Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

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.

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.

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.

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.

Electronic health literacy among older adults in the context of the COVID-19 pandemic: A mixed-methods study.

AIM: This study aimed to investigate eHealth literacy about coronavirus disease 2019 (COVID-19) among older adults during the pandemic. BACKGROUND: The COVID-19 pandemic promoted the development of online health care. Higher demand for accessing information from the Internet was seen. METHODS: This was a sequential explanatory mixed-method study, involving a survey of older adults to explore the status and influencing factors of eHealth literacy regarding COVID-19. Semi-structured interviews were used to understand experiences and challenges regarding information retrieval, judgment and utilization. RESULTS: A total of 337 older adults participated in the online questionnaire survey. Overall, older adults had slightly higher scores on eHealth literacy during the COVID-19 pandemic. Participants' location in the past month and current health issues were associated with eHealth literacy. Qualitative data were collected from nine older adults and included that some older adults retrieved health-related information during the pandemic. However, those who used non-smartphones described difficulties in information retrieval. A glut of misinformation has resulted in an 'infodemic', which has not only increased the difficulty of judging information but also posed challenges in information utilization for older adults. CONCLUSION: Improving older adults' eHealth literacy is essential in promoting an improved response to major public health events and in providing better health care for this group in the future. It is essential that government health agencies and health care providers provide evidence-based health information via social media platforms. Further efforts are needed to combine aspects of traditional and online health care services and provide reliable and updated online information and resources for older adults. IMPLICATIONS FOR NURSING MANAGEMENT: Providing evidence to eHealth literacy improvement and health management of older adults in the context of public health events.

Pretreatment with High Mobility Group Box-1 Monoclonal Antibody Prevents the Onset of Trigeminal Neuropathy in Mice with a Distal Infraorbital Nerve Chronic Constriction Injury.

Persistent pain following orofacial surgery is not uncommon. High mobility group box 1 (HMGB1), an alarmin, is released by peripheral immune cells following nerve injury and could be related to pain associated with trigeminal nerve injury. Distal infraorbital nerve chronic constriction injury (dIoN-CCI) evokes pain-related behaviors including increased facial grooming and hyper-responsiveness to acetone (cutaneous cooling) after dIoN-CCI surgery in mice. In addition, dIoN-CCI mice developed conditioned place preference to mirogabalin, suggesting increased neuropathic pain-related aversion. Treatment of the infraorbital nerve with neutralizing antibody HMGB1 (anti-HMGB1 nAb) before dIoN-CCI prevented both facial grooming and hyper-responsiveness to cooling. Pretreatment with anti-HMGB1 nAb also blocked immune cell activation associated with trigeminal nerve injury including the accumulation of macrophage around the injured IoN and increased microglia activation in the ipsilateral spinal trigeminal nucleus caudalis. The current findings demonstrated that blocking of HMGB1 prior to nerve injury prevents the onset of pain-related behaviors, possibly through blocking the activation of immune cells associated with the nerve injury, both within the CNS and on peripheral nerves. The current findings further suggest that blocking HMGB1 before tissue injury could be a novel strategy to prevent the induction of chronic pain following orofacial surgeries.

Impact on mental health and perceptions of psychological care among medical and nursing staff in Wuhan during the 2019 novel coronavirus disease outbreak: A cross-sectional study.

The severe 2019 outbreak of novel coronavirus disease (COVID-19), which was first reported in Wuhan, would be expected to impact the mental health of local medical and nursing staff and thus lead them to seek help. However, those outcomes have yet to be established using epidemiological data. To explore the mental health status of medical and nursing staff and the efficacy, or lack thereof, of critically connecting psychological needs to receiving psychological care, we conducted a quantitative study. This is the first paper on the mental health of medical and nursing staff in Wuhan. Notably, among 994 medical and nursing staff working in Wuhan, 36.9% had subthreshold mental health disturbances (mean PHQ-9: 2.4), 34.4% had mild disturbances (mean PHQ-9: 5.4), 22.4% had moderate disturbances (mean PHQ-9: 9.0), and 6.2% had severe disturbance (mean PHQ-9: 15.1) in the immediate wake of the viral epidemic. The noted burden fell particularly heavily on young women. Of all participants, 36.3% had accessed psychological materials (such as books on mental health), 50.4% had accessed psychological resources available through media (such as online push messages on mental health self-help coping methods), and 17.5% had participated in counseling or psychotherapy. Trends in levels of psychological distress and factors such as exposure to infected people and psychological assistance were identified. Although staff accessed limited mental healthcare services, distressed staff nonetheless saw these services as important resources to alleviate acute mental health disturbances and improve their physical health perceptions. These findings emphasize the importance of being prepared to support frontline workers through mental health interventions at times of widespread crisis.

Source Contributions of PM2.5 in the Severe Haze Episode in Hebei Cities.

Beijing-Tianjin-Hebei area is one of the most polluted areas in China. This paper used the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) and Model-3/Community Multiscale Air Quality (CMAQ) modeling system to quantify the source contribution to PM2.5 in Hebei cities in order to obtain an in-depth understanding haze process in January and February 2013, using the Multiresolution Emission Inventory for China (MEIC). The result showed that PM2.5 were mainly originated from the southern Hebei (SHB) with the fractions of 70.8% and 66.4% to Shijiazhuang, 70.6% and 63.9% to Xingtai, and 68.5% and 63.0% to Handan in January and February 2013, respectively. The northern Hebei (NHB) contributed 69.8% and 70.7% to Zhangjiakou, 68.7% and 66.2% to Chengde, and 57.7% and 59.6% to Qinhuangdao in January and February. In Cangzhou, Hengshui, and Langfang, regional joint policy making should be implemented due to the pollution of multiple sources. In Baoding and Tangshan, industrial emissions contributed 38.1% and 41.9% of PM2.5 to Baoding and 39.8% and 45.8% to Tangshan in January and February, respectively. Industrial and domestic emissions should be controlled in Tangshan and Baoding, especially for industrial emissions of NHB.

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.

Exploring emission spatiotemporal pattern and potential reduction capacity in China's aviation sector: Flight trajectory optimization perspective.

China's rapid expansion of civil aviation has led to an increase in pollution-related issues, causing adverse health effects on populations near airports and downwind. Accurately quantifying aviation emissions is essential for effective emission management. Here, we developed a high-resolution aviation emissions inventory for China by employing a bottom-up approach that relied on daily flight schedules. By using the Aeronautical Information Publication (AIP) to reproduce real-world flight routes rather than conventional great-circle routes, we improved the accuracy of emissions and investigated the potential for reducing these emissions. Our findings demonstrated substantial variations in domestic civil aviation emissions both spatially and temporally. Emissions peaked in most provinces during Chinese holidays, particularly the Chinese Lunar New Year and summer holidays, highlighting the importance of detailed activity data for accurate emissions calculations. Therefore, we recommend extensive utilization of real-world flight routes, particularly in areas with limited Automatic Dependent Surveillance-Broadcast (ADS-B) coverage since they provide more accurate representations of actual flight trajectories. Our study also identified regions like Shaanxi, Sichuan, Beijing, and their surroundings having considerable potential for emission reduction due to substantial deviations from great-circle routes. This approach can enhance the accuracy and spatiotemporal resolution of aviation emissions at national and global scales throughout the year, without relying on extensive, long-term real-time flight trajectories. Additionally, it provides a unique way to quantify the potential for emission reductions across provinces in civil aviation, ultimately contributing to mitigating pollution-related health impacts from aviation emissions and promoting a more sustainable aviation industry.

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.

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.

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.

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.

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.

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.

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.