surgical-protocols-for-deep-cervical-lymphovenous-anastomosis-rat

Surgical Protocols for Deep Cervical Lymphovenous Anastomosis in a Rat Model: Lymph Node and Lymphatic Vessel Anastomoses

Deep cervical lymphovenous anastomosis (dcLVA) has become a promising treatment strategy for Alzheimer&#39;s disease (AD), offering significant ...

Peking University Health Science Center

Ischemic stroke (IS) is one of the most common serious secondary diseases of atrial fibrillation (AF) within 1 year after its occurrence, both of which have manifestations of ischemia and hypoxia of the small vessels in the early phase of the condition. The fundus is a collection of capillaries, while the retina responds differently to light of different wavelengths. Predicting the risk of IS occurring secondary to AF, based on subtle differences in fundus images of different wavelengths, is yet to be explored. This study was conducted to predict the risk of IS occurring secondary to AF based on multi-spectrum fundus images using deep learning.

Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning.

Deep neural networks (DNNs) have been widely applied in medical image classification and achieve remarkable classification performance. These achievements heavily depend on large-scale accurately annotated training data. However, label noise is inevitably introduced in the medical image annotation, as the labeling process heavily relies on the expertise and experience of annotators. Meanwhile, DNNs suffer from overfitting noisy labels, degrading the performance of models. Therefore, in this work, we innovatively devise a noise-robust training approach to mitigate the adverse effects of noisy labels in medical image classification. Specifically, we incorporate contrastive learning and intra-group mixup attention strategies into vanilla supervised learning. The contrastive learning for feature extractor helps to enhance visual representation of DNNs. The intra-group mixup attention module constructs groups and assigns self-attention weights for group-wise samples, and subsequently interpolates massive noisy-suppressed samples through weighted mixup operation. We conduct comparative experiments on both synthetic and real-world noisy medical datasets under various noise levels. Rigorous experiments validate that our noise-robust method with contrastive learning and mixup attention can effectively handle with label noise, and is superior to state-of-the-art methods. An ablation study also shows that both components contribute to boost model performance. The proposed method demonstrates its capability of curb label noise and has certain potential toward real-world clinic applications.

Suppressing label noise in medical image classification using mixup attention and self-supervised learning.

 Citicoline can be used to reduce acute ischemic stroke injury via venous infusion, however, its protective effects in the brain extracellular space remain largely unknown. Herein, we investigated the brain protective effects of citicoline administered via the brain extracellular space and sought precise effective dosage range that can protect against ischemic injury after experimental ischemic stroke in rats. : Fifty-six Sprague-Dawley rats were randomly divided into control, intraperitoneal (IP), caudate-putamen (CPu)-25, CPu-40, CPu-50, CPu-60 and CPu-75 groups based on the infusion site and concentration of citicoline. Two hours after the administration of citicoline, the rats were subjected to a permanent middle cerebral artery occlusion to mimic acute ischemic stroke. Then, the brain infarct volume in rats after stroke was measured and their neurological deficiency was evaluated to explain the protective effects and effective dosage range of citicoline.  Compared to the control and IP groups, brain infarct volume of rats in CPu-40, CPu-50, and CPu-60 groups is significant smaller. Furthermore, the brain infarct volume of rats in CPu-50 is the least.  Here, we showed that citicoline can decrease the brain infarct volume, thus protecting the brain from acute ischemic stroke injury. We also found that the appropriate effective citicoline dose delivered via the brain extracellular space is 50 mM. Our study provides novel insights into the precise treatment of acute ischemic stroke by citicoline via the brain extracellular space, further guiding the treatment of brain disease.

Efficacy of Citicoline Delivered via Brain Extracellular Space against Experimental Acute Ischemic Stroke in Rats.

This study aims to address the issue of long scan durations required by traditional graphical analysis methods, such as the Logan plot and its variant, the reversible equilibrium (RE) Logan plot, for dynamic PET imaging of tracer kinetics.We propose a relative RE Logan model that builds on the principles of the Logan plot and its variant to significantly reduce scan time without compromising the accuracy of tracer kinetics analysis. The model is supported by theoretical evidence and experimental validations, including two computer simulations and one clinical data analysis.The proposed model demonstrates a significant linear relationship between the variableand the slopeof the RE Logan plot, and the variable' and the slopeDVT'of the relative RE Logan plot. The Pearson correlation coefficients () of the linear fitting of the' to theequal 0.9849 in the simulated data and 0.9912 in the clinical data. Similarly, thevalues for the linear fitting ofDVT'toequal 0.9989 and 0.9988 in the simulated data, and 0.9954 in the clinical data.These results demonstrate the model's capability to maintain strong linear relationships and produce parametric images comparable to the traditional RE Logan plot, but with the considerable advantage of shorter scan durations. This innovation holds significant potential for enhancing the efficiency and feasibility of PET imaging in clinical settings.

A novel relative-equilibrium graphical plot for rapid reversible tracer studies in dynamic PET imaging.

Ischemic stroke is a leading cause of death and disability worldwide. Inflammatory response after stroke determines the outcome of ischemic injury. A recent study has reported an efficient method, epidural arterial implantation (EAI), for accelerating interstitial fluid (ISF) drainage, which provides a promising strategy to clear pro-inflammatory cytokines in the brain extracellular space (ECS). In this study, the method of EAI was modified (m-EAI) to control its function of accelerating the ISF drainage at different time points following ischemic attack. The neuroprotective effect of m-EAI on ischemic stroke was evaluated with the transient middle cerebral artery occlusion (tMCAO) rat model. The results demonstrated the accumulation of IL-1β, IL-6, and TNF-α was significantly decreased by activating m-EAI at 7 d before and immediately after ischemic attack in tMCAO rats, accompanied with decreased infarct volume and improved neurological function. This study consolidates the hypothesis of exacerbated ischemic damage by inflammatory response and provides a new perspective to treat encephalopathy via brain ECS. Further research is essential to investigate whether m-EAI combined with neuroprotective drugs could enhance the therapeutic effect on ischemic stroke.

A novel neuroprotective method against ischemic stroke by accelerating the drainage of brain interstitial fluid.

In this paper, we propose a new super-expressive activation function called the Parametric Elementary Universal Activation Function (PEUAF). We demonstrate the effectiveness of PEUAF through systematic and comprehensive experiments on various industrial and image datasets, including CIFAR-10, Tiny-ImageNet, and ImageNet. The models utilizing PEUAF achieve the best performance across several baseline industrial datasets. Specifically, in image datasets, the models that incorporate mixed activation functions (with PEUAF) exhibit competitive test accuracy despite the low accuracy of models with only PEUAF. Moreover, we significantly generalize the family of super-expressive activation functions, whose existence has been demonstrated in several recent works by showing that any continuous function can be approximated to any desired accuracy by a fixed-size network with a specific super-expressive activation function. Specifically, our work addresses two major bottlenecks in impeding the development of super-expressive activation functions: the limited identification of super-expressive functions, which raises doubts about their broad applicability, and their often peculiar forms, which lead to skepticism regarding their scalability and practicality in real-world applications.

Don't fear peculiar activation functions: EUAF and beyond.

The stimulator of interferon genes (STING) pathway has emerged as a new immunotherapy strategy with potent local stimulation specificity, showing promising potential to counteract the immunosuppression in glioma. Herein, a tumor microenvironment (TME) responsive nanoagonists are developed based on an organic-inorganic copolymer composed of the polymer PC6AB coupled with manganous phosphate ionic oligomers (MnP). The degradation of nanoagonists into PC6AB and MnP in the acidic TME enables spatiotemporal control of their delivery to tumor cells and immune cells, respectively. PC6AB with membranolytic activity selectively interacts with tumor cell membranes to induce immunogenic cell death, while manganese metal can activate the STING pathway in immune cells and trigger downstream immunostimulatory signals. Nanoagonists can stimulate robust antitumor immunity after local injection into the brain extracellular space (ECS), showing significant therapeutic efficacy in mouse glioma. Nanoagonists can achieve spatiotemporal orchestration of STING activation in response to TME and enhance immune response against "cold" solid tumors, providing a promising approach for clinical immunotherapy.

Smart Organic-Inorganic Copolymer Nanoparticles Distinguish Between Microglia and Cancer Cells for Synergistic Immunotherapy in Glioma.

Ischemic stroke exacts a heavy toll in death and disability worldwide. After ischemic stroke, the accumulation of pathobiomolecules in the brain extracellular space (ECS) will exacerbate neurological damage and cognitive impairment. Photobiomodulation (PBM) has been demonstrated to improve cognitive function in Alzheimer's disease mouse models by accelerating molecular transportation in the brain ECS. This suggests that PBM may have a potential role in the accumulation of pathobiomolecules in the brain ECS following ischemic stroke. In this study, we developed a PBM therapy apparatus with custom parameters. By evaluating the treatment effect, we identified that 755 nm was the optimal light wavelength for ischemic stroke in rats with transient middle cerebral artery occlusion/reperfusion. Extracellular diffusion and interstitial fluid (ISF) drainage were measured using a tracer-based magnetic resonance imaging method. Our results showed that PBM accelerated molecular transportation in the brain ECS and ISF drainage, promoting the clearance of pro-inflammatory cytokines and reducing the deposition of pathological proteins. Consequently, the infarct volume decreased and neurological cognitive function was improved. Besides, the acceleration of ISF drainage was achieved by reducing expression and restoring polarization of aquaporin 4 (AQP4) in the peri-infarct area. In summary, we demonstrated that PBM could alleviate ischemia-reperfusion injury and prevent post-stroke cognitive impairment by accelerating molecular transportation in the brain ECS, paving a pathway for ischemic stroke treatment via the light-ECS interaction.

Accelerated Molecular Transportation in the Brain Extracellular Space with 755-nm Light Attenuates Post-Stroke Cognitive Impairment in Rats.

Large-animal models are playing a pivotal role in bridging the translational research gap. Positron emission tomography (PET) imaging is preferred in disease research involving large-animal models. Its ability to non-invasively monitor metabolic activity, receptor-ligand interactions, and pharmacokinetics in real time makes PET imaging an essential tool for evaluating therapeutic efficacy and advancing the development of targeted treatments. This review focuses on recent advancements in dedicated large-animal PET scanners, the utilization of large-animal models for simulating human diseases, and their applications in PET studies. It specifically highlights the critical role of PET imaging in facilitating the development of more effective and safer treatments for infections, chronic heart disease, diabetes, cancer, central nervous system disorders, and addiction, emphasizing its importance in the translational research landscape.

The Impact of PET Imaging on Translational Medicine: Insights from Large-Animal Disease Models.

T-cell immunoglobulin and mucin domain-3 (TIM3) is an inhibitory checkpoint glycoprotein expressed on immune cells, particularly tumor-infiltrating lymphocytes (TILs), and plays a critical role in suppressing antitumor immune responses. While dual blockade of TIM3 and programmed cell death protein 1 (PD1) has shown promising results in enhancing immune responses in advanced cancers, the lack of reliable, noninvasive methods for detecting TIM3 expression in tumors remains a major challenge. To address this, we developed and characterized a novel positron emission tomography (PET) tracer, [F]AlF-RESCA-HVCR2N2, based on a TIM3-specific nanobody labeled via [F]AlF radiochemistry. The tracer demonstrated high specificity and affinity for TIM3 ( = 4.644 nM) in vitro, enabling the clear visualization of TIM3-expressing lesions in tumor models through PET imaging. Dynamic imaging and kinetic analyses revealed favorable pharmacokinetics, and a preliminary correlation was observed between PET signal intensity and histologically assessed TIM3 expression in tumor tissues. These results highlight the potential of [F]AlF-RESCA-HVCR2N2 as a sensitive and noninvasive tool for quantifying TIM3 expression in vivo, with significant implications for patient stratification, treatment monitoring, and the development of TIM3-targeted immunotherapies.

Novel Radiofluorinated Nanobody PET Tracer for Preclinical Studies of TIM3 Expression.

Nectin4 is a potential therapeutic target for multiple cancer types. To develop a diagnostic radiotracer for nectin4, a nanobody-based PET tracer was selected from several candidate sequences and prepared via [F]AlF radiolabeling. The radiochemical quality was assessed, and tracer uptake was evaluated in cell lines and tumor models with varying nectin4 expression levels.  and  studies showed that the tracer exhibited a specific affinity for nectin4, with a binding affinity () of 6.77 nM in cell lines. The tracer enabled clear visualization of nectin4-expressing lesions, and its uptake profile and pharmacokinetic behavior were characterized using dynamic PET imaging and kinetic analysis. A correlation between PET signal intensity and histological nectin4 expression was also observed in tumor models. Thus, it may have the potential to become a nectin4 tracer for clinical applications.

A Radiofluorinated Nanobody PET Tracer to Dynamically Visualize Differential Nectin4 Expression .

The integration of deep learning in medical imaging has significantly advanced diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to inherent inter-modality variability. Here we present the Modality Projection Universal Model (MPUM), trained on 861 subjects, which dynamically adapts to diverse imaging modalities through a modality-projection strategy. MPUM achieves state-of-the-art, whole-body organ segmentation, providing rapid localization for computer-aided diagnosis and precise anatomical quantification to support clinical decision-making. A controller-based convolutional layer further enables saliency map visualization, enhancing model interpretability for clinical use. Beyond segmentation, MPUM reveals metabolic correlations along the brain-body axis and between distinct brain regions, providing insights into systemic and physiological interactions from a whole-body perspective. Here we show that this universal framework accelerates diagnosis, facilitates large-scale imaging analysis, and bridges anatomical and metabolic information, enabling discovery of cross-organ disease mechanisms and advancing integrative brain-body research.

Modality-projection universal model for comprehensive full-body medical imaging segmentation.

This work reports the performance evaluation of a newly developed small-animal positron emission tomography (PET) system based on lutetium-yttrium oxyorthosilicate (LYSO) crystals and multi-pixel photon counter (MPPC). Performance was evaluated, including spatial resolution, system sensitivity, energy resolution, scatter fraction (SF), noise-equivalent count rate (NECR), micro-Derenzo phantom imaging, and in vivo imaging of mice and rats. The system achieved a tangential spatial resolution of 0.9 mm in the axial direction at a quarter axial offset using the three-dimensional ordered-subsets expectation maximization (3D OSEM) reconstruction algorithm. The peak sensitivity was 8.74% within a 200-750 keV energy window, with an average energy resolution of 12.5%. Scatter fractions were 12.9% and 30.0% for mouse- and rat-like phantoms, respectively. The NECR reached 878.7 kcps at 57.6 MBq for the mouse phantom and 421.4 kcps at 63.2 MBq for the rat phantom. High-resolution phantom and in vivo images confirmed the system's capability for quantitative, high-sensitivity small-animal imaging, demonstrating its potential for preclinical molecular imaging studies.

Article	Year
Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning. Frontiers in cardiovascular medicine\| PubMed ID: 37600060	2023
Suppressing label noise in medical image classification using mixup attention and self-supervised learning. Physics in medicine and biology\| PubMed ID: 38636495	2024
Efficacy of Citicoline Delivered via Brain Extracellular Space against Experimental Acute Ischemic Stroke in Rats. International journal of medical sciences\| PubMed ID: 38818467	2024
A novel relative-equilibrium graphical plot for rapid reversible tracer studies in dynamic PET imaging. Physics in medicine and biology\| PubMed ID: 38996417	2024
A novel neuroprotective method against ischemic stroke by accelerating the drainage of brain interstitial fluid. Science China. Life sciences\| PubMed ID: 39115728	2024
Don't fear peculiar activation functions: EUAF and beyond. Neural networks : the official journal of the International Neural Network Society\| PubMed ID: 39987712	2025
Smart Organic-Inorganic Copolymer Nanoparticles Distinguish Between Microglia and Cancer Cells for Synergistic Immunotherapy in Glioma. Advanced science (Weinheim, Baden-Wurttemberg, Germany)\| PubMed ID: 40298877	2025
Accelerated Molecular Transportation in the Brain Extracellular Space with 755-nm Light Attenuates Post-Stroke Cognitive Impairment in Rats. Cyborg and bionic systems (Washington, D.C.)\| PubMed ID: 40330544	2025
The Impact of PET Imaging on Translational Medicine: Insights from Large-Animal Disease Models. Biomolecules\| PubMed ID: 40723791	2025
Novel Radiofluorinated Nanobody PET Tracer for Preclinical Studies of TIM3 Expression. Molecular pharmaceutics\| PubMed ID: 40911812	2025
A Radiofluorinated Nanobody PET Tracer to Dynamically Visualize Differential Nectin4 Expression . Journal of medicinal chemistry\| PubMed ID: 41037820	2025
Modality-projection universal model for comprehensive full-body medical imaging segmentation. Nature communications\| PubMed ID: 41136445	2025
Design and Performance Assessment of a High-Resolution Small-Animal PET System. Bioengineering (Basel, Switzerland)\| PubMed ID: 41155117	2025

Zhaoheng Xie

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