In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
To explore the formation mechanism of anisotropy in Ti-6Al-4V alloy fabricated by selective laser melting(SLM),the compressive mechanical properties,microhardness,microstructure,and crystallographic orientation of the...To explore the formation mechanism of anisotropy in Ti-6Al-4V alloy fabricated by selective laser melting(SLM),the compressive mechanical properties,microhardness,microstructure,and crystallographic orientation of the alloy across different planes were investigated.The anisotropy of SLM-fabricated Ti-6Al-4V alloys was analyzed,and the electron backscatter diffraction technique was used to investigate the influence of different grain types and orientations on the stress-strain distribution at various scales.Results reveal that in room-temperature compression tests at a strain rate of 10^(-3) s^(-1),both the compressive yield strength and microhardness vary along the deposition direction,indicating a certain degree of mechanical property anisotropy.The alloy exhibits a columnar microstructure;along the deposition direction,the grains appear equiaxed,and they have internal hexagonal close-packed(hcp)α/α'martensitic structure.α'phase has a preferential orientation approximately along the<0001>direction.Anisotropy arises from the high aspect ratio of columnar grains,along with the weak texture of the microstructure and low symmetry of the hcp crystal structure.展开更多
Herein,antibacterial silver‑doped fluorescent carbon dots(Ag‑CDs)were synthesized through a stepwise hydrothermal method,with polyethyleneimine(PEI),citric acid(CA),and silver nitrate(AgNO3)serving as precursors.The a...Herein,antibacterial silver‑doped fluorescent carbon dots(Ag‑CDs)were synthesized through a stepwise hydrothermal method,with polyethyleneimine(PEI),citric acid(CA),and silver nitrate(AgNO3)serving as precursors.The applicability and antimicrobial efficacy of these nanomaterials were systematically investigated for metal ion sensing.Experimental evidence demonstrated that the Ag‑CDs exhibited a pronounced fluorescence quenching response toward ferric ions(Fe^(3+)),enabling their quantitative determination via a linear concentration‑dependent relationship.These Ag‑CDs exhibited significant inhibitory effects on biofilm growth and disruption for both Escherichia coli and Staphylococcus aureus.Mechanism investigations indicate that Ag‑CDs induced the death of Escherichia coli and Pseudomonas aeruginosa by disrupting their bacterial morphology and structure,triggering the generation of intracellular reactive oxygen species(ROS),and impairing their antioxidant defense system.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
BACKGROUND Post-stroke depression(PSD)is associated with hypothalamic-pituitary-adrenal(HPA)axis dysfunction and neurotransmitter deficits.Selective serotonin reuptake inhibitors(SSRIs)are commonly used,but their effi...BACKGROUND Post-stroke depression(PSD)is associated with hypothalamic-pituitary-adrenal(HPA)axis dysfunction and neurotransmitter deficits.Selective serotonin reuptake inhibitors(SSRIs)are commonly used,but their efficacy is limited.This study investigated whether combining SSRIs with traditional Chinese medicine(TCM)Free San could enhance their therapeutic effects.AIM To evaluate the clinical efficacy and safety of combining SSRIs with Free San in treating PSD,and to assess its impact on HPA axis function.METHODS Ninety-two patients with PSD were enrolled and randomly divided into control groups(n=46)and study groups(n=46).The control group received the SSRI paroxetine alone,whereas the study group received paroxetine combined with Free San for 4 weeks.Hamilton Depression Scale and TCM syndrome scores were assessed before and after treatment.Serum serotonin,norepinephrine,cortisol,cor-ticotropin-releasing hormone,and adrenocorticotropic hormone were measured.The treatment responses and adverse reactions were recorded.RESULTS After treatment,the Hamilton Depression Scale and TCM syndrome scores were significantly lower in the study group than in the control group(P<0.05).Serum serotonin and norepinephrine levels were significantly higher in the study group than in the control group,whereas cortisol,corticotropin-releasing hormone,and adrenocorticotropic hormone levels were significantly lower(P<0.05).The total efficacy rates were 84.78%and 65.22%in the study and control groups,respectively(P<0.05).No significant differences in adverse reactions were observed between the two groups(P>0.05).CONCLUSION Combining SSRIs with Free San can enhance therapeutic efficacy,improve depressive symptoms,and regulate HPA axis function in patients with PSD with good safety and clinical application value.展开更多
GEMIN5 is a predominantly cytoplasmic multifunctional protein, known to be involved in recognizing snRNAs through its WD40 repeats domain placed at the N-terminus. A dimerization domain in the middle region acts as a ...GEMIN5 is a predominantly cytoplasmic multifunctional protein, known to be involved in recognizing snRNAs through its WD40 repeats domain placed at the N-terminus. A dimerization domain in the middle region acts as a hub for protein–protein interaction, while a non-canonical RNA-binding site is placed towards the C-terminus. The singular organization of structural domains present in GEMIN5 enables this protein to perform multiple functions through its ability to interact with distinct partners, both RNAs and proteins. This protein exerts a different role in translation regulation depending on its physiological state, such that while GEMIN5 down-regulates global RNA translation, the C-terminal half of the protein promotes translation of its mRNA. Additionally, GEMIN5 is responsible for the preferential partitioning of mRNAs into polysomes. Besides selective translation, GEMIN5 forms part of distinct ribonucleoprotein complexes, reflecting the dynamic organization of macromolecular complexes in response to internal and external signals. In accordance with its contribution to fundamental cellular processes, recent reports described clinical loss of function mutants suggesting that GEMIN5 deficiency is detrimental to cell growth and survival. Remarkably, patients carrying GEMIN5 biallelic variants suffer from neurodevelopmental delay, hypotonia, and cerebellar ataxia. Molecular analyses of individual variants, which are defective in protein dimerization, display decreased levels of ribosome association, reinforcing the involvement of the protein in translation regulation. Importantly, the number of clinical variants and the phenotypic spectrum associated with GEMIN5 disorders is increasing as the knowledge of the protein functions and the pathways linked to its activity augments. Here we discuss relevant advances concerning the functional and structural features of GEMIN5 and its separate domains in RNA-binding, protein interactome, and translation regulation, and how these data can help to understand the involvement of protein malfunction in clinical variants found in patients developing neurodevelopmental disorders.展开更多
Electrocatalytic nitrate reduction reaction(NO3RR)represents a sustainable and environmentally benign route for ammonia(NH3)synthesis.However,NO3RR is still limited by the competition from hydrogen evolution reaction(...Electrocatalytic nitrate reduction reaction(NO3RR)represents a sustainable and environmentally benign route for ammonia(NH3)synthesis.However,NO3RR is still limited by the competition from hydrogen evolution reaction(HER)and the high energy barrier in the hydrogenation step of nitrogen-containing intermediates.Here,we report a selective etching strategy to construct Ru M nanoalloys(M=Fe,Co,Ni,Cu)uniformly dispersed on porous nitrogen-doped carbon substrates for efficient neutral NH3electrosynthesis.Density functional theory calculations confirm that the synergic effect between Ru and transition metal M modulates the electronic structure of the alloy,significantly lowering the energy barrier for the conversion of*NO_(2)to*HNO_(2).Experimentally,the optimized Ru Fe-NC catalyst achieves 100%Faraday efficiency with a high yield rate of 0.83 mg h^(-1)mg^(-1)catat a low potential of-0.1 V vs.RHE,outperforming most reported catalysts.In situ spectroscopic analyses further demonstrate that the Ru M-NC effectively promotes the hydrogenation of nitrogen intermediates while inhibiting the formation of hydrogen radicals,thereby reducing HER competition.The Ru FeNC assembled Zn-NO_(3)^(-)battery achieved a high open-circuit voltage and an outstanding power density and capacity,which drive selective NO_(3)^(-)conversion to NH3.This work provides a powerful synergistic design strategy for efficient NH3electrosynthesis and a general framework for the development of advanced multi-component catalysts for sustainable nitrogen conversion.展开更多
Selective laser melting(SLM),as an additive manufacturing technology,has garnered widespread attention for its capability to fabricate components with complex geometries and to tailor the microstructure and mechanical...Selective laser melting(SLM),as an additive manufacturing technology,has garnered widespread attention for its capability to fabricate components with complex geometries and to tailor the microstructure and mechanical properties under specific conditions.However,the intrinsic influence mechanism of microstructure formation under non-equilibrium solidification conditions in SLM processes has not been clearly revealed.In the present work,the influence of Al concentration and process parameters on the microstructure forming mechanism of Al_(x)CoCrFeNi HEAs prepared by SLM is investigated by molecular dynamics simulation method.The simulation results show that the difference in Al content significantly affects the microstructure formation of HEAs,including the growth rate and morphology of columnar crystals,stress distribution at grain boundaries,and defect structure.In addition,the results show that increasing the substrate temperature improves the solidification formability,reduces microstructural defects,and helps reduce residual stress in Al_(x)CoCrFeNi HEAs.By analyzing the influence of heat and solute flow in the molten pool on the growth of columnar crystals,it is found that spatial fluctuations in Al concentration during the non-equilibrium solidification process inhibit the high cooling rates induced by steep temperature gradients.These findings promote the understanding of the forming mechanism of microstructure in HEAs prepared by SLM and provide theoretical guidance for designing high-performance SLM-fabricated HEAs.展开更多
Developing biomass platform compounds into high value-added chemicals is a key step in renewable resource utilization.Herein,we report porous carbon-supported Ni-ZnO nanoparticles catalyst(Ni-ZnO/AC)synthesized via lo...Developing biomass platform compounds into high value-added chemicals is a key step in renewable resource utilization.Herein,we report porous carbon-supported Ni-ZnO nanoparticles catalyst(Ni-ZnO/AC)synthesized via low-temperature coprecipitation,exhibiting excellent performance for the selective hydrogenation of 5-hydroxymethylfurfural(HMF).A linear correlation is first observed between solvent polarity(E_(T)(30))and product selectivity within both polar aprotic and protic solvent classes,suggesting that solvent properties play a vital role in directing reaction pathways.Among these,1,4-dioxane(aprotic)favors the formation of 2,5-bis(hydroxymethyl)furan(BHMF)with 97.5%selectivity,while isopropanol(iPrOH,protic)promotes 2,5-dimethylfuran production with up to 99.5%selectivity.Mechanistic investigations further reveal that beyond polarity,proton-donating ability is critical in facilitating hydrodeoxygenation.iPrOH enables a hydrogen shuttle mechanism where protons assist in hydroxyl group removal,lowering the activation barrier.In contrast,1,4-dioxane,lacking hydrogen bond donors,stabilizes BHMF and hinders further conversion.Density functional theory calculations confirm a lower activation energy in iPrOH(0.60 eV)compared to 1,4-dioxane(1.07 eV).This work offers mechanistic insights and a practical strategy for solvent-mediated control of product selectivity in biomass hydrogenation,highlighting the decisive role of solvent-catalyst-substrate interactions.展开更多
Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic...Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.展开更多
Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(...Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(2DOF)modal pushover-based scaling procedure(2DMPS)has been developed for a nonlinear dynamic analysis of asymmetric in-plan buildings.The 2DMPS procedure scales ground motions to approach close enough to a target value of the inelastic displacement of the first-mode inelastic 2DOF modal stick,extended for structures with significant contributions of higher modes.Further,4-,6-and 13-story RC SMRF buildings were selected for analyses using ground motion records scaled by the 2DMPS procedure,the modal pushover-based scaling method(MPS),and ASCE/SEI 7-16 scaling procedures.The median values of EDPs on scaled records closely matched the benchmark results.The bias in the EDP values due to the scaled records in every group regarding their median value was lower than the dispersion of the 21 unscaled records.These results generally demonstrate the accuracy and efficiency of the 2DMPS method.Additionally,the 2DOF modal stick’s inelastic response spectra are better suited for calculating seismic demands for one-way asymmetric-plan structures than the SDOF inelastic response spectra.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Translation is a crucial step in gene expression.Over the past decade,the development and application of ribosome profiling(Ribo-seq)have significantly advanced our understanding of translational regulation in vivo.Ho...Translation is a crucial step in gene expression.Over the past decade,the development and application of ribosome profiling(Ribo-seq)have significantly advanced our understanding of translational regulation in vivo.However,the analysis and visualization of Ribo-seq data remain challenging.Despite the availability of various analytical pipelines,improvements in comprehensiveness,accuracy,and user-friendliness are still necessary.In this study,we develop RiboParser/RiboShiny,a robust framework for analyzing and visualizing Ribo-seq data.Building on published methods,we optimize ribosome structure-based and start/stopbased models to improve the accuracy and stability of P-site detection,even in species with a high proportion of leaderless transcripts.Leveraging these improvements,RiboParser offers comprehensive analyses,including quality control,gene-level analysis,codon-level analysis,and the analysis of Ribo-seq variants.Meanwhile,RiboShiny provides a user-friendly and adaptable platform for data visualization,facilitating deeper insights into the translational landscape.Furthermore,the integration of standardized genome annotation renders our platform universally applicable to various organisms with sequenced genomes.This framework has the potential to significantly improve the precision and efficiency of Ribo-seq data interpretation,thereby deepening our understanding of translational regulation.展开更多
Lithium-oxygen(Li-O2)batteries are perceived as a promising breakthrough in sustainable electrochemical energy storage,utilizing ambient air as an energy source,eliminating the need for costly cathode materials,and of...Lithium-oxygen(Li-O2)batteries are perceived as a promising breakthrough in sustainable electrochemical energy storage,utilizing ambient air as an energy source,eliminating the need for costly cathode materials,and offering the highest theoretical energy density(~3.5 k Wh kg^(-1))among discussed candidates.Contributing to the poor cycle life of currently reported Li-O_(2)cells is singlet oxygen(1O_(2))formation,inducing parasitic reactions,degrading key components,and severely deteriorating cell performance.Here,we harness the chirality-induced spin selectivity effect of chiral cobalt oxide nanosheets(Co_(3)O_(4)NSs)as cathode materials to suppress 1O_(2)in Li-O_(2)batteries for the first time.Operando photoluminescence spectroscopy reveals a 3.7-fold and 3.23-fold reduction in 1O_(2)during discharge and charge,respectively,compared to conventional carbon paperbased cells,consistent with differential electrochemical mass spectrometry results,which indicate a near-theoretical charge-to-O_(2)ratio(2.04 e-/O_(2)).Density functional theory calculations demonstrate that chirality induces a peak shift near the Fermi level,enhancing Co 3d-O 2p hybridization,stabilizing reaction intermediates,and lowering activation barriers for Li_(2)O_(2)formation and decomposition.These findings establish a new strategy for improving the stability and energy efficiency of sustainable Li-O_(2)batteries,abridging the current gap to commercialization.展开更多
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal...Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.展开更多
The endoplasmic reticulum,a key cellular organelle,regulates a wide variety of cellular activities.Endoplasmic reticulum autophagy,one of the quality control systems of the endoplasmic reticulum,plays a pivotal role i...The endoplasmic reticulum,a key cellular organelle,regulates a wide variety of cellular activities.Endoplasmic reticulum autophagy,one of the quality control systems of the endoplasmic reticulum,plays a pivotal role in maintaining endoplasmic reticulum homeostasis by controlling endoplasmic reticulum turnover,remodeling,and proteostasis.In this review,we briefly describe the endoplasmic reticulum quality control system,and subsequently focus on the role of endoplasmic reticulum autophagy,emphasizing the spatial and temporal mechanisms underlying the regulation of endoplasmic reticulum autophagy according to cellular requirements.We also summarize the evidence relating to how defective or abnormal endoplasmic reticulum autophagy contributes to the pathogenesis of neurodegenerative diseases.In summary,this review highlights the mechanisms associated with the regulation of endoplasmic reticulum autophagy and how they influence the pathophysiology of degenerative nerve disorders.This review would help researchers to understand the roles and regulatory mechanisms of endoplasmic reticulum-phagy in neurodegenerative disorders.展开更多
Heat shock protein family B(small)member 8(HSPB8)is a 22 kDa ubiquitously expressed protein belonging to the family of small heat shock proteins.HSPB8 is involved in various cellular mechanisms mainly related to prote...Heat shock protein family B(small)member 8(HSPB8)is a 22 kDa ubiquitously expressed protein belonging to the family of small heat shock proteins.HSPB8 is involved in various cellular mechanisms mainly related to proteotoxic stress response and in other processes such as inflammation,cell division,and migration.HSPB8 binds misfolded clients to prevent their aggregation by assisting protein refolding or degradation through chaperone-assisted selective autophagy.In line with this function,the pro-degradative activity of HSPB8 has been found protective in several neurodegenerative and neuromuscular diseases characterized by protein misfolding and aggregation.In cancer,HSPB8 has a dual role being capable of exerting either a pro-or an anti-tumoral activity depending on the pathways and factors expressed by the model of cancer under investigation.Moreover,HSPB8 exerts a protective function in different diseases by modulating the inflammatory response,which characterizes not only neurodegenerative diseases,but also other chronic or acute conditions affecting the nervous system,such as multiple sclerosis and intracerebellar hemorrhage.Of note,HSPB8 modulation may represent a therapeutic approach in other neurological conditions that develop as a secondary consequence of other diseases.This is the case of cognitive impairment related to diabetes mellitus,in which HSPB8 exerts a protective activity by assuring mitochondrial homeostasis.This review aims to summarize the diverse and multiple functions of HSPB8 in different pathological conditions,focusing on the beneficial effects of its modulation.Drug-based and alternative therapeutic approaches targeting HSPB8 and its regulated pathways will be discussed,emphasizing how new strategies for cell and tissue-specific delivery represent an avenue to advance in disease treatments.展开更多
Clear aligner treatment is a novel technique in current orthodontic practice.Distinct from traditional fixed orthodontic appliances,clear aligners have different material features and biomechanical characteristics and...Clear aligner treatment is a novel technique in current orthodontic practice.Distinct from traditional fixed orthodontic appliances,clear aligners have different material features and biomechanical characteristics and treatment efficiencies,presenting new clinical challenges.Therefore,a comprehensive and systematic description of the key clinical aspects of clear aligner treatment is essential to enhance treatment efficacy and facilitate the advancement and wide adoption of this new technique.This expert consensus discusses case selection and grading of treatment difficulty,principle of clear aligner therapy,clinical procedures and potential complications,which are crucial to the clinical success of clear aligner treatment.展开更多
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
基金National Natural Science Foundation of China(51504138,51674118,52271177)Hunan Provincial Natural Science Foundation of China(2023JJ50181)Supported by State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology(P2024-022)。
文摘To explore the formation mechanism of anisotropy in Ti-6Al-4V alloy fabricated by selective laser melting(SLM),the compressive mechanical properties,microhardness,microstructure,and crystallographic orientation of the alloy across different planes were investigated.The anisotropy of SLM-fabricated Ti-6Al-4V alloys was analyzed,and the electron backscatter diffraction technique was used to investigate the influence of different grain types and orientations on the stress-strain distribution at various scales.Results reveal that in room-temperature compression tests at a strain rate of 10^(-3) s^(-1),both the compressive yield strength and microhardness vary along the deposition direction,indicating a certain degree of mechanical property anisotropy.The alloy exhibits a columnar microstructure;along the deposition direction,the grains appear equiaxed,and they have internal hexagonal close-packed(hcp)α/α'martensitic structure.α'phase has a preferential orientation approximately along the<0001>direction.Anisotropy arises from the high aspect ratio of columnar grains,along with the weak texture of the microstructure and low symmetry of the hcp crystal structure.
文摘Herein,antibacterial silver‑doped fluorescent carbon dots(Ag‑CDs)were synthesized through a stepwise hydrothermal method,with polyethyleneimine(PEI),citric acid(CA),and silver nitrate(AgNO3)serving as precursors.The applicability and antimicrobial efficacy of these nanomaterials were systematically investigated for metal ion sensing.Experimental evidence demonstrated that the Ag‑CDs exhibited a pronounced fluorescence quenching response toward ferric ions(Fe^(3+)),enabling their quantitative determination via a linear concentration‑dependent relationship.These Ag‑CDs exhibited significant inhibitory effects on biofilm growth and disruption for both Escherichia coli and Staphylococcus aureus.Mechanism investigations indicate that Ag‑CDs induced the death of Escherichia coli and Pseudomonas aeruginosa by disrupting their bacterial morphology and structure,triggering the generation of intracellular reactive oxygen species(ROS),and impairing their antioxidant defense system.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金Supported by Open Project of Jiangsu Province Key Laboratory of Integrated Traditional Chinese and Western Medicine for the Prevention and Treatment of Geriatric Diseases,No.202232.
文摘BACKGROUND Post-stroke depression(PSD)is associated with hypothalamic-pituitary-adrenal(HPA)axis dysfunction and neurotransmitter deficits.Selective serotonin reuptake inhibitors(SSRIs)are commonly used,but their efficacy is limited.This study investigated whether combining SSRIs with traditional Chinese medicine(TCM)Free San could enhance their therapeutic effects.AIM To evaluate the clinical efficacy and safety of combining SSRIs with Free San in treating PSD,and to assess its impact on HPA axis function.METHODS Ninety-two patients with PSD were enrolled and randomly divided into control groups(n=46)and study groups(n=46).The control group received the SSRI paroxetine alone,whereas the study group received paroxetine combined with Free San for 4 weeks.Hamilton Depression Scale and TCM syndrome scores were assessed before and after treatment.Serum serotonin,norepinephrine,cortisol,cor-ticotropin-releasing hormone,and adrenocorticotropic hormone were measured.The treatment responses and adverse reactions were recorded.RESULTS After treatment,the Hamilton Depression Scale and TCM syndrome scores were significantly lower in the study group than in the control group(P<0.05).Serum serotonin and norepinephrine levels were significantly higher in the study group than in the control group,whereas cortisol,corticotropin-releasing hormone,and adrenocorticotropic hormone levels were significantly lower(P<0.05).The total efficacy rates were 84.78%and 65.22%in the study and control groups,respectively(P<0.05).No significant differences in adverse reactions were observed between the two groups(P>0.05).CONCLUSION Combining SSRIs with Free San can enhance therapeutic efficacy,improve depressive symptoms,and regulate HPA axis function in patients with PSD with good safety and clinical application value.
基金partially supported by grants PID2020-115096RB-I00 and PID2023-148273NB-I00 from Ministerio de Ciencia y Universidad (MICIU/AEI)(to EMS)。
文摘GEMIN5 is a predominantly cytoplasmic multifunctional protein, known to be involved in recognizing snRNAs through its WD40 repeats domain placed at the N-terminus. A dimerization domain in the middle region acts as a hub for protein–protein interaction, while a non-canonical RNA-binding site is placed towards the C-terminus. The singular organization of structural domains present in GEMIN5 enables this protein to perform multiple functions through its ability to interact with distinct partners, both RNAs and proteins. This protein exerts a different role in translation regulation depending on its physiological state, such that while GEMIN5 down-regulates global RNA translation, the C-terminal half of the protein promotes translation of its mRNA. Additionally, GEMIN5 is responsible for the preferential partitioning of mRNAs into polysomes. Besides selective translation, GEMIN5 forms part of distinct ribonucleoprotein complexes, reflecting the dynamic organization of macromolecular complexes in response to internal and external signals. In accordance with its contribution to fundamental cellular processes, recent reports described clinical loss of function mutants suggesting that GEMIN5 deficiency is detrimental to cell growth and survival. Remarkably, patients carrying GEMIN5 biallelic variants suffer from neurodevelopmental delay, hypotonia, and cerebellar ataxia. Molecular analyses of individual variants, which are defective in protein dimerization, display decreased levels of ribosome association, reinforcing the involvement of the protein in translation regulation. Importantly, the number of clinical variants and the phenotypic spectrum associated with GEMIN5 disorders is increasing as the knowledge of the protein functions and the pathways linked to its activity augments. Here we discuss relevant advances concerning the functional and structural features of GEMIN5 and its separate domains in RNA-binding, protein interactome, and translation regulation, and how these data can help to understand the involvement of protein malfunction in clinical variants found in patients developing neurodevelopmental disorders.
基金financially supported by National Natural Science Foundation of China(22466010)Guizhou Provincial Basic Research Program(Natural Science)ZK[2023]47 and key program ZD[2025]075+6 种基金Innovation and Entrepreneurship Project for overseas Talents in Guizhou Province[2022]02Specific Natural Science Foundation of Guizhou University(X202207)the national undergraduate innovation and entrepreneurship training program(gzugc2023006gzusc2024012)SRT project of Guizhou university(2023SRT0292023SRT024)supported by Shanghai Technical Service Center of Science and Engineering Computing,Shanghai University。
文摘Electrocatalytic nitrate reduction reaction(NO3RR)represents a sustainable and environmentally benign route for ammonia(NH3)synthesis.However,NO3RR is still limited by the competition from hydrogen evolution reaction(HER)and the high energy barrier in the hydrogenation step of nitrogen-containing intermediates.Here,we report a selective etching strategy to construct Ru M nanoalloys(M=Fe,Co,Ni,Cu)uniformly dispersed on porous nitrogen-doped carbon substrates for efficient neutral NH3electrosynthesis.Density functional theory calculations confirm that the synergic effect between Ru and transition metal M modulates the electronic structure of the alloy,significantly lowering the energy barrier for the conversion of*NO_(2)to*HNO_(2).Experimentally,the optimized Ru Fe-NC catalyst achieves 100%Faraday efficiency with a high yield rate of 0.83 mg h^(-1)mg^(-1)catat a low potential of-0.1 V vs.RHE,outperforming most reported catalysts.In situ spectroscopic analyses further demonstrate that the Ru M-NC effectively promotes the hydrogenation of nitrogen intermediates while inhibiting the formation of hydrogen radicals,thereby reducing HER competition.The Ru FeNC assembled Zn-NO_(3)^(-)battery achieved a high open-circuit voltage and an outstanding power density and capacity,which drive selective NO_(3)^(-)conversion to NH3.This work provides a powerful synergistic design strategy for efficient NH3electrosynthesis and a general framework for the development of advanced multi-component catalysts for sustainable nitrogen conversion.
基金supported by the National Natural Science Foundation of China(Grant No.12102133).
文摘Selective laser melting(SLM),as an additive manufacturing technology,has garnered widespread attention for its capability to fabricate components with complex geometries and to tailor the microstructure and mechanical properties under specific conditions.However,the intrinsic influence mechanism of microstructure formation under non-equilibrium solidification conditions in SLM processes has not been clearly revealed.In the present work,the influence of Al concentration and process parameters on the microstructure forming mechanism of Al_(x)CoCrFeNi HEAs prepared by SLM is investigated by molecular dynamics simulation method.The simulation results show that the difference in Al content significantly affects the microstructure formation of HEAs,including the growth rate and morphology of columnar crystals,stress distribution at grain boundaries,and defect structure.In addition,the results show that increasing the substrate temperature improves the solidification formability,reduces microstructural defects,and helps reduce residual stress in Al_(x)CoCrFeNi HEAs.By analyzing the influence of heat and solute flow in the molten pool on the growth of columnar crystals,it is found that spatial fluctuations in Al concentration during the non-equilibrium solidification process inhibit the high cooling rates induced by steep temperature gradients.These findings promote the understanding of the forming mechanism of microstructure in HEAs prepared by SLM and provide theoretical guidance for designing high-performance SLM-fabricated HEAs.
基金the National Nature Science Foundation of China for Excellent Young Scientists Fund(32222058)Fundamental Research Foundation of CAF(CAFYBB2022QB001).
文摘Developing biomass platform compounds into high value-added chemicals is a key step in renewable resource utilization.Herein,we report porous carbon-supported Ni-ZnO nanoparticles catalyst(Ni-ZnO/AC)synthesized via low-temperature coprecipitation,exhibiting excellent performance for the selective hydrogenation of 5-hydroxymethylfurfural(HMF).A linear correlation is first observed between solvent polarity(E_(T)(30))and product selectivity within both polar aprotic and protic solvent classes,suggesting that solvent properties play a vital role in directing reaction pathways.Among these,1,4-dioxane(aprotic)favors the formation of 2,5-bis(hydroxymethyl)furan(BHMF)with 97.5%selectivity,while isopropanol(iPrOH,protic)promotes 2,5-dimethylfuran production with up to 99.5%selectivity.Mechanistic investigations further reveal that beyond polarity,proton-donating ability is critical in facilitating hydrodeoxygenation.iPrOH enables a hydrogen shuttle mechanism where protons assist in hydroxyl group removal,lowering the activation barrier.In contrast,1,4-dioxane,lacking hydrogen bond donors,stabilizes BHMF and hinders further conversion.Density functional theory calculations confirm a lower activation energy in iPrOH(0.60 eV)compared to 1,4-dioxane(1.07 eV).This work offers mechanistic insights and a practical strategy for solvent-mediated control of product selectivity in biomass hydrogenation,highlighting the decisive role of solvent-catalyst-substrate interactions.
基金funded by Deanship of Graduate studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01264).
文摘Automated essay scoring(AES)systems have gained significant importance in educational settings,offering a scalable,efficient,and objective method for evaluating student essays.However,developing AES systems for Arabic poses distinct challenges due to the language’s complex morphology,diglossia,and the scarcity of annotated datasets.This paper presents a hybrid approach to Arabic AES by combining text-based,vector-based,and embeddingbased similarity measures to improve essay scoring accuracy while minimizing the training data required.Using a large Arabic essay dataset categorized into thematic groups,the study conducted four experiments to evaluate the impact of feature selection,data size,and model performance.Experiment 1 established a baseline using a non-machine learning approach,selecting top-N correlated features to predict essay scores.The subsequent experiments employed 5-fold cross-validation.Experiment 2 showed that combining embedding-based,text-based,and vector-based features in a Random Forest(RF)model achieved an R2 of 88.92%and an accuracy of 83.3%within a 0.5-point tolerance.Experiment 3 further refined the feature selection process,demonstrating that 19 correlated features yielded optimal results,improving R2 to 88.95%.In Experiment 4,an optimal data efficiency training approach was introduced,where training data portions increased from 5%to 50%.The study found that using just 10%of the data achieved near-peak performance,with an R2 of 85.49%,emphasizing an effective trade-off between performance and computational costs.These findings highlight the potential of the hybrid approach for developing scalable Arabic AES systems,especially in low-resource environments,addressing linguistic challenges while ensuring efficient data usage.
文摘Advanced intensity measures(IMs)based on an inelastic deformation spectrum improved the evaluation of the median engineering demand parameters(EDPs)and reduced dispersion.In this regard,an optimized two-degreefreedom(2DOF)modal pushover-based scaling procedure(2DMPS)has been developed for a nonlinear dynamic analysis of asymmetric in-plan buildings.The 2DMPS procedure scales ground motions to approach close enough to a target value of the inelastic displacement of the first-mode inelastic 2DOF modal stick,extended for structures with significant contributions of higher modes.Further,4-,6-and 13-story RC SMRF buildings were selected for analyses using ground motion records scaled by the 2DMPS procedure,the modal pushover-based scaling method(MPS),and ASCE/SEI 7-16 scaling procedures.The median values of EDPs on scaled records closely matched the benchmark results.The bias in the EDP values due to the scaled records in every group regarding their median value was lower than the dispersion of the 21 unscaled records.These results generally demonstrate the accuracy and efficiency of the 2DMPS method.Additionally,the 2DOF modal stick’s inelastic response spectra are better suited for calculating seismic demands for one-way asymmetric-plan structures than the SDOF inelastic response spectra.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金supported by the National Key Research and Development Program of China(2022YFA0912100)the National Natural Science Foundation of China(32270098 and 32470073)+1 种基金the Fundamental Research Funds for the Central Universities(2662024JC015)the National Key Laboratory of Agricultural Microbiology(AML2024D02)to Z.Z.
文摘Translation is a crucial step in gene expression.Over the past decade,the development and application of ribosome profiling(Ribo-seq)have significantly advanced our understanding of translational regulation in vivo.However,the analysis and visualization of Ribo-seq data remain challenging.Despite the availability of various analytical pipelines,improvements in comprehensiveness,accuracy,and user-friendliness are still necessary.In this study,we develop RiboParser/RiboShiny,a robust framework for analyzing and visualizing Ribo-seq data.Building on published methods,we optimize ribosome structure-based and start/stopbased models to improve the accuracy and stability of P-site detection,even in species with a high proportion of leaderless transcripts.Leveraging these improvements,RiboParser offers comprehensive analyses,including quality control,gene-level analysis,codon-level analysis,and the analysis of Ribo-seq variants.Meanwhile,RiboShiny provides a user-friendly and adaptable platform for data visualization,facilitating deeper insights into the translational landscape.Furthermore,the integration of standardized genome annotation renders our platform universally applicable to various organisms with sequenced genomes.This framework has the potential to significantly improve the precision and efficiency of Ribo-seq data interpretation,thereby deepening our understanding of translational regulation.
基金supported by Basic Science Research Program(Priority Research Institute)through the NRF of Korea funded by the Ministry of Education(2021R1A6A1A10039823)by the Korea Basic Science Institute(National Research Facilities and Equipment Center)grant funded by the Ministry of Education(2020R1A6C101B194)。
文摘Lithium-oxygen(Li-O2)batteries are perceived as a promising breakthrough in sustainable electrochemical energy storage,utilizing ambient air as an energy source,eliminating the need for costly cathode materials,and offering the highest theoretical energy density(~3.5 k Wh kg^(-1))among discussed candidates.Contributing to the poor cycle life of currently reported Li-O_(2)cells is singlet oxygen(1O_(2))formation,inducing parasitic reactions,degrading key components,and severely deteriorating cell performance.Here,we harness the chirality-induced spin selectivity effect of chiral cobalt oxide nanosheets(Co_(3)O_(4)NSs)as cathode materials to suppress 1O_(2)in Li-O_(2)batteries for the first time.Operando photoluminescence spectroscopy reveals a 3.7-fold and 3.23-fold reduction in 1O_(2)during discharge and charge,respectively,compared to conventional carbon paperbased cells,consistent with differential electrochemical mass spectrometry results,which indicate a near-theoretical charge-to-O_(2)ratio(2.04 e-/O_(2)).Density functional theory calculations demonstrate that chirality induces a peak shift near the Fermi level,enhancing Co 3d-O 2p hybridization,stabilizing reaction intermediates,and lowering activation barriers for Li_(2)O_(2)formation and decomposition.These findings establish a new strategy for improving the stability and energy efficiency of sustainable Li-O_(2)batteries,abridging the current gap to commercialization.
文摘Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics.
基金supported by the National Natural Science Foundation of China,Nos.92049120 and 81870897STI2030-Major Projects,No.2021ZD0204001+6 种基金Guangdong Key Project for Development of New Tools for the Diagnosis and Treatment of Autism,No.2018B030335001the Natural Science Foundation of Jiangsu Province,No.BK20181436the National Major Scientific and Technological Special Project for Significant New Drug Development,No.2019ZX09301102the Discipline Construction Program of the Second Affiliated Hospital of Soochow University,No.XKTJ-TD202003Sino-German Cooperation Mobility Programme,No.M-0679the Science and Technology Project of Suzhou,No.SKY2022161Research Project of Neurological Diseases of the Second Affiliated Hospital of Soochow University Medical Center,No.ND2023A01(all to QHM)。
文摘The endoplasmic reticulum,a key cellular organelle,regulates a wide variety of cellular activities.Endoplasmic reticulum autophagy,one of the quality control systems of the endoplasmic reticulum,plays a pivotal role in maintaining endoplasmic reticulum homeostasis by controlling endoplasmic reticulum turnover,remodeling,and proteostasis.In this review,we briefly describe the endoplasmic reticulum quality control system,and subsequently focus on the role of endoplasmic reticulum autophagy,emphasizing the spatial and temporal mechanisms underlying the regulation of endoplasmic reticulum autophagy according to cellular requirements.We also summarize the evidence relating to how defective or abnormal endoplasmic reticulum autophagy contributes to the pathogenesis of neurodegenerative diseases.In summary,this review highlights the mechanisms associated with the regulation of endoplasmic reticulum autophagy and how they influence the pathophysiology of degenerative nerve disorders.This review would help researchers to understand the roles and regulatory mechanisms of endoplasmic reticulum-phagy in neurodegenerative disorders.
基金supported by:Fondazione Telethon-Italy(No.GGP19128 to AP)Fondazione Cariplo-Italy(No.2021-1544 to RC)+14 种基金Fondazione Italiana di Ricerca per la Sclerosi Laterale Amiotrofica(AriSLA)-Italy(No.MLOpathy to APTarget-RAN to AP)Association Française contre les Myopathies-France(AFM Telethon No.23236 to AP)Kennedy’s Disease Association-USA(2018 grant to RC2020 grant to MG)Ministero dell’Universitàe della Ricerca(MIUR)-Italy(PRIN-Progetti di ricerca di interesse nazionale(No.2017F2A2C5 to APNo.2022EFLFL8 to APNo.2020PBS5MJ to VCNo.2022KSJZF5 to VC)PRIN-Progetti di ricerca di interesse nazionale-bando 2022,PNRR finanziato dall’Unione europea-Next Generation EU,componente M4C2,investimento 1.1(No.P2022B5J32 to RC and No.P20225R4Y5 to VC)CN3:RNA-Codice Proposta:CN_00000041Tematica Sviluppo di terapia genica e farmaci con tecnologia a RNA(Centro Nazionale di Ricerca-CN3 National Center for Gene Therapy and Drugs based on RNA Technology to AP)Progetto Dipartimenti di Eccellenza(to DiSFeB)Ministero della Salute,Agenzia Italiana del Farmaco(AIFA)-Italy(Co_ALS to AP)Universitàdegli Studi di Milano(piano di sviluppo della ricerca(PSR)UNIMI-linea B(to RC and BT).
文摘Heat shock protein family B(small)member 8(HSPB8)is a 22 kDa ubiquitously expressed protein belonging to the family of small heat shock proteins.HSPB8 is involved in various cellular mechanisms mainly related to proteotoxic stress response and in other processes such as inflammation,cell division,and migration.HSPB8 binds misfolded clients to prevent their aggregation by assisting protein refolding or degradation through chaperone-assisted selective autophagy.In line with this function,the pro-degradative activity of HSPB8 has been found protective in several neurodegenerative and neuromuscular diseases characterized by protein misfolding and aggregation.In cancer,HSPB8 has a dual role being capable of exerting either a pro-or an anti-tumoral activity depending on the pathways and factors expressed by the model of cancer under investigation.Moreover,HSPB8 exerts a protective function in different diseases by modulating the inflammatory response,which characterizes not only neurodegenerative diseases,but also other chronic or acute conditions affecting the nervous system,such as multiple sclerosis and intracerebellar hemorrhage.Of note,HSPB8 modulation may represent a therapeutic approach in other neurological conditions that develop as a secondary consequence of other diseases.This is the case of cognitive impairment related to diabetes mellitus,in which HSPB8 exerts a protective activity by assuring mitochondrial homeostasis.This review aims to summarize the diverse and multiple functions of HSPB8 in different pathological conditions,focusing on the beneficial effects of its modulation.Drug-based and alternative therapeutic approaches targeting HSPB8 and its regulated pathways will be discussed,emphasizing how new strategies for cell and tissue-specific delivery represent an avenue to advance in disease treatments.
文摘Clear aligner treatment is a novel technique in current orthodontic practice.Distinct from traditional fixed orthodontic appliances,clear aligners have different material features and biomechanical characteristics and treatment efficiencies,presenting new clinical challenges.Therefore,a comprehensive and systematic description of the key clinical aspects of clear aligner treatment is essential to enhance treatment efficacy and facilitate the advancement and wide adoption of this new technique.This expert consensus discusses case selection and grading of treatment difficulty,principle of clear aligner therapy,clinical procedures and potential complications,which are crucial to the clinical success of clear aligner treatment.
