UNASSIGNED: This study aimed to outline the picture of the sexual functions of male patients with affective disorders as an important part of their lives.
UNASSIGNED: The sample consisted of 57 male patients diagnosed with mood disorders in remission. They were interviewed for demographic and clinical data, asked to fill in number of self-descriptive questionnaires\' Sexual Function of Man (SFM/K), the Montgomery-Åsberg Depression Scale (MADRS) and Young Mania Scale (YMRS), and the Alcohol Use Disorders Identification Test (AUDIT).
UNASSIGNED: Lower levels of sexual functioning were experienced by patients who had suffered from affective disorder for a longer time, and who had a diagnosis of recurrent depressive disorder (F33), in comparison with patients with bipolar disorder (F31). The most common sexual dysfunction was premature ejaculation, while the rarest was erectile dysfunction. An occurrence of any sexual disorder at least once in the past was reported by 66% of all patients. Participants did not have problems with alcohol usage.
UNASSIGNED: A worse quality of sexual functioning was associated with a longer history of affective disorder. Sexual dysfunction can be affected by even the most minor depressive and manic-depressive components. The tools used excluded non-heterosexual patients. Further research based on bigger samples is required.

OBJECTIVE: This study aimed to create and validate robust machine-learning-based prediction models for antipsychotic drug (risperidone) continuation in children and teenagers suffering from mania over one year and to discover potential variables for clinical treatment.
METHODS: The study population was collected from the national claims database in China. A total of 4,532 patients aged 4-18 who began risperidone therapy for mania between September 2013 and October 2019 were identified. The data were randomly divided into two datasets: training (80%) and testing (20%). Five regularly used machine learning methods were employed, in addition to the SuperLearner (SL) algorithm, to develop prediction models for the continuation of atypical antipsychotic therapy. The area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI) was utilized.
RESULTS: In terms of discrimination and robustness in predicting risperidone treatment continuation, the generalized linear model (GLM) performed the best (AUC: 0.823, 95% CI: 0.792-0.854, intercept near 0, slope close to 1.0). The SL model (AUC: 0.823, 95% CI: 0.791-0.853, intercept near 0, slope close to 1.0) also exhibited significant performance. Furthermore, the present findings emphasize the significance of several unique clinical and socioeconomic variables, such as the frequency of emergency room visits for nonmental health disorders.
CONCLUSIONS: The GLM and SL models provided accurate predictions regarding risperidone treatment continuation in children and adolescents with episodes of mania and hypomania. Consequently, applying prediction models in atypical antipsychotic medicine may aid in evidence-based decision-making.

BACKGROUND: Delirious mania (DM) is a severe psychiatric condition having rapid onset of delirium, mania, and psychosis. It is an emergency condition as it has acute onset and is characterized by extreme hyperactivity. Catatonic signs may also be present. Very few cases have been reported from India, hence making it imperative to study its clinical characteristics and possible treatment, which can help in providing care to such patients in emergency settings.
UNASSIGNED: This paper describes four cases with a diagnosis of DM - demography, clinical features, investigations, treatment. All the patients had an acute onset and rapid progression of symptoms, with clinical symptoms of talkativeness, increased psychomotor activity, decreased need for sleep, aggressive and violent behavior, increased libido, increased appetite with delusion of grandiosity, disorientation to time/place/person, impaired memory of recent events, impaired attention with fluctuating course, negativism, echolalia, and echopraxia.
CONCLUSIONS: There is a high likelihood of misdiagnosing DM in the absence of diagnostic guidelines. There should be an active search for the underlying aetiology in all cases of DM. Atypical antipsychotics and mood stabilizers may be used to treat less severe forms of DM. Modified electric convulsive treatment and intravenous benzodiazepines elicit a good response.
UNASSIGNED: Делириозная мания (ДМ) — это тяжелое психическое нарушение, характеризующееся быстрым возникновением и сочетанием делирия, мании и психоза, также возможны симптомы кататонии. Такое состояние является неотложным ввиду характерных для него острого начала и крайнего возбуждения. В Индии зарегистрировано очень мало случаев этого расстройства, поэтому важно изучать его клинические характеристики и приемлемые методы лечения, чтобы иметь возможность обеспечить таким пациентам адекватную неотложную помощь.
UNASSIGNED: В статье описано 4 случая пациентов с диагнозом ДМ — их демографические характеристики, клинические особенности, лабораторные и инструментальные данные, лечение. У всех пациентов отмечали острое начало и быстрое прогрессирование клинических симптомов. Частыми проявлениями были многоречивость, повышенная и психомоторная активность, сниженная потребность во сне, агрессивное и буйное поведение, усиление либидо, повышенный аппетит, бред величия, дезориентация во времени/пространстве/личности, нарушение памяти на недавние события, нарушение внимания по типу неустойчивости, негативизм, эхолалия, эхопраксия.
UNASSIGNED: В связи с отсутствием специфических диагностических рекомендаций для делириозной мании высока вероятность допущения диагностической ошибки. Во всех подобных случаях необходимо активно искать этиологические факторы, лежащие в основе ДМ. Для лечения менее тяжёлых форм ДМ могут применяться атипичные антипсихотические препараты и нормотимики. Хороший терапевтический эффект дают модифицированная электросудорожная терапия и внутривенное введение бензодиазепинов.

OBJECTIVE: The objective of this study is to explore and enhance the diagnostic process of unipolar and bipolar disorders. The primary focus is on leveraging automated processes to improve the accuracy and accessibility of diagnosis. The study aims to introduce an audio corpus collected from patients diagnosed with these disorders, annotated using the Clinical Global Impressions Scale (CGI) by psychiatrists.
METHODS: Traditional diagnostic methods rely on the clinician\'s expertise and consideration of co-existing mental disorders. However, this study proposes the implementation of automated processes in the diagnosis, providing quantitative measures and enabling prolonged observation of patients. The paper introduces a speech signal pipeline for CGI state classification, with a specific focus on selecting the most discriminative features. Acoustic features such as prosodies, MFCC, and LPC coefficients are examined in the study. The classification process utilizes common machine learning methods.
RESULTS: The results of the study indicate promising outcomes for the automated diagnosis of bipolar and unipolar disorders using the proposed speech signal pipeline. The audio corpus annotated with CGI by psychiatrists achieved a classification accuracy of 95% for the two-class classification. For the four- and seven-class classifications, the results were 77.3% and 73%, respectively, demonstrating the potential of the developed method in distinguishing different states of the disorders.

Objective: Manic and mixed episodes of bipolar disorder are important episodes of this disorder. The aim of the current study was to assess serum vitamin D (SVD) levels in patients with mania and mixed bipolar disorder, compared to healthy subjects. Method : The current cross-sectional study was conducted on 75 subjects, including healthy subjects (n = 25), patients with acute-phase mania (n = 25), and patients with mixed bipolar disorder (n = 25). The SVD levels were measured in all of the enrolled subjects. The Hamilton Depression Rating Scale (HDRS), Young Mania Rating Scale (YMRS), and Clinical Global Impression- Severity (CGI-S) were used to assess disease activity in patient groups. Data analysis was performed using SPSS version 18. For statistical analysis, analysis of variance (ANOVA), independent-sample t test, Pearson correlation, and Chi-square tests were utilized. P-values < 0.05 were considered statistically significant. Results: The results showed that the mean of SVD was significantly lower in mania and mixed bipolar patients compared to healthy subjects (P < 0.05). In addition, the number of subjects with SVD ≥ 20 ng/ml was higher in the healthy group compared to the patient groups (P < 0.05). Also, SVD was negatively correlated with the CGI-S (r = -0.311; P = 0.028), YMRS (r = -0.464; P = 0.001), and HDRS (r = -0.393; P = 0.005) in the total patient subjects. Conclusion: Prevalence of low SVD was considerably high in mania and mixed bipolar patients compared to healthy subjects. Additionally, meaningful negative correlations were found between SVD and disease activity-related variables including the HDRS, YMRS, and CGI-S.

BACKGROUND: Bipolar disorder is a mental illness that is chronic and has frequent relapses.
OBJECTIVE: The purpose of the research was to study the nursing care of patients with bipolar disorder in the mania phase.
METHODS: A qualitative study was employed in this study. The sample consisted of 10 nurses working in psychiatric clinics and data were collected through semi-structured interviews. Thematic analysis was applied for analysing the data.
RESULTS: Of the 10 participants, 70% were female and 30% were male. The mean age was 48.7 years. All participants were registered nurses and most of them held a Master of Science degree. Their work experience ranged from 10 to 30 years. Three main themes emerged when analysing the data obtained from the interviews with the nurses, those themes were a) Echoes of Vigilance: Navigating the journey, b) Amidst the Tempest: Attending to the Patients\' Complex Needs, and c) Restoring Balance: The Nurturing Hands of Bipolar Nursing Care, each of which could be divided into several sub-themes.
CONCLUSIONS:  Nursing care plays an important role in symptom improvement and disease control by providing patient support, managing pharmacotherapy, preventing suicidality, and educating patients about the disease and self-management strategies.

UNASSIGNED: Depressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA).
UNASSIGNED: Our results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%.
UNASSIGNED: These findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment.

UNASSIGNED: Deep brain stimulation (DBS) can be an effective therapy to control motor signs in patients with Parkinson\'s disease (PD). However, subthalamic nucleus (STN) DBS can induce undesirable psychiatric adverse effects, including elevated mood.
UNASSIGNED: We reported a video case of a 73-year-old male implanted with bilateral STN DBS who experienced stimulation-induced elevated mood. A correlation between mood changes and enhanced activation of the ventromedial region in the left STN was observed.
UNASSIGNED: This video case report illustrates STN DBS-induced elevated mood and enhances early symptom recognition for patients and diagnostic awareness for professionals.

Disturbed sleep comes in many forms. While the key role of sleep in mental health is undisputed, our understanding of the type of sleeping problems that manifest in the early stages of psychiatric disorders is limited. A sample without psychiatric diagnoses (N = 440, 341 women, 97 men, 2 non-binaries; Mage = 32.1, SD = 9.4, range 18-77) underwent a comprehensive assessment, evaluating eight sleep features and 13 questionnaires on common psychiatric complaints. Results revealed that traits of affect disorders, generalized anxiety, and ADHD had the worst sleep profiles, while autism disorder, eating disorder, and impulsivity traits showed milder sleep issues. Mania was the only trait associated with an overall better sleep profile. Across traits, insomnia and fatigue dominated and sleep variability was least prominent. These findings provide support for both transdiagnostic and disorder-specific targets for prevention and treatment.

UNASSIGNED: Given the importance of medication adherence among individuals with bipolar disorder (BD), this analysis from an ongoing randomized controlled trial (RCT) examined the relationship between BD symptoms, functioning and adherence in 69 poorly adherent adults with BD.
UNASSIGNED: Study inclusion criteria included being ≥ 18 years old with BD Type 1 or 2, difficulties with medication adherence and actively symptomatic as measured by Brief Psychiatric Rating Scale (BPRS) score ≥ 36, Young Mania Rating Scale (YMRS) > 8 or Montgomery Asberg Depression Rating Scale (MADRS) > 8. Adherence was measured in 2 ways: 1) the self-reported Tablets Routine Questionnaire (TRQ) and 2) electronic pill container monitoring (eCap pillbox). BD symptoms and functioning were measured with the MADRS, YMRS, Clinical Global Impressions Scale (CGI), and Global Assessment of Functioning (GAF). Only screening and baseline data were examined.
UNASSIGNED: Mean age was 42.32 (SD = 12.99) years, with 72.46% (n = 50) female and 43.48% (n = 30) non-white. Mean past 7-day percentage of days with missed BD medications using TRQ was 40.63% (SD = 32.61) and 30.30% (SD = 30.41) at screening and baseline, respectively. Baseline adherence using eCap was 42.16% (SD = 35.85) in those with available eCap data (n = 41). Worse adherence based on TRQ was significantly associated with higher MADRS (p = 0.04) and CGI (p = .03) but lower GAF (p = 0.02). eCAP measured adherence was not significantly associated with clinical variables.
UNASSIGNED: While depression and functioning were approximate markers of adherence, reliance on patient self-report or BD symptom presentation may give an incomplete picture of medication-taking behaviors.