Background/Aims: Liver transplantation is the most effective treatment for the sickest patients with acute-on-chronic liver failure (ACLF). However, the influence of donor age on liver transplantation, especially in ACLF patients, is still unclear. Methods: In this study, we used the data of the Scientific Registry of Transplant Recipients. We included patients with ACLF who received liver transplantation from January 1, 2007, to December 31, 2017, and the total number was 13,857. We allocated the ACLF recipients by age intogroup I (donor age ≤17 years, n=647); group II (donor age 18–59 years, n=11,423); and group III (donor age ≥60 years, n=1,787). Overall survival (OS), graft survival, and mortality were com-pared among the three age groups and the four ACLF grades. Cox regression was also analyzed. Results: The 1-, 3-, and 5-year OS rates were 89.6%, 85.5%, and 82.0% in group I; 89.4%, 83.4%, and 78.2% in group II; and 86.8%, 78.4%, and 71.4% in group III, respectively (p<0.001).When we analyzed the different effects of donor age on OS with different ACLF grades, in groupsII and III, we observed statistical differences. Finally, the cubic spline curve told us that the relative death rate changed linearly with increasing donor age. Conclusions Donor age is related to OS and graft survival of ACLF patients after transplanta-tion, and poorer results were associated with elderly donors. In addition, different donor ages have different effects on recipients with different ACLF grades.

Background/Aims: Liver transplantation is the most effective treatment for the sickest patients with acute-on-chronic liver failure (ACLF). However, the influence of donor age on liver transplantation, especially in ACLF patients, is still unclear. Methods: In this study, we used the data of the Scientific Registry of Transplant Recipients. We included patients with ACLF who received liver transplantation from January 1, 2007, to December 31, 2017, and the total number was 13,857. We allocated the ACLF recipients by age intogroup I (donor age ≤17 years, n=647); group II (donor age 18–59 years, n=11,423); and group III (donor age ≥60 years, n=1,787). Overall survival (OS), graft survival, and mortality were com-pared among the three age groups and the four ACLF grades. Cox regression was also analyzed. Results: The 1-, 3-, and 5-year OS rates were 89.6%, 85.5%, and 82.0% in group I; 89.4%, 83.4%, and 78.2% in group II; and 86.8%, 78.4%, and 71.4% in group III, respectively (p<0.001).When we analyzed the different effects of donor age on OS with different ACLF grades, in groupsII and III, we observed statistical differences. Finally, the cubic spline curve told us that the relative death rate changed linearly with increasing donor age. Conclusions Donor age is related to OS and graft survival of ACLF patients after transplanta-tion, and poorer results were associated with elderly donors. In addition, different donor ages have different effects on recipients with different ACLF grades.

Background/Aims: Liver transplantation is the most effective treatment for the sickest patients with acute-on-chronic liver failure (ACLF). However, the influence of donor age on liver transplantation, especially in ACLF patients, is still unclear. Methods: In this study, we used the data of the Scientific Registry of Transplant Recipients. We included patients with ACLF who received liver transplantation from January 1, 2007, to December 31, 2017, and the total number was 13,857. We allocated the ACLF recipients by age intogroup I (donor age ≤17 years, n=647); group II (donor age 18–59 years, n=11,423); and group III (donor age ≥60 years, n=1,787). Overall survival (OS), graft survival, and mortality were com-pared among the three age groups and the four ACLF grades. Cox regression was also analyzed. Results: The 1-, 3-, and 5-year OS rates were 89.6%, 85.5%, and 82.0% in group I; 89.4%, 83.4%, and 78.2% in group II; and 86.8%, 78.4%, and 71.4% in group III, respectively (p<0.001).When we analyzed the different effects of donor age on OS with different ACLF grades, in groupsII and III, we observed statistical differences. Finally, the cubic spline curve told us that the relative death rate changed linearly with increasing donor age. Conclusions Donor age is related to OS and graft survival of ACLF patients after transplanta-tion, and poorer results were associated with elderly donors. In addition, different donor ages have different effects on recipients with different ACLF grades.

Background/Aims: Liver transplantation is the most effective treatment for the sickest patients with acute-on-chronic liver failure (ACLF). However, the influence of donor age on liver transplantation, especially in ACLF patients, is still unclear. Methods: In this study, we used the data of the Scientific Registry of Transplant Recipients. We included patients with ACLF who received liver transplantation from January 1, 2007, to December 31, 2017, and the total number was 13,857. We allocated the ACLF recipients by age intogroup I (donor age ≤17 years, n=647); group II (donor age 18–59 years, n=11,423); and group III (donor age ≥60 years, n=1,787). Overall survival (OS), graft survival, and mortality were com-pared among the three age groups and the four ACLF grades. Cox regression was also analyzed. Results: The 1-, 3-, and 5-year OS rates were 89.6%, 85.5%, and 82.0% in group I; 89.4%, 83.4%, and 78.2% in group II; and 86.8%, 78.4%, and 71.4% in group III, respectively (p<0.001).When we analyzed the different effects of donor age on OS with different ACLF grades, in groupsII and III, we observed statistical differences. Finally, the cubic spline curve told us that the relative death rate changed linearly with increasing donor age. Conclusions Donor age is related to OS and graft survival of ACLF patients after transplanta-tion, and poorer results were associated with elderly donors. In addition, different donor ages have different effects on recipients with different ACLF grades.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Bone formation and deposition are initiated by sensory nerve infiltration in adaptive bone remodeling. Here, we focused on the role of Semaphorin 3A (Sema3A), expressed by sensory nerves, in mechanical loads-induced bone formation and nerve withdrawal using orthodontic tooth movement (OTM) model. Firstly, bone formation was activated after the 3rd day of OTM, coinciding with a decrease in sensory nerves and an increase in pain threshold. Sema3A, rather than nerve growth factor (NGF), highly expressed in both trigeminal ganglion and the axons of periodontal ligament following the 3rd day of OTM. Moreover, in vitro mechanical loads upregulated Sema3A in neurons instead of in human periodontal ligament cells (hPDLCs) within 24 hours. Furthermore, exogenous Sema3A restored the suppressed alveolar bone formation and the osteogenic differentiation of hPDLCs induced by mechanical overload. Mechanistically, Sema3A prevented overstretching of F-actin induced by mechanical overload through ROCK2 pathway, maintaining mitochondrial dynamics as mitochondrial fusion. Therefore, Sema3A exhibits dual therapeutic effects in mechanical loads-induced bone formation, both as a pain-sensitive analgesic and a positive regulator for bone formation.
Humans ; Bone Remodeling ; Cell Differentiation ; Osteogenesis ; Semaphorin-3A/pharmacology* ; Trigeminal Ganglion/metabolism*

Since the advent of coronary artery bypass grafting (CABG), the selection of bypass conduits has always been one of the most controversial topics in this field. Arterial conduits have received extensive attention due to their excellent biological features and high patency. In recent years, the application of arterial grafting and total arterial grafting in China keeps increasing in recent years, but there is still a gap compared to the Europe and America. Previous clinical studies have indicated the benefits of the total arterial grafting in terms of patency and long-term outcomes, but the advantage of multiple arterial grafting over other procedures is still in need to be confirmed with high-quality randomized controlled trials. This article reviews the clinical application and strategy of total-arterial CABG, aiming to provide objective reference for future clinical research and application.

Bone formation and deposition are initiated by sensory nerve infiltration in adaptive bone remodeling.Here,we focused on the role of Semaphorin 3A(Sema3A),expressed by sensory nerves,in mechanical loads-induced bone formation and nerve withdrawal using orthodontic tooth movement(OTM)model.Firstly,bone formation was activated after the 3rd day of OTM,coinciding with a decrease in sensory nerves and an increase in pain threshold.Sema3A,rather than nerve growth factor(NGF),highly expressed in both trigeminal ganglion and the axons of periodontal ligament following the 3rd day of OTM.Moreover,in vitro mechanical loads upregulated Sema3A in neurons instead of in human periodontal ligament cells(hPDLCs)within 24 hours.Furthermore,exogenous Sema3A restored the suppressed alveolar bone formation and the osteogenic differentiation of hPDLCs induced by mechanical overload.Mechanistically,Sema3A prevented overstretching of F-actin induced by mechanical overload through ROCK2 pathway,maintaining mitochondrial dynamics as mitochondrial fusion.Therefore,Sema3A exhibits dual therapeutic effects in mechanical loads-induced bone formation,both as a pain-sensitive analgesic and a positive regulator for bone formation.

Bone formation and deposition are initiated by sensory nerve infiltration in adaptive bone remodeling.Here,we focused on the role of Semaphorin 3A(Sema3A),expressed by sensory nerves,in mechanical loads-induced bone formation and nerve withdrawal using orthodontic tooth movement(OTM)model.Firstly,bone formation was activated after the 3rd day of OTM,coinciding with a decrease in sensory nerves and an increase in pain threshold.Sema3A,rather than nerve growth factor(NGF),highly expressed in both trigeminal ganglion and the axons of periodontal ligament following the 3rd day of OTM.Moreover,in vitro mechanical loads upregulated Sema3A in neurons instead of in human periodontal ligament cells(hPDLCs)within 24 hours.Furthermore,exogenous Sema3A restored the suppressed alveolar bone formation and the osteogenic differentiation of hPDLCs induced by mechanical overload.Mechanistically,Sema3A prevented overstretching of F-actin induced by mechanical overload through ROCK2 pathway,maintaining mitochondrial dynamics as mitochondrial fusion.Therefore,Sema3A exhibits dual therapeutic effects in mechanical loads-induced bone formation,both as a pain-sensitive analgesic and a positive regulator for bone formation.

Bone formation and deposition are initiated by sensory nerve infiltration in adaptive bone remodeling.Here,we focused on the role of Semaphorin 3A(Sema3A),expressed by sensory nerves,in mechanical loads-induced bone formation and nerve withdrawal using orthodontic tooth movement(OTM)model.Firstly,bone formation was activated after the 3rd day of OTM,coinciding with a decrease in sensory nerves and an increase in pain threshold.Sema3A,rather than nerve growth factor(NGF),highly expressed in both trigeminal ganglion and the axons of periodontal ligament following the 3rd day of OTM.Moreover,in vitro mechanical loads upregulated Sema3A in neurons instead of in human periodontal ligament cells(hPDLCs)within 24 hours.Furthermore,exogenous Sema3A restored the suppressed alveolar bone formation and the osteogenic differentiation of hPDLCs induced by mechanical overload.Mechanistically,Sema3A prevented overstretching of F-actin induced by mechanical overload through ROCK2 pathway,maintaining mitochondrial dynamics as mitochondrial fusion.Therefore,Sema3A exhibits dual therapeutic effects in mechanical loads-induced bone formation,both as a pain-sensitive analgesic and a positive regulator for bone formation.