Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the elderly.While its precise etiology remains unclear,researchers have identified diverse pathologica...Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the elderly.While its precise etiology remains unclear,researchers have identified diverse pathological characteristics and molecular pathways associated with its progression.Advances in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's disease.These non-coding RNAs regulate several biological processes critical to the advancement of the disease,offering promising potential as therapeutic targets and diagnostic biomarkers.Therefore,this review aims to investigate the underlying mechanisms of Alzheimer's disease onset,with a particular focus on microRNAs,long non-coding RNAs,and circular RNAs associated with the disease.The review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding RNAs.It comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease,as well as how these noncoding RNAs influence the disease's progression by regulating gene expression and protein functions.For example,miR-9 targets the UBE4B gene,promoting autophagy-mediated degradation of Tau protein,thereby reducing Tau accumulation and delaying Alzheimer's disease progression.Conversely,the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA,promoting the generation of amyloid-βand accelerating Alzheimer's disease development.Additionally,circular RNAs play significant roles in regulating neuroinflammatory responses.By integrating insights from these regulatory mechanisms,there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's disease.This review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs,potentially paving the way for early detection and novel treatment strategies.展开更多
Herein,manganese(Mn)‑doped poly(1,5‑diaminonaphthalene)(PN)electrode material(Mn@PN)was synthesized via chemical oxidative polymerization.The material′s distinctive vesicular architecture enables rapid ion transport ...Herein,manganese(Mn)‑doped poly(1,5‑diaminonaphthalene)(PN)electrode material(Mn@PN)was synthesized via chemical oxidative polymerization.The material′s distinctive vesicular architecture enables rapid ion transport while maintaining the structural stability of the electrode under continuous charge‑discharge cycles.Electrochemical characterization under a three‑electrode system revealed exceptional rate capability:Mn@PN delivered an ultrahigh specific capacitance of 10318 F·g^(-1) at a low current density of 3 A·g^(-1) and retained 9415 F·g^(-1)(91.2%retention compared to the value at 3 A·g^(-1))even at an ultrahigh current density of 50 A·g^(-1).Moreover,the material exhibited 97.4%capacitance retention after 9000 cycles at 30 A·g^(-1),corresponding with a low capacitance decay rate of 0.003‰per cycle,significantly outperforming conventional conductive polymers like polyaniline(PANI).An asymmetric supercapacitor assembled with Mn@PN as the positive electrode(Mn@PN||AC)achieved an energy density of 328 Wh·kg^(-1) at 15 A·g^(-1) and retained 80.7%of its initial specific capacitance after 4000 cycles at 20 A·g^(-1).展开更多
Long COVID is characterized by a group of persistent symptoms following the acute SARS-COV2 infection, which presented a multifaceted challenge to the healthcare systems all over the globe. The long COVID symptoms spa...Long COVID is characterized by a group of persistent symptoms following the acute SARS-COV2 infection, which presented a multifaceted challenge to the healthcare systems all over the globe. The long COVID symptoms span various organ systems including the respiratory, cardiovascular, gastrointestinal, and neurological manifestations. Mitochondrial dysfunction and immune dysregulation play crucial roles in the long COVID pathophysiology. Recently nutritional intervention gained much attention in managing post-viral syndromes. Effective interventions like supplementation of omega-3 fatty acid, macro and micro nutrients, and vitamins help to reduce systemic inflammation and counteract muscle wasting. Other approaches like nutritional recovery, dietetic interventions, continuous nutritional care post-hospital discharge, nutritional rehabilitation programs,whole-diet approaches like Mediterranean diet, plant-based diet, and caloric optimization, improve overall functional recovery. Physical activity and exercise regimes have been shown to improve fatigue, dyspnea, and cognitive function. Tailored exercise regimes may promote safe rehabilitation. Certain ineffective interventions,such as non-personalized approaches, high dose of antioxidants, use of herbal products that are not clinically validated need to be addressed. Dietary interventions such as personalized nutritional counseling have been demonstrated to improve physical performance in long COVID patients. Further research is needed to refine protocols and identify optimal combinations of dietary and movement-based therapies to support the recovery of long-COVID patients. This narrative review focuses on the ongoing researches that reveals the intricate relationship between nutrition and long COVID recovery and also establishes effective protocols for nutritional care.展开更多
Objective:The contribution of long non-coding RNAs(lncRNAs)associated with protein palmitoylation to the progression of hepatocellular carcinoma(HCC)remains largely unclear.This study sought to establish a prognostic ...Objective:The contribution of long non-coding RNAs(lncRNAs)associated with protein palmitoylation to the progression of hepatocellular carcinoma(HCC)remains largely unclear.This study sought to establish a prognostic signature based on palmitoylation-related lncRNAs and explore their functional implications in HCC.Methods:RNA sequencing and clinical data for HCC and normal tissues were sourced from the Cancer Genome Atlas(TCGA).Pearson correlation analysis was used to identify lncRNAs that were co-expressed with palmitoylation-related genes.Univariate Cox regression was applied to select lncRNAs with prognostic value,followed by the construction of a predictive model using the least absolute shrinkage and selection operator(LASSO)regression.A focused analysis was performed on one key lncRNA,AC009403.1.Expression levels of the final nine lncRNAs included in the model were further validated by reverse transcription quantitative polymerase chain reaction(RT-qPCR).Results:A prognostic model for HCC was developed using nine palmitoylation-associated lncRNAs:AC009403.1,AC010789.1,AC026402.2,AC107021.2,AC135050.6,AL353572.4,MKLN1-AS,PRRT3-AS1,and ZNF582-AS1.This model effectively stratified patients into high-and low-risk groups exhibiting significantly different overall survival(OS)and progression-free survival(PFS),with the low-risk group showing more favorable outcomes.The high-risk group was associated with an immunosuppressive microenvironment,higher tumor mutation burden(TMB),and increased sensitivity to certain chemotherapeutic drugs(e.g.,Sorafenib).Finally,RT-qPCR validation revealed that all nine lncRNAs were significantly upregulated in HCC tissues.Conclusion:The nine-lncRNA signature exhibits robust predictive power for HCC prognosis and provides novel insights into the mechanisms of lncRNA-regulated palmitoylation in HCC development.展开更多
The stability of fruit juice has consistently been an important concern in the food processing industry,which can be time-consuming and costly.Therefore,developing accurate stability early-warning model may serve as a...The stability of fruit juice has consistently been an important concern in the food processing industry,which can be time-consuming and costly.Therefore,developing accurate stability early-warning model may serve as a viable solution.Based on multiple light scattering technology,this paper collects the stability data as the training set of Triphala fruit juice over a three-month period and finds that the sediment amount reached 0.6 mg/mL,composed of ellagic acid and phlobaphene,with the solution's light transmittance fluctuating range(23%-76%)and the particle size(0.27-0.29μm)on day 75.The early warning model comprises a synergistic integration of long short-term memory and backpropagation neural network models.The model exhibits a mean absolute percentage error of 0.626%,an R^(2) of 0.911,and an accuracy of 85.71%.This model is capable of predicting key stability parameters,including sedimentation,transmittance,particle size,particle migration rate,and stability index,within a 90-day period in just 7 days,and thereby provide accurate early-stage stability alerts.展开更多
Exercise,as a non-pharmacological health intervention,has been widely recognized for its beneficial effects,yet its underlying molecular mechanisms remain incompletely understood.The duration,frequency,and intensity o...Exercise,as a non-pharmacological health intervention,has been widely recognized for its beneficial effects,yet its underlying molecular mechanisms remain incompletely understood.The duration,frequency,and intensity of exercise exert distinct physiological impacts on the human body[1].Notably,acute exercise(AE)primarily elicits immediate metabolic responses and immune activation to cope with environmental stimuli,whereas long-term exercise(LE)induces cumulative health benefits across multiple organ systems[2‒4].Aging represents a complex biological process that persists throughout the ontogenetic continuum and serves as a pivotal etiological determinant for numerous chronic pathologies.In the context of accelerating global demographic aging,the development of interventions to promote healthspan extension and modulate aging trajectories has become a paramount research imperative in geroscience.Currently,research on the relationship between exercise and aging is a hot topic.For example,exercise has been shown to modulate aging through pathways such as AMP-activated protein kinase(AMPK)[5].However,the precise molecular links remain elusive.In a recent breakthrough study,Geng et al.used a novel multi-omics strategy to pinpoint betaine,a glycine derivative from choline/diet that serves as both a hepatic methyl donor and a renal osmoprotectant,as a key exercise-induced molecule with anti-inflammatory and geroprotective effects mediated partially via TANK-binding kinase 1(TBK1)inhibition[6,7].This work represents a significant advance as it systematically maps the molecular divergence between acute and long-term exercise while establishing a direct link between renal metabolism and systemic senescence-delaying benefits.展开更多
Myalgic encephalomyelitis/chronic fatigue syndrome-an insidious disease:The recent COVID-19 pandemic has brought substantial attention to the overlapping symptoms between long COVID and myalgic encephalomyelitis/chron...Myalgic encephalomyelitis/chronic fatigue syndrome-an insidious disease:The recent COVID-19 pandemic has brought substantial attention to the overlapping symptoms between long COVID and myalgic encephalomyelitis/chronic fatigue syndrome(ME/CFS),a chronic and poorly understood neurological disorder(Shankar et al.,2024).展开更多
Hepatocellular carcinoma(HCC)remains one of the most prevalent and lethal malignancies worldwide.Long non-coding RNAs(lncRNAs)have emerged as crucial regulators of gene expression and cancer progression,yet the functi...Hepatocellular carcinoma(HCC)remains one of the most prevalent and lethal malignancies worldwide.Long non-coding RNAs(lncRNAs)have emerged as crucial regulators of gene expression and cancer progression,yet the functional diversity of RP11-derived lncRNAs—originally mapped to bacterial artificial chromosome(BAC)clones from the Roswell Park Cancer Institute—has only recently begun to be appreciated.This mini-review aims to systematically synthesize current findings on RP11-derived lncRNAs in HCC,outlining their genomic origins,molecular mechanisms,and biological significance.We highlight their roles in metabolic reprogramming,microRNA network modulation,and tumor progression,as well as their diagnostic and prognostic value in tissue and serum-based analyses.Finally,we discuss therapeutic opportunities and propose future directions to translate RP11-derived lncRNAs into clinically actionable biomarkers and targets for precision liver cancer therapy.展开更多
Ischemic stroke is a serious medical event that cannot be predicted in advance and can have longlasting effects on patients,families,and communities.A deeper understanding of the changes in gene expression and the fun...Ischemic stroke is a serious medical event that cannot be predicted in advance and can have longlasting effects on patients,families,and communities.A deeper understanding of the changes in gene expression and the fundamental molecular mechanisms involved could help address this critical issue.In recent years,research into regulatory long non-coding(lnc)RNAs,a diverse group of RNA molecules with regulatory functions,has emerged as a promising direction in the study of cerebral infarction.This review paper aims to provide a comprehensive exploration of the roles of regulatory lncRNAs in cerebral infarction,as well as potential strategies for their application in clinical settings.LncRNAs have the potential to act as“sponges”that attract specific microRNAs,thereby regulating the expression of microRNA target genes.These interactions influence various aspects of ischemic stroke,including reperfusion-induced damage,cell death,immune responses,autophagy,angiogenesis,and the generation of reactive oxygen species.We highlight several regulatory lncRNAs that have been utilized in animal model treatments,including lncRNA NKILA,lncRNA Meg8,and lncRNA H19.Additionally,we discuss lncRNAs that have been used as biomarkers for the diagnosis and prognosis of cerebral infarction,such as lncRNA FOXO3,lncRNA XIST,and lncRNA RMST.The lncRNAs hold potential for genetic-level treatments in patients.However,numerous challenges,including inefficiency,low targeting accuracy,and side effects observed in preliminary studies,indicate the need for thorough investigation.The application of lncRNAs in ischemic stroke presents challenges that require careful and extensive validation.展开更多
Objective:To analyze the clinical application value of autologous periosteum graft combined with platelet-rich plasma(PRP)in the treatment of long bone fractures in the extremities.Methods:A total of 40 patients with ...Objective:To analyze the clinical application value of autologous periosteum graft combined with platelet-rich plasma(PRP)in the treatment of long bone fractures in the extremities.Methods:A total of 40 patients with long bone fractures in the extremities admitted to Santai Hospital Affiliated to North Sichuan Medical College from January 2023 to January 2025 were included,including cases of upper extremity forearm fractures and lower extremity femoral and tibial fractures.The patients were evenly divided using a random number table,with the control group undergoing open reduction and internal fixation(ORIF)combined with autologous periosteum graft,and the observation group undergoing ORIF,autologous periosteum graft,and PRP injection.Surgical indicators,complication rates,excellent fracture healing rates,functional satisfaction,and joint range of motion were compared between the two groups.Results:The surgical indicators in the observation group were similar to those in the control group(p>0.05).The complication rate in the observation group was lower than that in the control group,while the excellent fracture healing rate and functional satisfaction were higher in the observation group(p<0.05).Conclusion:Autologous periosteum graft combined with PRP technology is safe and reliable for the treatment of long bone fractures in the extremities,with satisfactory clinical outcomes.展开更多
Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning...Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.展开更多
The published article titled“Long Noncoding RNA PVT1 PromotesMelanoma Progression via Endogenous Sponging miR-26b”has been retracted from Oncology Research,Vol.26,No.5,2018,pp.675–681.DOI:10.3727/096504017X14920318...The published article titled“Long Noncoding RNA PVT1 PromotesMelanoma Progression via Endogenous Sponging miR-26b”has been retracted from Oncology Research,Vol.26,No.5,2018,pp.675–681.DOI:10.3727/096504017X14920318811730 URL:https://www.techscience.com/or/v26n5/56680 Following the publication,concerns have been raised about a number of figures in this article.An unexpected area of similarity was identified in terms of the cellular data,where the results from differently performed experiments were intended to have been shown,although the areas immediately surrounding this area featured comparatively different distributions of cells.In addition,the western blots in this article were presented with atypical,unusually shaped and possibly anomalous protein bands in many cases.展开更多
The published article titled“Long Noncoding RNA(lncRNA)HOTAIR Affects Tumorigenesis andMetastasis of Non-Small Cell Lung Cancer by Upregulating miR-613”has been retracted from Oncology Research,Vol.26,No.5,2018,pp....The published article titled“Long Noncoding RNA(lncRNA)HOTAIR Affects Tumorigenesis andMetastasis of Non-Small Cell Lung Cancer by Upregulating miR-613”has been retracted from Oncology Research,Vol.26,No.5,2018,pp.725–734.DOI:10.3727/096504017X15119467381615 URL:https://www.techscience.com/or/v26n5/56685 Following the publication,concerns have been raised about a number of figures in this article.An unexpected area of similarity was identified in terms of the cellular data,where the results from differently performed experiments were intended to have been shown,although the areas immediately surrounding this area featured comparatively different distributions of cells.In addition,the western blots in this article were presented with atypical,unusually shaped and possibly anomalous protein bands in many cases.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Shrublands and grasslands,which constitute approximately 70%of Australia’s vegetation,play a critical role in global wildfire-prone regions.To advance the understanding of grass fire spread,a three-dimensional,physic...Shrublands and grasslands,which constitute approximately 70%of Australia’s vegetation,play a critical role in global wildfire-prone regions.To advance the understanding of grass fire spread,a three-dimensional,physicsbased fire model provides valuable insights into fire dynamics.However,such models are computationally intensive and time-consuming.To address these challenges,we constructed an extensive numerical database comprising 64,000 high-fidelity wildfire simulation cases and implemented a Long Short-Term Memory neural network architecture.The model demonstrates strong predictive performance,achieving a coefficient of determination(R2)of 0.96 on training data,indicating excellent agreement with the physics-based simulation outputs.By utilizing coordinates from five reference points to predict fire front movement,this approach offers a novel method for analysing fire dynamics in homogeneous fuel beds with an average deviation of less than 2.5%.Combining the strengths of physics-based modelling and deep learning,our research enhances fire spread prediction accuracy of over 95%while significantly reducing computational demands.Future efforts will focus on refining the model,expanding the dataset,and incorporating additional variables to improve predictive capabilities and operational applicability.展开更多
Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationall...Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.展开更多
Atherosclerosis,characterized by the formation of fibrofatty lesions in the arterial wall,remains a leading cause of global morbidity and mortality.Emerging evidence highlights the critical regulatory roles of long no...Atherosclerosis,characterized by the formation of fibrofatty lesions in the arterial wall,remains a leading cause of global morbidity and mortality.Emerging evidence highlights the critical regulatory roles of long non-coding RNAs(lncRNAs)and microRNAs(miRNAs)in atherogenesis.LncRNAs can function as competing endogenous RNAs(ceRNAs)by sponging miRNAs,thereby modulating the expression of downstream target mRNAs.This review summarizes current knowledge on lncRNA-miRNA-mRNA regulatory networks and their functional roles in the three major cell types involved in atherosclerotic plaque development:endothelial cells(ECs),vascular smooth muscle cells(VSMCs),and macrophages.In ECs,these networks are implicated in inflammation,apoptosis,proliferation,angiogenesis,pyroptosis,and autophagy.In VSMCs,they regulate proliferation,apoptosis,and migration.In macrophages,they influence lipid metabolism,inflammatory responses,oxidative stress,and autophagy.Although the ceRNA mechanism is predominant,some lncRNAs also act as primary transcripts for miRNAs.Additionally,exosome-mediated non-coding RNA delivery mediates intercellular crosstalk,further expanding the complexity of RNA-based regulation in atherosclerosis.Despite significant progress,challenges remain due to the complexity and context-specificity of these networks.Further research is essential to elucidate these mechanisms and explore their potential as therapeutic targets for atherosclerosis.展开更多
Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data ...Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data loss and anomalies frequently compromise data integrity in practical settings,significantly impacting system operational efficiency and security.Most existing data recovery methods require complete datasets for training,leading to substantial data and computational demands and limited generalization.To address these limitations,this study proposes a missing data imputation model based on an improved Generative Adversarial Network(BAC-GAN).Within the BAC-GAN framework,the generator utilizes Bidirectional Long Short-Term Memory(BiLSTM)networks and Multi-Head Attention mechanisms to capture temporal dependencies and complex relationships within power system data.The discriminator employs a Convolutional Neural Network(CNN)architecture to integrate local features with global structures,effectivelymitigating the generation of implausible imputations.Experimental results on two public datasets demonstrate that the BAC-GAN model achieves superior data recovery accuracy compared to five state-of-the-art and classical benchmarkmethods,with an average improvement of 17.7%in reconstruction accuracy.The proposedmethod significantly enhances the accuracy of grid fault diagnosis and provides reliable data support for the stable operation of smart grids,showing great potential for practical applications in power systems.展开更多
As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^(...As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^([1,2]).展开更多
文摘Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the elderly.While its precise etiology remains unclear,researchers have identified diverse pathological characteristics and molecular pathways associated with its progression.Advances in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's disease.These non-coding RNAs regulate several biological processes critical to the advancement of the disease,offering promising potential as therapeutic targets and diagnostic biomarkers.Therefore,this review aims to investigate the underlying mechanisms of Alzheimer's disease onset,with a particular focus on microRNAs,long non-coding RNAs,and circular RNAs associated with the disease.The review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding RNAs.It comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease,as well as how these noncoding RNAs influence the disease's progression by regulating gene expression and protein functions.For example,miR-9 targets the UBE4B gene,promoting autophagy-mediated degradation of Tau protein,thereby reducing Tau accumulation and delaying Alzheimer's disease progression.Conversely,the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA,promoting the generation of amyloid-βand accelerating Alzheimer's disease development.Additionally,circular RNAs play significant roles in regulating neuroinflammatory responses.By integrating insights from these regulatory mechanisms,there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's disease.This review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs,potentially paving the way for early detection and novel treatment strategies.
文摘Herein,manganese(Mn)‑doped poly(1,5‑diaminonaphthalene)(PN)electrode material(Mn@PN)was synthesized via chemical oxidative polymerization.The material′s distinctive vesicular architecture enables rapid ion transport while maintaining the structural stability of the electrode under continuous charge‑discharge cycles.Electrochemical characterization under a three‑electrode system revealed exceptional rate capability:Mn@PN delivered an ultrahigh specific capacitance of 10318 F·g^(-1) at a low current density of 3 A·g^(-1) and retained 9415 F·g^(-1)(91.2%retention compared to the value at 3 A·g^(-1))even at an ultrahigh current density of 50 A·g^(-1).Moreover,the material exhibited 97.4%capacitance retention after 9000 cycles at 30 A·g^(-1),corresponding with a low capacitance decay rate of 0.003‰per cycle,significantly outperforming conventional conductive polymers like polyaniline(PANI).An asymmetric supercapacitor assembled with Mn@PN as the positive electrode(Mn@PN||AC)achieved an energy density of 328 Wh·kg^(-1) at 15 A·g^(-1) and retained 80.7%of its initial specific capacitance after 4000 cycles at 20 A·g^(-1).
基金Chung Shan Medical University, Taichung city, Taiwan China, for its support。
文摘Long COVID is characterized by a group of persistent symptoms following the acute SARS-COV2 infection, which presented a multifaceted challenge to the healthcare systems all over the globe. The long COVID symptoms span various organ systems including the respiratory, cardiovascular, gastrointestinal, and neurological manifestations. Mitochondrial dysfunction and immune dysregulation play crucial roles in the long COVID pathophysiology. Recently nutritional intervention gained much attention in managing post-viral syndromes. Effective interventions like supplementation of omega-3 fatty acid, macro and micro nutrients, and vitamins help to reduce systemic inflammation and counteract muscle wasting. Other approaches like nutritional recovery, dietetic interventions, continuous nutritional care post-hospital discharge, nutritional rehabilitation programs,whole-diet approaches like Mediterranean diet, plant-based diet, and caloric optimization, improve overall functional recovery. Physical activity and exercise regimes have been shown to improve fatigue, dyspnea, and cognitive function. Tailored exercise regimes may promote safe rehabilitation. Certain ineffective interventions,such as non-personalized approaches, high dose of antioxidants, use of herbal products that are not clinically validated need to be addressed. Dietary interventions such as personalized nutritional counseling have been demonstrated to improve physical performance in long COVID patients. Further research is needed to refine protocols and identify optimal combinations of dietary and movement-based therapies to support the recovery of long-COVID patients. This narrative review focuses on the ongoing researches that reveals the intricate relationship between nutrition and long COVID recovery and also establishes effective protocols for nutritional care.
基金supported by the Science and Technology Program in Guangxi Province(2023JJD140004).
文摘Objective:The contribution of long non-coding RNAs(lncRNAs)associated with protein palmitoylation to the progression of hepatocellular carcinoma(HCC)remains largely unclear.This study sought to establish a prognostic signature based on palmitoylation-related lncRNAs and explore their functional implications in HCC.Methods:RNA sequencing and clinical data for HCC and normal tissues were sourced from the Cancer Genome Atlas(TCGA).Pearson correlation analysis was used to identify lncRNAs that were co-expressed with palmitoylation-related genes.Univariate Cox regression was applied to select lncRNAs with prognostic value,followed by the construction of a predictive model using the least absolute shrinkage and selection operator(LASSO)regression.A focused analysis was performed on one key lncRNA,AC009403.1.Expression levels of the final nine lncRNAs included in the model were further validated by reverse transcription quantitative polymerase chain reaction(RT-qPCR).Results:A prognostic model for HCC was developed using nine palmitoylation-associated lncRNAs:AC009403.1,AC010789.1,AC026402.2,AC107021.2,AC135050.6,AL353572.4,MKLN1-AS,PRRT3-AS1,and ZNF582-AS1.This model effectively stratified patients into high-and low-risk groups exhibiting significantly different overall survival(OS)and progression-free survival(PFS),with the low-risk group showing more favorable outcomes.The high-risk group was associated with an immunosuppressive microenvironment,higher tumor mutation burden(TMB),and increased sensitivity to certain chemotherapeutic drugs(e.g.,Sorafenib).Finally,RT-qPCR validation revealed that all nine lncRNAs were significantly upregulated in HCC tissues.Conclusion:The nine-lncRNA signature exhibits robust predictive power for HCC prognosis and provides novel insights into the mechanisms of lncRNA-regulated palmitoylation in HCC development.
基金supported by grants from the National Natural Science Foundation of China(82304871)China Postdoctoral Science Foundation(2023MD734100)Sichuan Science and Technology Program(2024NSFSC1846),Sichuan Science and Technology Program(2024YFFKO185)。
文摘The stability of fruit juice has consistently been an important concern in the food processing industry,which can be time-consuming and costly.Therefore,developing accurate stability early-warning model may serve as a viable solution.Based on multiple light scattering technology,this paper collects the stability data as the training set of Triphala fruit juice over a three-month period and finds that the sediment amount reached 0.6 mg/mL,composed of ellagic acid and phlobaphene,with the solution's light transmittance fluctuating range(23%-76%)and the particle size(0.27-0.29μm)on day 75.The early warning model comprises a synergistic integration of long short-term memory and backpropagation neural network models.The model exhibits a mean absolute percentage error of 0.626%,an R^(2) of 0.911,and an accuracy of 85.71%.This model is capable of predicting key stability parameters,including sedimentation,transmittance,particle size,particle migration rate,and stability index,within a 90-day period in just 7 days,and thereby provide accurate early-stage stability alerts.
基金funded by the National Natural Science Foundation of China(grant numbers 32371244 and 92057118 to M.H.)and the 2023 Shanghai Eastern Talent Plan Leading Project(to M.H.)supported by the innovative research team of high-level local universities in Shanghai(grant numbers SHSMUZDCX20212000 and SHSMU-ZDCX20211202 to M.H.)+1 种基金the Shanghai Frontiers Science Center of Cellular Homeostasis and Human Diseases,and the Fundamental Research Funds for the Central Universities to M.H.labsupported by the Natural Science Foundation of Guangxi Zhuang Autonomous Region(grant number 2025GXNSFAA069105 to M.H.).
文摘Exercise,as a non-pharmacological health intervention,has been widely recognized for its beneficial effects,yet its underlying molecular mechanisms remain incompletely understood.The duration,frequency,and intensity of exercise exert distinct physiological impacts on the human body[1].Notably,acute exercise(AE)primarily elicits immediate metabolic responses and immune activation to cope with environmental stimuli,whereas long-term exercise(LE)induces cumulative health benefits across multiple organ systems[2‒4].Aging represents a complex biological process that persists throughout the ontogenetic continuum and serves as a pivotal etiological determinant for numerous chronic pathologies.In the context of accelerating global demographic aging,the development of interventions to promote healthspan extension and modulate aging trajectories has become a paramount research imperative in geroscience.Currently,research on the relationship between exercise and aging is a hot topic.For example,exercise has been shown to modulate aging through pathways such as AMP-activated protein kinase(AMPK)[5].However,the precise molecular links remain elusive.In a recent breakthrough study,Geng et al.used a novel multi-omics strategy to pinpoint betaine,a glycine derivative from choline/diet that serves as both a hepatic methyl donor and a renal osmoprotectant,as a key exercise-induced molecule with anti-inflammatory and geroprotective effects mediated partially via TANK-binding kinase 1(TBK1)inhibition[6,7].This work represents a significant advance as it systematically maps the molecular divergence between acute and long-term exercise while establishing a direct link between renal metabolism and systemic senescence-delaying benefits.
基金supported by the Judith Jane Mason and Harold Stannett Williams Memorial Foundation National Medical Program(#Mason2210)to JX。
文摘Myalgic encephalomyelitis/chronic fatigue syndrome-an insidious disease:The recent COVID-19 pandemic has brought substantial attention to the overlapping symptoms between long COVID and myalgic encephalomyelitis/chronic fatigue syndrome(ME/CFS),a chronic and poorly understood neurological disorder(Shankar et al.,2024).
基金supported by the National Research Foundation of Korea(NRF),funded by the Ministry of Science and ICT(MSIT),Republic of Korea(grant numbers:RS-2022-NR070489 and RS-2023-00210847)the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health and Welfare,Republic of Korea(grant number HR21C1003).
文摘Hepatocellular carcinoma(HCC)remains one of the most prevalent and lethal malignancies worldwide.Long non-coding RNAs(lncRNAs)have emerged as crucial regulators of gene expression and cancer progression,yet the functional diversity of RP11-derived lncRNAs—originally mapped to bacterial artificial chromosome(BAC)clones from the Roswell Park Cancer Institute—has only recently begun to be appreciated.This mini-review aims to systematically synthesize current findings on RP11-derived lncRNAs in HCC,outlining their genomic origins,molecular mechanisms,and biological significance.We highlight their roles in metabolic reprogramming,microRNA network modulation,and tumor progression,as well as their diagnostic and prognostic value in tissue and serum-based analyses.Finally,we discuss therapeutic opportunities and propose future directions to translate RP11-derived lncRNAs into clinically actionable biomarkers and targets for precision liver cancer therapy.
基金supported by the China Postdoctoral Science Foundation,No.2022M712689the Natural Science Foundation of the Jiangsu Higher Education Institutions of China,No.22KJB1800029+1 种基金The University Student Innovation Project of Yangzhou University,No.XCX20240856The Jiangsu Provincial Science and Technology Talent Project,No.FZ20240964(all to TX).
文摘Ischemic stroke is a serious medical event that cannot be predicted in advance and can have longlasting effects on patients,families,and communities.A deeper understanding of the changes in gene expression and the fundamental molecular mechanisms involved could help address this critical issue.In recent years,research into regulatory long non-coding(lnc)RNAs,a diverse group of RNA molecules with regulatory functions,has emerged as a promising direction in the study of cerebral infarction.This review paper aims to provide a comprehensive exploration of the roles of regulatory lncRNAs in cerebral infarction,as well as potential strategies for their application in clinical settings.LncRNAs have the potential to act as“sponges”that attract specific microRNAs,thereby regulating the expression of microRNA target genes.These interactions influence various aspects of ischemic stroke,including reperfusion-induced damage,cell death,immune responses,autophagy,angiogenesis,and the generation of reactive oxygen species.We highlight several regulatory lncRNAs that have been utilized in animal model treatments,including lncRNA NKILA,lncRNA Meg8,and lncRNA H19.Additionally,we discuss lncRNAs that have been used as biomarkers for the diagnosis and prognosis of cerebral infarction,such as lncRNA FOXO3,lncRNA XIST,and lncRNA RMST.The lncRNAs hold potential for genetic-level treatments in patients.However,numerous challenges,including inefficiency,low targeting accuracy,and side effects observed in preliminary studies,indicate the need for thorough investigation.The application of lncRNAs in ischemic stroke presents challenges that require careful and extensive validation.
文摘Objective:To analyze the clinical application value of autologous periosteum graft combined with platelet-rich plasma(PRP)in the treatment of long bone fractures in the extremities.Methods:A total of 40 patients with long bone fractures in the extremities admitted to Santai Hospital Affiliated to North Sichuan Medical College from January 2023 to January 2025 were included,including cases of upper extremity forearm fractures and lower extremity femoral and tibial fractures.The patients were evenly divided using a random number table,with the control group undergoing open reduction and internal fixation(ORIF)combined with autologous periosteum graft,and the observation group undergoing ORIF,autologous periosteum graft,and PRP injection.Surgical indicators,complication rates,excellent fracture healing rates,functional satisfaction,and joint range of motion were compared between the two groups.Results:The surgical indicators in the observation group were similar to those in the control group(p>0.05).The complication rate in the observation group was lower than that in the control group,while the excellent fracture healing rate and functional satisfaction were higher in the observation group(p<0.05).Conclusion:Autologous periosteum graft combined with PRP technology is safe and reliable for the treatment of long bone fractures in the extremities,with satisfactory clinical outcomes.
基金funded by Science and Technology Research and Development Program Project of China Railway Group Limited(No.2023-Major-02)National Natural Science Foundation of China(Grant No.52378200)Sichuan Science and Technology Program(Grant No.2024NSFSC0017).
文摘Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.
文摘The published article titled“Long Noncoding RNA PVT1 PromotesMelanoma Progression via Endogenous Sponging miR-26b”has been retracted from Oncology Research,Vol.26,No.5,2018,pp.675–681.DOI:10.3727/096504017X14920318811730 URL:https://www.techscience.com/or/v26n5/56680 Following the publication,concerns have been raised about a number of figures in this article.An unexpected area of similarity was identified in terms of the cellular data,where the results from differently performed experiments were intended to have been shown,although the areas immediately surrounding this area featured comparatively different distributions of cells.In addition,the western blots in this article were presented with atypical,unusually shaped and possibly anomalous protein bands in many cases.
文摘The published article titled“Long Noncoding RNA(lncRNA)HOTAIR Affects Tumorigenesis andMetastasis of Non-Small Cell Lung Cancer by Upregulating miR-613”has been retracted from Oncology Research,Vol.26,No.5,2018,pp.725–734.DOI:10.3727/096504017X15119467381615 URL:https://www.techscience.com/or/v26n5/56685 Following the publication,concerns have been raised about a number of figures in this article.An unexpected area of similarity was identified in terms of the cellular data,where the results from differently performed experiments were intended to have been shown,although the areas immediately surrounding this area featured comparatively different distributions of cells.In addition,the western blots in this article were presented with atypical,unusually shaped and possibly anomalous protein bands in many cases.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金funded by the National Natural Science Foundation of China(NSFC No.52322610)Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N)Furthermore,this research was undertaken with the assistance of computational resources from the National Computational Infrastructure(NCI Australia),an NCRISenabled capability supported by the Australian Government.
文摘Shrublands and grasslands,which constitute approximately 70%of Australia’s vegetation,play a critical role in global wildfire-prone regions.To advance the understanding of grass fire spread,a three-dimensional,physicsbased fire model provides valuable insights into fire dynamics.However,such models are computationally intensive and time-consuming.To address these challenges,we constructed an extensive numerical database comprising 64,000 high-fidelity wildfire simulation cases and implemented a Long Short-Term Memory neural network architecture.The model demonstrates strong predictive performance,achieving a coefficient of determination(R2)of 0.96 on training data,indicating excellent agreement with the physics-based simulation outputs.By utilizing coordinates from five reference points to predict fire front movement,this approach offers a novel method for analysing fire dynamics in homogeneous fuel beds with an average deviation of less than 2.5%.Combining the strengths of physics-based modelling and deep learning,our research enhances fire spread prediction accuracy of over 95%while significantly reducing computational demands.Future efforts will focus on refining the model,expanding the dataset,and incorporating additional variables to improve predictive capabilities and operational applicability.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.
基金supported by the National Natural Science Foundation of China(No.82360024).
文摘Atherosclerosis,characterized by the formation of fibrofatty lesions in the arterial wall,remains a leading cause of global morbidity and mortality.Emerging evidence highlights the critical regulatory roles of long non-coding RNAs(lncRNAs)and microRNAs(miRNAs)in atherogenesis.LncRNAs can function as competing endogenous RNAs(ceRNAs)by sponging miRNAs,thereby modulating the expression of downstream target mRNAs.This review summarizes current knowledge on lncRNA-miRNA-mRNA regulatory networks and their functional roles in the three major cell types involved in atherosclerotic plaque development:endothelial cells(ECs),vascular smooth muscle cells(VSMCs),and macrophages.In ECs,these networks are implicated in inflammation,apoptosis,proliferation,angiogenesis,pyroptosis,and autophagy.In VSMCs,they regulate proliferation,apoptosis,and migration.In macrophages,they influence lipid metabolism,inflammatory responses,oxidative stress,and autophagy.Although the ceRNA mechanism is predominant,some lncRNAs also act as primary transcripts for miRNAs.Additionally,exosome-mediated non-coding RNA delivery mediates intercellular crosstalk,further expanding the complexity of RNA-based regulation in atherosclerosis.Despite significant progress,challenges remain due to the complexity and context-specificity of these networks.Further research is essential to elucidate these mechanisms and explore their potential as therapeutic targets for atherosclerosis.
基金supported by the National Natural Science Foundation of China(No.51977113)the Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.(No.5211JX240001).
文摘Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data loss and anomalies frequently compromise data integrity in practical settings,significantly impacting system operational efficiency and security.Most existing data recovery methods require complete datasets for training,leading to substantial data and computational demands and limited generalization.To address these limitations,this study proposes a missing data imputation model based on an improved Generative Adversarial Network(BAC-GAN).Within the BAC-GAN framework,the generator utilizes Bidirectional Long Short-Term Memory(BiLSTM)networks and Multi-Head Attention mechanisms to capture temporal dependencies and complex relationships within power system data.The discriminator employs a Convolutional Neural Network(CNN)architecture to integrate local features with global structures,effectivelymitigating the generation of implausible imputations.Experimental results on two public datasets demonstrate that the BAC-GAN model achieves superior data recovery accuracy compared to five state-of-the-art and classical benchmarkmethods,with an average improvement of 17.7%in reconstruction accuracy.The proposedmethod significantly enhances the accuracy of grid fault diagnosis and provides reliable data support for the stable operation of smart grids,showing great potential for practical applications in power systems.
基金supported by the National Natural Science Foundation of China (Grant No.52405033)。
文摘As unmanned underwater vehicles (UUVs) are increasingly designed to perform long-duration missions in highly complex and often extreme environments, traditional design methods face significant and growing challenges^([1,2]).
