scholarly journals Cytokine Signature and COVID-19 Prediction Models in the Two Waves of Pandemics.

2021 ◽  
Author(s):  
Serena Cabaro ◽  
Vittoria D'Esposito ◽  
Tiziana Di Matola ◽  
Silvia Sale ◽  
Michele Cennamo ◽  
...  
Keyword(s):  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Diagnostic Tools ◽  
Linear Discriminant ◽  
Cart Analysis ◽  
Diagnosis And Prognosis ◽  
Machine Learning Approach ◽  
Set Up ◽  
Definition Of

Abstract In Europe, two waves of infections with SARS-CoV-2 (COVID-19) have been observed to date. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in the two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed. CART analysis revealed that IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide non-invasive biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.

scholarly journals Cytokine signature and COVID-19 prediction models in the two waves of pandemics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Serena Cabaro ◽  
Vittoria D’Esposito ◽  
Tiziana Di Matola ◽  
Silvia Sale ◽  
Michele Cennamo ◽  
...  
Keyword(s):  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Diagnostic Tools ◽  
Linear Discriminant ◽  
Diagnosis And Prognosis ◽  
Machine Learning Approach ◽  
Set Up ◽  
Definition Of ◽  
Second Wave

AbstractIn Europe, multiple waves of infections with SARS-CoV-2 (COVID-19) have been observed. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed, as compared with the first. IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.

scholarly journals Validation and verification of predictive salivary biomarkers for oral health

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nagihan Bostanci ◽  
Konstantinos Mitsakakis ◽  
Beral Afacan ◽  
Kai Bao ◽  
Benita Johannsen ◽  
...  
Keyword(s):  
Oral Health ◽  
Predictive Accuracy ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
Regression Tree ◽  
High Sensitivity ◽  
Oral Health Status ◽  
Classification And Regression Tree ◽  
Cart Analysis ◽  
Salivary Biomarkers

AbstractOral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.

scholarly journals Cognitive and Behavioral Domains That Reliably Differentiate Normal Aging and Dementia in Down Syndrome

2021 ◽  
Vol 11 (9) ◽  
pp. 1128
Author(s):  
Jordan P. Harp ◽  
Lisa M. Koehl ◽  
Kathryn L. Van Pelt ◽  
Christy L. Hom ◽  
Eric Doran ◽  
...  
Keyword(s):  
Primary Care ◽  
Down Syndrome ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Vineland Adaptive Behavior Scales ◽  
Cart Analysis ◽  
Dementia Screening ◽  
Significant Difference ◽  
Unit Increase

Primary care integration of Down syndrome (DS)-specific dementia screening is strongly advised. The current study employed principal components analysis (PCA) and classification and regression tree (CART) analyses to identify an abbreviated battery for dementia classification. Scale- and subscale-level scores from 141 participants (no dementia n = 68; probable Alzheimer’s disease n = 73), for the Severe Impairment Battery (SIB), Dementia Scale for People with Learning Disabilities (DLD), and Vineland Adaptive Behavior Scales—Second Edition (Vineland-II) were analyzed. Two principle components (PC1, PC2) were identified with the odds of a probable dementia diagnosis increasing 2.54 times per PC1 unit increase and by 3.73 times per PC2 unit increase. CART analysis identified that the DLD sum of cognitive scores (SCS < 35 raw) and Vineland-II community subdomain (<36 raw) scores best classified dementia. No significant difference in the PCA versus CART area under the curve (AUC) was noted (D(65.196) = −0.57683; p = 0.57; PCA AUC = 0.87; CART AUC = 0.91). The PCA sensitivity was 80% and specificity was 70%; CART was 100% and specificity was 81%. These results support an abbreviated dementia screening battery to identify at-risk individuals with DS in primary care settings to guide specialized diagnostic referral.

scholarly journals Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kaizhou Huang ◽  
Feiyang Ji ◽  
Zhongyang Xie ◽  
Daxian Wu ◽  
Xiaowei Xu ◽  
...  
Keyword(s):  
High Risk ◽  
Hepatitis B ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Artificial Liver ◽  
Liver Support ◽  
Artificial Liver Support ◽  
Cart Analysis ◽  
Classification And Regression ◽  
Cart Model

Abstract Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2–39.5%) and high risk (63.8–91.1%). The CART model showed that patients lacking HE and with a PT ≤ 27.8 s and a TBil level ≤455 μmol/L experienced less 28-day mortality after ALSS therapy. For HBV-ACLF patients with HE and a PT > 27.8 s, mortality remained high after such therapy. Patients lacking HE with a PT ≤ 27.8 s and TBil level ≤ 455 μmol/L may benefit markedly from ALSS therapy. For HBV-ACLF patients at high risk, unnecessary ALSS therapy should be avoided. The CART model is a novel user-friendly tool for screening HBV-ACLF patient eligibility for ALSS therapy, and will aid clinicians via ACLF risk stratification and therapeutic guidance.

scholarly journals A Novel Comprehensive Clinical Stratification Model to Refine Prognosis of Glioblastoma Patients Undergoing Surgical Resection

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 386 ◽  
Author(s):  
Tamara Ius ◽  
Fabrizio Pignotti ◽  
Giuseppe Maria Della Pepa ◽  
Giuseppe La Rocca ◽  
Teresa Somma ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Classification Tree ◽  
Prognostic Score ◽  
Regression Tree ◽  
Image Data ◽  
Extent Of Resection ◽  
Classification And Regression Tree ◽  
Postoperative Treatment ◽  
Time Data ◽  
Cart Analysis

Despite recent discoveries in genetics and molecular fields, glioblastoma (GBM) prognosis still remains unfavorable with less than 10% of patients alive 5 years after diagnosis. Numerous studies have focused on the research of biological biomarkers to stratify GBM patients. We addressed this issue in our study by using clinical/molecular and image data, which is generally available to Neurosurgical Departments in order to create a prognostic score that can be useful to stratify GBM patients undergoing surgical resection. By using the random forest approach [CART analysis (classification and regression tree)] on Survival time data of 465 cases, we developed a new prediction score resulting in 10 groups based on extent of resection (EOR), age, tumor volumetric features, intraoperative protocols and tumor molecular classes. The resulting tree was trimmed according to similarities in the relative hazard ratios amongst groups, giving rise to a 5-group classification tree. These 5 groups were different in terms of overall survival (OS) (p < 0.000). The score performance in predicting death was defined by a Harrell’s c-index of 0.79 (95% confidence interval [0.76–0.81]). The proposed score could be useful in a clinical setting to refine the prognosis of GBM patients after surgery and prior to postoperative treatment.

scholarly journals Classification and regression tree (CART) analyses of genomic signatures reveal sets of tetramers that discriminate temperature optima of archaea and bacteria

Archaea ◽  
2008 ◽  
Vol 2 (3) ◽  
pp. 159-167 ◽  
Author(s):  
Betsey Dexter Dyer ◽  
Michael J. Kahn ◽  
Mark D. LeBlanc
Keyword(s):  
Environmental Conditions ◽  
Convergent Evolution ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Evolutionary Divergence ◽  
Cart Analysis ◽  
Genomic Signatures ◽  
Genome Wide ◽  
Classification And Regression ◽  
Temperature Optima

Classification and regression tree (CART) analysis was applied to genome-wide tetranucleotide frequencies (genomic signatures) of 195 archaea and bacteria. Although genomic signatures have typically been used to classify evolutionary divergence, in this study, convergent evolution was the focus. Temperature optima for most of the organisms examined could be distinguished by CART analyses of tetranucleotide frequencies. This suggests that pervasive (nonlinear) qualities of genomes may reflect certain environmental conditions (such as temperature) in which those genomes evolved. The predominant use of GAGA and AGGA as the discriminating tetramers in CART models suggests that purine-loading and codon biases of thermophiles may explain some of the results.

scholarly journals Identification of Wheat Cultivars by CART Analysis

2020 ◽  
pp. 1-8
Author(s):  
Ummey Shapla ◽  
M. Hasanuzzaman ◽  
Md. Omar Kayess
Keyword(s):  
Flag Leaf ◽  
Wheat Variety ◽  
Regression Tree ◽  
Spike Length ◽  
Classification And Regression Tree ◽  
Wheat Cultivars ◽  
Wheat Varieties ◽  
Cart Analysis ◽  
1000 Grain Weight

This experiment was conducted at the Research Farm of the Department of Genetics and Plant Breeding of Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh to develop biometrical methods based on some morphological traits for characterization of the selected wheat varieties. Among the selected wheat varieties BARI Gom 26 requires comparatively fewer days and BARI Gom 27 requires more days for 50% heading than other varieties. The BARI Gom 28, BARI Gom 29 and BARI Gom 30 are comparatively short (< 90 cm) whereas others are medium-sized (> 90 cm) plants. BARI Gom 27 has narrow flag leaf than others. BARI Gom 28 show short spike length while BARI Gom 22, BARI Gom 26 and BARI Gom 30 show nearly a similar length of the spike. The BARI Gom 25 is large-sized in length and breadth but the grain of BARI GOM 27 is comparatively small sized. BARI Gom 22, BARI Gom 23, BARI Gom 24, BARI Gom 25 and BARI Gom 26 are classified which have <7.5 mm length of the grain. The 1000 grain weight of BARI Gom 24 is more than other wheat varieties and comparatively less in BARI Gom 22 and BARI Gom 27. BARI Gom 24 can be identified with the height of >90 cm, breath of flag leaf is >1.2 cm, spike length is >10 cm and yield per plant is >20.196 g. Based on these variations, a classification and regression tree (CART) has been developed to identify the wheat variety easily and quickly.

scholarly journals Assessing the influence of environmental context on responses to noise exposure in the city of Toronto

2021 ◽  
Author(s):  
Desislava Stevanova
Keyword(s):  
Spatial Data ◽  
Green Space ◽  
Noise Exposure ◽  
Regression Tree ◽  
Environmental Context ◽  
Classification And Regression Tree ◽  
Bivariate Analysis ◽  
Night Time ◽  
Cart Analysis ◽  
The City

This thesis examines individual and community noise perception of environmental noise in three neighbourhoods in the city of Toronto. The significance of this research is based on a relative absence of literature on how noise sensitivity and annoyance are affected by non-acoustic factors such as the built environment, demographic, and socio-economic factors. Data from a neighbourhood noise survey (n=552) were combined with spatial data on exposures to noise. Bivariate analysis, multivariate regression, and classification and regression tree (CART) analysis were used. The results showed that participants in Downtown and Don Valley have similar noise responses (64% and 67% high annoyance) despite differences in noise exposure (LAeq 24h: 66.8 and 59.3). Estimation of Community Tolerance Levels (CTL) confirmed that participants exposed to lower sound levels have a lower tolerance of noise. Further results showed that a neighbourhood with high socioeconomic status and access to green space, and relatively low night time noise levels were still two times more likely to report high annoyance, compared with neighbourhood with moderate socio-economic status and lower access to green space. The results suggest that environmental context influences expectations and sensitivity to noise.

Sign in / Sign up

Export Citation Format

Share Document