已被取代 - 肺炎克雷伯菌生物反应器感染模型中黏菌素耐药性的快速演化

数据集获取

源站：https://datashare.ed.ac.uk/handle/10283/4789

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

SUPERSEDED - This item has been replaced by the one which can be found at https://datashare.ed.ac.uk/handle/10283/8712 ## .Colistin resistance remains an important antibiotic for the therapeutic management of drug-resistant Klebsiella pneumoniae. Despite the numerous reports of colistin resistance in clinical strains, it remains unclear exactly when and how different mutational events arise resulting in reduced colistin susceptibility. Using a bioreactor model of infection, we modelled the emergence of colistin resistance in a susceptible isolate of K. pneumoniae. Genotypic, phenotypic and mathematical analyses of the antibiotic challenged and un-challenged population indicates that after an initial decline, the population recovers within 24h due to a small number of “founder cells” which have single point mutations mainly in the regulatory genes encoding crrB and pmrB and whose phenotype exhibits colistin MICs up to 100-fold higher than the parental strain. Our work underlines that development of colistin resistance during treatment of susceptible K. pneumoniae infections is readily achieved having implications for the use of cationic antimicrobial peptides as a monotherapy. The dataset is related to the paper ‘Rapid Evolution of Colistin Resistance in a Bioreactor Model of Infection of Klebsiella pneumoniae’ (https://doi.org/10.1101/2022.10.03.510619).

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。