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Enregistrement W2129170345 · doi:10.1111/j.1360-0443.2009.02849.x

WHAT NEUROBIOLOGY TELLS US ABOUT ADDICTION

2010· letter· en· W2129170345 sur OpenAlexaboutno aff
Martin Y. Iguchi, Christopher J. Evans

Notice bibliographique

RevueAddiction · 2010
Typeletter
Langueen
DomaineNeuroscience
ThématiqueNeurotransmitter Receptor Influence on Behavior
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAddictionAddiction medicinePsychologyPsychiatryMedicine

Résumé

récupéré en direct d'OpenAlex

The paper by Harold Kalant, ‘What neurobiology can not tell us about addiction’1, rehashes arguments regarding the limitation of neurobiology research in understanding addiction. We agree that addiction research is limited by complexity at all levels of analysis, but we respectfully disagree with several key issues raised by the author. Let us start with the author's first question: ‘What is addiction?’. The author moves quickly past the DSM-IV-TR and other definitions to a statement by an ad hoc committee of the Royal Society of Canada that: ‘the only elements common to all definitions of addiction are a strongly established pattern of repeated self-administration of a drug in doses that reliably produce reinforcing psychoactive effects, and great difficulty in achieving voluntary long-term cessation of such use, even when the user is strongly motivated to stop’. The author concludes that the key phrase is ‘self-administration’ and that ‘addiction is not produced by a drug, but by self-administration of a drug’. He supports his conviction by contrasting the relatively low likelihood that a pain patient will become addicted when administered opioids by a health care professional, with the anecdote that Civil War veterans allowed to self-administer opiates often developed ‘soldier's disease’. 2 He acknowledges differences between pain patients and the soldiers, but his conclusion remains that the only important difference is self-administration. He concludes from this that models of addiction not based on self-administration are basically ‘flawed’. That addiction does not occur in all patients administered opioids by health care professionals is clear, but some do become addicted. At the same time, a great majority of patients who self-administer opioids do not become addicted. That said, we point to the many important factors that account for such differences beyond self-administration such as environmental contexts (e.g. war veterans often experienced severe traumas during and following the war, experienced high unemployment and possibly mental health problems), the likelihood and importance of co-administered substances, high levels of social disruption and displacement, as well as other pre-existing vulnerabilities (genetic and otherwise). We also point to conceptual/integrative models such as proposed by Koob and colleagues that place such factors into a wider context 3. We agree on the need to look at genetic and environmentally engraved phenotypes as drivers of addictive behavior. Differences between modes of drug administration have been researched extensively at the molecular, cellular and behavioral levels, and researchers are far from naive. For example, Robinson and colleagues demonstrated marked differences between experimenter and self-administered opioids in alteration of neuronal spine density in several brain regions 4. That experimenter-administered drug, which results in sensitization and place preference, is completely ‘flawed’ for understanding aspects of addiction is not our opinion. We agree that study of the genetics of addiction has the best chance in the areas of socially accepted drugs such as alcohol and nicotine. Recent identification of a region of chromosome 15 (containing the α5-α3-β4 nicotinic receptor genes) separately in smokers and lung-cancer patients provides evidence that genetic tools will have power to identify aspects of susceptibility 5. Also, success of treatment options has gained insight from genetics with the finding that naltrexone appears to be more successful in alcoholics with selective alleles of the mu-opioid receptor 6 The numbers needed for genome-wide association studies for relatively straightforward phenotypes has been greater than expected because the effect size for specific genes is often exceedingly small 7. The implication is that it will be extremely difficult to identify the genetic vulnerability to illicit drugs and complex psychological endophenotypes underlying susceptibility to addictive behavior 8. The author makes the important point that the control of proteins are at the heart of the issue, and the opportunity for genetic influence at multiple loci to modulate proteins has increased markedly as we begin to understand the complexity of epigenetics, promoters, protein trafficking/turnover and microRNA regulation. Finally, we agree that reductionistic or analytical approaches are placed into a more appropriate context when corralled within appropriate and integrated conceptual frames. This is true for all areas of science. The challenge is certainly one of making the science ‘fly’, as nothing brings focus like a plane about to crash. We agree that the field needs to think more conceptually and integratively, and we look forward to flying with Dr Kalant in the future. None.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesIntégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,526
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0030,009
Charge utile insuffisante (le modèle a refusé de juger)0,0030,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,269
Écart entre enseignants0,245 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2010
Routes d'admission1
Résumé présentoui

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