Objective To explore the impact of tumor-associated myeloid cell infiltration signatures on postoperative overall survival (OS) in patients with gastric cancer. Methods A retrospective analysis was performed on patients who underwent radical gastrectomy at Zhongshan Hospital, Fudan University from January 2005 to December 2019. Patients were randomly divided into a training set (n＝167) and an internal validation set (n＝168) at a 1:1 ratio. Meanwhile, 147 patients with gastric cancer treated at Zhongshan Hospital (Xiamen), Fudan University during the same period were enrolled as the external validation set (n＝147). Immunohistochemistry was used in all patients to evaluate the distribution characteristics of 18 immune markers in three regions of gastric cancer tissues, namely primary tumor (PT), invasive margin (IM), and normal tissue (NT). These markers include 11 myeloid cell markers (CD11b, CD33, CD11c, CD14, CD15, CD16, CD68, CD86, CD163, CD206, and CD66b) and 7 immune checkpoints (CD73, IDO, LAG3, PD-1, SIGLEC9, SIRPA, and TIM3). LASSO regression and Cox proportional hazards model were applied to screen immune markers associated with postoperative OS in gastric cancer patients, and a predictive model for 1-, 3-, and 5-year postoperative OS was established. Data from the internal and external validation set were used for internal and external verification of the predictive model. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) was calculated to assess the discriminative ability of the model. Results In the training set, LASSO regression and Cox proportional hazards model identified 4 immune marker signatures (CD14_PT, CD15_PT, CD206_PT, and SIGLEC9_PT) to construct the predictive model. ROC curve analysis showed that the AUCs of the model for predicting 1-, 3-, and 5-year postoperative OS were 0.74, 0.75, and 0.84, respectively. When the model was applied to the internal and external validation sets, the AUCs for 1-, 3-, and 5-year postoperative OS were 0.74, 0.72, 0.72 (internal validation set) and 0.72, 0.71, 0.74 (external validation set), respectively. Cox multivariate regression analysis showed that the risk score was an independent risk factor for postoperative OS in patients with gastric cancer. Conclusion The model constructed based on LASSO-Cox regression exhibits good predictive performance and can effectively predict postoperative OS in gastric cancer patients, which may serve as a basis for intensive treatment of high-risk patients after surgery.

Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to study white and gray matter (GM) micro-organization and structural connectivity in the brain. Super-resolution track-density imaging (TDI) is an image reconstruction method for dMRI data, which is capable of providing spatial resolution beyond the acquired data, as well as novel and meaningful anatomical contrast that cannot be obtained with conventional reconstruction methods. TDI has been used to reveal anatomical features in human and animal brains. In this study, we used short track TDI (stTDI), a variation of TDI with enhanced contrast for GM structures, to reconstruct direction-encoded color maps of fixed tree shrew brain. The results were compared with those obtained with the traditional diffusion tensor imaging (DTI) method. We demonstrated that fine microstructures in the tree shrew brain, such as Baillarger bands in the primary visual cortex and the longitudinal component of the mossy fibers within the hippocampal CA3 subfield, were observable with stTDI, but not with DTI reconstructions from the same dMRI data. The possible mechanisms underlying the enhanced GM contrast are discussed.
Animals ; Brain Mapping ; Diffusion Tensor Imaging ; methods ; Hippocampus ; diagnostic imaging ; Image Processing, Computer-Assisted ; methods ; Male ; Neural Pathways ; diagnostic imaging ; Tupaiidae ; anatomy & histology ; Visual Cortex ; diagnostic imaging