Depression, a leading contributor to global disability, lacks objective diagnostic biomarkers. This review evaluates functional near-infrared spectroscopy (fNIRS) as a portable neuroimaging tool for depression detection, highlighting its algorithmic innovations and clinical translation potential. Machine learning techniques effectively decode hemodynamic patterns of the prefrontal cortex during emotional or cognitive tasks to achieve high classification accuracy in controlled studies. Clinically, fNIRS identifies prefrontal cortex hypoactivation as correlated with symptom severity and tracks neuroplasticity during psychotherapy. However, heterogeneity across symptom subtypes, cultural backgrounds, and age groups limits the generalizability of the model. Technical challenges include signal noise from motion artifacts and interference from superficial tissues. Future research should prioritize standardized multicenter trials, multimodal integration to enhance biomarker specificity, and interpretable artificial intelligence frameworks for clinical translation. fNIRS demonstrates unique advantages for scalable, noninvasive depression screening but necessitates rigorous validation to transition from research to point-of-care applications. This review provides insights into the optimization of fNIRS-based tools for precision psychiatry.

Facial Action Units (FAUs) capture the micro-level facial movement patterns of individuals during emotional expression, emotional response, and social interaction processes. While effectively preventing the leakage of personal identity information, they provide a robust representation for depression recognition with the added advantage of privacy protection. However, existing studies lack the guidance of external emotional stimuli and fail to adequately model interpretability. To address this, we have constructed the Emotional Image Viewing Experiment (EIVE) dataset for depression recognition based on FAUs. Meanwhile, We present SFENet, a shapelet-based facial expression interpretability model for depression recognition. Specifically, we extract shapelets for each FAU from subject samples using perceptually important points and construct shapelet-based initialization convolution kernels. Building upon this kernel, we propose a shapelet-convolution-based historical information enhancement module that strengthens feature extraction through historical information aggregation and time-scale interaction. Additionally, we design a sliding-window-based shapelet-sample similarity loss to update shapelet features, optimizing model training by measuring the alignment between features and sample sequences. Interpretability is derived from the parameters initialized by the shapelet convolution kernel and fitted through model training. SFENet achieves strong competitive performance in both EIVE and AVEC 2014, revealing that the case group exhibits a stronger tendency for negative facial expression unit activation under different emotional conditions. Our work is available at https://github.com/yjy-97/SFENet.

Adolescent depressive disorder is a clinically heterogeneous condition with poorly understood molecular mechanisms. Protein N-glycosylation, a key post-translational modification involved in immune regulation and neuronal communication, has not been systematically investigated across depressive subtypes in youth. Understanding glycosylation alterations may reveal novel biochemical pathways underlying disease heterogeneity and pathophysiology. We applied a site-specific glycoproteomic approach integrating liquid chromatography-tandem mass spectrometry (LC-MS/MS) and StrucGP to profile serum N-glycosylation in adolescents with major depressive disorder (MDD), MDD with non-suicidal self-injury (NSSI), and MDD with suicide attempts (SA), compared with healthy controls. Serum samples were pooled by group (10 individuals per pool), and glycan alterations were identified using predefined fold-change thresholds. This approach is classified as an exploratory research strategy, which does not support statistical inference. All depressive subtypes displayed shared alterations, characterized by reduced bi-antennary glycans and LacNAc/Lewis structures, alongside elevated tetra-antennary sialylation, particularly N6H7S4. Candidate subtype-associated patterns were also observed: increased tri-antennary sialylation in NSSI, and selective upregulation of N6H7S4 on ORM2 in SA. Differentially glycosylated proteins, including SERPING1 and A2M, were enriched in immune, complement, and coagulation pathways. Notably, protein-level changes were minimal in label-free quantification, suggesting glycan-specific dysregulation. This exploratory study identifies candidate subtype-dependent alterations in N-glycosylation in adolescent depression, which may implicate glycan-mediated immune and neuroinflammatory mechanisms in its molecular heterogeneity. This study provides a preliminary, pooled-sample based catalog of candidate site-specific N-glycosylation alterations for adolescent depression, warranting validation in larger, individual-level cohorts.

Treatment-resistant depression (TRD) is a major challenge in clinical psychiatry, characterized by insufficient response to at least two different classes of antidepressants. Despite advances in treatment, many TRD patients continue to experience significant symptoms and reduced quality of life. The study aims to provide an exploratory assessment of toludesvenlafaxine hydrochloride sustained-release tablets for efficacy and safety in TRD. We conducted an open-label, single-arm, exploratory 8-week study in adults with TRD (n = 31) receiving Toludesvenlafaxine. The primary endpoint was the change in the Montgomery-Asberg Depression Rating Scale (MADRS) score from baseline to week 8. Secondary endpoints included changes in anxiety, quality of life, and functional status, as well as the assessment of safety. Within-group improvements were observed in MADRS scores at weeks 2, 4, and 8 compared with baseline (P < 0.0001), with an average reduction of -13.0 ± 9.6 at week 8. The MADRS response rate (≥ 50

IntroductionDepression is a prevalent mental disorder, and early screening and treatment are crucial for detecting depression. However, there are still some limitations in the currently proposed deep models based on audio-video data, for example, it is difficult to effectively extract and select useful multimodal information and features from audio-video data, and very few studies have been able to focus on three dimensions of information: time, channel, and space at the same time in depression detection. In addition, there are challenges in utilizing other tasks to enhance prediction accuracy. The resolution of these issues is crucial for constructing models of depression detection.MethodsIn this paper, we propose a multi-task representation learning based on vision and audio for depression detection model (DepITCM).The model comprises three main modules: a data preprocessing module, the Inception-Temporal-Channel Principal Component Analysis Module(ITCM Encoder), and a multi-task learning module. To efficiently extract rich feature representations from audio and video data, the ITCM Encoder employs a staged feature extraction strategy, transitioning from global to local features. This approach enables the capture of global features while emphasizing the fusion of temporal, channel, and spatial information in finer detail. Furthermore, inspired by multi-task learning strategies, this paper enhances the primary task of depression classification by incorporating a secondary task (regression task) to improve overall performance.ResultsWe conducted experiments on the AVEC2017 and AVEC2019 datasets. The results show that, in the classification task, our method achieved an F1 score of 0.823 and a classification accuracy of 0.823 on the AVEC2017 dataset, and an F1 score of 0.816 and a classification accuracy of 0.810 on the AVEC2019 dataset. In the regression task, the RMSE was 6.10 (AVEC2017) and 4.89 (AVEC2019), respectively. These results demonstrate that our method outperforms most existing methods in both classification and regression tasks. Furthermore, we demonstrate that the model proposed in this paper can effectively improve the performance of depression detection when using multi-task learning.DiscussionAlthough depression detection through multimodality has shown good results in previous studies. However, multi-task learning can utilize the complementary information between different tasks. Therefore, our work combines multimodal and multi-task learning to improve the accuracy of depression detection. Previous studies have mostly focused on the extraction of global features while ignoring the importance of local features. Based on the problems of previous studies, we have made corresponding improvements to provide a more comprehensive and effective solution for depression detection.

Clinical research has demonstrated that exploring behavioral signal differences between depressed patients and non-depressed people using audiovisual technology is an effective approach for achieving depression recognition. Hence, in this paper we propose an emotion word reading experiment (EWRE), and extract features from facial expressions and audios for depression recognition. Building upon this, we propose a depression recognition model (DEP-Former), which deeply integrates multimodal features. DEP-Former first designs a modality adapter to achieve emotion space mapping and the sharing of multimodal features, addressing cross-modal inconsistencies. Simultaneously, it proposes a mechanism of attention index sharing, exceeding the limitations of cognitive subjectivity by calculating confidence in key emotional information across modalities. Finally, we propose a multimodal cross-attention module and a Bernoulli distribution feature fusion prediction module to achieve deep integration of multilevel information, thereby enabling depression recognition. Compared with existing advanced multimodal models, DEP-Former demonstrates superior performance in EWRE, achieving an accuracy of 0.9500 and an F1 score of 0.9499, significantly enhancing depression recognition over the single-modality methods. Furthermore, its robust generalization ability is validated on the AVEC 2014 dataset. Through the attention query of the interpretability analysis module, we discover that depressed patients exhibit heightened sensitivity to negative emotional words, such as dismissal and tragedy. In contrast, healthy individuals tend to be more attuned to positive emotional words, including passion, purity, and justice. Additionally, depressed patients exhibit a degree of psychological state diversity, showing sensitivity to some positive emotional words as well. Our codes and data are available at https://github.com/QLUTEmoTechCrew/DEP-Former.

BACKGROUND:Clinical studies have shown that facial expressions and body posture in depressed patients differ significantly from those of healthy individuals. Combining relevant behavioral features with artificial intelligence technology can effectively improve the efficiency of depression detection, thereby assisting doctors in early identification of patients. This study aims to develop an end-to-end multimodal recognition model combining facial expressions and body posture via deep learning techniques, enabling rapid preliminary screening of depression. METHODS:We invited 146 subjects (73 in the patient group and 73 in the control group) to participate in an emotion-stimulus experiment for depression recognition. We focused on differentiating depression patients from the control group by analyzing changes in body posture and facial expressions under emotional stimuli. We first extracted images of body position and facial emotions from the video, then used a pre-trained ResNet-50 network to extract features. Additionally, we analyzed facial expression features using OpenFace for sequence analysis. Subsequently, various deep learning frameworks were combined to assess the severity of depression. RESULTS:We found that under different stimuli, facial expression units AU04, AU07, AU10, AU12, AU17, and AU26 had significant effects in the emotion-stimulus experiment, with these features generally being negative. The decision-level fusion model based on facial expressions and body posture achieved excellent results, with the highest accuracy of 0.904 and an F1 score of 0.901. CONCLUSIONS:The experimental results suggest that depression patients exhibit predominantly negative facial expressions. This study validates the emotion-stimulus experiment, demonstrating that combining facial expressions and body posture enables accurate preliminary depression screening.

Depression is a common mental health issue, and early detection is crucial for timely intervention. This study proposes an end-to-end EEG-based depression recognition model, AMCCBDep, which combines Attention-based Multi-scale Parallel Convolution (AMPC), Conformer, and Bidirectional Gated Recurrent Unit (BiGRU). The AMPC module captures temporal features through multiscale convolutions and extracts spatial features using depthwise separable convolutions, while applying the ECA attention mechanism to weigh key channels, enhancing the model's focus on crucial electrode channels. The Conformer module further captures both global and local temporal dependencies in EEG signals to ensure the capture of long-range dependencies and local patterns. The BiGRU module improves the model's ability to recognize depressive states by utilizing bidirectional modeling. We used the 128-channel resting-state EEG signals from the MODMA dataset, which includes data from 24 depression patients (13 males, 11 females, aged 16 to 56) and 29 healthy individuals (20 males, 9 females, aged 18 to 55). Experimental results show that the AMCCBDep model achieved an accuracy of 98.68% ± 0.45% on the MODMA dataset. The model evaluation results for both 128-channel and 16-channel configurations demonstrate that reducing the number of electrodes has a minimal impact on performance, suggesting that electrode reduction could be considered in practical applications. This model showcases strong potential in advancing depression detection in neuroscience, providing an efficient and scalable solution for clinical and practical applications. Future research will further optimize model performance and explore the impact of reducing the number of electrodes on clinical practice.

Facial Expression Recognition (FER) is a critical method for evaluating the emotional states of patients with mental disorders, playing a significant role in treatment monitoring. However, due to privacy constraints, facial expression data from patients with mental disorders is severely limited. Additionally, the more complex inter-class and intra-class similarities compared to healthy individuals make accurate recognition of facial expressions challenging. Therefore, we propose a Voluntary Facial Expression Mimicry (VFEM) experiment, which collected facial expression data from schizophrenia, depression, and anxiety. This experiment establishes the first dataset designed for facial expression recognition tasks exclusively composed of patients with mental disorders. Simultaneously, based on VFEM, we propose a Vision Transformer FER model tailored for Complex mental disorder patients (CmdVIT). CmdVIT integrates crucial facial expression features through both explicit and implicit mechanisms, including explicit visual center positional encoding and implicit sparse attention center loss function. These two key components enhance positional information and minimize the facial feature space distance between conventional attention and critical attention, effectively suppressing inter-class and intraclass similarities. In various FER tasks for different mental disorders in VFEM, CmdVIT achieves more competitive performance compared to contemporary benchmark models. Our works are available at https://github.com/yjy-97/CmdVIT.

Depression is a common mental health disorder. With current depression detection methods, specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment. Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized. Specialized physicians usually require extensive training and experience to capture changes in these features. Advancements in deep learning technology have provided technical support for capturing non-biological markers. Several researchers have proposed automatic depression estimation (ADE) systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening. This article summarizes commonly used public datasets and recent research on audio- and video-based ADE based on three perspectives: Datasets, deficiencies in existing research, and future development directions.

Background: Depression was a widespread and high-risk mental disorder that could significantly affectthoughts, emotions, and behaviors. While recent studies explored techniques for detecting depression throughbody posture, depression detection researches utilizing skeletal data recorded by Kinect of scale assessmentwere still ongoing. Our goal was to establish a novel approach for depression detection that utilizes the skeletaldata during scale assessment. Methods: Participants, which included a depression group and a control group, were assessed using theHamilton Rating Scale for Depression (HAMD-24), and we utilized Kinect V2 to capture human skeletal data.To extract the skeletal data and reduce noise, we developed program that has been open-sourced forprocessing Kinect recorded files. With skeletal data extraction, feature analysis, and data preprocessing, thethree human skeleton datasets were created using the hip joint as the splitting point, as follows: whole bodyskeleton joints dataset, upper body skeleton joints dataset, and lower body skeleton joints dataset. To enhancethe classification capacity of temporal and spatial features within skeletal data, we designed Temporal SpatialAttention (Dep-TSA) for depression detection. Results: According to the scale’s criteria, a total of 202 participants were included, with 89 depression patientsas the depression group, and 113 social recruited healthy individuals were constituted the control group. Theproposed Dep-TSA model shown outstanding results compared to other models, achieving an accuracy of72.13%, a specificity of 76.47% and a sensitivity of 66.67% on whole body skeleton joints dataset. It alsoattained accuracy of 67.32% on upper body skeleton joints dataset, and accuracy of 62.30% on lower bodyskeleton joints dataset. Conclusions: The proposed Dep-TSA module, proven effective for extracting raw spatial-temporal featuresand low-level features of body skeleton joints, achieved satisfactory classification accuracy across these threehandcrafted datasets. These findings suggested that using Kinect-recorded skeletal data during the HAMDassessment process could be an effective tool for depression analysis. In future research, our aim was to extendthis study to investigate the detection of the severity of depression and to combine it with more modalities formore efficient recognition.

BACKGROUND AND OBJECTIVES:Depression is a typical mental illness, and early screening can effectively prevent exacerbation of the condition. Many studies have found that the expressions of depressed patients are different from those of other subjects, and microexpressions have been used in the clinical detection of mental illness. However, there are few methods for the automatic detection of depression based on microexpressions.METHODS:A new dataset of 156 participants (76 in the case group and 80 in the control group) was created. All data were collected in the context of a new emotional stimulation experiment and doctor-patient conversation. We first analyzed the Average Number of Occurrences (ANO) and Average Duration (AD) of facial expressions in the case group and the control group. Then, we proposed a two-stream model SFTNet for identifying depression based on microexpressions, which consists of a single-temporal network (STNet) and a full-temporal network (FTNet). STNet is used to extract features from facial images at a single time node, FTNet is used to extract features from all-time nodes, and the decision network combines the two features to identify depression through decision fusion. The code for SFTNet is available at https://github.com/muzixingyun/SFTNet.RESULTS:We found that the AD of all subjects was less than 20 frames (2/3 seconds) and that the facial expressions of the control group were richer. SFTNet achieved excellent results on the emotional stimulus experimental dataset, with Accuracy, Precision and Recall of 0.873, 0.888 and 0.846, respectively. We also conducted experiments on the doctor-patient conversation dataset, and the Accuracy, Precision and Recall were 0.829, 0.817 and 0.837, respectively. SFTNet can also be applied to microexpression detection task with more accuracy than SOTA models.CONCLUSIONS:In the emotional stimulation experiment, the subjects in the case group are more likely to show negative emotions. Compared to SOTA models, our depression detection method is more accurate and can assist doctors in the diagnosis of depression.

Many clinical studies have shown that facial expression recognition and cognitive function are impaired in depressed patients. Different from spontaneous facial expression mimicry (SFEM), 164 subjects (82 in a case group and 82 in a control group) participated in our voluntary facial expression mimicry (VFEM) experiment using expressions of neutrality, anger, disgust, fear, happiness, sadness and surprise. Our research is as follows. First, we collected a large amount of subject data for VFEM. Second, we extracted the geometric features of subject facial expression images for VFEM and used Spearman correlation analysis, a random forest, and logistic regression-based recursive feature elimination (LR-RFE) to perform feature selection. The features selected revealed the difference between the case group and the control group. Third, we combined geometric features with the original images and improved the advanced deep learning facial expression recognition (FER) algorithms in different systems. We propose the E-ViT and E-ResNet based on VFEM. The accuracies and F1 scores were higher than those of the baseline models, respectively. Our research proved that it is effective to use feature selection to screen geometric features and combine them with a deep learning model for depression facial expression recognition.

Depression is a prevalent mental disorder worldwide. Early screening and treatment are crucial in preventing the progression of the illness. Existing emotion-based depression recognition methods primarily rely on facial expressions, while body expressions as a means of emotional expression have been overlooked. To aid in the identification of depression, we recruited 156 participants for an emotional stimulation experiment, gathering data on facial and body expressions. Our analysis revealed notable distinctions in facial and body expressions between the case group and the control group and a synergistic relationship between these variables. Hence, we propose a two-stream feature fusion model (TSFFM) that integrates facial and body features. The central component of TSFFM is the Fusion and Extraction (FE) module. In contrast to conventional methods such as feature concatenation and decision fusion, our approach, FE, places a greater emphasis on in-depth analysis during the feature extraction and fusion processes. Firstly, within FE, we carry out local enhancement of facial and body features, employing an embedded attention mechanism, eliminating the need for original image segmentation and the use of multiple feature extractors. Secondly, FE conducts the extraction of temporal features to better capture the dynamic aspects of expression patterns. Finally, we retain and fuse informative data from different temporal and spatial features to support the ultimate decision. TSFFM achieves an Accuracy and F1-score of 0.896 and 0.896 on the depression emotional stimulus dataset, respectively. On the AVEC2014 dataset, TSFFM achieves MAE and RMSE values of 5.749 and 7.909, respectively. Furthermore, TSFFM has undergone testing on additional public datasets to showcase the effectiveness of the FE module.

Antipsychotic medications are commonly used to treat schizophrenia, but they can have negative effects on lipid metabolism, leading to an increased risk of cardiovascular diseases, reduced life expectancy, and difficulties with treatment adherence. The specific mechanisms by which antipsychotics disrupt lipid metabolism are not well understood. Sterol regulatory element-binding proteins (SREBPs) are important transcriptional factors that regulate lipid metabolism. Proprotein convertase subtilisin/kexin type 9 (PCSK9), a gene regulated by SREBPs, plays a critical role in controlling levels of low-density lipoprotein cholesterol (LDL-C) and has become a focus of research on lipid-lowering drugs. Recent studies have shown that antipsychotic drugs can affect lipid metabolism through the SREBP/PCSK9 pathway. A deep understanding of the mechanism for this pathway in antipsychotic drug-related metabolic abnormalities will promote the prevention of lipid metabolism disorders in patients with schizophrenia and the development and application of new drugs.

Background: Considerable evidence has shown that facial expression mimicry is impaired in patients with depression. We aimed to evaluate voluntary expression mimicry by facial expression recognition for diagnosing depression.Methods: A total of 168 participants performed voluntary expression mimicry task, posing anger, disgust, fear, happiness, neutrality, sadness, and surprise. 9 healthy raters performed facial expression recognition task through the observer scoring method, and evaluated seven expressions imitated by participants. Emotional scores were calculated to measure any differences between two groups of participants and provided a basis for clinical diagnosis of depression.Results: Compared with the control group, the depression group had lower accuracy in imitating happiness. Compared with the control group, the depression group imitated a higher neutrality bias for sadness, surprise, happiness and disgust, while sadness and surprise had a lower happiness bias; for imitating happiness, the depression group showed higher anger, disgust, fear, neutrality, and surprise bias; for imitating neutrality, the depression group showed higher sadness bias, and lower happiness bias. Compared with the control group, the raters had a higher reaction time to recognize the happiness imitated by depression group, and it was positively correlated with severity of depression. The severity of depression was also negatively correlated with accuracy in imitating happiness, and positively correlated with neutrality bias of imitating surprise.Limitations: The ecological effectiveness of static stimulus materials is lower than that of dynamic stimuli. Without synchronized functional imaging, there is no way to link brain activation patterns.Conclusion: The ability of patients with depression to voluntarily imitate facial expressions declines, which is mainly reflected in accuracy, bias and recognizability. Our experiment has discovered deficits in these aspects of patients with depression, which will be used as a method for diagnosising depression.

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM Dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid Network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM Dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://github.com/muzixingyun/LI-FPN

Objective To investigate the efficacy of repetitive transcranial magnetic stimulation (rTMS) in the treatment of chronic insomnia and the improvement of cognitive function, sleep quality, sleep belief and attitude. Methods A total of 240 patients with chronic insomnia who met the inclusion criteria and were treated in the Shandong Mental Health Center from January to December 2019 were selected as the research objects. They were randomly divided into left dorsolateral prefrontal cortex (DLPFC) treatment group, right DLPFC treatment group, 1 cm behind the central point (Cz) treatment group and control group (sham stimulation group), with 60 cases in each group. Pittsburgh sleep quality index (PSQI) and sleep personal belief and attitude scale (DBAS) were used to evaluate the patients'' sleep quality, sleep belief and attitude before and at the end of treatment. The cognitive function of patients was evaluated in neuropsychological and electrophysiological aspects by Montreal Cognitive Assessment Scale (MoCA), symbol number conversion test (SDMT) and event-related potentials (ERPs). Results The PSQI scores of left DLPFC treatment group, right DLPFC treatment group and 1 cm after Cz treatment group were signifi cantly lower than those before treatment, and the DBAS scores were signifi cantly higher than those before treatment (P ＜0.05). There was no signifi cant diff erence between PSQI and DBAS before and after treatment in the control group (P ＞0.05). The PSQI and DBAS of right DLPFC treatment group were signifi cantly lower than those of left DLPFC treatment group and 1 cm after CZ treatment group; MoCA and SDMT in the left DLPFC treatment group, the right DLPFC treatment group and the treatment group 1 cm after CZ were signifi cantly higher than those before treatment (P＜0.05), but there was no signifi cant diff erence in MoCA and SDMT before and after treatment in the control group (P＞0.05); The P3 latency after treatment was signifi cantly shorter in the left DLPFC treatment group, the right DLPFC treatment group and the treatment group 1 cm after CZ, and the P3 amplitude was signifi cantly greater than that before treatment (P＜0.05). There was no signifi cant diff erence in P3 latency and P3 amplitude before and after treatment in the control group (P＞0.05). Conclusion rTMS can greatly improve the cognitive function, sleep quality, sleep belief and attitude of patients, and the eff ect is signifi cant. It is worthy to be applied in clinic.

Background:Most antipsychotic drugs are dopamine receptor antagonists that usually lead to abnormal increases in prolactin concentrations and the development of hyperprolactinemia (HPRL), which in turn causes sexual dysfunction in patients. Peony-Glycyrrhiza Decoction (PGD) enhanced dopamine D2 receptors (DRD2) and dopamine transporter (DAT) and significantly reversed the expression of DRD2 and DAT. Therefore, we hypothesized that PGD might effectively improve hyperprolactinemia and alleviate sexual dysfunction in patients.Methods:We performed an 8-week randomized controlled study on 62 subjects with schizophrenia who were randomized into two groups. The experimental group was treated with the PGD intervention, and the control group did not receive treatment. The primary outcome indicators were the levels of sex hormones and the total Arizona Sexual Experience Scale (ASEX) score.Results:There was a significant difference in PRL levels between the two groups at weeks 4 and 8. From the beginning to the end of the experiment, there was a significant increase in PRL levels in the control group, while there was no significant change in the experimental group. The ASEX scale assessed sexual function in both groups, and patients in the experimental group showed an improvement in sexual function at week 8. During the experiment, the two groups found no differences between Positive and Negative Syndrome Scale (PANSS) scores and Treatment Emergent Symptom Scale (TESS) scores.Conclusion:PGD significantly improved the patient's sexual function but was less effective in reducing prolactin levels and may prevent further increases in prolactin levels.

Relative limb movement is an important feature in assessing depression. In this study, we looked into whether a skeleton-mimetic task using natural stimuli may help people recognize depression. We innovatively used Kinect V2 to collect participant data. Sequential skeletal data was directly extracted from the original Kinect-3D and tetrad coordinates of the participant's 25 body joints. Two constructed skeletal datasets of whole-body joints (including binary classification and multi classification) were input into the proposed model for depression recognition after data preparation. We improved the temporal convolution network (TCN), creating novel spatial attention dilated TCN (SATCN) network that included a hierarchy of temporal convolution groups with different dilated convolution scales to capture important skeletal features and a spatial attention block for final result prediction. The depression and non-depression groups can be classified automatically with a maximum accuracy of 75.8% in the binary classification task, and 64.3% accuracy in the multi classification dataset to recognize more fine-grained identification of depression severity, according to experimental results. Our experiments and methods based on Kinect V2 can not only identify and screen depression patients but also effectively observe the recovery level of depression patients during the recovery process. For example, in the change from severe depression to moderate or mild depression multi classification dataset.