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Selective Serotonin Reuptake Inhibitors

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Predicting inadequate postoperative pain management in depressed patients: A machine learning approach.

PloS one 30726237
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...
Adult Aged Antidepressive Agents Depression Drug Prescriptions Electronic Health Records Female Humans Hydrocodone Machine Learning Male Middle Aged Models, Biological Natural Language Processing Pain Management Pain, Postoperative Postoperative Period Selective Serotonin Reuptake Inhibitors
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Design and development of a robotic predator as a stimulus in conditioned place aversion for the study of the effect of ethanol and citalopram in zebrafish.

Behavioural brain research 31614187
Zebrafish are becoming a species of choice in psychopharmacology, laying a promising path to refined pharmacological manipulations and high-throughput behavioral phenotyping. The field of robotics has the potential to accelerate progress along this p...
Animals Association Avoidance Learning Behavior, Animal Central Nervous System Depressants Citalopram Conditioning, Psychological Equipment Design Ethanol Fear Female Male Predatory Behavior Robotics Selective Serotonin Reuptake Inhibitors Spatial Behavior Zebrafish
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Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology 31831204
Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reuptake inhibitor (SSRI), which is the first-line depression treatment. Unfortunately, many depressed p...
Antidepressive Agents Biogenic Monoamines Drug Administration Schedule Humans Hydrocortisone Male Neural Networks, Computer Neuroendocrine Cells Neurotransmitter Agents Selective Serotonin Reuptake Inhibitors Testosterone
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Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.

Scientific reports 33436854
Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent res...
Amino Acid Sequence Antidepressive Agents Drug Evaluation, Preclinical Humans Molecular Conformation Neural Networks, Computer Pharmaceutical Preparations Pharmacology Selective Serotonin Reuptake Inhibitors
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Association of Selective Serotonin Reuptake Inhibitor Use With Abnormal Physical Movement Patterns as Detected Using a Piezoelectric Accelerometer and Deep Learning in a Nationally Representative Sample of Noninstitutionalized Persons in the US.

JAMA network open 35389502
IMPORTANCE: Selective serotonin reuptake inhibitors (SSRIs) are a common first-line treatment for some psychiatric disorders, including depression and anxiety; although they are generally well tolerated, SSRIs have known adverse effects, including mo...
Accelerometry Adult Cross-Sectional Studies Deep Learning Female Humans Nutrition Surveys Selective Serotonin Reuptake Inhibitors
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Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal.

Physical and engineering sciences in medicine 38358619
In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) pa...
Adult Deep Learning Depressive Disorder, Major Electroencephalography Female Humans Male Middle Aged Neural Networks, Computer Selective Serotonin Reuptake Inhibitors Signal Processing, Computer-Assisted Treatment Outcome
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Influence of panic disorder and paroxetine on brain functional hubs in drug-free patients.

Journal of psychopharmacology (Oxford, England) 39310938
BACKGROUND: The effects of panic disorder (PD) and pharmacotherapy on brain functional hubs in drug-free patients, and the utility of their degree centrality (DC) in diagnosing and predicting treatment response (TR) for PD, remained unclear.
Adult Brain Case-Control Studies Female Humans Magnetic Resonance Imaging Male Middle Aged Panic Disorder Paroxetine Selective Serotonin Reuptake Inhibitors Support Vector Machine Treatment Outcome Young Adult
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Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.

Molecules (Basel, Switzerland) 39942741
Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line treatment for depressive disorder: selective serotonin reuptake inhibitors and serotonin-norepinephrin...
Antidepressive Agents Humans Machine Learning Norepinephrine Plasma Membrane Transport Proteins Quantitative Structure-Activity Relationship Selective Serotonin Reuptake Inhibitors Serotonin and Noradrenaline Reuptake Inhibitors Serotonin Plasma Membrane Transport Proteins
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Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy.

Proceedings of the National Academy of Sciences of the United States of America 40035765
Cardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-driven screens have the potential to identify drugs and downstream pathways that modulate cardiomyocyte hypertrophy. Here, we developed LogiRx, a logic-bas...
Animals Cardiomegaly Citalopram Escitalopram Humans Machine Learning Male Mice Myocytes, Cardiac Selective Serotonin Reuptake Inhibitors Signal Transduction
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Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression.

Medical care 40266632
OBJECTIVES: To demonstrate an innovative method combining machine learning with comparative effectiveness research techniques and to investigate a hitherto unstudied question about the effectiveness of common prescribing patterns.
Adult Antidepressive Agents Comparative Effectiveness Research Depressive Disorder, Major Female Humans Machine Learning Male Middle Aged Practice Patterns, Physicians' Primary Health Care Selective Serotonin Reuptake Inhibitors United States United States Department of Veterans Affairs Veterans
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