Consumers' Behavioral Willingness to Use Green Financial Products: An Empirical Study within a Theoretical Framework.

In an era marked by the expansion of the Internet economy and the intensification of environmental concerns, the convergence of digital finance and green finance has emerged as a significant global trend. China's Alipay Ant Forest, an innovative green financial product, has successfully quantified carbon emission reductions resulting from users' green consumption patterns, establishing the first carbon account-based green financial product and pioneering an innovative "green finance plus gamification" model. However, the academic literature has not fully explained the underlying mechanisms that drive consumer engagement with such green financial products. This study, motivated by the academic question of what factors influence consumers' willingness to use green financial products, employs Ant Forest as a case study and develops a novel structural equation model based on self-determination theory, customer-perceived value, and the technology acceptance model. The model incorporates user type as a control variable and considers autonomy, gamification, and bonuses as key independent variables, with customer-perceived value serving as a mediating variable. Data collection involved 606 participants, enabling a comprehensive analysis of the factors influencing users' willingness to engage with green financial products. The findings support the proposed hypothesis, identifying several significant predictors of users' willingness to use green financial products, with the exception of age. This study advances the theoretical understanding of consumer behavior towards green financial products by integrating self-determination theory, customer-perceived value, and the technology acceptance model, while also offering practical insights for marketing strategies. It explores the interface between digital finance, environmental sustainability, and consumer behavior, highlighting opportunities for financial institutions to leverage Internet applications to promote green financial services and enhance their marketing approaches to influence consumer adoption.

PK Modeling of L-4-Boronophenylalanine and Development of Bayesian Predictive Platform for L-4-Boronophenylalanine PKs for Boron Neutron Capture Therapy.

L-4-[(10B)]Boronophenylalanine (BPA) is an amino acid analogue with a boron-10 moiety. It is most widely used as a boron carrier in boron neutron capture therapy. In this study, a Bayesian predictive platform of blood boron concentration based on a BPA pharmacokinetic (PK) model was developed. This platform is user-friendly and can predict the individual boron PK and optimal time window for boron neutron capture therapy in a simple way. The present study aimed to establish a PK model of L-4-boronophenylalanine and develop a Bayesian predictive platform for blood boron PKs for user-friendly estimation of boron concentration during neutron irradiation of neutron capture therapy. Whole blood boron concentrations from seven previous reports were graphically extracted and analyzed using the nonlinear mixed-effects modeling (NONMEM) approach. Model robustness was assessed using nonparametric bootstrap and visual predictive check approaches. The visual predictive check indicated that the final PK model is able to adequately predict observed concentrations. The Shiny package was used to input real-time blood boron concentration data, and during the following irradiation session blood boron was estimated with an acceptably short calculation time for the determination of irradiation time. Finally, a user-friendly Bayesian estimation platform for BPA PKs was developed to optimize individualized therapy for patients undergoing BNCT.

Aberrant splicing of FHIT transcripts in human gastric cancer cell lines.

Alterations of the FHIT gene occur as frequent events in several human cancers. Replacement of exogenous wild-type FHIT gene has been shown to induce suppression of tumorigenicity of human FHIT-negative cells in nude mice and aberrant FHIT transcripts have been observed in a variety of human solid tumors. In the presence study, we performed a nested reverse transcription-polymerase chain reaction (RT-PCR) analysis to identify aberrant FHIT transcripts in 6 gastric cancer cell lines. In addition to the wild-type FHIT transcript, small-sized transcripts with various number and lengths were observed in all of the cell lines examined. Sequence analysis confirmed that different types of truncated transcripts included exonic deletions, insertions of intron 5 sequences between exons, and combinations of both. Most of these transcripts lacked exon 5 in which translation initiation codon is located. Aberrant transcripts with partial exonic deletions, resulting from activation of cryptic splice sites, were also observed in 5 cell lines. Additionally, multi-step splice patterns, indicative of additional downstream processing, were observed in several cancer lines. Our results suggest that the aberrant FHIT transcripts in gastric cancer cell lines resulted from faulty splicing, including exon skipping, selection of cryptic splice site and additional downstream splice processing.