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BackgroundThis study aims to assess the causal relationship between immune cell characteristics and malignant tumors of bone and articular cartilage, focusing on the mediating role of metabolites. Using Mendelian randomization, we evaluated these relationships based on genetic variations to identify potential biomarkers and therapeutic targets.MethodsA two-sample Mendelian randomization analysis was conducted using GWAS data for immune cell features and 1,400 metabolites to investigate direct and mediating effects. Effective instrumental variables (IVs) were selected, and statistical analyses—including inverse variance weighting (IVW), weighted median, and mode-based methods—were performed using R software. This approach enabled the assessment of direct causal relationships as well as the potential mediating role of metabolites in the association between immune cell features and malignancies.ResultsSignificant causal relationships were identified between 26 immune phenotypes and the risk of malignant tumors of bone and articular cartilage. Notably, the HLA DR+ NK cell phenotype SSC-A showed a positive correlation with the risk of these malignancies. Further analysis revealed causal relationships with 67 metabolites, 38 of which were positively correlated and 29 negatively correlated. Mediation analysis highlighted the role of immune surveillance and metabolic dysregulation in tumor development, as evidenced by the association between the immune phenotype SSC-A on HLA DR+ NK cells and the metabolite 5-hydroxyhexanoate.ConclusionThe findings suggest significant causal relationships between immune phenotypes and malignant tumors of bone and articular cartilage, with metabolites potentially mediating these relationships. These insights lay the groundwork for further research and could contribute to the development of new biomarkers and treatment strategies.

Abstract Myocardial infarction (MI) remains the leading cause of death worldwide. Cardiac fibroblasts (CFs) are abundant in the heart and are responsible for cardiac repair post‐MI. NF‐κB‐repressing factor (NKRF) plays a significant role in the transcriptional inhibition of various specific genes. However, the NKRF action mechanism in CFs remains unclear in cardiac repair post‐MI. This study investigates the NKRF mechanism in cardiac remodeling and dysfunction post‐MI by establishing a CF‐specific NKRF‐knockout (NKRF‐CKO) mouse model. NKRF expression is downregulated in CFs in response to pathological cardiac remodeling in vivo and TNF‐α in vitro. NKRF‐CKO mice demonstrate worse cardiac function and survival and increased infarct size, heart weight, and MMP2 and MMP9 expression post‐MI compared with littermates. NKRF inhibits CF migration and invasion in vitro by downregulating MMP2 and MMP9 expression. Mechanistically, NKRF inhibits human antigen R (HuR) transcription by binding to the classical negative regulatory element within the HuR promoter via an NF‐κB‐dependent mechanism. This decreases HuR‐targeted Mmp2 and Mmp9 mRNA stability. This study suggests that NKRF is a therapeutic target for pathological cardiac remodeling.

Abstract Background Premature coronary artery disease (PCAD) has become more common in recent years and is often associated with poor outcomes. Triglyceride-glucose (TyG) index is a simple and reliable surrogate for insulin resistance (IR) and is an independent predictor of cardiovascular prognosis. However, the prognostic value of the TyG index in patients with PCAD remains uncertain. Thus, this study aimed to investigate the prognostic value and predictive performance of the TyG index in patients with PCAD. Methods A total of 526 young subjects (male

The physical properties of materials are critical to their functionality, and the ability to control these properties using external forces is a significant challenge. In this study, we investigate the effect of three high frequency acoustic wave vibration patterns on the structure and morphology of MOF particles. Our results indicate that while regular vibration patterns generated by SAW can alter particle morphology, hybrid waves and Lamb waves with irregular vibration patterns can synthesise MOF crystals with multi-level pores. The vibration pattern of acoustic waves is shown to be a critical factor in controlling the particle morphology process. These results provide new insights into the precise control of crystal structure and the theory of crystallisation by particle attachment (CPA).

Accurately recognizing insect pest in their larva phase is significant to take the early treatment on the infected crops, thus helping timely reduce the yield loss in agricultural products. The convolutional neural networks (CNNs)-based classification methods have become the most competitive methods to address many technical challenges related to image recognition in the field. Focusing on accurate and small models carried on mobile devices, this study proposed a novel pest classification method PCNet (Pest Classification Network) based on lightweight CNNs embedded attention mechanism. PCNet was designed with EfficientNet V2 as the backbone, and the coordinate attention mechanism (CA) was incorporated in this architecture to learn the inter-channel pest information and pest positional information of input images. Moreover, combining the feature maps output by mobile inverted bottleneck (MBConv) with the feature maps output by average pooling to develop the feature fusion module, which implements the feature fusion between shallow layers and deep layers to address the loss of insect pest features in the down-sampling procedures. In addition, a stochastic, pipeline-based data augmentation approach was adopted to randomly enhance data diversity and thus avoid model overfitting. The experimental results show that the PCNet model achieved recognition accuracy of 98.4 % on the self-built dataset consisting of 30 classes of larvae, which outperforms three classic CNN models (AlexNet, VGG16, and ResNet101), and four lightweight CNN models (ShuffleNet V2, MobileNet V3, EfficientNet V1 and V2). To further verify the robustness on different datasets, the proposed model was also tested on two other public datasets: IP102 and miniImageNet. The recognition accuracy of PCNet is 73.7 % on the IP102 dataset, outperforming other models and 94.0 % on miniImageNet dataset, which is only lower than that of ResNet101 and MobileNet V3. The number of PCNet parameters is 20.7 M, which is less than those of traditional classic CNN models. The satisfactory accuracy and small size of this model makes it suitable for real-time pest recognition in the field with resource constrained mobile devices. Our code will be available at https://github.com/pby521/PCNet/tree/master.

ABSTRACT Inflammatory bowel diseases (IBD) are characterized by chronic inflammation of the gastrointestinal tract and profound alterations to the gut microbiome. Adherent-invasive Escherichia coli (AIEC) is a mucosa-associated pathobiont that colonizes the gut of patients with Crohn’s disease, a form of IBD. Because AIEC exacerbates gut inflammation, strategies to reduce the AIEC bloom during colitis are highly desirable. To thrive in the inflamed gut, Enterobacteriaceae acquire the essential metal nutrient iron by producing and releasing siderophores. Here, we implemented an immunization-based strategy to target the siderophores enterobactin and its glucosylated derivative salmochelin to reduce the AIEC bloom in the inflamed gut. Using chemical (dextran sulfate sodium) and genetic (Il10−/− mice) IBD mouse models, we showed that immunization with enterobactin conjugated to the mucosal adjuvant cholera toxin subunit B potently elicited mucosal and serum antibodies against these siderophores. Siderophore-immunized mice exhibited lower AIEC gut colonization, diminished AIEC association with the gut mucosa, and reduced colitis severity. Moreover, Peyer’s patches and the colonic lamina propria harbored enterobactin-specific B cells that could be identified by flow cytometry. The beneficial effect of siderophore immunization was primarily B cell-dependent because immunized muMT−/− mice, which lack mature B lymphocytes, were not protected during AIEC infection. Collectively, our study identified siderophores as a potential therapeutic target to reduce AIEC colonization and its association with the gut mucosa, which ultimately may reduce colitis exacerbation. Moreover, this work provides the foundation for developing monoclonal antibodies against siderophores, which could provide a narrow-spectrum strategy to target the AIEC bloom in Crohn’s disease patients. IMPORTANCE Adherent-invasive Escherichia coli (AIEC) is abnormally prevalent in patients with ileal Crohn’s disease and exacerbates intestinal inflammation, but treatment strategies that selectively target AIEC are unavailable. Iron is an essential micronutrient for most living organisms, and bacterial pathogens have evolved sophisticated strategies to capture iron from the host environment. AIEC produces siderophores, small, secreted molecules with a high affinity for iron. Here, we showed that immunization to elicit antibodies against siderophores promoted a reduction of the AIEC bloom, interfered with AIEC association with the mucosa, and mitigated colitis in experimental mouse models. We also established a flow cytometry-based approach to visualize and isolate siderophore-specific B cells, a prerequisite for engineering monoclonal antibodies against these molecules. Together, this work could lead to a more selective and antibiotic-sparing strategy to target AIEC in Crohn’s disease patients.

Light traps have been widely used as effective tools to monitor multiple agricultural and forest insect pests simultaneously. However, the current detection methods of pests from light trapping images have several limitations, such as exhibiting extremely imbalanced class distribution, occlusion among multiple pest targets, and inter-species similarity. To address the problems, this study proposes an improved YOLOv3 model in combination with image enhancement to better detect crop pests in real agricultural environments. First, a dataset containing nine common maize pests is constructed after an image augmentation based on image cropping. Then, a linear transformation method is proposed to optimize the anchors generated by the k-means clustering algorithm, which can improve the matching accuracy between anchors and ground truths. In addition, two residual units are added to the second residual block of the original YOLOv3 network to obtain more information about the location of the underlying small targets, and one ResNet unit is used in the feature pyramid network structure to replace two DBL(Conv+BN+LeakyReLU) structures to enhance the reuse of pest features. Experiment results show that the mAP and mRecall of our proposed method are improved by 6.3% and 4.61%, respectively, compared with the original YOLOv3. The proposed method outperforms other state-of-the-art methods (SSD, Faster-rcnn, and YOLOv4), indicating that the proposed method achieves the best detection performance, which can provide an effective model for the realization of intelligent monitoring of maize pests.

BackgroundInsulin resistance (IR) has emerged as a risk factor for coronary heart disease (CAD), but there is currently insufficient data on the association of non-insulin-based IR indexes [triglyceride (TG)/high-density lipoprotein cholesterol (HDL-C) ratio, triglyceride and glucose (TyG) index, and metabolic score for IR (METS-IR)] with the presence and severity of CAD. Thus, the present study aimed to examine the relationship between these three non-insulin-based IR indexes and CAD, as well as to further compare the predictive values of each index.Materials and methodsIn total, 802 consecutive patients who underwent coronary angiography for suspected CAD from January 2016 to April 2017 were included in this study and were divided into the control group (n = 149) and CAD group (n = 653) according to the angiography results. The triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio, triglyceride and glucose index (TyG index), and METS-IR were calculated according to the corresponding formulas. The severity of CAD was evaluated using the Gensini score (GS). The relationship of the TG/HDL-C ratio, TyG index, and METS-IR with CAD was analyzed, and the predictive values of the indexes were compared.ResultsThe TG/HDL-C ratio, TyG index, and METS-IR in the CAD group were significantly higher than those in the control group. The TG/HDL-C ratio and METS-IR in the high GS group were significantly higher than those in the non-high GS group. Multivariate logistic regression analysis showed that the TG/HDL-C ratio and METS-IR were independent predictors for the presence of CAD {adjusted odds ratio (OR) [95% confidence interval (CI)]: 1.32 (1.02–1.70) and 1.65 (1.32–2.05), respectively}, whereas only the METS-IR was an independent predictor of the severity of CAD [adjusted OR (95% CI): 1.22 (1.02–1.47)]. Further subgroup analysis indicated that statistical significance was observed only among men, younger patients (≤ 60), and patients with prediabetes mellitus (PDM). Receiver operator characteristic (ROC) analysis showed that the METS-IR had the highest predictive value for the prediction of both the presence and severity of CAD.ConclusionThe TG/HDL-C ratio, TyG index, and METS-IR are valuable predictors of the presence and severity of CAD, and the METS-IR has the highest predictive value among the three non-insulin-based IR indexes.

One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly (Bemisia tabaci) and thrips (Frankliniella occidentalis) are two most prominent pests in greenhouses of northern China. Traditionally, growers estimate the population of these pests by counting insects caught on sticky traps, which is not only a challenging task but also an extremely time-consuming one. To alleviate this situation, this study proposed an automated detection approach to meet the need for continuous monitoring of pests in greenhouse conditions. Candidate targets were firstly located using a spectral residual model and then different color features were extracted. Ultimately, Whitefly and thrips were identified using a support vector machine classifier with an accuracy of 93.9 and 89.9%, a true positive rate of 93.1 and 80.1%, and a false positive rate of 9.9 and 12.3%, respectively. Identification performance was further tested via comparison between manual and automatic counting with a coefficient of determination, R2, of 0.9785 and 0.9582. The results show that the proposed method can provide a comparable performance with previous handcrafted feature-based methods, furthermore, it does not require the support of high-performance hardware compare with deep learning-based method. This study demonstrates the potential of developing a vision-based identification system to facilitate rapid gathering of information pertaining to numbers of small-sized pests in greenhouse agriculture and make a reliable estimation of overall population density.

Two-dimensional (2D) metal–organic framework (MOF) nanosheets have recently received extensive attention due to their ultra-thin thickness, large specific surface area, chemical and functional designability. In this study, an unconventional method using surface acoustic wave (SAW) technology is proposed to exfoliate large quantities and uniform layers of 2D MOF-Zn2(bim)4 nanosheets in a microfluidic system. We successfully demonstrated that the thickness of 2D MOF is effectively and accurately controlled by optimizing the SAW parameters. The mechanisms for the efficient exfoliation of 2D MOF nanosheets is attributed to both the electric and acoustic fields generated by the SAWs in the liquid. The electric field ionizes the methanol to produce H+ ions, which intercalate Zn2(bim)4 sheets and weaken the interlayer bonding, and the strong shear force generated by SAWs separates the MOF sheets. A yield of 66% for monolayer MOFs with a maximum size of 3.5 μm is achieved under the combined effect of electric and acoustic fields. This fast, low-energy exfoliation platform has the potential to provide a simple and scalable microfluidic exfoliation method for production of large-area and quantities of 2D MOFs.

BAW-based micromixers usually achieve mixing enhancement with acoustic-induced bubbles, while SAW-based micromixers usually enhance mixing efficiency by varying the configuration of IDTs and microchannels. In this paper, bubble-enhanced acoustic mixing induced by standing surface acoustic waves (SSAWs) in a microchannel is proposed and experimentally demonstrated. Significant enhancement in the mixing efficiency was achieved after the bubbles were stimulated in our acoustofluidic microdevice. With an applied voltage of 5 V, 50 times amplified, the proposed mixing microdevice could achieve 90.8% mixing efficiency within 60 s at a flow rate of 240 μL/h. The bubbles were generated from acoustic cavitation assisted by the temperature increase resulting from the viscous absorption of acoustic energy. Our results also suggest that a temperature increase is harmful to microfluidic devices and temperature monitoring. Regulation is essential, especially in chemical and biological applications.

In order to predict the bolt’s stress distribution under the pre-tightening state and the loosening process under the transverse load, a 3D model considering the bolt thread structure was established by finite element method (FEM). In this study, the looseness effect of transverse load amplitude, the friction coefficient of the bolt head load bearing surface and the thread contact surface are mainly studied. The result indicated that the stress under bolt pre-tightening is mainly concentrated on the contact part of the bolt’s head and bar. At the contact area of the load threads at one end and the transition part between the bolt bar and the thread, the stress and axial direction of the thread part are offset. Under the transverse displacement load, the sliding of the bolt can be divided into three stages: full contact, viscous contact, and full slip. The numerical model of bolt relaxation obtained in this paper can provide reference for the application, checking and prediction of threaded fasteners.

Temperature is an important parameter for many medical and biological applications. It is key to measuring the temperature of acoustofluidics devices for controlling the device’s temperature. In this paper, Rhodamine B was used to measure the temperature change of the microchannel induced by the SSAWs’ thermal effect in microfluidics. A thermocouple was integrated into the microfluidics device to calibrate the relationship between the fluorescent intensity ratios of Rhodamine B and the temperature. Then, the fluid temperature in the microchannel heated by the SSAWs was measured by the fluorescent signal intensity ratio in the acoustofluidics device. The fluid temperature with different input voltages and different flow rates was measured. The results show that SSAWs can heat the still fluid rapidly to 80 °c, and the flow rates will influence the temperature of the fluid. The results will be useful for precisely controlling the temperature of acoustofluidics devices.

Solvent vapor-driven soft actuators are promising devices for human-environment interaction and have gained considerable attention in recent years. To enhance the interaction with the solvents and deliver a fast response and superior sensitivity, polymer/MOF composites are promising candidates for the fabrication of solvent-driven actuators. This paper presents a multiple-solvent-driven intelligent MOF-polymer soft actuator, namely MIL-88B/PVDF asymmetric film, and compares it to analogs based on other MIL-88(Fe) series flexible MOFs. This comparison investigates the deformation-structure relationship and the solvent-driven mechanism. The deformation of the MIL-88(Fe)/PVDF composite films in response to various organic solvents has been studied, and the structure and composition of the MOF fillers and the composites were characterized by SEM, FT-IR, and XRD. Furthermore, the MIL-88B/PVDF composite, which shows ability to respond to multiple solvents and a fast deformation response to several organic solvents through the solvent swelling/adsorption of the MOF skeleton and the appropriate flexibility and group affinity from the terephthalic acid ligand, was used to fabricate an inverted opal gradient actuator composed of MIL-88B, carbon black (CB) and PVDF, namely MIL-88B/CB/PVDF. The MIL-88B/CB/PVDF film not only realizes excellent dual-responsive deformation and discoloration to methanol vapor with fast visible deformation within 1 s with a maximum crimp angle of up to 5581 (10 vol% concentration) and a wide color change range covering six viewing-angledependent high-saturation colors (red, orange, yellow, green, blue and purple) but also shows an approximate linear relationship between the maximum curl angle and the solvent concentration for three solvents (methanol, toluene and chloroform). In addition, several smart devices have also been fabricated, including methanol-driven bionic petals, a worm robot, an alertor and a LIG sensor. Therefore, such polymer/MOF soft actuators have great potential in bio-robots and this work provides a new design strategy for smart materials. [ABSTRACT FROM AUTHOR]