Precise identification of cancer subtypes from whole slide images (WSIs) is pivotal in tailoring patient-specific therapies. Under the weakly supervised multiple instance learning (MIL) paradigm, existing techniques frequently fall short in simultaneously capturing local tissue textures and long-range contextual relationships. To address these challenges, we introduce ConvMixerSSM, a hybrid model that integrates a ConvMixer block for local spatial representation, a state space model (SSM) block for capturing long-range dependencies, and a feature-gated block to enhance informative feature selection. The model was evaluated on the TCGA-NSCLC dataset and the CAMELYON16 dataset for cancer subtyping tasks. Extensive experiments, including comparisons with state-of-the-art MIL methods and ablation studies, were conducted to assess the contribution of each component. ConvMixerSSM achieved an AUC of 97.83%, an ACC of 91.82%, and an F1 score of 91.18%, outperforming existing MIL baselines on the TCGA-NSCLC dataset. The ablation study revealed that each block contributed positively to performance, with the full model showing the most balanced and superior results. Moreover, our visualization results further confirm that ConvMixerSSM can effectively identify tumor regions within WSIs, providing model interpretability and clinical relevance. These findings suggest that ConvMixerSSM has strong potential for advancing computational pathology applications in clinical decision-making.

Vitamin D₃ is an essential fat-soluble vitamin for the human body. Its metabolite, 25-hydroxyvitamin D₃ (25(OH)D₃), serves as the primary biomarker to assess vitamin D levels. The monitoring of 25(OH)D₃ concentration is crucial for human health assessment. While traditional detection methods offer high sensitivity and accuracy, they are operationally complex and costly. This review systematically summarizes the most recent progress in 25(OH)D₃ detection technologies. Special attention is given to the recognition modes of 25(OH)D₃ by antibodies, nucleic acids, and molecularly imprinted recognition elements. Subsequently, the design strategies of diverse types of biosensors, including fluorescent, colorimetric, and electrochemical biosensors, are analyzed. Moreover, the development of portable devices, smartphone software, and flexible wearable devices for detection applications is also examined. Biosensing detection platforms are compared from the perspectives of target recognition, signal conversion, signal output, and application scenarios. Additionally, the potential of biosensor detection platforms in clinical diagnosis, health management, and community health surveillance is further investigated. Finally, the future trends of intelligent, portable, accurate, and home-use 25(OH)D₃ detection systems are delineated. This review offers a comprehensive reference for researchers developing next-generation 25(OH)D₃ diagnostic sensors and provides insights for the early prevention and treatment of vitamin D deficiency-related diseases.