Anemia in ESKD Patient (End-Stage Kidney Disease): The Rare Cause Is in the Forefront

We reported a case of severe anemia in a patient with end-stage kidney disease (ESKD) on dialysis. The anemia developed when the patient is switched from hemodialysis (HD) to peritoneal dialysis (PD) when the intra-venous erythropoietin stimulating agent (ESA, Epogen) was changed into subcutaneous injection of darbepoetin. The patient’s hemoglobin dropped 2 grams in about two months during this period. Extensive work-up including, bleeding disorders, hemolysis, iron deficiency, infections including CMV, Epstein-Bar virus, parvo-19 virus infection were unrevealing The anti-Epogen neutralizing antibodies were not measured due to unavailability. Bone marrow biopsy and aspirate were negative for infiltrations or myelodysplastic syndrome (MDS). The leukocyte and platelet counts were normal. Even though anti-ESA antibodies were not measured in this case, all tentative causes of his anemia were excluded by laboratory investigations. The patient’s anemia was treated symptomatically with blood transfusion and discontinuation of the ESA treatment. He made a remarkable recovery.

Identifying Biomarkers for Diabetic Kidney Disease Using GraphSAGE Neural Network

Diabetic Kidney Disease (DKD) is a common chronic complication of diabetes. Despite advancements in accurately identifying biomarkers for detecting and diagnosing this harmful disease, there remains an urgent need for new biomarkers to enable early detection of DKD. In this study, we modeled publicly available transcriptome datasets as a graph problem and used GraphSAGE Neural Networks (GNNs) to identify potential biomarkers. The GraphSAGE model effectively learned representations that captured the intricate interactions, dependencies among genes, and disease-specific gene expression patterns necessary to classify samples as DKD and Control. We finally extracted the features of importance;the identified set of genes exhibited an impressive ability to distinguish between healthy and unhealthy samples, even though these genes differ from previous research findings. The unexpected biomarker variations in this study suggest more exploration and validation studies for discovering biomarkers in DKD. In conclusion, our study showcases the effectiveness of modeling transcriptome data as a graph problem, demonstrates the use of GraphSAGE models for biomarker discovery in DKD, and advocates for integrating advanced machine-learning techniques in DKD biomarker research, emphasizing the need for a holistic approach to unravel the intricacies of biological systems.

A Case Report: Calciphylaxis Presenting as Digital Ischemia in Patient with End Stage Kidney Disease on Peritoneal Dialysis

Calciphylaxis also known as Calcific uremic arteriolopathy (CUA), is a fatal disease with high mortality that mostly affects patients with end stage kidney disease (ESKD) on dialysis. In recent years the incidence of CUA has been increasing among patients on peritoneal dialysis (PD). The exact mechanism and risk factors are not well described.

New construction for expert system based on innovative knowledge discovery technology

Knowledge acquisition Is the bottleneck of expert system. To solve this problem, KD （D＆K）, which is a comprehensive knowledge discovery process model coopersting both database and knowledge base, and related technology are proposed. Then based on KD （D＆K） and related technology, the new construction of Expert System based on Knowledge Discovery （ESKD） Is proposed. As the key knowledge acqulsltlon component of ESKD, KD （D＆K） Is composed of KDD＊ and KDK＊. KDD＊- the new process model based on double bases cooperating mechanism; KDK＊- the new process model based on double-basis fusion mechanism are Introduced, respectively. The overall framework of ESKD Is proposed. Some sub-systems and dynamic knowledge base system are discussed. Flnelly, the effectiveness and advantages of ESKD are tested In a real-world agriculture database. We hope that ESKD may be useful for the new generation of expert systems.