Diabetes Diagnosis and Treatment Research Based on Machine Learning

: Diabetes mellitus (DM) is a type of chronic metabolic disease that has affected millions of people worldwide and is known with a defect in the amount of insulin secretion, insulin functions, or both. This deficiency leads to an increase in the amounts of glucose, which could be accompanied by long-term damages to other organs such as eyes, kidneys, heart, and nervous system. Thus, introducing an appropriate approach for diagnosis and treatment of different types of DM is the aim of several researches. By the emergence of nanotechnology and its application in medicine, new approaches were presented for these purposes. The object of this review article is to introduce different types of polymeric nanoparticles (PNPs), as one of the most important classes of nanoparticles, for diabetic management. To achieve this goal, at first, some of the conventional therapeutic and diagnostic methods of DM will be reviewed. Then, different types of PNPs, in two forms of natural and synthetic polymers with different properties, as a new method for DM treatment and diagnosis will be introduced. In the next section, the transport mechanisms of these types of nano-carriers across the epithelium, via paracellular and transcellular pathways will be explained. Finally, the clinical use of PNPs in the treatment and diagnosis of DM will be summarized. Based on the results of this literature review, PNPs could be considered one of the most promising methods for DM management.

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Urinary Tract Infection and Diabetes: Diagnosis and Treatment

The Kidney and Hypertension in Diabetes Mellitus ◽

10.1007/978-1-4757-6746-9_38 ◽

1994 ◽

pp. 401-405

Author(s):

René Vejlsgaard

Keyword(s):

Urinary Tract Infection ◽

Urinary Tract ◽

Tract Infection ◽

Diagnosis And Treatment ◽

Diabetes Diagnosis

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Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment

American Journal of Roentgenology ◽

10.2214/ajr.18.20328 ◽

2019 ◽

Vol 212 (1) ◽

pp. 52-56 ◽

Cited By ~ 4

Author(s):

Javier E. Villanueva-Meyer ◽

Peter Chang ◽

Janine M. Lupo ◽

Christopher P. Hess ◽

Adam E. Flanders ◽

...

Keyword(s):

Machine Learning ◽

Diagnosis And Treatment

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Identifying cognitive deficits in cocaine dependence using standard tests and machine learning

10.31234/osf.io/7qsz3 ◽

2018 ◽

Author(s):

Said Enrique Jiménez ◽

Diego Angeles-Valdez ◽

Viviana Villicaña ◽

Ernesto Reyes-Zamorano ◽

ruth alcala ◽

...

Keyword(s):

Machine Learning ◽

Cocaine Dependence ◽

Diagnosis And Treatment ◽

Cognitive Domains ◽

Cognitive Markers ◽

Multiple Observations ◽

Other Substances ◽

Planning Decision ◽

The Mind ◽

Treatment Improvement

Background and aims There is a growing need for detecting valid and generalizable markers due to a demand of accurate Cocaine Dependence diagnosis and treatment. Machine Learning (ML) is a modern statistical alternative to select from multiple observations the most reliable features, which allows precise and more effective categorization addressing the demand to improve diagnosis. The aim of the current study was to identify cognitive markers by using three ML algorithms, Elastic Net (GlmNet), Random forest (Rf) and Generalized Linear Model (Glm), with the purpose of classify Cocaine Dependence (CD) and Non-dependent controls (NDC) to make it generalizable for new samples. Methods Two independent samples were required, the first one consisted on 87 participants (53 CD and 34 NDC) and the second one conformed by 40 participants (20 CD and 20 NDC). All participants were evaluated with neuropsychological tests that included 40 variables assessing cognitive domains of flexibility, inhibition, working memory, problem solving, planning, decision making and theory of the mind. With the results of the cognitive evaluation the three ML algorithms were trained in the first sample and tested on the second one to classify into CD and NDC. Results Even though the three algorithms had a ROC performance over 50%, GlmNet was superior in both, training (ROC = 0.71) and testing set (ROC = 0.85) compared to Rf and Glm. Furthermore, GlmNet was capable of identifying eight predictors out of 40 from all the cognitive domains assessed. Conclusions ML is an effective approach for the identification of generalizable cognitive markers. Specific subsets resulted robust predictors for accurate classification of new cases, such as those from cognitive flexibility and inhibition domain. These findings are relevant in addictions field as they have highly beneficial potential for diagnosis and treatment improvement, not only for CD but also for other substances abuse.

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Natural Carbon Nanodots: Toxicity Assessment and Theranostic Biological Application

Pharmaceutics ◽

10.3390/pharmaceutics13111874 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1874

Author(s):

Ming-Hsien Chan ◽

Bo-Gu Chen ◽

Loan Thi Ngo ◽

Wen-Tse Huang ◽

Chien-Hsiu Li ◽

...

Keyword(s):

Carbon Source ◽

Large Scale ◽

New Technologies ◽

Cell Damage ◽

Carbon Sources ◽

Diagnosis And Treatment ◽

Carbon Nanodots ◽

Biological Application ◽

Treatment Research ◽

Natural Carbon

This review outlines the methods for preparing carbon dots (CDs) from various natural resources to select the process to produce CDs with the best biological application efficacy. The oxidative activity of CDs mainly involves photo-induced cell damage and the destruction of biofilm matrices through the production of reactive oxygen species (ROS), thereby causing cell auto-apoptosis. Recent research has found that CDs derived from organic carbon sources can treat cancer cells as effectively as conventional drugs without causing damage to normal cells. CDs obtained by heating a natural carbon source inherit properties similar to the carbon source from which they are derived. Importantly, these characteristics can be exploited to perform non-invasive targeted therapy on human cancers, avoiding the harm caused to the human body by conventional treatments. CDs are attractive for large-scale clinical applications. Water, herbs, plants, and probiotics are ideal carbon-containing sources that can be used to synthesize therapeutic and diagnostic CDs that have become the focus of attention due to their excellent light stability, fluorescence, good biocompatibility, and low toxicity. They can be applied as biosensors, bioimaging, diagnosis, and treatment applications. These advantages make CDs attractive for large-scale clinical application, providing new technologies and methods for disease occurrence, diagnosis, and treatment research.

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Integrated Bioinformatics and Machine Learning Algorithms Analyses Highlight Related Pathways and Genes Associated with Alzheimer's Disease

Current Bioinformatics ◽

10.2174/1574893617666211220154326 ◽

2021 ◽

Vol 17 ◽

Author(s):

Hui Zhang ◽

Qidong Liu ◽

Xiaoru Sun ◽

Yaru Xu ◽

Yiling Fang ◽

...

Keyword(s):

Machine Learning ◽

Network Analysis ◽

Predictive Model ◽

Differentially Expressed Genes ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Differentially Expressed ◽

Diagnosis And Treatment ◽

Ppi Network ◽

Key Genes

Background: The pathophysiology of Alzheimer's disease (AD) is still not fully studied. Objective: This study aimed to explore the differently expressed key genes in AD and build a predictive model of diagnosis and treatment. Methods: Gene expression data of the entorhinal cortex of AD, asymptomatic AD, and control samples from the GEO database were analyzed to explore the relevant pathways and key genes in the progression of AD. Differentially expressed genes between AD and the other two groups in the module were selected to identify biological mechanisms in AD through KEGG and PPI network analysis in Metascape. Furthermore, genes with a high connectivity degree by PPI network analysis were selected to build a predictive model using different machine learning algorithms. Besides, model performance was tested with five-fold cross-validation to select the best fitting model. Results: A total of 20 co-expression gene clusters were identified after the network was constructed. Module 1 (in black) and module 2 (in royal blue) were most positively and negatively correlated with AD, respectively. Total 565 genes in module 1 and 215 genes in module 2, respectively, overlapped in two differentially expressed genes lists. They were enriched in the G protein-coupled receptor signaling pathway, immune-related processes, and so on. 11 genes were screened by using lasso logistic regression, and they were considered to play an important role in predicting AD samples. The model built by the support vector machine algorithm with 11 genes showed the best performance. Conclusion: This result shed light on the diagnosis and treatment of AD.

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