Ethanol consumption is widely recognized for its detrimental effects on mental health, particularly its association with depressive disorders. This narrative review aims to explore the intricate molecular mechanisms underlying ethanol-induced depression, synthesizing findings from preclinical and clinical studies. We begin by providing an overview of the relationship between chronic ethanol consumption and depression, highlighting compelling evidence from diverse populations. Subsequently, we delve into insights from animal models that elucidate the pathophysiological changes triggered by prolonged ethanol exposure. Key mechanisms identified include oxidative stress, which contributes to cellular damage; neuroinflammation, characterized by the activation of glial cells and altered cytokine profiles; and disruptions in neurotrophic factors that impair neuronal growth and survival. Furthermore, we discuss the induction of apoptosis in neural cells and the significant impact of ethanol on neurotransmitter receptor remodeling and regulation, leading to altered synaptic transmission. While substantial progress has been made in understanding these mechanisms, we also acknowledge the limitations of current research methodologies and call for further investigations to translate these findings into effective therapeutic strategies for individuals affected by ethanol-induced depression. This review ultimately underscores the need for a comprehensive understanding of the molecular underpinnings of ethanol's impact on mood disorders, paving the way for improved interventions and preventative measures.

Major depressive disorder (MDD) is a leading cause of disability, and chronic inflammation is a contributing factor to its onset and progression. This study examined the relationship between genetic predisposition to inflammation and MDD risk in a nationally representative sample of U.S. military veterans, as well as psychosocial moderators of this association.

A composite polygenic risk score (PRS) for inflammatory biomarkers was derived from the UK Biobank and examined in relation to a positive MDD screen in 1,660 European-American veterans. The analysis adjusted for known correlates of inflammation and MDD, including medical conditions and cumulative trauma burden.

Each standard deviation increase in the inflammatory PRS was associated with more than two-fold increased odds of screening positive for MDD (OR = 2.51, 95% CI = 1.39-4.54). Interaction analyses revealed that optimism moderated this association; among those in the highest PRS tertile, individuals with high optimism were more than 30 times less likely to screen positive for MDD compared to those with low optimism (0.7% vs. 22.6%). Pathway-based analyses identified enrichment of immune- and brain-related gene sets, highlighting potential biological mechanisms linking inflammation and MDD.

Findings suggest genetic risk for inflammation contributes to MDD vulnerability and that optimism may buffer this risk.

Inflammation is a protective mechanism that also starts the healing process. However, inflammatory reaction may cause severe tissue damage. The increased influx of phagocytic leukocytes may produce excessive amount of reactive oxygen species, which leads to additional cell injury. Inflammatory response activates the leukocytes and thus induces tissue damage and prolongs inflammation. The inflammation-induced activation of the complement system may also contribute to cell injury. Non-steroidal anti-inflammatory drugs (NSAIDs) and glucocorticoids are chief agents for treating inflammation associated with the diseases. However, the unwanted side effects of NSAIDs (e.g., gastrointestinal disturbances, skin reactions, adverse renal effects, cardiovascular side effects) and glucocorticoids (e.g., suppression of immune system, Cushing's syndrome, osteoporosis, hyperglycemia) limit their use in patients. Natural herbs are important sources of anti-inflammatory drugs. The ingredients extracted from natural herbs display anti-inflammatory effects to work through multiple pathways with lower risk of adverse reaction. At present, the main anti-inflammatory natural agents include saponins, flavonoids, alkaloids, polysaccharides, and so on. The present article will review the anti-inflammatory effects of saponins including escin, ginsenosides, glycyrrhizin, astragaloside, Panax notoginseng saponins, saikosaponin, platycodin, timosaponin, ophiopogonin D, dioscin, senegenin.

Ketamine, a dissociative anaesthetic, has expanded its clinical use beyond anaesthesia to pain management and treatment-resistant depression. As an N-methyl-d-aspartate receptor antagonist, ketamine disrupts the excitatory neurotransmission via interaction with the opioid, alpha-amino-3-hydroxy-5-methyl-4-isooxazole-propionic acid receptor and serotonin pathways, contributing to its broad therapeutic potential. However, its use is not without risks. In patients with insulin resistance, ketamine's effect on glucose metabolism, mitochondrial function and oxidative stress are exacerbated. This paper explores ketamine's pharmacokinetics, cardiovascular impact and its cellular effects on cardiomyocytes, particularly in insulin-resistant individuals. The findings discussed emphasize the importance of careful administration and monitoring in these vulnerable populations to balance ketamine's therapeutic benefits against its potential risks in patients with underlying metabolic or cardiovascular conditions.

Major depressive disorder (MDD) is a multifactorial disorder involving genetic and environmental factors, with unclear pathogenesis. This study aims to explore the pathogenic pathway of MDD and its relationship with immune responses and to discover its potential targets by bioinformatics methods. We first applied gene set variation analysis (GSVA) and seven different immune infiltration algorithms to the GSE98793 dataset to determine the differences in signaling pathways, metabolic pathways, and immune cell infiltration between MDD patients and healthy controls. Differentially expressed genes between MDD patients and controls were obtained from five datasets (GSE98793, GSE32280, GSE38206, GSE39653, and GSE52790), and 113 machine learning methods were employed to construct MDD diagnostic models. Based on the constructed MDD diagnostic models, MDD patients were divided into high-risk and low-risk groups. GSVA and immune microenvironment analyses were conducted to investigate the differences between the two groups. Furthermore, potential drugs and therapeutic targets for the high-risk MDD group were explored to provide new insights and directions for the precise treatment of MDD. GSVA and immune infiltration results indicate that patients with MDD exhibit differences from normal individuals in various aspects, including biological processes, signaling pathways, metabolic processes, and immune cells. To investigate the functions and biological significance of differentially expressed genes in MDD patients, we performed GO and KEGG enrichment analyses on the differentially expressed genes from five databases (GSE98793, GSE32280, GSE38206, GSE39653, and GSE52790). By comparing the enrichment results across the five datasets, we found that the cell-killing signaling pathway was consistently present in the enriched signaling pathways of all datasets, suggesting that this pathway may play a crucial role in the pathogenesis of MDD. The random forest algorithm (AUC = 0.788) was selected as the optimal algorithm from 113 machine learning algorithms, leading to the development of a robust and predictive MDD algorithm, highlighting the important role of NPL in MDD. By dividing MDD into high and low-risk subgroups based on diagnostic model scores, enrichment pathways, and immunological results further demonstrated that high-risk MDD is associated with increased levels of reactive oxygen species, inflammation, and numbers of T cells and B cells. Through GSEA scoring, five upregulated pathways in the high-risk MDD group were identified, and multiple potential drugs such as Mibefradil, LY364947, ZLN005, STA- 5326, and vemurafenib were screened. Patients with MDD show differences in signaling pathways, metabolic pathways, and immune mechanisms. By constructing an MDD diagnostic model, we predicted the key genes of MDD and the characteristic pathways associated with a higher risk of MDD. This provides new insights for risk stratification identification and offers new perspectives for the clinical application of precision immunotherapy and drug development.

Several studies have reported controversial results regarding the association between mental health factors and the risk of herpes zoster. This study examined the effects of self-rated stress, sleep deprivation, depressive symptoms, and suicidal ideation on the risk of herpes zoster.

Data from the Korea Health Panel Survey (2008-2018) were analyzed. Associations between self-reported mental health factors and herpes zoster were determined using a generalized estimating equation model. Age, sex, socioeconomic status, health behaviors, and comorbidities were adjusted for in the model. The standardized incidence ratios (SIRs) were reported from the sensitivity analysis.

Of the 78,896 included person-years (mean age, 51.31 ± 16.90; females, n = 43,503 [55.14 %]), 1130 (1.43 %) developed herpes zoster. The adjusted odds ratios (aORs) for the association between severe self-rated stress and herpes zoster were 1.483 (95 % confidence interval [CI]: 1.161-1.895) and for sleep deprivation, 1.194 (95 % CI: 1.038-1.374). When stratified by sex, the association remained significant in females but not in males. For depressive symptoms and suicidal ideation, the adjusted models did not reveal significant associations with herpes zoster infection. In the sensitivity analysis, the SIRs for herpes zoster were higher with severe self-rated stress (1.37, 95 % CI: 1.07-1.68) and sleep deprivation (1.21, 95 % CI: 1.07-1.36).

Severe self-rated stress and sleep deprivation were associated with an increased risk of herpes zoster, whereas depressive symptoms and suicidal ideation were not. Further studies are required to confirm these findings and explore additional factors.

Cyclodextrin (CD)-based drug delivery systems have emerged as a promising strategy to overcome limitations commonly encountered in antidepressant therapy, including low bioavailability, poor solubility, and suboptimal penetration of the blood-brain barrier. This review synthesizes current evidence demonstrating that complexing various classes of antidepressants-such as tricyclic antidepressants (TCAs), selective serotonin reuptake inhibitors (SSRIs), and atypical antidepressants-with β-CD or its derivatives significantly enhances drug solubility and stability. In addition, encapsulation with CDs can diminish systemic toxicity and improve pharmacokinetics, thereby helping to optimize dosage regimens and reduce adverse effects. Analysis of published in vitro and in vivo studies indicates that CD formulations not only boost therapeutic efficacy but also enable sustained or targeted release, which is critical for drugs requiring precise plasma and tissue concentrations. When compared to other carriers (e.g., liposomes, polymeric nanoparticles, dendrimers), CD-based systems often stand out for their ease of formulation, biocompatibility, and cost-effectiveness, although limited drug-loading capacity can be a drawback. We recommend expanding in vivo trials to substantiate the clinical benefits of CD-antidepressant complexes, particularly for treatment-resistant cases or specific subpopulations (e.g., elderly and pediatric patients). Additional investigations should also explore hybrid systems-combining CDs with advanced nano- or macroparticles-to amplify their advantages and address any limitations. Ultimately, integrating CDs into antidepressant regimens holds substantial potential to refine therapy outcomes, reduce adverse events, and pave the way for more personalized, effective interventions for depression.

Previous studies have shown an association between depression and increased susceptibility to infection in the general population. However, there has been no prior research specifically examining the relationship between treatment-resistant depression (TRD) and severe bacterial infections (SBI) in adolescents and young adults. This retrospective observational cohort study utilized the Taiwan National Health Insurance Research Database (NHIRD) from 2001 to 2010. It included adolescents (12-19 years of age) and young adults (20-29 years of age) diagnosed with major depressive disorder (MDD), comprising 6958 cases of TRD and 27,832 cases of antidepressant-responsive depression (ARPD). The TRD and ARPD groups were further matched (4:1) by chronological age, age at diagnosis of depression, sex, residence, and family income. The primary outcomes were severe bacterial infections (SBI) and septicemia. Cox regression analysis was conducted to identify the risk of hospitalization due to SBI or septicemia during the follow-up period. Compared with controls, the ARPD group had increased risks of SBI (hazard ratio [HR] with 95% confidence interval [CI]: 3.90, 2.73-5.57) and septicemia (HR, 95% CI: 2.56, 1.34-4.91). Notably, the risks of SBI and septicemia appeared to be further elevated in the TRD group. The TRD group exhibited higher incidences of SBI (HR, 95% CI: 6.99, 4.73-10.34) and septicemia (HR, 95% CI: 2.85, 1.28-6.36) than the control group. Adolescents and young adults with TRD had 6.99-fold and 3.90-fold increased risks of SBI and septicemia compared to individuals without MDD, respectively. Therefore, healthcare providers need to be vigilant when monitoring and implementing preventive measures in this population.

Depression is a significant psychiatric disorder with particularly high prevalence among adolescents. This mental health condition can have severe consequences, including academic failure, social withdrawal, and suicidal behavior. Given the increasing rate of depression in this age group, understanding the underlying biological mechanisms is essential for early detection and intervention. Recent studies have suggested that immune markers play a role in the pathophysiology of depression, prompting further investigation of their potential association with depressive symptoms in adolescents.

To investigate the relationship between immune markers (monocytes, lymphocytes, and direct bilirubin) and the incidence and severity of depression among adolescents.

This cross-sectional study recruited 145 adolescent patients with depression [male (M)/female (F) = 38/107] from Jiangbin Hospital in Guangxi, Zhuang and 163 healthy controls (M/F = 77/86) from routine health check-ups. Blood samples were collected after an overnight fast. Depression severity was measured using the Zung Self-Rating Depression Scale. The inclusion criteria were age 12-24 years, diagnosis of depressive disorder (ICD-10), and no recent antidepressant use. The exclusion criteria included psychiatric comorbidities and serious somatic diseases. Key statistical methods included group comparisons and correlation analyses.

There was a higher prevalence of females in the depression group (P < 0.001). Significant age differences were observed between the groups (Z = 9.43, P < 0.001). The depression group had higher monocyte (Z = 3.43, P < 0.001) and lymphocyte (t = 2.29, P < 0.05) counts, and higher serum direct bilirubin levels (Z = 4.72, P < 0.001). Monocyte count varied significantly according to depression severity, with lower counts in the mild group (Z = -2.90, P < 0.05). A negative correlation between age and lymphocyte counts was observed (ρ = -0.22, P < 0.01). Logistic regression analysis showed that serum direct bilirubin levels significantly predicted depression.

The potential role of elevated levels of immune markers in the early detection of depression in adolescents has been highlighted. Therefore, it is necessary to explore further the relationships between these immune markers and depression.

The abundant expression of circular RNAs (circRNAs) in the central nervous system and their contribution to the pathogenesis of depression suggest that circRNAs are promising therapeutic targets for depression. This study explored the role and mechanism of circKat6b in esketamine's antidepressant effect. We found that intravenous administration of esketamine (5 mg/kg) treatment decreased the circKat6b expression in the astrocytes of hippocampus induced by a chronic unpredictable mild stress (CUMS) mouse model, while the overexpression of circKat6b in the hippocampus significantly attenuated the antidepressant effects of esketamine in depressed mice. RNA-sequencing, RT-PCR, and western blot experiments showed that the stat1 and p-stat1 expression were significantly upregulated in mouse astrocytes overexpressing circKat6b. In the CUMS mouse model, overexpression of circKat6b in the hippocampus significantly reversed the downregulation of p-stat1 protein expression caused by esketamine. Our findings demonstrated that a novel mechanism of the antidepressant like effect of esketamine may be achieved by reducing the expression of circKat6b in the astrocyte of the hippocampus of depressed mice.

Breast cancer is a significant traumatic experience that often leads to chronic stress and mental health challenges. Research has consistently shown that physical activity-especially exercise-can alleviate depressive symptoms; however, the specific biological mechanisms underlying these antidepressant effects remain unclear. In this review, we comprehensively summarize the biological mechanisms of depression and the antidepressant mechanisms of physical activity and explore the biological processes through which exercise exerts its antidepressant effects in breast cancer patients. We focus on the impact of physical activity on inflammation, the endocrine system, glutamate, and other aspects, all of which play crucial roles in the pathophysiology of depression. Moreover, we discuss the heterogeneity of depression in breast cancer patients and the complex interactions between its underlying mechanisms. Additionally, we propose that a deeper understanding of these mechanisms in the breast cancer population can guide the design and implementation of exercise-based interventions that maximize the antidepressant benefits of physical activity. Finally, we summarize the current research and propose future research directions.

The pathophysiology of major depressive disorder (MDD) involves multiple factors, including inflammatory processes. This study investigated the relationship between changes in the levels of cytokines and remission in patients with MDD following duloxetine treatment.

MDD patients were administered duloxetine for 16 weeks. Clinical evaluation and immunological monitoring were performed every 4 weeks.

Our results indicated that changes in serum levels of TNF-α and IL-6 were associated with remission following duloxetine treatment in MDD patients. There was a slight increase in TNF-α levels in the first four weeks following duloxetine treatment, which correlated significantly with patients who were in remission. Furthermore, patients in remission exhibited an initial increase in IL-6 levels in the first four weeks, followed by a decrease at 16 weeks.

These results suggest an important relationship between changes in cytokine levels and remission in patients with major depression after duloxetine treatment.

Major depressive disorder (MDD) is strongly associated with type 2 diabetes mellitus (T2DM). The kynurenine and serotonin pathways, as well as chronic low-grade inflammation, are being considered potential links between them. MDD associated with T2DM is less responsive to treatment than that without T2DM; however, the underlying mechanism remains unknown. We aimed to investigate the effects of inflammatory cytokines on the kynurenine and serotonin pathways in patients with comorbid MDD and T2DM and those with only MDD. We recruited 13 patients with comorbid MDD and T2DM and 27 patients with only MDD. We measured interleukin-6 and tumor necrosis factor-α (TNF-α) levels as inflammatory cytokines and metabolites of the kynurenine pathway and examined the relationship between the two. TNF-α levels were significantly higher in patients with comorbid MDD and T2DM than in those with only MDD in univariate (p = 0.044) and multivariate (adjusted p = 0.036) analyses. TNF-α showed a statistically significant effect modification (interaction) with quinolinic acid/tryptophan and serotonin in patients from both groups (β = 1.029, adjusted p < 0.001; β =  - 1.444, adjusted p = 0.047, respectively). Limitations attributed to the study design and number of samples may be present. All patients were Japanese with mild to moderate MDD; therefore, the generalizability of our findings may be limited. MDD with T2DM has more inflammatory depression components and activations of the kynurenine pathway by inflammatory cytokines than MDD without T2DM. Hence, administering antidepressants and anti-inflammatory drugs in combination may be more effective in patients with comorbid MDD and T2DM.

Depression affects several aspects of life, including socioeconomic status, relationships, behavior, emotions, and overall health. The etiology of depression is complex and influenced by various factors, with obesity emerging as a significant contributor. This narrative review aims to investigate the factors associated with the development of depression, with a particular focus on the role of obesity. The literature search was conducted on PubMed, Embase, and PsycINFO from May to July 2024. The review highlights the impact of environmental and socioeconomic conditions; lifestyle choices, including physical activity and dietary habits; stress; traumatic experiences; neurotransmitter imbalances; medical and psychological conditions; hormone fluctuations; and epigenetic factors on depression. A key emphasis is placed on the inflammatory processes linked to obesity, which may drive the bidirectional relationship between obesity and depression. The findings suggest that obesity is associated with an increased risk of depression, potentially due to chronic inflammation, neurochemical dysregulation, and the emotional and social challenges related to weight stigma and obesity management. Understanding these interconnected factors is important for developing targeted interventions to address both obesity and depression, leading to improved quality of life for those affected.

It is widely known that sex differences have a significant impact on patients with major depressive disorder (MDD). This study aims to evaluate the sex-related connection between serum trace elements and changes in neurometabolism in the anterior cingulate cortex (ACC) of MDD patients.

109 untreated MDD patients and 59 healthy controls underwent proton magnetic resonance spectroscopy (1H-MRS) under resting conditions. We measured metabolic ratios in the ACC from both sides. Additionally, venous blood samples were taken from all participants to detect calcium (Ca), phosphorus, magnesium (Mg), copper (Cu), ceruloplasmin (CER), zinc (Zn), and iron (Fe) levels. We performed association and interaction analyses to explore the connections between the disease and gender.

In individuals with MDD, the Cu/Zn ratio increased, while the levels of Mg, CER, Zn and Fe decreased. Male MDD patients had lower Cu levels, while female patients had an increased Cu/Zn ratio. We observed significant gender differences in Cu, CER and the Cu/Zn ratio in MDD. Male patients showed a reduced N-acetyl aspartate (NAA)/phosphocreatine + creatine (PCr + Cr) ratio in the left ACC. The NAA/PCr + Cr ratio decreased in the right ACC in patients with MDD. In the left ACC of male MDD patients, the Cu/Zn ratio was inversely related to the NAA/PCr + Cr ratio, and Fe levels were negatively associated with the GPC + PC/PCr + Cr ratio.

Our findings highlight gender-specific changes in Cu homeostasis among male MDD patients. The Cu/Zn ratio and Fe levels in male MDD patients were significantly linked to neurometabolic alterations in the ACC.

Anorexia nervosa (AN) is a complex disorder affecting mainly, but not only, teenagers. Researchers agree that AN is deeply associated with a pro-inflammatory state following an impaired immune system, resulting from altered levels of cytokines such as IL-1β and TNF-α, also impacted by the frequent depressive states. Thus, this case-control study aimed to evaluate the relationship between patients suffering from AN undergoing specialized eating disorder treatment for AN and pro-inflammatory cytokines. To reach our purpose, we assessed eating-related psychopathology and depressive symptoms and measured serum concentration of pro-inflammatory cytokines IL-1β, IL-6, IL-8, and TNF-α before and after 6 months of integrated therapy (which included psychopharmacotherapy, psychotherapy, and nutritional treatment), to define whether selected pro-inflammatory cytokines could be considered a pathophysiological marker of the disorder. A sample of 16 young female patients with early diagnosis of AN, and without any previous treatment, and 22 healthy controls matched by age, sex, and socioeconomic status were enrolled. After 6 months of integrated therapy, a significant decrease of all selected pro-inflammatory cytokines was detected. In addition, an improvement in the anxiety-depressant aspects was also noted. In conclusion, the results obtained suggest that pro-inflammatory cytokines are indeed related to the pathophysiology of AN. However, further investigations, involving larger samples of patients with distinct subtypes of AN, are essential to confirm the current findings.

Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratifications of predictor variables, leading to algorithms that are both more accurate and generalizable. However, the wider availability of machine learning tools capable of incorporating such biological knowledge remains limited. Addressing this gap, we introduce BioM2, a novel R package designed for biologically informed multistage machine learning. BioM2 uniquely leverages biological information to effectively stratify and aggregate high-dimensional biological data in the context of machine learning. Demonstrating its utility with genome-wide DNA methylation and transcriptome-wide gene expression data, BioM2 has shown to enhance predictive performance, surpassing traditional machine learning models that operate without the integration of biological knowledge. A key feature of BioM2 is its ability to rank predictor variables within biological categories, specifically Gene Ontology pathways. This functionality not only aids in the interpretability of the results but also enables a subsequent modular network analysis of these variables, shedding light on the intricate systems-level biology underpinning the predictive outcome. We have proposed a biologically informed multistage machine learning framework termed BioM2 for phenotype prediction based on omics data. BioM2 has been incorporated into the BioM2 CRAN package (https://cran.r-project.org/web/packages/BioM2/index.html).

Selenium binding protein 1 (SELENBP1) is involved in neurologic disorders, such as multiple sclerosis, spinal cord injury, Parkinson's disease, epilepsy, and schizophrenia. However, the role of SELENBP1 in the neurogenesis of depression, which is a neurologic disorder, and the underlying mechanisms of oxidative stress and inflammation in depression remain unknown. In this study, we evaluated the changes in the expression levels of SELENBP1 in the hippocampus of a mouse model of depression and in the serum of human patients with depression using the Gene Expression Omnibus database. These changes were validated using blood samples from human patients with depression and mouse models with chronic unpredictable mild stress (CUMS)-induced depressive-like behavior. We also investigated the effects of SELENBP1 knockout (KO) on inflammation, oxidative stress, and hippocampal neurogenesis in mice with CUMS-induced depression. Our results revealed that SELENBP1 levels was decreased in the blood of human patients with depression and in the hippocampus of mice with CUMS-induced depression. SELENBP1 KO increased CUMS-induced depressive behavior in mice and caused dysregulation of inflammatory cytokines and oxidative stress. This led to a decrease in the numbers of doublecortin- and Ki67-positive cells, which might aggravate CUMS-induced depressive symptoms. These findings suggest that SELENBP1 might be involved in the regulation of neurogenesis in mice with depression and could be served as a potential target for diagnosing and treating depression.

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Academic stress is a problem that affects students due to a number of factors that are considered stressors. These include academic overload and completion of assignments and exams, exacerbated by such external conditions as family, social and economic problems. Together, these can affect emotional and physical health, which may lead in the long term to developing a number of pathologies, given the alteration of immunological homoeostasis with which they are related.

To evaluate the effect of academic stress on the production of cytokines IL-6, TNF-α, IL-1β and IL-10 in Morphology students in the Faculty of Health Sciences of the Universidad del Cauca during an academic period.

A descriptive longitudinal study was carried out with a population of 78 students studying Morphology, among the subjects with the highest academic load in the Physiotherapy, Medicine, Nursing and Phonoaudiology programs in the Faculty. Academic stress was assessed in the students by applying the Academic Stress Questionnaire (ASQ), and through quantification of the IL-6, TNF-α, IL-1β and IL-10 cytokines using the ELISA (Enzyme-linked immunosorbent assay) technique in three "moments" of the academic semester: Moment 1: beginning of the academic semester; Moment 2: week of evaluations of 70% of the semester; Moment 3: week of final exams.

The students perceived stress as "normal" at Moment 1, while at Moments 2 and 3 it was perceived as "quite a lot", with percentages of 48.7% and 50%, respectively. The predominant stressors were: "methodological deficiencies", "academic overload", and "exams", for the three moments of the study. "Physical exhaustion" was the most prevalent stress response at all three moments, followed by "irascible behaviour" (Moment 2 and 3), and "sleep disturbances" (Moment 3). To cope with the stress, the students resorted mainly to "planning and management of personal resources" in the three moments of the study. A progressive increase in the pro-inflammatory cytokines IL-6 and TNF-α and a decrease in IL-10 were observed at all three moments. A correlation was found between some questions belonging to the "methodological deficiencies", "beliefs about performance", "sleep disturbances", "physical exhaustion" and "irascible behaviour" dimensions with IL-6, IL-1β, TNF-α and IL. -10.

The morphology students suffer increased stress indicators (perceived stress and pro-inflammatory cytokines) throughout the academic period. The "methodological deficiencies", "academic overload" and "exams" stressors, together with "physical exhaustion", "sleep disturbances" and "irascible behaviour", possibly influence the production of the IL-6, TNF-α and IL-10 cytokines.

The glymphatic system is cerebrospinal fluid-brain tissue fluid exchange flow mediated by aquaporin-4 (AQP4) on the end feet of astrocytes for a system, which is capable of rapidly removing brain metabolites and thus maintaining brain homeostasis, and is known as the central immune system. Dysfunction of the glymphatic system causes accumulation of misfolded and highly phosphorylated proteins (amyloid-β and Tau proteins), which destabilizes the proteins, and the body's neuroinflammatory factors are altered causing aging of the immune system and leading to neurodegenerative diseases. Damage to the glymphatic system and aging share common manifestations, as well as unstudied biological mechanisms that are also linked, such as mitochondria, oxidative stress, chronic inflammation, and sleep. In this paper, we first summarize the structure, function, and research methods of the glymphatic system and the relationship between the glymphatic system and the peripheral immune system, and second, sort out and summarize the factors of the glymphatic system in removing metabolites and resolving aging-related diseases and factors affecting aging, to explore its related biological mechanisms, and moreover, to provide a new way of thinking for treating or intervening aging-related diseases.

Major Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies.

This prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source "Pothos" air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels.

Following initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness.

This study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

Genome-wide association studies (GWAS) of psychiatric disorders (PD) yield numerous loci with significant signals, but often do not implicate specific genes. Because GWAS risk loci are enriched in expression/protein/methylation quantitative loci (e/p/mQTL, hereafter xQTL), transcriptome/proteome/methylome-wide association studies (T/P/MWAS, hereafter XWAS) that integrate xQTL and GWAS information, can link GWAS signals to effects on specific genes. To further increase detection power, gene signals are aggregated within relevant gene sets (GS) by performing gene set enrichment (GSE) analyses. Often GSE methods test for enrichment of "signal" genes in curated GS while overlooking their linkage disequilibrium (LD) structure, allowing for the possibility of increased false positive rates. Moreover, no GSE tool uses xQTL information to perform mendelian randomization (MR) analysis. To make causal inference on association between PD and GS, we develop a novel MR GSE (MR-GSE) procedure. First, we generate a "synthetic" GWAS for each MSigDB GS by aggregating summary statistics for x-level (mRNA, protein or DNA methylation (DNAm) levels) from the largest xQTL studies available) of genes in a GS. Second, we use synthetic GS GWAS as exposure in a generalized summary-data-based-MR analysis of complex trait outcomes. We applied MR-GSE to GWAS of nine important PD. When applied to the underpowered opioid use disorder GWAS, none of the four analyses yielded any signals, which suggests a good control of false positive rates. For other PD, MR-GSE greatly increased the detection of GO terms signals (2,594) when compared to the commonly used (non-MR) GSE method (286). Some of the findings might be easier to adapt for treatment, e.g., our analyses suggest modest positive effects for supplementation with certain vitamins and/or omega-3 for schizophrenia, bipolar and major depression disorder patients. Similar to other MR methods, when applying MR-GSE researchers should be mindful of the confounding effects of horizontal pleiotropy on statistical inference.

Objective: To investigate the antidepressant effect and potential mechanism of the Chufan Yishen Formula (CFYS) through network pharmacology, molecular docking, and experimental verification. Methods: The active ingredients and their target genes of CFYS were identified through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and TCM-ID. We obtained the differentially expressed genes in patients with depression from the GEO database and screened out the genes intersecting with the target genes of CFYS to construct the PPI network. The key pathways were selected through STRING and KEGG. Then, molecular docking and experimental verification were performed. Results: A total of 113 effective components and 195 target genes were obtained. After intersecting the target genes with the differentially expressed genes in patients with depression, we obtained 37 differential target genes, among which HMOX1, VEGFA, etc., were the key genes. After enriching the differential target genes by KEGG, we found that the "chemical carcinogenesis-reactive oxygen species" pathway was the key pathway for the CFYS antidepressant effect. Besides, VEGFA might be a key marker for depression. Experimental verification found that CFYS could significantly improve the behavioral indicators of rats with depression models, including improving the antioxidant enzyme activity and increasing VEGFA levels. The results are consistent with the network pharmacology analysis. Conclusions: CFYS treatment for depression is a multicomponent, multitarget, and multipathway complex process, which may mainly exert an antidepressant effect by improving the neuron antioxidant stress response and regulating VEGFA levels.

Depressive disorder is a complex mental health condition in which the etiopathogenesis involves several factors. Suitable biomarkers for the development of depression have not yet been established. Alterations in cytokines are assumed to be involved in the pathophysiology of depressive disorder. Adipokines (also known as adipocytokines) are important factors that not only regulate the energy balance but also regulate the inflammatory and immune responses. This study investigated the serum levels of adiponectin, leptin, resistin, chemerin, and fetuin A and the possible role of these adipokines in depressive disorder.

We recruited a total of 73 patients diagnosed with recurrent depressive disorder (rDD) and 54 age- and sex-matched healthy controls (HCs). Serum adipocytokines were determined using ELISA kits (R&D, USA). The serum levels of the investigated molecules between depressive patients and HCs were compared, and diagnostic values were evaluated using the receiver operating characteristic (ROC) curve method for discriminating depressive patients from HCs. Correlations between the molecules and clinical variables were also evaluated.

Patients with rDD had lower levels of serum adiponectin and chemerin and higher levels of serum leptin, resistin and fetuin A (p < 0.05) vs. controls. Moreover, ROC curve analysis showed that the area under the curve (AUC) values of above set of adipocytkines were >0.7, with a sensitivity and specificity over 80% in discriminating patients with rDD from HCs.

These results suggest that circulating adipocytokies may hold promise as biomarkers for the diagnosis of rDD.

Major depressive disorder (MDD) is a prevalent brain disorder affecting more than 2% of the world's population. Due to the lack of well-specific biomarkers, it is difficult to distinguish MDD from other diseases with similar clinical symptoms (such as Alzheimer's disease and cerebral thrombosis). In this work, we provided a strategy to address this issue by constructing a combinatorial biomarker of serum glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL). To achieve the convenient and sensitive detection of two proteins, we developed an electrochemical immunosandwich sensor using two metal-ion-doped carbon dots (Pb-CDs and Cu-CDs) as probes for signal output. Each probe contains approximately 300 Pb2+ or 200 Cu2+, providing excellent signal amplification. This method achieved detection limits of 0.3 pg mL-1 for GFAP and 0.2 pg mL-1 for NFL, lower than most of the reported detection limits. Analysis of real serum samples showed that the concentration ratio of GFAP to NFL, which is associated with the relative degree of brain inflammation and neurodegeneration, is suitable for not only distinguishing MDD from healthy individuals but also specifically distinguishing MDD from Alzheimer's disease and cerebral thrombosis. The good specificity gives the combinatorial GFAP/NFL biomarker broad application prospects in the screening, diagnosis, and treatment of MDD.

Many factors have contributed to rendering frailty an emerging, relevant, and very popular concept. First, many pandemics that have affected humanity in history, including COVID-19, most recently, have had more severe effects on frail people compared to non-frail ones. Second, the increase in human life expectancy observed in many developed countries, including Italy has led to a rise in the percentage of the older population that is more likely to be frail, which is why frailty is much a more common concern among geriatricians compared to other the various health-care professionals. Third, the stratification of people according to the occurrence and the degree of frailty allows healthcare decision makers to adequately plan for the allocation of available human professional and economic resources. Since frailty is considered to be fully preventable, there are relevant consequences in terms of potential benefits both in terms of the clinical outcome and healthcare costs. Frailty is becoming a popular, pervasive, and almost omnipresent concept in many different contexts, including clinical medicine, physical health, lifestyle behavior, mental health, health policy, and socio-economic planning sciences. The emergence of the new "science of frailty" has been recently acknowledged. However, there is still debate on the exact definition of frailty, the pathogenic mechanisms involved, the most appropriate method to assess frailty, and consequently, who should be considered frail. This narrative review aims to analyze frailty from many different aspects and points of view, with a special focus on the proposed pathogenic mechanisms, the various factors that have been considered in the assessment of frailty, and the emerging role of biomarkers in the early recognition of frailty, particularly on the role of mitochondria. According to the extensive literature on this topic, it is clear that frailty is a very complex syndrome, involving many different domains and affecting multiple physiological systems. Therefore, its management should be directed towards a comprehensive and multifaceted holistic approach and a personalized intervention strategy to slow down its progression or even to completely reverse the course of this condition.

Major depressive disorder (MDD) is generally among the most prevalent psychiatric illnesses. Significant advances have occurred in comprehension of the MDD biology. However, it is still essential to recognize new biomarkers for potential targeted treatment of patients with MDD. The present work deals with in-depth comparative computational analyses to obtain new insights, such as gene ontology and pathway enrichment analyses and weighted gene co-expression network analysis (WGCNA) through gene expression dataset. The expression of selected hub-genes was validated in MDD patients using quantitative real-time PCR (RT-qPCR). We found that MDD progression includes the turquoise module genes (p-value = 1e-18, r = 0.97). According to gene enrichment analysis, the cytokine-mediated signaling pathway mostly involves genes in this module. By selection of four candidate hub-genes (IL6, NRG1, TNF, and BDNF), RT-qPCR validation was performed. A significant NRG1 downregulation was revealed by the RT-qPCR outcomes in MDD. In MDD patients, TNF and IL6 expression were considerably higher, and no considerable differences were found in the BDNF expression. Ultimately, based on ROC analyses, IL6, NRG1, and TNF had a higher MDD diagnostic performance. Therefore, our study presents information on the intricate association between MDD development and cytokine-mediated signaling, thus providing new rationales to develop new therapeutic approaches.

Beta-hydroxybutyrate (BHB), an endogenous NLRP3 inflammasome inhibitor, has been shown to be associated with the pathophysiology of depression in rodents. However its active mechanism has not been revealed. Herein, we probed both the pathways and brain regions involved in BHB's antidepressant-like effects in a learned helplessness (LH) rat model of depression. A single bilateral infusion of BHB into the cerebral ventricles induced the antidepressant-like effects on the LH rats. The antidepressant-like effects of BHB were blocked by the TrkB inhibitor ANA-12 and the AMPA receptor antagonist NBQX, indicating that the antidepressant-like effects of BHB involve BDNF-TrkB signaling and AMPA receptor activation. Further, infusions of BHB into the prelimbic and infralimbic portions of medial prefrontal cortex, the dentate gyrus of hippocampus, and the basolateral region of amygdala produced the antidepressant-like effects on LH rats. However, infusions of BHB into the central region of amygdala, the CA3 region of hippocampus, and the shell and core regions of nucleus accumbens had no effect. Finally, a single bilateral infusion of BHB into the cerebral ventricles of naive rats strengthened learning ability on repeated active avoidance test where saline-infused animals failed to increase avoidance responses.

Since diagnosis of mood disorder heavily depends on signs and symptoms, emerging researches have been studying biomarkers with the attempt to improve diagnostic accuracy, but none of the findings have been broadly accepted. The purpose of the present study was to construct a preliminary diagnostic model to distinguish major depressive disorder (MDD) and bipolar disorder (BD) using potential commonly tested blood biomarkers.

Information of 721 inpatients with an ICD-10 diagnosis of MDD or BD were collected from the electronic medical record system. Variables in the nomogram were selected by best subset selection method after a prior univariable screening, and then constructed using logistic regression with inclusion of the psychotropic medication use. The discrimination, calibration and internal validation of the nomogram were evaluated by the receiver operating characteristic curve (ROC), the calibration curve, cross validation and subset validation method.

The nomogram consisted of five variables, including age, eosinophil count, plasma concentrations of prolactin, total cholesterol, and low-density lipoprotein cholesterol. The model could discriminate between MDD and BD with an area under the ROC curve (AUC) of 0.858, with a sensitivity of 0.716 and a specificity of 0.890.

The comprehensive nomogram constructed by the present study can be convenient to distinguish MDD and BD since the incorporating variables were common indicators in clinical practice. It could help avoid misdiagnoses and improve prognosis of the patients.

Increasing evidence suggests that inflammation appears to play a role in the genesis of depression. Berberine has potent anti-inflammatory effects and potential antidepressant activity, although the mechanism by which it works is yet unclear. Our study aimed to investigate the molecular mechanisms through which berberine treats depression and reduces inflammation.

The CUMS model and behavioral evaluation were utilized in this study to evaluate the efficacy of berberine in the treatment of depression. Berberine's effect on the inflammatory response in CUMS mice was evaluated via ELISA assays and western blotting. Nissl staining was used to observe hippocampal neuronal functional damage. Western blotting, ELISA, ubiquitination tests, and immunoprecipitation were utilized in conjunction with in vitro experiments to study the involvement of Trim65 in the antidepressant effects of berberine.

The results suggest that berberine effectively alleviates depressive symptoms, suppresses the expression of genes associated with the NLRP3 inflammasome (NLRP3, cleaved caspase-1, ASC, GSDMD-N, Pro-IL-1β, IL-1β, Pro-IL-18, and IL-18), and reduces hippocampal neuronal functional damage in CUMS mice. Further studies showed that knockdown of Trim65 reversed the effects of berberine and increased NLRP3 inflammasome activity. Finally, K285, an important site for Trim65 binding to NLRP3, was identified.

Our study describes the mechanism of berberine limiting NLRP3 inflammasome activity by promoting the conjugation of Trim65 to NLRP3 and NLRP3 ubiquitination, and suggests NLRP3 inflammasome activation as a prospective target for treating inflammation-associated disorders such as depression.

Depressive disorders are multifactorial diseases in that a variety of factors may play a role in their etiology, including inflammation and abnormalities in the thyroid hormone (TH) metabolism and levels. The purpose of this study was to evaluate iodothyronine deiodinases (DIOs) and DIO-interacting cytokines as possible biomarkers in the diagnosis of depressive disorders.

This study enrolled 73 patients diagnosed with recurrent depressive disorder (rDD) and 54 controls. The expressions of DIO1, DIO2, DIO3, IL1B, IL6, TNFA, and IFNG genes, encoding three types of DIOs (1, 2, and 3), interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ, were assessed using the polymerase chain reaction in blood cells and an enzymatic immunoassay method in serum. The levels of examined molecules between patients and controls were compared, and correlations and diagnostic values were evaluated.

Lower levels of DIO2 and higher levels of IL1B, IL6, and TNFA were found in patients compared to controls. The protein concentrations of DIO1 and DIO2 were lower, while that of DIO3 was higher, in patients than in controls. Serum IL-1β, IL-6, and TNF-α were also higher in patients than in controls. The area under the curve (AUC) of the IL-1β, IL-6, DIO1, and DIO3 proteins was >0.7 for discriminating patients with rDD from controls.

The expressions of genes for DIO2, IL-1β, IL-6, and TNF-α may have a role in the estimation of processes present in depressive disorders. We can cautiously claim that DIO1 and DIO3 and pivotal cytokines, mainly IL-1β and IL-6, may play a role in depression diagnosis, and further studies are suggested to explain the exact role of these molecules in larger samples with more precise methods.

Major depressive disorder (MDD) is a silent global health problem that can lead to suicide. MDD development is suggested to result from numerous risk factors, including genetic factors. A precise tool for MDD diagnosis is currently not available. Recently, inflammatory processes have been identified as being strongly involved in MDD development and the reactivation of human herpesvirus type 6 (HHV-6), upregulating cytokines such as TNF-α, which are associated with MDD. Therefore, this study aimed to determine the association of HHV-6 with genetic factors, especially TNF-α mutation, in MDD patients and their relatives compared to healthy controls. The Patient Health Questionnaire (PHQ-9) was used to evaluate MDD status, and 471 oral buccal samples were investigated for HHV-6 infection and viral copy number by qPCR. TNF-α (-308G/A) gene mutation and the cytokines TNF-α, IL-6, and IL-10 were analyzed by high-resolution melting (HRM) analysis and enzyme-linked immunosorbent assay (ELISA). Whole-exome sequencing of buccal samples was performed to analyze for genetic factors. The results showed significantly higher HHV-6 positivities and viral loads in MDD patients (15/59 (25.67%) and 14,473 ± 16,948 copies/µL DNA) and their relatives (blood relatives 17/36 (47.22%) and 8146 ± 5656 copies/µL DNA); non-blood relatives 7/16 (43.75%) and 20,721 ± 12,458 copies/µL DNA) compared to the healthy population (51/360 (14.17%) and 6303 ± 5791 copies/µL DNA). The TNF-α (-308G/A) mutation showed no significant difference. Surprisingly, 12/26 (46.15%) participants with the TNF-α (-308G/A) mutation showed HHV-6 positivities at higher rates than those with wild-type TNF-α (-308G) (70/267 (26.22%)). HHV-6-positive participants with TNF-α (-308G/A) showed higher levels of TNF-α, IL-6, and IL-10 than those of negative control. Exome analysis revealed that common mutations in immune genes were associated with depression. Therefore, this study unveiled the novel association of inflammatory gene TNF-α (-308G/A) mutations with HHV-6 reactivation, which could represent a combined risk factor for MDD. This result could induce further research on MDD development and clinical applications.

Chronic stress refers to continuous emotional changes and psychological pressure that individuals experience when they are unable to adjust and stabilize the internal environment over an extended period. It can increase the pressure on endocrine mediators and cytokines in the circulation, as well as tissues throughout the hypothalamic-pituitary-adrenaline (HPA) axis and sympathetic nervous system (SNS); thus, evolving the internal environment of the tumor. This review assesses several key issues, involving psychosocial factors, and integrates clinical, cellular, and molecular studies-as well as the latest research progress-to provide a mechanistic understanding regarding breast oncopsychology. We propose that chronic stress contributes to large individual diferences in the prognosis of breast cancer survivors because they change the basic physiological processes of the endocrine and immune systems, which in turn regulate tumor growth. The study of psychological and physiological reactions of breast cancer patients suggests a new idea for psychological intervention and clinical treatment for breast cancer patients.

Psoriatic disease is a chronic, systemic immune-mediated inflammatory disorder comprising three major domains, skin, vascular and bone/joint inflammation. It is known for a long time that psoriatic disease is associated with a number of conditions such as hypertension, dyslipidemia, diabetes (metabolic syndrome) and depression. Up to one out of five people with psoriasis show concomitant depression. In the past, this was attributed to psychological stress of suffering from a chronic condition that is often visible and itchy, leading to stigmatization and adding to a significant burden of disease. Recent data provide evidence that depression associated with psoriatic disease is linked to the specific inflammatory pattern with IL-23, IL-17 family cytokines, TNF, IL-6 and IL-8 causing neuroinflammation and subsequently depression or depressive behaviour and/or anxiety. Psoriatic disease shows a distinct pattern of immune cells (e.g. dendritic cells, Th17 cells, neutrophils), mediators (e.g. IL-17A/F, IL-23, TNF) and tissue-related factors in all major domains that is different from other inflammatory dermatoses. There is a striking similarity between the inflammatory pattern in psoriatic disease and neuroinflammation that leads to depression. A number of risk factors have been identified in psoriatic disease, the most important of which are obesity and tobacco smoking. Obesity is known as a major risk factor for depression and anxiety due to its inflammatory signature. Apart from psychotherapy and anti-depressive medication, targeted treatments for psoriasis, including TNF, IL-17 and IL-23 inhibitors, can improve depression/depressive symptoms. The review summarizes the current knowledge about depression as a comorbidity in psoriatic disease.

Major depressive disorder and bipolar disorder are the leading causes of global disability. Approximately 50% of patients fail to attain remission, prompting a pronounced focus on the significance of dietary patterns and specific nutrients within the pathophysiology of mood disorders. The connection between chronic diseases and mood disorders follows a bidirectional pattern: physical ailments are interrelated with affective disorders, and, concurrently, mood symptoms often precede chronic diseases and have the potential to worsen their prognosis. Nutraceuticals affect factors that could potentially impact the onset of mood disorders: monoamines and brain-derived neurotrophic factor (BDNF) concentrations, neuroinflammation, oxidative stress, and sleep quality. Furthermore, mood disorders rarely manifest in isolation. Typically, such patients concurrently experience other mental disorders or somatic comorbidities: obesity, hypertension, diabetes, polycystic ovary syndrome (PCOS), etc., where providing nutritional support is also pertinent. To optimize the therapeutic approach for individuals with mood disorders, incorporating nutritional support may not solely ameliorate symptoms stemming directly from the mental condition, but also indirectly through interventions targeting comorbidities.

Copper (Cu) is an essential trace element in the brain and serves as an important cofactor for numerous enzymes involved in a wide range of biochemical processes including neurobehavioral, mitochondrial respiration, and antioxidant effects. Recent studies have demonstrated that copper dyshomeostasis is tightly associated with the development of depression by inducing oxidative stress and inflammatory responses. However, these findings have remained controversial so far. Cumulative studies have shown a positive association, while some other studies showed no association and even a negative association between serum/plasma copper level and depression. Based on these conflicted results, the association was speculated to be due to the clinical features of the population, stages of the disease, severity of copper excess, and types of specimens detected in these studies. In addition, there was an inverse association between dietary copper intake and depression. Furthermore, increasing copper intake could influence dietary zinc and iron intake to prevent and treat depression. Thus, copper supplementation may be a good measure to manage depression. This review provided a deeper understanding of the potential applicability of copper in the prevention and treatment of depression.

Depression is a public health problem. Despite the availability of treatment options, its prevalence is increasing. A high rate of treatment failure is often reported, along with considerable side effects associated with synthetic antidepressants. Therefore, developing effective and safe antidepressants from traditional herbs or natural products as an alternative strategy is warranted to avoid side effects and increase drug efficacy. In traditional medicine, cardamom has traditionally been used to treat conditions like asthma, tooth and gum infections, cataracts, nausea, diarrhea, and even depression and anxiety as well as some problems with the heart, kidneys, and digestive system.

The current study aimed to evaluate the antidepressant activity of cardamom oil in a rat model of depression induced by reserpine and compare it with the activity of the antidepressant drug fluoxetine.

Depression-like symptoms were induced in male rats by daily i. p. injection of reserpine (0.2 mg/kg/d for 15 d followed by 0.1 mg/kg/d for 21 d to maintain the depressive state), and the rats were treated with cardamom oil (oral dose = 200 mg/kg/d) for 21 d along with the maintenance dose of reserpine. We performed behavioral tests (forced swimming test and open-field test) and evaluated biochemical markers of depression.

Our findings revealed that cardamom oil attenuated depression-like symptoms in reserpine-injected rats by improving the behavioral changes measured by the forced swimming test and the locomotor activities measured by the open-field test. In reserpine-injected rats, cardamom oil exerted antidepressant-like effects by modulating lower levels of brain monoamine neurotransmitters (serotonin, norepinephrine, and dopamine), GSH, and higher oxido-nitrosative stress parameters (malondialdehyde and nitric oxide). Moreover, cardamom oil alleviated depression-like behaviors by lowering monoamine oxidase activity and raising the activities of Na⁺/K⁺-ATPase and acetylcholinesterase and levels of a brain-derived neurotrophic factor in the cortex and hippocampus.

We recommend the use of cardamom oil as a safe and reliable treatment or an adjuvant for preventing depression-like symptoms in patients suffering from depression.

Deficiencies in standard of care antidepressants are driving novel drug discovery. A new age of antidepressant medications has emerged with the introduction of rapid-acting antidepressants with efficacy in treatment-resistant patients.

The newly approved medicines and those in clinical development for major depressive disorder (MDD) are documented in this scoping review of newly approved and emerging antidepressants. Compounds are evaluated for clinical efficacy, tolerability, and safety and compared to those of standard of care medicines.

A new age of antidepressant discovery relies heavily on glutamatergic mechanisms. New medicines based upon the model of ketamine have been delivered and are in clinical development. Rapid onset and the ability to impact treatment-resistant depression, raises the question of the best first-line medicines for patients. Drugs with improvements in tolerability are being investigated (e.g. mGlu_2/3 receptor antagonists, AMPA receptor potentiators, and novel NMDA receptor modulators). Multiple companies are working toward the identification of novel psychedelic drugs where the requirement for psychedelic activity is not fully known. Gaps still exist - methods for matching patients with specific medicines are needed, and medicines for the prevention of MDD and its disease progression need research attention.