:Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.

01 Oct 2024·Annals of African Medicine

Comparison of the Analgesic Duration Using Ultrasound-guided Popliteal Sciatic Nerve Block between Diabetics with Neuropathy and Nondiabetics without Neuropathy

Article

Author: Ribeiro, Karl Nicholas Sa ; Prasad, Ganesh ; Naik, Shilpa A. ; Misquith, Julie C. R.

Introduction:In India, the prevalence of diabetes mellitus neuropathy was reported to be as high as 30%. Eight percentage of the diabetic population suffer from foot ulceration and 1.8% have amputations. Popliteal nerve block can be potentially used for foot and ankle surgery with several advantages.Aim:To compare analgesic duration of an ultrasound (US)-guided popliteal sciatic nerve block between diabetics with neuropathy and nondiabetics without neuropathy.Patients and Methods:Participants were allocated into two groups for popliteal sciatic nerve blocks under US guidance. The primary outcome was the duration to onset of sensory and motor blockade. The secondary outcome was the duration to rescue analgesic and the visual analog scale scoring within 24 h. Hemodynamic outcomes were also monitored along with the above variables.Results:It was observed that the onset of sensory blockade was faster in participants with diabetes mellitus with peripheral neuropathy as compared to the nondiabetic participants and the duration for onset of motor blockade in dorsiflexion was faster in diabetic patients as compared to the nondiabetic patients (17.48 ± 3.21 min). However, there was no significant changes when comparing the onset of duration to loss of plantar flexion, in diabetics (17.86 ± 2.29 min) versus in nondiabetics (18.51 ± 3.32 min). The duration for rescue analgesics was found to be longer in diabetic participants (13.19 ± 2.14 h) as compared to the nondiabetic participants (11.44 ± 1.86 h). No differences were observed in the hemodynamic changes and the complications associated with local anesthetics in either group.Conclusion:Diabetic patients with neuropathy have faster onset of blockade when compared to nondiabetic patients without neuropathy which may be due to the degenerative condition of the peripheral nerves in them. The hemodynamic parameters do not play a role in defining the outcome of the block.

01 Oct 2024·International Dental Journal

Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review

Review

Author: Manoharan, Varsha ; Khanagar, Sanjeev B ; Abumelha, Abdulaziz S ; Almohareb, Thamer ; Mashyakhy, Mohammed ; Marwah, Nikhil ; Binalrimal, Sultan ; Alzahrani, Khaled M ; Almutairi, Basil ; Vishwanathaiah, Satish ; Maganur, Prabhadevi C ; Robaian, Ali

Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was undertaken to critically appraise developments and performance of artificial intelligence (AI) models designed for tooth numbering and detection using dento-maxillofacial radiographic images. In order to maintain the integrity of their methodology, the authors of this systematic review followed the diagnostic test accuracy criteria outlined in PRISMA-DTA. Electronic search was done by navigating through various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library for the articles published from 2018 to 2023. Sixteen articles that met the inclusion exclusion criteria were subjected to risk of bias assessment using QUADAS-2 and certainty of evidence was assessed using GRADE approach.AI technology has been mainly applied for automated tooth detection and numbering, to detect teeth in CBCT images, to identify dental treatment patterns and approaches. The AI models utilised in the studies included exhibited a highest precision of 99.4% for tooth detection and 98% for tooth numbering. The use of AI as a supplementary diagnostic tool in the field of dental radiology holds great potential.

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