健康受试者与囊性纤维化受试者的真实世界环境暴露研究

CF 是白种人中最常见的常染色体隐性遗传病,在法国约影响 7500 名患者,其病情进展取决于肺部急性加重,而环境因素如烟草烟雾、空气污染、温度变化和食物摄入等被认为是除遗传与表观遗传改变之外的重要影响因素

Centre Hospitalier Intercommunal CreteilCentre Hospitalier Intercommunal Creteil
clinicaltrials
浏览 11
其他预测Cystic Fibrosis, Pulmonary
Real World Environmental Exposure Study With Healthy and Cystic Fibrosis Subjects

基本信息

模态
其他
任务类型
预测
创建/更新时间
2026-10-12

资源简介

健康受试者与囊性纤维化受试者的真实世界环境暴露研究

该数据集来自 clinicaltrials.gov,聚焦囊性纤维化(Cystic Fibrosis, CF)这一肺部疾病。CF 是白种人中最常见的常染色体隐性遗传病,在法国约影响 7500 名患者,其病情进展取决于肺部急性加重,而环境因素如烟草烟雾、空气污染、温度变化和食物摄入等被认为是除遗传与表观遗传改变之外的重要影响因素。数据涵盖健康受试者与 CF 受试者的真实世界环境暴露信息,可用于探索环境暴露与 CF 肺部疾病表型变异之间的关系,支持疾病进展预测等研究(来源 clinicaltrials.gov)。

【数据内容】

源站描述(机器翻译,仅供参考):条件:囊性纤维化,肺部。囊性纤维化(CF)是导致白种人早期死亡的最常见常染色体隐性遗传病,在法国约影响 7500 名患者。疾病进展取决于肺部急性加重,即呼吸道症状的急性恶化,最终损害肺功能和生活质量。急性加重最常由肺部细菌感染引起,其影响包括咳嗽加重、痰量增加、抗生素使用增加、呼吸困难以及肺功能下降。CF 的表型变异性提示其他因素尤其是 CF 气道疾病的参与。除遗传和表观遗传改变外,环境因素——例如烟草烟雾、空气污染、温度变化、食物摄入——似乎是相关的候选因素。此前的综述已讨论了当前已知

【主题与分类】

主题标签:Cystic Fibrosis, Pulmonary。本站分类:呼吸系统疾病/其他。

【获取与许可】

源站页面:https://clinicaltrials.gov/study/NCT06339450

官方服务

专家保障全链路合规授权与标注交付

陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付,我们提供全链路闭环服务,让高质量临床数据即拿即用。

⚡️ 需要原始数据或标注支持?联系我们获取定制方案。

帮我联系

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/NCT06339450

标准获取流程:

  1. 访问源站链接,注册/登录账号;
  2. 进入数据集详情页,按页面指引申请权限或直接下载;
  3. 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。

数据说明

Conditions: Cystic Fibrosis, Pulmonary. Cystic fibrosis (CF) is the most common autosomal recessive disease that leads to early mortality in Caucasians and affects around 7500 patients in France. Progression of the disease depends on pulmonary exacerbations defined as acute deterioration of respiratory symptoms which ultimately impair lung function and quality of life. Most frequently caused by lung bacterial infections, exacerbations’ effects include increased cough, increased sputum production, increased use of antibiotics, dyspnea and decreased lung function. The phenotypic variability of CF suggests the implication of other contributors especially to the CF airway disease. Beside genetic and epigenetic alterations, environmental factors - e.g tobacco smoke, air pollution, temperature changes, food intake - appear as relevant candidates. A previous review has discussed current knowledge on the effects of air pollution on the course of CF disease. Although scarce, the existing epidemiological andexperimental literature suggests a link between exposure to air pollutants and adverse health effects.Although scarce, the existing epidemiological and experimental literature suggests a link between exposure to air pollutants and adverse health effects. The EU sponsored REMEDIA project (Impact of exposome on the course of lung diseases, Grant agreement ID 874753) contributes to the understanding of the influence of the exposome on chronic obstructive pulmonary disease (COPD) and CF. Objective of work package 3 within the REMEDIA project is the development of a mobile environmental sensor toolbox that is capable to assess the external exposome. The biomarkertoolbox was developed and tested in a proof-of-concept study carried out in healthy volunteers. The next step is to validate the collectionof exhaled breath condensate (EBC) in a real-life study. In this aim, the objective of the present study will be to assess the feasibility of EBC collection in CF patients and healthy individuals

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。

数据缺失?

依托陕西、四川两大基地,我们与超过十家三甲医院建立直接合作关系,覆盖合规授权、采集治理、专业标注、数据交付的全流程,为AI医疗团队提供即拿即用的高质量临床数据。

⚡️ 需要数据支持或标注服务?立即联系我们获取专业方案。

获取专属数据定制方案