Exploration of drug science最新文献

Malignant brain tumors are the leading cause of cancer-related death in children and remain a significant cause of morbidity and mortality throughout all demographics. Central nervous system (CNS) tumors are classically treated with surgical resection and radiotherapy in addition to adjuvant chemotherapy. However, the therapeutic efficacy of chemotherapeutic agents is limited due to the blood-brain barrier (BBB). Magnetic resonance guided focused ultrasound (MRgFUS) is a new and promising intervention for CNS tumors, which has shown success in preclinical trials. High-intensity focused ultrasound (HIFU) has the capacity to serve as a direct therapeutic agent in the form of thermoablation and mechanical destruction of the tumor. Low-intensity focused ultrasound (LIFU) has been shown to disrupt the BBB and enhance the uptake of therapeutic agents in the brain and CNS. The authors present a review of MRgFUS in the treatment of CNS tumors. This treatment method has shown promising results in preclinical trials including minimal adverse effects, increased infiltration of the therapeutic agents into the CNS, decreased tumor progression, and improved survival rates.

Despite recent advancements in the field of neuro-ophthalmology, the rising rates of neurological and ophthalmological conditions, mismatches between supply and demand of clinicians, and an aging population underscore the urgent need to explore new therapeutic approaches within the field. Glucagon-like peptide 1 receptor agonists (GLP-1RAs), traditionally used in the treatment of type 2 diabetes, are becoming increasingly appreciated for their diverse applications. Recently, GLP-1RAs have been approved for the treatment of obesity and recognized for their cardioprotective effects. Emerging evidence indicates some GLP-1RAs can cross the blood-brain barrier and may have neuroprotective effects. Therefore, this article aims to review the literature on the neurologic and neuro-ophthalmic role of glucagon-like peptide 1 (GLP-1). This article describes GLP-1 peptide characteristics and the mechanisms mediating its known role in increasing insulin, decreasing glucagon, delaying gastric emptying, and promoting satiety. This article identifies the sources and targets of GLP-1 in the brain and review the mechanisms which mediate its neuroprotective effects, as well as implications for Alzheimer's disease (AD) and Parkinson's disease (PD). Furthermore, the preclinical works which unravel the effects of GLP-1 in ocular dynamics and the preclinical literature regarding GLP-1RA use in the management of several neuro-ophthalmic conditions, including diabetic retinopathy (DR), glaucoma, and idiopathic intracranial hypertension (IIH) are discussed.

Aim: Conventional techniques to share and archive spinal imaging data raise issues with trust and security, with novel approaches being more greatly considered. Ethereum smart contracts present one such novel approach. Ethereum is an open-source platform that allows for the use of smart contracts. Smart contracts are packages of code that are self-executing and reside in the Ethereum state, defining conditions for programmed transactions. Though powerful, limited attempts have been made to showcase the clinical utility of such technologies, especially in the pre- and post-operative imaging arenas. Herein, we therefore aim to propose a proof-of-concept smart contract that stores intraoperative three-dimensional (3D) augmented reality surgical navigation (ARSN) data and was tested on a private, proof-of-authority network. To the author's best knowledge, the present study represents a first-use case of the Interplanetary File Storage protocol for storing and retrieving spine imaging smart contracts.

Methods: The content identifier hashes were stored inside the smart contracts while the interplanetary file system (IPFS) was used to efficiently store the image files. Insertion was achieved with four storage mappings, one for each of the following: fictitious patient data, specific diagnosis, patient identity document (ID), and Gertzbein grade. Inserted patient observations were then queried with wildcards. Insertion and retrieval times for different record volumes were collected.

Results: It took 276 milliseconds to insert 50 records and 713 milliseconds to insert 350 records. Inserting 50 records required 934 Megabyte (MB) of memory per insertion with patient data and imaging, while inserting 350 records required almost the same amount of memory per insertion. In a database of 350 records, the retrieval function needs about 1,026 MB to query a record with all three fields left blank, but only 970 MB to obtain the same observation from a database of 50 records.

Conclusions: The concept presented in this study exemplifies the clinical utility of smart contracts and off-chain data storage for efficient retrieval/insertion of ARSN data.