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.
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.
We are beginning a new era of
-integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro ...diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis
by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive
reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care.
Background
The COVID-19 pandemic began in early 2021 and placed significant strains on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for ...patients at elevated risk of morbidity and mortality.
Objective
The goal of this retrospective study of patients who tested positive with COVID-19 and were treated at NYU (New York University) Langone Health was to identify clinical markers predictive of disease severity in order to assist in clinical decision triage and to provide additional biological insights into disease progression.
Methods
The clinical activity of 3740 patients at NYU Langone Hospital was obtained between January and August 2020; patient data were deidentified. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to the intensive care unit (ICU).
Results
The XGBoost (eXtreme Gradient Boosting) model that was trained on clinical data from the final 24 hours excelled at predicting mortality (area under the curve AUC=0.92; specificity=86%; and sensitivity=85%). Respiration rate was the most important feature, followed by SpO2 (peripheral oxygen saturation) and being aged 75 years and over. Performance of this model to predict the deceased outcome extended 5 days prior, with AUC=0.81, specificity=70%, and sensitivity=75%. When only using clinical data from the first 24 hours, AUCs of 0.79, 0.80, and 0.77 were obtained for deceased, ventilated, or ICU-admitted outcomes, respectively. Although respiration rate and SpO2 levels offered the highest feature importance, other canonical markers, including diabetic history, age, and temperature, offered minimal gain. When lab values were incorporated, prediction of mortality benefited the most from blood urea nitrogen and lactate dehydrogenase (LDH). Features that were predictive of morbidity included LDH, calcium, glucose, and C-reactive protein.
Conclusions
Together, this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.
The integration of semiconductor nanoparticle quantum dots (QDs) into a modular, microfluidic biosensor for the multiplexed quantitation of three important cancer markers, carcinoembryonic antigen ...(CEA), cancer antigen 125 (CA125), and Her-2/
Neu (C-erbB-2) was achieved. The functionality of the integrated sample processing, analyte capture and detection modalities was demonstrated using both serum and whole saliva specimens. Here, nano-bio-chips that employed a fluorescence transduction signal with QD-labeled detecting antibody were used in combination with antigen capture by a microporous agarose bead array supported within a microfluidics ensemble so as to complete the sandwich-type immunoassay. The utilization of QD probes in this miniaturized biosensor format resulted in signal amplification 30 times relative to that of standard molecular fluorophores as well as affording a reduction in observed limits of detection by nearly 2 orders of magnitude (0.02
ng/mL CEA; 0.11
pM CEA) relative to enzyme-linked immunosorbent assay (ELISA). Assay validation studies indicate that measurements by the nano-bio-chip system correlate to standard methods at
R
2
=
0.94 and
R
2
=
0.95 for saliva and serum, respectively. This integrated nano-bio-chip assay system, in tandem with next-generation fluorophores, promises to be a sensitive, multiplexed tool for important diagnostic and prognostic applications.
A brief review of cytology in dentistry Srinivasan Rajsri, Kritika; K Durab, Safia; A Varghese, Ida ...
British dental journal,
02/2024, Letnik:
236, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Oral cytology is a non-invasive adjunctive diagnostic tool with a number of potential applications in the practice of dentistry. This brief review begins with a history of cytology in medicine and ...how cytology was initially applied in oral medicine. A description of the different technical aspects of oral cytology is provided, including the collection and processing of oral cytological samples, and the microscopic interpretation and reporting, along with their advantages and limitations. Applications for oral cytology are listed with a focus on the triage of patients presenting with oral potentially malignant disorders and oral mucosal infections. Furthermore, the utility of oral cytology roles across both expert (for example, secondary oral medicine or tertiary head and neck oncology services) and non-expert (for example, primary care general dental practice) clinical settings is explored. A detailed section covers the evidence-base for oral cytology as a diagnostic adjunctive technique in both the early detection and monitoring of patients with oral cancer and oral epithelial dysplasia. The review concludes with an exploration of future directions, including the integration of artificial intelligence for automated analysis and point of care 'smart diagnostics', thereby offering some insight into future opportunities for a wider application of oral cytology in dentistry.
The advent of the alternative sweeteners market has signaled a demand for chemosensors which target multiple saccharides and saccharide derivatives, in aqueous media at physiological pH. This demand ...has largely been unmet as existing molecular receptors for saccharides have generally not shown sufficient degrees of affinity and selectivity in aqueous media. A chemosensor array for saccharides and saccharide derivatives, fully operational in aqueous media at physiological pH, has been developed and is reported herein. Boronic acid based peptidic receptors, derived from a combinatorial library, served as the cross-reactive sensor elements in this array. The binding of saccharides to these receptors was assessed colorimetrically using an indicator uptake protocol in the taste-chip platform. The differential indicator uptake rates of these receptors in the presence of saccharides were exploited in order to identify patterns within the data set using linear discriminant analysis. This chemosensor array is capable of classifying disaccharides and monosaccharides as well as discriminating compounds within each saccharide group. Disaccharides have also been distinguished from closely related reduced-calorie counterparts. This linear discriminant analysis set was then employed as a training set for identifying a specific saccharide in a real-world beverage sample. The methodology developed here augers well for use in other real-world samples involving saccharides as well as for sensing other desired analytes.
Objective Interobserver agreement in the context of oral epithelial dysplasia (OED) grading has been notoriously unreliable and can impose barriers for developing new molecular markers and diagnostic ...technologies. This paper aimed to report the details of a 3-stage histopathology review and adjudication process with the goal of achieving a consensus histopathologic diagnosis of each biopsy. Study Design Two adjacent serial histologic sections of oral lesions from 846 patients were independently scored by 2 different pathologists from a pool of 4. In instances where the original 2 pathologists disagreed, a third, independent adjudicating pathologist conducted a review of both sections. If a majority agreement was not achieved, the third stage involved a face-to-face consensus review. Results Individual pathologist pair κ values ranged from 0.251 to 0.706 (fair-good) before the 3-stage review process. During the initial review phase, the 2 pathologists agreed on a diagnosis for 69.9% of the cases. After the adjudication review by a third pathologist, an additional 22.8% of cases were given a consensus diagnosis (agreement of 2 out of 3 pathologists). After the face-to-face review, the remaining 7.3% of cases had a consensus diagnosis. Conclusions The use of the defined protocol resulted in a substantial increase (30%) in diagnostic agreement and has the potential to improve the level of agreement for establishing gold standards for studies based on histopathologic diagnosis.
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.
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.
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.
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.
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.
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.
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