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

ABSTRACT The pharmacopeia used by physicians and laypeople in medieval Europe has largely been dismissed as placebo or superstition. While we now recognize that some of the materia medica used by medieval physicians could have had useful biological properties, research in this area is limited by the...

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Autores principales:	Erin Connelly, Charo I. del Genio, Freya Harrison
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	antibiotic resistance antimicrobial agents antimicrobial combinations Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/fc924ef7328d444b9c735cc5519e2576
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spelling	oai:doaj.org-article:fc924ef7328d444b9c735cc5519e25762021-11-15T15:56:57ZData Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents10.1128/mBio.03136-192150-7511https://doaj.org/article/fc924ef7328d444b9c735cc5519e25762020-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.03136-19https://doaj.org/toc/2150-7511ABSTRACT The pharmacopeia used by physicians and laypeople in medieval Europe has largely been dismissed as placebo or superstition. While we now recognize that some of the materia medica used by medieval physicians could have had useful biological properties, research in this area is limited by the labor-intensive process of searching and interpreting historical medical texts. Here, we demonstrate the potential power of turning medieval medical texts into contextualized electronic databases amenable to exploration by the use of an algorithm. We used established methodologies from network science to reveal patterns in ingredient selection and usage in a key text, the 15th-century Lylye of Medicynes, focusing on remedies to treat symptoms of microbial infection. In providing a worked example of data-driven textual analysis, we demonstrate the potential of this approach to encourage interdisciplinary collaboration and to shine a new light on the ethnopharmacology of historical medical texts. IMPORTANCE 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.Erin ConnellyCharo I. del GenioFreya HarrisonAmerican Society for Microbiologyarticleantibiotic resistanceantimicrobial agentsantimicrobial combinationsMicrobiologyQR1-502ENmBio, Vol 11, Iss 1 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	antibiotic resistance antimicrobial agents antimicrobial combinations Microbiology QR1-502
spellingShingle	antibiotic resistance antimicrobial agents antimicrobial combinations Microbiology QR1-502 Erin Connelly Charo I. del Genio Freya Harrison Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents
description	ABSTRACT The pharmacopeia used by physicians and laypeople in medieval Europe has largely been dismissed as placebo or superstition. While we now recognize that some of the materia medica used by medieval physicians could have had useful biological properties, research in this area is limited by the labor-intensive process of searching and interpreting historical medical texts. Here, we demonstrate the potential power of turning medieval medical texts into contextualized electronic databases amenable to exploration by the use of an algorithm. We used established methodologies from network science to reveal patterns in ingredient selection and usage in a key text, the 15th-century Lylye of Medicynes, focusing on remedies to treat symptoms of microbial infection. In providing a worked example of data-driven textual analysis, we demonstrate the potential of this approach to encourage interdisciplinary collaboration and to shine a new light on the ethnopharmacology of historical medical texts. IMPORTANCE 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.
format	article
author	Erin Connelly Charo I. del Genio Freya Harrison
author_facet	Erin Connelly Charo I. del Genio Freya Harrison
author_sort	Erin Connelly
title	Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents
title_short	Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents
title_full	Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents
title_fullStr	Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents
title_full_unstemmed	Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents
title_sort	data mining a medieval medical text reveals patterns in ingredient choice that reflect biological activity against infectious agents
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/fc924ef7328d444b9c735cc5519e2576
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Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents

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