scholarly journals Robust linear regression model of Ki-67 for mitotic rate in gastrointestinal stromal tumors

2014 ◽  
Vol 7 (3) ◽  
pp. 745-749 ◽  
Author(s):  
RALF KEMMERLING ◽  
DENIS WEYLAND ◽  
TOBIAS KIESSLICH ◽  
ROMANA ILLIG ◽  
ECKHARD KLIESER ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Gastrointestinal Stromal Tumors ◽  
Mitotic Rate ◽  
Ki 67 ◽  
Stromal Tumors ◽  
Robust Linear Regression

scholarly journals Immunohistochemical features of gastrointestinal stromal tumors and their role for differential diagnosis and prognosis

2021 ◽  
pp. 10-16
Author(s):  
Yana Miroshnichenko
Keyword(s):  
Differential Diagnosis ◽  
Tumor Cells ◽  
Gastrointestinal Stromal Tumors ◽  
Spindle Cell ◽  
Prognostic Markers ◽  
Mitotic Rate ◽  
Mesenchymal Tumors ◽  
Ki 67 ◽  
Stromal Tumors ◽  
Strong Expression

The aim. To clarify all most important immunohistochemical features of gastrointestinal stromal tumors with different histological patterns and analyze the role of expression of Ki-67, MMP-9, VEGF and p16ink4A as a predictive markers of tumor progression. Materials and methods. The study is based on analysis of 100 primary GISTs for description of their morphological features and 36 GISTs taken from this 100 for study of prognostic markers. Results. All spindle cell GISTs have shown diffuse expression of CD117 in tumor cells. The levels of CD117 expression varied from strong expression (3+) until mild expression (1+). Strong expression were seen in 75,8 % of spindle cell GISTs. Epithelioid GISTs demonstrated heterognous moderate or mild expression of CD117. All primary epithelioid GISTs from patients that had relapse of tumor in period from 1 till 3 years demonstrated focal mild expression of CD 117 in tumor cells. Expression of DOG-1 were seen in all 100 cases of GISTs, that were included in our study. The strong expression of DOG-1 (3+) were seen in all 45 GISTs that had low mitotic rate (≤5 mitoses per 50HPF) and not associated with their histological pattern. GISTs with high mitotic rate demonstrated heterogeneous expression of DOG-1 in tumors: moderate expression (2+) with patchy areas of strong expression (3+). Expression of CD56 was not found in spindle cell GISTs, but single tumor cells of epithelioid GISTs that had high mitotic rate demonstrated expression of this marker. The average expression of p16ink4A were higher in tumors that gave relapses compared with tumors without relapses (50,3 % versus 5,7 % respectively, U-test=16.5; p≤0,01).The average expression of MMP-9 also were significantly higher in GISTs that gave relapses: 63,2 % compared with 13,4 % in GISTs without relapse (U-test=16; p≤0 ,01).The strong VEGF expression was found in 66,7 % of GISTs that had relapses and only in 8,3 % of GISTs without relapses. 50 % of GISTs without relapses was negative for VEGF. Finally, the average expression of Ki-67 were 13,4 % in GISTs with relapses and 8,7 % in GISTs without them (U-test=16; p≤0,01). Conclusion. We highly recommend using DOG-1 for epithelioid GISTs. Additionally in epithelioid GISTs can be used CD56 that can give focal positive reaction in some tumour cells. The following minimal panel of markers for differential diagnosis of spindled GISTs from other mesenchymal tumors of gastrointestinal tract is proposed: CD117, DOG-1 and SMA, where the first too markers will demonstrated the moderate or strong diffuse expression and SMA can be occasionally positive in some tumor cells. p16ink4A, ki-67, VEGF and MMP-9 can be used as additional prognostic markers in GISTs.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

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.

scholarly journals Vitamin A and E Homologues Impacting the Fate of Acrylamide in Equimolar Asparagine-Glucose Model System

Antioxidants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 993
Author(s):  
Su Lee Kuek ◽  
Azmil Haizam Ahmad Tarmizi ◽  
Raznim Arni Abd Razak ◽  
Selamat Jinap ◽  
Maimunah Sanny
Keyword(s):  
Vitamin E ◽  
Linear Regression ◽  
Vitamin A ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model System ◽  
Concentration Increase ◽  
Acrylamide Formation ◽  
Β Carotene

This study aims to evaluate the influence of Vitamin A and E homologues toward acrylamide in equimolar asparagine-glucose model system. Vitamin A homologue as β-carotene (BC) and five Vitamin E homologues, i.e., α-tocopherol (AT), δ-tocopherol (DT), α-tocotrienol (ATT), γ-tocotrienol (GTT), and δ-tocotrienol (DTT), were tested at different concentrations (1 and 10 µmol) and subjected to heating at 160 °C for 20 min before acrylamide quantification. At lower concentrations (1 µmol; 431, 403, 411 ppm, respectively), AT, DT, and GTT significantly increase acrylamide. Except for DT, enhancing concentration to 10 µmol (5370, 4310, 4250, 3970, and 4110 ppm, respectively) caused significant acrylamide formation. From linear regression model, acrylamide concentration demonstrated significant depreciation over concentration increase in AT (Beta = −83.0, R2 = 0.652, p ≤ 0.05) and DT (Beta = −71.6, R2 = 0.930, p ≤ 0.05). This study indicates that different Vitamin A and E homologue concentrations could determine their functionality either as antioxidants or pro-oxidants.

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