Fluorescence imaging is a remarkable tool for molecular targeting and multicolor imaging, but it suffers from low resolution in centimeter-deep tissues. The recently developed ultrasound-switchable fluorescence (USF) imaging has overcome this challenge and achieved in vivo imaging in a mouse with help from the indocyanine green (ICG) dye encapsulated poly(N-isopropylacrylamide) (ICG-PNIPAM) contrast agent. However, the ICG-PNIPAM has shortcomings, such as concerns about cytotoxicity and blueshifted excitation and emission spectra. This study introduces a newly developed ICG-encapsulated liposome to broaden the contrast agent selection for USF imaging and resolve the issues mentioned above. The emission peak of the ICG-liposome is 836 nm with excellent biostability and USF imaging capability. Furthermore, the cell viability test verifies the low cytotoxicity feature. Eventually, both ex vivo and in vivo USF imaging are successfully achieved and 3D USF images are acquired. The ex vivo result confirms that the ICG-liposome maintains the thermoresponsive characteristic at the right lobe of the liver and is able to conduct the USF imaging. The further in vivo USF imaging demonstrates that although the whole liver emitted fluorescence, only the right lobe of the liver contains the working ICG-liposome.
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http://dx.doi.org/10.1002/adhm.201901457 | DOI Listing |
Biochim Biophys Acta Mol Basis Dis
January 2025
AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan; In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan. Electronic address:
The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep learning and advanced data analytics, in accelerating key stages of the drug discovery process: target identification, drug design, clinical trial optimization, and drug response prediction. Cutting-edge tools such as DrugnomeAI and PandaOmics have made substantial contributions to therapeutic target identification, while AI's predictive capabilities are driving personalized treatment strategies.
View Article and Find Full Text PDFBiomed Opt Express
December 2024
School of Optoelectronic, Chongqing University of Posts and Telecommunications, Chongqing, China.
In photoacoustic imaging (PAI), a delay-and-sum (DAS) beamforming reconstruction algorithm is widely used due to its ease of implementation and fast execution. However, it is plagued by issues such as high sidelobe artifacts and low contrast, that significantly hinder the ability to differentiate various structures in the reconstructed images. In this study, we propose an adaptive weighting factor called spatial coherence mean-to-standard deviation factor (scMSF) in DAS, which is extended into the spatial frequency domain.
View Article and Find Full Text PDFHead Neck
December 2024
Department of Otolaryngology-Head & Neck Surgery, University of South Florida, Tampa, Florida, USA.
Objective: We hypothesized that a method to segment human airways from clinical cases and import them into a case presentation environment in Virtual Reality (VR) could be developed to model and visualize complex airway stenosis for efficient surgical planning.
Methods: One normal and two pathological airways modeled from CT scans at a slice thickness of 0.625 mm were processed.
Urology
November 2024
Mayo Clinic Department of Urology, Rochester, MN. Electronic address:
Objective: To characterize and identify factors associated with long-term morbidity of definitive urosymphyseal fistula (USF) treatment.
Methods: Retrospective chart review of a single institution database identified 57 patients who underwent operative treatment of USF between 2009 and 2022 with at least 90 days of follow-up. Delayed complications were considered those occurring ≥90 days following surgery.
J Imaging Inform Med
December 2024
Department of Radiology, USF Health, Tampa, USA.
Small cohorts of certain disease states are common especially in medical imaging. Despite the growing culture of data sharing, information safety often precludes open sharing of these datasets for creating generalizable machine learning models. To overcome this barrier and maintain proper health information protection, foundational models are rapidly evolving to provide deep learning solutions that have been pretrained on the native feature spaces of the data.
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