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Background and Objectives: Effective diagnostics of depression state using instrumental methods of biopotentials measurement is promising both as a tool for increasing the efficiency of work of a diagnostician and for application in automated hardware and software therapy complexes, including those with biofeedback, allowing to create neuroadaptive systems for correction of psychoemotional and cognitive problems of patients. The purpose of this paper is to analyze the requirements to the methodology of biophysical data collection, hardware and software for their primary processing on the basis of open datasets of psychoemotional state determination, to formulate a methodology for the formation of multimodal datasets for the study of mental states, their changes, suitable for use in machine learning algorithms, to describe a possible method of realization of these requirements in hardware and software complexes. Materials and Methods: Open datasets of depressed patients were selected to analyze the main characteristics of datasets characterizing mental states. To formulate the main requirements, publications on the features of multimodal data application for the diagnosis of depressive disorders were reviewed. Results: The result of the work are the requirements to multimodal biopotential data for the study of psychoemotional states, the methodology and functional concept of hardware-software complex for their registration, the synchronization and recording in annotated form. Conclusion: The example of depressive disorder shows the usefulness and possibility of recording multimodal, synchronized annotated data on the psycho-emotional state of the subject for research, diagnostic purposes and application as a training sample in machine learning algorithms. The proposed methodology and the concept of the hardware-software complex allow to level out the main disadvantages of multimodal systems of biopotentials registration realized in the form of separate blocks and to supplement the instrumental data with annotation by state labels and time labels.

The purpose of this paper is to comparatively analyse the efficiency of using artificial neural networks with different convolutional and recurrent architectures in the task of depression diagnosis based on electroencephalogram (EEG) data. Open datasets were chosen as objects of the study and own EEG data of real patients with depression were collected. Methods. To solve the problem of identifying biomarkers of depressive disorder from EEG data, we used convolutional neural networks using two-dimensional or one-dimensional convolution operation, as well as hybrid models of convolutional and recurrent neural networks. To test the developed models of artificial neural networks, we selected open data sets, performed an experiment to collect our own data from real depressed patients, and merged the prepared data sets. The result of this work is analysis and comparison of the performance of different classifiers based on convolutional and recurrent neural network models. Conclusion. We show that the average accuracy of classification of depressive disorder in a test sample using cross-validation was 0.68. The results are consistent with the known results from the literature for small patient-disaggregated datasets. Although the accuracy obtained in this task is insufficient for practical application of the model, it can be argued that further research to improve the efficiency of the model is promising, as well as the need to create a sufficiently large representative dataset of depressed patients, which is an important scientific task for further construction of biophysical models of depressive disorders.

This study introduces the Divergent Selective Focused Multi-heads Self-Attention Network (DSFMANet), an innovative deep learning model devised to automatically predict Hamilton Depression Rating Scale-17 (HAMD-17) scores in patients with depression. This model introduces a multi-branch structure for sub-bands and artificially configures attention focus factors on various branches, resulting in distinct attention distributions for different sub-bands. Experimental results demonstrate that when DSFMANet processes sub-band data, its performance surpasses current benchmarks in key metrics such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). This success is particularly evident in terms of MSE and MAE, where the performance of sub-band data is significantly superior, highlighting the model's potential in accurately predicting HAMD-17 scores. Concurrently, the experiment also compared the model's performance with sub-band and full-band data, affirming the superiority of the selective focus attention mechanism in electroencephalography (EEG) signal processing. DSFMANet, when utilizing sub-band data, exhibits higher data processing efficiency and reduced model complexity. The findings of this study underscore the significance of employing deep learning models based on sub-band analysis in depression diagnosis. The DSFMANet model not only effectively enhances the accuracy of depression diagnosis but also offers valuable research directions for similar applications in the future. This deep learning-based automated approach can effectively ascertain the HAMD-17 scores of patients with depression, thus offering more accurate and reliable support for clinical decision-making.

Depression is a complex mental health disorder that presents significant challenges in diagnosis and treatment. This study proposes an innovative approach, leveraging artificial intelligence advancements, to enhance multimodal depression diagnosis. The diagnosis of depression often relies on subjective assessments and clinical interviews, leading to potential biases and inaccuracies. Additionally, integrating diverse data modalities, such as textual, imaging, and audio information, poses technical challenges due to data heterogeneity and high dimensionality. To address these challenges, this paper proposes the RLKT-MDD (Representation Learning and Knowledge Transfer for Multimodal Depression Diagnosis) model framework. Representation learning enables the model to autonomously discover meaningful patterns and features from diverse data sources, surpassing traditional feature engineering methods. Knowledge transfer facilitates the effective transfer of knowledge from related domains, improving the model's performance in depression diagnosis. Furthermore, we analyzed the interpretability of the representation learning process, enhancing the transparency and trustworthiness of the diagnostic process. We extensively experimented with the DAIC-WOZ dataset, a diverse collection of multimodal data from clinical settings, to evaluate our proposed approach. The results demonstrate promising outcomes, indicating significant improvements over conventional diagnostic methods. Our study provides valuable insights into cutting-edge techniques for depression diagnosis, enabling more effective and personalized mental health interventions.

Abstract Objectives Participants Methods Results Conclusion Explore associations between eating disorders (EDs) and sleep duration and the impact of depression symptoms (DSs) in American college students.College students from 2018 to 2019 Healthy Minds Study.Multivariate logistic regression evaluated associations between EDs (bulimia, anorexia, binge eating [BED]) and sleep duration. Stratified analyses assessed effect modification by DSs and gender.Students reporting any eating disorder had no greater risk of not recommended sleep duration during weekdays compared to those with no eating disorder history (Adjusted OR = 0.99, 95% CI: 0.89-1.13). However, subjects reporting BED were at greater risk of deficient sleep during weekends compared to those with no EDs (Adjusted OR = 1.26, 95% CI: 1.01-1.58). This association was not impacted by DSs. However, all EDs were associated with not-recommended sleep duration among those who identified as other gender identity in comparison to those with no EDs.Health educators should monitor academic performance in students with EDs for adequate sleep duration. [ABSTRACT FROM AUTHOR]

Estimates of the prevalence of depression in people with intellectual disability range from almost nonexistent to 39%. We analyzed the outcomes of three screening instruments to find out more about the prevalence in people with mild or borderline disability. 102 Dutch individuals residing in institutions or living independently took part. The Beck Depression Inventory, the Glasgow Depression Scale and the Signaallijst Depressie voor Zwakzinnigen were used. Using the standard cutoff values, the numbers of people identified as depressed by the tests were 31%, 44%, and 22%, respectively. These were high numbers, and they did not refer to the same cases. When scoring above cutoff on all three tests simultaneously was the criterion, 13.7% of the participants were identified as depressed. The combined use of several measuring instruments taught us that depression in people with intellectual disabilities might occur more frequently than often assumed: 13.7% might be the lower limit of its actual prevalence. People in this group still run the risk of being underdiagnosed. [ABSTRACT FROM AUTHOR]

Depression diagnosis is a challenging clinical task currently conducted mostly using subjective criteria. It is well known that depression alters the neural activity in the brain, so that the corresponding neurophysiological signature may be measured using non-invasive electroencephalography (EEG) signals. These, in turn, may be possible to decode using machine learning algorithms. Despite the extensive literature, the existing techniques rely on several channels and obtrusive systems. In this paper, and for the first time, the diagnostic power of each EEG channel for depression detection is analyzed using Neighborhood Component Analysis (NCA). Our results indicate that a mere two features collected from one EEG channel suffice for reliable diagnosis. To evaluate the performance of the proposed method, a dataset comprising seven minutes of EEG recording from 84 subjects is used. The data was divided into two separate sets, one for feature selection and one for diagnostic classification. We delineate brain regions that have the strongest discriminative power linked to depression diagnosis. Thus, we identified one electrode (i.e., AF4) located on the frontal lobe, which can be used to diagnose depression with high accuracy. After evaluation of a series of shallow machine learning methods, we achieved the classification accuracy of 80.8%, sensitivity of 60% and specificity of 99.7% with two features from one electrode. We also achieved the highest classification accuracy of 91.8%, the specificity of 93.5%, and sensitivity of 90% with two electrodes and three features. Our findings show that it is possible to significantly reduce the complexity of algorithms to diagnose depression with the motivation of use in highly accessible wearable devices. [ABSTRACT FROM AUTHOR]

Recent development trends in artificial intelligence applications have seen increasing interest in the design of automated systems for depression detection and diagnosis among the affective computing community. Particularly, active research has been conducted in depression diagnosis, based on multi-modal approaches in deep learning technology, which enable utilization of various information through fusion of varied data types. This study proposes a four-stream-based depression diagnosis model consisting of Bidirectional Long Short-Term Memory (Bi-LSTM) and convolutional neural networks (CNN), using speech and text data. One-dimensional features of audio signals are extracted using Mel Frequency Cepstral Coefficients and Gammatone Cepstral Coefficients, and two-dimensional features are extracted from Bark, equivalent rectangular bandwidth, and Log-Mel spectrograms, based on time-frequency transform. The extracted features are applied to Bi-LSTM and CNN-based transfer learning models. Word encoding was used for mapping of text to sequences with numeric indices, and word embedding used for representation of all words in numeric dense vectors. These were applied to Bi-LSTM and n-gram-based CNN models. Finally, an ensemble of the softmax values output from the four deep learning models was used to perform depression diagnosis, based on the proposed four-stream model. Using the proposed model, experiments were performed with the Extended Distress Analysis Interview Corpus Wizard of Oz depression database and other datasets. Experimental results showed improved performance by 10.7% to 11.9% over two-stream-based state-of-the-art methods. This demonstrates that the proposed model is effective for depression diagnosis.

Plain language summary We interviewed 12 midwives at pregnancy clinics in southern Sri Lanka about what happens if a pregnant woman gets depressed. Some midwives knew a lot about depression, while others did not know very much. They all had different ideas of what the signs of depression were, and what percentage of pregnant women are usually depressed. They have a rule book about how to be a midwife, but it is not clear about how they should find out if a pregnant woman has depression. However, all midwives agreed on what they should do if they meet a depressed woman during an antenatal appointment in their clinic, following the rule book closely. They said they need to tell their supervisor, who will tell a psychiatrist who can treat the depression. However, sometimes there are problems. Midwives said that they, their supervisor and the psychiatrist are all very busy and don’t have enough time to spend with pregnant women. Also, a lot of the women in their clinics don’t want to get treated because they feel embarrassed about having depression, and don’t want other people to know. Midwives told us they could do their jobs better if they could give a quick test for depression to every woman visiting their clinic. This would be an easy solution, because they already use a test like this for after women give birth.

This study introduces the Divergent Selective Focused Multi-heads Self-Attention Network (DSFMANet), an innovative deep learning model devised to automatically predict Hamilton Depression Rating Scale-17 (HAMD-17) scores in patients with depression. This model introduces a multi-branch structure for sub-bands and artificially configures attention focus factors on various branches, resulting in distinct attention distributions for different sub-bands. Experimental results demonstrate that when DSFMANet processes sub-band data, its performance surpasses current benchmarks in key metrics such as mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). This success is particularly evident in terms of MSE and MAE, where the performance of sub-band data is significantly superior, highlighting the model's potential in accurately predicting HAMD-17 scores. Concurrently, the experiment also compared the model's performance with sub-band and full-band data, affirming the superiority of the selective focus attention mechanism in electroencephalography (EEG) signal processing. DSFMANet, when utilizing sub-band data, exhibits higher data processing efficiency and reduced model complexity. The findings of this study underscore the significance of employing deep learning models based on sub-band analysis in depression diagnosis. The DSFMANet model not only effectively enhances the accuracy of depression diagnosis but also offers valuable research directions for similar applications in the future. This deep learning-based automated approach can effectively ascertain the HAMD-17 scores of patients with depression, thus offering more accurate and reliable support for clinical decision-making. ● New network model predicts depression severity from brain signals. ● Focused attention mechanism improves signal processing. ● Analyzing specific signal bands increases diagnostic accuracy. ● Achieves high reliability in mental health assessment. ● Model efficiently handles complex brain data. [ABSTRACT FROM AUTHOR]

Multichannel electroencephalogram (EEG) signals, which directly reflects the brain's inner workings state, is an powerful tool to diagnosis depression. For EEG-based depression diagnosis, appropriate features are important to get a good classification result. Considering the multichannel nature of the EEG signals, the extracted features are generally high-dimensional and contain many redundant features, which impairs the performance of the classifier. Therefore, it is necessary to perform dimensionality reduction. However, existing dimensionality reduction methods are not well suited for EEG data analysis. For this reason, a novel dimensionality reduction algorithm termed correntropy based sparse graph regularized nonnegative matrix factorization (RSGNMF) is proposed. Our RSGNMF solution finds the discriminative low-dimensional representations via joint optimization for better classification. Specifically, RSGNMF adopts correntropy replace the squared Euclidean distance (SED) into nonnegative matrix factorization (NMF) as similarity measure to increase the robustness for noise and outliers, and simultaneously integrating graph regularization and sparse constraints. In order to solve the optimization problem of RSGNMF, the half-quadratic technique was used and subsequently the multiplicative update rule was obtained. Furthermore, convergence and computational complexity of RSGNMF is analyzed. Experimental results reveal the effectiveness of RSGNMF in EEG depression diagnosis in comparison with other state-of-the-art NMF methods. It also shows the practical application value of our method in detecting depression. • A novel correntropy based sparse graph regularized NMF called RSGNMF is proposed. • RSGNMF uses correntropy to replace squared Euclidean distance as similarity measure. • RSGNMF integrates graph regularization to explore the geometric structure of data. • RSGNMF adaptively selects the most important features by sparsity constraints. • Experimental results reveal the effectiveness of RSGNMF in EEG depression diagnosis. [ABSTRACT FROM AUTHOR]

Background: This study investigated the mediating effects of coping strategies and psychological status on the relationship between illness perception and health-related quality of life (HRQoL) among patients with atrial fibrillation (AF)., Methods: This cross-sectional study enrolled 178 patients with AF who were admitted to a tertiary hospital in Beijing City in mainland from March 2020 and June 2022. Assessments were made for HRQoL using the Short Form Health Survey depressive symptoms using the Patient Health Questionnaire-9, anxiety using the Generalized Anxiety Disorder-7 (GAD-7), illness perception using the Brief Illness Perception Questionnaire (BIPQ), AF symptoms using the Atrial Fibrillation Severity Scale (AFSS), and coping strategies using the Brief-COPE Scale., Results: Significant correlations were observed between illness perception, emotional variables, coping strategies, and HRQoL scores. The regression analysis found that BIPQ, GAD, Maladaptive coping and Problem-focused coping are significant predictors of PCS (F = 20.906, R 2  = 0.326, p < 0.01) and MCS (F = 31.24, R 2  = 0.419, p < 0.01). Bootstrap samples were used to conduct mediation analysis. The indirect effects of GAD-7 and Problem-focused coping (PC) on the impact of BIPQ on QoL were significant. GAD accounted for 13.2-19.3% of the variance in the total effect across different models, while PC accounted for 22.1-25.8%. The results also indicated a significant chain effect in the illness percepitong-anxiety-coping style-QoL model, which can explain 4.3-10.2% of the total effect, respectively., Conclusions: The perception of illness significantly influenced HRQoL in patients with AF, as mediated by emotional symptoms and coping strategies. This highlights the importance of anxiety and problem-focused coping mechanisms. These findings underscore the need for a holistic, patient-centered approach to AF management that incorporates emotional well-being and coping strategies., Trial Registration: Retrospectively registered with ClinicalTrials.gov (NCT05974098). The date of registration: 1 August 2023., (© 2024. The Author(s).)

Background: Depressive disorders have substantial global implications, leading to various social consequences, including decreased occupational productivity and a high disability burden. Early detection and intervention for clinically significant depression have gained attention; however, the existing depression screening tools, such as the Center for Epidemiologic Studies Depression Scale, have limitations in objectivity and accuracy. Therefore, researchers are identifying objective indicators of depression, including image analysis, blood biomarkers, and ecological momentary assessments (EMAs). Among EMAs, user-generated text data, particularly from diary writing, have emerged as a clinically significant and analyzable source for detecting or diagnosing depression, leveraging advancements in large language models such as ChatGPT., Objective: We aimed to detect depression based on user-generated diary text through an emotional diary writing app using a large language model (LLM). We aimed to validate the value of the semistructured diary text data as an EMA data source., Methods: Participants were assessed for depression using the Patient Health Questionnaire and suicide risk was evaluated using the Beck Scale for Suicide Ideation before starting and after completing the 2-week diary writing period. The text data from the daily diaries were also used in the analysis. The performance of leading LLMs, such as ChatGPT with GPT-3.5 and GPT-4, was assessed with and without GPT-3.5 fine-tuning on the training data set. The model performance comparison involved the use of chain-of-thought and zero-shot prompting to analyze the text structure and content., Results: We used 428 diaries from 91 participants; GPT-3.5 fine-tuning demonstrated superior performance in depression detection, achieving an accuracy of 0.902 and a specificity of 0.955. However, the balanced accuracy was the highest (0.844) for GPT-3.5 without fine-tuning and prompt techniques; it displayed a recall of 0.929., Conclusions: Both GPT-3.5 and GPT-4.0 demonstrated relatively reasonable performance in recognizing the risk of depression based on diaries. Our findings highlight the potential clinical usefulness of user-generated text data for detecting depression. In addition to measurable indicators, such as step count and physical activity, future research should increasingly emphasize qualitative digital expression., (©Daun Shin, Hyoseung Kim, Seunghwan Lee, Younhee Cho, Whanbo Jung. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.09.2024.)

Background: Depression is common in spine surgery candidates and may influence postoperative outcomes. Ecological momentary assessments (EMAs) can overcome limitations of existing depression screening methods (e.g., recall bias, inaccuracy of historical diagnoses) by longitudinally monitoring depression symptoms in daily life. In this study, we compared EMA-based depression assessment with retrospective self-report (a 9-item Patient Health Questionnaire [PHQ-9]) and chart-based depression diagnosis in lumbar spine surgery candidates. We further examined the associations of each depression assessment method with surgical outcomes., Methods: Adult patients undergoing lumbar spine surgery (n = 122) completed EMAs quantifying depressive symptoms up to 5 times daily for 3 weeks preoperatively. Correlations (rank-biserial or Spearman) among EMA means, a chart-based depression history, and 1-time preoperative depression surveys (PHQ-9 and Psychache Scale) were analyzed. Confirmatory factor analysis was used to categorize PHQ-9 questions as somatic or non-somatic; subscores were compared with a propensity score-matched general population cohort. The associations of each screening modality with 6-month surgical outcomes (pain, disability, physical function, pain interference) were analyzed with multivariable regression., Results: The association between EMA Depression scores and a depression history was weak (r rb = 0.34 [95% confidence interval (CI), 0.14 to 0.52]). Moderate correlations with EMA-measured depression symptoms were observed for the PHQ-9 (r s = 0.51 [95% CI, 0.37 to 0.63]) and the Psychache Scale (r s = 0.68 [95% CI, 0.57 to 0.76]). Compared with the matched general population cohort, spine surgery candidates endorsed similar non-somatic symptoms but significantly greater somatic symptoms on the PHQ-9. EMA Depression scores had a stronger association with 6-month surgical outcomes than the other depression screening modalities did., Conclusions: A history of depression in the medical record is not a reliable indication of preoperative depression symptom severity. Cross-sectional depression assessments such as PHQ-9 have stronger associations with daily depression symptoms but may conflate somatic depression symptoms with spine-related disability. As an alternative to these methods, mobile health technology and EMAs provide an opportunity to collect real-time, longitudinal data on depression symptom severity, potentially improving prognostic accuracy., Level of Evidence: Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence., Competing Interests: Disclosure: This study was funded by grants from AO Spine North America, the Cervical Spine Research Society, the Scoliosis Research Society, the Foundation for Barnes-Jewish Hospital, the Washington University/BJC Healthcare Big Ideas Competition, and the National Institute of Mental Health (1F31MH124291-01A). The sponsors had no role in the study design, data collection, data interpretation, manuscript preparation, or decision to submit the manuscript for publication. The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article ( http://links.lww.com/JBJS/I120 )., (Copyright © 2024 by The Journal of Bone and Joint Surgery, Incorporated.)

Background: Depression is a prevalent and serious mental health disorder that significantly impacts daily life and functioning. Neurofilament Light chain (NfL), associated with axonal neuronal damage, has been identified as a promising biomarker, potentially aiding in early diagnosis of depression, personalized treatment, and tracking disease progression. This study used meta-analysis to evaluate the potential of plasma NfL as a biomarker for depression patients., Methods: A systematic search following the PRISMA guidelines was conducted across PubMed, Web of Science, Scopus, and Google Scholar databases to find relevant studies on plasma NfL levels in patients with depression. A random effects model meta-analysis was applied to determine its potential as a biomarker for differentiating patients from controls., Results: Our meta-analysis, based on four articles with six datasets, revealed that plasma NfL levels were notably higher in individuals with depression (228 cases) compared to healthy controls (118 individuals). The weighted mean difference (WMD) was 8.78 (95% CI: 5.28, 12.28; P < 0.01), indicating a significant effect size. Given the diverse confounding factors inherent in the included observational studies, the observed variability can be attributed to these influences. Due to the observed heterogeneity (heterogeneity Chi-Square: 54.91, p < 0.05), we performed a subgroup analysis. Subgroup analyses based on depression type and analysis method consistently supported the association between NfL and depression, strengthening the evidence., Conclusion: Our meta-analysis demonstrates that elevated NfL levels may serve as a promising biomarker for diagnosing depressive disorders. Further research on diverse subtypes and longitudinal changes is needed to validate its clinical utility., (© 2024. The Author(s).)

Background: The assessment of hopelessness plays a significant role in preventing various psychological disorders and major life events within the general population. However, the psychometric properties of the Beck Hopelessness Scale (BHS) have been a subject of controversy, primarily studied in clinical groups. The aim of the present study was to gain new psychometric insights and propose a new short version of the BHS for the general population using the Item Response Theory (IRT) approach., Methods: A total of 2164 Argentinean individuals completed the BHS alongside the Inventory of Suicide Orientation-30. We compared IRT models with two and three parameters for the original BHS version, exploring the removal of redundant and less informative items. Convergent and discriminant validity was also examined., Results: Our results support the 2PL model for the BHS-19. In addition, the BHS-10 short version adequately depicted the same range of the measured trait as the original version, showing reasonable measurement accuracy in the middle-high levels of the trait (marginal reliability = 0.70, Cronbach's α = 0.86). Notably, a positive correlation was found between the factorial score of BHS-10, BHS-19, and suicide orientation., Conclusions: In conclusion, our findings support the use of a simplified version as a practical and valuable tool for both research and clinical practice in the future., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Although anxiety and depression have been associated with adverse outcomes in chronic heart failure (HF), data on temporal evolution of these symptoms are scarce. We aimed to investigate the association between repeatedly measured depression and anxiety symptoms and clinical outcome in chronic HF patients., Methods: In this prospective observational study, outpatients with chronic HF were included and followed-up for a maximum of 2.5 years. The hospital anxiety and depression scale (HADS) questionnaire was conducted every six months. The primary endpoint was a composite of HF hospitalization, cardiovascular death, heart transplantation and left ventricular assist device (LVAD) implantation. Cox and joint models were used to investigate the association between the HADS score and the endpoint., Results: A total of 362 patients filled out a median (25th-75th percentile) of 3 [2-4] questionnaires each. Mean ± SD age was 63 ± 13 years, 72% were men. Anxiety scores remained relatively stable leading up to the endpoint, while depression scores increased. Higher baseline depression scores were significantly associated with the endpoint (hazard ratio [HR] 1.68 and 95% confidence interval [CI] 1.19-2.36 per log(score+1), p = 0.003), while higher baseline anxiety scores did not reach statistical significance (HR [95% CI] 1.34 [0.99-1.83], p = 0.061). When repeatedly measured, both higher anxiety (HR [95% CI] 1.57[1.07-2.30], p = 0.022) and depression (HR [95% CI] 2.04 [1.39-3.06], p < 0.001) scores were significantly associated with the endpoint., Conclusion: Serial measurements of depression and anxiety symptoms identify chronic HF patients with increased risk of adverse clinical outcomes. Screening for both disorders should be considered in clinical practice., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Depressive symptoms (DS) have become a global public health problem. However, a risk prediction model for DS in the elderly population has not been established. The purpose of this study was to develop and validate a predictive nomogram to screen for DS in the elderly population., Methods: A cross-sectional data of 3396 participants aged 60 and over were obtained from the China Health and Retirement Longitudinal Study 2018 (CHARLS). Participants were divided into the development and validation set. Predictive factors were selected through a single-factor analysis, and then a predictive model nomogram was established. The discrimination, calibration, and clinical validity were evaluated using the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow tests, and decision curve analyses (DCA)., Results: A total of 2379 and 1017 participants were included in the development and validation set, respectively. The analysis found that gender, residence, dyslipidemia, self-rated health, and ADL disability were risk factors for DS in older adults, and were included in the final model. This nomogram showed an acceptable predictive performance as evaluated by the area under the ROC curve with values of 0.684 (95 % confidence interval (CI): 0.663-0.706) and 0.687 (95 % CI: 0.655-0.719) in the development and validation set, respectively. The calibration curve indicated that the model was accurate, and DCA demonstrated a good clinical application value., Conclusion: Five factors were selected to establish a nomogram for predicting DS in older adults. The nomogram has a good evaluation performance and can be used as a reliable tool to predict DS among older adults., Competing Interests: Declaration of competing interest All authors declare that there are no conflicts of interest in this study., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Many late adolescents experience a state of psychological sub-health, requiring early recognition and intervention. This study aims to assess the psychological state of late Chinese adolescents and uncover developmental trend of mental health through network analysis., Method: We analyzed data from 9072 Chinese high school adolescents in Shandong Province surveyed in 2020-2021, and divided them into the normal, the suspected, and the abnormal groups based on Symptom Checklist 90 (SCL-90) scores. Network analysis was employed to identify the core symptoms and bridge symptoms across different states., Results: Anxiety and depression were the most central symptoms, without gender differences. Core symptoms, network structure, and network invulnerability varied across different psychological states. The abnormal group exhibited the highest value of natural connectivity, followed by the suspected and normal groups. This pattern extended to bridge networks. While not meeting diagnostic criteria, the suspected group demonstrated abnormalities in network edge invariance and global strength invariance., Limitations: The cross-sectional design cannot establish causality, and biases in self-report measurements cannot be ignored., Conclusion: Compared to traditional scale indicators, network structural characteristics may be a more sensitive assessment indicator., Competing Interests: Declaration of competing interest The authors have no conflicts of interest regarding this research study., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Administration mode of patient-reported outcome measures (PROMs) may influence responses. We assessed if Patient Health Questionnaire-9 (PHQ-9), Edinburgh Postnatal Depression Scale (EPDS) and Hospital Anxiety and Depression Scale - Depression subscale (HADS-D) item responses and scores were associated with administration mode. We compared (1) self-administration versus interview-administration; within self-administration (2) research or medical setting versus private; and (3) pen-and-paper versus electronic; and within interview-administration (4) in-person versus phone. We analysed individual participant data meta-analysis datasets with item-level data for the PHQ-9 (N = 34,529), EPDS (N = 16,813), and HADS-D (N = 16,768). We used multiple indicator multiple cause models to assess differential item functioning (DIF) by administration mode. We found statistically significant DIF for most items on all measures due to large samples, but influence on total scores was negligible. In 10 comparisons conducted across the PHQ-9, EPDS, and HADS-D, Pearson's correlations and intraclass correlation coefficients between latent depression symptom scores from models that did or did not account for DIF were between 0.995 and 1.000. Total PHQ-9, EPDS, and HADS-D scores did not differ materially across administration modes. Researcher and clinicians who evaluate depression symptoms with these questionnaires can select administration methods based on patient preferences, feasibility, or cost., Competing Interests: Declaration of competing interest All authors have completed the ICJME uniform disclosure form. Dr. Vigod declares that she receives royalties from UpToDate, outside the submitted work. Dr. Beck CT declares that she receives royalties for her Postpartum Depression Screening Scale published by Western Psychological Services. Dr. Bernstein declares that he has consulted to Abbvie Canada, Amgen Canada, Bristol Myers Squibb Canada, Eli Lilly, Ferring Canada, JAMP Pharmaceuticals, Janssen Canada, Pendopharm Canada, Pfizer Canada, Sandoz Canada, Takeda Canada. He has also received unrestricted educational grants from Abbvie Canada, Bristol Myers Squibb Canada, Eli Lilly, Ferring Canada, Organon Canada, Janssen Canada, Pfizer Canada, and Takeda Canada; as well as been on speaker's bureau of Abbvie Canada, Janssen Canada, Takeda Canada and Pfizer Canada, all outside the submitted work. Dr. Chan J CN is a steering committee member and/or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr. Chan LF declares personal fees and non-financial support from Otsuka, Lundbeck, and Johnson and Johnson; and non-financial support from Ortho-McNeil-Janssen, and Menarini, outside the submitted work. Dr. Hegerl declares that within the last three years, he was an advisory board member for Janssen and received a research grant from Medice, all outside the submitted work. Dr. Howard declares that she has received personal fees from NICE Scientific Advice, outside the submitted work. Dr. Inagaki declares that he has received personal fees from Meiji, Mochida, Takeda, Novartis, Yoshitomi, Pfizer, Eisai, Otsuka, MSD, Sumitomo Dainippon, Janssen, and Eli Lilly, all outside of the submitted work. Dr. Marrie declares that she is a co-investigator on a study funded in part by Biogen Idec and Roche (no funds to her), outside the submitted work. Dr. Pugh declares that she received salary support from Pfizer-Astella and Millennium, outside the submitted work. Dr. Sultan declares funding from Sanofi-Aventis Corporation, during conduct of the primary study. Dr. Sundström-Poromaa declares that she has served on advisory boards and acted as invited speaker at scientific meetings for MSD, Novo Nordisk, Bayer Health Care, and Lundbeck A/S. Dr. Lynne Wagner declares that she receives personal fees from Celgene, outside the submitted work. Dr. Yonkers declares that she receives royalties from UpTodate, outside the submitted work. All other authors declare no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; and no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: To examine the associations between self-reported screen time and symptoms of stress, anxiety, and depression in adolescents., Methods: A cross-sectional study was conducted with 982 adolescents aged between 12 and 15 years, enrolled in public schools in Jacarezinho (PR), Brazil. Screen time was assessed by the question "Considering a typical day, how much time do you spend watching TV, playing videogame, using computer or smartphone?" The DASS-21 questionnaire (short form) was used to assess symptoms of depression, anxiety, and stress. Crude and adjusted analyses (age, sex, and maternal level of education) between screen time and mental disorders symptoms were performed using general linear regression models, with Poisson distribution, with significance level at p<0.05., Results: Higher depressive symptoms were observed in adolescents who reported screen time of 4-6 hours/day (PR 1.35, 95%CI 1.13-1.61) and ≥6 hours/day (PR 1.88, 95%CI 1.62-2.19), compared with their pairs with <2 hours/day. The same was observed for anxiety symptoms with screen time of 4-6 hours/day (PR 1.23, 95%CI 1.04-1.46) and ≥6 hours/day (PR 1.50, 95%CI 1.28-1.77); and stress, with 4-6 hours/day (PR 1.25, 95%CI 1.08-1.44) and ≥6 hours/day (PR 1.49, 95%CI 1.30-1.71), also compared with their pairs with <2 hours/day., Conclusions: Screen time was positively associated with depressive, anxiety, and stress symptoms in adolescents. Special attention should be given to those who spend more than four hours a day in front of a screen.

Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learning to model and discover biomarkers of depression in UK Biobank data sets (depression n = 4,479, healthy control n = 19,821). CatBoost was employed for model construction, with Shapley Additive Explanations (SHAP) being utilized to interpret the resulting model. Model performance was corroborated through 5-fold cross-validation, and its diagnostic efficacy was evaluated based on the area under the receiver operating characteristic (AUC) curve. A total of 45 depression-related proteins were screened based on the top 20 important features output by the CatBoost model in six data sets. Of the nine diagnostic models for depression, the performance of the traditional risk factor model was improved after the addition of proteomic data, with the best model having an average AUC of 0.764 in the test sets. KEGG pathway analysis of 45 screened proteins showed that the most significant pathway involved was the cytokine-cytokine receptor interaction. It is feasible to explore diagnostic biomarkers of depression using data-driven machine learning methods and large-scale data sets, although the results require validation.

Background: Depression represents a pressing global public health concern, impacting the physical and mental well-being of hundreds of millions worldwide. Notwithstanding advances in clinical practice, an alarming number of individuals at risk for depression continue to face significant barriers to timely diagnosis and effective treatment, thereby exacerbating a burgeoning social health crisis., Objective: This study seeks to develop a novel online depression risk detection method using natural language processing technology to identify individuals at risk of depression on the Chinese social media platform Sina Weibo., Methods: First, we collected approximately 527,333 posts publicly shared over 1 year from 1600 individuals with depression and 1600 individuals without depression on the Sina Weibo platform. We then developed a hierarchical transformer network for learning user-level semantic representations, which consists of 3 primary components: a word-level encoder, a post-level encoder, and a semantic aggregation encoder. The word-level encoder learns semantic embeddings from individual posts, while the post-level encoder explores features in user post sequences. The semantic aggregation encoder aggregates post sequence semantics to generate a user-level semantic representation that can be classified as depressed or nondepressed. Next, a classifier is employed to predict the risk of depression. Finally, we conducted statistical and linguistic analyses of the post content from individuals with and without depression using the Chinese Linguistic Inquiry and Word Count., Results: We divided the original data set into training, validation, and test sets. The training set consisted of 1000 individuals with depression and 1000 individuals without depression. Similarly, each validation and test set comprised 600 users, with 300 individuals from both cohorts (depression and nondepression). Our method achieved an accuracy of 84.62%, precision of 84.43%, recall of 84.50%, and F1-score of 84.32% on the test set without employing sampling techniques. However, by applying our proposed retrieval-based sampling strategy, we observed significant improvements in performance: an accuracy of 95.46%, precision of 95.30%, recall of 95.70%, and F1-score of 95.43%. These outstanding results clearly demonstrate the effectiveness and superiority of our proposed depression risk detection model and retrieval-based sampling technique. This breakthrough provides new insights for large-scale depression detection through social media. Through language behavior analysis, we discovered that individuals with depression are more likely to use negation words (the value of "swear" is 0.001253). This may indicate the presence of negative emotions, rejection, doubt, disagreement, or aversion in individuals with depression. Additionally, our analysis revealed that individuals with depression tend to use negative emotional vocabulary in their expressions ("NegEmo": 0.022306; "Anx": 0.003829; "Anger": 0.004327; "Sad": 0.005740), which may reflect their internal negative emotions and psychological state. This frequent use of negative vocabulary could be a way for individuals with depression to express negative feelings toward life, themselves, or their surrounding environment., Conclusions: The research results indicate the feasibility and effectiveness of using deep learning methods to detect the risk of depression. These findings provide insights into the potential for large-scale, automated, and noninvasive prediction of depression among online social media users., (© Zhenwen Zhang, Jianghong Zhu, Zhihua Guo, Yu Zhang, Zepeng Li, Bin Hu. Originally published in JMIR Mental Health (https://mental.jmir.org).)

Background: Electroencephalography (EEG) is a promising tool for identifying the physiological biomarkers of fibromyalgia (FM). Evidence suggests differences in power band and density between individuals with FM and healthy controls. EEG changes appear to be associated with pain intensity; however, their relationship with the quality of pain has not been examined. We aimed to investigate whether abnormal EEG in the frontal and central points of the 10-20 EEG system in individuals with FM is associated with pain's sensory-discriminative and affective-motivational dimensions. The association between EEG and the two dimensions of emotional disorders (depression and anxiety) was also investigated., Methods: In this cross-sectional pilot study, pain experience (pain rating index [PRI]) and two dimensions of emotional disorders (depression and anxiety) were assessed using the McGill Pain Questionnaire (PRI-sensory and PRI-affective) and Hospital Anxiety and Depression Scale (HADS), respectively. In quantitative EEG analysis, the relative spectral power of each frequency band (delta, theta, alpha, and beta) was identified in the frontal and central points during rest., Results: A negative correlation was found between the relative spectral power for the delta bands in the frontal (r= -0.656; p = 0.028) and central points (r= -0.624; p = 0.040) and the PRI-affective scores. A positive correlation was found between the alpha bands in the frontal (r = 0.642; p = 0.033) and central points (r = 0.642; p = 0.033) and the PRI-affective scores. A negative correlation between the delta bands in the central points and the anxiety subscale of the HADS (r = -0.648; p = 0.031) was detected., Conclusion: The affective-motivational dimension of pain and mood disorders may be related to abnormal patterns of electrical activity in patients with FM., Trial Registration: Retrospectively registered on ClinicalTrials.gov (NCT05962658)., (© 2024. The Author(s).)

Breast cancer impacts the psychological well-being of women, leaving them at risk of developing depression, anxiety, and other stress-related disorders. The Depression Anxiety Stress Scales (DASS-21) is a widely used measure, although empirical evidence regarding its psychometric properties in the breast cancer population is limited. The purpose of this study was to conduct an exhaustive analysis of the psychometric properties of the DASS-21 in a sample of Spanish women diagnosed with breast cancer. Participants were 289 breast cancer patients who completed the DASS-21 and other questionnaires measuring life satisfaction, positive and negative affect, flourishing, perceived stress, and breast cancer-specific stressors. In terms of validity evidence based on the internal structure of the DASS-21, adequate fit indices were obtained for the model based on three first-order factors (depression, anxiety, stress) and one second-order factor (general psychological distress). Reliability coefficients (McDonald's omega) ranged from .84 to .95. Validity evidence based on relationships with other variables was also provided by moderate and strong correlations with well-being indicators and stress measures. The results support the use of the DASS-21 for measuring general psychological distress in the breast cancer context, where it may provide useful information for the design of psychological interventions with patients., (© 2024. The Author(s).)

Objective: To determine the psychometric properties of an Aboriginal and Torres Strait Islander-developed depressive symptom screening scale., Design: Prospective diagnostic accuracy study., Setting: Ten primary health care services or residential alcohol and other drug rehabilitation services in Australia that predominantly serve Aboriginal and Torres Strait Islander peoples., Participants: 500 adults (18 years or older) who identified as Aboriginal and/or Torres Strait Islander and were able to communicate sufficiently to respond to questionnaire and interview questions. Recruitment occurred between 25 March 2015 and 2 November 2016., Main Outcome Measure: Criterion validity of seven Aboriginal and Torres Strait Islander-developed items, using the adapted Patient Health Questionnaire 9 (aPHQ-9) and depression module of the Mini International Neuropsychiatric Interview (MINI) 6.0.0 as the criterion standards., Results: The seven-item scale had good internal consistency (α = 0.83) and correlated highly with the aPHQ-9 (ρ = 0.76). All items were significantly associated with diagnosis of a current major depressive episode. Discriminant function and decision tree analysis identified three items forming a summed scale that classified 85% of participants correctly. These three items showed equivalent sensitivity and specificity to the aPHQ-9 when compared with the MINI-identified diagnosis of a current major depressive episode., Conclusion: Three items developed by and for Aboriginal and Torres Strait Islander people may provide effective, efficient and culturally appropriate screening for depression in Aboriginal and Torres Strait Islander health care contexts., (© 2024 The Author(s). Medical Journal of Australia published by John Wiley & Sons Australia, Ltd on behalf of AMPCo Pty Ltd.)

Background: Depression is a common concern for patients seeking medical care. The Patient Health Questionnaire-9 (PHQ-9) is a tool used to diagnose and manage depression. Tracking individual symptom scores rather than the sum of multiple symptom scores has been found to be more predictive of depression treatment response., Methods: The records of 30 patients who had a follow-up visit in primary care were reviewed. We discuss 3 patient scenarios and present their PHQ-9 data as individual symptom scores, in the form of a color-coded heat map., Results: In the cases presented, medication side effects, anxiety, a thyroid disorder, and fibromyalgia were identified as possible influencers of the PHQ-9 survey scores., Dsicussion: A heat map helped clinicians understand the patient's clinical status in an efficient manner. We encourage the development of a PHQ-9 heat map in electronic medical record systems., (Copyright© Board of Regents of the University of Wisconsin System and The Medical College of Wisconsin, Inc.)

Background: People living in care homes often have problems with pain, anxiety and depression. Whether being on analgesia, anxiolytics or antidepressants has any bearing on pain severity and quality of life (QoL) in this population, requires further investigation., Objectives: (i) to examine the relationship between pain, anxiety and depression and medication use in care home residents and (ii) to compare those on medications to treat pain, anxiety and depression, and those who were not, and associations with pain severity and overall QoL., Methods: This was a secondary analysis of a randomised controlled trial testing a falls prevention intervention in care homes. We recorded pain, anxiety and depression, QoL measurements and prescribed medication use., Results: In 1589 participants, the mean age was 84.7 years (±9.3 SD), 32.2% were male and 67.3% had a diagnosis of dementia. 54.3% and 53.2% of participants had some level of pain and anxiety or depression respectively, regardless of prescribed medication use. There was a direct association between pain severity and being on any analgesia, opioid analgesia, and antidepressants, but no associations between pain severity and use of paracetamol and anxiolytics. QoL was best for residents with no pain and not on any analgesia, anxiolytics or antidepressants and worst for those with moderate-extreme pain and taking at least two of these classes of medications., Conclusion: Many care home residents live with pain, anxiety and depression. Addressing residents' pain may also increase their quality of life, but using medication alone to reach this goal may be inadequate., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Geriatrics Society.)

Objectives: This research simulates an adaptive version of the IDAS-II (IDAS-CAT)., Methods: 2021 participants from both community (n = 1692) and patients (n = 329) samples completed the IDAS-II. Item response theory metric properties of the IDAS-II full test and the 20-items of the general depression (GD) scale were obtained. The efficiency and accuracy of different computerized adaptive algorithms were simulated. Different subsamples completed additional external measures in order to gather evidence of validity of the scores estimated with the simulated adaptive algorithms selected., Results: Both unidimensional computerized adaptive testing algorithm selected for the GD scale and the bifactor model chosen for the full test, allow 70% reduction in the length of administration, maintaining a measurement error below 0.30 on the general and 0.50 on the specific factors. Results show high correlations of the scores estimated with the adaptive algorithms and the estimates based on the full test, as well as correlations with external criteria almost equal to those generated with the full test., Conclusions: IDAS-CAT could be a reliable and fast tool for measuring internalizing spectrum., (© 2024 The Author(s). International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.)

Background: Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by dopaminergic neuron degeneration and diverse motor and nonmotor symptoms. Early diagnosis and intervention are crucial but challenging due to reliance on clinical presentation. Recent research suggests potential biomarkers for early detection, including plasma netrin-1 (NTN-1), a protein implicated in neuronal survival., Methods: This cross-sectional study recruited 105 PD patients and 65 healthy controls, assessing plasma NTN-1 levels and correlating them with clinical characteristics. Statistical analyses explored associations between NTN-1 levels and PD symptoms, considering demographic factors., Results: PD patients exhibited significantly lower plasma NTN-1 levels compared to controls. NTN-1 demonstrated moderate potential as a PD biomarker. Positive correlations were found between NTN-1 levels and motor, depression, and cognitive symptoms. Multiple regression analysis revealed disease duration and NTN-1 levels as key factors influencing symptom severity. Gender also impacted symptom scores., Conclusion: Reduced plasma NTN-1 levels correlate with PD severity, suggesting its potential as a biomarker. However, further research is needed to elucidate the roles of NTN-1 in PD pathophysiology and validate its diagnostic and therapeutic implications. Understanding the involvement of NTN-1 may lead to personalized management strategies for PD., (© 2024 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

Depression treatment strategies are within the scope of pediatric practice and among the competencies recommended by the Academy of Pediatrics and The American Board of Pediatrics. Treatments that may be provided through collaborative care include nonpharmacologic therapies such as psychosocial treatments and evidence-based psychotherapies, and pharmacotherapy and monitoring processes for depression. Abundant support and guidance are available to pediatricians in depression care, including mental health consultation and online materials., (© American Academy of Pediatrics, 2024. All rights reserved.)

Objective: Elexacaftor/tezacaftor/ivacaftor (E/T/I) has provided life-changing pharmacotherapy for many people with cystic fibrosis (CF), but conflicting literature exists regarding the effect on mental health. While some reports suggest E/T/I may induce adverse psychiatric symptoms, others report improvements in mental health symptoms. To add to this growing body of knowledge, we retrospectively analyzed depression and anxiety symptoms before and after E/T/I initiation in adults with CF at a single large US CF center., Method: Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) scores recorded in a database were studied. Patients with scores collected before and after E/T/I initiation were included. Regression analyses described associations between score changes and age, race, ethnicity, sex, CFTR variant, and prior depression and/or anxiety diagnoses. Secondary analyses examined possible confounding effects of the COVID-19 pandemic., Results: There was no change in mean GAD-7 (0.5 ± 5.3, p = 0.41) or PHQ-9 (-0.02 ± 6.0, p = 0.97) scores following initiation of E/T/I (N = 86). A trend between a prior diagnosis of depression and worsening in PHQ-9 post-E/T/I was observed (OR 3.58; p = 0.054)., Conclusions: Treatment with E/T/I does not lead to changes in depression or anxiety symptoms at the population level in this single center cohort study. A prior diagnosis of depression trended towards an increased odds of worsening PHQ-9 scores after E/T/I initiation., (© 2024 The Author(s). The Clinical Respiratory Journal published by John Wiley & Sons Ltd.)

By young adulthood, 1 in 5 teens will experience an episode of major depression. The second leading cause of death among youths aged 15 to 24 years is suicide, most of which will have been caused by untreated or undiagnosed depression. Depression is a highly heritable condition: depressed children often have depressed parents. Support to caregivers is important because depressed parents can have negative effects on children's development and future mental health. Groups more vulnerable to mental health disorders such as depression include Black, Indigenous, and persons of color and lesbian, gay, bisexual, transgender, or queer/questioning, who in recent years have the highest rate of suicide attempts (Black teens, sexual minority youth), the highest increases in suicide rates (Black children and youths), and the highest suicide rates (American Indian/Alaskan native). They frequently experience more adverse childhood events, which increases the risk of depression and suicide attempts. Pediatricians are most likely to care for these vulnerable youths, who often are less engaged in specialty mental health care for a variety of reasons, including stigma and barriers to access. By offering behavioral and mental health care to vulnerable populations in primary care, mental health equity may be achieved. Screening for depression and assessment for suicide are within the scope of pediatric practice and among the competencies recommended by the American Academy of Pediatrics and The American Board of Pediatrics., (© American Academy of Pediatrics, 2024. All rights reserved.)

Self-report questionnaires are commonly used in depression research with little consideration of their reading ease. This study aimed to increase the reading ease of the commonly-used Quick Inventory of Depressive Symptoms Self Report (QIDS-SR) and assess the impact of the change in wording on the measure's psychometric properties. The study had three phases: 1) Flesh-Kincaid readability statistics of the original and modified wording were compared; 2) a sample of n = 95 participants rated the modified wording for perceived change in meaning and ease of understanding; 3) a second sample of n = 136 participants completed two versions of the QIDS-SR (original, modified, or one of each) alongside the Beck Depression Inventory (BDI). The internal consistency, test-retest reliability and convergent validity of the modified version were assessed. The modified QIDS-SR had significantly higher reading ease, was considered easier to understand and was not perceived to have a significant change in meaning. Its psychometric properties were unaffected. The wording of the questionnaire was successfully simplified to increase its accessibility and this had no notable impact on the psychometric properties of the measure., Competing Interests: Declaration of competing interest The authors have no conflict of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: To investigate the association between pain and sleep quality, anxiety, depression, body perception, and biological rhythm in chronic scrotal pain (CSP)., Material and Methods: This cross-sectional study of 140 CSP patients was conducted between January and October 2023. The patients were separated into groups according to the Visual Analogue Scale (VAS) score as group 1 (score<3, n=27), group 2 (score: 3-6, n=84), and group 3 (score>6, n=29). In addition to demographic data, the points were recorded from the Body Image Scale (BIS), Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN), Hospital Anxiety and Depression Scale (HADS), and the Pittsburgh Sleep Quality Index (PSQI)., Results: The median (IQR) age of patients was 33 (20) years and the median (IQR) duration of pain was 5 (9) months. Group 3 patients had more anxiety (24.1% vs. group 1: 22.2%; group 2: 8.3%; P=0.03) and depression (55.2% vs. group 1: 40.7%; group 2: 29.8%; P=0.04) symptoms. No statistical differences were determined between the groups according to the BIS (P=0.54). The median (IQR) total BRIAN score was higher in group 3 than group 1 [37 (11) vs. 33 (13), P=0.03]. Higher rates of poor sleep quality were determined in group 3 patients according to the PSQI (75.9% vs. group 1: 48.1%; group 2: 51.2%; P=0.04). The VAS score was found to be positively associated with pain duration, total BRIAN score and PSQI score (r=0.31, P<0.001; r=0.17, P=0.04; r=0.23, P=0.006, respectively). The PSQI score was found to be an independent predictor for a higher VAS score (HR: 1.14, P=0.01)., Conclusion: The results of this study suggest that poor sleep quality may influence the perceived intensity of pain., Level of Evidence: II., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Short empirically-supported scales or individual items are preferred in comprehensive surveys, brief screeners, and experience sampling studies. To that end, we examined the Short Almost Perfect Scale (SAPS) to evaluate empirical support for the interchangeability of items to measure perfectionistic strivings (Standards) and perfectionistic concerns (Discrepancy). Based on a large and diverse sample ( N  = 1,103) and tests of tau-equivalence (equal factor loadings) for each respective set of items, Study 1 advanced a subset of SAPS items to measure Standards (2 items) and Discrepancy (3 items). Cross-sectional gender and race/ethnicity invariance were supported, and in structural equations analyses, the SAPS5 factors were significantly associated with depression, state anxiety, life satisfaction, and gratitude. Study 2 cross-validated Study 1 measurement and structural findings with a new U.S. sample ( N  = 803). The three items representing the Discrepancy (perfectionistic concerns) factor also were supported in a cross-national comparison between the U.S. sample and a scale development sample in New Zealand ( N  = 3,921). For the most part, across both studies and all analyses, the three Discrepancy items were empirically interchangeable indicators of perfectionistic concerns and comparably strong predictors of psychological outcomes, supporting their use in studies or other contexts with space or time restrictions for measurement.

Background: This study evaluated parenting stress, anxiety, and depression symptoms and their associated factors in parents of children with chronic kidney disease (CKD)., Methods: This cross-sectional study compared parents of patients with CKD (0-18 years) with a matched control group of parents of healthy children. Both groups completed the Parenting Stress Index - Short Form, the Hospital Anxiety and Depression Scale, and a sociodemographic questionnaire., Results: The study group consisted of 45 parents (median age 39; 32 mothers) of CKD patients (median age 8; 36% female). Nearly 75% of children had CKD stages 2, 3, or 4, and 44.5% had congenital anomaly of the kidney and urinary tract. Five children (11%) were on dialysis, and 4 (9%) had a functioning kidney graft. Compared with parents of healthy children, more stress and anxiety symptoms were reported. Since the CKD diagnosis, 47% of parents perceived a deterioration of their own health, and 40% reduced work on a structural basis. Higher levels of stress, anxiety, and depression symptoms were associated with a more negative perception of own health, and more child medical comorbidities and school absence., Conclusions: This study showed higher levels of parenting stress and anxiety symptoms in parents of children with CKD compared with parents of healthy children. This was associated with a less positive perception of their own health, especially if the child had more medical comorbidities or more absence from school. Psychosocial interventions to reduce the parental burden should be integrated in the standard care of pediatric nephrology departments., (© 2024. The Author(s), under exclusive licence to International Pediatric Nephrology Association.)

Measurement tools from which valid interpretations can be made are critical for assessing early emerging depressive symptoms, as depressive symptoms in childhood are associated with increased risk for early-onset depressive disorder, recurrence, suicidality, and other psychopathology. The Depression Self-Rating Scale for Children (DSRS) is a widely used self-report scale assessing youth depressive symptoms. The relatively few studies investigating the DSRS' latent structure have yielded mixed results, and measurement invariance (MI) based on sex and age has not been examined. We examined the factor structure and MI of the DSRS across sex and age in a community sample of 6-9-year-olds ( N = 352; M age = 7.57 years, SD = .70). Consistent with the largest prior structural study of the DSRS, a two-factor structure, with factors reflecting elevated negative affect (NA) and low positive affect (PA), showed strong model fit. Although this structure was consistent across sex and age (i.e., configural invariance), loadings of DSRS items varied across sex and age (i.e., metric noninvariance). Allowing the loadings of items contributing to noninvariance to vary across groups improved model fit. Implications for the clinical and research utility of the DSRS and suggestions for future research are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Patients with ITP have been reported to experience higher levels of depression and anxiety than their healthy counterparts. The limited research conducted on this subject in the pediatric age group has demonstrated that patients have psychosocial difficulties, and their quality of life is adversely affected. The correlation of depressive symptoms with disease characteristics of cITP has never been investigated. This was a cross-sectional study in patients being treated for cITP. Communication with participants was done during routine outpatient visits or by telephone or e-mail, and a survey about demographics and the Children's Depression Inventory (CDI) and the Social Anxiety Scale for Children-Revised (SAS-CR) was administered prospectively. A total of 56 children with cITP were recruited. The mean CDI score was 17 (SD: ± 9.44). Approximately half of the patients had higher CDI scores than healthy Turkish children. Older age, time since diagnosis, a number of hospitalizations (both total and within the last year) were positively correlated with CDI scores. There was no significant correlation between SAS-CR scores and disease characteristics. Depressive symptom scores were higher in children with cITP compared with healthy children in this study. Psychological needs may be overlooked in the medical management of children with cITP., (© 2024. Japanese Society of Hematology.)

Background: The study seeks to assess serum neurofilament light chain (NfL) levels in paediatric narcolepsy-diagnosed patients. Moreover, it aims to explore the correlation between NfL levels and the severity of narcolepsy symptoms, sleep quality, and manifestations of anxiety and depression., Methods: This retrospective analysis included 98 paediatric narcolepsy cases and 100 controls matched for age and gender. The study focused on comparing serum NfL levels across these groups. Severity of EDS in patients was measured with the Epworth Sleepiness Scale (ESS). Moreover, the Pittsburgh Sleep Quality Index (PSQI), Hamilton Depression Rating Scale-24 (HAMD-24), and Hamilton Anxiety Scale-14 (HAMA-14) were used to assess narcolepsy symptoms, sleep quality, and psychological conditions., Results: Patients with paediatric narcolepsy had significantly higher serum NfL levels than controls (P < 0.05). Additionally, a positive correlation was found between serum NfL levels and ESS scores (P < 0.001). An independent link between serum NfL and paediatric narcolepsy was established via multiple logistic regression (OR = 0.943, 95 % CI = 0.921-0.993, P = 0.004). Moreover, serum NfL's diagnostic precision for paediatric narcolepsy was evident from the ROC curve area of 0.938 (95 % CI: 0.86-0.99, P < 0.001)., Conclusion: The study implies a positive correlation between increased serum NfL levels and the severity of paediatric narcolepsy. Nevertheless, the causative link between serum NfL levels and paediatric narcolepsy remains uncertain, highlighting the need for larger sample sizes and well-structured cohort studies to offer more definitive., Competing Interests: Declaration of competing interest The authors declared that they had no conflicts of interest., (Copyright © 2024 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.)

World Health Organization (WHO) has identified depression as a significant contributor to global disability, creating a complex thread in both public and private health. Electroencephalogram (EEG) can accurately reveal the working condition of the human brain, and it is considered an effective tool for analyzing depression. However, manual depression detection using EEG signals is time-consuming and tedious. To address this, fully automatic depression identification models have been designed using EEG signals to assist clinicians. In this study, we propose a novel automated deep learning-based depression detection system using EEG signals. The required EEG signals are gathered from publicly available databases, and three sets of features are extracted from the original EEG signal. Firstly, spectrogram images are generated from the original EEG signal, and 3-dimensional Convolutional Neural Networks (3D-CNN) are employed to extract deep features. Secondly, 1D-CNN is utilized to extract deep features from the collected EEG signal. Thirdly, spectral features are extracted from the collected EEG signal. Following feature extraction, optimal weights are fused with the three sets of features. The selection of optimal features is carried out using the developed Chaotic Owl Invasive Weed Search Optimization (COIWSO) algorithm. Subsequently, the fused features undergo analysis using the Self-Attention-based Gated Densenet (SA-GDensenet) for depression detection. The parameters within the detection network are optimized with the assistance of the same COIWSO. Finally, implementation results are analyzed in comparison to existing detection models. The experimentation findings of the developed model show 96% of accuracy. Throughout the empirical result, the findings of the developed model show better performance than traditional approaches.

Scores on the Patient Health Questionnaire-9 (PHQ-9) are frequently used to assess depression both in research and in clinical practice. The aim was to examine the validity of the PHQ-9 sum score by using Mokken scale analysis (Study I) and cognitive interviews (Study II) on the Icelandic version of PHQ-9. A primary care sample of 618 individuals was used in Study I. The results indicate that the PHQ-9 items are not close enough to perfectly unidimensional for their sum score to accurately order people on the depression severity dimension. In Study II, the sample consisted of 53 individuals, with 28 having a history of depression and 25 not. The findings reveal a number of issues concerning respondents' use of the PHQ-9. No systematic differences were found in the results of the two groups. The PHQ-9 sum score should thus be interpreted and used with great care. We provide scale revision recommendations to improve the quality of PHQ-9., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: To develop an index to assess capacity to work in relation to common mental disorders (CMDs) in the general working population and field test its psychometric properties., Methods: Content analysis of three qualitative studies on people (n = 49) with their own experiences of working with CMD guided the items selected for the index. Face and content validity and test-retest reliability were performed. The index was field tested in two versions with 26 and 17 items, respectively, among health care professionals regarding internal reliability, component structure and concurrent validity., Results: The final version of the Capacity to Work Index (C2WI; 17 items) was normally distributed in the field test with high internal reliability (Cronbach's alpha, 0.84). Missing responses were randomly distributed and nonspecific. Principal component analysis showed one clear component with negatively framed items. Concurrent validity showed high correlation with the WHO-5 Well-Being Scale (Pearson's r, 0.68), but lower correlation for the general health question (r, - 0.44), one item of the Work Ability Index (r, - 0.33), and the Stress of Conscience constructs (r, 0.44)., Conclusion: The C2WI showed promising psychometric qualities. Low and negative correlation with the item from Work Ability Index suggests that the C2WI measures additional dimensions, but further testing in larger and more diverse samples is required., (© 2023. The Author(s).)

Veterans with PTSD are at higher risk for suicide. This study examined the specific associations of PTSD symptom clusters with suicidal ideation (SI) and death ideation (DI), independently from depressive symptom clusters. Participants included 695 Israeli male outpatient military veterans (M = 25.35 years, SD = 5.65), divided into subsamples of probable PTSD (PTSD Checklist for DSM-5 [PCL-5] ≥ 33) and subthreshold PTSD scores (PCL-5 < 33). Data were extracted from medical chartsand self-report questionnaires. The main analyses included logistic regression to evaluate the associations between SI and DI (Brief Symptom Inventory, items 9 and 39) and PTSD symptom clusters (PCL-5), controlling for depressive symptom clusters (Beck Depression Inventory; cognitive-affective and somatization) in each subsample. The results showed that, for veterans with probable PTSD, the negative alterations in cognition and mood symptom cluster was positively correlated with SI and DI, while avoidance was negatively correlated with SI, independently from depressive symptoms clusters. In those with sub-syndromal PTSD, the re-experiencing cluster was positively correlated with DI, independently from the depressive symptom clusters. These findings highlight the importance of targeting PTSD components, such as negative alterations in cognition and mood symptoms experienced by veterans with PTSD, as part of suicide prevention efforts., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Hypermobility spectrum disorders (HSD) and hypermobile Ehlers-Danlos syndrome (hEDS) are often accompanied by varied and complex multisystemic comorbid symptoms/conditions. The Spider questionnaire was developed to evaluate the presence and impact of eight common multisystemic comorbidities. Thirty-one questions across eight symptom domains assess neuromusculoskeletal, pain, fatigue, cardiac dysautonomia, urogenital, gastrointestinal, anxiety, and depression symptoms. This study aimed to evaluate the Spider's construct validity in adults., Method: A cross-sectional observational study was conducted over four stages. Three international patient charities aided recruitment of participants through social media and website advertisements. Adults aged 18 to 65 years, with and without HSD/hEDS, were invited to participate. Validated, frequently used comparator questionnaires were used to establish convergent validity of Spider symptom domains. A control group was recruited for known-group validity analysis. Participants answered each Spider domain and the corresponding comparator questionnaire via surveys hosted by REDCap. Anonymous data were analysed using SPSS. Convergent validity was assessed through Spearman's correlational analysis and known-group validity through Mann-Whitney U analysis., Results: A total of 11,151 participants were recruited across the four stages. Statistically significant, moderate-to-strong correlations were found between all Spider domains and their comparators (p < 0.001, r = 0.63 to 0.80). Known-group validity analysis showed statistically significant differences (p < 0.001) between the hypermobile and control groups in all eight domains., Conclusions: Convergent and known-group validity of the Spider was established with adults. These results suggest the Spider can measure the presence and impact of multisystemic comorbid symptoms/conditions in adults with HSD/hEDS, providing a tool which guides multidisciplinary management. Key Points • The Spider questionnaire is a novel tool assessing the presence and impact of the multisystemic comorbid symptoms/conditions associated with HSD/hEDS. • Convergent and known-group validity of the Spider questionnaire was established in adults aged 18 to 65. • This tool provides a quick and easy method to visualise the symptom profile of those with HSD/hEDS to guide symptom management., (© 2024. The Author(s).)

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehensive consideration of depression's pathogenesis, leading to limited neuroscientific interpretability. To address these issues, we propose a hemisphere asymmetry network (HEMAsNet) inspired by the brain for depression recognition from EEG signals. HEMAsNet employs a combination of multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) blocks to extract temporal features from both hemispheres of the brain. Moreover, the model introduces a unique 'Callosum-like' block, inspired by the corpus callosum's pivotal role in facilitating inter-hemispheric information transfer within the brain. This block enhances information exchange between hemispheres, potentially improving depression recognition accuracy. To validate the performance of HEMAsNet, we first confirmed the asymmetric features of frontal lobe EEG in the MODMA dataset. Subsequently, our method achieved a depression recognition accuracy of 0.8067, indicating its effectiveness in increasing classification performance. Furthermore, we conducted a comprehensive investigation from spatial and frequency perspectives, demonstrating HEMAsNet's innovation in explaining model decisions. The advantages of HEMAsNet lie in its ability to achieve more accurate and interpretable recognition of depression through the simulation of physiological processes, integration of spatial information, and incorporation of the Callosum-like block.