A network analysis of daily stressors, subjective well-being, psychological distress and psychological capital during the COVID-19 pandemic.

BACKGROUND: There is already considerable evidence suggesting the potential existence of a comprehensive system that reflects how individuals utilize psychological capital (PsyCap) to preserve mental health amid daily stressors during the COVID-19 pandemic. To explore the underlying mechanism of this system, the current study is the first to use network analysis showing the dimension-level correlation patterns of daily stressors, subjective well-being (SWB), psychological distress and PsyCap during the pandemic. METHOD: We recruited 1556 participants in China and assessed daily stressors, SWB, psychological distress, and PsyCap through self-report questionnaires. A dimension-level network analysis was conducted to identify key dimensions and their associations. Relative importance analysis examined the contribution of each PsyCap dimension to SWB and psychological distress. RESULT: Depression, anxiety and stress demonstrated high strength, and life satisfaction exhibited the highest bridge strength in the network. The great majority dimensions of daily stressors had positive connections with stress and anxiety, while financial restrictions and dissatisfaction with education/occupation had negative connections with life satisfaction. Self-efficacy and optimism were positively connected with life satisfaction. Hope and resilience were negatively connected with stress, as well as hope and optimism were negatively connected with depression. Optimism and self-efficacy made the highest contribution to SWB, while resilience made the highest contribution to psychological distress among PsyCap dimensions. CONCLUSION: The findings elucidate the intricate relationship between daily stressors, SWB, psychological distress and PsyCap during the pandemic. Targeted interventions focusing on the specific PsyCap dimensions may enhance mental health outcomes in the post-COVID-19 era.

Linderalactone mitigates diabetic cardiomyopathy in mice via suppressing the MAPK/ATF6 pathway.

Diabetic cardiomyopathy (DCM) is a challenging diabetic complication that manifests as chronic inflammation. Yet, the mechanism underlying diabetes-associated myocardial injury is not fully understood. We investigated the pharmacological effects and mechanisms of linderalactone, a natural compound that can prevent diabetes-induced cardiomyopathy in mice. Diabetes was induced by a single dose of streptozotocin (120 mg/kg, i.p.). Diabetic mice were administrated with linderalactone (2.5 or 5 mg/kg) by gavage for five weeks. Harvested heart tissues were then subjected to RNA-sequencing analysis to explore the potential mechanism of linderalactone. Linderalactone prevented heart dysfunction by inhibiting myocardial hypertrophy, fibrosis, and inflammation, without altering blood glucose. RNA-sequencing indicated that linderalactone exerted its cardioprotective effects mainly by affecting the mitogen-activated protein kinase (MAPK)/ activating transcription factor 6 (ATF6) pathway. Linderalactone also suppressed endoplasmic reticulum (ER) stress mediated by the diabetes-activated MAPKs/ATF6 pathway, thereby reducing myocardial hypertrophy and inflammation in heart tissues and in cultured cardiomyocytes. Inhibition of MAPKs or a deficiency of ATF6 in cardiomyocytes mimicked the linderalactone-associated decreases in high glucose-induced hypertrophy and inflammation. Linderalactone showed beneficial effects in alleviating diabetic cardiomyopathy, in part by modulating the MAPK/ATF6 signaling pathway to mitigate myocardial hypertrophy and inflammation. Linderalactone may have clinical utility in the treatment for diabetes-associated cardiomyopathy.

Exploring the ability of vocal biomarkers in distinguishing depression from bipolar disorder, schizophrenia, and healthy controls.

Background: Vocal features have been exploited to distinguish depression from healthy controls. While there have been some claims for success, the degree to which changes in vocal features are specific to depression has not been systematically studied. Hence, we examined the performances of vocal features in differentiating depression from bipolar disorder (BD), schizophrenia and healthy controls, as well as pairwise classifications for the three disorders. Methods: We sampled 32 bipolar disorder patients, 106 depression patients, 114 healthy controls, and 20 schizophrenia patients. We extracted i-vectors from Mel-frequency cepstrum coefficients (MFCCs), and built logistic regression models with ridge regularization and 5-fold cross-validation on the training set, then applied models to the test set. There were seven classification tasks: any disorder versus healthy controls; depression versus healthy controls; BD versus healthy controls; schizophrenia versus healthy controls; depression versus BD; depression versus schizophrenia; BD versus schizophrenia. Results: The area under curve (AUC) score for classifying depression and bipolar disorder was 0.5 (F-score = 0.44). For other comparisons, the AUC scores ranged from 0.75 to 0.92, and the F-scores ranged from 0.73 to 0.91. The model performance (AUC) of classifying depression and bipolar disorder was significantly worse than that of classifying bipolar disorder and schizophrenia (corrected p < 0.05). While there were no significant differences in the remaining pairwise comparisons of the 7 classification tasks. Conclusion: Vocal features showed discriminatory potential in classifying depression and the healthy controls, as well as between depression and other mental disorders. Future research should systematically examine the mechanisms of voice features in distinguishing depression with other mental disorders and develop more sophisticated machine learning models so that voice can assist clinical diagnosis better.

The effect of exposure to COVID-19 on life satisfaction: The mediating role of hyperarousal and moderating/mediating role of affective forecasting.

BACKGROUND: As a global trauma event, the Coronavirus disease (COVID-19) hugely impacts people's mental health and well-being. The purpose of our study is threefold: first, investigate the relationship between exposure to COVID-19 and life satisfaction among a large sample in China; second, test the mediating role of hyperarousal in the association between exposure to COVID-19 and life satisfaction; third, explore the moderating/mediating role of affective forecasting in the association between hyperarousal and life satisfaction. METHOD: The current study recruited 5546 participants to complete a set of self-report questionnaires online from April 22, 2020 to April 24, 2020. The moderated mediation and chain mediation model analyses were conducted using the SPSS software and PROCESS macro program. RESULTS: Exposure to COVID-19 negatively predicted life satisfaction (Effect = -0.058, p < 0.001). This relationship was partially mediated by the hyperarousal level (Effect = -0.018, CI = [-0.024, -0.013]). The moderating role of forecasted positive affect (PA)/forecasted negative affect (NA) between hyperarousal and life satisfaction was significant (ß = 0.058, CI = [0.035, 0.081]; ß = 0.037, CI = [0.014, 0.06]). The chain mediating effect of hyperarousal and forecasted PA/forecasted NA on the relationship between exposure to COVID-19 and life satisfaction was also significant (Effect = -0.003, CI = [-0.004, -0.002]; Effect = -0.006, CI = [-0.008, -0.004]). LIMITATIONS: The cross-sectional design precludes causal inference. CONCLUSION: Greater exposure to COVID-19 was associated with more severe hyperarousal symptoms, decreased life satisfaction. Forecasted PA and forecasted NA could moderate and mediate the negative impact of hyperarousal on life satisfaction. The moderating/mediating role of forecasted PA/NA implied that future interventions targeting at improving affective forecasting and reducing hyperarousal might be helpful to improve life satisfaction during the post-COVID-19 era.

Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches.

Depression is one of the most common mental illnesses but remains underdiagnosed. Suicide, as a core symptom of depression, urgently needs to be monitored at an early stage, i.e., the suicidal ideation (SI) stage. Depression and subsequent suicidal ideation should be supervised on social media. In this research, we investigated depression and concomitant suicidal ideation by identifying individuals' linguistic characteristics through machine learning approaches. On Weibo, we sampled 487,251 posts from 3196 users from the depression super topic community (DSTC) as the depression group and 357,939 posts from 5167 active users on Weibo as the control group. The results of the logistic regression model showed that the SCLIWC (simplified Chinese version of LIWC) features such as affection, positive emotion, negative emotion, sadness, health, and death significantly predicted depression (Nagelkerke's R2 = 0.64). For model performance: F-measure = 0.78, area under the curve (AUC) = 0.82. The independent samples' t-test showed that SI was significantly different between the depression (0.28 ± 0.5) and control groups (-0.29 ± 0.72) (t = 24.71, p < 0.001). The results of the linear regression model showed that the SCLIWC features, such as social, family, affection, positive emotion, negative emotion, sadness, health, work, achieve, and death, significantly predicted suicidal ideation. The adjusted R2 was 0.42. For model performance, the correlation between the actual SI and predicted SI on the test set was significant (r = 0.65, p < 0.001). The topic modeling results were in accordance with the machine learning results. This study systematically investigated depression and subsequent SI-related linguistic characteristics based on a large-scale Weibo dataset. The findings suggest that analyzing the linguistic characteristics on online depression communities serves as an efficient approach to identify depression and subsequent suicidal ideation, assisting further prevention and intervention.

A Biological Insight into the Susceptibility to Influenza Infection in Junior Rats by Comprehensive Analysis of lncRNA Profiles.

Long noncoding RNAs (lncRNAs) have been reported to participate in regulating many biological processes, including immune response to influenza A virus (IAV). However, the association between lncRNA expression profiles and influenza infection susceptibility has not been well elucidated. Here, we analyzed the expression profiles of lncRNAs, miRNAs, and mRNAs among IAV-infected adult rat (IAR), normal adult rat (AR), IAV-infected junior rat (IJR), and normal junior rat (JR) by RNA sequencing. Compared with differently expressed lncRNAs (DElncRNAs) between AR and IAR, 24 specific DElncRNAs were found between IJR and JR. Then, based on the fold changes and P value, the top 5 DElncRNAs, including 3 upregulated and 2 downregulated lncRNAs, were chosen to establish a ceRNA network for further disclosing their regulatory mechanisms. To visualize the differentially expressed genes in the ceRNA network, GO and KEGG pathway analysis was performed to further explore their roles in influenza infection of junior rats. The results showed that the downregulated DElncRNA-target genes were mostly enriched in the IL-17 signaling pathway. It indicated that the downregulated lncRNAs conferred the susceptibility of junior rats to IAV via mediating the IL-17 signaling pathway.

Protective effects of acarbose against insulitis in multiple low-dose streptozotocin-induced diabetic mice.

AIMS: The development of type 1 diabetes is associated with inflammatory lesion of the pancreatic islets, known as insulitis. In this study, we focused on the protective effects of acarbose against insulitis in streptozotocin (STZ)-induced diabetic mice and the underlying mechanisms. MAIN METHODS: The mouse models were established via intraperitoneal injection of multiple low-dose STZ. Blood glucose level and body weight were measured. The severity of insulitis and inflammatory parameters in pancreatic tissues were evaluated. Insulin levels in pancreas and serum were also assessed. In vitro, MIN6 ß cells were exposed to pro-inflammatory cytokines to assess the protective effects of acarbose. Cell function and apoptosis were evaluated. KEY FINDINGS: We found that acarbose administration by gavage reduced the severity of insulitis and improved insulin levels in the experimental diabetic mice. ELISA revealed decreased levels of the inflammatory response markers IL-1ß and TNF-α in mouse pancreatic tissues following acarbose treatment. In vitro, acarbose increased cell viability, decreased cell apoptosis, and improved GSIS in MIN6 ß cells exposed to pro-inflammatory cytokines. In addition, caspase-3 level and p-p53/p53 ratio in ß cells were reduced by acarbose treatment. SIGNIFICANCE: Taken together, these results revealed a novel function of acarbose in attenuating insulitis. The protective effects of acarbose elicited in vitro and in vivo were shown to be mediated, at least in part, through its anti-inflammatory action.

A Clinical Study of Early Intervention with Coralline Hydroxyapatite on Fresh Extraction Sockets.

To clinically, histologically and radiologically evaluate the effects of early intervention on fresh extraction sockets, 30 patients were selected and assigned evenly into group A (blood clot) and group B (coralline hydroxyapatite). All participants underwent clinical examination. Oral plaster casts and panoramic radiographs were undertaken before tooth extraction and at 3-4 months after socket grafting. A 6-8 mm sample of longitudinal bone core was obtained using a 2 mm diameter trephine 3-4 month's post-operatively. Group A had a greater decrease in interdental papilla height, which was about 2-3 times more than that of group B (P >0.001). The buccal-lingual alveolar ridge width also absorbed more in group A (P >0.001). Radiological findings were similar. Group B had less new bone formation (P >0.001), and the residual rate was a little high. There was no statistical difference between the two groups in terms of new bone formation rating grade. Clinical, histological and radiological tissue differences were observed between sockets with and without biomaterial grafts 3-4 months after surgery. This indicates that early intervention of extraction sockets is clinically advantageous.

Comparisons Between Different Methods in Measuring Enzyme Similarity for Metabolic Network Alignment

Metabolic network alignments enable comparison of the similarities and differences between pathways in two metabolic networks and help to uncover the conserved sub-blocks therein. Such analysis is important in the understanding of metabolic networks and species evolution. The fundamental parts of metabolic network alignment algorithms all involve comparisons of the similarity between two enzymes as a similarity measure of network nodes. As a result, the study of methods for measuring enzyme similarity becomes highly relevant. Currently, two approaches are mainly used to measure enzyme similarity. One of the methods is based on similarity measures of gene or protein sequences; the other is based on enzyme classification. In this study, multiple metabolic network alignments were performed using both the methods. The results showed that, in general, the sequence similarity method yielded higher accuracy, especially with respect to reflecting evolutionary distances.

Elementary Flux Mode Analysis Revealed Cyclization Pathway as a Powerful Way for NADPH Regeneration of Central Carbon Metabolism.

NADPH regeneration capacity is attracting growing research attention due to its important role in resisting oxidative stress. Besides, NADPH availability has been regarded as a limiting factor in production of industrially valuable compounds. The central carbon metabolism carries the carbon skeleton flux supporting the operation of NADPH-regenerating enzyme and offers flexibility in coping with NADPH demand for varied intracellular environment. To acquire an insightful understanding of its NADPH regeneration capacity, the elementary mode method was employed to compute all elementary flux modes (EFMs) of a network representative of central carbon metabolism. Based on the metabolic flux distributions of these modes, a cluster analysis of EFMs with high NADPH regeneration rate was conducted using the self-organizing map clustering algorithm. The clustering results were used to study the relationship between the flux of total NADPH regeneration and the flux in each NADPH producing enzyme. The results identified several reaction combinations supporting high NADPH regeneration, which are proven to be feasible in cells via thermodynamic analysis and coincident with a great deal of previous experimental report. Meanwhile, the reaction combinations showed some common characteristics: there were one or two decarboxylation oxidation reactions in the combinations that produced NADPH and the combination constitution included certain gluconeogenesis pathways. These findings suggested cyclization pathways as a powerful way for NADPH regeneration capacity of bacterial central carbon metabolism.

CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics.

The Central Carbon Metabolic Flux Database (CeCaFDB, available at http://www.cecafdb.org) is a manually curated, multipurpose and open-access database for the documentation, visualization and comparative analysis of the quantitative flux results of central carbon metabolism among microbes and animal cells. It encompasses records for more than 500 flux distributions among 36 organisms and includes information regarding the genotype, culture medium, growth conditions and other specific information gathered from hundreds of journal articles. In addition to its comprehensive literature-derived data, the CeCaFDB supports a common text search function among the data and interactive visualization of the curated flux distributions with compartmentation information based on the Cytoscape Web API, which facilitates data interpretation. The CeCaFDB offers four modules to calculate a similarity score or to perform an alignment between the flux distributions. One of the modules was built using an inter programming algorithm for flux distribution alignment that was specifically designed for this study. Based on these modules, the CeCaFDB also supports an extensive flux distribution comparison function among the curated data. The CeCaFDB is strenuously designed to address the broad demands of biochemists, metabolic engineers, systems biologists and members of the -omics community.

[Therapeutic efficacy of chemotherapy combined with radiotherapy in 527 patients with stage III and IV non-small cell lung cancer].

BACKGROUND: Chemotherapy is very important in treatment of advanced non-small cell lung cancer (NSCLC), and the third-generation cisplatin-based chemotherapy regimens have been the standard treatment for advanced NSCLC. The aim of this study is to compare the efficacy and toxicity among four different chemotherapeutic regimens combined with radiotherapy in patients with stage III/IV NSCLC. METHODS: A total of 527 patients with stage III/IV NSCLC were enrolled, among whom there were 243 patients received cisplatin/vinorelbine (NP group), 163 patients for cisplatin/paclitaxel (TP group), 65 patients for cisplatin/gemcitabine (GP group) and 56 patients for cisplatin/docetaxel (DP group). The efficacy, side effects, median time to progression (TTP), median survival time (MST), 1- and 2-year survival rate were compared. RESULTS: The response rate was 46.9% in the NP arm, 44.8% in the TP arm, 47.7% in the GP arm and 42.9% in the DP arm (P > 0.05). The response rate of patients with radiochemotherapy was 69.9%, and 40.8% for those with chemotherapy alone (P < 0.05). In group NP, TP, GP and DP, median TTP was 5.7, 5.3, 5.9 and 5.5 months (P > 0.05) respectively, MST was 10.4, 10.6, 11.5 and 10.4 months (P > 0.05) respectively, 1-year survival rate was 41.9%, 41.1%, 43.1% and 42.9% (P > 0.05) respectively, and 2-year survival rate was 21.3%, 19.4%, 23.1% and 23.2% (P > 0.05) respectively. CONCLUSIONS: The third-generation cisplatin-based chemotherapy regimens may be the standard treatment for advanced NSCLC, and their combination with radiotherapy may improve the therapeutic efficacy and prolong the survival of patients.

[Randomized clinical trial of IEP and EP regimens in the treatment of patients with small cell lung cancer].

BACKGROUND: To observe and compare the efficacy and safety of IEP and EP regimens for small cell lung cancer (SCLC). METHODS: Sixty-four patients with SCLC pathologically proved were randomly divided into IEP group ( n =32) and EP group ( n =32). RESULTS: All the 64 patients were evaluable for response and toxicity. In IEP group, the total responsive rate, responsive rates of limited-stage patients and extensive-stage patients were 84.4%(27/32), 100.0%(15/15) and 70.6%(12/17) respectively; while in EP group, those were 75.0%(24/32), 85.7%(12/14) and 66.7% (12/18) respectively. The median duration of remission was 6 months and 1-year survival rate was 62.5% in IEP group, and 5 months and 56.2% in EP group. There was no significant difference in response rate, median duration of remission and 1-year survival between the two groups ( P > 0.05). The main toxicity was myelosuppression. Incidences of leukopenia at grade III-IV, nausea, vomiting and alopecia were significantly higher in the IEP arm than those in the EP arm ( P < 0.01 ). CONCLUSIONS: High response rates and tolerable toxicities are attainable for small cell lung cancer treated with IEP and EP. IEP regimen shows a similar response rate compared with EP regimen. They might be considered as relevant regimens in initial patients with small cell lung cancer.