Expression of cysLT1 and cysLT2 Receptor in Chronic Hyperplastic Eosinophilic Sinusitis

Elevated production of cysteinyl leukotrienes (cysLTs) from sinus tissues and abundant sinus eosinophils are characteristic features of chronic hyperplastic eosinophilic sinusitis (CHS). CysLTs exert their action through G-protein-coupled receptors named cysLTs receptor type I (cysLT1R) and type II (cysLT2R). These expressions of cysLT receptors in the sinus mucosa have yet to be clarified and the relationship between eosinophilia and the expression of these receptors remains obscure. We compared the expressions of cysLT1R and cysLT2R in the sinus mucosa in patients with CHS, non-eosinophilic chronic sinusitis (NECS), and control sinus tissues; and analyzed the correlation between the expression of CysLTRs and the presence of sinus eosinophils by immunohistochemistry and real-time PCR. A significantly higher percentage of eosinophils expressing cysLT2R protein was observed in patients with CHS compared with NECS and controls. In addition, cysLT2R mRNA expression in CHS was significantly higher than in NECS and controls. Furthermore, a positive correlation was observed between cysLT2R mRNA expression and the number of infiltrated eosinophils. In contrast, the cysLT1R mRNA expression did not differ significantly among these groups. The effect of cysLTs on sinus eosinophils may be mediated through the cysLT2R in patients with CHS. These results may suggest the therapeutic benefit of cysLT2R antagonists in CHS

A Genome-Wide Association Meta-Analysis of Circulating Sex Hormone-Binding Globulin Reveals Multiple Loci Implicated in Sex Steroid Hormone Regulation

WOS:000306840400020Peer reviewe

Numerical Approximation of Valuation Equations Incorporating Stochastic Volatility Models

<p>This dissertation studies the problem of controlling far field boundary errors arising in partial differential equation approaches for pricing financial contracts written on stochastic volatility models. Feynman-Kac type results are obtained by relating finite domain Dirichlet problems to options bearing barrier features. We then adopt a probabilistic framework to show convergence for strictly sublinear contracts even when the underlying process is a local martingale, and for linear contracts when it is a proper martingale. By restricting the stochastic volatility models to a smaller class, upper bounds for the far field boundary errors are derived for linear contracts. Convergence does not hold for linear contracts dependent on strict local martingales. While rigorous results for this case are unavailable, we conjecture inverse second order convergence in the far boundary distance when appropriate Neumann boundary conditions are imposed. Effective use of a finite difference alternating direction implicit algorithm is discussed. This scheme is implemented to test convergence theories and conjectures on well known models, such as the Bessel model and the Heston model.</p

ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods

The incidence of maritime accidents can be significantly reduced by identifying the deck officer’s fatigue levels. The development of car driver fatigue detectors has employing electroencephalogram (EEG)-based technologies in recent years and made it possible to swiftly and accurately determine the level of a driver’s fatigue. However, individual variability and the sensitivity of EEG signals reduce the detection precision. Recently, another type of video-based technology for detecting driver fatigue by recording changes in the drivers’ eye characteristics has also been explored. In order to improve the classification performance of EEG-based approaches, this paper introduces the ADTIDO (Automatic Detect the TIred Deck Officers) algorithm, an EEG-based classification method of deck officers’ fatigue level, which combines a video-based approach to record the officer’s eye closure time for each time window. This paper uses a Discrete Wavelet Transformer (DWT) and decomposes the EEG signals into six sub-signals, from which we extract various EEG-based features, e.g., MAV, SD, and RMS. Unlike the traditional video-based method of calculating the Eyelid Closure Degree (ECD), this paper then obtains the ECD values from the EEG signals. The ECD-EEG fusion features are then created and used as the inputs for a classifier by combining the ECD and EEG feature sets. In addition, the present work develops the definition of “fatigue” at the individual level based on the real-time operational reaction time of the deck officer. To verify the efficacy of this research, the authors conducted their trials by using the EEG signals gathered from 21 subjects. It was found that Bidirectional Gated Recurrent Unit (Bi-GRU) networks outperform other classifiers, reaching a classification accuracy of 90.19 percent, 1.89 percent greater than that of only using EEG features as inputs. By combining the ADTIDO channel findings, the classification accuracy of deck officers’ fatigue levels finally reaches 95.74 percent

Impact of airborne pollen concentration and meteorological factors on the number of outpatients with allergic rhinitis

The prevalence of allergic rhinitis (AR) caused by pollen allergen is high in northern China. This study analyzed the allergen detection results of patients with AR in Beijing Tongren Hospital from 2016 to 2019, and evaluated the association between AR and seasonal airborne pollen concentration and meteorological factors in Beijing, China. We found that AR patients caused by pollen accounted for 61.18% (16 793/27 449) in AR patients. Among them, Artemisia pollen sensitive patients accounted for 48.54% (13 325/27 449) of AR. We also found that the number of outpatients diagnosed with AR is strongly correlated with seasonal airborne pollen concentration and is influenced by meteorological factors, such as temperature and humidity. These results may help clinicians and healthcare workers to be prepared for this influx of outpatients in the relevant seasons

Selection of Patients and Anesthetic Types for Endovascular Treatment in Acute Ischemic Stroke: A Meta-Analysis of Randomized Controlled Trials.

BACKGROUND:and Purpose Recent randomized controlled trials have demonstrated consistent effectiveness of endovascular treatment (EVT) for acute ischemic stroke, leading to update on stroke management guidelines. We conducted this meta-analysis to assess the efficacy and safety of EVT overall and in subgroups stratified by age, baseline stroke severity, brain imaging feature, and anesthetic type. METHODS:Published randomized controlled trials comparing EVT and standard medical care alone were evaluated. The measured outcomes were 90-day functional independence (modified Rankin Scale ≤2), all-cause mortality, and symptomatic intracranial hemorrhage. RESULTS:Nine trials enrolling 2476 patients were included (1338 EVT, 1138 standard medical care alone). For patients with large vessel occlusions confirmed by noninvasive vessel imaging, EVT yielded improved functional outcome (pooled odds ratio [OR], 2.02; 95% confidence interval [CI], 1.64-2.50), lower mortality (OR, 0.75; 95% CI, 0.58-0.97), and similar symptomatic intracranial hemorrhage rate (OR, 1.12; 95% CI, 0.72-1.76) compared with standard medical care. A higher proportion of functional independence was seen in patients with terminus intracranial artery occlusion (±M1) (OR, 3.16; 95% CI, 1.64-6.06), baseline Alberta Stroke Program Early CT score of 8-10 (OR, 2.11; 95% CI, 1.25-3.57) and age ≤70 years (OR, 3.01; 95% CI, 1.73-5.24). EVT performed under conscious sedation had better functional outcomes (OR, 2.08; 95% CI, 1.47-2.96) without increased risk of symptomatic intracranial hemorrhage or short-term mortality compared with general anesthesia. CONCLUSIONS:Vessel-imaging proven large vessel occlusion, a favorable scan, and younger age are useful predictors to identify anterior circulation stroke patients who may benefit from EVT. Conscious sedation is feasible and safe in EVT based on available data. However, firm conclusion on the choice of anesthetic types should be drawn from more appropriate randomized controlled trials

Research on Design of Intelligent Background Differential Model for Training Target Monitoring

The detection of moving targets is to detect the change area in a sequence of images and extract the moving targets from the background image. It is the basis. Whether the moving targets can be correctly detected and segmented has a huge impact on the subsequent work. Aiming at the problem of high failure rate in the detection of sports targets under complex backgrounds, this paper proposes a research on the design of an intelligent background differential model for training target monitoring. This paper proposes a background difference method based on RGB colour separation. The colour image is separated into independent RGB three-channel images, and the corresponding channels are subjected to the background difference operation to obtain the foreground image of each channel. In order to retain the difference of each channel, the information of the foreground images of the three channels is fused to obtain a complete foreground image. The feature of the edge detection is not affected by light; the foreground image is corrected. From the experimental results, the ordinary background difference method uses grey value processing, and some parts of the target with different colours but similar grey levels to the background cannot be extracted. However, the method in this paper can better solve the defect of misdetection. At the same time, compared with traditional methods, it also has a higher detection efficiency