SUMOylation Fine-Tunes Endothelial HEY1 in the Regulation of Angiogenesis.

BACKGROUND: Angiogenesis, which plays a critical role in embryonic development and tissue repair, is controlled by a set of angiogenic signaling pathways. As a TF (transcription factor) belonging to the basic helix-loop-helix family, HEY (hairy/enhancer of split related with YRPW motif)-1 (YRPW motif, abbreviation of 4 highly conserved amino acids in the motif) has been identified as a key player in developmental angiogenesis. However, the precise mechanisms underlying HEY1's actions in angiogenesis remain largely unknown. Our previous studies have suggested a potential role for posttranslational SUMOylation in the dynamic regulation of vascular development and organization. METHODS: Immunoprecipitation, mass spectrometry, and bioinformatics analysis were used to determine the biochemical characteristics of HEY1 SUMOylation. The promoter-binding capability of HEY1 was determined by chromatin immunoprecipitation, dual luciferase, and electrophoretic mobility shift assays. The dimerization pattern of HEY1 was determined by coimmunoprecipitation. The angiogenic capabilities of endothelial cells were assessed by CCK-8 (cell counting kit-8), 5-ethynyl-2-deoxyuridine staining, wound healing, transwell, and sprouting assays. Embryonic and postnatal vascular growth in mouse tissues, matrigel plug assay, cutaneous wound healing model, oxygen-induced retinopathy model, and tumor angiogenesis model were used to investigate the angiogenesis in vivo. RESULTS: We identified intrinsic endothelial HEY1 SUMOylation at conserved lysines by TRIM28 (tripartite motif containing 28) as the unique E3 ligase. Functionally, SUMOylation facilitated HEY1-mediated suppression of angiogenic RTK (receptor tyrosine kinase) signaling and angiogenesis in primary human endothelial cells and mice with endothelial cell-specific expression of wild-type HEY1 or a SUMOylation-deficient HEY1 mutant. Mechanistically, SUMOylation facilitates HEY1 homodimer formation, which in turn preserves HEY1's DNA-binding capability via recognition of E-box promoter elements. Therefore, SUMOylation maintains HEY1's function as a repressive TF controlling numerous angiogenic genes, including RTKs and Notch pathway components. Proangiogenic stimuli induce HEY1 deSUMOylation, leading to heterodimerization of HEY1 with HES (hairy and enhancer of split)-1, which results in ineffective DNA binding and loss of HEY1's angiogenesis-suppressive activity. CONCLUSIONS: Our findings demonstrate that reversible HEY1 SUMOylation is a molecular mechanism that coordinates endothelial angiogenic signaling and angiogenesis, both in physiological and pathological milieus, by fine-tuning the transcriptional activity of HEY1. Specifically, SUMOylation facilitates the formation of the HEY1 transcriptional complex and enhances its DNA-binding capability in endothelial cells.

Protein-Controlled Split DNAzyme to Enhance Catalytic Activity: Design and Performance.

In this study, we utilized proteins to control the assembly of split DNAzyme to establish protein-controlled split DNAzymes (Pc SD), with the aim of enhancing its catalytic activity. To achieve this, simultaneous recognition of protein by affinity ligands at both ends of split DNAzyme fragments induced an increased local concentration of each split fragment, leading to reassembly of the split catalytic core with a rigid conformation and higher affinity to its cofactor. As a result, under protein control, Pc SD exhibits unexpected cleavage efficiency compared to free split DNAzyme. To further explore the catalytic features, we then systematically positioned split sites within the catalytic core of three popular DNAzyme-based Pc SDs, thus revealing the important nucleic acids that influence Pc SDs activity. Based on the excellent analytical performance of Pc SD for streptavidin (with a LOD of 0.1 pM in buffer),we equipped Pc SD with antibodies as rapid diagnostic tools for inpatient care (AFP as biomarker) with a minimized workflow (with a LOD of 2 pM in 5% human serum). The results of this study offer fundamental insights into external factors for boosting DNAzyme catalysis and will be promising for applications that utilize split DNAzymes.

Heteromultivalent DNA Enhances the Assembly Yield of Hybrid Nanoparticles and Facilitates Dynamic Disassembly for Bioanalysis Using ICP-MS.

To obtain enhanced physical and biological properties, various nanoparticles are typically assembled into hybrid nanoparticles through the binding of multiple homologous DNA strands to their complementary counterparts, commonly referred to as homomultivalent assembly. However, the poor binding affinity and limited controllability of homomultivalent disassembly restrict the assembly yield and dynamic functionality of the hybrid nanoparticles. To achieve a higher binding affinity and flexible assembly choice, we utilized the paired heteromultivalency DNA to construct hybrid nanoparticles and demonstrate their excellent assembly characteristics and dynamic applications. Specifically, through heteromultivalency, DNA-functionalized magnetic beads (MBs) and gold nanoparticles (AuNPs) were efficiently assembled. By utilizing ICP-MS, the assembly efficiency of AuNPs on MBs was directly monitored, enabling quantitative analysis and optimization of heteromultivalent binding events. As a result, the enhanced assembly yield is primarily attributed to the fact that heteromultivalency allows for the maximization of effective DNA probes on the surface of nanoparticles, eliminating steric hindrance interference. Subsequently, with external oligonucleotides as triggers, it was revealed that the disassembly mechanism of hybrid nanoparticles was initiated, which was based on an increased local concentration rather than toehold-mediated displacement of paired heteromultivalency DNA probes. Capitalizing on these features, an output platform was then established based on ICP-MS signals that several Boolean operations and analytical applications can be achieved by simply modifying the design sequences. The findings provide new insights into DNA biointerface interaction, with potential applications to complex logic operations and the construction of large DNA nanostructures.

Blood biomarkers for post-stroke cognitive impairment: A systematic review and meta-analysis.

BACKGROUND AND PURPOSE: Post-stroke cognitive impairment (PSCI) is a frequent consequence of stroke, which affects the quality of life and prognosis of stroke survivors. Numerous studies have indicated that blood biomarkers may be the key determinants for predicting and diagnosing cognitive impairment, but the results remain varied. Therefore, this meta-analysis aims to summarize potential biomarkers associated with PSCI. METHODS: PubMed, Web of Science, Embase, and Cochrane Library were comprehensively searched for studies exploring blood biomarkers associated with PSCI from inception to 15 April 2022. RESULTS: 63 studies were selected from 4,047 references, which involves 95 blood biomarkers associated with the PSCI. We meta-analyzed 20 potential blood biomarker candidates, the results shown that the homocysteine (Hcy) (SMD = 0.35; 95 %CI: 0.20-0.49; P < 0.00001), c-reactive protein (CRP) (SMD = 0.49; 95 %CI: 0.20-0.78; P = 0.0008), uric acid (UA) (SMD = 0.41; 95 %CI: 0.06-0.76; P = 0.02), interleukin 6 (IL-6) (SMD = 0.92; 95 % CI: 0.27-1.57; P = 0.005), cystatin C (Cys-C) (SMD = 0.58; 95 %CI: 0.28-0.87; P = 0.0001), creatinine (SMD = 0.39; 95 %CI: 0.23-0.55; P < 0.00001) and tumor necrosis factor alpha (TNF-α) (SMD = 0.45; 95 %CI: 0.08-0.82; P = 0.02) levels were significantly higher in patients with PSCI than in the non-PSCI group. CONCLUSION: Based on our findings, we recommend that paramedics focus on the blood biomarkers levels of Hcy, CRP, UA, IL-6, Cys-C, creatinine and TNF-α in conjunction with neuroimaging and neuropsychological assessment to assess the risk of PSCI, which may help with early detection and timely preventive measures. At the same time, other potential blood biomarkers should be further validated in future studies.

Exploring optimal pathways for enterprise procurement management systems based on fast neural modeling and semantic segmentation.

Corporate procurement management assumes a pivotal role within the contemporary business landscape, yet confronts an array of challenges as markets continue to evolve and globalize. Conventional procurement management systems frequently grapple with issues of inefficiency, resource depletion, and noncompliance, necessitating the exploration of innovative avenues for optimization. This paper delves into the realm of risk mitigation associated with collusion behavior in the administration of intelligent procurement systems, presenting a novel procurement collusion identification model founded on a convolutional neural network (CNN) with reinforcement learning techniques. This framework commences with the application of a CNN and Long Short-Term Memory (LSTM) network for in-depth feature analysis and initial identification of historical procurement data, subsequently leveraging reinforcement learning methodologies to enhance the model's autonomy and intelligence for the purpose of optimization. Throughout the experimental phase, diverse domains of procurement data were meticulously selected for analysis. The empirical findings unequivocally demonstrate the model's proficiency, with an average recognition accuracy of 95.1% across five publicly available datasets. This performance surpasses existing machine learning methodologies employed in contemporary research and common recognition networks, thereby offering a pioneering reference point for the intelligent administration and optimization of future procurement systems.

Imaging classification of prostate cancer with extracapsular extension and its impact on positive surgical margins after laparoscopic radical prostatectomy.

Abstract: To explore the impact of different imaging classifications of prostate cancer (PCa) with extracapsular extension (EPE) on positive surgical margins (PSM) after laparoscopic radical prostatectomy. Methods: Clinical data were collected for 114 patients with stage PT3a PCa admitted to Ningbo Yinzhou No. 2 Hospital from September 2019 to August 2023. Radiologists classified the EPE imaging of PCa into Type I, Type II, and Type III. A chi-square test or t-test was employed to analyze the factors related to PSM. Multivariate regression analysis was conducted to determine the factors associated with PSM. Receiver operating characteristic curve analysis was used to calculate the area under the curve and evaluate the diagnostic performance of our model. Clinical decision curve analysis was performed to assess the clinical net benefit of EPE imaging classification, biopsy grade group (GG), and combined model. Results: Among the 114 patients, 58 had PSM, and 56 had negative surgical margins. Multivariate analysis showed that EPE imaging classification and biopsy GG were risk factors for PSM after laparoscopic radical prostatectomy. The areas under the curve for EPE imaging classification and biopsy GG were 0.677 and 0.712, respectively. The difference in predicting PSM between EPE imaging classification and biopsy GG was not statistically significant (P>0.05). However, when used in combination, the diagnostic efficiency significantly improved, with an increase in the area under the curve to 0.795 (P<0.05). The clinical decision curve analysis revealed that the clinical net benefit of the combined model was significantly higher than that of EPE imaging classification and biopsy GG. Conclusions: EPE imaging classification and biopsy GG were associated with PSM after laparoscopic radical prostatectomy, and their combination can significantly improve the accuracy of predicting PSM.

Construction of a streptavidin-based dual-localized DNAzyme walker for disease biomarker detection.

A dual-localized DNAzyme walker (dlDW) was constructed by utilizing multiple split DNAzymes with probes, and their substrates are separately localized on streptavidin and AuNPs, serving as walking pedals and tracks, respectively. Based on dlDW, biosensing platform was successfully constructed and showed great potential application in clinical disease diagnosis.

Molecular dynamics study on the physical compatibility of SEBS/plasticizer blend systems.

CONTEXT: Thermoplastic elastomer styrene-ethylene-butylene-styrene block copolymer (SEBS) has excellent mechanical properties and aging resistance, so it has good application prospects in thermoplastic solid propellants. The selection of plasticizer is one of the keys to the formulation design of thermoplastic solid propellant. The compatibility of the plasticizer with the polymer determines the plasticizer's ability to plasticize the polymer's molecular chain segments. Herein, the compatibility of four plasticizers with SEBS was investigated, and the results declared that the order of compatibility between SEBS and the four plasticizers is SEBS/WO > SEBS/DOS > SEBS/DEP > SEBS/TA. METHODS: Physical compatibility of SEBS binder with plasticizer triacetin (TA), diethyl phthalate (DEP), dioctyl sebacate (DOS), and 26# industrial white oil (WO) was simulated using molecular dynamics (MD) method via Materials Studio 8.0, and the simulation results were verified experimentally. The results showed that the compatibility of SEBS with these plasticizers can be comprehensively evaluated by analyzing solubility parameters, radial distribution functions, and blend miscibility simulations.

Deep learning with diffusion MRI as in vivo microscope reveals sex-related differences in human white matter microstructure.

Biological sex is a crucial variable in neuroscience studies where sex differences have been documented across cognitive functions and neuropsychiatric disorders. While gross statistical differences have been previously documented in macroscopic brain structure such as cortical thickness or region size, less is understood about sex-related cellular-level microstructural differences which could provide insight into brain health and disease. Studying these microstructural differences between men and women paves the way for understanding brain disorders and diseases that manifest differently in different sexes. Diffusion MRI is an important in vivo, non-invasive methodology that provides a window into brain tissue microstructure. Our study develops multiple end-to-end classification models that accurately estimates the sex of a subject using volumetric diffusion MRI data and uses these models to identify white matter regions that differ the most between men and women. 471 male and 560 female healthy subjects (age range, 22-37 years) from the Human Connectome Project are included. Fractional anisotropy, mean diffusivity and mean kurtosis are used to capture brain tissue microstructure characteristics. Diffusion parametric maps are registered to a standard template to reduce bias that can arise from macroscopic anatomical differences like brain size and contour. This study employ three major model architectures: 2D convolutional neural networks, 3D convolutional neural networks and Vision Transformer (with self-supervised pretraining). Our results show that all 3 models achieve high sex classification performance (test AUC 0.92-0.98) across all diffusion metrics indicating definitive differences in white matter tissue microstructure between males and females. We further use complementary model architectures to inform about the pattern of detected microstructural differences and the influence of short-range versus long-range interactions. Occlusion analysis together with Wilcoxon signed-rank test is used to determine which white matter regions contribute most to sex classification. The results indicate that sex-related differences manifest in both local features as well as global features / longer-distance interactions of tissue microstructure. Our highly consistent findings across models provides new insight supporting differences between male and female brain cellular-level tissue organization particularly in the central white matter.

Subject-Agnostic Transformer-Based Neural Speech Decoding from Surface and Depth Electrode Signals.

Objective: This study investigates speech decoding from neural signals captured by intracranial electrodes. Most prior works can only work with electrodes on a 2D grid (i.e., Electrocorticographic or ECoG array) and data from a single patient. We aim to design a deep-learning model architecture that can accommodate both surface (ECoG) and depth (stereotactic EEG or sEEG) electrodes. The architecture should allow training on data from multiple participants with large variability in electrode placements and the trained model should perform well on participants unseen during training. Approach: We propose a novel transformer-based model architecture named SwinTW that can work with arbitrarily positioned electrodes, by leveraging their 3D locations on the cortex rather than their positions on a 2D grid. We train both subject-specific models using data from a single participant as well as multi-patient models exploiting data from multiple participants. Main Results: The subject-specific models using only low-density 8x8 ECoG data achieved high decoding Pearson Correlation Coefficient with ground truth spectrogram (PCC=0.817), over N=43 participants, outperforming our prior convolutional ResNet model and the 3D Swin transformer model. Incorporating additional strip, depth, and grid electrodes available in each participant (N=39) led to further improvement (PCC=0.838). For participants with only sEEG electrodes (N=9), subject-specific models still enjoy comparable performance with an average PCC=0.798. The multi-subject models achieved high performance on unseen participants, with an average PCC=0.765 in leave-one-out cross-validation. Significance: The proposed SwinTW decoder enables future speech neuroprostheses to utilize any electrode placement that is clinically optimal or feasible for a particular participant, including using only depth electrodes, which are more routinely implanted in chronic neurosurgical procedures. Importantly, the generalizability of the multi-patient models suggests the exciting possibility of developing speech neuroprostheses for people with speech disability without relying on their own neural data for training, which is not always feasible.

Identification and functional analysis of the DOF gene family in Populus simonii: implications for development and stress response.

Background: Populus simonii, a notable native tree species in northern China, demonstrates impressive resistance to stress, broad adaptability, and exceptional hybridization potential. DOF family is a class of specific transcription factors that only exist in plants, widely participating in plant growth and development, and also playing an important role in abiotic stress response. To date, there have been no reported studies on the DOF gene family in P. simonii, and the expression levels of this gene family in different tissues of poplar, as well as its expression patterns under cold, heat, and other stress conditions, remain unclear. Methods: In this study, DOF gene family were identified from the P. simonii genome, and various bioinformatics data on the DOF gene family, gene structure, gene distribution, promoters and regulatory networks were analyzed. Quantitative real time PCR technology was used to verify the expression patterns of the DOF gene family in different poplar tissues. Results: This research initially pinpointed 41 PSDOF genes in P. simonii genome. The chromosomal localization results revealed that the PSDOF genes is unevenly distributed among 19 chromosomes, with the highest number of genes located on chromosomes 4, 5, and 11. A phylogenetic tree was constructed based on the homology between Arabidopsis thaliana and P. simonii, dividing the 41 PSDOF genes into seven subgroups. The expression patterns of PSDOF genes indicated that specific genes are consistently upregulated in various tissues and under different stress conditions, suggesting their pivotal involvement in both plant development and response to stress. Notably, PSDOF35 and PSDOF28 serve as pivotal hubs in the interaction network, playing a unique role in coordinating with other genes within the family. Conclusion: The analysis enhances our comprehension of the functions of the DOF gene family in tissue development and stress responses within P. simonii. These findings provide a foundation for future exploration into the biological roles of DOF gene family.

Moisture Power Generation: From Material Selection to Device Structure Optimization.

Moisture power generation (MPG) technology, producing clean and sustainable energy from a humid environment, has drawn significant attention and research efforts in recent years as a means of easing the energy crisis. Despite the rapid progress, MPG technology still faces numerous challenges with the most significant one being the low power-generating performance of individual MPG devices. In this review, we introduce the background and underlying principles of MPG technology while thoroughly explaining how the selection of suitable materials (carbons, polymers, inorganic salts, etc.) and the optimization of the device structure (pore structure, moisture gradient structure, functional group gradient structure, and electrode structure) can address the existing and anticipated challenges. Furthermore, this review highlights the major scientific and engineering hurdles on the way to advancing MPG technology and offers potential insights for the development of high-performance MPG systems.

Exploring the five-paced viper (Deinagkistrodon acutus) venom proteome by integrating a combinatorial peptide ligand library approach with shotgun LC-MS/MS

Snake venoms are complex mixtures of toxic proteins or peptides encoded by various gene families that function synergistically to incapacitate prey. In the present study, in order to unravel the proteomic repertoire of Deinagkistrodon acutus venom, some trace abundance components were analyzed. Methods Shotgun proteomic approach combined with shotgun nano-LC-ESI-MS/MS were employed to characterize the medically important D. acutus venom, after collected samples were enriched with the combinatorial peptide ligand library (CPLL). Results This avenue helped us find some trace components, undetected before, in D. acutus venom. The results indicated that D. acutus venom comprised 84 distinct proteins from 10 toxin families and 12 other proteins. These results are more than twice the number of venom components obtained from previous studies, which were only 29 distinct proteins obtained through RP-HPLC for the venom of the same species. The present results indicated that in D. acutus venom, the most abundant components (66.9%) included metalloproteinases, serine proteinases, and C-type lectin proteins; the medium abundant components (13%) comprised phospholipases A2 (PLA2) and 5'-nucleotidases and nucleases; whereas least abundant components (6%) were aminopeptidases, L-amino acid oxidases (LAAO), neurotoxins and disintegrins; and the trace components. The last were undetected before the use of conventional shotgun proteomics combined with shotgun nano-LC-ESI-MS/MS, such as cysteine-rich secretory proteins Da-CRPa, phospholipases B-like 1, phospholipases B (PLB), nerve growth factors (NGF), glutaminyl-peptide cyclortransferases (QC), and vascular non-inflammatory molecules 2 (VNN2). Conclusion These findings demonstrated that the CPLL enrichment method worked well in finding the trace toxin proteins in D. acutus venom, in contrast with the previous venomic characterization of D. acutus by conventional LC-MS/MS. In conclusion, this approach combined with the CPLL enrichment was effective for allowing us to explore the hidden D. acutus venomic profile and extended the list of potential venom toxins.(AU)

Exploring the five-paced viper (Deinagkistrodon acutus) venom proteome by integrating a combinatorial peptide ligand library approach with shotgun LC-MS/MS

Abstract Background Snake venoms are complex mixtures of toxic proteins or peptides encoded by various gene families that function synergistically to incapacitate prey. In the present study, in order to unravel the proteomic repertoire of Deinagkistrodon acutus venom, some trace abundance components were analyzed. Methods Shotgun proteomic approach combined with shotgun nano-LC-ESI-MS/MS were employed to characterize the medically important D. acutus venom, after collected samples were enriched with the combinatorial peptide ligand library (CPLL). Results This avenue helped us find some trace components, undetected before, in D. acutus venom. The results indicated that D. acutus venom comprised 84 distinct proteins from 10 toxin families and 12 other proteins. These results are more than twice the number of venom components obtained from previous studies, which were only 29 distinct proteins obtained through RP-HPLC for the venom of the same species. The present results indicated that in D. acutus venom, the most abundant components (66.9%) included metalloproteinases, serine proteinases, and C-type lectin proteins; the medium abundant components (13%) comprised phospholipases A2 (PLA2) and 5-nucleotidases and nucleases; whereas least abundant components (6%) were aminopeptidases, L-amino acid oxidases (LAAO), neurotoxins and disintegrins; and the trace components. The last were undetected before the use of conventional shotgun proteomics combined with shotgun nano-LC-ESI-MS/MS, such as cysteine-rich secretory proteins Da-CRPa, phospholipases B-like 1, phospholipases B (PLB), nerve growth factors (NGF), glutaminyl-peptide cyclortransferases (QC), and vascular non-inflammatory molecules 2 (VNN2). Conclusion These findings demonstrated that the CPLL enrichment method worked well in finding the trace toxin proteins in D. acutus venom, in contrast with the previous venomic characterization of D. acutus by conventional LC-MS/MS. In conclusion, this approach combined with the CPLL enrichment was effective for allowing us to explore the hidden D. acutus venomic profile and extended the list of potential venom toxins.