This study investigated the dissolution behavior of BCS class II ionizable weak base Saquinavir and its mesylate salt in the multi-compartment transfer setup employing different composition of dissolution media. The dissolution behavior of Saquinavir was studied by using a two-compartment transfer model representing the transfer of drug from the stomach (donor compartment) to the upper intestine (acceptor compartment). Various buffers like phosphate, bicarbonate, FaSSIF, and FeSSIF were employed. The dissolution was also studied in the concomitant presence of the additional solute, i.e., Quercetin. Further, the dissolution profiles of Saquinavir and its mesylate salt were simulated by GastroPlus, and the simulated dissolution profiles were compared against the experimental ones. The formation of in situ HCl salt and water-soluble amorphous phosphate aggregates was confirmed in the donor and acceptor compartments of the transfer setup, respectively. As the consequence of the lower solubility product of HCl salt of Saquinavir, the solubility advantage of mesylate salt was vanished leading to the lower than the predicted dissolution in the acceptor compartment. However, the formation of water-soluble aggregates in the presence of the phosphate salts was observed leading to the higher than the predicted dissolution of the free base in the transfer setup. Interestingly, the formation of such water-soluble aggregates was found to be hindered in the concomitant presence of an ionic solute resulting in the lower dissolution rates. The in situ generation of salts and aggregates in the transfer model lead to the inconsistent prediction of dissolution profiles by GastroPlus.
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http://dx.doi.org/10.1208/s12249-019-1563-0 | DOI Listing |
In unsupervised transfer learning for medical image segmentation, where existing algorithms face the challenge of error propagation due to inaccessible source domain data. In response to this scenario, source-free domain transfer algorithm with reduced style sensitivity (SFDT-RSS) is designed. SFDT-RSS initially pre-trains the source domain model by using the generalization strategy and subsequently adapts the pre-trained model to target domain without accessing source data.
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Institute of Marine Economics and Management, Shandong University of Finance and Economics, Lixia District, Second Ring East Road, Jinan, 7366250000, China.
Biodiversity is crucial for maintaining ecosystem stability and achieving sustainable development. However, global biodiversity loss is a common challenge faced by most countries. Therefore, based on the data from the International Union for Conservation of Nature (IUCN) Red List of Threatened Species and the Eora database, we used the multi-regional input-output (MRIO) model to calculate biodiversity loss in 188 countries.
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Department of Surgery, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands.
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Department of Medical Imaging and Radiological Science, I-Shou University, Kaohsiung City 824005, Taiwan.
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagnostic accuracy. This study aims to enhance breast cancer detection through a cross-modality fusion approach combining mammography and ultrasound imaging, using advanced convolutional neural network (CNN) architectures.
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Department of Health Science, College of Health and Human Services, California State University Long Beach, Long Beach, CA 90840, USA.
Key populations are particularly vulnerable to human immunodeficiency virus (HIV) infection. Nearly half of Tajikistan's gross domestic product (GDP) originates from labor migrant transfers. While not officially designated as a key population, over 300,000 migrants return to Tajikistan every year at increased risk for HIV due to absence or interruption of treatment, change in risky behaviors, and other factors.
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