Frailty and chronic kidney disease: a systematic review

Objective Frailty is associated with increased vulnerability to poor health. There is growing interest in understanding the association between frailty and chronic kidney disease (CKD). This systematic review explored how frailty is measured in patients with CKD and the association between frailty and adverse outcomes across different stages of renal impairment. Study design Systematic analysis of peer reviewed articles. Data sources Pubmed, Medline, Web of Science and Cochrane were used to identify the articles. Data synthesis Articles published before the 17th of September 2016, that measured frailty in patients with CKD was eligible for the systematic review. Two independent researchers assessed the eligibility of the articles. Quality of the articles was assessed using the Epidemiological Appraisal Instrument. Results The literature search yielded 540 articles, of which 32 met the study criteria and were included in the review (n\ua0=\ua036,076, age range: 50–83 years). Twenty-three (72%) studies used or adapted the Fried phenotype to measure frailty. The prevalence of frailty ranged from 7% in community-dwellers (CKD Stages 1–4) to 73% in a cohort of patients on haemodialysis. The incidence of frailty increased with reduced glomerular filtration rate. Frailty was associated with an increased risk of mortality and hospitalization. Conclusion Frailty is prevalent in patients with CKD and it is associated with an increased risk of adverse health outcomes. There are differences in the methods used to assess frailty and this hinders comparisons between studies

Identifying Induced Bias in Machine Learning

The last decade has witnessed an unprecedented rise in the application of machine learning in high-stake automated decision-making systems such as hiring, policing, bail sentencing, medical screening, etc. The long-lasting impact of these intelligent systems on human life has drawn attention to their fairness implications. A majority of subsequent studies targeted the existing historically unfair decision labels in the training data as the primary source of bias and strived toward either removing them from the dataset (de-biasing) or avoiding learning discriminatory patterns from them during training. In this thesis, we show label bias is not a necessary condition for unfair outcomes from a machine learning model. We develop theoretical and empirical evidence showing that biased model outcomes can be introduced by a range of different data properties and components of the machine learning development pipeline.In this thesis, we first prove that machine learning models are expected to introduce bias even when the training data doesn’t include label bias. We use the proof-by-construction technique in our formal analysis. We demonstrate that machine learning models, trained to optimize for joint accuracy, introduce bias even when the underlying training data is free from label bias but might include other forms of disparity. We identify two data properties that led to the introduction of bias in machine learning. They are the group-wise disparity in the feature predictivity and the group-wise disparity in the rates of missing values. The experimental results suggest that a wide range of classifiers trained on synthetic or real-world datasets are prone to introducing bias under feature disparity and missing value disparity independently from or in conjunction with the label bias. We further analyze the trade-off between fairness and established techniques to improve the generalization of machine learning models such as adversarial training, increasing model complexity, etc. We report that adversarial training sacrifices fairness to achieve robustness against noisy (typically adversarial) samples. We propose a fair re-weighted adversarial training method to improve the fairness of the adversarially trained models while sacrificing minimal adversarial robustness. Finally, we observe that although increasing model complexity typically improves generalization accuracy, it doesn’t linearly improve the disparities in the prediction rates.This thesis unveils a vital limitation of machine learning that has yet to receive significant attention in FairML literature. Conventional FairML literature reduces the ML fairness task to as simple as de-biasing or avoiding learning discriminatory patterns. However, the reality is far away from it. Starting from deciding on which features collect up to algorithmic choices such as optimizing robustness can act as a source of bias in model predictions. It calls for detailed investigations on the fairness implications of machine learning development practices. In addition, identifying sources of bias can facilitate pre-deployment fairness audits of machine learning driven automated decision-making systems.</p

Epstein-Barr virus-associated lymphomas decoded

Epstein–Barr virus (EBV)-associated lymphomas cover a range of histological B- and T-cell non-Hodgkin and Hodgkin lymphoma subtypes. The role of EBV on B-cell malignant pathogenesis and its impact on the tumour microenvironment are intriguing but incompletely understood. Both the International Consensus Classification (ICC) and 5th Edition of the World Health Organization (WHO-HAEM5) proposals give prominence to the distinct clinical, prognostic, genetic and tumour microenvironmental features of EBV in lymphoproliferative disorders. There have been major advances in our biological understanding, in how to harness features of EBV and its host immune response for targeted therapy, and in using EBV as a method to monitor disease response. In this article, we showcase the latest developments and how they may be integrated to stimulate new and innovative approaches for further lines of investigation and therapy.</p

JutePestDetect: An intelligent approach for jute pest identification using fine-tuned transfer learning

In certain Asian countries, Jute is one of the primary sources of income and Gross Domestic Product (GDP) for the agricultural sector. Like many other crops, Jute is prone to pest infestations, and its identification is typically made visually in countries like Bangladesh, India, Myanmar, and China. In addition, this method is time-consuming, challenging, and somewhat imprecise, which poses a substantial financial risk. To address this issue, the study proposes a high-performing and resilient transfer learning (TL) based JutePestDetect model to identify jute pests at the early stage. Firstly, we prepared jute pest dataset containing 17 classes and around 380 photos per pest class, which were evaluated after manual and automatic pre-processing and cleaning, such as background removal and resizing. Subsequently, five prominent pre-trained models—DenseNet201, InceptionV3, MobileNetV2, VGG19, and ResNet50—were selected from a previous study to design the JutePestDetect model. Each model was revised by replacing the classification layer with a global average pooling layer and incorporating a dropout layer for regularization. To evaluate the models' performance, various metrics such as precision, recall, F1 score, ROC curve, and confusion matrix were employed. These analyses provided additional insights for determining the efficacy of the models. Among them, the customized regularized DenseNet201-based proposed JutePestDetect model outperformed the others, achieving an impressive accuracy of 99%. As a result, our proposed method and strategy offer an enhanced approach to pest identification in the case of Jute, which can significantly benefit farmers worldwide

Complete genome sequence and analysis of Wolinella succinogenes

To understand the origin and emergence of pathogenic bacteria, knowledge of the genetic inventory from their nonpathogenic relatives is a prerequisite. Therefore, the 2.11-megabase genome sequence of Wolinella succinogenes, which is closely related to the pathogenic bacteria Helicobacter pylori and Campylobacter jejuni, was determined. Despite being considered nonpathogenic to its bovine host, W. succinogenes holds an extensive repertoire of genes homologous to known bacterial virulence factors. Many of these genes have been acquired by lateral gene transfer, because part of the virulence plasmid pVir and an N-linked glycosylation gene cluster were found to be syntenic between C. jejuni and genomic islands of W. succinogenes. In contrast to other host-adapted bacteria, W. succinogenes does harbor the highest density of bacterial sensor kinases found in any bacterial genome to date, together with an elaborate signaling circuitry of the GGDEF family of proteins. Because the analysis of the W. succinogenes genome also revealed genes related to soil- and plant-associated bacteria such as the nif genes, W. succinogenes may represent a member of the epsilon proteobacteria with a life cycle outside its host