Unlocking microbiota potential: the role of organic copper in enhancing healthy white shrimp (Penaeus vannamei) farming.

Background: Microbiota sequencing has emerged a powerful tool for advancing aquatic nutrition research. However, few studies have comprehensively investigated the host microbiota's response to trace minerals. This study examined the role of organic copper supplementation in promoting the health of farmed white shrimp (Penaeus vannamei) from a microbiota perspective.

Collaborative Learning of Augmentation and Disentanglement for Semi-Supervised Domain Generalized Medical Image Segmentation.

This paper explores a challenging yet realistic scenario: semi-supervised domain generalization (SSDG) that includes label scarcity and domain shift problems. We pinpoint that the limitations of previous SSDG methods lie in 1) neglecting the difference between domain shifts existing within a training dataset (intra-domain shift, IDS) and those occurring between training and testing datasets (cross-domain shift, CDS) and 2) overlooking the interplay between label scarcity and domain shifts, resulting in these methods merely stitching together semi-supervised learning (SSL) and domain generalization (DG) techniques. Considering these limitations, we propose a novel perspective to decompose SSDG into the combination of unsupervised domain adaptation (UDA) and DG problems.

Fabrication of sodium alginate nanofibrous membranes with bead-on-string structure for effective air filtration.

Airborne dust particles carrying pathogens threaten public health, driving demand for eco-friendly air filters. While sodium alginate (SA) offers sustainability advantages over petroleum-based nanofibers, its conventional smooth, densely packed membranes suffer from limited performance. Inspired by biological micro/nanostructures, we engineered bead-on-string SA nanofibers via green electrospinning.

Intervention-based Mixup Augmentation for Multiple Instance Learning in Whole-Slide Image Survival Analysis.

In the field of computational pathology, multiple instance learning (MIL) has become a critical modeling framework for analyzing gigapixel whole-slide images (WSIs). Data scarcity, particularly in survival analyses requiring long-term follow-up, is a prevalent challenge that is often mitigated through data augmentation. Although data augmentation methods for multi-instance classification have progressed, they remain insufficient for multi-instance regression in survival analysis.

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories.

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine . However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide.

Dietary enzyme complex improve the growth performance, digestive ability, antioxidant capacity and immunity, and glycometabolism in Penaeus vannamei fed high-plant-protein diets.

In this study, six isonitrogenous and isolipid diets were formulated. A diet containing 12 % fishmeal was used as the fishmeal control group (PC). By using composite plant protein sources (soybean meal, peanut meal, and low-gossypol cottonseed meal) to partially replace fishmeal, a high-plant-protein diet with 4 % fishmeal was formed.

Ovatodiolide alleviates renal fibrosis through regulating metabolic reprogramming via targeting glucose-6-phosphate dehydrogenase.

Background: Ovatodiolide (Ova) is a bioactive compound from Anisomeles indica (l.) Kuntze, which has been traditionally utilized to tonify the kidney function. There is limited targeted therapies available for renal fibrosis (RF) in chronic kidney disease (CKD).

Organic Trace Mineral Source Enhances the Bioavailability, Health Status, and Gut Microbiota Community in White Shrimp ().

Trace mineral supplementation is critical for shrimp aquaculture sustainability, yet the bioavailability of conventional inorganic forms remains suboptimal. The study investigated the effects of inorganic (IM) and organic (OM) trace mineral premixes at varying doses on growth, physiological responses, and gut microbiota in white shrimp (). Five experimental diets were formulated: a basal control (no supplementation), full-dose IM (IM100), half-dose IM (IM50), half-dose OM (OM50), and one-third dose OM (OM33).

Few-shot small vessel segmentation using a detail-preserving network enhanced by discriminator.

Accurate segmentation of small vessels, such as coronary and pulmonary arteries, is crucial for early detection and treatment of vascular diseases. However, challenges persist due to the vessel's small size, complex structures, morphological variations, and limited annotated data. To address these challenges, we propose a detail-preserving network enhanced by a discriminator to improve the few-shot small vessel segmentation performance.

Uncertainty Quantification and Quality Control for Heatmap-Based Landmark Detection Models.

Uncertainty quantification is a vital aspect of explainable artificial intelligence that fosters clinician trust in medical applications and facilitates timely interventions, leading to safer and more reliable outcomes. Although deep learning models have reached clinically acceptable accuracy in anatomical landmark detection, their predictions remain susceptible to contextual noise due to the small size of the target structures, making uncertainty quantification more challenging than in classification and segmentation tasks. This paper presents an end-to-end uncertainty quantification method tailored for heatmap-based anatomical landmark detection models, designed to improve both interpretability and controllability in clinical applications.

Evaluation of the combined impact of aflatoxin B1 and deoxynivalenol fed to Penaeus vannamei and mitigation properties provided by a yeast cell wall extract.

The purpose of this study was to examine how white shrimp (Penaeus vannamei) responded to varying concentrations of combined mycotoxins (aflatoxin B1 (AFB1) and deoxynivalenol (DON)) as well as the mitigating effects of an adsorbent (yeast cell wall extract, YCWE). The experiment comprised of five groups receiving isonitrogenous and isolipidic diets: CON (control group), LDA (CON with 20 μg/kg AFB1 and 1500 μg/kg DON), HDA (CON with 100 μg/kg AFB1 and 3000 μg/kg DON), LDAM (LDA with 0.2 % YCWE), and HDAM (HDA with 0.

CQENet: A segmentation model for nasopharyngeal carcinoma based on confidence quantitative evaluation.

Accurate segmentation of the tumor regions of nasopharyngeal carcinoma (NPC) is of significant importance for radiotherapy of NPC. However, the precision of existing automatic segmentation methods for NPC remains inadequate, primarily manifested in the difficulty of tumor localization and the challenges in delineating blurred boundaries. Additionally, the black-box nature of deep learning models leads to insufficient quantification of the confidence in the results, preventing users from directly understanding the model's confidence in its predictions, which severely impacts the clinical application of deep learning models.

Antera 3D skin image analysis system for objective evaluation of clinical treatment efficacy in a patient with facial vitiligo.

Objective: To evaluate the changes of skin surface parameters in a patient with vitiligo using Antera 3D Skin Image Analysis System, and to observe the clinical efficacy of the treatment of facial vitiligo.

Methods: A patient with facial vitiligo was treated with intradermal injection of psoralen solution into the lesion combined with 308 nm excimer laser therapy. Vitiligo lesion images were captured using the Antera 3D camera before treatment and every 2 weeks after the start of the treatment.

Exploring Attention and Self-Supervised Learning Mechanism for Graph Similarity Learning.

Graph similarity estimation is a challenging task due to the complex graph structures. Though important and well-studied, three critical aspects are yet to be fully handled in a unified framework: 1) how to learn richer cross-graph interactions from a pairwise node perspective; 2) how to map the similarity matrix into a similarity score by exploiting the inherent structure in the similarity matrix; and 3) how to establish a self-supervised learning mechanism for graph similarity learning. To solve these issues, we explore multiple attention and self-supervised mechanisms for graph similarity learning in this work.

Heterogeneous Graph Representation Learning Framework for Resting-State Functional Connectivity Analysis.

Brain functional connectivity analysis is important for understanding brain development and brain disorders. Recent studies have suggested that the variations of functional connectivity among multiple subnetworks are closely related to the development of diseases. However, the existing works failed to sufficiently capture the complex correlation patterns among the subnetworks and ignored the learning of heterogeneous structural information across the subnetworks.

Clinical evaluation of hypercoagulability in advanced malignant tumors using thromboelastography and conventional coagulation tests.

This study compares the diagnostic value of conventional coagulation tests (CCTs) and thromboelastography (TEG) for high blood coagulation states in advanced malignant tumors and to explore the diagnostic efficacy of their combination. In this study, 120 patients with advanced malignancy were divided into hypercoagulable state (n = 62) and non-hypercoagulable state (n = 58) groups. Traditional coagulation tests or CCTs (including prothrombin time, activated partial thromboplastin time, international normalized ratio, fibrinogen, D-dimer, and platelet count) were conducted.

ECGEFNet: A two-branch deep learning model for calculating left ventricular ejection fraction using electrocardiogram.

Left ventricular systolic dysfunction (LVSD) and its severity are correlated with the prognosis of cardiovascular diseases. Early detection and monitoring of LVSD are of utmost importance. Left ventricular ejection fraction (LVEF) is an essential indicator for evaluating left ventricular function in clinical practice, the current echocardiography-based evaluation method is not avaliable in primary care and difficult to achieve real-time monitoring capabilities for cardiac dysfunction.

CMR-BENet: A confidence map refinement boundary enhancement network for left ventricular myocardium segmentation.

Background And Objective: Left ventricular myocardium segmentation is of great significance for clinical diagnosis, treatment, and prognosis. However, myocardium segmentation is challenging as the medical image quality is disturbed by various factors such as motion, artifacts, and noise. Its accuracy largely depends on the accurate identification of edges and structures.

CorLabelNet: a comprehensive framework for multi-label chest X-ray image classification with correlation guided discriminant feature learning and oversampling.

Recent advancements in deep learning techniques have significantly improved multi-label chest X-ray (CXR) image classification for clinical diagnosis. However, most previous studies neither effectively learn label correlations nor take full advantage of them to improve multi-label classification performance. In addition, different labels of CXR images are usually severely imbalanced, resulting in the model exhibiting a bias towards the majority class.

Precomputed low-frequency lighting in cinematic volume rendering.

Cinematic Rendering (CR) employs physical models such as ray tracing and global illumination to simulate real-world light phenomena, producing high-quality images with rich details. In the medical field, CR can significantly aid doctors in accurate diagnosis and preoperative planning. However, doctors require efficient real-time rendering when using CR, which presents a challenge due to the substantial computing resources demanded by CR's ray tracing and global illumination models.

Structure preservation constraints for unsupervised domain adaptation intracranial vessel segmentation.

Unsupervised domain adaptation (UDA) has received interest as a means to alleviate the burden of data annotation. Nevertheless, existing UDA segmentation methods exhibit performance degradation in fine intracranial vessel segmentation tasks due to the problem of structure mismatch in the image synthesis procedure. To improve the image synthesis quality and the segmentation performance, a novel UDA segmentation method with structure preservation approaches, named StruP-Net, is proposed.

Contour-constrained branch U-Net for accurate left ventricular segmentation in echocardiography.

Using echocardiography to assess the left ventricular function is one of the most crucial cardiac examinations in clinical diagnosis, and LV segmentation plays a particularly vital role in medical image processing as many important clinical diagnostic parameters are derived from the segmentation results, such as ejection function. However, echocardiography typically has a lower resolution and contains a significant amount of noise and motion artifacts, making it a challenge to accurate segmentation, especially in the region of the cardiac chamber boundary, which significantly restricts the accurate calculation of subsequent clinical parameters. In this paper, our goal is to achieve accurate LV segmentation through a simplified approach by introducing a branch sub-network into the decoder of the traditional U-Net.

Age-specific ASPECTS atlas of Chinese subjects across different age groups for assessing acute ischemic stroke.

The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a valuable and easy-to-use method for assessing acute ischemic stroke. It aids in identifying suitable candidates for thrombolytic therapies and evaluating treatment effectiveness. However, ASPECTS evaluation primarily relies on visual observation in current clinical practice, lacking a common standardized space.

Associations between multimorbidity patterns and sarcopenia transitions in Chinese older adults.

Aim: Previous studies have shown that chronic diseases are strongly linked to the development of sarcopenia. Few studies have assessed the relationship between multimorbidity patterns and sarcopenia. This study aimed to investigate the impact of multimorbidity patterns on sarcopenia transitions in Chinese older adults.

Frailty detection with routine blood tests using data from the english longitudinal study of ageing (ELSA).

Purpose: Frailty is a rising global health issue in ageing society. Easily accessible and sensitive tools are needed for frailty monitoring while routine blood factors can be potential candidates.

Methods: Data from 1907 participants (aged 60 years or above) were collected from the 4th to 9th wave of the English longitudinal study of ageing.