MicroRNA-145-5p inhibits the tumorigenesis of breast cancer through SENP2-regulated ubiquitination of ERK2.

Breast carcinoma exhibits the highest incidence among various cancers and is the foremost cause of mortality in women. Increasing evidence shows that SUMOylation of proteins plays a critical role in the progression of breast cancer; however, the role of SENP2 and its molecular mechanism in breast cancer remain underexplored. Here, we discerned that SENP2 promoted the tumorigenesis of breast cancer both in vitro and in vivo.

Surface-Enhanced Raman Scattering-Based Multimodal Techniques: Advances and Perspectives.

Surface-enhanced Raman scattering (SERS) spectroscopy is a versatile molecular fingerprinting technique with rapid signal readout, high aqueous compatibility, and portability. To translate SERS for real-world applications, it is pertinent to overcome inherent challenges, including high sample variability and heterogeneity, matrix effects, and nonlinear SERS signal responses of different analytes in complex (bio)chemical matrices with numerous interfering species. In this perspective, we highlight emerging SERS-based multimodal techniques to address the key roadblocks to improving the sensitivity, specificity, and reliability of (bio)chemical detection, bioimaging, theragnosis, and theragnostic.

LC3-associated phagocytosis and human diseases: Insights from mechanisms to therapeutic potential.

LC3-associated phagocytosis (LAP) is a distinct type of autophagy that involves the sequestration of extracellular material by phagocytes. Beyond the removal of dead cells and cellular debris from eukaryotic cells, LAP is also involved in the removal of a variety of pathogens, including bacteria, fungi, and viruses. These events are integral to multiple physiological and pathological processes, such as host defense, inflammation, and tissue homeostasis.

Impact of Surface Enhanced Raman Spectroscopy in Catalysis.

Catalysis stands as an indispensable cornerstone of modern society, underpinning the production of over 80% of manufactured goods and driving over 90% of industrial chemical processes. As the demand for more efficient and sustainable processes grows, better catalysts are needed. Understanding the working principles of catalysts is key, and over the last 50 years, surface-enhanced Raman Spectroscopy (SERS) has become essential.

A Novel Infection-Related Inflammatory Response-Related Genes Signature Predicts the Prognosis of Esophageal Squamous Cell Carcinoma.

Background: Our previous research showed that () infection can activate the inflammatory signaling pathway and promotes the malignancy development of esophageal squamous cell carcinoma (ESCC). However, the prognostic significance of inflammatory response-related genes (IRRGs) in -infected ESCC requires further elucidation. Hence, our study constructed a prognostic signature based on and IRRGs to forecast the survival of patients with ESCC, which may provide insight into new treatment options for ESCC patients.

SENP3 mediates the deSUMOylation and degradation of YAP1 to regulate the progression of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a prevalent malignancy in women, casting a formidable shadow on their well-being. Positioned within the nucleolus, SUMO-specific protease 3 (SENP3) assumes a pivotal role in the realms of development and tumorigenesis. However, the participation of SENP3 in TNBC remains a mystery.

Plasmonic nanoparticle sensors: current progress, challenges, and future prospects.

Plasmonic nanoparticles (NPs) have played a significant role in the evolution of modern nanoscience and nanotechnology in terms of colloidal synthesis, general understanding of nanocrystal growth mechanisms, and their impact in a wide range of applications. They exhibit strong visible colors due to localized surface plasmon resonance (LSPR) that depends on their size, shape, composition, and the surrounding dielectric environment. Under resonant excitation, the LSPR of plasmonic NPs leads to a strong field enhancement near their surfaces and thus enhances various light-matter interactions.

Discovery Small-Molecule p300 Inhibitors Derived from a Newly Developed Indazolone-Focused DNA-Encoded Library.

The DNA-encoded library (DEL) is a robust tool for chemical biology and drug discovery. In this study, we developed a DNA-compatible light-promoted reaction that is highly efficient and plate-compatible for DEL construction based on the formation of the indazolone scaffold. Employing this high-efficiency approach, we constructed a DEL featuring an indazolone core, which enabled the identification of a novel series of ligands specifically targeting E1A-binding protein (p300) after DEL selection.

Quantitative Proteomics Provided Insights into the Protective Effects of Heat Acclimation on the Rat Hypothalamus after Exertional Heatstroke.

Background: The effects of heat acclimation (HA) on the hypothalamus after exertional heatstroke (EHS) and the specific mechanism have not been fully elucidated, and this study aimed to address these questions.

Methods: In the present study, rats were randomly assigned to the control, EHS, HA, or HA + EHS groups (n = 9). Hematoxylin and eosin (H&E) staining was used to examine pathology.

Harnessing Cooperative Multivalency in Thioguanine for Surface-Enhanced Raman Scattering (SERS)-Based Differentiation of Polyfunctional Analytes Differing by a Single Functional Group.

Small-molecule receptors are increasingly employed to probe various functional groups for (bio)chemical analysis. However, differentiation of polyfunctional analogs sharing multiple functional groups remains challenging for conventional mono- and bidentate receptors because their insufficient number of binding sites limits interactions with the least reactive yet property-determining functional group. Herein, we introduce 6-thioguanine (TG) as a supramolecular receptor for unique tridentate receptor-analyte complexation, achieving ≥97 % identification accuracy among 16 polyfunctional analogs across three classes: glycerol derivatives, disubstituted propane, and vicinal diols.

SENP3-regulated Nodal signaling plays a potential role in cardiac left-right asymmetry development.

Congenital heart disease (CHD) is a type of major defect that occurs during embryonic development. Although significant advances have been made in the treatment of CHD, its etiology and molecular mechanism remain unclear. To identify the critical role of SUMOylation in cardiac development, we generated SENP3 knockout mice and showed that SENP3 knockout mice die on embryonic day 8.

Forward-predictive SERS-based chemical taxonomy for untargeted structural elucidation of epimeric cerebrosides.

Achieving untargeted chemical identification, isomeric differentiation, and quantification is critical to most scientific and technological problems but remains challenging. Here, we demonstrate an integrated SERS-based chemical taxonomy machine learning framework for untargeted structural elucidation of 11 epimeric cerebrosides, attaining >90% accuracy and robust single epimer and multiplex quantification with <10% errors. First, we utilize 4-mercaptophenylboronic acid to selectively capture the epimers at molecular sites of isomerism to form epimer-specific SERS fingerprints.

Creating 3D Nanoparticle Structural Space via Data Augmentation to Bidirectionally Predict Nanoparticle Mixture's Purity, Size, and Shape from Extinction Spectra.

Nanoparticle (NP) characterization is essential because diverse shapes, sizes, and morphologies inevitably occur in as-synthesized NP mixtures, profoundly impacting their properties and applications. Currently, the only technique to concurrently determine these structural parameters is electron microscopy, but it is time-intensive and tedious. Here, we create a three-dimensional (3D) NP structural space to concurrently determine the purity, size, and shape of 1000 sets of as-synthesized Ag nanocubes mixtures containing interfering nanospheres and nanowires from their extinction spectra, attaining low predictive errors at 2.

Surface-Enhanced Raman Scattering-Based Surface Chemotaxonomy: Combining Bacteria Extracellular Matrices and Machine Learning for Rapid and Universal Species Identification.

Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprinting and multiplexing capabilities, as well as portability and speed of readout. Here, we develop a SERS-based surface chemotaxonomy that uses bacterial extracellular matrices (ECMs) as proxy biosignatures to hierarchically classify bacteria based on their shared surface biochemical characteristics to eventually identify six distinct bacterial species at >98% classification accuracy.

A Comprehensive Review of Digital Twin from the Perspective of Total Process: Data, Models, Networks and Applications.

With the rapid development of industrial digitalization and intelligence, there is an urgent need to accurately depict the physical world in digital space, and, in turn, regulate and optimize the behavior of physical entities based on massive data collection and analysis. As a technology that combines virtual space and physical space, digital twin can satisfy all of the above needs, and has attracted widespread attention. Due to the promising application prospects of digital twins, both academia and industry have launched research in this field, and related studies have been conducted from different perspectives.

SUMOylation of Smad2 mediates TGF-β-regulated endothelial-mesenchymal transition.

Endothelial-mesenchymal transition (EndoMT) is a complex biological process in which endothelial cells are transformed into mesenchymal cells, and dysregulated EndoMT causes a variety of pathological processes. Transforming growth factor beta (TGF-β) signaling effectively induces the EndoMT process in endothelial cells, and Smad2 is the critical protein of the TGF-β signaling pathway. However, whether small ubiquitin-like modifier modification (SUMOylation) is involved in EndoMT remains unclear.

Achieving Molecular Recognition of Structural Analogues in Surface-Enhanced Raman Spectroscopy: Inducing Charge and Geometry Complementarity to Mimic Molecular Docking.

Molecular recognition of complex isomeric biomolecules remains challenging in surface-enhanced Raman scattering (SERS) spectroscopy due to their small Raman cross-sections and/or poor surface affinities. To date, the use of molecular probes has achieved excellent molecular sensitivities but still suffers from poor spectral specificity. Here, we induce "charge and geometry complementarity" between probe and analyte as a key strategy to achieve high spectral specificity for effective SERS molecular recognition of structural analogues.

Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network.

With the continuous development of intelligent vehicles, people's demand for services has also rapidly increased, leading to a sharp increase in wireless network traffic. Edge caching, due to its location advantage, can provide more efficient transmission services and become an effective method to solve the above problems. However, the current mainstream caching solutions only consider content popularity to formulate caching strategies, which can easily lead to cache redundancy between edge nodes and lead to low caching efficiency.

Targeting the JAK2-STAT3 pathway to inhibit cGAS-STING activation improves neuronal senescence after ischemic stroke.

Neuroinflammation after cerebral ischemia is a key event in progressive brain injury after ischemic stroke. The JAK2/STAT3 pathway is pivotal for neuroinflammation; however, its role in brain senescence after ischemic stroke is unclear. Here, we report that inflammation is increased in the brains of C57BL/6 stroke mice.

Intrusion Detection Method for Internet of Vehicles Based on Parallel Analysis of Spatio-Temporal Features.

The problems with network security that the Internet of Vehicles (IoV) faces are becoming more noticeable as it continues to evolve. Deep learning-based intrusion detection techniques can assist the IoV in preventing network threats. However, previous methods usually employ a single deep learning model to extract temporal or spatial features, or extract spatial features first and then temporal features in a serial manner.