Objective: To compare the in vivo pharmacokinetics of curcumin hydroxypropyl-β-cyclodextrin phospholipid complex, curcumin hydroxypropyl-β-cyclodextrin and curcumin phospholipid complex, and to discuss the advantage of hydroxypropyl-β-cyclodextrin phospholipid complex as carrier.
Methods: Drawing blood after SD rats were oral administered with the above preparations and free drug at 50 mg/kg( corresponding to curcumin) , and the blood concentration were determined by HPLC.
Results: The AUC0-∞ of curcumin hydroxypropyl-β-cyclodextrin phospholipid complex was(1 126. 20 ± 323. 24) g/(L . h), which was 5. 89, 1. 49 and 1. 17 times as curcumin (191. 08 ± 43. 27) µg/( L . h), curcumin phospholipid complex(754. 93 ± 55. 33) µg/(L . h), curcumin hydroxypropyl-β- cyclodextrin(961. 21 ± 253. 65) µg/(L . h).
Conclusion: The curcumin hydroxypropyl-β-cyclodextrin phospholipid complex has a better absorption property than curcumin phospholipid complex and curcumin hydroxypropyl-β-cyclodextrin, which is more beneficial to improve the bioavailability.
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Sensors (Basel)
December 2024
Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M5S 3H6, Canada.
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Research Group on Technology Applied to Exercise Physiology-GTAFE, Health Sciences Postgraduate Program, São Francisco University, Bragança Paulista 12916-900, SP, Brazil.
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LAQV/Requimte, Departamento de Química e Bioquímica, Faculdade de Ciências da Universidade do Porto Rua do Campo Alegre, s/n 4169-007 Porto Portugal
Snake venom-secreted phospholipases A (svPLAs) are critical, highly toxic enzymes present in almost all snake venoms. Upon snakebite envenomation, svPLAs hydrolyze cell membrane phospholipids and induce pathological effects such as paralysis, myonecrosis, inflammation, or pain. Despite its central importance in envenomation, the chemical mechanism of svPLAs is poorly understood, with detrimental consequences for the design of small-molecule snakebite antidotes, which is highly undesirable given the gravity of the epidemiological data that ranks snakebite as the deadliest neglected tropical disease.
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Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
The intricate nature of microbiota sequencing data-high dimensionality and sparsity-presents a challenge in identifying informative and reproducible microbial features for both research and clinical applications. Addressing this, we introduce PreLect, an innovative feature selection framework that harnesses microbes' prevalence to facilitate consistent selection in sparse microbiota data. Upon rigorous benchmarking against established feature selection methodologies across 42 microbiome datasets, PreLect demonstrated superior classification capabilities compared to statistical methods and outperformed machine learning-based methods by selecting features with greater prevalence and abundance.
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