Background: The reparative regeneration of jawbone defects poses a significant challenge within the field of dentistry. Despite being the gold standard, autogenous bone materials are not without drawbacks, including a heightened risk of postoperative infections. Consequently, the development of innovative materials that can surpass the osteogenic capabilities of autologous bone has emerged as a pivotal area of research. Methods: Mesenchymal stem cells (MSCs), known for their multilineage differentiation potential, were isolated from human umbilical cords and transfected with miR-181a. The osteogenic differentiation of miR-181a/MSC was investigated. Then, physicochemical properties of miR-181a/MSC-loaded nano-hydroxyapatite (nHAC) scaffolds were characterized, and their efficacy and underlying mechanism in rat calvarial defect repair were explored. Results: miR-181a overexpression in MSCs significantly promoted osteogenic differentiation, as evidenced by increased alkaline phosphatase activity and expression of osteogenic markers. The miR-181a/MSC-loaded nHAC scaffolds exhibited favorable bioactivity and accelerated bone tissue repair and collagen secretion in vivo. Mechanistic studies reveal that miR-181a directly targeted the TP53/SLC7A11 pathway, inhibiting ferroptosis and enhancing the osteogenic capacity of MSCs. Conclusions: The study demonstrates that miR-181a/MSC-loaded nHAC scaffolds significantly enhance the repair of bone defects by promoting osteogenic differentiation and inhibiting ferroptosis. These findings provide novel insights into the molecular mechanisms regulating MSC osteogenesis and offer a promising therapeutic strategy for bone defect repair.

The batch effect is a nonbiological variation that arises from technical differences across different batches of data during the data generation process for acquisition-related reasons, such as collection of images at different sites or using different scanners. This phenomenon can affect the robustness and generalizability of computational pathology- or radiology-based cancer diagnostic models, especially in multicenter studies. To address this issue, we developed an open-source platform, Batch Effect Explorer (BEEx), that is designed to qualitatively and quantitatively determine whether batch effects exist among medical image datasets from different sites. A suite of tools was incorporated into BEEx that provide visualization and quantitative metrics based on intensity, gradient, and texture features to allow users to determine whether there are any image variables or combinations of variables that can distinguish datasets from different sites in an unsupervised manner. BEEx was designed to support various medical imaging techniques, including microscopy and radiology. Four use cases clearly demonstrated the ability of BEEx to identify batch effects and validated the effectiveness of rectification methods for batch effect reduction. Overall, BEEx is a scalable and versatile framework designed to read, process, and analyze a wide range of medical images to facilitate the identification and mitigation of batch effects, which can enhance the reliability and validity of image-based studies. Significance: BEEx is a prescreening tool for image-based analyses that allows researchers to evaluate batch effects in multicenter studies and determine their origin and magnitude to facilitate development of accurate AI–based cancer models.

Accurate detection of deeply invasive rectal cancer remains a major clinical challenge: conventional optical endoscopy is limited to superficial mucosal visualization, while endoscopic ultrasound lacks sufficient functional contrast to distinguish malignant from benign tissues. Herein, we developed an integrated endoscopic microwave-induced thermoacoustic/ultrasound dual-modal imaging system. Its core is a miniaturized coaxial probe integrating a 3.05[Formula: see text]GHz circularly polarized omnidirectional antenna and a 15[Formula: see text]MHz focused ultrasound transducer, with FPGA timing synchronization to achieve precise co-registration of dual-modal signals. The system provides intrinsic functional contrast by exploiting the dielectric property differences between tumor and normal tissues, achieving thermoacoustic imaging over a 3[Formula: see text]cm axial scanning range with radial penetration exceeding 1.5[Formula: see text]cm in tissue-mimicking phantoms. Meanwhile, it enables precise anatomical localization via high-resolution ultrasound imaging. Ex vivo porcine rectum experiments successfully resolved the layered structure of the rectal wall, including the mucosa, submucosa, and muscularis propria, with measured conductivity and acoustic impedance values in good agreement with theoretical predictions. T1–T3 staged tumor phantom experiments demonstrated that ultrasound imaging could accurately localize tumor invasion depth within each tissue layer, while thermoacoustic signal intensity increased progressively with the conductivity gradient associated with tumor progression. This system can simultaneously provide complementary functional and structural information without ionizing radiation, offering a novel technical approach for early rectal cancer diagnosis, accurate T-staging, and treatment response monitoring.

The batch effect is a nonbiological variation that arises from technical differences across different batches of data during the data generation process for acquisition-related reasons, such as collection of images at different sites or using different scanners. This phenomenon can affect the robustness and generalizability of computational pathology- or radiology-based cancer diagnostic models, especially in multicenter studies. To address this issue, we developed an open-source platform, Batch Effect Explorer (BEEx), that is designed to qualitatively and quantitatively determine whether batch effects exist among medical image datasets from different sites. A suite of tools was incorporated into BEEx that provide visualization and quantitative metrics based on intensity, gradient, and texture features to allow users to determine whether there are any image variables or combinations of variables that can distinguish datasets from different sites in an unsupervised manner. BEEx was designed to support various medical imaging techniques, including microscopy and radiology. Four use cases clearly demonstrated the ability of BEEx to identify batch effects and validated the effectiveness of rectification methods for batch effect reduction. Overall, BEEx is a scalable and versatile framework designed to read, process, and analyze a wide range of medical images to facilitate the identification and mitigation of batch effects, which can enhance the reliability and validity of image-based studies.Significance: BEEx is a prescreening tool for image-based analyses that allows researchers to evaluate batch effects in multicenter studies and determine their origin and magnitude to facilitate development of accurate AI–based cancer models.