A novel missense mutation c.1381T>C: p.(S461P) in POLE causes multiple molecular features of endometrial carcinoma in China: a case report

BackgroundAccurately determining the pathogenicity of newly discovered POLE mutations is crucial for the precise molecular classification of endometrial carcinoma.MethodsIn one patient with endometrial carcinoma, next-generation sequencing (NGS) was performed to detect variants in POLE, TP53, BRCA1/2, CTNNB1, EPCAM, MLH1, MSH2, MSH6, and PMS2, as well as microsatellite instability (MSI) status in the tumor tissues. Variant interpretation followed ACMG/AMP guidelines, integrating evidence from literature, established guidelines, public databases, and clinical studies. Immunohistochemistry was used to evaluate MLH1, PMS2, MSH2, MSH6, and p53 protein expression in tumor tissues.ResultsWe successfully identified a novel potential missense mutation, c.1381T>C: p.(S461P), in exon 14 of POLE. This variant, reported here for the first time in endometrial carcinoma, was preliminarily classified as likely pathogenic based on available evidence. Additional variants were detected: TP53: c.844C>T: p.(R282W), TP53: c.711G>A: p.(M237I), and MSH6: c.3103C>T: p.(R1035*). The MSI status was classified as MSI-L. Immunohistochemistry revealed MLH1 (+), PMS2 (+), MSH2 (+), MSH6 (−), and p53 expression consistent with a mixed pattern (80% tumor region wild type, 20% region mutant subtype).ConclusionThis is the first report of the POLE (c.1381T>C: p.(S461P)) variant in endometrial carcinoma. We analyzed its potential pathogenic mechanism, which may contribute to the complex molecular phenotype of POLEmut + MMRd + p53abn tumors, and expanded the POLE mutation spectrum by adding a new likely pathogenic site

Teacher cognition in teaching intercultural communicative competence: A qualitative study on preservice Chinese language teachers in Hong Kong SAR, China

The purpose of this study is to examine preservice Chinese language teachers’ cognition in teaching intercultural communicative competence. In the study we collected data through in-depth interviews with seven preservice teachers in a Master of Education program (Teaching Chinese as a Second Language, TCSL) at a university in Hong Kong SAR, China. The findings indicated that the participants had a relatively positive attitude and inclination toward the development of students’ intercultural communicative competence, while their conceptualizations of culture tended to be static and ambiguous. In addition, the participants’ objectives in teaching intercultural communicative competence were found to be more attitude-than knowledge- or skill-oriented. The study offers valuable insights that preservice language teachers’ cognition plays a crucial role in their future professional development and calls for curricular innovations with intercultural aims in teacher education programs

Association of mutation profiles with metastasis in patients with non-small cell lung cancer

ObjectiveThis study focused on the analysis of the correlation between common gene mutation types and metastatic sites in NSCLC patients.MethodsWe retrospectively studied 1586 NSCLC patients and used fluorescence Polymerase chain reaction (PCR) to detect EGFR, ALK, ROS1, RET, MET, BRAF, HER2, KRAS, NRAS, and PIK3CA gene mutations, and also investigated sex, smoking status, age at diagnosis, histological type and TNM stage. In addition, we analyzed the site of metastasis in patients with stage IV NSCLC.ResultsThe EGFR-mutation group more frequently metastasized to lung (18.9%, P = 0.004), brain (18.9%, P = 0.001) and bone (27.1%, P = 0.004) than wild-type patients. ALK-mutation group (71.0%, P < 0.001), BRAF-mutation group (82.4%, P = 0.005) and NRAS-mutation group (100%, P = 0.025) were more likely to metastasize than the wild-type group. In the ALK mutation, lung metastasis (24.2%, P = 0.013), brain (24.2%, P = 0.007), bone metastasis (32.3%, P = 0.024), liver metastasis (19.4%, P = 0.001), and pleural metastasis (29.0%, P = 0.021) were common. In the KRAS-mutation group, lung metastasis (21.7%, P = 0.012) and brain metastasis (23.3%, P = 0.001) were more common. Less metastasis occurred in the HER2-mutation group (28.3%, P = 0.014). There was no difference in the RET, MET and PIK3CA mutations.ConclusionPatients with ALK mutant, BRAF mutant or NRAS mutant were more prone to metastasis, while the HER 2 mutation group was less metastatic. Patients with EGFR mutant NSCLC are more likely to develop bone, lung, or brain metastasis

A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model

Remarkable strides in computational pathology have been made in the task-agnostic foundation model that advances the performance of a wide array of downstream clinical tasks. Despite the promising performance, there are still several challenges. First, prior works have resorted to either vision-only or vision-captions data, disregarding invaluable pathology reports and gene expression profiles which respectively offer distinct knowledge for versatile clinical applications. Second, the current progress in pathology FMs predominantly concentrates on the patch level, where the restricted context of patch-level pretraining fails to capture whole-slide patterns. Here we curated the largest multimodal dataset consisting of H\&E diagnostic whole slide images and their associated pathology reports and RNA-Seq data, resulting in 26,169 slide-level modality pairs from 10,275 patients across 32 cancer types. To leverage these data for CPath, we propose a novel whole-slide pretraining paradigm which injects multimodal knowledge at the whole-slide context into the pathology FM, called Multimodal Self-TAught PRetraining (mSTAR). The proposed paradigm revolutionizes the workflow of pretraining for CPath, which enables the pathology FM to acquire the whole-slide context. To our knowledge, this is the first attempt to incorporate multimodal knowledge at the slide level for enhancing pathology FMs, expanding the modelling context from unimodal to multimodal knowledge and from patch-level to slide-level. To systematically evaluate the capabilities of mSTAR, extensive experiments including slide-level unimodal and multimodal applications, are conducted across 7 diverse types of tasks on 43 subtasks, resulting in the largest spectrum of downstream tasks. The average performance in various slide-level applications consistently demonstrates significant performance enhancements for mSTAR compared to SOTA FMs.Comment: 45 pages, 9 figure

Lateralization Value of Low Frequency Band Beamformer Magnetoencephalography Source Imaging in Temporal Lobe Epilepsy

Objective: In presurgical evaluation of temporal lobe epilepsy (TLE), selection of the resection side is challenging when bilateral temporal epileptiform discharges or structural abnormalities are present. We aim to evaluate the lateralization value of beamformer analysis of magnetoencephalography (MEG) in TLE.Methods: MEG data from 14 TLE patients were analyzed through beamformer analysis. We measured the hemispherical power distribution of beamformer sources and calculated the lateralization index (LI). We calculated the LI at multiple frequencies to explore the frequency dependency and at the delta frequency to define laterality. LI values ranging from −1 to −0.05 indicated right hemispheric dominance. LI values ranging from 0.05 to 1 indicated left hemispheric dominance. LI values ranging from −0.05 to 0.05 defined bilaterality. We measured the power of beamformer sources with a 9-s duration to explore time dependency.Results: The beamformer analysis showed that 10/14 patients had power dominance ipsilateral to resection. The delta frequency band had a higher lateralization value than other frequency bands. A time-dependent power fluctuation was found in the delta frequency band.Conclusions: MEG beamformer analysis, especially in the delta band, might efficiently provide additional information regarding lateralization in TLE

EWEK-QA: Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems

The emerging citation-based QA systems are gaining more attention especially in generative AI search applications. The importance of extracted knowledge provided to these systems is vital from both accuracy (completeness of information) and efficiency (extracting the information in a timely manner). In this regard, citation-based QA systems are suffering from two shortcomings. First, they usually rely only on web as a source of extracted knowledge and adding other external knowledge sources can hamper the efficiency of the system. Second, web-retrieved contents are usually obtained by some simple heuristics such as fixed length or breakpoints which might lead to splitting information into pieces. To mitigate these issues, we propose our enhanced web and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the content of the extracted knowledge fed to the system. This has been done through designing an adaptive web retriever and incorporating KGs triples in an efficient manner. We demonstrate the effectiveness of EWEK-QA over the open-source state-of-the-art (SoTA) web-based and KG baseline models using a comprehensive set of quantitative and human evaluation experiments. Our model is able to: first, improve the web-retriever baseline in terms of extracting more relevant passages (>20\%), the coverage of answer span (>25\%) and self containment (>35\%); second, obtain and integrate KG triples into its pipeline very efficiently (by avoiding any LLM calls) to outperform the web-only and KG-only SoTA baselines significantly in 7 quantitative QA tasks and our human evaluation

Turicibacter fermentation enhances the inhibitory effects of Antrodia camphorata supplementation on tumorigenic serotonin and Wnt pathways and promotes ROS-mediated apoptosis of Caco-2 cells

Introduction: Diet-induced obesity has been shown to decrease the abundance of Turicibacter, a genus known to play a role in the serotonin signaling system, which is associated with colorectal tumorigenesis, making the presence of Turicibacter potentially influential in the protection of intestinal tumorigenesis. Recently, Antrodia camphorata (AC), a medicinal fungus native to Taiwan, has emerged as a promising candidate for complementary and alternative cancer therapy. Small molecules and polysaccharides derived from AC have been reported to possess health-promoting effects, including anti-cancer properties.Methods: Bacterial culture followed with cell culture were used in this study to determine the role of Turicibacter in colorectal tumorigenesis and to explore the anti-cancer mechanism of AC with Turicibacter fermentation.Results:Turicibacter fermentation and the addition of AC polysaccharide led to a significant increase in the production of nutrients and metabolites, including α-ketoglutaric acid and lactic acid (p < 0.05). Treatment of Turicibacter fermented AC polysaccharide was more effective in inhibiting serotonin signaling-related genes, including Tph1, Htr1d, Htr2a, Htr2b, and Htr2c (p < 0.05), and Wnt-signaling related protein and downstream gene expressions, such as phospho-GSK-3β, active β-catenin, c-Myc, Ccnd1, and Axin2 (p < 0.05). Additionally, it triggered the highest generation of reactive oxygen species (ROS), which activated PI3K/Akt and MAPK/Erk signaling and resulted in cleaved caspase-3 expression. In comparison, the treatment of AC polysaccharide without Turicibacter fermentation displayed a lesser effect.Discussion: Our findings suggest that AC polysaccharide effectively suppresses the tumorigenic serotonin and Wnt-signaling pathways, and promotes ROS-mediated apoptosis in Caco-2 cells. These processes are further enhanced by Turicibacter fermentation

Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation

Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath). The generalization ability of foundation models is crucial for the success in various downstream clinical tasks. However, current foundation models have only been evaluated on a limited type and number of tasks, leaving their generalization ability and overall performance unclear. To address this gap, we established a most comprehensive benchmark to evaluate the performance of off-the-shelf foundation models across six distinct clinical task types, encompassing a total of 39 specific tasks. Our findings reveal that existing foundation models excel at certain task types but struggle to effectively handle the full breadth of clinical tasks. To improve the generalization of pathology foundation models, we propose a unified knowledge distillation framework consisting of both expert and self knowledge distillation, where the former allows the model to learn from the knowledge of multiple expert models, while the latter leverages self-distillation to enable image representation learning via local-global alignment. Based on this framework, a Generalizable Pathology Foundation Model (GPFM) is pretrained on a large-scale dataset consisting of 190 million images from around 86,000 public H&E whole slides across 34 major tissue types. Evaluated on the established benchmark, GPFM achieves an impressive average rank of 1.36, with 29 tasks ranked 1st, while the the second-best model, UNI, attains an average rank of 2.96, with only 4 tasks ranked 1st. The superior generalization of GPFM demonstrates its exceptional modeling capabilities across a wide range of clinical tasks, positioning it as a new cornerstone for feature representation in CPath