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Cited by 27History of Medicine StudiesEthnobotanical and Medicinal Plants StudiesHistory of Science and Medicine

The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.

Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents

Open access
Connelly, Erin, del Genio, Charo I., Harrison, Freya·2020·mBio
DOI 10.1128/mbio.03136-19PMC PMC7018648

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Abstract

We used established methodologies from network science to identify patterns in medicinal ingredient combinations in a key medieval text, the 15th-century Lylye of Medicynes , focusing on recipes for topical treatments for symptoms of microbial infection. We conducted experiments screening the antimicrobial activity of selected ingredients. These experiments revealed interesting examples of ingredients that potentiated or interfered with each other’s activity and that would be useful bases for future, more detailed experiments. Our results highlight (i) the potential to use methodologies from network science to analyze medieval data sets and detect patterns of ingredient combination, (ii) the potential of interdisciplinary collaboration to reveal different aspects of the ethnopharmacology of historical medical texts, and (iii) the potential development of novel therapeutics inspired by premodern remedies in a time of increased need for new antibiotics.

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Integrity signals

Registry

Crossref · 10.1128/mbio.03136-19

Cites

106 of 106 references verified0 citation errors · 0 not checkable

Statistics

Figures

FIG 1 Community co-occurrence of ingredients. Every pixel in the figure corresponds to a pair of ingredients. The saturation of the color is proportional to the number of times that the pair of ingredients is found in the same community, according to the scale on the right-hand border of the figure.
FIG 1 Community co-occurrence of ingredients. Every pixel in the figure corresponds to a pair of ingredients. The saturation of the color is proportional to the number of times that the pair of ingredients is found in the same community, according to the scale on the right-hand border of the figure.CC BY · PubMed Central
FIG 2 Example of an ingredient network. The nodes in yellow are ingredients of a recipe for the treatment of fistula in lacrimali; those in blue are ingredients of a recipe for the treatment of pascionibus oris. Ingredients found in both recipes are colored both yellow and blue. Thicker links join pairs of ingredients that appear in both recipes.
FIG 2 Example of an ingredient network. The nodes in yellow are ingredients of a recipe for the treatment of fistula in lacrimali; those in blue are ingredients of a recipe for the treatment of pascionibus oris. Ingredients found in both recipes are colored both yellow and blue. Thicker links join pairs of ingredients that appear in both recipes.CC BY · PubMed Central
FIG 3 Fictitious example of the process for identifying relevant combinations of ingredients via thresholding and community detection. (A) The starting network. (B, C) Apply the thresholding procedure and choose the partitioned network with maximal modularity (q{c}, equation 2). (D) Draw a heat map to visualize the strengths of associations between ingredient pairs. Darker pixels indicate a stronger association. For example, in this example, network ingredients 1 and 3 are more strongly connected by their recipe co-occurrences than are ingredients 1 and 2 or ingredients 2 and 3. (E) Identify the most strongly associated group of ingredients and manually search the database for recipes including these combinations.
FIG 3 Fictitious example of the process for identifying relevant combinations of ingredients via thresholding and community detection. (A) The starting network. (B, C) Apply the thresholding procedure and choose the partitioned network with maximal modularity (q{c}, equation 2). (D) Draw a heat map to visualize the strengths of associations between ingredient pairs. Darker pixels indicate a stronger association. For example, in this example, network ingredients 1 and 3 are more strongly connected by their recipe co-occurrences than are ingredients 1 and 2 or ingredients 2 and 3. (E) Identify the most strongly associated group of ingredients and manually search the database for recipes including these combinations.CC BY · PubMed Central

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Citations

Cites

Show all 106 references

Research works printed but not linked: 5

  1. Voigts L. 2003. The master of the King’s stillatories. In Stratford J (ed), The Lancastrian Court: Proceedings of the 2001 Harlaxton Symposium. Shaun Tyas, Donington, Lincolnshire, United Kingdom.
  2. Connelly E del Genio CI Harrison F. 2020. Data mining a medieval medical text reveals patterns in ingredient choice that reflect biological activity against the causative agents of specified infections. bioRxiv doi:10.1101/368779. · DOI · Paper page
  3. . 1559. Opus lilium medicinae. Apud Gulielmum Rouillium, Lyon, France.
  4. . 1497. Practica seu Lilium Medicinae. Johannes and Gregorius de Gregoriis for Benedictus Fontana, Venice, Italy.
  5. 10.1211/jpp/61.10.0003 · DOI

Metadata

Authors
Connelly, E., del Genio, C. I., & Harrison, F.
Affiliations
  • University of Pennsylvania (US)
  • University of Warwick (GB)
Corresponding author
  • Connelly, Erin
  • del Genio, Charo I.
  • Harrison, Freya
ORCID
Venue
mBio
Publisher
American Society for Microbiology
Published
2020-02-25
Volume
11
Issue
1
ISSN
  • 2150-7511 (ISSN-L)
  • 2161-2129
DOI
10.1128/mbio.03136-19
PMC
PMC7018648
OpenAlex
W3005612885
Cited by
27
References
106
Citations by year
2026: 3 · 2025: 5 · 2024: 5 · 2023: 3 · 2022: 4 · 2021: 2 · 2020: 4
FWCI
53.87
Type
journal article
Language
en
Open access
Licence
Locations
  • mBio (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Pure (Coventry University) (acceptedVersion, cc-by)
  • Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • University of Pennsylvania
  • University of Warwick
Topics
  • Social Sciences › Arts and Humanities › History › History of Medicine Studies
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Ethnobotanical and Medicinal Plants Studies
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › History of Science and Medicine
Keywords
medieval medicine, antimicrobial activity, topical treatment, microbial infection, ethnopharmacology, historical medical texts, network analysis, antibiotic development
MeSH terms
Anti-Bacterial Agents, Electronic Data Processing, Communicable Disease Control, Communicable Diseases, Humans, Materia Medica, Reference Books, Medical, Ethnopharmacology, History, Medieval, Data Mining

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CoChat. (2026). Citation check: Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents. https://app.cochat.ai/papers/10.1128/mbio.03136-19
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Connelly, E., del Genio, C. I., & Harrison, F. (2020). Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents. mBio. https://doi.org/10.1128/mbio.03136-19

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