Current Drug TherapyPub Date : 2024-07-19DOI: 10.2174/0115748855312609240628110440
Nagma Shahin, Parag Jain, Ajazuddin, K. Nagori
{"title":"Advancements in Natural Alkaloid-Loaded Drug Delivery Systems for Enhanced Peptic Ulcer Treatment: A Review","authors":"Nagma Shahin, Parag Jain, Ajazuddin, K. Nagori","doi":"10.2174/0115748855312609240628110440","DOIUrl":"https://doi.org/10.2174/0115748855312609240628110440","url":null,"abstract":"\u0000\u0000Peptic ulcers, which damage the gastrointestinal tract lining, are a significant global health\u0000issue. The traditional approaches to treating peptic ulcers have poor bioavailability, unstable formulations,\u0000and undesirable side effects. Natural alkaloids have garnered increased interest as potential\u0000therapeutic agents in recent years due to their diverse pharmacological actions and decreased toxicity\u0000profiles. This manuscript summarizes recent progress in natural alkaloids for peptic ulcer treatment,\u0000highlighting new drug delivery methods. Natural alkaloids, originating from different plants, have\u0000anti-inflammatory, antioxidant, and antibacterial properties, potentially accelerating the healing of\u0000peptic ulcers. Moreover, medicines don't always function properly because they degrade too fast,\u0000don't dissolve well, and have other problems, such as insufficient bioavailability. Creative liposome\u0000delivery systems, microspheres, nanocarriers, and other customized delivery strategies have shown\u0000promise in overcoming these barriers. By distributing the medication gradually and precisely, these\u0000innovative techniques improve the medication and effectiveness at the ulcer site. As a result, natural\u0000ingredients work better and provide better treatment results.\u0000","PeriodicalId":11004,"journal":{"name":"Current Drug Therapy","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Current Drug TherapyPub Date : 2024-07-19DOI: 10.2174/0115748855307754240711065309
Tanishk Thakur, Naresh Rana, Shruti Jain
{"title":"Internet of Healthcare Things Based Detection of EEG Epileptic Seizures:\u0000A Smart System","authors":"Tanishk Thakur, Naresh Rana, Shruti Jain","doi":"10.2174/0115748855307754240711065309","DOIUrl":"https://doi.org/10.2174/0115748855307754240711065309","url":null,"abstract":"\u0000\u0000A seizure is a sudden and uncontrolled electrical activity in the brain that\u0000can cause a variety of symptoms, depending on the location and severity of the abnormal activity. It\u0000can be a symptom of an underlying neurological disorder or can occur without an apparent cause.\u0000Epilepsy is one of the most common causes of seizures. Overactive electrical discharges disrupt normal brain electrical activity and interfere with nerve cell communication.\u0000\u0000\u0000\u0000A comprehensive analysis of the literature revealed that several CAD system\u0000designs have shown to be useful to radiologists in routine medical practice as second-opinion aids for\u0000epileptic seizure detection in circumstances where a clear differentiation cannot be formed subjectively.\u0000CAD systems are made to help radiologists by automating the examination of medical data and images, improving the efficiency and accuracy of diagnosis. These systems examine patterns in medical\u0000imaging using machine learning approaches, which can be quite helpful in spotting small abnormalities that the human eye can miss. Moreover, the objective of this study was to design a smart\u0000healthcare system using a combination of DWT, Hjorth, and statistical parameters for seizure detection.\u0000\u0000\u0000\u0000In this research article, the authors proposed the framework of the Internet of Healthcare\u0000Things (IoHT) for performing seizure detection. The authors used different pre-processing techniques\u0000and extracted different features like Hjorth, wavelets, and statistics, which were classified using different machine-learning techniques. This novel methodology combines a number of technologies and\u0000techniques to improve seizure detection's precision and dependability.\u0000\u0000\u0000\u0000DWT + Hjorth + Statistical parameters with bior 1.5 as the pre-processing technique yielding\u0000the best outcomes. 86% accuracy was obtained with kNN for k = 5, 93% accuracy was obtained with\u0000a linear kernel for an SVM classifier, and 95.5% accuracy was obtained using a decision tree and\u0000logistic regression. The authors also considered another dataset for validation and received 96.83%\u0000accuracy with decision tree and logistic regression classifiers considering the bior1.5 wavelet filter\u0000as a preprocessing technique.\u0000\u0000\u0000\u0000The IoHT framework offers a multi-modal, adaptive method of seizure detection that\u0000enables the dynamic modification of detection parameters and the incorporation of extra sensor signals. This improves seizure detection's precision and dependability, which has important implications\u0000for patient care and monitoring. This work shows how IoHT and machine learning can be combined\u0000to build a reliable, real-time seizure detection system. These developments, which make it possible\u0000for prompt interventions and individualized treatment plans, can significantly improve the quality of\u0000care for individuals with epilepsy.\u0000","PeriodicalId":11004,"journal":{"name":"Current Drug Therapy","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}