A photo-responsive self-healing hydrogel loaded with immunoadjuvants and MoS2 nanosheets for combating post-resection breast cancer recurrence.

Tumor recurrence after surgical resection remains a significant challenge in breast cancer treatment. Immune checkpoint blockade therapy, as a promising alternative therapy, faces limitations in combating tumor recurrence due to the low immune response rate. In this study, we developed an implantable photo-responsive self-healing hydrogel loaded with MoS2 nanosheets and the immunoadjuvant R837 (PVA-MoS2-R837, PMR hydrogel) for in situ generation of tumor-associated antigens at the post-surgical site of the primary tumor, enabling sustained and effective activation of the immune response. This PMR hydrogel exhibited potential for near-infrared (NIR) light response, tissue adhesion, self-healing, and sustained adjuvant release. When implanted at the site after tumor resection, NIR irradiation triggered a photothermal effect, resulting in the ablation of residual cancer cells. The in situ-generated tumor-associated antigens promoted dendritic cell (DC) maturation. In a mouse model, PMR hydrogel-mediated photothermal therapy combined with immune checkpoint blockade effectively inhibited the recurrence of resected tumors, providing new insights for combating post-resection breast cancer recurrence.

Two-Stage CNN Model for Joint Demosaicing and Denoising of Burst Bayer Images.

In the classical image processing pipeline, demosaicing and denoising are separated steps that may interfere with each other. Joint demosaicing and denoising utilizes the shared image prior information to guide the image recovery process. It is expected to have better performance by the joint optimization of the two problems. Besides, learning recovered images from burst (continuous exposure images) can further improve image details. This article proposes a two-stage convolutional neural network model for joint demosaicing and denoising of burst Bayer images. The proposed CNN model consists of a single-frame joint demosaicing and denoising module, a multiframe denoising module, and an optional noise estimation module. It requires a two-stage training scheme to ensure that the model converges to a good solution. Experiments on multiframe Bayer images with simulated Gaussian noise show that the proposed method has obvious performance advantages and speed advantages compared with similar approaches. Experiments on actual multiframe Bayer images verify the denoising effect and detail retention ability of the proposed method.

A nanoparticle-containing polycaprolactone implant for combating post-resection breast cancer recurrence.

The recurrence and metastasis of tumor after surgery is the main cause of death for patients with breast cancer. Systemic chemotherapy suffered from low delivery efficiency to tumors and the side effects of chemo drugs. Localized chemotherapy using drug-containing implants is an alternative, while the reconstruction of breast tissue is generally considered after chemotherapy, resulting in a second surgery for patients. Here, we describe a strategy using implantable drug-containing polymeric scaffolds to deliver chemo drugs directly to the post-resection site, and simultaneously provide mechanical support and regenerative niche for breast tissue reconstruction. When doxorubicin was loaded in mesoporous silica nanoparticles and subsequently incorporated into polycaprolactone scaffolds (DMSN@PCL), a 9-week sustained drug release was achieved post implantation in mice. The local recurrence of residual tumor after surgery was significantly inhibited within 4 weeks in a post-surgical mouse model bearing xenograft MDA-MB-231 tumor. DMSN@PCL scaffolds exhibited good biocompatibility in mice during the treatment. We believe our strategy holds great promise as an adjuvant localized chemotherapy in clinics for combating post-resection breast cancer recurrence.

Accelerating thrombolysis using a precision and clot-penetrating drug delivery strategy by nanoparticle-shelled microbubbles.

Conventional thrombolytic drugs for vascular blockage such as tissue plasminogen activator (tPA) are challenged by the low bioavailability, off-target side effects and limited penetration in thrombi, leading to delayed recanalization. We hypothesize that these challenges can be addressed with the targeted and controlled delivery of thrombolytic drugs or precision drug delivery. A porous and magnetic microbubble platform is developed to formulate tPA. This system can maintain the tPA activity during circulation, be magnetically guided to the thrombi, and then remotely activated for drug release. The ultrasound stimulation also improves the drug penetration into thrombi. In a mouse model of venous thrombosis, the residual thrombus decreased by 67.5% when compared to conventional injection of tPA. The penetration of tPA by ultrasound was up to several hundred micrometers in thrombi. This strategy not only improves the therapeutic efficacy but also accelerates the lytic rate, enabling it to be promising in time-critical thrombolytic therapy.

Perfluorooctane sulfonate enhances mRNA expression of PPARγ and ap2 in human mesenchymal stem cells monitored by long-retained intracellular nanosensor.

Perfluorooctane sulfonate (PFOS) has been widely used as a surface coating for household products. It still exists in living environments despite being restricted, due to its bioaccumulation and long half-life. Studies have shown that PFOS has the ability to induce adipogenic differentiation of human cells. Human mesenchymal stem cells (hMSCs) distributed within the adipose tissue might be a potential target of accumulated PFOS. However, traditional end-point toxicity assays failed to examine the subtle changes of cellular function exposed to low-dose persistent organic pollutants in real time. In the present work, highly sensitive and long-retained (more than 30 days) fluorescence based polymeric nanosensors were developed and employed for real-time assessment of cellular functions. hMSCs were engineered with sensor molecules encapsulated poly (lactic-co-glycolic acid) (PLGA) particles. Once internalized by hMSCs, PLGA particles continuously release and replenish sensor molecules to cytoplasm, resulting in prolonged fluorescence signal against photo bleaching and dilution by exocytosis. With this method, the dynamic changes of viability, ROS induction, and adipogenic differentiation related mRNA expression of hMSCs were monitored. PFOS with the concentration as low as 0.1 µM can induce cellular ROS and enhance the PPARγ and ap2 mRNA expression, suggesting the effect on promoting adipogenic differentiation of hMSCs.

Online Meta Adaptation for Fast Video Object Segmentation.

Conventional deep neural networks based video object segmentation (VOS) methods are dominated by heavily fine-tuning a segmentation model on the first frame of a given video, which is time-consuming and inefficient. In this paper, we propose a novel method which rapidly adapts a base segmentation model to new video sequences with only a couple of model-update iterations, without sacrificing performance. Such attractive efficiency benefits from the meta-learning paradigm which leads to a meta-segmentation model and a novel continuous learning approach which enables online adaptation of the segmentation model. Concretely, we train a meta-learner on multiple VOS tasks such that the meta model can capture their common knowledge and gains the ability to fast adapt the segmentation model to new video sequences. Furthermore, to deal with unique challenges of VOS tasks from temporal variations in the video, e.g., object motion and appearance changes, we propose a principled online adaptation approach that continuously adapts the segmentation model across video frames by exploiting temporal context effectively, providing robustness to annoying temporal variations. Integrating the meta-learner with the online adaptation approach, the proposed VOS model achieves competitive performance against the state-of-the-arts and moreover provides faster per-frame processing speed.

Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning.

In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, real-world noise is signal dependent, or the noise level is not constant over the whole image. In this paper, we attempt to estimate the precise and pixelwise noise level instead of a simple global scalar. To the best of our knowledge, this is the first work on the problem. Particularly, we propose a deep convolutional neural network named "deep residual noise estimator" (DRNE) for pixelwise noise-level estimation. We carefully design the architecture of the DRNE, which consists of a stack of customized residual blocks without any pooling or interpolation operation. The proposed DRNE formulates the process of noise estimation as pixel-to-pixel prediction. The experimental results show that the DRNE can achieve better performance on nonhomogeneous noise estimation than state-of-the-art methods. In addition, the DRNE can bring denoising performance gains in removing signal-dependent Gaussian noise when working with recent deep learning denoising methods.

[Studies on thermal denaturation of peanut allergen Ara h1 and its interaction with reducing sugars].

The thermal denaturation of peanut allergen Ara h1, its interaction with reducing sugars and the corresponding changes in allergenicity were investigated by circular dichroism (CD), fluorescence and ELISA method comprehensively. The experimental results indicate that the secondary structure of Ara h1 changes significantly along with decreasing alpha-helical structure and its allergenicity with the temperature higher than 85 degrees C, and that both xylose and fructose can stabilize Ara h1 protein structure through interacting with Ara h1 protein and decrease its allergenicity obviously. This study should be helpful to the further understanding of sensitization mechanism of food allergy and be useful for the guidance on reasonable manufacturing of peanut foods.