The cure rate for chronic neurodegenerative diseases remains low,creating an urgent need for improved intervention methods.Recent studies have shown that enhancing mitochondrial function can mitigate the effects of th...The cure rate for chronic neurodegenerative diseases remains low,creating an urgent need for improved intervention methods.Recent studies have shown that enhancing mitochondrial function can mitigate the effects of these diseases.This paper comprehensively reviews the relationship between mitochondrial dysfunction and chronic neurodegenerative diseases,aiming to uncover the potential use of targeted mitochondrial interventions as viable therapeutic options.We detail five targeted mitochondrial intervention strategies for chronic neurodegenerative diseases that act by promoting mitophagy,inhibiting mitochondrial fission,enhancing mitochondrial biogenesis,applying mitochondria-targeting antioxidants,and transplanting mitochondria.Each method has unique advantages and potential limitations,making them suitable for various therapeutic situations.Therapies that promote mitophagy or inhibit mitochondrial fission could be particularly effective in slowing disease progression,especially in the early stages.In contrast,those that enhance mitochondrial biogenesis and apply mitochondria-targeting antioxidants may offer great benefits during the middle stages of the disease by improving cellular antioxidant capacity and energy metabolism.Mitochondrial transplantation,while still experimental,holds great promise for restoring the function of damaged cells.Future research should focus on exploring the mechanisms and effects of these intervention strategies,particularly regarding their safety and efficacy in clinical settings.Additionally,the development of innovative mitochondria-targeting approaches,such as gene editing and nanotechnology,may provide new solutions for treating chronic neurodegenerative diseases.Implementing combined therapeutic strategies that integrate multiple intervention methods could also enhance treatment outcomes.展开更多
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.展开更多
Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative con...Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.展开更多
The prolonged and intricate history of oncological treatments has transitioned significantly since the introduction of chemotherapy.Substantial therapeutic benefits in cancer therapy have been achieved by the integrat...The prolonged and intricate history of oncological treatments has transitioned significantly since the introduction of chemotherapy.Substantial therapeutic benefits in cancer therapy have been achieved by the integration of conventional treatments with molecular biosciences and omics technologies.Human epidermal growth factor receptor,hormone receptors,and angiogenesis factors are among the established therapies in tumor reduction and managing side effects.Novel targeted therapies like KRAS G12C,Claudin-18 isoform 2(CLDN18.2),Trophoblast cell-surface antigen 2(TROP2),and epigenetic regulators emphasize their promise in advancing precision medicine.However,in many cases,the resistance mechanisms associated with these interventions render them ineffective in carrying out their functions.The purpose of this review is to provide a comprehensive and up-to-date examination of both established and emerging drug targets and mechanisms of treatment resistance in oncology.This review seeks to elucidate recent advancements,address persisting challenges,and explore opportunities for innovative developments in cancer target research.Additionally,it explores the growing role of artificial intelligence in reshaping cancer drug discovery and development frameworks as potential avenues for future research.In conclusion,innovative approaches in oncology,supported by pharmacological research,ongoing clinical trials,molecular biosciences,and artificial intelligence,are poised to significantly transform cancer treatment.展开更多
Naphthalene,anthracene and pyridone endoperoxides are known to thermally release singlet oxygen.However,in the cycloreversion reaction,singlet oxygen is produced stoichiometrically;therefore,multiple singlet oxygen re...Naphthalene,anthracene and pyridone endoperoxides are known to thermally release singlet oxygen.However,in the cycloreversion reaction,singlet oxygen is produced stoichiometrically;therefore,multiple singlet oxygen releasing modules are expected to be very useful in inducing apoptosis of cancer cells.Herein,we present a potential therapeutic agent presenting three-pyridone endoperoxide modules and a mitochondria targeting group.Compared to previously reported pyridone-based monofunctional endoperoxides,the triple endoperoxide is highly effective as evidenced by assays and fluorescence microscopy.展开更多
In recent years,proteolysis-targeting chimeras(PROTACs)have gained widespread attention as an emerging therapeutic approach.PROTACs are bifunctional molecules composed of a target protein-binding ligand,an E3 ubiquiti...In recent years,proteolysis-targeting chimeras(PROTACs)have gained widespread attention as an emerging therapeutic approach.PROTACs are bifunctional molecules composed of a target protein-binding ligand,an E3 ubiquitin ligase ligand,and a linker connecting these ligands.By harnessing the cell’s intrinsic ubiquitin-proteasome system(UPS),they promote the ubiquitination of specific target proteins,leading to their degradation and therapeutic effects.PROTACs show exceptional promise in targeting conventional“undruggable”targets compared to traditional small-molecule inhibitors.This review provides an overview of PROTACs,including their molecular mechanism of action,therapeutic benefits,development history,key design aspects,current research and development challenges,and future trends in nextgeneration PROTAC technology.展开更多
Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-through...Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.展开更多
Triple-negative breast cancer(TNBC)presents significant diagnostic and therapeutic challenges due to the lack of targeted treatments,rapid progression,high recurrence and metastasis rates,and overall poorer prognosis....Triple-negative breast cancer(TNBC)presents significant diagnostic and therapeutic challenges due to the lack of targeted treatments,rapid progression,high recurrence and metastasis rates,and overall poorer prognosis.Herein,the targeted theranostic platform of cysteine-modified gold nanodots-sulfhydrated luteinizing hormone releasing hormone(CGN-SLR)nanosystem was designed for target recognition and precise dual-mode imaging-guided photothermal therapy(PTT)against TNBC.On the one hand,the CGN-SLR nanosystem can serve as an ideal targeting fluorescent probe and computed tomography(CT)enhancer to facilitate the accurate diagnosis and surgical guidance of TNBC.On the other hand,the CGN-SLR nanosystem with great targeting and PTT ability can significantly inhibit the growth of TNBC,without causing harm to normal tissues and healthy organs.It provides an effective strategy for the diagnosis and treatment of TNBC through the rational design of multifunctional nanoplatform with target recognition,multiple imaging guidance/monitoring,and high-efficiency PTT.展开更多
We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precisio...We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precision sampling strategy based on prostate cancer’s spatial distribution,aligning with personalized diagnosis trends.展开更多
Metabolic reprogramming involving branched-chain amino acids(BCAAs)—leucine,isoleucine,and valine—is increasingly recognized as pivotal in cancer progression,metastasis,and immune modulation.This review comprehensiv...Metabolic reprogramming involving branched-chain amino acids(BCAAs)—leucine,isoleucine,and valine—is increasingly recognized as pivotal in cancer progression,metastasis,and immune modulation.This review comprehensively explores how cancer cells rewire BCAA metabolism to enhance proliferation,survival,and therapy resistance.Tumors manipulate BCAA uptake and catabolism via high expression of transporters like L-type amino acid transporter 1(LAT1)and enzymes including branched chain amino acid transaminase 1(BCAT1),branched chain amino acid transaminase 2(BCAT2),branched-chain alpha-keto acid dehydrogenase(BCKDH),and branched chain alpha-keto acid dehydrogenase kinase(BCKDK).These alterations sustain energy production,biosynthesis,redox homeostasis,and oncogenic signaling(especially mammalian target of rapamycin complex 1[mTORC1]).Crucially,tumor-driven BCAA depletion also shapes an immunosuppressive microenvironment,impairing anti-tumor immunity by limiting essential nutrients for T cells and natural killer(NK)cells.Innovative therapeutic strategies targeting BCAA pathways—ranging from selective small-molecule inhibitors(e.g.,LAT1 and BCAT1/2)to dietary modulation—have shown promising preclinical and early clinical efficacy,highlighting their potential to exploit metabolic vulnerabilities in cancer cells while bolstering immune responses.By integrating multi-omics data and precision targeting approaches,this review underscores the translational significance of BCAA metabolic reprogramming,positioning it as a novel frontier in cancer treatment.展开更多
Delivery carriers serve as a highly efficient approach for precision nutrition and medicine;however,artificial delivery carriers are prone to triggering the immune response and have the disadvantages of poor stability...Delivery carriers serve as a highly efficient approach for precision nutrition and medicine;however,artificial delivery carriers are prone to triggering the immune response and have the disadvantages of poor stability and low bioavailability.Extracellular vesicles(EVs),nucleus-free biological particles composed of phospholipid bilayers secreted by living cells,are a new generation of targeted delivery carriers.In recent years,an increasing number of species have been reported to contain EVs.Among them,food-derived extracellular vesicles(FDEVs)show outstanding comprehensive properties.FDEVs are considered to have great application potential due to their wide range of sources,high yields,absence of human pathogenic pathogens,and ethical concerns.In this review,the preparation,nomenclature,physicochemical characteristics,and preservation methods of FDEVs are discussed,as well as their potential protein markers,bioactivities,and applications as novel targeted delivery carriers of FDEVs from animals,plants,and microorganisms.We also summarized the adverse consequences of FDEVs in current studies,and put forward the problems and challenges in the process of FDEVs research and commercialization.In short,the importance of FDEVs has been highlighted,and FDEVs have good application prospects as a new class of targeted delivery carriers.The current problems should be paid attention to and actively solved.展开更多
Oncology Research Editorial Office Published:19 January 2026 The published article titled“miR-202 Promotes Cell Apoptosis in Esophageal Squamous Cell Carcinoma by Targeting HSF2”has been retracted from Oncology Rese...Oncology Research Editorial Office Published:19 January 2026 The published article titled“miR-202 Promotes Cell Apoptosis in Esophageal Squamous Cell Carcinoma by Targeting HSF2”has been retracted from Oncology Research,Vol.25,No.2,2017,pp.215-223.DOI:10.3727/096504016X14732772150541 URL:https://www.techscience.com/or/v25n2/56800.展开更多
Food-grade biopolymers and nanotechnology have been increasingly used to revolutionize the delivery of bioactive compounds by enhancing stability,bioavailability,and controlled release.Within the scope of nanoencapsul...Food-grade biopolymers and nanotechnology have been increasingly used to revolutionize the delivery of bioactive compounds by enhancing stability,bioavailability,and controlled release.Within the scope of nanoencapsulation systems,this review explores food-derived polymers such as vicilin,zein,gluten,cruciferin,inulin,and others.These biopolymers are ideal since they encapsulate numerous functional compounds,such as vitamins,probiotics,essential oils,and polyphenols,because they are biocompatible,amphiphilic,and biodegradable.The specific physical and chemical properties of each polymer,extraction procedures,and nanoencapsulation techniques applied therein(e.g.,ionic gelation and spray drying)are described.The review highlights advanced targeting systems like pH-sensitive,magnetic delivery.Additional applications include those in synergistic nutraceutical systems,oral administration of vaccination,and intelligent food packaging.All these findings demonstrate that food polymers are increasingly more viable as functional nanocarriers by way of increasing bioactive delivery and the shifting requirements of personalized and health-based dietary regimes.展开更多
Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services.Although meshes for electrostatic interaction computations can be reconstru...Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services.Although meshes for electrostatic interaction computations can be reconstructed from point clouds,they are usually too dense,leading to high computational costs.This paper presents an optimization method for converting dense meshes into optimal meshes,enabling fast and accurate computation of the electrostatic interaction by point clouds.First,the dense mesh reconstructed from point clouds is simplified into a coarse mesh using local operators.Second,the simplified mesh is refined by an iterative strategy that integrates a lightweight method of moments and an impedance matrix inheritance technique,ultimately yielding an optimal mesh for computing the electrostatic interaction.Simulation results show that our method effectively optimizes dense meshes,making electrostatic interaction computations using point clouds approximately 63.4 times more efficient than the previous method.展开更多
The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framewo...The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framework based on YOLOv8,with the aim of enhancing detection accuracy and the ability to recognize multi-scale targets in blurry and complex underwater environments.A streamlined Vision Transformer(ViT)model is used as the feature extraction backbone,which retains global self-attention feature extraction and accelerates training efficiency.In addition,a detection head named Dynamic Head(DyHead)is introduced,which enhances the efficiency of processing various target sizes through multi-scale feature fusion and adaptive attention modules.Furthermore,a dynamic loss function adjustment method called SlideLoss is employed.This method utilizes sliding window technology to adaptively adjust parameters,which optimizes the detection of challenging targets.The experimental results on the RUOD dataset show that the proposed improved model not only significantly enhances the accuracy of target detection but also increases the efficiency of target detection.展开更多
To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle(UAV)aerial imagery,which often lead to missed and false detections,we propose Multi-scale Feature Fusion ...To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle(UAV)aerial imagery,which often lead to missed and false detections,we propose Multi-scale Feature Fusion YOLO(MFF-YOLO),an enhanced algorithm based on YOLOv8s.Our approach introduces a Multi-scale Feature Fusion Strategy(MFFS),comprising the Multiple Features C2f(MFC)module and the Scale Sequence Feature Fusion(SSFF)module,to improve feature integration across different network levels.This enables more effective capture of fine-grained details and sequential multi-scale features.Furthermore,we incorporate Inner-CIoU,an improved loss function that uses auxiliary bounding boxes to enhance the regression quality of small object boxes.To ensure practicality for UAV deployment,we apply the Layer-adaptive Magnitude-based pruning(LAMP)method to significantly reduce model size and computational cost.Experiments on the VisDrone2019 dataset show that MFF-YOLO achieves a 5.7% increase in mean average precision(mAP)over the baseline,while reducing parameters by 8.5 million and computation by 17.5%.The results demonstrate that our method effectively improves detection performance in UAV aerial scenarios.展开更多
Oncology Research Editorial Office Published:19 January 2026 The published article titled“miR-126-5p Restoration Promotes Cell Apoptosis in Cervical Cancer by Targeting Bcl2l2”has been retracted from Oncology Resear...Oncology Research Editorial Office Published:19 January 2026 The published article titled“miR-126-5p Restoration Promotes Cell Apoptosis in Cervical Cancer by Targeting Bcl2l2”has been retracted from Oncology Research,Vol.25,No.4,2017,pp.463-470.DOI:10.3727/096504016X14685034103879 URL:https://www.techscience.com/or/v25n4/56826.展开更多
The published article titled“MicroRNA-133b Inhibits Proliferation,Cellular Migration,and Invasion via Targeting LASP1 in Hepatocarcinoma Cells”has been retracted from Oncology Research,Vol.25,No.8,2017,pp.1269–1282.
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
基金partly supported by the Yan’an University Qin Chuanyuan“Scientist+Engineer”Team Special Fund,No.2023KXJ-012(to YL)Yan’an University Transformation of Scientific and Technological Achievements Fund,No.2023CGZH-001(to YL)+2 种基金College Students Innovation and Entrepreneurship Training Program,Nos.D2023158,202410719056(to XS,JM)Yan’an University Production and Cultivation Project,No.CXY202001(to YL)Kweichow Moutai Hospital Research and Talent Development Fund Project,No.MTyk2022-25(to XO)。
文摘The cure rate for chronic neurodegenerative diseases remains low,creating an urgent need for improved intervention methods.Recent studies have shown that enhancing mitochondrial function can mitigate the effects of these diseases.This paper comprehensively reviews the relationship between mitochondrial dysfunction and chronic neurodegenerative diseases,aiming to uncover the potential use of targeted mitochondrial interventions as viable therapeutic options.We detail five targeted mitochondrial intervention strategies for chronic neurodegenerative diseases that act by promoting mitophagy,inhibiting mitochondrial fission,enhancing mitochondrial biogenesis,applying mitochondria-targeting antioxidants,and transplanting mitochondria.Each method has unique advantages and potential limitations,making them suitable for various therapeutic situations.Therapies that promote mitophagy or inhibit mitochondrial fission could be particularly effective in slowing disease progression,especially in the early stages.In contrast,those that enhance mitochondrial biogenesis and apply mitochondria-targeting antioxidants may offer great benefits during the middle stages of the disease by improving cellular antioxidant capacity and energy metabolism.Mitochondrial transplantation,while still experimental,holds great promise for restoring the function of damaged cells.Future research should focus on exploring the mechanisms and effects of these intervention strategies,particularly regarding their safety and efficacy in clinical settings.Additionally,the development of innovative mitochondria-targeting approaches,such as gene editing and nanotechnology,may provide new solutions for treating chronic neurodegenerative diseases.Implementing combined therapeutic strategies that integrate multiple intervention methods could also enhance treatment outcomes.
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
基金supported by the Fundamental Research Funds for the Central Universities of China(FRF-TP-24-058A)with additional support from the National Key Laboratory of Helicopter Aeromechanics(2024-ZSJ-LB-02-02).
文摘Considering the impact of terminal impact time constraints and the state information of maneuvering targets on the guidance accuracy in multi-UAV cooperative guidance,this paper proposes an impact time cooperative control guidance law(ITCCG)that combines the optimal error dynamics with an improved adaptive cubature Kalman filter(IACKF)algorithm.First,a terminal impact time feedback term is introduced into proportional navigation guidance based on the relative virtual guidance model,and terminal time control is achieved through optimal error dynamics.Then,the Huber loss function is used to reduce the impact of measurement outliers,and the diagonal decomposition is applied to address the issue of non-positive definite matrices that cannot undergo Cholesky decomposition.Finally,the ITCCG and IACKF algorithms combined achieve multi-UAV time-cooperated guidance based on maneuvering target state estimation.Simulation results show that the proposed algorithm effectively reduces the target state estimation error and achieves cooperative guidance within the desired time frame.
文摘The prolonged and intricate history of oncological treatments has transitioned significantly since the introduction of chemotherapy.Substantial therapeutic benefits in cancer therapy have been achieved by the integration of conventional treatments with molecular biosciences and omics technologies.Human epidermal growth factor receptor,hormone receptors,and angiogenesis factors are among the established therapies in tumor reduction and managing side effects.Novel targeted therapies like KRAS G12C,Claudin-18 isoform 2(CLDN18.2),Trophoblast cell-surface antigen 2(TROP2),and epigenetic regulators emphasize their promise in advancing precision medicine.However,in many cases,the resistance mechanisms associated with these interventions render them ineffective in carrying out their functions.The purpose of this review is to provide a comprehensive and up-to-date examination of both established and emerging drug targets and mechanisms of treatment resistance in oncology.This review seeks to elucidate recent advancements,address persisting challenges,and explore opportunities for innovative developments in cancer target research.Additionally,it explores the growing role of artificial intelligence in reshaping cancer drug discovery and development frameworks as potential avenues for future research.In conclusion,innovative approaches in oncology,supported by pharmacological research,ongoing clinical trials,molecular biosciences,and artificial intelligence,are poised to significantly transform cancer treatment.
基金supported by the National Natural Science Foundation of China(22007008,22178048).
文摘Naphthalene,anthracene and pyridone endoperoxides are known to thermally release singlet oxygen.However,in the cycloreversion reaction,singlet oxygen is produced stoichiometrically;therefore,multiple singlet oxygen releasing modules are expected to be very useful in inducing apoptosis of cancer cells.Herein,we present a potential therapeutic agent presenting three-pyridone endoperoxide modules and a mitochondria targeting group.Compared to previously reported pyridone-based monofunctional endoperoxides,the triple endoperoxide is highly effective as evidenced by assays and fluorescence microscopy.
文摘In recent years,proteolysis-targeting chimeras(PROTACs)have gained widespread attention as an emerging therapeutic approach.PROTACs are bifunctional molecules composed of a target protein-binding ligand,an E3 ubiquitin ligase ligand,and a linker connecting these ligands.By harnessing the cell’s intrinsic ubiquitin-proteasome system(UPS),they promote the ubiquitination of specific target proteins,leading to their degradation and therapeutic effects.PROTACs show exceptional promise in targeting conventional“undruggable”targets compared to traditional small-molecule inhibitors.This review provides an overview of PROTACs,including their molecular mechanism of action,therapeutic benefits,development history,key design aspects,current research and development challenges,and future trends in nextgeneration PROTAC technology.
基金the Deanship of Research and Graduate Studies at King Khalid University,KSA,for funding this work through the Large Research Project under grant number RGP2/164/46.
文摘Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets.
基金supported by the Natural Science Foundation of Jilin Province(No.SKL202302002).
文摘Triple-negative breast cancer(TNBC)presents significant diagnostic and therapeutic challenges due to the lack of targeted treatments,rapid progression,high recurrence and metastasis rates,and overall poorer prognosis.Herein,the targeted theranostic platform of cysteine-modified gold nanodots-sulfhydrated luteinizing hormone releasing hormone(CGN-SLR)nanosystem was designed for target recognition and precise dual-mode imaging-guided photothermal therapy(PTT)against TNBC.On the one hand,the CGN-SLR nanosystem can serve as an ideal targeting fluorescent probe and computed tomography(CT)enhancer to facilitate the accurate diagnosis and surgical guidance of TNBC.On the other hand,the CGN-SLR nanosystem with great targeting and PTT ability can significantly inhibit the growth of TNBC,without causing harm to normal tissues and healthy organs.It provides an effective strategy for the diagnosis and treatment of TNBC through the rational design of multifunctional nanoplatform with target recognition,multiple imaging guidance/monitoring,and high-efficiency PTT.
文摘We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precision sampling strategy based on prostate cancer’s spatial distribution,aligning with personalized diagnosis trends.
基金supported by a grant from the Dalian Science and Technology Innovation Fund Program(No.2024JJ13PT070)United Foundation for Dalian Institute of Chemical Physics,Chinese Academy of Sciences and the Second Hospital of Dalian Medical University(No.DMU-2&DICP UN202410)Dalian Life and Health Field Guidance Program Project(No.2024ZDJH01PT084).
文摘Metabolic reprogramming involving branched-chain amino acids(BCAAs)—leucine,isoleucine,and valine—is increasingly recognized as pivotal in cancer progression,metastasis,and immune modulation.This review comprehensively explores how cancer cells rewire BCAA metabolism to enhance proliferation,survival,and therapy resistance.Tumors manipulate BCAA uptake and catabolism via high expression of transporters like L-type amino acid transporter 1(LAT1)and enzymes including branched chain amino acid transaminase 1(BCAT1),branched chain amino acid transaminase 2(BCAT2),branched-chain alpha-keto acid dehydrogenase(BCKDH),and branched chain alpha-keto acid dehydrogenase kinase(BCKDK).These alterations sustain energy production,biosynthesis,redox homeostasis,and oncogenic signaling(especially mammalian target of rapamycin complex 1[mTORC1]).Crucially,tumor-driven BCAA depletion also shapes an immunosuppressive microenvironment,impairing anti-tumor immunity by limiting essential nutrients for T cells and natural killer(NK)cells.Innovative therapeutic strategies targeting BCAA pathways—ranging from selective small-molecule inhibitors(e.g.,LAT1 and BCAT1/2)to dietary modulation—have shown promising preclinical and early clinical efficacy,highlighting their potential to exploit metabolic vulnerabilities in cancer cells while bolstering immune responses.By integrating multi-omics data and precision targeting approaches,this review underscores the translational significance of BCAA metabolic reprogramming,positioning it as a novel frontier in cancer treatment.
基金supported by the National Natural Science Foundation of China(82373277).
文摘Delivery carriers serve as a highly efficient approach for precision nutrition and medicine;however,artificial delivery carriers are prone to triggering the immune response and have the disadvantages of poor stability and low bioavailability.Extracellular vesicles(EVs),nucleus-free biological particles composed of phospholipid bilayers secreted by living cells,are a new generation of targeted delivery carriers.In recent years,an increasing number of species have been reported to contain EVs.Among them,food-derived extracellular vesicles(FDEVs)show outstanding comprehensive properties.FDEVs are considered to have great application potential due to their wide range of sources,high yields,absence of human pathogenic pathogens,and ethical concerns.In this review,the preparation,nomenclature,physicochemical characteristics,and preservation methods of FDEVs are discussed,as well as their potential protein markers,bioactivities,and applications as novel targeted delivery carriers of FDEVs from animals,plants,and microorganisms.We also summarized the adverse consequences of FDEVs in current studies,and put forward the problems and challenges in the process of FDEVs research and commercialization.In short,the importance of FDEVs has been highlighted,and FDEVs have good application prospects as a new class of targeted delivery carriers.The current problems should be paid attention to and actively solved.
文摘Oncology Research Editorial Office Published:19 January 2026 The published article titled“miR-202 Promotes Cell Apoptosis in Esophageal Squamous Cell Carcinoma by Targeting HSF2”has been retracted from Oncology Research,Vol.25,No.2,2017,pp.215-223.DOI:10.3727/096504016X14732772150541 URL:https://www.techscience.com/or/v25n2/56800.
文摘Food-grade biopolymers and nanotechnology have been increasingly used to revolutionize the delivery of bioactive compounds by enhancing stability,bioavailability,and controlled release.Within the scope of nanoencapsulation systems,this review explores food-derived polymers such as vicilin,zein,gluten,cruciferin,inulin,and others.These biopolymers are ideal since they encapsulate numerous functional compounds,such as vitamins,probiotics,essential oils,and polyphenols,because they are biocompatible,amphiphilic,and biodegradable.The specific physical and chemical properties of each polymer,extraction procedures,and nanoencapsulation techniques applied therein(e.g.,ionic gelation and spray drying)are described.The review highlights advanced targeting systems like pH-sensitive,magnetic delivery.Additional applications include those in synergistic nutraceutical systems,oral administration of vaccination,and intelligent food packaging.All these findings demonstrate that food polymers are increasingly more viable as functional nanocarriers by way of increasing bioactive delivery and the shifting requirements of personalized and health-based dietary regimes.
基金supported by the National Natural Science Foundation of China(No.62003269).
文摘Computing electrostatic interaction on non-cooperative targets with unknown meshes is crucial for electrostatic-based space on-orbit services.Although meshes for electrostatic interaction computations can be reconstructed from point clouds,they are usually too dense,leading to high computational costs.This paper presents an optimization method for converting dense meshes into optimal meshes,enabling fast and accurate computation of the electrostatic interaction by point clouds.First,the dense mesh reconstructed from point clouds is simplified into a coarse mesh using local operators.Second,the simplified mesh is refined by an iterative strategy that integrates a lightweight method of moments and an impedance matrix inheritance technique,ultimately yielding an optimal mesh for computing the electrostatic interaction.Simulation results show that our method effectively optimizes dense meshes,making electrostatic interaction computations using point clouds approximately 63.4 times more efficient than the previous method.
基金supported by the National Natural Science Foundation of China(No.52106080)the Jilin City Science and Technology Innovation Development Plan Project(No.20240302014)+2 种基金the Jilin Provincial Department of Education Science and Technology Research Project(No.JJKH20230135K)the Jilin Province Science and Technology Development Plan Project(No.YDZJ202401640ZYTS)the Northeast Electric Power University Teaching Reform Research Project(No.J2427)。
文摘The continuous decrease in global fishery resources has increased the importance of precise and efficient underwater fish monitoring technology.First,this study proposes an improved underwater target detection framework based on YOLOv8,with the aim of enhancing detection accuracy and the ability to recognize multi-scale targets in blurry and complex underwater environments.A streamlined Vision Transformer(ViT)model is used as the feature extraction backbone,which retains global self-attention feature extraction and accelerates training efficiency.In addition,a detection head named Dynamic Head(DyHead)is introduced,which enhances the efficiency of processing various target sizes through multi-scale feature fusion and adaptive attention modules.Furthermore,a dynamic loss function adjustment method called SlideLoss is employed.This method utilizes sliding window technology to adaptively adjust parameters,which optimizes the detection of challenging targets.The experimental results on the RUOD dataset show that the proposed improved model not only significantly enhances the accuracy of target detection but also increases the efficiency of target detection.
基金supported by the National Natural Science Foundation of China(No.61976028).
文摘To address the challenges of small target detection and significant scale variations in unmanned aerial vehicle(UAV)aerial imagery,which often lead to missed and false detections,we propose Multi-scale Feature Fusion YOLO(MFF-YOLO),an enhanced algorithm based on YOLOv8s.Our approach introduces a Multi-scale Feature Fusion Strategy(MFFS),comprising the Multiple Features C2f(MFC)module and the Scale Sequence Feature Fusion(SSFF)module,to improve feature integration across different network levels.This enables more effective capture of fine-grained details and sequential multi-scale features.Furthermore,we incorporate Inner-CIoU,an improved loss function that uses auxiliary bounding boxes to enhance the regression quality of small object boxes.To ensure practicality for UAV deployment,we apply the Layer-adaptive Magnitude-based pruning(LAMP)method to significantly reduce model size and computational cost.Experiments on the VisDrone2019 dataset show that MFF-YOLO achieves a 5.7% increase in mean average precision(mAP)over the baseline,while reducing parameters by 8.5 million and computation by 17.5%.The results demonstrate that our method effectively improves detection performance in UAV aerial scenarios.
文摘Oncology Research Editorial Office Published:19 January 2026 The published article titled“miR-126-5p Restoration Promotes Cell Apoptosis in Cervical Cancer by Targeting Bcl2l2”has been retracted from Oncology Research,Vol.25,No.4,2017,pp.463-470.DOI:10.3727/096504016X14685034103879 URL:https://www.techscience.com/or/v25n4/56826.
文摘The published article titled“MicroRNA-133b Inhibits Proliferation,Cellular Migration,and Invasion via Targeting LASP1 in Hepatocarcinoma Cells”has been retracted from Oncology Research,Vol.25,No.8,2017,pp.1269–1282.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
