Exploring mechanisms of anhedonia in depression through neuroimaging and data-driven approaches.

BACKGROUND: Anhedonia is a core symptom of depression that is closely related to prognosis and treatment outcomes. However, accurate and efficient treatments for anhedonia are lacking, mandating a deeper understanding of the underlying mechanisms. METHODS: A total of 303 patients diagnosed with depression and anhedonia were assessed by the Snaith-Hamilton Pleasure Scale (SHAPS) and magnetic resonance imaging (MRI). The patients were categorized into a low-anhedonia group and a high-anhedonia group using the K-means algorithm. A data-driven approach was used to explore the differences in brain structure and function with different degrees of anhedonia based on MATLAB. A random forest model was used exploratorily to test the predictive ability of differences in brain structure and function on anhedonia in depression. RESULTS: Structural and functional differences were apparent in several brain regions of patients with depression and high-level anhedonia, including in the temporal lobe, paracingulate gyrus, superior frontal gyrus, inferior occipital gyrus, right insular gyrus, and superior parietal lobule. And changes in these brain regions were significantly correlated with scores of SHAPS. CONCLUSIONS: These brain regions may be useful as biomarkers that provide a more objective assessment of anhedonia in depression, laying the foundation for precision medicine in this treatment-resistant, relatively poor prognosis group.

Perineural Treatment with High Mobility Group Box-1 Monoclonal Antibody Prevents Initiation of Pain-Like Behaviors in Female Mice with Trigeminal Neuropathy.

Post-traumatic trigeminal neuropathy (PTTN) is a type of chronic pain caused by damage to the trigeminal nerve. A previous study reported that pretreatment with anti-high mobility group box-1 (HMGB1) neutralizing antibodies (nAb) prevented the onset of PTTN following distal infraorbital nerve chronic constriction injury (dIoN-CCI) in male mice. Clinical evidence indicates a high incidence of PTTN in females. Although our previous study found that perineural HMGB1 is crucial in initiation of PTTN in male mice, it is currently unknown whether HMGB1 is also involved in the pathogenesis of PTTN in female mice. Therefore, in the current study, we examined the effect of anti-HMGB1 nAb on pain-like behavior in female mice following dIoN-CCI surgery. We found that dIoN-CCI surgery enhanced reactivity to mechanical and cold stimuli in female mice, which was suppressed by treatment with anti-HMGB1 nAb. Moreover, the increase in macrophages after dIoN-CCI was significantly attenuated by pretreatment with anti-HMGB1 nAb. Furthermore, anti-HMGB1 nAb treatment inhibited microglial activation in the trigeminal spinal tract nucleus. These data suggest that HMGB1 also plays a crucial role in the onset of PTTN after nerve injury in female mice. Thus, anti-HMGB1 nAb could be a novel therapeutic agent for inhibiting the onset of PTTN in female and male mice.

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation.

BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathological mechanisms underlying depression and candidate clinical biomarkers. METHODS: Depression-associated metabolomics was studied in 2 datasets from the UK Biobank database: participants with lifetime depression (N = 123,459) and participants with current depression (N = 94,921). The Whitehall II cohort (N = 4744) was used for external validation. CatBoost machine learning was used for modeling, and Shapley additive explanations were used to interpret the model. Fivefold cross-validation was used to validate model performance, training the model on 3 of the 5 sets with the remaining 2 sets for validation and testing, respectively. Diagnostic performance was assessed using the area under the receiver operating characteristic curve. RESULTS: In the lifetime depression and current depression datasets and sex-specific analyses, 24 significantly associated metabolic biomarkers were identified, 12 of which overlapped in the 2 datasets. The addition of metabolic features slightly improved the performance of a diagnostic model using traditional (nonmetabolomics) risk factors alone (lifetime depression: area under the curve 0.655 vs. 0.658 with metabolomics; current depression: area under the curve 0.711 vs. 0.716 with metabolomics). CONCLUSIONS: The machine learning model identified 24 metabolic biomarkers associated with depression. If validated, metabolic biomarkers may have future clinical applications as supplementary information to guide early and population-based depression detection.

Prediction of anxious depression using multimodal neuroimaging and machine learning.

Anxious depression is a common subtype of major depressive disorder (MDD) associated with adverse outcomes and severely impaired social function. It is important to clarify the underlying neurobiology of anxious depression to refine the diagnosis and stratify patients for therapy. Here we explored associations between anxiety and brain structure/function in MDD patients. A total of 260 MDD patients and 127 healthy controls underwent three-dimensional T1-weighted structural scanning and resting-state functional magnetic resonance imaging. Demographic data were collected from all participants. Differences in gray matter volume (GMV), (fractional) amplitude of low-frequency fluctuation ((f)ALFF), regional homogeneity (ReHo), and seed point-based functional connectivity were compared between anxious MDD patients, non-anxious MDD patients, and healthy controls. A random forest model was used to predict anxiety in MDD patients using neuroimaging features. Anxious MDD patients showed significant differences in GMV in the left middle temporal gyrus and ReHo in the right superior parietal gyrus and the left precuneus than HCs. Compared with non-anxious MDD patients, patients with anxious MDD showed significantly different GMV in the left inferior temporal gyrus, left superior temporal gyrus, left superior frontal gyrus (orbital part), and left dorsolateral superior frontal gyrus; fALFF in the left middle temporal gyrus; ReHo in the inferior temporal gyrus and the superior frontal gyrus (orbital part); and functional connectivity between the left superior temporal gyrus(temporal pole) and left medial superior frontal gyrus. A diagnostic predictive random forest model built using imaging features and validated by 10-fold cross-validation distinguished anxious from non-anxious MDD with an AUC of 0.802. Patients with anxious depression exhibit dysregulation of brain regions associated with emotion regulation, cognition, and decision-making, and our diagnostic model paves the way for more accurate, objective clinical diagnosis of anxious depression.

Psychosocial factors of insomnia in depression: a network approach.

BACKGROUND: Insomnia symptoms in patients with major depressive disorder (MDD) are common and deleterious. Childhood trauma, personality traits, interpersonal distress, and social support contribute to insomnia, but how they interact to affect insomnia remains uncertain. METHODS: A total of 791 patients with MDD completed the Insomnia Severity Index, Eysenck Personality Questionnaire, Interpersonal Relationship Comprehensive Diagnostic Scale, Childhood Trauma Questionnaire, Social Support Rating Scale and Hamilton Depression Scale-17. This study utilized network analyses to identify the central symptoms of insomnia and their associations with psychosocial factors. RESULTS: Worrying about sleep was identified as the central symptom in the insomnia network, insomnia and associated personality network, insomnia and associated interpersonal disturbance network, insomnia and associated childhood trauma network, insomnia and associated social support network, and the integrated network of insomnia symptoms and associated psychosocial factors. In the networks of insomnia symptoms and individual psychosocial factors, most psychosocial factors (other than childhood trauma) were directly or indirectly related to insomnia symptoms; however, neuroticism was the only factor directly associated with insomnia symptoms before and after controlling for covariates. In the final integrated network of insomnia symptoms and psychosocial factors, neuroticism was a bridge node and mediated the relationships of social support and interpersonal disturbances with insomnia symptoms, which is clearly presented in the shortest pathways. CONCLUSIONS: Worrying about sleep and neuroticism were prominent in the integrated network of insomnia symptoms and associated psychosocial factors, and the edge between them connected psychosocial factors and insomnia symptoms in MDD patients.

The Mediation Role of Sleep Disturbances between Vitamin D and Depressive Symptoms: A Cross-Sectional Study.

Depression and sleep disturbances are highly prevalent health problems that have been suggested to be associated with vitamin D deficiency. This study investigated whether sleep disturbances affect the association between vitamin D and depressive symptoms. A total of 425 patients with depression were included in this study. Spearman correlation coefficients were chosen to assess the relation between vitamin D concentrations and depressive symptomatology (according to the PHQ-9 and HAMD-17 scores). The GLM Mediation Model in the Medmod module for data analysis in Jamovi 2.2.5 was used to analyze the mediation models for sleep disturbances. Vitamin D concentrations were significantly correlated with PHQ-9 and HAMD-17 scale scores. In addition, item 3 was suggested to have a mediating effect between vitamin D and depressive symptoms in the mediating model of PHQ-9, and item 4 was suggested to have a mediating effect between vitamin D and depressive symptoms in the mediating model of HAMD-17. Sleep disturbances (especially difficulty falling asleep) are mediators between vitamin D and depressive symptoms, suggesting that increasing vitamin D levels at the right time to regulate sleep disturbances may improve depression symptoms, yet further research is necessary.

Striatum-related spontaneous coactivation patterns predict treatment response on positive symptoms of drug-naive first-episode schizophrenia with risperidone monotherapy.

Background: Evidence from functional magnetic resonance imaging (fMRI) studies of schizophrenia suggests that interindividual variation in the stationary striatal functional circuit may be correlated with antipsychotic treatment response. However, little is known about the role of the dynamic striatum-related network in predicting patients' clinical improvement. The spontaneous coactivation pattern (CAP) technique has recently been found to be important for elucidating the non-stationary nature of functional brain networks. Methods: Forty-two drug-naive first-episode schizophrenia patients underwent fMRI and T1W imaging before and after 8 weeks of risperidone monotherapy. The striatum was divided into 3 subregions, including the putamen, pallidum, and caudate. Spontaneous CAPs and CAP states were utilized to measure the dynamic characteristics of brain networks. We used DPARSF and Dynamic Brain Connectome software to analyze each subregion-related CAP and CAP state for each group and then compared the between-group differences in the neural network biomarkers. We used Pearson's correlation analysis to determine the associations between the neuroimaging measurements with between-group differences and the improvement in patients' psychopathological symptoms. Results: In the putamen-related CAPs, patients showed significantly increased intensity in the bilateral thalamus, bilateral supplementary motor areas, bilateral medial, and paracingulate gyrus, left paracentral lobule, left medial superior frontal gyrus, and left anterior cingulate gyrus compared with healthy controls. After treatment, thalamic signals in the putamen-related CAP 1 showed a significant increase, while the signals of the medial and paracingulate gyrus in the putamen-related CAP 3 revealed a significant decrease. The increase in thalamic signal intensity in the putamen-related CAP 1 was significantly and positively correlated with the percentage reduction in PANSS_P. Conclusion: This study is the first to combine striatal CAPs and fMRI to explore treatment response-related biomarkers in the early phase of schizophrenia. Our findings suggest that dynamic changes in CAP states in the putamen-thalamus circuit may be potential biomarkers for predicting patients' variation in the short-term treatment response of positive symptoms.

Assessment of brain imaging and cognitive function in a modified rhesus monkey model of depression.

Depression incurs a huge personal and societal burden, impairing cognitive and social functioning and affecting millions of people worldwide. A better understanding of the biological basis of depression could facilitate the development of new and improved therapies. Rodent models have limitations and do not fully recapitulate human disease, hampering clinical translation. Primate models of depression help to bridge this translational gap and facilitate research into the pathophysiology of depression. Here we optimized a protocol for administering unpredictable chronic mild stress (UCMS) to non-human primates and evaluated the influence of UCMS on cognition using the classical Wisconsin General Test Apparatus (WGTA) method. We used resting-state functional MRI to explore changes in amplitude of low-frequency fluctuations and regional homogeneity in rhesus monkeys. Our work highlights that the UCMS paradigm effectively induces behavioral and neurophysiological (functional MRI) changes in monkeys but without significantly impacting cognition. The UCMS protocol requires further optimization in non-human primates to authentically simulate changes in cognition associated with depression.

Longitudinal multi-omics alterations response to 8-week risperidone monotherapy: Evidence linking cortical thickness, transcriptomics and epigenetics.

Background: Antipsychotic treatment-related alterations of cortical thickness (CT) and clinical symptoms have been previously corroborated, but less is known about whether the changes are driven by gene expression and epigenetic modifications. Methods: Utilizing a prospective design, we recruited 42 treatment-naive first-episode schizophrenia patients (FESP) and 38 healthy controls. Patients were scanned by TI weighted imaging before and after 8-week risperidone monotherapy. CT estimation was automatically performed with the FreeSurfer software package. Participants' peripheral blood genomic DNA methylation (DNAm) status, quantified by using Infinium® Human Methylation 450K BeadChip, was examined in parallel with T1 scanning. In total, CT measures from 118 subjects and genomic DNAm status from 114 subjects were finally collected. Partial least squares (PLS) regression was used to detect the spatial associations between longitudinal CT variations after treatment and cortical transcriptomic data acquired from the Allen Human Brain Atlas. Canonical correlation analysis (CCA) was then performed to identify multivariate associations between DNAm of PLS1 genes and patients' clinical improvement. Results: We detected the significant PLS1 component (2,098 genes) related to longitudinal alterations of CT, and the PLS1 genes were significantly enriched in neurobiological processes, and dopaminergic- and cancer-related pathways. Combining Laplacian score and CCA analysis, we further linked DNAm of 33 representative genes from the 2,098 PLS1 genes with patients' reduction rate of clinical symptoms. Conclusions: This study firstly revealed that changes of CT and clinical behaviors after treatment may be transcriptionally and epigenetically underlied. We define a "three-step" roadmap which represents a vital step toward the exploration of treatment- and treatment response-related biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care.

Inflammatory bowel disease and the long-term risk of depression: A prospective cohort study of the UK biobank.

OBJECTIVE: Depression is more common in patients with chronic inflammatory diseases, but whether inflammatory bowel disease (IBD), a chronic, relapsing immune-mediated disease, is associated with a higher risk of depression remains uncertain. METHOD: We studied 497,134 participants in the UK Biobank, including 3561 IBD patients. Multivariate Cox proportional risk models were constructed to investigate the risk associated with IBD and depression adjusting for potential confounding factors including sociodemographic, lifestyle, and family history variables. RESULTS: The average age of participants was 56.54 ± 8.09 years; 54.3% were female and 90.4% were white. Over a mean follow-up period of 13.3 years, the cumulative incidence of depression was 8.2% (95% CI: 7.3%-9.1%) in IBD patients compared with 4.9% (95% CI: 4.9%-5.0%) in individuals without IBD. Compared with non-IBD participants, the adjusted hazard ratio (HR) for depression among IBD patients was 1.56 (95% CI: 1.39-1.76), with an adjusted HR of 1.54 (95% CI: 1.25-1.90) in Crohn's disease and 1.52 (95% CI: 1.30-1.78) in ulcerative colitis, respectively. CONCLUSION: IBD patients had a significantly higher risk of depression than non-IBD participants after adjusting for multiple confounding factors. We recommend screening for depression in middle-aged adults with IBD and no established history of depression.

Screening Children's Intellectual Disabilities with Phonetic Features, Facial Phenotype and Craniofacial Variability Index.

BACKGROUND: Intellectual Disability (ID) is a kind of developmental deficiency syndrome caused by congenital diseases or postnatal events. This syndrome could be intervened as soon as possible if its early screening was efficient, which may improve the condition of patients and enhance their self-care ability. The early screening of ID is always achieved by clinical interview, which needs in-depth participation of medical professionals and related medical resources. METHODS: A new method for screening ID has been proposed by analyzing the facial phenotype and phonetic characteristic of young subjects. First, the geometric features of subjects' faces and phonetic features of subjects' voice are extracted from interview videos, then craniofacial variability index (CVI) is calculated with the geometric features and the risk of ID is given with the measure of CVI. Furthermore, machine learning algorithms are utilized to establish a method for further screening ID based on facial features and phonetic features. RESULTS: The proposed method using three feature sets, including geometric features, CVI features and phonetic features was evaluated. The best performance of accuracy was closer to 80%. CONCLUSIONS: The results using the three feature sets revealed that the proposed method may be applied in a clinical setting in the future after continuous improvement.

Comparative analysis of nitrate evolution patterns during pollution episodes: Method development and results from Tianjin, China.

Particulate nitrate plays an increasingly important role in the formation of air pollution process, while the main mechanisms of nitrate concentration change are different in each stage, same as the driving factors. In this study, we proposed an episode-based analysis to illustrate the typical nitrate evolution patterns and identify the possible impacting factors in different evolution stages. Applying into twelve air pollution episodes, three typical patterns of nitrate evolution were abstracted, and the corresponding conceptual models were constructed. All the pollution episodes were grouped by their evolving shapes, which were driven by physical and chemical processes. Episodes started slowly typically arose from gradual pollutant accumulation, both locally and regionally, and chemical formation under high humidity. Type 1 ("hump-shaped type"), accounting for 66.3 % of the total episode durations, including two "peak" concentrations, displays a rapid growth rate which could up to 4.6 µg m-3 h-1 in average, mainly relying on the sharp drop in the planetary boundary layer height. Short scavenging processes and thoroughly dissipated stages of the pollution episodes always accompanied by strong north wind affected by Siberia-Mongolia cold current. Type 2 ("triangle-shaped type", 24.3 %) shows a gentle growth rate and short duration. Compared with Type 1, chemical process may be more important "source" for the increase of nitrate concentration during Type 2. Type 3 ("trapezoid-shaped type", 9.4 %) presents a long platform stage, during which high humidity (RH > 90 %) provides favorable conditions for wet removal and secondary production, and the updraft can carry pollutants to high altitude. The source and sink are roughly balanced for Type 3. Our study highlights the importance of pattern identification for understanding the nitrate evolution behavior, it may also provide insights for pollution prediction and scientific mitigation strategies.

Measuring childhood trauma in young adults with depression: A latent profile analysis.

Childhood traumas are important risk factors for depression in young adults. However, the co-occurrence of childhood traumas is complex, and the specific effects of different types of childhood traumas on depression need further exploration. The aim of this study was to assess the co-occurrence of childhood traumas and the impact of different profiles of childhood trauma on depression. A total of 1053 young adults with depression in China participated. PHQ-9, SHAPS, GAD-7, CTQ-SF, and NLES were evaluated. Latent profile analysis (LPA) was conducted to identify profiles of childhood trauma. The effects of different childhood trauma profiles on depression, anxiety, and anhedonia were assessed using stepwise linear regression. LPA suggested three profiles: no or low childhood traumas, moderate childhood trauma with emotional abuse and childhood neglect, and high childhood trauma with high levels of all trauma types. Regression analyses suggested that high levels of emotional abuse and childhood neglect significantly affected anhedonia. Childhood adverse events cluster in young adults with depression, allowing grouping into three distinct profiles. Specific childhood trauma patterns predict anhedonia symptoms in adult depression.

Blockade of receptor for advanced glycation end-products with azeliragon ameliorates streptozotocin-induced diabetic neuropathy.

Treatment options for diabetic neuropathy are suboptimal, so development of a new therapeutic strategy is urgent. We focused on the role of receptor for advanced glycation end-products (RAGE) in diabetic neuropathy. We elaborated the effects of azeliragon (orally available small-molecule antagonist of RAGE) on streptozotocin (STZ)-induced mechanical hypersensitivity in mice. A reduction in mechanical nociceptive threshold observed 28 days after STZ treatment was improved by single administration of azeliragon (10 and 30 mg/kg) at 3 h, but this effect disappeared at 24 h. Conversely, repeat administration (three times; days 28, 30, and 32) of azeliragon (30 mg/kg) enhanced the antinociceptive effect significantly compared with that obtained upon single administration, and this effect persisted at least up to 24 h. The antinociceptive effect of azeliragon (30 mg/kg) was almost comparable with that of pregabalin (30 mg/kg). These drug treatments had no effect on blood glucose levels. Our findings suggest that RAGE might be an effective target for diabetic neuropathy treatment.

Virtual histology of morphometric similarity network after risperidone monotherapy and imaging-epigenetic biomarkers for treatment response in first-episode schizophrenia.

BACKGROUND: Antipsychotic treatment has been conceived to alter brain connectivity, but it is unclear how the changes of network phenotypes relate to the underlying transcriptomics. Given DNA methylation (DNAm) may alter transcriptional levels, we further integrated an imaging-transcriptomic-epigenetic analysis to explore multi-omics treatment response biomarkers. METHODS: Forty-two treatment-naive first-episode schizophrenia patients were scanned by TI weighted (T1W) imaging and DTI before and after 8-week risperidone monotherapy, and their peripheral blood genomic DNAm values were examined in parallel with MRI scanning. Morphometric similarity network (MSN) quantified with DTI and T1W data were used as a marker of treatment-related alterations in interareal cortical connectivity. We utilized partial least squares (PLS) to examine spatial associations between treatment-related MSN variations and cortical transcriptomic data obtained from the Allen Human Brain Atlas. RESULTS: Longitudinal MSN alterations were related to treatment response on cognitive function and general psychopathology symptoms, while DNAm values of 59 PLS1 genes were on negative and positive symptoms. Virtual-histology transcriptomic analysis linked the MSN alterations with the neurobiological, cellular and metabolic pathways or processes, and assigned MSN-related genes to multiple cell types, specifying neurons and glial cells as contributing most to the transcriptomic associations of longitudinal changes in MSN. CONCLUSIONS: We firstly reveal how brain-wide transcriptional levels and cell classes capture molecularly validated cortical connectivity alterations after antipsychotic treatment. Our findings represent a vital step towards the exploration of treatment response biomarkers on the basis of multiple omics rather than a single omics type as a strategy for advancing precise care.

The relationship between vitamin D levels in seasonal variations and Chinese patients with first-episode drug-naive depression.

BACKGROUND: Vitamin D deficiency is highly prevalent worldwide and is associated with various diseases, including depression. Previous studies on vitamin D and depression have different conclusions. OBJECTIVES: Our study aimed to examine the association between vitamin D levels in seasonal variation and depression. METHODS: A total of 324 patients with first-episode depression aged 18-50 years were recruited for our study. Vitamin D levels were recorded, and PHQ-9 scale evaluation was performed in different seasons. Seasonal variations in vitamin D levels and depressive symptoms were examined. RESULTS: The cohort comprised 77 males and 247 females. 98.1% of patients had insufficient or deficient vitamin D levels. The median vitamin D level was 12 ng/mL; 14.5 ng/mL in summer and 13 ng/mL in autumn, which was significantly higher than 9 ng/mL in spring, and the correlation between vitamin D level and PHQ-9 score was more significant in spring but not in summer and autumn. LIMITATIONS: Our study used cross-sectional data and could not examine the causal relationship of the vitamin D level and depressive symptoms. There are also some possible influencing factors, such as the dietary habits, outdoor sports, and the use of sunscreen were not investigated. CONCLUSION: Observational data showed that the vitamin D level of depression is lower than the normal (30 ng/mL), and it is closely related to depressive symptoms in spring. The seasonal variations in vitamin D levels might play a critical role in Chinese patients with first-episode depression.

Psychosocial factors associated with anxious depression.

BACKGROUND: Anxious depression is a common subtype of major depressive disorder (MDD) associated with adverse outcomes and severely impaired social function. The aim of this study was to explore the relationships between child maltreatment, family functioning, social support, interpersonal problems, dysfunctional attitudes, and anxious depression. METHODS: Data were collected from 809 MDD patients. The Hamilton Anxiety Scale (HAMA), Hamilton Depression Scale-17 (HAMD-17), Family Assessment Device (FAD), Childhood Trauma Questionnaire (CTQ), Social Support Rating Scale (SSRS), Interpersonal Relationship Integrative Diagnostic Scale (IRIDS), and Dysfunctional Attitudes Scale (DAS) were administered and recorded. Anxious depression was defined as an anxiety/somatization factor score ≥ 7 on the HAMD-17. Chi-squared tests, Mann-Whitney U tests, distance correlations, and structural equation models were used for data analysis. RESULTS: Two-fifths of MDD patients had comorbid anxiety, and there were significant differences in child maltreatment, family functioning, social support, interpersonal problems, and dysfunctional attitudes between groups. Of these factors, interpersonal relationships were most related to anxiety in MDD patients, and dysfunctional attitudes mediated the relationship between interpersonal relationships and anxiety in MDD patients. LIMITATIONS: This study used cross-sectional data with no further follow-up to assess patient outcomes. This study did not include information about pharmacological treatments. A larger sample size is needed to validate the results. CONCLUSIONS: Psychosocial factors were significantly associated with anxious depression. Interpersonal relationships and dysfunctional attitudes have a direct effect on anxious depression, and interpersonal relationships also mediate the effects of anxious depression via dysfunctional attitudes.

A lightweight CNN and Transformer hybrid model for mental retardation screening among children from spontaneous speech.

Mental retardation (MR) is a group of mental disorders characterized by low intelligence and social adjustment difficulties. Early diagnosis is beneficial for the timely intervention of children with MR to ease the degree of disability. Children with MR always have impaired speech functions compared to normal children, which is significant for clinical diagnosis. On the basis of this, our study proposes a spontaneous speech-based framework (MT-Net) for screening MR, which merges mobile inverted bottleneck convolutional blocks (MBConv) and visual Transformer blocks. MT-Net takes log-mel spectrograms converted from raw interview speech as data source, and utilizes MBConv and visual Transformer to learn low-level and high-level features well. In addition, SpecAugment, a data augmentation strategy, has been used to expand our audio dataset to further enhance the performance of MT-Net. The experimental results show that our proposed MT-Net outperforms Transformer networks (ViT) and convolutional neural networks (ResNet18, MobileNetV2, EfficientNetV2), achieving accuracy of 91.60% after using SpecAugment. Our proposed MT-Net has fewer parameters, low computing consumption and high prediction accuracy, which is expected to be an auxiliary screening tool for MR.

Upregulation of Nav1.6 Mediated by the p38 MAPK Pathway in the Dorsal Root Ganglia Contributes to Cancer-Induced Bone Pain in Rats.

Cancer-induced bone pain (CIBP) occurs frequently among advanced cancer patients. Voltage-gated sodium channels (VGSCs) have been associated with chronic pain, but how VGSCs function in CIBP is poorly understood. Here, we aimed to investigate the specific role of VGSCs in the dorsal root ganglia (DRGs) in CIBP. A CIBP rat model was generated by the intratibial inoculation of MRMT-1 breast carcinoma cells. Transcriptome sequencing was conducted to assess the gene expression profiles. The expression levels of key genes and differentiated genes related to activated pathways were measured by Western blotting and qPCR. We implanted a catheter intrathecally for the administration of lentivirus and drugs. Then, the changes in the mechanical withdrawal threshold (MWT) were measured. We identified 149 differentially expressed mRNAs (DEmRNAs) in the DRGs of CIBP model rats. The expression of Nav1.6, which was among these DEmRNAs, was significantly upregulated. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEmRNAs showed that they were mainly enriched in the mitogen-activated protein kinase (MAPK) pathway. The decrease in MWT induced by bone cancer was attenuated by Nav1.6 knockdown. Western blot analysis revealed that a p38 inhibitor decreased the expression of Nav1.6 and attenuated pain behavior. Our study shows that the upregulation of Nav1.6 expression by p38 MAPK in the DRGs of rats contributes to CIBP.

Two stream Non-Local CNN-LSTM network for the auxiliary assessment of mental retardation.

At present, the assessment of mental retardation is mainly based on clinical interview, which requires the participation of experienced psychiatrist and is laborious. Studies have shown that there are correlations between mental retardation and abnormal behaviors (such as, hyperkinetic, tics, stereotypes, etc.). On the basis of this fact, a two stream Non-Local CNN-LSTM network has been proposed to learn the features of upper body behavior and facial expression of patients, thus, to achieve the preliminary screening of mental retardation. Specifically, RGB and optical flow are extracted separately from interview videos, and a two stream network based on contribution mechanism is designed to effectively fuse the information of two kinds of images, which may update the network in a new approach of alternating iteration training to find the optimal model. Besides, by introducing non-local mechanism and adopting it to the network, the global feature sensing can be established more effectively to reduce the background interference for video clip in a short time zone. Experiments on clinical video dataset show that the performance of proposed model is better than other prevalent deep learning methods of behavioral feature learning, the accuracy reaches 89.15% in basic experiment, and is further improved to 89.52% in the supplementary experiment. Furthermore, the experimental results show that this method still has a lot of room for improvement. In general, our work indicates that the proposed model has potential value for the clinical diagnosis and screening of mental retardation.