Chitosan as a promising materials for the construction of nanocarriers for diabetic retinopathy: an updated review

Umpierrez GE, Pasquel FJ. Management of inpatient hyperglycemia and diabetes in older adults. Diabetes Care. 2017;40(4):509–17.

Article  PubMed  PubMed Central  Google Scholar 

Sun Z, et al. Optical coherence tomography angiography in diabetic retinopathy: an updated review. Eye. 2021;35(1):149–61.

Article  PubMed  Google Scholar 

Markan A, et al. Novel imaging biomarkers in diabetic retinopathy and diabetic macular edema. Ther Adv Ophthalmol. 2020;12:2515841420950513.

PubMed  PubMed Central  Google Scholar 

Palermo NE, et al. Stress hyperglycemia during surgery and anesthesia: pathogenesis and clinical implications. Curr Diab Rep. 2016;16:1–7.

Article  CAS  Google Scholar 

Chen JY, et al. Paradoxical association of hyperglycemia and surgical complications among patients with and without diabetes. JAMA Surg. 2022;157(9):765–70.

Article  PubMed  PubMed Central  Google Scholar 

Davies MJ, et al. Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2018;41(12):2669–701.

Article  PubMed  PubMed Central  Google Scholar 

Fong DS, et al. Diabet retinopathy. Diabetes Care. 2004;27(10):2540–53.

Article  PubMed  Google Scholar 

Taylor SI, Yazdi ZS, Beitelshees AL. Pharmacological treatment of hyperglycemia in type 2 diabetes. J Clin Investig. 2021;131(2):e142243.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Heintz E, et al. Prevalence and healthcare costs of diabetic retinopathy: a population-based register study in Sweden. Diabetologia. 2010;53:2147–54.

Article  CAS  PubMed  Google Scholar 

Kompella UB, et al. Nanomedicines for back of the eye drug delivery, gene delivery, and imaging. Progr Retinal Eye Res. 2013;36:172–98.

Article  CAS  Google Scholar 

Tremolada G, et al. The role of angiogenesis in the development of proliferative diabetic retinopathy: impact of intravitreal anti-VEGF treatment. J Diabetes Res. 2012;2012:1-8. https://doi.org/10.1155/2012/728325.

Mohamed Q, Gillies MC, Wong TY. Management of diabetic retinopathy: a systematic review. JAMA. 2007;298(8):902–16.

Article  CAS  PubMed  Google Scholar 

Yang Y, et al. The stress hyperglycemia ratio, an index of relative hyperglycemia, as a predictor of clinical outcomes after percutaneous coronary intervention. Int J Cardiol. 2017;241:57–63.

Article  PubMed  Google Scholar 

Sheng B, et al. An overview of artificial intelligence in diabetic retinopathy and other ocular diseases. Front Public Health. 2022;10:971943.

Article  PubMed  PubMed Central  Google Scholar 

Inzucchi SE, et al. Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the study of diabetes. Diabetes Care. 2015;38(1):140–9.

Article  PubMed  Google Scholar 

Huang Y, et al. Dysbiosis and implication of the gut microbiota in diabetic retinopathy. Front Cell Infect Microbiol. 2021;11:646348.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jiwani N, Gupta K, Afreen N. A convolutional neural network approach for diabetic retinopathy classification. In: 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT). Indore: IEEE; 2022.

Math L, Fatima R. Adaptive machine learning classification for diabetic retinopathy. Multimedia Tools Appl. 2021;80(4):5173–86.

Article  Google Scholar 

Stitt AW, et al. The progress in understanding and treatment of diabetic retinopathy. Prog Retin Eye Res. 2016;51:156–86.

Article  PubMed  Google Scholar 

Tsiknakis N, et al. Deep learning for diabetic retinopathy detection and classification based on fundus images: a review. Comput Biol Med. 2021;135:104599.

Article  PubMed  Google Scholar 

Vujosevic S, et al. Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol. 2020;8(4):337–47.

Article  PubMed  Google Scholar 

Ansari P, et al. Diabetic retinopathy: an overview on mechanisms, pathophysiology and pharmacotherapy. Diabetology. 2022;3(1):159–75.

Article  Google Scholar 

Lechner J, O’Leary OE, Stitt AW. The pathology associated with diabetic retinopathy. Vision Res. 2017;139:7–14.

Article  PubMed  Google Scholar 

Amoaku WM, et al. Diabetic retinopathy and diabetic macular oedema pathways and management: UK Consensus Working Group. Eye. 2020;34(Suppl 1):1–51.

Article  PubMed  PubMed Central  Google Scholar 

Reddy GT, et al. An ensemble based machine learning model for diabetic retinopathy classification. In: 2020 international conference on emerging trends in information technology and engineering (ic-ETITE). Vellore: IEEE; 2020.

Jenkins AJ, et al. Biomarkers in diabetic retinopathy. Rev Diabet Stud. 2015;12(1–2):159.

Article  PubMed  PubMed Central  Google Scholar 

Demb JB, Singer JH. Functional circuitry of the retina. Annu Rev Vis Sci. 2015;1:263–89.

Article  PubMed  PubMed Central  Google Scholar 

Country MW. Retinal metabolism: a comparative look at energetics in the retina. Brain Res. 2017;1672:50–7.

Article  CAS  PubMed  Google Scholar 

Gundluru N, et al. Enhancement of detection of diabetic retinopathy using Harris hawks optimization with deep learning model. Comput Intell Neuroscien. 2022;2022:8512469.

Google Scholar 

Davis BM, et al. Glaucoma: the retina and beyond. Acta Neuropathol. 2016;132:807–26.

Article  PubMed  PubMed Central  Google Scholar 

Chen M, et al. Immune regulation in the aging retina. Prog Retin Eye Res. 2019;69:159–72.

Article  CAS  PubMed  Google Scholar 

Reichenbach A, Bringmann A. Glia of the human retina. Glia. 2020;68(4):768–96.

Article  PubMed  Google Scholar 

Gunasekeran DV, et al. Artificial intelligence for diabetic retinopathy screening, prediction and management. Curr Opin Ophthalmol. 2020;31(5):357–65.

Article  PubMed  Google Scholar 

Holmes D. Reconstructing the retina. Nature. 2018;561(7721):S2–3.

Article  ADS  CAS  PubMed  Google Scholar 

Kassani SH, et al. Diabetic retinopathy classification using a modified xception architecture. In: 2019 IEEE international symposium on signal processing and information technology (ISSPIT). Ajman: IEEE; 2019.

Das S, et al. Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed Signal Process Control. 2021;68:102600.

Article  Google Scholar 

Pang L, et al. Understanding diabetic neuropathy: focus on oxidative stress. Oxid Med Cell Longev. 2020;2020:1–13.

Google Scholar 

Rosenberger DC, et al. Challenges of neuropathic pain: focus on diabetic neuropathy. J Neural Transm. 2020;127(4):589–624.

Article  PubMed  Google Scholar 

Feldman EL, et al. Diabetic neuropathy. Nat Rev Dis Primers. 2019;5(1):41.

Article  PubMed  Google Scholar 

Bodman MA, Varacallo M. Peripheral diabetic neuropathy. In: StatPearls. Treasure Island: StatPearls Publishing; 2023.

Nascimento OJ, Pupe CC, Cavalcanti EB. Diabetic neuropathy. Rev Dor. 2016;17:46–51.

Article  Google Scholar 

Schreiber AK, et al. Diabetic neuropathic pain: physiopathology and treatment. World J Diabetes. 2015;6(3):432.

Article  PubMed  PubMed Central  Google Scholar 

Richner M, et al. Functional and structural changes of the blood-nerve-barrier in diabetic neuropathy. Front Neuro Sci. 2019;12:1038.

Article  Google Scholar 

Kumar A, Mittal R. Nrf2: a potential therapeutic target for diabetic neuropathy. Inflammopharmacology. 2017;25:393–402.

Article  CAS  PubMed  Google Scholar 

Sasaki H, et al. Spectrum of diabetic neuropathies. Diabetol Int. 2020;11:87–96.

Article  PubMed  PubMed Central  Google Scholar 

Gonçalves NP, et al. Schwann cell interactions with axons and microvessels in diabetic neuropathy. Nat Reviews Neurol. 2017;13(3):135–47.

Article  Google Scholar 

Sinclair SH, Schwartz SS. Diabetic retinopathy–an underdiagnosed and undertreated inflammatory, neuro-vascular complication of diabetes. Front Endocrinol. 2019;10:843.

Article  Google Scholar 

Gadekallu TR, et al. Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics. 2020;9(2):274.

Article 

Comments (0)

No login
gif