FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385610614> ?p ?o ?g. }

• W4385610614 endingPage "125052" @default.
• W4385610614 startingPage "125052" @default.
• W4385610614 abstract "Diabetic kidney disease (DKD) is one of the most common kidney diseases worldwide. It is estimated that approximately 537 million adults worldwide have diabetes, and up to 30%–40% of diabetic patients are at risk of developing nephropathy. The pathogenesis of DKD is complex, and its onset is insidious. Currently, the clinical diagnosis of DKD primarily relies on the increase of urinary albumin and the decrease in glomerular filtration rate in diabetic patients. However, the excretion of urinary albumin is influenced by various factors, such as physical activity, infections, fever, and high blood glucose, making it challenging to achieve an objective and accurate diagnosis. Therefore, there is an urgent need to develop an efficient, rapid, and cost-effective auxiliary diagnostic technology for diabetic nephropathy. There is an urgent need to develop an efficient, fast, and low-cost auxiliary diagnostic technology for DKD. In this study, an improved Dual Branch Attention Network (DBAN) was developed to quickly identify DKD. Serum Raman spectroscopy samples were collected from 32 DKD patients and 32 healthy volunteers. The collected data were preprocessed using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, and the DBAN was used to classify the serum Raman spectroscopy data of DKD. The model consists of a dual-branch structure that extracts features using Convolutional Neural Network (CNN) and bottleneck layer modules. The attention module allows the model to learn features specifically, and lateral connections are added between the dual branches to achieve multi-level and multi-scale fusion of shallow and deep features, as well as local and global features, improving the classification accuracy of the experiment. The results of the study showed that compared to traditional deep learning algorithms such as Artificial Neural Network (ANN), CNN, GoogleNet, ResNet, and AlexNet, our proposed DBAN classification model achieved 95.4% accuracy, 98.0% precision, 96.5% sensitivity, and 97.2% specificity, demonstrating the best classification performance. This is the best method for identifying DKD, and has important reference value for the diagnosis of DKD patients, as well as improving the accuracy of medical auxiliary diagnosis." @default.
• W4385610614 created "2023-08-06" @default.
• W4385610614 creator A5008322177 @default.
• W4385610614 creator A5032903415 @default.
• W4385610614 creator A5033339297 @default.
• W4385610614 creator A5034608423 @default.
• W4385610614 creator A5041438685 @default.
• W4385610614 creator A5044497095 @default.
• W4385610614 creator A5047166646 @default.
• W4385610614 creator A5049944728 @default.
• W4385610614 creator A5063253432 @default.
• W4385610614 creator A5079557669 @default.
• W4385610614 date "2024-01-01" @default.
• W4385610614 modified "2023-09-26" @default.
• W4385610614 title "DBAN: An improved dual branch attention network combined with serum Raman spectroscopy for diagnosis of diabetic kidney disease" @default.
• W4385610614 cites W1500925570 @default.
• W4385610614 cites W1584599469 @default.
• W4385610614 cites W1979406233 @default.
• W4385610614 cites W2000738544 @default.
• W4385610614 cites W2016365402 @default.
• W4385610614 cites W2041379055 @default.
• W4385610614 cites W2048719990 @default.
• W4385610614 cites W2071851716 @default.
• W4385610614 cites W2084218274 @default.
• W4385610614 cites W2111255529 @default.
• W4385610614 cites W2126870113 @default.
• W4385610614 cites W2145027776 @default.
• W4385610614 cites W2159387972 @default.
• W4385610614 cites W2166051277 @default.
• W4385610614 cites W2568131087 @default.
• W4385610614 cites W2749488004 @default.
• W4385610614 cites W2898309056 @default.
• W4385610614 cites W2981344742 @default.
• W4385610614 cites W3008019394 @default.
• W4385610614 cites W3009865587 @default.
• W4385610614 cites W3150538710 @default.
• W4385610614 cites W4200172532 @default.
• W4385610614 cites W4205858292 @default.
• W4385610614 cites W4306768310 @default.
• W4385610614 cites W4310346562 @default.
• W4385610614 cites W4315618953 @default.
• W4385610614 cites W4323315132 @default.
• W4385610614 doi "https://doi.org/10.1016/j.talanta.2023.125052" @default.
• W4385610614 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37574605" @default.
• W4385610614 hasPublicationYear "2024" @default.
• W4385610614 type Work @default.
• W4385610614 citedByCount "0" @default.
• W4385610614 crossrefType "journal-article" @default.
• W4385610614 hasAuthorship W4385610614A5008322177 @default.
• W4385610614 hasAuthorship W4385610614A5032903415 @default.
• W4385610614 hasAuthorship W4385610614A5033339297 @default.
• W4385610614 hasAuthorship W4385610614A5034608423 @default.
• W4385610614 hasAuthorship W4385610614A5041438685 @default.
• W4385610614 hasAuthorship W4385610614A5044497095 @default.
• W4385610614 hasAuthorship W4385610614A5047166646 @default.
• W4385610614 hasAuthorship W4385610614A5049944728 @default.
• W4385610614 hasAuthorship W4385610614A5063253432 @default.
• W4385610614 hasAuthorship W4385610614A5079557669 @default.
• W4385610614 hasConcept C126322002 @default.
• W4385610614 hasConcept C126894567 @default.
• W4385610614 hasConcept C134018914 @default.
• W4385610614 hasConcept C149635348 @default.
• W4385610614 hasConcept C159641895 @default.
• W4385610614 hasConcept C185592680 @default.
• W4385610614 hasConcept C2779922275 @default.
• W4385610614 hasConcept C2780091579 @default.
• W4385610614 hasConcept C2780513914 @default.
• W4385610614 hasConcept C2781184683 @default.
• W4385610614 hasConcept C41008148 @default.
• W4385610614 hasConcept C555293320 @default.
• W4385610614 hasConcept C71924100 @default.
• W4385610614 hasConcept C77411442 @default.
• W4385610614 hasConceptScore W4385610614C126322002 @default.
• W4385610614 hasConceptScore W4385610614C126894567 @default.
• W4385610614 hasConceptScore W4385610614C134018914 @default.
• W4385610614 hasConceptScore W4385610614C149635348 @default.
• W4385610614 hasConceptScore W4385610614C159641895 @default.
• W4385610614 hasConceptScore W4385610614C185592680 @default.
• W4385610614 hasConceptScore W4385610614C2779922275 @default.
• W4385610614 hasConceptScore W4385610614C2780091579 @default.
• W4385610614 hasConceptScore W4385610614C2780513914 @default.
• W4385610614 hasConceptScore W4385610614C2781184683 @default.
• W4385610614 hasConceptScore W4385610614C41008148 @default.
• W4385610614 hasConceptScore W4385610614C555293320 @default.
• W4385610614 hasConceptScore W4385610614C71924100 @default.
• W4385610614 hasConceptScore W4385610614C77411442 @default.
• W4385610614 hasFunder F4320325419 @default.
• W4385610614 hasLocation W43856106141 @default.
• W4385610614 hasLocation W43856106142 @default.
• W4385610614 hasOpenAccess W4385610614 @default.
• W4385610614 hasPrimaryLocation W43856106141 @default.
• W4385610614 hasRelatedWork W2013701055 @default.
• W4385610614 hasRelatedWork W2061728276 @default.
• W4385610614 hasRelatedWork W2134392906 @default.
• W4385610614 hasRelatedWork W2356203034 @default.
• W4385610614 hasRelatedWork W2377006731 @default.
• W4385610614 hasRelatedWork W2381954214 @default.
• W4385610614 hasRelatedWork W2472562742 @default.

• next