Discussing pathologic mechanisms of Diabetic retinopathy &amp; therapeutic potentials of curcumin and β-glucogallin in the management of Diabetic retinopathy

Rohilla, Manni; Rishabh,; Bansal, Seema; Garg, Anjali; Dhiman, Sachin; Dhankhar, Sanchit; Saini, Monika; Chauhan, Samrat; Alsubaie, Nawal; Batiha, Gaber El-Saber; Albezrah, Nisreen Khalid Aref; Singh, Thakur Gurjeet

doi:10.1016/j.biopha.2023.115881

Cited by 5 publications

(1 citation statement)

References 140 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This disorder becomes the most common cause of visual impairment and blindness in people with diabetes, affecting 75% of this population over a 15-year period. Many risk factors, such as poor glycemic control, hypertension, dyslipidemia, prolonged diabetes, and genetic predispositions, contribute to the progression of diabetic kidney disease (DR) [1], [2]. The fact that the incidence of diabetes-related vision loss has been declining in recent decades highlights the fact that, despite its severity, a significant portion of vision loss attributable to DR can be prevented.…”

Section: Introductionmentioning

confidence: 99%

A Novel Iterative Hyperparameter Optimization Method for Enhanced Diabetic Retinopathy Screening

Karadayi Ataş

2024

CTBEB

View full text Add to dashboard Cite

A major contributor to blindness in people of working age worldwide is diabetic retinopathy (DR), which is mostly caused by vascular damage brought on by high blood sugar in those who have diabetes mellitus. Since early detection greatly reduces the risk of vision loss, it is essential for the effective management of DR. This work presents a novel method that uses artificial intelligence (AI) to improve fundus image analysis for early diagnosis of diabetes mellitus (DR). Understanding how important high-quality images are to precise diagnosis, our approach starts with a sophisticated preprocessing stage. This stage focuses on improving the pictures by removing erroneous dark patches and other distortions, readying the dataset for additional examination. After preprocessing, we utilize an innovative hybrid hyperparameter optimization model that creatively combines the best features of grid and random search techniques. In order to identify early indicators of DR, such as microaneurysms and vessel changes, this model seeks to optimize the parameters of machine learning algorithms. Through a methodical analysis of the preprocessed fundus images, our model more effectively and accurately detects patterns suggestive of early-stage DR. The results of this study not only show that using AI in conjunction with careful preprocessing and hyperparameter optimization is effective in diagnosing DR, but they also open up new avenues for future investigation into the use of AI in early disease detection. In the end, this study makes a substantial contribution to the field of medical imaging and presents a viable treatment option for preventing vision loss in diabetic patients through early diagnostic intervention.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Iterative Hyperparameter Optimization Method for Enhanced Diabetic Retinopathy Screening

Karadayi Ataş

2024

CTBEB

View full text Add to dashboard Cite

show abstract

Microbial synthesis of gallic acid and its glucoside β‐glucogallin

Guo,

Ren,

et al. 2024

Biotech & Bioengineering

View full text Add to dashboard Cite

Gallic acid (GA) and β‐glucogallin (BGG) are natural products with diverse uses in pharmaceutical, food, chemical and cosmetic industries. They are valued for their wide‐ranging properties such as antioxidant, antibacterial, antidiabetic, and anticancer properties. Despite their significant importance, microbial production of GA and BGG faces challenges such as limited titers and yields, along with the incomplete understanding of BGG biosynthesis pathways in microorganisms. To address these challenges, we developed a recombinant Escherichia coli strain capable of efficiently producing GA. Our approach involved screening efficient pathway enzymes, integrating biosynthetic pathway genes into the genome while balancing carbon flux via adjusting expression levels, and strengthening the shikimate pathway to remove bottlenecks. The resultant strain achieved impressive results, producing 51.57 g/L of GA with a carbon yield of 0.45 g/g glucose and a productivity of 1.07 g/L/h. Furthermore, we extended this microbial platform to biosynthesize BGG by screening GA 1‐O‐glucosyltransferase, leading to the de novo production of 92.42 mg/L of BGG. This work establishes an efficient chassis for producing GA at an industrial level and provides a microbial platform for generating GA derivatives.

show abstract

Role of Artificial Intelligence in Diabetic Wound Screening and Early Detection

Dhankhar,

Garg,

Chauhan

et al. 2024

CBIOT

View full text Add to dashboard Cite

The morbidity and death rates linked to diabetes mellitus are substantially increased by foot ulcers, a prevalent consequence of the disease. Proper wound management is essential for controlling foot ulcers. This includes monitoring the ulcers' healing progress through clinical reviews, changing dressings as needed, treating infections with the right medications, and ensuring that the ulcer is offloaded correctly. Taking pictures of the ulcer was a dependable way to track how diabetic foot ulcers were healing in the past. Images of foot ulcers have recently experienced a tremendous change due to the emergence of digital cameras in cell phones. Artificial intelligence (AI) and other recent developments in digital health technology present a great chance to improve the efficiency of diabetes care, which might reduce the growth in healthcare costs associated with diabetes. Patients with diabetes can alleviate the burden on clinics and patients' transportation demands by electronically sharing photos of their ulcers, which diabetes care providers can remotely monitor. Improved remote monitoring of diabetic foot ulcers using smartphone apps is now possible with the help of a new generation of AI-powered solutions. This clinical update review aims to gather information on this trending topic so that medical professionals can be current on all the latest advancements in the field.

show abstract

Discussing pathologic mechanisms of Diabetic retinopathy & therapeutic potentials of curcumin and β-glucogallin in the management of Diabetic retinopathy

Cited by 5 publications

References 140 publications

A Novel Iterative Hyperparameter Optimization Method for Enhanced Diabetic Retinopathy Screening

A Novel Iterative Hyperparameter Optimization Method for Enhanced Diabetic Retinopathy Screening

Microbial synthesis of gallic acid and its glucoside β‐glucogallin

Role of Artificial Intelligence in Diabetic Wound Screening and Early Detection

Contact Info

Product

Resources

About