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ObjectivePancreatic ductal adenocarcinoma (PDAC) shows a remarkable predilection for liver metastasis. Pro-oncogenic secretome delivery and trafficking via exosomes are crucial for pre-metastatic microenvironment formation and metastasis. This study aimed to explore the underlying mechanisms of how PDAC-derived exosomes (Pex) modulate the liver microenvironment and promote metastasis.DesignC57BL/6 mice were ‘educated’ by tail vein Pex injection. The intrasplenic injection liver metastasis and PDAC orthotopic transplantation models were used to evaluate liver metastasis. Stable cell lines CD44v6 (CD44 variant isoform 6) or C1QBP (complement C1q binding protein) knockdown or overexpression was established using lentivirus transfection or gateway systems. A total of 142 patients with PDAC in Huashan Hospital were retrospectively enrolled. Prognosis and liver metastasis were predicted using Kaplan-Meier survival curves and logistic regression models.ResultsPex tail vein injection induced the deposition of liver fibrotic extracellular matrix, which promoted PDAC liver metastasis. Specifically, the exosomal CD44v6/C1QBP complex was delivered to the plasma membrane of hepatic satellite cells (HSCs), leading to phosphorylation of insulin-like growth factor 1 signalling molecules, which resulted in HSC activation and liver fibrosis. Expression of Pex CD44v6 and C1QBP in PDAC patients with liver metastasis was significantly higher than in PDAC patients without liver metastasis, and simultaneous high expression of exosomal CD44v6 and C1QBP correlated with a worse prognosis and a higher risk of postoperative PDAC liver metastasis.ConclusionThe Pex-derived CD44v6/C1QBP complex is essential for the formation of a fibrotic liver microenvironment and PDAC liver metastasis. Highly expressed exosomal CD44v6 and C1QBP are promising biomarkers for predicting prognosis and liver metastasis in patients with PDAC.

Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time‐consuming and labor‐intensive. This paper introduces an innovative Virtual AI Teacher system (VATE) designed to autonomously analyze and correct student Errors. Leveraging advanced large language models (LLMs), the system utilizes student drafts as a primary source for error analysis, thereby enhancing the understanding of the student's learning process. It incorporates sophisticated prompt engineering and maintains an error pool to reduce computational overhead. The AI‐driven system also features a real‐time dialogue component for efficient student interaction. Our approach demonstrates significant advantages over traditional and machine learning‐based error correction methods, including reduced educational costs, high scalability, and superior generalizability. The system has been deployed on the Squirrel AI learning platform for elementary mathematics education, where it achieves 78.3 accuracy in error analysis and shows a marked improvement in student learning efficiency. Satisfaction surveys indicate a strong positive reception, highlighting the system's potential to transform educational practices.

Glypican-3 (GPC3) is a cell-surface heparan sulfate proteoglycan highly expressed in hepatocellular carcinoma (HCC). We have generated a group of high-affinity mouse monoclonal antibodies targeting GPC3. Here, we report the humanization and testing of these antibodies for clinical development. We compared the affinity and cytotoxicity of recombinant immunotoxins containing mouse single-chain variable regions fused with a Pseudomonas toxin. To humanize the mouse Fvs, we grafted the combined KABAT/IMGT complementarity determining regions (CDR) into a human IgG germline framework. Interestingly, we found that the proline at position 41, a non-CDR residue in heavy chain variable regions (VH), is important for humanization of mouse antibodies. We also showed that two humanized anti-GPC3 antibodies (hYP7 and hYP9.1b) in the IgG format induced antibody-dependent cell-mediated cytotoxicity and complement-dependent-cytotoxicity in GPC3-positive cancer cells. The hYP7 antibody was tested and showed inhibition of HCC xenograft tumor growth in nude mice. This study successfully humanizes and validates high affinity anti-GPC3 antibodies and sets a foundation for future development of these antibodies in various clinical formats in the treatment of liver cancer.

This paper extends our previously published work on the virtual AI teacher (VATE) system, presented at IAAI‐25. VATE is designed to autonomously analyze and correct student errors in mathematical problem‐solving using advanced large language models (LLMs). By incorporating student draft images as a primary input for reasoning, the system provides fine‐grained error cause analysis and supports real‐time, multi‐round AI—student dialogues. In this extended version, we introduce a new snap‐to‐solve module for handling low‐reasoning tasks using edge‐deployed LLMs, enabling faster and partially offline interaction. We also include expanded benchmarking experiments, including human expert evaluations and ablation studies, to assess model performance and learning outcomes. Deployed on the Squirrel AI platform, VATE demonstrates high accuracy (78.3%) in error analysis and improves student learning efficiency, with strong user satisfaction. These results suggest that VATE is a scalable, cost‐effective solution with the potential to transform educational practices.

Glypican-3 (GPC3) is a cell surface heparan sulfate proteoglycan that is being evaluated as an emerging therapeutic target in hepatocellular carcinoma (HCC). GPC3 has been shown to interact with several extracellular signaling molecules, including Wnt, HGF, and Hedgehog. Here, we reported a cell surface transmembrane protein (FAT1) as a new GPC3 interacting protein. The GPC3 binding region on FAT1 was initially mapped to the C-terminal region (Q14517, residues 3662-4181), which covered a putative receptor tyrosine phosphatase (RTP)-like domain, a Laminin G-like domain, and five EGF-like domains. Fine mapping by ELISA and flow cytometry showed that the last four EGF-like domains (residues 4013-4181) contained a specific GPC3 binding site, whereas the RTP domain (residues 3662-3788) and the downstream Laminin G-2nd EGF-like region (residues 3829-4050) had non-specific GPC3 binding. In support of their interaction, GPC3 and FAT1 behaved concomitantly or at a similar pattern, e.g. having elevated expression in HCC cells, being up-regulated under hypoxia conditions, and being able to regulate the expression of EMT-related genes Snail, Vimentin, and E-Cadherin and promoting HCC cell migration. Taken together, our study provides the initial evidence for the novel mechanism of GPC3 and FAT1 in promoting HCC cell migration.

Objective This study aimed to describe the temporal trends and risk factors of kidney cancer (KC) burden from 1990 to 2021, evaluate its age, period, and cohort effects, and project the disease burden over the next 15 years. Methods Data were derived from the 2021 Global Burden of Disease (GBD) study. A joinpoint regression model was used to estimate the average annual percentage change (AAPC) in KC prevalence and mortality, while age-period-cohort analysis was applied to estimate age, period, and cohort effects. We extended the Bayesian age-period-cohort (BAPC) model to predict the disease burden of KC from 2022 to 2036. Results In 2021, the number of incident KC cases in China reached 65,799 (4.62 cases per 100,000 total population). Additionally, KC resulted in 24,867 deaths (1.75 deaths per 100,000 total population).The incidence rate of KC continued to rise from 1.38 per 100,000 in 1990 to 4.62 per 100,000 in 2021, with males consistently exceeding females in case numbers. Meanwhile, KC mortality rose from 0.77 per 100,000 in 1990 to 1.75 per 100,000 in 2021.Throughout the study period, the average annual percent changes (AAPC) in incidence and mortality were 3.92% and 2.61%, respectively. Males exhibited higher prevalence and mortality of KC. In the Age-Period-Cohort (APC) analysis, the risk of KC was observed to increase with advancing age in the age dimension. Period effects analysis revealed an overall downward trajectory in all age groups. Cohort-level analysis indicated that early birth cohorts had higher susceptibility, with those born before 1920–1925 exhibiting a higher risk profile that subsequently decreased over time. Smoking and high body mass index (BMI) were the primary risk factors for KC-related disability-adjusted life years (DALYs) and mortality, while the contribution of occupational exposure to trichloroethylene was relatively minor. By 2036, the age-standardized incidence and mortality of KC are projected to rise to 4.58 and 1.31 per 100,000, respectively. Conclusion To alleviate the disease burden of KC, comprehensive strategies are required, including risk factor prevention in primary care settings, KC screening for the elderly and high-risk populations, and access to high-quality medical services.

Magnetoelectric materials, which encompass coupled magnetic and electric polarizabilities within a single phase, hold great promises for magnetic controlled electronic components or electric-field controlled spintronics. However, the realization of ideal magnetoelectric materials remains tough due to the inborn competion between ferroelectricity and magnetism in both levels of symmetry and electronic structure. Herein, we introduce a methodology for constructing single phase paramagnetic ferroelectric molecule [TMCM][FeCl 4 ], which shows low-magnetic-field magnetoelectricity at room temperature. By applying a low magnetic field (≤1 kOe), the halogen Cl‧‧‧Cl distance and the volume of [FeCl 4 ] − anions could be manipulated. This structural change causes a characteristic magnetostriction hysteresis, resulting in a substantial deformation of ~10 −4 along the a -axis under an in-plane magnetic field of 2 kOe. The magnetostrictive effect is further qualitatively simulated by density functional theory calculations. Furthermore, this mechanical deformation significantly dampens the ferroelectric polarization by directly influencing the overall dipole configuration. As a result, it induces a remarkable α 31 component (~89 mV Oe −1 cm −1 ) of the magnetoelectric tensor. And the magnetoelectric coupling, characterized by the change of polarization, reaches ~12% under 40 kOe magnetic field. Our results exemplify a design methodology that enables the creation of room-temperature magnetoelectrics by leveraging the potent effects of magnetostriction. The authors report a molecular ferroelectric (TMCM)[FeCl 4 ], which shows strong magnetostrictive and magnetoelectric effects at room temperature. The spin-lattice coupling of FeCl 4 and flexible structure of organic cations are responsible for these effects.

Digital inclusive finance is an important means for China to improve the financial inclusion rate and promote common prosperity. Rural development issues are related to China's social stability and national strength, and they are also key issues in the process of building a moderately prosperous society in all respects. Since the 2010s, China has initiated pilot policies for rural e-commerce. As a crucial policy for rural revitalization and rural economic development, whether the e-commerce to rural areas policy can drive the development of digital inclusive finance in Chinese rural areas deserves significant attention. This paper delves into the impact of this policy on digital inclusive finance in rural areas. Based on quasi-natural experiment data including 2844 counties in China, we employ a staggered difference-in-differences (DiD) model and find that after the arrival of the e-commerce boom, the digital inclusive finance index increased largely. Further mechanism analysis shows that this policy facilitated faster growth in farmers' income, improved their consumption structure, and promoted innovation in rural areas. Moreover, the policy significantly enhanced digital inclusive finance in larger cities and cities with more developed digital infrastructure. Our manuscript can help the Chinese government optimize relevant policies and reveal the importance of e-commerce in rural development. This study focuses on whether the prosperous development of rural e-commerce can enhance its digital inclusive finance. Based on the quasi natural experiment of e-commerce entering rural areas, we constructed a DID model. After conducting a series of mechanism analyses and empirical tests, the study found that the policy of e-commerce entering rural areas can improve the development of digital inclusive finance in rural areas, and this is achieved through promoting rural income growth, improving rural consumption structure, and driving innovation. The development of rural areas is the foundation and guarantee of China, and the development of digital inclusive finance in rural areas provides richer and more convenient financial service channels for rural residents. Studying the impact mechanism and heterogeneity of e-commerce policies in rural areas can not only help policy makers refine targeted policies, but also help rural residents more easily accept the new technologies and changes brought about by the e-commerce wave.

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on the kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN’s recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R 2 scores up to 0.908, 0.823, and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like Feed-forward neural network (FFNN), support vector regression (SVR) and general regression neural network (GRNN), the predictive accuracy of the kPCA-RNN model was at least 8%, 17% and 12% better than the comparative models in these three cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

Background In medical education, Lecture Based Learning (LBL) is the most common way of disseminating information. Team Based Learning (TBL), a new teaching method, is a teacher-guided method that employs teams in a class, showing suitability for medical education. Two teaching methods represent distinct educational approaches, each with its own set of advantages and limitations. In this study, we performed a systemic review on the efficacy of TBL pedagogy in medical education. Methods MEDLINE, EMBASE, the Cochrane Library, and Web of Science database were searched through July 2022. Standard mean difference (SMD) and 95% confidence intervals (CIs) were calculated. Results The analysis included 33 studies. Our analysis revealed that students utilizing the TBL method exhibited significantly higher pre-( SMD = 0.51, 95%CI 0.11 to 0.92) /post-test (SMD = 0.96, 95%CI 0.70 to 1.22) scores than students with LBL. Students in TBL classes had better development of scores, retention (SMD = 1.03, 95%CI 0.38 to 1.69), engagement (SMD = 2.26, 95%CI 0.23 to 4.29) and higher satisfactory rate (SMD = 1.08, 95%CI 0.87 to 1.29). However, students required more time to independently complete reading materials and preparatory tasks. Conclusion Our study indicates the gratifying effectiveness of TBL application in medical education. TBL pedagogy is compatible with the present medical education and should be generalized in more classrooms.