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Computer-aided diagnostic (CAD) systems for color fundus images play a critical role in the early detection of fundus diseases, including diabetes, hypertension, and cerebrovascular disorders. Although deep learning has substantially advanced automatic segmentation techniques in this field, several challenges persist, such as limited labeled datasets, significant structural variations in blood vessels, and persistent dataset discrepancies, which continue to hinder progress. These challenges lead to inconsistent segmentation performance, particularly for small vessels and branch regions. To address these limitations, we propose an enhanced HarDNet-based model that integrates HarDNet modules, Receptive Field Block (RFB) modules (designed to capture multi-scale contextual information), and Dense Aggregation modules. This innovative architecture enables the network to effectively extract multi-scale features and improve segmentation accuracy, especially for small and complex structures. The proposed model achieves superior performance in retinal vessel segmentation tasks, with accuracies of 0.9685 (±0.0035) on the DRIVE dataset and 0.9744 (±0.0029) on the CHASE_DB1 dataset, surpassing state-of-the-art models such as U-Net, ResU-Net, and R2U-Net. Notably, the model demonstrates exceptional capability in segmenting tiny vessels and branch regions, producing results that closely align with the gold standard. This highlights its significant advantage in handling intricate vascular structures. The robust and accurate performance of the proposed model underscores its effectiveness and reliability in medical image analysis, providing valuable technical support for related research and applications.

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

BackgroundThe aim of this work is to combine radiological and pathological information of tumor to develop a signature for pretreatment prediction of discrepancies of pathological response at several centers and restage patients with locally advanced rectal cancer (LARC) for individualized treatment planning.Patients and MethodsA total of 981 consecutive patients with evaluation of response according to tumor regression grade (TRG) who received nCRT were retrospectively recruited from four hospitals (primary cohort and external validation cohort 1–3); both pretreatment multiparametric MRI (mp-MRI) and whole slide image (WSI) of biopsy specimens were available for each patient. Quantitative image features were extracted from mp-MRI and WSI and used to construct a radiopathomics signature (RPS) powered by an artificial-intelligence model. Models based on mp-MRI or WSI alone were also constructed for comparison.ResultsThe RPS showed overall accuracy of 79.66–87.66% in validation cohorts. The areas under the curve of RPS at specific response grades were 0.98 (TRG0), 0.93 (≤ TRG1), and 0.84 (≤ TRG2). RPS at each grade of pathological response revealed significant improvement compared with both signatures constructed without combining multiscale tumor information (P < 0.01). Moreover, RPS showed relevance to distinct probabilities of overall survival and disease-free survival in patients with LARC who underwent nCRT (P < 0.05).ConclusionsThe results of this study suggest that radiopathomics, combining both radiological information of the whole tumor and pathological information of local lesions from biopsy, could potentially predict discrepancies of pathological response prior to nCRT for better treatment planning.

Photodynamic therapy (PDT) has been widely employed in tumor treatment due to its effectiveness. However, the tumor hypoxic microenvironment which is caused by abnormal vasculature severely limits the efficacy of PDT. Furthermore, the abnormal vasculature has been implicated in the failure of immunotherapy. In this study, a novel nanoparticle denoted as Combo‐NP is introduced, composed of a biodegradable NIR II fluorescent pseudo‐conjugate polymer featuring disulfide bonds within its main chain, designated as TPA‐BD, and the vascular inhibitor Lenvatinib. Combo‐NP exhibits dual functionality by not only inducing cytotoxic reactive oxygen species (ROS) to directly eliminate tumor cells but also eliciting immunogenic cell death (ICD). This ICD response, in turn, initiates a robust cascade of immune reactions, thereby augmenting the generation of cytotoxic T lymphocytes (CTLs). In addition, Combo‐NP addresses the issue of tumor hypoxia by normalizing the tumor vasculature. This normalization process enhances the efficacy of PDT while concurrently fostering increased CTLs infiltration within the tumor microenvironment. These synergistic effects synergize to potentiate the photodynamic‐immunotherapeutic properties of the nanoparticles. Furthermore, when combined with anti‐programmed death‐ligand 1 (PD‐L1), they showcase notable inhibitory effects on tumor metastasis. The findings in this study introduce an innovative nanomedicine strategy aimed at triggering systemic anti‐tumor immune responses for the treatment of Uveal melanoma. A nanoparticle (Combo‐NP) that contains biodegradable NIR‐II‐fluorescent pseudo conjugate polymer with disulfide bonds in the main chain (TPA‐BD) and a vascular inhibitor Lenvatinib (Len) is designed. Combo‐NP not only kill tumors by generating reactive oxygen species (ROS), but also trigger immunogenic cell death (ICD) initiating cascade immune responses leading to the production of cytotoxic T lymphocytes (CTLs). Moreover, Combo‐NP alleviate hypoxia by normalizing the tumor vessels, thereby improving the Photodynamic therapy (PDT)efficiency and increasing the infiltration of CTLs. These effects combine to amplify the photodynamic‐immunotherapy of the nanoparticles exhibiting promising inhibitory effects on tumor metastasis and inducing an abscopal immune response when combine with anti‐programmed death‐ligand 1 (PD‐L1) immunotherapy.

•Couples exhibit significantly greater prefrontal alpha synchronization compared to close friends even in a non-interactive and natural context.•Low-relationship-quality couples required heightened neural compensation to maintain robust behavioral synchronization, indicating the significance of relationship quality on couple emotional coordination.•Support vector machine analysis highlights the crucial role of prefrontal activity in differentiating couples from friends, suggesting its importance in romantic emotional coordination.•This research contributes to our understanding of the neural mechanisms underlying emotional coordination within romantic relationships, shedding light on the intricacies of romantic emotions. Emotions are fundamental to social interaction and deeply intertwined with interpersonal dynamics, especially in romantic relationships. Although the neural basis of interaction processes in romance has been widely explored, the underlying emotions and the connection between relationship quality and neural synchronization remain less understood. Our study employed EEG hyperscanning during a non-interactive video-watching paradigm to compare the emotional coordination between romantic couples and close friends. Couples showed significantly greater behavioral and prefrontal alpha synchronization than friends. Notably, couples with low relationship quality required heightened neural synchronization to maintain robust behavioral synchronization. Further support vector machine analysis underscores the crucial role of prefrontal activity in differentiating couples from friends. In summary, our research addresses gaps concerning how intrinsic emotions linked to relationship quality influence neural and behavioral synchronization by investigating a natural non-interactive context, thereby advancing our understanding of the neural mechanisms underlying emotional coordination in romantic relationships.

Given the rapid spread of COVID-19, an updated risk-stratify prognostic tool could help clinicians identify the high-risk patients with worse prognoses. We aimed to develop a non-invasive and easy-to-use prognostic signature by chest CT to individually predict poor outcome (death, need for mechanical ventilation, or intensive care unit admission) in patients with COVID-19. From November 29, 2019 to February 19, 2020, a total of 492 patients with COVID-19 from four centers were retrospectively collected. Since different durations from symptom onsets to the first CT scanning might affect the prognostic model, we designated the 492 patients into two groups: 1) the early-phase group: CT scans were performed within one week after symptom onset (0-6 days, n = 317); and 2) the late-phase group: CT scans were performed one week later after symptom onset (≥7 days, n = 175). In each group, we divided patients into the primary cohort (n = 212 in the early-phase group, n = 139 in the late-phase group) and the external independent validation cohort (n = 105 in the early-phase group, n = 36 in the late-phase group) according to the centers. We built two separate radiomics models in the two patient groups. Firstly, we proposed an automatic segmentation method to extract lung volume for radiomics feature extraction. Secondly, we applied several image preprocessing procedures to increase the reproducibility of the radiomics features: 1) applied a low-pass Gaussian filter before voxel resampling to prevent aliasing; 2) conducted ComBat to harmonize radiomics features per scanner; 3) tested the stability of the features in the radiomics signature by several image transformations, such as rotating, translating, and growing/shrinking. Thirdly, we used least absolute shrinkage and selection operator (LASSO) to build the radiomics signature (RadScore). Afterward, we conducted a Fine-Gray competing risk regression to build the clinical model and the clinic-radiomics signature (CrrScore). Finally, performances of the three prognostic signatures (clinical model, RadScore, and CrrScore) were estimated from the two aspects: 1) cumulative poor outcome probability prediction; 2) 28-day poor outcome prediction. We also did stratified analyses to explore the potential association between the CrrScore and the poor outcomes regarding different age, type, and comorbidity subgroups. In the early-phase group, the CrrScore showed the best performance in estimating poor outcome (C-index = 0.850), and predicting the probability of 28-day poor outcome (AUC = 0.862). In the late-phase group, the RadScore alone achieved similar performance to the CrrScore in predicting poor outcome (C-index = 0.885), and 28-day poor outcome probability (AUC = 0.976). Moreover, the RadScore in both groups successfully stratified patients with COVID-19 into low- or high-RadScore groups with significantly different survival time in the training and validation cohorts (all < 0.05). The CrrScore in both groups can also significantly stratify patients with different prognoses regarding different age, type, and comorbidities subgroups in the combined cohorts (all < 0.05). This research proposed a non-invasive and quantitative prognostic tool for predicting poor outcome in patients with COVID-19 based on CT imaging. Taking the insufficient medical recourse into account, our study might suggest that the chest CT radiomics signature of COVID-19 is more effective and ideal to predict poor outcome in the late-phase COVID-19 patients. For the early-phase patients, integrating radiomics signature with clinical risk factors can achieve a more accurate prediction of individual poor prognostic outcome, which enables appropriate management and surveillance of COVID-19.

Background Stroke is one of the leading causes of adult disability, often resulting in motor dysfunction and brain network reorganization. Brain-computer interface (BCI) systems offer a novel approach to post-stroke motor rehabilitation, with motor imagery (MI) serving as a key paradigm that requires decoding left and right-hand MI differences to optimize system performance. However, the neural dynamics underlying these differences, especially from the perspective of Electroencephalography(EEG) microstate, remain poorly understood in acute stroke patients. Methods This study enrolled 14 acute stroke patients and recorded their EEG data during left and right-hand MI tasks. Four EEG microstate (A, B, C, and D) were analyzed to extract temporal feature parameters, including Duration, Occurrence Coverage, and transition probabilities(TP). Significant features were used to construct classification models using Linear Discriminant Analysis(LDA), Support Vector Machines(SVM), and K-Nearest Neighbors(KNN) algorithms. Results Microstate analysis revealed significant differences in temporal features of microstate A and C during left and right-hand MI tasks. During left-hand MI, microstate A exhibited longer Duration( P fdr =0.032), higher Occurrence( P fdr =0.018), and greater Coverage( P fdr =0.004) compared to the right-hand, whereas microstate C showed the opposite pattern( P fdr =0.044, P fdr =0.004, P fdr =0.004). Additionally, the TP from microstate B→A, D→A and D→C also demonstrated significant differences( P fdr =0.04, P fdr <0.001, P fdr =0.006). Among classification models, the KNN algorithm achieved the highest accuracy of 75.00%, outperforming LDA and SVM. Fisher analysis indicated that the Occurrence of microstate C was the most discriminative feature for distinguishing between left and right-hand MI tasks in acute stroke patients. Conclusion Differences in EEG microstate features during left and right-hand MI tasks in acute stroke patients may reflect lateralized mechanisms of brain network reorganization. Microstate features hold significant potential for both post-stroke brain function assessment and the optimization of BCI systems. These features could enhance adaptive BCI strategies in acute stroke rehabilitation.

Seasonal solar thermal-energy storage systems used for space heating applications is a promising technology to reduce greenhouse gas emissions. A novel solar heating system with seasonal and cascade thermal-energy storage based on zeolite water is proposed in this study. The system’s efficiency is improved through cascade storage and the release of solar energy. The energy storage density is improved through the deep coupling of daily energy storage and cross-seasonal energy storage. A mathematical model of the system-performance analysis is established. The system performances in the non-heating and heating seasons and throughout the year are analyzed by considering the Chifeng City of China as an application case. The results indicate that the average collection efficiency of the proposed system is 2.88% higher in the non-heating season and 7.4% higher in the heating season than that of the reference system. Furthermore, the utilization efficiency of the proposed system is 37.16%, which is 3.26% higher than that of the reference system. Further, the proposed system has a supply heat of 2135 GJ in the heating season, which is 9.66% higher than the reference system. This study provides a solution for the highly efficient solar energy utilization for large-scale space-heating applications.

Background Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) is very important for treatment plan decision in locally advanced rectal cancer (LARC). The aim of this study was to investigate whether self-attention mechanism based multi-sequence fusion strategy applied to multiparametric magnetic resonance imaging (MRI) based deep learning or hand-crafted radiomics model construction can improve prediction of response to nCRT in LARC. Methods This retrospective analysis enrolled 422 consecutive patients with LARC who received nCRT before surgery at two hospitals. All patients underwent multiparametric MRI scans with three imaging sequences. Tumor regression grade (TRG) was used to assess the response of nCRT based on the resected specimen. Patients were separated into 2 groups: poor responders (TRG 2, 3) versus good responders (TRG 0, 1). A self-attention mechanism, namely channel attention, was applied to fuse the three sequence information for deep learning and radiomics models construction. For comparison, other two models without channel attention were also constructed. All models were developed in the same hospital and validated in the other hospital. Results The deep learning model with channel attention mechanism achieved area under the curves (AUCs) of 0.898 in the internal validation cohort and 0.873 in the external validation cohort, which was the best performed model in all cohorts. More importantly, both the deep learning and radiomics model that applied channel attention mechanism performed better than those without channel attention mechanism. Conclusions The self-attention mechanism based multi-sequence fusion strategy can improve prediction of response to nCRT in LARC.

Skin cutaneous melanoma (SKCM) is a malignant tumor with high mortality rate in human, and its occurrence and development are jointly regulated by genes and the environment. However, the specific pathogenesis of SKCM is not completely understood. In recent years, an increasing number of studies have reported the important role of competing endogenous RNA (ceRNA) regulatory networks in various tumors; however, the complexity and specific biological effects of the ceRNA regulatory network of SKCM remain unclear. In the present study, we obtained a ceRNA regulatory network of long non-coding RNAs, microRNAs, and mRNAs related to the phosphatase and tensin homolog ( PTEN ) in SKCM and identified the potential diagnostic and prognostic markers related to SKCM. We extracted the above three types of RNA involved in SKCM from The Cancer Genome Atlas database. Through bioinformatics analysis, the OIP5-AS1 -hsa-miR-186-5p/hsa-miR-616-3p/hsa-miR-135a-5p/hsa-miR-23b-3p/hsa-miR-374b-5p- PTPRC / IL7R / CD69 and MALAT1 -hsa-miR-135a-5p/hsa-miR-23b-3p/hsa-miR-374b-5p- IL7R / CD69 ceRNA networks were found to be related to the prognosis of SKCM. Finally, we determined the OIP5-AS1-PTPRC / IL7R / CD69 and MALAT1-IL7R / CD69 axes in ceRNA as a clinical prognostic model using correlation and Cox regression analyses. Additionally, we explored the possible role of these two axes in affecting gene expression and immune microenvironment changes and the occurrence and development of SKCM through methylation and immune infiltration analyses. In summary, the ceRNA-based OIP5-AS1-PTPRC / IL7R / CD69 and MALAT1-IL7R / CD69 axes may be a novel and important approach for the diagnosis and prognosis of SKCM.