Modeling blood-brain barrier formation and cerebral cavernous malformations in human PSC-derived organoids.

The human blood-brain barrier (hBBB) is a highly specialized structure that regulates passage across blood and central nervous system (CNS) compartments. Despite its critical physiological role, there are no reliable in vitro models that can mimic hBBB development and function. Here, we constructed hBBB assembloids from brain and blood vessel organoids derived from human pluripotent stem cells. We validated the acquisition of blood-brain barrier (BBB)-specific molecular, cellular, transcriptomic, and functional characteristics and uncovered an extensive neuro-vascular crosstalk with a spatial pattern within hBBB assembloids. When we used patient-derived hBBB assembloids to model cerebral cavernous malformations (CCMs), we found that these assembloids recapitulated the cavernoma anatomy and BBB breakdown observed in patients. Upon comparison of phenotypes and transcriptome between patient-derived hBBB assembloids and primary human cavernoma tissues, we uncovered CCM-related molecular and cellular alterations. Taken together, we report hBBB assembloids that mimic the core properties of the hBBB and identify a potentially underlying cause of CCMs.

Temperature-Controlled Selective Formation of Silver Nanoclusters and Their Transformation to the Same Product.

Herein, two atomically precise silver nanoclusters, Ag54 and Ag33, directed by inner anion templates (CrO4 2- and/or Cl-), are initially isolated as a mixed phase from identical reactants across a wide temperature range (20-80 °C). Interestingly, fine-tuning the reaction temperature can realize pure phase synthesis of the two nanoclusters; that is, a metastable Ag54 is kinetically formed at a low temperature (20 °C), whereas such a system is steered towards a thermodynamically stable Ag33 at a relatively high temperature (80 °C). Electrospray ionization mass spectrometry illustrates that the stability of Ag33 is superior to that of Ag54, which is further supported by density functional theory calculations. Importantly, the difference in structural stability can influence the pathway of 1,4-bis(pyrid-4-yl)benzene induced transformation reaction starting from Ag54 and Ag33. The former undergoes a dramatic breakage-reorganization process to form an Ag31 dimer (Ag31), while the same product can be also achieved from the latter following a noninvasive ligand exchange process. Both the Ag54 and Ag33 have the potential for further remote laser ignition applications. This work not only demonstrates how temperature controls the isolation of a specific phase, but also sheds light on the structural transformation pathway of nanoclusters with different stability.

Depression heightened the association of the systemic immune-inflammation index with all-cause mortality among osteoarthritis patient.

BACKGROUND: Systemic immune-inflammatory index (SII) has been recognized as a novel inflammatory indicator in numerous diseases. It remains unknown how SII affects all-cause mortality among patients with osteoarthritis (OA). In this prospective cohort study, we intended to examine the relationship of SII with all-cause mortality among OA populations and assess the interaction between depression and SII. METHODS: Data was collected from National Health and Nutrition Examination Survey (NHANES) in 2005-2018. The National Death Index (NDI) provided vital status records. Multivariable Cox regression analyses with cubic spines were applied to estimate the association between SII and all-cause and CVD mortality. Stratified analysis and interaction tests assessed the interaction of SII and depression on all-cause mortality. RESULTS: In total 3174 OA adults were included. The lowest quartile Q1 (HR:1.44, 95%CI:1.02-2.04) and highest quartile Q4 (HR:1.44, 95%CI:1.02-2.04) of SII presented a higher risk of death compared with those in second quartile Q2 (Ref.) and third quartile Q3 (HR:1.23, 95%CI:0.89-1.68. Restricted cubic splines analysis revealed a U-shaped association of SII with all-cause mortality, the inflection points were 412.93 × 109/L. The interaction test observed a more significant relationship of SII with all-cause mortality in depression patients than in non-depression patients, indicating that depression can modify this association. LIMITATIONS: First, the observational study design failed to make causal inferences. Second, the baseline SII cannot reflect the long-term level of inflammation. Finally, there may be potential bias. CONCLUSION: SII was U-shaped associated with all-cause mortality in OA patients, and this association was significantly heightened by depression.

Oligosaccharides from black ginseng innovatively prepared by low-temperature steam-heating process ameliorate cognitive impairment in Alzheimer's disease mice via the Keap-1/Nrf2 pathway.

BACKGROUND: Our objective in this study was to evaluate the effectiveness of oligosaccharides extracted from black ginseng (OSBG), innovatively prepared by a low-temperature steam-heating process, in the improvement of learning and memory impairment in mice, as well as the mechanism(s). RESULTS: Eight carbohydrates involving isomaltose and maltotetraose were detected in black gensing; monosaccharide residues including mannose and rhamnose were also discovered. OSBG-treated mice showed significant amelioration in recognition and spatial memory deficits compared to the scopolamine group. OSBG could decrease acetylcholinesterase activity in a tissue-dependent fashion but not in a dose-dependent manner. Furthermore, in contrast, OSBG administration resulted in significant upregulation superoxide dismutase, glutathione, glutathione peroxidase (GPx), and Kelch-like ECH-associated protein 1, downregulation of malondialdehyde and nuclear factor erythroid 2-related factor 2 in the tissues. Finally, at the genus level, we observed that the OSBG interventions increased the relative abundance of probiotics (e.g., Barnesiella, Staphylococcus, Clostridium_XlVb) and decreased pernicious bacteria such as Eisenbergiella and Intestinimonas, compared to the Alzheimer's disease mouse model group. Herein, our results demonstrate that OSBG restores the composition of the scopolamine-induced intestinal microbiota in mice, providing homeostasis of gut microbiota and providing evidence for microbiota-regulated therapeutic potential. CONCLUSION: Our results showed for the first time a clear role for OSBG in improving scopolamine-induced memory impairment by inhibiting cholinergic dysfunction in a tissue-dependent manner. Additionally, OSBG administration relieved oxidative stress by activating the Keap-1/Nrf2 pathway and modulating the gut microbiota. Collectively, OSBG may be a promising target for neuroprotective antioxidants for improving memory and cognition in Alzheimer's disease patients. © 2024 Society of Chemical Industry.

LINC00960 affects osteosarcoma treatment and prognosis by regulating the tumor immune microenvironment.

Background: Osteosarcoma (OS), the commonest primary malignant bone tumor, is mainly seen in children and teenagers. LINC00960, a newly discovered long intergenic non-protein coding RNA, has been shown to be important in certain cancers. The objective of this study was to assess LINC00960's prognostic and therapeutic value and analyze its mechanism of action in osteosarcoma. Methods: With the transcriptome information of 85 osteosarcomas from the TARGET database, the Cox regression analyses, K-M curve, and ROC curve, were conducted for survival and prognostic analysis. The functional analysis was conducted using GO, KEGG, GSEA, and GSVA. The ESTIMATE, ssGSEA, MCP-counter, ImmuCellAI algorithms, and immune checkpoint correlation analysis were performed for immune-related analysis. The single-cell RNA sequencing data of 6 osteosarcoma patients was obtained from the Gene Expression Omnibus database. The Tumor Immune Dysfunction and Exclusion algorithm and the "pRRophetic" R package were performed to predict the response to immunotherapy and chemotherapy. Results: LINC00960 overexpression is associated with osteosarcoma metastasis and poor prognosis. Based on the LINC00960 expression, the nomogram prediction model was created, which showed good accuracy and precision to predict the overall survival of osteosarcoma. Single-cell and immune-related analysis showed that LINC00960 is mainly highly expressed in the tumor-exhausted CD8 T cells in osteosarcoma. In osteosarcoma, the expression of LIC00960 was favorably connected with immune checkpoint-related genes and negatively correlated with immune infiltration. TIDE analysis indicated that low LINC00960 expression patients might have a better response to immunotherapy. Drug sensitivity analysis showed that high LINC00960 expression patients might have better responses to Bleomycin and Doxorubicin. Conclusion: LINC00960 has the potential to be a novel biomarker for predicting overall survival in osteosarcoma patients and to guide more individualized treatment and clinical decision-making.

Soil microbiota plays a key regulatory role in the outbreak of tobacco root rot.

Introduction: Root rot caused by the fungal pathogen Fusarium sp. poses significant challenges to tobacco cultivation in China, leading to major economic setbacks. The interplay between this pathogen and the wider soil microbial community remains poorly understood. Methods: High-throughput sequencing technology was utilized to evaluate soil prokaryotic, fungal, and protistan communities. We compared microbial communities in infected soils to those in healthy soils from the same field. Additionally, the influence of pH on the microbial communities was assessed. Results: Infected soils displayed elevated levels of soil nutrients but diminished observed richness across prokaryotic, fungal, and protistan groups. The pathogenic fungi Fusarium solani f sp. eumartii's abundance was notably increased in infected soils. Infection with F. solani significantly altered the soil's microbial community structure and interactions, manifested as a decrease in network scale and the number of keystone species. An evaluation of prokaryotes' role in F. solani's invasion revealed an increased number of connecting nodes in infected soils. Additionally, relationships between predatory protists and fungi were augmented, whereas predation on F. solani declined. Discussion: The study underscores the significance of comprehending the interactions among soil microorganisms and brings to light the susceptibility of soil microbial communities to pathogen invasion. It offers insights into the multifaceted relationships and potential vulnerabilities within the soil ecosystem in the context of Fusarium sp. invasion.

High-Resolution Characterization of Human Brain Cortex with High-Fidelity Spatial Transcriptomic Slides (HiFi-Slides).

Spatial transcriptomic tools and platforms help researchers to inspect tissues and cells with fine details of how they differentiate in expressions and how they orient themselves. With the higher resolution we get and higher throughput of expression targets, spatial analysis can truly become the core player for cell clustering, migration study, and, eventually, the novel model for pathological study. We present the demonstration of HiFi-slide, a whole transcriptomic sequencing technique that recycles used sequenced-by-synthesis flow cell surfaces to a high-resolution spatial mapping tool that can be directly applied to tissue cell gradient analysis, gene expression analysis, cell proximity analysis, and other cellular-level spatial studies.

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19.

Background: Corona Virus Disease 2019 (COVID-19) and Osteoarthritis (OA) are diseases that seriously affect the physical and mental health and life quality of patients, particularly elderly patients. However, the association between COVID-19 and osteoarthritis at the genetic level has not been investigated. This study is intended to analyze the pathogenesis shared by OA and COVID-19 and to identify drugs that could be used to treat SARS-CoV-2-infected OA patients. Methods: The four datasets of OA and COVID-19 (GSE114007, GSE55235, GSE147507, and GSE17111) used for the analysis in this paper were obtained from the GEO database. Common genes of OA and COVID-19 were identified through Weighted Gene Co-Expression Network Analysis (WGCNA) and differential gene expression analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key genes, which were analyzed for expression patterns by single-cell analysis. Finally, drug prediction and molecular docking were carried out using the Drug Signatures Database (DSigDB) and AutoDockTools. Results: Firstly, WGCNA identified a total of 26 genes common between OA and COVID-19, and functional analysis of the common genes revealed the common pathological processes and molecular changes between OA and COVID-19 are mainly related to immune dysfunction. In addition, we screened 3 key genes, DDIT3, MAFF, and PNRC1, and uncovered that key genes are possibly involved in the pathogenesis of OA and COVID-19 through high expression in neutrophils. Finally, we established a regulatory network of common genes between OA and COVID-19, and the free energy of binding estimation was used to identify suitable medicines for the treatment of OA patients infected with SARS-CoV-2. Conclusion: In the present study, we succeeded in identifying 3 key genes, DDIT3, MAFF, and PNRC1, which are possibly involved in the development of both OA and COVID-19 and have high diagnostic value for OA and COVID-19. In addition, niclosamide, ciclopirox, and ticlopidine were found to be potentially useful for the treatment of OA patients infected with SARS-CoV-2.

Superior capsular reconstruction using the long head of the biceps to treat massive rotator cuff tears improves patients shoulder pain, mobility and function.

PURPOSE: Arthroscopic superior capsule reconstruction (SCR) with the long head of the biceps (LHBT) was performed to restore structural stability, force couple balance, and shoulder joint function. This study aimed to evaluate the functional outcomes of SCR using the LHBT over at least 24 months of follow-up. METHOD: This retrospective study included 89 patients with massive rotator cuff tears who underwent SCR using the LHBT, met the inclusion criteria and underwent follow up for at least 24 months. The preoperative and postoperative shoulder range of motion (forward flexion, external rotation, and abduction), acromiohumeral interval (AHI), visual analog scale (VAS) score, American Shoulder and Elbow Surgeons (ASES) score and Constant-Murley score were obtained, and the tear size, and Goutallier and Hamada grades were also investigated. RESULTS: Compared with those measured preoperatively, the range of motion, AHI, and VAS, Constant-Murley, and ASES scores were significantly improved immediately postoperatively (P < 0.001) and at the 6-month, 12-month, and final follow-ups (P < 0.001). At the last follow-up, the postoperative ASES score and Constant-Murley score increased from 42.8 ± 7.6 to 87.4 ± 6.1, and 42.3 ± 8.9 to 84.9 ± 10.7, respectively; with improvements of 51 ± 21.7 in forward flexion, 21.0 ± 8.1 in external rotation, and 58.5 ± 22.5 in abduction. The AHI increased 2.1 ± 0.8 mm and the VAS score significantly changed from 6.0 (5.0, 7.0) to 1.0 (0.0, 1.0), at the final follow-up. Eleven of the 89 patients experienced retears, and one patient needed reoperation. CONCLUSION: In this study with at least 24-months of follow-up, SCR using the LHBT for massive rotator cuff tears could effectively relieve shoulder pain, restore shoulder function and increase shoulder mobility to some extent. LEVEL OF EVIDENCE: IV.

TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network.

The end-to-end image fusion framework has achieved promising performance, with dedicated convolutional networks aggregating the multi-modal local appearance. However, long-range dependencies are directly neglected in existing CNN fusion approaches, impeding balancing the entire image-level perception for complex scenario fusion. In this paper, therefore, we propose an infrared and visible image fusion algorithm based on the transformer module and adversarial learning. Inspired by the global interaction power, we use the transformer technique to learn the effective global fusion relations. In particular, shallow features extracted by CNN are interacted in the proposed transformer fusion module to refine the fusion relationship within the spatial scope and across channels simultaneously. Besides, adversarial learning is designed in the training process to improve the output discrimination via imposing competitive consistency from the inputs, reflecting the specific characteristics in infrared and visible images. The experimental performance demonstrates the effectiveness of the proposed modules, with superior improvement against the state-of-the-art, generalising a novel paradigm via transformer and adversarial learning in the fusion task.

LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images.

Deep learning based fusion methods have been achieving promising performance in image fusion tasks. This is attributed to the network architecture that plays a very important role in the fusion process. However, in general, it is hard to specify a good fusion architecture, and consequently, the design of fusion networks is still a black art, rather than science. To address this problem, we formulate the fusion task mathematically, and establish a connection between its optimal solution and the network architecture that can implement it. This approach leads to a novel method proposed in the paper of constructing a lightweight fusion network. It avoids the time-consuming empirical network design by a trial-and-test strategy. In particular we adopt a learnable representation approach to the fusion task, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model. The low-rank representation (LRR) objective is the foundation of our learnable model. The matrix multiplications, which are at the heart of the solution are transformed into convolutional operations, and the iterative process of optimisation is replaced by a special feed-forward network. Based on this novel network architecture, an end-to-end lightweight fusion network is constructed to fuse infrared and visible light images. Its successful training is facilitated by a detail-to-semantic information loss function proposed to preserve the image details and to enhance the salient features of the source images. Our experiments show that the proposed fusion network exhibits better fusion performance than the state-of-the-art fusion methods on public datasets. Interestingly, our network requires a fewer training parameters than other existing methods.

Towards Robust Visual Object Tracking with Independent Target-Agnostic Detection and Effective Siamese Cross-Task Interaction.

Advanced Siamese visual object tracking architectures are jointly trained using pair-wise input images to perform target classification and bounding box regression. They have achieved promising results in recent benchmarks and competitions. However, the existing methods suffer from two limitations: First, though the Siamese structure can estimate the target state in an instance frame, provided the target appearance does not deviate too much from the template, the detection of the target in an image cannot be guaranteed in the presence of severe appearance variations. Second, despite the classification and regression tasks sharing the same output from the backbone network, their specific modules and loss functions are invariably designed independently, without promoting any interaction. Yet, in a general tracking task, the centre classification and bounding box regression tasks are collaboratively working to estimate the final target location. To address the above issues, it is essential to perform target-agnostic detection so as to promote cross-task interactions in a Siamese-based tracking framework. In this work, we endow a novel network with a target-agnostic object detection module to complement the direct target inference, and to avoid or minimise the misalignment of the key cues of potential template-instance matches. To unify the multi-task learning formulation, we develop a cross-task interaction module to ensure consistent supervision of the classification and regression branches, improving the synergy of different branches. To eliminate potential inconsistencies that may arise within a multi-task architecture, we assign adaptive labels, rather than fixed hard labels, to supervise the network training more effectively. The experimental results obtained on several benchmarks, i.e., OTB100, UAV123, VOT2018, VOT2019, and LaSOT, demonstrate the effectiveness of the advanced target detection module, as well as the cross-task interaction, exhibiting superior tracking performance as compared with the state-of-the-art tracking methods.

Discriminative Dictionary Pair Learning With Scale-Constrained Structured Representation for Image Classification.

The dictionary pair learning (DPL) model aims to design a synthesis dictionary and an analysis dictionary to accomplish the goal of rapid sample encoding. In this article, we propose a novel structured representation learning algorithm based on the DPL for image classification. It is referred to as discriminative DPL with scale-constrained structured representation (DPL-SCSR). The proposed DPL-SCSR utilizes the binary label matrix of dictionary atoms to project the representation into the corresponding label space of the training samples. By imposing a non-negative constraint, the learned representation adaptively approximates a block-diagonal structure. This innovative transformation is also capable of controlling the scale of the block-diagonal representation by enforcing the sum of within-class coefficients of each sample to 1, which means that the dictionary atoms of each class compete to represent the samples from the same class. This implies that the requirement of similarity preservation is considered from the perspective of the constraint on the sum of coefficients. More importantly, the DPL-SCSR does not need to design a classifier in the representation space as the label matrix of the dictionary can also be used as an efficient linear classifier. Finally, the DPL-SCSR imposes the l2,p -norm on the analysis dictionary to make the process of feature extraction more interpretable. The DPL-SCSR seamlessly incorporates the scale-constrained structured representation learning, within-class similarity preservation of representation, and the linear classifier into one regularization term, which dramatically reduces the complexity of training and parameter tuning. The experimental results on several popular image classification datasets show that our DPL-SCSR can deliver superior performance compared with the state-of-the-art (SOTA) dictionary learning methods. The MATLAB code of this article is available at https://github.com/chenzhe207/DPL-SCSR.

Fast Self-Guided Multi-View Subspace Clustering.

Multi-view subspace clustering is an important topic in cluster analysis. Its aim is to utilize the complementary information conveyed by multiple views of objects to be clustered. Recently, view-shared anchor learning based multi-view clustering methods have been developed to speed up the learning of common data representation. Although widely applied to large-scale scenarios, most of the existing approaches are still faced with two limitations. First, they do not pay sufficient consideration on the negative impact caused by certain noisy views with unclear clustering structures. Second, many of them only focus on the multi-view consistency, yet are incapable of capturing the cross-view diversity. As a result, the learned complementary features may be inaccurate and adversely affect clustering performance. To solve these two challenging issues, we propose a Fast Self-guided Multi-view Subspace Clustering (FSMSC) algorithm which skillfully integrates the view-shared anchor learning and global-guided-local self-guidance learning into a unified model. Such an integration is inspired by the observation that the view with clean clustering structures will play a more crucial role in grouping the clusters when the features of all views are concatenated. Specifically, we first learn a locally-consistent data representation shared by all views in the local learning module, then we learn a globally-discriminative data representation from multi-view concatenated features in the global learning module. Afterwards, a feature selection matrix constrained by the l2,1 -norm is designed to construct a guidance from global learning to local learning. In this way, the multi-view consistent and diverse information can be simultaneously utilized and the negative impact caused by noisy views can be overcame to some extent. Extensive experiments on different datasets demonstrate the effectiveness of our proposed fast self-guided learning model, and its promising performance compared to both, the state-of-the-art non-deep and deep multi-view clustering algorithms. The code of this paper is available at https://github.com/chenzhe207/FSMSC.

Radical-Scavenging and Subchondral Bone-Regenerating Nanomedicine for Osteoarthritis Treatment.

Osteoarthritis (OA) is characterized by cartilage degradation and subchondral bone remodeling. However, most available studies focus on either cartilage degradation or subchondral bone lesion, alone, and rarely pay attention to the synergy of these two pathological changes. Herein, a dual-functional medication is developed to simultaneously protect cartilage and achieve subchondral bone repair. Black phosphorus nanosheets (BPNSs), with a strong reactive oxygen species (ROS)-scavenging capability and high biocompatibility, also present a notable promoting effect in osteogenesis. BPNSs efficiently eliminate the intracellular ROS and, thus, protect the inherent homeostasis between cartilage matrix anabolism and catabolism. RNA sequencing results of BPNSs-treated OA chondrocytes further reveal the restoration of chondrocyte function, activation of antioxidant enzymes, and regulation of inflammation. Additional in vivo assessments solidly confirm that BPNSs inhibit cartilage degradation and prevent OA progression. Meanwhile, histological evaluation and microcomputed tomography (micro-CT) scanning analysis verify the satisfying disease-modifying effects of BPNSs on OA. Additionally, the excellent biocompatibility of BPNSs enables them as a competitive candidate for OA treatment. This distinct disease-modifying treatment of OA on the basis of BPNSs provides an insight and paradigm on the dual-functional treatment strategy focusing on both cartilage degradation and subchondral bone lesion in OA and explores a broader biomedical application of BPNS nanomedicine in orthopedics.

U-SPDNet: An SPD manifold learning-based neural network for visual classification.

With the development of neural networking techniques, several architectures for symmetric positive definite (SPD) matrix learning have recently been put forward in the computer vision and pattern recognition (CV&PR) community for mining fine-grained geometric features. However, the degradation of structural information during multi-stage feature transformation limits their capacity. To cope with this issue, this paper develops a U-shaped neural network on the SPD manifolds (U-SPDNet) for visual classification. The designed U-SPDNet contains two subsystems, one of which is a shrinking path (encoder) making up of a prevailing SPD manifold neural network (SPDNet (Huang and Van Gool, 2017)) for capturing compact representations from the input data. Another is a constructed symmetric expanding path (decoder) to upsample the encoded features, trained by a reconstruction error term. With this design, the degradation problem will be gradually alleviated during training. To enhance the representational capacity of U-SPDNet, we also append skip connections from encoder to decoder, realized by manifold-valued geometric operations, namely Riemannian barycenter and Riemannian optimization. On the MDSD, Virus, FPHA, and UAV-Human datasets, the accuracy achieved by our method is respectively 6.92%, 8.67%, 1.57%, and 1.08% higher than SPDNet, certifying its effectiveness.

Enhanced robust spatial feature selection and correlation filter learning for UAV tracking.

Spatial boundary effect can significantly reduce the performance of a learned discriminative correlation filter (DCF) model. A commonly used method to relieve this effect is to extract appearance features from a wider region of a target. However, this way would introduce unexpected features from background pixels and noises, which will lead to a decrease of the filter's discrimination power. To address this shortcoming, this paper proposes an innovative method called enhanced robust spatial feature selection and correlation filter Learning (EFSCF), which performs jointly sparse feature learning to handle boundary effects effectively while suppressing the influence of background pixels and noises. Unlike the ℓ2-norm-based tracking approaches that are prone to non-Gaussian noises, the proposed method imposes the ℓ2,1-norm on the loss term to enhance the robustness against the training outliers. To enhance the discrimination further, a jointly sparse feature selection scheme based on the ℓ2,1 -norm is designed to regularize the filter in rows and columns simultaneously. To the best of the authors' knowledge, this has been the first work exploring the structural sparsity in rows and columns of a learned filter simultaneously. The proposed model can be efficiently solved by an alternating direction multiplier method. The proposed EFSCF is verified by experiments on four challenging unmanned aerial vehicle datasets under severe noise and appearance changes, and the results show that the proposed method can achieve better tracking performance than the state-of-the-art trackers.

Resistance Mechanisms of Sitobion miscanthi (Hemiptera: Aphididae) to Malathion Revealed by Synergist Assay.

A resistant strain (MRS) of Sitobion miscanthi was cultured by continuous selection with malathion for over 40 generations. The MRS exhibited 32.7-fold resistance to malathion compared to the susceptible strain (MSS) and 13.5-fold, 2.9-fold and 4.8-fold cross-resistance for omethoate, methomyl and beta-cypermethrin, respectively. However, no cross-resistance was found to imidacloprid in this resistant strain. The realized heritability for malathion resistance was 0.02. Inhibitors of esterase activity, both triphenyl phosphate (TPP) and S,S,S,-tributyl phosphorotrithioate (DEF) as synergists, exhibited significant synergism to malathion in the MRS strain, with 11.77-fold and 5.12-fold synergistic ratios, respectively, while piperonyl butoxide (PBO) and diethyl maleate (DEM) showed no significant synergism in the MRS strain. The biochemical assay indicated that carboxylesterase activity was higher in MRS than in MSS. These results suggest that the increase in esterase activity might play an important role in S. miscanthi resistance to malathion. Imidacloprid could be used as an alternative for malathion in the management of wheat aphid resistance.

Icaritin, a metabolite of Icarrin, Alleviates non-alcoholic fatty liver disease via inhibition of lipogenesis and ER stress.

Non-alcoholic fatty liver disease (NAFLD) is one of the most serious global public health concerns. However, there are currently no effective drugs for treatment of this disease. Icariin (ICA), a small-molecule natural product extracted from Epimedium brevicornu Maxim, offers various pharmacological activities. In the present work, we wondered whether ICA can attenuate NAFLD in db/db mice treated with ICA for 8 weeks and how ICA exerts an influence on NAFLD. In db/db mice, ICA treatment had a robust effect on inhibition of lipogenesis associated with NAFLD amelioration by decreasing liver lipid deposition, together with ameliorating insulin sensitivity, glucose tolerance, and fasting serum glucose. Of note, ICA-treated rats showed a much higher concentration of icaritin (ICT) in plasma, a major metabolite of ICA, about 2000 times higher than that of ICA by liquid chromatography mass spectrometry (LC-MS). Interestingly, ICT, rather than ICA, can dramatically decrease hepatic lipogenesis-related markers in oleate acid/palmitate acid (OA/PA)-induced steatosis in primary hepatocytes (PH) and HepG2 cells, and hepatic lipid accumulation in db/db mice, demonstrating the inhibitory effect of ICT on lipogenesis. Mechanistically, we found that anti-lipogenic activities of ICT were related to reducing endoplasmic reticulum (ER) stress as evidenced by Western blot, qPCR, and other assays in thapsigargin (THP) induced-ER stress models. To our knowledge, this is the first report showing the unexpected and key role for ICT on the prevention of NAFLD in db/db mice through an ER stress mechanism.

A Zn8 Double-Cavity Metallacalix[8]arene as Molecular Sieve to Realize Self-Cleaning Intramolecular Tandem Transformation of Li-S Chemistry.

Toward the well-explored lithium-sulfur (Li-S) catalytic chemistry, the slow adsorption-migration-conversion kinetics of lithium polysulfides on catalytic materials and Li2 S deposition-induced passivation of active sites limit the rapid and complete conversion of sulfur. Conceptively, molecular architectures can provide atom-precise models to understand the underlying active sites responsible for selective adsorption and conversion of LiPSs and Li2 S2 /Li2 S species. Here, an octanuclear Zn(II) (Zn8 ) cluster is presented, which features a metallacalix[8]arene with double cavities up and down the Zn8 ring. The central Zn8 ring and the specific double cavities with organic ligands of different electronegativity and bonding environments render active sites with variable steric hindrance and interaction toward the sulfur-borne species. An intramolecular tandem transformation mechanism is realized exclusively by Zn8 cluster, which promotes the self-cleaning of active sites and continuous electrochemical reaction. Notably, the external azo groups and internal Zn/O sites of Zn8 cluster in sequence stimulate the adsorption and conversion of long chain Li2 Sx (x ≥ 4) and short chain Li2 S/Li2 S2 , contributing to remarkable rate performance and cycling stability. This work pioneers the application of metallacalix[n]arene clusters with atom-precise structure in Li-S batteries, and the proposed mechanism advances the molecule-level understanding of Li-S catalytic chemistry.