ArticleSistemas e Tecnologias de Informacao : atas de 12a Conferencia Iberica de Sistemas e Tecnologias de Informacao (CISTI'2017) : 21 a 24 de junho de 2017, Lisboa, Portugal = Information systems and technologies : proceedings of the 12th Ib...2017
Digital Image Analysis of Cells and Computational Tools for the Study of Mechanism of RSV Entry to Human Bronchial Epithelium.
Article in Sistemas e Tecnologias de Informacao : atas de 12a Conferencia Iberica de Sistemas e Tecnologias de Informacao (CISTI'2017) : 21 a 24 de junho de 2017, Lisboa, Portugal = Information systems and technologies : proceedings of the 12th Ib..., 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed, 1 citations in OpenAlex.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
this paper presents a research proposal which has been developed as a doctoral thesis in the PhD program in Computer Systems Engineering at the Universidad del Norte since August 2015. This research focuses on the analysis of cell images of the human bronchial epithelium infected with the Respiratory Syncytial Virus in order to understand the mechanisms of entry of the virus into the human body. Due to the large amount of information that is processed, it is necessary to use computational tools to finally differentiate between infected and uninfected cells.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.