Loss Of Interest In Activities Research Articles

Depressed mood as a transdiagnostic target relevant to anxiety and/or psychosis: a scoping review protocol

IntroductionDepressed mood is a psychological state characterised by sadness or loss of interest in activities. Depressed mood is a highly prevalent symptom across major mental disorders. However, there is limited...

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder.

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.

A COMPARATIVE STUDY TO ASSESS THE LEVEL OF DEPRESSION AMONG THE ELDERLY LIVING IN OLD AGE HOMES AND COMMUNITY, PUDUCHERRY

Depression is a state of mental illness. It is characterized by deep, long lasting feelings of sadness or despair. Depression can change an individual’s thinking/feelings and also affects his/her social behaviour and sense of physical well-being. It can affect people of any age group, including young children and teens. It can run in families and usually starts between the ages of 15 and 30 years. Women and elderly people are more commonly affected than men. There are several types of depression such as major depression it is a change in mood that lasts for weeks or months. It is one of the most severe types of depression. Dysthymia is a less severe form of depression but usually lasts for several years. Psychotic depression a severe form of depression associated with hallucinations and delusions .Seasonal depression, occurring only at certain time of the year usually winter, also known as winter blues’. Some of the common factors that may cause depression are genetics , trauma and high levels of stress, mental illnesses such as schizophrenia and substance abuse, postpartum depression (women may develop depression after the birth of the baby), serious medical conditions such as heart disease, cancer and HIV, use of certain medications, alcohol and drug abuse, individuals with low self-esteem, trauma and high levels of stress due to financial problems, breakup of a relationship or loss of a loved one. The signs and symptoms of depression include feeling of sadness and loneliness, loss of interest in activities once found enjoyable, feeling of hopelessness, worthlessness or excessive guilt, fatigue or loss of energy, sleeping too little or too much, loss of appetite, restlessness and being easily annoyed. Diagnose the depression based on the detailed history and sign and symptoms of the individual. Laboratory tests such as blood tests to screen for alcohol/drugs in blood. Psychological evaluation of the individual’s thoughts, feelings and behaviour patterns. The most common treatments of depression are Counselling & Psychotherapy, Medication, Electro convulsive therapy. Medications such as anti-depressants are given to help balance chemicals in the brain known as ‘neuro transmitters’. Electroconvulsive therapy (ECT) is carried out in case the treatment with other therapies such as medications has failed. Preventive measures include regular exercise, healthy diet and stable relationships. Statement Of The Problem: A comparative study to assess the level of depression among the elderly living in old age homes and community, Puducherry’’. Objectives: To assess the level of depression among the elderly. • To compare the level of depression among elderly residing in old age home and those living in community. • To associate the level of depression among elderly with their selected demographic variables. Materials and Methods: The investigator obtained formal permission from Hospital authority and Institutional Ethical Committee of Sri Manakula Vinayagar Medical College and Hospital. The study was conducted at help age India old age home and thirubhuvanai community area, Puducherry. The period of data collection was four weeks .The study was conducted quantitative research approach ,descriptive research design and convenient sampling technique was used to select the sample .The sample size was 60 elderly depression patient, 30 were inmates of old age home and 30 were living in community. The investigator explained the procedure to the clients and assessed level of depression among elderly people’s. After obtaining oral consent primarily the demographic data was obtained from the samples. Standardized Hamilton depression rating scale prepared by the investigator was used to assess the level of depression among the elderly living in old age homes and community. This scale consists of 17 items. The data variables were collected from the elderly residing at help age India and thirubhuvanai community area .This was continued for 15-20 minutes in a day. Sample is reassessed to analyze the intervention on depression. Data analysis was done using descriptive and statistical analysis. Result: The findings shows that, the mean score of the level of depression among the elderly living in the old age homes was 16.47 ± 4.637 and the community was 12.73 ± 6.341.The calculated independent ‘t’ test value of t = -2.603 shows statistically significant difference between Comparison between the level of depression among the elderly living in old age homes and community respectively. Conclusion: Out of 60 samples in Frequency and percentage wise distribution of level of depression among the elderly living in old age homes and community .In old age homes, Majority of the elderly10 (33.3%) had moderate level of depression, 9 (30%) had severe level of depression, 8 (26.7%) had mild level of depression, 2 (6.7%) had extremely severe level of depression and 1 (3.3%) had normal level.

Fusion of Topic Modeling and RoBERTa for Detecting Signs of Depression from Social Media

Depression, or Major Depressive Disorder, is a serious and common medical condition that affects people worldwide. It negatively affects the person's feelings, thoughts, and actions. Depression causes a loss of interest in activities he enjoyed in the past. It can lead to physical and emotional problems that hamper the daily activities at work and home. In recent years, much research has been done to identify Depression through various modalities of image, speech, and text through artificial intelligence. Social media is an important medium where many discussions and mentions happen about Depression. The current study proposes a novel approach to understand how the depressed and non-depressed communicate differently with the help of Topic Modeling with latent-Dirichlet allocation (LDA) and also detect depression with the help of Robustly Optimized BERT Pretraining Approach (RoBERTa). The current study achieved an accuracy of 66.4% for the depression detection model, which outperformed the previous approaches with similar methodology. The current study is helpful for self-diagnosis of signs of Depression at very early stages.

The dynamic interaction between symptoms and pharmacological treatment in patients with major depressive disorder: the role of network intervention analysis

IntroductionThe Major Depressive Disorder (MDD) is a mental health disorder that affects millions of people worldwide. It is characterized by persistent feelings of sadness, hopelessness, and a loss of interest in activities that were once enjoyable. MDD is a major public health concern and is the leading cause of disability, morbidity, institutionalization, and excess mortality, conferring high suicide risk. Pharmacological treatment with Selective Serotonin Reuptake Inhibitors (SSRIs) and Serotonin Noradrenaline Reuptake Inhibitors (SNRIs) is often the first choice for their efficacy and tolerability profile. However, a significant percentage of depressive individuals do not achieve remission even after an adequate trial of pharmacotherapy, a condition known as treatment-resistant depression (TRD).MethodsTo better understand the complexity of clinical phenotypes in MDD we propose Network Intervention Analysis (NIA) that can help health psychology in the detection of risky behaviors, in the primary and/or secondary prevention, as well as to monitor the treatment and verify its effectiveness. The paper aims to identify the interaction and changes in network nodes and connections of 14 continuous variables with nodes identified as "Treatment" in a cohort of MDD patients recruited for their recent history of partial response to antidepressant drugs. The study analyzed the network of MDD patients at baseline and after 12 weeks of drug treatment.ResultsAt baseline, the network showed separate dimensions for cognitive and psychosocial-affective symptoms, with cognitive symptoms strongly affecting psychosocial functioning. The MoCA tool was identified as a potential psychometric tool for evaluating cognitive deficits and monitoring treatment response. After drug treatment, the network showed less interconnection between nodes, indicating greater stability, with antidepressants taking a central role in driving the network. Affective symptoms improved at follow-up, with the highest predictability for HDRS and BDI-II nodes being connected to the Antidepressants node.ConclusionNIA allows us to understand not only what symptoms enhance after pharmacological treatment, but especially the role it plays within the network and with which nodes it has stronger connections.

Self-esteem and optimism in patients with major depression disorder: a cross-sectional study.

Depressive disorders are common mental illnesses characterized by persistent feelings of sadness, hopelessness, and loss of interest in activities. Self-esteem refers to the appraisal of one's worth and personality, whereas optimism reflects a positive attitude and the expectation of positive outcomes. Therefore, the present study aims to determine the average self-esteem and optimism of patients with depression in 2022. The present study is a single center and prospective descriptive-analytical study in which 121 patients out of 154 participants with major depression using convenience sampling were studied. Recruitment started on 10 June 2022 and ended on 12 July 2022. Data were collected using a demographic questionnaire, the Rosenberg Self-Esteem Scale (RSES), and the Revised Life Orientation Test (LOT -R). Data analysis was performed using statistical tests and the Pearson correlation coefficient with SPSS software version 21. The mean and SD of patients' self-esteem scores were 14.68±1.30, and the mean and SD for optimism scores were 9.90±1.68. Pearson correlation analysis showed that educational level had a significant inverse relationship with patients' self-esteem scores (r=-0.009, P=0.03), and sex had a significant relationship with patients' optimism scores (r=0.175, P=0.008). Considering the importance of self-esteem and optimism in the recovery and return to society of patients with major depression, it is necessary to take measures to strengthen and increase self-esteem and optimism in these patients.

An Open-Source Platform for Real-Time Preliminary Diagnosis amongst Adults using Data Analytics

Depression can be defined as a mental health disorder characterized by persistently depressed mood, loss of interest in activities, causing significant impairment in daily life. Technical intervention to screen depression in non-clinical population which records, classify depression on the basis of severity and provide features or predictors that discriminate the classification of depression among non-clinical population comprising of college students is the main area of the study. Beck Depression Inventory – II (BDI-II), as per Diagnostic and Statistical manual of Mental disorder (DSM IV) is used to screen depression and its severity. Indicators are determined on the basis of how well the features or predictors can discriminate the classes of depression severity. Providing quality indicators which help in supporting the process can be considered as symptoms for screening depression. Descriptive analytics is used in order to find the underlying pattern of the responses captured, factor analysis groups variables on the basis of correlation between patterns of the responses to reduce dimension. The approach for supervised descriptive analysis method that takes BDI-II questions as features and refine the features using information gain and linear discriminant analysis as feature selection algorithm. The classification of severity of depression is done using Support vector machine (SVM)..

Pengaruh Kekerasasn Seksual Yang Difasilitasi Teknologi Terhadap Depresi Pada Wanita Dewasa Awal Pengguna Media Sosial

This study aims to determine whether or not there is an effect of technology-facilitated sexual violence on depression in early adult women using social media. Technology-facilitated sexual violence is a series sexually aggressive or harmfully harassing behavior using communication technologies. Depression is a mood disorder characterized by feelings of sadness or loss of interest in activities for at least 2 weeks. This research was conducted on 150 female subjects aged 18-25 years who owned and used social media and had been victims of technology-facilitated sexual violence. The method used was a quantitative survey, in which participants were asked to fill out the Technology Facilitated Sexual Violence Scale and Beck Depression Inventory-II. The analysis used was multiple linear regression with the help of the statistical program IBM SPSS 25.00 for mac. The results showed that technology-facilitated sexual violence simultaneously plays a role of 36.5% of depression.

A behavioral analysis of the combinational effect of Piperine & Bupropion HCl for the treatment of depression

Aim: The goal of this study is to determine that treating depression with Piperine and Bupropion HCl in combination is more successful than with these medicines alone. Background: -Depression is a mood disorder referred to as the collection of conditions connected with the elevation or lowering of the mood marked by melancholy, loss of interest in activities, with little or no social life. Despite the enormous amount of research, efficient treatment of depression is still one of the most desired aspects of managing depression. Objective: This study is focused on establishing the effectiveness of combinational therapy through animal behavioral models. Method: The study was initiated by acclimatizing animals for one week in conventional laboratory settings. Depression was induced in animals through the forced swim method. Animal behavioral models were utilized to investigate the efficacy of the drug combination for the treatment of depression. Result: Positive results were reported for the group treated with the drug combination and were significant (p < 0.05) to every other group that participated in the study. No mortality was found in any of the study groups. Conclusion: This study supports the efficacy of combinational therapy of Piperine and Bupropion HCl for treating depression.

ВПЛИВ ПОСТТРАВМАТИЧНОГО СТРЕСОВОГО РОЗЛАДУ НА СЕКСУАЛЬНЕ ФУНКЦІОНУВАННЯ ВІЙСЬКОВИХ ТА ВЕТЕРАНІВ

The article highlights the peculiarities of the relationship between post-traumatic stress disorder and various types of sexual dysfunctions that may occur in veterans and military personnel. In particular, foreign studies of previous years have been analyzed, statistical data on the prevalence of sexual dysfunctions among different cohorts of military personnel and veterans, as well as the statistical prevalence of sexual dysfunctions against the background of post-traumatic stress disorder among military and civilian populations have been compared. Attention is drawn to the fact that the level of sexual dysfunction among military personnel with post-traumatic stress disorder is higher than among the civilian population. Additional, aggravating factors that can complicate or inhibit the scientific study of this issue, as well as collectively burden the anamnesis of the specified group of subjects, are indicated. Attention is focused on the lack of relevant systematic research among female veterans and female military personnel. The cause-and-effect relationship of sexual dysfunctions with post-traumatic stress disorder is determined, in addition, the prevalence of sexual disorders against the background of post-traumatic stress disorder or other conditions, such as depression, anxiety, etc., is also outlined. The typical symptoms of post-traumatic stress disorder, which can directly or indirectly lead to various kinds of sexual disorders, are analyzed: difficulties with experiencing positive emotions, alienation, feeling of separation from others, loss of interest in activities that previously caused positive emotions, etc. The impact of partnership on the specifics of experiencing sexual dysfunction against the background of post-traumatic stress disorder is highlighted. The factors that can complicate the process of discussing intimate issues both by clients or patients, and by psychologists or doctors are indicated. General recommendations for starting the discussion of sexual problems were given and the expediency of the direction of sexual rehabilitation in the process of complex rehabilitation measures, which are provided to veterans and military personnel regardless of their age and gender, was updated.

Systems Pharmacology-Based Research on the Mechanism of Danzhi Xiaoyao San in the Treatment of Depression

Depression is a common and serious mental disorder characterized by persistent feeling of sadness and loss of interest in activities. Despite conventional treatments can improve depression symptoms and prevent a recurrence, complementary and alternative therapies are necessary to be involved because of the detrimental effects of the current therapy. Traditional Chinese Medicine (TCM) formulas are considered to be a potential complementary method with special advantages for treating the multiple pathogenesis of depression in a holistic way. However, the underlying pharmacological mechanism of TCM formula on depression therapy remained elusive. Therefore, in current study, a systems pharmacology approach integrating Absorption, Distribution, Metabolism, and Excretion (ADME) filtering, target fishing, gene enrichment analysis, network pharmacology, and pathway analysis was developed to comprehensively explore the multiple therapeutic mechanisms of Danzhi Xiaoyao San (DZXYS) for the treatment of depression. Finally, 111 active components and 112 potential targets of DZXYS were identified through ADME filtering and target fishing. Gene enrichment analysis found that DZXYS played a significant role in treating depression through the regulation of multiple pathogenesis including neurotransmitter systems imbalance, brain derived neurotrophic factor, hypothalamic-pituitary-adrenal axis dysregulation, inflammation, and immune. Moreover, the compound-target and target-pathway networks were constructed to elucidate the therapeutic mechanism of DZXYS from the system perspective. This study proposes a promising way for facilitating in-depth understanding of the complicated therapeutic mechanism of TCM on depression.

The prevalence and correlates of depression among older adults in greater Kumasi of the Ashanti region

BackgroundApproximately two million Ghanaians suffer from mental disorders including depression. The WHO defines it as an illness characterized by constant sadness and loss of interest in activities that a person usually enjoys doing and this condition is the leading cause of mental disorders; however, the burden of depression on the aged population is fairly unknown. A better appreciation of depression and its predictors is necessary to design appropriate policy interventions. Therefore, this study aims to assess the prevalence and correlates of depression among older people in the Greater Kumasi of the Ashanti region.MethodsA cross-sectional study design with a multi-stage sampling approach was employed to recruit and collect data from 418 older adults aged 60 years and above at the household level in four enumeration areas (EAs) within the Asokore Mampong Municipality. Households within each EAs were mapped and listed by trained resident enumerators to create a sampling frame. Data was collected electronically with Open Data Kit application over 30 days through face-to-face interaction using the Geriatric Depression Scale (GDS). The results were summarized using descriptive and inferential statistics. A multivariable logistics regression using a forward and backward stepwise approach was employed to identify the predictors of depression in the study sample. All analyses were performed using STATA software version 16, and the significance level was maintained at a p-value < 0.05 and presented at a 95% confidence interval.ResultsThe study achieved a response rate of 97.7% from the estimated sample size of 428 respondents. The mean age was 69.9 (SD = 8.8), and the distribution was similar for both sexes (p = 0.25). The prevalence of depression in this study was 42.1% and dominated by females, older adults (> 80 years), and lower economic class respondents. The rate was 43.4% for both consumers of alcohol and smokers with a history of stroke (41.2%) and taking medication for chronic conditions (44.2%). The predictors of depression in our study were being single, low class [aOR = 1.97; 95% CI = 1.18–3.27] and having other chronic conditions [aOR = 1.86; 95% CI = 1.59–4.62], and the inability to manage ones’ own affairs [aOR = 0.56; 95% CI = 0.32–0.97].ConclusionThe study provides data that can inform policy decisions on the care of the elderly with depression in Ghana and other similar countries, confirming the need to provide support efforts towards high-risk groups such as single people, people with chronic health conditions, and lower-income people. Additionally, the evidence provided in this study could serve as baseline data for larger and longitudinal studies.

Depression Detection using AI, ML and NLP

Depression is a serious mental illness that can be debilitating. It can interfere with daily activities and cause feelings of sadness, hopelessness, and despair. It is a mental health condition that can range in severity from mild to severe. The severity of depression can be assessed based on the number, intensity, and duration of symptoms, as well as their impact on an individual's daily functioning. Mild depression typically involves symptoms such as feeling sad, low energy, and a lack of motivation. These symptoms may not significantly interfere with an individual's ability to function in their daily life, and they may still be able to maintain their social and occupational obligations. Moderate depression involves more severe and persistent symptoms, such as a sense of hopelessness, persistent feelings of sadness, and difficulty concentrating or making decisions. These symptoms may make it challenging to fulfill daily responsibilities, and may result in social isolation or problems at work or school. Severe depression involves symptoms that significantly impair an individual's ability to function in their daily life, such as suicidal thoughts, complete loss of interest in activities, and difficulty with basic tasks such as personal hygiene or eating. Severe depression is a medical emergency and requires immediate professional intervention to prevent harm to oneself or others

Prevalence of major depressive disorder and its associated factors among students of private higher secondary schools of science stream, Rajkot city, Gujarat: A cross-sectional study

Background: Major Depressive Disorder (MDD) is a mood disorder characterized by persistent feelings of sadness, hopelessness, worthlessness, and a loss of interest in activities. There is an emerging concern about the prevalence of MDD among high school students. Aims and Objectives: The objective of the study was to estimate the prevalence of MDD among students of private higher secondary schools and to find association between MDD with sociodemographic variables and other predictors of MDD. Materials and Methods: A cross-sectional study was conducted among the students of private higher secondary school of science stream in Rajkot city of Gujarat. A total of 1219 students were enrolled in the study. The students were screened for MDD by Patient Health Questionnaire (PHQ-9) modified for teens. Severity of the depression according to PHQ-9 scale had been described as Minimal Depression, Mild Depression, Moderate Depression, Moderately Severe Depression, and Severe Depression. Data were entered and analyzed using Microsoft excel 2007 and Epi info version 7.1.5.2. The Chi-square test was used to check association between MDD and its various predictors. Results: The prevalence of MDD was found to be 3.20% with highest proportion (9.21%) in Muslim students. Significant association found between MDD and its predictors such as physical illness, flow of ongoing study, and performance in study and addiction. Conclusion: It is recommended that the students should be assessed periodically to know their depression status and academic performance-related problems. Responsible factors should be identified and addressed with the help of parents, teachers, psychologists, and psychiatrists for prompt resolvement.

The prevalence of post-traumatic stress disorder (PTSD) among emergency medical services personnel in Riyadh, Saudi Arabia

Background: Emergency medical services personnel are not aware of the significance and the prevalence of post-traumatic stress disorder (PTSD) among them and its effect on their mental well-being and the quality of patient care. Objective: The current study aimed to assess the prevalence of PTSD and its associated factors among emergency medical services personnel in Riyadh, Saudi Arabia. Methods: A descriptive cross-sectional study was conducted including all accessible emergency medical services personnel working at five hospitals in Riyadh during the period from November to December 2022. Data were collected using the three-phase method including identification of eligible care providers, approaching using an interview-based questionnaire, and recruitment of the questionnaires that were administered to all pre-hospital care providers. Results: A total of 138 emergency personnel were included. A total of 74 (53.6%) were emergency specialists, 12 (8.7%) were emergency technicians, 39 (28.3%) were interns or physicians, and 13 (9.4%) were nurses or paramedics. The most endorsed symptoms included loss of interest in activities that the participants used to enjoy (45.7%), followed by feeling very upset when something reminded them of the stressful experience (44.2%), and trouble falling or staying asleep. A total of 42 (30.4%) participants experienced PTSD (95% CI: 23.1%-38.2%). Conclusion: The current study revealed that nearly one out of each three emergency personnel experienced symptoms coping with PTSD. The higher rate was estimated among old age and participants with higher experience years. Avoiding memories and sleep disturbance were the most reported symptoms.

Expert system for depression detection in teenagers

Depression (major depressive disorder) is a common and serious medical illness that negatively affects how you feel, think, and act. Fortunately, it is treatable. Depression causes sadness and/or a loss of interest in activities once enjoyed. It can lead to various emotional and physical problems and decrease a person’s ability to function at work and home. Teenagers often experience this disorder because their emotions are still unstable. They also don’t have many chances to consult with a psychologist and doctor. Through this research, an expert system was created that implements knowledge from experts related to depression so that teenagers can do a self-test when needed. The expert system was developed using the Certainty Factor method for the knowledge inference engine. The system was tested iteratively and could achieve similar results with a domain expert. We hope that the system can detect early symptoms of depression among teenagers and minimize the negative impact it may cause.

Role of TNF and Hs CRP as novel biomarkers in etiopathogenesis of depression: A case-control study

Depression is a psychiatric disorder characterized by depressed mood, loss of interest in activities and loss of energy that lasts for two weeks or more. Depression affects individual emotional and psychological well-being with low self-esteem and ultimately resulted in social isolation. Nearly half of all cases of depression remain undetected for years or inadequately controlled. TNF-α has a vital role in inflammation, cell proliferation, differentiation, apoptosis, innate and adaptive immunity and have pathological role in several diseases. Early assessment of TNF-α and hs-CRP levels in depression patients could modify the disease progression and limit co-morbidities associated with it. Therefore, we assessed the level of TNF-α and hs-CRP in our study and correlated it with the disease severity.: to study is to estimate TNF-α and hs-CRP levels in depressed patients and evaluate the relationship between the serum level of TNF- α and hs-CRP in the etiopathogenesis of depression. This study was conducted at a tertiary level post graduate teaching institute of central India. 50 newly diagnosed drug naive cases of depression of age group 18-50 years, attending Psychiatry OPD. And 50 apparently healthy controls matched for same age and sex were taken. The subjects were enrolled for the study after taking ethics committee approval and obtaining written consent. The data collected was expressed as mean ± SD and analysed by using SPSS version 20. P-value of &amp;#60; 0.05 was accepted as statistically significant. The mean value of serum TNF-α among control group (11.209 ± 6.058 Pg/ml) which was lower as compared to case group (33.816 ± 21.581 Pg/ml). The difference among the two groups was found to be statistically significant as p value is less than 0.001. The mean Serum hs-CRP concentration was found to be higher in case group (7.524 ± 1.842 mg/l) as compare to control group (3.720 ± 1.238 mg/l). The difference among the two groups was found to be statistically significant as p value is &amp;#60;0.05. The difference among the three groups was found to be statistically significant as p value is less than 0.001. We observed a strong association between depression and increased serum TNF-α, with raised hs-CRP. There are several researches potentiating the vital role of TNF- α and hs-CRPin depression. Large scale prospective studies are needed to establish the cause effect relationship and role of inflammatory cytokines in depression and to find newer treatment modalities like TNF-α blockers that might be helpful for consideration of new therapeutic approach Further studies are essential to detect the association. Nevertheless we found that depression might be responsible for elevated TNF-α and hs-CRP levels. We suggest screening of all the patients of depression for serum TNF-α estimation at initial OPD visit.

The Mindfulness Meditation Effect on Anxiety in nursing Students level II of nursing study Program STIKes Santa Elisabeth Medan 2022

&#x0D; &#x0D; &#x0D; &#x0D; Mindfulness meditation is an intervention with mindfulness and mind exercises that aims to relax the mind, increase concentration, and find joy and joy. Mindfulness meditation can reduce anxiety. Anxiety is a psychological and physiological state that is disturbed, due to excessive feelings of a stressor that appears, so that it can cause cognitive, somatic, emotional and behavioral discomfort accompanied by symptoms such as feelings of sadness, emptiness, hopelessness, guilt, worthlessness. irritability, restlessness, loss of interest in activities even though they are pleasurable. This study aims to identify the Mindfulness Meditation Effect on Anxiety in nursing Students level II of nursing study Program STIKes Santa Elisabeth Medan 2022. This research method uses a one group pretest-posttest design. The sampling technique was purposive sampling with a total of 47 respondents. The results of the statistical test showed that there was an effect of mindfulness meditation on anxiety in sophomore students at the Santa Elisabeth Medical School Study Program, Medan in 2022 with the results of the Wilcoxon sign rank test, obtained p value = 0.001. Mindfulness meditation as an alternative to dealing with anxiety in students can reduce students' anxiety levels thereby increasing concentration and relaxing the mind..Further researchers are advised to conduct research on mindfulness meditation with the duration and frequency of the intervention extended by 2-3 cycles to make it more optimal.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

Ginsenoside Rh2 administration produces crucial antidepressant-like effects in a CUMS-induced mice model of depression.

IntroductionThe most striking feature of depression is sadness and a loss of interest in activities, which represents a major cause of disability globally. Therefore, it is necessary to identify novel antidepressants for clinical practice. Ginsenoside Rh2 (Rh2) is one of the major bioactive ginsenosides that can be extracted from Panax ginseng and has been demonstrated to improve both memory and learning. The purpose of this study was to provide broad insight into the mechanisms underlying depression and gain greater insights into antidepressant therapy.MethodsIn this study, we first established an effective and feasible depression animal model of chronic unpredictable mild stress (CUMS) and behavioral testing was evaluated by the forced swim test (FST), the tail suspension test (TST) and the sucrose preference test. Following pretreatment with Rh2 (10 and 20 mg/kg), the immobility time of mice was reduced without affecting locomotor activity in both the FST and TST. Western blotting and immunofluorescence were used to investigate the activation of the hippocampal BDNF signaling pathway and hippocampal neurogenesis.ResultsDifferent concentrations of Rh2 significantly reduced depressive‐like symptoms in CUMS‐induced mice and downregulated the effects of the BDNF signaling cascade and neurogenesis in the hippocampus. Furthermore, the administration of K252a completely prevented the antidepressant‐like activity of Rh2 in mice.ConclusionThe results indicated that Rh2 possesses the antidepression action via the positive regulation of the BDNF‐TrkB pathway.

A novel approach to investigate depression symptoms in the aging population using generalizability theory.

As depression is common in older people and confers significant risk for dementia, its accurate assessment is essential. The 15-item Geriatric Depression Scale (GDS-15) is a widely used assessment tool for measuring depression in aged populations, and its psychometric properties have been recently improved using Rasch analysis. However, its temporal reliability and ability to distinguish between dynamic and enduring symptoms of depression have not been examined using the appropriate methodology. Generalizability theory (G theory) is a suitable method to distinguish between enduring and dynamic symptoms of depression and to evaluate the reliability of the GDS-15 scores and sources of measurement error. We applied G theory to the longitudinal GDS-15 data of 354 participants from the Sydney Memory and Ageing Study, collected biennially over 10 years, from individuals aged 70 years and older. The GDS-15 demonstrated strong reliability and generalizability of its test scores in measuring enduring symptoms of depression (Ga = 0.82, Gr = 0.90) across the sample population and occasions, and indicated that depression scores significantly increased over time. In addition, three identified dynamic symptoms of depression, namely helplessness, hopelessness, and loss of interest in activities, did not affect the overall reliability of the GDS-15. Thus, the GDS-15 is a reliable measure for assessing enduring symptoms of depression and can be used to evaluate the efficacy of depression treatments and monitor depression levels over time in older adults. (PsycInfo Database Record (c) 2022 APA, all rights reserved).