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Ecological momentary assessment (EMA) for mental disorders, using application-based (app) technology capable of passive and ambient data collection, has been insufficiently evaluated and validated with rigorous, adequately-powered, high-quality studies. Herein, we sought to validate the mind.me application for the assessment of depressive symptoms in adults. Adults (ages 18–65) who self-identified as having clinically significant depressive symptoms [i.e. Patient Health Questionnaire 9 (PHQ-9) ≥ 5] utilized the mind.me app—a mobile phone technology that collects data passively and continuously, and is capable of integrating broad multimodal data [e.g., location variance (e.g. GPS), behavioural (e.g. social network activity), and communication data (e.g. SMS texting, phone calls)]. The primary outcome was predictive accuracy (i.e. convergent validity with depressive symptom measurement, as captured by the PHQ-9). 200 subjects were enrolled in the study (mean age 46 ± 12.71). The average PHQ-9 score was 12.8 ± 6.9. The predictive accuracy of the mind.me app was 0.91 ± 0.06. The sensitivity was 0.98 and the specificity was 0.93. The mind.me app was rated by 200 users as highly usable and informative to their illness. The mind.me app exhibits robust predictive accuracy in detecting depressive symptoms in adults with clinically relevant depressive symptoms. The mind.me app more specifically demonstrates convergence with the PHQ-9.

Aim To compare the neurodevelopment of children with unilateral cerebral palsy (CP) with middle cerebral artery (MCA) and periventricular venous infarctions (PVIs). Method In this cross‐sectional study, children with unilateral CP completed a neurological exam, unimanual Quality of Upper Extremity Skills Test, hand usage questionnaires, and IQ test. Neuroimaging was obtained from health records. Results Two hundred and forty‐five participants with unilateral CP had neuroimaging (151 [61.9%] male, ages 2–18y, median=7y 6mo, interquartile range [IQR]=6y 7mo, with 93.6% in Gross Motor Function Classification System level I/II and 78.8% in Manual Ability Classification System level I/II). Ninety‐seven (39.6%) had MCA injuries and 106 (43.3%) had periventricular white matter injuries, of which 48 (45.3%) were PVIs. Median Quality of Upper Extremity Skills Test for the MCA group was 49.2 (IQR=55.8), PVI 79.9 (IQR=23.6) (Mann–Whitney U=988.50, p, Video Podcast: https://youtu.be/79w_s82qhr0

Background Major Depressive Disorder (MDD) is a common and debilitating mood disorder. Individuals with MDD are often misdiagnosed or diagnosed in an untimely manner, exacerbating existing functional impairments. Ecological momentary assessment (EMA) involves the repeated sampling of an individual's symptoms within their natural environment and has been demonstrated to assist in illness assessment and characterization. Capturing data in this way would set the stage for improved treatment outcomes and serve as a complementary resource in the management and treatment of depressive symptoms. Methods Online databases PubMed/MedLine and PsycINFO were searched using PRISMA guidelines and combinations of the following keywords: EMA, depression, smartphone app, diagnosing, symptoms, phone, app, ecological momentary assessment, momentary assessment, data mining, unobtrusive, passive data, GPS, sensor. Results A total of nineteen original articles were identified using our search parameters and ten articles met the inclusion criteria for full-text review. Among the ten relevant studies, three studies evaluated feasibility, seven evaluated detection, and three evaluated treatment of MDD. Limitations Limitations include that the design of all of the studies included in this review are non-randomized. It should be noted that most of the studies included were pilot studies and/or exploratory trials lacking a control group. Conclusions Available evidence suggests that the use of passive smartphone-based applications may lead to improved management of depressive symptoms. This review aids the creation of new EMA applications, highlights the potential of EMA usage in clinical settings and drug development, emphasizes the importance for regulation of applications in the mental health field, and provides insight into future directions.

The authors regret an error in one of the extracted data points in the meta-analysis. The classification accuracy for Serretti et al. (2007) was corrected to 64% ( Table 3b ). The overall results before and after this correction remain directionally consistent and are summarized below ( Figures 2 and 3 ; Table 2 ; results subsection 3.6 ). The authors apologise for any inconvenience caused.

Orexins are neuropeptides that are postulated to play a central role in the regulation of the sleep-wake cycle, appetite, affect, and reward circuitry. The objectives of the current review are to comprehensively evaluate (1) the potential role of orexins in the pathophysiology of major depressive disorders (MDD) and (2) the orexin system as a novel target in the treatment of MDD. Dysfunction of the sleep-wake cycle is observed as a central feature of MDD pathophysiology. Orexin system disturbances produce sleep-wake dysfunction, as observed in MDD. Orexin antagonists have been shown to treat insomnia effectively without disrupting normal sleep architecture in both preclinical (e.g., animal models) and clinical studies. Orexin antagonists are generally safe, well-tolerated, and associated with an acceptable long-term adverse effect profile with relatively low propensity for tolerance or dependence. Orexin antagonists have also been shown to possess antidepressant-like properties in some animal models of MDD. Extant evidence indicates that orexin-modulating treatments exert pleiotropic effects on multiple neural systems implicated in the phenomenology of mood disorders and suggests orexins as a promising target for investigation and intervention in mood disorders. To date, no human clinical trials evaluating the antidepressant effects of orexin antagonists in MDD have been completed. Given the promising results from preclinical studies, clinical trials are merited to evaluate the antidepressant effects of orexin antagonists in MDD.

Depressive symptoms and episodes dominate the course of bipolar disorder. The morbidity of bipolar disorder is disproportionately mediated by depressive symptoms; economic costs of bipolar disorder are also disproportionately due to unremitting depressive symptoms. Relatively few treatment options have established unequivocal efficacy in the treatment of bipolar depression. Herein we review evidence regarding the efficacy of the D

Major depressive disorder (MDD) is a debilitating disorder with increasing prevalence globally. Despite the development of novel treatments for MDD, many patients present with treatment resistant depression (TRD), identified by treatment non-response following one or more adequate trials of an antidepressant. Rapastinel may prove to be a viable treatment for TRD; it has the potential to produce a rapid antidepressant response without serious adverse events and improve functional symptoms. Areas covered: We review the efficacy of rapastinel via completed and on-going clinical trials. The online databases Pubmed, clinicaltrials.gov and clinicaltrialsregister.eu were searched for rapastinel (GLYX-13) treatment in subjects with MDD. Nine clinical trials were identified. Expert opinion: Rapastinel is a novel and potentially transformative treatment for individuals with TRD. There is a limited number of clinical studies so far, but this compound has the potential to provide rapid, reliable and robust antidepressant effects without psychotomimetic and other unwanted side effects. Alternative formulations such as the oral formulation, provide the opportunity for rapastinel to be administered less frequently, i.e. once weekly. Furthermore, the beneficial effects on measures of cognition and suicidality so far, represent a tremendous advantage.

Convergent evidence demonstrates that immune dysfunction (e.g. chronic low-grade inflammatory activation) plays an important role in the development and progression of mood disorders. The Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway is a pleiotropic cellular cascade that transduces numerous signals, including signals from the release of cytokines and growth factors. The JAK/STAT signaling pathway is involved in mediating several functions of the central nervous system, including neurogenesis, synaptic plasticity, gliogenesis, and microglial activation, all of which have been implicated in the pathophysiology of mood disorders. In addition, the antidepressant actions of current treatments have been shown to be mediated by JAK/STAT-dependent mechanisms. To date, two JAK inhibitors (JAKinibs) have been approved by the U.S. Food and Drug Administration and are primarily indicated for the treatment of inflammatory conditions such as rheumatoid arthritis. Indirect evidence from studies in populations with inflammatory conditions indicates that JAKinibs significantly improve measures of mood and quality of life. There is also direct evidence from studies in populations with depressive disorders, suggesting that JAK/STAT pathways may be involved in the pathophysiology of depression and that the inhibition of specific JAK/STAT pathways (i.e. via JAKinibs) may be a promising novel treatment for depressive disorders.

Metabolomics is a subcategory of the “omics,” related to metabolites in respect to endogenous and exogenous processes. Specifically, metabolomics holds promise in the field of psychiatry through informing personalized medicine. With the use of metabolomic measures, there is an opportunity to create an overall objective classification of psychiatric disorders that includes various neurobiological measures, in addition to the current behavioral methods. Potential areas of growth through metabolomics include disease diagnosis, treatment response, and drug discovery. Ultimately, while there have been advances in the application of metabolomics in psychiatry, more are required in order to introduce this technique fluidly into daily clinical practice.

Weight disturbances and their associated risk factors are seen in a large number of those with a diagnosis of bipolar disorder (BD) and have shown to be the primary contributor to premature mortality. Consequently, the term metabolic syndrome (MetS) is often applied to this population denoting the increased risk of cardiovascular diseases and type 2 diabetes. Contributing to the development of MetS in BD are analogous risk factors such as social determinants, environmental exposures, and illness treatment. In particular, it has been well studied that medication often used for BD (e.g., antipsychotics and mood stabilizers) contributes grossly to weight gain and metabolic disturbances; however, they are still commonly used due to their effectiveness in treating psychiatric symptoms. The presence of MetS can have a large effect on the lifestyle of those with BD and can influence a number of areas, for example, impacting illness prognosis by contributing to a greater burden of illness and mediating cognitive dysfunctions that can be apparent both structurally and functionally. Toward management of metabolic symptoms, a variety of interventions are available which include behavioral, pharmacological, and surgical. Specifically, one-third of patients requesting bariatric surgery (BS) for weight management have a diagnosis of BD. Bariatric surgery has shown to alleviate more than metabolic disturbances but also improve cognitive functioning. Unfortunately, while BS appears to be a long-term solution for weight management, it is often not applied for those with BD, as there is conflicting evidence surrounding post-surgery psychiatric effects.

Aim We sought to compare alterations in serum bioenergetic markers within a well-characterized sample of adults with schizophrenia at baseline and after 8 weeks of pharmacological treatment with the hypothesis that treatment would be associated with significant changes in bioenergetic markers given the role of bioenergetic dysfunction in schizophrenia. Methods We recruited adults with schizophrenia (n = 122) who had not received pharmacological treatment for at least 1 month prior to enrollment, including drug-naive (i.e., first-episode) participants and treatment non-adherent participants. Pre- and post-treatment serum samples were analyzed using liquid chromatography-tandem mass spectrometry. Results Metabolites with the greatest change, when comparing pre- and post-treatment levels, were identified revealing 14 water-soluble metabolites of interest. The composition of these metabolites was: amino acids (n = 6), carnitines (n = 4), polar lipids (n = 3), and organic acid (n = 1). All amino acids and lysophosphatidylcholines (LysoPC) were increased, while the four carnitines - oleoylcarnitine, L-palmitoylcarnitine, linoleyl carnitine, and L-acetylcarnitine - were decreased post-treatment. Of these metabolite biomarkers, six - oleoylcarnitine, linoleyl carnitine, L-acetylcarnitine, LysoPC(15:0), D-glutamic acid, and L-arginine - were identified as having most consistently and predictably changed after 8 weeks of treatment. Conclusion The current study identified several bioenergetic markers that consistently change with pharmacological treatment. These bioenergetic changes may provide further insights into the pathophysiology of schizophrenia along with furthering our understanding of the mechanisms subserving both the effects (e.g., antipsychotic effects) and side-effects (e.g., metabolic syndrome) of antipsychotics.

During the past decade, there has been renewed interest in the relationship between brain-based disorders, the gut microbiota, and the possible beneficial effects of probiotics. Emerging evidence suggests that modifying the composition of the gut microbiota via probiotic supplementation may be a viable adjuvant treatment option for individuals with major depressive disorder (MDD). Convergent evidence indicates that persistent low-grade inflammatory activation is associated with the diagnosis of MDD as well as the severity of depressive symptoms and probability of treatment response. The objectives of this review are to (1) evaluate the evidence supporting an anti-inflammatory effect of probiotics and (2) describe immune system modulation as a potential mechanism for the therapeutic effects of probiotics in populations with MDD. A narrative review of studies investigating the effects of probiotics on systemic inflammation was conducted. Studies were identified using PubMed/Medline, Google Scholar, and clinicaltrials.gov (from inception to November 2017) using the following search terms (and/or variants): probiotic, inflammation, gut microbiota, and depression. The available evidence suggests that probiotics should be considered a promising adjuvant treatment to reduce the inflammatory activation commonly found in MDD. Several controversial points remain to be addressed including the role of leaky gut, the role of stress exposure, and the role of blood-brain-barrier permeability. Taken together, the results of this review suggest that probiotics may be a potentially beneficial, but insufficiently studied, antidepressant treatment intervention.

Treatment resistant depression (TRD) represents approximately 20% of all individuals receiving care for major depressive disorder. The opioidergic system is identified as a novel target which hitherto has not been sufficiently investigated in adults with TRD. The combination product buprenorphine + samidorphan is an opioid modulatory agent which has demonstrated replicated evidence of efficacy in TRD without abuse liability. Areas covered: Databases Pubmed, Google Scholar and clinicaltrials.gov were searched from inception through December 2017 for clinical trial information, pharmacokinetics, and pharmacodynamics of buprenorphine + samidorphan. Herein we provide a summary of the available information. Eight clinical trials were identified for inclusion, of the eight trials, five trials had available results and are included in detail in our review. Expert opinion: Buprenorphine + samidorphan has demonstrated efficacy in TRD. Extant evidence surrounding the safety and tolerability profile of buprenorphine + samidorphan does not identify any significant safety concerns. Additional studies are needed in order to assess the long-term safety and efficacy of this product.

Technology is becoming increasingly intertwined with our lives every year. As technology advances, it offers promising new methods to help detect, manage, and improve the care of major depressive disorder (MDD). Unlike other specialties in medicine, psychiatry has been slow to adopt new technologies. Other areas of medicine, primarily cardiology and oncology, have made use of technological methods to patients' great benefits. In psychiatry, new technological methods can predict which antidepressants will be most effective, provide therapies to patients, and empower patients to manage their own medical records. We offer an overview to new technologies and their applications to psychiatry for novices. Our particular focus is big data, machine learning, mobile applications, and blockchain technology. We summarise the uses of technology as assisting physicians in decision making, facilitating patient-patient interaction, and securely storing and managing health-care data. We suggest possible advantages and challenges to adopting these methods. Continued research and technological innovation is needed to improve the psychiatrist's toolbox and to monitor the adoption and consequences of new technologies.

Major depressive disorder (MDD) is the leading cause of disability worldwide with a heterogeneous symptom profile. Levomilnacipran extended release (ER) (Fetzima), a SNRI, has been approved by the Food and Drug Administration for treatment of MDD. While categorized as a SNRI, in contradistinction to other approved SNRIs, levomilnacipran exhibits differential affinity for the norepinephrine reuptake transporter when compared to the serotonin reuptake transporter. Areas covered: Completed clinical trials which focused on levomilnacipran ER administered in those with MDD were included in this drug evaluation. Expert opinion: Levomilnacipran ER, like all other first-line antidepressants exhibits significant efficacy in reducing total symptom severity. Levomilnacipran ER is particularly effective at improving measures of motivation, energy, and interest. Head to head comparative trials are not available with other antidepressants, and consequently, there are no claims of superior efficacy when compared to alternative antidepressants. Notwithstanding, it would be a viable and testable hypothesis that differential efficacy in favor of levomilnacipran may be obtained across select dimensions of depressive symptoms (e.g., fatigue and lack of motivation). Unfortunately, rigorous studies evaluating levomilnacipran for cognitive function in MDD have not been conducted. Levomilnacipran ER is generally well tolerated with minimal propensity for metabolic and weight disturbance.

Introduction: To determine, via narrative, non-systematic review of pre-clinical and clinical studies, whether the effect of cannabis on hepatic biotransformation pathways would be predicted to res...

The high and increasing prevalence of medical marijuana consumption in the general population invites the need for quality evidence regarding its safety and efficacy. Herein, we synthesize extant literature pertaining to the phytocannabinoid cannabidiol (CBD) and its brain effects. The principle phytocannabinoid Δ9-tetrahydrocannabinol (Δ9-THC) and CBD are the major pharmacologically active cannabinoids. The effect of CBD on brain systems as well as on phenomenological measures (e.g. cognitive function) are distinct and in many cases opposite to that of Δ9-THC. Cannabidiol is without euphoriant properties, and exerts antipsychotic, anxiolytic, anti-seizure, as well as anti-inflammatory properties. It is essential to parcellate phytocannabinoids into their constituent moieties as the most abundant cannabinoid have differential effects on physiologic systems in psychopathology measures. Disparate findings and reports related to effects of cannabis consumption reflect differential relative concentration of Δ9-THC and CBD. Existing literature, notwithstanding its deficiencies, provides empirical support for the hypothesis that CBD may exert beneficial effects on brain effector systems/substrates subserving domain-based phenomenology. Interventional studies with purified CBD are warranted with a call to target-engagement proof-of-principle studies using the research domain criteria (RDoC) framework.

BACKGROUND: We lack knowledge of the developmental profiles of different brain injuries in hemiplegic cerebral palsy (HCP). This is important because children with specific injury patterns may respond differently to rehabilitation interventions. OBJECTIVES: To assess the relative proportion of brain injury patterns in HCP and compare the developmental profile of children with middle cerebral artery (MCA) and periventricular venous infarctions (PVI). DESIGN/METHODS: Children aged 2–18 years with a diagnosis of HCP were recruited from 9 children’s rehabilitation hospitals and informed consent was obtained. Developmental and neuroimaging information were collected from 6 sources: 1) data extraction from the health record, 2) brain imaging categorized by a neuroradiologist, 3) administration of the Quality of Upper Extremity Skills Test (QUEST) and classification of Gross Motor Function Classification System (GMFCS) and Manual Ability Classification System (MACS) by an occupational therapist, 4) child/parent questionnaires on hand usage: the Children’s Hand-use Experience Questionnaire (CHEQ) or Pediatric Upper Extremity Motor Activity Log (PMAL), 5) physician-administered sensory exam and 6) full scale intelligence quotient (IQ). Two groups comprising the most prevalent brain injury patterns were compared using a cross-sectional study design. RESULTS: Of 321 recruited, 246 (76.6%) had neuroimaging and were included in the analyses. The mean age was 8.30± 4.28, GMFCS I (n=181, 77.0%) and II (n=39, 16.6%), MACS I (n=82, 35.3%), II (n= 101, 43.5%). Neuroimaging revealed MCA infarctions (n=98, 39.8%), periventricular white matter lesions (n=110, 44.7%) of which periventricular venous infarction (PVI) was present in n=41, (16.7%), miscellaneous (n=8, 3.3%), unilateral malformations (n=19, 7.7%), non-MCA arterial infarctions (n=3, 1.2%), and normal imaging (n=8, 3.3%). Comparing PVI to MCA, the QUEST total score was higher in PVI, with 79.43±16.96 compared to 49.79±31.39 in MCA (t = 5.48, p value

BACKGROUND Ecological momentary assessment (EMA) for mental disorders has been insufficiently evaluated and validated with rigorous, adequately-powered, high-quality studies. OBJECTIVE Herein, we sought to validate the mind.me application (app) for the assessment of depressive symptoms in adults. METHODS Adults (ages 18-65) who self-identified as having clinically significant depressive symptoms [i.e. Patient Health Questionnaire 9 (PHQ-9) ≥ 5] utilized the mind.me app—a mobile phone technology that collects data passively and continuously, and is capable of integrating broad multimodal data [e.g., location variance (e.g. GPS), behavioural (e.g. social network activity), and communication data (e.g. SMS texting, phone calls)]. The primary outcome was predictive accuracy (i.e. convergent validity with depressive symptom measurement, as captured by the PHQ-9). RESULTS 200 subjects were enrolled in the study (mean age 46 ±12.71). The average PHQ-9 score was 12.8 ±6.9. The predictive accuracy of the mind.me app was 0.91 ±0.06. The sensitivity was 0.98 and the specificity was 0.93. The mind.me app was rated by 200 users as highly usable and informative to their illness. CONCLUSIONS The mind.me app exhibits robust predictive accuracy in detecting depressive symptoms in adults with clinically relevant depressive symptoms. The mind.me app is one of the first rigorously validated EMA tools for adults capable of detecting depressive symptoms. Future vistas aim to evaluate mind.me’s capability of impacting healthcare and cost outcomes in individuals with mood disorders. The mind.me app provides a unique opportunity for enhanced assay sensitivity in mood disorder research.

Cognitive dysfunction is a major component of major depressive disorder (MDD). No 'gold-standard' tool exists for the assessment of cognitive dysfunction for adults with MDD. The use of measurement-based care to improve treatment outcomes invites the need for a systematic screening, evaluation and measurement tool. The aim herein was to provide a succinct summary of literature documenting clinical implication of cognitive dysfunction in MDD, and a review of available screening, diagnostic and measurement tools for cognitive dysfunction in MDD is provided. We also take the opportunity to introduce a screening tool (ie, the THINC-it tool) targeted at addressing the unmet needs. We found that there are limitations to the current measurement scales; for example, many are not targeted for MDD and not all digitally available tests are free of charge. Furthermore, the spectrum of cognitive dysfunction in MDD is poorly represented by the existing tests and as such, there is a lack of sensitivity in the ability to screen a patient with MDD for a cognitive dysfunction. Recognising and addressing the limitations in the current screening techniques for cognitive dysfunction as well as being presented with the current tools available provides the ability to perform an educated cognitive screening for a patient with MDD.

Learning objectives After participating in this activity, learners should be better able to:• Characterize cognitive dysfunction in patients with major depressive disorder.• Evaluate approaches to treating cognitive dysfunction in patients with major depressive disorder. Abstract Cognitive dysfunction is a core psychopathological domain in major depressive disorder (MDD) and is no longer considered to be a pseudo-specific phenomenon. Cognitive dysfunction in MDD is a principal determinant of patient-reported outcomes, which, hitherto, have been insufficiently targeted with existing multimodal treatments for MDD. The neural structures and substructures subserving cognitive function in MDD overlap with, yet are discrete from, those subserving emotion processing and affect regulation. Several modifiable factors influence the presence and extent of cognitive dysfunction in MDD, including clinical features (e.g., episode frequency and illness duration), comorbidity (e.g., obesity and diabetes), and iatrogenic artefact. Screening and measurement tools that comport with the clinical ecosystem are available to detect and measure cognitive function in MDD. Notwithstanding the availability of select antidepressants capable of exerting procognitive effects, most have not been sufficiently studied or rigorously evaluated. Promising pharmacological avenues, as well as psychosocial, behavioral, chronotherapeutic, and complementary alternative approaches, are currently being investigated.

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 predict therapeutic outcomes in mood disorders populations.We searched Ovid MEDLINE/PubMed from inception to February 8, 2018 for relevant studies that included adults with bipolar or unipolar depression; assessed therapeutic outcomes with a pharmacological, neuromodulatory, or manual-based psychotherapeutic intervention for depression; applied a machine learning algorithm; and reported predictors of therapeutic response. A random-effects meta-analysis of proportions and meta-regression analyses were conducted.We identified 639 records: 75 full-text publications were assessed for eligibility; 26 studies (n=17,499) and 20 studies (n=6325) were included in qualitative and quantitative review, respectively. Classification algorithms were able to predict therapeutic outcomes with an overall accuracy of 0.82 (95% confidence interval [CI] of [0.77, 0.87]). Pooled estimates of classification accuracy were significantly greater (p 0.01) in models informed by multiple data types (e.g., composite of phenomenological patient features and neuroimaging or peripheral gene expression data; pooled proportion [95% CI] = 0.93[0.86, 0.97]) when compared to models with lower-dimension data types (pooledproportion=0.68[0.62,0.74]to0.85[0.81,0.88]).Most studies were retrospective; differences in machine learning algorithms and their implementation (e.g., cross-validation, hyperparameter tuning); cannot infer importance of individual variables fed into learning algorithm.Machine learning algorithms provide a powerful conceptual and analytic framework capable of integrating multiple data types and sources. An integrative approach may more effectively model neurobiological components as functional modules of pathophysiology embedded within the complex, social dynamics that influence the phenomenology of mental disorders.

Objective:Risk factors for suicide can be broadly categorized as sociodemographic, clinical and treatment. There is interest in environmental risk and protection factors for suicide. Emerging evidence suggests a link between environmental factors in the form of air pollution and aeroallergens in relation to suicidality.Methods:Herein, we conducted a systematic review of 15 articles which have met inclusion criteria on the aforementioned effects.Results:The majority of the reviewed articles reported an increased suicide risk alongside increased air pollutants or aeroallergens (i.e. pollen) increase; however, not all environmental factors were explored equally. In specific, studies that were delimited to evaluating particulate matter (PM) reported a consistent association with suicidality. We also provide a brief description of putative mechanisms (e.g. inflammation and neurotransmitter dysregulation) that may mediate the association between air pollution, aeroallergens and suicidality.Conclusion:Available evidence suggests that exposure to harmful air quality may be associated with suicidality. There are significant public health implications which are amplified in regions and countries with greater levels of air pollution and aeroallergens. In addition, those with atopic sensitivity may represent a specific subgroup that is at risk.

To determine, via narrative, non-systematic review of pre-clinical and clinical studies, whether the effect of cannabis on hepatic biotransformation pathways would be predicted to result in clinically significant drug-drug interactions (DDIs) with commonly prescribed psychotropic agents.A non-systematic literature search was conducted using the following databases: PubMed, PsycInfo, and Scopus from inception to January 2017. The search term cannabis was cross-referenced with the terms drug interactions, cytochrome, cannabinoids, cannabidiol, and medical marijuana. Pharmacological, molecular, and physiologic studies evaluating the pharmacokinetics of ΔΔ

Objective: Antipsychotics (APs) are associated with metabolic syndrome, with increases in leptin proposed as an underlying mechanism of AP-induced weight gain. Currently available meta-analyses on this topic have limited their populations of interest to those diagnosed with schizophrenia. The purpose of this meta-analysis is to explore the relationship between leptin levels and AP use across multiple psychiatric diagnoses, and also in healthy controls. Method: Systematic electronic searches were conducted using PubMed and OVID: Medline. Longitudinal studies were included if showing leptin levels before and after AP use. We included participants with any psychiatric disorders and mentally healthy participants, if exposed to AP use. The differences in leptin levels were evaluated using Hedges' g with a random effects model. Results: Forty-two studies were found (36 schizophrenia, 2 bipolar disorder, 1 anorexia nervosa, and 3 healthy controls), encompassing 66 study arms and 1,156 participants. The meta-analysis showed that regardless of diagnoses, leptin levels increase with AP use (Hedges' g = 0.811, p ≤.001). Conclusion: Leptin increases induced by APs are present across all diagnoses. More comprehensive research is needed to understand the relationship between AP use and leptin levels across multiple diagnoses.