Perigenual anterior cingulate cortex and its structural covariance as predictors for future body fat gain in young adults.

OBJECTIVE: This study aimed to examine whether baseline gray matter (GM) volume and structural covariance patterns could predict body fat gain over 1 to 2 years in a relatively large sample. METHODS: Voxel-based morphometry (VBM) analysis was applied to examine the association between baseline GM volume and body fat gain in 502 participants over 1 to 2 years. Furthermore, this study tested whether the structural covariances between the regions identified as seeds from VBM analysis and the rest of the brain were associated with future body fat gain. RESULTS: A significant positive association was observed between baseline GM volume in the perigenual anterior cingulate cortex (pgACC) and body fat gain over 1 to 2 years. Furthermore, relative to those with lower future body fat gain, pgACC covaried more extensively with the middle frontal gyrus, middle temporal gyrus, inferior temporal gyrus, and cerebellum in participants with higher future body fat gain. CONCLUSIONS: Using VBM and structural covariance network analysis, the current study revealed that higher GM volume of pgACC and its increased structural covariances with specific brain regions were associated with future weight gain, which may guide the development of more effective prevention and treatment interventions for obesity.

Risk of insomnia during COVID-19: effects of depression and brain functional connectivity.

Normal sleepers may be at risk for insomnia during COVID-19. Identifying psychological factors and neural markers that predict their insomnia risk, as well as investigating possible courses of insomnia development, could lead to more precise targeted interventions for insomnia during similar public health emergencies. Insomnia severity index of 306 participants before and during COVID-19 were employed to determine the development of insomnia, while pre-COVID-19 psychometric and resting-state fMRI data were used to explore corresponding psychological and neural markers of insomnia development. Normal sleepers as a group reported a significant increase in insomnia symptoms after COVID-19 outbreak (F = 4.618, P = 0.0102, df = 2, 609.9). Depression was found to significantly contribute to worse insomnia (ß = 0.066, P = 0.024). Subsequent analysis found that functional connectivity between the precentral gyrus and middle/inferior temporal gyrus mediated the association between pre-COVID-19 depression and insomnia symptoms during COVID-19. Cluster analysis identified that postoutbreak insomnia symptoms followed 3 courses (lessened, slightly worsened, and developed into mild insomnia), and pre-COVID-19 depression symptoms and functional connectivities predicted these courses. Timely identification and treatment of at-risk individuals may help avoid the development of insomnia in the face of future health-care emergencies, such as those arising from COVID-19 variants.

Comparative transcriptomic analyzes of human lung epithelial cells infected with wild-type SARS-CoV-2 and its variant with a 12-bp missing in the E gene

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that caused a global outbreak of coronavirus disease 2019 (COVID-19) pandemic. To elucidate the mechanism of SARS-CoV-2 replication and immunogenicity, we performed a comparative transcriptome profile of mRNA and long non-coding RNAs (lncRNAs) in human lung epithelial cells infected with the SARS-CoV-2 wild-type strain (8X) and the variant with a 12-bp deletion in the E gene (F8). In total, 3,966 differentially expressed genes (DEGs) and 110 differentially expressed lncRNA (DE-lncRNA) candidates were identified. Of these, 94 DEGs and 32 DE-lncRNAs were found between samples infected with F8 and 8X. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzes revealed that pathways such as the TNF signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved. Furthermore, we constructed a lncRNA-protein-coding gene co-expression interaction network. The KEGG analysis of the co-expressed genes showed that these differentially expressed lncRNAs were enriched in pathways related to the immune response, which might explain the different replication and immunogenicity properties of the 8X and F8 strains. These results provide a useful resource for studying the pathogenesis of SARS-CoV-2 variants.

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection

Background SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility. Methods This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy. Results Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities;lower Ct value at recurrence;more persistent cough;and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials. Conclusion Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

Thermal-annealing-regulated plasmonic enhanced fluorescence platform enables accurate detection of antigen/antibody against infectious diseases

Plasmonic enhanced fluorescence (PEF) technology is a powerful strategy to improve the sensitivity of immunofluorescence microarrays (IFMA), however, current approaches to constructing PEF platforms are either expensive/time-consuming or reliant on specialized instruments. Here, we develop a completely alternative approach relying on a two-step protocol that includes the self-assembly of gold nanoparticles (GNPs) at the water—oil interface and subsequent annealing-assisted regulation of gold nanogap. Our optimized thermal-annealing GNPs (TA-GNP) platform generates adequate hot spots, and thus produces high-density electromagnetic coupling, eventually enabling 240-fold fluorescence enhancement of probed dyes in the near-infrared region. For clinical detection of human samples, TA-GNP provides super-high sensitivity and low detection limits for both hepatitis B surface antigen and SARS-CoV-2 binding antibody, coupled with a much-improved detection dynamic range up to six orders of magnitude. With fast detection, high sensitivity, and low detection limit, TA-GNP could not only substantially improve the outcomes of IFMA-based precision medicine but also find applications in fields of proteomic research and clinical pathology. Electronic Supplementary Material Supplementary material (UV—Vis absorption and transmission spectra of GNPs, SEM, microscopy and digital images of PEF platforms, and fluorescence images of IFMA on PEF platforms) is available in the online version of this article at 10.1007/s12274-022-5035-6.

Detection method for antibodies against SARS-CoV-2 and clinical application

To determine if a method to detect antibodies against SARS-CoV-2 can be applied clinically. In this retrospective study, the sera samples of 39 patients with newly diagnosed coronavirus disease 2019 (COVID- 19) and 90 healthy people were analyzed by antibody-detection reagents within enzyme-linked immunosorbent assays. The sera samples of confirmed cases at different onset times and 40 suspected cases were also tested. Then. we combined the results of antibody tests. nucleic-acid tests, and patient data. The sensitivity and specificity for SARS-COV-2-specific total antibodies was 92.31% and 100%, respectively. The production time of total antibodies in serum samples increased with time. and the median detection time was 13 days. The result of antibody testing of one confirmed case preceded the result of the nucleic-acid test. Moreover, the antibodies 0f 40 suspected cases were all negative. Detection of the total antibodies against SARS-CoV-2 can be used as an auxiliary diagnostic indicator of infection by this virus, as well as a supplementary means to exclude suspected cases/populations in areas with a high prevalence of negative detection of the nucleic acids of SARS-CoV-2.

Evaluation of the Spatial Effect of Network Resilience in the Yangtze River Delta: An Integrated Framework for Regional Collaboration and Governance under Disruption

Public health emergencies are characterized by significant uncertainty and robust transmission, both of which will be exacerbated by population mobility, threatening urban security. Enhancing regional resilience in view of these risks is critical to the preservation of human lives and the stability of socio-economic development. Network resilience (NR) is widely accepted as a strategy for reducing the risk of vulnerability and maintaining regional sustainability. However, past assessments of it have not sufficiently focused on its spatial effect and have overlooked both its internal evolution characteristics and external threats which may affect its function and effectiveness. Therefore, we used the Yangtze River Delta Region (YRDR) as a case study and conceptualized an integrated framework to evaluate the spatial pattern and mechanisms of NR under the superposition of the COVID-19 pandemiv and major holidays. The results indicated that the topology of a population mobility network has a significant effect on its resilience. Accordingly, the network topology indexes differed from period to period, which resulted in a decrease of 17.7% in NR. For network structure, the Shanghai-Nanjing and Shanghai-Hangzhou development axes were dependent, and the network was redundant. In the scenario where 20% of the cities were disrupted, the NR was the largest. Furthermore, the failure of dominant nodes and the emergence of vulnerable nodes were key factors that undermined the network's resilience. For network processes, NR has spatial effects when it is evolute and there is mutual inhibition between neighboring cities. The main factors driving changes in resilience were found to be GDP, urbanization rate, labor, and transportation infrastructure. Therefore, we propose a trans-scale collaborative spatial governance system covering 'region-metropolitan-city';which can evaluate the uncertain disturbances caused by the network cascade effect and provide insights into the sustainable development of cities and regions.

Robust humoral and cellular immune responses in long-term convalescent COVID-19 individuals following one-dose SARS-CoV-2 inactivated vaccination

COVID-19, caused by SARS-CoV-2, has resulted in hundreds of millions of infections and millions of deaths worldwide. Preliminary results exhibited excellent efficacy of SARS-CoV-2 vaccine in preventing hospitalization and severe disease. However, data on inactivated vaccine-induced immune responses of naturally infected patients are limited. Here, we characterized SARS-CoV-2 RBD-specific IgG (anti-S-RBD IgG) and neutralizing antibodies (NAbs) against SARS-CoV-2 wild type and variants of concerns (VOCs), as well as RBD-specific IgG-secreting B cells and antigen-specific T cells respectively in 51 SARS-CoV-2 recovered subjects and 63 healthy individuals. In SARS-CoV-2 recovered patients, a single dose vaccine is sufficient to reactivate robust anti-S-RBD IgG and NAbs. The neutralizing capacity against VOCs increased significantly post-vaccination no matter healthy individuals or SARS-CoV-2 recovered patients. In addition, RBD-specific IgG-secreting B cells in SARS-CoV-2 recovered patients were significantly higher than that in healthy vaccine recipients. After the vaccine booster, the frequencies of specific IFN-γ+ CD4+ T cell, IL-2+ CD4+ T cell, and TNF-α+ CD4+ T cell responses were significantly increased in SARS-CoV-2 recovered patients. Our data highlighted the safety and utility of SARS-CoV-2 inactivated vaccine and demonstrated that robust humoral and cellular immune response can be reactivated by one-dose inactivated vaccine in SARS-CoV-2 recovered patients.

Resting-state functional connectome predicts individual differences in depression during COVID-19 pandemic.

Stressful life events are significant risk factors for depression, and increases in depressive symptoms have been observed during the COVID-19 pandemic. The aim of this study is to explore the neural makers for individuals' depression during COVID-19, using connectome-based predictive modeling (CPM). Then we tested whether these neural markers could be used to identify groups at high/low risk for depression with a longitudinal dataset. The results suggested that the high-risk group demonstrated a higher level and increment of depression during the pandemic, as compared to the low-risk group. Furthermore, a support vector machine (SVM) algorithm was used to discriminate major depression disorder patients and healthy controls, using neural features defined by CPM. The results confirmed the CPM's ability for capturing the depression-related patterns with individuals' resting-state functional connectivity signature. The exploration for the anatomy of these functional connectivity features emphasized the role of an emotion-regulation circuit and an interoception circuit in the neuropathology of depression. In summary, the present study augments current understanding of potential pathological mechanisms underlying depression during an acute and unpredictable life-threatening event and suggests that resting-state functional connectivity may provide potential effective neural markers for identifying susceptible populations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Harmonized-Multinational qEEG norms (HarMNqEEG).

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

Maladaptive changes in delay discounting in males during the COVID-19 pandemic: the predictive role of functional connectome.

The Coronavirus disease of 2019 (COVID-19) and measures to curb it created population-level changes in male-dominant impulsive and risky behaviors such as violent crimes and gambling. One possible explanation for this is that the pandemic has been stressful, and males, more so than females, tend to respond to stress by altering their focus on immediate versus delayed rewards, as reflected in their delay discounting rates. Delay discounting rates from healthy undergraduate students were collected twice during the pandemic. Discounting rates of males (n=190) but not of females (n=493) increased during the pandemic. Using machine learning, we show that prepandemic functional connectome predict increased discounting rates in males (n=88). Moreover, considering that delay discounting is associated with multiple psychiatric disorders, we found the same neural pattern that predicted increased discounting rates in this study, in secondary datasets of patients with major depression and schizophrenia. The findings point to sex-based differences in maladaptive delay discounting under real-world stress events, and to connectome-based neuromarkers of such effects. They can explain why there was a population-level increase in several impulsive and risky behaviors during the pandemic and point to intriguing questions about the shared underlying mechanisms of stress responses, psychiatric disorders and delay discounting.

The Effects of Digital Transformation on Firm Performance: Evidence from China’s Manufacturing Sector

With vast potentials in improving operations and stimulating growth, digital transformation has aroused much attention from firms across the world. However, the high costs associated with the transformation can not be ignored. Limited research has looked into the organizational performance effects of digital transformation. After examining the benefits and costs of digital transformation, this research makes an empirical study on the impact of digital transformation on firm operational and financial performance. The panel data from 2010 to 2020 of 2254 manufacturing companies in China suggests that the intensity of digital transformation is in positive correlation with the process-based operating performance, and in the U-shaped correlation with the profit-oriented financial performance. Further, we find that digital transformation has a much more lasting impact on operating performance than on financial performance. The conditions required (i.e., policy and innovation environment) to improve the operating performance via digital transformation are more easing. This research shows the differentiated effect of digital transformation on different dimensions of organizational performance and provides guidance for companies to set the goals for digital transformation.

Stronger functional network connectivity and social support buffer against negative affect during the COVID-19 outbreak and after the pandemic peak.

Health and financial uncertainties, as well as enforced social distancing, during the COVID-19 pandemic have adversely affected the mental health of people. These impacts are expected to continue even after the pandemic, particularly for those who lack support from family and friends. The salience network (SN), default mode network (DMN), and frontoparietal network (FPN) function in an interconnected manner to support information processing and emotional regulation processes in stressful contexts. In this study, we examined whether functional connectivity of the SN, DMN, and FPN, measured using resting-state functional magnetic resonance imaging before the pandemic, is a neurobiological marker of negative affect (NA) during the COVID-19 pandemic and after its peak in a large sample (N = 496, 360 females); the moderating role of social support in the brain-NA association was also investigated. We found that participants reported an increase in NA during the pandemic compared to before the pandemic, and the NA did not decrease, even after the peak period. People with higher connectivity within the SN and between the SN and the other two networks reported less NA during and after the COVID-19 outbreak peak, and the buffer effect was stronger if their social support was greater. These findings suggest that the functional networks that are responsible for affective processing and executive functioning, as well as the social support from family and friends, play an important role in protecting against NA under stressful and uncontrollable situations.

Facile fabrication of phenylenediamine residue derived N, O co-doped hierarchical hyperporous carbon for high-efficient chloroxylenol removal

The chloroxylenol (PCMX) has shown well virucidal efficacy against COVID-19, but the large-scale utilization of which will undoubtedly pose extra environmental threaten. In the present study, the recycled industrial phenylenediamine residue was used and an integrated strategy of “carbonization-casting-activation” using super low-dose of activator and templates was established to achieve in-situ N/O co-doping and facile synthesis of a kind of hierarchical hyperporous carbons (HHPC). The sample of HHPC-1.25-0.5 obtained with activator and template to residue of 1.25 and 0.5 respectively shows super-high specific surface area of 3602 m2/g and volume of 2.81 cm3/g and demonstrates remarkable adsorption capacity of 1475 mg/g for PCMX in batch and of 1148 mg/g in dynamic column adsorption test. In addition, the HHPC-1.25-0.5 exhibits excellent reusability and tolerance for PCMX adsorption under various ionic backgrounds and real water matrix conditions. The combined physio-chemistry characterization, kinetic study and DFT calculation reveal that the enhanced high performances originate from the hierarchical pore structure and strong electrostatic interaction between PCMX and surface rich pyridinic-N and carbonyl groups.

Neural connectome prospectively encodes the risk of post-traumatic stress disorder (PTSD) symptom during the COVID-19 pandemic.

BACKGROUND: The novel coronavirus (COVID-19) pandemic has affected humans worldwide and led to unprecedented stress and mortality. Detrimental effects of the pandemic on mental health, including risk of post-traumatic stress disorder (PTSD), have become an increasing concern. The identification of prospective neurobiological vulnerability markers for developing PTSD symptom during the pandemic is thus of high importance. METHODS: Before the COVID-19 outbreak (September 20, 2019-January 11, 2020), some healthy participants underwent resting-state functional connectivity MRI (rs-fcMRI) acquisition. We assessed the PTSD symptomology of these individuals during the peak of COVID-19 pandemic (February 21, 2020-February 28, 2020) in China. This pseudo-prospective cohort design allowed us to test whether the pre-pandemic neural connectome status could predict the risk of developing PTSD symptom during the pandemic. RESULTS: A total of 5.60% of participants (n = 42) were identified as being high-risk to develop PTSD symptom and 12.00% (n = 90) exhibited critical levels of PTSD symptoms during the COVID-19 pandemic. Pre-pandemic measures of functional connectivity (the neural connectome) prospectively classified those with heightened risk to develop PTSD symptom from matched controls (Accuracy = 76.19%, Sensitivity = 80.95%, Specificity = 71.43%). The trained classifier generalized to an independent sample. Continuous prediction models revealed that the same connectome could accurately predict the severity of PTSD symptoms within individuals (r 2 = 0.31p<.0). CONCLUSIONS: This study confirms COVID-19 break as a crucial stressor to bring risks developing PTSD symptom and demonstrates that brain functional markers can prospectively identify individuals at risk to develop PTSD symptom.

Predisposing Variations in Fear-Related Brain Networks Prospectively Predict Fearful Feelings during the 2019 Coronavirus (COVID-19) Pandemic.

The novel coronavirus (COVID-19) pandemic has led to a surge in mental distress and fear-related disorders, including posttraumatic stress disorder (PTSD). Fear-related disorders are characterized by dysregulations in fear and the associated neural pathways. In the present study, we examined whether individual variations in the fear neural connectome can predict fear-related symptoms during the COVID-19 pandemic. Using machine learning algorithms and back-propagation artificial neural network (BP-ANN) deep learning algorithms, we demonstrated that the intrinsic neural connectome before the COVID-19 pandemic could predict who would develop high fear-related symptoms at the peak of the COVID-19 pandemic in China (Accuracy rate = 75.00%, Sensitivity rate = 65.83%, Specificity rate = 84.17%). More importantly, prediction models could accurately predict the level of fear-related symptoms during the COVID-19 pandemic by using the prepandemic connectome state, in which the functional connectivity of lvmPFC (left ventromedial prefrontal cortex)-rdlPFC (right dorsolateral), rdACC (right dorsal anterior cingulate cortex)-left insula, lAMY (left amygdala)-lHip (left hippocampus) and lAMY-lsgACC (left subgenual cingulate cortex) was contributed to the robust prediction. The current study capitalized on prepandemic data of the neural connectome of fear to predict participants who would develop high fear-related symptoms in COVID-19 pandemic, suggesting that individual variations in the intrinsic organization of the fear circuits represent a neurofunctional marker that renders subjects vulnerable to experience high levels of fear during the COVID-19 pandemic.

Social media exposure during COVID-19 lockdowns could lead to emotional overeating via anxiety: The moderating role of neuroticism.

China was a major hotspot during the beginning of the COVID-19 pandemic. Several studies have reported changes in residents' eating behaviors and appetite during city wide lockdowns and home confinements. However, few have investigated how neuroticism interacts with the impact of COVID-19 to influence eating behaviors during city lockdowns. Thus, the current study aims to establish a pathway model to understand social media exposure, negative affect, neuroticism, and their interaction with eating behaviors during the COVID-19 lockdowns. We present data from 1,128 participants (Mage = 24.34 ± 10.48 years) who completed an online survey between February 17 and 27, 2020. The extent of respondents' social media exposure, negative affect, eating behaviors, and desire for high-calorie food during city lockdowns, as well as the personality trait of neuroticism, were measured. Results show that city lockdowns and home confinements had a negative impact on residents' eating behaviors and appetite. Forty-eight percent of respondents showed moderate to constant emotional overeating, and respondents' desire for high-calorie food significantly increased. Correlation analysis showed that emotional overeating is positively associated with social media exposure, neuroticism, and anxiety. Then, a moderated mediation model was established, showing that heavy social media exposure could lead to emotional overeating through anxiety, and the association between social media exposure and anxiety varies depending on the extent of neuroticism. The current study provides novel insight into how the interaction of a personality trait and the stressful situation of COVID-19 influence people's negative emotions and eating behaviors.