Identifying the predictors of severe psychological distress by auto-machine learning methods.

Social stress in daily life and the COVID-19 pandemic have greatly impacted the mental health of the population. Early detection of a predisposition to severe psychological distress is essential for timely interventions. This paper analyzed 4036 samples participating in the 2019-2020 National Health Information Trends Survey (HINTS) and identified 57 candidate predictors of severe psychological distress based on univariate chi-square and t-test analyses. Five machine learning methods, namely logistic regression (LR), automatic generalized linear models (Auto-GLM), automatic random forests (Auto-Random Forests), automatic deep neural networks (Auto-Deep learning) and automatic gradient boosting machines (Auto-GBM), were employed to model synthetic minority oversampling technique-based (SMOTE) resampled data and identify predictors of severe psychological distress. Predictors were evaluated by odds ratios in logistic models and variable importance in the other models. Forty-seven variables were identified as significant predictors of severe psychological distress, including 13 sociodemographic variables and 34 variables related to individual lifestyle and behavioral habits. Among them, new potentially relevant variables related to an individual's level of concern and trust in cancer information, exposure to health care providers, and cancer screening and awareness are included. The performance of each model was evaluated using five-fold cross-validation. The optimal model performance-wise was Auto-GBM with an accuracy of 89.75%, a precision of 89.68%, a recall of 89.31%, an F1-score of 89.48% and an AUC of 95.57%. Significant predictors of severe psychological distress were identified in this study and the value of machine learning methods in predicting severe psychological distress is demonstrated, thereby enhancing pre-prediction and clinical decision-making of severe psychological distress problems.

SARS-CoV-2 Epitopes following Infection and Vaccination Overlap Known Neutralizing Antibody Sites.

Identification of epitopes targeted following virus infection or vaccination can guide vaccine design and development of therapeutic interventions targeting functional sites, but can be laborious. Herein, we employed peptide microarrays to map linear peptide epitopes (LPEs) recognized following SARS-CoV-2 infection and vaccination. LPEs detected by nonhuman primate (NHP) and patient IgMs after SARS-CoV-2 infection extensively overlapped, localized to functionally important virus regions, and aligned with reported neutralizing antibody binding sites. Similar LPE overlap occurred after infection and vaccination, with LPE clusters specific to each stimulus, where strong and conserved LPEs mapping to sites known or likely to inhibit spike protein function. Vaccine-specific LPEs tended to map to sites known or likely to be affected by structural changes induced by the proline substitutions in the mRNA vaccine's S protein. Mapping LPEs to regions of known functional importance in this manner may accelerate vaccine evaluation and discovery of targets for site-specific therapeutic interventions.

Transcriptome Analysis of Peripheral Blood Mononuclear Cells in SARS-CoV-2 Naïve and Recovered Individuals Vaccinated With Inactivated Vaccine.

The urgent approval of the use of the inactivated COVID-19 vaccine is essential to reduce the threat and burden of the epidemic on global public health, however, our current understanding of the host immune response to inactivated vaccine remains limited. Herein, we performed serum IgG antibody detection and transcriptomics analysis on 20 SARS-CoV-2 naïve individuals who received multiple doses of inactivated vaccine and 5 SARS-CoV-2 recovered individuals who received single dose of inactivated vaccine. Our research revealed the important role of many innate immune pathways after vaccination, identified a significant correlation with the third dose of booster vaccine and proteasome-related genes, and found that SARS-CoV-2 recovered individuals can produces a strong immune response to a single dose of inactivated vaccine. These results help us understand the reaction mechanism of the host's molecular immune system to the inactivated vaccine, and provide a basis for the choice of vaccination strategy.

Inhibitor screening using microarray identifies the high capacity of neutralizing antibodies to Spike variants in SARS-CoV-2 infection and vaccination.

Rationale: Mutations of SARS-CoV-2, which is responsible for coronavirus disease 2019 (COVID-19), could impede drug development and reduce the efficacy of COVID-19 vaccines. Here, we developed a multiplexed Spike-ACE2 Inhibitor Screening (mSAIS) assay that can measure the neutralizing effect of antibodies across numerous variants of the coronavirus's Spike (S) protein simultaneously. Methods: The SARS-CoV-2 spike variant protein microarrays were prepared by printing 72 S variants onto a chemically-modified glass slides. The neutralization potential of purified anti-S antibodies and serum from convalescent COVID-19 patients and vaccinees to S variants were assessed with the mSAIS assay. Results: We identified new S mutations that are sensitive and resistant to neutralization. Serum from both infected and vaccinated groups with a high titer of neutralizing antibodies (NAbs) displayed a broader capacity to neutralize S variants than serum with low titer NAbs. These data were validated using serum from a large vaccinated cohort (n = 104) with a tiled S peptide microarray. In addition, similar results were obtained using a SARS-CoV-2 pseudovirus neutralization assay specific for wild-type S and five prevalent S variants (D614G, B.1.1.7, B.1.351, P.1, B.1.617.2), thus demonstrating that high antibody diversity is associated with high NAb titers. Conclusions: Our results demonstrate the utility of the mSAIS platform in screening NAbs. Moreover, we show that heterogeneous antibody populations provide a more protective effect against S variants, which may help direct COVID-19 vaccine and drug development.

Embryonic Origin and Subclonal Evolution of Tumor-Associated Macrophages Imply Preventive Care for Cancer.

Macrophages are widely distributed in tissues and function in homeostasis. During cancer development, tumor-associated macrophages (TAMs) dominatingly support disease progression and resistance to therapy by promoting tumor proliferation, angiogenesis, metastasis, and immunosuppression, thereby making TAMs a target for tumor immunotherapy. Here, we started with evidence that TAMs are highly plastic and heterogeneous in phenotype and function in response to microenvironmental cues. We pointed out that efforts to tear off the heterogeneous "camouflage" in TAMs conduce to target de facto protumoral TAMs efficiently. In particular, several fate-mapping models suggest that most tissue-resident macrophages (TRMs) are generated from embryonic progenitors, and new paradigms uncover the ontogeny of TAMs. First, TAMs from embryonic modeling of TRMs and circulating monocytes have distinct transcriptional profiling and function, suggesting that the ontogeny of TAMs is responsible for the functional heterogeneity of TAMs, in addition to microenvironmental cues. Second, metabolic remodeling helps determine the mechanism of phenotypic and functional characteristics in TAMs, including metabolic bias from macrophages' ontogeny in macrophages' functional plasticity under physiological and pathological conditions. Both models aim at dissecting the ontogeny-related metabolic regulation in the phenotypic and functional heterogeneity in TAMs. We argue that gleaning from the single-cell transcriptomics on subclonal TAMs' origins may help understand the classification of TAMs' population in subclonal evolution and their distinct roles in tumor development. We envision that TAM-subclone-specific metabolic reprogramming may round-up with future cancer therapies.

Dynamic landscape mapping of humoral immunity to SARS-CoV-2 identifies non-structural protein antibodies associated with the survival of critical COVID-19 patients.

A comprehensive analysis of the humoral immune response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential in understanding COVID-19 pathogenesis and developing antibody-based diagnostics and therapy. In this work, we performed a longitudinal analysis of antibody responses to SARS-CoV-2 proteins in 104 serum samples from 49 critical COVID-19 patients using a peptide-based SARS-CoV-2 proteome microarray. Our data show that the binding epitopes of IgM and IgG antibodies differ across SARS-CoV-2 proteins and even within the same protein. Moreover, most IgM and IgG epitopes are located within nonstructural proteins (nsps), which are critical in inactivating the host's innate immune response and enabling SARS-CoV-2 replication, transcription, and polyprotein processing. IgM antibodies are associated with a good prognosis and target nsp3 and nsp5 proteases, whereas IgG antibodies are associated with high mortality and target structural proteins (Nucleocapsid, Spike, ORF3a). The epitopes targeted by antibodies in patients with a high mortality rate were further validated using an independent serum cohort (n = 56) and using global correlation mapping analysis with the clinical variables that are associated with COVID-19 severity. Our data provide fundamental insight into humoral immunity during SARS-CoV-2 infection. SARS-CoV-2 immunogenic epitopes identified in this work could also help direct antibody-based COVID-19 treatment and triage patients.

Longitudinal and proteome-wide analyses of antibodies in COVID-19 patients reveal features of the humoral immune response to SARS-CoV-2.

Introduction: The SARS-CoV-2 pandemic has endangered global health, the world economy, and societal values. Despite intensive measures taken around the world, morbidity and mortality remain high as many countries face new waves of infection and the spread of new variants. Worryingly, more and more variants are now being identified, such as 501Y.V1 (B.1.1.7) in the UK, 501Y.V2 (B.1.351) in South Africa, 501Y.V3 in Manaus, Brazil, and B.1.617/B.1.618 in India, which could lead to a severe epidemic rebound. Moreover, some variants have a stronger immune escape ability. To control the new SARS-CoV-2 variant, we may need to develop and redesign new vaccines repeatedly. So it is important to investigate how our immune system combats and responds to SARS-CoV-2 infection to develop safe and effective medical interventions. Objectives: In this study, we performed a longitudinal and proteome-wide analysis of antibodies in the COVID-19 patients to revealed some immune processes of COVID-19 patients against SARS-CoV-2 and found some dominant epitopes of a potential vaccine. Methods: Microarray assay, Antibody depletion assays, Neutralization assay. Results: We profiled a B-cell linear epitope landscape of SARS-CoV-2 and identified the epitopes specifically recognized by either IgM, IgG, or IgA. We found that epitopes more frequently recognized by IgM are enriched in non-structural proteins. We further identified epitopes with different immune responses in severe and mild patients. Moreover, we identified 12 dominant epitopes eliciting antibodies in most COVID-19 patients and identified five key amino acids of epitopes. Furthermore, we found epitope S-82 and S-15 are perfect immunogenic peptides and should be considered in vaccine design. Conclusion: This data provide useful information and rich resources for improving our understanding of viral infection and developing a novel vaccine/neutralizing antibodies for the treatment of SARS-CoV-2.

Predictive Value of First-Trimester Glycosylated Hemoglobin Levels in Gestational Diabetes Mellitus: A Chinese Population Cohort Study.

This study was aimed at exploring the predictive value of first-trimester glycosylated hemoglobin (HbA1c) levels in the diagnosis of gestational diabetes mellitus (GDM). A total of 744 pregnant women registered at the Peking University International Hospital between March 2017 and March 2019 were included in this study. Data on personal characteristics and biochemical indicators of the pregnant women were collected during the first trimester. The International Association of Diabetes and Pregnancy Study Groups has adopted specific diagnostic criteria as the gold standard for the diagnosis of GDM. Receiver operating characteristic (ROC) curve statistics were used to assess the predictive value of first-trimester HbA1c levels in the diagnosis of GDM. HbA1c levels in the first trimester were significantly higher in the GDM group than in the non-GDM group (5.23% ± 0.29% vs. 5.06 ± 0.28%, P < 0.05). The first-trimester HbA1c level was an independent risk factor for gestational diabetes. The area under the ROC curve (AUC) of HbA1c for GDM was 0.655 (95% confidence interval 0.620-0.689, P < 0.001). The positive likelihood ratio was the highest at HbA1c = 5.9%, sensitivity was 2.78, and specificity was 99.83%. There was no statistical difference in AUC between fasting blood glucose and HbA1c (P = 0.407). First-trimester HbA1c levels can be used to predict GDM. The risk of GDM was significantly increased in pregnant women with first-trimester HbA1c levels > 5.9%. There was no statistical difference between first-trimester HbA1c and fasting blood glucose levels in predicting GDM.

Clinical manifestations of blood cell parameters and inflammatory factors in 92 patients with COVID-19.

BACKGROUND: Although there are many COVID-19 case series studies, few studies report the relationship between variations in blood cell parameters and inflammatory factors and disease severity. This study aims to describe the dynamic trends in COVID-19 blood cell parameters and inflammatory factors. METHODS: Ninety-two patients with confirmed COVID-19 at Jingzhou Central Hospital, Hubei Province, China, between January 23, 2020, and April 10, 2020, were enrolled. Epidemiological data, clinical information, and laboratory test results were collected and analyzed. RESULTS: As patient age increased, disease severity increased (P<0.0001). The inflammatory factor C-reactive protein (CRP) showed a gradual increasing trend with disease aggravation. Based on dynamic change graphs, CRP in all patients with severe and critical COVID-19 initially increased and then decreased; however, CRP in patients with a good prognosis did not increase again after the initial decline (<20 mg/L). CRP in patients with a poor prognosis returned to a high level (>50 mg/L) 1 week after the initial decrease and continued to fluctuate at a high level. Lymphocyte count (LYM#) in patients with severe and critical disease was significantly lower (<1×109/L) than that in patients with moderate disease; LYM# was significantly increased 3 weeks after disease onset in patients with a good prognosis (>1×109/L), but patients with a poor prognosis continued to have a low LYM#. CONCLUSIONS: CRP and LYM# showed strong correlation with disease progression, suggesting that these parameters could be used to monitor changes in patient condition.

Development and comparison of novel multiple cross displacement amplification (MCDA) assays with other nucleic acid amplification methods for SARS-CoV-2 detection.

The development of alternative isothermal amplification assays including multiple cross displacement amplification (MCDA) may address speed and portability limitations of real-time PCR (rt-PCR) methods for SARS-CoV-2 detection. We developed a novel SARS-CoV-2 MCDA assay and compared its speed and sensitivity to loop-mediated isothermal amplification (LAMP) and rt-PCR. Two MCDA assays targeting SARS-CoV-2 N gene and ORF1ab were designed. The fastest time to detection and sensitivity of MCDA was compared to LAMP and rt-PCR using DNA standards and transcribed RNA. For the N gene, MCDA was faster than LAMP and rt-PCR by 10 and 20 min, respectively with fastest time to detection at 5.2 min. rt-PCR had the highest sensitivity with the limit of detection at 10 copies/µl compared with MCDA (100 copies/µl) and LAMP (500 copies/µl). For ORF1ab, MCDA and LAMP had similar speed with fastest time to detection at 9.7 and 8.4 min, respectively. LAMP was more sensitive for ORF1ab detection with 50 copies/µl compared to MCDA (500 copies/µl). In conclusion, different nucleic acid amplification methods provide different advantages. MCDA is the fastest nucleic acid amplification method for SARS-CoV-2 while rt-PCR is the most sensitive. These advantages should be considered when determining the most suitable nucleic acid amplification methods for different applications.

Red cell distribution width (RDW): a prognostic indicator of severe COVID-19.

BACKGROUND: The global mortality rate for coronavirus disease 2019 (COVID-19) is 3.68%, but the mortality rate for critically ill patients is as high as 50%. Therefore, the exploration of prognostic predictors for patients with COVID-19 is vital for prompt clinical intervention. Our study aims to explore the predictive value of hematological parameters in the prognosis of patients with severe COVID-19. METHODS: Ninety-eight patients who were diagnosed with COVID-19 at Jingzhou Central Hospital and Central Hospital of Wuhan, Hubei Province, were included in this study. RESULTS: The median age of the patients was 59 [28-80] years; the median age of patients with a good prognosis was 56 [28-79] years, and the median age of patients with a poor outcome was 67 [35-80] years. The patients in the poor outcome group were older than the patients in the good outcome group (P<0.05). The comparison of hematological parameters showed that lymphocyte count (Lym#), red blood cells (RBCs), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) were significantly lower in the poor outcome group than in the good outcome group (P<0.05). Further, the red cell volume distribution width-CV (RDW-CV) and red cell volume distribution width-SD (RDW-SD) were significantly higher in the poor outcome group than in the good outcome group (P<0.0001). Receiver operating characteristic (ROC) curves showed RDW-SD, with an area under the ROC curve (AUC) of 0.870 [95% confidence interval (CI) 0.796-0.943], was the most significant single parameter for predicting the prognosis of severe patients. When the cut-off value was 42.15, the sensitivity and specificity of RDW-SD for predicting the prognosis of severe patients were 73.1% and 80.2%, respectively. Reticulocyte (RET) channel results showed the RET level was significantly higher in critical patients than in moderate patients and severe patients (P<0.05), which may be one cause of the elevated RDW in patients with a poor outcome. CONCLUSIONS: In this study, the hematological parameters of COVID-19 patients were statistically analyzed. RDW was found to be a prognostic predictor for patients with severe COVID-19, and the increase in RET may contribute to elevated RDW.

Analysis of the COVID-19 Epidemic Transmission Network in Mainland China: K-Core Decomposition Study.

BACKGROUND: Since the outbreak of COVID-19 in December 2019 in Wuhan, Hubei Province, China, frequent interregional contacts and the high rate of infection spread have catalyzed the formation of an epidemic network. OBJECTIVE: The aim of this study was to identify influential nodes and highlight the hidden structural properties of the COVID-19 epidemic network, which we believe is central to prevention and control of the epidemic. METHODS: We first constructed a network of the COVID-19 epidemic among 31 provinces in mainland China; after some basic characteristics were revealed by the degree distribution, the k-core decomposition method was employed to provide static and dynamic evidence to determine the influential nodes and hierarchical structure. We then exhibited the influence power of the above nodes and the evolution of this power. RESULTS: Only a small fraction of the provinces studied showed relatively strong outward or inward epidemic transmission effects. The three provinces of Hubei, Beijing, and Guangzhou showed the highest out-degrees, and the three highest in-degrees were observed for the provinces of Beijing, Henan, and Liaoning. In terms of the hierarchical structure of the COVID-19 epidemic network over the whole period, more than half of the 31 provinces were located in the innermost core. Considering the correlation of the characteristics and coreness of each province, we identified some significant negative and positive factors. Specific to the dynamic transmission process of the COVID-19 epidemic, three provinces of Anhui, Beijing, and Guangdong always showed the highest coreness from the third to the sixth week; meanwhile, Hubei Province maintained the highest coreness until the fifth week and then suddenly dropped to the lowest in the sixth week. We also found that the out-strengths of the innermost nodes were greater than their in-strengths before January 27, 2020, at which point a reversal occurred. CONCLUSIONS: Increasing our understanding of how epidemic networks form and function may help reduce the damaging effects of COVID-19 in China as well as in other countries and territories worldwide.

SARS-CoV-2 Proteome Microarray for Mapping COVID-19 Antibody Interactions at Amino Acid Resolution.

Comprehensive profiling of humoral antibody response to severe acute respiratory syndrome (SARS) coronavirus-2 (CoV-2) proteins is essential in understanding the host immunity and in developing diagnostic tests and vaccines. To address this concern, we developed a SARS-CoV-2 proteome peptide microarray to analyze antibody interactions at the amino acid resolution. With the array, we demonstrate the feasibility of employing SARS-CoV-1 antibodies to detect the SARS-CoV-2 nucleocapsid phosphoprotein. The first landscape of B-cell epitopes for SARS-CoV-2 IgM and IgG antibodies in the serum of 10 coronavirus disease of 2019 (COVID-19) patients with early infection is also constructed. With array data and structural analysis, a peptide epitope for neutralizing antibodies within the SARS-CoV-2 spike receptor-binding domain's interaction interface with the angiotensin-converting enzyme 2 receptor was predicted. All the results demonstrate the utility of our microarray as a platform to determine the changes of antibody responses in COVID-19 patients and animal models as well as to identify potential targets for diagnosis and treatment.

COVID-19 diagnostic testing: Technology perspective.

The corona virus disease 2019 (COVID-19) is a highly contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). More than 18 million people were infected with a total of 0.7 million deaths in ∼188 countries. Controlling the spread of SARS-CoV-2 is therefore inherently dependent on identifying and isolating infected individuals, especially since COVID-19 can result in little to no symptoms. Here, we provide a comprehensive review of the different primary technologies used to test for COVID-19 infection, discuss the advantages and disadvantages of each technology, and highlight the studies that have employed them. We also describe technologies that have the potential to accelerate SARS-CoV-2 detection in the future, including digital PCR, CRISPR, and microarray. Finally, remaining challenges in COVID-19 diagnostic testing are discussed, including (a) the lack of universal standards for diagnostic testing; (b) the identification of appropriate sample collection site(s); (c) the difficulty in performing large population screening; and (d) the limited understanding of SARS-COV-2 viral invasion, replication, and transmission.

Serum Protein Profiling Reveals a Landscape of Inflammation and Immune Signaling in Early-stage COVID-19 Infection.

Coronavirus disease 2019 (COVID-19) is a highly contagious infection and threating the human lives in the world. The elevation of cytokines in blood is crucial to induce cytokine storm and immunosuppression in the transition of severity in COVID-19 patients. However, the comprehensive changes of serum proteins in COVID-19 patients throughout the SARS-CoV-2 infection is unknown. In this work, we developed a high-density antibody microarray and performed an in-depth proteomics analysis of serum samples collected from early COVID-19 (n = 15) and influenza (n = 13) patients. We identified a large set of differentially expressed proteins (n = 132) that participate in a landscape of inflammation and immune signaling related to the SARS-CoV-2 infection. Furthermore, the significant correlations of neutrophil and lymphocyte with the CCL2 and CXCL10 mediated cytokine signaling pathways was identified. These information are valuable for the understanding of COVID-19 pathogenesis, identification of biomarkers and development of the optimal anti-inflammation therapy.

Preliminary study to identify severe from moderate cases of COVID-19 using combined hematology parameters.

BACKGROUND: The third fatal coronavirus is the novel coronavirus (SARS-CoV-2) that causes novel coronavirus pneumonia (COVID-19) which first broke out in December 2019. Patients will develop rapidly if there is no any intervention, so the risk identification of severe patients is critical. The aim of this study was to investigate the characteristics and rules of hematology changes in patients with COVID-19, and to explore the possibility differentiating moderate and severe patients using conventional hematology parameters or combined parameters. METHODS: The clinical data of 45 moderate and severe type patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in Jingzhou Central Hospital from January 23 to February 13, 2020 were collected. The epidemiological indexes, clinical symptoms, and laboratory test results of the patients were retrospectively analyzed. Those parameters with significant differences between moderate and severe cases were analyzed, and the combination parameters with the best diagnostic performance were selected using the linear discriminant analysis (LDA) method. RESULTS: Of the 45 patients with the novel 2019 corona virus (COVID-19) (35 moderate and 10 severe cases), 23 were male and 22 were female, with ages ranging from 16 to 62 years. The most common clinical symptoms were fever (89%) and dry cough (60%). As the disease progressed, white blood cell count (WBC), neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), red blood cell distribution width-coefficient of variation (RDW-CV), and red cell volume distribution width-standard deviation (RDW-SD) parameters in the severe group were significantly higher than those in the moderate group (P<0.05); meanwhile, lymphocyte count (Lym#), eosinophil count (Eos#), high fluorescent cell percentage (HFC%), red blood cell count (RBC), hemoglobin (HGB), and hematocrit (HCT) parameters in the severe group were significantly lower than those in the moderate group (P<0.05). For NLR parameter, it's area under the curve (AUC), cutoff, sensitivity and specificity were 0.890, 13.39, 83.3% and 82.4% respectively; meanwhile, for PLR parameter, it's AUC, cutoff, sensitivity and specificity were 0.842, 267.03, 83.3% and 74.0% respectively. The combined parameters of NLR and RDW-SD had the best diagnostic efficiency (AUC =0.938), and when the cutoff value was 1.046, the sensitivity and the specificity were 90.0% and 84.7% respectively, followed by the combined parameter NLR&RDW-CV (AUC =0.923). When the cut-off value was 0.62, the sensitivity and the specificity for distinguishing severe type from moderate cases of COVID-19 were 90.0% and 82.4% respectively. CONCLUSIONS: The combined NLR and RDW-SD parameter is the best hematology index. It may help clinicians to predict the severity of COVID-19 patients and can be used as a useful indicator to help prevent and control the epidemic.