scholarly journals Biomarkers of Acute and Chronic Kidney Disease

2019 ◽  
Vol 81 (1) ◽  
pp. 309-333 ◽  
Author(s):  
William R. Zhang ◽  
Chirag R. Parikh
Keyword(s):  
Kidney Disease ◽  
Kidney Diseases ◽  
Improve Patient Care ◽  
Cellular Responses ◽  
Systemic Circulation ◽  
Low Molecular Weight ◽  
Clinical Settings ◽  
Chronic Kidney Diseases ◽  
Improve Patient ◽  
Low Molecular Weight Proteins

The current unidimensional paradigm of kidney disease detection is incompatible with the complexity and heterogeneity of renal pathology. The diagnosis of kidney disease has largely focused on glomerular filtration, while assessment of kidney tubular health has notably been absent. Following insult, the kidney tubular cells undergo a cascade of cellular responses that result in the production and accumulation of low-molecular-weight proteins in the urine and systemic circulation. Modern advancements in molecular analysis and proteomics have allowed the identification and quantification of these proteins as biomarkers for assessing and characterizing kidney diseases. In this review, we highlight promising biomarkers of kidney tubular health that have strong underpinnings in the pathophysiology of kidney disease. These biomarkers have been applied to various specific clinical settings from the spectrum of acute to chronic kidney diseases, demonstrating the potential to improve patient care.

Systems analysis of plasma IgG intact N-glycopeptides from patients with chronic kidney diseases via EThcD-sceHCD-MS/MS

The Analyst ◽  
2021 ◽  
Author(s):  
Yong Zhang ◽  
Shanshan Zheng ◽  
Yonghong Mao ◽  
Wei Cao ◽  
Lijun Zhao ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Immune Response ◽  
Kidney Disease ◽  
Immunoglobulin G ◽  
Systems Analysis ◽  
Kidney Diseases ◽  
Site Specific ◽  
Chronic Kidney Diseases

Immunoglobulin G (IgG) molecules modulate an immune response. However, site-specific N-glycosylation signatures of plasma IgG in patients with chronic kidney disease (CKD) remain unclear. This study aimed to propose a...

scholarly journals An Approach to Classify Chronic Kidney Diseases using Scintigraphy Images

2019 ◽  
Author(s):  
Pedro Pedrosa Rebouças Filho ◽  
Suane Pires Pinheiro Da Silva ◽  
Jefferson Silva Almeida ◽  
Elene Firmeza Ohata ◽  
Shara Shami Araujo Alves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Chronic Kidney Diseases ◽  
Occurrence Matrix

Chronic kidney diseases cause over a million deaths worldwide every year. One of the techniques used to diagnose the diseases is renal scintigraphy. However, the way that is processed can vary depending on hospitals and doctors, compromising the reproducibility of the method. In this context, we propose an approach to process the exam using computer vision and machine learning to classify the stage of chronic kidney disease. An analysis of different features extraction methods, such as Gray-Level Co-occurrence Matrix, Structural Co-occurrence Matrix, Local Binary Patters (LBP), Hu's Moments and Zernike's Moments in combination with machine learning methods, such as Bayes, Multi-layer Perceptron, k-Nearest Neighbors, Random Forest and Support Vector Machines (SVM), was performed. The best result was obtained by combining LBP feature extractor with SVM classifier. This combination achieved accuracy of 92.00% and F1-score of 91.00%, indicating that the proposed method is adequate to classify chronic kidney disease in two stages, being a high risk of developing end-stage renal failure and other outcomes, and otherwise.

scholarly journals Data Analysis on Chronic Kidney Disease Prognosis

2020 ◽  
Vol 8 (6S) ◽  
pp. 130-132
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Data Analysis ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Chronic Kidney Diseases ◽  
Optimum Result ◽  
Disease Prognosis

We have taken our dataset from UCI Machine Learning Repository. Our study is about Chronic Kidney Diseases based on 24 input attributes to produce one output attribute i.e. a patient is suffering from chronic kidney disease or not. We have used three major attributes in our study i.e. PCV, RBCC and Hemoglobin with respect to Age for optimum result. These attributes play major role in our study.

scholarly journals Relationship between xerosis and pruritus in patients with chronic kidney disease

2017 ◽  
Vol 71 (0) ◽  
pp. 0-0
Author(s):  
Katarzyna Kiliś-Pstrusińska ◽  
Elżbieta Wojtowicz-Prus ◽  
Jacek Szepietowski
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Treatment Options ◽  
Skin Lesions ◽  
Chronic Kidney Diseases ◽  
Skin Changes ◽  
Uremic Pruritus ◽  
Common Skin

Xerosis and pruritus are the most common skin disorders in patients with chronic kidney diseases (CKD). The prevalence and intensity of those skin changes are higher in patients undergoing dialysis, independent of its type, compared to patients treated conservatively. However, they can occur even in the early stages of CKD and be very bothersome for the sufferers. The problem of dry skin in CKD patients, its characteristics, reasons and relationship between xerosis and pruritus have been described. The current views on the pathogenesis of chronic kidney disease-associated pruritus (CKD-P), formerly known as uremic pruritus, have been discussed. This article summarizes the available treatment options for CKD-P, including both topical and systemic therapies. The authors direct attention to the need for skin lesions treatment in order to prevent their progression and to improve the quality of patients’ life.

scholarly journals Intelligent Prediction Techniques for Chronic Kidney Disease Data Analysis

2021 ◽  
Vol 11 (2) ◽  
pp. 19-37
Author(s):  
Shanmugarajeshwari V. ◽  
Ilayaraja M.
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Clinical Decision ◽  
Great Accuracy ◽  
Attribute Selection ◽  
Risk Level ◽  
Chronic Kidney Diseases ◽  
Medical Databases ◽  
Prediction Techniques

Information is stored in various domains like finance, banking, hospital, education, etc. Nowadays, data stored in medical databases are growing rapidly. The proposed approach entails three parts comparable to preprocessing, attribute selection, and classification C5.0 algorithms. This work aims to design a machine-based diagnostic approach using various techniques. These algorithms improve the efficiency of mining risk factors of chronic kidney diseases, but there are also have some shortcomings. To overcome these issues and improve an effectual clinical decision support system exhausting classification methods over a large volume of the dataset for making better decisions and predictions, this paper presents grouping classification assembly through consuming the C5.0 algorithm, pointing towards assembling time to acquire great accuracy to identify an early diagnosis of chronic kidney disease patients with risk level by analyzing the chronic kidney disease dataset.

scholarly journals Butyrate producing microbiota are reduced in chronic kidney diseases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bei Gao ◽  
Adarsh Jose ◽  
Norma Alonzo-Palma ◽  
Taimur Malik ◽  
Divya Shankaranarayanan ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiota ◽  
Disease Severity ◽  
Kidney Diseases ◽  
Health Concern ◽  
Chronic Kidney Diseases ◽  
Component Systems ◽  
Advanced Stages ◽  
Two Component Systems

AbstractChronic kidney disease is a major public health concern that affects millions of people globally. Alterations in gut microbiota composition have been observed in patients with chronic kidney disease. Nevertheless, the correlation between the gut microbiota and disease severity has not been investigated. In this study, we performed shot-gun metagenomics sequencing and identified several taxonomic and functional signatures associated with disease severity in patients with chronic kidney disease. We noted that 19 microbial genera were significantly associated with the severity of chronic kidney disease. The butyrate-producing bacteria were reduced in patients with advanced stages of chronic kidney diseases. In addition, functional metagenomics showed that two-component systems, metabolic activity and regulation of co-factor were significantly associated with the disease severity. Our study provides valuable information for the development of microbiota-oriented therapeutic strategies for chronic kidney disease.

scholarly journals The role of metabolic acidosis in chronic kidney diseases

2010 ◽  
Vol 4 (3) ◽  
pp. 367-372
Author(s):  
James C. M. Chan
Keyword(s):  
Chronic Kidney Disease ◽  
Metabolic Acidosis ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Original Research ◽  
Due Diligence ◽  
Acid Base ◽  
Chronic Kidney Diseases ◽  
Balance Development

Abstract Background and objectives: This review focuses on three areas, basic acid-base physiology especially concerning hydrogen ion balance, development of acidosis in chronic kidney disease (CKD), and the consequences of acidosis. We highlight what is well established, what is less certain, and what is unknown. Method and results: The literature on acidosis in CKD were searched from 2004 to 2010 utilizing PubMed, Google Scholar, and Ovid to augment the classic work on acid base physiology over the past three decades. The original research in endogenous acid production and net acid excretion were reviewed. Touching upon the development of metabolic acidosis in CKD, we focused on the consequences of chronic metabolic acidosis on growth and other important variables. Finally, we recognize the significant issue of patients’ medical non-compliance and presented treatment strategy to counter this problem. Conclusion: The correction of acidosis in chronic kidney disease needs no advocacy. The case is made conclusively. Patient non-compliance because of the medication that needs to be taken several times a day is a problem, requiring due diligence.

Autophagy in Kidney Disease

2020 ◽  
Vol 82 (1) ◽  
pp. 297-322 ◽  
Author(s):  
Mary E. Choi
Keyword(s):  
Kidney Disease ◽  
Kidney Diseases ◽  
Ischemia Reperfusion ◽  
Kidney Injury ◽  
Therapeutic Benefit ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Chronic Kidney Diseases ◽  
Cellular Processes ◽  
Autophagy Pathway

Autophagy is a cellular homeostatic program for the turnover of cellular organelles and proteins, in which double-membraned vesicles (autophagosomes) sequester cytoplasmic cargos, which are subsequently delivered to the lysosome for degradation. Emerging evidence implicates autophagy as an important modulator of human disease. Macroautophagy and selective autophagy (e.g., mitophagy, aggrephagy) can influence cellular processes, including cell death, inflammation, and immune responses, and thereby exert both adaptive and maladaptive roles in disease pathogenesis. Autophagy has been implicated in acute kidney injury, which can arise in response to nephrotoxins, sepsis, and ischemia/reperfusion, and in chronic kidney diseases. The latter includes comorbidities of diabetes and recent evidence for chronic obstructive pulmonary disease–associated kidney injury. Roles of autophagy in polycystic kidney disease and kidney cancer have also been described. Targeting the autophagy pathway may have therapeutic benefit in the treatment of kidney disorders.

A tactical framework for EMR adoption

2019 ◽  
Vol 32 (3) ◽  
pp. 148-152
Author(s):  
James Lambley ◽  
Craig Kuziemsky
Keyword(s):  
Information Technologies ◽  
Implementation Strategies ◽  
Improve Patient Care ◽  
Theoretical Models ◽  
Clinical Settings ◽  
Human Diversity ◽  
Health Information Technologies ◽  
Emr Adoption ◽  
Legacy Information Systems ◽  
Improve Patient

Hospitals and other health settings across Canada are transitioning from paper or legacy information systems to Electronic Medical Records (EMR) systems to improve patient care and service delivery. The literature speaks to benefits of EMR systems, but also challenges, such as adverse patient events and provider workflow interruptions. Theoretical models have been proposed to help understand the complex interaction between health information technologies and the healthcare environment, but a shortcoming is the transition from conceptual models to actual clinical settings. The health ecosystem is filled with human diversity and organizational culture considerations that cannot be separated from technical implementation strategies. This paper analyzes literature on EMR implementation and adoption to develop a tactical framework for EMR adoption. The framework consists of six categories, each with a set of seed questions to consider when leading technology adoption projects.

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