Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportunities. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have ① more imbalance between the numbers of the two intra-component HBs and ② more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the curve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical analysis. Finally, we validate the models using the experimental data of 34 systems. The extra trees forest model outperforms the other models in the validation, with an ROC-AUC of 0.88. Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

Intestinal homeostasis is maintained by specialized host cells and the gut microbiota. Wnt/β-catenin signaling is essential for gastrointestinal development and homeostasis, and its dysregulation has been implicated in inflammation and colorectal cancer. Axin1 negatively regulates activated Wnt/β-catenin signaling, but little is known regarding its role in regulating host-microbial interactions in health and disease. Here, we aim to demonstrate that intestinal Axin1 determines gut homeostasis and host response to inflammation. Axin1 expression was analyzed in human inflammatory bowel disease datasets. To explore the effects and mechanism of intestinal Axin1 in regulating intestinal homeostasis and colitis, we generated new mouse models with conditional knockout in intestinal epithelial cell (IEC; ) and Paneth cell (PC; ) to compare with control ( ; LoxP: locus of X-over, P1) mice. We found increased Axin1 expression in the colonic epithelium of human inflammatory bowel disease (IBD). mice exhibited altered goblet cell spatial distribution, PC morphology, reduced lysozyme expression, and enriched (). The absence of intestinal epithelial and PC Axin1 decreased susceptibility to dextran sulfate sodium (DSS)-induced colitis . and mice became more susceptible to DSS-colitis after cohousing with control mice. Treatment with reduced DSS-colitis severity. Antibiotic treatment did not change the IEC proliferation in the mice. However, the intestinal proliferative cells in mice with antibiotic treatment were reduced compared with those in mice without treatment. These data suggest non-colitogenic effects driven by the gut microbiome. In conclusion, we found that the loss of intestinal Axin1 protects against colitis, likely driven by epithelial Axin1 and Axin1-associated . Our study demonstrates a novel role of Axin1 in mediating intestinal homeostasis and the microbiota. Further mechanistic studies using specific Axin1 mutations elucidating how Axin1 modulates the microbiome and host inflammatory response will provide new therapeutic strategies for human IBD.

Seasonal influenza activity typically peaks in the winter months but plummeted globally during the current coronavirus disease 2019 (COVID-19) pandemic. Unraveling lessons from influenza's unprecedented low profile is critical in informing preparedness for incoming influenza seasons. Here, we explored a country-specific inference model to estimate the effects of mask-wearing, mobility changes (international and domestic), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interference in China, England, and the United States. We found that a one-week increase in mask-wearing intervention had a percent reduction of 11.3%-35.2% in influenza activity in these areas. The one-week mobility mitigation had smaller effects for the international (1.7%-6.5%) and the domestic community (1.6%-2.8%). In 2020-2021, the mask-wearing intervention alone could decline percent positivity by 13.3-19.8. The mobility change alone could reduce percent positivity by 5.2-14.0, of which 79.8%-98.2% were attributed to the deflected international travel. Only in 2019-2020, SARS-CoV-2 interference had statistically significant effects. There was a reduction in percent positivity of 7.6 (2.4-14.4) and 10.2 (7.2-13.6) in northern China and England, respectively. Our results have implications for understanding how influenza evolves under non-pharmaceutical interventions and other respiratory diseases and will inform health policy and the design of tailored public health measures.

The number of coronavirus disease 2019 (COVID-19) cases continues to surge, overwhelming healthcare systems and causing excess mortality in many countries. Testing of infectious populations remains a key strategy to contain the COVID-19 outbreak, delay the exponential spread of the disease, and flatten the epidemic curve. Using the Omicron variant outbreak as a background, this study aimed to evaluate the effectiveness of testing strategies with different test combinations and frequencies, analyze the factors associated with testing effectiveness, and optimize testing strategies based on these influencing factors. We developed a stochastic, agent-based, discrete-time susceptible-latent-infectious-recovered model simulating a community to estimate the association between three levels of testing strategies and COVID-19 transmission. Antigen testing and its combination strategies were more efficient than polymerase chain reaction (PCR)-related strategies. Antigen testing also showed better performance in reducing the demand for hospital beds and intensive care unit beds. The delay in the turnaround time of test results had a more significant impact on the efficiency of the testing strategy compared to the detection limit of viral load and detection-related contacts. The main advantage of antigen testing strategies is the short turnaround time, which is also a critical factor to be optimized to improve PCR strategies. After modifying the turnaround time, the strategies with less frequent testing were comparable to daily testing. The choice of testing strategy requires consideration of containment goals, test efficacy, community prevalence, and economic factors. This study provides evidence for the selection and optimization of testing strategies in the post-pandemic era and provides guidance for optimizing healthcare resources.

During the coronavirus disease 2019 (COVID-19) emergency, many hospitals were built or renovated around the world to meet the challenges posed by the rising number of infected cases. Environmental management in the hospital life cycle is vital in preventing nosocomial infection and includes many infection control procedures. In certain urgent situations, a hospital must be completed quickly, and work process approval and supervision must therefore be accelerated. Thus, many works cannot be checked in detail. This results in a lack of work liability control and increases the difficulty of ensuring the fulfillment of key infection prevention measures. This study investigates how blockchain technology can transform the work quality inspection workflow to assist in nosocomial infection control under a fast delivery requirement. A blockchain-based life-cycle environmental management framework is proposed to track the fulfillment of crucial infection control measures in the design, construction, and operation stages of hospitals. The proposed framework allows for work quality checking after the work is completed, when some work cannot be checked on time. Illustrative use cases are selected to demonstrate the capabilities of the developed solution. This study provides new insights into applying blockchain technology to address the challenge of environmental management brought by rapid delivery requirements.

Recombinant adenovirus serotype 5 (Ad5) vector has been widely applied in vaccine development targeting infectious diseases, such as Ebola virus disease and coronavirus disease 2019 (COVID-19). However, the high prevalence of preexisting anti-vector immunity compromises the immunogenicity of Ad5-based vaccines. Thus, there is a substantial unmet need to minimize preexisting immunity while improving the insert-induced immunity of Ad5 vectors. Herein, we address this need by utilizing biocompatible nanoparticles to modulate Ad5-host interactions. We show that positively charged human serum albumin nanoparticles ((+)HSAnp), which are capable of forming a complex with Ad5, significantly increase the transgene expression of Ad5 in both coxsackievirus-adenovirus receptor-positive and -negative cells. Furthermore, in charge- and dose-dependent manners, Ad5/(+)HSAnp complexes achieve robust (up to 227-fold higher) and long-term (up to 60 days) transgene expression in the lungs of mice following intranasal instillation. Importantly, in the presence of preexisting anti-Ad5 immunity, complexed Ad5-based Ebola and COVID-19 vaccines significantly enhance antigen-specific humoral response and mucosal immunity. These findings suggest that viral aggregation and charge modification could be leveraged to engineer enhanced viral vectors for vaccines and gene therapies.

Xuanfeibaidu Formula (XFBD) is a Chinese medicine used in the clinical treatment of coronavirus disease 2019 (COVID-19) patients. Although XFBD has exhibited significant therapeutic efficacy in clinical practice, its underlying pharmacological mechanism remains unclear. Here, we combine a comprehensive research approach that includes network pharmacology, transcriptomics, and bioassays in multiple model systems to investigate the pharmacological mechanism of XFBD and its bioactive substances. High-resolution mass spectrometry was combined with molecular networking to profile the major active substances in XFBD. A total of 104 compounds were identified or tentatively characterized, including flavonoids, terpenes, carboxylic acids, and other types of constituents. Based on the chemical composition of XFBD, a network pharmacology-based analysis identified inflammation-related pathways as primary targets. Thus, we examined the anti-inflammation activity of XFBD in a lipopolysaccharide-induced acute inflammation mice model. XFBD significantly alleviated pulmonary inflammation and decreased the level of serum proinflammatory cytokines. Transcriptomic profiling suggested that genes related to macrophage function were differently expressed after XFBD treatment. Consequently, the effects of XFBD on macrophage activation and mobilization were investigated in a macrophage cell line and a zebrafish wounding model. XFBD exerts strong inhibitory effects on both macrophage activation and migration. Moreover, through multimodal screening, we further identified the major components and compounds from the different herbs of XFBD that mediate its anti-inflammation function. Active components from XFBD, including , , and , were then found to strongly downregulate macrophage activation, and polydatin, isoliquiritin, and acteoside were identified as active compounds. Components of and were found to substantially inhibit endogenous macrophage migration, while the presence of ephedrine, atractylenolide I, and kaempferol was attributed to these effects. In summary, our study explores the pharmacological mechanism and effective components of XFBD in inflammation regulation via multimodal approaches, and thereby provides a biological illustration of the clinical efficacy of XFBD.

Traditional Chinese medicine (TCM) has been successfully applied worldwide in the treatment of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the pharmacological mechanisms underlying this success remain unclear. Hence, the aim of this review is to combine pharmacological assays based on the theory of TCM in order to elucidate the potential signaling pathways, targets, active compounds, and formulas of herbs that are involved in the TCM treatment of COVID-19, which exhibits combatting viral infections, immune regulation, and amelioration of lung injury and fibrosis. Extensive reports on target screening are elucidated using virtual prediction via docking analysis or network pharmacology based on existing data. The results of these reports indicate that an intricate regulatory mechanism is involved in the pathogenesis of COVID-19. Therefore, more pharmacological research on the natural herbs used in TCM should be conducted in order to determine the association between TCM and COVID-19 and account for the observed therapeutic effects of TCM against COVID-19.

Subwavelength manipulation of light waves with high precision can enable new and exciting applications in spectroscopy, sensing, and medical imaging. For these applications, miniaturized spectrometers are desirable to enable the on-chip analysis of spectral information. In particular, for imaging-based spectroscopic sensing mechanisms, the key challenge is to determine the spatial-shift information accurately (i.e., the spatial displacement introduced by wavelength shift or biological or chemical surface binding), which is similar to the challenge presented by super-resolution imaging. Here, we report a unique "rainbow" trapping metasurface for on-chip spectrometers and sensors. Combined with super-resolution image processing, the low-setting 4× optical microscope system resolves a displacement of the resonant position within 35 nm on the plasmonic rainbow trapping metasurface with a tiny area as small as 0.002 mm. This unique feature of the spatial manipulation of efficiently coupled rainbow plasmonic resonances reveals a new platform for miniaturized on-chip spectroscopic analysis with a spectral resolution of 0.032 nm in wavelength shift. Using this low-setting 4× microscope imaging system, we demonstrate a biosensing resolution of 1.92 × 10 exosomes per milliliter for A549-derived exosomes and distinguish between patient samples and healthy controls using exosomal epidermal growth factor receptor (EGFR) expression values, thereby demonstrating a new on-chip sensing system for personalized accurate bio/chemical sensing applications.

Many microorganisms have mechanisms that protect cells against attack from viruses. The fermentation components of sp. 1647 exhibit potent anti-influenza A virus (IAV) activity. This strain was isolated from soil in southern China in the 1970s, but the chemical nature of its antiviral substance(s) has remained unknown until now. We used an integrated multi-omics strategy to identify the antiviral agents from this streptomycete. The antibiotics and Secondary Metabolite Analysis Shell (antiSMASH) analysis of its genome sequence revealed 38 biosynthetic gene clusters (BGCs) for secondary metabolites, and the target BGCs possibly responsible for the production of antiviral components were narrowed down to three BGCs by bioactivity-guided comparative transcriptomics analysis. Through bioinformatics analysis and genetic manipulation of the regulators and a biosynthetic gene, cluster 36 was identified as the BGC responsible for the biosynthesis of the antiviral compounds. Bioactivity-based molecular networking analysis of mass spectrometric data from different recombinant strains illustrated that the antiviral compounds were a class of structural analogues. Finally, 18 pseudo-tetrapeptides with an internal ureido linkage, omicsynins A1-A6, B1-B6, and C1-C6, were identified and/or isolated from fermentation broth. Among them, 11 compounds (omicsynins A1, A2, A6, B1-B3, B5, B6, C1, C2, and C6) are new compounds. Omicsynins B1-B4 exhibited potent antiviral activity against IAV with the 50% inhibitory concentration (IC) of approximately 1 µmol∙L and a selectivity index (SI) ranging from 100 to 300. Omicsynins B1-B4 also showed significant antiviral activity against human coronavirus HCoV-229E. By integrating multi-omics data, we discovered a number of novel antiviral pseudo-tetrapeptides produced by sp. 1647, indicating that the secondary metabolites of microorganisms are a valuable source of novel antivirals.

The essential role of immunoglobulin G (IgG) in immune system regulation and combatting infectious diseases cannot be fully recognized without an understanding of the changes in its -glycans attached to the asparagine 297 of the Fc domain that occur under such circumstances. These glycans impact the antibody stability, half-life, secretion, immunogenicity, and effector functions. Therefore, in this study, we analyzed and compared the total IgG glycome-at the level of individual glycan structures and derived glycosylation traits (sialylation, galactosylation, fucosylation, and bisecting -acetylglucosamine (GlcNAc))-of 64 patients with influenza, 77 patients with coronavirus disease 2019 (COVID-19), and 56 healthy controls. Our study revealed a significant decrease in IgG galactosylation, sialylation, and bisecting GlcNAc (where the latter shows the most significant decrease) in deceased COVID-19 patients, whereas IgG fucosylation was increased. On the other hand, IgG galactosylation remained stable in influenza patients and COVID-19 survivors. IgG glycosylation in influenza patients was more time-dependent: In the first seven days of the disease, sialylation increased and fucosylation and bisecting GlcNAc decreased; in the next 21 days, sialylation decreased and fucosylation increased (while bisecting GlcNAc remained stable). The similarity of IgG glycosylation changes in COVID-19 survivors and influenza patients may be the consequence of an adequate immune response to enveloped viruses, while the observed changes in deceased COVID-19 patients may indicate its deviation.

By providing a means of separating the airborne emissions of patients from the air breathed by healthcare workers (HCWs), vented individual patient (VIP) hoods, a form of local exhaust ventilation (LEV), offer a new approach to reduce hospital-acquired infection (HAI). Results from recent studies have demonstrated that, for typical patient-emitted aerosols, VIP hoods provide protection at least equivalent to that of an N95 mask. Unlike a mask, hood performance can be easily monitored and HCWs can be alerted to failure by alarms. The appropriate use of these relatively simple devices could both reduce the reliance on personal protective equipment (PPE) for infection control and provide a low-cost and energy-efficient form of protection for hospitals and clinics. Although the development and deployment of VIP hoods has been accelerated by the coronavirus disease 2019 (COVID-19) pandemic, these devices are currently an immature technology. In this review, we describe the state of the art of VIP hoods and identify aspects in need of further development, both in terms of device design and the protocols associated with their use. The broader concept of individual patient hoods has the potential to be expanded beyond ventilation to the provision of clean conditions for individual patients and personalized control over other environmental factors such as temperature and humidity.

Most studies of coronavirus disease 2019 (COVID-19) progression have focused on the transfer of patients within secondary or tertiary care hospitals from regular wards to intensive care units. Little is known about the risk factors predicting the progression to severe COVID-19 among patients in community isolation, who are either asymptomatic or suffer from only mild to moderate symptoms. Using a multivariable competing risk survival analysis, we identify several important predictors of progression to severe COVID-19-rather than to recovery-among patients in the largest community isolation center in Wuhan, China from 6 February 2020 (when the center opened) to 9 March 2020 (when it closed). All patients in community isolation in Wuhan were either asymptomatic or suffered from mild to moderate COVID-19 symptoms. We performed competing risk survival analysis on time-to-event data from a cohort study of all COVID-19 patients ( = 1753) in the isolation center. The potential predictors we investigated were the routine patient data collected upon admission to the isolation center: age, sex, respiratory symptoms, gastrointestinal symptoms, general symptoms, and computed tomography (CT) scan signs. The main outcomes were time to severe COVID-19 or recovery. The factors predicting progression to severe COVID-19 were: male sex (hazard ratio (HR) = 1.29, 95% confidence interval (CI) 1.04-1.58,  = 0.018), young and old age, dyspnea (HR = 1.58, 95% CI 1.24-2.01,  < 0.001), and CT signs of ground-glass opacity (HR = 1.39, 95% CI 1.04-1.86,  = 0.024) and infiltrating shadows (HR = 1.84, 95% CI 1.22-2.78,  = 0.004). The risk of progression was found to be lower among patients with nausea or vomiting (HR = 0.53, 95% CI 0.30-0.96,  = 0.036) and headaches (HR = 0.54, 95% CI 0.29-0.99,  = 0.046). Our results suggest that several factors that can be easily measured even in resource-poor settings (dyspnea, sex, and age) can be used to identify mild COVID-19 patients who are at increased risk of disease progression. Looking for CT signs of ground-glass opacity and infiltrating shadows may be an affordable option to support triage decisions in resource-rich settings. Common and unspecific symptoms (headaches, nausea, and vomiting) are likely to have led to the identification and subsequent community isolation of COVID-19 patients who were relatively unlikely to deteriorate. Future public health and clinical guidelines should build on this evidence to improve the screening, triage, and monitoring of COVID-19 patients who are asymtomatic or suffer from mild to moderate symptoms.

Regular coronavirus disease 2019 (COVID-19) epidemic prevention and control have raised new requirements that necessitate operation-strategy innovation in urban rail transit. To alleviate increasingly serious congestion and further reduce the risk of cross-infection, a novel two-stage distributionally robust optimization (DRO) model is explicitly constructed, in which the probability distribution of stochastic scenarios is only partially known in advance. In the proposed model, the mean-conditional value-at-risk (CVaR) criterion is employed to obtain a tradeoff between the expected number of waiting passengers and the risk of congestion on an urban rail transit line. The relationship between the proposed DRO model and the traditional two-stage stochastic programming (SP) model is also depicted. Furthermore, to overcome the obstacle of model solvability resulting from imprecise probability distributions, a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form. A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming (MILP) solver is developed to improve the computational efficiency of large-scale instances. Finally, a series of numerical examples with real-world operation data are executed to validate the proposed approaches.

The rapid spread of the coronavirus disease (COVID-19) pandemic in over 200 countries poses a substantial threat to human health. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, can be discharged with feces into the drainage system. However, a comprehensive understanding of the occurrence, presence, and potential transmission of SARS-CoV-2 in sewers, especially in community sewers, is still lacking. This study investigated the virus occurrence by viral nucleic acid testing in vent stacks, septic tanks, and the main sewer outlets of community where confirmed patients had lived during the outbreak of the epidemic in Wuhan, China. The results indicated that the risk of long-term emission of SARS-CoV-2 to the environment via vent stacks of buildings was low after confirmed patients were hospitalized. SARS-CoV-2 were mainly detected in the liquid phase, as opposed to being detected in aerosols, and its RNA in the sewage of septic tanks could be detected for only four days after confirmed patients were hospitalized. The surveillance of SARS-CoV-2 in sewage could be a sensitive indicator for the possible presence of asymptomatic patients in the community, though the viral concentration could be diluted more than 10 times, depending on the sampling site, as indicated by the () test. The comprehensive investigation of the community sewage drainage system is helpful to understand the occurrence characteristics of SARS-CoV-2 in sewage after excretion with feces and the feasibility of sewage surveillance for COVID-19 pandemic monitoring.

Regulatory science is a discipline that uses comprehensive methods of natural science, social science, and humanities to provide support for administrative decision-making through the development of new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of regulated products. During the pandemics induced by infectious diseases, such as H1N1 flu, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS), regulatory science strongly supported the development of drugs and vaccines to respond to the viruses. In particular, with the support of research on drug regulatory science, vaccines have played a major role in the prevention and control of coronavirus disease 2019 (COVID-19). This review summarizes the overall state of the vaccine industry, research and development (R&D) of COVID-19 vaccines in China, and the general state of regulatory science and supervision for vaccines in China. Further, this review highlights how regulatory science has promoted the R&D of Chinese COVID-19 vaccines, with analyses from the aspects of national-level planning, relevant laws and regulations, technical guidelines, quality control platforms, and post-marketing supervision. Ultimately, this review provides a reference for the formulation of a vaccine development strategy in response to the current pandemic and the field of vaccine development in the post-pandemic era, as well as guidance on how to better respond to emerging and recurring infectious diseases that may occur in the future.

The coronavirus disease 2019 (COVID-19) pandemic has caused a surge in demand for face masks, with the massive consumption of masks leading to an increase in resource-related and environmental concerns. In this work, we fabricated meltblown polypropylene (mb-PP)-based high-performance planar face masks and investigated the effects of six commonly used disinfection methods and various mask-wearing periods on the reusability of these masks. The results show that, after three cycles of treatment using hot water at 70 °C for 30 min, which is one of the most scalable, user-friendly methods for viral disinfection, the particle filtration efficiency (PFE) of the mask remained almost unchanged. After mask wearing for 24 h and subsequent disinfection using the same treatment procedures, the PFE decreased to 91.3%; the average number of bacterial and fungal colonies was assessed to be 9.2 and 51.6 colony-forming units per gram (CFU∙g ), respectively; and coliform and pyogenic bacteria were not detected. Both the PFE and the microbial indicators are well above the standard for reusable masks after disinfection. Schlieren photography was then used to assess the capabilities of used and disinfected masks during use; it showed that the masks exhibit a high performance in suppressing the spread of breathed air.

COVID-19, an infectious pulmonary disease caused by the SARS-CoV-2 virus, has profoundly impacted the world, motivating researchers across a broad spectrum of academic disciplines to gain a deeper understanding and develop effective therapies to this disease. This article presents an engineering perspective on how microfluidic technologies may address some of the challenges presented by COVID-19 and other pulmonary diseases. In particular, this article highlights urgent needs in pulmonary medicine, with an emphasis on technological innovations in the microfluidic manipulation of particles and fluids, and how these innovations may contribute to the study, diagnosis, and therapy of pulmonary diseases.