Robert Egger
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View article: Comprehensive characterization of granular fibrotic and cellular features in liver tissue enabled by deep learning models
Comprehensive characterization of granular fibrotic and cellular features in liver tissue enabled by deep learning models Open
Background & Aims Histologic staging of metabolic dysfunction-associated steatohepatitis (MASH) requires semiquantitative assessment of hepatocellular ballooning, steatosis, lobular inflammation, and fibrosis. We hypothesize that quantitat…
View article: Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis
Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis Open
Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollme…
View article: AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis
AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis Open
While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, …
View article: Analytical and Clinical Validation of AIM-NASH: A Digital Pathology Tool for Artificial Intelligence-based Measurement of Nonalcoholic Steatohepatitis Histology
Analytical and Clinical Validation of AIM-NASH: A Digital Pathology Tool for Artificial Intelligence-based Measurement of Nonalcoholic Steatohepatitis Histology Open
Metabolic-dysfunction associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollme…
View article: Deep-learning quantified cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer types
Deep-learning quantified cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer types Open
While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, …
View article: Brief synaptic inhibition persistently interrupts firing of fast-spiking interneurons
Brief synaptic inhibition persistently interrupts firing of fast-spiking interneurons Open
Neurons perform input-output operations that integrate synaptic inputs with intrinsic electrical properties; these operations are generally constrained by the brevity of synaptic events. Here, we report that sustained firing of CA1 hippoca…
View article: 1277 Identification of clinically relevant spatial tissue phenotypes in large-scale multiplex immunofluorescence data via unsupervised graph learning in non-small cell lung cancer
1277 Identification of clinically relevant spatial tissue phenotypes in large-scale multiplex immunofluorescence data via unsupervised graph learning in non-small cell lung cancer Open
Background Multiplex immunofluorescence (mIF) allows simultaneous spatial interrogation of multiple cell- and tissue-based biomarkers from patient cohorts at scale using whole-slide images (WSI). Identification of spatially-derived insight…
View article: Brief synaptic inhibition persistently interrupts firing of fast-spiking interneurons
Brief synaptic inhibition persistently interrupts firing of fast-spiking interneurons Open
Summary Neurons perform input-output operations that integrate synaptic inputs with intrinsic electrical properties, operations generally constrained by the brevity of synaptic events. Here we report that sustained firing of CA1 hippocampa…
View article: Thalamus drives two complementary input strata of the neocortex in parallel
Thalamus drives two complementary input strata of the neocortex in parallel Open
Sensory information enters the neocortex via thalamocortical axons that define the major ‘input’ layer 4. The same thalamocortical axons, however, additionally innervate the deep ‘output’ layers 5/6. How such bistratification impacts corti…
View article: Morphological characterization of HVC projection neurons in the zebra finch (<i>Taeniopygia guttata</i>)
Morphological characterization of HVC projection neurons in the zebra finch (<i>Taeniopygia guttata</i>) Open
Singing behavior in the adult male zebra finch is dependent upon the activity of a cortical region known as HVC (proper name). The vast majority of HVC projection neurons send primary axons to either the downstream premotor nucleus RA (rob…
View article: EM connectomics reveals axonal target variation in a sequence-generating network
EM connectomics reveals axonal target variation in a sequence-generating network Open
The sequential activation of neurons has been observed in various areas of the brain, but in no case is the underlying network structure well understood. Here we examined the circuit anatomy of zebra finch HVC, a cortical region that gener…
View article: Author response: EM connectomics reveals axonal target variation in a sequence-generating network
Author response: EM connectomics reveals axonal target variation in a sequence-generating network Open
Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods References Decision letter Author response Article and author information Metrics Abstract The sequential activation of neurons has been o…
View article: Simulation of sensory-evoked signal flow in anatomically realistic models of neural networks
Simulation of sensory-evoked signal flow in anatomically realistic models of neural networks Open
In this thesis, a new concept for development and simulation of anatomically and functionally constrained models of signal flow in neural networks is described. This approach consists of the following tools: 1. A standardized anatomical re…