# Analyze Diabetes Research Trends with MeSH Terms

**Use case:** 

Collect recent articles tagged with specific MeSH terms related to diabetes complications to understand research focus and gaps. This data is crucial for academ

## Input

```json
{
  "searchTerms": [
    "diabetes complications"
  ],
  "searchUrls": [],
  "pmidList": [],
  "maxItemsPerTerm": 75,
  "dateFrom": "2022/01/01",
  "articleType": "any",
  "freeFullTextOnly": false,
  "language": "any",
  "meshFilter": [
    "Diabetic Nephropathies",
    "Diabetic Retinopathy",
    "Diabetic Neuropathies"
  ],
  "includeCitedByCount": true
}
```

## Output

```json
{
  "pmid": {
    "label": "PMID",
    "format": "text"
  },
  "title": {
    "label": "Title",
    "format": "text"
  },
  "authors": {
    "label": "Authors",
    "format": "array"
  },
  "journal": {
    "label": "Journal",
    "format": "text"
  },
  "publicationDate": {
    "label": "Published",
    "format": "date"
  },
  "doi": {
    "label": "DOI",
    "format": "text"
  },
  "articleUrl": {
    "label": "Article URL",
    "format": "link"
  },
  "tags": {
    "label": "Tags",
    "format": "array"
  }
}
```

## About this Actor

This example demonstrates how to use [PubMed Search Scraper](https://apify.com/crawlerbros/pubmed-search-scraper) with a specific input configuration. Visit the [Actor detail page](https://apify.com/crawlerbros/pubmed-search-scraper) to learn more, explore other use cases, and run it yourself.


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For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/crawlerbros/pubmed-search-scraper.md

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).