Effects of methamphetamine use in the microbiome: A scoping review
Bibliographic record
Abstract
Methamphetamine is a synthetic psychostimulant drug in the brain, smoked, injected or snorted. It is neurotoxic and its use elicits a wide range of effects like hyper-activity, euphoria, increased blood pressure, sleep deprivation, alertness, loss of appetite and weight, dry mouth, high heart rate, and cognitive impairment which causes anxiety, aggression, irritability, panic, hallucinations, memory loss, trust issues, paranoia, chronic mood changes, seizure. Also, it can cause heart attack, stroke and death. The association between methamphetamine, sexually transmitted infections and human immunodeficiency virus has mainly been attributed to injection drug use, high sexual drive, long-lasting sexual encounters, which may influence unprotected sexual behaviours with many intimate partners. Previous evidence has shown that methamphetamine alters the composition, diversity, and immune function of the gut and oral microbiome. Human and animal model studies on methamphetamine-induced microbiota dysbiosis have focused on gut and oral microbiota with scarce information on its effect on microbiota. Additionally, human studies have focused on men who have sex with men. However, the effects of methamphetamine on microbiome and sexually transmitted infections are poorly understood. Methamphetamine use can potentially affect the structure and function of the microbiome negatively. Microbiome is an important tool we can use to understand the interaction between substance use and abuse and diseases. Findings may inform microbiome-based therapies and public health policies. Our preliminary search did not find any scoping review publication on the effects of methamphetamine on the microbiome. This research, therefore, aims to describe the effects of methamphetamine on the microbiome in humans and animal models. This review will help us to identify existing gaps and future areas of research related to methamphetamine use and the microbiome.
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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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.558 | 0.255 |
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; both teacher heads agree on what is shown here.
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".