D2-RecST: Dual-domain joint reconstruction strategy for fluorescence molecular tomography based on image domain and perception domain

TSP-1 increases autophagy level in cartilage by upregulating HSP27 which delays progression of osteoarthritis

This study aimed to determine whether Thrombospondin-1 (TSP-1) can be used as a biomarker to diagnose early osteoarthritis (OA) and whether it has a chondroprotective effect against OA. We examined TSP-1 expression in cartilage, synovial fluid, and serum at different time points after anterior cruciate ligament transection (ACLT) surgery in rats. Subsequently, TSP-1 was overexpressed or silenced to detect its effects on extracellular matrix (ECM) homeostasis, autophagy level, proliferation and apoptosis in chondrocytes. Adenovirus encoding TSP-1 was injected into the knee joints of ACLT rats to test its effect against OA. Combined with proteomic analysis, the molecular mechanism of TSP-1 in cartilage degeneration was explored. Intra-articular injection of an adenovirus carrying the TSP-1 sequence showed chondroprotective effects against OA. Moreover, TSP-1 expression decreases with OA progression and can effectively promote cartilage proliferation, inhibit apoptosis, and helps to sustain the balance between ECM anabolism and catabolism. Overexpression of TSP-1 also can increase autophagy by upregulating Heat Shock Protein 27 (HSP27, hspb1), thereby enhancing its effect as a stimulator of autophagy. TSP-1 is a hopeful strategy for OA treatment.

Two-stage deep learning method for sparse-view fluorescence molecular tomography reconstruction

Fluorescence molecular tomography (FMT) is a preclinical optical tomographic imaging technique that can trace various physiological and pathological processes at the cellular or even molecular level. Reducing the number of FMT projection views can improve the data acquisition speed, which is significant in applications such as dynamic problems. However, a reduction in the number of projection views will dramatically aggravate the ill-posedness of the FMT inverse problem and lead to significant degradation of the reconstructed images. To deal with this problem, we have proposed a deep-learning-based reconstruction method for sparse-view FMT that only uses four perpendicular projection views and divides the image reconstruction into two stages: image restoration and inverse Radon transform. In the first stage, the projection views of the surface fluorescence are restored to eliminate the blur derived from photon diffusion through a fully convolutional neural network. In the second stage, another convolutional neural network is used to implement the inverse Radon transform between the restored projections from the first stage and the reconstructed transverse slices. Numerical simulation and phantom and mouse experiments are carried out. The results show that the proposed method can effectively deal with the image reconstruction problem of sparse-view FMT.