Electropositive Citric Acid-Polyethyleneimine Carbon Dots Carrying the PINK1 Gene Regulate ATP-Related Metabolic Dysfunction in APP/PS1-N2a Cells.

(1) Background: Alzheimer's disease (AD) is characterized by ß-amyloid (Aß) peptide accumulation and mitochondrial dysfunction during the early stage of disease. PINK1 regulates the balance between mitochondrial homeostasis and bioenergy supply and demand via the PINK1/Parkin pathway, Na+/Ca2+ exchange, and other pathways. (2) Methods: In this study, we synthesized positively charged carbon dots (CA-PEI CDs) using citric acid (CA) and polyethyleneimine (PEI) and used them as vectors to express PINK1 genes in the APP/PS1-N2a cell line to determine mitochondrial function, electron transport chain (ETC) activity, and ATP-related metabolomics. (3) Results: Our findings showed that the CA-PEI CDs exhibit the characteristics of photoluminescence, low toxicity, and concentrated DNA. They are ideal biological carriers for gene delivery. PINK1 overexpression significantly increased the mitochondrial membrane potential in APP/PS1-N2a cells and reduced reactive-oxygen-species generation and Aß1-40 and Aß1-42 levels. An increase in the activity of NADH ubiquinone oxidoreductase (complex I, CI) and cytochrome C oxidase (complex IV, CIV) induces the oxidative phosphorylation of mitochondria, increasing ATP generation. (4) Conclusions: These findings indicate that the PINK gene can alleviate AD by increasing bioenergetic metabolism, reducing Aß1-40 and Aß1-42, and increasing ATP production.

Causal effects of immune cells in glioblastoma: a Bayesian Mendelian Randomization study.

Background: Glioblastoma (GBM) is a highly malignant brain tumor, and immune cells play a crucial role in its initiation and progression. The immune system's cellular components, including various types of lymphocytes, macrophages, and dendritic cells, among others, engage in intricate interactions with GBM. However, the precise nature of these interactions remains to be conclusively determined. Method: In this study, a comprehensive two-sample Mendelian Randomization (MR) analysis was conducted to elucidate the causal relationship between immune cell features and the incidence of GBM. Utilizing publicly available genetic data, we investigated the causal associations between 731 immune cell signatures and the risk of GBM. Subsequently, we conducted a reverse Mendelian randomization analysis to rule out reverse causation. Finally, it was concluded that there is a unidirectional causal relationship between three subtypes of immune cells and GBM. Comprehensive sensitivity analyses were employed to validate the results robustness, heterogeneity, and presence of horizontal pleiotropy. To enhance the accuracy of our results, we concurrently subjected them to Bayesian analysis. Results: After conducting MR analyses, we identified 10 immune phenotypes that counteract glioblastoma, with the most protective being FSC-A on Natural Killer T cells (OR = 0.688, CI = 0.515-0.918, P = 0.011). Additionally, we found 11 immune cell subtypes that promote GBM incidence, including CD62L- HLA DR++ monocyte % monocyte (OR = 1.522, CI = 1.004-2.307, P = 0.048), CD4+CD8+ T cell % leukocyte (OR = 1.387, CI = 1.031-1.866, P = 0.031). Following the implementation of reverse MR analysis, where glioblastoma served as the exposure variable and the outcomes included 21 target immune cell subtypes, we discerned that only three cell subtypes (CD45 on CD33+ HLA DR+ CD14dim, CD33+ HLA DR+ Absolute Count, and IgD+ CD24+ B cell Absolute Count) exhibited a unidirectional causal association with glioblastoma. Conclusion: Our study has genetically demonstrated the close relationship between immune cells and GBM, guiding future clinical research.

Association of preoperative coronavirus disease 2019 with mortality, respiratory morbidity and extrapulmonary complications after elective, noncardiac surgery: An observational cohort study.

STUDY OBJECTIVE: To assess the impact of preoperative infection with the contemporary strain of severe acute respiratory coronavirus 2 (SARS-CoV-2) on postoperative mortality, respiratory morbidity and extrapulmonary complications after elective, noncardiac surgery. DESIGN: An ambidirectional observational cohort study. SETTING: A tertiary and teaching hospital in Shanghai, China. PATIENTS: All adult patients (≥ 18 years of age) who underwent elective, noncardiac surgery under general anesthesia at Huashan Hospital of Fudan University from January until March 2023 were screened for eligibility. A total of 2907 patients were included. EXPOSURE: Preoperative coronavirus disease 2019 (COVID-19) positivity. MEASUREMENTS: The primary outcome was 30-day postoperative mortality. The secondary outcomes included postoperative pulmonary complications (PPCs), myocardial injury after noncardiac surgery (MINS), acute kidney injury (AKI), postoperative delirium (POD) and postoperative sleep quality. Multivariable logistic regression was used to assess the risk of postoperative mortality and morbidity imposed by preoperative COVID-19. MAIN RESULTS: The risk of 30-day postoperative mortality was not associated with preoperative COVID-19 [adjusted odds ratio (aOR), 95% confidence interval (CI): 0.40, 0.13-1.28, P = 0.123] or operation timing relative to diagnosis. Preoperative COVID-19 did not increase the risk of PPCs (aOR, 95% CI: 0.99, 0.71-1.38, P = 0.944), MINS (aOR, 95% CI: 0.54, 0.22-1.30; P = 0.168), or AKI (aOR, 95% CI: 0.34, 0.10-1.09; P = 0.070) or affect postoperative sleep quality. Patients who underwent surgery within 7 weeks after COVID-19 had increased odds of developing delirium (aOR, 95% CI: 2.26, 1.05-4.86, P = 0.036). CONCLUSIONS: Preoperative COVID-19 or timing of surgery relative to diagnosis did not confer any added risk of 30-day postoperative mortality, PPCs, MINS or AKI. However, recent COVID-19 increased the risk of POD. Perioperative brain health should be considered during preoperative risk assessment for COVID-19 survivors.

Enhancing Oil Recovery in Low-Permeability Reservoirs Using a Low-Molecular Weight Amphiphilic Polymer.

Polymer flooding has achieved considerable success in medium-high permeability reservoirs. However, when it comes to low-permeability reservoirs, polymer flooding suffers from poor injectivity due to the large molecular size of the commonly used high-molecular-weight (high-MW) partially hydrolyzed polyacrylamides (HPAM). Herein, an amphiphilic polymer (LMWAP) with a low MW (3.9 × 106 g/mol) was synthesized by introducing an amphiphilic monomer (Allyl-OP-10) and a chain transfer agent into the polymerization reaction. Despite the low MW, LMWAP exhibited better thickening capability in brine than its counterparts HPAM-1800 (MW = 1.8 × 107 g/mol) and HPAM-800 (MW = 8 × 106 g/mol) due to the intermolecular hydrophobic association. LMWAP also exhibited more significant shear-thinning behavior and stronger elasticity than the two counterparts. Furthermore, LMWAP possesses favorable oil-water interfacial activity due to its amphiphilicity. The oil-water interfacial tension (IFT) could be reduced to 0.88 mN/m and oil-in-water (O/W) emulsions could be formed under the effect of LMWAP. In addition, the reversible hydrophobic association endows the molecular chains of LMWAP with dynamic association-disassociation transition ability. Therefore, despite the similar hydrodynamic sizes in brine, LMWAP exhibited favorable injectivity under low-permeability conditions, while the counterpart HPAM-1800 led to fatal plugging. Furthermore, LMWAP could enhance oil recovery up to 21.5%, while the counterpart HPAM-800 could only enhance oil recovery by up to 11.5%, which could be attributed to the favorable interfacial activity of LMWAP.

Predicting Natural Evolution in the RBD Region of the Spike Glycoprotein of SARS-CoV-2 by Machine Learning.

Machine learning (ML) is a key focus in predicting protein mutations and aiding directed evolution. Research on potential virus variants is crucial for vaccine development. In this study, the machine learning software PyPEF was employed to conduct mutation analysis within the receptor-binding domain (RBD) of the Spike glycoprotein of SARS-CoV-2. Over 48,960,000 variants were predicted. Eight prospective variants that could surface in the future underwent modeling and molecular dynamics simulations. The study forecasts that the latest variant, ISOY2P5O1, may potentially emerge around 17 November 2023, with an approximate window of uncertainty of ±22 days. The ISOY8P5O2 variant displayed an increased binding capacity in the dry assay, with a total predicted binding energy of -110.306 kcal/mol. This represents an 8.25% enhancement in total binding energy compared to the original SARS-CoV-2 strain discovered in Wuhan (-101.892 kcal/mol). Reverse research confirmed the structural significance of mutation sites using ML models, particularly in the context of protein folding. The study validated regression methods (SVR, RF, and PLS) with different data structures. This study investigates the effectiveness of the "ML-Guided Design Correctly Predicts Combinatorial Effects Strategy" compared to the "ML-Guided Design Correctly Predicts Natural Evolution Prediction Strategy". To enhance machine learning, we created a timestamping algorithm and two auxiliary programs using advanced techniques to rapidly process extensive data, surpassing batch sequencing capabilities. This study not only advances machine learning in guiding protein evolution but also holds potential for forecasting future viruses and vaccine development.

Mapping dynamic spatial patterns of brain function with spatial-wise attention.

Objective: Using functional magnetic resonance imaging (fMRI) and deep learning to discover the spatial pattern of brain function, or functional brain networks (FBNs) has been attracted many reseachers. Most existing works focus on static FBNs or dynamic functional connectivity among fixed spatial network nodes, but ignore the potential dynamic/time-varying characteristics of the spatial networks themselves. And most of works based on the assumption of linearity and independence, that oversimplify the relationship between blood-oxygen level dependence signal changes and the heterogeneity of neuronal activity within voxels.Approach: To overcome these problems, we proposed a novel spatial-wise attention (SA) based method called Spatial and Channel-wise Attention Autoencoder (SCAAE) to discover the dynamic FBNs without the assumptions of linearity or independence. The core idea of SCAAE is to apply the SA to generate FBNs directly, relying solely on the spatial information present in fMRI volumes. Specifically, we trained the SCAAE in a self-supervised manner, using the autoencoder to guide the SA to focus on the activation regions. Experimental results show that the SA can generate multiple meaningful FBNs at each fMRI time point, which spatial similarity are close to the FBNs derived by known classical methods, such as independent component analysis.Main results: To validate the generalization of the method, we evaluate the approach on HCP-rest, HCP-task and ADHD-200 dataset. The results demonstrate that SA mechanism can be used to discover time-varying FBNs, and the identified dynamic FBNs over time clearly show the process of time-varying spatial patterns fading in and out.Significance: Thus we provide a novel method to understand human brain better. Code is available athttps://github.com/WhatAboutMyStar/SCAAE.

Spatial-temporal convolutional attention for discovering and characterizing functional brain networks in task fMRI.

Functional brain networks (FBNs) are spatial patterns of brain function that play a critical role in understanding human brain function. There are many proposed methods for mapping the spatial patterns of brain function, however they oversimplify the underlying assumptions of brain function and have various limitations such as linearity and independence. Additionally, current methods fail to account for the dynamic nature of FBNs, which limits their effectiveness in accurately characterizing these networks. To address these limitations, we present a novel deep learning and spatial-wise attention based model called Spatial-Temporal Convolutional Attention (STCA) to accurately model dynamic FBNs. Specifically, we train STCA in a self-supervised manner by utilizing a Convolutional Autoencoder to guide the STCA module in assigning higher attention weights to regions of functional activity. To validate the reliability of the results, we evaluate our approach on the HCP-task motor behavior dataset, the experimental results demonstrate that the STCA derived FBNs have higher spatial similarity with the templates and that the spatial similarity between the templates and the FBNs derived by STCA fluctuates with the task design over time, suggesting that STCA can reflect the dynamic changes of brain function, providing a powerful tool to better understand human brain function. Code is available at https://github.com/SNNUBIAI/STCAE.

Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy.

BACKGROUND: RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined. METHODS: In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis. RESULTS: A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells. CONCLUSION: In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.

Simulation study of an x-ray sub-picosecond resolution detection system based on time-domain amplification.

This study proposes what we believe to be a novel x-ray detection system that achieves a temporal resolution of 930 fs with photorefractive and four-wave mixing effects. The system comprises two parts: a signal-conversion system and signal-acquisition system. The signal-conversion system is based on the photorefractive effect, which converts x-ray evolution into the variation of infrared interference intensity. The signal-conversion sensor consists of ultra-fast response LT-GaAs and a high-resolution interference cavity, achieving a resolution of  767 fs. The signal-acquisition system consists of a time-domain amplification system based on four-wave mixing and a high-resolution signal-recording system with a resolution of 21 ps, providing a temporal resolution of 525 fs.

Organelle-dependent polyprotein designs enable stoichiometric expression of nitrogen fixation components targeted to mitochondria.

Introducing nitrogen fixation (nif  ) genes into eukaryotic genomes and targeting Nif components to mitochondria or chloroplasts is a promising strategy for engineering nitrogen-fixing plants. A prerequisite for achieving nitrogen fixation in crops is stable and stoichiometric expression of each component in organelles. Previously, we designed a polyprotein-based nitrogenase system depending on Tobacco Etch Virus protease (TEVp) to release functional Nif components from five polyproteins. Although this system satisfies the demand for specific expression ratios of Nif components in Escherichia coli, we encountered issues with TEVp cleavage of polyproteins targeted to yeast mitochondria. To overcome this obstacle, a version of the Nif polyprotein system was constructed by replacing TEVp cleavage sites with minimal peptide sequences, identified by knowledge-based engineering, that are susceptible to cleavage by the endogenous mitochondrial-processing peptidase. This replacement not only further reduces the number of genes required, but also prevents potential precleavage of polyproteins outside the target organelle. This version of the polyprotein-based nitrogenase system achieved levels of nitrogenase activity in E. coli, comparable to those observed with the TEVp-based polyprotein nitrogenase system. When applied to yeast mitochondria, stable and balanced expression of Nif components was realized. This strategy has potential advantages, not only for transferring nitrogen fixation to eukaryotic cells, but also for the engineering of other metabolic pathways that require mitochondrial compartmentalization.

A new abietane diterpenoid from Lycopodium complanatum.

A new abietane diterpenoid, 1ß, 11-epoxyabieta-12-hydroxy-8, 11, 13-triene-7-one (1), along with three known compounds (2-4), was isolated from Lycopodium complanatum. Their structures were confirmed by the analysis of 1D, 2D NMR and HRESIMS data, and comparison with previous spectral data. All compounds were tested for inhibitory activities against A549, HepG2 and MCF-7 tumor cell lines. [Figure: see text].

Current global research on mandibular defect: A bibliometric analysis from 2001 to 2021.

Background: Mandibular defects can result from congenital deformities, trauma, tumor resection, and osteomyelitis. The shape was irregular because the lower jaw was radians. This involves teeth and jaw functions; therefore, the difficulty of bone repair is greater than that in other body parts. Several standard treatments are available, but they result in various problems, such as difficulties in skin flap transplantation and possible zone dysfunction, artificial material boneless combining ability, and a long treatment period. This study aimed to introduce the present status of research on mandibular defects to analyze the current introduction and predict future research trends through a bibliometric study. Methods: From 2001 to 2021, publications on mandibular defects were collected for bibliometric visualization using VOSviewer, CiteSpace, and Scimago Graphica software based on the Web of Science Core Collection. Results: This study analyzed 4,377 articles, including 1,080 published in the United States, 563 in China, and 359 in Germany, with an increase in the number of articles published over the past 20 years. Wikesjoe and Ulf Mai E had the most publications (p = 36) and citations (citations = 1,553). Shanghai Jiaotong University published the highest number of papers among the research institutions (p = 88). The most productive journal was Journal of Oral and Maxillofacial Surgery, and the cited literature was primarily classified as dentistry, dermatology, and surgery. Cluster Analysis of Co-occurrence Keywords revealed that highest number of core words were mandibular defects, mandibular reconstruction, and bone regeneration. The highest cited words were head and neck cancer, accuracy, and osteogenic differentiation. High-frequency terms of Cluster Analysis of References were osteosynthesis plate, tissue engineering, and rapid distraction rate. Conclusion: Over the past 20 years, the number of studies on mandibular defects has gradually increased. New surgical procedures are increasingly being used in clinical practice. Current frontier topics mainly focus on areas such as computer-aided design, 3D printing of osteotomy and reconstruction guide plates, virtual surgical planning, and bone tissue engineering.

Learning-based interfered fluid avoidance guidance for hypersonic reentry vehicles with multiple constraints.

To address the problem of no-fly zone avoidance for hypersonic reentry vehicles in the multiple constraints gliding phase, a learning-based avoidance guidance framework is proposed. First, the reference heading angle determination problem is solved efficiently and skillfully by introducing a nature-inspired methodology based on the concept of the interfered fluid dynamic system (IFDS), in which the distance and relative position relationships of all no-fly zones can be comprehensively considered, and additional rules are no longer needed. Then, by incorporating the predictor-corrector method, the heading angle corridor, and bank angle reversal logic, a fundamental interfered fluid avoidance guidance algorithm is proposed to steer the vehicle toward the target zone while avoiding no-fly zones. In addition, a learning-based online optimization mechanism is used to optimize the IFDS parameters in real time to improve the avoidance guidance performance of the proposed algorithm in the entire gliding phase. Finally, the adaptability and robustness of the proposed guidance algorithm are verified via comparative and Monte Carlo simulations.

Identification of an EMT-related gene-based prognostic signature in osteosarcoma.

BACKGROUND: The correlation between epithelial-mesenchymal transition (EMT) and osteosarcoma (OS) has been widely reported. Integration of the EMT-related genes to predict the prognosis is significant for investigating the mechanism of EMT in OS. Here, we aimed to construct a prognostic EMT-related gene signature for OS. METHODS: Transcriptomic and survival data of OS patients were downloaded from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO). We performed univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and stepwise multivariate Cox regression analysis to construct EMT-related gene signatures. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) were applied to evaluate its predictive performance. GSVA, ssGSEA, ESTIMATE, and scRNA-seq were performed to investigate the tumor microenvironment, and the correlation between IC50 of drugs and ERG score was investigated. Furthermore, Edu and transwell experiments were conducted to assess the malignancy of OS cells. RESULTS: We constructed a novel EMT-related gene signature (including CDK3, MYC, UHRF2, STC2, COL5A2, MMD, and EHMT2) for outcome prediction of OS. According to the signature, patients stratified into high- and low-ERG-score groups exhibited significantly different prognoses. ROC curves and Kaplan-Meier analysis revealed a promising performance of the signature with external validation. GSVA, ssGSEA, ESTIMATE algorithm, and scRNA-seq excavated EMT-related pathways and suggested the correlation between ERG score and immune activation. Notably, the pivotal gene CDK3 was upregulated in OS tissue and positively related to OS cell proliferation and migration. CONCLUSION: Our EMT-related gene signature might reference OS risk stratification and guide clinical strategies as an independent prognostic factor in OS.

A Peptide-Conjugated Probe with Cleavage-Induced Morphological Change for Treatment on Tumor Cell Membrane.

Despite the promising advancements of in situ forming nanoassembly for the inhibition of tumor growth and metastasis, the lack of sufficient triggering sites and hardly controlling the forming position restrict their further developments. Herein, a smart transformable peptide-conjugated probe (DMFA) with enzyme cleavage-induced morphological change is designed for treatment on the tumor cell membrane. Specifically, after self-assembling into nanoparticles and anchoring on the cell membrane with sufficient interaction sites rapidly and stably, DMFA will be efficiently cleaved into α-helix forming part (DP) and ß-sheet forming part (LFA) by overexpressed matrix metalloproteinase-2. Thus, the promoted Ca2+ influx by DP-induced cell membrane breakage and decreased Na+ /K+ -ATPase activity by LFA-assembled nanofibers wrapping the cells can inhibit PI3K-Akt signaling pathway, leading to the inhibition of tumor cell growth and metastasis. This peptide-conjugated probe undergoes in situ morphological transformation on the cell membrane, exhibiting great potential in tumor therapy.

Duck plague virus pUL15 performs a nonspecial cleavage activity through its C terminal nuclease domain in vitro.

Duck plague virus (DPV), also known as anatid herpesvirus, is a double-stranded DNA virus and a member of α herpesvirus. DPV pUL15 is a homolog of herpes simplex virus 1 (HSV-1) pUL15, a terminase large subunit, and plays a key role in the cleavage and packaging of the viral concatemeric genome. However, the sequence similarity between DPV pUL15 and its homologs is low, and it is not sure if DPV pUL15 has the potential to cleave the concatemeric genome as same as its homologs. Here, we expressed the C terminal domain of DPV pUL15 to explore the nuclease function of DPV pUL15. The main results showed that (Ⅰ) DPV pUL15 C-terminal domain possesses nonspecific nuclease activity and lacks the DNA binding ability. (Ⅱ) DPV pUL15 nuclease activity needs to coordinate with divalent metal ions and tends to be more active at high temperatures. (Ⅲ) Even though the structure of DPV pUL15 nuclease domain is relatively conserved, the mutations of conserved amino acids on the nuclease domain do not significantly inhibit the nuclease activity.

Linear ubiquitination of LKB1 activates AMPK pathway to inhibit NLRP3 inflammasome response and reduce chondrocyte pyroptosis in osteoarthritis.

Background: Osteoarthritis (OA) is the most common chronic disease. It is characterized by high levels of clinical heterogeneity and low inflammation. Therefore, elucidation of the mechanisms that regulate gene expression is critical for developing effective OA therapies. This study aimed to explore the role of LKB1/AMPK in the progression of OA. Methods: Anterior cruciate ligament transection (ACLT) was performed on Sprague Dawley (SD) rats right knee to construct OA model, followed by AICAR [AMP-activated protein kinase (AMPK) activator] treatment. The level changes [AMPK, IL-10, IL-13, IL-1ß, TNF-α, IL-6, ASC, Caspase-1, Ki67, and hibit Nod-like receptor protein 3 (NLRP3)] and the degree of tissue injury were assessed by western blot, Immunohistochemical (IHC), Enzyme-linked immunosorbent assay (ELISA), Hematoxylin-eosin staining (HE), Immunofluorescence (IF), Terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay, and Safranin O and Fast Green staining (S-O). Human chondrocytes were induced by LPS to construct a cellular inflammatory model, and then transfected with oe-AMPK or oe-HOIL-1-interacting protein (HOIP). Cell viability/apoptotic and intracellular content of AMPK, HOIP, IL-1ß, IL-10, IL-13, TNF-α, IL-6, ASC, NLRP3 and Caspase-1 were measured by western blot, ELISA, CCK-8, IF, flow cytometry and TUNEL assays. Results: After AICAR treatment with OA rats, the expression of p-AMPK, IL-10, IL-13, Ki67 and Bcl-2 increased, the level of NLRP3 inflammasome, TNF-α, IL-6, Bax and Caspase-3 levels were decreased, and tissue damage and apoptosis were significantly alleviated. After transfected with oe-LKB1, chondrocyte activity and LKB1 linear ubiquitination were enhanced, and the level of HOIP, p-AMPK, IL-10 and IL-13 were increased. In contrast, NLRP3 inflammasome (ASC, NLRP3, Caspase-1, IL-1ß, and cleaved Caspase-1), TNF-α, and IL-6 levels decreased, apoptosis rate and TUNEL positive rate were attenuated. Conclusion: LKB1/AMPK pathway significantly ameliorated NLRP3 inflammasome response and chondrocyte injury. Activation of AMPK pathway by linear ubiquitination of LKB1 may be a potential target for OA treatment. The translational potential of this article: This study highlights the importance of the LKB1/AMPK pathway in NLRP3 inflammatory body response and chondrocyte injury. Activation of LKB1 by modulating linear ubiquitination may be a potential target for OA treatment.

Unsupervised Person Re-Identification With Wireless Positioning Under Weak Scene Labeling.

Existing unsupervised person re-identification methods only rely on visual clues to match pedestrians under different cameras. Since visual data is essentially susceptible to occlusion, blur, clothing changes, etc., a promising solution is to introduce heterogeneous data to make up for the defect of visual data. Some works based on full-scene labeling introduce wireless positioning to assist cross-domain person re-identification, but their GPS labeling of entire monitoring scenes is laborious. To this end, we propose to explore unsupervised person re-identification with both visual data and wireless positioning trajectories under weak scene labeling, in which we only need to know the locations of the cameras. Specifically, we propose a novel unsupervised multimodal training framework (UMTF), which models the complementarity of visual data and wireless information. Our UMTF contains a multimodal data association strategy (MMDA) and a multimodal graph neural network (MMGN). MMDA explores potential data associations in unlabeled multimodal data, while MMGN propagates multimodal messages in the video graph based on the adjacency matrix learned from histogram statistics of wireless data. Thanks to the robustness of the wireless data to visual noise and the collaboration of various modules, UMTF is capable of learning a model free of the human label on data. Extensive experimental results conducted on two challenging datasets, i.e., WP-ReID and Campus4K demonstrate the effectiveness of the proposed method.

Applications of Carbon Dots for the Treatment of Alzheimer's Disease.

There are currently approximately 50 million victims of Alzheimer's disease (AD) worldwide. The exact cause of the disease is unknown at this time, but amyloid plaques and neurofibrillary tangles in the brain are hallmarks of the disease. Current drug treatments for AD may slow the progression of the disease and improve the quality of life of patients, but they are often only minimally effective and are not cures. A major obstacle to developing and delivering more effective drug therapies is the presence of the blood-brain barrier (BBB), which prevents many compounds with therapeutic potential from reaching the central nervous system. Nanotechnology may provide a solution to this problem. Among the medical nanomaterials currently being studied, carbon dots (CDs) have attracted widespread attention because of their ability to cross the BBB, non-toxicity, and potential for drug/gene delivery.

Brasesquilignan A-E, Five New Furofurans Lignans from Selaginella braunii Baker.

Five new furofurans lignans, Brasesquilignan A-E (1-5), were isolated from the aqueous ethanol extract of Selaginella braunii Baker. Their structures were elucidated by extensive analysis of NMR and HRESIMS data. Their absolute configurations were determined by CD spectra, enzymatic hydrolysis, and GCMS analysis. Furthermore, all compounds were evaluated for anti-proliferative activities against various human cancer cellsin vitro. Compounds 2 and 3 exhibited weak inhibitorypotency against five human cancer cells.