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
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
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
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
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
由于患者个体差异、采集协议多样性和数据损坏等因素,现有基于磁共振成像(Magnetic resonance imaging,MRI)的脑肿瘤分割方法存在模态数据丢失问题,导致分割精度不高。为此,本文提出了一种基于U-Net和Transformer结合的不完整多模态脑...由于患者个体差异、采集协议多样性和数据损坏等因素,现有基于磁共振成像(Magnetic resonance imaging,MRI)的脑肿瘤分割方法存在模态数据丢失问题,导致分割精度不高。为此,本文提出了一种基于U-Net和Transformer结合的不完整多模态脑肿瘤分割(Incomplete multimodal brain tumor segmentation based on the combination of U-Net and Transformer,IM TransNet)方法。首先,针对脑肿瘤MRI的4个不同模态设计了单模态特定编码器,提升模型对各模态数据的表征能力。其次,在U-Net中嵌入双重注意力的Transformer模块,克服模态缺失引起的信息不完整问题,减少U-Net的长距离上下文交互和空间依赖性局限。在U-Net的跳跃连接中加入跳跃交叉注意力机制,动态关注不同层级和模态的特征,即使在模态缺失时,也能有效融合特征并进行重建。此外,针对模态缺失引起的训练不平衡问题,设计了辅助解码模块,确保模型在各种不完整模态子集上均能稳定高效地分割脑肿瘤。最后,基于公开数据集BRATS验证模型的性能。实验结果表明,本文提出的模型在增强型肿瘤、肿瘤核心和全肿瘤上的平均Dice评分分别为63.19%、76.42%和86.16%,证明了其在处理不完整多模态数据时的优越性和稳定性,为临床实践中脑肿瘤的准确、高效和可靠分割提供了一种可行的技术手段。展开更多
BACKGROUND Vessels encapsulating tumor clusters(VETC)represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma(HCC).However,it seems that no one have focu...BACKGROUND Vessels encapsulating tumor clusters(VETC)represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma(HCC).However,it seems that no one have focused on predicting VETC status in small HCC(sHCC).This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC(≤3 cm)patients.AIM To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients.METHODS A total of 309 patients with sHCC,who underwent segmental resection and had their VETC status confirmed,were included in the study.These patients were recruited from three different hospitals:Hospital 1 contributed 177 patients for the training set,Hospital 2 provided 78 patients for the test set,and Hospital 3 provided 54 patients for the validation set.Independent predictors of VETC were identified through univariate and multivariate logistic analyses.These independent predictors were then used to construct a VETC prediction model for sHCC.The model’s performance was evaluated using the area under the curve(AUC),calibration curve,and clinical decision curve.Additionally,Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence,just as it is with the actual VETC status and early recurrence.RESULTS Alpha-fetoprotein_lg10,carbohydrate antigen 199,irregular shape,non-smooth margin,and arterial peritumoral enhancement were identified as independent predictors of VETC.The model incorporating these predictors demonstrated strong predictive performance.The AUC was 0.811 for the training set,0.800 for the test set,and 0.791 for the validation set.The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets.Furthermore,the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC.Finally,early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group,regardless of whether considering the actual or predicted VETC status.CONCLUSION Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC(≤3 cm)patients,and it holds potential for predicting early recurrence.This model equips clinicians with valuable information to make informed clinical treatment decisions.展开更多
Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive c...Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.展开更多
Peptide-drug conjugates have achieved considerable development and application as a novel strategy for targeted delivery of anticancer drugs. Bioactive peptides induced calcium deposition can irreversibly assist inhib...Peptide-drug conjugates have achieved considerable development and application as a novel strategy for targeted delivery of anticancer drugs. Bioactive peptides induced calcium deposition can irreversibly assist inhibition of tumors. However, active regulation of calcium level through signal transduction of bioactive substances has not been reported yet. In this study, novel neuropeptide-doxorubicin conjugates(NP-DOX) with lysosome-specific acid response were described for neuropeptide Y_1 receptor(Y_1R)-overexpressed triple-negative breast cancer. The delivery mechanism of NP-DOX was clarified that diverse pathways were involved, including intracellular and intercellular transport. Importantly, up-regulation of Y_1 R-mediated intracellular calcium level via second messenger inositol triphosphate was presented in NP-DOX treated MDA-MB-231 cells. In vivo antitumor efficacy demonstrated that NP-DOX showed less organ toxicity and enhanced tumor inhibition benefited from its controlled release and Y_1R-mediated calcium deposition, compared with free DOX. This bioconjugate is a proof-of-concept confirming that neuropeptide-mediated control of signaling responses in neuropeptide-drug conjugates enables great potential for further applications in tumor chemotherapy.展开更多
Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,p...Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.展开更多
For a significant duration,enhancing the efficacy of cancer therapy has remained a critical concern.Magnetotactic bacteria(MTB),often likened to micro-robots,hold substantial promise as a drug delivery system.MTB,clas...For a significant duration,enhancing the efficacy of cancer therapy has remained a critical concern.Magnetotactic bacteria(MTB),often likened to micro-robots,hold substantial promise as a drug delivery system.MTB,classified as anaerobic,aquatic,and gram-negative microorganisms,exhibit remarkable motility and precise control over their internal biomineralization processes.This unique ability results in the formation of magnetic nanoparticles arranged along filamentous structures in a catenary fashion,enclosed within a membrane.These bacteria possess distinctive biochemical properties that facilitate their precise positioning within complex environments.By harnessing these biochemical attributes,MTB could potentially offer substantial advantages in the realm of cancer therapy.This article reviews the drug delivery capabilities of MTB in tumor treatment and explores various applications based on their inherent properties.The objective is to provide a comprehensive understanding of MTB-driven drug delivery and stimulate innovative insights in this field.展开更多
Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatin...Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.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.
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.
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.
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.
文摘由于患者个体差异、采集协议多样性和数据损坏等因素,现有基于磁共振成像(Magnetic resonance imaging,MRI)的脑肿瘤分割方法存在模态数据丢失问题,导致分割精度不高。为此,本文提出了一种基于U-Net和Transformer结合的不完整多模态脑肿瘤分割(Incomplete multimodal brain tumor segmentation based on the combination of U-Net and Transformer,IM TransNet)方法。首先,针对脑肿瘤MRI的4个不同模态设计了单模态特定编码器,提升模型对各模态数据的表征能力。其次,在U-Net中嵌入双重注意力的Transformer模块,克服模态缺失引起的信息不完整问题,减少U-Net的长距离上下文交互和空间依赖性局限。在U-Net的跳跃连接中加入跳跃交叉注意力机制,动态关注不同层级和模态的特征,即使在模态缺失时,也能有效融合特征并进行重建。此外,针对模态缺失引起的训练不平衡问题,设计了辅助解码模块,确保模型在各种不完整模态子集上均能稳定高效地分割脑肿瘤。最后,基于公开数据集BRATS验证模型的性能。实验结果表明,本文提出的模型在增强型肿瘤、肿瘤核心和全肿瘤上的平均Dice评分分别为63.19%、76.42%和86.16%,证明了其在处理不完整多模态数据时的优越性和稳定性,为临床实践中脑肿瘤的准确、高效和可靠分割提供了一种可行的技术手段。
基金Supported by the Project of Shanghai Municipal Commission of Health,No.2022LJ024.
文摘BACKGROUND Vessels encapsulating tumor clusters(VETC)represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma(HCC).However,it seems that no one have focused on predicting VETC status in small HCC(sHCC).This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC(≤3 cm)patients.AIM To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients.METHODS A total of 309 patients with sHCC,who underwent segmental resection and had their VETC status confirmed,were included in the study.These patients were recruited from three different hospitals:Hospital 1 contributed 177 patients for the training set,Hospital 2 provided 78 patients for the test set,and Hospital 3 provided 54 patients for the validation set.Independent predictors of VETC were identified through univariate and multivariate logistic analyses.These independent predictors were then used to construct a VETC prediction model for sHCC.The model’s performance was evaluated using the area under the curve(AUC),calibration curve,and clinical decision curve.Additionally,Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence,just as it is with the actual VETC status and early recurrence.RESULTS Alpha-fetoprotein_lg10,carbohydrate antigen 199,irregular shape,non-smooth margin,and arterial peritumoral enhancement were identified as independent predictors of VETC.The model incorporating these predictors demonstrated strong predictive performance.The AUC was 0.811 for the training set,0.800 for the test set,and 0.791 for the validation set.The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets.Furthermore,the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC.Finally,early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group,regardless of whether considering the actual or predicted VETC status.CONCLUSION Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC(≤3 cm)patients,and it holds potential for predicting early recurrence.This model equips clinicians with valuable information to make informed clinical treatment decisions.
基金Research Fund of Macao Polytechnic University(RP/FCSD-01/2022).
文摘Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.
基金financially supported by the Key R&D Program of Zhejiang Province (No.2020C03110)the National Natural Science Foundation of China (Nos.T2222021, 32011530115,32025021)+1 种基金the Science&Technology Bureau of Ningbo City (Nos.2020Z094, 2021Z072)Excellent Member of Youth Innovation Promotion Association Foundation of CAS (No.Y2021079)。
文摘Peptide-drug conjugates have achieved considerable development and application as a novel strategy for targeted delivery of anticancer drugs. Bioactive peptides induced calcium deposition can irreversibly assist inhibition of tumors. However, active regulation of calcium level through signal transduction of bioactive substances has not been reported yet. In this study, novel neuropeptide-doxorubicin conjugates(NP-DOX) with lysosome-specific acid response were described for neuropeptide Y_1 receptor(Y_1R)-overexpressed triple-negative breast cancer. The delivery mechanism of NP-DOX was clarified that diverse pathways were involved, including intracellular and intercellular transport. Importantly, up-regulation of Y_1 R-mediated intracellular calcium level via second messenger inositol triphosphate was presented in NP-DOX treated MDA-MB-231 cells. In vivo antitumor efficacy demonstrated that NP-DOX showed less organ toxicity and enhanced tumor inhibition benefited from its controlled release and Y_1R-mediated calcium deposition, compared with free DOX. This bioconjugate is a proof-of-concept confirming that neuropeptide-mediated control of signaling responses in neuropeptide-drug conjugates enables great potential for further applications in tumor chemotherapy.
基金supported by the Researchers Supporting Program at King Saud University.Researchers Supporting Project number(RSPD2024R867),King Saud University,Riyadh,Saudi Arabia.
文摘Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.
基金supported by the National Natural Science Foundation of China(No.3190110313 to K.Ma)Special Foundation of President of the Chinese Academy of Sciences(No.YZJJ2022QN_(4)4)+2 种基金HFIPS Director’s Fund(Nos.E16CWK123X1YZJJQY202201)the Heye Health Technology Chong Ming Project(No.HYCMP-2022012 to Y.Wang)。
文摘For a significant duration,enhancing the efficacy of cancer therapy has remained a critical concern.Magnetotactic bacteria(MTB),often likened to micro-robots,hold substantial promise as a drug delivery system.MTB,classified as anaerobic,aquatic,and gram-negative microorganisms,exhibit remarkable motility and precise control over their internal biomineralization processes.This unique ability results in the formation of magnetic nanoparticles arranged along filamentous structures in a catenary fashion,enclosed within a membrane.These bacteria possess distinctive biochemical properties that facilitate their precise positioning within complex environments.By harnessing these biochemical attributes,MTB could potentially offer substantial advantages in the realm of cancer therapy.This article reviews the drug delivery capabilities of MTB in tumor treatment and explores various applications based on their inherent properties.The objective is to provide a comprehensive understanding of MTB-driven drug delivery and stimulate innovative insights in this field.
基金Institutional Fund Projects under Grant No.(IFPIP:801-830-1443)The author gratefully acknowledges technical and financial support provided by the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.
