FEED-囊性纤维化(FEED-CF)
约半数成年 CF 患者会发展为糖尿病,而 CF 现行营养标准推荐不受限的高热量高脂饮食,这与用于预防或治疗其他类型糖尿病的饮食策略相矛盾,并可能导致饮食质量偏低、添加糖摄入远超一般人群建议

基本信息
资源简介
FEED-囊性纤维化(FEED-CF)
FEED-Cystic Fibrosis(FEED-CF)是一个来自 clinicaltrials.gov 的数据集,关注囊性纤维化(CF)及囊性纤维化相关糖尿病。约半数成年 CF 患者会发展为糖尿病,而 CF 现行营养标准推荐不受限的高热量高脂饮食,这与用于预防或治疗其他类型糖尿病的饮食策略相矛盾,并可能导致饮食质量偏低、添加糖摄入远超一般人群建议。研究者还发现 CF 患者腹部器官周围的内脏脂肪多于健康对照。该数据集可用于研究与 CF 饮食、糖尿病风险和内脏脂肪积累相关的营养与代谢问题(来源 clinicaltrials.gov)。
【数据内容】
源站描述(机器翻译,仅供参考):病症:囊性纤维化、囊性纤维化相关糖尿病。囊性纤维化(CF)是一种遗传病,约半数成年 CF 患者会发展为糖尿病。在无 CF 人群中成功预防或治疗其他类型糖尿病的饮食策略,与当前针对 CF 患者的营养建议相矛盾。由于营养不良,CF 的营养标准治疗是处方不受限的高热量、高脂饮食。然而,标准 CF 饮食意味着低质量饮食,添加糖过量,远超一般人群建议。此外,研究者已表明,与健康对照相比,CF 患者腹部器官周围(称为内脏脂肪)有更多脂肪。假设是,典型的高糖、高脂 CF 饮食在 CF 患者的糖尿病风险和内脏脂肪积累中起作用。在这
【主题与分类】
主题标签:Cystic Fibrosis、Cystic Fibrosis-related Diabetes。本站分类:内分泌、营养和代谢疾病/糖尿病。
【获取与许可】
源站页面:https://clinicaltrials.gov/study/NCT05766774
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载,适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档,适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务(收技术服务费,非数据销售)
暂未开放当前数据集主要来源为 Kaggle 公开托管,完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
源站:https://clinicaltrials.gov/study/NCT05766774
标准获取流程:
- 访问源站链接,注册/登录账号;
- 进入数据集详情页,按页面指引申请权限或直接下载;
- 部分平台提供 SDK,例如:
- OpenDataLab:
pip install openxlab后openxlab.dataset.get(...) - ModelScope:
pip install modelscope后modelscope download - 天池: 登录后页面提供直链下载
以源站「数据下载/使用文档」页面为准。
- OpenDataLab:
数据说明
Conditions: Cystic Fibrosis, Cystic Fibrosis-related Diabetes. Approximately half of adults with cystic fibrosis (CF), a genetic disease, will develop diabetes. Dietary strategies shown to be successful in preventing or treating other forms of diabetes in people without CF contradict current nutritional recommendations for people with CF. The nutrition standard-of-care in CF is prescription of an unrestricted high-calorie, high-fat diet because of malnutrition. However, the standard CF diet translates to low-quality diets, with excess added sugars well-above general population recommendations. Also the investigators have shown that people with CF have more fat around their abdominal organs (called visceral fat) compared to healthy controls. The hypothesis is that the typical high-sugar, high-fat CF diet plays a role in diabetes risk and visceral fat accumulation in people with CF. In this study, the investigators will test if a low-added sugar diet improves risk markers for diabetes and decreases visceral fat over 8 weeks. The study will recruit 60 participants with CF. A total of 30 participants will get a low-added sugar, high-fat diet and the other 30 will get a standard CF diet with no sugar restrictions. Participants will be randomized to the diet group they are assigned. All foods will be provided for 8 weeks. There will be a total of 4 study visits at the Emory Hospital clinical research unit. These will include: 1) a screening visit with an oral glucose tolerance test with blood draws to determine if they already have diabetes, 2) a baseline visit for an insulin secretion test (called glucose-potentiated arginine (GPA) stimulation test) to assess risk for diabetes, as well as magnetic resonance imaging (MRI) testing to measure visceral fat, 3) a 4-week visit for another oral glucose tolerance test and in-person check-in, and 4) an 8-week visit for another GPA and MRI. Blood samples will be collected and banked. In addition to all meals provided for 8 weeks, participants will be compensated for their time and effort. Par
数据加载示例(图像类)
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
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