AIMC Topic: Antidepressive Agents
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What do patients learn about psychotropic medications on the web? A natural language processing study.
Journal of affective disorders
Sep 10, 2019
BACKGROUND: Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information a...
Can Machine Learning help us in dealing with treatment resistant depression? A review.
Journal of affective disorders
Aug 13, 2019
BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression ...
Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.
PloS one
Feb 6, 2019
Widely-prescribed prodrug opioids (e.g., hydrocodone) require conversion by liver enzyme CYP-2D6 to exert their analgesic effects. The most commonly prescribed antidepressant, selective serotonin reuptake inhibitors (SSRIs), inhibits CYP-2D6 activity...
Recent Developments in the Treatment of Depression.
Behavior therapy
Jan 17, 2019
The cognitive and behavioral interventions can be as efficacious as antidepressant medications and more enduring, but some patients will be more likely to respond to one than the other. Recent work has focused on developing sophisticated selection al...
A rapid LC-MS/MS method for simultaneous determination of quetiapine and duloxetine in rat plasma and its application to pharmacokinetic interaction study.
Journal of food and drug analysis
Aug 14, 2018
Combinations of new antidepressants like duloxetine and second-generation antipsychotics like quetiapine are used in clinical treatment of major depressive disorder, as well as in forensic toxicology scenarios. The drug-drug interaction (DDI) between...
Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review.
Journal of affective disorders
Aug 14, 2018
BACKGROUND: No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that pre...
Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants.
NeuroImage. Clinical
Aug 11, 2018
BACKGROUND: Clinically approved antidepressants modulate the brain's emotional valence circuits, suggesting that the response of these circuits could serve as a biomarker for screening candidate antidepressant drugs. However, it is necessary that the...
Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.
PloS one
Jun 7, 2018
Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. We extended the work in predictive modeling of treatment resistant...
The Cost-Effectiveness of Using PARO, a Therapeutic Robotic Seal, to Reduce Agitation and Medication Use in Dementia: Findings from a Cluster-Randomized Controlled Trial.
Journal of the American Medical Directors Association
Jan 9, 2018
OBJECTIVES: To examine the within-trial costs and cost-effectiveness of using PARO, compared with a plush toy and usual care, for reducing agitation and medication use in people with dementia in long-term care.
Fast and easy extraction of antidepressants from whole blood using ionic liquids as extraction solvent.
Talanta
Dec 16, 2017
This study aims to prove that ionic liquids (ILs) can be used as extraction solvents in a liquid-liquid microextraction, coupled to LC-MS/MS, for the quantification of a large group of antidepressants in whole blood samples. The sample preparation pr...
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