RESUMEN

Brain disorder diagnosis via resting-state functional magnetic resonance imaging (rs-fMRI) is usually limited due to the complex imaging features and sample size. For brain disorder diagnosis, the graph convolutional network (GCN) has achieved remarkable success by capturing interactions between individuals and the population. However, there are mainly three limitations: 1) The previous GCN approaches consider the non-imaging information in edge construction but ignore the sensitivity differences of features to non-imaging information. 2) The previous GCN approaches solely focus on establishing interactions between subjects (i.e., individuals and the population), disregarding the essential relationship between features. 3) Multisite data increase the sample size to help classifier training, but the inter-site heterogeneity limits the performance to some extent. This paper proposes a knowledge-aware multisite adaptive graph Transformer to address the above problems. First, we evaluate the sensitivity of features to each piece of non-imaging information, and then construct feature-sensitive and feature-insensitive subgraphs. Second, after fusing the above subgraphs, we integrate a Transformer module to capture the intrinsic relationship between features. Third, we design a domain adaptive GCN using multiple loss function terms to relieve data heterogeneity and to produce the final classification results. Last, the proposed framework is validated on two brain disorder diagnostic tasks. Experimental results show that the proposed framework can achieve state-of-the-art performance.

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Skin lesion is one of the most common diseases, and most categories are highly similar in morphology and appearance. Deep learning models effectively reduce the variability between classes and within classes, and improve diagnostic accuracy. However, the existing multi-modal methods are only limited to the surface information of lesions in skin clinical and dermatoscopic modalities, which hinders the further improvement of skin lesion diagnostic accuracy. This requires us to further study the depth information of lesions in skin ultrasound. In this paper, we propose a novel skin lesion diagnosis network, which combines clinical and ultrasound modalities to fuse the surface and depth information of the lesion to improve diagnostic accuracy. Specifically, we propose an attention-guided learning (AL) module that fuses clinical and ultrasound modalities from both local and global perspectives to enhance feature representation. The AL module consists of two parts, attention-guided local learning (ALL) computes the intra-modality and inter-modality correlations to fuse multi-scale information, which makes the network focus on the local information of each modality, and attention-guided global learning (AGL) fuses global information to further enhance the feature representation. In addition, we propose a feature reconstruction learning (FRL) strategy which encourages the network to extract more discriminative features and corrects the focus of the network to enhance the model's robustness and certainty. We conduct extensive experiments and the results confirm the superiority of our proposed method. Our code is available at: https://github.com/XCL-hub/AGFnet.

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Colorectal cancer (CRC) is the third most common cancer worldwide and the second most common cause of cancer death. Nanotherapies are able to selectively target the delivery of cancer therapeutics, thus improving overall antitumor efficiency and reducing conventional chemotherapy side effects. Mesoporous silica nanoparticles (MSNs) have attracted the attention of many researchers due to their remarkable advantages and biosafety. We offer insights into the recent advances of MSNs in CRC treatment and their potential clinical application value.

RESUMEN

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).

Asunto(s)

Enfermedad de Alzheimer , Imagen Multimodal , Redes Neurales de la Computación , Enfermedad de Alzheimer/diagnóstico por imagen , Humanos , Imagen Multimodal/métodos , Aprendizaje Automático , Interpretación de Imagen Asistida por Computador/métodos , Neuroimagen/métodos , Imagen por Resonancia Magnética/métodos

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Multi-center disease diagnosis aims to build a global model for all involved medical centers. Due to privacy concerns, it is infeasible to collect data from multiple centers for training (i.e., centralized learning). Federated Learning (FL) is a decentralized framework that enables multiple clients (e.g., medical centers) to collaboratively train a global model while retaining patient data locally for privacy. However, in practice, the data across medical centers are not independently and identically distributed (Non-IID), causing two challenging issues: (1) catastrophic forgetting at clients, i.e., the local model at clients will forget the knowledge received from the global model after local training, causing reduced performance; and (2) invalid aggregation at the server, i.e., the global model at the server may not be favorable to some clients after model aggregation, resulting in a slow convergence rate. To mitigate these issues, an innovative Federated learning using Model Projection (FedMoP) is proposed, which guarantees: (1) the loss of local model on global data does not increase after local training without accessing the global data so that the performance will not be degenerated; and (2) the loss of global model on local data does not increase after aggregation without accessing local data so that convergence rate can be improved. Extensive experimental results show that our FedMoP outperforms state-of-the-art FL methods in terms of accuracy, convergence rate and communication cost. In particular, our FedMoP also achieves comparable or even higher accuracy than centralized learning. Thus, our FedMoP can ensure privacy protection while outperforming centralized learning in accuracy and communication cost.

Asunto(s)

Aprendizaje Automático , Humanos , Redes Neurales de la Computación , Algoritmos

RESUMEN

Previous studies have demonstrated that Eyes Absent 4 (EYA4) influences the proliferation and migration of tumor cells. Notably, studies have established that EYA4 can also limit tumor sensitivity to chemotherapeutic agents. The objective of this study was to investigate the effect of EYA4 in conferring drug resistance in osteosarcoma (OS). Bioinformatics, histological, and cellular analyses revealed that the expression level of EYA4 was higher in OS tissues than in healthy tissues/cells and in resistant tissues/cells compared with sensitive tissues/cells. In vitro and in vivo experiments demonstrated that EYA4 knockdown increased the sensitivity of OS to doxorubicin (DOX). Conversely, overexpression of EYA4 decreased the sensitivity of OS to DOX. Exploration of the resistance mechanism exposed that EYA4 facilitates DNA double-strand break (DSB) repair, a typical mode of DNA damage repair (DDR). Subsequently, our findings indicated that EYA4 could directly interact with histone H2AX to activate the DDR pathway. Taken together, our observations indicated that EYA4 may serve as a target molecule for reversing drug resistance in OS patients.

Asunto(s)

Antibióticos Antineoplásicos , Neoplasias Óseas , Daño del ADN , Reparación del ADN , Doxorrubicina , Resistencia a Antineoplásicos , Ratones Desnudos , Osteosarcoma , Doxorrubicina/farmacología , Humanos , Osteosarcoma/tratamiento farmacológico , Osteosarcoma/metabolismo , Osteosarcoma/patología , Osteosarcoma/genética , Resistencia a Antineoplásicos/efectos de los fármacos , Animales , Reparación del ADN/efectos de los fármacos , Reparación del ADN/fisiología , Ratones , Neoplasias Óseas/tratamiento farmacológico , Neoplasias Óseas/genética , Neoplasias Óseas/patología , Neoplasias Óseas/metabolismo , Antibióticos Antineoplásicos/farmacología , Línea Celular Tumoral , Daño del ADN/efectos de los fármacos , Transactivadores/genética , Transactivadores/metabolismo , Femenino , Ratones Endogámicos BALB C , Masculino , Ensayos Antitumor por Modelo de Xenoinjerto/métodos

RESUMEN

Here, we report the systematical synthesis of zeolite-templated carbon (ZTC) supported Ru and Rh mono- or bi-metallic electrocatalysts towards hydrogen evolution reaction (HER). The zeolite A or ZSM-5 derived ZTC supports and metal sites were adjusted, and all electrocatalysts outperformed the commercial Pt/C electrocatalyst for HER performance. In particular, the RhRu/(ZTC/ZSM5) sample exhibited superior catalytic performance with the overpotential of 24.8 mV@10 mA ⋅ cm-2, and outstanding stability with 1 mV drop after 20000 cyclic voltammetry circles. This work offers a simple impregnation method for the synthesis of highly performed HER electrocatalysts supported on porous zeolite-templated carbon.

RESUMEN

BACKGROUND: Accurate segmentation of gastric tumors from CT scans provides useful image information for guiding the diagnosis and treatment of gastric cancer. However, automated gastric tumor segmentation from 3D CT images faces several challenges. The large variation of anisotropic spatial resolution limits the ability of 3D convolutional neural networks (CNNs) to learn features from different views. The background texture of gastric tumor is complex, and its size, shape and intensity distribution are highly variable, which makes it more difficult for deep learning methods to capture the boundary. In particular, while multi-center datasets increase sample size and representation ability, they suffer from inter-center heterogeneity. METHODS: In this study, we propose a new cross-center 3D tumor segmentation method named Hierarchical Class-Aware Domain Adaptive Network (HCA-DAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale context features from the CT images with anisotropic resolution, and a hierarchical class-aware domain alignment (HCADA) module for adaptively aligning multi-scale context features across two domains by integrating a class attention map with class-specific information. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers and validate its segmentation performance in both in-center and cross-center test scenarios. RESULTS: Our baseline segmentation network (i.e., AsTr) achieves best results compared to other 3D segmentation models, with a mean dice similarity coefficient (DSC) of 59.26%, 55.97%, 48.83% and 67.28% in four in-center test tasks, and with a DSC of 56.42%, 55.94%, 46.54% and 60.62% in four cross-center test tasks. In addition, the proposed cross-center segmentation network (i.e., HCA-DAN) obtains excellent results compared to other unsupervised domain adaptation methods, with a DSC of 58.36%, 56.72%, 49.25%, and 62.20% in four cross-center test tasks. CONCLUSIONS: Comprehensive experimental results demonstrate that the proposed method outperforms compared methods on this multi-center database and is promising for routine clinical workflows.

Asunto(s)

Imagenología Tridimensional , Redes Neurales de la Computación , Neoplasias Gástricas , Tomografía Computarizada por Rayos X , Humanos , Neoplasias Gástricas/diagnóstico por imagen , Neoplasias Gástricas/patología , Imagenología Tridimensional/métodos , Tomografía Computarizada por Rayos X/métodos , Aprendizaje Profundo

RESUMEN

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Asunto(s)

Enfermedad de Alzheimer , Imagen Multimodal , Humanos , Enfermedad de Alzheimer/diagnóstico por imagen , Imagen Multimodal/métodos , Encéfalo/diagnóstico por imagen , Neuroimagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Imagen por Resonancia Magnética/métodos , Aprendizaje Automático , Diagnóstico Precoz , Algoritmos , Imagenología Tridimensional/métodos

RESUMEN

INTRODUCTION: This study aimed to assess the efficacy and safety of adalimumab in pediatric patients with chronic non-infectious posterior uveitis and panuveitis (not associated with juvenile idiopathic arthritis). METHODS: The medical records of children (< 18 years old) with chronic non-infectious posterior uveitis and panuveitis were collected and analyzed in this retrospective cohort study. Children were allocated to a conventional adalimumab-free treatment (CT) or adalimumab (ADA) group based on whether they additionally received adalimumab. RESULTS: In total, 69 children (138 eyes) were included, with 21 (42 eyes) and 48 (96 eyes) in the CT and ADA groups, respectively. During the average follow-up period of 24 months, the improvement in all ocular parameters (best-corrected visual acuity, intraocular inflammation, fluorescein angiography score) was better in the ADA group than in the CT group, except for changes in central macular thickness, which did not significantly differ between the groups. The mean time of first alleviation, which was after 1.03 ± 0.12 months of therapy, was earlier in the ADA group than in the CT group (2.30 ± 0.46 months). In the ADA group, 90.6% of children had remission within 3 months, and 47.9% had no relapse during follow-up. Cough and cold were the most common adverse events in the ADA group; however, the number of adverse events was similar between both the groups. CONCLUSIONS: Adalimumab was effective in the treatment of chronic noninfectious posterior uveitis and panuveitis in pediatric patients, and disease inactivity was accomplished in the majority of the patients, thereby improving visual outcomes and maintaining disease stability. Adverse events were limited and tolerable.

RESUMEN

Intervertebral disc degeneration (IDD) is a well-established cause of disability, and extensive evidence has identified the important role played by regulatory noncoding RNAs, specifically circular RNAs (circRNAs) and microRNAs (miRNAs), in the progression of IDD. To elucidate the molecular mechanism underlying IDD, we established a circRNA/miRNA/mRNA network in IDD through standardized analyses of all expression matrices. Our studies confirmed the differential expression of the transcription factors early B-cell factor 1 (EBF1), circEYA3, and miR-196a-5p in the nucleus pulposus (NP) tissues of controls and IDD patients. Cell proliferation, apoptosis, and extracellular mechanisms of degradation in NP cells (NPC) are mediated by circEYA3. MiR-196a-5p is a direct target of circEYA3 and EBF1. Functional analysis showed that miR-196a-5p reversed the effects of circEYA3 and EBF1 on ECM degradation, apoptosis, and proliferation in NPCs. EBF1 regulates the nuclear factor kappa beta (NF-кB) signalling pathway by activating the IKKß promoter region. This study demonstrates that circEYA3 plays an important role in exacerbating the progression of IDD by modulating the NF-κB signalling pathway through regulation of the miR196a-5p/EBF1 axis. Consequently, a novel molecular mechanism underlying IDD development was elucidated, thereby identifying a potential therapeutic target for future exploration.

Asunto(s)

Degeneración del Disco Intervertebral , MicroARNs , Humanos , FN-kappa B/genética , FN-kappa B/metabolismo , Degeneración del Disco Intervertebral/genética , Degeneración del Disco Intervertebral/metabolismo , MicroARNs/genética , MicroARNs/metabolismo , Transducción de Señal , ARN Circular/genética , Transactivadores/metabolismo

RESUMEN

BACKGROUND: Cell senescence is a sign of aging and plays a significant role in the pathogenesis of age-related disorders. For cell therapy, senescence may compromise the quality and efficacy of cells, posing potential safety risks. Mesenchymal stem cells (MSCs) are currently undergoing extensive research for cell therapy, thus necessitating the development of effective methods to evaluate senescence. Senescent MSCs exhibit distinctive morphology that can be used for detection. However, morphological assessment during MSC production is often subjective and uncertain. New tools are required for the reliable evaluation of senescent single cells on a large scale in live imaging of MSCs. RESULTS: We have developed a successful morphology-based Cascade region-based convolution neural network (Cascade R-CNN) system for detecting senescent MSCs, which can automatically locate single cells of different sizes and shapes in multicellular images and assess their senescence state. Additionally, we tested the applicability of the Cascade R-CNN system for MSC senescence and examined the correlation between morphological changes with other senescence indicators. CONCLUSIONS: This deep learning has been applied for the first time to detect senescent MSCs, showing promising performance in both chronic and acute MSC senescence. The system can be a labor-saving and cost-effective option for screening MSC culture conditions and anti-aging drugs, as well as providing a powerful tool for non-invasive and real-time morphological image analysis integrated into cell production.

Asunto(s)

Aprendizaje Profundo , Células Madre Mesenquimatosas , Proliferación Celular , Senescencia Celular , Células Cultivadas

RESUMEN

BACKGROUND: Aging is a holistic change that has a major impact on the immune system, and immunosenescence contributes to the overall progression of aging. The bone marrow is the most important hematopoietic immune organ, while the spleen, as the most important extramedullary hematopoietic immune organ, maintains homeostasis of the human hematopoietic immune system (HIS) in cooperation with the bone marrow. However, the overall changes in the HIS during aging have not been described. Here, we describe a hematopoietic immune map of the spleen and bone marrow of young and old mice using single-cell sequencing and flow cytometry techniques. RESULTS: We observed extensive, complex changes in the HIS during aging. Compared with young mice, the immune cells of aged mice showed a marked tendency toward myeloid differentiation, with the neutrophil population accounting for a significant proportion of this response. In this change, hypoxia-inducible factor 1-alpha (Hif1α) was significantly overexpressed, and this enhanced the immune efficacy and inflammatory response of neutrophils. Our research revealed that during the aging process, hematopoietic stem cells undergo significant changes in function and composition, and their polymorphism and differentiation abilities are downregulated. Moreover, we found that the highly responsive CD62L + HSCs were obviously downregulated in aging, suggesting that they may play an important role in the aging process. CONCLUSIONS: Overall, aging extensively alters the cellular composition and function of the HIS. These findings could potentially give high-dimensional insights and enable more accurate functional and developmental analyses as well as immune monitoring in HIS aging.

RESUMEN

Recently, leveraging deep neural networks for automated colorectal polyp segmentation has emerged as a hot topic due to the favored advantages in evading the limitations of visual inspection, e.g., overwork and subjectivity. However, most existing methods do not pay enough attention to the uncertain areas of colonoscopy images and often provide unsatisfactory segmentation performance. In this paper, we propose a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. Specifically, considering that polyps vary greatly in size and shape, we first adopt a pyramid vision transformer encoder to learn multi-scale feature representations. Then, a simple yet effective boundary exploration module (BEM) is proposed to explore boundary cues from the low-level features. To make the network focus on the ambiguous area where the prediction score is biased to neither the foreground nor the background, we further introduce a boundary uncertainty aware module (BUM) that explores error-prone regions from the high-level features with the assistance of boundary cues provided by the BEM. Through the top-down hybrid deep supervision, our BUNet implements coarse-to-fine polyp segmentation and finally localizes polyp regions precisely. Extensive experiments on five public datasets show that BUNet is superior to thirteen competing methods in terms of both effectiveness and generalization ability.

Asunto(s)

Pólipos del Colon , Humanos , Pólipos del Colon/diagnóstico por imagen , Incertidumbre , Aprendizaje , Señales (Psicología) , Generalización Psicológica , Procesamiento de Imagen Asistido por Computador

RESUMEN

Neuroinflammation is associated with poor outcomes in patients with spinal cord injury (SCI). Recent studies have demonstrated that stimulator of interferon genes (Sting) plays a key role in inflammatory diseases. However, the role of Sting in SCI remains unclear. In the present study, it is found that increased Sting expression is mainly derived from activated microglia after SCI. Interestingly, knockout of Sting in microglia can improve the recovery of neurological function after SCI. Microglial Sting knockout restrains the polarization of microglia toward the M1 phenotype and alleviates neuronal death. Furthermore, it is found that the downregulation of mitofusin 2 (Mfn2) expression in microglial cells leads to an imbalance in mitochondrial fusion and division, inducing the release of mitochondrial DNA (mtDNA), which mediates the activation of the cGas-Sting signaling pathway and aggravates inflammatory response damage after SCI. A biomimetic microglial nanoparticle strategy to deliver MASM7 (named MSNs-MASM7@MI) is established. In vitro, MSNs-MASM7@MI showed no biological toxicity and effectively delivered MASM7. In vivo, MSNs-MASM7@MI improves nerve function after SCI. The study provides evidence that cGas-Sting signaling senses Mfn2-dependent mtDNA release and that its activation may play a key role in SCI. These findings provide new perspectives and potential therapeutic targets for SCI treatment.

Asunto(s)

Microglía , Traumatismos de la Médula Espinal , Humanos , Microglía/metabolismo , ADN Mitocondrial/genética , ADN Mitocondrial/metabolismo , Regulación hacia Abajo , Inflamación/metabolismo , Traumatismos de la Médula Espinal/metabolismo , Nucleotidiltransferasas/metabolismo

RESUMEN

Since microRNAs (miRNAs) are valuable biomarkers for disease diagnosis and prognosis, the pursuit of enhanced detection sensitivity through signal amplification strategies has emerged as a prominent focus in low-abundance miRNA detection research. DNA walkers, as dynamic DNA nanodevice, have gained significant attention for their applications as signal amplification strategies. To overcome the limitations of unipedal DNA walkers with a restricted signal amplification efficiency, there is a great need for multi-pedal DNA walkers that offer improved walking and signal amplification capabilities. Here, we employed a combination of catalytic hairpin assembly (CHA) and APE1 enzymatic cleavage reactions to construct a tripedal DNA walker, driving its movement to establish a cascade signal amplification system for the electrochemical detection of miRNA-155. The biosensor utilizes tumor cell-endogenous microRNA-155 and APE1 as dual-trigger for DNA walker formation and walking movement, leading to highly efficient and controllable signal amplification. The biosensor exhibited high sensitivity, with a low detection limit of 10 pM for microRNA-155, and successfully differentiated and selectively detected microRNA-155 from other interfering RNAs. Successful detection in 20 % serum samples indicates its potential clinical application. In addition, we harnessed strand displacement reactions to create a gentle yet efficient electrode regeneration strategy, to addresses the time-consuming challenges during electrode modification processes. We have successfully demonstrated the stability of current signals even after multiple cycles of electrode regeneration. This study showcased the high-efficiency amplification potential of multi-pedal DNA walkers and the effectiveness and versatility of strand displacement in biosensing applications. It opens a promising path for developing regenerable electrochemical biosensors. This regenerable strategy for electrochemical biosensors is both label-free and cost-effective, and holds promise for detecting various disease-related RNA targets beyond its current application.

Asunto(s)

Técnicas Biosensibles , MicroARNs , Técnicas Electroquímicas , Técnicas de Amplificación de Ácido Nucleico , ADN/genética , MicroARNs/genética , Límite de Detección

RESUMEN

Accurate segmentation of gastric tumors from computed tomography (CT) images provides useful image information for guiding the diagnosis and treatment of gastric cancer. Researchers typically collect datasets from multiple medical centers to increase sample size and representation, but this raises the issue of data heterogeneity. To this end, we propose a new cross-center 3D tumor segmentation method named unsupervised scale-aware and boundary-aware domain adaptive network (USBDAN), which includes a new 3D neural network that efficiently bridges an Anisotropic neural network and a Transformer (AsTr) for extracting multi-scale features from the CT images with anisotropic resolution, and a scale-aware and boundary-aware domain alignment (SaBaDA) module for adaptively aligning multi-scale features between two domains and enhancing tumor boundary drawing based on location-related information drawn from each sample across all domains. We evaluate the proposed method on an in-house CT image dataset collected from four medical centers. Our results demonstrate that the proposed method outperforms several state-of-the-art methods.

Asunto(s)

Neoplasias Gástricas , Humanos , Neoplasias Gástricas/diagnóstico por imagen , Anisotropía , Concienciación , Suministros de Energía Eléctrica , Hospitales

RESUMEN

As an early sign of thyroid cancer, thyroid nodules are the most common nodular lesions. As a non-invasive imaging method, ultrasound is widely used in the diagnosis of benign and malignant thyroid nodules. As there is no obvious difference in appearance between the two types of thyroid nodules, and the contrast with the surrounding muscle tissue is too low, it is difficult to distinguish the benign and malignant nodules. Therefore, a dense nodal Swin-Transformer(DST) method for the diagnosis of thyroid nodules is proposed in this paper. Image segmentation is carried out through patch, and feature maps of different sizes are constructed in four stages, which consider different information of each layer of features. In each stage block, a dense connection mechanism is used to make full use of multi-layer features and effectively improve the diagnostic performance. The experimental results of multi-center ultrasound data collected from 17 hospitals show that the accuracy of the proposed method is 87.27%, the sensitivity is 88.63%, and the specific effect is 85.16%, which verifies that the proposed algorithm has the potential to assist clinical practice.

Asunto(s)

Neoplasias de la Tiroides , Nódulo Tiroideo , Humanos , Nódulo Tiroideo/diagnóstico por imagen , Nódulo Tiroideo/patología , Sensibilidad y Especificidad , Diagnóstico Diferencial , Ultrasonografía/métodos

RESUMEN

Fibromyalgia syndrome (FMS) is a type of rheumatology that seriously affects the normal life of patients. Due to the complex clinical manifestations of FMS, it is challenging to detect FMS. Therefore, an automatic FMS diagnosis model is urgently needed to assist physicians. Brain functional connectivity networks (BFCNs) constructed by resting-state functional magnetic resonance imaging (rs-fMRI) to describe brain functions have been widely used to identify individuals with relevant diseases from normal control (NC). Therefore, we propose a novel model based on BFCN and graph convolutional network (GCN) for automatic FMS diagnosis. Firstly, a novel fused BFCN method is proposed by fusing Pearson's correlation (PC) and low-rank (LR) BFCN, which retains information and reduces data redundancy to construct BFCN. Then we combine the feature of BFCN with non-image information of subjects to obtain nodes and adjacency matrices, which builds a graph with edge attention. Finally, the graph is sent to the GCN layer for FMS diagnosis. Our model is evaluated on the in-house FMS dataset to achieve 82.48% accuracy. The experimental results show that our method outperforms the state-of-the-art competing methods.

Asunto(s)

Fibromialgia , Médicos , Humanos , Fibromialgia/diagnóstico por imagen , Encéfalo/diagnóstico por imagen

RESUMEN

Contrast-enhanced ultrasound (CEUS) video plays an important role in post-ablation treatment response assessment in patients with hepatocellular carcinoma (HCC). However, the assessment of treatment response using CEUS video is challenging due to issues such as high inter-frame data repeatability, small ablation area and poor imaging quality of CEUS video. To address these issues, we propose a two-stage diagnostic framework for post-ablation treatment response assessment in patients with HCC using CEUS video. The first stage is a location stage, which is used to locate the ablation area. At this stage, we propose a Yolov5-SFT to improve the location results of the ablation area and a similarity comparison module (SCM) to reduce data repeatability. The second stage is an assessment stage, which is used for the evaluation of postoperative efficacy. At this stage, we design an EfficientNet-SK to improve assessment accuracy. The Experimental results on the self-collected data show that the proposed framework outperforms other selected algorithms, and can effectively assist doctors in the assessment of post-ablation treatment response.

Asunto(s)

Carcinoma Hepatocelular , Neoplasias Hepáticas , Humanos , Carcinoma Hepatocelular/diagnóstico por imagen , Carcinoma Hepatocelular/cirugía , Neoplasias Hepáticas/diagnóstico por imagen , Neoplasias Hepáticas/cirugía , Medios de Contraste , Tomografía Computarizada por Rayos X , Ultrasonografía/métodos