Optimization of spatial resolution and scattering effects for biomedical fluorescence imaging by using sub-regions of the shortwave infrared spectrum

Non-destructive prediction and pixel-level visualization of polysaccharide-based properties in ancient paper using SWNIR hyperspectral imaging and machine learning

Ancient documents and artworks are invaluable cultural heritage artworks that require careful preservation. Traditional methods for assessing their physical and chemical properties-such as tearing index, tensile index, water absorption, and pH-are often destructive, risking irreversible damage. This study introduces a novel, non-destructive approach using Short-Wave Near-Infrared (SWNIR) hyperspectral imaging (HSI) combined with advanced machine learning models. By integrating spectral preprocessing, feature selection, and machine learning techniques-including Back Propagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), and Convolutional Neural Networks (CNN)-with Sparrow Search Algorithm (SSA) optimization and Gray Level Co-occurrence Matrix (GLCM) texture feature extraction, the resulting SSA-BP-UVE-GLCM model achieved high predictive accuracy (R2 ≥ 0.98). This framework enables precise, pixel-level predictions of paper properties, influenced by polysaccharides like cellulose, offering a non-invasive analysis that supports targeted restoration strategies and advances the conservation of cultural heritage. The findings enhance non-invasive testing and classification methods for polysaccharide-based materials, providing a foundation for further exploration of environmental impacts on artwork integrity using sophisticated machine learning algorithms.

Near-infrared dye IRDye800CW-NHS coupled to Trastuzumab for near-infrared II fluorescence imaging in tumor xenograft models of HER-2-positive breast cancer

Near-infrared II fluorescent probes targeting tumors for diagnostic purposes have received much attention in recent years. In this study, a fluorescent probe for the NIR-II was constructed by using IRDye800CW-NHS fluorescent dye with Trastuzumab, which was investigated for its ability to target HER-2-positive breast cancer in xenograft mice models. This probe was compared with Trastuzumab-ICG which was synthesized using a similar structure, ICG-NHS. The results demonstrated that the IRDye800CW-NHS had significantly stronger fluorescence in the NIR-I and NIR-II than ICG-NHS in the aqueous phase. And the different metabolic modes of IRDye800CW-NHS and ICG-NHS were revealed in bioimaging experiments. IRDye800CW-NHS was mainly metabolised by the kidneys, while ICG-NHS was mainly metabolised by the liver. After coupling with Trastuzumab, Trastuzumab-800CW (TMR = 5.35 ± 0.39) not only had a stronger tumor targeting ability than Trastuzumab-ICG (TMR = 4.42 ± 0.10) based on the calculated maximum tumor muscle ratio (TMR), but also had a comparatively lower hepatic uptake and faster metabolism. Histopathology analysis proved that both fluorescent probes were non-toxic to various organ tissues. These results reveal the excellent optical properties of IRDye800CW-NHS, and the great potential of coupling with antibodies to develop fluorescent probes that will hopefully be applied to intraoperative breast cancer navigation in humans.

NIR-II Aza-BODIPY Dyes Bioconjugated to Monoclonal Antibody Trastuzumab for Selective Imaging of HER2-Positive Ovarian Cancer

Using fluorescence-guided surgery (FGS) to cytoreductive surgery helps achieving complete resection of microscopic ovarian tumors. The use of visible and NIR-I fluorophores has led to beneficial results in clinical trials; however, involving NIR-II dyes seems to outperform those benefits due to the deeper tissue imaging and higher signal/noise ratio attained within the NIR-II optical window. In this context, we developed NIR-II emitting dyes targeting human epidermal growth factor receptor 2 (HER2)-positive ovarian tumors by coupling water-soluble NIR-II aza-BODIPY dyes to the FDA-approved anti-HER2 antibody, namely, trastuzumab. These bioconjugated NIR-II-emitting dyes displayed a prolonged stability in serum and a maintained affinity toward HER2 in vitro. We obtained selective targeting of HER2 positive tumors (SKOV-3) in vivo, with a favorable tumor accumulation. We demonstrated the fluorescence properties and the specific HER2 binding of the bioconjugated dyes in vivo and thus their potential for NIR-II FGS in the cancer setting.

Versatile Types of Inorganic/Organic NIR-IIa/IIb Fluorophores: From Strategic Design toward Molecular Imaging and Theranostics

In vivo imaging in the second near-infrared window (NIR-II, 1000-1700 nm), which enables us to look deeply into living subjects, is producing marvelous opportunities for biomedical research and clinical applications. Very recently, there has been an upsurge of interdisciplinary studies focusing on developing versatile types of inorganic/organic fluorophores that can be used for noninvasive NIR-IIa/IIb imaging (NIR-IIa, 1300-1400 nm; NIR-IIb, 1500-1700 nm) with near-zero tissue autofluorescence and deeper tissue penetration. This review provides an overview of the reports published to date on the design, properties, molecular imaging, and theranostics of inorganic/organic NIR-IIa/IIb fluorophores. First, we summarize the design concepts of the up-to-date functional NIR-IIa/IIb biomaterials, in the order of single-walled carbon nanotubes (SWCNTs), quantum dots (QDs), rare-earth-doped nanoparticles (RENPs), and organic fluorophores (OFs). Then, these novel imaging modalities and versatile biomedical applications brought by these superior fluorescent properties are reviewed. Finally, challenges and perspectives for future clinical translation, aiming at boosting the clinical application progress of NIR-IIa and NIR-IIb imaging technology are highlighted.

Deep learning: step forward to high-resolution in vivo shortwave infrared imaging

Shortwave infrared window (SWIR: 1000-1700 nm) represents a major improvement compared to the NIR-I region (700-900 nm) in terms of temporal and spatial resolutions in depths down to 4 mm. SWIR is a fast and cheap alternative to more precise methods such as X-ray and opto-acoustic imaging. Main obstacles in SWIR imaging are the noise and scattering from tissues and skin that reduce the precision of the method. We demonstrate that the combination of SWIR in vivo imaging in the NIR-IIb region (1500-1700 nm) with advanced deep learning image analysis allows to overcome these obstacles and making a large step forward to high resolution imaging: it allows to precisely segment vessels from tissues and noise, provides morphological structure of the vessels network, with learned pseudo-3D shape, their relative position, dynamic information of blood vascularization in depth in small animals and distinguish the vessels types: artieries and veins. For demonstration we use neural network IterNet that exploits structural redundancy of the blood vessels, which provides a useful analysis tool for raw SWIR images.