scholarly journals Plasma chemokines as immune biomarkers for diagnosis of pediatric tuberculosis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nathella Pavan Kumar ◽  
Syed Hissar ◽  
Kannan Thiruvengadam ◽  
Velayuthum V. Banurekha ◽  
Sarath Balaji ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Operating Characteristics ◽  
Tb Treatment ◽  
Immune Biomarkers ◽  
Receiver Operating Characteristics Curve ◽  
Tree Models ◽  
Pediatric Tuberculosis ◽  
Control Study

Abstract Background Diagnosing tuberculosis (TB) in children is challenging due to paucibacillary disease, and lack of ability for microbiologic confirmation. Hence, we measured the plasma chemokines as biomarkers for diagnosis of pediatric tuberculosis. Methods We conducted a prospective case control study using children with confirmed, unconfirmed and unlikely TB. Multiplex assay was performed to examine the plasma CC and CXC levels of chemokines. Results Baseline levels of CCL1, CCL3, CXCL1, CXCL2 and CXCL10 were significantly higher in active TB (confirmed TB and unconfirmed TB) in comparison to unlikely TB children. Receiver operating characteristics curve analysis revealed that CCL1, CXCL1 and CXCL10 could act as biomarkers distinguishing confirmed or unconfirmed TB from unlikely TB with the sensitivity and specificity of more than 80%. In addition, combiROC exhibited more than 90% sensitivity and specificity in distinguishing confirmed and unconfirmed TB from unlikely TB. Finally, classification and regression tree models also offered more than 90% sensitivity and specificity for CCL1 with a cutoff value of 28 pg/ml, which clearly classify active TB from unlikely TB. The levels of CCL1, CXCL1, CXCL2 and CXCL10 exhibited a significant reduction following anti-TB treatment. Conclusion Thus, a baseline chemokine signature of CCL1/CXCL1/CXCL10 could serve as an accurate biomarker for the diagnosis of pediatric tuberculosis.

scholarly journals Groundwater Augmentation through the Site Selection of Floodwater Spreading Using a Data Mining Approach (Case study: Mashhad Plain, Iran)

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1405 ◽  
Author(s):  
Seyed Naghibi ◽  
Mehdi Vafakhah ◽  
Hossein Hashemi ◽  
Biswajeet Pradhan ◽  
Seyed Alavi
Keyword(s):  
Data Mining ◽  
Regression Tree ◽  
Roc Curves ◽  
Flood Damage ◽  
Boosted Regression Trees ◽  
Classification And Regression Tree ◽  
Operating Characteristics ◽  
National Guidelines ◽  
Conditioning Factors ◽  
Suitability Maps

It is a well-known fact that sustainable development goals are difficult to achieve without a proper water resources management strategy. This study tries to implement some state-of-the-art statistical and data mining models i.e., weights-of-evidence (WoE), boosted regression trees (BRT), and classification and regression tree (CART) to identify suitable areas for artificial recharge through floodwater spreading (FWS). At first, suitable areas for the FWS project were identified in a basin in north-eastern Iran based on the national guidelines and a literature survey. Using the same methodology, an identical number of FWS unsuitable areas were also determined. Afterward, a set of different FWS conditioning factors were selected for modeling FWS suitability. The models were applied using 70% of the suitable and unsuitable locations and validated with the rest of the input data (i.e., 30%). Finally, a receiver operating characteristics (ROC) curve was plotted to compare the produced FWS suitability maps. The findings depicted acceptable performance of the BRT, CART, and WoE for FWS suitability mapping with an area under the ROC curves of 92, 87.5, and 81.6%, respectively. Among the considered variables, transmissivity, distance from rivers, aquifer thickness, and electrical conductivity were determined as the most important contributors in the modeling. FWS suitability maps produced by the proposed method in this study could be used as a guideline for water resource managers to control flood damage and obtain new sources of groundwater. This methodology could be easily replicated to produce FWS suitability maps in other regions with similar hydrogeological conditions.

scholarly journals Discovery and Validation of a Three-Cytokine Plasma Signature as a Biomarker for Diagnosis of Pediatric Tuberculosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Nathella Pavan Kumar ◽  
Syed Hissar ◽  
Kannan Thiruvengadam ◽  
Velayuthum V. Banurekha ◽  
N. Suresh ◽  
...  
Keyword(s):  
Plasma Levels ◽  
Validation Cohort ◽  
Diagnostic Study ◽  
Operating Characteristics ◽  
Discovery Cohort ◽  
Roc Curve Analysis ◽  
Immune Biomarkers ◽  
Cytokine Plasma ◽  
Pediatric Tb ◽  
Pediatric Tuberculosis

Pediatric TB poses challenge in diagnosis due to the paucibacillary nature of the disease. We conducted a prospective diagnostic study to identify immune biomarkers of pediatric TB and controls (discovery cohort) and obtained a separate “validation” cohort of confirmed cases of pediatric TB and controls. Multiplex ELISA was performed to examine the plasma levels of cytokines. Discovery and validation cohorts revealed that baseline plasma levels of IFNγ, TNFα, IL-2, and IL-17A were significantly higher in active TB (confirmed TB and unconfirmed TB) in comparison to unlikely TB children. Receiver operating characteristics (ROC) curve analysis revealed that IFNγ, IL-2, TNFα, and IL-17A (in the discovery cohort) and TNFα and IL-17A (in the validation cohort) could act as biomarkers distinguishing confirmed or unconfirmed TB from unlikely TB with the sensitivity and specificity of more than 90%. In the discovery cohort, cytokines levels were significantly diminished following anti-tuberculosis treatment. In both the cohorts, combiROC models offered 100% sensitivity and 98% to 100% specificity for a three-cytokine signature of TNFα, IL-2, and IL-17A, which can distinguish confirmed or unconfirmed TB children from unlikely TB. Thus, a baseline cytokine signature of TNFα, IL-2, and IL-17A could serve as an accurate biomarker for the diagnosis of pediatric tuberculosis.

scholarly journals Low and very low birth weight in puppies: definitions, risk factors and survival in a large-scale population

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Amélie Mugnier ◽  
Sylvie Chastant-Maillard ◽  
Hanna Mila ◽  
Faouzi Lyazrhi ◽  
Florine Guiraud ◽  
...  
Keyword(s):  
Risk Factors ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Neonatal Mortality ◽  
Litter Size ◽  
Large Scale ◽  
Very Low Birth Weight ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Operating Characteristics

Abstract Background Neonatal mortality (over the first three weeks of life) is a major concern in canine breeding facilities as an economic and welfare issue. Since low birth weight (LBW) dramatically increases the risk of neonatal death, the risk factors of occurrence need to be identified together with the chances and determinants of survival of newborns at-risk. Results Data from 4971 puppies from 10 breeds were analysed. Two birth weight thresholds regarding the risk of neonatal mortality were identified by breed, using respectively Receiver Operating Characteristics and Classification and Regression Tree method. Puppies were qualified as LBW and very low birth weight (VLBW) when their birth weight value was respectively between the two thresholds and lower than the two thresholds. Mortality rates were 4.2, 8.8 and 55.3%, in the normal, LBW and VLBW groups, accounting for 48.7, 47.9 and 3.4% of the included puppies, respectively. A separate binary logistic regression approach allowed to identify breed, gender and litter size as determinants of LBW. The increase in litter size and being a female were associated with a higher risk for LBW. Survival for LBW puppies was reduced in litters with at least one stillborn, compared to litters with no stillborn, and was also reduced when the dam was more than 6 years old. Concerning VLBW puppies, occurrence and survival were influenced by litter size. Surprisingly, the decrease in litter size was a risk factor for VLBW and also reduced their survival. The results of this study suggest that VLBW and LBW puppies are two distinct populations. Moreover, it indicates that events and factors affecting intrauterine growth (leading to birth weight reduction) also affect their ability to adapt to extrauterine life. Conclusion These findings could help veterinarians and breeders to improve the management of their facility and more specifically of LBW puppies. Possible recommendations would be to only select for reproduction dams of optimal age and to pay particular attention to LBW puppies born in small litters. Further studies are required to understand the origin of LBW in dogs.

P4-044: IDENTIFICATION OF PHENOTYPIC CLUSTERS OF AD PATIENTS AND CONTROLS USING CLASSIFICATION AND REGRESSION TREE MODELS

2014 ◽  
Vol 10 ◽  
pp. P798-P799
Author(s):  
Charlotte Teunissen ◽  
Niki S.M. Schoonenboom ◽  
Pieter Jelle Visser ◽  
Wiesje M. Van der Flier ◽  
Dirk Knol ◽  
...  
Keyword(s):  
Regression Tree ◽  
Classification And Regression Tree ◽  
Tree Models ◽  
Classification And Regression

scholarly journals Relation between carotid vulnerable plaques and peripheral leukocyte: A case-control study of comparison utilizing multi-parametric CEUS

2019 ◽  
Author(s):  
Xianghong Luo ◽  
Wanbin Li ◽  
Yun Bai ◽  
Lianfang Du ◽  
Rong Wu ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Peripheral Leukocyte ◽  
Regression Tree Analysis ◽  
Vulnerable Plaques ◽  
Asymptomatic Patients ◽  
Contrast Enhanced ◽  
The Relationship ◽  
Control Study

Abstract Background: This study evaluates carotid vulnerable plaques using contrast-enhanced ultrasound (CEUS) and explores the relationship between vulnerable plaques and leukocytes.Methods: Sixty-two symptomatic and 54 asymptomatic patients underwent CEUS. The images were analyzed using time-intensity and fitting curves, and peak (PTIC), mean (MTIC), peak (PFC), sharpness (SFC), and area under the curve (AUCFC) were obtained. The relations between CEUS parameters and leukocytes were analyzed.Results: In the symptomatic group, total leukocytes and neutrophils were higher, while lymphocyte was decreased; PTIC, MTIC, PFC, SFC,, and AUCFC were significantly higher; MTIC and AUCFC were negatively correlated with lymphocytes, and MTIC was positively correlated with neutrophils. Classification and regression tree analysis showed that MTIC at a cutoff of 20.8 and AUCFC at a cutoff of 8.8 resulted in a predictive of acute cerebral infarction, accuracy of 84.3%, sensitivity of 87.1%, and specificity of 81.5%.Conclusions: The variation in the perivascular leucocyte is significantly related to intraplaque inflammatory activities, CEUS is a feasible monitor of intraplaque neovascularization, so CEUS combined with perivascular leucocyte could be helpful as a warning for vulnerable plaques.

scholarly journals Classification and Regression Tree and Computer Adaptive Testing in Cardiac Rehabilitation: Instrument Validation Study (Preprint)

2018 ◽  
Author(s):  
Linda Peute ◽  
Thom Scheeve ◽  
Monique Jaspers
Keyword(s):  
Cardiac Rehabilitation ◽  
Data Quality ◽  
Sensitivity And Specificity ◽  
Computerized Adaptive Testing ◽  
Regression Tree ◽  
Adaptive Testing ◽  
Classification And Regression Tree ◽  
Web Based ◽  
Classification And Regression ◽  
Multiple Domains

BACKGROUND There is a need for shorter-length assessments that capture patient questionnaire data while attaining high data quality without an undue response burden on patients. Computerized adaptive testing (CAT) and classification and regression tree (CART) methods have the potential to meet these needs and can offer attractive options to shorten questionnaire lengths. OBJECTIVE The objective of this study was to test whether CAT or CART was best suited to reduce the number of questionnaire items in multiple domains (eg, anxiety, depression, quality of life, and social support) used for a needs assessment procedure (NAP) within the field of cardiac rehabilitation (CR) without the loss of data quality. METHODS NAP data of 2837 CR patients from a multicenter Cardiac Rehabilitation Decision Support System (CARDSS) Web-based program was used. Patients used a Web-based portal, MyCARDSS, to provide their data. CAT and CART were assessed based on their performances in shortening the NAP procedure and in terms of sensitivity and specificity. RESULTS With CAT and CART, an overall reduction of 36% and 72% of NAP questionnaire length, respectively, was achieved, with a mean sensitivity and specificity of 0.765 and 0.817 for CAT, 0.777 and 0.877 for classification trees, and 0.743 and 0.40 for regression trees, respectively. CONCLUSIONS Both CAT and CART can be used to shorten the questionnaires of the NAP used within the field of CR. CART, however, showed the best performance, with a twice as large overall decrease in the number of questionnaire items of the NAP compared to CAT and the highest sensitivity and specificity. To our knowledge, our study is the first to assess the differences in performance between CAT and CART for shortening questionnaire lengths. Future research should consider administering varied assessments of patients over time to monitor their progress in multiple domains. For CR professionals, CART integrated with MyCARDSS would provide a feedback loop that informs the rehabilitation progress of their patients by providing real-time patient measurements.

Testing different thresholds for patient global assessment in defining remission for rheumatoid arthritis: are the current ACR/EULAR Boolean criteria optimal?

2020 ◽  
Vol 79 (4) ◽  
pp. 445-452 ◽  
Author(s):  
Paul Studenic ◽  
David Felson ◽  
Maarten de Wit ◽  
Farideh Alasti ◽  
Tanja A Stamm ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Global Assessment ◽  
Activity Index ◽  
Recursive Partitioning ◽  
Regression Tree ◽  
Disease Activity Index ◽  
Patient Global Assessment ◽  
Classification And Regression Tree ◽  
Operating Characteristics ◽  
The Impact

ObjectivesThis study aimed to evaluate different patient global assessment (PGA) cut-offs required in the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) Boolean remission definition for their utility in rheumatoid arthritis (RA).MethodsWe used data from six randomised controlled trials in early and established RA. We increased the threshold for the 0–10 score for PGA gradually from 1 to 3 in steps of 0.5 (Boolean1.5 to Boolean3.0) and omitted PGA completely (BooleanX) at 6 and 12 months. Agreement with the index-based (Simplified Disease Activity Index (SDAI)) remission definition was analysed using kappa, recursive partitioning (classification and regression tree (CART)) and receiver operating characteristics. The impact of achieving each definition on functional and radiographic outcomes after 1 year was explored.ResultsData from 1680 patients with early RA and 920 patients with established RA were included. The proportion of patients achieving Boolean remission increased with higher thresholds for PGA from 12.4% to 19.7% in early and 5.9% to 12.3% in established RA at 6 months. Best agreement with SDAI remission occurred at PGA cut-offs of 1.5 and 2.0, while agreement decreased with higher PGA (CART: optimal agreement at PGA≤1.6 cm; sensitivity of PGA≤1.5 95%). Changing PGA thresholds at 6 months did not affect radiographic progression at 12 months (mean ꙙsmTSS for Boolean, 1.5, 2.0, 2.5, 3.0, BooleanX: 0.35±5.4, 0.38±5.14, 0.41±5.1, 0.37±4.9, 0.34±4.9, 0.27±4.7). However, the proportion attaining HAQ≤0.5 was 90.2%, 87.9%, 85.2%, 81.1%, 80.7% and 73.1% for the respective Boolean definitions.ConclusionIncreasing the PGA cut-off to 1.5 cm would provide high consistency between Boolean with the index-based remission; the integer cut-off of 2.0 cm performed similarly.

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