Proof of concept design for a toothbrush with on-board vacuum to reduce oral aspirates.

BACKGROUND: Over the course of brushing, aerosolised particles develop in the mouth. In individuals who do not have the ability to expel these oral aspirates, they can be inhaled and cause aspiration pneumonia. This article showcases a novel vacuum toothbrush, termed "ToothVac," and provides findings from its first human trial. METHODS: The ToothVac device suctions saliva and aspirates during brushing, storing them in a removable reservoir at the bottom of the brush, to minimise the risk of inhalation and subsequent infection. Further descriptions of the various components of the ToothVac are included. This trial involved 18 participants who brushed using the ToothVac with the vacuum suction turned on and then off. RESULTS: The volume of saliva produced was measured and compared. The ToothVac significantly reduced the amount of saliva that was produced by these participants when brushing. CONCLUSION: The device has potential clinical potential in that it may reduce the risk of aspiration pneumonia and related lung infections. Potential future research may include clinical trials for specific indications or marketing for oral aspirate removal, as well as optimisation of brush design using injection moulding for scalable manufacturing.

Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has resulted in significant morbidity and mortality; large numbers of patients require intensive care, which is placing strain on health care systems worldwide. There is an urgent need for a COVID-19 disease severity assessment that can assist in patient triage and resource allocation for patients at risk for severe disease. OBJECTIVE: The goal of this study was to develop, validate, and scale a clinical decision support system and mobile app to assist in COVID-19 severity assessment, management, and care. METHODS: Model training data from 701 patients with COVID-19 were collected across practices within the Family Health Centers network at New York University Langone Health. A two-tiered model was developed. Tier 1 uses easily available, nonlaboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts the probability of mortality using biomarker measurements (C-reactive protein, procalcitonin, D-dimer) and age. Both the Tier 1 and Tier 2 models were validated using two external datasets from hospitals in Wuhan, China, comprising 160 and 375 patients, respectively. RESULTS: All biomarkers were measured at significantly higher levels in patients who died vs those who were not hospitalized or discharged (P<.001). The Tier 1 and Tier 2 internal validations had areas under the curve (AUCs) of 0.79 (95% CI 0.74-0.84) and 0.95 (95% CI 0.92-0.98), respectively. The Tier 1 and Tier 2 external validations had AUCs of 0.79 (95% CI 0.74-0.84) and 0.97 (95% CI 0.95-0.99), respectively. CONCLUSIONS: Our results demonstrate the validity of the clinical decision support system and mobile app, which are now ready to assist health care providers in making evidence-based decisions when managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics and sites, where application of these tools could lead to improvements in patient outcomes and cost containment.

Programmable Bio-nanochip Platform: A Point-of-Care Biosensor System with the Capacity To Learn.

The combination of point-of-care (POC) medical microdevices and machine learning has the potential transform the practice of medicine. In this area, scalable lab-on-a-chip (LOC) devices have many advantages over standard laboratory methods, including faster analysis, reduced cost, lower power consumption, and higher levels of integration and automation. Despite significant advances in LOC technologies over the years, several remaining obstacles are preventing clinical implementation and market penetration of these novel medical microdevices. Similarly, while machine learning has seen explosive growth in recent years and promises to shift the practice of medicine toward data-intensive and evidence-based decision making, its uptake has been hindered due to the lack of integration between clinical measurements and disease determinations. In this Account, we describe recent developments in the programmable bio-nanochip (p-BNC) system, a biosensor platform with the capacity for learning. The p-BNC is a "platform to digitize biology" in which small quantities of patient sample generate immunofluorescent signal on agarose bead sensors that is optically extracted and converted to antigen concentrations. The platform comprises disposable microfluidic cartridges, a portable analyzer, automated data analysis software, and intuitive mobile health interfaces. The single-use cartridges are fully integrated, self-contained microfluidic devices containing aqueous buffers conveniently embedded for POC use. A novel fluid delivery method was developed to provide accurate and repeatable flow rates via actuation of the cartridge's blister packs. A portable analyzer instrument was designed to integrate fluid delivery, optical detection, image analysis, and user interface, representing a universal system for acquiring, processing, and managing clinical data while overcoming many of the challenges facing the widespread clinical adoption of LOC technologies. We demonstrate the p-BNC's flexibility through the completion of multiplex assays within the single-use disposable cartridges for three clinical applications: prostate cancer, ovarian cancer, and acute myocardial infarction. Toward the goal of creating "sensors that learn", we have developed and describe here the Cardiac ScoreCard, a clinical decision support system for a spectrum of cardiovascular disease. The Cardiac ScoreCard approach comprises a comprehensive biomarker panel and risk factor information in a predictive model capable of assessing early risk and late-stage disease progression for heart attack and heart failure patients. These marker-driven tests have the potential to radically reduce costs, decrease wait times, and introduce new options for patients needing regular health monitoring. Further, these efforts demonstrate the clinical utility of fusing data from information-rich biomarkers and the Internet of Things (IoT) using predictive analytics to generate single-index assessments for wellness/illness status. By promoting disease prevention and personalized wellness management, tools of this nature have the potential to improve health care exponentially.

Cardiac ScoreCard: A Diagnostic Multivariate Index Assay System for Predicting a Spectrum of Cardiovascular Disease.

Clinical decision support systems (CDSSs) have the potential to save lives and reduce unnecessary costs through early detection and frequent monitoring of both traditional risk factors and novel biomarkers for cardiovascular disease (CVD). However, the widespread adoption of CDSSs for the identification of heart diseases has been limited, likely due to the poor interpretability of clinically relevant results and the lack of seamless integration between measurements and disease predictions. In this paper we present the Cardiac ScoreCard-a multivariate index assay system with the potential to assist in the diagnosis and prognosis of a spectrum of CVD. The Cardiac ScoreCard system is based on lasso logistic regression techniques which utilize both patient demographics and novel biomarker data for the prediction of heart failure (HF) and cardiac wellness. Lasso logistic regression models were trained on a merged clinical dataset comprising 579 patients with 6 traditional risk factors and 14 biomarker measurements. The prediction performance of the Cardiac ScoreCard was assessed with 5-fold cross-validation and compared with reference methods. The experimental results reveal that the ScoreCard models improved performance in discriminating disease versus non-case (AUC = 0.8403 and 0.9412 for cardiac wellness and HF, respectively), and the models exhibit good calibration. Clinical insights to the prediction of HF and cardiac wellness are provided in the form of logistic regression coefficients which suggest that augmenting the traditional risk factors with a multimarker panel spanning a diverse cardiovascular pathophysiology provides improved performance over reference methods. Additionally, a framework is provided for seamless integration with biomarker measurements from point-of-care medical microdevices, and a lasso-based feature selection process is described for the down-selection of biomarkers in multimarker panels.

Simultaneous Quantitative SARS-CoV-2 Antigen and Host Antibody Detection and Pre-Screening Strategy at the Point of Care.

As COVID-19 pandemic public health measures are easing globally, the emergence of new SARS-CoV-2 strains continue to present high risk for vulnerable populations. The antibody-mediated protection acquired from vaccination and/or infection is seen to wane over time and the immunocompromised populations can no longer expect benefit from monoclonal antibody prophylaxis. Hence, there is a need to monitor new variants and its effect on vaccine performance. In this context, surveillance of new SARS-CoV-2 infections and serology testing are gaining consensus for use as screening methods, especially for at-risk groups. Here, we described an improved COVID-19 screening strategy, comprising predictive algorithms and concurrent, rapid, accurate, and quantitative SARS-CoV-2 antigen and host antibody testing strategy, at point of care (POC). We conducted a retrospective analysis of 2553 pre- and asymptomatic patients who were tested for SARS-CoV-2 by RT-PCR. The pre-screening model had an AUC (CI) of 0.76 (0.73-0.78). Despite being the default method for screening, body temperature had lower AUC (0.52 [0.49-0.55]) compared to case incidence rate (0.65 [0.62-0.68]). POC assays for SARS-CoV-2 nucleocapsid protein (NP) and spike (S) receptor binding domain (RBD) IgG antibody showed promising preliminary results, demonstrating a convenient, rapid (<20 min), quantitative, and sensitive (ng/mL) antigen/antibody assay. This integrated pre-screening model and simultaneous antigen/antibody approach may significantly improve accuracy of COVID-19 infection and host immunity screening, helping address unmet needs for monitoring vaccine effectiveness and severe disease surveillance.

A Rapid and Sensitive Microfluidics-Based Tool for Seroprevalence Immunity Assessment of COVID-19 and Vaccination-Induced Humoral Antibody Response at the Point of Care.

As of 8 August 2022, SARS-CoV-2, the causative agent of COVID-19, has infected over 585 million people and resulted in more than 6.42 million deaths worldwide. While approved SARS-CoV-2 spike (S) protein-based vaccines induce robust seroconversion in most individuals, dramatically reducing disease severity and the risk of hospitalization, poorer responses are observed in aged, immunocompromised individuals and patients with certain pre-existing health conditions. Further, it is difficult to predict the protection conferred through vaccination or previous infection against new viral variants of concern (VoC) as they emerge. In this context, a rapid quantitative point-of-care (POC) serological assay able to quantify circulating anti-SARS-CoV-2 antibodies would allow clinicians to make informed decisions on the timing of booster shots, permit researchers to measure the level of cross-reactive antibody against new VoC in a previously immunized and/or infected individual, and help assess appropriate convalescent plasma donors, among other applications. Utilizing a lab-on-a-chip ecosystem, we present proof of concept, optimization, and validation of a POC strategy to quantitate COVID-19 humoral protection. This platform covers the entire diagnostic timeline of the disease, seroconversion, and vaccination response spanning multiple doses of immunization in a single POC test. Our results demonstrate that this platform is rapid (~15 min) and quantitative for SARS-CoV-2-specific IgG detection.

Location of biomarkers and reagents within agarose beads of a programmable bio-nano-chip.

The slow development of cost-effective medical microdevices with strong analytical performance characteristics is due to a lack of selective and efficient analyte capture and signaling. The recently developed programmable bio-nano-chip (PBNC) is a flexible detection device with analytical behavior rivaling established macroscopic methods. The PBNC system employs ≈300 µm-diameter bead sensors composed of agarose "nanonets" that populate a microelectromechanical support structure with integrated microfluidic elements. The beads are an efficient and selective protein-capture medium suitable for the analysis of complex fluid samples. Microscopy and computational studies probe the 3D interior of the beads. The relative contributions that the capture and detection of moieties, analyte size, and bead porosity make to signal distribution and intensity are reported. Agarose pore sizes ranging from 45 to 620 nm are examined and those near 140 nm provide optimal transport characteristics for rapid (<15 min) tests. The system exhibits efficient (99.5%) detection of bead-bound analyte along with low (≈2%) nonspecific immobilization of the detection probe for carcinoembryonic antigen assay. Furthermore, the role analyte dimensions play in signal distribution is explored, and enhanced methods for assay building that consider the unique features of biomarker size are offered.

Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19.

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19.

SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.

Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions.

BACKGROUND: The effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early-stage cancer and improving outcomes. In the current study, the authors described cytopathology tools, including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation. METHODS: Data were acquired from a multisite clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of 4 cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer across a spectrum of histopathologic diagnoses ranging from benign, to increasing grades of oral epithelial dysplasia (OED), to OSCC using demographics, lesion characteristics, and cell phenotypes. Cytopathology software was developed to assist pathologists in reviewing brush cytology test results, including high-content cell analyses, data visualization tools, and results reporting. RESULTS: Cell phenotypes were determined accurately through an automated cytological assay and machine learning approach (99.3% accuracy). Significant differences in cell phenotype distributions across diagnostic categories were found in 3 phenotypes (type 1 ["mature squamous"], type 2 ["small round"], and type 3 ["leukocytes"]). The clinical algorithms resulted in acceptable performance characteristics (area under the curve of 0.81 for benign vs mild dysplasia and 0.95 for benign vs malignancy). CONCLUSIONS: These new cytopathology tools represent a practical solution for rapid PMOL assessment, with the potential to facilitate screening and longitudinal monitoring in primary, secondary, and tertiary clinical care settings.

Sensors that Learn: The Evolution from Taste Fingerprints to Patterns of Early Disease Detection.

The McDevitt group has sustained efforts to develop a programmable sensing platform that offers advanced, multiplexed/multiclass chem-/bio-detection capabilities. This scalable chip-based platform has been optimized to service real-world biological specimens and validated for analytical performance. Fashioned as a sensor that learns, the platform can host new content for the application at hand. Identification of biomarker-based fingerprints from complex mixtures has a direct linkage to e-nose and e-tongue research. Recently, we have moved to the point of big data acquisition alongside the linkage to machine learning and artificial intelligence. Here, exciting opportunities are afforded by multiparameter sensing that mimics the sense of taste, overcoming the limitations of salty, sweet, sour, bitter, and glutamate sensing and moving into fingerprints of health and wellness. This article summarizes developments related to the electronic taste chip system evolving into a platform that digitizes biology and affords clinical decision support tools. A dynamic body of literature and key review articles that have contributed to the shaping of these activities are also highlighted. This fully integrated sensor promises more rapid transition of biomarker panels into wide-spread clinical practice yielding valuable new insights into health diagnostics, benefiting early disease detection.

Integration of semiconductor quantum dots into nano-bio-chip systems for enumeration of CD4+ T cell counts at the point-of-need.

Recent humanitarian efforts have led to the widespread release of antiretroviral drugs for the treatment of the more than 33 million HIV afflicted people living in resource-scarce settings. Here, the enumeration of CD4+ T lymphocytes is required to establish the level at which the immune system has been compromised. The gold standard method used in developed countries, based on flow cytometry, though widely accepted and accurate, is precluded from widespread use in resource-scarce settings due to its high expense, high technical requirements, difficulty in operation-maintenance and the lack of portability for these sophisticated laboratory-confined systems. As part of continuing efforts to develop practical diagnostic instrumentation, the integration of semiconductor nanocrystals (quantum dots, QDs) into a portable microfluidic-based lymphocyte capture and detection device is completed. This integrated system is capable of isolating and counting selected lymphocyte sub-populations (CD3+CD4+) from whole blood samples. By combining the unique optical properties of the QDs with the sample handling capabilities and cost effectiveness of novel microfluidic systems, a practical, portable lymphocyte measurement modality that correlates nicely with flow cytometry (R2 = 0.97) has been developed. This QD-based system reduces the optical requirements significantly relative to molecular fluorophores and the mini-CD4 counting device is projected to be suitable for use in both point-of-need and resource-scarce settings.

Innovative Programmable Bio-Nano-Chip Digitizes Biology Using Sensors That Learn Bridging Biomarker Discovery and Clinical Implementation.

The lack of standard tools and methodologies and the absence of a streamlined multimarker approval process have hindered the translation rate of new biomarkers into clinical practice for a variety of diseases afflicting humankind. Advanced novel technologies with superior analytical performance and reduced reagent costs, like the programmable bio-nano-chip system featured in this article, have potential to change the delivery of healthcare. This universal platform system has the capacity to digitize biology, resulting in a sensor modality with a capacity to learn. With well-planned device design, development, and distribution plans, there is an opportunity to translate benchtop discoveries in the genomics, proteomics, metabolomics, and glycomics fields by transforming the information content of key biomarkers into actionable signatures that can empower physicians and patients for a better management of healthcare. While the process is complicated and will take some time, showcased here are three application areas for this flexible platform that combines biomarker content with minimally invasive or non-invasive sampling, such as brush biopsy for oral cancer risk assessment; serum, plasma, and small volumes of blood for the assessment of cardiac risk and wellness; and oral fluid sampling for drugs of abuse testing at the point of need.

Challenges and opportunities for translating medical microdevices: insights from the programmable bio-nano-chip.

This perspective highlights the major challenges for the bioanalytical community, in particular the area of lab-on-a-chip sensors, as they relate to point-of-care diagnostics. There is a strong need for general-purpose and universal biosensing platforms that can perform multiplexed and multiclass assays on real-world clinical samples. However, the adoption of novel lab-on-a-chip/microfluidic devices has been slow as several key challenges remain for the translation of these new devices to clinical practice. A pipeline of promising medical microdevice technologies will be made possible by addressing the challenges of integration, failure to compete with cost and performance of existing technologies, requisite for new content, and regulatory approval and clinical adoption.

Membrane-based on-line optical analysis system for rapid detection of bacteria and spores.

We report here the adaptation of our electronic microchip technology towards the development of a new method for detecting and enumerating bacterial cells and spores. This new approach is based on the immuno-localization of bacterial spores captured on a membrane filter microchip placed within a flow cell. A combination of microfluidic, optical, and software components enables the integration of staining of the bacterial species with fully automated assays. The quantitation of the analyte signal is achieved through the measurement of a collective response or alternatively through the identification and counting of individual spores and particles. This new instrument displays outstanding analytical characteristics, and presents a limit of detection of approximately 500 spores when tested with Bacillus globigii (Bg), a commonly used simulant for Bacillus anthracis (Ba), with a total analysis time of only 5 min. Additionally, the system performed well when tested with real postal dust samples spiked with Bg in the presence of other common contaminants. This new approach is highly customizable towards a large number of relevant toxic chemicals, environmental factors, and analytes of relevance to clinical chemistry applications.

A multiplexable, microfluidic platform for the rapid quantitation of a biomarker panel for early ovarian cancer detection at the point-of-care.

Point-of-care (POC) diagnostic platforms have the potential to enable low-cost, large-scale screening. As no single biomarker is shed by all ovarian cancers, multiplexed biomarker panels promise improved sensitivity and specificity to address the unmet need for early detection of ovarian cancer. We have configured the programmable bio-nano-chip (p-BNC)-a multiplexable, microfluidic, modular platform-to quantify a novel multi-marker panel comprising CA125, HE4, MMP-7, and CA72-4. The p-BNC is a bead-based immunoanalyzer system with a credit-card-sized footprint that integrates automated sample metering, bubble and debris removal, reagent storage and waste disposal, permitting POC analysis. Multiplexed p-BNC immunoassays demonstrated high specificity, low cross-reactivity, low limits of detection suitable for early detection, and a short analysis time of 43 minutes. Day-to-day variability, a critical factor for longitudinally monitoring biomarkers, ranged between 5.4% and 10.5%, well below the biologic variation for all four markers. Biomarker concentrations for 31 late-stage sera correlated well (R(2) = 0.71 to 0.93 for various biomarkers) with values obtained on the Luminex platform. In a 31 patient cohort encompassing early- and late-stage ovarian cancers along with benign and healthy controls, the multiplexed p-BNC panel was able to distinguish cases from controls with 68.7% sensitivity at 80% specificity. Utility for longitudinal biomarker monitoring was demonstrated with prediagnostic plasma from 2 cases and 4 controls. Taken together, the p-BNC shows strong promise as a diagnostic tool for large-scale screening that takes advantage of faster results and lower costs while leveraging possible improvement in sensitivity and specificity from biomarker panels.

Next Generation Programmable Bio-Nano-Chip System for On-Site Detection in Oral Fluids.

Current on-site drug of abuse detection methods involve invasive sampling of blood and urine specimens, or collection of oral fluid, followed by qualitative screening tests using immunochromatographic cartridges. Test confirmation and quantitative assessment of a presumptive positive are then provided by remote laboratories, an inefficient and costly process decoupled from the initial sampling. Recently, a new noninvasive oral fluid sampling approach that is integrated with the chip-based Programmable Bio-Nano-Chip (p-BNC) platform has been developed for the rapid (~ 10 minutes), sensitive detection (~ ng/ml) and quantitation of 12 drugs of abuse. Furthermore, the system can provide the time-course of select drug and metabolite profiles in oral fluids. For cocaine, we observed three slope components were correlated with cocaine-induced impairment using this chip-based p-BNC detection modality. Thus, this p-BNC has significant potential for roadside drug testing by law enforcement officers. Initial work reported on chip-based drug detection was completed using 'macro' or "chip in the lab" prototypes, that included metal encased "flow cells", external peristaltic pumps and a bench-top analyzer system instrumentation. We now describe the next generation miniaturized analyzer instrumentation along with customized disposables and sampling devices. These tools will offer real-time oral fluid drug monitoring capabilities, to be used for roadside drug testing as well as testing in clinical settings as a non-invasive, quantitative, accurate and sensitive tool to verify patient adherence to treatment.

Programmable bio-nano-chip system: a flexible point-of-care platform for bioscience and clinical measurements.

The development of integrated instrumentation for universal bioassay systems serves as a key goal for the lab-on-a-chip community. The programmable bio-nano-chip (p-BNC) system is a versatile multiplexed and multiclass chemical- and bio-sensing system for bioscience and clinical measurements. The system is comprised of two main components, a disposable cartridge and a portable analyzer. The customizable single-use plastic cartridges, which now can be manufactured in high volumes using injection molding, are designed for analytical performance, ease of use, reproducibility, and low cost. These labcard devices implement high surface area nano-structured biomarker capture elements that enable high performance signaling and are index-matched to real-world biological specimens. This detection modality, along with the convenience of on-chip fluid storage in blisters and self-contained waste, represents a standard process to digitize biological signatures at the point-of-care. A companion portable analyzer prototype has been developed to integrate fluid motivation, optical detection, and automated data analysis, and it serves as the human interface for complete assay automation. In this report, we provide a systems-level perspective of the p-BNC universal biosensing platform with an emphasis on flow control, device integration, and automation. To demonstrate the flexibility of the p-BNC, we distinguish diseased and non-case patients across three significant disease applications: prostate cancer, ovarian cancer, and acute myocardial infarction. Progress towards developing a rapid 7 minute myoglobin assay is presented using the fully automated p-BNC system.

Enhancement of performance in porous bead-based microchip sensors: Effects of chip geometry on bio-agent capture.

Measuring low concentrations of clinically-important biomarkers using porous bead-based lab-on-a-chip (LOC) platforms is critical for the successful implementation of point-of-care (POC) devices. One way to meet this objective is to optimize the geometry of the bead holder, referred to here as a micro-container. In this work, two geometric micro-containers were explored, the inverted pyramid frustum (PF) and the inverted clipped pyramid frustum (CPF). Finite element models of this bead array assay system were developed to optimize the micro-container and bead geometries for increased pressure, to increase analyte capture in porous bead-based fluorescence immunoassays. Custom micro-milled micro-container structures containing an inverted CPF geometry resulted in a 28% reduction in flow-through regions from traditional anisotropically-etched pyramidal geometry derived from Si-111 termination layers. This novel "reduced flow-through" design resulted in a 33% increase in analyte penetration into the bead and twofold increase in fluorescence signal intensity as demonstrated with C-Reactive Protein (CRP) antigen, an important biomarker of inflammation. A consequent twofold decrease in the limit of detection (LOD) and the limit of quantification (LOQ) of a proof-of-concept assay for the free isoform of Prostate-Specific Antigen (free PSA), an important biomarker for prostate cancer detection, is also presented. Furthermore, a 53% decrease in the bead diameter is shown to result in a 160% increase in pressure and 2.5-fold increase in signal, as estimated by COMSOL models and confirmed experimentally by epi-fluorescence microscopy. Such optimizations of the bead micro-container and bead geometries have the potential to significantly reduce the LODs and reagent costs for spatially programmed bead-based assay systems of this type.

Application of programmable bio-nano-chip system for the quantitative detection of drugs of abuse in oral fluids.

OBJECTIVE: There is currently a gap in on-site drug of abuse monitoring. Current detection methods involve invasive sampling of blood and urine specimens, or collection of oral fluid, followed by qualitative screening tests using immunochromatographic cartridges. While remote laboratories then may provide confirmation and quantitative assessment of a presumptive positive, this instrumentation is expensive and decoupled from the initial sampling making the current drug-screening program inefficient and costly. The authors applied a noninvasive oral fluid sampling approach integrated with the in-development chip-based Programmable bio-nano-chip (p-BNC) platform for the detection of drugs of abuse. METHOD: The p-BNC assay methodology was applied for the detection of tetrahydrocannabinol, morphine, amphetamine, methamphetamine, cocaine, methadone and benzodiazepines, initially using spiked buffered samples and, ultimately, using oral fluid specimen collected from consented volunteers. RESULTS: Rapid (∼10min), sensitive detection (∼ng/mL) and quantitation of 12 drugs of abuse was demonstrated on the p-BNC platform. Furthermore, the system provided visibility to time-course of select drug and metabolite profiles in oral fluids; for the drug cocaine, three regions of slope were observed that, when combined with concentration measurements from this and prior impairment studies, information about cocaine-induced impairment may be revealed. CONCLUSIONS: This chip-based p-BNC detection modality has significant potential to be used in the future by law enforcement officers for roadside drug testing and to serve a variety of other settings, including outpatient and inpatient drug rehabilitation centers, emergency rooms, prisons, schools, and in the workplace.