A Bacterial Responsive Microneedle Dressing with Hydrogel Backing Layer for Chronic Wound Treatment.

The treatment of chronic wounds still presents great challenges due to being infected by biofilms and the damaged healing process. The current treatments do not address the needs of chronic wounds. In this study, a highly effective dressing (Dox-DFO@MN Hy) for the treatment of chronic wounds is described. This dressing combines the advantages of microneedles (MNs) and hydrogels in the treatment of chronic wounds. MNs is employed to debride the biofilms and break down the wound barrier, providing rapid access to therapeutic drugs from hydrogel backing layer. Importantly, to kill the pathogenic bacteria in the biofilms specifically, Doxycycline hydrochloride (Dox) is wrapped into the polycaprolactone (PCL) microspheres that have lipase-responsive properties and loaded into the tips of MNs. At the same time, hydrogel backing layer is used to seal the wound and accelerate wound healing. Benefiting from the combination of two advantages of MNs and hydrogel, the dressing significantly reduces the bacteria in the biofilms and effectively promotes angiogenesis and cell migration in vitro. Overall, Dox-DFO@MN Hy can effectively treat chronic wounds infected with biofilms, providing a new idea for the treatment of chronic wounds.

Cervical cell nuclei segmentation based on GC-UNet.

Cervical cancer diagnosis hinges significantly on precise nuclei segmentation at early stages, which however, remains largely elusive due to challenges such as overlapping cells and blurred nuclei boundaries. This paper presents a novel deep neural network (DNN), the Global Context UNet (GC-UNet), designed to adeptly handle intricate environments and deliver accurate cell segmentation. At the core of GC-UNet is DenseNet, which serves as the backbone, encoding cell images and capitalizing on pre-existing knowledge. A unique context-aware pooling module, equipped with a gating model, is integrated for effective encoding of ImageNet pre-trained features, ensuring essential features at different levels are retained. Further, a decoder grounded in a global context attention block is employed to foster global feature interaction and refine the predicted masks.