囊性纤维化远程痰液采集

源站描述指出,CF 标准诊疗需通过痰培养进行感染监测与检测,但 Elexacaftor/Tezacaftor/Ivacaftor(ETI)的引入和远程医疗的采用减少了呼吸道培养数量并改变了采样类型;既往能咳出痰的 ETI 使用者如今在门诊更难产生痰液,导致成人口咽(OP)拭子采样增加,而 OP 拭子

University of PennsylvaniaUniversity of Pennsylvania
clinicaltrials
浏览 8
其他Cystic FibrosisInfections
Remote Sputum Collection in Cystic Fibrosis

基本信息

模态
其他
创建/更新时间
2026-10-12

资源简介

囊性纤维化远程痰液采集

该数据集来自 clinicaltrials.gov,主题为囊性纤维化(CF)患者的远程痰液采集,涉及 Cystic Fibrosis 与 Infections。源站描述指出,CF 标准诊疗需通过痰培养进行感染监测与检测,但 Elexacaftor/Tezacaftor/Ivacaftor(ETI)的引入和远程医疗的采用减少了呼吸道培养数量并改变了采样类型;既往能咳出痰的 ETI 使用者如今在门诊更难产生痰液,导致成人口咽(OP)拭子采样增加,而 OP 拭子对细菌检测敏感性不足。尽管 ETI 带来临床改善,CF 气道慢性细菌感染仍持续存在,因此需要额外的非侵入性感染监测方法(来源 clinicaltrials.gov)。

【数据内容】

源站描述(机器翻译,仅供参考):病症:Cystic Fibrosis、Infections。CF 患者临床标准诊疗是通过痰培养进行感染监测与检测。然而,过去三年中,Elexacaftor/Tezacaftor/Ivacaftor(ETI)的引入和远程医疗就诊的采用减少了呼吸道培养的数量,并改变了呼吸道采样的类型。使用 ETI 的 CF 患者中,既往能够咳出痰液者如今在门诊就诊时更不易产生痰液。因此,成人中口咽(OP)拭子采样有所增加。OP 拭子对细菌检测缺乏敏感性。尽管 ETI 使用带来临床改善(如肺功能、肺部急性加重率下降),CF 气道中的慢性细菌感染仍持续存在。因此,需要额外的非侵入性感染监测方法。

【主题与分类】

主题标签:Cystic Fibrosis、Infections。本站分类:呼吸系统疾病。

【获取与许可】

源站页面:https://clinicaltrials.gov/study/NCT06950892

官方服务

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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。

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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。

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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。

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使用方式

数据集获取

源站:https://clinicaltrials.gov/study/NCT06950892

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, Infections. The clinical standard of care of people with CF is conducting sputum culture for infection surveillance and detection. However, the introduction of Elexacaftor/Tezacaftor/Ivacaftor (ETI) and adoption of telehealth encounters have reduced the number of respiratory cultures and shifted the types of respiratory sampling over the past three years. People with CF on ETI who were previously able to expectorate sputum are now less likely to produce sputum during clinic visits. As a result, there has been an increase in oropharyngeal (OP) swab sampling in adults. OP swabs lack sensitivity for bacterial detection. Despite clinical improvements related to ETI use (eg lung function, decreased pulmonary exacerbation rates), chronic bacterial infection persists in CF airways. Therefore, additional non-invasive methods for infection surveillance are needed. The investigators propose remote collection of sputum is a promising option for detecting bacteria and fungi in people with CF. Yet, delayed processing and temperature storage conditions may impact the recovery of CF pathogens in remotely collected sputa. To address this, the investigators propose an observational study testing the accuracy and validity of remotely collected sputum. The study will achieve this by comparing CF pathogen detection by culture in sputum samples collected at home to those obtained through induced sputum and OP swabs during clinic visits. The aims of the study are to: 1. Determine the diagnostic performance of home-collected sputum samples for CF pathogen detection compared to clinic-based induced sputum collection. 2. Determine if CF pathogen detection is greater in home-collected sputum compared to OP swab sampling. 3. Determine if storage temperature conditions (ambient versus 4°C) during overnight shipping influence bacterial identification and abundance in home-collected sputum. The study will enroll 150 participants. The sensitivity, specificity, overall accuracy, positive predictive value (PPV

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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