Celastrol-Induced Nur77 Interaction with TRAF2 Alleviates Inflammation by Promoting Mitochondrial Ubiquitination and Autophagy.

Mitochondria play an integral role in cell death, autophagy, immunity, and inflammation. We previously showed that Nur77, an orphan nuclear receptor, induces apoptosis by targeting mitochondria. Here, we report that celastrol, a potent anti-inflammatory pentacyclic triterpene, binds Nur77 to inhibit inflammation and induce autophagy in a Nur77-dependent manner. Celastrol promotes Nur77 translocation from the nucleus to mitochondria, where it interacts with tumor necrosis factor receptor-associated factor 2 (TRAF2), a scaffold protein and E3 ubiquitin ligase important for inflammatory signaling. The interaction is mediated by an LxxLL motif in TRAF2 and results not only in the inhibition of TRAF2 ubiquitination but also in Lys63-linked Nur77 ubiquitination. Under inflammatory conditions, ubiquitinated Nur77 resides at mitochondria, rendering them sensitive to autophagy, an event involving Nur77 interaction with p62/SQSTM1. Together, our results identify Nur77 as a critical intracellular target for celastrol and unravel a mechanism of Nur77-dependent clearance of inflamed mitochondria to alleviate inflammation.

Electroacupuncture suppresses neuronal ferroptosis to relieve chronic neuropathic pain.

Growing evidence supports the analgesic efficacy of electroacupuncture (EA) in managing chronic neuropathic pain (NP) in both patients and NP models induced by peripheral nerve injury. However, the underlying mechanisms remain incompletely understood. Ferroptosis, a novel form of programmed cell death, has been found to be activated during NP development, while EA has shown potential in promoting neurological recovery following acute cerebral injury by targeting ferroptosis. In this study, to investigate the detailed mechanism underlying EA intervention on NP, male Sprague-Dawley rats with chronic constriction injury (CCI)-induced NP model received EA treatment at acupoints ST36 and GV20 for 14 days. Results demonstrated that EA effectively attenuated CCI-induced pain hypersensitivity and mitigated neuron damage and loss in the spinal cord of NP rats. Moreover, EA reversed the oxidative stress-mediated spinal ferroptosis phenotype by upregulating reduced expression of xCT, glutathione peroxidase 4 (GPX4), ferritin heavy chain (FTH1) and superoxide dismutase (SOD) levels, and downregulating increased expression of acyl-CoA synthetase long-chain family member 4 (ACSL4), malondialdehyde levels and iron overload. Furthermore, EA increased the immunofluorescence co-staining of GPX4 in neurons cells of the spinal cord of CCI rats. Mechanistic analysis unveiled that the inhibition of antioxidant pathway of Nrf2 signalling via its specific inhibitor, ML385, significantly countered EA's protective effect against neuronal ferroptosis in NP rats while marginally diminishing its analgesic effect. These findings suggest that EA treatment at acupoints ST36 and GV20 may protect against NP by inhibiting neuronal ferroptosis in the spinal cord, partially through the activation of Nrf2 signalling.

Bundle-specific tractogram distribution estimation using higher-order streamline differential equation.

Streamline tractography locally traces peak directions extracted from fiber orientation distribution (FOD) functions, lacking global information about the trend of the whole fiber bundle. Therefore, it is prone to producing erroneous tracks while missing true positive connections. In this work, we propose a new bundle-specific tractography (BST) method based on a bundle-specific tractogram distribution (BTD) function, which directly reconstructs the fiber trajectory from the start region to the termination region by incorporating the global information in the fiber bundle mask. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion vectorial field. At the global level, the tractography process is simplified as the estimation of BTD coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) for qualitative and quantitative evaluation. Results demonstrate that our approach reconstructs complex fiber geometry more accurately. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts.

Inhibition of ANGPTL8 protects against diabetes-associated cognitive dysfunction by reducing synaptic loss via the PirB signaling pathway.

BACKGROUND: Type 2 diabetes mellitus (T2D) is associated with an increased risk of cognitive dysfunction. Angiopoietin-like protein 8 (ANGPTL8) is an important regulator in T2D, but the role of ANGPTL8 in diabetes-associated cognitive dysfunction remains unknown. Here, we explored the role of ANGPTL8 in diabetes-associated cognitive dysfunction through its interaction with paired immunoglobulin-like receptor B (PirB) in the central nervous system. METHODS: The levels of ANGPTL8 in type 2 diabetic patients with cognitive dysfunction and control individuals were measured. Mouse models of diabetes-associated cognitive dysfunction were constructed to investigate the role of ANGPTL8 in cognitive function. The cognitive function of the mice was assessed by the Barnes Maze test and the novel object recognition test, and levels of ANGPTL8, synaptic and axonal markers, and pro-inflammatory cytokines were measured. Primary neurons and microglia were treated with recombinant ANGPTL8 protein (rA8), and subsequent changes were examined. In addition, the changes induced by ANGPTL8 were validated after blocking PirB and its downstream pathways. Finally, mice with central nervous system-specific knockout of Angptl8 and PirB-/- mice were generated, and relevant in vivo experiments were performed. RESULTS: Here, we demonstrated that in the diabetic brain, ANGPTL8 was secreted by neurons into the hippocampus, resulting in neuroinflammation and impairment of synaptic plasticity. Moreover, neuron-specific Angptl8 knockout prevented diabetes-associated cognitive dysfunction and neuroinflammation. Mechanistically, ANGPTL8 acted in parallel to neurons and microglia via its receptor PirB, manifesting as downregulation of synaptic and axonal markers in neurons and upregulation of proinflammatory cytokine expression in microglia. In vivo, PirB-/- mice exhibited resistance to ANGPTL8-induced neuroinflammation and synaptic damage. CONCLUSION: Taken together, our findings reveal the role of ANGPTL8 in the pathogenesis of diabetes-associated cognitive dysfunction and identify the ANGPTL8-PirB signaling pathway as a potential target for the management of this condition.

2D Ordered Mesoporous Lamellar Hetero-Nanochannels with Asymmetric Wettability for Controllable Ion Transport.

Heterogeneous membranes play a crucial role in osmotic energy conversion by effectively reducing concentration polarization. However, most heterogeneous membranes mitigate concentration polarization through an asymmetric charge distribution, resulting in compromised ion selectivity. Herein, hetero-nanochannels with asymmetric wettability composed of 2D mesoporous carbon and graphene oxide are constructed. The asymmetric wettability of the membrane endows it with the ability to suppress the concentration polarization without degrading the ion selectivity, as well as achieving a diode-like ion transport feature. As a result, enhanced osmotic energy harvesting is achieved with a power density of 6.41 W m-2 . This represents a substantial enhancement of 102.80-137.85% when compared to homogeneous 2D membranes, surpassing the performance of the majority of reported 2D membranes. Importantly, the membrane can be further used for high-performance ionic power harvesting by regulating ion transport, exceeding previously reported data by 89.1%.

Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer.

MOTIVATION: Finding molecules with desired pharmaceutical properties is crucial in drug discovery. Generative models can be an efficient tool to find desired molecules through the distribution learned by the model to approximate given training data. Existing generative models (i) do not consider backbone structures (scaffolds), resulting in inefficiency or (ii) need prior patterns for scaffolds, causing bias. Scaffolds are reasonable to use, and it is imperative to design a generative model without any prior scaffold patterns. RESULTS: We propose a generative model-based molecule generator, Sc2Mol, without any prior scaffold patterns. Sc2Mol uses SMILES strings for molecules. It consists of two steps: scaffold generation and scaffold decoration, which are carried out by a variational autoencoder and a transformer, respectively. The two steps are powerful for implementing random molecule generation and scaffold optimization. Our empirical evaluation using drug-like molecule datasets confirmed the success of our model in distribution learning and molecule optimization. Also, our model could automatically learn the rules to transform coarse scaffolds into sophisticated drug candidates. These rules were consistent with those for current lead optimization. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/zhiruiliao/Sc2Mol. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Preoperative diffusion tensor imaging: Fiber-trajectory-distribution-based tractography to identify facial nerve in vestibular schwannoma.

PURPOSE: Tractography of the facial nerve based on diffusion MRI is instrumental before surgery for the resection of vestibular schwannoma, but no excellent methods usable for the suppression of motion and image noise have been proposed. The aim of this study was to effectively suppress noise and provide accurate facial nerve reconstruction by extend a fiber trajectory distribution function based on the fourth-order streamline differential equations. METHODS: Preoperative MRI from 33 patients with vestibular schwannoma who underwent surgical resection were utilized in this study. First, T1WI and T2WI were used to obtain mask images and regions of interest. Second, probabilistic tractography was employed to obtain the fibers representing the approximate facial nerve pathway, and these fibers were subsequently translated into orientation information for each voxel. Last, the voxel orientation information and the peaks of the fiber orientation distribution were combined to generate a fiber trajectory distribution function, which was used to parameterize the anatomical information. The parameters were determined by minimizing the cost between the trajectory of fibers and the estimated directions. RESULTS: Qualitative and visual analyses were used to compare facial nerve reconstruction with intraoperative recordings. Compared with other methods (SD_Stream, iFOD1, iFOD2, unscented Kalman filter, parallel transport tractography), the fiber-trajectory-distribution-based tractography provided the most accurate facial nerve reconstructions. CONCLUSION: The fiber-trajectory-distribution-based tractography can effectively suppress the effect of noise. It is a more valuable aid for surgeons before vestibular schwannoma resection, which may ultimately improve the postsurgical patient's outcome.

Comprehensive meta-analysis of the effects of oral medroxyprogesterone acetate plus conjugated equine oestrogens on the lipid profile in women: Insights from randomized controlled trials.

BACKGROUND: Menopause is associated with elevated cardiovascular risk due to the loss of the cardioprotective effect of oestrogens. Postmenopausal women are often prescribed hormone replacement therapy (HRT) in order to control menopause symptoms and correct hormone imbalances; however, HRT can impact serum lipids' concentrations. At present, data on the effect of the administration of medroxyprogesterone acetate plus conjugated equine oestrogens (MPACEE) on the lipid profile in females are uncertain, as the investigations conducted so far have produced conflicting results. Thus, we aimed to clarify the impact of MPACEE prescription on the serum lipids' values in women by means of a systematic review and meta-analysis of randomized controlled trials (RCTs). METHODS: We employed a random-effects model based on the DerSimonian and Laird method to determine the combined estimates of the intervention's impact on the lipid profile. The computation of the weighted mean difference (WMD) and its corresponding 95% confidence interval (CI) relied on the mean and standard deviation values from both the MPACEE and control group, respectively. RESULTS: A total of 53 RCTs were included in the meta-analysis with 68 RCT arms on total cholesterol (TC), 70 RCT arms on low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG), and 69 RCT arms on high-density lipoprotein cholesterol (HDL-C). Administration of MPACEE resulted in a significant reduction of TC (WMD = -11.93 mg/dL; 95% CI: -13.42, -10.44; p < .001) and LDL-C (WMD = -16.61 mg/dL; 95% CI: -17.97, -15.26; p < .001) levels, and a notable increase in HDL-C (WMD = 3.40 mg/dL; 95% CI: 2.93, 3.86; p < .001) and TG (WMD = 10.28 mg/dL; 95% CI: 7.92, 12.64; p < .001) concentrations. Subgroup analysis revealed that changes in the lipid profile were influenced by several factors: body mass index (for TC, HDL-C, TG), MPACEE dosages (for TC, LDL-C, HDL-C, TG), age (for TC, LDL-C, HDL-C, TG), durations of the intervention (for TC, LDL-C, HDL-C, TG), continuous/sequential administration of MPACEE (continuous for TC; sequential for LDL-C, TG) administration of MPACEE and serum lipids' concentrations before enrolment in the RCT (for TC, LDL-C, HDL-C, TG). CONCLUSIONS: MPACEE administration can influence serum lipids' concentrations in females by raising HDL-C and TG levels and reducing LDL-C and TC values. Therefore, postmenopausal women who suffer from hypercholesterolaemia might benefit from this type of HRT.

Dectin-1 aggravates neutrophil inflammation through caspase-11/4-mediated macrophage pyroptosis in asthma.

BACKGROUND: The pattern recognition receptor Dectin-1 was initially discovered to play a pivotal role in mediating pulmonary antifungal immunity and promoting neutrophil-driven inflammation. Recent studies have revealed that Dectin-1 is overexpressed in asthma, but the specific mechanism remains elusive. Additionally, Dectin-1 has been implicated in promoting pyroptosis, a hallmark of severe asthma airway inflammation. Nevertheless, the involvement of the non-classical pyroptosis signal caspase-11/4 and its upstream regulatory mechanisms in asthma has not been completely explored. METHODS: House dust mite (HDM)-induced mice was treated with Dectin-1 agonist Curdlan, Dectin-1 inhibitor Laminarin, and caspase-11 inhibitor wedelolactone separately. Subsequently, inflammatory cells in bronchoalveolar lavage fluid (BALF) were analyzed. Western blotting was performed to measure the protein expression of caspase-11 and gasdermin D (GSDMD). Cell pyroptosis and the expression of chemokine were detected in vitro. The correlation between Dectin-1 expression, pyroptosis factors and neutrophils in the induced sputum of asthma patients was analyzed. RESULTS: Curdlan appeared to exacerbate neutrophil airway inflammation in asthmatic mice, whereas wedelolactone effectively alleviated airway inflammation aggravated by Curdlan. Moreover, Curdlan enhanced the release of caspase-11 activation fragments and N-terminal fragments of gasdermin D (GSDMD-N) stimulated by HDM both in vivo or in vitro. In mouse alveolar macrophages (MH-S cells), Curdlan/HDM stimulation resulted in vacuolar degeneration and elevated lactate dehydrogenase (LDH) release. In addition, there was an upregulation of neutrophil chemokines CXCL1, CXCL3, CXCL5 and their receptor CXCR2, which was suppressed by wedelolactone. In asthma patients, a positive correlation was observed between the expression of Dectin-1 on macrophages and caspase-4 (the human homology of caspase-11), and the proportion of neutrophils in induced sputum. CONCLUSION: Dectin-1 activation in asthma induced caspase-11/4 mediated macrophage pyroptosis, which subsequently stimulated the secretion of chemokines, leading to the exacerbation of airway neutrophil inflammation.

A slow-releasing donor of hydrogen sulfide inhibits neuronal cell death via anti-PANoptosis in rats with spinal cord ischemia‒reperfusion injury.

BACKGROUND: Spinal cord ischemia‒reperfusion injury (SCIRI) can lead to paraplegia, which leads to permanent motor function loss. It is a disastrous complication of surgery and causes tremendous socioeconomic burden. However, effective treatments for SCIRI are still lacking. PANoptosis consists of three kinds of programmed cell death, pyroptosis, apoptosis, and necroptosis, and may contribute to ischemia‒reperfusion-induced neuron death. Previous studies have demonstrated that hydrogen sulfide (H2S) exerts a neuroprotective effect in many neurodegenerative diseases. However, whether H2S is anti-PANoptosis and neuroprotective in the progression of acute SCIRI remains unclear. Thus, in this study we aimed to explore the role of H2S in SCIRI and its underlying mechanisms. METHODS: Measurements of lower limb function, neuronal activity, microglia/macrophage function histopathological examinations, and biochemical levels were performed to examine the efficacy of H2S and to further demonstrate the mechanism and treatment of SCIRI. RESULTS: The results showed that GYY4137 (a slow-releasing H2S donor) treatment attenuated the loss of Nissl bodies after SCIRI and improved the BBB score. Additionally, the number of TUNEL-positive and cleaved caspase-3-positive cells was decreased, and the upregulation of expression of cleaved caspase-8, cleaved caspase-3, Bax, and Bad and downregulation of Bcl-2 expression were reversed after GYY4137 administration. Meanwhile, both the expression and activation of p-MLKL, p-RIP1, and p-RIP3, along with the number of PI-positive and RIP3-positive neurons, were decreased in GYY4137-treated rats. Furthermore, GYY4137 administration reduced the expression of NLRP3, cleaved caspase-1 and cleaved GSDMD, decreased the colocalization NeuN/NLRP3 and Iba1/interleukin-1ß-expressing cells, and inhibited proinflammatory factors and microglia/macrophage polarization. CONCLUSIONS: H2S ameliorated spinal cord neuron loss, prevented motor dysfunction after SCIRI, and exerted a neuroprotective effect via the inhibition of PANoptosis and overactivated microglia-mediated neuroinflammation in SCIRI.

The Effect of Lipid Composition on the Liposomal Delivery of Camptothecin Developed by Active Click Loading.

In the present study, we investigated the role of lipid composition of camptothecin (CPT)-loaded liposomes (CPT-Lips) to adjust their residence time, drug distribution, and therefore the toxicities and antitumor activity. The CPT was loaded into liposomes using a click drug loading method, which utilized liposomes preloaded with GSH and then exposed to CPT-maleimide. The method produced CPT-Lips with a high encapsulation efficiency (>95%) and sustained drug release. It is shown that the residence times of CPT-Lips in the body were highly dependent on lipid compositions with an order of non-PEGylated liposomes of unsaturated lipids < non-PEGylated liposomes of saturated lipids < PEGylated liposomes of saturated lipids. Interestingly, the fast clearance of CPT-Lips resulted in significantly decreased toxicities but did not cause a significant decrease in their in vivo antitumor activity. These results suggested that the lipid composition could effectively adjust the residence time of CPT-Lips in the body and further optimize their therapeutic index, which would guide the development of a liposomal formulation of CPT.

Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning.

Proteolysis-targeting chimeras (PROTACs) are hetero-bifunctional molecules that induce the degradation of target proteins by recruiting an E3 ligase. PROTACs have the potential to inactivate disease-related genes that are considered undruggable by small molecules, making them a promising therapy for the treatment of incurable diseases. However, only a few hundred proteins have been experimentally tested for their amenability to PROTACs, and it remains unclear which other proteins in the entire human genome can be targeted by PROTACs. In this study, we have developed PrePROTAC, an interpretable machine learning model based on a transformer-based protein sequence descriptor and random forest classification. PrePROTAC predicts genome-wide targets that can be degraded by CRBN, one of the E3 ligases. In the benchmark studies, PrePROTAC achieved a ROC-AUC of 0.81, an average precision of 0.84, and over 40% sensitivity at a false positive rate of 0.05. When evaluated by an external test set which comprised proteins from different structural folds than those in the training set, the performance of PrePROTAC did not drop significantly, indicating its generalizability. Furthermore, we developed an embedding SHapley Additive exPlanations (eSHAP) method, which extends conventional SHAP analysis for original features to an embedding space through in silico mutagenesis. This method allowed us to identify key residues in the protein structure that play critical roles in PROTAC activity. The identified key residues were consistent with existing knowledge. Using PrePROTAC, we identified over 600 novel understudied proteins that are potentially degradable by CRBN and proposed PROTAC compounds for three novel drug targets associated with Alzheimer's disease.

End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins.

Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain "dark"-i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space.

Phosphine-Catalyzed (4 + 2) Annulation of Allenoates Bearing Acidic Hydrogen with 1,1-Dicyanoalkenes.

α-succinimide-substituted allenoates were employed as phosphine acceptors in phosphine-catalyzed (4 + 2) annulation with 1,1-dicyanoalkenes. They served as C4 synthons in the annulation reaction under mild reaction conditions and produced hexahydroisoindole derivatives in moderate to high yields with good to excellent diastereoselectivities.

Oxidative Cascade Iodocyclization of 1,n-Dienes: Synthesis of Iodinated Benzo[b]azepine and Benzo[b]azocine Derivatives.

An oxidative cascade iodocyclization of 1,7- or 1,8-dienes has been realized under mild conditions. By employing I2 as an iodine source, this protocol provides a concise and efficient approach to a great deal of biologically significant iodinated benzo[b]azepine and benzo[b]azocine derivatives in moderate to good yields. The gram-scale synthesis and further transformation of products render the approach practical and attractive. Radical trapping and deuterium-labeling experiments help to understand the mechanism.

Nanoscale Insights into the Interaction Mechanism Underlying the Adsorption and Retention of Heavy Metal Ions by Humic Acid.

The mobility and distribution of heavy metal ions (HMs) in aquatic environments are significantly influenced by humic acid (HA), which is ubiquitous. A quantitative understanding of the interaction mechanism underlying the adsorption and retention of HMs by HA is of vital significance but remains elusive. Herein, the interaction mechanism between HA and different types of HMs (i.e., Cd(II), Pb(II), arsenate, and chromate) was quantitatively investigated at the nanoscale. Based on quartz crystal microbalance with dissipation tests, the adsorption capacities of Pb(II), Cd(II), As(V), and Cr(VI) ionic species on the HA surface were measured as ∼0.40, ∼0.25, ∼0.12, and ∼0.02 nmol cm-2, respectively. Atomic force microscopy force results showed that the presence of Pb(II)/Cd(II) cations suppressed the electrostatic double-layer repulsion during the approach of two HA surfaces and the adhesion energy during separation was considerably enhanced from ∼2.18 to ∼5.05/∼4.18 mJ m-2. Such strong adhesion stems from the synergistic metal-HA complexation and cation-π interaction, as evidenced by spectroscopic analysis and theoretical simulation. In contrast, As(V)/Cr(VI) oxo-anions could form only weak hydrogen bonds with HA, resulting in similar adhesion energies for HA-HA (∼2.18 mJ m-2) and HA-As(V)/Cr(VI)-HA systems (∼2.26/∼1.96 mJ m-2). This work provides nanoscale insights into quantitative HM-HA interactions, improving the understanding of HMs biogeochemical cycling.

Conversion of Retinoids along the Marine Food Chain Contributes to Adverse Impacts on the Spine, Liver, and Intestinal Health of the Marine Medaka (Oryzias melastigma).

Marine microalgae serve as an aquaculture bait. To enhance algal cell growth and breeding profits, high-intensity light conditions are standard for cultivating bait microalgae, potentially altering microalgal metabolite production. This research revealed that Thalassiosira pseudonana, when subjected to high-intensity light conditions, accumulated significant quantities of retinal (RAL) that transferred through the food chain and transformed into all-trans retinoic acid (atRA) in marine medaka. The study further explored the toxic effects on individual fish and specific tissues, as well as the mechanisms behind this toxicity. The accumulation of atRA in the liver, intestine, and spinal column resulted in structural damage and tissue inflammation, as well as oxidative stress. It also down-regulated the gene transcription levels of key pathways involved in immune function and growth. Furthermore, it disrupted the homeostasis of the intestinal microbial communities. The implications for wildlife and human health, which are influenced by the regulation of microalgal metabolite accumulation and their transfer via the food chain, require further investigation and could hold broader significance.

Repurposing ibudilast to mitigate Alzheimer's disease by targeting inflammation.

Alzheimer's disease is a multifactorial disease that exhibits cognitive deficits, neuronal loss, amyloid plaques, neurofibrillary tangles and neuroinflammation in the brain. Hence, a multi-target drug would improve treatment efficacy. We applied a new multi-scale predictive modelling framework that integrates machine learning with biophysics and systems pharmacology to screen drugs for Alzheimer's disease using patients' tissue samples. Our predictive modelling framework identified ibudilast as a drug with repurposing potential to treat Alzheimer's disease. Ibudilast is a multi-target drug, as it is a phosphodiesterase inhibitor and toll-like receptor 4 (TLR4) antagonist. In addition, we predict that ibudilast inhibits off-target kinases (e.g. IRAK1 and GSG2). In Japan and other Asian countries, ibudilast is approved for treating asthma and stroke due to its anti-inflammatory potential. Based on these previous studies and on our predictions, we tested for the first time the efficacy of ibudilast in Fisher transgenic 344-AD rats. This transgenic rat model is unique as it exhibits hippocampal-dependent spatial learning and memory deficits and Alzheimer's disease pathology, including hippocampal amyloid plaques, tau paired-helical filaments, neuronal loss and microgliosis, in a progressive age-dependent manner that mimics the pathology observed in Alzheimer's disease patients. Following long-term treatment with ibudilast, transgenic rats were evaluated at 11 months of age for spatial memory performance and Alzheimer's disease pathology. We demonstrate that ibudilast-treatment of transgenic rats mitigated hippocampal-dependent spatial memory deficits, as well as hippocampal (hilar subregion) amyloid plaque and tau paired-helical filament load, and microgliosis compared to untreated transgenic rat. Neuronal density analysed across all hippocampal regions was similar in ibudilast-treated transgenic compared to untreated transgenic rats. Interestingly, RNA sequencing analysis of hippocampal tissue showed that ibudilast-treatment affects gene expression levels of the TLR and ubiquitin-proteasome pathways differentially in male and female transgenic rats. Based on the TLR4 signalling pathway, our RNA sequencing data suggest that ibudilast-treatment inhibits IRAK1 activity by increasing expression of its negative regulator IRAK3, and/or by altering TRAF6 and other TLR-related ubiquitin ligase and conjugase levels. Our results support that ibudilast can serve as a repurposed drug that targets multiple pathways including TLR signalling and the ubiquitin/proteasome pathway to reduce cognitive deficits and pathology relevant to Alzheimer's disease.

Molecular Interaction Mechanisms Between Lubricant-Infused Slippery Surfaces and Mussel-Inspired Polydopamine Adhesive and DOPA Moiety.

Lubricant-infused slippery surfaces have recently emerged as promising antifouling coatings, showing potential against proteins, cells, and marine mussels. However, a comprehensive understanding of the molecular binding behaviors and interaction strength of foulants to these surfaces is lacking. In this work, mussel-inspired chemistry based on catechol-containing chemicals including 3,4-dihydroxyphenylalanine (DOPA) and polydopamine (PDA) is employed to investigate the antifouling performance and repellence mechanisms of fluorinated-based slippery surface, and the correlated interaction mechanisms are probed using atomic force microscopy (AFM). Intermolecular force measurements and deposition experiments between PDA and the surface reveal the ability of lubricant film to inhibit the contact of PDA particles with the substrate. Moreover, the binding mechanisms and bond dissociation energy between a single DOPA moiety and the lubricant-infused slippery surface are quantitatively investigated employing single-molecule force spectroscopy based on AFM (SM-AFM), which reveal that the infused lubricant layer can remarkably influence the dissociation forces and weaken the binding strength between DOPA and underneath per-fluorinated monolayer surface. This work provides new nanomechanical insights into the fundamental antifouling mechanisms of the lubricant-infused slippery surfaces against mussel-derived adhesive chemicals, with important implications for the design of lubricant-infused materials and other novel antifouling platforms for various bioengineering and engineering applications.

EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.

Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.