scholarly journals Identification of serum cytokine clusters associated with outcomes in ovarian clear cell carcinoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Akira Yabuno ◽  
Hirokazu Matsushita ◽  
Tetsutaro Hamano ◽  
Tuan Zea Tan ◽  
Daisuke Shintani ◽  
...  
Keyword(s):  
Regression Models ◽  
Clear Cell ◽  
Multivariate Analyses ◽  
Linear Regression Models ◽  
Serum Cytokine ◽  
Cluster Analyses ◽  
Primary Surgery ◽  
Tnf Α ◽  
Immunological Interactions ◽  
Clinical Biology

Abstract Serum cytokine and chemokine networks may reflect the complex systemic immunological interactions in cancer patients. Studying groups of cytokines and their networks may help to understand their clinical biology. A total of 178 cases of ovarian cancer were analyzed in this study, including 73 high-grade serous (HGSC), 66 clear cell (CCC) and 39 endometrioid carcinomas. Suspension cytokine arrays were performed with the patients’ sera taken before the primary surgery. Associations between each cytokine and clinicopathological factors were analyzed in all patients using multivariate linear regression models, and cluster analyses were performed for each histotype. In the multivariate analyses, twelve of 27 cytokines were correlated with histotypes. Cluster analyses in each histotype revealed 2 cytokine signatures S1 and S2 in HGSC, and similarly C1 and C2 in CCC. Twenty-two of 27 cytokines were commonly clustered in HGSC and CCC. Signature S1 and C1 included IL-2,6,8,15, chemokines and angiogenic factors, whereas signature S2 and C2 included IL-4,5,9,10,13, TNF-α and G-CSF. Four subgroups based on a high or low level for each signature were identified, and this cluster-based classification demonstrated significantly different progression-free and overall survivals for CCC patients (P = 0.00097 and P = 0.017).

scholarly journals The trade-off behaviours between virtual and physical activities during the first wave of the COVID-19 pandemic period

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Elisa Bin ◽  
Claudia Andruetto ◽  
Yusak Susilo ◽  
Anna Pernestål
Keyword(s):  
Regression Models ◽  
Multivariate Analyses ◽  
Personal Characteristics ◽  
Lifestyle Changes ◽  
Internet Usage ◽  
Linear Regression Models ◽  
Demographic Groups ◽  
Restriction Policy ◽  
Online Forms

Abstract Introduction The first wave of COVID-19 pandemic period has drastically changed people’s lives all over the world. To cope with the disruption, digital solutions have become more popular. However, the ability to adopt digitalised alternatives is different across socio-economic and socio-demographic groups. Objective This study investigates how individuals have changed their activity-travel patterns and internet usage during the first wave of the COVID-19 pandemicperiod, and which of these changes may be kept. Methods An empirical data collection was deployed through online forms. 781 responses from different countries (Italy, Sweden, India and others) have beencollected, and a series of multivariate analyses was carried out. Two linear regression models are presented, related to the change of travel activities andinternet usage, before and during the pandemic period. Furthermore, a binary regression model is used to examine the likelihood of the respondents to adoptand keep their behaviours beyond the pandemic period. Results The results show that the possibility to change the behaviour matter. External restrictions and personal characteristics are the driving factors of the reductionin ones' daily trips. However, the estimation results do not show a strong correlation between the countries' restriction policy and the respondents' likelihoodto adopt the new and online-based behaviours for any of the activities after the restriction period. Conclusion The acceptance and long-term adoption of the online alternatives for activities are correlated with the respondents' personality and socio-demographicgroup, highlighting the importance of promoting alternatives as a part of longer-term behavioural and lifestyle changes.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jaffer Okiring ◽  
Adrienne Epstein ◽  
Jane F. Namuganga ◽  
Victor Kamya ◽  
Asadu Sserwanga ◽  
...  
Keyword(s):  
Regression Models ◽  
Malaria Incidence ◽  
Temporal Changes ◽  
Linear Regression Models ◽  
High Burden ◽  
Exponential Regression ◽  
Malaria Morbidity ◽  
Laboratory Test Results ◽  
Combining Data ◽  
Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

Sign in / Sign up

Export Citation Format

Share Document