Effects of D-Limonene on aldose reductase and protein glycation in diabetic rats

Kumar, Manjesh; Poornima,; Mamidala, Estari; Al‐Ghanim, Khalid A.; Al‐Misned, Fahad A.; Ahmed, Zubair; Mahboob, Shahid

doi:10.1016/j.jksus.2020.01.043

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…Terpene structures, which belong to an important class of natural products have been extensively reported for antidiabetic activity and some of them are already under various stages of pre-clinical and clinical evaluation geared towards developing antidiabetic agents [10,14]. The antidiabetic potential of plant-derived terpenes could be attributed to varying mechanisms of action which include: increasing insulin sensitivity [14,39], inhibiting pancreatic amylase and glucosidase enzymes [40,41], reducing oxidative stress [42], and inhibiting the development of diabetic complications [43,44]. Several studies have revealed that, terpene-rich bioactive extracts of several African medicinal plants have shown antidiabetic activity by targeting key proteins associated with type II diabetes in vitro and in vivo as reviewed by Panigrahy, Bhatt [14].…”

Section: Introductionmentioning

confidence: 99%

Triterpenes from African Antidiabetic Herbs as Inhibitors of DPP-4 and PTP1B Targets: Molecular Modeling Investigation

Ogunyemi,

Gyebi,

Olawale

et al. 2023

Preprint

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African indigenous herbs and medicinal plants are extensively reported for their protective and therapeutic potential against Diabetes. However, most of the constituent phytochemicals that may account for the therapeutic activity are largely uninvestigated. Exploring novel phytochemical–target interactions in silico could help to provide useful insights into the mechanism underpinning the biological activities of these plants and their constituent phytochemicals. The aim of this study was to explore the interactions of terpene struc-tures previously reported from selected African antidiabetic with two emerging drug targets in diabetes. Structure-based virtual screening was used to screen 107 terpene structures against DPP-4 and PTP1B enzymes. The MD simulation, MM-GBSA free energy calculation and DFT were used to clarify the inter-actions of the in silico hits with the target enzymes. Molecular docking, post-docking Prime MM-GBSA analysis revealed the top terpenes with high binding affinity with the active site regions of DPP-4 and PTP1B. Ensemble docking revealed three triterpenes viz: cucurbitacin B (T1), 6-Oxoisoiguesterin (T4) and 20-Epi-isoiguesterinol (T2) as in silico hits which exhibit strong interaction potential with critical residues that define the catalytic triad (Ser630 and His740); oxyanion cavity (Ser631); hydrophobic S1 pocket (Tyr662) and the charged S2 pocket (Arg125) in the active site region of DPP-4. The interactions of these compounds with DPP-4 exhibited structural stability and conformational flexibility during 100 ns full atomistic MD simulation as indicated by the structure dynamics parameters including RMSD, RoG, SASA and hydrogen bond number. The post-MD MM/GBSA calculations further revealed the stability of the triterpene-DPP-4 complexes. Furthermore, Frontier molecular orbital calculations showed that, the triterpenes possess high interaction potential with the enzyme. The triterpenes showed desirable ADMET properties and drug-likeness. Therefore, cucurbitacin B, 6-Oxoisoiguesterin and 20-Epi-isoiguesterinol are recommended for experimental validation.

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Section: Introductionmentioning

confidence: 99%

Triterpenes from African Antidiabetic Herbs as Inhibitors of DPP-4 and PTP1B Targets: Molecular Modeling Investigation

Ogunyemi,

Gyebi,

Olawale

et al. 2023

Preprint

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“…For example, diabetes and hyperglycemia can result in a variety of health concerns, including vision problems. Diabetes mellitus can affect several tissues in the eye system, and cataracts are among the most common causes of vision impairment [4]. Therefore, it is expected that the demand for assistive devices will grow.…”

Section: Introductionmentioning

confidence: 99%

LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino

et al. 2022

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Smart wearable technologies such as fitness trackers are creating many new opportunities to improve the quality of life for everyone. It is usually impossible for visually impaired people to orientate themselves in large spaces and navigate an unfamiliar area without external assistance. The design space for assistive technologies for the visually impaired is complex, involving many design parameters including reliability, transparent object detection, handsfree operations, high-speed real-time operations, low battery usage, low computation and memory requirements, ensuring that it is lightweight, and price affordability. State-of-the-art visually impaired devices lack maturity, and they do not fully meet user satisfaction, thus more effort is required to bring innovation to this field. In this work, we develop a pair of smart glasses called LidSonic that uses machine learning, LiDAR, and ultrasonic sensors to identify obstacles. The LidSonic system comprises an Arduino Uno device located in the smart glasses and a smartphone app that communicates data using Bluetooth. Arduino collects data, manages the sensors on smart glasses, detects objects using simple data processing, and provides buzzer warnings to visually impaired users. The smartphone app receives data from Arduino, detects and identifies objects in the spatial environment, and provides verbal feedback about the object to the user. Compared to image processing-based glasses, LidSonic requires much less processing time and energy to classify objects using simple LiDAR data containing 45-integer readings. We provide a detailed description of the system hardware and software design, and its evaluation using nine machine learning algorithms. The data for the training and validation of machine learning models are collected from real spatial environments. We developed the complete LidSonic system using off-the-shelf inexpensive sensors and a microcontroller board costing less than USD 80. The intention is to provide a design of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. This work is expected to open new directions for smart glasses design using open software tools and off-the-shelf hardware.

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“…Diabetes and hyperglycemia, for instance, can cause a range of health issues, including visual impairment. Several tissues of the ocular system can be affected by diabetes, and cataracts are one of the most prevalent causes of vision impairment [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Busaeed

Katib

Albeshri

et al. 2022

Sensors

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Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system’s hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.

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Effects of D-Limonene on aldose reductase and protein glycation in diabetic rats

Cited by 10 publications

References 25 publications

Triterpenes from African Antidiabetic Herbs as Inhibitors of DPP-4 and PTP1B Targets: Molecular Modeling Investigation

Triterpenes from African Antidiabetic Herbs as Inhibitors of DPP-4 and PTP1B Targets: Molecular Modeling Investigation

LidSonic for Visually Impaired: Green Machine Learning-Based Assistive Smart Glasses with Smart App and Arduino

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

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