Nursing workforce allocation in the intensive care units of COVID-19-designated hospitals: A nationwide cross-sectional survey in China.

AIM: To explore the nursing workforce allocation in intensive care units (ICUs) of COVID-19-designated hospitals during the epidemic peak in China. DESIGN: A nationwide cross-sectional online survey. METHODS: A total of 37 head nurses and 262 frontline nurses in 37 ICUs of COVID-19-designated tertiary hospitals located in 22 cities of China were surveyed. The self-reported human resource allocation questionnaire was used to assess the nursing workforce allocation. RESULTS: The average patient-to-nurse ratio was 1.89 ± 1.14, and the median working hours per shift was 5 h. The top four majors of front-line nurses in ICUs were respiratory (31.30%), lemology (27.86%), intensive care (21.76%) and emergency (17.18%). We also found that a smaller average patient-to-nurse ratio (odds ratio [OR]: 0.328, 95% CI: 0.108, 1.000), longer average weekly rest time per person (OR: 0.193, 95% CI: 0.051, 0.729) and larger proportion of 6-9 working years (OR: 0.002, 95% CI: 0.001, 1.121) decreased the occurrence of nursing adverse events.

Supramolecular Nanofibers Block SARS-CoV-2 Entry into Human Host Cells.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection relies on its spike protein binding to angiotensin-converting enzyme 2 (ACE2) on host cells to initiate cellular entry. Blocking the interactions between the spike protein and ACE2 offers promising therapeutic opportunities to prevent infection. We report here on peptide amphiphile supramolecular nanofibers that display a sequence from ACE2 in order to promote interactions with the SARS-CoV-2 spike receptor binding domain. We demonstrate that displaying this sequence on the surface of supramolecular assemblies preserves its α-helical conformation and blocks the entry of a pseudovirus and its two variants into human host cells. We also found that the chemical stability of the bioactive structures was enhanced in the supramolecular environment relative to the unassembled peptide molecules. These findings reveal unique advantages of supramolecular peptide therapies to prevent viral infections and more broadly for other targets as well.

Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics

Differential equations-based epidemic compartmental models and deep neural networks-based artificial intelligence (AI) models are powerful tools for analyzing and fighting the transmission of COVID-19. However, the capability of compartmental models is limited by the challenges of parameter estimation, while AI models fail to discover the evolutionary pattern of COVID-19 and lack explainability. This paper aims to provide a novel method (called Epi-DNNs) by integrating compartmental models and deep neural networks (DNNs) to model the complex dynamics of COVID-19. In the proposed Epi-DNNs method, the neural network is designed to express the unknown parameters in the compartmental model and the Runge–Kutta method is implemented to solve the ordinary differential equations (ODEs) so as to give the values of the ODEs at a given time. Specifically, the discrepancy between predictions and observations is incorporated into the loss function, then the defined loss is minimized and applied to identify the best-fitted parameters governing the compartmental model. Furthermore, we verify the performance of Epi-DNNs on the real-world reported COVID-19 data on the Omicron epidemic in Shanghai covering February 25 to May 27, 2022. The experimental findings on the synthesized data have revealed its effectiveness in COVID-19 transmission modeling. Moreover, the inferred parameters from the proposed Epi-DNNs method yield a predictive compartmental model, which can serve to forecast future dynamics.

Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics.

Differential equations-based epidemic compartmental models and deep neural networks-based artificial intelligence (AI) models are powerful tools for analyzing and fighting the transmission of COVID-19. However, the capability of compartmental models is limited by the challenges of parameter estimation, while AI models fail to discover the evolutionary pattern of COVID-19 and lack explainability. This paper aims to provide a novel method (called Epi-DNNs) by integrating compartmental models and deep neural networks (DNNs) to model the complex dynamics of COVID-19. In the proposed Epi-DNNs method, the neural network is designed to express the unknown parameters in the compartmental model and the Runge-Kutta method is implemented to solve the ordinary differential equations (ODEs) so as to give the values of the ODEs at a given time. Specifically, the discrepancy between predictions and observations is incorporated into the loss function, then the defined loss is minimized and applied to identify the best-fitted parameters governing the compartmental model. Furthermore, we verify the performance of Epi-DNNs on the real-world reported COVID-19 data on the Omicron epidemic in Shanghai covering February 25 to May 27, 2022. The experimental findings on the synthesized data have revealed its effectiveness in COVID-19 transmission modeling. Moreover, the inferred parameters from the proposed Epi-DNNs method yield a predictive compartmental model, which can serve to forecast future dynamics.

ApoE4 associated with severe COVID-19 outcomes via downregulation of ACE2 and imbalanced RAS pathway.

BACKGROUND: Recent numerous epidemiology and clinical association studies reported that ApoE polymorphism might be associated with the risk and severity of coronavirus disease 2019 (COVID-19), and yielded inconsistent results. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection relies on its spike protein binding to angiotensin-converting enzyme 2 (ACE2) receptor expressed on host cell membranes. METHODS: A meta-analysis was conducted to clarify the association between ApoE polymorphism and the risk and severity of COVID-19. Multiple protein interaction assays were utilized to investigate the potential molecular link between ApoE and the SARS-CoV-2 primary receptor ACE2, ApoE and spike protein. Immunoblotting and immunofluorescence staining methods were used to access the regulatory effect of different ApoE isoform on ACE2 protein expression. RESULTS: ApoE gene polymorphism (ε4 carrier genotypes VS non-ε4 carrier genotypes) is associated with the increased risk (P = 0.0003, OR = 1.44, 95% CI 1.18-1.76) and progression (P < 0.00001, OR = 1.85, 95% CI 1.50-2.28) of COVID-19. ApoE interacts with both ACE2 and the spike protein but did not show isoform-dependent binding effects. ApoE4 significantly downregulates ACE2 protein expression in vitro and in vivo and subsequently decreases the conversion of Ang II to Ang 1-7. CONCLUSIONS: ApoE4 increases SARS-CoV-2 infectivity in a manner that may not depend on differential interactions with the spike protein or ACE2. Instead, ApoE4 downregulates ACE2 protein expression and subsequently the dysregulation of renin-angiotensin system (RAS) may provide explanation by which ApoE4 exacerbates COVID-19 disease.

Interference reduction isothermal nucleic acid amplification strategy for COVID-19 variant detection.

Common reference methods for COVID-19 variant diagnosis include viral sequencing and PCR-based methods. However, sequencing is tedious, expensive, and time-consuming, while PCR-based methods have high risk of insensitive detection in variant-prone regions and are susceptible to potential background signal interference in biological samples. Here, we report a loop-mediated interference reduction isothermal nucleic acid amplification (LM-IR-INA) strategy for highly sensitive single-base mutation detection in viral variants. This strategy exploits the advantages of nicking endonuclease-mediated isothermal amplification, luminescent iridium(III) probes, and time-resolved emission spectroscopy (TRES). Using the LM-IR-INA strategy, we established a luminescence platform for diagnosing COVID-19 D796Y single-base substitution detection with a detection limit of 2.01 × 105 copies/µL in a linear range of 6.01 × 105 to 3.76 × 108 copies/µL and an excellent specificity with a variant/wild-type ratio of significantly less than 0.0625%. The developed TRES-based method was also successfully applied to detect D796Y single-base substitution sequence in complicated biological samples, including throat and blood, and was a superior to steady-state technique. LM-IR-INA was also demonstrated for detecting the single-base substitution D614G as well as the multiple-base mutation H69/V70del without mutual interference, indicating that this approach has the potential to be used as a universal viral variant detection strategy.

The COVID-19 pandemic and Alzheimer's disease: mutual risks and mechanisms.

Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a life-threatening disease, especially in elderly individuals and those with comorbidities. The predominant clinical manifestation of COVID-19 is respiratory dysfunction, while neurological presentations are increasingly being recognized. SARS-CoV-2 invades host cells primarily via attachment of the spike protein to the angiotensin-converting enzyme 2 (ACE2) receptor expressed on cell membranes. Patients with Alzheimer's disease (AD) are more susceptible to SARS-CoV-2 infection and prone to severe clinical outcomes. Recent studies have revealed some common risk factors for AD and COVID-19. An understanding of the association between COVID-19 and AD and the potential related mechanisms may lead to the development of novel approaches to treating both diseases. In the present review, we first summarize the mechanisms by which SARS-CoV-2 invades the central nervous system (CNS) and then discuss the associations and potential shared key factors between COVID-19 and AD, with a focus on the ACE2 receptor, apolipoprotein E (APOE) genotype, age, and neuroinflammation.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

Chinese expert consensus on intestinal microecology and management of digestive tract complications related to tumor treatment (version 2022).

The human gut microbiota represents a complex ecosystem that is composed of bacteria, fungi, viruses, and archaea. It affects many physiological functions including metabolism, inflammation, and the immune response. The gut microbiota also plays a role in preventing infection. Chemotherapy disrupts an organism's microbiome, increasing the risk of microbial invasive infection; therefore, restoring the gut microbiota composition is one potential strategy to reduce this risk. The gut microbiome can develop colonization resistance, in which pathogenic bacteria and other competing microorganisms are destroyed through attacks on bacterial cell walls by bacteriocins, antimicrobial peptides, and other proteins produced by symbiotic bacteria. There is also a direct way. For example, Escherichia coli colonized in the human body competes with pathogenic Escherichia coli 0157 for proline, which shows that symbiotic bacteria compete with pathogens for resources and niches, thus improving the host's ability to resist pathogenic bacteria. Increased attention has been given to the impact of microecological changes in the digestive tract on tumor treatment. After 2019, the global pandemic of novel coronavirus disease 2019 (COVID-19), the development of novel tumor-targeting drugs, immune checkpoint inhibitors, and the increased prevalence of antimicrobial resistance have posed serious challenges and threats to public health. Currently, it is becoming increasingly important to manage the adverse effects and complications after chemotherapy. Gastrointestinal reactions are a common clinical presentation in patients with solid and hematologic tumors after chemotherapy, which increases the treatment risks of patients and affects treatment efficacy and prognosis. Gastrointestinal symptoms after chemotherapy range from nausea, vomiting, and anorexia to severe oral and intestinal mucositis, abdominal pain, diarrhea, and constipation, which are often closely associated with the dose and toxicity of chemotherapeutic drugs. It is particularly important to profile the gastrointestinal microecological flora and monitor the impact of antibiotics in older patients, low immune function, neutropenia, and bone marrow suppression, especially in complex clinical situations involving special pathogenic microbial infections (such as clostridioides difficile, multidrug-resistant Escherichia coli, carbapenem-resistant bacteria, and norovirus).

Retrospective Modeling of the Omicron Epidemic in Shanghai, China: Exploring the Timing and Performance of Control Measures.

BACKGROUND: In late February 2022, the Omicron epidemic swept through Shanghai, and the Shanghai government responded to it by adhering to a dynamic zero-COVID strategy. In this study, we conducted a retrospective analysis of the Omicron epidemic in Shanghai to explore the timing and performance of control measures based on the eventual size and duration of the outbreak. METHODS: We constructed an age-structured and vaccination-stratified SEPASHRD model by considering populations that had been detected or controlled before symptom onset. In addition, we retrospectively modeled the epidemic in Shanghai from 26 February 2022 to 31 May 2022 across four periods defined by events and interventions, on the basis of officially reported confirmed (58,084) and asymptomatic (591,346) cases. RESULTS: According to our model fitting, there were about 785,123 positive infections, of which about 57,585 positive infections were symptomatic infections. Our counterfactual assessment found that precise control by grid management was not so effective and that citywide static management was still needed. Universal and enforced control by citywide static management contained 87.65% and 96.29% of transmission opportunities, respectively. The number of daily new and cumulative infections could be significantly reduced if we implemented static management in advance. Moreover, if static management was implemented in the first 14 days of the epidemic, the number of daily new infections would be less than 10. CONCLUSIONS: The above research suggests that dynamic zeroing can only be achieved when strict prevention and control measures are implemented as early as possible. In addition, a lot of preparation is still needed if China wants to change its strategy in the future.

Clinical Application of Thromboelastography in Acute Ischemic Stroke.

Acute ischemic stroke (AIS), characterized by high morbidity and mortality, has imposed a considerable burden on society. Despite rapid development in the treatment of AIS, there is still a high risk of recurrence. Furthermore, there is a time delay in waiting for the results of conventional coagulation tests in candidate patients for intravenous thrombolysis therapy. Heterogeneous responses to antiplatelet, intravascular thrombolysis, and endovascular therapies also worsen the situation. Thromboelastography (TEG), as a global and portable detection method for hemostasis, facilitates clinicians in disease monitoring, treatment evaluation, and prognosis prediction in AIS. In this narrative review, we provided a comprehensive summary of the clinical application of TEG in ischemic stroke and gave insights to further studies.

Interference reduction isothermal nucleic acid amplification strategy for COVID-19 variant detection

Interference reduction isothermal nucleic acid amplification strategy for COVID-19 variant detection.ga1

Atomically dispersed Mn boosting photoelectrochemical SARS-CoV-2 spike protein immunosensing on carbon nitride.

The sudden outbreak of coronavirus disease (COVID-19) triggered by SARS-CoV-2 infection has created a terrifying situation around the world. The spike protein of SARS-CoV-2 can act as an early biomarker for COVID-19. Therefore, controlling the spread of COVID-19 requires a low-cost, fast-response, and sensitive monitoring technique of spike protein. Herein, a photoelectrochemical (PEC) immunosensor for the detection of spike protein was constructed using the nanobody and an Mn (Ⅱ) modified graphitic carbon nitride (Mn/g-C3N4). The introduction of atomically dispersed Mn (Ⅱ) can accelerate the effective transfer and separation of photogenerated electron-hole pairs, which significantly boosts PEC performance of g-C3N4, thereby improving the detection sensitivity. As a recognition site, nanobody can achieve high-affinity binding to the spike protein, leading to a high sensitivity. The linear detection range of the proposed PEC immunosensor was 75 fg mL-1 to 150 pg mL-1, and the limit of detection was calculated to be 1.22 fg mL-1. This stable and feasible PEC immunosensor would be a promising diagnostic tool for sensitively detecting spike protein of SARS-CoV-2.

Modified State-Dependent Queuing Model for the Capacity Analysis of Metro Rail Transit Station Corridor during COVID-19

The COVID-19 pandemic policies have had a significant impact on the daily commuter flow at the metro rail transit stations. In this study, we propose a modified state-dependent M(n)/G(n)/C/C queuing model for the analysis of commuter flow in the corridor of metro rail transit stations in the COVID-19 situation in order to ensure safe social distance. The proposed model is a finite capacity queuing system with state-dependent commuter arrivals and state-dependent service rates based on the flow–density relationship. First, a mathematical queuing model is developed by using the birth–death process (BDP) and the expected number of commuters, and average area occupied per commuter and blocking probabilities are computed. Then, the accuracy of the proposed model is verified by a discrete-event simulation (DES) framework. (1) The proposed model's results are compared to those of the existing M/G(n)/C/C model. The proposed modified model's sensitivity analysis revealed that the anticipated number of commuters in the corridor remains smaller when the arrival rate is state-dependent. (2) In accordance with COVID-19 protocol, when the facility is congested, commuters are discouraged from entering and a safe social distance is maintained between them. (3) No commuters are impeded, and adequate throughput is ensured from the corridor. The proposed model will assist the metro rail transit station operators in making intelligent decisions regarding the operations in the COVID-19 situation.

Research on Influencing Factors of Knowledge Hiding Behavior in Socialized Q&A Communities: Taking Zhihu as an Example

With the normalization of epidemic prevention and control, the expression of the public’s demand for health information on online platforms continues to increase, while knowledge hiding behavior has seriously hindered the communication and dissemination of epidemic prevention knowledge and has a negative impact on public communication and access to health information in the socialized Q&A communities. Therefore, further stimulating diving users' activity and reducing their knowledge hiding behavior have become the key to the sustainable development of epidemic prevention and control and communities. Based on the social cognition theory, from the perspective of individual cognition and external environment, this study constructs a theoretical model of the influencing factors of users’ knowledge hiding behavior in the socialized Q&A communities in the post-epidemic era and puts forward relevant assumptions. 151 effective questionnaires are collected and an empirical analysis is carried out by using the structural equation model. The results show that outcome expectation, community atmosphere, and requesting negatively affect knowledge hiding behavior;self-efficacy, outcome expectation, and community atmosphere negatively affect the three different types of knowledge hiding behavior, which are evasive hiding, playing dumb, and rationalized hiding;community atmosphere positively affects outcome expectation, which plays a significant intermediary effect between community atmosphere and knowledge hiding behavior. The research content and relevant conclusions of this study deepen and expand the connotation and extension of knowledge hiding behavior in the negative performance of Q&A communities. From the perspective of practical application, it can also effectively reduce knowledge hiding behavior, grasp the development direction of public health needs, and strengthen the dissemination of epidemic prevention and control knowledge.

Atomically Dispersed Mn Boosting Photoelectrochemical SARS-CoV-2 Spike Protein Immunosensing on Carbon Nitride

The sudden outbreak of coronavirus disease (COVID-19) triggered by SARS-CoV-2 infection has created a terrifying situation around the world. The Spike protein of SARS-CoV-2 may act as an early biomarker for leading to receptor binding and virus entry. Therefore, controlling the spread of COVID-19 requires a low-cost, fast-response, and sensitive monitoring technique of spike protein. Herein, a photoelectrochemical (PEC) immunosensor for the detection of spike protein was constructed using the nanobody and an Mn (Ⅱ) modified graphitic carbon nitride (Mn/g-C3N4). The introduction of atomically dispersed Mn (Ⅱ) accelerates the effective transfer and separation of photogenerated electron-hole pairs, which significantly boosts PEC performance of g-C3N4, thereby improving the detection sensitivity. As a recognition site, nanobody can achieve high-affinity binding to the spike protein for detection. The linear detection range of the proposed PEC immunosensor was 75 fg mL–1 to 150 pg mL–1, and the limit of detection was calculated to be 1.22 fg mL–1. This stable and feasible PEC immunosensor is a promising diagnostic tool for sensitively detecting SARS-CoV-2 spike protein. Graphical

COVID-19 and cognitive impairment: neuroinvasive and blood‒brain barrier dysfunction.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global pandemic. Although COVID-19 was initially described as a respiratory disease, there is growing evidence that SARS-CoV-2 is able to invade the brains of COVID-19 patients and cause cognitive impairment. It has been reported that SARS-CoV-2 may have invasive effects on a variety of cranial nerves, including the olfactory, trigeminal, optic, and vagus nerves, and may spread to other brain regions via infected nerve endings, retrograde transport, and transsynaptic transmission. In addition, the blood-brain barrier (BBB), composed of neurovascular units (NVUs) lining the brain microvasculature, acts as a physical barrier between nerve cells and circulating cells of the immune system and is able to regulate the transfer of substances between the blood and brain parenchyma. Therefore, the BBB may be an important structure for the direct and indirect interaction of SARS-CoV-2 with the brain via the blood circulation. In this review, we assessed the potential involvement of neuroinvasion under the SARS-CoV-2 infection, and the potential impact of BBB disorder under SARS-CoV-2 infection on cognitive impairment.

The landscape of potential health benefits of carotenoids as natural supportive therapeutics in protecting against Coronavirus infection.

The Coronavirus Disease-2019 (COVID-19) pandemic urges researching possibilities for prevention and management of the effects of the virus. Carotenoids are natural phytochemicals of anti-oxidant, anti-inflammatory and immunomodulatory properties and may exert potential in aiding in combatting the pandemic. This review presents the direct and indirect evidence of the health benefits of carotenoids and derivatives based on in vitro and in vivo studies, human clinical trials and epidemiological studies and proposes possible mechanisms of action via which carotenoids may have the capacity to protect against COVID-19 effects. The current evidence provides a rationale for considering carotenoids as natural supportive nutrients via antioxidant activities, including scavenging lipid-soluble radicals, reducing hypoxia-associated superoxide by activating antioxidant enzymes, or suppressing enzymes that produce reactive oxygen species (ROS). Carotenoids may regulate COVID-19 induced over-production of pro-inflammatory cytokines, chemokines, pro-inflammatory enzymes and adhesion molecules by nuclear factor kappa B (NF-κB), renin-angiotensin-aldosterone system (RAS) and interleukins-6- Janus kinase-signal transducer and activator of transcription (IL-6-JAK/STAT) pathways and suppress the polarization of pro-inflammatory M1 macrophage. Moreover, carotenoids may modulate the peroxisome proliferator-activated receptors γ by acting as agonists to alleviate COVID-19 symptoms. They also may potentially block the cellular receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human angiotensin-converting enzyme 2 (ACE2). These activities may reduce the severity of COVID-19 and flu-like diseases. Thus, carotenoid supplementation may aid in combatting the pandemic, as well as seasonal flu. However, further in vitro, in vivo and in particular long-term clinical trials in COVID-19 patients are needed to evaluate this hypothesis.

A dynamic modeling study on the effects of Wuhan traffic control and centralized quarantine measures on COVID-19 epidemic. (Special Issue: Spatio-temporal dynamics analysis, risk assessment and emergency management for COVID-19 epidemic.) [Chinese]

Objective: To quantitatively evaluate the effects of traffic control and centralized quarantine measures on COVID-19 epidemic in Wuhan, so as to provide scientific basis for epidemic prevention and control. Methods The SEIAHR model was established based on SEIR dynamic model, which took into account the characteristics of asymptomatic carriers and unconfirmed quarantined patients. Based on the timing of prevention measures, the epidemic was divided into three stages, the parameters were fitted, the basic reproduction numbers of different stages were calculated, and the development trend of epidemic was predicted. Results The R0 decreased dramatically. The R0 of the three stages were 3.684 1(95%CI: 3.106 1-4.048 0), 2.178 8(95%CI: 1.725 8-3.577 6)and 0.362 5(95%CI: 0.349 9-0.367 6), respectively. Due to the traffic control travel and centralized quarantine, the peak of the disease moved forward from April 19 to March 14, 2020. The scale of the epidemic had also been reduced by prevention and control measures. Conclusion The traffic control and centralized quarantine measures implemented in Wuhan were effective for the epidemic control, which can provide reference for other countries.