Sensitivity enhanced tunable plasmonic biosensor using two-dimensional twisted bilayer graphene superlattice

This study theoretically demonstrated an insight for designing a novel tunable plasmonic biosensor, which was created by simply stacking a twisted bilayer graphene (TBG) superlattice onto a plasmonic gold thin film. To achieve ultrasensitive biosensing, the plasmonic biosensor was modulated by Goos-Hänchen (GH) shift. Interestingly, our proposed biosensor exhibited tunable biosensing ability, largely depending on the twisted angle. When the relative twisted angle was optimized to be 55.3°, such a configuration: 44 nm Au film/1-TBG superlattice could produce an ultralow reflectivity of 2.2038 × 10-9and ultra-large GH shift of 4.4785 × 104μm. For a small refractive index (RI) increment of 0.0012 RIU (refractive index unit) in sensing interface, the optimal configuration could offer an ultra-high GH shift detection sensitivity of 3.9570 × 107μm/RIU. More importantly, the optimal plasmonic configuration demonstrated a theoretical possibility of quantitatively monitoring severe acute respiratory syndrome coronavirus (SARS-CoV-2) and human hemoglobin. Considering an extremely small RI change as little as 3 × 10-7RIU, a good linear response between detection concentration of SARS-CoV-2 and changes in differential GH shift was studied. For SARS-CoV-2, a linear detection interval was obtained from 0 to 2 nM. For human hemoglobin, a linear detection range was achieved from 0 to 0.002 g/L. Our work will be important to develop novel TBG-enhanced biosensors for quantitatively detecting microorganisms and biomolecules in biomedical application. © 2023 the author(s), published by De Gruyter, Berlin/Boston 2023.

Different associations of parental involvement with children's learning of Chinese, English, and math: a three-wave longitudinal study

Due to the impact of COVID-19, children and their parents are spending more time at home, which increases parent–child interactions. The goals of the present study were to examine the mediating effects of children's learning engagement on the relationships of parental involvement in Chinese, English, and math performance and to investigate whether parent-perceived parental involvement and child-perceived parental involvement consistently affected children's academic performance. Data were collected from 253 Chinese primary school students (117 boys, Mage = 10.53) during the COVID-19 pandemic. We included parental involvement perceived by the parents and by the children to comprehensively describe parental involvement (in wave 2);we collected children's learning engagement (wave 2);and we compared children's Chinese, English and math academic performances before (wave 1) and after (wave 3) China's first wave of COVID-19 in 2020. The results showed that after controlling for gender, age, and SES, the parental involvement perceived by parents could be directly and positively related to children's learning engagement, and it also indirectly influenced children's learning engagement through the children's perceived parental involvement. Learning engagement was a mediator of the relationship between parental involvement and children's academic performance. Parental involvement significantly predicted children's Chinese and English performances through their learning engagement, while parental involvement failed to predict children's mathematics performances during the COVID-19 pandemic. The current research provides insights into the underlying mechanisms of how parental involvement affects children's academic performances during school closures and hopes to guide parents and schools to consider how to cooperate and continue to use rapidly developing digital education resources amid the long-term impact of COVID-19 to provide children using more effective and suitable guidance in the future. © 2022, Instituto Universitário de Ciências Psicológicas, Sociais e da Vida.

Hemophagocytosis in a Severe COVID-19 Patient

The novel coronavirus (SARS-CoV-2) infection has become a heavy burden on global health. Although the coronavirus disease 2019 (COVID-19) may adversely affect multiple organs and systems of infected patients, to the best of our knowledge, there is little investigation of the SARS-CoV-2's impact on bone marrow. Our clinical and cytological findings in this case of severe COVID-19 infection provide novel insights into the pathogenesis of SARS-CoV-2 infection in the hematopoietic system. We recommend that physicians consider SARS-CoV-2 infection's effect on bone marrow in patients who are slow to recover and suggest that a better understanding of the bone marrow morphology in COVID-19-infected patients is needed. © 2022 Journal of Bone and Joint Surgery Inc.. All rights reserved.

Research on digital transformation of enterprise human resource management based on DEA-Malmquist model

The outbreak of COVID-19 in 2019, as well as the development of hot big data, cloud computing, artificial intelligence, and other technologies to promote the enterprise into the digital era, accelerated the progress of society, will continue to promote the development of society, vigorously developing digital economy is an important direction of enterprise economic growth. To push the basis of the rapid economic development is to realize the digital transformation, to realize the core of the digital transformation is the realization of digital transformation of enterprises, as the core power of enterprise development, human resource management, digital transformation, not only the need for human resources also need to speed up the pace of digital transformation of human resources, to support the realization of the digital transformation of enterprises. In order to realize the digital transformation of enterprise human resource management and the contactless human resource management of enterprises, this study constructed a DEA-Malmquist personnel efficiency evaluation model and classified enterprise employees in sequence. According to the enterprise contribution degree of different sequences of personnel and the possibility of online service, the paper puts forward the change of employee demand after digital transformation. In this study, the constructed model is applied to the practice of G company's human resource management digital transformation. According to the model, different employee needs of the company after digital transformation are predicted. Through the analysis of relevant data of G Company, the validity of the model is verified. © 2022 Technical Committee on Control Theory, Chinese Association of Automation.

The Impact of COVID-19 and the Rapid Adoption of Tele health on Provider Burnout in a Pediatric Healthcare Organization

Background: Clinician burnout is a serious problem in healthcare, which could be exacerbated by the consequences of the ongoing global COVID-19 pandemic, including the abrupt shifts in clinical practice (e.g., rapid increase in telehealth services). It is within this context that we conducted a survey of clinicians at a safety-net pediatric hospital to understand how these factors may have affected providers' experiences with and perceptions of burnout. Methods: The brief structured questionnaire was designed by a multidisciplinary team and was sent to all 378 providers at CHOC Children's in June 2020. Questions included demographics, experiences with and perceptions of burnout, and factors that may contribute to or mitigate burnout. After performing descriptive analyses, we used a binomial logistic regression model to test the primary hypotheses of this study: burnout is predicted by both providers' perceptions of the impact of the COVID-19 pandemic and their perceptions of the consequent transition to telehealth. Provider burnout was measured by the statement, “I am experiencing burnout,” with response options on a Likert scale from Strongly agree to Strongly disagree. This was converted to a binary variable for our analyses. Of note, and as others have done, we chose not to use the 22-item validated measure of burnout to avoid over-burdening clinicians. The study was reviewed and approved by the CHOC Institutional Review Board (#200675). Results: Eighty-four providers responded (22%), which is typical for surveys of clinicians. The majority of respondents identified as female (57%). Additionally, 70% of participants were between 35 and 54 years old, with about 10% <35 years and 20% >54 years. Almost all respondents (92%) were married or in a domestic partnership. Approximately 46% of respondents were Specialists, 33% General Pediatricians, and 20% Hospitalists. Fifty-six percent of respondents reported that they were experiencing burnout (N=84). Table 1 summarizes key descriptive results, and Figure 1 presents the results of the multivariable logistic regression model predicting self-reported provider burnout. The data show that, when controlling for the other variables in the model, the self-reported experience of burnout is predicted by three perceptions: COVID-19 has exacerbated provider burnout (p<0.001), the benefits of telehealth do not outweigh the challenges (p=0.045), and there is insufficient institutional support to reduce burnout (p<0.001). We also identified a statistically significant interaction, with those perceiving both sufficient institutional support and telehealth to be beneficial being less likely to report experiencing burnout (p=0.045). Conclusion: Increased institutional support should be provided to clinicians both now, as clinical practice continues to evolve during the COVID-19 pandemic, and more proactively during the inevitable future periods of crisis in an effort to reduce clinician burnout in these difficult times. Future research should assess the effectiveness of different institutional measures on clinician wellbeing.

Case-control study on severe risk factors in COVID-19 patients in Fujian Province

Objective To analyze the epidemiological characteristics and laboratory tests of coro-navirus diseases 2019(COVID-19) cases in Fujian province and to explore the risk factors for their progression to severe cases. Methods The clinical and epidemiological data of COVID-19 confirmed patients with clinical final outcome (including recovery death, etc.) from January 22 to March 7 in 2020 in Fujian were collected. The risk factors of the severe cases were analyzed by univariate and multivariate Logistic regression. Results Up to March 7, 2020, a total of 231 patients were collected in Fujian province, among which, 39(16.88%) were severe and critical cases. The univariate analysis showed that most patients in the severe group had underlying diseases (71. 80%), which was significantly higher than that in the non-severe group (34. 40%) (%2 = 18. 808, P<0. 001). Among them, hypertension, cardiovascular disease, lung disease, other chronic diseases and other factors were statistically different between the two groups (all P<0. 05). Then, numbers of hematological tests were statistical differences between the two groups. Multivariate Logistic regression analysis revealed that age =5 65 years old (OR =17. 067, 95%CI: 2. 640-110. 327), low level of lymphocyte (OR = 4. 731, 95%>CI: 1. 175-19. 046), liver dysfunction (OR = 12. 458, 95% CI: 2. 559-60. 649), high level of calcitonin (OR = 3. 577, 95% CI: 1. 733-7. 384) and high level of C-reaction protein (OR = 2. 354, 95%CI:1. 012-5. 478) were risk factors for the progression of COVID-19 patients to severe illness. The obtained regression equation fits the training sample well (AUC = 0. 941). Conclusions Low level of lymphocyte,liver dysfunction, high level of calcitonin and high level of C-reaction protein could be used as the early warning indicators for severe cases. More attention should be paid to elderly patients age

Towards Robust Cross-domain Image Understanding with Unsupervised Noise Removal

Deep learning has made a tremendous impact on various applications in multimedia, such as media interpretation and multimodal retrieval. However, deep learning models usually require a large amount of labeled data to achieve satisfactory performance. In multimedia analysis, domain adaptation studies the problem of cross-domain knowledge transfer from a label rich source domain to a label scarce target domain, thus potentially alleviates the annotation requirement for deep learning models. However, we find that contemporary domain adaptation methods for cross-domain image understanding perform poorly when source domain is noisy. Weakly Supervised Domain Adaptation (WSDA) studies the domain adaptation problem under the scenario where source data can be noisy. Prior methods on WSDA remove noisy source data and align the marginal distribution across domains without considering the fine-grained semantic structure in the embedding space, which have the problem of class misalignment, e.g., features of cats in the target domain might be mapped near features of dogs in the source domain. In this paper, we propose a novel method, termed Noise Tolerant Domain Adaptation (NTDA), for WSDA. Specifically, we adopt the cluster assumption and learn cluster discriminatively with class prototypes (centroids) in the embedding space. We propose to leverage the location information of the data points in the embedding space and model the location information with a Gaussian mixture model to identify noisy source data. We then design a network which incorporates the Gaussian mixture noise model as a sub-module for unsupervised noise removal and propose a novel cluster-level adversarial adaptation method based on the Generative Adversarial Network (GAN) framework which aligns unlabeled target data with the less noisy class prototypes for mapping the semantic structure across domains. Finally, we devise a simple and effective algorithm to train the network from end to end. We conduct extensive experiments to evaluate the effectiveness of our method on both general images and medical images from COVID-19 and e-commerce datasets. The results show that our method significantly outperforms state-of-the-art WSDA methods. © 2021 Owner/Author.

Into the Unobservables: A Multi-range Encoder-decoder Framework for COVID-19 Prediction

The ongoing COVID-19 pandemic has dramatically changed people's daily lives. A robust forecasting model for COVID-19 infections is essential for governments and institutions to plan timely and perform accurate interventions. Mainstream solutions for COVID-19 prediction fit reported data only by considering observed cases. However, the neglected facts that positive samples are incomplete and many facts of the novel disease are unknown may be prone to cause severe error accumulation, especially in long-term predictions. To fully understand the spreading patterns of the virus, we propose an encoder-decoder framework: (i) in the encoder we embed historical case data into multiple expose-infection ranges and learn message passing between time slices and across ranges with coarse-grained human mobility data incorporated;(ii) in the decoder, we decode the embedded features based on reported cases as well as deaths to jointly consider the effect of both observed and hidden data. We model the spreading of disease in over 60 counties of California and New York, which are two of the most metropolitan areas in the US. The proposed framework significantly outperforms state-of-the-art baselines on JHU COVID-19 dataset on both weekly prediction and daily prediction tasks. We design detailed ablation studies to verify the effectiveness of each key module and find the model not only works with the assistance of mobility data but also with purely cases and deaths, which implies its broad application scenarios. © 2021 ACM.

Racial and Ethnic Disparities in COVID-19-Related Infection in Patients with Psoriasis: A Cross-Sectional Study

Psoriasis severity may vary due to demographics and geography, yet data on the risk, racial disparities, and outcomes for COVID-19 in patients with psoriasis is limited. We evaluated rates of COVID-19 infection, hospitalization, and mortality among psoriasis patients in a California-based population through a cross-sectional study utilizing the University of California COVID Research Data Set (UC CORDS). Psoriasis diagnosis, COVID-19 testing, demographics, hospitalizations, and mortality were collected. Specific biologic (adalimumab, ustekinumab, secukinumab, guselkumab, and etanercept) and systemic (cyclosporine and methotrexate) treatment for at least 30 days prior to COVID-19 testing were identified. Data from 290,838 patients is included in UC CORDS with a 3.59% positive COVID-19 test rate. Of these, 3,566 patients had a diagnosis of psoriasis, with a 2.44% positive infection rate;lower than the 3.56% infection rate for those without psoriasis (p=0.00021). There were no significant differences in hospitalization or mortality rate for COVID-19-positive psoriasis patients compared to those without psoriasis (p=0.5523, p=0.1152, respectively). Lastly, no significant difference in infection rate for psoriasis patients on systemic (2.92%, p=0.579) or biologic agents (2.46%, p=0.986) in comparison to psoriasis patients not on these treatments. UC CORDS data showed higher COVID-19 infection rates in non-white (3.05%) and Hispanic (8.35%) patients compared to non-Hispanic Caucasians (1.95%, p<0.00001). No significant differences were seen in infection, hospitalization, or mortality rates between non-Hispanic races (Caucasian, African American, and Asian). Overall, psoriasis patients did not have increased risk for COVID-19 infection, hospitalization, or mortality, regardless of treatment modality.

Severe Coronavirus disease 2019 successfully treated with glucocorticoid and high-dose intravenous immunoglobulin: A case report and analysis of clinical experience

Objective To report a case of severe Coronavirus disease 2019 (COVID-19) that had been successfully treated with glucocorticoid and intravenous immunoglobulin therapy. Methods and results The patient was a healthcare provider in Wuhan City who was taking care of COVID-19 patients before the onset of the disease. He started to cough with a little white sticky sputum on January 16, 2020 and had a fever on January 22 (up to 38.5 ℃) before admission. CT results showed mild exudation in both lungs. Oral oseltamivir and intravenous moxifloxacin, cefoperazone and sulbactam sodium were given in addition to nutritional support. On January 26, the patient had chest tightness and shortness of breath. A swab test was positive for severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) nucleic acid, and chest CT results showed moderate exudation in both lungs. On January 28, shortness of breath worsened and intravenous methylprednisolone (40 mg, qd) and immunoglobulin (10 g, qd) were given. On January 30, shortness of breath further worsened;he had a body temperature of 40.7 ℃, pulse oxygen saturation (SpO2) of 83% with oxygen inhalation at 10 L/min, and lymphocyte count of 0.5×109/L. The dose of methylprednisolone and immunoglobulin were adjusted to 40 mg, q12h and 20 g, qd, respectively. Subcutaneous injection of thymalfasin (1.6 mg, qd) was added. Then the body temperature returned to normal, and symptoms such as chest tightness and shortness of breath were gradually improved. On January 31, SpO2 was 88% with oxygen inhalation at 10 L/min and a chest CT results revealed large amount of exudation in both lungs. On February 2, SpO2 was 95% with oxygen inhalation at 5 L/min and the dose of methylprednisolone was then gradually reduced. A chest CT results on February 3 revealed improved lung inflammation, and a throat swab on February 4 and 9 was negative for SARS-CoV-2 nucleic acid. Conclusion Glucocorticoid should be used with caution in patients with early and mild COVID-19. However, appropriate dosage of glucocorticoid can be used to modulate lung inflammation in patients with decompensated respiratory failure. Additionally, large dose of immunoglobulin can be given if necessary.

Metabolic associated fatty liver disease increases coronavirus disease 2019 disease severity in nondiabetic patients

BACKGROUND AND AIM: Coronavirus disease 2019 (COVID-19) has attracted increasing worldwide attention. While diabetes is known to aggravate COVID-19 severity, it is not known whether nondiabetic patients with metabolic dysfunction are also more prone to more severe disease. The association of metabolic associated fatty liver disease (MAFLD) with COVID-19 severity in nondiabetic patients was investigated here. METHODS: The study cohort comprised 65 patients with (i.e. cases) and 65 patients without MAFLD (i.e. controls). Each case was randomly matched with one control by sex (1:1) and age (+/-5 years). The association between the presence of MAFLD (as exposure) and COVID-19 severity (as the outcome) was assessed by binary logistic regression analysis. RESULTS: In nondiabetic patients with COVID-19, the presence of MAFLD was associated with a four-fold increased risk of severe COVID-19;the risk increased with increasing numbers of metabolic risk factors. The association with COVID-19 severity persisted after adjusting for age, sex, and coexisting morbid conditions. CONCLUSION: Health-care professionals caring for nondiabetic patients with COVID-19 should be cognizant of the increased likelihood of severe COVID-19 in patients with MAFLD.

[Advances on the asymptomatic infection of COVID-19].

The COVID-19 outbreak in China has been gradually controlled. At present, the management and risk assessment of asymptomatic infected cases has become an urgent problem to be addressed. Asymptomatic case is mainly detected by close contact screening, cluster epidemic investigation, infection source tracking investigation, and active detection of target population. Currently, research on the spread risk from asymptomatic cases was limited, and lacking the data relates to the distribution of asymptomatic cases in large community population. Pathogen detection using PCR is suitable for screening in close contacts of confirmed cases and should be started as early as possible. The antibody test is more suitable for screening in general population where the source of infection is unclear. The management of asymptomatic cases now in China focuses on isolation and medical observation according to the guideline of "early detection, early report, early isolation and early treatment" .

[Clinical features and outcome of treatment for novel coronavirus pneumonia: a meta-analysis].

Objective: To investigate the clinical features and outcome of treatment for novel coronavirus pneumonia. Methods: Literature on novel coronavirus pneumonia was retrieved from PubMed and EMBASE databases. The relevant data was extracted and a meta-analysis was performed using StatsDirect statistical software V.2.8.0 to calculate the combined odds ratio. Results: Seven studies were included, consisting of 1594 cases. The meta-analysis result showed that the most common clinical symptoms of the novel coronavirus pneumonia were fever (91.6%) and cough (64.5%), followed by dyspnea (32.8%) and sputum (28.1%). Headache (10.5%), sore throat (11.2%), hemoptysis (3.2%), diarrhea (6.6%) and the other symptoms were relatively rare. Aspartate aminotransferase (29%), alanine transaminase (22.7%), and total bilirubin (11.7%) levels were elevated, except for serum albumin levels (80.4%). The common therapeutic agents used were antibiotics (87.7%), antiviral drugs (75.5%), and glucocorticoids (26.6%), while antifungal agents (7.7%) were used in few. Mechanical ventilation (13.4%), extracorporeal membrane oxygenation (1.9%), and continuous renal replacement therapy (3.8%) were used in severe cases. The rate of mortality in hospital was 7.7%, respectively. Heterogeneity between studies was significant; however, subgroup and sensitivity analysis had failed to identify clear sources of heterogeneity. Conclusion: Fever, cough and liver dysfunction are the main clinical manifestations of this disease and the mortality rate is low.