This work proposes developing a water-soluble-chlorophyll-binding protein (WSCP) from Chenopodium album plant species (CaWSCP) as a contrast agent for cancer targeting with photoacoustic (PA) imaging. CaWSCP exhibits red-shifted absorption, good solubility, and photoconvertible properties that can generate enhanced contrast images while integrated SpyTag and SpyCatcher system enables active targeting of cancer cells. In vitro and in vivo experiments demonstrate the non-cytotoxic nature of WSCP and validate the efficacy of the SpyTag-SpyCatcher system for cancer cell targeting. Moreover, in vivo imaging confirm the contrast-enhancing capability of CaWSCP, positioning it as a promising candidate for PA imaging in cancer diagnostics.
This work introduces a total-peptide-based contrast agent for deep-tissue photoacoustic imaging, addressing the limitations of conventional imaging techniques. Chlorophyll a (Chla) was selected for its light-harvesting ability and solubilized using the Leipidium virginicum water-soluble chlorophyll-binding protein (LvP). LvP-chla enables clear visualization in vivo, discernible from the blood via spectroscopic PA imaging. LvP-chla showed promise as a favorable candidate for clinical photoacoustic imaging applications.
Photoacoustic tomography (PAT) is a promising hybrid imaging technique with clinical potential, but it faces challenges due to limited-view reconstruction. This research develops a deep learning-based approach using a multi-view imaging system and a Uformer network to reconstruct high-resolution images from limited-angle input data. The results show state-of-the-art performance compared to conventional restoration models, highlighting the potential of this method for improving PAT in clinical settings. This novel strategy helps overcome limited-data challenges and contributes to the development of innovative imaging solutions for clinical applications.
In this work, a multiwavelength autofluorescence virtual instant stain (MAVIS) workflow is proposed to provide a multiple virtual staining solution to facilitate clinical diagnosis. Multiple ultraviolet excitation and visible emission wavelengths are used to highlight different biomolecules while a weakly supervised algorithm provides a robust and accurate virtual staining with adjacent tissue slices. The result of MAVIS with three histochemical stains on human tissue slices achieves a multi-scale structural similarity index measure > 0.6, demonstrating the potential of multiple virtual staining as a rapid and low-cost alternative to the current histological workflow.
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