The influence of single-point mutation D614G on the binding process between human angiotensin-converting enzyme 2 and the SARS-CoV-2 spike protein-an atomistic simulation study† † Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d3ra00198a

SARS-CoV-2 has continuously evolved as changes in the genetic code occur during replication of the genome, with some of the mutations leading to higher transmission among human beings. The spike aspartic acid-614 to glycine (D614G) substitution in the spike represents a "more transmissible form of SARS-CoV-2” and occurs in all SARS-CoV-2 mutants. However, the underlying mechanism of the D614G substitution in virus infectivity has remained unclear. In this paper, we adopt molecular simulations to study the contact processes of the D614G mutant and wild-type (WT) spikes with hACE2. The interaction areas with hACE2 for the two spikes are completely different by visualizing the whole binding processes. The D614G mutant spike moves towards the hACE2 faster than the WT spike. We have also found that the receptor-binding domain (RBD) and N-terminal domain (NTD) of the D614G mutant extend more outwards than those of the WT spike. By analyzing the distances between the spikes and hACE2, the changes of number of hydrogen bonds and interaction energy, we suggest that the increased infectivity of the D614G mutant is not possibly related to the binding strength, but to the binding velocity and conformational change of the mutant spike. This work reveals the impact of D614G substitution on the infectivity of the SARS-CoV-2, and hopefully could provide a rational explanation of interaction mechanisms for all the SARS-CoV-2 mutants. SARS-CoV-2 has continuously evolved as changes in the genetic code occur during replication of the genome, with some of the mutations leading to higher transmission among human beings.

Big data and machine learning driven bioprocessing - Recent trends and critical analysis.

Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.

The influence of single-point mutation D614G on the binding process between human angiotensin-converting enzyme 2 and the SARS-CoV-2 spike protein-an atomistic simulation study.

SARS-CoV-2 has continuously evolved as changes in the genetic code occur during replication of the genome, with some of the mutations leading to higher transmission among human beings. The spike aspartic acid-614 to glycine (D614G) substitution in the spike represents a "more transmissible form of SARS-CoV-2" and occurs in all SARS-CoV-2 mutants. However, the underlying mechanism of the D614G substitution in virus infectivity has remained unclear. In this paper, we adopt molecular simulations to study the contact processes of the D614G mutant and wild-type (WT) spikes with hACE2. The interaction areas with hACE2 for the two spikes are completely different by visualizing the whole binding processes. The D614G mutant spike moves towards the hACE2 faster than the WT spike. We have also found that the receptor-binding domain (RBD) and N-terminal domain (NTD) of the D614G mutant extend more outwards than those of the WT spike. By analyzing the distances between the spikes and hACE2, the changes of number of hydrogen bonds and interaction energy, we suggest that the increased infectivity of the D614G mutant is not possibly related to the binding strength, but to the binding velocity and conformational change of the mutant spike. This work reveals the impact of D614G substitution on the infectivity of the SARS-CoV-2, and hopefully could provide a rational explanation of interaction mechanisms for all the SARS-CoV-2 mutants.

Reply to Chan, et al.

We appreciate the comments from Chan et al. for our study, and have carefully responded to the comments of Chan et al. and are very grateful for their praise of our research. We agree that smoking might be a risk factor of the severity of COVID-19 as mentioned by Chan et al., but in our study, smoking was not so robust compared with our conclusion. Also, we strongly agreed with the opinion of Chan, et al. that COVID-19 patients with diabetes or other chronic diseases might worsen the situation of the disease. But these factors were out of the scope of our study and we had published other research on this topic related to diabetes. Because of the limited sample size and original medical records, our study could not cover many factors suggested as Chan, et al. But we wish our study will be a useful and meaningful pilot study for the future studies. This article is protected by copyright. All rights reserved.

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation.

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants; the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

A Fuzzy-Set Configurational Examination of Governance Capability under Certainty and Uncertainty Conditions: Evidence from the Chinese Provincial Cases of Early COVID-19 Containing Practice

It is a complex task for provincial governments to sustain the effectiveness of the governance system in containing the spread of COVID-19 in the early stages. This study aims to examine the complex causal combinations of certainty, uncertainty and governance capabilities leading to high and low effectiveness of governance across 30 Chinese provincial administrative regions. The fuzzy-set qualitative comparative analysis (fsQCA) shows that: (1) Two paths lead to a high level of governance effectiveness. One is condition-based, while the other is mainly based on the expertise of health directors and low-spreading control conditions. (2) Two paths lead to a low level of governance effectiveness. Because of a high level of spreading control difficulty, most provinces take the first path. (3) The SARS experience in 2003 may not be a necessary condition to improve the governance effectiveness of the COVID-19 outbreak. Provinces could achieve good governance effectiveness even if they had no prior SARS experience. The findings enhance the understanding of the emergency response to a public health crisis in a country with a strong government by clarifying various effective and ineffective configurations. It also reflects China's existing public health emergency system to maintain sustainable governance under varying degrees of certainty and uncertainty.

Review of an intelligent indoor environment monitoring and management system for COVID-19 risk mitigation

The coronavirus disease (COVID-19) outbreak has turned the world upside down bringing about a massive impact on society due to enforced measures such as the curtailment of personal travel and limitations on economic activities. The global pandemic resulted in numerous people spending their time at home, working, and learning from home hence exposing them to air contaminants of outdoor and indoor origins. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which spreads by airborne transmission. The viruses found indoors are linked to the building's ventilation system quality. The ventilation flow in an indoor environment controls the movement and advection of any aerosols, pollutants, and Carbon Dioxide (CO2) created by indoor sources/occupants;the quantity of CO2 can be measured by sensors. Indoor CO2 monitoring is a technique used to track a person's COVID-19 risk, but high or low CO2 levels do not necessarily mean that the COVID-19 virus is present in the air. CO2 monitors, in short, can help inform an individual whether they are breathing in clean air. In terms of COVID-19 risk mitigation strategies, intelligent indoor monitoring systems use various sensors that are available in the marketplace. This work presents a review of scientific articles that influence intelligent monitoring development and indoor environmental quality management system. The paper underlines that the non-dispersive infrared (NDIR) sensor and ESP8266 microcontroller support the development of low-cost indoor air monitoring at learning facilities.

Recursive Zero-COVID model and quantitation of control efforts of the Omicron epidemic in Jilin province.

Since the beginning of March 2022, the epidemic due to the Omicron variant has developed rapidly in Jilin Province. To figure out the key controlling factors and validate the model to show the success of the Zero-COVID policy in the province, we constructed a Recursive Zero-COVID Model quantifying the strength of the control measures, and defined the control reproduction number as an index for describing the intensity of interventions. Parameter estimation and sensitivity analysis were employed to estimate and validate the impact of changes in the strength of different measures on the intensity of public health preventions qualitatively and quantitatively. The recursive Zero-COVID model predicted that the dates of elimination of cases at the community level of Changchun and Jilin Cities to be on April 8 and April 17, respectively, which are consistent with the real situation. Our results showed that the strict implementation of control measures and adherence of the public are crucial for controlling the epidemic. It is also essential to strengthen the control intensity even at the final stage to avoid the rebound of the epidemic. In addition, the control reproduction number we defined in the paper is a novel index to measure the intensity of the prevention and control measures of public health.

A trial of arbidol hydrochloride in adults with COVID-19.

BACKGROUND: To date, there is no effective medicine to treat coronavirus disease 2019 (COVID-19), and the antiviral efficacy of arbidol in the treatment for COVID-19 remained equivocal and controversial. The purpose of this study was to evaluate the efficacy and safety of arbidol tablets in the treatment of COVID-19. METHODS: This was a prospective, open-label, controlled and multicenter investigator-initiated trial involving adult patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Patients were stratified 1:2 to either standard-of-care (SOC) or SOC plus arbidol tablets (oral administration of 200 mg per time, three times a day for 14 days). The primary endpoint was negative conversion of SARS-CoV-2 within the first week. The rates and 95% confidential intervals were calculated for each variable. RESULTS: A total of 99 patients with laboratory-confirmed SARS-CoV-2 infection were enrolled; 66 were assigned to the SOC plus arbidol tablets group, and 33 to the SOC group. The negative conversion rate of SARS-CoV-2 within the first week in patients receiving arbidol tablets was significantly higher than that of the SOC group (70.3% [45/64] vs. 42.4% [14/33]; difference of conversion rate 27.9%; 95% confidence interval [CI], 7.7%-48.1%; P  = 0.008). Compared to those in the SOC group, patients receiving arbidol tablets had a shorter duration of clinical recovery (median 7.0 days vs. 12.0 days; hazard ratio [HR]: 1.877, 95% CI: 1.151-3.060, P = 0.006), symptom of fever (median 3.0 days vs. 12.0 days; HR: 18.990, 95% CI: 5.350-67.410, P < 0.001), as well as hospitalization (median 12.5 days vs. 20.0 days; P < 0.001). Moreover, the addition of arbidol tablets to SOC led to more rapid normalization of declined blood lymphocytes (median 10.0 days vs. 14.5 days; P > 0.05). The most common adverse event in the arbidol tablets group was the elevation of transaminase (5/200, 2.5%), and no one withdrew from the study due to adverse events or disease progression. CONCLUSIONS: SOC plus arbidol tablets significantly increase the negative conversion rate of SARS-CoV-2 within the first week anas, accelerate the recovery of COVID-19 patients. During the treatment with arbidol tablets, we find no significant serious adverse events. TRIAL REGISTRATION: Chinese Clinical Trial Registry, NCT04260594, www.clinicaltrials.gov/ct2/show/NCT04260594?term=NCT04260594&draw=2&rank=1.

Sensing of SARS-CoV-2 by pDCs and their subsequent production of IFN-I contribute to macrophage-induced cytokine storm during COVID-19.

Lung-infiltrating macrophages create a marked inflammatory milieu in a subset of patients with COVID-19 by producing a cytokine storm, which correlates with increased lethality. However, these macrophages are largely not infected by SARS-CoV-2, so the mechanism underlying their activation in the lung is unclear. Type I interferons (IFN-I) contribute to protecting the host against SARS-CoV-2 but may also have some deleterious effect, and the source of IFN-I in the lungs of infected patients is not well defined. Plasmacytoid dendritic cells (pDCs), a key cell type involved in antiviral responses, can produce IFN-I in response to SARS-CoV-2. We observed the infiltration of pDCs in the lungs of SARS-CoV-2-infected patients, which correlated with strong IFN-I signaling in lung macrophages. In patients with severe COVID-19, lung macrophages expressed a robust inflammatory signature, which correlated with persistent IFN-I signaling at the single-cell level. Hence, we observed the uncoupling in the kinetics of the infiltration of pDCs in the lungs and the associated IFN-I signature, with the cytokine storm in macrophages. We observed that pDCs were the dominant IFN-α-producing cells in response to the virus in the blood, whereas macrophages produced IFN-α only when in physical contact with infected epithelial cells. We also showed that IFN-α produced by pDCs, after the sensing of SARS-CoV-2 by TLR7, mediated changes in macrophages at both transcriptional and epigenetic levels, which favored their hyperactivation by environmental stimuli. Together, these data indicate that the priming of macrophages can result from the response by pDCs to SARS-CoV-2, leading to macrophage activation in patients with severe COVID-19.

Effects of coronavirus disease 2019 lockdown on metabolic syndrome and its components among Chinese employees: A retrospective cohort study

Objective Coronavirus disease 2019 (COVID-19) and the accompanying isolation have changed resident life rhythms and behaviors. This study investigated the effects of the COVID-19 pandemic on metabolic syndrome (MetS) and its components in employees in southwestern China. Methods This retrospective cohort study included 3,777 employees of five institutions who underwent physical examinations at the Affiliated Hospital of Southwest Medical University for three consecutive years from 2018 to 2020. We collected data on participant age and sex and measured the component indices of metabolic syndrome, including waist circumference, blood pressure (systolic and diastolic), fasting blood glucose level, and blood lipid (triglyceride and high-density lipoprotein cholesterol) level. We applied t-, chi-square, Mann–Whitney U, and Friedman's M tests to compare metabolic variables at different times. Results The incidence of MetS in 2020 was 18.6%, significantly higher than the prevalence of 15.7% before the epidemic. The number of abnormal MetS components following the COVID-19 lockdown was much greater than those in 2018 (P < 0.001) and 2019 (P < 0.001), with no significant variations between the two years (P = 0.142). All metabolic parameters, except for fasting blood glucose, were significantly worse than those pre-lockdown. The increase in the prevalence of MetS and all its abnormal components except for fasting glucose from 2019 to 2020 was significantly higher than that from 2018 to 2019. The change values between 2019–2020 and 2018–2019 for all indices except for diastolic blood pressure did not differ significantly between men and women. For all component indicators except for waist circumference, we observed no significant age differences in the growth differentials between the two periods (2019–2020 and 2018–2019). Conclusions COVD-19 lockdown have increased metabolic health risks among Chinese adults. Targeted measures, such as health education, are urgently needed to address poor metabolic health caused by the COVID-19 pandemic.

Nanocarriers for effective delivery: modulation of innate immunity for the management of infections and the associated complications.

Innate immunity is the first line of defense against invading pathogens. Innate immune cells can recognize invading pathogens through recognizing pathogen-associated molecular patterns (PAMPs) via pattern recognition receptors (PRRs). The recognition of PAMPs by PRRs triggers immune defense mechanisms and the secretion of pro-inflammatory cytokines such as TNF-α, IL-1ß, and IL-6. However, sustained and overwhelming activation of immune system may disrupt immune homeostasis and contribute to inflammatory disorders. Immunomodulators targeting PRRs may be beneficial to treat infectious diseases and their associated complications. However, therapeutic performances of immunomodulators can be negatively affected by (1) high immune-mediated toxicity, (2) poor solubility and (3) bioactivity loss after long circulation. Recently, nanocarriers have emerged as a very promising tool to overcome these obstacles owning to their unique properties such as sustained circulation, desired bio-distribution, and preferred pharmacokinetic and pharmacodynamic profiles. In this review, we aim to provide an up-to-date overview on the strategies and applications of nanocarrier-assisted innate immune modulation for the management of infections and their associated complications. We first summarize examples of important innate immune modulators. The types of nanomaterials available for drug delivery, as well as their applications for the delivery of immunomodulatory drugs and vaccine adjuvants are also discussed.

A multi-objective humanitarian pickup and delivery vehicle routing problem with drones

This paper applies the truck and drone cooperative delivery model to humanitarian logistics and proposes a multi-objective humanitarian pickup and delivery vehicle routing problem with drones (m-HPDVRPD) which contains two subproblems: cooperative routing subproblem and relief supplies allocation subproblem. The m-HPDVRPD is formulated as a multi-objective mixed integer linear programming (MILP) model with two objectives, which simultaneously minimizes the maximum cooperative routing time and maximizes the minimum fulfillment rate of demand nodes. We develop a hybrid multi-objective evolutionary algorithm with specialized local search operators (HMOEAS) and a hybrid multi-objective ant colony algorithm (HACO) for the problem. A set of numerical experiments are performed to compare the performance of the two algorithms and their three variants. And the experimental results prove that HMOEAS is more effective than other methods. Taking the Corona Virus Disease 2019 (COVID-19) epidemic in Wuhan as a case, we compare the truck-drone cooperative delivery model with the truck-only delivery and the drone-only delivery models, and find that our model has advantages in the delivery efficiency of anti-epidemic materials. Moreover, based on the real-world case, a sensitivity analysis is conducted to investigate the impact of different drone parameters on the efficiency of truck-drone cooperative delivery. [ FROM AUTHOR] Copyright of Annals of Operations Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The clinical and bioinformatics analysis for the role of antihypertension drugs on mortality among patients with hypertension hospitalized with COVID-19.

Comorbidities such as hypertension could exacerbate symptoms of coronaviral disease 2019 (COVID)-19 infection. Patients with hypertension may receive both anti-COVID-19 and antihypertension therapies when infected with COVID-19. However, it is not clear how different classes of anti-hypertension drugs impact the outcome of COVID-19 treatment. Herein, we explore the association between the inpatient use of different classes of anti-hypertension drugs and mortality among patients with hypertension hospitalized with COVID-19. We totally collected data from 278 patients with hypertension diagnosed with COVID-19 admitted to hospitals in Wuhan from February 1 to April 1, 2020. A retrospective study was conducted and single-cell RNA-sequencing (RNA-Seq) analysis of treatment-related genes was performed. The results showed that Angiotensin II receptor blocker (ARB) and calcium channel blocker (CCB) drugs significantly increased the survival rate but the use of angiotensin-converting enzyme inhibitor/ß-block/diuretic drugs did not affect the mortality caused by COVID-19. Based on the analysis of four public data sets of single-cell RNA-Seq on COVID-19 patients, we concluded that JUN, LST1 genes may play a role in the effect of ARB on COVID-19-related mortality, whereas CALM1 gene may contribute to the effect of CCB on COVID-19-related mortality. Our results provide guidance on the selection of antihypertension drugs for hypertensive patients infected with COVID-19.

In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning.

The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources; however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM2.5 at 20, NO2 at 20, and O3 at 22 error rates.

Exploring the Relationship among Human Activities, COVID-19 Morbidity, and At-Risk Areas Using Location-Based Social Media Data: Knowledge about the Early Pandemic Stage in Wuhan.

It is significant to explore the morbidity patterns and at-risk areas of the COVID-19 outbreak in megacities. In this paper, we studied the relationship among human activities, morbidity patterns, and at-risk areas in Wuhan City. First, we excavated the activity patterns from Sina Weibo check-in data during the early COVID-19 pandemic stage (December 2019~January 2020) in Wuhan. We considered human-activity patterns and related demographic information as the COVID-19 influencing determinants, and we used spatial regression models to evaluate the relationships between COVID-19 morbidity and the related factors. Furthermore, we traced Weibo users' check-in trajectories to characterize the spatial interaction between high-morbidity residential areas and activity venues with POI (point of interest) sites, and we located a series of potential at-risk places in Wuhan. The results provide statistical evidence regarding the utility of human activity and demographic factors for the determination of COVID-19 morbidity patterns in the early pandemic stage in Wuhan. The spatial interaction revealed a general transmission pattern in Wuhan and determined the high-risk areas of COVID-19 transmission. This article explores the human-activity characteristics from social media check-in data and studies how human activities played a role in COVID-19 transmission in Wuhan. From that, we provide new insights for scientific prevention and control of COVID-19.

In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning

The COVID-19 pandemic raises awareness of how the fatal spreading of infectious disease impacts economic, political, and cultural sectors, which causes social implications. Across the world, strategies aimed at quickly recognizing risk factors have also helped shape public health guidelines and direct resources;however, they are challenging to analyze and predict since those events still happen. This paper intends to invesitgate the association between air pollutants and COVID-19 confirmed cases using Deep Learning. We used Delhi, India, for daily confirmed cases and air pollutant data for the dataset. We used LSTM deep learning for training the combination of COVID-19 Confirmed Case and AQI parameters over the four different lag times of 1, 3, 7, and 14 days. The finding indicates that CO is the most excellent model compared with the others, having on average, 13 RMSE values. This was followed by pressure at 15, PM2.5 at 20, NO2 at 20, and O3 at 22 error rates.

Potential "Therapeutic" Effects of Tocotrienol-Rich Fraction (TRF) and Carotene "Against" Bleomycin-Induced Pulmonary Fibrosis in Rats via TGF-ß/Smad, PI3K/Akt/mTOR and NF-κB Signaling Pathways.

BACKGROUND: Pulmonary fibrosis (PF) is a chronic, progressive, and, ultimately, terminal interstitial disease caused by a variety of factors, ranging from genetics, bacterial, and viral infections, to drugs and other influences. Varying degrees of PF and its rapid progress have been widely reported in post-COVID-19 patients and there is consequently an urgent need to develop an appropriate, cost-effective approach for the prevention and management of PF. AIM: The potential "therapeutic" effect of the tocotrienol-rich fraction (TRF) and carotene against bleomycin (BLM)-induced lung fibrosis was investigated in rats via the modulation of TGF-ß/Smad, PI3K/Akt/mTOR, and NF-κB signaling pathways. DESIGN/METHODS: Lung fibrosis was induced in Sprague-Dawley rats by a single intratracheal BLM (5 mg/kg) injection. These rats were subsequently treated with TRF (50, 100, and 200 mg/kg body wt/day), carotene (10 mg/kg body wt/day), or a combination of TRF (200 mg/kg body wt/day) and carotene (10 mg/kg body wt/day) for 28 days by gavage administration. A group of normal rats was provided with saline as a substitute for BLM as the control. Lung function and biochemical, histopathological, and molecular alterations were studied in the lung tissues. RESULTS: Both the TRF and carotene treatments were found to significantly restore the BLM-induced alterations in anti-inflammatory and antioxidant functions. The treatments appeared to show pneumoprotective effects through the upregulation of antioxidant status, downregulation of MMP-7 and inflammatory cytokine expressions, and reduction in collagen accumulation (hydroxyproline). We demonstrated that TRF and carotene ameliorate BLM-induced lung injuries through the inhibition of apoptosis, the induction of TGF-ß1/Smad, PI3K/Akt/mTOR, and NF-κB signaling pathways. Furthermore, the increased expression levels were shown to be significantly and dose-dependently downregulated by TRF (50, 100, and 200 mg/kg body wt/day) treatment in high probability. The histopathological findings further confirmed that the TRF and carotene treatments had significantly attenuated the BLM-induced lung injury in rats. CONCLUSION: The results of this study clearly indicate the ability of TRF and carotene to restore the antioxidant system and to inhibit proinflammatory cytokines. These findings, thus, revealed the potential of TRF and carotene as preventive candidates for the treatment of PF in the future.

An automated nucleic acid detection platform using digital microfluidics with an optimized Cas12a system.

Outbreaks of both influenza virus and the novel coronavirus SARS-CoV-2 are serious threats to human health and life. It is very important to establish a rapid, accurate test with large-scale detection potential to prevent the further spread of the epidemic. An optimized RPA-Cas12a-based platform combined with digital microfluidics (DMF), the RCD platform, was established to achieve the automated, rapid detection of influenza viruses and SARS-CoV-2. The probe in the RPA-Cas12a system was optimized to produce maximal fluorescence to increase the amplification signal. The reaction droplets in the platform were all at the microliter level and the detection could be accomplished within 30 min due to the effective mixing of droplets by digital microfluidic technology. The whole process from amplification to recognition is completed in the chip, which reduces the risk of aerosol contamination. One chip can contain multiple detection reaction areas, offering the potential for customized detection. The RCD platform demonstrated a high level of sensitivity, specificity (no false positives or negatives), speed (≤30 min), automation and multiplexing. We also used the RCD platform to detect nucleic acids from influenza patients and COVID-19 patients. The results were consistent with the findings of qPCR. The RCD platform is a one-step, rapid, highly sensitive and specific method with the advantages of digital microfluidic technology, which circumvents the shortcomings of manual operation. The development of the RCD platform provides potential for the isothermal automatic detection of nucleic acids during epidemics. Electronic Supplementary Material: Supplementary material is available in the online version of this article at 10.1007/s11426-021-1169-1.