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Helicobacter pylori Detection Based on Synergistic Electromagnetic and Chemical Enhancement of Surface-Enhanced Raman Scattering in 3D Hotspot-Activated Gold Nanorods/Nano Mica Platelets/ZnO Quantum Dots

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ADVANCED SCIENCE 2025年 e2503562页

作者： Lu, Ming-Chang Yang, Yung-Chi Lee, Chia-Jung Chiu, Chih-Wei Natl Taiwan Univ Sci & Technol Dept Mat Sci & Engn Taipei 10607 Taiwan Taipei Med Univ Coll Pharm PhD Program Clin Drug Dev Herbal Med Taipei 11031 Taiwan

Gold nanorods (AuNRs) with a controllable aspect ratio are anchored on the surface of delaminated nano mica platelets (NMPs) in the presence of a cationic interfacial activator and protective agent enabling the positive charging of the AuNR and nanohybrid surfaces. The high anionic charge and specific surface area of NMPs stabilize AuNR growth and benefit the adsorption of anionic analytes. The nanohybrids (AuNRs/NMPs) exhibit a 3D hotspot effect due to self-assembly and feature regularly arranged AuNRs, thus enabling Raman signal enhancement and sensitive (limit of detection (LOD) = 10-9 m, Raman enhancement factor (EF) = 2.0 x 108) and reproducible (relative standard deviation (RSD) = 8.82%) adenine detection based on surface-enhanced Raman scattering (SERS). The further incorporation of ZnO quantum dots (QDs) affords nanohybrids (AuNRs/NMPs/ZnO QDs) that exhibit electromagnetic and chemical signal enhancement mechanisms and enable more sensitive and reproducible adenine detection (LOD = 10-10 m, EF = 1.6 x 109, RSD = 7.66%). AuNRs/NMPs/ZnO QDs are subsequently used for the selective and sensitive SERS-based detection of Helicobacter pylori (LOD = 90 CFU mL-1). Thus, this work paves the way for the noninvasive, nonfluorescent labeling, rapid, sensitive, selective, and reproducible detection of H. pylori.

关键词： gold nanorods Helicobacter pylori nano mica platelets surface-enhanced Raman scattering zinc oxide quantum dots

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Interpreting forces as deep learning gradients improves quality of predicted protein structures

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BIOPHYSICAL JOURNAL 2024年第17期123卷 2730-2739页

作者： King, Jonathan Edward Koes, David Ryan Univ Pittsburgh Carnegie Mellon Univ Joint PhD Program Computat Biol Pittsburgh PA USA Univ Pittsburgh Computat & Syst Biol Pittsburgh PA 15260 USA

Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a combination of increased accuracy and physical intuition. We propose a new method to train deep learning protein structure prediction models using molecular dynamics force fields to work toward these goals. Our custom PyTorch loss function, OpenMM-Loss, represents the potential energy of a predicted structure. OpenMM-Loss can be applied to any all-atom representation of a protein structure capable of mapping into our software package, SidechainNet. We demonstrate our method's efficacy by finetuning OpenFold. We show that subsequently predicted protein structures, both before and after a relaxation procedure, exhibit comparable accuracy while displaying lower potential energy and improved structural quality as assessed by MolProbity metrics.

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The serine protease DPP9 and the redox sensor KEAP1 form a mutually inhibitory complex

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JOURNAL OF BIOLOGICAL CHEMISTRY 2025年第1期301卷 108034页

作者： Tsamouri, Lydia P. Hsiao, Jeffrey C. Bachovchin, Daniel A. Mem Sloan Kettering Canc Ctr Weill Cornell Grad Sch Med Sci Pharmacol Program New York NY 10065 USA Mem Sloan Kettering Canc Ctr Chem Biol Program New York NY 10065 USA Mem Sloan Kettering Canc Ctr Triinst PhD Program Chem Biol New York NY 10065 USA

Synthetic inhibitors of the serine protease DPP9 activate the related NLRP1 and CARD8 inflammasomes and stimulate powerful innate immune responses. Thus, it seems plausible that a biomolecule similarly inhibits DPP9 and triggers inflammasome activation during infection, but one has not yet been discovered. Here, we wanted to identify and characterize DPP9-binding proteins to potentially uncover physiologically relevant mechanisms that control DPP9's activity. Notably, we found that the redox sensor protein KEAP1 binds to DPP9 in an inactive conformation and stabilizes this non-native fold. At the same time, this inactive form of DPP9 reciprocally inhibits the ability of KEAP1 to bind to and degrade the transcription factor NRF2, thereby inducing an antioxidant response. Although we discovered several experimental conditions, for example new protein expression and chemical denaturation, that force DPP9 out of its folded dimeric state and into a KEAP1-binding state, the key danger-related stimulus that causes this critical DPP9 conformational change is not yet known. Regardless, our data now reveal that an endogenous DPP9 inhibition mechanism does in fact exist, and moreover that DPP9, like the other NLRP1 regulator thioredoxin-1, is directly coupled to the intracellular redox potential. Overall, we expect this work will provide the foundation to discover additional biomolecules that regulate DPP9's activity, the DPP9-KEAP1 interaction, the intracellular redox environment, and the NLRP1 and CARD8 inflammasomes.

关键词： DPP9 KEAP1 inhibition NRF2 protease inflammasome redox NLRP1 CARD8

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Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

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Neural Regeneration Research 2024年第4期19卷 769-773页

作者： Mario Daidone Sergio Ferrantelli Antonino Tuttolomondo Internal Medicine and Stroke Care Ward Department of Health PromotionMother and Child CareInternal Medicine and Medical SpecialtiesUniversity of Palermo Molecular and Clinical Medicine PhD Program University of Palermo

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient *** recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine *** abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke *** introduced machine learning algorithms have been extensively employed in all the fields of stroke *** learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient *** the tremendous potential of machine learning in stroke medicine,several challenges must be *** include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and *** addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke *** learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved *** research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of *** review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic *** the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling dis

关键词： cerebrovascular disease deep learning machine learning reinforcement learning stroke stroke therapy supervised learning unsupervised learning

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The Health Belief Model in the Context of Alcohol Protective Behavioral Strategies

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PSYCHIATRY-INTERPERSONAL AND BIOLOGICAL PROCESSES 2023年第1期86卷 1-16页

作者： De Leon, Ardhys N. Peterson, Roselyn Dvorak, Robert D. Leary, Angelina V. Kramer, Matthew P. Burr, Emily K. Toth, Ethan M. Pinto, Daniel Univ Cent Florida Clin Psychol PhD Program Orlando FL USA Univ South Dakota Clin Psychol Vermillion SD USA

Objective: Alcohol use continues to be prevalent and problematic among young adult samples. Protective behavioral strategies (PBS), which are harm reduction strategies utilized while drinking, have been linked to decreased alcohol use and subsequent alcohol-related problems. An individual's likelihood of adopting PBS and other health behaviors, according to The Health Belief Model (HBM), is dependent on perceived susceptibility to and severity of adverse health outcomes, as well as perceived benefits and barriers related to implementing those behaviors. The present study examined whether the perceived effectiveness of PBS in the context of the HBM leads to an increase in PBS use. Method: The analytic sample (n =694 college students, M-age =20.21, SD =4.37, 63.26% female, 72.05% Caucasian) self-reported demographics, weekly alcohol consumption (i.e., frequency, intensity, and quantity), alcohol-related problems, use of PBS, and perceived effectiveness of PBS use. A latent variable model was used to test the effect of perceived PBS effectiveness on PBS use, alcohol consumption, and alcohol-related problems. Results: Perceived PBS effectiveness was associated with a higher likelihood of using PBS subtypes (Manner of Drinking, Stopping/Limiting Drinking, and Serious Harm Reduction), which in turn was associated with reductions in alcohol consumption and problems. Conclusions: These findings suggest that increasing perceptions of PBS effectiveness may lead to more PBS use, decreased alcohol consumption, and fewer alcohol-related problems. Future research could implement longitudinal methodology to assess attempts to increase perceived effectiveness of PBS use and potentially establish a causal link between these perceptions, PBS use, and alcohol-related outcomes.

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G protein-coupled receptoretargeted proteolysis-targeting chimeras in cancer therapeutics

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MOLECULAR PHARMACOLOGY 2025年第2期107卷 100013页

作者： Saca, Victoria R. Huber, Thomas Sakmar, Thomas P. Rockefeller Univ Lab Chem Biol & Signal Transduct 1230 York Ave New York NY 10065 USA Triinst PhD Program Chem Biol New York NY USA

G protein-coupled receptors (GPCRs) comprise a family of heptahelical membrane proteins that mediate intracellular and intercellular transmembrane signaling. Defects in GPCR signaling pathways are implicated in the pathophysiology of many diseases, including cardiovascular disease, endocrinopathies, immune disorders, and cancer. Although GPCRs are attractive drug targets, only a small number of Food and Drug Administration-approved anticancer therapeutics target GPCRs. Targeted protein degradation (TPD) technology allows for the direct modulation of the cellular expression level of a protein of interest. TPD methods such as proteolysis-targeting chimeras (PROTACs) use the ubiquitin-proteasome system to degrade a protein of interest selectively. Although the PROTAC system has not been widely applied to GPCRs and other membrane proteins, there is evidence that PROTACs or other TPD methods could be applied to the GPCRome. Current GPCR PROTACs show the feasibility of using PROTACs to degrade GPCRs;however, the degradation mechanism for some of these GPCR PROTACs is uncertain. Additional studies aimed at elucidating the degradation mechanism of GPCRs with PROTACs are necessary. Discovery of new allosteric intracellular small molecule binders of GPCRs will be required for the development of intracellularly oriented PROTACs. Promising early results in targeted degradation of GPCRs suggest that TPD drug discovery platforms will be useful in developing PROTACs targeting pathological GPCRs. Significance Statement: Aberrant signaling of G protein-coupled receptors (GPCRs) can contribute to the pathophysiology of cancer. Although GPCRs are generally highly attractive drug targets, many individual GPCRs are currently undrugged using traditional drug discovery approaches. Targeted protein degradation technologies, such as proteolysis-targeting chimeras, provide a new approach to drug discovery for targeting previously undruggable GPCRs relevant to the molecular pathophysiology of

关键词： Proteolysis-targeting chimeras G protein-coupled receptor Membrane receptor Therapeutic small molecule Pharmacology

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Tuning the ¹O2 Oxidation of a Phenol at the Air/Solid Interface of a Nanoparticle: Hydrophobic Surface Increases Oxophilicity

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LANGMUIR 2023年第31期39卷 11134-11144页

作者： Durantini, Andreïs M. Lapoot, Lloyd Jabeen, Shakeela Ghosh, Goutam Bipu, Johirul Essang, Serah Singh, Britney C. Greer, Alexander Brooklyn Coll Dept Chem Brooklyn NY 11210 USA CUNY PhD Program Chem Grad Ctr New York NY 10016 USA Univ Nacl Rio Cuarto Fac Ciencias Exactas Fis Quim & Nat Dept Quim IDAS CONICET Cordoba Argentina CUNY PhD Program Biochem Grad Ctr New York NY 10016 USA

Although silica surfaces have been used in organic oxidationsforthe production of peroxides, studies of airborne singlet oxygen atinterfaces are limited and have not found widespread advantages. Here,with prenyl phenol-coated silica and delivery of singlet oxygen (O-1(2)) through the gas phase, we uncover significantselectivity for dihydrofuran formation over allylic hydroperoxideformation. The hydrophobic particle causes prenyl phenol to producean iso-hydroperoxide intermediate with an internallyprotonated oxygen atom, which leads to dihydrofuran formation as wellas O atom transfer. In contrast, hydrophilic particles cause prenylphenol to produce allylic hydroperoxide, due to phenol OH hydrogenbonding with SiOH surface groups. Mechanistic insight is providedby air/nanoparticle interfaces coated with the prenyl phenol, in whichproduct yield was 6-fold greater on the hydrophobic nanoparticlescompared to the hydrophilic nanoparticles and total rate constants(ASI-k (T)) of O-1(2) were13-fold greater on the hydrophobic vs hydrophilic nanoparticles. Aslope intersection method was also developed that uses the airborne O-1(2) lifetime (& tau;(airborne)) and surface-associated O-1(2) lifetime (& tau;(surf)) to quantitate O-1(2) transitioning from volatile to non-volatileand surface boundary (surface & BULL;& BULL;& BULL;O-1(2)). Further mechanistic insights on the selectivity of the reactionof prenyl phenol with O-1(2) was provided by densityfunctional theory calculations.

关键词： Phenols

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Structural basis for the recognition of the bacterial tyrosine kinase Wzc by its cognate tyrosine phosphatase Wzb

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2022年第26期119卷 e2201800119-e2201800119页

作者： Alphonse, Sebastien Djemil, Imane Piserchio, Andrea Ghose, Ranajeet CUNY City Coll Dept Chem & Biochem New York NY 10031 USA CUNY Grad Ctr PhD Program Biochem New York NY 10016 USA CUNY Grad Ctr PhD Program Chem New York NY 10016 USA CUNY Grad Ctr PhD Program Phys New York NY 10016 USA

Bacterial tyrosine kinases (BY-kinases) comprise a family of protein tyrosine kinases that are structurally distinct from their functional counterparts in eukaryotes and are highly conserved across the bacterial kingdom. BY-kinases act in concert with their counteracting phosphatases to regulate a variety of cellular processes, most notably the synthesis and export of polysaccharides involved in biofilm and capsule biogenesis. Biochemical data suggest that BY-kinase function involves the cyclic assembly and disassembly of oligomeric states coupled to the overall phosphorylation levels of a C-terminal tyrosine cluster. This process is driven by the opposing effects of intermolecular autophosphorylation, and dephosphorylation catalyzed by tyrosine phosphatases. In the absence of structural insight into the interactions between a BY-kinase and its phosphatase partner in atomic detail, the precise mechanism of this regulatory process has remained poorly defined. To address this gap in knowledge, we have determined the structure of the transiently assembled complex between the catalytic core of the Escherichia coli (K-12) BY-kinase Wzc and its counteracting low-molecular weight protein tyrosine phosphatase (LMW-PTP) Wzb using solution NMR techniques Unambiguous distance restraints from paramagnetic relaxation effects were supplemented with ambiguous interaction restraints from static spectral perturbations and transient chemical shift changes inferred from relaxation dispersion measurements and used in a computational docking protocol for structure determination. This structure presents an atomic picture of the mode of interaction between an LMW-PTP and its BY-kinase substrate, and provides mechanistic insight into the phosphorylation-coupled assembly/disassembly process proposed to drive BY-kinase function.

关键词： bacterial tyrosine kinase low-molecular weight protein tyrosine phosphatase solution NMR relaxation dispersion paramagnetic relaxation enhancement

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Schistosoma mansoni soluble egg antigen suppresses colorectal cancer growth in vitro and in vivo

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JOURNAL OF MICROBIOLOGY IMMUNOLOGY AND INFECTION 2025年第2期58卷 241-250页

作者： Lam, Ho Yin Pekkle Liang, Ting-Ruei Jiang, Shinn-Jong Peng, Shih-Yi Tzu Chi Univ Coll Med Dept Biochem 701 Zhongyang RdSec 3 Hualien 970 Taiwan Tzu Chi Univ PhD Program Pharmacol & Toxicol Hualien Taiwan

Background: Colorectal cancer (CRC) is the third most common malignant disease around the world. Because the hosts' immunity plays a great part in regulating tumor cells' growth and progression, immunotherapies have therefore aroused great interest in treating cancers. Currently, scientists have investigated the use of Schistosoma- derived soluble egg antigens (SEA), which is known as a strong immune modulator, in treating a series of immune-related diseases. Methods: In this study, we investigated the anti-tumor effect of SEA against CRC using in vitro cell lines, HCT-116 and DLD-1, as well as in vivo mouse xenograft model. Approaches such as migration assay, invasion assay, and western blotting were done to analyze the anti-tumor effect of SEA. Furthermore, qRT-PCR and ELISA were performed to identify the immune profile of SEA-treated cells as well as SEA-treated xenograft mice. Results: In vitro studies suggested that SEA can dose-dependently inhibit the growth and progression of HCT-116 and DLD-1 cells. This inhibition was accompanied by a reduction of epithelial-mesenchymal transition (EMT), inflammasome inactivation, and apoptosis. SEA also downregulated the expression of IL-4 and IL-10 in the CRC cells, which may be the reason why their growth and progression were suppressed. In vivo studies showed a similar beneficial effect of SEA, as local administration of 25 mu g SEA significantly inhibits tumor cell growth. SEA treatment also shifts the host's immunity from a pro-tumorigenic response to an anti-tumor response. Conclusion: In conclusion, SEA may provide a beneficial effect against CRC, and further investigation may give promise in CRC treatment.

关键词： Colorectal cancer Schistosoma Soluble egg antigens Immunotherapies

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GTL regression: a linear model with skewed and thick-tailed disturbances

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2023年第6期52卷 2290-2309页

作者： Vijverberg, Wim Hasebe, Takuya CUNY Grad Ctr PhD Program Econ New York NY 10021 USA IZA Bonn Germany Sophia Univ Fac Liberal Arts Tokyo Japan

A maximum likelihood estimator of a linear regression model is efficient relative to the customary Ordinary Least Squares (OLS) estimator when disturbances are skewed and/or thick-tailed. In order to model skewed and thick-tailed disturbances, we specify a highly flexible Generalized Tukey Lambda (GTL) distribution that can closely mimic many other unimodal distributions. The GTL-based maximum likelihood regression estimator is consistent and asymptotically normal. A Monte Carlo study demonstrates the potential gains of this GTL-based estimator over the OLS estimator, and as a real-life application, an analysis of speeding tickets illustrates how GTL regression might modify standard OLS estimation results. For the applied data analyst, an LM test statistic is suggested as a straightforward post-estimation diagnostic of whether the standard OLS regression approach is suitable for the data at hand. Stata do-files are provided to perform the OLS post-estimation LM test and to implement GTL regression models.

关键词： Generalized Tukey lambda distribution Linear regression Robust estimation

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