Neurodegenerative disorders (NDDs) are prevalent chronic conditions characterized by progressive synaptic loss and pathological protein alterations. Increasing evidence suggested that mitochondrial quality control (MQC) serves as the key cellular process responsible for clearing misfolded proteins and impaired mitochondria. Herein, we provided a comprehensive analysis of the mechanisms through which MQC mediates the onset and progression of NDDs, emphasizing mitochondrial dynamic stability, the clearance of damaged mitochondria, and the generation of new mitochondria. In addition, traditional Chinese medicines (TCMs) and their active monomers targeting MQC in NDD treatment have been demonstrated. Consequently, we compiled the TCMs that show great potential in the treatment of NDDs by targeting MQC, aiming to offer novel insights and a scientific foundation for the use of MQC stabilizers in NDD prevention and treatment.

Objective:To explore the causal relationship between human immune cells and hypertrophic scar (HS) using two-sample bidirectional Mendelian randomization (MR) method.Methods:This study was based on two-sample MR method, and the datasets of 731 immune cells and HS were obtained from the genome-wide association study (GWAS) catalog database and Finngen database, respectively. A significance threshold was established to discern single nucleotide polymorphism (SNP) significantly correlated with immune cells or HS, thereby eliminating the impact of weak instrumental variable bias. The inverse variance weighted (IVW) method (meanwhile, the Benjamini-Hochberg (BH) procedure of false discovery rate (FDR) to adjust P values) was used for preliminary detection of the causal relationship between immune cells and HS and screen the immune cells that had a significant causal relationship with HS. Further, the causal relationship between the selected immune cells and HS was detected through five two-sample MR methods: IVW method, weighted median method, simple mode method, weighted mode method, and MR-Egger method, and the scatter plot was drawn. SNPs conformed to the hypothesis were subjected to Cochran Q test for heterogeneity assessment, MR-Egger regression coupled with MR-PRESSO to eliminate horizontal pleiotropic effects, and a leave-one-out analysis was also conducted to determine if significant results were driven by individual SNP. Finally, the IVW method contained in the two-sample MR analysis was utilized to inversely examine the causal relationship between HS and immune cells. Results:The number of SNPs in 731 immune cells reaching the significance threshold varied from 7 to 1 786, while in HS, 119 SNPs met the significance threshold, with the F values of all SNPs being greater than 10, suggesting a low likelihood of bias from weak instrumental variables. The IVW method revealed that 60 types of immune cells potentially had a causal relationship with HS (with all P values <0.05), and after adjustment using the BH method, only CD45RA and CD39 positive regulatory T cell (Treg) maintained a potentially strong causal relationship with HS ( PFDR<0.05). The IVW method (with odds ratio of 1.16 and 95% confidence interval of 1.08-1.24, P<0.05, PFDR<0.05), weighted median method (with odds ratio of 1.16 and 95% confidence interval of 1.05-1.28, P<0.05), weighted mode method (with odds ratio of 1.14 and 95% confidence interval of 1.02-1.27, P<0.05), and MR-Egger method (with odds ratio of 1.18 and 95% confidence interval of 1.07-1.30, P<0.05) of scatter plot all suggested a causal relationship between the 14 SNPs of CD45RA and CD39 positive Treg and risk of HS, only simple mode method of scatter plot suggested a not obvious relationship between the 14 SNPs of CD45RA and CD39 positive Treg and risk of HS ( P>0.05). Cochran Q test indicated no heterogeneity in the causal relationship between CD45RA on CD39 positive Treg and HS ( P>0.05). MR-Egger regression and MR-PRESSO analyses showed that there was no horizontal pleiotropy in the significant causal relationship between CD45RA and CD39 positive Treg and HS ( P>0.05). Leave-one-out analysis confirmed that the significant causal relationship between CD45RA and CD39 positive Treg and HS remained stable after sequentially removing individual SNP. Reverse two-sample MR analysis showed that HS had no potential causal relationship with any of the 731 types of immune cells ( P>0.05). Conclusions:From the perspective of genetics, it is revealed that immune cells CD45RA and CD39 positive Treg may increase the risk of HS.

Objective:To automatically classify hypertensive heart disease (HHD) and hypertrophic cardiomyopathy (HCM) based on mul-titask learning algorithm using native T1 mapping images.Methods:A total of 203 patients admitted to Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University from January 2017 to December 2021 were enrolled, including 53 patients with HHD, 121 patients with HCM, and 29 patients with normal control (NC). Native T1 mapping images of all enrolled patients were acquired using MRI and processed by a multi-task learning algorithm. The classification performance of each model was validated using ten-fold crossover, confusion matrix, and receiver operator characteristic (ROC) curves. The Resnet 50 model based on the original images was established as a control.Results:The ten-fold crossover validation results showed that the MTL-1 024, MTL-64, and MTL-all models showed better performance in terms of area under the curve (AUC), accuracy, sensitivity, and specificity compared to the Resnet 50 model. In the classification task, the MTL-64 model showed the best performance in terms of AUC (0.942 1), while the MTL-all model reached the highest value in terms of accuracy (0.852 2). In the segmentation task, the MTL-64 model achieved the best results with the Dice coefficient (0.879 7). The confusion matrix plot showed that the MTL model outperforms the Resnet 50 model based on the original image in terms of overall performance. The ROC graphs of all MTL models were significantly higher than the original image input Resnet 50 model.Conclusions:Multi-task learning-based native T1 mapping images are effective for automatic classification of HHD and HCM.

[This corrects the article DOI: 10.1016/j.apsb.2024.04.021.].

As one of the most malignant tumors worldwide, lung cancer, fueled by metastasis, has shown rising mortality rates. However, effective clinical strategies aimed at preventing metastasis are lacking owing to its dynamic multi-step, complicated, and progressive nature. Immunotherapy has shown promise in treating cancer metastasis by reversing the immunosuppressive network of the tumor microenvironment. However, drug resistance inevitably develops due to inadequate delivery of immunostimulants and an uncontrolled immune response. Consequently, adverse effects occur, such as autoimmunity, from the non-specific immune activation and non-specific inflammation in off-target organs. Nanocarriers that improve drug solubility, permeability, stability, bioavailability, as well as sustained, controlled, and targeted delivery can effectively overcome drug resistance and enhance the therapeutic effect while reducing adverse effects. In particular, nanomedicine-based immunotherapy can be utilized to target tumor metastasis, presenting a promising therapeutic strategy for lung cancer. Nanotechnology strategies that boost the immunotherapy effect are classified based on the metastatic cascade related to the tumor immune microenvironment; the breaking away of primary tumors, circulating tumor cell dissemination, and premetastatic niche formation cause distant secondary site colonization. In this review, we focus on the opportunities and challenges of integrating immunotherapy with nanoparticle formulation to establish nanotechnology-based immunotherapy by modulating the tumor microenvironment for preclinical and clinical applications in the management of patients with metastatic lung cancer. We also discuss prospects for the emerging field and the clinical translation potential of these techniques.
Humans ; Lung Neoplasms/therapy* ; Tumor Microenvironment ; Neoplasms/drug therapy* ; Immunotherapy/methods*

Objective:To design a lung function prediction method that combines transfer learning and multimodal feature fusion, aiming to improve the accuracy of lung function prediction in patients with idiopathic pulmonary fibrosis (IPF).Methods:CT images and clinical text data were reprocessed, and an adaptive module was designed to find the most suitable lung function attenuation function for IPF patients. The feature extraction module was utilized to comprehensively extract features. The feature extraction module comprises three sub-modules, including CT feature extraction, clinical text feature extraction, and lung function feature extraction. A multimodal feature prediction network was used to comprehensively evaluate the attenuation of lung function. The pre-trained model was fine-tuned to improve the predictive performance of the model.Results:Based on the OSIC pulmonary fibrosis progression competition dataset, it is found through the adaptive module that the linear attenuation hypothesis is more in line with the trend of pulmonary function decline in patients. Different modal data prediction experiments show that the model incorporating clinical text features has better predictive ability than the model using only CT images. The model combining CT images, clinical text features, and lung function features have optimal predictive results. The lung function prediction method combining transfer learning and multimodal feature fusion has modified version of the Laplace log likelihood (LLLm) of ?6.706 5, root mean squared error (RMSE) of 184.5, and mean absolute error (MAE) of 146.2, which outperforms other methods in terms of performance. The pre-trained model has higher prediction accuracy compared to the zero base training model.Conclusions:The lung function prediction method designed by combining transfer learning and multimodal feature fusion can effectively predict the lung function status of IPF patients at different weeks, providing important support for patient health management and disease diagnosis.

Osteoporotic vertebral compression fracture (OVCF) can lead to lower back pain and may be even accompanied by scoliosis, neurological dysfunction and other complications, which will affect the daily activities and life quality of patients. Vertebral augmentation is an effective treatment method for OVCF, but it cannot correct unbalance of bone metabolism or improve the osteoporotic status, causing complications like lower back pain, limited spinal activities and vertebral refracture. The post-operative systematic and standardized rehabilitation treatments can improve curative effect and therapeutic efficacy of anti-osteoporosis, reduce risk of vertebral refracture, increase patient compliance and improve quality of life. Since there still lack relevant clinical treatment guidelines for postoperative rehabilitation treatments following vertebral augmentation for OVCF, the current treatments are varied with uneven therapeutic effect. In order to standardize the postoperative rehabilitation treatment, the Spine Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized relevant experts to refer to relevant literature and develop the "Guideline for postoperative rehabilitation treatment following vertebral augmentation for osteoporotic vertebral compression fracture (2022 version)" based on the clinical guidelines published by the American Academy of Orthopedic Surgeons (AAOS) as well as on the principles of scientificity, practicality and advancement. The guideline provided evidence-based recommendations on 10 important issues related to postoperative rehabilitation treatments of OVCF.