Depression is one of the main mental disorders. Pharmacological treatment of depression is often associated with delayed effects or insufficient efficacy. Consequently, there is a need to discover new therapeutic methods to cope with depression faster and more effectively. Several lines of evidence indicate that the use of probiotic therapy reduces depressive symptoms. Nonetheless, the exact mechanisms linking the gut microbiota and the central nervous system, as well as the potential mechanisms of action for probiotics, are still not entirely clarified. The aim of this review was to systematically summarize the available knowledge according to PRISMA guidelines on the molecular mechanisms linking probiotics and healthy populations with subclinical depression or anxiety symptoms, as well as depressed patients with or without comorbid somatic illnesses. The standardized mean difference (SMD) with 95% confidence intervals (CI) was calculated. Twenty records were included. It has been found that probiotic administration is linked to a significant increase in BDNF levels during probiotic treatment compared to the placebo (SMD = 0.37, 95% CI [0.07, 0.68], p = 0.02) when considering the resolution of depressive symptoms in depressed patients with or without comorbid somatic illnesses. CRP levels were significantly lower (SMD = −0.47, 95% CI [0.75, −0.19], p = 0.001), and nitric oxide levels were significantly higher (SMD = 0.97, 95% CI [0.58, 1.36], p < 0.0001) in probiotic-treated patients compared to the placebo, however, only among depressed patients with somatic co-morbidities. There were no significant differences in IL-1β, IL-6, IL-10, TNF-α, and cortisol levels after probiotic administration between the intervention and control groups (all p > 0.05). Firm conclusions on the effectiveness of probiotics and their possible association with inflammatory markers in the healthy population (only with subclinical depressive or anxiety symptoms) cannot be drawn. The advent of clinical trials examining the long-term administration of probiotics could evaluate the long-term effectiveness of probiotics in treating depression and preventing its recurrence.

The effects of celecoxib on a broad spectrum of mood disorders and on inflammatory parameters have not yet been comprehensively evaluated. The aim of this study was to systematically summarize the available knowledge on this topic. Data from both preclinical and clinical studies were analyzed, considering the efficacy and safety of celecoxib in the treatment of mood disorders, as well as the correlation of inflammatory parameters with the effect of celecoxib treatment. Forty-four studies were included. We found evidence supporting the antidepressant efficacy of celecoxib in a dose of 400 mg/day used for 6 weeks as an add-on treatment in major depression (SMD = −1.12 [95%Cl: −1.71,−0.52], p = 0.0002) and mania (SMD = −0.82 [95% CI:−1.62,−0.01], p = 0.05). The antidepressant efficacy of celecoxib in the above dosage used as sole treatment was also confirmed in depressed patients with somatic comorbidity (SMD = −1.35 [95% CI:−1.95,−0.75], p < 0.0001). We found no conclusive evidence for the effectiveness of celecoxib in bipolar depression. Celecoxib at a dose of 400 mg/d used for up to 12 weeks appeared to be a safe treatment in patients with mood disorders. Although an association between celecoxib response and inflammatory parameters has been found in preclinical studies, this has not been confirmed in clinical trials. Further studies are needed to evaluate the efficacy of celecoxib in bipolar depression, as well as long-term studies evaluating the safety and efficacy of celecoxib in recurrent mood disorders, studies involving treatment-resistant populations, and assessing the association of celecoxib treatment with inflammatory markers.

Background: Smartphones allow for real-time monitoring of patients’ behavioral activities in a naturalistic setting. These data are suggested as markers for the mental state of patients with bipolar disorder (BD). Objective: We assessed the relations between data collected from smartphones and the clinically rated depressive and manic symptoms together with the corresponding affective states in patients with BD. Methods: BDmon, a dedicated mobile app, was developed and installed on patients’ smartphones to automatically collect the statistics about their phone calls and text messages as well as their self-assessments of sleep and mood. The final sample for the numerical analyses consisted of 51 eligible patients who participated in at least two psychiatric assessments and used the BDmon app (mean participation time, 208 [SD 132] days). In total, 196 psychiatric assessments were performed using the Hamilton Depression Rating Scale and the Young Mania Rating Scale. Generalized linear mixed-effects models were applied to quantify the strength of the relation between the daily statistics on the behavioral data collected automatically from smartphones and the affective symptoms and mood states in patients with BD. Results: Objective behavioral data collected from smartphones were found to be related with the BD states as follows: (1) depressed patients tended to make phone calls less frequently than euthymic patients (β=−.064, P=.01); (2) the number of incoming answered calls during depression was lower than that during euthymia (β=−.15, P=.01) and, concurrently, missed incoming calls were more frequent and increased as depressive symptoms intensified (β=4.431, P<.001; β=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (β=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to that in the euthymic state (β=3.53, P=.01) and positively correlated to the severity of symptoms (β=2.991, P=.02); (5) the variability of the duration of the outgoing calls was higher in manic/mixed states (β=.0012, P=.045) and positively correlated to the severity of symptoms (β=.0017, P=.02); and (6) the number and length of the sent text messages was higher in manic/mixed states as compared to that in the euthymic state (β=.031, P=.01; β=.015, P=.01; respectively) and positively correlated to the severity of manic symptoms (β=.116, P<.001; β=.022, P<.001; respectively). We also observed that self-assessment of mood was lower in depressive (β=−1.452, P<.001) and higher in manic states (β=.509, P<.001). Conclusions: Smartphone-based behavioral parameters are valid markers for assessing the severity of affective symptoms and discriminating between mood states in patients with BD. This technology opens a way toward early detection of worsening of the mental state and thereby increases the patient’s chance of improving in the course of the illness.