Increase of Circulating CXCL9 and CXCL11 Associated with Euthyroid or Subclinically Hypothyroid Autoimmune Thyroiditis

Context: Recently, CXCL9 and CXCL11 have been shown to be involved in autoimmune thyroid disorders; however, no data are present about CXCL9 and CXCL11 circulating levels in thyroid autoimmunity. Objective: Our objective was to evaluate circulating CXCL9 and CXCL11 in autoimmune thyroiditis (AIT). Design and Patients or Other Participants: Serum CXCL9 and CXCL11 have been measured in 141 consecutive patients with newly diagnosed AIT (AIT-p), 70 euthyroid controls, and 35 patients with nontoxic multinodular thyroid. The three groups were similar in gender distribution and age; among the AIT-p, 26% had subclinical hypothyroidism. Results: Serum CXCL9 and CXCL11 levels were significantly (P < 0.0001 for both) higher in AIT-p (143 ± 164 and 121 ± 63 pg/ml, respectively) than in controls (68 ± 37 and 65 ± 19 pg/ml, respectively) or patients with multinodular thyroid (87 ± 43 and 71 ± 20 pg/ml, respectively). Among AIT-p, CXCL9 and CXCL11 levels were significantly higher in patients older than 50 yr or those with a hypoechoic ultrasonographic pattern or with hypothyroidism. In a multiple linear regression model including age, thyroid volume, hypoechogenicity, hypervascularity, TSH, anti-thyroglobulin, and anti-thyroid peroxidase, only age and TSH were significantly (P < 0.05) related to serum CXCL9 or CXCL11 levels. In a multiple linear regression model of CXCL9 vs. age, TSH, and CXCL11, TSH (P = 0.032) and CXCL11 (P = 0.001) were significantly and independently related to CXCL9. Conclusions: We first show that circulating CXCL9 and CXCL11 are increased in patients with thyroiditis and hypothyroidism and are related to each other. These results underline the importance of a Th1 immune attack in the initiation of AIT.

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An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666191218124350 ◽

2020 ◽

Vol 10 (4) ◽

pp. 473-483

Author(s):

Pundra Chandra Shaker Reddy ◽

Alladi Sureshbabu

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Weather Prediction ◽

Big Data Analytics ◽

Multiple Linear Regression Model ◽

Seasonal Rainfall ◽

Climate Data ◽

Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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A Multiple Linear Regression Model for Maintainability Analysis Considering Maintainability Design Characteristics

Proceedings of the 4th International Conference on Computer Science and Application Engineering ◽

10.1145/3424978.3424988 ◽

2020 ◽

Author(s):

Zhenya Wu ◽

Jianping Hao ◽

Peng Wang ◽

Chenguang Liu ◽

Zhen Li

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Multiple Linear Regression Model ◽

Design Characteristics ◽

Maintainability Analysis

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Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

Clinical Neuroradiology ◽

10.1007/s00062-021-01067-5 ◽

2021 ◽

Author(s):

Olivia Fösleitner ◽

Véronique Schwehr ◽

Tim Godel ◽

Fabian Preisner ◽

Philipp Bäumer ◽

...

Keyword(s):

Skeletal Muscle ◽

Body Weight ◽

Sciatic Nerve ◽

Linear Regression ◽

Peripheral Nerve ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Magnetization Transfer ◽

Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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