A cytomics-on-a-chip platform and diagnostic model stratifies risk for oral lichenoid conditions.

OBJECTIVE: A small fraction of oral lichenoid conditions (OLC) have potential for malignant transformation. Distinguishing OLCs from other oral potentially malignant disorders (OPMDs) can help prevent unnecessary concern or testing, but accurate identification by nonexpert clinicians is challenging due to overlapping clinical features. In this study, the authors developed a 'cytomics-on-a-chip' tool and integrated predictive model for aiding the identification of OLCs. STUDY DESIGN: All study subjects underwent both scalpel biopsy for histopathology and brush cytology. A predictive model and OLC Index comprising clinical, demographic, and cytologic features was generated to discriminate between subjects with lichenoid (OLC+) (N = 94) and nonlichenoid (OLC-) (N = 237) histologic features in a population with OPMDs. RESULTS: The OLC Index discriminated OLC+ and OLC- subjects with area under the curve (AUC) of 0.76. Diagnostic accuracy of the OLC Index was not significantly different from expert clinician impressions, with AUC of 0.81 (P = .0704). Percent agreement was comparable across all raters, with 83.4% between expert clinicians and histopathology, 78.3% between OLC Index and expert clinician, and 77.3% between OLC Index and histopathology. CONCLUSIONS: The cytomics-on-a-chip tool and integrated diagnostic model have the potential to facilitate both the triage and diagnosis of patients presenting with OPMDs and OLCs.

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.

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.

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.

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.

'Cytology-on-a-chip' based sensors for monitoring of potentially malignant oral lesions.

UNLABELLED: Despite significant advances in surgical procedures and treatment, long-term prognosis for patients with oral cancer remains poor, with survival rates among the lowest of major cancers. Better methods are desperately needed to identify potential malignancies early when treatments are more effective. OBJECTIVE: To develop robust classification models from cytology-on-a-chip measurements that mirror diagnostic performance of gold standard approach involving tissue biopsy. MATERIALS AND METHODS: Measurements were recorded from 714 prospectively recruited patients with suspicious lesions across 6 diagnostic categories (each confirmed by tissue biopsy -histopathology) using a powerful new 'cytology-on-a-chip' approach capable of executing high content analysis at a single cell level. Over 200 cellular features related to biomarker expression, nuclear parameters and cellular morphology were recorded per cell. By cataloging an average of 2000 cells per patient, these efforts resulted in nearly 13 million indexed objects. RESULTS: Binary "low-risk"/"high-risk" models yielded AUC values of 0.88 and 0.84 for training and validation models, respectively, with an accompanying difference in sensitivity+specificity of 6.2%. In terms of accuracy, this model accurately predicted the correct diagnosis approximately 70% of the time, compared to the 69% initial agreement rate of the pool of expert pathologists. Key parameters identified in these models included cell circularity, Ki67 and EGFR expression, nuclear-cytoplasmic ratio, nuclear area, and cell area. CONCLUSIONS: This chip-based approach yields objective data that can be leveraged for diagnosis and management of patients with PMOL as well as uncovering new molecular-level insights behind cytological differences across the OED spectrum.

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.

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.

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.

Hot embossed polyethylene through-hole chips for bead-based microfluidic devices.

Over the past decade, there has been a growth of interest in the translation of microfluidic systems into real-world clinical practice, especially for use in point-of-care or near patient settings. While initial fabrication advances in microfluidics involved mainly the etching of silicon and glass, the economics of scaling of these materials is not amendable for point-of-care usage where single-test applications force cost considerations to be kept low and throughput high. As such, materials base more consistent with point-of-care needs is required. In this manuscript, the fabrication of a hot embossed, through-hole low-density polyethylene ensembles derived from an anisotropically etched silicon wafer is discussed. This semi-opaque polymer that can be easily sterilized and recycled provides low background noise for fluorescence measurements and yields more affordable cost than other thermoplastics commonly used for microfluidic applications such as cyclic olefin copolymer (COC). To fabrication through-hole microchips from this alternative material for microfluidics, a fabrication technique that uses a high-temperature, high-pressure resistant mold is described. This aluminum-based epoxy mold, serving as the positive master mold for embossing, is casted over etched arrays of pyramidal pits in a silicon wafer. Methods of surface treatment of the wafer prior to casting and PDMS casting of the epoxy are discussed to preserve the silicon wafer for future use. Changes in the thickness of polyethylene are observed for varying embossing temperatures. The methodology described herein can quickly fabricate 20 disposable, single use chips in less than 30 min with the ability to scale up 4 times by using multiple molds simultaneously. When coupled as a platform supporting porous bead sensors, as in the recently developed Programmable Bio-Nano-Chip, this bead chip system can achieve limits of detection, for the cardiac biomarker C-reactive protein, of 0.3 ng/mL, thereby demonstrating that the approach is compatible with high performance, real-world clinical measurements in the context of point-of-care testing.

Porous bead-based diagnostic platforms: bridging the gaps in healthcare.

Advances in lab-on-a-chip systems have strong potential for multiplexed detection of a wide range of analytes with reduced sample and reagent volume; lower costs and shorter analysis times. The completion of high-fidelity multiplexed and multiclass assays remains a challenge for the medical microdevice field; as it struggles to achieve and expand upon at the point-of-care the quality of results that are achieved now routinely in remote laboratory settings. This review article serves to explore for the first time the key intersection of multiplexed bead-based detection systems with integrated microfluidic structures alongside porous capture elements together with biomarker validation studies. These strategically important elements are evaluated here in the context of platform generation as suitable for near-patient testing. Essential issues related to the scalability of these modular sensor ensembles are explored as are attempts to move such multiplexed and multiclass platforms into large-scale clinical trials. Recent efforts in these bead sensors have shown advantages over planar microarrays in terms of their capacity to generate multiplexed test results with shorter analysis times. Through high surface-to-volume ratios and encoding capabilities; porous bead-based ensembles; when combined with microfluidic elements; allow for high-throughput testing for enzymatic assays; general chemistries; protein; antibody and oligonucleotide applications.

Effects of sample delivery on analyte capture in porous bead sensors.

Sample delivery is a crucial aspect of point-of-care applications where sample volumes need to be low and assay times short, while providing high analytical and clinical sensitivity. In this paper, we explore the influence of the factors surrounding sample delivery on analyte capture in an immunoassay-based sensor array manifold of porous beads resting in individual wells. We model using computational fluid dynamics and a flow-through device containing beads sensitized specifically to C-reactive protein (CRP) to explore the effects of volume of sample, rate of sample delivery, and use of recirculation vs. unilateral delivery on the effectiveness of the capture of CRP on and within the porous bead sensor. Rate of sample delivery lends to the development of a time-dependent, shrinking depletion region around the bead exterior. Our findings reveal that at significantly high rates of delivery, unique to porous bead substrates, capture at the rim of the bead is reaction-limited, while capture in the interior of the bead is transport-limited. While the fluorescence signal results from the aggregate of captured material throughout the bead, multiple kinetic regimes exist within the bead. Further, under constant pressure conditions dictated by the array architecture, we reveal the existence of an optimal flow rate that generates the highest signal, under point-of-care constraints of limited-volume and limited-time. When high sensitivity is needed, recirculation can be implemented to overcome the analyte capture limitations due to volume and time constraints. Computational simulations agree with experimental results performed under similar conditions.

Programmable bio-nanochip technology for the diagnosis of cardiovascular disease at the point-of-care.

Cardiovascular disease remains the leading cause of death in the world and continues to serve as the major contributor to healthcare costs. Likewise, there is an ever-increasing need and demand for novel and more efficient diagnostic tools for the early detection of cardiovascular disease, especially at the point-of-care (POC). This article reviews the programmable bio-nanochip (P-BNC) system, a new medical microdevice approach with the capacity to deliver both high performance and reduced cost. This fully integrated, total analysis system leverages microelectronic components, microfabrication techniques, and nanotechnology to noninvasively measure multiple cardiac biomarkers in complex fluids, such as saliva, while offering diagnostic accuracy equal to laboratory-confined reference methods. This article profiles the P-BNC approach, describes its performance in real-world testing of clinical samples, and summarizes new opportunities for medical microdevices in the field of cardiac diagnostics.