Development of a peptide targeting dopamine transporter to improve ADHD-like deficits
Bibliographic record
Abstract
Attention-deficit hyperactivity disorder (ADHD) is a neurocognitive disorder characterized by hyperactivity, inattention, working memory deficits and impulsivity. Its worldwide prevalence is estimated to be 3–5% in children and adolescents. The mainstay treatment for ADHD is stimulant medications (e.g. methylphenidate), which increase synaptic dopamine by directly blocking dopamine transporter (DAT). Although these pharmacological agents are effective, they are often associated with various side effects including risks for future substance use disorders in ADHD patients. Here, we investigated an interaction between DAT and dopamine D2 receptor (D2R) as a novel target to develop potential therapeutics for the treatment of ADHD by using an interfering peptide (TAT-DAT NT ) to dissociate this protein complex. We found that TAT-DAT NT promotes locomotor behavior in Sprague-Dawley rats. Furthermore, using in vivo microdialysis and high-performance liquid chromatography, we found that the disruption of D2R-DAT elevates extracellular dopamine level. More importantly, the interfering peptide, TAT-DAT NT , attenuates hyperactivity and improves spontaneous alternation behavior in spontaneously hypertensive rats (SHR) ------ a common animal model of ADHD. This work presents a different means (i.e. other than direct blockade by a DAT inhibitor) to regulate the activity of DAT and dopaminergic neurotransmission, and a potential target site for future development of ADHD treatments.
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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".