Abstract Background Corona Virus Disease 2019 (COVID-19) is the most prevalent global pandemic in recent times. Graves disease (GD), an autoimmune thyroid disease, is a clinical syndrome caused by excessive thyroid hormones. Our study is to understand the current epidemiological situation of COVID-19 infection in GD patients, and to analyze whether COVID-19 will affect the thyroid function, thyroid autoantibody and metabolism of GD patients. Methods 109 GD patients were followed by Shanghai General Hospital Thyroid Disease Center (TDC) from November 2022 to June 2023. There were three groups defined, i.e., pre, one-month after and three months after infection with COVID-19. SPSS was used to analyze the recruited data. Results 109 GD patients are infected with COVID-19 (72.48%), uncontrolled GD patients with high FT3 had a higher COVID-19 infection rate (79.31%). As for thyroid function in 35 GD patients with antithyroid drug (ATD) maintenance stage, there were significant differences in FT3, FT4, TT3 and TT4 before and after being infected with COVID-19. What’s more, there’s a significant difference between GD patients in one month and three months after COVID-19 infection of high TSAb group ( p = 0.048) but no significant difference between pre and one month. What’s more, there were significant differences in TT3, TT4 of GD patients after infected COVID-19 in non. And Phosphorus (P), 25-hydroxyvitamin D (25-OH-D3), Procollagen type 1 N-terminal propeptide (P1NP) in GD patients were be affected by COVID-19 infection. Conclusion GD patients with uncontrolled thyroid function group are susceptible to COVID-19. COVID-19 may affect the thyroid function of GD in TT3, TT4, TSAb high level group infection. COVID-19 vaccine is conducive to the stability of GD patients' condition. And COVID-19 may affect the bone metabolism in GD patients before and after COVID-19 infection. But there is no effect on glucose metabolism or lipid metabolism.
Cardiovascular disease (CVD) remains the leading cause of death and disability globally. A wide range of CVDs have been reported, each of which diverges significantly, exhibiting sophisticated types of pathogenesis (e.g., inflammatory, oxidative stress, and disorders in cardiomyocyte metabolism). Compared with conventional treatments in modern medicine, traditional Chinese medicine (TCM) can exhibit comparative advantages in the treatment of CVDs. TCM can be utilized to develop effective strategies for addressing the challenges of CVD, with fewer side effects and higher therapeutic efficiency. Astragaloside IV (AS-IV) has been confirmed as one of the major active ingredients found in Astragalus membranaceus (a Chinese herbal medicine that has been extensively employed clinically for the treatments of CVDs). Since recent studies have shown that AS-IV in CVD treatments has achieved promising results, the substance has aroused great attention and further discussions in the field. The present review aims to summarize the recent pharmacological advances in employing AS-IV in the treatment of CVDs.
Acute lung injury (ALI) and acute respiratory distress syndrome are life-threatening conditions induced by inflammatory responses, in which cell-free DNA (cfDNA) plays a pivotal role. This study investigated the therapeutic potential of biodegradable cationic nanoparticles (cNPs) in alleviating ALI. Using a mouse model of lipopolysaccharide-induced ALI, we examined the impact of intravenously administered cNPs. Our findings indicate that cNPs possess robust DNA binding capability, enhanced accumulation in inflamed lungs, and a favorable safety profile in vivo. Furthermore, cNPs attenuate the inflammatory response in LPS-induced ALI mice by scavenging cfDNA, mainly derived from neutrophil extracellular traps, and activating the macrophage-mediated cGAS-STING pathway. The findings suggest a potential treatment for ALI by targeting cfDNA with cNPs. This approach has demonstrated efficacy in mitigating lung injury and merits further exploration.
The potential function of long non-coding RNAs (lncRNAs) in human hepatic ischemia-reperfusion injury (HIRI) remains to be clarified.Clinical samples of transplanted liver tissues from 26 patients undergoing liver transplantation (LT) and normal liver tissues from seven patients undergoing hepatic hemangiomactomy (Con) were collected. Typical samples were subjected to whole transcriptome sequencing (RNA-seq). Differentially expressed genes between groups were identified by DEGseq and were analyzed by enrichment analysis including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis. Transcription of five lncRNAs including NONHSAG039942, NONHSAG071405, NONHSAG027516, LXLOC_058190, and LXLOC_024376 that presented significant difference in RNA-sequencing were validated by a quantitative real-time PCR (qRT-PCR), for which the subcellular localization and the binding ability to known human RNA-binding proteins (RBPs) were respectively predicted by LncLocator and catRAPID genomics v2.1.We identified 2917 lncRNAs and 2811 mRNAs that were differentially expressed (p < 0.05 and log2 fold change > 1 or < -1) between groups (LT vs. Con). NONHSAG039942, NONHSAG071405, LXLOC_058190, and LXLOC_024376 were validated by qRT-PCR to be significantly increased in the LT group, and were all predicted to be localized in cytoplasm or cytosol. NONHSAG039942, NONHSAG071405, and LXLOC_058190 held an RBP interaction propensity score of 98.07%, 76.95%, and 152.99%, respectively, with heterogeneous-nuclear ribonucleoprotein U (HNRNPU). Pathways significantly activated in transplant livers that involved HNRNPU as a core enrichment gene included hypoxia, ACE2 expression, apoptosis, spliceosome formation, etc. CONCLUSIONS: NONHSAG039942, NONHSAG071405, and LXLOC_058190 were significantly increased in transplant livers after reperfusion and their role in HIRI may be associated with HNRNPU, a core protein that participates in hypoxia and chromatin accessibility.
Abstract Background Misplacement of double-lumen endobronchial tubes (DLTs) during bronchial intubation, especially when bronchoscopy guidance is not applicable, threatens effective lung isolation and brings about airway injury during reposition. We aimed to examine whether a novel maneuver called right tracheal displacement (RTD) can reduce left-sided DLT misplacement during first-attempt intubation without bronchoscopy guidance. Methods Patients that underwent thoracic surgeries requiring one-lung ventilation during November 2020 to January 2021 were recruited and randomized into control and RTD group, with 54 cases in each group. The primary outcomes included the incidence of DLT misplacement and the time to complete desired bronchial intubation. The secondary outcomes included mucosal injury, sore throat and hoarseness upon emergence and at 24 h post-operatively. Result The incidence of DLT misplacement in RTD group was significantly lower compared to control group (0% vs. 16.7%) The time to complete bronchial intubation was also significantly shortened in RTD group compared to control (52.88 ± 9.36 s vs. 63.04 ± 20.02 s). The incidence of mucosal injury, sore throat and hoarseness were comparable between two groups. Conclusion RTD maneuver can effectively improve the success rate of first-attempt proper DLT positioning and shorten the time required by bronchial intubation. Trial registration This prospective, double-blind, randomized study has completed the registration of the Chinese Clinical Trial Center at 2/11/2020 with the registration number ChiCTR2000040212. It was conducted from 26/11/2020 to 31/7/2021 in third affiliated hospital of Sun Yat-sen university.
Pulmonary hypertension (PH) is a progressive cardiovascular disease, which may lead to severe cardiopulmonary dysfunction. As one of the main PH disease groups, pulmonary artery hypertension (PAH) is characterized by pulmonary vascular remodeling and right ventricular dysfunction. Increased pulmonary artery resistance consequently causes right heart failure, which is the major reason for morbidity and mortality in this disease. Although various treatment strategies have been available, the poor clinical prognosis of patients with PAH reminds us that further studies of the pathological mechanism of PAH are still needed. Inflammation has been elucidated as relevant to the initiation and progression of PAH, and plays a crucial and functional role in vascular remodeling. Many immune cells and cytokines have been demonstrated to be involved in the pulmonary vascular lesions in PAH patients, with the activation of downstream signaling pathways related to inflammation. Consistently, this influence has been found to correlate with the progression and clinical outcome of PAH, indicating that immunity and inflammation may have significant potential in PAH therapy. Therefore, we reviewed the pathogenesis of inflammation and immunity in PAH development, focusing on the potential targets and clinical application of anti-inflammatory and immunosuppressive therapy.
Abstract Background Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods. Methods Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ML models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model. Results 591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality ( P < 0.05). Among the six ML models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%; specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na + , TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time. Conclusion Our study firstly demonstrated that the XGBoost model with 14 common variables might predict postoperative pneumonia in OLT patients.
Abstract Background Pneumonia is the most frequently encountered postoperative pulmonary complications (PPC) after orthotopic liver transplantation (OLT), which cause high morbidity and mortality rates. We aimed to develop a model to predict postoperative pneumonia in OLT patients using machine learning (ML) methods. Methods Data of 786 adult patients underwent OLT at the Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2019 was retrospectively extracted from electronic medical records and randomly subdivided into a training set and a testing set. With the training set, six ML models including logistic regression (LR), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost) and gradient boosting machine (GBM) were developed. These models were assessed by the area under curve (AUC) of receiver operating characteristic on the testing set. The related risk factors and outcomes of pneumonia were also probed based on the chosen model. Results 591 OLT patients were eventually included and 253 (42.81%) were diagnosed with postoperative pneumonia, which was associated with increased postoperative hospitalization and mortality ( P < 0.05). Among the six ML models, XGBoost model performed best. The AUC of XGBoost model on the testing set was 0.734 (sensitivity: 52.6%; specificity: 77.5%). Pneumonia was notably associated with 14 items features: INR, HCT, PLT, ALB, ALT, FIB, WBC, PT, serum Na + , TBIL, anesthesia time, preoperative length of stay, total fluid transfusion and operation time. Conclusion Our study firstly demonstrated that the XGBoost model with 14 common variables can predict postoperative pneumonia in OLT patients.
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