Oral delivery of camptothecin using cyclodextrin/poly(anhydride) nanoparticles

Camptothecin (CPT), a molecule that shows powerful anticancer activity, is still not used in clinic due to its high hydrophobicity and poor active form's stability. In order to solve these drawbacks, the combination between poly(anhydride) nanoparticles and cyclodextrins was evaluated. CPT-loaded nanoparticles, prepared in the presence of 2-hydroxypropyl-β-cyclodextrin, (HPCD-NP) displayed a mean size close to 170nm and a payload of 50μg per mg (25 times higher than the one of the control nanoparticles). CPT was not released from nanoparticles under gastric conditions. However, under intestinal conditions, about 50% of the drug content was released as a burst, whereas the remained drug was released following a zero-order kinetic. Pharmacokinetic studies revealed that the CPT plasma levels, from orally administered nanoparticles, were high and sustained up to 48h. The CPT oral bioavailability was 7-fold higher than the value obtained with the control, whereas its clearance was significantly lower than for the aqueous suspension. These observations may be directly related to a prolonged residence time of nanoparticles in close contact with the intestinal epithelium, the presence of the cyclodextrin that decreases the CPT transformation into its inactive form and the generation of an acidic microenvironment during the degradation of the poly(anhydride) that would prevent the transformation of the active lactone into the inactive carboxylate conformation

Differential expression spectrum and targeted gene prediction of tRNA-derived small RNAs in idiopathic pulmonary arterial hypertension

Background: Idiopathic pulmonary arterial hypertension (PAH) is a potentially fatal pulmonary vascular disease with an extremely poor natural course. The limitations of current treatment and the unclear etiology and pathogenesis of idiopathic PAH require new targets and avenues of exploration involved in the pathogenesis of PAH. tRNA-derived small RNAs (tsRNAs), a new type of small non-coding RNAs, have a significant part in the progress of diverse diseases. However, the potential functions behind tsRNAs in idiopathic PAH remain unknown.Methods: Small RNA microarray was implemented on three pairs of plasma of idiopathic PAH patients and healthy controls to investigate and compare tsRNAs expression profiles. Validation samples were used for real-time polymerase chain reaction (Real-time PCR) to verify several dysregulated tsRNAs. Bioinformatic analysis was adopted to determine potential target genes and mechanisms of the validated tsRNAs in PAH.Results: Microarray detected 816 statistically significantly dysregulated tsRNAs, of which 243 tsRNAs were upregulated and 573 were downregulated in PAH. Eight validated tsRNAs in the results of Real-time PCR were concordant with the small RNA microarray: four upregulated (tRF3a-AspGTC-9, 5’tiRNA-31-GluCTC-16, i-tRF-31:54-Val-CAC-1 and tRF3b-TyrGTA-4) and four downregulated (5’tiRNA-33-LysTTT-4, i-tRF-8:32-Val-AAC-2, i-tRF-2:30-His-GTG-1, and i-tRF-15:31-Lys-CTT-1). The Gene Ontology analysis has shown that the verified tsRNAs are related to cellular macromolecule metabolic process, regulation of cellular process, and regulation of cellular metabolic process. It is disclosed that potential target genes of verified tsRNAs are widely involved in PAH pathways by Kyoto Encyclopedia of Genes and Genomes.Conclusion: This study investigated tsRNA profiles in idiopathic PAH and found that the dysregulated tsRNAs may become a novel type of biomarkers and possible targets for PAH

Enhancing bank marketing strategies with ensemble learning: Empirical analysis.

In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing strategy often leads to the homogenization of customer demand, making it challenging to distinguish among various products. To address this issue, this paper presents a customer demand learning model based on financial datasets and optimizes the distribution model of bank big data channels through induction to rectify the imbalance in bank customer transaction data. By comparing the prediction models of random forest model and support vector machine (SVM), this paper analyzes the ability of the prediction model based on ensemble learning to significantly enhance the market segmentation of e-commerce banks. The empirical results reveal that the accuracy of random forest model reaches 92%, while the accuracy of SVM model reaches 87%. This indicates that the ensemble learning model has higher accuracy and forecasting ability than the single model. It enables the bank marketing system to implement targeted marketing, effectively maintain the relationship between customers and banks, and significantly improve the success probability of product marketing. Meanwhile, the marketing model based on ensemble learning has achieved a sales growth rate of 20% and improved customer satisfaction by 30%. This demonstrates that the implementation of the ensemble learning model has also significantly elevated the overall marketing level of bank e-commerce services. Therefore, this paper offers valuable academic guidance for bank marketing decision-making and holds important academic and practical significance in predicting bank customer demand and optimizing product marketing strategy

Data interaction structure diagram of ensemble learning model.

Data interaction structure diagram of ensemble learning model.</p

Data transmission delay curve of different types of bank marketing ability prediction models.

Data transmission delay curve of different types of bank marketing ability prediction models.</p

S1 Data -

In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing strategy often leads to the homogenization of customer demand, making it challenging to distinguish among various products. To address this issue, this paper presents a customer demand learning model based on financial datasets and optimizes the distribution model of bank big data channels through induction to rectify the imbalance in bank customer transaction data. By comparing the prediction models of random forest model and support vector machine (SVM), this paper analyzes the ability of the prediction model based on ensemble learning to significantly enhance the market segmentation of e-commerce banks. The empirical results reveal that the accuracy of random forest model reaches 92%, while the accuracy of SVM model reaches 87%. This indicates that the ensemble learning model has higher accuracy and forecasting ability than the single model. It enables the bank marketing system to implement targeted marketing, effectively maintain the relationship between customers and banks, and significantly improve the success probability of product marketing. Meanwhile, the marketing model based on ensemble learning has achieved a sales growth rate of 20% and improved customer satisfaction by 30%. This demonstrates that the implementation of the ensemble learning model has also significantly elevated the overall marketing level of bank e-commerce services. Therefore, this paper offers valuable academic guidance for bank marketing decision-making and holds important academic and practical significance in predicting bank customer demand and optimizing product marketing strategy.</div

Variation curve of F1 value predicted by different types of models for bank marketing ability.

Variation curve of F1 value predicted by different types of models for bank marketing ability.</p

Data processing and operation flow of initial learner.

Data processing and operation flow of initial learner.</p