TheMarker: a comprehensive database of therapeutic biomarkers

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

A task-specific encoding algorithm for RNAs and RNA-associated interactions based on convolutional autoencoder

RNAs play essential roles in diverse physiological and pathological processes by interacting with other molecules (RNA/protein/compound), and various computational methods are available for identifying these interactions. However, the encoding features provided by existing methods are limited and the existing tools does not offer an effective way to integrate the interacting partners. In this study, a task-specific encoding algorithm for RNAs and RNA-associated interactions was therefore developed. This new algorithm was unique in (a) realizing comprehensive RNA feature encoding by introducing a great many of novel features and (b) enabling task-specific integration of interacting partners using convolutional autoencoder-directed feature embedding. Compared with existing methods/tools, this novel algorithm demonstrated superior performances in diverse benchmark testing studies. This algorithm together with its source code could be readily accessed by all user at: https://idrblab.org/corain/ and https://github.com/idrblab/corain/.

ADCdb: the database of antibody-drug conjugates

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

Acquisition sequences and reconstruction methods for fast chemical exchange saturation transfer imaging

Chemical exchange saturation transfer (CEST) imaging is an emerging molecular magnetic resonance imaging (MRI) technique that has been developed and employed in numerous diseases. Based on the unique saturation transfer principle, a family of CEST-detectable biomolecules in vivo have been found capable of providing valuable diagnostic information. However, CEST MRI needs a relatively long scan time due to the common long saturation labeling module and typical acquisition of multiple frequency offsets and signal averages, limiting its widespread clinical applications. So far, a plethora of imaging schemes and techniques has been developed to accelerate CEST MRI. In this review, the key acquisition and reconstruction methods for fast CEST imaging are summarized from a practical and systematic point of view. The first acquisition sequence section describes the major development of saturation schemes, readout patterns, ultrafast z-spectroscopy, and saturation-editing techniques for rapid CEST imaging. The second reconstruction method section lists the important advances of parallel imaging, compressed sensing, sparsity in the z-spectrum, and algorithms beyond the Fourier transform for speeding up CEST MRI.

INTEDE 2.0: the metabolic roadmap of drugs

The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.

RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction

Ribonucleic acids (RNAs) involve in various physiological/pathological processes by interacting with proteins, compounds, and other RNAs. A variety of powerful computational methods have been developed to predict such valuable interactions. However, all these methods rely heavily on the 'digitalization' (also known as 'encoding') of RNA-associated interacting pairs into a computer-recognizable descriptor. In other words, it is urgently needed to have a powerful tool that can not only represent each interacting partner but also integrate both partners into a computer-recognizable interaction. Herein, RNAincoder (deep learning-based encoder for RNA-associated interactions) was therefore proposed to (a) provide a comprehensive collection of RNA encoding features, (b) realize the representation of any RNA-associated interaction based on a well-established deep learning-based embedding strategy and (c) enable large-scale scanning of all possible feature combinations to identify the one of optimal performance in RNA-associated interaction prediction. The effectiveness of RNAincoder was extensively validated by case studies on benchmark datasets. All in all, RNAincoder is distinguished for its capability in providing a more accurate representation of RNA-associated interactions, which makes it an indispensable complement to other available tools. RNAincoder can be accessed at https://idrblab.org/rnaincoder/.

Rational Electrochemical Design of Cuprous Oxide Hierarchical Microarchitectures and Their Derivatives for SERS Sensing Applications

Rational morphology control of inorganic microarchitectures is important in diverse fields, requiring precise regulation of nucleation and growth processes. While wet chemical methods have achieved success regarding the shape-controlled synthesis of micro/nanostructures, accurately controlling the growth behavior in real time remains challenging. Comparatively, the electrodeposition technique can immediately control the growth behavior by tuning the overpotential, whereas it is rarely used to design complex microarchitectures. Here, the electrochemical design of complex Cu2O microarchitectures step-by-step by precisely controlling the growth behavior is demonstrated. The growth modes can be switched between the thermodynamic and kinetic modes by varying the overpotential. Cl- ions preferably adhered to {100} facets to modulate growth rates of these facets is proved. The discovered growth modes to prepare Cu2O microarchitectures composed of multiple building units inaccessible with existing methods are employed. Polyvinyl alcohol (PVA) additives can guarantee all pre-electrodeposits simultaneously evolve into uniform microarchitectures, instead of forming undesired microstructures on bare electrode surfaces in following electrodeposition processes is discovered. The designed Cu2O microarchitectures can be converted into noble metal microstructures with shapes unchanged, which can be used as surface-enhanced Raman scattering substrates. An electrochemical avenue toward rational design of complex inorganic microarchitectures is opened up.

The Alterations of Gut Microbiome and Lipid Metabolism in Patients with Spinal Muscular Atrophy

INTRODUCTION

Spinal muscular atrophy (SMA) can cause multiple system dysfunction, especially lipid metabolic disorders, for which management strategies are currently lacking. Microbes are related to metabolism and the pathogenesis of neurological diseases. This study aimed to preliminarily explore the alterations in the gut microbiota in SMA and the potential relationship between altered microbiota and lipid metabolic disorders.

METHODS

Fifteen patients with SMA and 17 gender- and age-matched healthy controls were enrolled in the study. Feces and fasting plasma samples were collected. 16S ribosomal RNA sequencing and nontargeted metabolomics analysis were performed to explore the correlation between microbiota and differential lipid metabolites.

RESULTS

No significant difference was found in microbial diversity (α- and β-diversity) between the SMA and control groups, with both groups having a relatively similar community structure. However, compared to the control group, the SMA group showed an increased relative abundance of the genera Ruminiclostridium, Gordonibacter, Enorma, Lawsonella, Frisingicoccus, and Anaerofilum and a decreased abundance of the genera Catabacter, Howardella, Marine_Methylotrophic_Group_3, and Lachnospiraceae_AC2044_group. The concurrent metabolomic analysis showed that the SMA group had 56 different kinds of lipid metabolite levels than did the control group. Additionally, the Spearman correlation suggested a correlation between the altered differential lipid metabolites and the above-mentioned altered microbiota.

CONCLUSIONS

The gut microbiome and lipid metabolites differed between the patients with SMA and the control subjects. The altered microbiota may be related with the lipid metabolic disorders in SMA. However, further study is necessary to clarify the mechanism of lipid metabolic disorders and develop management strategies to improve the related complications in SMA.

Accelerating CEST imaging using a model-based deep neural network with synthetic training data

PURPOSE

To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil CEST data.

THEORY AND METHODS

Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-ω domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state-of-the-art reconstruction methods.

RESULTS

The proposed CEST-VN method generated high-quality CEST source images and amide proton transfer-weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST-specific loss function and data-sharing block used.

CONCLUSIONS

The proposed CEST-VN method can offer high-quality CEST source images and amide proton transfer-weighted maps from highly undersampled multi-coil data by integrating the deep learning prior and multi-coil sensitivity encoding model.

ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.

Porous Film Coating Enabled by Polyvinyl Pyrrolidone (PVP) for Enhanced Air Permeability of Fabrics: The Effect of PVP Molecule Weight and Dosage

This study developed a versatile and facile method for creating pores and tuning the porous structure in the polymer latex films by selectively etching the added functional polyvinyl pyrrolidone (PVP) molecules. The pore formed in the latex films had a similar morphology to that of PVP aggregation before etching. This observation promotes us to regulate the pore morphology that determines the film's property, such as air permeability through varying the PVP molecule weight and dosage. To this end, the effects of PVP molecule weight and dosage on the pore formation were systematically studied. The results showed that the average pore size of porous film decreased from >10 μm to sub-micron (about 0.4 μm) as the molecular weight or the dosage of PVP increased. This was ascribed to the strong adsorption affinity of PVP molecule onto the latex particle surface, which further hindered the diffusion and self-assembly of PVP molecule. In addition, this interaction became much stronger when the higher molecule weight of PVP or the higher dosage of PVP was employed, leading to the decreased size of PVP aggregation, as well as the formed pores in the latex films. Furthermore, the addition of PVP had little effect on the color of coated fabric based on the results of CIE L*a*b* measurement. The proposed facile method can be used to improve the air permeability of coated fabrics.

A Multiscale Recognition Method for the Optimization of Traffic Signs Using GMM and Category Quality Focal Loss

Effective traffic sign recognition algorithms can assist drivers or automatic driving systems in detecting and recognizing traffic signs in real-time. This paper proposes a multiscale recognition method for traffic signs based on the Gaussian Mixture Model (GMM) and Category Quality Focal Loss (CQFL) to enhance recognition speed and recognition accuracy. Specifically, GMM is utilized to cluster the prior anchors, which are in favor of reducing the clustering error. Meanwhile, considering the most common issue in supervised learning (i.e., the imbalance of data set categories), the category proportion factor is introduced into Quality Focal Loss, which is referred to as CQFL. Furthermore, a five-scale recognition network with a prior anchor allocation strategy is designed for small target objects i.e., traffic sign recognition. Combining five existing tricks, the best speed and accuracy tradeoff on our data set (40.1% mAP and 15 FPS on a single 1080Ti GPU), can be achieved. The experimental results demonstrate that the proposed method is superior to the existing mainstream algorithms, in terms of recognition accuracy and recognition speed.

Hypocretin-1/Hypocretin Receptor 1 Regulates Neuroplasticity and Cognitive Function through Hippocampal Lactate Homeostasis in Depressed Model

Cognitive dysfunction is not only a common symptom of major depressive disorder, but also a more common residual symptom after antidepressant treatment and a risk factor for chronic and recurrent disease. The disruption of hypocretin regulation is known to be associated with depression, however, their exact correlation is remains to be elucidated. Hypocretin-1 levels are increased in the plasma and hypothalamus from chronic unpredictable mild stress (CUMS) model mice. Excessive hypocretin-1 conducted with hypocretin receptor 1 (HCRTR1) reduced lactate production and brain-derived neurotrophic factor (BDNF) expression by hypoxia-inducible factor-1α (HIF-1α), thus impairing adult hippocampal neuroplasticity, and cognitive impairment in CUMS model. Subsequently, it is found that HCRTR1 antagonists can reverse these changes. The direct effect of hypocretin-1 on hippocampal lactate production and cognitive behavior is further confirmed by intraventricular injection of hypocretin-1 and microPET-CT in rats. In addition, these mechanisms are further validated in astrocytes and neurons in vitro. Moreover, these phenotypes and changes in molecules of lactate transport pathway can be duplicated by specifically knockdown of HCRTR1 in hippocampal astrocytes. In summary, the results provide molecular and functional insights for involvement of hypocretin-1-HCRTR1 in altered cognitive function in depression.

SoCube: an innovative end-to-end doublet detection algorithm for analyzing scRNA-seq data

Doublets formed during single-cell RNA sequencing (scRNA-seq) severely affect downstream studies, such as differentially expressed gene analysis and cell trajectory inference, and limit the cellular throughput of scRNA-seq. Several doublet detection algorithms are currently available, but their generalization performance could be further improved due to the lack of effective feature-embedding strategies with suitable model architectures. Therefore, SoCube, a novel deep learning algorithm, was developed to precisely detect doublets in various types of scRNA-seq data. SoCube (i) proposed a novel 3D composite feature-embedding strategy that embedded latent gene information and (ii) constructed a multikernel, multichannel CNN-ensembled architecture in conjunction with the feature-embedding strategy. With its excellent performance on benchmark evaluation and several downstream tasks, it is expected to be a powerful algorithm to detect and remove doublets in scRNA-seq data. SoCube is freely provided as an end-to-end tool on the Python official package site PyPi (https://pypi.org/project/socube/) and open-source on GitHub (https://github.com/idrblab/socube/).

Saturation-prolongated and inhomogeneity-mitigated chemical exchange saturation transfer imaging with parallel transmission

Chemical exchange saturation transfer (CEST) imaging benefits from a longer saturation duration and a higher saturation duty cycle. Dielectric shading effects occur when the radiofrequency (RF) wavelength approaches the object size. Here, we proposed a simultaneous parallel transmission-based CEST (pTx-CEST) sequence to prolongate the saturation duration at a 100% duty cycle and improve the RF saturation homogeneity in CEST imaging. The simultaneous pTx-CEST sequence was implemented by switching the CEST saturation module from the non-pTx to pTx mode, using the pTx functionality with both transmit channels being driven simultaneously (instead of time-interleaved). The optimization of amplitude ratio and phase difference settings between RF channels for best B1 homogeneity was performed in phantoms of two different sizes mimicking the human brain and abdomen. The optimal amplitude and phase settings generating the best B1 homogeneity in the phantoms were used in pTx-CEST scans of the human study. The comparison of the maximum achievable saturation duration between the non-pTx-CEST and pTx-CEST sequences was performed in a protein phantom, healthy volunteers, and a metastatic brain tumor patient. The optimal amplitude ratio and phase difference setting between transmit channels manifested circular and elliptical polarization in the head-sized and abdomen-sized phantoms. In the brain, the maximum saturation durations achieved at a 100% duty cycle using the simultaneous pTx-CEST sequence were prolonged to 2240, 3220, and 4200 ms compared with 980 ms using the non-pTx-CEST sequence at repetition times of 3, 4, and 5 s, respectively. The longer saturation duration helped improve the image contrast between the tumor and the normal tissue in the patient. The optimized elliptical polarization mode saturation pulses yielded improved uniformity of CEST signals acquired from the human abdomen. The proposed simultaneous pTx-CEST sequence enabled essentially arbitrarily long saturation duration at a 100% duty cycle and helped reduce the dielectric shading effects with the optimized RF setting.

The Mechanisms of Polysaccharides from Tonic Chinese Herbal Medicine on the Enhancement Immune Function: A Review

Tonic Chinese herbal medicine is a type of traditional Chinese medicine, and its primary function is to restore the body's lost nutrients, improve activity levels, increase disease resistance, and alleviate physical exhaustion. The body's immunity can be strengthened by its polysaccharide components, which also have a potent immune-system-protecting effect. Several studies have demonstrated that tonic Chinese herbal medicine polysaccharides can improve the body's immune response to tumor cells, viruses, bacteria, and other harmful substances. However, the regulatory mechanisms by which various polysaccharides used in tonic Chinese herbal medicine enhance immune function vary. This study examines the regulatory effects of different tonic Chinese herbal medicine polysaccharides on immune organs, immune cells, and immune-related cytokines. It explores the immune response mechanism to understand the similarities and differences in the effects of tonic Chinese herbal medicine polysaccharides on immune function and to lay the foundation for the future development of tonic Chinese herbal medicine polysaccharide products.

Numerical fitting of Extrapolated semisolid Magnetization transfer Reference signals: Improved detection of ischemic stroke

PURPOSE

To propose a novel Numerical fitting method of the Extrapolated semisolid Magnetization transfer Reference (NEMR) signal for quantifying the CEST effect.

THEORY AND METHODS

Modified two-pool Bloch-McConnell equations were used to numerically fit the magnetization transfer (MT) and direct water saturation (DS) signals at far off-resonance frequencies, which was subsequently extrapolated into the frequency range of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) pools. Then the subtraction of the fitted two-pool z-spectrum and the experimentally acquired z-spectrum yielded APT^# and NOE^# signals mostly free of MT and DS contamination. Several strategies were used to accelerate the NEMR fitting. Furthermore, the proposed NEMR method was compared with the conventional extrapolated semisolid magnetization transfer reference (EMR) and magnetization transfer ratio asymmetry (MTR_asym ) methods in simulations and stroke patients.

RESULTS

The combination of RF downsampling, MT lineshape look-up table, and conversion of MATLAB code to C code accelerated the NEMR fitting by over 2700-fold. Monte-Carlo simulations showed that NEMR had higher accuracy than EMR and eliminated the requirement of the steady-state condition. In ischemic stroke patients, the NEMR maps at 1 μT removed hypointense artifacts seen on EMR and MTR_asym images, and better depicted stroke lesions than EMR. For NEMR, NOE^# yielded significantly (p < 0.05) stronger signal contrast between stroke and normal tissues than APT^# at 1 μT.

CONCLUSION

The proposed NEMR method is suitable for arbitrary saturation settings and can remove MT and DS contamination from the CEST signal for improved detection of ischemic stroke.

A Spring Compensation Method for a Low-Cost Biped Robot Based on Whole Body Control

At present, the research and application of biped robots is more and more popular. The popularity of biped robots can be better promoted by improving the motion performance of low-cost biped robots. In this paper, the method of the Linear Quadratic Regulator (LQR) is used to track a robot's center of mass (COM). At the same time, the whole-body-control method and value function generated in the process of tracking COM are used to construct the quadratic programming (QP) model of a biped robot. Through the above method, the torque feedforward of the robot is obtained in the Drake simulation platform. The torque feedforward information of the robot is transformed into position feedforward information by spring compensation. In this paper, open loop control and spring compensation are used, respectively, to make the robot perform simple actions. Generally, after the compensation method of spring compensation is adopted, the roll angle and pitch angle of the upper body of the robot are closer to 0 after the robot performs an action. However, as the selected motion can introduce more forward and lateral motions, the robot needs more spring clearance compensation to improve performance. For improving the motion performance of a low-cost biped robot, the experimental results show that the spring compensation method is both reasonable and effective.

Early Oral Administration of Ginseng Stem-Leaf Saponins Enhances the Peyer's Patch-Dependent Maternal IgA Antibody Response to a PEDV Inactivated Vaccine in Mice, with Gut Microbiota Involvement

Neonatal piglets during the first week of life are highly susceptible to porcine epidemic diarrhoea virus (PEDV) infection, with mortality rates reaching 80-100%. Passive lactogenic immunity remains the most effective way to protect neonates from infection. Although safe, inactivated vaccines provide little or no passive protection. Here, we administered ginseng stem-leaf saponins (GSLS) to mice before parenteral immunization with an inactivated PEDV vaccine to investigate the effect of GSLS on the gut-mammary gland (MG)-secretory IgA axis. Early oral GSLS administration potently increased PEDV-specific IgA plasma cell generation in the intestine, facilitated intestinal IgA plasma cell migration to the MG by enhancing the chemokine receptor (CCR)10-chemokine ligand (CCL)28 interaction, and ultimately promoted specific IgA secretion into milk, which was dependent on Peyer's patches (PPs). Additionally, GSLS improved the gut microbiota composition, especially increasing probiotic abundance, and these microflora members promoted the GSLS-enhanced gut-MG-secretory IgA axis response and were regulated by PPs. In summary, our findings highlight the potential of GSLS as an oral adjuvant for PEDV-inactivated vaccines and provide an attractive vaccination strategy for lactogenic immunity induction in sows. Further studies are required to evaluate the mucosal immune enhancement efficacy of GSLS in pigs.

Indirect Competitive Enzyme-Linked Immunosorbent Assay Based on Broad-Spectrum Antibody for Simultaneous Determination of Thirteen Fluoroquinolone Antibiotics in Rana catesbeianus

Fluoroquinolone (FQ) is a type of widely used antibiotic in agriculture and aquaculture, and exposure to low doses of FQs may result in the transfer of resistance between animal and human pathogens. Based on the optimization of the operating parameters, an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) standard curve was constructed for the simultaneous detection of 13 FQs, including enrofloxacin (ENR), ciprofloxacin (CIP), sarafloxacin (SAR), ofloxacin (OFL), norfloxacin (NOR), pefloxacin mesylate (PM), pefloxacin (PEF), enoxacin (ENX), marbofloxacin (MAR), fleroxacin (FLE), lomefloxacin (LOM), danofloxacin (DAN), and difloxacin (DIF). The limit of detection (LOD, computed as IC₁₀) and sensitivity (IC₅₀) of the ic-ELISA for ENR were 0.59 μg/L and 19.23 μg/L, respectively. The precision and dependability of the detection results of this ic-ELISA were properly verified by HPLC in Rana catesbeianus samples. This indicated that the established ic-ELISA approach could be utilized to determine the FQs in Rana catesbeianus. In addition, this ic-ELISA, based on a broad-spectrum antibody, provides a technical reference and potential strategy for an immunoassay of hazard factors with similar structure.

Biotinylated Tn5 transposase-mediated CUT&Tag efficiently profiles transcription factor-DNA interactions in plants

In contrast to CUT&Tag approaches for profiling bulk histone modifications, current CUT&Tag methods for analysing specific transcription factor (TF)-DNA interactions remain technically challenging due to TFs having relatively low abundance. Moreover, an efficient CUT&Tag strategy for plant TFs is not yet available. Here, we first applied biotinylated Tn5 transposase-mediated CUT&Tag (B-CUT&Tag) to produce high-quality libraries for interrogating TF-DNA interactions. B-CUT&Tag combines streptavidin-biotin-based DNA purification with routine CUT&Tag, optimizing the removal of large amounts of intact chromatin not targeted by specific TFs. The biotinylated chromatin fragments are then purified for construction of deep sequencing libraries or qPCR analysis. We applied B-CUT&Tag to probe genome-wide DNA targets of Squamosa promoter-binding-like protein 9 (SPL9), a well-established TF in Arabidopsis; the resulting profiles were efficient and consistent in demonstrating its well-established target genes in juvenile-adult transition/flowering, trichome development, flavonoid biosynthesis, wax synthesis and branching. Interestingly, our results indicate functions of AtSPL9 in modulating growth-defence trade-offs. In addition, we established a method for applying qPCR after CUT&Tag (B-CUT&Tag-qPCR) and successfully validated the binding of SPL9 in Arabidopsis and PHR2 in rice. Our study thus provides a convenient and highly efficient CUT&Tag strategy for profiling TF-chromatin interactions that is widely applicable to the annotation of cis-regulatory elements for crop improvement.

Comparative characterization of microRNA-71 of Echinococcus granulosus exosomes

Cystic echinococcosis (CE) is a global zoonotic disease caused by Echinococcus granulosus, posing a great threat to human and animal health. MiRNAs are small regulatory noncoding RNA involved in the pathogenesis of parasitic diseases, possibly via exosomes. Egr-miR-71 has been identified as one of the miRNAs in the blood of CE patients, but its secretory characteristics and functions remains unclear. Herein, we studied the secretory and biological activity of exosomal egr-miR-71 and its immunoregulatory functions in sheep peripheral blood mononuclear cells (PBMCs). Our results showed that egr-miR-71 was enriched in the exosome secreted by protoscoleces with biological activity. These egr-miR-71-containing exosomes were easily internalized and then induced the dysregulation of cytokines (IL-10 and TNF-α), nitric oxide (NO) and key components (CD14 and IRF5) in the LPS/TLR4 pathway in the coincubated sheep PBMCs. Similarly, egr-miR-71 overexpression also altered the immune functions but exhibited obvious differences in regulation of the cytokines and key components, preferably inhibiting proinflammatory cytokines (IL-1α, IL-1β and TNF-α). These results demonstrate that exosomal egr-miR-71 is bioactive and capacity of immunomodulation of PBMCs, potentially being involved in immune responses during E. granulosus infection.

Mitochondria-associated endoplasmic reticulum membranes involve in oxidative stress-induced intestinal barrier injury and mitochondrial dysfunction under diquat exposing

Many factors induced by environmental toxicants have made oxidative stress a risk factor for the intestinal barrier injury and growth restriction, which is serious health threat for human and livestock and induces significant economic loss. It is well-known that diquat-induced oxidative stress is implicated in the intestinal barrier injury. Although some studies have shown that mitochondria are the primary target organelle of diquat, the underlying mechanism remains incompletely understood. Recently, mitochondria-associated endoplasmic reticulum membranes (MAMs) have aroused increasing concerns among scholars, which participate in mitochondrial dynamics and signal transduction. In this study, we investigated whether MAMs involved in intestinal barrier injury and mitochondrial dysfunction induced by diquat-induced oxidative stress in piglets and porcine intestinal epithelial cells (IPEC-J2 cells). The results showed that diquat induced growth restriction and impaired intestinal barrier. The mitochondrial reactive oxygen species (ROS) was increased and mitochondrial membrane potential was decreased following diquat exposure. The ultrastructure of mitochondria and MAMs was also disturbed. Meanwhile, diquat upregulated endoplasmic reticulum stress marker protein and activated PERK pathway. Furthermore, loosening MAMs alleviated intestinal barrier injury, decrease of antioxidant enzyme activity and mitochondrial dysfunction induced by diquat in IPEC-J2 cells, while tightening MAMs exacerbated diquat-induced mitochondrial dysfunction. These results suggested that MAMs may be associated with the intestinal barrier injury and mitochondrial dysfunction induced by diquat in the jejunum of piglets.

Temporomandibular disorders and mental health: shared etiologies and treatment approaches

The biopsychosocial model suggests that temporomandibular disorders (TMDs) often coexist with mental health disorders, particularly depression and anxiety, affecting a significant portion of the global population. The interplay between TMDs and mental health disorders contributes to a complex comorbidity, perpetuating a cycle of mutual influence and reinforcement. This review investigates the neurobiological mechanisms and epidemiological evidence supporting the shared etiology of TMDs and mental health disorders, exploring potential shared vulnerabilities and bidirectional causal relationships. Shared vulnerabilities between TMDs and mental health disorders may stem from genetic and epigenetic predispositions, psychosocial factors, and behavioral aspects. Inflammatory cytokines, neurotransmitters, neurotrophins, and neuropeptides play pivotal roles in both peripheral and central sensitization as well as neuroinflammation. Brain imaging studies suggest that TMDs and mental health disorders exhibit overlapping brain regions indicative of reward processing deficits and anomalies within the triple network model. Future research efforts are crucial for developing a comprehensive understanding of the underlying mechanisms and confirming the reciprocal causal effects between TMDs and mental health disorders. This review provides valuable insights for oral healthcare professionals, stressing the importance of optimizing treatment strategies for individuals dealing with concurrent TMDs and mental health issues through a personalized, holistic, and multidisciplinary approach.