RESUMEN
The distinctive characteristics of near-infrared fluorescent organic molecules render them indispensable across diverse applications, from energy harvesting to bioimaging and sensing technologies. In this work, we continue our investigation on the chalcone derivative, 4-dimethylamino-2'-hydroxychalcone (nDHC, n = 1; where n is the number of olefinic bonds), by expanding the number of central double bonds (n = 2 (2DHC) and n = 3 (3DHC)). Additionally, we also synthesized the structurally related chalcones lacking the OH group (DC, 2DC, 3DC) in order to obtain a comprehensive understanding of their effects on the intramolecular charge transfer (ICT). The results show remarkable bathochromic shifts in absorption and fluorescence peaks in solution as n increases. These shifts, 20 nm and 35 nm for absorption and 100 nm and 200 nm for fluorescence in 2DHC and 3DHC, respectively, signify enhanced ICT and a significant increase in the excited state's dipole moment. The presence of OH groups notably amplifies these shifts due to additional electron donation, influencing solute-solvent interactions in solution. Femtosecond fluorescence upconversion and transient absoprtion techniques unraveled distinct dynamics in these derivatives, exhibiting the dominance of vibrational cooling, solvation, and intramolecular motions, particularly in the larger conjugated systems 3DHC and 3DC. The observed changes in the femtosecond transinet absorption spectra suggest the existence of new active states in extended conjugation systems, indicating diverse intramolecular conformational states contributing to their relaxation dynamics. The results of this study provide invaluable insights into excited-state spectroscopy, offering a roadmap for tailoring chalcone derivatives for specific applications.
RESUMEN
Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.