Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0011570 (depression)
 172,036 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Nitric oxide (NO) may be a neurotransmitter related to major depressive disorder (MDD) because the selective neuronal NO synthase (NOS) inhibitor, 7-nitroindazole, induces dose-dependent antidepressant-like effects. However, its role in MDD is not yet known. The purpose of our study was to determine if antidepressants improve depression via the NO pathway using an acute depressive rat model induced by L-arginine (AR). Three types of antidepressants were examined, fluoxetine (FLX, 10 mg/kg), milnacipran (MIL, 30 mg/kg), and mirtazapine (MIR, 10 mg/kg), in a depressive model that used AR (750 mg/kg) pretreatment. mRNA expression levels of three NOS subtypes were analyzed by real-time PCR, as well as serum NO levels. Significant increases in iNOS mRNA expression levels were found in brain regions after AR treatment, although the eNOS gene tended to decrease with AR injection. After antidepressant treatment, there were no mRNA expression changes in either nNOS or iNOS. However, eNOS mRNA expression significantly increased with FLX (cerebellum, P=0.011; hippocampus, P=0.011; midbrain, P=0.011; pons, P=0.013; striatum, P=0.011; and thalamus, P<0.001). There was a statistically significant increase in serum NO levels with MIL treatment (P=0.011). We conclude that changes in eNOS mRNA levels in the brain with FLX treatment, and amount of serum NO with MIL treatment may be related to antidepressant effects of both agents, but further experiments are needed to confirm involvement of the NO system in MDD.
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PMID:Antidepressant action via the nitric oxide system: A pilot study in an acute depressive model induced by arginin. 2600 4

Although social anxiety and depression are common, they are often underdiagnosed and undertreated, in part due to difficulties identifying and accessing individuals in need of services. Current assessments rely on client self-report and clinician judgment, which are vulnerable to social desirability and other subjective biases. Identifying objective, nonburdensome markers of these mental health problems, such as features of speech, could help advance assessment, prevention, and treatment approaches. Prior research examining speech detection methods has focused on fully supervised learning approaches employing strongly labeled data. However, strong labeling of individuals high in symptoms or state affect in speech audio data is impractical, in part because it is not possible to identify with high confidence which regions of a long speech indicate the person's symptoms or affective state. We propose a weakly supervised learning framework for detecting social anxiety and depression from long audio clips. Specifically, we present a novel feature modeling technique named NN2Vec that identifies and exploits the inherent relationship between speakers' vocal states and symptoms/affective states. Detecting speakers high in social anxiety or depression symptoms using NN2Vec features achieves F-1 scores 17% and 13% higher than those of the best available baselines. In addition, we present a new multiple instance learning adaptation of a BLSTM classifier, named BLSTM-MIL. Our novel framework of using NN2Vec features with the BLSTM-MIL classifier achieves F-1 scores of 90.1% and 85.44% in detecting speakers high in social anxiety and depression symptoms.
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PMID:A Weakly Supervised Learning Framework for Detecting Social Anxiety and Depression. 3118 83