English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The Toll-Like receptor 4 (TLR4) plays an important role in immunity, tissue repair, and regeneration. The objective of the present work was to evaluate the association of TLR4 single nucleotide polymorphisms (SNPs) rs4986790, rs4986791, rs11536858 (merged into rs10759931), rs1927911, and rs1927914 with increased diabetic foot ulcer (DFU) risk in patients with type 2 diabetes mellitus (T2DM). PCR-RFLP was used for genotyping TLR4 SNPs in 125 T2DM patients with DFU and 130 controls. The haplotypes and linkage disequilibrium between the SNPs were determined using Haploview software. Multivariate linear regression (MLR) and artificial neural network (ANN) modeling was done to observe their predictability for the risk of DFU in T2DM patients. Risk genotypes of all SNPs except rs1927914 were significantly associated with DFU. Haplotype ACATC ( P  value = 9.3 E  − 5) showed strong association with DFU risk. Two haplotypes ATATC ( P  value = 0.0119) and ATGTT ( P  value = 0.0087) were found to be protective against DFU. In conclusion TLR4 SNPs and their haplotypes may increase the risk of impairment of wound healing in T2DM patients. ANN model (83%) is found to be better than the MLR model (76%) and can be used as a tool for the DFU risk assessment in T2DM patients.

biomed research international

Association of Toll-Like Receptor 4 Polymorphisms with Diabetic Foot Ulcers and Application of Artificial Neural Network in DFU Risk Assessment in Type 2 Diabetes Patients