shubham singh
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Background
The overall survival rates are below 10% in patients with metastatic renal cell carcinoma (RCC) posing a significant health challenge. There is a pressing need for novel and simple predictors of metastasis in patients with RCC to aid in early detection, thereby having prognostic and therapeutic implications.
ObjectivesTo assess the efficacy of various inflammatory markers such as neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), Systemic Inflammatory Response Index (SIRI), and Systemic Immune-Inflammatory Index (SII), in predicting metastasis amongst individuals diagnosed with RCC.
MethodsA retrospective study was undertaken at a tertiary hospital, focusing on individuals diagnosed with RCC. Patients were divided into two groups: Those with and without metastases. Patient demographics and clinical, pathological, and laboratory data were collected. The researchers evaluated the predictive capabilities of NLR, PLR, SIRI, and SII using ROC curves, with cut-off points determined via the Youden Index.
ResultsOf the 91 patients, 25 (27.5%) had metastatic RCC. Significant differences in NLR (P = 0.047), PLR (P = 0.004), SIRI (P = 0.006), and SII (P = 0.005) were noted between metastatic and non-metastatic groups. The ROC analysis showed that SIRI and SII had the highest predictive capacity with areas under the curve (AUCs) of 0.687 and 0.693, respectively. Logistic regression demonstrated NLR, PLR, SII, and SIRI as independent predictors of metastasis in RCC, with a combined predictive accuracy of 83.5%.
ConclusionsNeutrophil-lymphocyte ratio, PLR, SIRI, and SII are reliable predictors of metastasis in RCC, with their combined use enhancing predictive accuracy. These hematological parameters can be easily derived from routine blood tests, could help in the early diagnosis of metastases, and tailor the management of RCC, improving patient outcomes. Further multicentric studies are recommended to validate these findings and help integrate them into clinical practice.
Keywords: Metastasis, Neutrophil-Lymphocyte Ratio, Platelet-Lymphocyte Ratio, Renal Cell Carcinoma, Systemic Inflammatory Response Index, Systemic Immune-Inflammatory Index -
The issue of pollution in urban cities is a major problem these days especiallyin cities like the New Delhi is detected with more number of toxic gases in air, whic h has deduced the air quality of New Delhi. Thus, predictive analytics play a significant role in predicting the future instances of air quality based on the historical data. Forecasting the air quality of these cities is mandatory to overcome its consequences. Several machines learning algorithm is widely used these days to predict the future instances. Such as random forest, support vector machine, regression, classification, and so on. Main pollutants which present in the air are PM2.5, PM10, CO, NO2, SO2and O3 . In this paper we have focused mainly on data set of New Delhi for predicting ambient air pollution and quality using several machines learning algorithm.
Keywords: Air pollution, Machine learning, Supportvector machine, Regression, Classification -
Stochastic programming is often used to solve optimization problems where parameters are uncertain. In this article, we have proposed a mathematical model for a three-stage transportation problem, where the parameters, namely transport costs, demand, unload capacity and external purchasing costs are uncertain. In order to remove the uncertainty, we have proposed a new transformation technique to reformulate the uncertain model deterministically with the help of Essen inequality. The obtained equivalent deterministic model is nonlinear. Furthermore, we have provided a theorem to ensure that the deterministic model gives a feasible solution. Finally, a numerical example, following uniform random variables, is presented to illustrate the model and methodology.Keywords: stochastic optimization, Chance constraints programming, Essen inequality
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