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EverFocus Electronics edsr 100, v.1.2
Edsr 100, V.1.2, supplied by EverFocus Electronics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/modeling+process/digital+image+processing+system+everfocus+model+edsr+100+v+1+2/pmc04356708-81-12-13
Average 90 stars, based on 1 article reviews
edsr 100, v.1.2 - by Bioz Stars, 2026-10
90/100 stars

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Article Title: Evaluation of swallowing in children with vomiting after feeding.
Article Snippet: Vomiting after feeding is a symptom of gastroesophageal reflux (GER) and of eosinophilic esophagitis (EE), which are considered to be a cause of infant feeding disorder.. The objective of the present study was to evaluate swallowing in children with feeding disorder manifested by vomiting after feeding.. Using clinical and videofluoroscopic methods we studied the swallowing of 37 children with vomiting after feeding (mean age = 15.4 months), and of 15 healthy children (mean age = 20.5 months).

Article Title: Oral and pharyngeal bolus transit in gastroesophageal reflux disease
Article Snippet: Gastroesophageal reflux disease (GERD) is a frequent cause of esophageal and extraesophageal symptoms [1].. The most frequent esophageal symptoms are heartburn and acid regurgitation, and established extraesophageal manifestations of the disease are cough, laryngitis, asthma and dental erosion, with proposed association with pharyngitis, sinusitis, idiopathic pulmonary fibrosis and recurrent otitis media [1, 2].. Dysphagia is a symptom referred by some patients [2– 4], which may be a consequence of abnormal clearance of a swallowed bolus along the esophagus.

Article Title: Oral and pharyngeal bolus transit in patients with chronic obstructive pulmonary disease
Article Snippet: The images were recorded in digital form on the unit EDSR 100, v.1.2 Everfocus ® (Taipei, Taiwan) and analyzed on a monitor of the same manufacturer.

Article Title: ORAL AND PHARYNGEAL TRANSIT IN FUNCTIONAL HEARTBURN
Article Snippet: The images were recorded at 30 frames/second on the unit EDSR 100, v1.2 Everfocus (Taipei, Taiwan), and analyzed on the monitor DVR of the same manufacturer, with the digital clock of the apparatus indicating time in minutes, seconds and the number of frames on each video frame.



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Overview of Methodological Workflows for Multi-Omics and Spatial Transcriptomics Analysis. a Nicheformer Model for Gene Expression Integration: The Nicheformer model processes tokenized gene expression data and assay-specific markers using transformer embeddings, producing unified outputs for gene ranking and modality integration. This enables accurate predictions for gene regulatory networks (GRN) and drug response analysis . b LocalCLiP for Spatial Transcriptomics: LocalCLiP utilizes a local transformer model to integrate spatial transcriptomics data, using KNN for image patch analysis and gene expression prediction, providing insights into tissue-specific molecular patterns . c BioTask Executor for Task-Specific Analysis: The BioTask Executor handles various biological tasks, from zero-shot learning to GRN inference and drug response prediction, by preprocessing data, initializing pretrained models (e.g., SCGPT, Geneformer), and fine-tuning them for task-specific applications . d Human-8CATAC-CorpuS for Multi-Tissue Analysis: The Human-8CATAC-CorpuS dataset, with 5 million cells from 31 tissues, is used to train models for gene expression prediction and cCRE signal reconstruction, enabling comprehensive analysis of tissue-specific regulatory elements . The schematics were adapted from [ , , ] and

Journal: Journal of Translational Medicine

Article Title: Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems

doi: 10.1186/s12967-025-07091-0

Figure Lengend Snippet: Overview of Methodological Workflows for Multi-Omics and Spatial Transcriptomics Analysis. a Nicheformer Model for Gene Expression Integration: The Nicheformer model processes tokenized gene expression data and assay-specific markers using transformer embeddings, producing unified outputs for gene ranking and modality integration. This enables accurate predictions for gene regulatory networks (GRN) and drug response analysis . b LocalCLiP for Spatial Transcriptomics: LocalCLiP utilizes a local transformer model to integrate spatial transcriptomics data, using KNN for image patch analysis and gene expression prediction, providing insights into tissue-specific molecular patterns . c BioTask Executor for Task-Specific Analysis: The BioTask Executor handles various biological tasks, from zero-shot learning to GRN inference and drug response prediction, by preprocessing data, initializing pretrained models (e.g., SCGPT, Geneformer), and fine-tuning them for task-specific applications . d Human-8CATAC-CorpuS for Multi-Tissue Analysis: The Human-8CATAC-CorpuS dataset, with 5 million cells from 31 tissues, is used to train models for gene expression prediction and cCRE signal reconstruction, enabling comprehensive analysis of tissue-specific regulatory elements . The schematics were adapted from [ , , ] and

Article Snippet: The schematic were adapted from [ ] and [ ] with permission Fig. 3 Overview of Methodological Workflows for Multi-Omics and Spatial Transcriptomics Analysis. a Nicheformer Model for Gene Expression Integration: The Nicheformer model processes tokenized gene expression data and assay-specific markers using transformer embeddings, producing unified outputs for gene ranking and modality integration.

Techniques: Biomarker Discovery, Gene Expression