Gut Microbiota and Treatment-Resistant Schizophrenia: Many Questions, Fewer Answers
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Abstract
PharmacogenomicsVol. 23, No. 5 EditorialGut microbiota and treatment-resistant schizophrenia: many questions, fewer answersMirko Manchia, Alessio Squassina, Federica Tozzi, Athos Antoniades & Bernardo CarpinielloMirko Manchia *Author for correspondence: E-mail Address: mirkomanchia@unica.ithttps://orcid.org/0000-0003-4175-6413Department of Medical Sciences and Public Health, Section of Psychiatry, University of Cagliari, Cagliari, 09127, ItalyUnit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, 09127, ItalyDepartment of Pharmacology, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada, Alessio SquassinaDepartment of Biomedical Science, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Monserrato, 09042, ItalyDepartment of Psychiatry, Dalhousie University, Halifax, Nova Scotia, B3H 2E2, Canada, Federica TozziResearch and Development, Stremble Ventures, Limassol, 3095, Cyprus, Athos AntoniadesResearch and Development, Stremble Ventures, Limassol, 3095, Cyprus & Bernardo CarpinielloDepartment of Medical Sciences and Public Health, Section of Psychiatry, University of Cagliari, Cagliari, 09127, ItalyUnit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, 09127, ItalyPublished Online:3 Mar 2022https://doi.org/10.2217/pgs-2022-0018AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleKeywords: antipsychoticsdrug metabolismFASTQmicroorganismresistanceschizophreniaReferences1. 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Neuropsychopharmacol. 56, 39–49 (2021).Crossref, Medline, Google Scholar25. Nunes A, Trappenberg T, Alda M. The definition and measurement of heterogeneity. Transl. Psychiatry 10(1), 299 (2020).Crossref, Medline, Google ScholarFiguresReferencesRelatedDetailsCited ByCytokine Imbalance as a Biomarker of Treatment-Resistant Schizophrenia26 September 2022 | International Journal of Molecular Sciences, Vol. 23, No. 19 Vol. 23, No. 5 Follow us on social media for the latest updates Metrics Downloaded 70 times History Received 14 February 2022 Accepted 15 February 2022 Published online 3 March 2022 Published in print April 2022 Information© 2022 Future Medicine LtdKeywordsantipsychoticsdrug metabolismFASTQmicroorganismresistanceschizophreniaAuthor contributionsM Manchia wrote the first draft and performed the literature search. A Squassina, A Antoniades and F Tozzi contributed to draft preparation and critically revised the manuscript. B Carpiniello co-wrote the manuscript and critically revised it.Financial & competing interests disclosureThis work was partly funded by Fondo Integrativo per la Ricerca 2018, granted to A Squassina, and by Fondo Integrativo per la Ricerca 2020, granted to M Manchia and B Carpiniello. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".