Chemical & Biomedical Imaging最新文献

Liver fibrosis, a progressive condition marked by excessive extracellular matrix deposition and activation of hepatic stellate cells, often develops asymptomatically in its early stages, leading to delayed clinical intervention. Conventional imaging techniques typically fail to detect mild fibrosis, resulting in diagnosis only at advanced stages such as cirrhosis, when therapeutic efficacy is significantly compromised. Recent advances in molecular imaging have facilitated the development of targeted contrast agents that enhance diagnostic sensitivity by selectively binding to fibrosis-specific biomarkers or responding to pathological microenvironmental changes. These include both nonresponsive probes that accumulate in fibrotic tissue and activatable probes sensitive to enzymes, small molecules, and other fibrosis-associated signals. This review systematically summarizes these emerging strategies and evaluates their potential for improving early diagnosis, staging accuracy, and therapeutic monitoring, thereby guiding future development and applications in hepatic fibrosis management.

Calcifications across the body offer snapshots of the surrounding ionic environment at the time of their formation. Links between prostate calcification chemistry and cancer are becoming of increasing interest, particularly in identifying biomarkers for disease. This study utilizes X-ray fluorescence mapping of 72 human prostate calcifications, measured at the I18 beamline at the Diamond Light Source, to determine the links between calcifications and their environment. This paper offers the first investigation of the elemental heterogeneity of prostate calcifications, demonstrating lower relative levels of minor elements at the calcification center compared to the edge but higher levels of zinc. Importantly, this study uniquely presents links between average Fe, Cr, Mn, Cu, and Ni ratios and grade Group (a classification system for urological tumors, specifically for prostate cancer), highlighting a potential avenue of exploration for biomarkers in prostate calcifications.

Cholangiocarcinoma, a form of liver bile duct cancer, is challenging to detect due to its critically low 5-year survival rate. Conventional imaging modalities, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are widely used, but recent advancements in Hyperspectral Imaging (HSI) offer a promising, non-invasive alternative for cancer diagnosis. However, supervised learning methods often require large annotated datasets that can be difficult to obtain. To alleviate this limitation, we propose an unsupervised learning strategy using Generative Adversarial Networks (GANs) for cholangiocarcinoma detection. This approach, named Unsupervised Spectral and Spatial Attention-based GAN (USSGAN), employs an unsupervised Spectral-Spatial attention-based GAN to classify and segment cancerous regions without relying on labeled training data. The integration of an adaptive step size into Tasmanian Devil Optimization (TDO) enhances the convergence speed and effectively captures diverse cancerous features. Enhanced Tasmanian Devil Optimization (ETDO) further improves segmentation performance, making the framework robust and computationally efficient. The proposed method was tested on a publicly available multidimensional choledochal cholangiocarcinoma dataset, achieving superior performance compared with existing techniques in the literature. USSGAN demonstrated high accuracy across key metrics such as overall accuracy (OA), average accuracy (AA), and Cohen's Kappa. Ablation studies confirmed the critical contributions of the proposed enhancements to the overall performance. With an overall accuracy of 98.03%, the USSGAN closely aligns with the assessments of experienced pathologists while maintaining minimal computational requirements. Its lightweight nature ensures real-time deployment, providing results within a minute, making it a practical and effective solution for clinical applications.

Metal-organic frameworks (MOFs), such as HKUST-1, have been used in many applications such as catalysis, gas capture, and more. However, one major limitation hindering their application is inherent chemical instability, and conducting in situ studies on their degradation with sufficient spatial-temporal resolution remains a challenge. In this work, we employ optical microscopy to quantitatively monitor the degradation of HKUST-1 under alkaline and acidic reducing environments with video-rate temporal resolution. By color-mapping the degradation progress over different time intervals with alkaline hole (h⁺) scavengers (sodium ascorbate, NaAs), we observe a sigmoidal time-dependent degradation trend. The results reveal the presence of confined regions with faster degradation. It is discovered that degradation begins with the chemical reduction of HKUST-1 into Cu₂O nanoparticles, followed by self-photoreduction into Cu₂O/Cu. Furthermore, it is observed that there is a h⁺ scavenger concentration and laser-wavelength-dependent degradation. At higher concentrations and irradiation energy, there is faster degradation in the HKUST-1 framework. Under acidic reducing conditions with lactic acid (LA), the degradation rate constant is 22% higher than that under alkaline conditions, while the valence state of Cu remains unchanged. This can be attributed to distinct degradation mechanisms at different pH levels, in which acidolysis and metal-ligand disruption dominate in the presence of LA, while HKUST-1 degradation is primarily redox-driven in NaAs solution. These findings offer mechanistic insight into the degradation behavior of HKUST-1 and provide valuable guidance for optimizing MOF stability in practical applications.

Quantitative imaging of luminescent signals, ranging from electrochemiluminescence (ECL) and chemiluminescence to colorimetric assays, is increasingly performed using consumer-grade digital cameras and smartphones. However, device-dependent variability, nonlinear signal encoding, and the absence of standardized workflows hinder reproducibility and quantification accuracy. This work presents a generalized methodology for robust signal quantification in luminescent systems using digital imaging, with ECL as a model case. By combining synchronized electrochemical control, manual optimization of imaging parameters, gamma correction, and color space transformations, accurate device-independent analysis is enabled. Using Ru-(bpy)₃ ²⁺/TPrA as a test system, we evaluate RGB, CIEXYZ, and CIELAB color spaces, identifying optimal channels for sensitivity and dynamic range. Our performance assessment underscores the importance of transfer function selection and supports both linear and nonlinear quantification models. Results show that linearized r and X color channels offer broad dynamic ranges with moderate sensitivity, while encoded R and a* channels provide higher sensitivity at low concentrations, requiring nonlinear modeling to extend their quantification range. This scalable approach enables standardized, high-throughput optical analysis using low-cost camera platforms, with broad applications in diagnostics, biosensing, and analytical chemistry.

Copper is an essential trace element for normal development and function throughout the body, including the central nervous system (CNS). Alterations to cellular copper levels result in severe neurological consequences and are linked to a range of CNS disorders, positioning treatments that restore copper balance as promising therapies for these disorders. However, despite the clear relationship between copper balance and CNS health, there are limited tools to measure copper levels in vivo in humans. This constitutes a significant challenge for both diagnosing disorders of copper imbalance and monitoring the efficacy of copper-altering treatments for these disorders. Here we report the synthesis and characterization of Fluorine-labeled Naphthalimide Copper sensor 1 (F-NpCu1), a fluorescent sensor for copper that contains a fluorine atom for future radiolabeling for clinical application. We demonstrate that the probe exhibits good stability and is highly selective for copper above other transition metals present in biological tissues. Copper binding promotes covalent bond formation between the sensor and proximal cellular proteins. F-NpCu1 is nontoxic and can be measured using fluorescence microscopy in living cells and fixed tissue sections from both mouse brain and pancreas. Furthermore, F-NpCu1 exhibits good blood-brain-barrier permeability and can report differences in brain copper levels induced by copper modulating therapies in living mice using intravital fluorescence microscopy. This study represents a promising advance toward the development of the first clinical tool for measuring copper in living humans, including in the CNS, with radiolabeling studies underway to develop ¹⁸F-NpCu1 for PET imaging of copper in vivo.

Interfacial electron transfer governs electrochemical heterogeneity at the single-entity level. Herein, we investigated the electronic coupling event during electrodissolution processes of single silver nanoentities on a Au electrode through a synchronized electrochemical-optical tracking platform. By implementing strategic control of interfacial gap distances and electrolyte composition, a marked differentiation of single-particle reaction dynamics can be achieved. The integration of superlocalization methodology reveals position-correlated optical centroid shifts during electrodissolution processes, demonstrating heterogeneous oxidation dynamics arising from spatially nonuniform surface oxide formation. Crucially, SAM-mediated gap regulation enables the precise regulation of interfacial electric field enhancement. Our methodology resolves electronic coupling heterogeneity at subnanowire scale while proving molecular interlayer-dependent modulation of coupling lifetimes. This electrochemical-optical imaging strategy establishes nanoscale spatial mapping of electrochemical dynamics, quantitative correlation between interfacial structure and coupling efficiency, and real-time tracking of transient electronic states. These findings demonstrate the capability of advanced optical imaging methodologies in elucidating structure-activity relationships at nanoscale interfaces, providing mechanistic insights for single-entity electrochemistry and nanoscale energy conversion systems.