BAFF Binding to T Cell-Expressed BAFF-R Promotes Follicular Helper T Cell Proliferation and GC B Cell Responses.

Chen, M.; Wang, D.; Wang, Ju; Li, Q.; Stohl, William; Zheng, Song Guo; He, Xiaoshun; Guo, Ziyue

doi:10.1097/00007890-201407151-00003

Cited by 4 publications

(1 citation statement)

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“…Altman first introduced the non-parametric technique for data classification in 1991 [ 44 ]. Initially, the K-NN method was designed to address both classification and regression issues [ 45 ].…”

Section: Methodsmentioning

confidence: 99%

MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor

Khan,

Ayoub,

Gulzar

et al. 2023

J. Imaging

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The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis of brain tumors requires the use of an automated method. This study aims at the early detection of brain tumors using brain magnetic resonance imaging (MRI) data and efficient learning paradigms. In visual feature extraction, convolutional neural networks (CNN) have achieved significant breakthroughs. The study involves features extraction by deep convolutional layers for the efficient classification of brain tumor victims from the normal group. The deep convolutional neural network was implemented to extract features that represent the image more comprehensively for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classifications. In this paper, we experimented with five machine learnings (ML) to heighten the understanding and enhance the scope and significance of brain tumor classification. Further, we proposed an ensemble of three high-performing individual ML models, namely Extreme Gradient Boosting, Ada-Boost, and Random Forest (XG-Ada-RF), to derive binary class classification output for detecting brain tumors in images. The proposed voting classifier, along with convoluted features, produced results that showed the highest accuracy of 95.9% for tumor and 94.9% for normal. Compared to individual methods, the proposed ensemble approach demonstrated improved accuracy and outperformed the individual methods.

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Section: Methodsmentioning

confidence: 99%

MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor

Khan,

Ayoub,

Gulzar

et al. 2023

J. Imaging

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Liver Transplant Tourism

Hindi¹,

Congly²,

Tang³

et al. 2020

Liver Transpl

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Liver transplant tourism is travel for transplantation involving organ trafficking and/or transplant commercialism. Various medical, financial, and organizational factors play a role in transplant care including waiting lists, Model for End-Stage Liver Disease scores, and financial aid. We outline the international experiences with transplant tourism (TT) and its effect on their medical communities and patients. For clinicians providing care to patients involved in TT, we also discuss pretransplant counseling and posttransplant care. Liver Transplantation 26 276-282 2020 AASLD. liveR tRAnsplAntAtion, vol. 26, no. 2, 2020 Hindi et Al. Review ARticle | 277 TT ReformAt the Declaration of Istanbul (DoI) summit in 2008, international transplant researchers and politicians defined specific acts related to the process of illegal organ transplantation. The DoI acknowledged TT and provided ethical and social transplant guidelines for individual countries in an effort to curb TT. Moreover, the differences in definitions for travel for transplantation and TT were distinguished. Travel for transplantation is the movement of organs, donors, recipients, or transplant professionals across jurisdictional borders Zaid Hindi and Mayur Brahmania were responsible for the study concept and design and for drafting the manuscript.

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