The Papermill Text Comparison API is a tool that identifies similarities between papers and past products. While it can alert users to potential cases of misconduct, its output should not be considered evidence. Instead, it serves as a useful tool for identifying possible instances of plagiarism or other forms of misconduct.
Run in Postman
{"message": [{"id": "retracted_article_id2", "title": "Silencing circANKRD36 protects H9c2 cells against lipopolysaccharide-induced injury via up-regulating miR-138", "abstract": "Background: Myocarditis refers to the inflammatory process that affects the muscle tissue of the heart. Persistent myocardial inflammation promotes myocardial cell damage, which ultimately leads to heart failure or death. Therefore, we aimed to explore the functional impacts of circANKRD36 on myocarditis. Methods: H9c2 cells were pre-treated with lipopolysaccharide (LPS). Viability and apoptosis were evaluated utilizing CCK-8 assay and flow cytometry. Inflammatory cytokines mRNA expression and production were assessed by qRT-PCR and ELISA. Western blot was utilized to distinguish apoptosis and inflammatory related factors expression. Sequentially, the above mentioned parameters were reassessed when overexpressed miR-138. Results: LPS declined viability and as well as raised apoptosis and inflammatory injury in H9c2 cells. Silencing circular RNA ANKRD36 (si-circANKRD36) alleviated apoptosis and inflammatory injury induced by LPS. MiR-138 expression was suppressed by LPS and elevated by si-circANKRD36. Increase of viability and inflammatory injury induced by si-circANKRD36 was alleviated by down-regulation of miR-138. Eventually, si-circANKRD36 inhibited p38MAPK/NF-\u03baB pathway which activated by LPS via up-regulating miR-138. Conclusion: Si-circANKRD36 exerted its anti-apoptosis and anti-inflammatory function under the condition of LPS in H9c2 cells through p38MAPK/NF-\u03baB pathway and up-regulation of miR-138.", "message": {"status": "red", "message": "This article is highly similar to other papers which are believed to have originated from paper-mills. This does not necessarily mean that this paper originated in a paper-mill. We recommend taking special care to check that this paper meets your criteria for consideration before peer-review."}}], "status_code": 200}
curl --location --request POST 'https://zylalabs.com/api/1915/papermill+text+comparison+api/1621/document+posting' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"id": "retracted_article_id2",
"title": "Silencing circANKRD36 protects H9c2 cells against lipopolysaccharide-induced injury via up-regulating miR-138",
"abstract": "Background: Myocarditis refers to the inflammatory process that affects the muscle tissue of the heart. Persistent myocardial inflammation promotes myocardial cell damage, which ultimately leads to heart failure or death. Therefore, we aimed to explore the functional impacts of circANKRD36 on myocarditis. Methods: H9c2 cells were pre-treated with lipopolysaccharide (LPS). Viability and apoptosis were evaluated utilizing CCK-8 assay and flow cytometry. Inflammatory cytokines mRNA expression and production were assessed by qRT-PCR and ELISA. Western blot was utilized to distinguish apoptosis and inflammatory related factors expression. Sequentially, the above mentioned parameters were reassessed when overexpressed miR-138. Results: LPS declined viability and as well as raised apoptosis and inflammatory injury in H9c2 cells. Silencing circular RNA ANKRD36 (si-circANKRD36) alleviated apoptosis and inflammatory injury induced by LPS. MiR-138 expression was suppressed by LPS and elevated by si-circANKRD36. Increase of viability and inflammatory injury induced by si-circANKRD36 was alleviated by down-regulation of miR-138. Eventually, si-circANKRD36 inhibited p38MAPK/NF-κB pathway which activated by LPS via up-regulating miR-138. Conclusion: Si-circANKRD36 exerted its anti-apoptosis and anti-inflammatory function under the condition of LPS in H9c2 cells through p38MAPK/NF-κB pathway and up-regulation of miR-138."
}'
After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Papermill Text Comparison API simply include your bearer token in the Authorization header.
| Header | Description |
|---|---|
Authorization
|
Required
Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.
|
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About the API:
The Papermill Text Comparison API is a tool designed to help detect cases of potential misconduct in academic publishing. It compares papers submitted to a database of previously submitted papers, known as "papermill-products," to identify similarities and alert users to possible instances of plagiarism or other forms of misconduct.
One important note is that the output of this API should not be considered evidence of misconduct. Instead, it serves as a helpful tool to flag potential issues that require further investigation.
The API takes article metadata as input and classifies articles into three categories: red, orange, and green. A "red" classification means that the query article is highly similar to past papermill-papers, while an "orange" classification indicates that there is some degree of similarity. A "green" classification means that no similar past papermill-papers are known.
The Papermill Text Comparison API can be used by academic publishers, universities, and other organizations to help ensure the integrity of their research publications. By identifying potential instances of misconduct early on, the API can help prevent issues such as plagiarism from going unnoticed.
It is worth noting that the API should be used as one tool in a broader suite of measures to ensure the integrity of academic publications. While the API can help detect potential issues, it cannot replace the need for thorough peer review and other forms of quality control.
Overall, the Papermill Text Comparison API is a valuable tool for academic publishers, researchers, and other stakeholders in the academic community. By providing alerts for potentially problematic submissions, the API can help ensure the integrity and quality of research publications.
Pass any text that you want to analyze.
The Papermill Text Comparison API takes article metadata as input and classifies articles into ‘red’, ‘orange’, and ‘green’.
‘red’ means that there are past papermill papers that are highly similar to the query article.
‘orange’ means that there are past papermill papers that are a little bit similar.
‘green’ means that no similar past papermill papers are known.
Academic Publishers: Academic publishers can use the Papermill Text Comparison API to check for similarities between submitted papers and previously published works in their database. The API can help publishers identify cases of plagiarism and other forms of misconduct.
Universities: Universities can use the API to check for similarities between student papers and past submissions in their own databases, as well as in external databases such as the ones used by academic publishers.
Research Institutes: Research institutes can use the API to check for similarities between research proposals and previously published works. This can help ensure that research proposals are original and not simply a repetition of previously published work.
Journal Editors: Journal editors can use the API to check for similarities between submitted papers and previously published works in their journals. This can help ensure that the journal publishes only original research.
Research Funders: Research funders can use the API to check for similarities between research proposals and previously published works. This can help ensure that grant funds are not used to repeat previously published work and that grant funds are used for original research.
Besides API call limitations per month, there is a limitation of:
- 10 requests per day on the Basic Plan.
The API returns a classification of the submitted article based on its similarity to past papermill papers. The classifications are 'red' (high similarity), 'orange' (some similarity), and 'green' (no known similar papers). Additionally, it provides metadata about similar articles, including titles and abstracts.
The key fields in the response data include 'id' (identifier of the similar article), 'title' (title of the similar article), and 'abstract' (summary of the similar article). These fields help users understand the context of the similarities.
The response data is structured as a JSON object containing a 'message' array. Each entry in the array represents a similar article, with fields for 'id', 'title', and 'abstract', allowing users to easily parse and utilize the information.
The API provides information on the similarity of submitted articles to previously published works, including classifications of similarity and details about articles that are similar. This helps users identify potential misconduct.
The API compares submitted articles against a database of previously submitted papers, known as "papermill-products." This database includes works primarily in biomedical science, where papermills have produced numerous similar papers.
Users can customize their requests by providing specific article metadata as input. This metadata may include the title, authors, and abstract of the article being analyzed, allowing for tailored comparisons against the database.
Typical use cases include academic publishers checking submitted papers for originality, universities verifying student submissions, and research funders ensuring grant proposals are unique. These applications help maintain the integrity of academic work.
Users can utilize the returned data by analyzing the classification and reviewing the details of similar articles. A 'red' classification may prompt further investigation, while 'green' indicates originality, aiding in decision-making for publication or funding.
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