The osteoporotic hip and vertebral fractures caused excess mortality rates in this population of Mainland China. The current diagnosis and medical treatment following the fragility fractures is still insufficient in Mainland China.

BackgroundThe combination of immune checkpoint blockade and chemotherapy has revolutionized the treatment of advanced gastric cancer (GC). It is crucial to unravel chemotherapy-induced tumor microenvironment (TME) modulation and identify which immunotherapy would improve antitumor effect.MethodsIn this study, tumor-associated immune cells (TAICs) infiltration in residual tumor after neoadjuvant chemotherapy (NAC) together with 1075 cases of treatment-naïve GC patients was analyzed first. Then we performed multiplex fluorescence staining of a panel of immune markers (CD3, CD4, CD8, FOXP3 and PDL1) and T cell receptor β-chain sequencing to phenotype and enumerate T cell subpopulations and clonal expansion in paired GC samples (prechemotherapy and postchemotherapy) from another cohort of 30 cases of stage II/III GC patients.ResultsInfiltration of CD68+ macrophages in residual tumors after NAC was significantly decreased compared with treatment-naïve GC patients, while no significant difference observed with respect to other immune markers. In residual tumors, post-NAC CD8 +T cells and CD68+ macrophages levels were significantly associated with chemotherapy response. Post-NAC CD8+ T cell levels remained as an independent predictor for favorable prognosis. Furthermore, when comparing the paired samples before and after NAC from 30 cases of stage II/III GC patients, we found FOXP3+ regulatory T cells proportion significantly decreased after chemotherapy. Pre-NAC FOXP3+ T reg cells level was much richer in the response group and decreased more significantly in the stromal compartment. CD8+ cytotoxic T lymphocytes levels were elevated after chemotherapy, which was more significant in the group treated with XELOX regimen and in patients with better response, consistent with the TCR diversity elevation.ConclusionsThese findings have deepened our understanding of the immune modulating effect of chemotherapy and suggest that the immune profile of specimens after standard chemotherapy should be considered for the personalized immunotherapy to ultimately improve clinical outcome in patients with GC.

Treatment procedures for anterior disc displacement (ADD) of temporomandibular joint (TMJ) are far from reaching a consensus. The aim of the study was to evaluate disc status changes of anterior disc displacement with reduction (ADDWR) and without reduction (ADDWoR) comparatively, to get a better understanding of the disease progress without intervention. This longitudinal retrospective study included 217 joints in 165 patients, which were divided into ADDWR group and ADDWoR group based on magnetic resonance imaging (MRI) examination. The joints were assessed quantitatively for disc length and displacement distance at initial and follow-up visits. Disc morphology, which was classified in 5 types, was also evaluated. Paired t-test and Wilcoxon signed rank test were used to assess intra-group differences and independent t-test for inter-group differences. Moreover, analysis of covariance was applied to analyze influential factors for changes in disc length and displacement distance. According to our results, discs tended to become shorter, move further forward and distort more seriously in ADDWoR group than in ADDWR group after follow-up. Moreover, discs were prone to become shorter and more anteriorly displaced in teenagers, type I and III morphologies, advanced Wilkes stages, or those with joint effusion. Follow-up period seemed to be not critical.

Sleep stage classification, including wakefulness (W), rapid eye movement (REM), and nonrapid eye movement (NREM) which includes three sleep stages that describe the depth of sleep, is one of the most critical steps in effective diagnosis and treatment of sleep-related disorders. Clinically, sleep staging is performed by domain experts through visual inspection of polysomnography (PSG) recordings, which is time-consuming, labor-intensive and often subjective in nature. Therefore, this study develops an automatic sleep staging system, which uses single channel electroencephalogram (EEG) signal, for convenience of wearing and less interference in the sleep, to do automatic identification of various sleep stages. To achieve the automatic sleep staging system, this study proposes a two-layer stacked ensemble model, which combines the advantages of random forest (RF) and LightGBM (LGB), where RF focuses on reducing the variance of the proposed model while LGB focuses on reducing the bias of the proposed model. Particularly, the proposed model introduces a class balance strategy to improve the N1 stage recognition rate. In order to evaluate the performance of the proposed model, experiments are performed on two datasets, including Sleep-EDF database (SEDFDB) and Sleep-EDF Expanded database (SEDFEDB). In the SEDFDB, the overall accuracy (ACC), weight F1-score (WF1), Cohen's Kappa coefficient (Kappa), sensitivity of N1 (SEN-N1) obtained by proposed model are 91.2%, 0.916, 0.864 and 72.52% respectively using subject-non-independent test (SNT). In parallel, the ACC, WF1, Kappa, SEN-N1 obtained by proposed model are 82.4%, 0.751, 0.719 and 27.15% respectively using subject-independent test (SIT). Experimental results show that the performance of the proposed model are competitive with the state-of-the-art methods and results, and the recognition rate of N1 stage is significantly improved. Moreover, in the SEDFEDB, the experimental results indicate the robustness and generality of the proposed model. INDEX TERMS Sleep stage classification, single channel EEG signal, two-layer stacked ensemble model, random forest, LightGBM.