This data file describes the synthetic protocol for preparation of the original 2,6-di(bromomethyl)-3,5-bis(alkoxycarbonyl)-4-aryl-1,4-dihydropyridines. In total, 6 unpublished compounds were obtained and characterised. The 2,6-di(bromomethyl)-1,4-dihydropyridines are mainly used as intermediates for synthesis of various lipid-like compounds based on 1,4-dihydropyridine cycle. All the structures of 2,6-di(bromomethyl)-1,4-dihydropyridines were confirmed by Nuclear Magnetic Resonance (NMR, including H NMR and C NMR) data. The data provided herein are directly related to the previously published research article - "Novel cationic amphiphilic 1,4-dihydropyridine derivatives for DNA delivery" [1] where three derivatives (2,6-di(bromomethyl)-4-phenyl-1,4-dihydropyridines ) from six presented in this data file were used as starting materials in synthesis of amphiphilic 1,4-dihydropyridines without any purification and characterisation. Synthesis of other three 2,6-di(bromomethyl)-3,5-bis(alkoxycarbonyl)-4-aryl-1,4-dihydropyridines and their characterisation are reported herein at the first time. Information provided in this data file can be used in organic synthesis by other chemists to develop synthetic strategies for the construction of various cationic 1,4-dihydropyridine derivatives and related heterocycles.
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http://dx.doi.org/10.1016/j.dib.2020.105532 | DOI Listing |
Sci Rep
January 2025
Department of Endodontics, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
The preservation of the original configurations of root canals during endodontic preparation is crucial for treatment success. Nickel-titanium (NiTi) rotary systems have been refined to optimize canal shaping while minimizing iatrogenic errors. This study aimed to evaluate and compare the shaping efficacy of the novel R-Motion (RM) and the established WaveOne Gold (WG) systems using micro-computed tomography (micro-CT).
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January 2025
Lianshui People's Hospital of Kangda college Affiliated to Nanjing Medical University, Hong Ri Dong Road, Lianshui County, 223499, Jiangsu, China.
The Cardiometabolic Index (CMI) is a well-recognized risk factor for a range of cardiovascular diseases and diabetes mellitus. However, the population-level characteristics of CMI and its potential association with mortality risk among individuals over 40 years of age have not been investigated. This study aims to assess the association between CMI and both all-cause and cardiovascular mortality among the middle-aged and elderly population.
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January 2025
Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Müunster, Müunster, Germany.
Objective: Acute kidney injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series, requires researchers to implement classification algorithms of their own which is resource intensive and might impact study quality by introducing different interpretations of edge cases. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation.
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January 2025
School of Public Health and Social Sciences, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.
Background: Adherence to HIV treatment regimens involves the consistent and correct intake of all prescribed medications. The implementation of antiretroviral therapy (ART) program has significantly reduced mortality among adolescents living with HIV. However, adherence to ART is lower among adolescents compared to other sub-populations and even lower in sub-Saharan Africa.
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January 2025
NCCA, Bournemouth University, Poole, United Kingdom.
Medical volume data are rapidly increasing, growing from gigabytes to petabytes, which presents significant challenges in organisation, storage, transmission, manipulation, and rendering. To address the challenges, we propose an end-to-end architecture for data compression, leveraging advanced deep learning technologies. This architecture consists of three key modules: downsampling, implicit neural representation (INR), and super-resolution (SR).
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