Abstract
As the scale and depth of artificial intelligence network models continue to increase, their accuracy in albumin recognition tasks has increased rapidly. However, today's small medical datasets are the main reason for the poor recognition of artificial intelligence techniques in this area. The sample size in this article is based on the data analysis and research on urine albumin detection of diabetes in the EI database. It is assumed that the observation group has at least 20 mg UAER difference from the control group, and the standard deviation of the UAER change from baseline to 12 weeks is 30 mg. Therefore, the sample size of the two groups is 77 cases. Assuming that the rate of loss to follow-up during the follow-up period is 20%, at least 92 patients are needed. The final enrollment in this study is 100 patients. Studies have shown that DR is used as an indicator to diagnose NDRD, and its OR value is as high as 28.198, indicating that non-DR can be used as an indicator to distinguish DN from NDRD. The meta-analysis found that DR has a sensitivity of 0.65 and a specificity of 0.75 in distinguishing DN from NDRD in patients with type 2 diabetes, and it is emphasized that PDR is highly specific in the diagnosis of DN. Using a meta-analysis to systematically analyze 45 studies, it was found that the sensitivity of DR to diagnose DN was 0.67, the specificity was 0.78, and the specificity of PDR to predict DN was 0.99, indicating that DR is a good indicator for predicting DN, and the team's latest research has also verified this point of view. They have established a new model for diagnosing DN. In addition to including traditional proteinuria, glycosylated hemoglobin, FR, blood pressure, and other indicators into the diagnostic model, it will also include the presence or absence of DR. The final external verification accuracy rate of this model is 0.875.
Highlights
With the continuous development of proteomics, the proteomics technology system based on protein separation technology, biological mass spectrometry technology, protein interaction technology, and bioinformatics technology has solved the difficulty of accurately characterizing the proteome expression profile and is the DKD research system establishment, and improvement of DKD provided technical support and laid a good foundation for DKD functional proteomics research
Its disadvantage is a large amount of data, the complexity, and difficulty of data analysis, and the peptide is the protein sequence coverage rate identified by the center which is very low, less than 20%, and the enzyme digestion process will cause the loss of important information such as posttranslational modifications (PTMs), which hinders the accurate functional analysis of the protein
Proteomics research on DKD clinical samples under different physiological and pathological conditions can comprehensively explore the key proteins that are significantly related to DKD; through in-depth analysis and verification of these proteins, we can more intuitively understand the molecular mechanism of DKD development, and obtaining candidate markers related to the DKD process and potential therapeutic targets for subsequent diseases will lay the foundation for the early diagnosis of DKD and the exploration of new treatment methods
Summary
With the continuous development of proteomics, the proteomics technology system based on protein separation technology, biological mass spectrometry technology, protein interaction technology, and bioinformatics technology has solved the difficulty of accurately characterizing the proteome expression profile and is the DKD (diabetic kidney disease) research system establishment, and improvement of DKD provided technical support and laid a good foundation for DKD functional proteomics research. E results showed that haptoglobin (HPT) as a biomarker with significant content differences can distinguish healthy individuals from DKD patients [2]. Based on LC-MS/MS and random forest (rRF) algorithms, An et al distinguish the urine peptide profiles of patients with DKD at different stages of type 2 diabetes [4]. Groop PH screened 8 kinds of LMW proteins unique to diabetic patients, using these proteins, analyzed the urine peptide sequence to predict the proteolytic characteristics associated with DKD, and explore the differential regulation mechanism of inflammation and complement system in DKD [8]. Hwang characterized the proteins in human urinary exosomes through LC-MS technology and found that the proteome in the DKD group and the healthy control group had significantly different levels of expression [9]. The model can automatically detect the lesion level of the patient’s albumin membrane albumin. e artificial intelligence algorithm is used to automatically extract the characteristics of albumin, and the traditional support vector machine (SVM), K nearest neighbor algorithm, and other steps that require feature detection and manual feature extraction of albumin are discarded, reducing human factors. e phenomenon of misdiagnosis can greatly shorten the diagnosis time of diabetes, which is of great significance for the early prevention and treatment of patients with diabetes
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