29 research outputs found

    Understanding the Mechanism of Deep Learning Frameworks in Lesion Detection for Pathological Images with Breast Cancer

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    With the advances of scanning sensors and deep learning algorithms, computational pathology has drawn much attention in recent years and started to play an important role in the clinical workflow. Computer-aided detection (CADe) systems have been developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing misdetections. In this study, we conducted four experiments to demonstrate that the features learned by deep learning models are interpretable from a pathological perspective. In addition, classifiers such as the support vector machine (SVM) and random forests (RF) were used in experiments to replace the fully connected layers and decompose the end-to-end framework, verifying the validity of feature extraction in the convolutional layers. The experimental results reveal that the features learned from the convolutional layers work as morphological descriptors for specific cells or tissues, in agreement with the diagnostic rules in practice. Most of the properties learned by the deep learning models summarized detection rules that agree with those of experienced pathologists. The interpretability of deep features from a clinical viewpoint not only enhances the reliability of AI systems, enabling them to gain acceptance from medical experts, but also facilitates the development of deep learning frameworks for different tasks in pathological analytics

    Subtype-Based Analysis of Cell-in-Cell Structures in Esophageal Squamous Cell Carcinoma

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    Cell-in-cell (CIC) structures are defined as the special structures with one or more cells enclosed inside another one. Increasing data indicated that CIC structures were functional surrogates of complicated cell behaviors and prognosis predictor in heterogeneous cancers. However, the CIC structure profiling and its prognostic value have not been reported in human esophageal squamous cell Carcinoma (ESCC). We conducted the analysis of subtyped CIC-based profiling in ESCC using “epithelium-macrophage-leukocyte” (EML) multiplex staining and examined the prognostic value of CIC structure profiling through Kaplan-Meier plotting and Cox regression model. Totally, five CIC structure subtypes were identified in ESCC tissue and the majority of them was homotypic CIC (hoCIC) with tumor cells inside tumor cells (TiT). By univariate and multivariate analyses, TiT was shown to be an independent prognostic factor for resectable ESCC, and patients with higher density of TiT tended to have longer post-operational survival time. Furthermore, in subpopulation analysis stratified by TNM stage, high TiT density was associated with longer overall survival (OS) in patients of TNM stages III and IV as compared with patients with low TiT density (mean OS: 51 vs 15 months, P = 0.04) and T3 stage (mean OS: 57 vs 17 months, P=0.024). Together, we reported the first CIC structure profiling in ESCC and explored the prognostic value of subtyped CIC structures, which supported the notion that functional pathology with CIC structure profiling is an emerging prognostic factor for human cancers, such as ESCC

    Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

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    Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

    Fault distance estimation-based protection scheme for DC microgrids

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    The DC microgrid has become a typical distribution network due to its excellent performances. However, a well-designed protection scheme still remains to be a challenge for DC microgrids. This paper proposes a fault distance estimation-based protection scheme for DC loop-type microgrids relying on two-terminal electrical quantities. Different from the traditional methods, a small inductor is implemented at each head of the line instead of obtaining the derivative of fault current using current difference value. Thus, the derivative of currents can be calculated accurately with the measured voltages on the small inductors during faults. The presented protection method is able to realise fault location and fault isolation quickly and accurately. Furthermore, it is immune to fault resistances. The presented protection scheme has been tested and verified through PSCAD/EMTDC simulations. The results prove that the new protection scheme possesses the excellent performance

    The expression and function of long noncoding RNAs in hepatocellular carcinoma

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    Abstract With the deepening of the genome project study, attention on noncoding RNAs is increasing. Long noncoding RNAs (lncRNAs) have become a new research hotspot. A growing number of studies have revealed that lncRNAs are involved in tumorigenesis and tumor suppressor pathways. Aberrant expressions of lncRNAs have been found in a variety of human tumors including hepatocellular carcinoma (HCC). In this review, we provide a brief introduction to lncRNA and highlight recent research on the functions and clinical significance of lncRNAs in HCC

    I Bibiena per i Gesuiti di Vienna

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    DA (D-blood group of Palm and Agouti, also known as Dark Agouti) and F344 (Fischer) are two inbred rat strains with differences in several phenotypes, including susceptibility to autoimmune disease models and inflammatory responses. While these strains have been extensively studied, little information is available about the DA and F344 genomes, as only the Brown Norway (BN) and spontaneously hypertensive rat strains have been sequenced to date. Here we report the sequencing of the DA and F344 genomes using next-generation Illumina paired-end read technology and the first de novo assembly of a rat genome. DA and F344 were sequenced with an average depth of 32-fold, covered 98.9% of the BN reference genome, and included 97.97% of known rat ESTs. New sequences could be assigned to 59 million positions with previously unknown data in the BN reference genome. Differences between DA, F344, and BN included 19 million positions in novel scaffolds, 4.09 million single nucleotide polymorphisms (SNPs) (including 1.37 million new SNPs), 458,224 short insertions and deletions, and 58,174 structural variants. Genetic differences between DA, F344, and BN, including high-impact SNPs and short insertions and deletions affecting >2500 genes, are likely to account for most of the phenotypic variation between these strains. The new DA and F344 genome sequencing data should facilitate gene discovery efforts in rat models of human disease
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