Prognostic value and association of the age-adjusted Charlson Comorbidity Index with sarcopenia within patients with gastric cancer after radical resection.

PURPOSE: The association between the age-adjusted Charlson Comorbidity Index (ACCI) and sarcopenia in patients with gastric cancer (GC) remains ambiguous. This study aimed to investigate the association between the ACCI and sarcopenia and the prognostic value in patients with GC after radical resection. In addition, this study aimed to develop a novel prognostic scoring system based on these factors. METHODS: Univariate and multivariate Cox regression analyses were used to determine prognostic factors in patients undergoing radical GC resection. Based on the ACCI and sarcopenia, a new prognostic score (age-adjusted Charlson Comorbidity Index and Sarcopenia [ACCIS]) was established, and its prognostic value was assessed. RESULTS: This study included 1068 patients with GC. Multivariate analysis revealed that the ACCI and sarcopenia were independent risk factors during the prognosis of GC (P = 0.001 and P < 0.001, respectively). A higher ACCI score independently predicted sarcopenia (P = 0.014). A high ACCIS score was associated with a greater American Society of Anesthesiologists score, higher pathologic TNM (pTNM) stage, and larger tumor size (all P < 0.05). Multivariate analysis demonstrated that the ACCIS independently predicted the prognosis for patients with GC (P < 0.001). By incorporating the ACCIS score into a prognostic model with sex, pTNM stage, tumor size, and tumor differentiation, we constructed a nomogram to predict the prognosis accurately (concordance index of 0.741). CONCLUSION: The ACCI score and sarcopenia are significantly correlated in patients with GC. The integration of the ACCI score and sarcopenia markedly enhances the accuracy of prognostic predictions in patients with GC.

Machine Learning Radiomics Liver Function Model for Prognostic Prediction After Radical Resection of Advanced Gastric Cancer: A Retrospective Study.

PURPOSE: We aimed to establish a machine learning radiomics liver function model to explore how liver function affects the prognosis of patients with gastric cancer (GC). METHODS: Patients with advanced GC were retrospectively enrolled in this study. Eight machine learning radiomic models were constructed by extracting radiomic features from portal-vein-phase contrast-enhanced computed tomography (CE-CT) images. Clinicopathological features were determined using univariate and multifactorial Cox regression analyses. These features were used to construct a GC survival nomogram. RESULTS: A total of 510 patients with GC were split into training and test cohorts in an 8:2 ratio. Kaplan-Meier analysis showed that patients with type I liver function had a better prognosis. Fifteen significant features were retained to establish the machine learning model. LightBGM showed the best predictive performance in the training (area under the receiver operating characteristic curve [AUC] 0.978) and test cohorts (AUC 0.714). Multivariate analysis revealed that gender, age, liver function, Nutritional Risk Screening 2002 (NRS-2002) score, tumor-lymph node-metastasis stage, tumor size, and tumor differentiation were independent risk factors for GC prognosis. The survival nomogram based on machine learning radiomics, instead of liver biochemical indicators, still had high accuracy (C-index of 0.771 vs. 0.773). CONCLUSION: The machine learning radiomics liver function model has high diagnostic value in predicting the influence of liver function on prognosis in patients with GC.

Surface Functionalization with Proanthocyanidins Provides an Anti-Oxidant Defense Mechanism That Improves the Long-Term Stability and Osteogenesis of Titanium Implants.

PURPOSE: Aseptic loosening is a major complication after total joint replacement. Reactive oxygen species generated by local tissue cells and liberated from implant surfaces have been suggested to cause implant failures. Surface modification of titanium (Ti)-based implants with proanthocyanidins (PAC) is a promising approach for the development of anti-oxidant defense mechanism to supplement the mechanical functions of Ti implants. In this study, a controlled PAC release system was fabricated on the surface of Ti substrates using the layer-by-layer (LBL) assembly. MATERIALS AND METHODS: Polyethyleneimine (PEI) base layer was fabricated to enable layer-by-layer (LBL) deposition of hyaluronic acid/chitosan (HA/CS) multi-layers without or with the PAC. Surface topography and wettability of the fabricated HA/CS-PAC substrates were characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier-transform infrared spectroscopy (FTIR) and contact angle measurement. PAC release profiles were investigated using drug release assays. MC3T3-E1 pre-osteoblast cells were used to assess the osteo-inductive effects of HA/CS-PAC substrates under conditions H2O2-induced oxidative stress in vitro. A rat model of femoral intramedullary implantation evaluated the osseo-integration and osteo-inductive potential of the HA/CS-PAC coated Ti implants in vivo. RESULTS: SEM, AFM, FTIR and contact angle measurements verified the successful fabrication of Ti surfaces with multi-layered HA/CS-PAC coating. Drug release assays revealed controlled and sustained release of PAC over 14 days. In vitro, cell-based assays showed high tolerability and enhanced the osteogenic potential of MC3T3-E1 cells on HA/CS-PAC substrates when under conditions of H2O2-induced oxidative stress. In vivo evaluation of femoral bone 14 days after femoral intramedullary implantation confirmed the enhanced osteo-inductive potential of the HA/CS-PAC coated Ti implants. CONCLUSION: Multi-layering of HA/CS-PAC coating onto Ti-based surfaces by the LBL deposition significantly enhances implant osseo-integration and promotes osteogenesis under conditions of oxidative stress. This study provides new insights for future applications in the field of joint arthroplasty.