Global contextual representation via graph-transformer fusion for hepatocellular carcinoma prognosis in whole-slide images

Current methods of digital pathological images typically employ small image patches to learn local representative features to overcome the issues of computationally heavy and memory limitations. However, the global contextual features are not fully considered in whole-slide images (WSIs). Here, we designed a hybrid model that utilizes Graph Neural Network (GNN) module and Transformer module for the representation of global contextual features, called TransGNN. GNN module built a WSI-Graph for the foreground area of a WSI for explicitly capturing structural features, and the Transformer module through the self-attention mechanism implicitly learned the global context information. The prognostic markers of hepatocellular carcinoma (HCC) prognostic biomarkers were used to illustrate the importance of global contextual information in cancer histopathological analysis. Our model was validated using 362 WSIs from 355 HCC patients diagnosed from The Cancer Genome Atlas (TCGA). It showed impressive performance with a Concordance Index (C-Index) of 0.7308 (95% Confidence Interval (CI): (0.6283–0.8333)) for overall survival prediction and achieved the best performance among all models. Additionally, our model achieved an area under curve of 0.7904, 0.8087, and 0.8004 for 1-year, 3-year, and 5-year survival predictions, respectively. We further verified the superior performance of our model in HCC risk stratification and its clinical value through Kaplan–Meier curve and univariate and multivariate COX regression analysis. Our research demonstrated that TransGNN effectively utilized the context information of WSIs and contributed to the clinical prognostic evaluation of HCC.</p

Effects of leukocyte-rich platelet-rich plasma and leukocyte-poor platelet-rich plasma on the healing of bone-tendon interface of rotator cuff in a mice model

Platelet-rich plasma (PRP) is widely used clinically to treat tendon injuries, and often contains leukocytes. However, the debate regarding the concentration of leukocytes in PRP is still ongoing. This study aimed to evaluate the therapeutic effects of leukocyte-rich platelet-rich plasma (LR-PRP) and leukocyte-poor platelet-rich plasma (LP-PRP) on the healing of the bone-tendon interface (BTI) of the rotator cuff. A total of 102 C57BL/6 mice were used. Thirty mice were used to prepare the PRP, while 72 underwent acute supraspinatus tendon injury repair. The animals were then randomly assigned to three groups: LR-PRP, LP-PRP and control groups. The mice were euthanized at 4 and 8 weeks postoperatively, and histological, immunological and biomechanical analyses were performed. The histological results showed that the fusion effect at the bone-tendon interface at 4 and 8 weeks after surgery was greater in the PRP groups and significantly increased at 4 weeks; however, at 8 weeks, the area of the fibrocartilage layer in the LP-PRP group increased significantly. M2 macrophages were observed at the repaired insertion for all the groups at 4 weeks. At 8 weeks, M2 macrophages withdrew back to the tendon in the control group, but some M2 macrophages were retained at the repaired site in the LR-PRP and LP-PRP groups. Enzyme-linked immunoassay results showed that the concentrations of IL-1β and TNF-α in the LR-PRP group were significantly higher than those in the other groups at 4 and 8 weeks, while the concentrations of IL-1β and TNF-α in the LP-PRP group were significantly lower than those in the control group. The biomechanical properties of the BTI were significantly improved in the PRP group. Significantly higher failure load and ultimate strength were seen in the LR-PRP and LP-PRP groups than in the control group at 4 and 8 weeks postoperatively. Thus, LR-RPR can effectively enhance the early stage of bone-tendon interface healing after rotator cuff repair, and LP-PRP could enhance the later stages of healing after rotator cuff injury