猪伤口愈合的高分辨率时间转录组学和成像数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Ksenia Zlobina, Hsin-Ya Yang, Manasa Kesapragada, Fan Lu, Anthony Gallegos, Guillermo Villa-Martinez, Moyasar A Alhamo, Kan Zhu, Cynthia Recendez, Craig Collins, Marco Rolandi, Athena Soulika, Elham Aslankoohi, Min Zhao, Marcella Gomez, R Rivkah Isseroff
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引用次数: 0

摘要

伤口愈合是一个涉及多种细胞类型的动态过程。从愈合的伤口中收集样本并研究它们的转录组学可以对潜在的过程提供更深入的了解。近年来,已经进行了几项实验,以收集人类和动物伤口的转录组数据。然而,这些数据的时间分辨率往往不能充分匹配过程的动态,而且空间方面经常被忽视。在这里,我们展示了从猪伤口愈合实验中收集的数据集,包括伤口边缘和中心的基因表达谱,以及伤口的照片。照片提供了非侵入性数据,使用人工智能方法进行图像分析的进步正在积极地融入医疗实践。通过在同一实验中收集这些综合数据,可以帮助构建智能伤口诊断和治疗算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A high-resolution temporal transcriptomic and imaging dataset of porcine wound healing.

Wound healing is a dynamic process involving various cell types. Collecting samples from healing wounds and investigating their transcriptomics can provide deeper insights into the underlying processes. In recent years, several experiments have been conducted to gather transcriptomic data from wounds in both humans and animals. However, the temporal resolution of such data often does not adequately match the dynamics of the process, and spatial aspects are frequently overlooked. Here, we present a dataset collected from an experiment on wound healing in pigs, including gene expression profiles at the wound edge and center, and photographs of the wounds. Photographs provide non-invasive data, and advancements in image analysis using artificial intelligence methods are actively being integrated into medical practice. Being collected within the same experiment, these comprehensive data can aid in building intelligent wound diagnostics and treatment algorithms.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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