Introduction: Regulatory T cell (Treg) is a subtype of T lymphocyte that plays a crucial role in establishing immunologic self-tolerance and maintaining immune homeostasis. In this study, we set out to investigate the percentage and absolute count of Tregs in major depressive disorder (MDD) patients and their correlation with disease severity. Materials & Methods: This is a case-control study consisting of 47 MDD patients and 47 healthy controls. MDD patients were treated with antidepressant drugs according to their physician’s choice. The severity of MDD was assessed using Beck Depression Inventory (BDI) and Montgomery-Asberg Depression Rating Scale (MADRS) at the time of recruitment. Healthy controls completed the Depression Anxiety Scoring System (DASS21) questionnaire to ensure they were in good mental health without history of MDD. The percentage and absolute count of CD4+ CD25+ Tregs and CD4+ CD25+ FOXP3+ Tregs were identified by multiparameter flow cytometry. Results: The percentage and absolute count of CD4+ CD25+ Treg cells were significantly higher in MDD patients than in healthy controls (P<0.001, in both cases). Likewise, the percentage and absolute count of CD4+ CD25+ FOXP3+ Treg cells were also significantly higher in MDD patients compared to healthy controls (P=0.003 and P=0.002, respectively). However, there was no significant correlation between the percentage and absolute count of CD4+ CD25+ Treg and CD4+ CD25+ FOXP3+ Treg cells with BDI or MADRS score. Conclusions: Our results suggest that antidepressant treatments contributed to an upregulation of Tregs in MDD patients.
Major depressive disorder

No abstract available.
Depressive Disorder, Major*

OBJECTIVE: Previous studies indicate that emotion regulation problems in major depressive disorder (MDD) may be caused by difficulties in preferring useful emotions according to their goals. We investigated expected emotional usefulness and emotional preference in individuals with MDD (MDDs) and healthy controls (HCs). METHODS: Participants were given an interpersonal scenario with two different goals (confrontation and collaboration) and rated their willingness to participate in emotion-provoking activities and the expected usefulness of a particular emotion. RESULTS: MDDs were similar to HCs in expected emotional usefulness but showed different patterns of emotional preference. HCs preferred happiness to negative emotions across goals whereas MDDs did not show such pattern. In addition, HCs displayed goal-appropriate preferences whereas MDDs did not prefer certain emotions for specific goals. CONCLUSION: Although MDDs seemed to understand how useful an emotion can be, they did not show preference for goal-appropriate emotions. Interventions should address why MDDs have difficulty engaging in goal-appropriate emotions despite having full knowledge of the utility of emotions in achieving goals.
Depression ; Depressive Disorder, Major* ; Happiness

A case of rapid stabilization using electroconvulsive therapy (ECT) for a major depressive disordered (MDD) patient with life-threatening low body mass index (BMI) is reported. This case report focuses on a 55-year-old Malay housewife with underlying hyperthyroidism in a euthyroid state who presented with MDD with mood congruent psychotic features, which were precipitated by the death of her husband. Her BMI was only 11 kg/m2 due to severe anorexia, and she was highly suicidal. Peripheral total parenteral nutrition was started and ECT was commenced for rapid stabilization on top of tablet escitalopram 15 mg nocte. Full remission was achieved after nine ECTs and steady healthy weight gain was achieved throughout admission. The patient was discharged at BMI of 13 kg/m2 with good appetite. ECT was safe for very low BMI MDD patient.
Electroconvulsive Therapy ; Depressive Disorder, Major

The patient is a 36-year-old- male who was bullied by peers and was emotionally abused by his father for being effeminate and preferring to play with dolls. These adverse childhood experiences made him vulnerable to depression. He told himself that he could no longer rely on anyone else but himself and took pride in his independence. He gave his best with his endeavors and did not settle for mediocrity, frequently reviewing his work. During the pandemic, he experienced several hardships such as disruption of activities, inability to meet the financial needs of his family and unemployment. He found himself struggling but surviving. While in a work-from-home arrangement, he was so frustrated about his internet connection that he complained on social media. The internet company then threatened him of possible legal charges and felt stuck in a hopeless situation. This affected his day-to-day activities until he felt so overwhelmed that he attempted suicide by ingesting multiple medications. He was then brought to the emergency room and was admitted. He was diagnosed to have Major Depressive Disorder and was started on antidepressants. Psychotherapy focused on identifying stressors and strengthening adaptive coping mechanisms while he was admitted at an isolation facility. He then followed up at the outpatient department of a tertiary government hospital in Mindanao with noted improvement in mood and functionality overtime. During admission, he was also diagnosed to have Diabetes Mellitus and COVID-19, which added to his burden as these were the biologic factors that were correlated to his depression. The case highlighted the interplay between the effects of multiple traumatic experiences in a vulnerable individual and thus necessitating a holistic management.
Depressive Disorder, Major ; Psychiatry ; Suicide

Background: Major depressive disorder is a mood disorder that has affected many people worldwide. It is characterized by persistently low or depressed mood, anhedonia or decreased interest in pleasurable activities, feelings of guilt or worthlessness, lack of energy, poor concentration, appetite changes, psychomotor retardation or agitation, sleep disturbances, or suicidal thoughts. Objective: The objective of the study was to predict the presence of major depressive disorder using a variety of machine learning classification algorithms (logistic regression, Naive Bayes, support vector machine, random forest, adaptive boosting, and extreme gradient boosting) on a publicly available depression dataset. Methodology: After data pre-processing, several experiments were performed to assess the recursive feature elimination with cross validation as a feature selection method and synthetic minority over-sampling technique to address dataset imbalance. Several machine learning algorithms were applied on an anonymized publicly available depression dataset. Feature importance of the top performing models were also generated. All simulation experiments were implemented via Python 3.8 and its machine learning libraries (Scikit-learn, Keras, Tensorflow, Pandas, Matplotlib, Seaborn, NumPy). Results: The top performing model was obtained by logistic regression with excellent performance metrics (91% accuracy, 93% sensitivity, 85% specificity, 93% recall, 93% F1-score, and 0.78 Matthews correlation coefficient). Feature importance scores of the most relevant attribute were also generated for the best model. Conclusion The findings suggest the utility of data science techniques powered by machine learning models to make a diagnosis of major depressive disorders with acceptable results. The potential deployment of these machine learning models in clinical practice can further enhance the diagnostic acumen of health professionals. Using data analytics and machine learning, data scientists can have a better understanding of mental health illness contributing to prompt and improved diagnosis thereby leading to the institution of early intervention and medical treatments ensuring the best quality of care for our patients.
Depressive Disorder, Major ; Machine Learning

OBJECTIVE: Staging of psychiatric disorders is gaining momentum and the purpose of this review is to examine whether major mood disorders can be defined according to stages. METHODS: In April 2018 the PubMed electronic data base was scrutinized by a combination of various search terms like “major depressive disorder and staging,”“bipolar disorder and neuroprogression,” etc. To incorporate the latest findings the search was limited to the last 10 years. Both original and review articles were examined by reading the abstracts, and papers which were found to be particularly applicable were read in full and their reference lists were also consulted. RESULTS: A significant increase occurred in the number of papers published on the topic of staging of mood disorders. Staging formats were found for both major mood disorders, with the caveat that many more articles were discovered for bipolar disorder. Current evidence points to allostatic load and neuroprogression as the basis for staging of mood disorders. CONCLUSION: Principal affective illnesses may be characterized by distinct stages, for instance early, intermediate and late. These phases inform the management so that clinicians should incorporate the staging schema into everyday practice and implement treatment strategies according to the phase of the illness.
Allostasis ; Bipolar Disorder ; Depressive Disorder ; Depressive Disorder, Major ; Mood Disorders*

Anxiety* ; Depressive Disorder ; Depressive Disorder, Major* ; Electroencephalography ; Humans

Research Question: Is Beck’s Depression Inventory Scale – Visayan (BDI-Visayan) an accurate and reliable depression screening tool among ambulatory hemodialysis patients validated against Semi-structured Clinical Interview for Depression for DSM IV? Background: Depressed dialysis patients are twice likely to die or require hospitalization. Unfortunately, there is a lack of a depression screening tool validated for Filipino patients. Objectives: Development and validation of Beck’s Depression Inventory Scale -Visayan version as a depression screening tool for ambulatory maintenance hemodialysis Filipino patients. General Study Design: This is a cross-cultural instrument translation and cross-sectional validation study. Participants: Using non-probability convenient sampling, patients >18years old with eGFR <60mL/min/1.73m2 based on CKD-Epi equation and on hemodialysis for ≥3 months were enrolled. Patients with hearing, speech or cognitive deficits, acute kidney injury, dementia, delirium or psychiatric disorders were excluded. Interventions: BDI Visayan was developed using combined translation technique with depression defined as a score of ≥14. Outcome measures: Structured Clinical Interview for Depression (SCID) for DSM IV was used as the gold standard. Analysis: Sensitivity, specificity, predictive values, and likelihood ratios of BDI-Visayan were compared to SCID. Cronbach’s alpha, Receiver Operator Characteristics and Area Under the Curve were used to determine reliability, optimal cut-off score, and overall accuracy, respectively. Results: BDI-Visayan has high reliability with Cronbach’s alpha of 0.904 and an accuracy of 0.80 AUC. The optimal cut-off for BDI-Visayan for major depressive disorder for ambulatory hemodialysis patients is 20 with 75% sensitivity, 55% specificity, 22% positive predictive value, 93% negative predictive value, 3.92 positive likelihood ratio, and 0.31 negative likelihood ratio. Conclusions BDI Visayan is a reliable and accurate depression screening tool for ambulatory maintenance hemodialysis Filipino patients with higher specificity at an optimum cut-off score of 20.
Humans ; Depressive Disorder, Major ; Renal Dialysis

Depression ; Depressive Disorder, Major ; Employment ; Humans