Wound image and transcriptome datasets of swine acute wounds

Wound healing progresses through overlapping phases: hemostasis, inflammation, proliferation, and remodeling. Continuous characterization of these transitions remains limited. Here, we employed a swine excisional wound model to monitor cellular dynamics across the healing timeline. Both non-invasive imaging and wound biopsy samples from the wound edge and center were acquired. Wound photographs were analyzed using advanced artificial intelligence methods. Wound biopsy samples were subject to RNA sequencing to generate gene expression profiles for the course of healing. By combining the image and the gene expression analyses, we were able to create the comprehensive data for wound healing, which can serve as ground truth for building wound diagnostic and treatment algorithms. , Six domestic pigs (Yorkshire-mix breed, females, 45-50 Kg) were utilized and divided to 2 groups (3 animals/group) for wound biopsy collection in a rotation order (group 1 biopsy on post-op day 1, 3, 5, 7, 11, 15, and 21/endpoint; group 2 biopsy on day 2, 4, 6, 9, 13, 16, and 19/endpoint).  Twelve full-thickness, excisional wounds at 2cm in diameter were created bilaterally on each side of the dorsum of each animal after shaving, depilation, and skin preparation.  Wound images (day 0-21) were captured with a DSLR camera at a set distance of 1 foot above the wounds, and a photo scale (Medline NE1 Wound Assessment Tool) was used for calibration in each image.  To determine the wound healing timeline, open wound areas were analyzed by manual tracking in ImageJ or automatically cropped and analyzed by algorithms. RNA samples (a total of 150 samples of 72 paired samples from the wound edge and center, and 6 healthy skin samples as controls) were extracted from the wound biopsi..., , # Wound image and transcriptome datasets of swine acute wounds Dataset DOI: [10.5061/dryad.0rxwdbsbr](10.5061/dryad.0rxwdbsbr) **Description of the data and file structure** The dataset contains observations from an acute wound healing experiment in pigs. Wound photographs and RNAseq data were collected at multiple time points from day 0 to day 21 post-wound onset. Tissue samples for RNAseq were taken from both wound edges and wound centers. Experiment description: Six domestic pigs (Yorkshire-mix breed, females, 45-50 Kg, pig ID: 1323, 1324, 1325, 1326, 1327, and 1328) were utilized. Twelve full-thickness, circular excision wounds (2 cm in diameter, wound ID: A, B, C, D, E, F, G, H, I, J, K, and L) were created in each animal. Baseline (Day 0) wound images and the excised skin tissue were collected. During the post-operative period, wound images and biopsy samples from the wound edge and wound center were collected on post-operative days 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15, 16, 19...,伤口愈合通过一系列相互重叠的阶段推进：止血（hemostasis）、炎症（inflammation）、增殖（proliferation）以及重塑（remodeling）。目前对这些过渡阶段的持续表征仍存在局限。本研究采用猪切除伤口模型（swine excisional wound model），对愈合全过程中的细胞动态变化进行监测。研究同时采集了无创成像数据以及来自伤口边缘与中心的伤口活检样本，并通过先进人工智能（artificial intelligence, AI）方法对伤口照片进行分析。对伤口活检样本开展RNA测序（RNA sequencing, RNA-seq），以获取愈合过程中的基因表达谱（gene expression profiles）。通过结合成像与基因表达分析，我们构建了伤口愈合的综合数据集，该数据集可作为开发伤口诊断与治疗算法的基准真值（ground truth）。 六头家猪（约克夏混系品种，雌性，体重45~50 kg）被用于本实验，并被分为2组（每组3头动物），按轮换顺序采集伤口活检样本：第1组在术后第1、3、5、7、11、15及21天（终点）采样；第2组在术后第2、4、6、9、13、16及19天（终点）采样。在每头动物背部双侧剃毛、脱毛并进行皮肤准备后，创建12个直径2 cm的全层切除伤口。 使用数码单反相机（DSLR camera）在伤口上方1英尺的固定高度拍摄术后第0~21天的伤口图像，每张图像均采用伤口评估标尺（Medline NE1 Wound Assessment Tool）进行校准。为确定伤口愈合时间线，可通过ImageJ软件手动追踪开放伤口面积，或通过算法自动裁剪并分析该面积。 从伤口活检样本中提取RNA样本（共150份样本，其中包括72份伤口边缘与中心的配对样本，以及6份健康皮肤样本作为对照）…… # 猪急性伤口的伤口影像与转录组数据集 Dataset DOI: [10.5061/dryad.0rxwdbsbr](10.5061/dryad.0rxwdbsbr) **数据与文件结构说明** 本数据集包含猪急性伤口愈合实验的观测数据。在伤口形成后第0天至第21天的多个时间点采集了伤口照片与RNA测序数据。RNA测序所用的组织样本取自伤口边缘与伤口中心。 实验说明： 本研究使用6头家猪（约克夏混系品种，雌性，体重45~50 kg，猪编号：1323、1324、1325、1326、1327、1328）。每头动物均创建12个直径2 cm的全层圆形切除伤口（伤口编号：A、B、C、D、E、F、G、H、I、J、K、L）。采集基线（第0天）伤口图像与切除的皮肤组织样本。 术后期间，在术后第1、2、3、4、5、6、7、9、11、13、15、16、19……天采集伤口图像以及来自伤口边缘与中心的活检样本。