Inflammatory disease of cervix uteri - Female, 45 - Cell image [4050730010688731] - Open Research Data - Bridge of Knowledge

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Inflammatory disease of cervix uteri - Female, 45 - Cell image [4050730010688731]

Description

This is the histopathological image of CERVIX UTERI cell sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.

The detailed information about the patient, sample, and diagnosis are as follows:

Patient:

Age: 45

Clinical description: Prophylaxis. IUD

Gender: Female

Diagnosis:

Classification: ICD-10_10-20

Classification code: Chapter XIV - Diseases of the genitourinary system

Diagnosis: Inflammatory disease of cervix uteri

Result of the histopathological examination: NILM (Negative for Intraepithelial Lesion or Malignancy). Bacterial vaginosis. Papsmear contains multiple superficial epithelial cells, single groups of glandular cervical cells and single cells from the transformation zone. Among them multiple granulocytes partly adhering to the epithelial cells.

Sample:

Material: SurePath

Collecting method: Exfoliative cytology

Topography: FEMALE GENITAL ORGANS

Organ: CERVIX UTERI

Tissue: Internal os

Type of staining: positive/HC

Staining: Papanicolaou

Antibody: Not applicable

Technology:

Equipment: Pannoramic 250 3DHistech

Lens: 20x

Organization:

Source: Medical University Gdańsk

Dataset file

b8dc1b99-7e43-4fbb-92e9-cc059fbcdee3
21.8 GB, S3 ETag , downloads: 19
The file hash is calculated from the formula
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.

Example script for calculation:
https://github.com/antespi/s3md5

File details

License:
Creative Commons: by-nc-sa 4.0 open in new tab
CC BY-NC-SA
Non-commercial - Share-alike
Raw data:
Data contained in dataset was not processed.
Software:
CaseViewer 2.3

Details

Year of publication:
2022
Verification date:
2021-04-12
Creation date:
2021
Dataset language:
English
Fields of science:
  • medical sciences (Medical and Health Sciences )
DOI:
DOI ID 10.34808/4cjb-k473 open in new tab
Ethical papers:
NKBBN/421-306/2020
Verified by:
Medical University of Gdańsk

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