-up.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Haze Air Pollution Health Impacts of Breath-Borne VOCs

Here, we investigated the use of breath-borne volatile organic compounds (VOCs) for rapid monitoring of air pollution health effects on humans. Forty-seven healthy college students were recruited, and their exhaled breath samples (n = 235) were collected and analyzed for VOCs before, on, and after two separate haze pollution episodes using gas chromatography-ion mobility spectrometry (GC-IMS). Using a paired t-test and machine learning model (Gradient Boosting Machine, GBM), six exhaled VOC species including propanol and isoprene were revealed to differ significantly among pre-, on-, and post-exposure in both haze episodes, while none was found between clean control days. The GBM model was shown capable of differentiating between pre- and on-exposure to haze pollution with a precision of 90-100% for both haze episodes. However, poor performance was detected for the same model between two different clean days. In addition to gender and particular haze occurrence influences, correlation analysis revealed that NH₄⁺, NO₃^-, acetic acid, mesylate, CO, NO₂, PM_2.5, and O₃ played important roles in the changes in breath-borne VOC fingerprints following haze air pollution exposure. This work has demonstrated direct evidence of human health impacts of haze pollution while identifying potential breath-borne VOC biomarkers such as propanol and isoprene for haze air pollution exposure.

Micro- and nanosensors for detecting blood pathogens and biomarkers at different points of sepsis care

Severe infections can cause a dysregulated response leading to organ dysfunction known as sepsis. Sepsis can be lethal if not identified and treated right away. This requires measuring biomarkers and pathogens rapidly at the different points where sepsis care is provided. Current commercial approaches for sepsis diagnosis are not fast, sensitive, and/or specific enough for meeting this medical challenge. In this article, we review recent advances in the development of diagnostic tools for sepsis management based on micro- and nanostructured materials. We start with a brief introduction to the most popular biomarkers for sepsis diagnosis (lactate, procalcitonin, cytokines, C-reactive protein, and other emerging protein and non-protein biomarkers including miRNAs and cell-based assays) and methods for detecting bacteremia. We then highlight the role of nano- and microstructured materials in developing biosensors for detecting them taking into consideration the particular needs of every point of sepsis care (e.g., ultrafast detection of multiple protein biomarkers for diagnosing in triage, emergency room, ward, and intensive care unit; quantitative detection to de-escalate treatment; ultrasensitive and culture-independent detection of blood pathogens for personalized antimicrobial therapies; robust, portable, and web-connected biomarker tests outside the hospital). We conclude with an overview of the most utilized nano- and microstructured materials used thus far for solving issues related to sepsis diagnosis and point to new challenges for future development.

Nanoparticle transfer biosensors for the non-invasive detection of SARS-CoV-2 antigens trapped in surgical face masks

Detecting SARS-CoV-2 antigens in respiratory tract samples has become a widespread method for screening new SARS-CoV-2 infections. This requires a nasopharyngeal swab performed by a trained healthcare worker, which puts strain on saturated healthcare services. In this manuscript we describe a new approach for non-invasive COVID-19 diagnosis. It consists of using mobile biosensors for detecting viral antigens trapped in surgical face masks worn by patients. The biosensors are made of filter paper containing a nanoparticle reservoir. The nanoparticles transfer from the biosensor to the mask on contact, where they generate colorimetric signals that are quantified with a smartphone app. Sample collection requires wearing a surgical mask for 30 min, and the total assay time is shorter than 10 min. When tested in a cohort of 27 patients with mild or no symptoms, an area under the receiving operating curve (AUROC) of 0.99 was obtained (96.2 % sensitivity and 100 % specificity). Serial measurements revealed a high sensitivity and specificity when masks were worn up to 6 days after diagnosis. Surgical face masks are inexpensive and widely available, which makes this approach easy to implement anywhere. The excellent sensitivity, even when tested with asymptomatic patient samples, along with the mobile detection scheme and non-invasive sampling procedure, makes this biosensor design ideal for mass screening.

Paper biosensors for detecting elevated IL-6 levels in blood and respiratory samples from COVID-19 patients

Decentralizing COVID-19 care reduces contagions and affords a better use of hospital resources. We introduce biosensors aimed at detecting severe cases of COVID-19 in decentralized healthcare settings. They consist of a paper immunosensor interfaced with a smartphone. The immunosensors have been designed to generate intense colorimetric signals when the sample contains ultralow concentrations of IL-6, which has been proposed as a prognosis biomarker of COVID-19. This is achieved by combining a paper-based signal amplification mechanism with polymer-filled reservoirs for dispensing antibody-decorated nanoparticles and a bespoken app for color quantification. With this design we achieved a low limit of detection (LOD) of 10^-3 pg mL^-1 and semi-quantitative measurements in a wide dynamic range between 10^-3 and 10² pg mL^-1 in PBS. The assay time is under 10 min. The low LOD allowed us to dilute blood samples and detect IL-6 with an LOD of 1.3 pg mL^-1 and a dynamic range up to 10² pg mL^-1. Following this protocol, we were able to stratify COVID-19 patients according to different blood levels of IL-6. We also report on the detection of IL-6 in respiratory samples (bronchial aspirate, BAS) from COVID-19 patients. The test could be easily adapted to detect other cytokines such as TNF-α and IL-8 by changing the antibodies decorating the nanoparticles accordingly. The ability of detecting cytokines in blood and respiratory samples paves the way for monitoring local inflammation in the lungs as well as systemic inflammation levels in the body.

Electrochemical sensing: A prognostic tool in the fight against COVID-19

The COVID-19 pandemic has devastated the world, despite all efforts in infection control and treatment/vaccine development. Hospitals are currently overcrowded, with health statuses of patients often being hard to gauge. Therefore, methods for determining infection severity need to be developed so that high-risk patients can be prioritized, resources can be efficiently distributed, and fatalities can be prevented. Electrochemical prognostic biosensing of various biomarkers may hold promise in solving these problems as they are low-cost and provide timely results. Therefore, we have reviewed the literature and extracted the most promising biomarkers along with their most favourable electrochemical sensors. The biomarkers discussed in this paper are C-reactive protein (CRP), interleukins (ILs), tumour necrosis factor alpha (TNFα), interferons (IFNs), glutamate, breath pH, lymphocytes, platelets, neutrophils and D-dimer. Metabolic syndrome is also discussed as comorbidity for COVID-19 patients, as it increases infection severity and raises chances of becoming infected. Cannabinoids, especially cannabidiol (CBD), are discussed as a potential adjunct therapy for COVID-19 as their medicinal properties may be desirable in minimizing the neurodegenerative or severe inflammatory damage caused by severe COVID-19 infection. Currently, hospitals are struggling to provide adequate care; thus, point-of-care electrochemical sensor development needs to be prioritized to provide an approximate prognosis for hospital patients. During and following the immediate aftermath of the pandemic, electrochemical sensors can also be integrated into wearable and portable devices to help patients monitor recovery while returning to their daily lives. Beyond the COVID-19 pandemic, these sensors will also prove useful for monitoring inflammation-based diseases such as cancer and cardiovascular disease.

Exhaled breath analysis in disease detection

Investigating the use of exhaled breath analysis to diagnose and monitor different diseases has attracted much interest in recent years. This review introduces conventionally used methods and some emerging technologies aimed at breath analysis and their relevance to lung disease, airway inflammation, gastrointestinal disorders, metabolic disorders and kidney diseases. One section correlates breath components and specific diseases, whereas the other discusses some unique ideas, strategies, and devices to analyze exhaled breath for the diagnosis of some common diseases. This review aims to briefly introduce the potential application of exhaled breath analysis for the diagnosis and screening of various diseases, thereby providing a new avenue for the detection of non-invasive diseases.

Surface-modified mesoporous nanofibers for microfluidic immunosensor with an ultra-sensitivity and high signal-to-noise ratio

How to balance the sensitivity and signal-to-noise ratio of immunosensor remains many challenges during various diseases diagnosis. Here we develop a new microfluidic immunosensor based on surface-modified mesoporous nanofibers, and simultaneously realize an ultra-sensitivity and high signal-to-noise ratio for the detection of multiple biomarkers. In the current study, we fabricated titanium dioxide (TiO₂)-based mesoporous electrospinning nanofibers, and modified nanofiber surface with both octadecylphosphonic acid (OPA) and poly(ethylene oxide)-poly(propylene oxide) triblock copolymer (PEO-PPO-PEO). Such nanofibers as solid substrate are covered on microfluidic channels. The porosity of our nanofibers dramatically increased the adsorption capability of antibodies, realizing an ultra sensitivity of biomarker detection. PEO-PPO-PEO modification can significantly block non-specific absorptions, obtaining a satisfied signal-to-noise ratio. For the detection of HIV p24 and interleukin 5 (IL-5), our immunosensor increased 6.41 and 6.93 fold in sensitivity and improved 504.66% and 512.80% in signal-to-noise ratio, in compared with gold standard immunoassay (ELISA) used in the clinic. Our immunosensor also broaden the linear range for the detection of HIV p24 (0.86-800 pg/ml) and IL-5 (0.70-800 pg/ml), in compared with ELISA which is 5.54-500 pg/ml for HIV p24 and 4.84-500 pg/ml for IL-5. Our work provided a guideline for the construction of advanced point-of-care immunosensor with an ultra-sensitivity and high signal-to-noise ratio for disease diagnosis.

On airborne transmission and control of SARS-Cov-2

The COVID-19 pandemic is creating a havoc situation across the globe that modern society has ever seen. Despite of their paramount importance, the transmission routes of SARS-Cov-2 still remain debated among various sectors. Evidences compiled here strongly suggest that the COVID-19 could be transmitted via air in inadequately ventilated environments. Existing experimental data showed that coronavirus survival was negatively impacted by ozone, high temperature and low humidity. Here, regression analysis showed that the spread of SARS-Cov-2 was reduced by increasing ambient ozone concentration level from 48.83 to 94.67 μg/m³ (p-value = 0.039) and decreasing relative humidity from 23.33 to 82.67% (p-value = 0.002) and temperature from -13.17 to 19 °C) (p-value = 0.003) observed for Chinese cities during Jan-March 2020. Besides using these environmental implications, social distancing and wearing a mask are strongly encouraged to maximize the fight against the COVID-19 airborne transmission. At no other time than now are the scientists in various disciplines around the world badly needed by the society to collectively confront this disastrous pandemic.

Ambient PM Toxicity Is Correlated with Expression Levels of Specific MicroRNAs

Uncertainties regarding optimized air pollution control remain as the underlying mechanisms of city-specific ambient particulate matter (PM)-induced health effects are unknown. Here, water-soluble extracts of PMs collected from four global cities via automobile air-conditioning filters were consecutively injected three times by an amount of 1, 2, and 2 mg into the blood circulation of Wistar rats after filtration by a 0.45 μm pore size membrane. Acute health effects, such as immune and inflammatory responses and hemorrhage in alveoli, were observed right after the PM extraction injection. Significant differences between cities in biomarker tumor necrosis factor-α (TNF-α) and monocyte chemoattractant protein-1 (MCP-1) levels were detected following the second and third PM injections. Rats' inflammatory responses varied substantially with the injections of city-specific PMs. Repeated PM extract exposure rendered the rats more vulnerable to subsequent challenges, and downregulation of certain microRNAs was observed in rats. Among the studied miRNAs, miR-125b, and miR-21 were most sensitive to the PM exposure, exhibiting a negative dose-response-type relationship with a source-specific PM (oxidative potential) toxicity (r² = 0.63 and 0.57; p-values < 0.05). The results indicated that city-specific PMs could induce different health effects by selectively regulating different miRNAs, and that certain microRNAs, e.g., miR-125b and miR-21, may be externally mediated to neutralize PM-related health damages.

Nanosensors based on LSPR are able to serologically differentiate dengue from Zika infections

The Flaviviridae virus family was named after the Yellow-fever virus, and the latin term flavi means “of golden color”. Dengue, caused by Dengue virus (DENV), is one of the most important infectious diseases worldwide. A sensitive and differential diagnosis is crucial for patient management, especially due to the occurrence of serological cross-reactivity to other co-circulating flaviviruses. This became particularly important with the emergence of Zika virus (ZIKV) in areas were DENV seroprevalence was already high. We developed a sensitive and specific diagnostic test based on gold nanorods (GNR) functionalized with DENV proteins as nanosensors. These were able to detect as little as one picogram of anti-DENV monoclonal antibodies and highly diluted DENV-positive human sera. The nanosensors could differentiate DENV-positive sera from other flavivirus-infected patients, including ZIKV, and were even able to distinguish which DENV serotype infected individual patients. Readouts are obtained in ELISA-plate spectrophotometers without the need of specific devices.

Rats Sniff Off Toxic Air

Breathing air is a fundamental human need, yet its safety, when challenged by various harmful or lethal substances, is often not properly guarded. For example, air toxicity is currently monitored only for a single or a limited number of known toxicants, thus failing to warn against possible hazardous air fully. Here, we discovered that, within minutes, living rats emitted distinctive profiles of volatile organic compounds (VOCs) via breath when exposed to various airborne toxicants such as endotoxin, O₃, ricin, and CO₂. Compared to background indoor air, when exposed to ricin or endotoxin aerosols, breath-borne VOC levels, especially that of carbon disulfide, were shown to decrease, while their elevated levels were observed for exposure to O₃ and CO₂. A clear contrast in breath-borne VOC profiles of rats exposed to different toxicants was observed with a statistical significance. Differences in microRNA regulations such as miR-33, miR-146a, and miR-155 from rats' blood samples revealed different mechanisms used by rats in combating different air toxicant challenges. Similar to dogs, rats were found here to be able to sniff off toxic air by releasing a specific breath-borne VOC profile. The discovered science opens a new arena for online monitoring of air toxicity and health effects of pollutants.