Association of lung-intestinal microecology and lung cancer therapy.

In recent years, the incidence of lung cancer is increasing. Lung cancer has become one of the most malignant tumors with the highest incidence in the world, which seriously affects people's health. The most important cause of death of lung cancer is metastasis. Therefore, it is crucial to understand the mechanism of lung cancer progression and metastasis. This review article discusses the physiological functions, pathological states and disorders of the lung and intestine based on the concepts of traditional Chinese medicine (TCM), and analyzes the etiology and mechanisms of lung cancer formation from the perspective of TCM. From the theory of "the exterior and interior of the lung and gastrointestinal tract", the theory of "the lung-intestinal axis" and the progression and metastasis of lung cancer, we proposed e "lung-gut co-treatment" therapy for lung cancer. This study provides ideas for studying the mechanism of lung cancer and the comprehensive alternative treatment for lung cancer patients.

Prevalence and risk factor for long COVID in children and adolescents: A meta-analysis and systematic review

Background Millions of COVID-19 pediatric survivors are facing the risk of long COVID after recovery from acute COVID-19. The primary objective of this study was to systematically review the available literature and determine the pooled prevalence of, and risk factors for long COVID among the pediatric survivors. Methods Studies that assessed the prevalence of, or risk factors associated with long COVID among pediatric COVID-19 survivors were systematically searched in PubMed, Embase, Cochrane Library, medRxiv and bioRxiv up to December 11th, 2022. Random effects model was performed to estimate the pooled prevalence of long COVID among pediatric COVID-19 patients. Subgroup analyses and meta-regression on the estimated prevalence of long COVID were performed by stratification with follow-up duration, mean age, sex ratio, percentage of multisystem inflammatory syndrome, hospitalization rate at baseline, and percentage of severe illness. Results Based on 40 studies with 12,424 individuals, the pooled prevalence of any long COVID was 23.36% ([95% CI 15.27-32.53]). The generalized symptom (19.57%, [95% CI 9.85-31.52]) was reported most commonly, followed by respiratory (14.76%, [95% CI 7.22-24.27]), neurologic (13.51%, [95% CI 6.52-22.40]), and psychiatric (12.30%, [95% CI 5.38-21.37]). Dyspnoea (22.75%, [95% CI 9.38-39.54]), fatigue (20.22%, [95% CI 9.19-34.09]), and headache (15.88%, [95% CI 6.85-27.57]) were most widely reported specific symptoms. The prevalence of any symptom during 3-6, 6-12, and >12 months were 26.41% ([95% CI 14.33-40.59]), 20.64% ([95% CI 17.06-24.46]), and 14.89% ([95% CI 6.09-26.51]), respectively. Individuals with aged over ten years, multisystem inflammatory syndrome, or had severe clinical symptoms exhibited higher prevalence of long COVID in multi-systems. Factors such as older age, female, poor physical or mental health, or had severe infection or more symptoms were more likely to have long COVID in pediatric survivors. Conclusions Nearly one quarter of pediatric survivors suffered multisystem long COVID, even at 1 year after infection. Ongoing monitoring, comprehensive prevention and intervention is warranted for pediatric survivors, especially for individuals with high risk factors.

Non-linear feature analysis of public emotion evolution for online teaching during the COVID-19 pandemic

PurposeNowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.Design/methodology/approachThe article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.FindingsIt was found that the sequence has typical non-linear chaotic characteristics;its correlation dimension indicates that it contains obvious fractal characteristics;the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.Originality/valueBased on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

Covid‐19‐tested food labels

While the transmission of virus SARS‐CoV‐2 via food is rare, some Chinese food retailers are considering a Covid‐19‐tested food label. However, how consumers may support such a label is unknown. We quantify Chinese consumers' willingness to pay (WTP) for food carrying a Covid‐19‐tested label using an online choice experiment. We find that the WTPs for such a label are always positive for all food products considered. The amount of WTP depends on the entities authenticating the labels, country of origin of the food, and consumers' socio‐demographic status. Contrary to expectation, the knowledge on Covid‐19 does not affect consumer preferences for the Covid‐19‐tested food labels. Our benefit and cost analysis suggests a possible large benefit of creating and administering a Covid‐19‐tested food label. This study provides insights for policymakers, global food manufacturers, and retailers to create marketing strategies to alleviate consumer food safety concerns associated with Covid‐19. [ABSTRACT FROM AUTHOR] Copyright of Canadian Journal of Agricultural Economics is the property of Wiley-Blackwell 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Prevalence and risk factor for long COVID in children and adolescents: A meta-analysis and systematic review.

BACKGROUND: Millions of COVID-19 pediatric survivors are facing the risk of long COVID after recovery from acute COVID-19. The primary objective of this study was to systematically review the available literature and determine the pooled prevalence of, and risk factors for long COVID among the pediatric survivors. METHODS: Studies that assessed the prevalence of, or risk factors associated with long COVID among pediatric COVID-19 survivors were systematically searched in PubMed, Embase, and Cochrane Library up to December 11th, 2022. Random effects model was performed to estimate the pooled prevalence of long COVID among pediatric COVID-19 patients. Subgroup analyses and meta-regression on the estimated prevalence of long COVID were performed by stratification with follow-up duration, mean age, sex ratio, percentage of multisystem inflammatory syndrome, hospitalization rate at baseline, and percentage of severe illness. RESULTS: Based on 40 studies with 12,424 individuals, the pooled prevalence of any long COVID was 23.36 % ([95 % CI 15.27-32.53]). The generalized symptom (19.57 %, [95 % CI 9.85-31.52]) was reported most commonly, followed by respiratory (14.76 %, [95 % CI 7.22-24.27]), neurologic (13.51 %, [95 % CI 6.52-22.40]), and psychiatric (12.30 %, [95% CI 5.38-21.37]). Dyspnea (22.75 %, [95% CI 9.38-39.54]), fatigue (20.22 %, [95% CI 9.19-34.09]), and headache (15.88 %, [95 % CI 6.85-27.57]) were most widely reported specific symptoms. The prevalence of any symptom during 3-6, 6-12, and> 12 months were 26.41 % ([95 % CI 14.33-40.59]), 20.64 % ([95 % CI 17.06-24.46]), and 14.89 % ([95 % CI 6.09-26.51]), respectively. Individuals with aged over ten years, multisystem inflammatory syndrome, or had severe clinical symptoms exhibited higher prevalence of long COVID in multi-systems. Factors such as older age, female, poor physical or mental health, or had severe infection or more symptoms were more likely to have long COVID in pediatric survivors. CONCLUSIONS: Nearly one quarter of pediatric survivors suffered multisystem long COVID, even at 1 year after infection. Ongoing monitoring, comprehensive prevention and intervention is warranted for pediatric survivors, especially for individuals with high risk factors.

COVID-19's effect on the spatial integration of fish markets: Evidence from carp price in China.

While curbing the spread of Coronavirus Disease 2019 (COVID-19), lockdown policies and "stay-at-home" restrictions caused massive supply chain disruptions worldwide. This led to breaks in spatial market integration, which could further lead to market inefficiency and resource misallocation. Taking daily price data from 2016 to 2021, this study investigates COVID-19's effect on the spatial market integration of fish in China using cointegration tests. We find a high degree of spatial market integration for fish in China before the COVID-19 pandemic. Further, our results show that COVID-19's effect on the spatial market integration of fish varies spatially in China. Specifically, COVID-19 reduces the degree of spatial market integration in most provinces, especially those with high infection rates. Meanwhile, the degree of spatial market integration in provinces with low infection rates remains high. Therefore, the government should be regionally specific when formulating market recovery policies.

Non-linear feature analysis of public emotion evolution for online teaching during the COVID-19 pandemic

Purpose Nowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era. Design/methodology/approach The article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis. Findings It was found that the sequence has typical non-linear chaotic characteristics;its correlation dimension indicates that it contains obvious fractal characteristics;the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic. Originality/value Based on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

UBR5 Acts as an Antiviral Host Factor against MERS-CoV via Promoting Ubiquitination and Degradation of ORF4b.

Within the past 2 decades, three highly pathogenic human coronaviruses have emerged, namely, severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The health threats and economic burden posed by these tremendously severe coronaviruses have paved the way for research on their etiology, pathogenesis, and treatment. Compared to SARS-CoV and SARS-CoV-2, MERS-CoV genome encoded fewer accessory proteins, among which the ORF4b protein had anti-immunity ability in both the cytoplasm and nucleus. Our work for the first time revealed that ORF4b protein was unstable in the host cells and could be degraded by the ubiquitin proteasome system. After extensive screenings, it was found that UBR5 (ubiquitin protein ligase E3 component N-recognin 5), a member of the HECT E3 ubiquitin ligases, specifically regulated the ubiquitination and degradation of ORF4b. Similar to ORF4b, UBR5 can also translocate into the nucleus through its nuclear localization signal, enabling it to regulate ORF4b stability in both the cytoplasm and nucleus. Through further experiments, lysine 36 was identified as the ubiquitination site on the ORF4b protein, and this residue was highly conserved in various MERS-CoV strains isolated from different regions. When UBR5 was knocked down, the ability of ORF4b to suppress innate immunity was enhanced and MERS-CoV replication was stronger. As an anti-MERS-CoV host protein, UBR5 targets and degrades ORF4b protein through the ubiquitin proteasome system, thereby attenuating the anti-immunity ability of ORF4b and ultimately inhibiting MERS-CoV immune escape, which is a novel antagonistic mechanism of the host against MERS-CoV infection. IMPORTANCE ORF4b was an accessory protein unique to MERS-CoV and was not present in SARS-CoV and SARS-CoV-2 which can also cause severe respiratory disease. Moreover, ORF4b inhibited the production of antiviral cytokines in both the cytoplasm and the nucleus, which was likely to be associated with the high lethality of MERS-CoV. However, whether the host proteins regulate the function of ORF4b is unknown. Our study first determined that UBR5, a host E3 ligase, was a potential host anti-MERS-CoV protein that could reduce the protein level of ORF4b and diminish its anti-immunity ability by inducing ubiquitination and degradation. Based on the discovery of ORF4b-UBR5, a critical molecular target, further increasing the degradation of ORF4b caused by UBR5 could provide a new strategy for the clinical development of drugs for MERS-CoV.

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform.

Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform

Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.

Shanghai expert consensus on clinical protocol for traditional Chinese medicine treatment of COVID-19 among the elderly population (second edition).

This document is the revised edition of the previously issued Shanghai Expert Consensus on Clinical Protocol for Traditional Chinese Medicine Treatment of COVID-19 among the Elderly Population. Based on the clinical experience and the Protocol for Diagnosis and Treatment of COVID-19 (Trial 9th Edition), this revised edition provides treatment approaches and recommendations to proactively cope with Omicron variant and increase the therapeutic efficacy for coronavirus disease 2019 among the elderly population in Shanghai, China.

A systematic review and meta-analysis on prevalence of and risk factors associated with depression, anxiety and insomnia in infectious diseases, including COVID-19: a call to action.

Infectious disease epidemics have become more frequent and more complex during the 21st century, posing a health threat to the general public and leading to psychological symptoms. The current study was designed to investigate the prevalence of and risk factors associated with depression, anxiety and insomnia symptoms during epidemic outbreaks, including COVID-19. We systematically searched the PubMed, Embase, Web of Science, OVID, Medline, Cochrane databases, bioRxiv and medRxiv to identify studies that reported the prevalence of depression, anxiety or insomnia during infectious disease epidemics, up to August 14th, 2020. Prevalence of mental symptoms among different populations including the general public, health workers, university students, older adults, infected patients, survivors of infection, and pregnant women across all types of epidemics was pooled. In addition, prevalence of mental symptoms during COVID-19 was estimated by time using meta-regression analysis. A total of 17,506 papers were initially retrieved, and a final of 283 studies met the inclusion criteria, representing a total of 948,882 individuals. The pooled prevalence of depression ranged from 23.1%, 95% confidential intervals (95% CI: [13.9-32.2]) in survivors to 43.3% (95% CI: [27.1-59.6]) in university students, the pooled prevalence of anxiety ranged from 25.0% (95% CI: [12.0-38.0]) in older adults to 43.3% (95% CI: [23.3-63.3]) in pregnant women, and insomnia symptoms ranged from 29.7% (95% CI: [24.4-34.9]) in the general public to 58.4% (95% CI: [28.1-88.6]) in university students. Prevalence of moderate-to-severe mental symptoms was lower but had substantial variation across different populations. The prevalence of mental problems increased over time during the COVID-19 pandemic among the general public, health workers and university students, and decreased among infected patients. Factors associated with increased prevalence for all three mental health symptoms included female sex, and having physical disorders, psychiatric disorders, COVID infection, colleagues or family members infected, experience of frontline work, close contact with infected patients, high exposure risk, quarantine experience and high concern about epidemics. Frequent exercise and good social support were associated with lower risk for these three mental symptoms. In conclusion, mental symptoms are common during epidemics with substantial variation across populations. The population-specific psychological crisis management are needed to decrease the burden of psychological problem and improve the mental wellbeing during epidemic.

Economic Evaluation of First-Line Camrelizumab for Advanced Non-small-cell Lung Cancer in China.

Background: As the first domestic PD-1 antibody approved for lung cancer in China, camrelizumab has exhibited proven effectiveness for non-small-cell lung cancer (NSCLC) patients. However, the cost-effectiveness of this new regimen remains to be investigated. Objective: To evaluate the cost-effectiveness of camrelizumab combination therapy vs. chemotherapy for previously untreated patients with advanced, non-squamous NSCLC without Alk or Egfr genomic aberrations from the perspective of China's healthcare system. Methods: Based on the CameL trial, the study developed a three-health state Markov model to evaluate the cost-effectiveness of adding camrelizumab to chemotherapy compared to chemotherapy alone in NSCLC patients. The analysis models were conducted for patients unselected by PD-L1 tumor expression (the base case) and the patient subgroup with PD-L1-expressing tumors (≥1%). Primary model outcomes included the costs in US dollars and health outcomes in quality-adjusted life-years (QALYs) as well as the incremental cost-effectiveness ratio (ICER) under a willingness-to-pay threshold of $31,500 per QALY. Additionally, a scenario analysis that adjusted within-trial crossover was employed to evaluate camrelizumab combination therapy compared to chemotherapy without subsequent use of PD1/PD-L1 antibodies. Results: Camrelizumab combination therapy was more costly and provided additional 0.11 QALYs over chemotherapy in the base case analysis (0.86 vs. 0.75 QALYs), 0.12 QALYs over chemotherapy in the subgroup analysis (0.99 vs. 0.88 QALYs), and 0.34 QALYs over chemotherapy in the scenario analysis (0.86 vs. 0.52 QALYs). Correspondingly, the ICER was $63,080 per QALY, $46,311 per QALY, and $30,591 per QALY, in the base case, the subgroup, and the scenario analysis, respectively. One-way sensitivity analyses revealed that ICERs of the base case and the subgroup analysis were most sensitive to the cost of camrelizumab, the cost of pemetrexed. Besides, the base case and subgroup analysis were more sensitive to the risk of neutrophil count decreased in the camrelizumab and the utility of stable disease, respectively. Conclusion: Although camrelizumab combination therapy is not cost-effective as first-line therapy for NSCLC patients in China in the base case, adjusting within-trial crossover would move the treatment regimen toward cost-effectiveness in the scenario analysis.

Immunocapture Magnetic Beads Enhanced the LAMP-CRISPR/Cas12a Method for the Sensitive, Specific, and Visual Detection of Campylobacter jejuni.

Campylobacter jejuni is one of the most important causes of food-borne infectious disease, and poses challenges to food safety and public health. Establishing a rapid, accurate, sensitive, and simple detection method for C. jejuni enables early diagnosis, early intervention, and prevention of pathogen transmission. In this study, an immunocapture magnetic bead (ICB)-enhanced loop-mediated isothermal amplification (LAMP) CRISPR/Cas12a method (ICB-LAMP-CRISPR/Cas12a) was developed for the rapid and visual detection of C. jejuni. Using the ICB-LAMP-CRISPR/Cas12a method, C. jejuni was first captured by ICB, and the bacterial genomic DNA was then released by heating and used in the LAMP reaction. After the LAMP reaction, LAMP products were mixed and detected by the CRISPR/Cas12a cleavage mixture. This ICB-LAMP-CRISPR/Cas12a method could detect a minimum of 8 CFU/mL of C. jejuni within 70 min. Additionally, the method was performed in a closed tube in addition to ICB capture, which eliminates the need to separate preamplification and transfer of amplified products to avoid aerosol pollution. The ICB-LAMP-CRISPR/Cas12a method was further validated by testing 31 C. jejuni-positive fecal samples from different layer farms. This method is an all-in-one, simple, rapid, ultrasensitive, ultraspecific, visual detection method for instrument-free diagnosis of C. jejuni, and has wide application potential in future work.

Host E3 ligase HUWE1 attenuates the proapoptotic activity of the MERS-CoV accessory protein ORF3 by promoting its ubiquitin-dependent degradation.

With the outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), coronaviruses have begun to attract great attention across the world. Of the known human coronaviruses, however, Middle East respiratory syndrome coronavirus (MERS-CoV) is the most lethal. Coronavirus proteins can be divided into three groups: nonstructural proteins, structural proteins, and accessory proteins. While the number of each of these proteins varies greatly among different coronaviruses, accessory proteins are most closely related to the pathogenicity of the virus. We found for the first time that the ORF3 accessory protein of MERS-CoV, which closely resembles the ORF3a proteins of severe acute respiratory syndrome coronavirus and SARS-CoV-2, has the ability to induce apoptosis in cells in a dose-dependent manner. Through bioinformatics analysis and validation, we revealed that ORF3 is an unstable protein and has a shorter half-life in cells compared to that of severe acute respiratory syndrome coronavirus and SARS-CoV-2 ORF3a proteins. After screening, we identified a host E3 ligase, HUWE1, that specifically induces MERS-CoV ORF3 protein ubiquitination and degradation through the ubiquitin-proteasome system. This results in the diminished ability of ORF3 to induce apoptosis, which might partially explain the lower spread of MERS-CoV compared to other coronaviruses. In summary, this study reveals a pathological function of MERS-CoV ORF3 protein and identifies a potential host antiviral protein, HUWE1, with an ability to antagonize MERS-CoV pathogenesis by inducing ORF3 degradation, thus enriching our knowledge of the pathogenesis of MERS-CoV and suggesting new targets and strategies for clinical development of drugs for MERS-CoV treatment.

The Impact of Quarantine on Pain Sensation among the General Population in China during the COVID-19 Pandemic.

During the pandemic era, quarantines might potentially have negative effects and disproportionately exacerbate health condition problems. We conducted this cross-sectional, national study to ascertain the prevalence of constant pain symptoms and how quarantines impacted the pain symptoms and identify the factors associated with constant pain to further guide reducing the prevalence of chronic pain for vulnerable people under the pandemic. The sociodemographic data, quarantine conditions, mental health situations and pain symptoms of the general population were collected. After adjusting for potential confounders, long-term quarantine (≥15 days) exposures were associated with an increased risk of constant pain complaints compared to those not under a quarantine (Odds Ratio (OR): 1.26; 95% Confidence Interval (CI): 1.03, 1.54; p = 0.026). Risk factors including unemployment (OR: 1.55), chronic disease history (OR: 2.38) and infection with COVID-19 (OR: 2.15), and any of mental health symptoms including depression, anxiety, insomnia and PTSD (OR: 5.44) were identified by a multivariable logistic regression. Additionally, mediation analysis revealed that the effects of the quarantine duration on pain symptoms were mediated by mental health symptoms (indirect effects: 0.075, p < 0.001). These results advocated that long-term quarantine measures were associated with an increased risk of experiencing pain, especially for vulnerable groups with COVID-19 infection and with mental health symptoms. The findings also suggest that reducing mental distress during the pandemic might contribute to reducing the burden of pain symptoms and prioritizing interventions for those experiencing a long-term quarantine.

Resource Allocation for Multiuser Molecular Communication Systems Oriented to the Internet of Medical Things.

Communication between nanomachines is still an important topic in the construction of the Internet of Bio-Nano Things (IoBNT). Currently, molecular communication (MC) is expected to be a promising technology to realize IoBNT. To effectively serve the IoBNT composed of multiple nanomachine clusters, it is imperative to study multiple-access MC. In this article, based on the molecular division multiple access technology, we propose a novel multiuser MC system, where information molecules with different diffusion coefficients are first employed. Aiming at the user fairness in the considered system, we investigate the optimization of molecular resource allocation, including the assignment of the types of molecules and the number of molecules of a type. Specifically, three performance metrics are considered, namely, min-max fairness for error probability, max-min fairness for achievable rate, and weighted sum-rate maximization. Moreover, we propose two assignment strategies for types of molecules, i.e., best-to-best (BTB) and best-to-worst (BTW). Subsequently, for a two-user scenario, we analytically derive the optimal allocation for the number of molecules when types of molecules are fixed for all users. In contrast, for a three-user scenario, we prove that the BTB and BTW schemes with the optimal allocation for the number of molecules can provide the lower and upper bounds on system performance, respectively. Finally, numerical results show that the combination of BTW and the optimal allocation for the number of molecules yields better performance than the benchmarks.

Microbiome Profiling Using Shotgun Metagenomic Sequencing Identified Unique Microorganisms in COVID-19 Patients With Altered Gut Microbiota.

COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut-lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman's correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.

Patterns of mental health problems before and after easing COVID-19 restrictions: Evidence from a 105248-subject survey in general population in China.

BACKGROUND: The COVID-19 pandemic has alarming implications for individual and population level mental health. Although the future of COVID-19 is unknown at present, more countries or regions start to ease restrictions. The findings from this study have provided the empirical evidence of prevalence and patterns of mental disorders in Chinese general population before and after easing most COVID-19 restrictions, and information of the factors associated with these patterns. METHODS: A cross-sectional population-based online survey was carried out from February to March 2020 in the general population across all provinces in China. The 12-item General Health Questionnaire (GHQ-12) was incorporated in the survey. Latent class analyses were performed to investigate the patterns of mental disorders and multinomial logistic regressions were used to examine how individual and regional risk factors can predict mental disorder patterns. RESULTS: Four distinctive patterns of mental health were revealed in the general population. After the ease of most COVID-19 restrictions, the prevalence of high risk of mental disorders decreased from 25.8% to 20.9% and prevalence of being high risk of unhappiness and loss of confidence decreased from 10.1% to 8.1%. However, the prevalence of stressed, social dysfunction and unhappy were consistently high before and after easing restrictions. Several regional factors, such as case mortality rate and healthcare resources, were associated with mental health status. Of note, healthcare workers were less likely to have mental disorders, compared to other professionals and students. CONCLUSIONS: The dynamic management of mental health and psychosocial well-being is as important as that of physical health both before and after the ease of COVID-19 restrictions. Our findings may help in mental health interventions in other countries and regions while easing COVID-19 restrictions.

Intermittent Hypoxic Preconditioning: A Potential New Powerful Strategy for COVID-19 Rehabilitation.

COVID-19 is a highly infectious respiratory virus, which can proliferate by invading the ACE2 receptor of host cells. Clinical studies have found that the virus can cause dyspnea, pneumonia and other cardiopulmonary system damage. In severe cases, it can lead to respiratory failure and even death. Although there are currently no effective drugs or vaccines for the prevention and treatment of COVID-19, the patient's prognosis recovery can be effectively improved by ameliorating the dysfunction of the respiratory system, cardiovascular systems, and immune function. Intermittent hypoxic preconditioning (IHP) as a new non-drug treatment has been applied in the clinical and rehabilitative practice for treating chronic obstructive pulmonary disease (COPD), diabetes, coronary heart disease, heart failure, hypertension, and other diseases. Many clinical studies have confirmed that IHP can improve the cardiopulmonary function of patients and increase the cardiorespiratory fitness and the tolerance of tissues and organs to ischemia. This article introduces the physiological and biochemical functions of IHP and proposes the potential application plan of IHP for the rehabilitation of patients with COVID-19, so as to provide a better prognosis for patients and speed up the recovery of the disease. The aim of this narrative review is to propose possible causes and pathophysiology of COVID-19 based on the mechanisms of the oxidative stress, inflammation, and immune response, and to provide a new, safe and efficacious strategy for the better rehabilitation from COVID-19.