Fetal intracranial tumors are rare,accounting for approximately 0.5%–1.9%of all pediatric tumors,though the true incidence may be underestimated.These tumors often present with distinct histopathological features,ima...Fetal intracranial tumors are rare,accounting for approximately 0.5%–1.9%of all pediatric tumors,though the true incidence may be underestimated.These tumors often present with distinct histopathological features,imaging characteristics,and clinical behavior compared to their postnatal counterparts.This review summarizes the current understanding of the prenatal diagnosis and characterization of fetal brain tumors,with a particular focus on the role of fetal magnetic resonance imaging(MRI).We discuss the advantages of advanced MR sequences in enhancing lesion detection and anatomical delineation following suspicious findings on obstetric ultrasound.Common tumor types encountered in utero—including teratomas,as-trocytomas,medulloblastomas,choroid plexus papillomas,and craniopharyngiomas—are reviewed in terms of imaging fea-tures,differential diagnosis,and clinical implications.Furthermore,the review addresses the diagnostic challenges,prognostic considerations,and the potential role of fetal MRI in guiding perinatal management and parental counseling.展开更多
Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progr...Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.展开更多
BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the cor...BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.展开更多
Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intell...Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intelligence(AI)and deep learning,there has been potential to improve diagnostic accuracy,especially with Magnetic Resonance Imaging(MRI).However,traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation.Thus,our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model.The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification.The proposed model is first trained and later evaluated using the BraTS 2020 dataset.In our proposed model preprocessing consists of normalization,noise reduction,and data augmentation to improve model robustness.The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution.We have performed experimentation to measure efficiency.For this,we have used various metrics including accuracy,sensitivity,and curve(AUC-ROC).The proposed model achieved a high accuracy of 94%,a sensitivity of 93%,a specificity of 92%,and an AUC-ROC of 0.98,outperforming traditional diagnostic models in brain tumor detection.The proposed model accurately identifies tumor regions,while dilated convolutions enhanced the segmentation accuracy,especially for complex tumor structures.The proposed model demonstrates significant potential for clinical application,providing reliable and precise brain tumor detection in MRI.展开更多
Although the two compounds quercetin and kaempferol components of TCM were verified as useful anticancer compounds,their molecular mechanisms are not well discussed.The present work aims to demystify the antitumor mec...Although the two compounds quercetin and kaempferol components of TCM were verified as useful anticancer compounds,their molecular mechanisms are not well discussed.The present work aims to demystify the antitumor mechanisms of TCM compounds.Therefore,network pharmacology and pharmacophore screening were adopted with molecular docking to identify the bioactive compounds possessing excellent oral bioavailability and drug-likeness.The method of pharmacophore screening was then employed to examine molecular interactions occurred between the compounds and targets.The gene-disease associations were collected from the DisGeNET database.The STRING database was utilized to cluster overlapping targets.The key targets were identified,and molecular docking with quercetin and kaempferol was performed against these targets to further characterize drug binding affinities,which verified strong binding affinities comparable with the known anticancer drugs.The multitarget inhibitor was identified and exerted a powerful inhibitory effect on tumor cells,as demonstrated by the CCK-8 assay.Quercetin and kaempferol components derived from TCM with good oral bioavailability and drug-likeness held promise for effective antitumor treatment,especially for tumors resistant to other treatment.展开更多
Background Brain tumors are challenging to diagnose and treat,and require accurate and early therapeutic intervention.Magnetic Resonance Imaging(MRI)scans can visualize the internal structure of the brain.Often,deep l...Background Brain tumors are challenging to diagnose and treat,and require accurate and early therapeutic intervention.Magnetic Resonance Imaging(MRI)scans can visualize the internal structure of the brain.Often,deep learning is applied to images for the early and accurate detection of tumor cells.However,these models lack accuracy and efficacy in practical applications.Hybrid or modified models can facilitate better classification and provide insights into early-stage cancer detection.Methods This study demonstrates a parallel architecture that uses MRI images and integrates transformer-based frameworks with Convolutional Neural Networks(CNNs)to better classify distinct types of brain tumors.The proposed architecture,SwinResDual(SwRD),combines a Residual Network(ResNet)and a Swin Transformer in parallel to extract key features from input images.Using augmented MRI scans,31,464 scans for multiclass classification,and 30,000 scans for binary classification,the architecture simultaneously processed images through the ResNet50 and Swin Transformer branches,leveraging their strengths in hierarchical feature extraction and global context modeling to efficiently capture local and global image features.The final classifications are obtained by merging these features and passing them through a linear classifier.This approach identifies strong and varied characteristics and provides a precise brain tumor diagnosis.Results In the extensive evaluation,the model performed with an accuracy of 99.79% and a cross-validation accuracy of 100%for multiclass classification,along with 99.97% accuracy in binary classification.Conclusions In conclusion,the findings demonstrate great promise for brain tumor detection and advanced medical imaging diagnostics.展开更多
Various genetic association studies have identified numerous single nucleotide polymorphisms(SNPs)associated with nasopharyngeal carcinoma(NPC)risk.However,these studies have predominantly focused on common variants,l...Various genetic association studies have identified numerous single nucleotide polymorphisms(SNPs)associated with nasopharyngeal carcinoma(NPC)risk.However,these studies have predominantly focused on common variants,leaving the contribution of rare variants to the“missing heritability”largely unexplored.Here,we integrate genotyping data from 3925 NPC cases and 15,048 healthy controls to identify a rare SNP,rs141121474,resulting in a Glu510Lys mutation in KLHDC4 gene linked to increased NPC risk.Subsequent analyses reveal that KLHDC4 is highly expressed in NPC and correlates with poorer prognosis.Functional characterizations demonstrate that KLHDC4 acts as an oncogene in NPC cells,enhancing their migratory and metastatic capabilities,with these effects being further augmented by the Glu510Lys mutation.Mechanistically,the Glu510Lys mutant exhibits increased interaction with Vimentin compared to the wild-type KLHDC4(KLHDC4-WT),leading to elevated Vimentin protein stability and modulation of the epithelial-mesenchymal transition process,thereby promoting tumor metastasis.Moreover,Vimentin knockdown significantly mitigates the oncogenic effects induced by overexpression of both KLHDC4-WT and the Glu510Lys variant.Collectively,our findings highlight the critical role of the rare KLHDC4 variant rs141121474 in NPC progression and propose its potential as a diagnostic and therapeutic target for NPC patients.展开更多
BACKGROUND Rectal cancer is one of the common digestive system malignant tumors around the world.Its early diagnosis and staging are crucial for rectal cancer treatment and prognosis.In recent years,tumor markers have...BACKGROUND Rectal cancer is one of the common digestive system malignant tumors around the world.Its early diagnosis and staging are crucial for rectal cancer treatment and prognosis.In recent years,tumor markers have gradually received attention in early screening,treatment monitoring and prognostic evaluation of cancer,but their predictive role in rectal cancer staging and differentiation is still unclear.AIM To assess the prognostic value of tumor markers alpha-fetoprotein(AFP)cancer antigen 72-4(CA72-4),carbohydrate antigen 19-9(CA19-9),and carcinoembryonic antigen(CEA),alongside multimodal magnetic resonance imaging(MRI),for staging and differentiating rectal cancer in patients.METHODS This study retrospectively analyzed 167 patients with rectal cancer who were treated at our institution from January 2020 to December 2024.Each patient underwent serological testing and multimodal MRI for diagnosis.Histopathological examination after surgical resection or imaging based on follow-up was used as the gold standard.According to the T stage and differentiation degree,patients were divided into low stage group(T1-T2)and high stage group(T3-T4).In addition,they were divided into low-differentiation groups and high-differentiation groups according to their differentiation degree.We compared the accuracy,sensitivity and specificity of tumor marker levels and MRI in rectal cancer stage and differentiation.RESULTS The study's findings indicate that in the context of rectal cancer T staging,there is substantial concordance between MRI and clinicopathological assessments,with a Kappa coefficient of 0.789(P<0.001).Similarly,for various degrees of tumor differentiation,MRI and clinicopathological evaluations demonstrated substantial agreement,with a Kappa coefficient of 0.651(P<0.001).Notably,the concentrations of tumor markers CA19-9,CA72-4,CEA,and AFP were significantly elevated in the T3-T4 stage compared to the T1-T2 stage.Furthermore,these markers were significantly higher in the low-differentiation group compared to the high-differentiation group(P<0.05).The combined use of tumor markers and MRI for preoperative T staging of rectal cancer yielded a diagnostic sensitivity of 93.7%and a specificity of 94.6%,as evidenced by the receiver operating characteristic analysis,with an area under the curve of 0.947.For tumor differentiation,the diagnostic sensitivity and specificity were 93.6%and 97.1%,respectively,with an area under the curve of 0.978(95%confidence interval:0.946-1.000),surpassing the accuracy of individual detection methods.CONCLUSION The CA19-9,CA72-4,CEA and AFP tumor markers combined with multimodal MRI have high sensitivity and specificity in diagnosing rectal cancer stage and differentiation.Their diagnostic efficacy is significantly better than that of single tests,which can effectively improve the predictive ability of rectal cancer stage and differentiation,provide a more reliable diagnostic reference for clinical practice,and have important clinical significance.展开更多
The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors po...The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors possess high changes in terms of size,shape,and amount,and hence the classification process acts as a more difficult research problem.This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods.The effectiveness of the suggested method depends on the coyote optimization algorithm,also known as the LOBO algorithm,which optimizes the weights of the deep-convolutional neural network classifier.The accuracy,sensitivity,and specificity indices,which are obtained to be 92.40%,94.15%,and 91.92%,respectively,are used to validate the effectiveness of the suggested method.The result suggests that the suggested strategy is superior for effectively classifying brain tumors.展开更多
Single-atom nanozymes(SAzymes)hold significant potential for tumor catalytic therapy,but their effectiveness is often compromised by low catalytic efficiency within tumor microenvironment.This efficiency is mainly inf...Single-atom nanozymes(SAzymes)hold significant potential for tumor catalytic therapy,but their effectiveness is often compromised by low catalytic efficiency within tumor microenvironment.This efficiency is mainly influenced by key factors including hydrogen peroxide(H_(2)O_(2))availability,acidity,and temperature.Simultaneous optimization of these key factors presents a significant challenge for tumor catalytic therapy.In this study,we developed a comprehensive strategy to refine single-atom catalytic kinetics for enhancing tumor catalytic therapy through dual-enzyme-driven cascade reactions.Iridium(Ir)SAzymes with high catalytic activity and natural enzyme glucose oxidase(GOx)were utilized to construct the cascade reaction system.GOx was loaded by Ir SAzymes due to its large surface area.Then,the dual-enzyme-driven cascade reaction system was modified by cancer cell membranes for improving biocompatibility and achieving tumor homologous targeting ability.GOx catalysis reaction could produce abundant H2O2 and lower the local p H,thereby optimizing key reaction-limiting factors.Additionally,upon laser irradiation,Ir SAzymes could raise local temperature,further enhancing the catalytic efficiency of dual-enzyme system.This comprehensive optimization maximized the performance of Ir SAzymes,significantly improving the efficiency of catalytic therapy.Our findings present a strategy of refining single-atom catalytic kinetics for tumor homologous-targeted catalytic therapy.展开更多
A brain tumor is a disease in which abnormal cells form a tumor in the brain.They are rare and can take many forms,making them difficult to treat,and the survival rate of affected patients is low.Magnetic resonance im...A brain tumor is a disease in which abnormal cells form a tumor in the brain.They are rare and can take many forms,making them difficult to treat,and the survival rate of affected patients is low.Magnetic resonance imaging(MRI)is a crucial tool for diagnosing and localizing brain tumors.However,themanual interpretation of MRI images is tedious and prone to error.As artificial intelligence advances rapidly,DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors.In this study,we introduce a deep convolutional neural network(DCNN)framework for brain tumor classification that uses EfficientNet-B6 as the backbone architecture and adds additional layers.The model achieved an accuracy of 99.10%on the public Brain Tumor MRI datasets,and we performed an ablation study to determine the optimal batch size,optimizer,loss function,and learning rate to maximize the accuracy and robustness of the model,followed by K-Fold cross-validation and testing the model on an independent dataset,and tuning Hyperparameters with Bayesian Optimization to further enhance the performance.When comparing our model to other deep learning(DL)models such as VGG19,MobileNetv2,ResNet50,InceptionV3,and DenseNet201,aswell as variants of the EfficientNetmodel(B1–B7),the results showthat our proposedmodel outperforms all othermodels.Our investigational results demonstrate superiority in terms of precision,recall/sensitivity,accuracy,specificity,and F1-score.Such innovations can potentially enhance clinical decision-making and patient treatment in neurooncological settings.展开更多
The advent of targeted T-cell therapy,with chimeric antigen receptor(CAR)T-cell therapy as the most prominent example,has yielded significant clinical efficacy for both relapsed and refractory hematological malignanci...The advent of targeted T-cell therapy,with chimeric antigen receptor(CAR)T-cell therapy as the most prominent example,has yielded significant clinical efficacy for both relapsed and refractory hematological malignancies.However,this form of T-cell immunotherapy is often accompanied by severe systemic toxicities,suboptimal response rates,and host immune rejection in clinical sethings,which detracts from its therapeutic utility.展开更多
The phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)pathway is one of the most frequently dysregulated signaling networks in oral squamous cell carcinoma(OSCC).Although the tumor microenvironment(TME)and epig...The phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)pathway is one of the most frequently dysregulated signaling networks in oral squamous cell carcinoma(OSCC).Although the tumor microenvironment(TME)and epigenetic modifiers are recognized to play a pivotal role in aberrant activation of the PI3K/AKT pathway in OSCC,the available evidence is fragmentary and a comprehensive analysis is warranted.This review evaluates the intricate mechanisms by which various components of the TME facilitate proliferation,apoptosis evasion,invasion,migration,angiogenesis,metastasis,as well as therapy resistance in OSCC through activation of PI3K/AKT signalling.The review has also analysed how epigenetic modifiers such as DNA methylation,histone modifications,and noncoding RNAs that have emerged as key players in orchestrating OSCC development and progression influence the PI3K/AKT pathway.Preclinical studies and clinical trials on the efficacy of PI3K/AKT inhibitors as viable options for OSCC treatment are discussed.Overall,this review supports the tenet that the PI3K/AKT pathway,which functions as a central hub through crosstalk with several oncogenic signaling pathways and overarching impact on all the hallmark traits of cancer,offers immense potential as a biomarker and oncotherapeutic target for OSCC.展开更多
In this editorial,we highlight the study by Xiao et al.Despite progress in the management of diabetic foot ulcers(DFUs),impaired wound healing remains a significant clinical challenge.Recent studies have highlighted t...In this editorial,we highlight the study by Xiao et al.Despite progress in the management of diabetic foot ulcers(DFUs),impaired wound healing remains a significant clinical challenge.Recent studies have highlighted the critical role of epigenetic modifications in diabetic wound healing,with particular emphasis on DNA and RNA methylation pathways.This editorial discusses the findings of Xiao et al,who identified the Wilms tumor 1-associated protein(WTAP)-DNA methyltransferase 1(DNMT1)axis as a pivotal regulator of endothelial dys-function in DFUs.WTAP,a regulatory subunit of N6-methyladenosine(m6A)methyltransferase,is upregulated under high-glucose conditions and drives the excessive expression of DNMT1 via m6A modification.This contributes to im-paired angiogenesis,reduced cell viability,and delayed wound closure.WTAP knockdown restored endothelial function and significantly improved wound healing in a diabetic mouse model.Furthermore,DNMT1 overexpression ab-rogated the benefits of WTAP suppression,confirming its downstream effector role.Thus,targeting the WTAP-DNMT1 axis provides a new avenue for DFU management.Moreover,epigenetic interventions that modulate both the m6A and RNA methylation pathways could restore endothelial function and enhance tissue repair in patients with diabetes.展开更多
Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors pr...Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection.While U-Net-based architectures have demonstrated strong performance in medical image segmentation,there remains room for improvement in feature extraction and localization accuracy.In this study,we propose a novel hybrid model designed to enhance 3D brain tumor segmentation.The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder.Additionally,to enhance the model’s generalization ability,Squeeze and Excitation attention mechanism is integrated.We introduce Gabor filter banks into the encoder to further strengthen the model’s ability to extract robust and transformation-invariant features from the complex and irregular shapes typical in medical imaging.This approach,which is not well explored in current U-Net-based segmentation frameworks,provides a unique advantage by enhancing texture-aware feature representation.Specifically,Gabor filters help extract distinctive low-level texture features,reducing the effects of texture interference and facilitating faster convergence during the early stages of training.Our model achieved Dice scores of 0.881,0.846,and 0.819 for Whole Tumor(WT),Tumor Core(TC),and Enhancing Tumor(ET),respectively,on the BraTS 2020 dataset.Cross-validation on the BraTS 2021 dataset further confirmed the model’s robustness,yielding Dice score values of 0.887 for WT,0.856 for TC,and 0.824 for ET.The proposed model outperforms several state-of-the-art existing models,particularly in accurately identifying small and complex tumor regions.Extensive evaluations suggest integrating advanced preprocessing with an attention-augmented hybrid architecture offers significant potential for reliable and clinically valuable brain tumor segmentation.展开更多
BACKGROUND Diabetic wound injury is a significant and common complication in individuals with diabetes.N6-methyladenosine(m6A)-related epigenetic regulation is widely involved in the pathogenesis of diabetes complicat...BACKGROUND Diabetic wound injury is a significant and common complication in individuals with diabetes.N6-methyladenosine(m6A)-related epigenetic regulation is widely involved in the pathogenesis of diabetes complications.However,the function of m6A methyltransferase Wilms tumor 1-associated protein(WTAP)in diabetic wound healing remains elusive.AIM To investigate the potential epigenetic regulatory mechanism of WTAP during diabetic wound healing.METHODS Human umbilical vein endothelial cells(HUVECs)were induced with high glucose(HG)to establish in vitro cell model.Male BALB/c mice were intraperitoneally injected with streptozotocin to mimic diabetes,and full-thickness excision was made to mimic diabetic wound healing.HG-induced HUVECs and mouse models were treated with WTAP siRNAs and DNA methyltransferase 1(DNMT1)overexpression vectors.Cell viability and migration ability were detected by cell counting kit-8 and Transwell assays.In vitro angiogenesis was measured using a tube formation experiment.The images of wounds were captured,and re-epithelialization and collagen deposition of skin tissues were analyzed using hematoxylin and eosin staining and Masson’s trichrome staining.RESULTS The expression of several m6A methyltransferases,including METTL3,METTL14,METTL16,KIAA1429,WTAP,and RBM15,were measured.WTAP exhibited the most significant elevation in HG-induced HUVECs compared with the normal control.WTAP depletion notably restored cell viability and enhanced tube formation ability and migration of HUVECs suppressed by HG.The unclosed wound area of mice was smaller in WTAP knockdowntreated mice than in control mice at nine days post-wounding,along with enhanced re-epithelialization rate and collagen deposition.The m6A levels on DNMT1 mRNA in HUVECs were repressed by WTAP knockdown in HUVECs.The mRNA levels and expression of DNMT1 were inhibited by WTAP depletion in HUVECs.Overexpression of DNMT1 in HUVECs notably reversed the effects of WTAP depletion on HG-induced HUVECs.CONCLUSION WTAP expression is elevated in HG-induced HUVECs and epigenetically regulates the m6A modification of DNMT1 to impair diabetic wound healing.展开更多
Immunotherapy has brought unprecedented breakthroughs to advanced malignant tumors,yet the immune microenvironment shaped by the tumor stroma has often been underestimated in the traditional focus on the“immune check...Immunotherapy has brought unprecedented breakthroughs to advanced malignant tumors,yet the immune microenvironment shaped by the tumor stroma has often been underestimated in the traditional focus on the“immune checkpoint-T cell”axis.Collagen not only constitutes a mechanical barrier that distinguishes between the periphery and core of solid tumors but also systematically remodels the orientation of metabolism,vasculature,and immune cell phenotypic plasticity through its spatial density,fiber arrangement,and crosslinking patterns(F igure 1)[1,2].Abundant evidence suggests that over-accumulated types I and III collagen drive CD8+T cell exhaustion,NK cell functional inhibition,and tumor-associated macrophage polarization through ligand-receptor networks involving LAIR-1,DDR2,andβ1/β3 integrins[3-6].Mechanistically,collagen engagement of LAIR-1 delivers inhibitory signals in effector lymphocytes,promoting dysfunctional or exhausted states[7-9].In parallel,collagen-β1/β3 integrin signaling activates mechanotransduction pathways(e.g.,FAK/SRC),reducing T-cell motility and immune-tumor contact,while DDR2 activation supports matrix-remodeling programs that limit lymphocyte trafficking.展开更多
This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 20...This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.展开更多
Colorectal cancer(CRC)is ranked as the third most common tumor globally,representing approximately 10%of all cancer cases,and is the second primary cause of cancer-associated mortality.Existing therapeutic approaches ...Colorectal cancer(CRC)is ranked as the third most common tumor globally,representing approximately 10%of all cancer cases,and is the second primary cause of cancer-associated mortality.Existing therapeutic approaches demonstrate limited efficacy against CRC,partially due to the immunosuppressive tumor microenvironment(TME).In recent years,substantial evidence indicates that dysbiosis of the gut microbiota and its metabolic products is closely associated with the initiation,progression,and prognostic outcomes of CRC.In this minireview,we systematically elaborate on how these microbes and their metabolites directly impair intestinal epithelial integrity,activate cancer-associated fibroblasts,remodel tumor vasculature,and critically,sculpt an immunosuppressive landscape by modulating T cells,dendritic cells,and tumor-associated macrophages.We highlight the translational potential of targeting the gut microbiota,including fecal microbiota transplantation,probiotics,and engineered microbial systems,to reprogram the TME and overcome resistance to immunotherapy and chemotherapy.A deeper understanding of the microbiota-TME axis is essential for developing novel diagnostic and therapeutic paradigms for CRC.展开更多
We read with great interest the investigation of Kang et al related the applications of the multiparametric magnetic resonance imaging-based predictive model for assessing chemotherapy efficacy in colorectal cancer pa...We read with great interest the investigation of Kang et al related the applications of the multiparametric magnetic resonance imaging-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.The authors focused on decision-making based on the integration of tumor differentiation,signal intensity ratio,margin distance,and magnetic resonance imaging-detected lymph node metastasis.Indeed,these multiparameter predictive models could also be used for diagnosis as an alternative to invasive tissue examination methods.However,progress in this field enables us to shift the paradigm to radiology biopsies,particularly given the nonlinear effects of various radiation sources.展开更多
基金supported by the Medical Innovation Research Special Project of Science and Technology Commission of Shanghai Municipality(Grant/Award Number:23Y11907800)Fundamental Research Funds for the Central Universities(Grant/Award Number:YG2023ZD22)Shanghai Key Laboratory of Child Brain and Development(Grant/Award Number:24dz2260100).
文摘Fetal intracranial tumors are rare,accounting for approximately 0.5%–1.9%of all pediatric tumors,though the true incidence may be underestimated.These tumors often present with distinct histopathological features,imaging characteristics,and clinical behavior compared to their postnatal counterparts.This review summarizes the current understanding of the prenatal diagnosis and characterization of fetal brain tumors,with a particular focus on the role of fetal magnetic resonance imaging(MRI).We discuss the advantages of advanced MR sequences in enhancing lesion detection and anatomical delineation following suspicious findings on obstetric ultrasound.Common tumor types encountered in utero—including teratomas,as-trocytomas,medulloblastomas,choroid plexus papillomas,and craniopharyngiomas—are reviewed in terms of imaging fea-tures,differential diagnosis,and clinical implications.Furthermore,the review addresses the diagnostic challenges,prognostic considerations,and the potential role of fetal MRI in guiding perinatal management and parental counseling.
文摘Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.
文摘BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.
基金supported by the European University of Atlantic.
文摘Brain tumors pose significant diagnostic challenges due to their diverse types and complex anatomical locations.Due to the increase in precision image-based diagnostic tools,driven by advancements in artificial intelligence(AI)and deep learning,there has been potential to improve diagnostic accuracy,especially with Magnetic Resonance Imaging(MRI).However,traditional state-of-the-art models lack the sensitivity essential for reliable tumor identification and segmentation.Thus,our research aims to enhance brain tumor diagnosis in MRI by proposing an advanced model.The proposed model incorporates dilated convolutions to optimize the brain tumor segmentation and classification.The proposed model is first trained and later evaluated using the BraTS 2020 dataset.In our proposed model preprocessing consists of normalization,noise reduction,and data augmentation to improve model robustness.The attention mechanism and dilated convolutions were introduced to increase the model’s focus on critical regions and capture finer spatial details without compromising image resolution.We have performed experimentation to measure efficiency.For this,we have used various metrics including accuracy,sensitivity,and curve(AUC-ROC).The proposed model achieved a high accuracy of 94%,a sensitivity of 93%,a specificity of 92%,and an AUC-ROC of 0.98,outperforming traditional diagnostic models in brain tumor detection.The proposed model accurately identifies tumor regions,while dilated convolutions enhanced the segmentation accuracy,especially for complex tumor structures.The proposed model demonstrates significant potential for clinical application,providing reliable and precise brain tumor detection in MRI.
文摘Although the two compounds quercetin and kaempferol components of TCM were verified as useful anticancer compounds,their molecular mechanisms are not well discussed.The present work aims to demystify the antitumor mechanisms of TCM compounds.Therefore,network pharmacology and pharmacophore screening were adopted with molecular docking to identify the bioactive compounds possessing excellent oral bioavailability and drug-likeness.The method of pharmacophore screening was then employed to examine molecular interactions occurred between the compounds and targets.The gene-disease associations were collected from the DisGeNET database.The STRING database was utilized to cluster overlapping targets.The key targets were identified,and molecular docking with quercetin and kaempferol was performed against these targets to further characterize drug binding affinities,which verified strong binding affinities comparable with the known anticancer drugs.The multitarget inhibitor was identified and exerted a powerful inhibitory effect on tumor cells,as demonstrated by the CCK-8 assay.Quercetin and kaempferol components derived from TCM with good oral bioavailability and drug-likeness held promise for effective antitumor treatment,especially for tumors resistant to other treatment.
文摘Background Brain tumors are challenging to diagnose and treat,and require accurate and early therapeutic intervention.Magnetic Resonance Imaging(MRI)scans can visualize the internal structure of the brain.Often,deep learning is applied to images for the early and accurate detection of tumor cells.However,these models lack accuracy and efficacy in practical applications.Hybrid or modified models can facilitate better classification and provide insights into early-stage cancer detection.Methods This study demonstrates a parallel architecture that uses MRI images and integrates transformer-based frameworks with Convolutional Neural Networks(CNNs)to better classify distinct types of brain tumors.The proposed architecture,SwinResDual(SwRD),combines a Residual Network(ResNet)and a Swin Transformer in parallel to extract key features from input images.Using augmented MRI scans,31,464 scans for multiclass classification,and 30,000 scans for binary classification,the architecture simultaneously processed images through the ResNet50 and Swin Transformer branches,leveraging their strengths in hierarchical feature extraction and global context modeling to efficiently capture local and global image features.The final classifications are obtained by merging these features and passing them through a linear classifier.This approach identifies strong and varied characteristics and provides a precise brain tumor diagnosis.Results In the extensive evaluation,the model performed with an accuracy of 99.79% and a cross-validation accuracy of 100%for multiclass classification,along with 99.97% accuracy in binary classification.Conclusions In conclusion,the findings demonstrate great promise for brain tumor detection and advanced medical imaging diagnostics.
基金supported by the National Natural Science Foundation(82261160657,82102490,and 81572781)the Guangdong Basic and Applied Basic Research Foundation(2024A1515013061)+2 种基金the Sci-Tech Project Foundation of Guangzhou City(2023A04J2141)Chang Jiang Scholars Program(J.-X.B.)the Hong Kong Research Grant Council(RGC)Theme-based Research Scheme Funds(T12-703/22-R and T12-703/23-N).
文摘Various genetic association studies have identified numerous single nucleotide polymorphisms(SNPs)associated with nasopharyngeal carcinoma(NPC)risk.However,these studies have predominantly focused on common variants,leaving the contribution of rare variants to the“missing heritability”largely unexplored.Here,we integrate genotyping data from 3925 NPC cases and 15,048 healthy controls to identify a rare SNP,rs141121474,resulting in a Glu510Lys mutation in KLHDC4 gene linked to increased NPC risk.Subsequent analyses reveal that KLHDC4 is highly expressed in NPC and correlates with poorer prognosis.Functional characterizations demonstrate that KLHDC4 acts as an oncogene in NPC cells,enhancing their migratory and metastatic capabilities,with these effects being further augmented by the Glu510Lys mutation.Mechanistically,the Glu510Lys mutant exhibits increased interaction with Vimentin compared to the wild-type KLHDC4(KLHDC4-WT),leading to elevated Vimentin protein stability and modulation of the epithelial-mesenchymal transition process,thereby promoting tumor metastasis.Moreover,Vimentin knockdown significantly mitigates the oncogenic effects induced by overexpression of both KLHDC4-WT and the Glu510Lys variant.Collectively,our findings highlight the critical role of the rare KLHDC4 variant rs141121474 in NPC progression and propose its potential as a diagnostic and therapeutic target for NPC patients.
基金Supported by the Zhejiang Provincial Natural Science Foundation of China,No.LTGY24H160006Jiaxing Medical Key Discipline,No.2023-ZC-015.
文摘BACKGROUND Rectal cancer is one of the common digestive system malignant tumors around the world.Its early diagnosis and staging are crucial for rectal cancer treatment and prognosis.In recent years,tumor markers have gradually received attention in early screening,treatment monitoring and prognostic evaluation of cancer,but their predictive role in rectal cancer staging and differentiation is still unclear.AIM To assess the prognostic value of tumor markers alpha-fetoprotein(AFP)cancer antigen 72-4(CA72-4),carbohydrate antigen 19-9(CA19-9),and carcinoembryonic antigen(CEA),alongside multimodal magnetic resonance imaging(MRI),for staging and differentiating rectal cancer in patients.METHODS This study retrospectively analyzed 167 patients with rectal cancer who were treated at our institution from January 2020 to December 2024.Each patient underwent serological testing and multimodal MRI for diagnosis.Histopathological examination after surgical resection or imaging based on follow-up was used as the gold standard.According to the T stage and differentiation degree,patients were divided into low stage group(T1-T2)and high stage group(T3-T4).In addition,they were divided into low-differentiation groups and high-differentiation groups according to their differentiation degree.We compared the accuracy,sensitivity and specificity of tumor marker levels and MRI in rectal cancer stage and differentiation.RESULTS The study's findings indicate that in the context of rectal cancer T staging,there is substantial concordance between MRI and clinicopathological assessments,with a Kappa coefficient of 0.789(P<0.001).Similarly,for various degrees of tumor differentiation,MRI and clinicopathological evaluations demonstrated substantial agreement,with a Kappa coefficient of 0.651(P<0.001).Notably,the concentrations of tumor markers CA19-9,CA72-4,CEA,and AFP were significantly elevated in the T3-T4 stage compared to the T1-T2 stage.Furthermore,these markers were significantly higher in the low-differentiation group compared to the high-differentiation group(P<0.05).The combined use of tumor markers and MRI for preoperative T staging of rectal cancer yielded a diagnostic sensitivity of 93.7%and a specificity of 94.6%,as evidenced by the receiver operating characteristic analysis,with an area under the curve of 0.947.For tumor differentiation,the diagnostic sensitivity and specificity were 93.6%and 97.1%,respectively,with an area under the curve of 0.978(95%confidence interval:0.946-1.000),surpassing the accuracy of individual detection methods.CONCLUSION The CA19-9,CA72-4,CEA and AFP tumor markers combined with multimodal MRI have high sensitivity and specificity in diagnosing rectal cancer stage and differentiation.Their diagnostic efficacy is significantly better than that of single tests,which can effectively improve the predictive ability of rectal cancer stage and differentiation,provide a more reliable diagnostic reference for clinical practice,and have important clinical significance.
文摘The categorization of brain tumors is a significant issue for healthcare applications.Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease.Brain tumors possess high changes in terms of size,shape,and amount,and hence the classification process acts as a more difficult research problem.This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods.The effectiveness of the suggested method depends on the coyote optimization algorithm,also known as the LOBO algorithm,which optimizes the weights of the deep-convolutional neural network classifier.The accuracy,sensitivity,and specificity indices,which are obtained to be 92.40%,94.15%,and 91.92%,respectively,are used to validate the effectiveness of the suggested method.The result suggests that the suggested strategy is superior for effectively classifying brain tumors.
基金financially supported by National Natural Science Foundation of China(U23A2097,82372116,22474079,22104094,82302362)Shenzhen Medical Research Fund(B2302047)+3 种基金Basic Research Program of Shenzhen(KQTD20190929172538530,JCYJ20220818095806014,JCYJ20240813142810014)Natural Science Foundation of Guangdong Province(2024A1515012677)Research Team Cultivation Program of Shenzhen University(2023QNT017,2023QNT019)Shenzhen University 2035 Program for Excellent Research(2024C004)。
文摘Single-atom nanozymes(SAzymes)hold significant potential for tumor catalytic therapy,but their effectiveness is often compromised by low catalytic efficiency within tumor microenvironment.This efficiency is mainly influenced by key factors including hydrogen peroxide(H_(2)O_(2))availability,acidity,and temperature.Simultaneous optimization of these key factors presents a significant challenge for tumor catalytic therapy.In this study,we developed a comprehensive strategy to refine single-atom catalytic kinetics for enhancing tumor catalytic therapy through dual-enzyme-driven cascade reactions.Iridium(Ir)SAzymes with high catalytic activity and natural enzyme glucose oxidase(GOx)were utilized to construct the cascade reaction system.GOx was loaded by Ir SAzymes due to its large surface area.Then,the dual-enzyme-driven cascade reaction system was modified by cancer cell membranes for improving biocompatibility and achieving tumor homologous targeting ability.GOx catalysis reaction could produce abundant H2O2 and lower the local p H,thereby optimizing key reaction-limiting factors.Additionally,upon laser irradiation,Ir SAzymes could raise local temperature,further enhancing the catalytic efficiency of dual-enzyme system.This comprehensive optimization maximized the performance of Ir SAzymes,significantly improving the efficiency of catalytic therapy.Our findings present a strategy of refining single-atom catalytic kinetics for tumor homologous-targeted catalytic therapy.
基金funded by the King Saud University,Riyadh,Saudi Arabia,for funding this work through the Researchers Supporting Research Funding program,(ORF-2025-1268).
文摘A brain tumor is a disease in which abnormal cells form a tumor in the brain.They are rare and can take many forms,making them difficult to treat,and the survival rate of affected patients is low.Magnetic resonance imaging(MRI)is a crucial tool for diagnosing and localizing brain tumors.However,themanual interpretation of MRI images is tedious and prone to error.As artificial intelligence advances rapidly,DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors.In this study,we introduce a deep convolutional neural network(DCNN)framework for brain tumor classification that uses EfficientNet-B6 as the backbone architecture and adds additional layers.The model achieved an accuracy of 99.10%on the public Brain Tumor MRI datasets,and we performed an ablation study to determine the optimal batch size,optimizer,loss function,and learning rate to maximize the accuracy and robustness of the model,followed by K-Fold cross-validation and testing the model on an independent dataset,and tuning Hyperparameters with Bayesian Optimization to further enhance the performance.When comparing our model to other deep learning(DL)models such as VGG19,MobileNetv2,ResNet50,InceptionV3,and DenseNet201,aswell as variants of the EfficientNetmodel(B1–B7),the results showthat our proposedmodel outperforms all othermodels.Our investigational results demonstrate superiority in terms of precision,recall/sensitivity,accuracy,specificity,and F1-score.Such innovations can potentially enhance clinical decision-making and patient treatment in neurooncological settings.
基金supported by the National Natural Science Foundation of China(52173150)the Guangzhou Science and Technology Program City-University Joint Funding Project(2023A03J0001)the Postdoctoral Fellowship Program of CPSF(GZC20233297)。
文摘The advent of targeted T-cell therapy,with chimeric antigen receptor(CAR)T-cell therapy as the most prominent example,has yielded significant clinical efficacy for both relapsed and refractory hematological malignancies.However,this form of T-cell immunotherapy is often accompanied by severe systemic toxicities,suboptimal response rates,and host immune rejection in clinical sethings,which detracts from its therapeutic utility.
文摘The phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)pathway is one of the most frequently dysregulated signaling networks in oral squamous cell carcinoma(OSCC).Although the tumor microenvironment(TME)and epigenetic modifiers are recognized to play a pivotal role in aberrant activation of the PI3K/AKT pathway in OSCC,the available evidence is fragmentary and a comprehensive analysis is warranted.This review evaluates the intricate mechanisms by which various components of the TME facilitate proliferation,apoptosis evasion,invasion,migration,angiogenesis,metastasis,as well as therapy resistance in OSCC through activation of PI3K/AKT signalling.The review has also analysed how epigenetic modifiers such as DNA methylation,histone modifications,and noncoding RNAs that have emerged as key players in orchestrating OSCC development and progression influence the PI3K/AKT pathway.Preclinical studies and clinical trials on the efficacy of PI3K/AKT inhibitors as viable options for OSCC treatment are discussed.Overall,this review supports the tenet that the PI3K/AKT pathway,which functions as a central hub through crosstalk with several oncogenic signaling pathways and overarching impact on all the hallmark traits of cancer,offers immense potential as a biomarker and oncotherapeutic target for OSCC.
基金Supported by the Kuwait Foundation for the Advancement of Sciences and Dasman Diabetes Institute,No.RACB-2021-007.
文摘In this editorial,we highlight the study by Xiao et al.Despite progress in the management of diabetic foot ulcers(DFUs),impaired wound healing remains a significant clinical challenge.Recent studies have highlighted the critical role of epigenetic modifications in diabetic wound healing,with particular emphasis on DNA and RNA methylation pathways.This editorial discusses the findings of Xiao et al,who identified the Wilms tumor 1-associated protein(WTAP)-DNA methyltransferase 1(DNMT1)axis as a pivotal regulator of endothelial dys-function in DFUs.WTAP,a regulatory subunit of N6-methyladenosine(m6A)methyltransferase,is upregulated under high-glucose conditions and drives the excessive expression of DNMT1 via m6A modification.This contributes to im-paired angiogenesis,reduced cell viability,and delayed wound closure.WTAP knockdown restored endothelial function and significantly improved wound healing in a diabetic mouse model.Furthermore,DNMT1 overexpression ab-rogated the benefits of WTAP suppression,confirming its downstream effector role.Thus,targeting the WTAP-DNMT1 axis provides a new avenue for DFU management.Moreover,epigenetic interventions that modulate both the m6A and RNA methylation pathways could restore endothelial function and enhance tissue repair in patients with diabetes.
基金the National Science and Technology Council(NSTC)of the Republic of China,Taiwan,for financially supporting this research under Contract No.NSTC 112-2637-M-131-001.
文摘Accurate and efficient brain tumor segmentation is essential for early diagnosis,treatment planning,and clinical decision-making.However,the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection.While U-Net-based architectures have demonstrated strong performance in medical image segmentation,there remains room for improvement in feature extraction and localization accuracy.In this study,we propose a novel hybrid model designed to enhance 3D brain tumor segmentation.The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder.Additionally,to enhance the model’s generalization ability,Squeeze and Excitation attention mechanism is integrated.We introduce Gabor filter banks into the encoder to further strengthen the model’s ability to extract robust and transformation-invariant features from the complex and irregular shapes typical in medical imaging.This approach,which is not well explored in current U-Net-based segmentation frameworks,provides a unique advantage by enhancing texture-aware feature representation.Specifically,Gabor filters help extract distinctive low-level texture features,reducing the effects of texture interference and facilitating faster convergence during the early stages of training.Our model achieved Dice scores of 0.881,0.846,and 0.819 for Whole Tumor(WT),Tumor Core(TC),and Enhancing Tumor(ET),respectively,on the BraTS 2020 dataset.Cross-validation on the BraTS 2021 dataset further confirmed the model’s robustness,yielding Dice score values of 0.887 for WT,0.856 for TC,and 0.824 for ET.The proposed model outperforms several state-of-the-art existing models,particularly in accurately identifying small and complex tumor regions.Extensive evaluations suggest integrating advanced preprocessing with an attention-augmented hybrid architecture offers significant potential for reliable and clinically valuable brain tumor segmentation.
文摘BACKGROUND Diabetic wound injury is a significant and common complication in individuals with diabetes.N6-methyladenosine(m6A)-related epigenetic regulation is widely involved in the pathogenesis of diabetes complications.However,the function of m6A methyltransferase Wilms tumor 1-associated protein(WTAP)in diabetic wound healing remains elusive.AIM To investigate the potential epigenetic regulatory mechanism of WTAP during diabetic wound healing.METHODS Human umbilical vein endothelial cells(HUVECs)were induced with high glucose(HG)to establish in vitro cell model.Male BALB/c mice were intraperitoneally injected with streptozotocin to mimic diabetes,and full-thickness excision was made to mimic diabetic wound healing.HG-induced HUVECs and mouse models were treated with WTAP siRNAs and DNA methyltransferase 1(DNMT1)overexpression vectors.Cell viability and migration ability were detected by cell counting kit-8 and Transwell assays.In vitro angiogenesis was measured using a tube formation experiment.The images of wounds were captured,and re-epithelialization and collagen deposition of skin tissues were analyzed using hematoxylin and eosin staining and Masson’s trichrome staining.RESULTS The expression of several m6A methyltransferases,including METTL3,METTL14,METTL16,KIAA1429,WTAP,and RBM15,were measured.WTAP exhibited the most significant elevation in HG-induced HUVECs compared with the normal control.WTAP depletion notably restored cell viability and enhanced tube formation ability and migration of HUVECs suppressed by HG.The unclosed wound area of mice was smaller in WTAP knockdowntreated mice than in control mice at nine days post-wounding,along with enhanced re-epithelialization rate and collagen deposition.The m6A levels on DNMT1 mRNA in HUVECs were repressed by WTAP knockdown in HUVECs.The mRNA levels and expression of DNMT1 were inhibited by WTAP depletion in HUVECs.Overexpression of DNMT1 in HUVECs notably reversed the effects of WTAP depletion on HG-induced HUVECs.CONCLUSION WTAP expression is elevated in HG-induced HUVECs and epigenetically regulates the m6A modification of DNMT1 to impair diabetic wound healing.
文摘Immunotherapy has brought unprecedented breakthroughs to advanced malignant tumors,yet the immune microenvironment shaped by the tumor stroma has often been underestimated in the traditional focus on the“immune checkpoint-T cell”axis.Collagen not only constitutes a mechanical barrier that distinguishes between the periphery and core of solid tumors but also systematically remodels the orientation of metabolism,vasculature,and immune cell phenotypic plasticity through its spatial density,fiber arrangement,and crosslinking patterns(F igure 1)[1,2].Abundant evidence suggests that over-accumulated types I and III collagen drive CD8+T cell exhaustion,NK cell functional inhibition,and tumor-associated macrophage polarization through ligand-receptor networks involving LAIR-1,DDR2,andβ1/β3 integrins[3-6].Mechanistically,collagen engagement of LAIR-1 delivers inhibitory signals in effector lymphocytes,promoting dysfunctional or exhausted states[7-9].In parallel,collagen-β1/β3 integrin signaling activates mechanotransduction pathways(e.g.,FAK/SRC),reducing T-cell motility and immune-tumor contact,while DDR2 activation supports matrix-remodeling programs that limit lymphocyte trafficking.
文摘This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.
基金Supported by National Natural Science Foundation of China,No.82170638Natural Science Foundation of the Science and Technology Commission of Shanghai Municipality,No.23ZR1458300+1 种基金Key Discipline Project of Shanghai Municipal Health System,No.2024ZDXK0004and Pujiang Project of Shanghai Magnolia Talent Plan,No.24PJD098.
文摘Colorectal cancer(CRC)is ranked as the third most common tumor globally,representing approximately 10%of all cancer cases,and is the second primary cause of cancer-associated mortality.Existing therapeutic approaches demonstrate limited efficacy against CRC,partially due to the immunosuppressive tumor microenvironment(TME).In recent years,substantial evidence indicates that dysbiosis of the gut microbiota and its metabolic products is closely associated with the initiation,progression,and prognostic outcomes of CRC.In this minireview,we systematically elaborate on how these microbes and their metabolites directly impair intestinal epithelial integrity,activate cancer-associated fibroblasts,remodel tumor vasculature,and critically,sculpt an immunosuppressive landscape by modulating T cells,dendritic cells,and tumor-associated macrophages.We highlight the translational potential of targeting the gut microbiota,including fecal microbiota transplantation,probiotics,and engineered microbial systems,to reprogram the TME and overcome resistance to immunotherapy and chemotherapy.A deeper understanding of the microbiota-TME axis is essential for developing novel diagnostic and therapeutic paradigms for CRC.
基金Supported by Russian Science Foundation,No.24-64-00028.
文摘We read with great interest the investigation of Kang et al related the applications of the multiparametric magnetic resonance imaging-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.The authors focused on decision-making based on the integration of tumor differentiation,signal intensity ratio,margin distance,and magnetic resonance imaging-detected lymph node metastasis.Indeed,these multiparameter predictive models could also be used for diagnosis as an alternative to invasive tissue examination methods.However,progress in this field enables us to shift the paradigm to radiology biopsies,particularly given the nonlinear effects of various radiation sources.
