Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of PET and anatomical ...information from CT. Tumor segmentation is a critical element of PET-CT but at present, the performance of existing automated methods for this challenging task is low. Segmentation tends to be done manually by different imaging experts, which is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT modalities, with the underlying assumption that each modality contains complementary information. However, these methods do not fully exploit the high PET tumor sensitivity that can guide the segmentation. We introduce a deep learning-based framework in multimodal PET-CT segmentation with a multimodal spatial attention module (MSAM). The MSAM automatically learns to emphasize regions (spatial areas) related to tumors and suppress normal regions with physiologic high-uptake from the PET input. The resulting spatial attention maps are subsequently employed to target a convolutional neural network (CNN) backbone for segmentation of areas with higher tumor likelihood from the CT image. Our experimental results on two clinical PET-CT datasets of non-small cell lung cancer (NSCLC) and soft tissue sarcoma (STS) validate the effectiveness of our framework in these different cancer types. We show that our MSAM, with a conventional U-Net backbone, surpasses the state-of-the-art lung tumor segmentation approach by a margin of 7.6% in Dice similarity coefficient (DSC).
Based on the actual processing of
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paper surface sizing, we used bone glue solution with mass fractions of 0.5–2.5% to apply manual drag-dye surface sizing. Organic elemental analysis, scanning ...electron microscopy, surface pH determination, water contact angle measurement, dynamic vapour sorption, and thermogravimetric analysis were used to characterize the bone glue content, surface properties and thermal stability of the sized
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paper. Different bone glue concentrations were retained uniformly and effectively on
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paper. The surface morphology of
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paper changed significantly after sizing due to the bone glue coating on the fibres and the fibre network. With an increase in sizing concentration from 1.0 to 1.5%, the hydrophobicity of the sizing
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paper’s surface increased considerably. The weak acidity of bone glue solution lowered
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paper’s surface pH. Increasing bone glue concentration can boost
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paper’s saturation moisture absorption. When bone glue concentration was over 1.0%, sized
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paper’s moisture absorption rate increased, while sized
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paper with a concentration below 1.0% showed a slight inhibition of moisture absorption within a certain humidity range. The sized
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paper with a concentration of 1.0% had a more stable and slower hygroscopic reaction in the absorption–desorption cycle. As bone glue concentration increased,
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paper’s initial moisture content decreased and then increased, with the lowest value for sized
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paper at 1.0% concentration. The sized
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paper with a concentration of 1.0% bone glue showed the best thermal stability. Surface sizing should have little effect on
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paper’s calcium carbonate content, but bone glue complicated its thermal behaviour.
Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in ...accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.
α-Amylase is abundant in plants and animals. α-Amylase inhibitors can reduce endogenous α-amylase activity, playing an essential role in agricultural pest control, and preventing and treating human ...disease. In the agricultural field, α-Amylase inhibitors can restrict pest that relies on the starch of crops. Acarbose is an α-amylase inhibitor used to treat diabetes. Some α-amylase inhibitors are represented by antinutritional factors, while others are proteinaceous. Depending on their structures and sources, researchers have divided them into seven types: The knottin-like type, the γ-thionin-like type, the cereal type, the Kunitz type, the thaumatin-like type, and the lectin-like type. This paper introduces the methods for separating, purifying, and detecting proteinaceous α-amylase inhibitors while examining the structure and inhibition mechanism of several proteinaceous α-amylase inhibitors. Finally, it explores the potential applications of α-amylase inhibitors.
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•Protein supplements play a vital role during the inflammatory phase of skin repair.•Soy peptide performed better on fibroblast proliferation than the soy protein.•Soy peptide ...performed better on collagen synthesis than the soy protein.•Soy peptide showed a better performance of wound tissue repair.
The aim of this study was to assess the effect of soy protein and soy peptide on skin repair process in male Wistar rats. Soy protein and soy peptide were administered orally by gavage to the rats post-operatively for 16 days. The supplementation of soy protein and soy peptide resulted in improvement of leucocytes production, decrease in serum inflammation index (IL-1β, IL-6, TNF-α) as well as increase of plasma protein concentration. Additionally, the excessive production of inflammatory factors in peptide group were significantly suppressed compared with the protein group at day 4, which indicated that soy peptide had a better impact on promoting the process of skin repair. Histopathological analysis indicated that peptide group performed better on fibroblast proliferation and collagen synthesis than the protein group, and eventually manifests as more collagen deposition and a better skin repair rate. In conclusion, soy peptide displayed the potential to promote skin regeneration.
Abstract
Environmental filtering is deemed to play a predominant role in regulating the abundance and distribution of animals during the urbanization process. However, the current knowledge about the ...effects of urbanization on the population densities of terrestrial mammals is limited. In this study, we compared two invasive mammals (dogs
Canis lupus familiaris
and cats
Felis silvestris
) and three indigenous mammals (Siberian weasels
Mustela sibirica
, Amur hedgehogs
Erinaceus amurensis
, and Tolai hares
Lepus tolai
) in response to urbanization using camera trap distance sampling (CTDS) in the rural–urban landscape of Tianjin, China. We used generalized additive mixed models (GAMMs) to test the specific responses of their densities to levels of urbanization. Invasive dogs (2.63 individuals/km
2
, 95% CI: 0.91–7.62) exhibited similar density estimations to cats (2.15 individuals/km
2
, 95% CI: 1.31–3.50). Amur hedgehogs were the most abundant species (6.73 individuals/km
2
, 95% CI: 3.15–14.38), followed by Tolai hares (2.22 individuals/km
2
, 95% CI: 0.87–5.68) and Siberian weasels (2.15 individuals/km
2
, 95% CI: 1.06–4.36). The densities of cats, Siberian weasels, and Amur hedgehogs increased with the level of urbanization. The population densities of dogs and cats were only influenced by urban‐related variables, while the densities of Siberian weasels and Amur hedgehogs were influenced by both urban‐related variables and nature‐related variables. Our findings highlight that the CTDS is a suitable and promising method for wildlife surveys in rural–urban landscapes, and urban wildlife management needs to consider the integrated repercussions of urban‐ and nature‐related factors, especially the critical impacts of green space habitats at finer scales.
Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly ...sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are extremely labor-intensive and prone to errors. As a result, a robust and automated method for analyzing atrial structures in 3-D is of high interest. We have, therefore, developed AtriaNet, a 16-layer convolutional neural network (CNN), on 154 3-D LGE-MRIs with a spatial resolution of 0.625 mm × 0.625 mm × 1.25 mm from patients with AF, to automatically segment the left atrial (LA) epicardium and endocardium. AtriaNet consists of a multi-scaled, dualpathway architecture that captures both the local atrial tissue geometry and the global positional information of LA using 13 successive convolutions and three further convolutions for merging. By utilizing computationally efficient batch prediction, AtriaNet was able to successfully process each 3-D LGE-MRI within 1 min. Furthermore, benchmarking experiments have shown that AtriaNet has outperformed the state-of-the-art CNNs, with a DICE score of 0.940 and 0.942 for the LA epicardium and endocardium, respectively, and an inter-patient variance of <;0.001. The estimated LA diameter and volume computed from the automatic segmentations were accurate to within 1.59 mm and 4.01 cm 3 of the ground truths. Our proposed CNN was tested on the largest known data set for LA segmentation, and to the best of our knowledge, it is the most robust approach that has ever been developed for segmenting LGE-MRIs. The increased accuracy of atrial reconstruction and analysis could potentially improve the understanding and treatment of AF.
Accurate characterization of visual attributes such as spiculation, lobulation, and calcification of lung nodules in computed tomography (CT) images is critical in cancer management. The ...characterization of these attributes is often subjective, which may lead to high inter- and intra-observer variability. Furthermore, lung nodules are often heterogeneous in the cross-sectional image slices of a 3D volume. Current state-of-the-art methods that score multiple attributes rely on deep learning-based multi-task learning (MTL) schemes. These methods, however, extract shared visual features across attributes and then examine each attribute without explicitly leveraging their inherent intercorrelations. Furthermore, current methods treat each slice with equal importance without considering their relevance or heterogeneity, which limits performance. In this study, we address these challenges with a new convolutional neural network (CNN)-based MTL model that incorporates multiple attention-based learning modules to simultaneously score 9 visual attributes of lung nodules in CT image volumes. Our model processes entire nodule volumes of arbitrary depth and uses a slice attention module to filter out irrelevant slices. We also introduce cross-attribute and attribute specialization attention modules that learn an optimal amalgamation of meaningful representations to leverage relationships between attributes. We demonstrate that our model outperforms previous state-of-the-art methods at scoring attributes using the well-known public LIDC-IDRI dataset of pulmonary nodules from over 1,000 patients. Our model also performs competitively when repurposed for benign-malignant classification. Our attention modules provide easy-to-interpret weights that offer insights into the predictions of the model.
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To evaluate the clinical value of microRNA (miR) and circulating tumor RNA (ctDNA) in the diagnosis of epithelial ovarian cancer (EOC) by meta-analysis and indirect comparison based on common ...reference criteria.
The PubMed, EMBASE, MEDLINE, Cochrane Library, Chinese biology medicine (CBM), China national knowledge infrastructure (CNKI), Wanfang, and Chinese Weipu (VIP) databases were searched by computer. The retrieval time limit was from the date of establishment of the database to September 2020. Two researchers independently screened the literature and extracted the basic data according to the inclusion and exclusion criteria formulated in advance, and evaluated the literature quality according to the quality assessment of diagnostic accuracy research (quadas-2). The Meta disc 1.4 and Stata 12.0 software programs were used for meta-analysis to calculate the combined sensitivity, combined specificity, combined positive likelihood ratio, combined negative likelihood ratio and combined diagnostic odds ratio (DOR). The summary receiver operating characteristic (SROC) curve was drawn using Revman 5.3 software, and the stability of the results was evaluated by sensitivity analysis. The publication bias was evaluated by Deek's funnel asymmetric test. The relative diagnostic odds ratio (RDOR) results of indirect comparison between microRNA and ctDNA were obtained using R software.
Nineteen articles were included, including a total of 1,351 EOC patients and 1,194 controls. The heterogeneity test showed that there was obvious heterogeneity caused by non-threshold effect. The random effects model was used for meta-analysis of microRNA in the diagnosis of EOC. The results showed that there was no significant difference between microRNA and ctDNA in the accuracy of EOC diagnosis. The asymmetric test of Deek's funnel chart showed that there was no significant publication bias.
There are some limitations in this study, there is no blind diagnostic test, and the intensity of indirect comparison evidence is lower than that of direct comparison evidence. The accuracy of diagnostic tests and the imperfection of mesh meta-analysis statistical methods. MicroRNA and ctDNA have similar clinical diagnostic value for EOC.
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