scholarly journals CD177 Enhances the Detection of Myelodysplastic Syndrome by Flow Cytometry

2020 ◽  
Vol 153 (4) ◽  
pp. 554-565 ◽  
Author(s):  
Khaled Alayed ◽  
Jeremy B Meyerson ◽  
Ebenezer S Osei ◽  
Georgeta Blidaru ◽  
June Schlegelmilch ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Multivariate Analysis ◽  
Myelodysplastic Syndrome ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Receiver Operating Characteristic Area ◽  
Flow Cytometric ◽  
Increased Sensitivity ◽  
Characteristic Area ◽  
Highly Correlated

Abstract Objectives Previously we demonstrated that a decreased percentage of CD177-positive granulocytes detected by flow cytometry (FCM) was associated with myelodysplastic syndrome (MDS). Here we expand on those findings to more rigorously evaluate the utility of CD177 for the detection of MDS. Methods Two hundred patient samples (100 MDS and 100 controls) were evaluated for granulocyte expression of CD177 and 11 other flow cytometric parameters known to be associated with MDS. Results We show that CD177, as a single analyte, is highly correlated with MDS with a receiver operating characteristic area under curve value of 0.8. CD177 expression below 30% demonstrated a sensitivity of 51% and a specificity of 94% for detecting MDS with a positive predictive value of 89.5%. In multivariate analysis of 12 MDS-associated FCM metrics, CD177 and the Ogata parameters were significant indicators of MDS, and CD177 increased sensitivity of the Ogata score by 16% (63%-79%) for predicting MDS. Finally, diagnostic criteria incorporating these parameters with a 1% blast cutoff level and CD177 resulted in a sensitivity of 90% and specificity of 91% for detecting MDS. Conclusions The findings indicate CD177 is a useful FCM marker for MDS.

scholarly journals Machine Learning Models for Diagnostic Classification of Hepatitis C Tests

2021 ◽  
Vol 10 (1) ◽  
pp. 70
Author(s):  
Oladosu Oyebisi Oladimeji ◽  
Abimbola Oladimeji ◽  
Oladimeji Olayanju
Keyword(s):  
Hepatitis C ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Matthews Correlation Coefficient ◽  
Receiver Operating Characteristic Area ◽  
Precision Recall Curve ◽  
Characteristic Area ◽  
F Measure ◽  
Machine Learning Models ◽  
Recall Curve

Introduction: Hepatitis C is a chronic infection caused by hepatitis c virus - a blood borne virus. Therefore, the infection occurs through exposure to small quantities of blood. It has been estimated by World Health Organization (WHO) to have affected 71 million people worldwide. This infection costs individual, groups and government a lot because no vaccine has been gotten yet for the treatment. This disease is likely to continue to affect more people because it’s long asymptotic phase which makes its early detection not feasible.Material and Methods: In this study, we have presented machine learning models to automatically classify the diagnosis test of hepatitis and also ranked the test features in order to know how they contribute to the classification which help in decision making process by the health care industry. The synthetic minority oversampling technique (SMOTE) was used to solve the problem of imbalance dataset.Results: The models were evaluated based on metrics such as Matthews correlation coefficient, F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve.  We found that using SMOTE techniques helped raise performance of the predictive models. Also, random forest (RF) had the best performance based on Matthews correlation coefficient (0.99), F-measure (0.99), Precision-Recall curve (1.00) and Receiver Operating Characteristic Area Under Curve (0.99).Conclusion: This discovery has the potential to impact on clinical practice, when health workers aim at classifying diagnosis result of disease at its early stage.

Serum syndecan-1, hyaluronan and thrombomodulin levels in patients with lupus nephritis

Rheumatology ◽  
2020 ◽  
Author(s):  
Kelvin Y C Yu ◽  
Susan Yung ◽  
Mel K M Chau ◽  
Colin S O Tang ◽  
Desmond Y H Yap ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Receiver Operating Characteristic ◽  
Healthy Subjects ◽  
Area Under Curve ◽  
Operating Characteristic ◽  
Receiver Operating Characteristic Area ◽  
Characteristic Area ◽  
Renal Lupus ◽  
Receiver Operating

Abstract Objectives We investigated circulating syndecan-1, HA and thrombomodulin levels in patients with biopsy-proven Class III/IV ± V LN and their clinico-pathological associations. Patients with non-renal SLE or non-lupus chronic kidney disease, and healthy subjects served as controls. Methods Serum syndecan-1, HA and thrombomodulin levels were determined by ELISAs. Results Syndecan-1, HA and thrombomodulin levels were significantly higher during active LN compared with remission (P < 0.01, for all), and correlated with the level of proteinuria, estimated glomerular filtration rate, anti-dsDNA antibodies, complement 3 and serum creatinine. Longitudinal studies showed that syndecan-1 and thrombomodulin levels increased prior to clinical renal flare by 3.6 months, while HA level increased at the time of nephritic flare, and the levels decreased in parallel with treatment response. Receiver operating characteristic curve analysis showed that syndecan-1 and thrombomodulin levels distinguished patients with active LN from healthy subjects, LN patients in remission, patients with active non-renal lupus and patients with non-lupus chronic kidney disease (receiver operating characteristic area under curve of 0.98, 0.91, 0.82 and 0.95, respectively, for syndecan-1; and area under curve of 1.00, 0.84, 0.97 and 0.79, respectively, for thrombomodulin). HA level distinguished active LN from healthy subjects, LN patients in remission and non-lupus chronic kidney disease (receiver operating characteristic area under curve of 0.82, 0.71 and 0.90, respectively) but did not distinguish between renal vs non-renal lupus. Syndecan-1 and thrombomodulin levels correlated with the severity of interstitial inflammation, while HA level correlated with chronicity grading in kidney biopsies of active LN. Conclusion Our findings suggest potential utility of serum syndecan-1, thrombomodulin and HA levels in clinical management, and their potential contribution to LN pathogenesis.

Diagnostic Utility of Flow Cytometry in Myelodysplastic Syndrome: A Retrospective Validation From a Single Centre.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 4846-4846
Author(s):  
Pervin Topcuoglu ◽  
Klara Dalva ◽  
Sule Mine Bakanay ◽  
Sinem Civriz Bozdag ◽  
Onder Arslan ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Flow Cytometry ◽  
Myelodysplastic Syndrome ◽  
Antigen Expression ◽  
Pathological Examination ◽  
Light Scatter ◽  
Myeloid Lineage ◽  
Hematopoietic Stem ◽  
Altered Expression ◽  
Flow Cytometric

Abstract Abstract 4846 Myelodysplastic syndrome (MDS) is heterogeneous clonal hematopoietic stem cell disorder characterized by cytopenia(s) and dysplasia in one or more cell lineage. Though flow cytometry (FCM) is an important diagnostic tool in hematopoietic cell disorders, a prominent immunophenotyping feature in MDS may not be determined. In this study, we retrospectively evaluated flow cytometric features of bone marrow samples diagnosed as MDS with clinical and hematological findings. Patients-Method Between Feb 2004 and March 2009, flow cytometric parameters of 73 patients (M/F: 50/23) with MDS were re-analyzed. Median age was 59 years (17-89 ys). Our general principles are to evaluate quality of bone marrow samples, to determine proportion of the cells and features of their light scatter, and to give percentage of the blast. When detected a finding of dysplasia in the first analysis, the second step includes the determination of the maturation of the cells and the presence of the aberrant antigen expression. Results The samples were interpreted as MDS in % 76.7, MDS-RAEB-1 or RAEB-2 in %16.4, myeloproliferative disorder in %1.4 and non-diagnostic in %6.8 of the cases by flow cytometric examination. We detected variable degrees of hypogranulation in myeloid lineage in %82.2 of the samples by the light scatter features of the cells: 85% of severe and 15% of moderate or mild hypogranulation. The ratio of myeloid and lymphoid was changing from 0.3 to 17.5 (median 2). The decreasing of this ratio (<1) was observed in 19.4% of the samples. We detected altered expression of mature granulocyte. These included decreasing or lack of expression in CD15 45/73 (61.4%), CD13 38/70 (54.3%), CD16 53/67 (79.1%), CD11b 51/71 (71.8%), CD24 44/69 (65.2%), CD10 23/72 (31.9%) and MPO 14/72 (19.4%). Besides, bright expression of CD33 in 53.5% of the samples was observed. CD36 and CD56 in myeloid lineage were co-expressed in about 50 % of the samples. In 80.8 % of the samples dysplasia in erythroid compartment could be evaluated: Expression of CD71 according to glycophorin A (ratio <1) was decreased in 23.7 %. When we made similar analysis in the samples without RAEB-1 and -2 as pathological examination of bone marrow, 13.4 % of the samples could not be evaluated in favor of dysplasia. Of the samples with dysplasia hypogranulation, aberrant antigen expression of myeloid lineage and eryhtroid dysplasia were observed in 92.1%, 34.1% and 31.5%, respectively. In conclusion, FCM events may help to the differantial diagnosis of MDS especially when combining with clinical events. Improving of the analysis by focusing on the blast characteristics may be a standard approach to evaluate for low risk MDS. Disclosures No relevant conflicts of interest to declare.

scholarly journals Feasibility and visualization of deep learning detection and classification of inferior vena cava filters

2020 ◽  
Author(s):  
Brian J. Park ◽  
Vlasios S. Sotirchos ◽  
Jason Adleberg ◽  
S. William Stavropoulos ◽  
Tessa S. Cook ◽  
...  
Keyword(s):  
Deep Learning ◽  
Inferior Vena Cava ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Inferior Vena ◽  
Vena Cava ◽  
Area Under The Curve ◽  
Receiver Operating Characteristic Area ◽  
Inferior Vena Cava Filters ◽  
Characteristic Area

AbstractPurposeThis study assesses the feasibility of deep learning detection and classification of 3 retrievable inferior vena cava filters with similar radiographic appearances and emphasizes the importance of visualization methods to confirm proper detection and classification.Materials and MethodsThe fast.ai library with ResNet-34 architecture was used to train a deep learning classification model. A total of 442 fluoroscopic images (N=144 patients) from inferior vena cava filter placement or removal were collected. Following image preprocessing, the training set included 382 images (110 Celect, 149 Denali, 123 Günther Tulip), of which 80% were used for training and 20% for validation. Data augmentation was performed for regularization. A random test set of 60 images (20 images of each filter type), not included in the training or validation set, was used for evaluation. Total accuracy and receiver operating characteristic area under the curve were used to evaluate performance. Feature heatmaps were visualized using guided backpropagation and gradient-weighted class activation mapping.ResultsThe overall accuracy was 80.2% with mean receiver operating characteristic area under the curve of 0.96 for the validation set (N=76), and 85.0% with mean receiver operating characteristic area under the curve of 0.94 for the test set (N=60). Two visualization methods were used to assess correct filter detection and classification.ConclusionsA deep learning model can be used to automatically detect and accurately classify inferior vena cava filters on radiographic images. Visualization techniques should be utilized to ensure deep learning models function as intended.

scholarly journals Optimal Deep Learning Model to Identify the Development of Pomegranate Fruit in Farms

2020 ◽  
Vol 9 (3) ◽  
pp. 2352-2356
Keyword(s):  
Operating Characteristic ◽  
Market Value ◽  
Water Shortage ◽  
Receiver Operating Characteristic Area ◽  
Characteristic Area ◽  
Post Harvesting ◽  
Accuracy Of Results ◽  
Cultivation Techniques ◽  
Pomegranate Fruit ◽  
Deep Learning Model

In this study, estimating the maturing condition in gardens helps to enhance the process of post-harvesting. Collecting fruits on the basis of their developmental stage will minimize storage costs and maximize market value. Additionally, estimated ripeness of the fruits can be more useful for indicators for detecting water shortage and to determine the water used during irrigation. The purpose of the study is to develop the new direction of technology to detect the ripeness stage between two classes: ripe and unripe. We employ deep Neural Network (DNN) classifiers for the prediction of ripe and unripe class. The results of our proposed classifiers give the sensitivity 96.2%, specificity 94.2% with accuracy of results 94.5%, over a dataset of 200 images of each class. The ROC (receiver operating characteristic) area values curve close to 0.98 in all-class during training. We believe this is a notable performance that allows a suitable non-intrusive maturing prediction that will enhance cultivation techniques.

Clinical and Host Biological Factors Predict Colectomy Risk in Children Newly Diagnosed With Ulcerative Colitis

2021 ◽  
Author(s):  
Jeffrey S Hyams ◽  
Michael Brimacombe ◽  
Yael Haberman ◽  
Thomas Walters ◽  
Greg Gibson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Confidence Interval ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Newly Diagnosed ◽  
Receiver Operating Characteristic Area ◽  
Characteristic Area ◽  
Receiver Operating

Abstract Background Develop a clinical and biological predictive model for colectomy risk in children newly diagnosed with ulcerative colitis (UC). Methods This was a multicenter inception cohort study of children (ages 4-17 years) newly diagnosed with UC treated with standardized initial regimens of mesalamine or corticosteroids (CS) depending upon initial disease severity. Therapy escalation to immunomodulators or infliximab was based on predetermined criteria. Patients were phenotyped by clinical activity per the Pediatric Ulcerative Colitis Activity Index (PUCAI), disease extent, endoscopic/histologic severity, and laboratory markers. In addition, RNA sequencing defined pretreatment rectal gene expression and high density DNA genotyping by the Affymetrix UK Biobank Axiom Array. Coprimary outcomes were colectomy over 3 years and time to colectomy. Generalized linear models, Cox proportional hazards multivariate regression modeling, and Kaplan-Meier plots were used. Results Four hundred twenty-eight patients (mean age 13 years) started initial theapy with mesalamine (n = 136), oral CS (n = 144), or intravenous CS (n = 148). Twenty-five (6%) underwent colectomy at ≤1 year, 33 (9%) at ≤2 years, and 35 (13%) at ≤3 years. Further, 32/35 patients who had colectomy failed infliximab. An initial PUCAI ≥ 65 was highly associated with colectomy (P = 0.0001). A logistic regression model predicting colectomy using the PUCAI, hemoglobin, and erythrocyte sedimentation rate had a receiver operating characteristic area under the curve of 0.78 (95% confidence interval [0.73, 0.84]). Addition of a pretreatment rectal gene expression panel reflecting activation of the innate immune system and response to external stimuli and bacteria to the clinical model improved the receiver operating characteristic area under the curve to 0.87 (95% confidence interval [0.82, 0.91]). Conclusions A small group of children newly diagnosed with severe UC still require colectomy despite current therapies. Our gene signature observations suggest additional targets for management of those patients not responding to current medical therapies.

A Five Parameter Based Flow Cytometric Scoring System Refines the Revised International Prognostic Scoring System for Myelodysplastic Syndromes

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 3849-3849
Author(s):  
Canan Alhan ◽  
Theresia M. Westers ◽  
Eline M.P. Cremers ◽  
Claudia Cali ◽  
Gert J Ossenkoppele ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Multivariate Analysis ◽  
Myelodysplastic Syndromes ◽  
Scoring System ◽  
Risk Group ◽  
Myeloid Cells ◽  
Low Risk ◽  
Added Value ◽  
Flow Cytometric ◽  
Myeloid Progenitors

Abstract Abstract 3849 The International Prognostic Scoring System (IPSS) has found widespread application in the clinical practice of myelodysplastic syndromes (MDS) for estimation of prognosis and treatment decision making. Increasing knowledge on components that encompass the IPSS has led to the design of the revised IPSS (IPSS-R, Greenberg, Blood 2012). Furthermore, it was recognized that techniques such as flow cytometry and molecular analyses should be validated for their prognostic value in MDS; once proven the added value, they might be part of future prognostic scoring systems. Over the past years, flow cytometry based scoring systems have been proposed to refine prognostication of MDS (Wells, Blood 2003; Matarraz, Cytometry 2010). The aim of our study was to construct a simple and widely applicable hazard-adapted flow cytometry based scoring system for the prognosis of myelodysplastic syndromes and investigate whether it is of added value to the IPSS-R. Bone marrow of 96 patients with MDS was analyzed by flow cytometry. Patients were assigned to an IPSS-R category based on bone marrow blast count, cytogenetics and depth of cytopenias. Patients that received a hematopoietic stem cell transplantation or chemotherapy were censored from start of treatment. All samples were processed according to European LeukemiaNet guidelines (van de Loosdrecht, Haematologica 2009 and Leukemia 2012). Aberrancies with regard to count, expression level of markers and lineage infidelity marker expression on myeloid progenitors, B cell progenitors, maturing erythroid, monocytic and myeloid cells were analyzed. The optimal cut-off level per variable was determined based on significance and hazard ratio (HR) of overall survival (OS) and univariate analysis. In the second step, multivariate analysis was performed with the variables that had a p-value ≤0.1. The parameters that were most predictive of OS from multivariate analysis were increased CD45 expression of myeloid progenitors, asynchronous expression of CD11b on myeloid progenitors, percentage of CD34negCD117pos mature myeloid cells, percentage of mature myeloid cells with CD14 expression and CD45 expression on monocytes. In the next step, a flow cytometric scoring system based on these five parameters was constructed. Each variable was scored for in a weighed manner and the weight was determined by the size of the HR. From this a MDS flow cytometric scoring system (MFCS) was designed that identified four risk groups. i.e. patients with a normal flow score (0–0.5 points), low (1 point), intermediate (>1–2 points) and patients with a high score (>2–4.5 points). The MFCS identified patients with significantly different OS, normal score, median OS 60.6 months (range 0.6–198.6 months), vs. low score, median OS 26.8 months (range 2.5–89.2 months), vs. intermediate score, median OS 19.1 months (range 1.6–62.3 months), vs. high score, median OS 7.9 months (range 0.5–15.1 months), p<0.001. In concordance with the IPSS-R cohort, the largest number of patients was within the low risk group, 41.7% (40/96). Interestingly, within this IPSS-R risk group, the MFCS was able to identify prognostic subgroups. The median OS of IPSS-R low risk patients with a normal MFCS score was not reached, compared with low risk patients with a low MFCS, median OS 40.6 months (range 22.5–89.2 months) and compared with intermediate MFCS, median OS 19.1 months (range 12.5–43.6 months), p<0.001. In a multivariate analysis the MFCS combined with the IPSS-R were best predictive for OS rather than the scores alone (p=0.002 and p=<0.001, respectively). In conclusion, we designed a simple, five parameter MDS flow cytometric (MFCS) score based on hazard. The MFSC score was able to identify prognostic subgroups within a well defined IPSS-R risk group. Furthermore, the MFCS combined with the IPSS-R offered refined prognostication for MDS. This implies that flow cytometric analysis has an added value for prognostication and should be part of a prognostic scoring system for MDS. In a currently ongoing prospective multi center study, these new scoring models will be validated. Disclosures: Alhan: Celgene: Honoraria. Ossenkoppele:Celgene: Consultancy, Honoraria. van de Loosdrecht:Celgene: Consultancy, Honoraria.

scholarly journals Validation of a Blood-Based Laboratory Test to Aid in the Confirmation of a Diagnosis of Schizophrenia

2010 ◽  
Vol 5 ◽  
pp. BMI.S4877 ◽  
Author(s):  
Emanuel Schwarz ◽  
Rauf Izmailov ◽  
Michael Spain ◽  
Anthony Barnes ◽  
James P. Mapes ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Area Under The Curve ◽  
Healthy Controls ◽  
Receiver Operating Characteristic Area ◽  
Specific Prediction ◽  
Characteristic Area ◽  
Disease Signature ◽  
Based Medicine ◽  
And Control ◽  
Independent Cohort

We describe the validation of a serum-based test developed by Rules-Based Medicine which can be used to help confirm the diagnosis of schizophrenia. In preliminary studies using multiplex immunoassay profiling technology, we identified a disease signature comprised of 51 analytes which could distinguish schizophrenia (n = 250) from control (n = 230) subjects. In the next stage, these analytes were developed as a refined 51-plex immunoassay panel for validation using a large independent cohort of schizophrenia (n = 577) and control (n = 229) subjects. The resulting test yielded an overall sensitivity of 83% and specificity of 83% with a receiver operating characteristic area under the curve (ROC-AUC) of 89%. These 51 immunoassays and the associated decision rule delivered a sensitive and specific prediction for the presence of schizophrenia in patients compared to matched healthy controls.

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