Large Language Models (LLMs) show significant potential in AI mathematical tutoring, yet current evaluations often rely on simplistic metrics or narrow pedagogical scenarios, failing to assess comprehensive, multi-turn teaching effectiveness. In this paper, we introduce KMP-Bench, a comprehensive K-8 Mathematical Pedagogical Benchmark designed to assess LLMs from two complementary perspectives. The first module, KMP-Dialogue, evaluates holistic pedagogical capabilities against six core principles (e.g., Challenge, Explanation, Feedback), leveraging a novel multi-turn dialogue dataset constructed by weaving together diverse pedagogical components. The second module, KMP-Skills, provides a granular assessment of foundational tutoring abilities, including multi-turn problem-solving, error detection and correction, and problem generation. Our evaluations on KMP-Bench reveal a key disparity: while leading LLMs excel at tasks with verifiable solutions, they struggle with the nuanced application of pedagogical principles. Additionally, we present KMP-Pile, a large-scale (150K) dialogue dataset. Models fine-tuned on KMP-Pile show substantial improvement on KMP-Bench, underscoring the value of pedagogically-rich training data for developing more effective AI math tutors.

Periodontitis and dental caries are common oral diseases affecting billions globally. While observational studies suggest links between these conditions and lung cancer, causality remains uncertain. This study used two-sample Mendelian randomization (MR) to explore causal relationships between dental traits (periodontitis, dental caries) and lung cancer subtypes, and to assess mediation by pulmonary function. Genetic instruments were derived from the largest available genome-wide association studies, including data from 487,823 dental caries and 506,594 periodontitis cases, as well as lung cancer data from the Transdisciplinary Research of Cancer in Lung consortium. Inverse-variance weighting was the main analytical method; lung function mediation was assessed using the delta method. The results showed a significant positive causal effect of dental caries on overall lung cancer and its subtypes. Specifically, a one-standard-deviation increase in dental caries incidence was associated with a 188.0 % higher risk of squamous cell lung carcinoma ($\text{OR}=2.880,95 \% \text{CI}=1.236- 6.713, \mathrm{p}=0.014)$, partially mediated by declines in forced vital capacity (FVC) and forced expiratory volume in one second (FEV1), accounting for 5.124 % and 5.890 % of the total effect. No causal effect was found for periodontitis. These findings highlight a causal role of dental caries in lung cancer risk and support integrating dental care and pulmonary function monitoring into cancer prevention strategies. The project will be open-sourced upon acceptance.

Combining images from multi-modalities is beneficial for exploring various information in computer vision, especially in the medical domain. As an essential part of clinical diagnosis, multi-modal brain tumor segmentation presents a set of distinct challenges for accurately delineating both the normal anatomy and the pathologic deviations caused by the tumor. In this paper, we aim to fuse information on different imaging modalities with the medical domain knowledge to segment tumors. We present MASM, a novel Modality Aware and Shift Mixer that integrates intra-modality and inter-modality dependencies of multi-modal images for effective and robust brain tumor segmentation. Specifically, we introduce a Modality-Aware (MA) module according to neuroimaging studies for modeling the specific modality pair relationships at low levels, and a Modality-Shift (MS) module with specific mosaic patterns is developed to explore the complex relationships that are not addressed by the MA module across modalities efficiently. Experimentally, we outperform previous state-of-the-art approaches on the public Brain Tumor Segmentation dataset. Further qualitative experiments demonstrate the effectiveness and robustness of MASM.