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Record W3016028452 · doi:10.1097/qad.0000000000002481

Reply to: Benefits of cannabis use for metabolic disorders and survival in people living with HIV with or without hepatitis C

2020· letter· en· W3016028452 on OpenAlexaff
Cecilia T. Costiniuk, Mohammad‐Ali Jenabian

Bibliographic record

VenueAIDS · 2020
Typeletter
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversité du Québec à MontréalRoyal Victoria HospitalMcGill UniversityRoyal Victoria Regional Health CentreUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsCannabisObservational studyMedicineNeurocognitiveEffects of cannabisEndocannabinoid systemMoodHepatitis CInternal medicineNonalcoholic fatty liver diseaseDiseaseMetabolic syndromePsychiatryFatty liverObesityCognitionCannabidiol

Abstract

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We appreciate the interest shown by Santos et al.[1] regarding our editorial review. We agree with them that numerous observational studies point towards the beneficial effects of cannabis on hepatic fibrosis in persons living with HIV/hepatitis C virus (HCV). However, there are discrepancies in the findings between earlier cross-sectional observational studies [2–4] and later prospective longitudinal evaluations and a meta-analysis of liver disease progression among persons with HIV/HCV infection [5,6]. These later studies and meta-analysis suggest that cannabis use did not increase the prevalence or progression of hepatic fibrosis and, in fact, was associated with reduced prevalence of nonalcoholic fatty liver disease in cannabis users [5,6]. As suggested by Brunet et al.[5], earlier studies were biased by reverse causality, referring to the fact that patients modify their behaviour due to illness as the sicker, more symptomatic patients could be using more marijuana to relieve symptoms ad liver disease progresses. Difficulty in understanding whether cannabis has positive versus negative effects on different conditions likely relates to the fact that multiple factors affect the endocannabinoid system. Observational studies cannot control for all of these variables and multiple interactions between variables. Indeed, high-fat diet, alcohol intake and potentially obesity augment production of 2-arachidonoyl glycerol (2-AG) and arachidonyl ethanolamide which, in turn, activate CB1 and CB2 [7–9]. Furthermore, people with HIV and HIV/HCV on effective antiretroviral therapy have high rates of mood [10] and sleep disorders [11], neurocognitive aberrancies [12] and metabolic dysregulation [13], all of which are affected by the endocannabinoid system [14]. Therefore, it is plausible that the endocannabinoid system is dysregulated in people with HIV monoinfection and HIV/HCV coinfection, but this hypothesis has not yet been tested. Moreover, the impact of phytocannabinoids intake on the regulation of endocannabinoids during these infections remains unclear. Cannabinoid receptors are expressed on the gut epithelum. 2-AG, palmitoylethanolamide and N-arachidonoylethanolamine (anandamide) modulate epithelial barrier permeability and function, influencing microbial translocation into the bloodstream [15]. Given the central importance of gut dysfunctionality in HIV pathogenesis [16] and liver injury [17,18], one may hypothezie that the gut endocannabinoid system is disrupted in HIV and HIV/HCV infection. Altogether and going forward, efforts to better characterize the endocannabinoid system in health and in HIV and HIV/HCV infection, in addition to the effects of exogenous cannabinoid administration on the endocannabinoid system, are needed. Randomized controlled clinical trials, with biological specimens collected pre and post cannabinoid administration in clinically characterized human participants, will help us to understand the biology of the endocannabinoid system and pathways affected in gut-liver axis and in the context of hepatic fibrosis progression. These data will inform the design of therapeutic strategies to potentially slow or reverse the damage of HIV and HIV/HCV-associated liver disease and other comorbidities. Acknowledgements Conflicts of interest There are no conflicts of interest.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.017
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0170.019
Insufficient payload (model declined to judge)0.0120.007

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 head0.017
GPT teacher head0.267
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations1
Published2020
Admission routes1
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