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.

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.

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.

Modeling analyte transport and capture in porous bead sensors.

Porous agarose microbeads, with high surface to volume ratios and high binding densities, are attracting attention as highly sensitive, affordable sensor elements for a variety of high performance bioassays. While such polymer microspheres have been extensively studied and reported on previously and are now moving into real-world clinical practice, very little work has been completed to date to model the convection, diffusion, and binding kinetics of soluble reagents captured within such fibrous networks. Here, we report the development of a three-dimensional computational model and provide the initial evidence for its agreement with experimental outcomes derived from the capture and detection of representative protein and genetic biomolecules in 290 µm porous beads. We compare this model to antibody-mediated capture of C-reactive protein and bovine serum albumin, along with hybridization of oligonucleotide sequences to DNA probes. These results suggest that, due to the porous interior of the agarose bead, internal analyte transport is both diffusion and convection based, and regardless of the nature of analyte, the bead interiors reveal an interesting trickle of convection-driven internal flow. On the basis of this model, the internal to external flow rate ratio is found to be in the range of 1:170 to 1:3100 for beads with agarose concentration ranging from 0.5% to 8% for the sensor ensembles here studied. Further, both model and experimental evidence suggest that binding kinetics strongly affect analyte distribution of captured reagents within the beads. These findings reveal that high association constants create a steep moving boundary in which unbound analytes are held back at the periphery of the bead sensor. Low association constants create a more shallow moving boundary in which unbound analytes diffuse further into the bead before binding. These models agree with experimental evidence and thus serve as a new tool set for the study of bioagent transport processes within a new class of medical microdevices.

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.

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.

Programmable nano-bio-chip sensors: analytical meets clinical.

There have been many recent advances in the nano-bio-chip analysis methodology with implications for a number of high-morbidity diseases including HIV, cancer, and heart disease. (To listen to a podcast about this article, please go to the Analytical Chemistry multimedia page at pubs.acs.org/page/ancham/audio/index.html .).

Current development of saliva/oral fluid-based diagnostics.

Saliva can be easily obtained in medical and non-medical settings, and contains numerous bio-molecules, including those typically found in serum for disease detection and monitoring. In the past two decades, the achievements of high-throughput approaches afforded by biotechnology and nanotechnology allow for disease-specific salivary biomarker discovery and establishment of rapid, multiplex, and miniaturized analytical assays. These developments have dramatically advanced saliva-based diagnostics. In this review, we discuss the current consensus on development of saliva/oral fluid-based diagnostics and provide a summary of recent research advancements of the Texas-Kentucky Saliva Diagnostics Consortium. In the foreseeable future, current research on saliva based diagnostic methods could revolutionize health care.

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.

Use of saliva-based nano-biochip tests for acute myocardial infarction at the point of care: a feasibility study.

BACKGROUND: For adults with chest pain, the electrocardiogram (ECG) and measures of serum biomarkers are used to screen and diagnose myocardial necrosis. These measurements require time that can delay therapy and affect prognosis. Our objective was to investigate the feasibility and utility of saliva as an alternative diagnostic fluid for identifying biomarkers of acute myocardial infarction (AMI). METHODS: We used Luminex and lab-on-a-chip methods to assay 21 proteins in serum and unstimulated whole saliva procured from 41 AMI patients within 48 h of chest pain onset and from 43 apparently healthy controls. Data were analyzed by use of logistic regression and area under curve (AUC) for ROC analysis to evaluate the diagnostic utility of each biomarker, or combinations of biomarkers, in screening for AMI. RESULTS: Both established and novel cardiac biomarkers demonstrated significant differences in concentrations between patients with AMI and controls without AMI. The saliva-based biomarker panel of C-reactive protein, myoglobin, and myeloperoxidase exhibited significant diagnostic capability (AUC = 0.85, P < 0.0001) and in conjunction with ECG yielded strong screening capacity for AMI (AUC = 0.96) comparable to that of the panel (brain natriuretic peptide, troponin-I, creatine kinase-MB, myoglobin; AUC = 0.98) and far exceeded the screening capacity of ECG alone (AUC approximately 0.6). En route to translating these findings to clinical practice, we adapted these unstimulated whole saliva tests to a novel lab-on-a-chip platform for proof-of-principle screens for AMI. CONCLUSIONS: Complementary to ECG, saliva-based tests within lab-on-a-chip systems may provide a convenient and rapid screening method for cardiac events in prehospital stages for AMI patients.

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.

Cell-based sensor for analysis of EGFR biomarker expression in oral cancer.

Oral cancer is the sixth most common cancer worldwide and has been marked by high morbidity and poor survival rates that have changed little over the past few decades. Beyond prevention, early detection is the most crucial determinant for successful treatment and survival of cancer. Yet current methodologies for cancer diagnosis based upon pathological examination alone are insufficient for detecting early tumor progression and molecular transformation. To address this clinical need, we have developed a cell-based sensor to detect oral cancer biomarkers, such as the epidermal growth factor receptor (EGFR) whose over-expression is associated with early oral tumorigenesis and aggressive cancer phenotypes. The lab-on-a-chip (LOC) sensor utilizes an embedded track-etched membrane, which functions as a micro-sieve, to capture and enrich cells from complex biological fluids or biopsy suspensions. Once captured, "on-membrane" immunofluorescent assays reveal the presence and isotype of interrogated cells via automated microscopy and fluorescent image analysis. Using the LOC sensor system, with integrated capture and staining technique, EGFR assays were completed in less than 10 minutes with staining intensity, homogeneity, and cellular localization patterns comparable to conventional labeling methods. Further examination of EGFR expression in three oral cancer cell lines revealed a significant increase (p < 0.05) above control cells with EGFR expression similar to normal squamous epithelium. Results obtained in the microfluidic sensor system correlated well with flow cytometry (r(2) = 0.98), the "gold standard" in quantitative protein expression analysis. In addition, the LOC sensor detected significant differences between two of the oral cancer cell lines (p < 0.01), accounting for disparity of approximately 34 000 EGFR per cell according to quantitative flow cytometry. Taken together, these results support the LOC sensor system as a suitable platform for rapid detection of oral cancer biomarkers and characterization of EGFR over-expression in oral malignancies. Application of this technique may be clinically useful in cancer diagnostics for early detection, prognostic evaluation, and therapeutic selection. Having demonstrated the functionality of this integrated microfluidic sensor system, further studies using clinical samples from oral cancer patients are now warranted.

Lab-on-a-chip methods for point-of-care measurements of salivary biomarkers of periodontitis.

Salivary secretions contain a variety of molecules that reflect important pathophysiological activities. Quantitative changes of specific salivary biomarkers could have significance in the diagnosis and management of both oral and systemic diseases. Modern point-of-care technologies with enhanced detection capabilities are needed to implement a significant advancement in salivary diagnostics. One such promising technology is the recently described lab-on-a-chip (LOC) assay system, in which assays are performed on chemically sensitized beads populated into etched silicon wafers with embedded fluid handling and optical detection capabilities. Using this LOC system, complex assays can be performed with small sample volumes, short analysis times, and markedly reduced reagent costs. This report describes the use of LOC methodologies to assess the levels of interleukin-1beta (IL-1beta), C-reactive protein (CRP), and matrix metalloproteinase-8 (MMP-8) in whole saliva, and the potential use of these biomarkers for diagnosing and categorizing the severity and extent of periodontitis. This study demonstrates that the results achieved by the LOC approach are in agreement with those acquired with standard enzyme-linked immunosorbent assay (ELISA), with significant IL-1beta and MMP-8 elevations in whole saliva of periodontitis patients. Furthermore, because of the superior detection capacities associated with the LOC approach, unlike those with ELISA, significant differences in CRP levels between periodontitis patients and normal subjects are observed. Finally, principal component analysis (PCA) is performed to yield an efficient method to discriminate between periodontally healthy and unhealthy patients, thus increasing the diagnostic value of these biomarkers for periodontitis when examined with the integrated LOC sensor system.

Microchip-based enumeration of human white blood cells.

The advent of flow cytometry has considerably changed the ways in which medical testing is conducted. However, the cost of flow cytometers, their large size, and their maintenance needs make them scarce in resource-poor settings and available almost only in clinical pathology laboratories in developed countries. Because cell enumeration is a basic and crucial support of diagnosis, prognosis, and treatment, an alternative cell-counting method that would potentially be cost-effective, portable, and suitable for use in resource-poor settings is warranted. We describe here a protocol for conducting cell-counting experiments in a simple microfluidic structure. This protocol describes how to build a simple microfluidic cell and perform a total white blood cell (WBC) count through capture and immunolabeling of the WBCs with an anti-CD45 antibody.

A microchip-based assay for interleukin-6.

The electronic taste chip (ETC) assay system is a lab-on-a-chip technology that offers a microchip platform on which bead-based immunoassays are performed. Each bead within the array serves as its own independent self-contained "microreactor" system, with its selectivity determined by the specificity of the antibody that it hosts. The bead-loaded chip is sandwiched between two optically transparent polymethylmethacrylate inserts, packaged within a metal casing described here as the "flow cell." This flow cell allows for delivery of sample and detecting reagents to the microchip and the associated beads. Images of fluorescent beads are captured with a digital video chip and analyzed to facilitate detection and, ultimately, quantitation of analytes in complex fluids. This chapter describes the application of the ETC system for the detection and measurement of interleukin (IL)-6.