Natural Product Quercetin-3-methyl ether Promotes Colorectal Cancer Cell Apoptosis by Downregulating Intracellular Polyamine Signaling.

Dysregulation of cellular metabolism is a key marker of cancer, and it is suggested that metabolism should be considered as a targeted weakness of colorectal cancer. Increased polyamine metabolism is a common metabolic change in tumors. Thus, targeting polyamine metabolism for anticancer therapy, particularly polyamine blockade therapy, has gradually become a hot topic. Quercetin-3-methyl ether is a natural compound existed in various plants with diverse biological activities like antioxidant and antiaging. Here, we reported that Quercetin-3-methyl ether inhibits colorectal cancer cell viability, and promotes apoptosis in a dose-dependent and time-dependent manner. Intriguingly, the polyamine levels, including spermidine and spermine, in colorectal cancer cells were reduced upon treatment of Quercetin-3-methyl ether. This is likely resulted from the downregulation of SMOX, a key enzyme in polyamine metabolism that catalyzes the oxidation of spermine to spermidine. These findings suggest Quercetin-3-methyl ether decreases cellular polyamine level by suppressing SMOX expression, thereby inducing colorectal cancer cell apoptosis. Our results also reveal a correlation between the anti-tumor activity of Quercetin-3-methyl ether and the polyamine metabolism modulation, which may provide new insights into a better understanding of the pharmacological activity of Quercetin-3-methyl ether and how it reprograms cellular polyamine metabolism.

Why Do People Believe in Vaccine Misinformation? The Roles of Perceived Familiarity and Evidence Type.

The proliferation of health misinformation poses a significant threat to public health, making it increasingly important to understand why misinformation is accepted. The illusory truth effect, which refers to the increased believability of a message due to repeated exposure, has been widely studied. However, there is limited research on this effect in the context of COVID-19 vaccine misinformation. This paper aims to examine the role of perceived familiarity with COVID-19 vaccine misinformation on various message perceptions, including perceived accuracy, agreement, perceived message effectiveness, and determinants of vaccination, including vaccine attitude and vaccination intention. Furthermore, it explores the impact of misinformation evidence (statistical vs. narrative) on the magnitude of the effects of perceived familiarity. To investigate these factors, a between-subjects experimental study was conducted, employing a 2 (Familiarity: strong vs. weak) × 3 (Evidence type: statistical, narrative, and both evidence) + 1 (Control: a message about drinking water) design. The results revealed that perceived familiarity with COVID-19 vaccine misinformation significantly predicted perceived accuracy, which was found to be negatively correlated with vaccine attitudes and vaccination intentions. Moreover, statistical evidence presented in misinformation was perceived as more persuasive in perceived message effectiveness, compared to narrative and mixed evidence. Interestingly, the effects of perceived familiarity were not contingent on the type of evidence used in COVID-19 vaccine misinformation. These findings emphasize the importance of avoiding the repetition of misinformation, reducing the processing fluency associated with misinformation correction, and educating individuals on how to critically evaluate statistical evidence when encountering (mis)information.

A novel dextran-grafted tetrapeptide resin for antibody purification.

Short peptide biomimetic affinity chromatography as a novel antibody separation chromatography is a potential alternative to protein A chromatography. However, if directly attaching ligand to matrix, the adsorption capacity and mass transfer rate would be affected by pore blockage and steric effect. Grafting resin is an effective method to solve this problem by using polymer as a bridge between matrix and ligand. In this work, a novel resin was prepared by grafting a tetrapeptide to the dextran-grafted matrix. Then, the adsorption properties for human immunoglobin G and BSA were determined. The results showed the saturation adsorption capacity could reach to 133 mg/g resin at pH 8.9 with a significantly low dissociation constant (0.03 mg/mL). The influence of flow rates to dynamic binding capacity of this resin was less than that of the non-grafted resin. The separation performance of the resin showed monoclonal antibody could be well isolated from the Chinese hamster ovary culture supernatant at pH 9.0 with the purity of 93.0% and yield of 84.7% by one step. Overall, this resin could achieve higher binding capacity by the possible of gaining higher ligand density, indicating its potential significance for separation in larger scale systems.

An analytical model for squeeze-film damping of perforated torsional microplates resonators.

Squeeze-film damping plays a significant role in the performance of micro-resonators because it determines their quality factors. Perforations in microstructures are often used to control the squeeze-film damping in micro-resonators. To model the perforation effects on the squeeze-film damping, many analytical models have been proposed, however, most of the previous models have been concerned with the squeeze-film damping due to the normal motion between the perforated vibrating plate and a fixed substrate, while there is a lack of works that model the squeeze-film damping of perforated torsion microplates, which are also widely used in MEMS devices. This paper presents an analytical model for the squeeze-film damping of perforated torsion microplates. The derivation in this paper is based on a modified Reynolds equation that includes compressibility and rarefaction effects. The pressure distribution under the vibrating plate is obtained using the double sine series. Closed-form expressions for the stiffness and the damping coefficients of the squeeze-film are derived. The accuracy of the model is verified by comparing its results with the finite element method (FEM) results and the experimental results available in the literature. The regime of validity and limitations of the present model are assessed.

Smoke-Aware Global-Interactive Non-Local Network for Smoke Semantic Segmentation.

Compared with other objects, smoke semantic segmentation (SSS) is more difficult and challenging due to some special characteristics of smoke, such as non-rigid, translucency, variable mode and so on. To achieve accurate positioning of smoke in real complex scenes and promote the development of intelligent fire detection, we propose a Smoke-Aware Global-Interactive Non-local Network (SAGINN) for SSS, which harness the power of both convolution and transformer to capture local and global information simultaneously. Non-local is a powerful means for modeling long-range context dependencies, however, friendliness to single-scale low-resolution features limits its potential to produce high-quality representations. Therefore, we propose a Global-Interactive Non-local (GINL) module, leveraging global interaction between multi-scale key information to improve the robustness of feature representations. To solve the interference of smoke-like objects, a Pyramid High-level Semantic Aggregation (PHSA) module is designed, where the learned high-level category semantics from classification aids model by providing additional guidance to correct the wrong information in segmentation representations at the image level and alleviate the inter-class similarity problem. Besides, we further propose a novel loss function, termed Smoke-aware loss (SAL), by assigning different weights to different objects contingent on their importance. We evaluate our SAGINN on extensive synthetic and real data to verify its generalization ability. Experimental results show that SAGINN achieves 83% average mIoU on the three testing datasets (83.33%, 82.72% and 82.94%) of SYN70K with an accuracy improvement of about 0.5%, 0.002 mMse and 0.805 Fß on SMOKE5K, which can obtain more accurate location and finer boundaries of smoke, achieving satisfactory results on smoke-like objects.

Image Super-Resolution via Efficient Transformer Embedding Frequency Decomposition With Restart.

Recently, transformer-based backbones show superior performance over the convolutional counterparts in computer vision. Due to quadratic complexity with respect to the token number in global attention, local attention is always adopted in low-level image processing with linear complexity. However, the limited receptive field is harmful to the performance. In this paper, motivated by Octave convolution, we propose a transformer-based single image super-resolution (SISR) model, which explicitly embeds dynamic frequency decomposition into the standard local transformer. All the frequency components are continuously updated and re-assigned via intra-scale attention and inter-scale interaction, respectively. Specifically, the attention in low resolution is enough for low-frequency features, which not only increases the receptive field, but also decreases the complexity. Compared with the standard local transformer, the proposed FDRTran layer simultaneously decreases FLOPs and parameters. By contrast, Octave convolution only decreases FLOPs of the standard convolution, but keeps the parameter number unchanged. In addition, the restart mechanism is proposed for every a few frequency updates, which first fuses the low and high frequency, then decomposes the features again. In this way, the features can be decomposed in multiple viewpoints by learnable parameters, which avoids the risk of early saturation for frequency representation. Furthermore, based on the FDRTran layer with restart mechanism, the proposed FDRNet is the first transformer backbone for SISR which discusses the Octave design. Sufficient experiments show our model reaches state-of-the-art performance on 6 synthetic and real datasets. The code and the models are available at https://github.com/catnip1029/FDRNet.

Saliency Guided Deep Neural Network for Color Transfer With Light Optimization.

Color transfer aims to change the color information of the target image according to the reference one. Many studies propose color transfer methods by analysis of color distribution and semantic relevance, which do not take the perceptual characteristics for visual quality into consideration. In this study, we propose a novel color transfer method based on the saliency information with brightness optimization. First, a saliency detection module is designed to separate the foreground regions from the background regions for images. Then a dual-branch module is introduced to implement color transfer for images. Finally, a brightness optimization operation is designed during the fusion of foreground and background regions for color transfer. Experimental results show that the proposed method can implement the color transfer for images while keeping the color consistency well. Compared with other existing studies, the proposed method can obtain significant performance improvement. The source code and pre-trained models are available at https://github.com/PlanktonQAQ/SCTNet.

A Bio-inspired Model for Bee Simulations.

As eusocial creatures, bees display unique macro collective behavior and local body dynamics that hold potential applications in various fields, such as computer animation, robotics, and social behavior. Unlike birds and fish, bees fly in a low-aligned zigzag pattern. Additionally, bees rely on visual cues for foraging and predator avoidance, exhibiting distinctive local body oscillations, such as body lifting, thrusting, and swaying. These inherent features pose significant challenges for realistic bee simulations in practical animation applications. In this paper, we present a bio-inspired model for bee simulations capable of replicating both macro collective behavior and local body dynamics of bees. Our approach utilizes a visually-driven system to simulate a bee's local body dynamics, incorporating obstacle perception and body rolling control for effective collision avoidance. Moreover, we develop an oscillation rule that captures the dynamics of the bee's local bodies, drawing on insights from biological research. Our model extends beyond simulating individual bees' dynamics; it can also represent bee swarms by integrating a fluid-based field with the bees' innate noise and zigzag motions. To fine-tune our model, we utilize pre-collected honeybee flight data. Through extensive simulations and comparative experiments, we demonstrate that our model can efficiently generate realistic low-aligned and inherently noisy bee swarms.

Role of serum neuron-specific enolase levels in the early diagnosis and prognosis of sepsis-associated encephalopathy: a systematic review and meta-analysis.

Background: Sepsis-associated encephalopathy (SAE) is one of the most ubiquitous complications of sepsis and is characterized by cognitive impairment, poor prognosis, and a lack of uniform clinical diagnostic criteria. Therefore, this study investigated the early diagnostic and prognostic value of serum neuron-specific enolase (NSE) in SAE. Methods: This systematic review and meta-analysis systematically searched for clinical trials with serum NSE information in patients with sepsis in the PubMed, Web of Science, Embase, and Cochrane databases from their inception to April 10, 2023. Included studies were assessed for quality and risk of bias using The Quality Assessment of Diagnostic Accuracy-2 tool. The meta-analysis of the included studies was performed using Stata 17.0 and Review Manager version 5.4. Findings: Eleven studies were included in this meta-analysis involving 1259 serum samples from 947 patients with sepsis. Our results showed that the serum NSE levels of patients with SAE were higher than those of the non-encephalopathy sepsis group (mean deviation, MD,12.39[95% CI 8.27-16.50, Z = 5.9, p < 0.00001]), and the serum NSE levels of patients with sepsis who died were higher than those of survivors (MD,4.17[95% CI 2.66-5.68, Z = 5.41, p < 0.00001]). Conclusion: Elevated serum NSE levels in patients with sepsis are associated with the early diagnosis of SAE and mortality; therefore, serum NSE probably is a valid biomarker for the early diagnosis and prognosis of patients with SAE. Systematic review registration: This study was registered in PROSPERO, CRD42023433111.

Latest advances and clinical application prospects of resveratrol therapy for neurocognitive disorders.

Neurocognitive disorders, such as Alzheimer's disease, vascular dementia, and postoperative cognitive dysfunction, are non-psychiatric brain syndromes in which a significant decline in cognitive function causes great trauma to the mental status of the patient. The lack of effective treatments for neurocognitive disorders imposes a considerable burden on society, including a substantial economic impact. Over the past few decades, the identification of resveratrol, a natural plant compound, has provided researchers with an opportunity to formulate novel strategies for the treatment of neurocognitive disorders. This is because resveratrol effectively protects the brain of those with neurocognitive disorders by targeting some mechanisms such as inflammation and oxidative stress. This article reviews the status of recent research investigating the use of resveratrol for the treatment of different neurocognitive disorders. By examining the possible mechanisms of action of resveratrol and the shared mechanisms of different neurocognitive disorders, treatments for neurocognitive disorders may be further clarified.