Major Depressive Suicide Research Articles

Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution of EPHX2: A Pilot Integrative Machine Learning Study

Suicide is a major public health problem caused by a complex interaction of various factors. Major depressive disorder (MDD) is the most prevalent psychiatric disorder associated with suicide; therefore, it is essential to prioritize suicide prediction and prevention within this population. Integrated information from different dimensions, including personality, cognitive function, and social and genetic factors, is necessary to improve the performance of predictive models. Besides, recent studies have indicated the critical roles for EPHX2/P2X2 in the pathophysiology of MDD. Our previous studies found an association of EPHX2 and P2X2 with suicide in MDD. This study is aimed at (1) establishing predictive models with integrated information to distinguish MDD from healthy volunteers, (2) estimating the suicide risk of MDD, and (3) determining the contribution of EPHX2/P2X2. This cross-sectional study was conducted on 472 prospectively collected participants. The machine learning (ML) technique using Extreme Gradient Boosting (XGBoost) classifier was employed to evaluate the performance and relative importance of the extracted characteristics in recognising patients with MDD and depressed suicide attempters (DSA). In independent validation set, the model with clinical and cognitive information could recognise MDD with an area under the receiver operating characteristic curve (AUC) of 0.938 (95% confidence interval (CI), 0.898–0.977), and genetic information did not improve classification performance. The model with clinical, cognitive, and genetic information resulted in a significantly higher AUC of 0.801 (95% CI, 0.719–0.884) for identifying DSA than the model with only clinical information, in which the three single nucleotide polymorphisms of EPHX2 showed important roles. This study successfully established step-by-step predictive ML models to estimate the risk of suicide attempts in MDD. We found that EPHX2 can help improve the performance of suicidal predictive models. This trial is registered with NCT05575713.

Plasma Apo-E mediated corticospinal tract abnormalities and suicidality in patients with major depressive disorder.

This study aims to explore the link between Apo-E, brain white matter, and suicide in patients with major depressive disorder (MDD) to investigate the potential neuroimmune mechanisms of Apo-E that may lead to suicide. Thirty-nine patients with MDD (22 patients with suicidality) and 57 age, gender, and education-matched healthy controls participated in this study, provided plasma Apo-E samples, and underwent diffusion tensor imaging scans. Plasma Apo-E levels and white matter microstructure were analyzed among the MDD with suicidality, MDD without suicidality, and HC groups using analysis of variance with post hoc Bonferroni correction and tract-based spatial statistics (TBSS) with threshold-free cluster enhancement correction. Mediation analysis investigated the relationship between Apo-E, brain white matter, and suicidality in MDD. The MDD with suicidality subgroup had higher depressive and suicide scores, longer disease course, and lower plasma Apo-E levels than MDD without suicidality. TBSS revealed that the MDD non-suicide subgroup showed significantly increased mean diffusivity in the left corticospinal tract and body of the left corpus callosum, as well as increased axial diffusivity in the left anterior corona radiata and the right posterior thalamic radiation compared to the suicidal MDD group. The main finding was that the increased MD of the left corticospinal tract contributed to the elevated suicide score, with Apo-E mediating the effect. Preliminary result that Apo-E's mediating role between the left corticospinal tract and the suicide factor suggests the neuroimmune mechanism of suicide in MDD. The study was registered on ClinicalTrials.gov (NCT03790085).

Grey matter abnormalities in major depressive disorder patients with suicide attempts: A systematic review of age-specific differences

BackgroundPrevious studies have reported alterations in brain structure in major depressive disorder (MDD) patients with suicide attempts. However, age-related changes in suicidal MDD patients remain unclear. MethodsWe performed a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Embase, PubMed, and Web of Science were searched to identify relevant studies from inception to January 2023. All voxel-based and surface-based morphometry studies comparing suicidal MDD patients to MDD or healthy controls were included. Studies were then grouped by age range (old, middle-age, adolescent) and the commonalities and age-related structural brain alterations were summarized. The included studies were evaluated using the Newcastle-Ottawa Scale (NOS). ResultsA total of 17 studies met the inclusion criteria, including 3 of late-life depression (LLD) patients, 11 of middle-aged depression (MAD) patients, and 3 of adolescent depression (AOD) patients. The majority of studies had moderate to high NOS scores, indicating good quality. Patients in all three age groups exhibited extensive alterations in the lateral, medial, and orbital regions of the frontal lobes. Furthermore, suicidal MAD patients showed a specific decrease in the gray matter volume of the dorsolateral prefrontal cortex compared to suicidal LLD patients. Cortical thickness and left angular gyrus volume were decreased in suicidal MAD and suicidal LLD patients, but increased in suicidal AOD patients. ConclusionThis systematic review summarizes structural brain changes in suicidal MDD patients at three age groups: elderly, middle-aged, and adolescent. These findings help elucidate the common circuitry of MDD related to suicide over the lifespan and highlight unique circuitry associated with different ages. These findings may help predict the risk of suicide in MDD patients at different ages.

Connectomics-based resting-state functional network alterations predict suicidality in major depressive disorder

Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD). However, dynamic alterations and dysfunction of resting-state networks (RSNs) in MDD patients with suicidality have remained unclear. Thus, we investigated whether subjects with different severity of suicidal ideation and suicidal behavior may have different disturbances in brain RSNs and whether these changes could be used as the diagnostic biomarkers to discriminate MDD with or without suicidal ideation and suicidal behavior. Then a multicenter, cross-sectional study of 528 MDD patients with or without suicidality and 998 healthy controls was performed. We defined the probability of dying by the suicide of the suicidality components as a ‘suicidality gradient’. We constructed ten RSNs, including default mode (DMN), subcortical (SUB), ventral attention (VAN), and visual network (VIS). The network connections of RSNs were analyzed among MDD patients with different suicidality gradients and healthy controls using ANCOVA, chi-squared tests, and network-based statistical analysis. And support vector machine (SVM) model was designed to distinguish patients with mild-to-severe suicidal ideation, and suicidal behavior. We found the following abnormalities with increasing suicidality gradient in MDD patients: within-network connectivity values initially increased and then decreased, and one-versus-other network values decreased first and then increased. Besides, within- and between-network connectivity values of the various suicidality gradients are mainly negatively correlated with HAMD anxiety and positively correlated with weight. We found that VIS and DMN-VIS values were affected by age (p < 0.05), cingulo-opercular network, and SUB-VAN values were statistically influenced by sex (p < 0.05). Furthermore, the SVM model could distinguish MDD patients with different suicidality gradients (AUC range, 0.73–0.99). In conclusion, we have identified that disrupted brain connections were present in MDD patients with different suicidality gradient. These findings provided useful information about the pathophysiological mechanisms of MDD patients with suicidality.

Thalamo-cortical circuits associated with trait- and state-repetitive negative thinking in major depressive disorder

BackgroundRepetitive negative thinking (RNT), often referred to as rumination in the mood disorders literature, is a symptom dimension associated with poor prognosis and suicide in major depressive disorder (MDD). Given the transdiagnostic nature of RNT, this study aimed to evaluate the hypothesis that neurobiological substrates of RNT in MDD may share the brain mechanisms underlying obsessions, particularly those involving cortico-striatal-thalamic-cortical (CSTC) circuits. MethodsThirty-nine individuals with MDD underwent RNT induction during fMRI. Trait-RNT was measured by the Ruminative Response Scale (RRS) and state-RNT was measured by a visual analogue scale. We employed a connectome-wide association analysis examining the association between RNT intensity with striatal and thalamic connectivity. ResultsA greater RRS score was associated with hyperconnectivity of the right mediodorsal thalamus with prefrontal cortex, including lateral orbitofrontal cortex, along with Wernicke's area and posterior default mode network nodes (t = 4.66–6.70). A greater state-RNT score was associated with hyperconnectivity of the right laterodorsal thalamus with bilateral primary sensory and motor cortices, supplementary motor area, and Broca's area (t = 4.51–6.57). Unexpectedly, there were no significant findings related to the striatum. ConclusionsThe present results suggest RNT in MDD is subserved by abnormal connectivity between right thalamic nuclei and cortical regions involved in both visceral and higher order cognitive processing. Emerging deep-brain neuromodulation methods may be useful to establish causal relationships between dysfunction of right thalamic-cortical circuits and RNT in MDD.

Decreased beta 1 (12-15 Hertz) power modulates the transfer of suicidal ideation to suicide in major depressive disorder.

Suicide prevention for major depressive disorder (MDD) is a worldwide challenge, especially for suicide attempt (SA). Viewing suicide as a state rather than a lifetime event provided new perspectives on suicide research. This study aimed to verify and complement SAs biomarkers of MDD with a recent SA sample. This study included 189 participants (60 healthy controls; 47 MDD patients with non-suicide (MDD-NSs), 40 MDD patients with suicide ideation (MDD-SIs) and 42 MDD patients with SA (MDD-SAs)). MDD patients with an acute SA time was determined to be within 1 week since the last SA. SUICIDALITY Part in MINI was applied to evaluate suicidality. Absolute powers in 14 frequency bands were extracted from subject's resting-state electroencephalography data and compared within four groups. The relationship among suicidality, the number of SA and powers in significant frequency bands were investigated. MDD-SIs had increased powers in delta, theta, alpha and beta band on the right frontocentral channels compared to MDD-NSs, while MDD-SAs had decreased powers in delta, beta and gamma bands on widely the right frontocentral and parietooccipital channels compared to MDD-SIs. Beta 1 power was the lowest in MDD-SAs and was modulated by the number of SA. The correlation between suicidality and beta 1 power was negative in MDD-SAs and positive in MDD-SIs. Reduced beta 1 (12-15 Hz) power could be essential in promoting suicidal behaviour in MDD. Research on recent SA samples contributes to a better understanding of suicide mechanisms and preventing suicidal behaviour in MDD.

Determinants and Outcomes of Suicidal Behavior Among Patients With Major Depressive Disorder

Major depressive disorder (MDD) is an important risk factor of suicidal behavior, but the added burden of suicidal behavior and MDD on the patient and societal level, including all-cause mortality, is not well studied. Also, the contribution of various prognostic factors for suicidal behavior has not been quantified in larger samples. To describe the clinical and societal outcomes, including all-cause mortality, of suicidal behavior in patients with MDD and to explore associated risk factors and clinical management to inform future research and guidelines. This population-based cohort study used health care data from the Stockholm MDD Cohort. Patients aged 18 years or older with episodes of MDD diagnosed between January 1, 2012, and December 31, 2017, in any health care setting were included. The dates of the data analysis were February 1 to November 1, 2022. Patients with MDD with and without records of suicidal behavior. The main outcome was all-cause mortality. Secondary outcomes were comorbid conditions, medications, health care resource utilization (HCRU), and work loss. Using Region Stockholm registry variables, a risk score for factors associated with suicidal behavior within 1 year after the start of an MDD episode was calculated. A total of 158 169 unipolar MDD episodes were identified in 145 577 patients; 2240 (1.4%) of these episodes, in 2219 patients, included records of suicidal behavior (mean [SD] patient age, 40.9 [18.6] years; 1415 episodes [63.2%] in women and 825 [36.8%] in men). A total of 11 109 MDD episodes in 9574 matched patients with MDD without records of suicidal behavior were included as controls (mean [SD] patient age, 40.8 [18.5] years; 7046 episodes [63.4%] in women and 4063 [36.6%] in men). The all-cause mortality rate was 2.5 per 100 person-years at risk for the MDD-SB group and 1.0 per 100 person-years at risk for the MDD-non-SB group, based on 466 deaths. Suicidal behavior was associated with higher all-cause mortality (hazard ratio, 2.62 [95% CI, 2.15-3.20]), as well as with HCRU and work loss, compared with the matched controls. Patients with MDD and suicidal behavior were younger and more prone to have psychiatric comorbid conditions, such as personality disorders, substance use, and anxiety, at the start of their episode. The most important factors associated with suicidal behavior within 1 year after the start of an MDD episode were history of suicidal behavior and age, history of substance use and sleep disorders, and care setting in which MDD was diagnosed. This cohort study's findings suggest that high mortality, morbidity, HCRU, and work loss associated with MDD may be substantially accentuated in patients with MDD and suicidal behavior. Use of medication aimed at decreasing the risk of all-cause mortality during MDD episodes should be systematically evaluated to improve long-term outcomes.

Predicting Suicide Attempts among Major Depressive Disorder Patients with Structural Neuroimaging: A Machine Learning Approach

IntroductionEvery year at least one million people die by suicide, with major depressive disorder (MDD) being one of the major causes of suicide deaths. Current suicide risk assessments rely on subjective information, are time consuming, low predictive, and poorly reliable. Thus, finding objective biomarkers of suicidality is crucial to move clinical practice towards a precision psychiatry framework, enhancing suicide risk detection and prevention for MDD.ObjectivesThe present study aimed at applying machine learning (ML) algorithms on both grey matter and white-matter voxel-wise data to discriminate MDD suicide attempters (SA) from non-attempters (nSA).Methods91 currently depressed MDD patients (24 SA, 67 nSA) underwent a structural MRI session. T1-weighted images and diffusion tensor imaging scans were respectively pre-processed using Computational Atlas Toolbox 12 (CAT12) and spatial tract-based statistics (TBSS) on FSL, to obtain both voxel-based morphometry (VBM) and fractional anisotropy (FA) measures. Three classification models were built, entering whole-brain VBM and FA maps alone into a Support Vector Machine (SVM) and combining both modalities into a Multiple Kernel Learning (MKL) algorithm. All models were trained through a 5-fold nested cross-validation with subsampling to calculate reliable estimates of balanced accuracy, specificity, sensitivity, and area under the receiver operator curve (AUC).ResultsModels’ performances are summarized in Table 1.Table 1.Models’ performances.Input featuresAlgorithmSpecificitySensitivityBalanced accuracyAUCVBMSVM55.00%50.00%52.50%0.55FASVM72.00%54.00%63.00%0.62VBM and FAMKL68.00%54.00%61.00%0.58Abbreviations: AUC, area under the receiver operator curve; FA, fractional anisotropy; VBM, voxel-based morphometry.ConclusionsOverall, although overcoming the random classification accuracy (i.e., 50%), performances of all models classifying SA and nSA MDD patients were moderate, possibly due to the imbalanced numerosity of classes, with SVM on FA reaching the highest accuracy. Thus, future studies may enlarge the sample and add different features (e.g., functional neuroimaging data) to develop an objective and reliable predictive model to assess and hence prevent suicide risk among MDD patients.Disclosure of InterestNone Declared

Childhood maltreatment and suicidality in major depressive disorder – a machine learning approach

Childhood maltreatment and suicidality in major depressive disorder – a machine learning approach

Suicide risk stratification among major depressed patients based on a machine learning approach and whole-brain functional connectivity

BackgroundSuicide risk stratification and individual-level prediction among major depressive disorder (MDD) is important but unrecognized. Here, we construct models to detect suicidality in MDD using machine learning (ML) and whole-brain functional connectivity (FC). MethodsA cross-sectional assessment was conducted on 200 subjects, including 126 MDD with high suicide risk (HSR; 73 patients with suicidal ideation [SI], 53 patients with suicidal attempts [SA]), 36 patients with low suicide risk (LSR) and 38 healthy controls (HCs). Whole-brain FC features were calculated, the least absolute shrinkage and selection operator (LASSO) method was used for feature selection. A support vector machine (SVM) was performed to build models to distinguish MDD from HCs, and for suicide risk stratification among MDD. Leave-one-out cross-validation (LOOCV) was performed for validation. ResultsThe models constructed using SVM on whole-brain FC had powerful classification efficiency in screening MDD from HCs (accuracy = 88.50 %), and in suicide risk stratification among MDD patients (with accuracy = 84.56 % and 74.60 % in classifying patients with HSR or LSR, and SA or SI, respectively). Subsequent analysis demonstrated that intra-network dysconnectivity in the sensorimotor network and inter-network dysconnectivity between the default and dorsal attention network could characterize HSR and SA in MDD, separately. LimitationsThis study was a single center cohort study without external validation. ConclusionThese findings indicate ML approaches are useful in suicide risk stratification among MDD based on whole-brain FC, which may help to identify individuals with different suicide risks in MDD and provide an individual-level prediction.

Investigation of the Relationship among Cortisol, Pro-inflammatory Cytokines, and the Degradation of Tryptophan into Kynurenine in Patients with Major Depression and Suicidal Behavior.

The increased degradation of tryptophan (Trp) along the kynurenine (Kyn) pathway due to inflammation and/or activation of the hypothalamic-pituitary-adrenal (HPA) axis has been reported among the biological factors involved in the pathophysiology of major depressive disorder (MDD) and suicide. However, the interaction among these multiple factors is not yet completely clarified. We studied plasma levels of Trp, Kyn, cortisol and proinflammatory cytokines (IL-1, IL- 6, IL-12, IL-20) and calculated the ratio Kyn/Trp as an index of the breakdown of Trp into Kyn in 31 suicidal MDD patients and 67 non-suicidal MDD patients. We confirmed that suicidal MDD patients have reduced plasma Trp, higher Kyn and Kyn/Trp ratio, and no difference in cortisol levels than non-suicidal MDD patients. IL-1 and IL-12 levels were significantly higher in suicidal MDD than in non-suicidal MDD (p=0.034 and p=0.023, respectively), whereas Il-6 and IL-20 levels were equal in the two groups. The Kyn/Trp ratio was positively correlated with a pro-inflammatory cytokines index (r=0.309, p=0.002) and cortisol (r=0.368, p=0.001). Notably, the variance in the Kyn/Trp ratio explained by the model including both cortisol and inflammatory parameters as dependent variables, substantially improved compared with the models in which the two parameters were considered separately. These findings show that both cortisol and proinflammatory cytokines are involved in the enhanced breakdown of Trp into Kyn occurring in suicidal MDD patients, thus adding new knowledge on the biological mechanisms leading to the activation of the Kyn pathway in MDD and suicide.

Temporal Stability of Dynamic Default Mode Network Connectivity Negatively Correlates with Suicidality in Major Depressive Disorder.

Previous studies have demonstrated that the suicidality in patients with major depressive disorder (MDD) is related to abnormal brain functional connectivity (FC) patterns. However, little is known about its relationship with dynamic functional connectivity (dFC) based on the assumption that brain FCs fluctuate over time. Temporal stabilities of dFCs within the whole brain and nine key networks were compared between 52 MDD patients and 21 age, sex-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging and temporal correlation coefficients. The alterations in MDD were further correlated with the scores of suicidality item in the Hamilton Rating Scale for Depression (HAMD). Compared with HCs, the MDD patients showed a decreased temporal stability of dFC as indicated by a significantly decreased temporal correlation coefficient at the global level, as well as within the default mode network (DMN) and subcortical network. In addition, temporal correlation coefficients of the DMN were found to be significantly negatively correlated with the HAMD suicidality item scores in MDD patients. These results suggest that MDD may be characterized by excessive temporal fluctuations of dFCs within the DMN and subcortical network, and that decreased stability of DMN connectivity may be particularly associated with the suicidality in MDD.

Efficacy and Safety of Subcutaneous Esketamine in the Treatment of Suicidality in Major Depressive Disorder and Bipolar Depression.

Affective disorders account for most cases of suicide. The pharmacological arsenal to treat suicidality is limited and available agents take too long to take effect. A large body of evidence shows optimal results of ketamine for treating depression, but the evidence concerning suicidality has not been fully described. We report the first real-world study of severely depressed patients presenting with suicide ideation who were treated with repeated administration of subcutaneous esketamine. We analyzed data from 70 acutely depressed subjects diagnosed with resistant major depressive disorder or bipolar depression. Subjects were administered subcutaneous esketamine once a week for 6 weeks. The primary efficacy endpoint, the change from baseline to 24-h post-administration 6 in the item 10 Montgomery-Åsberg Depression Rating Scale score, was analyzed using a mixed-effects repeated-measures model. There were significant effects for time on item 10 Montgomery-Åsberg Depression Rating Scale scores (p < 0.0001) but not for a time × diagnosis interaction (p = 0.164) from baseline to the end of the study. Efficacy of esketamine did not differ between groups (major depressive disorder vs bipolar depression) at any timepoint. Statistical significance on suicidality scores was observed from 24 h after the first administration (p < 0.001), and a further reduction was observed with repeated administrations. Esketamine was safe and well tolerated. Mean heart rate remained stable during the administrations and the blood pressure increase was self-limited. Repeated subcutaneous esketamine administration had significant anti-suicidality effects in both major depressive disorder and bipolar groups, with a rapid onset of action and a good tolerability profile. Large randomized controlled trials are warranted to confirm these preliminary findings.

Identification of potential blood biomarkers associated with suicide in major depressive disorder

Suicides have increased to over 48,000 deaths yearly in the United States. Major depressive disorder (MDD) is the most common diagnosis among suicides, and identifying those at the highest risk for suicide is a pressing challenge. The objective of this study is to identify changes in gene expression associated with suicide in brain and blood for the development of biomarkers for suicide. Blood and brain were available for 45 subjects (53 blood samples and 69 dorsolateral prefrontal cortex (DLPFC) samples in total). Samples were collected from MDD patients who died by suicide (MDD-S), MDDs who died by other means (MDD-NS) and non-psychiatric controls. We analyzed gene expression using RNA and the NanoString platform. In blood, we identified 14 genes which significantly differentiated MDD-S versus MDD-NS. The top six genes differentially expressed in blood were: PER3, MTPAP, SLC25A26, CD19, SOX9, and GAR1. Additionally, four genes showed significant changes in brain and blood between MDD-S and MDD-NS; SOX9 was decreased and PER3 was increased in MDD-S in both tissues, while CD19 and TERF1 were increased in blood but decreased in DLPFC. To our knowledge, this is the first study to analyze matched blood and brain samples in a well-defined population of MDDs demonstrating significant differences in gene expression associated with completed suicide. Our results strongly suggest that blood gene expression is highly informative to understand molecular changes in suicide. Developing a suicide biomarker signature in blood could help health care professionals to identify subjects at high risk for suicide.

Higher Interleukin 13 differentiates patients with a positive history of suicide attempts in major depressive disorder

• Inflammatory components (PCA) differentiate patients vs controls. • Elastic net results prompted IL-13 as new marker of suicide in major depression. • A positive history of suicide attempts is associated to higher level of IL-13. Every 40 seconds one person dies from suicide worldwide, half of which are related to depression. Neuroinflammation has been reported in neuropsychiatric conditions, including major depressive disorder (MDD). Preliminary findings suggested a link between inflammation and suicidal behavior. Nevertheless, to the best of our knowledge, no previous study explored how a wide range of cytokines may differentiate lifetime suicidal behaviors (i.e. positive history of suicide attempts) in patients affected by MDD. In 28 healthy controls (HC), 28 MDD patients with a positive history of attempting suicide (SA), and 28 MDD patients with a negative history (nSA), we determined concentrations of immune analytes using an ultra-sensitive Pro Human Cytokine Group 27-Plex Panel and Chemokine immunoassay panels (Bioplex). To deal with the 27 highly correlated cytokines we applied two different statistical approaches that reduce the dimensionality of data: Principal Component Analysis, exploring if suicidality may be related to a comprehensive derived inflammatory state, and elastic-net penalized regression that allows considering as predictor every single cytokine. Five inflammatory components were identified: none predicted suicidal behavior. Elastic net results prompted IL-13 as a new candidate marker of suicide in patients with MDD. The retrospective design of the study and the small sample size preclude the possible causal interpretation and generalizability of reported results. Exploring a wide range of cytokines may provide a new inflammatory signature of suicide useful for understanding pathophysiological underpinnings, developing new therapeutic targets, and allowing the identification of at-risk factors.

Anti-NMDAR Encephalitis: Multidisciplinary Development of a Clinical Practice Guideline.

Anti-NMDAR Encephalitis: Multidisciplinary Development of a Clinical Practice Guideline.

Machine learning model for predicting Major Depressive Disorder using RNA-Seq data: optimization of classification approach.

Considering human brain disorders, Major Depressive Disorder (MDD) is seen as a lethal disease in which a person goes to the extent of suicidal behavior. Physical detection of MDD patients is less precise but machine learning can aid in improved classification of disease. The present research included three RNA-seq data classes to classify DEGs and then train key gene data using a randomforest machine learning method. The three classes in the sample are 29 CON (sudden death healthy control), 21 MDD-S (a Major DepressiveDisorder Suicide) being included in the second group, and 9 MDD (non-suicides MDD) which are included in the third group. With PCA analysis, 99 key genes were obtained. 47.1% data variability is given by these 99 genes. The model training of 99 genes indicated improved classification. The RF classification model has an accuracy of 61.11% over test data and 97.56% over train data. It was also noticed that the RF method offered greater accuracy than the KNN method. 99 genes were annotated using DAVID and ClueGo packages. Some of the important pathways and function observed in the study were glutamatergic synapse, GABA receptor activation, long-term synaptic depression, and morphine addiction. Out Of 99 genes, four genes, namely DLGAP1, GNG2, GRIA1, and GRIA4, were found to be predominantly involved in the glutamatergic synapse pathway. Another substantial link was observed in the GABA receptor activation involving the following two genes, GABBR2 and GNG2. Also, the genes found responsible for long-term synaptic depressionwere GRIA1, MAPT, and PTEN. There was another finding of morphine addiction which comprises three genes, namely GABBR2, GNG2, and PDE4D. For massive datasets, this approach will act as the gold standard. The cases of CON, MDD, and MDD-S are physically distinct. There was dysregulation in the expression level of 12 genes. The 12 genes act as a possible biomarker for Major Depressive Disorder and open up a new path for depressed subjects to explore further.

Serum Lipid Levels and Suicide Attempts Within 2 Weeks in Patients With Major Depressive Disorder: Is There a Relationship?

The potential correlation between serum lipid profiles and suicidal tendencies has been previously reported, however, it is unclear whether serum lipid profiles have definite relevance to recently attempted suicides in individuals suffering from major depressive disorder (MDD). In this study, the relationship between blood lipids and suicide attempts in first-episode MDD patients in research were used to examine whether there is a connection. The cross-sectional study recruited 580 patients at the time of their first episode, measuring up to the diagnostic standard of MDD. Baseline demographic, clinical data, and blood lipid level data were collected. Depression severity was measured with the Hamilton Depression Rating Scale (HAMD). Our results revealed that the level of TC may be identified as a promising and effective biomarker for first-episode MDD suicide risk, suggesting that screening of serum lipid profiles in depressive patients is essential for suicide prevention.

Feasability of Utilizing a Rapid Screening Tool for Suicide and Major Depressive Disorder in Appalachian Acute Care Settings

INTRODUCTION Patients who commit suicide will often visit the emergency department (ED) shortly before death for reasons other than those related to the suicide. These visits may be a prime opportunity to identify patients with symptoms of major depressive disorder (MDD) that contribute to suicidal ideation. This pilot study investigated the feasibility of a recently developed tool, the CAD-MDD-S, a combination of the Computerized Adaptive Diagnostic Screening Tool for Major Depressive Disorder (CAD-MDD) and four screening questions from the Columbia-Suicide Severity Rating Scale (C-SSRS),to identify patients with suicidal thoughts, intent, plans, and behaviors, and/or MDD. METHOD CAD-MDD-S was administered to ambulatory adult patients (18+ years) who presented at an academic ED or student health services clinic for non-mental health concerns. The goal was to study the feasibility and efficiency of the screening tool, assessing the potential impact the tool may have in identifying at risk patients in these clinical settings. RESULTS From August 2015 to November 2016, 345 patients agreed to complete the screening tool (89% agree rate), with 76 screening positive for MDD, 13 for risk of suicide, and 7 for both. The median screening time to completion was six minutes, with 87% of patients taking less than 10 minutes. CONCLUSIONS This pilot study demonstrated that the ad- ministration of the CAD-MDD-S diagnostic screening tool was feasible and efficient in emergency settings, and the time to completion did not present a substantial burden to the patient. Future studies are needed to confirm these findings in other populations with larger samples.

Suicidal Major Depression in a Healthcare Worker Associated with the COVID-19 Pandemic: A Case Report

Novel coronavirus disease (COVID-19) pandemic has been affecting the whole world since December 2019. During the pandemic, healthcare professionals are at the forefront of the fight against COVID-19, and many healthcare workers died during this fight. Healthcare workers, who are at the forefront of the pandemic, are under intense mental and physical stress along this period. This disease makes increase psychiatric symptoms for general population and especially in health professionals at pandemic conditions. In particular, the symptoms of burnout, depression, and anxiety have rised significantly. With this case report, we present a physician who had symptoms that started during the COVID-19 pandemic, had depression accompanied by suicidal thoughts, and went into complete remission with electroconvulsive therapy. The mental health effects of healthcare workers who have had COVID-19 are discussed within the frame of this case report.