The Effects of Efficacy Framing in News Information and Health Anxiety on Coronavirus-Disease-2019-Related Cognitive Outcomes and Interpretation Bias

Within the coronavirus-disease-2019 (COVID-19) pandemic, disease-related information is omnipresent in the media, whereas information about how to manage the pandemic is less often covered. Under the context where threat is present, this study investigated whether and how the strength of efficacy framing (i.e., the perspective adopted by a communicating text that emphasizes one’s possibilities to cope with an external threat) of COVID-19-related news, as well as its interaction with trait health anxiety under the COVID-19 context, related to people’s COVID-19-related cognitive outcomes. One hundred and ninety-three participants reported demographics, trait health anxiety, and COVID-19-related behaviors (e.g., precautionary measures, information-seeking behaviors). They then either read high-efficacy (n = 112; e.g., cure rate) or low-efficacy (n = 81; e.g., mortality rate) information about COVID-19. Afterward, their tendency to interpret illness and COVID-19-related information more negatively, and other COVID-19-related cognitions (e.g., risk perception, behavioral change intentions) were assessed. High-efficacy framing resulted in lower-risk perception and marginally weaker COVID-19-related interpretation bias, compared with low-efficacy framing. There was some evidence of an interaction with health anxiety such that high-efficacy framing, compared with low-efficacy framing, was associated with greater intention to adopt protective behaviors, particularly for individuals with higher levels of health anxiety.</p

Palladium, platinum, selenium and tellurium enrichment in the Jiguanzui-Taohuazui Cu-Au Deposit, Edong Ore District: Distribution and comparison with Cu-Mo deposits

The Jiguanzui-Taohuazui Cu-Au deposit is located in the Edong ore district, Middle–Lower Yangtze River metallogenic belt, eastern China. The deposit is palladium, platinum, selenium and tellurium enriched; however, the distribution of these metals is unclear. Three mineral assemblages of ore in the deposit have been identified, namely: a magnetite-bornite-chalcopyrite-(hematite) assemblage (Mt-Bn-Cp-Hm), a chalcopyrite-pyrite assemblage (Cp-Py), and a pyrite-chalcopyrite-(sphalerite) assemblage (Py-Cp-Sph). Forty-eight bulk ore assay results show high concentrations of up to 66.9 ppb for Pd, 5.9 ppb for Pt, 150 ppm for Se and 249 ppm for Te. The high temperature Mt-Bn-Cp-Hm assemblage (530–380 °C) is enriched in Pt and Pd, whereas the Py-Cp-Sph assemblage in the marble-replacement ore (300–220 °C) hosts the major Se and Te mineralization. Palladium, Pt, and Se are mostly hosted in sulfide minerals, whereas Te is hosted in tellurides and Bi-Te-S sulfosalt minerals. Building on previous experimental and thermodynamic calculations, we propose the major controls on the Pd and Pt distribution in the deposit are temperature and salinity, whereas the Se and Te mineralization is promoted by the precipitation of major sulfide phases such as pyrite, chalcopyrite and sphalerite. A comparison of the ores from the Jiguanzui-Taohuazui Cu-Au and Tongshankou Cu-Mo deposits in the Edong ore district shows that the Cu-Au deposit has higher PGE and Te, but similar Se concentrations. This scenario is consistent with the average grades and bulk ore contents of these elements from global (oxidized) porphyry (±skarn) Cu deposits. This suggests that the saturation of magmatic sulfides in the magma chamber as a result of higher proportion of crustal S-rich and/or reduced material contamination can be detrimental for PGE and Te enrichment processes, and thus, Cu-Au porphyry (±skarn) deposits have more potential for higher Pd and Te concentrations than the Cu-Mo deposits

The Impact of Interpretation Biases on Psychological Responses to the COVID-19 Pandemic:a Prospective Study

BACKGROUND: This study investigates the longitudinal role of interpretation biases in the development and maintenance of health anxiety during the pandemic. Individual differences in behavioural responses to the virus outbreak and decision-making were also examined. METHODS: Two hundred seventy-nine individuals from a pre-pandemic study of interpretation bias and health anxiety completed an online survey during the third wave of the COVID-19 pandemic in Hong Kong. Participants’ health anxiety, interpretation biases, and COVID-specific behaviours (i.e. practice of social distancing, adherence to preventive measures, information seeking), and health decision-making were assessed. RESULTS: Pre-pandemic tendencies to interpret ambiguous physical sensations as signals for illness did not predict health anxiety during the pandemic, b = −0.020, SE = 0.024, t = −0.843, p = .400, 99% CI [−0.082, 0.042], but were associated with a preference for risky treatment option for COVID-19, b = 0.026, SE = 0.010, Wald = 2.614, p = .009, OR = 1.026, 99% CI [1.001, 1.054]. Interpretation biases and health anxiety symptoms during the pandemic were associated with each other and were both found to be significant predictors of practice of social distancing, adherence to preventive measures, and information seeking behaviour. CONCLUSIONS: This study adds to the growing evidence of the role of interpretation biases in health anxiety and the way that people respond to the ongoing pandemic. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12529-022-10079-5

Evaluation of electroacupuncture as a non-pharmacological therapy for astrocytic structural aberrations and behavioral deficits in a post-ischemic depression model in mice

BackgroundAscending clinical evidence supports that electroacupuncture (EA) is effective in treating post-ischemic depression (PID), but little is known about how it works at the cellular level. Astrocytes are exquisitely sensitive to their extracellular environment, and under stressful conditions, they may experience aberrant structural remodeling that can potentially cause neuroplastic disturbances and contribute to subsequent changes in mood or behavior.ObjectivesThis study aimed to investigate the effect of EA on behavioral deficits associated with PID in mice and verify the hypothesis that astrocytic morphology may be involved in this impact.MethodsWe established a PID animal model induced by transient bilateral common carotid artery occlusion (BCCAO, 20 min) and chronic restraint stress (CRS, 21 days). EA treatment (GV20 + ST36) was performed for 3 weeks, from Monday to Friday each week. Depressive- and anxiety-like behaviors and sociability were evaluated using SPT, FST, EPM, and SIT. Immunohistochemistry combined with Sholl and cell morphological analysis was utilized to assess the process morphology of GFAP+ astrocytes in mood-related regions. The potential relationship between morphological changes in astrocytes and behavioral output was detected by correlation analysis.ResultsBehavioral assays demonstrated that EA treatment induced an overall reduction in behavioral deficits, as measured by the behavioral Z-score. Sholl and morphological analyses revealed that EA prevented the decline in cell complexity of astrocytes in the prefrontal cortex (PFC) and the CA1 region of the hippocampus, where astrocytes displayed evident deramification and atrophy of the branches. Eventually, the correlation analysis showed there was a relationship between behavioral emotionality and morphological changes.ConclusionOur findings imply that EA prevents both behavioral deficits and structural abnormalities in astrocytes in the PID model. The strong correlation between behavioral Z-scores and the observed morphological changes confirms the notion that the weakening of astrocytic processes may play a crucial role in depressive symptoms, and astrocytes could be a potential target of EA in the treatment of PID

MoNET: an R package for multi-omic network analysis

The increasing availability of multi-omic data has enabled the discovery of disease biomarkers in different scales. Understanding the functional interaction between multi-omic biomarkers is becoming increasingly important due to its great potential for providing insights of the underlying molecular mechanism.Leveraging multiple biological network databases, we integrated the relationship between single nucleotide polymorphisms (SNPs), genes/proteins and metabolites, and developed an R package Multi-omic Network Explorer Tool (MoNET) for multi-omic network analysis. This new tool enables users to not only track down the interaction of SNPs/genes with metabolome level, but also trace back for the potential risk variants/regulators given altered genes/metabolites. MoNET is expected to advance our understanding of the multi-omic findings by unveiling their transomic interactions and is likely to generate new hypotheses for further validation.The MoNET package is freely available on https://github.com/JW-Yan/MONET.Supplementary data are available at Bioinformatics online

Association Between Three-Dimensional Transrectal Ultrasound Findings and Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: An Observational Study

BackgroundThere is a significant demand for the development of non-surgical methods for the evaluation of complete response to tumor therapy. Predicting ability and image quality of routine imaging has not been satisfactory. To avoid the deficiencies, we assessed the capability of three-dimensional transrectal ultrasound in predicting the response to neoadjuvant chemoradiotherapy in rectal cancer patients.MethodsThe inclusion criteria were patients with locally advanced rectal adenocarcinoma, receiving capecitabine-based neoadjuvant chemoradiotherapy, distance from anal verge (≤6 cm), clinical stage T3-4 and/or N+ without evidence of distant metastasis, and restaging ycT0-3a (T3a &lt;5 mm) after the end of neoadjuvant chemoradiotherapy. Three-dimensional transrectal ultrasound was performed 7 weeks after neoadjuvant chemoradiotherapy to discern the patients with complete response from the others. Eight main parameters were obtained from three-dimensional transrectal ultrasound: thickness of muscularis on the residual side, thickness of contralateral muscularis, angle of residual arc, regularity of the shape, integrity of the mucosal layer, blurring of the margin, internal echo, and posterior echo. The association between tumor response and three-dimensional transrectal ultrasound parameters was analyzed, and a model was developed by logistic regression.ResultsBetween 2014 and 2019, 101 patients were recruited; 72 cases received total mesorectal excision, and 29 cases underwent watch-and-wait. Among the three-dimensional transrectal ultrasound parameters, the adjusted-thickness of the muscularis (P&lt;0.01), angle of the residual arc (P&lt;0.01), and regularity of the residual shape (P&lt;0.01) were strongly associated with tumor response. In the dataset with total mesorectal excision cases (TME dataset), the residual adjusted-thickness (odds ratio [OR]=4.88, 95% confidence interval [CI]=1.44–16.6, P=0.01) and regularity of the residual shape (OR=5.00, 95% CI=1.13–22.2, P=0.03) were kept in the final logistic model. The area under the curve of the logistic model was 0.84. Among these parameters, residual adjusted-thickness correlated significantly with tumor response. Additionally, we observed similar results in the whole population of 101 cases (whole dataset) and in the cross-validation.ConclusionThree-dimensional transrectal ultrasound model is a valuable method for predicting tumor response in rectal cancer patients undergoing neoadjuvant chemoradiotherapy, which should be included as a factor for evaluating clinical complete response.Trial RegistrationThis trial was registered with ClinicalTrials.gov, number NCT02605265. Registered 9 November 2015 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT0260526

Inferring trajectories of psychotic disorders using dynamic causal modeling

Introduction: Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data. Methods: A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation. Results: Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values. Conclusion: DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment

Heparan Sulfate Facilitates Spike Protein-Mediated SARS-CoV-2 Host Cell Invasion and Contributes to Increased Infection of SARS-CoV-2 G614 Mutant and in Lung Cancer

The severe acute respiratory syndrome (SARS)-like coronavirus disease (COVID-19) is caused by SARS-CoV-2 and has been a serious threat to global public health with limited treatment. Cellular heparan sulfate (HS) has been found to bind SARS-CoV-2 spike protein (SV2-S) and co-operate with cell surface receptor angiotensin-converting enzyme 2 (ACE2) to mediate SARS-CoV-2 infection of host cells. In this study, we determined that host cell surface SV2-S binding depends on and correlates with host cell surface HS expression. This binding is required for SARS-Cov-2 virus to infect host cells and can be blocked by heparin lyase, HS antagonist surfen, heparin, and heparin derivatives. The binding of heparin/HS to SV2-S is mainly determined by its overall sulfation with potential, minor contribution of specific SV2-S binding motifs. The higher binding affinity of SV2-S G614 mutant to heparin and upregulated HS expression may be one of the mechanisms underlying the higher infectivity of the SARS-CoV-2 G614 variant and the high vulnerability of lung cancer patients to SARS-CoV-2 infection, respectively. The higher host cell infection by SARS-CoV-2 G614 variant pseudovirus and the increased infection caused by upregulated HS expression both can be effectively blocked by heparin lyase and heparin, and possibly surfen and heparin derivatives too. Our findings support blocking HS-SV2-S interaction may provide one addition to achieve effective prevention and/treatment of COVID-19

Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model

PurposeThe aim of this study was to investigate the value of a deep learning model (DLM) based on breast tumor ultrasound image segmentation in predicting pathological response to neoadjuvant chemotherapy (NAC) in breast cancer.MethodsThe dataset contains a total of 1393 ultrasound images of 913 patients from Renmin Hospital of Wuhan University, of which 956 ultrasound images of 856 patients were used as the training set, and 437 ultrasound images of 57 patients underwent NAC were used as the test set. A U-Net-based end-to-end DLM was developed for automatically tumor segmentation and area calculation. The predictive abilities of the DLM, manual segmentation model (MSM), and two traditional ultrasound measurement methods (longest axis model [LAM] and dual-axis model [DAM]) for pathological complete response (pCR) were compared using changes in tumor size ratios to develop receiver operating characteristic curves.ResultsThe average intersection over union value of the DLM was 0.856. The early-stage ultrasound-predicted area under curve (AUC) values of pCR were not significantly different from those of the intermediate and late stages (p&lt; 0.05). The AUCs for MSM, DLM, LAM and DAM were 0.840, 0.756, 0.778 and 0.796, respectively. There was no significant difference in AUC values of the predictive ability of the four models.ConclusionUltrasonography was predictive of pCR in the early stages of NAC. DLM have a similar predictive value to conventional ultrasound for pCR, with an add benefit in effectively improving workflow