construction-lung-adenocarcinoma-xenograft-model-mice-evaluation

Construction of Lung Adenocarcinoma Xenograft Model in Mice and Evaluation of the Efficiency of XY Decoction through MAPK Pathway

Lung adenocarcinoma is increasing worldwide, prompting the exploration and development of effective treatment methods. In recent years, the therapeutic ...

dynamic-multiparameter-platelet-function-assessment-using-capacitive

Dynamic Multiparameter Platelet Function Assessment Using a Capacitive Biosensor

Platelets play a fundamental role in blood clotting through a series of regulated responses, including adhesion, spreading, granular secretion, ...

First Affiliated Hospital to Changchun University of Chinese Medicine

University of Washington

Rapid and uniform rewarming has been proved to be beneficial, and sometimes indispensable for the survival of cryopreserved biomaterials, inhibiting ice-recrystallization-devitrification and thermal stress-induced fracture (especially in large samples). To date, the convective water bath remains the gold standard rewarming method for small samples in the clinical settings, but it failed in the large samples (e.g., cryopreserved tissues and organs) due to damage caused by the slow and nonuniform heating. A single-mode electromagnetic resonance (SMER) system was developed to achieve ultrafast and uniform rewarming for large samples. In this study, we investigated the heating effects of the SMER system and compared the heating performance with water bath and air warming. A numerical model was established to further analyze the temperature change and distribution at different time points during the rewarming process. Overall, the SMER system achieved rapid heating at 331.63 ± 8.59°C min while limiting the maximum thermal gradient to <9°C min, significantly better than the other two warming methods. The experimental results were highly consistent, indicating SMER is a promising rewarming technology for the successful cryopreservation of large biosamples.

Single-Mode Electromagnetic Resonance Rewarming for the Cryopreservation of Samples with Large Volumes: A Numerical and Experimental Study.

Age-related macular degeneration (AMD) and Stargardt disease are the leading causes of blindness for the elderly and young adults respectively. Geographic atrophy (GA) of AMD and Stargardt atrophy are their end-stage outcomes. Efficient methods for segmentation and quantification of these atrophic lesions are critical for clinical research. In this study, we developed a deep convolutional neural network (CNN) with a trainable self-attended mechanism for accurate GA and Stargardt atrophy segmentation. Compared with traditional post-hoc attention mechanisms which can only visualize CNN features, our self-attended mechanism is embedded in a fully convolutional network and directly involved in training the CNN to actively attend key features for enhanced algorithm performance. We applied the self-attended CNN on the segmentation of AMD and Stargardt atrophic lesions on fundus autofluorescence (FAF) images. Compared with a preexisting regular fully convolutional network (the U-Net), our self-attended CNN achieved 10.6% higher Dice coefficient and 17% higher IoU (intersection over union) for AMD GA segmentation, and a 22% higher Dice coefficient and a 32% higher IoU for Stargardt atrophy segmentation. With longitudinal image data having over a longer time, the developed self-attended mechanism can also be applied on the visual discovery of early AMD and Stargardt features.

Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks.

Liver failure is the 12th leading cause of death worldwide. Protein-bound toxins such as bilirubin are responsible for many complications of the disease. Binder dialysis systems use albumin or another binding molecule in dialysate and detoxifying sorbent columns to remove these toxins. Systems like the molecular adsorbent recirculating system and BioLogic-DT have existed since the 1990s, but survival benefits in randomized controlled trials have not been consistent. New binder dialysis systems, including open albumin dialysis and the Advanced Multi-Organ Replacement system, are being developed. Optimal conditions for binder dialysis have not been established. We developed and validated a computational model of bound solute dialysis. It predicted the impact of changing between two test setups using different polysulfone dialyzers (F3 and F6HPS). We then predicted the impact of varying the dialysate flow rate on toxin removal. We found that bilirubin removal declines with dialysate flow rate. This can be explained through a linear decline in free bilirubin membrane permeability. Our model quantifies this decline through a single parameter (polysulfone dialyzers). Validation for additional dialyzers and flow rates will be needed. This model will benefit clinical trials by predicting optimal dialyzer and flow rate conditions. Accounting for toxin adsorption onto the dialyzer membrane may improve results further.

ArticleTotal : 2	Year
Publication title	2025
Publication title	2025

Article	Year
Single-Mode Electromagnetic Resonance Rewarming for the Cryopreservation of Samples with Large Volumes: A Numerical and Experimental Study. Biopreservation and biobanking\| PubMed ID: 35984939	2022
Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks. Scientific reports\| PubMed ID: 36028647	2022
Predicting the Impact of Polysulfone Dialyzers and Binder Dialysate Flow Rate on Bilirubin Removal. Bioengineering (Basel, Switzerland)\| PubMed ID: 39768080	2024

Ziyuan Wang

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