Published: Jan. 1, 2024
Language: Английский
Frontiers in Endocrinology, Journal Year: 2024, Volume and Issue: 15
Published: April 29, 2024
Background Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods diagnosing treating these conditions have faced limitations, the emergence Machine Learning (ML) technologies heralds new era, offering promise revolutionizing diabetic care through enhanced precision tailored treatment strategies. Objective This review aims to explore transformative impact ML managing complications, highlighting its potential advance diagnostic accuracy therapeutic approaches by leveraging developments medical imaging, biomarker detection, clinical biomechanics. Methods A meticulous literature search was executed across PubMed, Scopus, Google Scholar databases identify pertinent articles published up March 2024. The strategy carefully crafted, employing combination keywords such “Machine Learning,” “Diabetic Foot,” Foot Ulcers,” Care,” “Artificial Intelligence,” “Predictive Modeling.” offers an in-depth analysis foundational principles algorithms that constitute ML, placing special emphasis their relevance sciences, particularly within specialized domain pathology. Through incorporation illustrative case studies schematic diagrams, endeavors elucidate intricate computational methodologies involved. Results has proven be invaluable deriving critical insights from complex datasets, enhancing both planning for management. highlights efficacy decision-making, underscored comparative analyses prognostic assessments applications care. Conclusion culminates prospective assessment trajectory realm We believe despite challenges limitations ethical considerations, remains at forefront paradigms management are globally applicable precision-oriented. technological evolution unprecedented possibilities opportunities patient
Language: Английский
Citations
15
Frontiers in Molecular Biosciences, Journal Year: 2024, Volume and Issue: 11
Published: July 9, 2024
Background Diabetic foot ulcers are the most common and serious complication of diabetes mellitus, high morbidity, mortality, disability which greatly diminish quality life patients impose a heavy socioeconomic burden. Thus, it is urgent to identify potential biomarkers targeted drugs for diabetic ulcers. Methods In this study, we downloaded datasets related from gene expression omnibus. Dysregulation mitophagy-related genes was identified by differential analysis weighted co-expression network analysis. Multiple machine algorithms were utilized hub genes, novel artificial neural model assisting in diagnosis constructed based on their transcriptome patterns. Finally, that can target using Enrichr platform molecular docking methods. Results 702 differentially expressed ulcers, enrichment showed these associated with mitochondria energy metabolism. Subsequently, hexokinase-2, small ribosomal subunit protein us3, l-lactate dehydrogenase A chain as multiple learning validated diagnostic performance validation cohort independent present study (The areas under roc curve 0.671, 0.870, 0.739, respectively). Next, training good, 0.924 0.840, respectively. retinoic acid estradiol promising anti-diabetic targeting hexokinase-2 (−6.6 −7.2 kcal/mol), us3 (−7.5 −8.3 (−7.6 −8.5 kcal/mol). Conclusion The chain, emphasized critical roles treatment through dimensions, providing
Language: Английский
Citations
4
Revista română de medicină de laborator, Journal Year: 2025, Volume and Issue: 33(1), P. 41 - 50
Published: Jan. 1, 2025
Abstract Background Osteoarthritis (OA), a common degenerative joint disease, is pathologically characterized by pain and functional limitation. Cuprotosis-related genes (CRGs) exert vital biological effects on various diseases, but their functions in OA remain largely unknown. We aimed to explore the potential role of CRGs establish diagnostic classifier. Methods The Gene Expression Omnibus database was firstly employed collect data sets several controls samples. Batch correction conducted using RobustRankAggreg sva package remove systematic errors between different batches sequencing. limma utilized screen differentially expressed genes, were identified through Pearson correlation analysis. Results A total 2,033 after analyzing sets. Through Least Absolute Shrinkage Selection Operator COX model support vector machine-recursive feature elimination classifier, 6 crucial finally determined, including biglycan, Ephrin-A3, leukemia inhibitory factor, natural killer cell granule protein 7, stimulator chondrogenesis 1 tumor necrosis alpha-induced 3. integrated analysis these revealed that they had high prediction performance. area under curve 0.772 training set 0.693 validation set. These exhibited significant correlations with infiltration M2 macrophages, resting mast cells other immune cells. Conclusions classifier for successfully constructed based CRGs, associations are found microenvironment OA.
Language: Английский
Citations
0
Journal of Applied Toxicology, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 20, 2025
The increasing prevalence of environmental pollutants has raised public concern about their potential role in diseases such as atherosclerosis (AS). Existing studies suggest that chemicals, including bisphenol S (BPS), may adversely affect cardiovascular health, but the specific mechanisms remain unclear. This study aims to elucidate effects BPS on AS and underlying mechanisms. Through an extensive search databases ChEMBL, STITCH, SwissTargetPrediction, SuperPred, SEA, GEO, we identified 34 targets related BPS-induced AS. A target network was constructed using STRING platform Cytoscape software. GO KEGG functional enrichment analysis DAVID database revealed promote occurrence by interfering with critical biological processes glutathione metabolism, nitrogen tyrosine metabolism. followed selection 4 core targets-aminopeptidase n (ANPEP), alcohol dehydrogenase 5 (ADH5), lysosomal pro-x carboxypeptidase (PRCP), microsomal s-transferase 1 (MGST1)-using five machine learning methods. These play a pivotal Furthermore, molecular docking confirmed tight binding between these targets. In conclusion, this provides theoretical framework for understanding contributes scientific evidence development prevention treatment strategies triggered exposure.
Language: Английский
Citations
0
Gene, Journal Year: 2025, Volume and Issue: unknown, P. 149374 - 149374
Published: Feb. 1, 2025
Language: Английский
Citations
0
Phytotherapy Research, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 12, 2024
ABSTRACT Diabetic wound (DW) represent a significant clinical challenge and often fail to heal effectively. Loureirin B (LB), flavonoid extracted from dragon's blood, has shown potential by influencing macrophage polarization promoting healing. However, its mechanisms efficacy in DW remain be explored. This study employed single‐cell RNA sequencing analyze the classification of cells diabetic foot ulcers identify related influenced macrophages. Molecular docking was used predict interactions LB with key proteins TGFβ/Smad signaling pathway. The effects on healing were further validated through vitro vivo experiments using model. Single‐cell analysis identified specific subtypes involved process highlighted role suggested action within In studies showed that under high glucose conditions, promoted pro‐inflammatory M1 healing‐promoting M2 ECM production fibroblasts activating TGF‐β/Smad signaling. vivo, treatment enhanced rates mice fibroblast synthesis regulates promote These findings suggest could therapeutic agent for improving healing, emphasizing need explore human subjects.
Language: Английский
Citations
1
American Journal of Otolaryngology, Journal Year: 2024, Volume and Issue: 46(1), P. 104556 - 104556
Published: Dec. 10, 2024
Idiopathic sudden sensorineural hearing loss (ISSNHL) is a of unknown etiology, with complex and diverse prognostic factors. Aim This study aims to identify independent factors for ISSNHL construct validate nomogram based on these
Language: Английский
Citations
0
Current Diabetes Reviews, Journal Year: 2024, Volume and Issue: 21(2)
Published: May 7, 2024
Diabetes mellitus (DM) frequently results in Diabetic Nephropathy (DN), which has a significant negative impact on the quality of life diabetic patients. Sphingolipid metabolism is associated with diabetes, but its relationship DN unclear. Therefore, screening biomarkers related to sphingolipid crucial for treating DN.
Language: Английский
Citations
0
Translational Cancer Research, Journal Year: 2024, Volume and Issue: 13(9), P. 4973 - 4984
Published: Sept. 1, 2024
Bladder cancer is one of the most commonly diagnosed urinary cancers worldwide. Although muscle-invasive bladder (MIBC) accounts for only 25% cases, it has a high recurrence rate and poor prognosis, especially among high-grade cases. Despite existence some molecular markers, there clear clinical need robust prediction model that can assist in patient management therapeutic decision-making. Therefore, we aimed to use public databases develop such an effective assessment model.
Language: Английский
Citations
0
Frontiers in Endocrinology, Journal Year: 2024, Volume and Issue: 15
Published: Nov. 13, 2024
Introduction Adamantinomatous craniopharyngioma (ACP) is difficult to cure completely and prone recurrence after surgery. Ferroptosis as an iron-dependent programmed cell death, may be a critical process in ACP. The study aimed screen diagnostic markers related ferroptosis ACP improve accuracy. Methods Gene expression profiles of were obtained from the gene omnibus (GEO) database. Limma package was used analyze differently expressed genes (DEGs). intersection DEGs ferroptosis-related factors (DEFRGs). Enrichment analysis processed, including Ontology (GO), Kyoto Encyclopedia Genes Genomes (KEGG), disease ontology (DO), set enrichment (GSEA), Set Variation Analysis (GSVA) analysis. Machine learning algorithms undertaken for screening associated with levels DEFRGs verified patients. A nomogram drawn predict relationship between key DEFRG risk disease. groups then clustered by consensus clustering Results screened normal samples. Ferroptosis-related FerrDb V2 GeneCard databases. correlation also confirmed. total 6 overlapped obtained. Based on results nomogram, CASP8, KRT16, KRT19, TP63 protective disease, while GOT1 TFAP2C factors. According DEFRGs, matrix differentiated, number clusters stable. TP63, upregulated patients, downregulated. affect multiple marker genes. combination these might biomarker diagnosis via participating process. Discussion genes, potential ACP, which lays theoretical foundation diagnosis.
Language: Английский
Citations
0