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.

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.

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*

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.

Emerging and re-emerging RNA viruses occasionally cause epidemics and pandemics worldwide, such as the on-going outbreak of the novel coronavirus SARS-CoV-2. Herein, we identified two potent inhibitors of human DHODH, S312 and S416, with favorable drug-likeness and pharmacokinetic profiles, which all showed broad-spectrum antiviral effects against various RNA viruses, including influenza A virus, Zika virus, Ebola virus, and particularly against SARS-CoV-2. Notably, S416 is reported to be the most potent inhibitor so far with an EC of 17 nmol/L and an SI value of 10,505.88 in infected cells. Our results are the first to validate that DHODH is an attractive host target through high antiviral efficacy in vivo and low virus replication in DHODH knock-out cells. This work demonstrates that both S312/S416 and old drugs (Leflunomide/Teriflunomide) with dual actions of antiviral and immuno-regulation may have clinical potentials to cure SARS-CoV-2 or other RNA viruses circulating worldwide, no matter such viruses are mutated or not.
Animals ; Antiviral Agents ; pharmacology ; therapeutic use ; Betacoronavirus ; drug effects ; physiology ; Binding Sites ; drug effects ; Cell Line ; Coronavirus Infections ; drug therapy ; virology ; Crotonates ; pharmacology ; Cytokine Release Syndrome ; drug therapy ; Drug Evaluation, Preclinical ; Gene Knockout Techniques ; Humans ; Influenza A virus ; drug effects ; Leflunomide ; pharmacology ; Mice ; Mice, Inbred BALB C ; Orthomyxoviridae Infections ; drug therapy ; Oseltamivir ; therapeutic use ; Oxidoreductases ; antagonists & inhibitors ; metabolism ; Pandemics ; Pneumonia, Viral ; drug therapy ; virology ; Protein Binding ; drug effects ; Pyrimidines ; biosynthesis ; RNA Viruses ; drug effects ; physiology ; Structure-Activity Relationship ; Toluidines ; pharmacology ; Ubiquinone ; metabolism ; Virus Replication ; drug effects

Objective:To explore the effects of planned aerobic-resistance exercise on nutritional status and intra-dialytic hypotension of maintenance hemodialysis (MHD) patients during dialysis.Methods:From January 2018 to October 2018, we selected 106 hemodialysis patients in the First Affiliated Hospital of Zhengzhou University by convenience sampling. All of patients were divided into control group and observation group with method of the random number table, 53 cases in each group. Control group carried out the routine management model. On the basis of that in control group, observation group implemented the planned aerobic-resistance exercise during dialysis for three months. We compared the hemoglobin (Hb) , serum prealbumin (PA) , body cell mass (BCM) , bone mineral content (BMC) , skeletal muscle content (SMC) , cardiopulmonary function and incidence of intra-dialytic hypotension of patients between two groups.Results:Three months after intervention, the Hb, PA, BCM, BMC and SMC of patients in observation group were (99.77±6.29) g/L, (344.55±37.12) mg/L, (31.42±3.15) kg, (3.08±0.41) kg and (27.85±2.51) kg respectively higher than those in control group with statistical differences ( P<0.05) ; the maximal oxygen uptake and left ventricular ejection fraction of observation group were (14.93±2.82) ml/ (kg·min) and (58.21±5.22) % respectively higher than those in control group with statistical differences ( P<0.05) . During intervention, the incidence of intra-dialytic hypotension of observation group was 9.05% (187/2 067) lower than that in control group [19.45% (402/2 067) ]with a statistical difference (χ 2=91.520, P<0.01) . Conclusions:Planned aerobic-resistance exercise during dialysis can improve the nutritional status, cardiopulmonary function and endurance of MHD patients, and reduce the incidence of intra-dialytic hypotension which is worthy of clinical promotion.

Objective: To explore the association between the exposure to major air pollutants in pre-pregnancy and early pregnancy (peri-conceptional period) and gestational diabetes mellitus (GDM). Methods: From March 2015 to April 2018, 4 817 pregnancies were recruited at three prenatal check-ups hospital in Hefei (Hefei First People′s Hospital, Hefei. Maternal and Child Care Hospital and the First Affiliated Hospital of Anhui Medical University), China. Questionnaire was used to collect the demographic data, the health status and lifestyle of pregnant women. GDM was diagnosed according to the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2017 Edition). Logistic regression was used to investigate the association of exposure to major air pollutants (PM_2.5, PM₁₀, SO₂, CO and NO₂) during different periods of pre-pregnancy (12 weeks before pregnancy) and first trimester (12 weeks after last menstruation) and duration of exposure to high levels of pollutants with GDM. Results: The mean±SD of the age of subjects was (29.14±4.19) years old and the prevalence of GDM was 21.4% (n=1 030). The results of multivariate logistic regression analysis showed that after adjusting for confounding factors, the risk of GDM increased gradually with the prolonged exposure time of high-concentration pollutants compared with pregnant women who were not exposed to high pollution during the pre-pregnancy (χ²=61.28, P_trend<0.001) with the OR (95%CI) values for exposure time of 1, 2, and 3 months about 1.42 (1.10-1.84), 1.73 (1.29-2.33), and 2.51 (1.75-3.59), respectively. In the pre-pregnancy period, in every 10 μg/m³ increase of PM_2.5 and PM₁₀, the OR (95%CI) values of GDM were 1.14 (1.08-1.20) and 1.13 (1.08-1.19), respectively; for each increase of 1 μg/m³ and 0.10 mg/m³ of SO₂ and CO, the OR (95% CI) values of GDM were 1.03 (1.01-1.05) and 1.07 (1.01-1.13), respectively. For every 1 μg/m³ increase in the average concentration of SO₂ in the first trimester, the OR (95%CI) value of GDM was 1.02 (1.01-1.05). Conclusion PM_2.5, PM₁₀, SO₂ and CO exposure during the pre-pregnancy and SO₂ exposure in first trimester were positively correlated with the risk of GDM.