Stormkrogsgaard8786

The primary outcome was death, which was compared between patients who did and did not receive QPD (QPD and NoQPD groups, respectively). Propensity score matching (PSM) was used to identify cohorts.

In total, 239 and 522 participants were enrolled in the QPD and NoQPD groups, respectively. After PSM at a 1  1 ratio, 446 patients meeting the criteria were included in the analysis with 223 in each arm. In the QPD and NoQPD groups, 7 (3.2%) and 29 (13.0%) patients died, and those in the QPD group had a significantly lower risk of death (hazard ratio (HR) 0.29, 95% CI 0.13-0.67) than those in the NoQPD group (

 = 0.004). Furthermore, the survival time was significantly longer in the QPD group than in the NoQPD group (

 < 0.001).

The use of QPD may reduce the risk of death in patients with COVID-19 pneumonia.

The use of QPD may reduce the risk of death in patients with COVID-19 pneumonia.Alzheimer's disease (AD) is the most common cause for dementia worldwide. Until recently, all approved treatments for AD were symptomatic and not disease modifying. On 7 June 2021, the US FDA approved aducanumab, a human IgG1 anti-Aβ monoclonal antibody selective for Aβ aggregates, as the first disease-modifying treatment for AD. Aducanumab is approved in the United States for the treatment of mild cognitive impairment or mild-dementia stage of AD. In this Editorial, we review the trial data for aducanumab in the treatment of AD and the controversies that its approval has generated.Adipogenic differentiation from stem cells has become a research target due to the increasing interest in obesity. It has been indicated that adipocytes can secrete palmitic acid methyl ester (PAME), which is able to regulate stem cell proliferation. However, the effects of PAME on adipogenic differentiation in stem cell remain unclear. Here, we present that the adipogenic differentiation medium supplemented with PAME induced the differentiation of rat adipose tissue-derived mesenchymal stem cells (rAD-MSCs) into adipocyte. rAD-MSCs were treated with PAME for 12 days and then subjected to various analyses. The results from the present study show that PAME significantly increased the levels of adipogenic differentiation markers, PPARγ and Gpd1, and enhanced adipogenic differentiation in rAD-MSCs. Furthermore, the level of GPR40/120 protein increased during induction of adipocyte differentiation in rAD-MSCs. Cotreatment with PAME and a GPR40/120 antagonist together inhibited the PAME-enhanced adipogenic differentiation. Moreover, PAME significantly increased phosphorylation of extracellular signal-regulated kinases (ERK), but not AKT and mTOR. Cotreatment with PAME and a GPR40/120 antagonist together inhibited the PAME-enhanced ERK phosphorylation and adipogenic differentiation. PAME also increased the intracellular Ca2+ levels. Cotreatment with PAME and a Ca2+ chelator or a phospholipase C (PLC) inhibitor prevented the PAME-enhanced ERK phosphorylation and adipogenic differentiation. check details Our data suggest that PAME activated the GPR40/120/PLC-mediated pathway, which in turn increased the intracellular Ca2+ levels, thereby activating the ERK, and eventually enhanced adipogenic differentiation in rAD-MSCs. The findings from the present study might help get insight into the physiological roles and molecular mechanism of PAME in regulating stem cell differentiation.Endometrial cancer (EC) is commonly diagnosed cancer in women, and the prognosis of advanced types of EC is extremely poor. Kinesin family member 2C (KIF2C) has been reported as an oncogene in cancers. However, its pathophysiological roles and the correlation with tumor-infiltrating lymphocytes in EC remain unclear. The mRNA and protein levels of KIF2C in EC tissues were detected by qRT-PCR, Western blot (WB), and IHC. CCK8, Transwell, and colony formation assay were applied to assess the effects of KIF2C on cell proliferation, migration, and invasion. Cell apoptosis and cell cycle were analyzed by flow cytometry. The antitumor effect was further validated in the nude mouse xenograft cancer model and humanized mouse model. KIF2C expression was higher in EC. Knockdown of KIF2C prolonged the G1 phases and inhibited EC cell proliferation, migration, and invasion in vitro. Bioinformatics analysis indicated that KIF2C is negatively correlated with the infiltration level of CD8+ T cells but positively with the poor prognosis of EC patients. The apoptosis of CD8+ T cell was inhibited after the knockdown of KIF2C and was further inhibited when it is combined with anti-PD1. Conversely, compared to the knockdown of KIF2C expression alone, the combination of anti-PD1 further promoted the apoptosis of Ishikawa and RL95-2 cells. Moreover, the knockdown of KIF2C inhibited the expression of Ki-67 and the growth of tumors in the nude mouse xenograft cancer model. Our study found that the antitumor efficacy was further evaluated by the combination of anti-PD1 and KIF2C knockdown in a humanized mouse model. This study indicated that KIF2C is a novel prognostic biomarker that determines cancer progression and also a target for the therapy of EC and correlated with tumor immune cells infiltration in EC.

The optimal technique for nasojejunal tube (NJT) placement in terms of facilitating early enteral nutrition (EN) in patients with acute pancreatitis (AP) is unclear. In this study, we aimed to evaluate the impact of two common techniques on EN implementation and clinical outcomes in a group of AP patients.

This is a retrospective study. All the data were extracted from an electronic database from August 2015 to October 2017. Patients with a diagnosis of AP requiring NJT placement were retrospectively analyzed. The primary outcome was the successful procedural rate.

A total of 53 eligible patients were enrolled, of whom 30 received an ultrasound-assisted technique and the rest received the endoscopy method (

= 23). There was no difference in success rates of initial placement procedures between the two groups (93.3% and 95.7% in the ultrasound-assisted group and endoscopy group, respectively). The mean amount of EN delivery within the first three days after NJT placement was significantly higher in the ultrasound-assisted group (841.4 kcal (95% CI 738.8, 944 kcal) vs. 652.5 kcal (95% CI 562.5, 742.6 kcal),

= 0.018). Moreover, a slight increased postprocedural intra-abdominal pressure (IAP) was observed in patients undergoing endoscopic procedures, but not in the ultrasound-assisted group, especially at 6 hours after NJT placement (0.35 vs. -2.01 from baseline,

< 0.05). For clinical outcomes, we observed no difference between groups.

Compared with endoscopic procedures, ultrasound-assisted NJT placement possesses the acceptable success rates of initial placement procedures.

Compared with endoscopic procedures, ultrasound-assisted NJT placement possesses the acceptable success rates of initial placement procedures.Every graph G=(V, E) considered in this paper consists of a finite set V of vertices and a finite set E of edges, together with an incidence function that associates each edge e ∈ E of G with an unordered pair of vertices of G which are called the ends of the edge e. A graph is said to be a planar graph if it can be drawn in the plane so that its edges intersect only at their ends. A proper k-vertex-coloring of a graph G=(V, E) is a mapping c V⟶S (S is a set of k colors) such that no two adjacent vertices are assigned the same colors. The famous Four Color Theorem states that a planar graph has a proper vertex-coloring with four colors. However, the current known proof for the Four Color Theorem is computer assisted. In addition, the correctness of the proof is still lengthy and complicated. In 2010, a simple O(n 2) time algorithm was provided to 4-color a 3-colorable planar graph. In this paper, we give an improved linear-time algorithm to either output a proper 4-coloring of G or conclude that G is not 3-colorable when an arbitrary planar graph G is given. Using this algorithm, we can get the proper 4-colorings of 3-colorable planar graphs, planar graphs with maximum degree at most five, and claw-free planar graphs.The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neural networks and gene expression data has become a hot topic. However, most classifiers may face the challenges of overfitting and low classification accuracy when dealing with small sample size and high-dimensional biological data. In this paper, the Cascade Flexible Neural Forest (CFNForest) Model was proposed to accomplish cancer subtype classification. CFNForest extended the traditional flexible neural tree structure to FNT Group Forest exploiting a bagging ensemble strategy and could automatically generate the model's structure and parameters. In order to deepen the FNT Group Forest without introducing new hyperparameters, the multilayer cascade framework was exploited to design the FNT Group Forest model, which transformed features between levels and improved the performance of the model. The proposed CFNForest model also improved the operational efficiency and the robustness of the model by sample selection mechanism between layers and setting different weights for the output of each layer. To accomplish cancer subtype classification, FNT Group Forest with different feature sets was used to enrich the structural diversity of the model, which make it more suitable for processing small sample size datasets. The experiments on RNA-seq gene expression data showed that CFNForest effectively improves the accuracy of cancer subtype classification. The classification results have good robustness.Most visitors come to visit museums; in reality, few immersive solutions support the senses experience. Virtual reality (VR) technology attaches the virtual information from the real environment. Applying the VR technology in the 3D relic information display and visualization in the museum field is a hot research issue. However, most current solutions of relics are one-sided, only focusing on the virtual exhibition, lack of associations with actual function, and senses experience, especially the large artistic cultural relics. The scenario-based virtual exhibition solution is an available approach to allow visitors to imitate ancient artist and provide relatively experience in the form of content and sense organ of ancient art. It converts large relics into "digital large relics" and enables experiencing performance of ancient civilization in person. The solution presents relics to the visitors in a more direct and vivid manner and with innovative forms, strong interaction, and intelligence, thereby improving the interests and satisfaction among visitors in this type of relic exhibition. Besides, it also provides visitors with a convenient way to experience and learn ritual and culture. Evaluation and conclusion can be drawn that most participants appreciated this solution in clear interface and completion aspects.Objectives To establish and validate a nomogram integrating radiomics signatures from ultrasound and clinical factors to discriminate between benign, borderline, and malignant serous ovarian tumors. Materials and methods In this study, a total of 279 pathology-confirmed serous ovarian tumors collected from 265 patients between March 2013 and December 2016 were used. The training cohort was generated by randomly selecting 70% of each of the three types (benign, borderline, and malignant) of tumors, while the remaining 30% was included in the validation cohort. From the transabdominal ultrasound scanning of ovarian tumors, the radiomics features were extracted, and a score was calculated. The ability of radiomics to differentiate between the grades of ovarian tumors was tested by comparing benign vs borderline and malignant (task 1) and borderline vs malignant (task 2). These results were compared with the diagnostic performance and subjective assessment by junior and senior sonographers. Finally, a clinical-feature alone model and a combined clinical-radiomics (CCR) model were built using predictive nomograms for the two tasks.