Carlsoncherry6947

Surface-Enhanced Raman Scattering (SERS) is a powerful analysis technology, attracting more and more attention due to its high sensitivity and selectivity. Herein, we report a simple seed-mediated method to synthesize Au@Ag nanoparticles (NPs) as a multifunctional biosensor for the label-free detection of hydrogen peroxide (H2O2) and glucose by SERS. Au@Ag NPs, as an ultrasensitive SERS substrate, show the dual activities (peroxidase-like and GOx-like activities). Under the condition of pH 4.0 NaAc buffer solution, the glucose and H2O can be catalyzed by Au@Ag NPs to produce glucose acid and H2O2, and then H2O2 can oxidize 3,3',5,5'-tetramethylbenzidine (TMB) to form a blue oxidation product oxidic TMB (oxTMB) which exhibits strong SERS signals at 1188, 1330, 1605 cm-1. Thus, we have developed a new SERS strategy for analysis of glucose with a detection limit of 5 × 10-10molL-1, suggesting that Au@Ag NPs have the potential for biosensor, immunoassay and medical treatment.Computational Growth and Remodeling (G&R) models have been widely used to capture the pathological development of arterial diseases and have shown promise for aiding clinical diagnosis, prognosis prediction, and staging classification. However, due to the high complexity of the arterial adaptation mechanism, high-fidelity arterial G&R simulation usually takes hours or even days, which hinders its application in clinical practice. To remedy this problem, we develop a computationally efficient arterial G&R simulation framework that comprehensively combines the physics-based G&R simulations and data-driven machine learning approaches. The proposed framework greatly enhances the computational efficiency of arterial G&R simulations, thereby enabling more time-consuming arterial applications, including personalized parameter estimation and arterial disease progression prediction. In particular, we achieve significant computational cost reduction mainly through two methods (1) constructing a Multifidelity Surrogate (MFS) to approximate multifidelity G&R simulations by using a cokriging approach and (2) developing a novel iterative optimization algorithm for personalized parameter estimation. The proposed framework is demonstrated by estimating G&R model parameters and predicting individual aneurysm growth using follow-up CT images of Abdominal Aortic Aneurysms (AAAs) from 21 patients. Results show that the personalized parameters are satisfactorily estimated and the growth of AAAs is predicted within the clinically relevant time frame, i.e., less than 2 h, without a loss of accuracy.The role of hydroxychloroquine (HCQ) in early outpatient management of mild coronavirus disease 2019 (COVID-19) needs further investigation. This study was a multicenter, population-based national retrospective-cohort investigation of 28,759 adults with mild COVID-19 seen at the network of Comprehensive Healthcare Centers (CHC) between March and September 2020 throughout Iran. The baseline characteristics and outcome variables were extracted from the national integrated health system database. A total of 7295 (25.37%) patients who presented with mild COVID-19 within 3-7 days of symptoms onset received HCQ (400 mg twice daily on day 1 followed by 200 mg twice daily for the next four days and were then followed for 14 days). The main outcome measures were hospitalization or death for six months follow-up. COVID-19-related hospitalizations or deaths occurred in 523 (7.17%) and 27 (0.37%) respectively, in HCQ recipients and 2382 (11.10%) and 287 (1.34%) respectively, in non-recipients. The odds of hospitalization or death was reduced by 38% (odds ratio [OR] = 0.62; 95% confidence interval [CI] 0.56-0.68, p = less then 0.001) and 73% (OR = 0.27; 95% CI 0.18-0.41, p = less then 0.001) in HCQ recipients and non-recipients. These effects were maintained after adjusting for age, comorbidities, and diagnostic modality. No serious HCQ-related adverse drug reactions were reported. In our large outpatient national cohort of adults with mild COVID-19 disease who were given HCQ early in the course of the disease, the odds of hospitalization or death was reduced significantly regardless of age or comorbidities.An important factor in periodontitis pathogenesis relates to a network of interactions of various cytokines. Thrombospondin-1 (TSP-1) is upregulated in several inflammatory diseases. We previously found that Porphyromonas gingivalis lipopolysaccharide (P. gingivalis LPS)-induced TSP-1 production, and that TSP-1 simultaneously and effectively elevated inflammatory cytokines in THP-1 macrophages. This suggests that TSP-1 plays an important role in the pathology of periodontitis. However, the function of TSP-1 on oral cells is largely unknown. This study aimed to elucidate the underlying molecular mechanisms of TSP-1 in human periodontal fibroblasts (hPDLFs). We demonstrated that TSP-1 is highly expressed in the gingival crevicular fluid of patients with chronic periodontitis and in the inflammatory gingival tissues of rats. TSP-1 overexpression or treatment with recombinant human TSP-1(rTSP-1) promoted the expression of MMP-2, MMP-9 and RANKL/OPG in hPDLFs, while anti-TSP-1 inhibited cytokines production from P. gingivalis LPS-treated hPDLFs. Additional experiments showed that SB203580 (a special p38MAPK inhibitor) inhibited MMP-2, MMP-9 and RANKL/OPG expression induced by rTSP-1. Thus, TSP-1 effectively promoted P. gingivalis LPS-induced periodontal tissue (extracellular matrix (ECM) and alveolar bone) destruction by the p38MAPK signalling pathway, indicating that it may be a potential therapeutic target against periodontitis.Since adipose tissue (AT) can upregulate pro-inflammatory interleukins (ILs) via storing extra lipids in obesity, obesity is considered the leading cause of chronic low-grade inflammation. These ILs can pave the way for the infiltration of immune cells into the AT, ultimately resulting in low-grade inflammation and dysregulation of adipocytes. IL-1, which is divided into two subclasses, i.e., IL-1α and IL-1β, is a critical pro-inflammatory factor. In obesity, IL-1α and IL-1β can promote insulin resistance via impairing the function of adipocytes and promoting inflammation. The current study aims to review the detailed molecular mechanisms and the roles of IL-1α and IL-1β and their antagonist, interleukin-1 receptor antagonist(IL-1Ra), in developing obesity-related inflammatory complications, i.e., type II diabetes (T2D), non-alcoholic steatohepatitis (NASH), atherosclerosis, and cognitive disorders. Besides, the current study discusses the recent advances in natural drugs, synthetic agents, and gene therapy approaches to treat obesity-related inflammatory complications via suppressing IL-1.Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a single radiologist, poses a challenge in consistently maintaining high interpretation accuracy. The introduction of large-scale public datasets has led to a series of novel systems for automated abnormality classification. However, the labels of these datasets were obtained using natural language processed medical reports, yielding a large degree of label noise that can impact the performance. In this study, we propose novel training strategies that handle label noise from such suboptimal data. Prior label probabilities were measured on a subset of training data re-read by 4 board-certified radiologists and were used during training to increase the robustness of the training model to the label noise. Furthermore, we exploit the high comorbidity of abnormalities observed in chest radiography and incorporate this information to further reduce the impact of label noise. Additionally, anatomical knowledge is incorporated by training the system to predict lung and heart segmentation, as well as spatial knowledge labels. To deal with multiple datasets and images derived from various scanners that apply different post-processing techniques, we introduce a novel image normalization strategy. Experiments were performed on an extensive collection of 297,541 chest radiographs from 86,876 patients, leading to a state-of-the-art performance level for 17 abnormalities from 2 datasets. With an average AUC score of 0.880 across all abnormalities, our proposed training strategies can be used to significantly improve performance scores.Tanreqing injection (TRQI), a drug approved by the National Drug Regulatory Authority of China (China SFDA, number Z20030045), is widely used clinically to treat respiratory diseases. However, as a complex system, the pharmacological mechanism of TRQI for the treatment of respiratory diseases is still unclear. TRQI contains three Chinese medicines that make up the classic Chinese compound formulas Shuang-Huang-Lian (SHL). Moreover, it is known that SHL components are beneficial for characterizing the chemical compounds of TRQI. Therefore, in this study, we applied UHPLC/Q-TOF-MS/MS analysis based on multiple chemical compound libraries to identify the chemical profiles of TRQI and used network pharmacology to predict the potential targets of TRQI active compounds. First, three chemical libraries related to TRQI were created, including the TRQI in-house library, SHL in-house library, and targeted Metlin library. An integrated TRQI library was established by combining three chemical libraries for the identificaI; GABBR1, MAPK3, GRM5, FOS, DRD2, GRM1, VEGFA, GRM3 and 92 other potential core targets for the treatment of respiratory diseases by modulating pathways in cancer, the calcium signaling pathway, cAMP signaling pathway, estrogen signaling pathway and TNF-α signaling pathway.Pemigatinib is an oral, selective, potent, competitive inhibitor acting on fibroblast growth factor receptor (FGFR)1, FGFR2, and FGFR3, which has obtained accelerated approval in the USA through a test approved by the USA FDA. It is not only significant in the therapy of adult recurrent, unresectable, metastatic or locally advanced cholangiocarcinoma, but also plays an important role in treating adult patients with FGFR2 fusion or other rearrangements. The aim of our research was to establish and verify a reliable and quick ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) assay to determine the level of pemigatinib in rat plasma. The analyte was prepared using a simple and convenient approach with acetonitrile for protein crash, and then separated from the matrix on a Waters Acquity UPLC BEH C18 column (2.1 mm × 50 mm, 1.7 μm) in a gradient elution program, where the mobile phase was consisted of acetonitrile and 0.1 % formic acid in water and was set at 0.40 mL/min flow rate. Selective reaction monitoring (SRM) was used to conducted for UPLC-MS/MS dectection with ion transitions at m/z 488.01 → 400.98 for pemigatinib and m/z 447.00 → 361.94 for erdafitinib (Internal standard, IS), respectively. This method had good linearity in a 0.5-1000 ng/mL calibration range for pemigatinib, where the lower limit of quantification (LLOQ) was validated at 0.5 ng/mL. The precision of pemigatinib for intra- and inter-day was less than 13.3 %, and the accuracy was determined to be from -4.8%-11.2%. Glesatinib During the assay in plasma samples, the analyte was found to be stable. Besides, matrix effect and recovery of the analyte and IS were acceptable. The novel optimized UPLC-MS/MS assay was also suitable for determining the concentration level of pemigatinib in a pharmacokinetic study after a single dose of 1.35 mg/kg pemigatinib orally to the rats.