Cosinusoidal encoding multiplexed structured illumination multispectral ghost imaging.

The information dimension obtained by multispectral ghost imaging is more abundant than in single-band ghost imaging. Existing multispectral ghost imaging systems still meet some shortages, such as complex structure or reconstruction time-consuming. Here, an approach of cosinusoidal encoding multiplexed structured illumination multispectral ghost imaging is proposed. It can capture the multispectral image of the target object within one projection cycle with a single-pixel detector while maintaining high imaging efficiency and low time-consuming. The core of the proposed approach is the employed novel encoding strategy which is apt to decode and reconstruct the multispectral image via the Fourier transform. Specifically, cosinusoidal encoding matrices with specific frequency characteristics are fused with the orthogonal Hadamard basis patterns to form the multiplexed structured illumination patterns. A broadband photomultiplier is employed to collect the backscattered signals of the target object interacted by the corresponding structured illumination. The conventional linear algorithm is applied first to recover the mixed grayscale image of the imaging scene. Given the specific frequency distribution of the constructed cosinusoidal encoding matrices, the mixed grayscale image can be converted to the frequency domain for further decoding processing. Then, the pictures of multiple spectral components can be obtained with some manipulations by applying Fourier transform. A series of numerical simulations and experiments verified our proposed approach. The present cosinusoidal encoding multiplexed structured illumination can also be introduced in many other fields of high-dimensional information acquisition, such as high-resolution imaging and polarization ghost imaging.

Brain Structural Connectivity Guided Vision Transformers for Identification of Functional Connectivity Characteristics in Preterm Neonates.

Preterm birth is the leading cause of death in children under five years old, and is associated with a wide sequence of complications in both short and long term. In view of rapid neurodevelopment during the neonatal period, preterm neonates may exhibit considerable functional alterations compared to term ones. However, the identified functional alterations in previous studies merely achieve moderate classification performance, while more accurate functional characteristics with satisfying discrimination ability for better diagnosis and therapeutic treatment is underexplored. To address this problem, we propose a novel brain structural connectivity (SC) guided Vision Transformer (SCG-ViT) to identify functional connectivity (FC) differences among three neonatal groups: preterm, preterm with early postnatal experience, and term. Particularly, inspired by the neuroscience-derived information, a novel patch token of SC/FC matrix is defined, and the SC matrix is then adopted as an effective mask into the ViT model to screen out input FC patch embeddings with weaker SC, and to focus on stronger ones for better classification and identification of FC differences among the three groups. The experimental results on multi-modal MRI data of 437 neonatal brains from publicly released Developing Human Connectome Project (dHCP) demonstrate that SCG-ViT achieves superior classification ability compared to baseline models, and successfully identifies holistically different FC patterns among the three groups. Moreover, these different FCs are significantly correlated with the differential gene expressions of the three groups. In summary, SCG-ViT provides a powerfully brain-guided pipeline of adopting large-scale and data-intensive deep learning models for medical imaging-based diagnosis.

Self-supervised clustering analysis of colorectal cancer biomarkers based on multi-scale whole slides image and mass spectrometry imaging fused images.

Mass spectrometry imaging (MSI) is widely used for unlabeled molecular co-localization in biological samples and is also commonly used for screening cancer biomarkers. The main issues affecting the screening of cancer biomarkers are: 1) low-resolution MSI and pathological slices cannot be accurately matched; 2) a large amount of MSI data cannot be directly analyzed without manual annotation. This paper proposes a self-supervised cluster analysis method for colorectal cancer biomarkers based on multi-scale whole slide images (WSI) and MSI fusion images without manual annotation, which can accurately determine the correlation between molecules and lesion areas. This paper uses the combination of WSI multi-scale high-resolution and MSI high-dimensional data to obtain high-resolution fusion images. This method can observe the spatial distribution of molecules in pathological slices and use this method as an evaluation index for self-supervised screening of cancer biomarkers. The experimental results show that the method proposed in this chapter can train the image fusion model with a small amount of MSI and WSI data, and the mean Pixel Accuracy (mPA) and mean Intersection over Union (mIoU) evaluation metrics of the fused images can reach 0.9587 and 0.8745. And self-supervised clustering using MSI features and fused image features can obtain good classification results, and the precision, recall, and F1-score values of the self-supervised model reach 0.9074, 0.9065, and 0.9069, respectively. This method effectively combines the advantages of WSI and MSI, which will significantly expand the application scenarios of MSI and facilitate the screening of disease markers.

How to improve photodynamic therapy-induced antitumor immunity for cancer treatment?

Photodynamic therapy (PDT) is a promising method of tumor ablation and function-preserving oncological intervention, which is minimally invasive, repeatable, and has excellent function and cosmetic effect, with no cumulative toxicity. More importantly, PDT can induce immunogenic cell death and local inflammation, thus stimulating the body's immune response. However, the weak immunity induced by PDT alone is insufficient to trigger a systemic immune response towards cancer cells. To overcome this obstacle, multiple strategies have been investigated, including tumor microenvironment remodeling, tumor vaccines, subcellular-targeted PDT, and synergistic therapies. This review summarizes the latest progress in the development of strategies to improve the PDT-induced immune effect for enhanced cancer treatment.

Pd/Cu-Catalyzed Cascade C(sp3)-H Arylation and Intramolecular C-N Coupling: A One-Pot Synthesis of 3,4-2 H-Quinolinone Skeletons.

In this letter, we successfully explored a cascade Pd/Cu-catalyzed intermolecular C(sp3)-H arylation of amides and intramolecular C-N coupling reaction. This method provides a one-pot strategy to synthesize 3,4-2 H-quinolinone with good regioselectivity of C-H arylation and C-N coupling from C-I and C-X bonds from readily available starting materials.