Biomarkers

Huss, Ralf

doi:10.1016/b978-0-12-410396-2.00019-0

Cited by 29 publications

(10 citation statements)

References 19 publications

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“…[11][12][13] 2.2 | Classification of biomarkers Disease biomarkers are either static or dynamic, and based on the desired characteristics described above, they typically are subclassified as functional, biochemical, cellular, genomic, proteomic, metabolomic, microbiomic, behavioral, or outcome biomarkers. [14][15][16] Of these, functional, biochemical, and genomic biomarkers currently are the most practical to use and most widely available and are currently of particular clinical interest for CIE management in dogs ( Figure 1).…”

Section: Clinical Utility Of Biomarkersmentioning

confidence: 99%

Clinical utility of currently available biomarkers in inflammatory enteropathies of dogs

Heilmann

Steiner

2018

Veterinary Internal Medicne

108

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Chronic inflammatory enteropathies (CIE) in dogs are a group of disorders that are characterized by chronic persistent or recurrent signs of gastrointestinal disease and histologic evidence of mucosal inflammation. These CIEs are classified as either food‐responsive, antibiotic‐responsive, or immunosuppressant‐responsive enteropathy. Patients not clinically responding to immunomodulatory treatment are grouped as nonresponsive enteropathy and dogs with intestinal protein loss as protein‐losing enteropathy. Disease‐independent clinical scoring systems were established in dogs for assessment of clinical disease severity and patient monitoring during treatment. Histopathologic and routine clinicopathologic findings are usually not able to distinguish the subgroups of CIE. Treatment trials are often lengthy and further diagnostic tests are usually at least minimally invasive. Biomarkers that can aid in defining the presence of disease, site of origin, severity of the disease process, response to treatment, or a combination of these would be clinically useful in dogs with CIE. This article summarizes the following biomarkers that have been evaluated in dogs with CIE during the last decade, and critically evaluates their potential clinical utility in dogs with CIE: functional biomarkers (cobalamin, methylmalonic acid, folate, α1‐proteinase inhibitor, immunoglobulin A), biochemical biomarkers (C‐reactive protein, perinuclear anti‐neutrophilic cytoplasmic antibodies, 3‐bromotyrosine, N‐methylhistamine, calprotectin, S100A12, soluble receptor of advanced glycation end products, cytokines and chemokines, alkaline phosphatase), microbiomic biomarkers (microbiome changes, dysbiosis index), metabolomic biomarkers (serum metabolome), genetic biomarkers (genomic markers, gene expression changes), and cellular biomarkers (regulatory T cells). In addition, important performance criteria of diagnostic tests are briefly reviewed.

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Section: Clinical Utility Of Biomarkersmentioning

confidence: 99%

Clinical utility of currently available biomarkers in inflammatory enteropathies of dogs

Heilmann

Steiner

2018

Veterinary Internal Medicne

108

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“…One way to examine the extent to which stress impacts stress-related physiological mechanisms in BC survivors is to evaluate the secretion patterns of associated biomarkers. Several biomarkers are considered to be very reliable and sensitive indicators of an individual’s biological response (biochemical, physiological, cellular, or behavioural responses) to different types of stressors [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Salivary secretory immunoglobulin A reactivity: a comparison to cortisol and -amylase patterns in the same breast cancer survivors

Lambert

Couture-Lalande

Brennan

et al. 2018

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IntroductionOne way to examine the extent to which the stress associated with a breast cancer experience (BC) impacts stress-related physiological mechanisms is to study the secretion patterns of associated biomarkers. Unlike cortisol and α-amylase (sAA), biomarkers of immune functioning such as secretory immunoglobulin A (SIgA) have rarely been examined in BC survivors.Aim of the studyThis study had two principal aims: the first was to evaluate the basal secretion profiles of SIgA as well as its response to an acute stressor as a marker of immune health in BC survivors and women with no history of BC, and the second was to determine how SIgA stress-related patterns compare to published cortisol and sAA patterns in the same women.ResultsOverall, the findings indicate that BC survivors exhibit a blunted cortisol reaction to an acute stressor, a generally elevated diurnal sAA concentration pattern, and normal SIgA profiles, compared to women with no history of cancer. This study serves as a foundation for future research to elucidate the relationships between BC experience variables, stress biomarkers, and health outcomes in BC survivors.

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“…Biomarkers should also have some additional features such as interpretability, deployability and generalizability (Woo & Wager, 2015). As Huss stated, depending on several criteria, biomarkers can be classified as either imaging biomarkers such as MRI, PET, Computed Tomography, EEG or molecular biomarkers such as genetic variations, mutations, proteins and lipids (Huss, 2015). Interpretable biomarkers in neuroimaging should be meaningful and associated to other parameters such as genetic or clinical.…”

Section: Biomarker Research On Fnirsmentioning

confidence: 99%

Diagnostic Machine Learning Applications on Clinical Populations using Functional Near Infrared Spectroscopy: A Review

Eken

Nassehi

Eroğul

2023

Preprint

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Objective : There is a growing interest in functional near-infrared spectroscopy (fNIRS) among researchers due to its remarkable advantages such as ease of use, being inexpensive and less tolerance to motion artifacts compared to other neuroimaging modalities. Also, its interaction with machine learning (ML) approaches was inevitable like other neuroimaging modalities for different purposes such as diagnostic classification of diseases or prediction of disease severity due to the lack of robust and objective biomarkers. A review of literature is carried out to understand the evolution of biomarker research on clinical populations and clinical states by combining fNIRS and ML. Approach : In this review, article search was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) standard and 62 studies were evaluated and we provided a general overview by using fNIRS, particularly in clinical populations and some clinically relevant conditions on healthy populations. Also, potential biomarkers that were found in these studies and some popular ML algorithms that had been used for the prediction or classification of fNIRS data were discussed. Main Results : There is an increasing trend to perform ML applications on fNIRS data on biomarker research related to different clinical fields. Among these studies, few were able to have a notable number of participants for classification and clinical state prediction. Oxy-hemoglobin was used more than deoxy-hemoglobin in ML-based studies as a potential feature source. Significance : Using ML on fNIRS data might be a promising approach to revealing specific biomarkers for either diagnostic classification of diseases or prediction of clinical conditions.

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Biomarkers

Cited by 29 publications

References 19 publications

Clinical utility of currently available biomarkers in inflammatory enteropathies of dogs

Clinical utility of currently available biomarkers in inflammatory enteropathies of dogs

Salivary secretory immunoglobulin A reactivity: a comparison to cortisol and -amylase patterns in the same breast cancer survivors

Diagnostic Machine Learning Applications on Clinical Populations using Functional Near Infrared Spectroscopy: A Review

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