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Record W2992739852 · doi:10.1099/mic.0.000853

Metabolic networks of the human gut microbiota

2019· review· en· W2992739852 on OpenAlexaff
Susannah Selber‐Hnatiw, Tarin Sultana, William Ka Fai Tse, Niki Abdollahi, Sheyar Abdullah, Jalal Al Rahbani, Diala Alazar, Nekoula Jean Alrumhein, Saro Aprikian, Rimsha Arshad, Jean-Daniel Azuelos, Daphney Bernadotte, Natalie Beswick, Hana Chazbey, Kelsey Church, Emaly Ciubotaru, Lora D'Amato, Tavia Del Corpo, Jasmine Deng, Briana Laura Di Giulio, Diana Diveeva, Elias Elahie, James Gordon Marcel Frank, Emma Furze, Rebecca E. Garner, Vanessa Gibbs, Rachel Goldberg-Hall, Chaim Jacob Goldman, Fani-Fay Goltsios, Kevin Gorjipour, Taylor Grant, Brittany M. Greco, Nadir Guliyev, Andrew Habrich, Hillary Hyland, Nabila Ibrahim, Tania Iozzo, Anastasia Jawaheer-Fenaoui, Julia Jane Jaworski, Maneet Kaur Jhajj, Jermaine D. Jones, Rodney Joyette, Samad Kaudeer, Shawn Kelley, Shayesteh Kiani, Marylin Koayes, Abby Johanna Amy-Aminta Léna Kpata, Shannon Maingot, Sara De Martin, Kelly Mathers, Sean McCullogh, Kelly McNamara, James D. Mendonça, Karamat Mohammad, Sharara Arezo Momtaz, Thiban Navaratnarajah, Kathy Nguyen-Duong, Mustafa Omran, Angela Ortiz, Anjali Patel, Kahlila Paul-Cole, Paul-Arthur Plaisir, Jessica Alexandra Porras Marroquin, Ashlee Danielle Prévost, Angela B. V. Quach, Aries John Rafal, Rewaparsad Ramsarun, Sami Rhnima, Lydia Rili, Naomi Safir, Eugenie Samson, Rebecca Rose Sandiford, Stefano Secondi, Stephanie Shahid, Mojdeh Shahroozi, Fily Sidibé, Megan R. B. Smith, Alina Maria Sreng Flores, Anabel Suarez Ybarra, Rebecca Sénéchal, Tarek Taifour, Lawrence C.H. Tang, Adam Trapid, Maxim Tremblay Potvin, Justin Wainberg, Dani Ni Wang, Mischa Weissenberg, Allison White, Gabrielle Wilkinson, Brittany Williams, Joshua Roth Wilson, Johanna Zoppi, Katerina Zouboulakis, Chiara Gamberi

Bibliographic record

VenueMicrobiology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsConcordia University
Fundersnot available
KeywordsDysbiosisBiologyGut floraHost (biology)Adaptation (eye)DiseaseGut–brain axisGeneticsImmunologyNeuroscienceMedicineInternal medicine

Abstract

fetched live from OpenAlex

The human gut microbiota controls factors that relate to human metabolism with a reach far greater than originally expected. Microbial communities and human (or animal) hosts entertain reciprocal exchanges between various inputs that are largely controlled by the host via its genetic make-up, nutrition and lifestyle. The composition of these microbial communities is fundamental to supply metabolic capabilities beyond those encoded in the host genome, and contributes to hormone and cellular signalling that support the dynamic adaptation to changes in food availability, environment and organismal development. Poor functional exchange between the microbial communities and their human host is associated with dysbiosis, metabolic dysfunction and disease. This review examines the biology of the dynamic relationship between the reciprocal metabolic state of the microbiota-host entity in balance with its environment (i.e. in healthy states), the enzymatic and metabolic changes associated with its imbalance in three well-studied diseases states such as obesity, diabetes and atherosclerosis, and the effects of bariatric surgery and exercise.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.320
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2019
Admission routes1
Has abstractyes

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