Modeling and optimization of triclosan biodegradation by the newly isolated Bacillus sp. DL4: Kinetics and metagenomics analysis (preprint)

Overusing triclosan (TCS) endangered ecological safety and human health, and the pandemic of COVID-19 aggravates the accumulation of TCS in the aquatic environment. Therefore, reducing residual TCS concentrations in the environment is an urgent issue. An aerobic bacterium, Bacillus sp. DL4 was isolated with the capability of TCS biodegradation. Response surface methodology (RSM) and artificial neural network (ANN) were carried out to optimize and verify the different condition variables. All the variables were linear and the interaction of the three factors significantly affected TCS removal at the quadratic level (p < 0.001). Under the optimal conditions (35℃, initial pH 7.31, and 5% strain DL4), the TCS removal rate of 95.89 ± 0.68% was observed and found to be consistent with the predicted values from RSM and ANN models. In addition, statistical comparisons between the models indicated that the ANN model had a stronger predictive capability than the RSM model. Kinetic studies showed that TCS degradation was consistent with a pseudo-first-order kinetic model. Whole genome sequencing indicated that many functional genes were involved in and facilitated TCS degradation. Main metabolite products were detected and identified during the biodegradation process by LC-MS, and a possible degradation pathway was tentatively hypothesized. Overall, this study provides a theoretical foundation for the characterization and mechanism of TCS biodegradation in the environment by Bacillus sp. DL4.

Impact of comorbidities on the serological response to COVID-19 vaccination in Taiwan (preprint)

Background/Aims Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is one of the best policies to control COVID-19 pandemic. The serological response to COVID-19 vaccination in Taiwanese patients with different comorbidities is elusive. Methods Uninfected subjects who received 3 doses of mRNA vaccines (BNT162b2 [Pfizer-BioNTech, BNT] and mRNA-1273 [Moderna]), viral vector-based vaccines (ChAdOx1-S (AZD1222, AZ) or protein subunit vaccines (Medigen COVID-19 vaccine) were prospectively enrolled. The SARS-CoV-2-IgG spike antibody level was determined within three months after the 3rd dose of vaccination. The Charlson Comorbidity Index (CCI) was applied to determine the association between vaccine titers and underlying comorbidities. Results A total of 824 subjects were enrolled in the current study. The proportions of CCI scores of 0-1, 2-3 and >4 were 52.8% (n=435), 31.3% (n=258) and 15.9% (n=131), respectively. The most commonly used vaccination combination was AZ-AZ-Moderna (39.2%), followed by Moderna-Moderna-Moderna (27.8%). The mean vaccination titer was 3.11 log BAU/mL after a median of 48 days after the 3rd dose. Factors associated with potentially effective neutralization capacity included an age ≥60 years (odds ratio [OR]/95% confidence interval [CI], 0.49/0.34–0.72; P <0.001), female gender (OR/CI, 1.78/1.26–2.53; P =0.001), Moderna-based vaccination (compared to AZ-based vaccination; OR/CI, 6.49/3.90–10.82; P <0.001) and a CCI score ≥4 (OR/CI, 0.55/0.35–0.85; P =0.01). There was a decreasing trend in antibody titers with increasing CCI scores (trend P<0.001). Linear regression analysis revealed that AZ-based vaccination (β: 0.341, CI: 0.144, 0.21, P<0.001) and higher CCI scores (β: -0.055, CI: -0.096, -0.014, P=0.009) independently correlated with low IgG spike antibody levels. Conclusions Subjects with more comorbidities had a poor response to 3 doses of COVID-19 vaccination.

Warfarin anticoagulation management during the COVID-19 pandemic: The role of internet clinic and machine learning

Background: Patients who received warfarin require constant monitoring by hospital staff. However, social distancing and stay-at-home orders, which were universally adopted strategies to avoid the spread of COVID-19, led to unprecedented challenges. This study aimed to optimize warfarin treatment during the COVID-19 pandemic by determining the role of the Internet clinic and developing a machine learning (ML) model to predict anticoagulation quality. Methods: This retrospective study enrolled patients who received warfarin treatment in the hospital anticoagulation clinic (HAC) and “Internet + Anticoagulation clinic” (IAC) of the Nanjing Drum Tower Hospital between January 2020 and September 2021. The primary outcome was the anticoagulation quality of patients, which was evaluated by both the time in therapeutic range (TTR) and international normalized ratio (INR) variability. Anticoagulation quality and incidence of adverse events were compared between HAC and IAC. Furthermore, five ML algorithms were used to develop the anticoagulation quality prediction model, and the SHAP method was introduced to rank the feature importance. Results: Totally, 241 patients were included, comprising 145 patients in the HAC group and 96 patients in the IAC group. In the HAC group and IAC group, 73.1 and 69.8% (p = 0.576) of patients achieved good anticoagulation quality, with the average TTR being 79.9 ± 20.0% and 80.6 ± 21.1%, respectively. There was no significant difference in the incidence of adverse events between the two groups. Evaluating the five ML models using the test set, the accuracy of the XGBoost model was 0.767, and the area under the receiver operating characteristic curve was 0.808, which showed the best performance. The results of the SHAP method revealed that age, education, hypertension, aspirin, and amiodarone were the top five important features associated with poor anticoagulation quality. Conclusion: The IAC contributed to a novel management method for patients who received warfarin during the COVID-19 pandemic, as effective as HAC and with a low risk of virus transmission. The XGBoost model could accurately select patients at a high risk of poor anticoagulation quality, who could benefit from active intervention.

Proteomic Analysis of Human Milk Reveals Nutritional and Immune Benefits in the Colostrum from Mothers with COVID-19

Despite the well-known benefits of breastfeeding and the World Health Organization's breastfeeding recommendations for COVID-19 infected mothers, whether these mothers should be encouraged to breastfeed is under debate due to concern about the risk of virus transmission and lack of evidence of breastmilk's protective effects against the virus. Here, we provide a molecular basis for the breastfeeding recommendation through mass spectrometry (MS)-based proteomics and glycosylation analysis of immune-related proteins in both colostrum and mature breastmilk collected from COVID-19 patients and healthy donors. The total protein amounts in the COVID-19 colostrum group were significantly higher than in the control group. While casein proteins in COVID-19 colostrum exhibited significantly lower abundances, immune-related proteins, especially whey proteins with antiviral properties against SARS-CoV-2, were upregulated. These proteins were detected with unique site-specific glycan structures and improved glycosylation diversity that are beneficial for recognizing epitopes and blocking viral entry. Such adaptive differences in milk from COVID-19 mothers tended to fade in mature milk from the same mothers one month postpartum. These results suggest that feeding infants colostrum from COVID-19 mothers confers both nutritional and immune benefits, and provide molecular-level insights that aid breastmilk feeding decisions in cases of active infection.

Assessing the spread risk of COVID-19 associated with multi-mode transportation networks in China

The spatial spread of COVID-19 during early 2020 in China was primarily driven by outbound travelers leaving the epicenter, Wuhan, Hubei province. Existing studies focus on the influence of aggregated out-bound population flows originating from Wuhan;however, the impacts of different modes of transportation and the network structure of transportation systems on the early spread of COVID-19 in China are not well understood. Here, we assess the roles of the road, railway, and air transportation networks in driving the spatial spread of COVID-19 in China. We find that the short-range spread within Hubei province was dominated by ground traffic, notably, the railway transportation. In contrast, long-range spread to cities in other provinces was mediated by multiple factors, including a higher risk of case importation associated with air transportation and a larger outbreak size in hub cities located at the center of transportation networks. We further show that, although the dissemination of SARS-CoV-2 across countries and continents is determined by the worldwide air transportation network, the early geographic dispersal of COVID-19 within China is better predicted by the railway traffic. Given the recent emergence of multiple more transmissible variants of SARS-CoV-2, our findings can support a better assessment of the spread risk of those variants and improve future pandemic preparedness and responses.

Ligand/Receptor-mediated Cell Communication Alteration Causes the Lung Inflammation Microenvironment and the Redistribution of Inter-tissue Immune Cells in COVID-19 Patients (preprint)

How SARS-CoV-2 causes disturbances of the lung microenvironment and systemic immune response remains a mystery. Here, we first analyze detailedly paired single-cell transcriptome data of the lungs, blood and bone marrow of two patients who died of COVID-19. Second, our results demonstrate that SARS-CoV-2 infection significantly increases the cellular communication frequency between AT1/AT2 cells and highly inflammatory myeloid cells, and induces the pulmonary inflammation microenvironment, and drives the disorder of fibroblasts, club and ciliated cells, thereby causing the increase of pulmonary fibrosis and mucus accumulation. Third, our works reveal that the increase of the lung T cell infiltration is mainly recruited by myeloid cells through certain ligands/receptors (ANXA1/FPR1, C5AR1/RPS19 and CCL5/CCR1), rather than AT1/AT2. Fourth, we find that some ligands and receptors such as ANXA1/FPR1, CD74/COPA, CXCLs/CXCRs, ALOX5/ALOX5AP, CCL5/CCR1, are significantly activated and shared among patients’ lungs, blood and bone marrow, implying that dysregulated ligands and receptors may cause the migration, redistribution and the inflammatory storm of immune cells in different tissues. Overall, our study reveals a latent mechanism by which the disorders of ligands and receptors caused by SARS-CoV-2 infection drive cell communication alteration, the pulmonary inflammatory microenvironment and systemic immune responses across tissues in COVID-19 patients.

Optimal allocations to heterogeneous agents with an application to stimulus checks (preprint)

A planner allocates discrete transfers of size $D_g$ to $N$ heterogeneous groups labeled $g$ and has CES preferences over the resulting outcomes, $H_g(D_g)$. We derive a closed-form solution for optimally allocating a fixed budget subject to group-specific inequality constraints under the assumption that increments in the $H_g$ functions are non-increasing. We illustrate our method by studying allocations of "support checks" from the U.S. government to households during both the Great Recession and the COVID-19 pandemic. We compare the actual allocations to optimal ones under alternative constraints, assuming the government focused on stimulating aggregate consumption during the 2008--2009 crisis and focused on welfare during the 2020--2021 crisis. The inputs for this analysis are obtained from versions of a life-cycle model with heterogeneous households, which predicts household-type-specific consumption and welfare responses to tax rebates and cash transfers.

Ligand/Receptor-mediated Cell Communication Alteration Causes the Lung Inflammation Microenvironment and the Redistribution of Inter-tissue Immune Cells in COVID-19 Patients (preprint)

How SARS-CoV-2 causes disturbances of the lung microenvironment and systemic immune response remains a mystery. Here, we first analyze detailedly paired single-cell transcriptome data of the lungs, blood and bone marrow of two patients who died of COVID-19. Second, our results demonstrate that SARS-CoV-2 infection significantly increases the cellular communication frequency between AT1/AT2 cells and highly inflammatory myeloid cells, and induces the pulmonary inflammation microenvironment, and drives the disorder of fibroblasts, club and ciliated cells, thereby causing the increase of pulmonary fibrosis and mucus accumulation. Third, our works reveal that the increase of the lung T cell infiltration is mainly recruited by myeloid cells through certain ligands/receptors (ANXA1/FPR1, C5AR1/RPS19 and CCL5/CCR1), rather than AT1/AT2. Fourth, we find that some ligands and receptors such as ANXA1/FPR1, CD74/COPA, CXCLs/CXCRs, ALOX5/ALOX5AP, CCL5/CCR1, are significantly activated and shared among patients’ lungs, blood and bone marrow, implying that dysregulated ligands and receptors may cause the migration, redistribution and the inflammatory storm of immune cells in different tissues. Overall, our study reveals a latent mechanism by which the disorders of ligands and receptors caused by SARS-CoV-2 infection drive cell communication alteration, the pulmonary inflammatory microenvironment and systemic immune responses across tissues in COVID-19 patients.

Optimal Allocations to Heterogeneous Agents with An Application to the COVID-19 Stimulus Checks (preprint)

This paper derives a closed-form solution for optimally allocating resources among heterogeneous individuals subject to individual-specific allocation constraints assuming the planner has CES-preferences and returns to allocations are non-increasing. We derive the optimal allocation of stimulus checks when the government's objective is to i) minimize inequality in consumption, ii) maximize stimulus, and iii) trade off these goals. The inputs for this analysis are obtained from a life-cycle model with heterogeneous households, which predicts household-type-specific consumption responses to different stimulus check amounts. Compared to the actual 2021-policy, optimal policies under all levels of inequality-aversion allocate more to low-income families with children.

Digital virus manipulation chip with a large array of all-dielectric nanocavities (preprint)

Spatial manipulation of a precise number of viruses for host cell infection is essential for the study of virus pathogenesis and evolution. Albeit optical tweezers have been advanced to the atomic level via optical cooling, it remains a formidable challenge to efficiently trap and move viruses in an aqueous environment, being restricted by insufficient strength of optical forces and a lack of precise spatial manipulation techniques. Here, we demonstrate giant optical forces produced by the enhancement of light in engineered arrays of nanocavities for trapping and digitally moving viruses down to 40 nm in size. By employing the virus hopping and flexibility of moving the laser position, we demonstrate a digital virus manipulation chip with a large trapping area, enabling single or massive virus transporting, positioning, and concentrating. Our work paves the way to efficient and precise manipulation of either single viruses or their massive ensembles, opening a wide range of novel opportunities for virus pathogenesis, virus diagnostics, vaccine, and antiviral drug development, being also important to tackle the current COVID-19 outbreaks. 

Sequential ER stress and inflammatory responses are induced by SARS-CoV-2 ORF3 through ERphagy (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have resulted in a number of severe cases of COVID-19 and deaths worldwide. However, knowledge of SARS-CoV-2 infection, diseases and therapy remains limited, underlining the urgency of fundamental studies and drug development. Studies have shown that induction of autophagy and hijacking of autophagic machinery are essential for infection and replication of SARS-CoV-2; however, the mechanism of this manipulation and function of autophagy during SARS-CoV-2 infection remain unclear. In the present study, we identified ORF3 as an inducer of autophagy and revealed that ORF3 localizes to the ER and induces FAM134B-related ERphagy through the HMGB1-Beclin1 pathway. As a consequence, ORF3 induces ER stress and inflammatory responses through ERphagy and sensitizes cells to ER stress-induced cell death, suggesting that SARS-CoV-2 ORF3 hijacks ERphagy and then harms ER homeostasis to induce inflammatory responses through excessive ER stress. These findings reveal a sequential induction of ERphagy, ER stress and acute inflammatory responses during SARS-CoV-2 infection and provide therapeutic potential for ERphagy and ER stress-related drugs for COVID-19 treatment and prevention. ImportanceSARS-CoV-2 infection and replication require autophagosome-like double-membrane vacuoles. Inhibition of autophagy suppresses viral replication, indicating the essential role of autophagy in SARS-CoV-2 infection. However, how SARS-CoV-2 hijacks autophagy and the function of autophagy in the disease progression remain unknown. Here, we reveal that SARS-CoV-2 ORF3 induces ERphagy and consequently induces ER stress to trigger acute inflammatory responses and enhance sensitivity to ER stress-induced apoptosis. Our studies uncover ERphagy-induced inflammatory responses during SARS-CoV-2 infection and provide a promising therapeutic approach for treating SARS-CoV-2 infection and inflammatory responses in COVID-19 by manipulating autophagy and ER stress.

Optimal Allocation of the COVID-19 Stimulus Checks (preprint)

Congress spent $250B sending stimulus checks to individuals. Could the same stimulus have been achieved for less, assuming the government’s information is restricted to 2019 tax returns? Using a life-cycle consumption-saving model with heterogeneous consumers, we calculate the consumption responses to $100 increments of cash transfers by, e.g., marital status, income, and number of children. We find the optimal allocation under different constraints using a new algorithm that can rank an arbitrarily-large number of possible allocations. The optimal policy roughly doubles the amount for low-income and younger consumers and can achieve the same stimulus at almost half the cost.

Suppression of MDA5-mediated antiviral immune responses by NSP8 of SARS-CoV-2 (preprint)

Melanoma differentiation-associated gene-5 (MDA5) acts as a cytoplasmic RNA sensor to detect viral dsRNA and mediates type I interferon (IFN) signaling and antiviral innate immune responses to infection by RNA viruses. Upon recognition of viral dsRNA, MDA5 is activated with K63-linked polyubiquitination and then triggers the recruitment of MAVS and activation of TBK1 and IKK, subsequently leading to IRF3 and NF-{kappa}B phosphorylation. Great numbers of symptomatic and severe infections of SARS-CoV-2 are spreading worldwide, and the poor efficacy of treatment with type I interferon and antiviral agents indicates that SARS-CoV-2 escapes from antiviral immune responses via an unknown mechanism. Here, we report that SARS-CoV-2 nonstructural protein 8 (NSP8) acts as an innate immune suppressor and inhibits type I IFN signaling to promote infection of RNA viruses. It downregulates the expression of type I IFNs, IFN-stimulated genes and proinflammatory cytokines by binding to MDA5 and impairing its K63-linked polyubiquitination. Our findings reveal that NSP8 mediates innate immune evasion during SARS-CoV-2 infection and may serve as a potential target for future therapeutics for SARS-CoV-2 infectious diseases.

Efficient network immunization under limited knowledge (preprint)

Targeted immunization or attacks of large-scale networks has attracted significant attention by the scientific community. However, in real-world scenarios, knowledge and observations of the network may be limited thereby precluding a full assessment of the optimal nodes to immunize (or remove) in order to avoid epidemic spreading such as that of current COVID-19 epidemic. Here, we study a novel immunization strategy where only n nodes are observed at a time and the most central between these n nodes is immunized (or attacked). This process is continued repeatedly until 1-p fraction of nodes are immunized (or attacked). We develop an analytical framework for this approach and determine the critical percolation threshold pc and the size of the giant component P{infty} for networks with arbitrary degree distributions P(k). In the limit of n[->]{infty} we recover prior work on targeted attack, whereas for n=1 we recover the known case of random failure. Between these two extremes, we observe that as n increases, pc increases quickly towards its optimal value under targeted immunization (attack) with complete information. In particular, we find a new scaling relationship between |pc({infty}) -pc (n)| and n as |pc({infty}) -pc (n)|~n-1exp(- n). For Scale-free (SF) networks, where P(k)~k-{gamma},2

Efficient network immunization under limited knowledge (preprint)

Targeted immunization or attacks of large-scale networks has attracted significant attention by the scientific community. However, in real-world scenarios, knowledge and observations of the network may be limited thereby precluding a full assessment of the optimal nodes to immunize (or remove) in order to avoid epidemic spreading such as that of current COVID-19 epidemic. Here, we study a novel immunization strategy where only $n$ nodes are observed at a time and the most central between these $n$ nodes is immunized (or attacked). This process is continued repeatedly until $1-p$ fraction of nodes are immunized (or attacked). We develop an analytical framework for this approach and determine the critical percolation threshold $p_c$ and the size of the giant component $P_{\infty}$ for networks with arbitrary degree distributions $P(k)$. In the limit of $n\to\infty$ we recover prior work on targeted attack, whereas for $n=1$ we recover the known case of random failure. Between these two extremes, we observe that as $n$ increases, $p_c$ increases quickly towards its optimal value under targeted immunization (attack) with complete information. In particular, we find a new scaling relationship between $|p_c(\infty)-p_c(n)|$ and $n$ as $|p_c(\infty)-p_c(n)|\sim n^{-1}\exp(-\alpha n)$. For Scale-free (SF) networks, where $P(k)\sim k^{-\gamma}, 2

Clinical characteristics of refractory COVID-19 pneumonia in Wuhan, China ;Clinical Infectious Diseases ;Oxford Academic

Background Since December 2019, novel coronavirus (SARS-CoV-2)-infected pneumonia (COVID-19) occurred in Wuhan, and rapidly spread throughout China. This study aimed to clarify the characteristics of patients with refractory COVID-19. Methods In this retrospective single-center study, we included 155 consecutive patients with confirmed COVID-19 in Zhongnan Hospital of Wuhan University from January 1st to February 5th. The cases were divided into general and refractory COVID-19 groups according to the clinical efficacy after hospitalization, and the difference between groups were compared. Results Compared with general COVID-19 patients (45.2%), refractory patients had an older age, male sex, more underlying comorbidities, lower incidence of fever, higher levels of maximum temperature among fever cases, higher incidence of breath shortness and anorexia, severer disease assessment on admission, high levels of neutrophil, aspartate aminotransferase (AST), lactate dehydrogenase (LDH) and C-reactive protein, lower levels of platelets and albumin, and higher incidence of bilateral pneumonia and pleural effusion (P<0.05). Refractory COVID-19 patients were more likely to receive oxygen, mechanical ventilation, expectorant, and adjunctive treatment including corticosteroid, antiviral drugs and immune enhancer (P<0.05). After adjustment, those with refractory COVID-19 were also more likely to have a male sex and manifestations of anorexia and fever on admission, and receive oxygen, expectorant and adjunctive agents (P<0.05) when considering the factors of disease severity on admission, mechanical ventilation, and ICU transfer. Conclusion Nearly 50% COVID-19 patients could not reach obvious clinical and radiological remission within 10 days after hospitalization. The patients with male sex, anorexia and no fever on admission predicted poor efficacy.