Distribution of Plasma One-Carbon Metabolism Factors and Amino Acids Profile in Depression State Treated with Paroxetine: A Model Study.
Bibliographic record
Abstract
OBJECTIVE: Stress may have an important role in the origin and progress of depression and can impair metabolic homeostasis. The one-carbon cycle (1-CC) metabolism and amino acid (AA) profile are some of the consequences related to stress. In this study, we investigated the Paroxetine treatment effect on the plasma metabolite alterations induced by forced swim stress-induced depression in mice. MATERIALS AND METHODS: In this experimental study that was carried out in 2021, thirty male NMRI mice (6-8 weeks age, 30 ± 5 g) were divided into five groups: control, sham, paroxetine treatment only (7 mg/kg BW/day), depression induction, and Paroxetine+depression. Mice were subjected to a forced swim test (FST) to induce depression and then were treated with Paroxetine, for 35 consecutive days. The swimming and immobility times were recorded during the interventions. Then, animals were sacrificed, plasma was prepared and the concentration of 1-CC factors and twenty AAs was measured by spectrophotometry and high-performance liquid chromatography system (HPLC) techniques. Data were analyzed by SPSS, using One-Way ANOVA and Pearson Correlation, and P<0.05 was considered significant. RESULTS: Plasma concentrations of phenylalanine, glutamate, aspartate, arginine, ornithine, citrulline, threonine, histidine, and alanine were significantly reduced in the depression group in comparison with the control group. The Homocysteine (Hcy) plasma level was increased in the Paroxetine group which can be associated with hyperhomocysteinemia. Moreover, vitamin B12, phenylalanine, glutamate, ornithine, citrulline, and glycine plasma levels were significantly reduced in the depression group after Paroxetine treatment. CONCLUSION: This study has demonstrated an impairment in the plasma metabolites' homeostasis in depression and normal conditions after Paroxetine treatment, although, further studies are required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".