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

    Christodoulides, Nicolaos / McRae, Michael P / Simmons, Glennon W / Modak, Sayli S / McDevitt, John T

    Micromachines

    2019  Volume 10, Issue 4

    Abstract: 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 ... ...

    Abstract 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.
    Language English
    Publishing date 2019-04-16
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2620864-7
    ISSN 2072-666X
    ISSN 2072-666X
    DOI 10.3390/mi10040251
    Database MEDical Literature Analysis and Retrieval System OnLINE

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

    McRae, Michael P / Simmons, Glennon W / Christodoulides, Nicolaos J / Lu, Zhibing / Kang, Stella K / Fenyo, David / Alcorn, Timothy / Dapkins, Isaac P / Sharif, Iman / Vurmaz, Deniz / Modak, Sayli S / Srinivasan, Kritika / Warhadpande, Shruti / Shrivastav, Ravi / McDevitt, John T

    Lab on a chip

    2020  Volume 20, Issue 12, Page(s) 2075–2085

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Algorithms ; Biomarkers ; COVID-19 ; Comorbidity ; Coronavirus Infections/diagnosis ; Coronavirus Infections/physiopathology ; Critical Care ; Decision Support Systems, Clinical/organization & administration ; Humans ; Image Processing, Computer-Assisted ; Immunoassay/methods ; Machine Learning ; Pandemics ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/physiopathology ; Point-of-Care Systems ; Predictive Value of Tests ; Risk Factors ; Severity of Illness Index ; Software ; Treatment Outcome
    Chemical Substances Biomarkers
    Keywords covid19
    Language English
    Publishing date 2020-06-03
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2056646-3
    ISSN 1473-0189 ; 1473-0197
    ISSN (online) 1473-0189
    ISSN 1473-0197
    DOI 10.1039/d0lc00373e
    Database MEDical Literature Analysis and Retrieval System OnLINE

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

    McRae, Michael P / Simmons, Glennon W / Christodoulides, Nicolaos J / Lu, Zhibing / Kang, Stella K / Fenyo, David / Alcorn, Timothy / Dapkins, Isaac P / Sharif, Iman / Vurmaz, Deniz / Modak, Sayli S / Srinivasan, Kritika / Warhadpande, Shruti / Shrivastav, Ravi / McDevitt, John T

    medRxiv : the preprint server for health sciences

    2020  

    Abstract: 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 ... ...

    Abstract 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.
    Keywords covid19
    Language English
    Publishing date 2020-04-22
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2020.04.16.20068411
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions.

    McRae, Michael P / Modak, Sayli S / Simmons, Glennon W / Trochesset, Denise A / Kerr, A Ross / Thornhill, Martin H / Redding, Spencer W / Vigneswaran, Nadarajah / Kang, Stella K / Christodoulides, Nicolaos J / Murdoch, Craig / Dietl, Steven J / Markham, Roger / McDevitt, John T

    Cancer cytopathology

    2020  Volume 128, Issue 3, Page(s) 207–220

    Abstract: 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 ... ...

    Abstract 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.
    MeSH term(s) Adult ; Algorithms ; Biomarkers, Tumor/metabolism ; Carcinoma, Squamous Cell/diagnosis ; Carcinoma, Squamous Cell/metabolism ; Cytodiagnosis/instrumentation ; Cytodiagnosis/methods ; Early Detection of Cancer/methods ; Female ; Humans ; Machine Learning ; Male ; Mass Screening/methods ; Middle Aged ; Models, Theoretical ; Mouth Neoplasms/diagnosis ; Mouth Neoplasms/metabolism ; Point-of-Care Systems ; Prospective Studies ; ROC Curve ; Software
    Chemical Substances Biomarkers, Tumor
    Language English
    Publishing date 2020-02-07
    Publishing country United States
    Document type Journal Article ; Multicenter Study ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2594979-2
    ISSN 1934-6638 ; 1934-662X
    ISSN (online) 1934-6638
    ISSN 1934-662X
    DOI 10.1002/cncy.22236
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
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  5. Article: Rapid Point-of-Care Isothermal Amplification Assay for the Detection of Malaria without Nucleic Acid Purification.

    Modak, Sayli S / Barber, Cheryl A / Geva, Eran / Abrams, William R / Malamud, Daniel / Ongagna, Yhombi Serge Yvon

    Infectious diseases

    2016  Volume 9, Page(s) 1–9

    Abstract: Malaria remains one of the most prevalent infectious diseases and results in significant mortality. Isothermal amplification (loop-mediated isothermal amplification) is used to detect malarial DNA at levels of ~1 parasite/µL blood in ≤30 minutes without ... ...

    Abstract Malaria remains one of the most prevalent infectious diseases and results in significant mortality. Isothermal amplification (loop-mediated isothermal amplification) is used to detect malarial DNA at levels of ~1 parasite/µL blood in ≤30 minutes without the isolation of parasite nucleic acid from subject's blood or saliva. The technique targets the mitochondrial cytochrome oxidase subunit 1 gene and is capable of distinguishing Plasmodium falciparum from Plasmodium vivax. Malarial diagnosis by the gold standard microscopic examination of blood smears is generally carried out only after moderate-to-severe symptoms appear. Rapid diagnostic antigen tests are available but generally require infection levels in the range of 200-2,000 parasites/µL for a positive diagnosis and cannot distinguish if the disease has been cleared due to the persistence of circulating antigen. This study describes a rapid and simple molecular assay to detect malarial genes directly from whole blood or saliva without DNA isolation.
    Language English
    Publishing date 2016-01-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2551443-X
    ISSN 1178-6337
    ISSN 1178-6337
    DOI 10.4137/IDRT.S32162
    Database MEDical Literature Analysis and Retrieval System OnLINE

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

    McRae, Michael P / Simmons, Glennon W / Christodoulides, Nicolaos J / Lu, Zhibing / Kang, Stella K / Fenyo, David / Alcorn, Timothy / Dapkins, Isaac P / Sharif, Iman / Vurmaz, Deniz / Modak, Sayli S / Srinivasan, Kritika / Warhadpande, Shruti / Shrivastav, Ravi / McDevitt, John T

    Lab Chip

    Abstract: 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 ... ...

    Abstract 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.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #506003
    Database COVID19

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

    McRae, Michael P. / Simmons, Glennon W. / Christodoulides, Nicolaos J. / Lu, Zhibing / Kang, Stella K. / Fenyo, David / Alcorn, Timothy / Dapkins, Isaac P. / Sharif, Iman / Vurmaz, Deniz / Modak, Sayli S. / Srinivasan, Kritika / Warhadpande, Shruti / Shrivastav, Ravi / McDevitt, John T.

    Lab on a Chip

    2020  Volume 20, Issue 12, Page(s) 2075–2085

    Abstract: The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. ...

    Abstract The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality.
    Keywords Biochemistry ; Bioengineering ; General Chemistry ; Biomedical Engineering ; covid19
    Language English
    Publisher Royal Society of Chemistry (RSC)
    Publishing country uk
    Document type Article ; Online
    ZDB-ID 2056646-3
    ISSN 1473-0189 ; 1473-0197
    ISSN (online) 1473-0189
    ISSN 1473-0197
    DOI 10.1039/d0lc00373e
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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