Progress in understanding the health promoting effect of egg yolk immunoglobulin

As an important immuneoactive component in eggs, yolk immunoglobulin (IgY) shows great competitiveness in research and production due to its good stability, high safety, low cost, easy availability, strong immune activity, and no drug resistance. This article highlights the significant advantages of IgY as a good antibiotic substitute in the prevention and treatment of viral and bacterial diseases. Also, IgY has great potential in the regulation of nutrient metabolism balance, intestinal microflora and immune homeostasis by affecting key rate-limiting enzymes, and relevant receptors and inflammatory factors specifically. Proper diet and targeted delivery of foodborne IgY may be a new perspective on inflammation regulation, disease control, nutritional balance or homeostasis, and oral microencapsulated IgY is expected to be a new approach against increasing public health emergencies (such as COVID-19 pandemic).

Antibody response to SARS-CoV-2 WT and Omicron BA.4/5 of inactivated COVID-19 vaccine in patients with lung cancer after second and booster immunization.

COVID-19 inactivated vaccine-induced humoral responses in patients with lung cancer (LCs) to SARS-CoV-2 wild-type (WT) strain and variants BA.4/5 after the primary 2-dose and booster vaccination remained unknown. We conducted a cross-sectional study in 260 LCs, 140 healthy controls (HC) and additional 40 LCs with serial samples by detecting total antibodies, IgG anti-RBD and neutralizing antibodies (NAb) toward WT and BA.4/5. SARS-CoV-2-specific antibody responses were augmented by the booster dose of inactivated vaccines in LCs, whereas they were lower than that in HCs. Enhanced humoral responses waned over time after triple injection, notably in NAb against WT and BA.4/5. The NAb against BA.4/5 was much lower than WT. Age ≥ 65 was risk factor for immunization of NAb to WT. Undergoing treatment resulted in a lower antibody response than those without and radiotherapy was a also risk factor for seroconversion of NAb to WT. Lower lymphocyte counts contributed to a lower titer of IgG anti-RBD and NAb against BA.4/5 in LCs than HCs. Specifically, total B cells, CD4+T cells and CD8+T counts were correlated with the humoral response. These results should be taken into consideration for the elderly patients under treatment.

COVID-19 vaccination status, side effects, and perceptions among breast cancer survivors: a cross-sectional study in China.

Introduction: Breast cancer is the most prevalent malignancy in patients with coronavirus disease 2019 (COVID-19). However, vaccination data of this population are limited. Methods: A cross-sectional study of COVID-19 vaccination was conducted in China. Multivariate logistic regression models were used to assess factors associated with COVID-19 vaccination status. Results: Of 2,904 participants, 50.2% were vaccinated with acceptable side effects. Most of the participants received inactivated virus vaccines. The most common reason for vaccination was "fear of infection" (56.2%) and "workplace/government requirement" (33.1%). While the most common reason for nonvaccination was "worry that vaccines cause breast cancer progression or interfere with treatment" (72.9%) and "have concerns about side effects or safety" (39.6%). Patients who were employed (odds ratio, OR = 1.783, p = 0.015), had stage I disease at diagnosis (OR = 2.008, p = 0.019), thought vaccines could provide protection (OR = 1.774, p = 0.007), thought COVID-19 vaccines were safe, very safe, not safe, and very unsafe (OR = 2.074, p < 0.001; OR = 4.251, p < 0.001; OR = 2.075, p = 0.011; OR = 5.609, p = 0.003, respectively) were more likely to receive vaccination. Patients who were 1-3 years, 3-5 years, and more than 5 years after surgery (OR = 0.277, p < 0.001; OR = 0.277, p < 0.001, OR = 0.282, p < 0.001, respectively), had a history of food or drug allergies (OR = 0.579, p = 0.001), had recently undergone endocrine therapy (OR = 0.531, p < 0.001) were less likely to receive vaccination. Conclusion: COVID-19 vaccination gap exists in breast cancer survivors, which could be filled by raising awareness and increasing confidence in vaccine safety during cancer treatment, particularly for the unemployed individuals.

Booster shot of inactivated SARS-CoV-2 vaccine induces potent immune responses in people living with HIV.

This study aimed to investigate the immunogenicity to SARS-CoV-2 and evasive subvariants BA.4/5 in people living with HIV (PLWH) following a third booster shot of inactivated SARS-CoV-2 vaccine. We conducted a cross-sectional study in 318 PLWH and 241 healthy controls (HC) using SARS-CoV-2 immunoassays. Vaccine-induced immunological responses were compared before and after the third dose. Serum levels of IgG anti-RBD and inhibition rate of NAb were significantly elevated at the "post-third dose" sampling time compared with the pre-third dose in PLWH, but were relatively decreased in contrast with those of HCs. Induced humoral and cellular responses attenuated over time after triple-dose vaccination. The neutralizing capacity against BA.4/5 was also intensified but remained below the positive inhibition threshold. Seropositivity of SARS-CoV-2-specific antibodies in PLWH was prominently lower than that in HC. We also identified age, CD4 cell counts, time after the last vaccination, and WHO staging type of PLWH as independent factors associated with the seropositivity of antibodies. PLWH receiving booster shot of inactivated vaccines generate higher antibody responses than the second dose, but lower than that in HCs. Decreased anti-BA.4/5 responses than that of WT impede the protective effect of the third dose on Omicron prevalence.

Roles of biomarkers in anti-MDA5-positive dermatomyositis, associated interstitial lung disease, and rapidly progressive interstitial lung disease.

BACKGROUND: Anti-melanoma differentiation-associated gene 5 (MDA5)-positive dermatomyositis (MDA5+ DM) is significantly associated with interstitial lung disease (ILD), especially rapidly progressive ILD (RPILD) due to poor prognosis, resulting in high mortality rates. However, the pathogenic mechanism of MDA5+ DM-RPILD is unclear. Although some MDA5+ DM patients have a chronic course of ILD, many do not develop RPILD. Therefore, the related biomarkers for the early diagnosis, disease activity monitoring, and prediction of the outcome of RPILD in MDA5+ DM patients should be identified. Blood-based biomarkers are minimally invasive and can be easily detected. METHODS: Recent relative studies related to blood biomarkers in PubMed were reviewed. RESULTS: An increasing number of studies have demonstrated that dysregulated expression of blood biomarkers related to ILD such as ferritin, Krebs von den Lungen-6 (KL-6), surfactant protein-D (SP-D), and cytokines, and some tumor markers in MDA5+ DM may provide information in disease presence, activity, treatment response, and prognosis. These studies have highlighted the great potentials of blood biomarker values for MDA5+ DM-ILD and MDA5+ DM-RPILD. This review provides an overview of recent studies related to blood biomarkers, besides highlighted protein biomarkers, including antibody (anti-MDA5 IgG subclasses and anti-Ro52 antibody), genetic (exosomal microRNAs and neutrophil extracellular traps related to cell-free DNA), and immune cellular biomarkers in MDA5+ DM, MDA5+ DM-ILD, and MDA5+ DM-RPILD patients, hopefully elucidating the pathogenesis of MDA5+ DM-ILD and providing information on the early diagnosis, disease activity monitoring, and prediction of the outcome of the ILD, especially RPILD. CONCLUSIONS: Therefore, this review may provide insight to guide treatment decisions for MDA5+ DM-RPILD patients and improve outcomes.

Spectrum and Style Transformation Framework for Omni-Domain COVID-19 Diagnosis

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic and profoundly affects almost all people around the world. Thus, many automatic diagnosis methods based on computed tomography (CT) images have been proposed to reduce the workload of radiologists. Most of the existing methods focus on the in-domain predictions, i.e., the training and testing have similar distributions, which is impractical in real-world situations, since the CT images can be collected from different devices and in different hospitals. To improve the diagnosis performance of COVID-19 for both in-domain and out-of-domain data, this paper proposes a spectrum and style transformation framework for omni-domain COVID-19 diagnosis. To achieve this, we first present a spectrum transform module, which helps to discover the discriminating features of each domain to recognize the in-domain data. Then, we formulate a cross-domain stylization module, which learns the cross-domain knowledge to enhance the model generalization capability to deal with out-of-domain data. Moreover, our framework is a plug-and-play module that can be easily integrated into existing deep models. We evaluate our framework on four COVID-19 datasets and show our method consistently improves the diagnosis performance of various methods on both in-domain and out-of-domain data.

Real-world effectiveness and protection of SARS-CoV-2 vaccine among patients hospitalized for COVID-19 in Xi'an, China, December 8, 2021, to January 20, 2022: A retrospective study.

Introduction: In December 2021, a large-scale epidemic broke out in Xi'an, China, due to SARS-CoV-2 infection. This study reports the effect of vaccination on COVID-19 and evaluates the impact of different vaccine doses on routine laboratory markers. Methods: The laboratory data upon admission, of 231 cases with COVID-19 hospitalized from December 8, 2021 to January 20, 2022 in Xi'an, including blood routine, lymphocyte subtypes, coagulative function tests, virus specific antibodies and blood biochemical tests were collected and analyzed. Results: Of the 231 patients, 21 were not vaccinated, 158 were vaccinated with two doses and 52 with three doses. Unvaccinated patients had a higher proportion of moderate and severe symptoms than vaccinated patients, while two-dose vaccinated patients had a higher proportion than three-dose vaccinated patients. SARS-CoV-2 specific IgG levels were significantly elevated in vaccinated patients compared with unvaccinated patients. Particularly, unvaccinated patients had lower counts and percentages of lymphocytes, eosinophils and CD8+ T-lymphocytes, and elevated coagulation-related markers. In addition, vaccination had no effect on liver and kidney function. Conclusions: Vaccination against SARS-CoV-2, inducing high IgG level and increased CD8+ T cells and eosinophils, and regulating coagulation function, can significantly attenuate symptoms of COVID-19, suggesting that the vaccine remains protective against SARS-CoV-2.

Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts.

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F-score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance.

Estimation of COVID-19 mortality in the United States using Spatio-temporal Conway Maxwell Poisson model.

Spatio-temporal Poisson models are commonly used for disease mapping. However, after incorporating the spatial and temporal variation, the data do not necessarily have equal mean and variance, suggesting either over- or under-dispersion. In this paper, we propose the Spatio-temporal Conway Maxwell Poisson model. The advantage of Conway Maxwell Poisson distribution is its ability to handle both under- and over-dispersion through controlling one special parameter in the distribution, which makes it more flexible than Poisson distribution. We consider data from the pandemic caused by the SARS-CoV-2 virus in 2019 (COVID-19) that has threatened people all over the world. Understanding the spatio-temporal pattern of the disease is of great importance. We apply a spatio-temporal Conway Maxwell Poisson model to data on the COVID-19 deaths and find that this model achieves better performance than commonly used spatio-temporal Poisson model.

Risk factors and prediction nomogram model for psychosocial and behavioural problems among children and adolescents during the COVID-19 pandemic: A national multicentre study: Risk Factors of Childhood Psychosocial Problems.

BACKGROUND: We aimed to explore the risk profiles attributable to psychosocial and behavioural problems during the coronavirus disease 2019 pandemic. To this end, we created a risk-prediction nomogram model. METHODS: A national multicentre study was conducted through an online questionnaire involving 12,186 children (6-11 years old) and adolescents (12-16 years old). Respondents' psychosocial and behavioural functioning were assessed using the Achenbach Child Behaviour Checklist (CBCL). Data were analysed using STATA software and R-language. RESULTS: The positive detection rate of psychological problems within Wuhan was greater than that outside Wuhan for schizoid (P = 0.005), and depression (P = 0.030) in children, and for somatic complaints (P = 0.048), immaturity (P = 0.023), and delinquent behaviour (P = 0.046) in adolescents. After graded multivariable adjustment, seven factors associated with psychological problems in children and adolescents outside Wuhan were parent-child conflict (odds ratio (OR): 4.94, 95% confidence interval (95% CI): 4.27-5.72), sleep problems (OR: 4.05, 95% CI: 3.77-4.36), online study time (OR: 0.41, 95% CI: 0.37-0.47), physical activity time (OR: 0.510, 95% CI: 0.44-0.59), number of close friends (OR: 0.51, 95% CI: 0.44-0.6), time spent playing videogames (OR: 2.26, 95% CI: 1.90-2.69) and eating disorders (OR: 2.71, 95% CI: 2.35-3.11) (all P < 0.001). Contrastingly, within Wuhan, only the first four factors, namely, parent-child conflict (5.95, 2.82-12.57), sleep problems (4.47, 3.06-6.54), online study time (0.37, 0.22-0.64), and physical activity time (0.42, 0.22-0.80) were identified (all P < 0.01). Accordingly, nomogram models were created with significant attributes and had decent prediction performance with C-indexes over 80%. LIMITATION: A cross-sectional study and self-reported measures. CONCLUSIONS: Besides the four significant risk factors within and outside Wuhan, the three additional factors outside Wuhan deserve special attention. The prediction nomogram models constructed in this study have important clinical and public health implications for psychosocial and behavioural assessment.

Psychosocial and behavioral problems of children and adolescents in the early stage of reopening schools after the COVID-19 pandemic: a national cross-sectional study in China.

This study aims to explore the psychosocial and behavioral problems of children and adolescents in the early stage of reopening schools. In this national cross-sectional study, a total of 11072 students from China were naturally divided into two groups based on their schooling status: reopened schools (RS) and home schooling (HS) group. The psychosocial and behavioral functioning were measured by Achenbach Child Behaviour Checklist (CBCL) and compared in these two groups. Multivariable logistic regression analyses were conducted to explore the independent predictors associated with the psychosocial and behavioral problems. Our results showed that the students in the RS group had more adverse behaviors than that of HS group. The RS group had the higher rates of parent-offspring conflict, prolonged homework time, increased sedentary time and sleep problems (all p < 0.001). When separate analyses were conducted in boys and girls, the RS group had the higher scores for (1) overall behavioral problems (p = 0.02 and p = 0.01), internalizing (p = 0.02 and p = 0.02) and externalizing (p = 0.02 and p = 0.004) behaviors in the 6-11 age group; (2) externalizing (p = 0.049 and p = 0.006) behaviors in the 12-16 age group. Multivariable regression showed parent-offspring conflict and increased sedentary time were the most common risk factors, while physical activity and number of close friends were protective factors for behavior problems in RS students (p < 0.01 or 0.05). The present study revealed that students' psychosocial and behavioral problems increased in the early stage of schools reopened unexpectedly. These findings suggest that close attention must be paid and holistic strategies employed in the school reopening process of post-COVID-19 period.

Add-On Chinese Medicine for Coronavirus Disease 2019 (ACCORD): A Retrospective Cohort Study of Hospital Registries.

Chinese medicine (CM) was extensively used to treat COVID-19 in China. We aimed to evaluate the real-world effectiveness of add-on semi-individualized CM during the outbreak. A retrospective cohort of 1788 adult confirmed COVID-19 patients were recruited from 2235 consecutive linked records retrieved from five hospitals in Wuhan during 15 January to 13 March 2020. The mortality of add-on semi-individualized CM users and non-users was compared by inverse probability weighted hazard ratio (HR) and by propensity score matching. Change of biomarkers was compared between groups, and the frequency of CMs used was analyzed. Subgroup analysis was performed to stratify disease severity and dose of CM exposure. The crude mortality was 3.8% in the semi-individualized CM user group and 17.0% among the non-users. Add-on CM was associated with a mortality reduction of 58% (HR = 0.42, 95% CI: 0.23 to 0.77, [Formula: see text] = 0.005) among all COVID-19 cases and 66% (HR = 0.34, 95% CI: 0.15 to 0.76, [Formula: see text] = 0.009) among severe/critical COVID-19 cases demonstrating dose-dependent response, after inversely weighted with propensity score. The result was robust in various stratified, weighted, matched, adjusted and sensitivity analyses. Severe/critical patients that received add-on CM had a trend of stabilized D-dimer level after 3-7 days of admission when compared to baseline. Immunomodulating and anti-asthmatic CMs were most used. Add-on semi-individualized CM was associated with significantly reduced mortality, especially among severe/critical cases. Chinese medicine could be considered as an add-on regimen for trial use.

A rapid screening classifier for diagnosing COVID-19.

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction. Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians. Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.