scholarly journals Clinical Evaluation of Pathognomonic Salivary Protease Fingerprinting for Oral Disease Diagnosis

2021 ◽  
Vol 11 (9) ◽  
pp. 866
Author(s):  
Garrit Koller ◽  
Eva Schürholz ◽  
Thomas Ziebart ◽  
Andreas Neff ◽  
Roland Frankenberger ◽  
...  
Keyword(s):  
Disease Status ◽  
Disease Diagnosis ◽  
Matrix Metalloproteases ◽  
Supervised Machine Learning ◽  
Oral Disease ◽  
Peptide Cleavage ◽  
Disease States ◽  
Substrate Cleavage ◽  
Specific Activities ◽  
Learning Analysis

Dental decay (Caries) and periodontal disease are globally prevalent diseases with significant clinical need for improved diagnosis. As mediators of dental disease-specific extracellular matrix degradation, proteases are promising analytes. We hypothesized that dysregulation of active proteases can be functionally linked to oral disease status and may be used for diagnosis. To address this, we examined a total of 52 patients with varying oral disease states, including healthy controls. Whole mouth saliva samples and caries biopsies were collected and subjected to analysis. Overall proteolytic and substrate specific activities were assessed using five multiplexed, fluorogenic peptides. Peptide cleavage was further described by inhibitors targeting matrix metalloproteases (MMPs) and cysteine, serine, calpain proteases (CSC). Proteolytic fingerprints, supported by supervised machine-learning analysis, were delineated by total proteolytic activity (PepE) and substrate preference combined with inhibition profiles. Caries and peridontitis showed increased enzymatic activities of MMPs with common (PepA) and divergent substrate cleavage patterns (PepE), suggesting different MMP contribution in particular disease states. Overall, sensitivity and specificity values of 84.6% and 90.0%, respectively, were attained. Thus, a combined analysis of protease derived individual and arrayed substrate cleavage rates in conjunction with inhibitor profiles may represent a sensitive and specific tool for oral disease detection.

scholarly journals Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 642
Author(s):  
Yi-Da Wu ◽  
Ruey-Kai Sheu ◽  
Chih-Wei Chung ◽  
Yen-Ching Wu ◽  
Chiao-Chi Ou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Excellent Agreement ◽  
Antinuclear Antibody ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Observer Agreement ◽  
Supervised Machine Learning ◽  
International Consensus ◽  
Testing Data

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

scholarly journals Platelets in Healthy and Disease States: From Biomarkers Discovery to Drug Targets Identification by Proteomics

2020 ◽  
Vol 21 (12) ◽  
pp. 4541 ◽  
Author(s):  
Erica Gianazza ◽  
Maura Brioschi ◽  
Roberta Baetta ◽  
Alice Mallia ◽  
Cristina Banfi ◽  
...  
Keyword(s):  
Drug Targets ◽  
Disease Diagnosis ◽  
Post Translational Modifications ◽  
Disease States ◽  
Recent Advances ◽  
Health And Disease ◽  
Proteome Changes ◽  
Platelet Proteomics ◽  
The Impact ◽  
Platelet Functions

Platelets are a heterogeneous small anucleate blood cell population with a central role both in physiological haemostasis and in pathological states, spanning from thrombosis to inflammation, and cancer. Recent advances in proteomic studies provided additional important information concerning the platelet biology and the response of platelets to several pathophysiological pathways. Platelets circulate systemically and can be easily isolated from human samples, making proteomic application very interesting for characterizing the complexity of platelet functions in health and disease as well as for identifying and quantifying potential platelet proteins as biomarkers and novel antiplatelet therapeutic targets. To date, the highly dynamic protein content of platelets has been studied in resting and activated platelets, and several subproteomes have been characterized including platelet-derived microparticles, platelet granules, platelet releasates, platelet membrane proteins, and specific platelet post-translational modifications. In this review, a critical overview is provided on principal platelet proteomic studies focused on platelet biology from signaling to granules content, platelet proteome changes in several diseases, and the impact of drugs on platelet functions. Moreover, recent advances in quantitative platelet proteomics are discussed, emphasizing the importance of targeted quantification methods for more precise, robust and accurate quantification of selected proteins, which might be used as biomarkers for disease diagnosis, prognosis and therapy, and their strong clinical impact in the near future.

The disease status of catatonia

2002 ◽  
Vol 25 (5) ◽  
pp. 590-591 ◽  
Author(s):  
Irwin Savodnik
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Status ◽  
Logical Status ◽  
Disease States

Georg Northoff encounters a problem regarding the logical status of “catatonia.” Whereas Parkinson's disease (PD) is a disease on the basis of Virchowian criteria, catatonia is not. PD is associated with pathognomonic neurological lesions. Catatonia does not require any such association. The diagnosis is rendered using social criteria rather than neuropathological ones. Therefore, Northoff is not comparing two disease states at all.

ACTOR: a latent Dirichlet model to compare expressed isoform proportions to a reference panel

2019 ◽  
Author(s):  
Sean D. McCabe ◽  
Andrew B. Nobel ◽  
Michael I. Love
Keyword(s):  
Relative Proportion ◽  
Disease Status ◽  
R Package ◽  
Tissue Expression ◽  
Reference Panel ◽  
Tissue Type ◽  
Disease States ◽  
Dirichlet Model ◽  
Public Datasets ◽  
Splicing Patterns

AbstractThe relative proportion of RNA isoforms expressed for a given gene has been associated with disease states in cancer, retinal diseases, and neurological disorders. Examination of relative isoform proportions can help determine biological mechanisms, but such analyses often require a per-gene investigation of splicing patterns. Leveraging large public datasets produced by genomic consortia as a reference, one can compare splicing patterns in a dataset of interest with those of a reference panel in which samples are divided into distinct groups (tissue of origin, disease status, etc). We propose ACTOR, a latent Dirichlet model with Dirichlet Multinomial observations to compare expressed isoform proportions in a dataset to an independent reference panel. We use a variational Bayes procedure to estimate posterior distributions for the group membership of one or more samples. Using the Genotype-Tissue Expression (GTEx) project as a reference dataset, we evaluate ACTOR on simulated and real RNA-seq datasets to determine tissue-type classifications of genes. ACTOR is publicly available as an R package at https://github.com/mccabes292/actor.

scholarly journals Dynamic Changes in the Subgingival Microbiome and Their Potential for Diagnosis and Prognosis of Periodontitis

mBio ◽  
2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Baochen Shi ◽  
Michaela Chang ◽  
John Martin ◽  
Makedonka Mitreva ◽  
Renate Lux ◽  
...  
Keyword(s):  
Disease Progression ◽  
Clinical Examination ◽  
Disease Diagnosis ◽  
Taxonomic Composition ◽  
Oral Disease ◽  
Dynamic Changes ◽  
Diagnosis And Prognosis ◽  
Microbiome Profile ◽  
Before And After

ABSTRACTThe human microbiome influences and reflects the health or disease state of the host. Periodontitis, a disease affecting about half of American adults, is associated with alterations in the subgingival microbiome of individual tooth sites. Although it can be treated, the disease can reoccur and may progress without symptoms. Without prognostic markers, follow-up examinations are required to assess reoccurrence and disease progression and to determine the need for additional treatments. To better identify and predict the disease progression, we aim to determine whether the subgingival microbiome can serve as a diagnosis and prognosis indicator. Using metagenomic shotgun sequencing, we characterized the dynamic changes in the subgingival microbiome in periodontitis patients before and after treatment at the same tooth sites. At the taxonomic composition level, the periodontitis-associated microorganisms were significantly shifted from highly correlated in the diseased state to poorly correlated after treatment, suggesting that coordinated interactions among the pathogenic microorganisms are essential to disease pathogenesis. At the functional level, we identified disease-associated pathways that were significantly altered in relative abundance in the two states. Furthermore, using the subgingival microbiome profile, we were able to classify the samples to their clinical states with an accuracy of 81.1%. Follow-up clinical examination of the sampled sites supported the predictive power of the microbiome profile on disease progression. Our study revealed the dynamic changes in the subgingival microbiome contributing to periodontitis and suggested potential clinical applications of monitoring the subgingival microbiome as an indicator in disease diagnosis and prognosis.IMPORTANCEPeriodontitis is a common oral disease. Although it can be treated, the disease may reoccur without obvious symptoms. Current clinical examination parameters are useful in disease diagnosis but cannot adequately predict the outcome of individual tooth sites after treatment. A link between the subgingival microbiota and periodontitis was identified previously; however, it remains to be investigated whether the microbiome can serve as a diagnostic and prognostic indicator. In this study, for the first time, we characterized the subgingival microbiome of individual tooth sites before and after treatment using a large-scale metagenomic analysis. Our longitudinal study revealed changes in the microbiota in taxonomic composition, cooccurrence of subgingival microorganisms, and functional composition. Using the microbiome profiles, we were able to classify the clinical states of subgingival plaque samples with a high accuracy. Follow-up clinical examination of sampled sites indicates that the subgingival microbiome profile shows promise for the development of diagnostic and prognostic tools.

scholarly journals Supervised machine learning models applied to disease diagnosis and prognosis

2019 ◽  
Vol 6 (4) ◽  
pp. 405-423
Author(s):  
Maria C Mariani ◽  
◽  
Osei K Tweneboah ◽  
Md Al Masum Bhuiyan ◽  
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Diagnosis And Prognosis ◽  
Machine Learning Models

scholarly journals Machine Learning Classification of Disrotatory IRC and Conrotatory Non-IRC Trajectory Motion for Cyclopropyl Radical Ring Opening

2021 ◽  
Author(s):  
Steven Maley ◽  
Jesse Melville ◽  
Spencer Yu ◽  
Cal Hargis ◽  
Reid Hamilton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ring Opening ◽  
Supervised Machine Learning ◽  
Qualitative Assessment ◽  
Vibrational Modes ◽  
Radical Ring ◽  
Machine Learning Classification ◽  
Ring Opening Reaction ◽  
Learning Analysis

Transition-state features from trajectories were used for supervised machine learning analysis of the cyclopropyl radical ring opening reaction. Quantitative and qualitative assessment of features controlling disrotatory IRC versus conrotatory non-IRC motion and revealed that there are two key vibrational modes where their directional combination provides prediction of pathway motion. <br>

scholarly journals GIV/Girdin and Exo70 Constitute the Core of the Mammalian Polarized Exocytic Machinery

2019 ◽  
Author(s):  
Cristina Rohena ◽  
Navin Rajapakse ◽  
I-Chung Lo ◽  
Peter Novick ◽  
Debashis Sahoo ◽  
...  
Keyword(s):  
Matrix Metalloproteases ◽  
Regulatory Control ◽  
List Type ◽  
Linear Motif ◽  
Linear Interaction ◽  
Spatial Control ◽  
Disease States ◽  
Cargo Proteins ◽  
And Migration ◽  
Polarized Exocytosis

SUMMARYPolarized exocytosis is a fundamental process by which membrane and cargo proteins are delivered to the plasma membrane with precise spatial control; it is essential for cell growth, morphogenesis, and migration. Although the need for the octameric exocyst complex is conserved from yeast to humans, what imparts spatial control is known only in yeast, i.e., a polarity scaffold without mammalian homolog, called Bem1p. We demonstrate that polarity scaffold GIV/Girdin fulfills the key criteria and functions of its yeast counterpart Bem1p. Both Bem1p and GIV bind yeast and mammalian Exo70 proteins via similar short-linear interaction motifs, but each preferentially binds its evolutionary counterpart. In cells where this GIV•Exo-70 interaction is selectively disrupted, delivery of the metalloprotease MT1-MMP to podosomes, collagen degradation and haptotaxis through basement membrane matrix were impaired. GIV’s interacting partners reveal other components of polarized exocytosis in mammals. Findings not only expose how GIV “upgrades” the exocytic process in mammals, but also how the ability to regulate exocytosis shapes GIV’s ability to fuel metastasis.GRAPHIC ABSTRACTGraphic Abstract: Schematic comparing the components of polarized exocytosis, i.e., the major polarity scaffold in yeast (Bem1p; left) and humans (Girdin; right) and the various cellular components and signaling mechanisms that are known to converge on them.The eTOC blurbPolarized exocytosis is a precision-controlled process that is enhanced in disease states, e.g., cancer invasion; what imparts polarity was unknown. Authors reveal how the process underwent an evolutionary upgrade from yeast to humans by pinpointing GIV/Girdin as the polarity scaffold which orchestrates the exocytosis of matrix metalloproteases during cell invasion.HIGHLIGHTSGIV (human) and Bem1p (yeast) bind Exo70; are required for exocytosisGIV binds and aids PM localization Exo70 via a conserved short linear motifBinding facilitates MT1-MMP delivery to podosomes, ECM degradation, invasionRegulatory control over polarized exocytosis is upgraded during evolution

Augmenting Embedding with Domain Knowledge for Oral Disease Diagnosis Prediction

2018 ◽  
pp. 236-250
Author(s):  
Guangkai Li ◽  
Songmao Zhang ◽  
Jie Liang ◽  
Zhanqiang Cao ◽  
Chuanbin Guo
Keyword(s):  
Domain Knowledge ◽  
Disease Diagnosis ◽  
Oral Disease
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