A critical analysis of health system in Nepal; Perspective's based on COVID-19 response.

Background: Nepal moved from a unitary government to a federal system of government in 2015 under its constitution. Nepal is a federal democratic republic governed by three levels of government: a federal, provincial, and local level. The response to COVID-19 in Nepal has been majorly led and controlled by the federal government. All three levels of government are performing their responsibilities; however, they face various challenges in responding to COVID-19. This study aimed to critically analyze Nepal's health system in the context of the COVID-19 response. Methods: We conducted semi-structured in-depth interviews by telephone among the policymakers, health workers, and stakeholders at the federal, provincial, and local levels (n = 41) between January to July 2021. The interviews were audio recorded, transcribed into English, and coded using inductive-deductive approaches. Results: COVID-19 considerably impacted routine health care, mainly maternity services and immunization. Inadequate financial resources, inadequate human resources, and the lack of ventilators, ICUs, and X-ray services were the significant challenges in tackling and managing COVID-19 effectively. Conclusion: The study found that all three levels of government perform their roles and responsibilities and effectively manage the pandemic. The federal and provincial governments focused more on the plans and policy development, while the local government demonstrated greater accountability in implementing those plans and policies. Therefore, all three tiers of government need to coordinate together for preparing and communicating information in times of emergency. Besides, it is imperative to empower local governments to maintain Nepal's federal health system.

Applications and prospects of cryo-EM in drug discovery.

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy (cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence (AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of medium-resolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

Nesting the SIRV model with NAR, LSTM and statistical methods to fit and predict COVID-19 epidemic trend in Africa.

OBJECTIVE: Compared with other regions in the world, the transmission characteristics of the COVID-19 epidemic in Africa are more obvious, has a unique transmission mode in this region; At the same time, the data related to the COVID-19 epidemic in Africa is characterized by low data quality and incomplete data coverage, which makes the prediction method of COVID-19 epidemic suitable for other regions unable to achieve good results in Africa. In order to solve the above problems, this paper proposes a prediction method that nests the in-depth learning method in the mechanism model. From the experimental results, it can better solve the above problems and better adapt to the transmission characteristics of the COVID-19 epidemic in African countries. METHODS: Based on the SIRV model, the COVID-19 transmission rate and trend from September 2021 to January 2022 of the top 15 African countries (South Africa, Morocco, Tunisia, Libya, Egypt, Ethiopia, Kenya, Zambia, Algeria, Botswana, Nigeria, Zimbabwe, Mozambique, Uganda, and Ghana) in the accumulative number of COVID-19 confirmed cases was fitted by using the data from Worldometer. Non-autoregressive (NAR), Long-short term memory (LSTM), Autoregressive integrated moving average (ARIMA) models, Gaussian and polynomial functions were used to predict the transmission rate ß in the next 7, 14, and 21 days. Then, the predicted transmission rate ßs were substituted into the SIRV model to predict the number of the COVID-19 active cases. The error analysis was conducted using root-mean-square error (RMSE) and mean absolute percentage error (MAPE). RESULTS: The fitting curves of the 7, 14, and 21 days were consistent with and higher than the original curves of daily active cases (DAC). The MAPE between the fitted and original 7-day DAC was only 1.15% and increased with the longer of predict days. Both the predicted ß and DAC of the next 7, 14, and 21 days by NAR and LSTM nested models were closer to the real ones than other three ones. The minimum RMSEs for the predicted number of COVID-19 active cases in the next 7, 14, and 21 days were 12,974, 14,152, and 12,211 people, respectively when the order of magnitude for was 106, with the minimum MAPE being 1.79%, 1.97%, and 1.64%, respectively. CONCLUSION: Nesting the SIRV model with NAR, LSTM, ARIMA methods etc. through functionalizing ß respectively could obtain more accurate fitting and predicting results than these models/methods alone for the number of confirmed COVID-19 cases in Africa in which nesting with NAR had the highest accuracy for the 14-day and 21-day predictions. The nested model was of high significance for early understanding of the COVID-19 disease burden and preparedness for the response.

A Review of Non-Invasive Drug Delivery through Respiratory Routes.

With rapid and non-invasive characteristics, the respiratory route of administration has drawn significant attention compared with the limitations of conventional routes. Respiratory delivery can bypass the physiological barrier to achieve local and systemic disease treatment. A scientometric analysis and review were used to analyze how respiratory delivery can contribute to local and systemic therapy. The literature data obtained from the Web of Science Core Collection database showed an increasing worldwide tendency toward respiratory delivery from 1998 to 2020. Keywords analysis suggested that nasal and pulmonary drug delivery are the leading research topics in respiratory delivery. Based on the results of scientometric analysis, the research hotspots mainly included therapy for central nervous systems (CNS) disorders (Parkinson's disease, Alzheimer's disease, depression, glioblastoma, and epilepsy), tracheal and bronchial or lung diseases (chronic obstructive pulmonary disease, asthma, acute lung injury or respiratory distress syndrome, lung cancer, and idiopathic pulmonary fibrosis), and systemic diseases (diabetes and COVID-19). The study of advanced preparations contained nano drug delivery systems of the respiratory route, drug delivery barriers investigation (blood-brain barrier, BBB), and chitosan-based biomaterials for respiratory delivery. These results provided researchers with future research directions related to respiratory delivery.

AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2.

The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN-FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2.

Development and validation of a high throughput SARS-CoV-2 whole genome sequencing workflow in a clinical laboratory.

Monitoring new mutations in SARS-CoV-2 provides crucial information for identifying diagnostic and therapeutic targets and important insights to achieve a more effective COVID-19 control strategy. Next generation sequencing (NGS) technologies have been widely used for whole genome sequencing (WGS) of SARS-CoV-2. While various NGS methods have been reported, one chief limitation has been the complexity of the workflow, limiting the scalability. Here, we overcome this limitation by designing a laboratory workflow optimized for high-throughput studies. The workflow utilizes modified ARTIC network v3 primers for SARS-CoV-2 whole genome amplification. NGS libraries were prepared by a 2-step PCR method, similar to a previously reported tailed PCR method, with further optimizations to improve amplicon balance, to minimize amplicon dropout for viral genomes harboring primer-binding site mutation(s), and to integrate robotic liquid handlers. Validation studies demonstrated that the optimized workflow can process up to 2688 samples in a single sequencing run without compromising sensitivity and accuracy and with fewer amplicon dropout events compared to the standard ARTIC protocol. We additionally report results for over 65,000 SARS-CoV-2 whole genome sequences from clinical specimens collected in the United States between January and September of 2021, as part of an ongoing national genomics surveillance effort.

Discovering trends and hotspots of biosafety and biosecurity research via machine learning.

Coronavirus disease 2019 (COVID-19) has infected hundreds of millions of people and killed millions of them. As an RNA virus, COVID-19 is more susceptible to variation than other viruses. Many problems involved in this epidemic have made biosafety and biosecurity (hereafter collectively referred to as 'biosafety') a popular and timely topic globally. Biosafety research covers a broad and diverse range of topics, and it is important to quickly identify hotspots and trends in biosafety research through big data analysis. However, the data-driven literature on biosafety research discovery is quite scant. We developed a novel topic model based on latent Dirichlet allocation, affinity propagation clustering and the PageRank algorithm (LDAPR) to extract knowledge from biosafety research publications from 2011 to 2020. Then, we conducted hotspot and trend analysis with LDAPR and carried out further studies, including annual hot topic extraction, a 10-year keyword evolution trend analysis, topic map construction, hot region discovery and fine-grained correlation analysis of interdisciplinary research topic trends. These analyses revealed valuable information that can guide epidemic prevention work: (1) the research enthusiasm over a certain infectious disease not only is related to its epidemic characteristics but also is affected by the progress of research on other diseases, and (2) infectious diseases are not only strongly related to their corresponding microorganisms but also potentially related to other specific microorganisms. The detailed experimental results and our code are available at https://github.com/KEAML-JLU/Biosafety-analysis.

AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2

The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN–FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2.

Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study

ObjectiveTo delineate the clinical characteristics of patients with coronavirus disease 2019 (covid-19) who died.DesignRetrospective case series.SettingTongji Hospital in Wuhan, China.ParticipantsAmong a cohort of 799 patients, 113 who died and 161 who recovered with a diagnosis of covid-19 were analysed. Data were collected until 28 February 2020.Main outcome measuresClinical characteristics and laboratory findings were obtained from electronic medical records with data collection forms.ResultsThe median age of deceased patients (68 years) was significantly older than recovered patients (51 years). Male sex was more predominant in deceased patients (83;73%) than in recovered patients (88;55%). Chronic hypertension and other cardiovascular comorbidities were more frequent among deceased patients (54 (48%) and 16 (14%)) than recovered patients (39 (24%) and 7 (4%)). Dyspnoea, chest tightness, and disorder of consciousness were more common in deceased patients (70 (62%), 55 (49%), and 25 (22%)) than in recovered patients (50 (31%), 48 (30%), and 1 (1%)). The median time from disease onset to death in deceased patients was 16 (interquartile range 12.0-20.0) days. Leukocytosis was present in 56 (50%) patients who died and 6 (4%) who recovered, and lymphopenia was present in 103 (91%) and 76 (47%) respectively. Concentrations of alanine aminotransferase, aspartate aminotransferase, creatinine, creatine kinase, lactate dehydrogenase, cardiac troponin I, N-terminal pro-brain natriuretic peptide, and D-dimer were markedly higher in deceased patients than in recovered patients. Common complications observed more frequently in deceased patients included acute respiratory distress syndrome (113;100%), type I respiratory failure (18/35;51%), sepsis (113;100%), acute cardiac injury (72/94;77%), heart failure (41/83;49%), alkalosis (14/35;40%), hyperkalaemia (42;37%), acute kidney injury (28;25%), and hypoxic encephalopathy (23;20%). Patients with cardiovascular comorbidity were more likely to develop cardiac complications. Regardless of history of cardiovascular disease, acute cardiac injury and heart failure were more common in deceased patients.ConclusionSevere acute respiratory syndrome coronavirus 2 infection can cause both pulmonary and systemic inflammation, leading to multi-organ dysfunction in patients at high risk. Acute respiratory distress syndrome and respiratory failure, sepsis, acute cardiac injury, and heart failure were the most common critical complications during exacerbation of covid-19.

Improving Data Surveillance Resilience Beyond COVID-19: Experiences of Primary heAlth Care quAlity Cohort In ChinA (ACACIA) Using Unannounced Standardized Patients.

We analyzed COVID-19 influences on the design, implementation, and validity of assessing the quality of primary health care using unannounced standardized patients (USPs) in China. Because of the pandemic, we crowdsourced our funding, removed tuberculosis from the USP case roster, adjusted common cold and asthma cases, used hybrid online-offline training for USPs, shared USPs across provinces, and strengthened ethical considerations. With those changes, we were able to conduct fieldwork despite frequent COVID-19 interruptions. Furthermore, the USP assessment tool maintained high validity in the quality checklist (criteria), USP role fidelity, checklist completion, and physician detection of USPs. Our experiences suggest that the pandemic created not only barriers but also opportunities to innovate ways to build a resilient data collection system. To build data system reliance, we recommend harnessing the power of technology for a hybrid model of remote and in-person work, learning from the sharing economy to pool strengths and optimize resources, and dedicating individual and group leadership to problem-solving and results. (Am J Public Health. 2022;112(6):913-922. https://doi.org/10.2105/AJPH.2022.306779).

Effectiveness of the use of a high-flow nasal cannula to treat COVID-19 patients and risk factors for failure: a meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread globally, and many patients with severe cases have received oxygen therapy through a high-flow nasal cannula (HFNC). OBJECTIVES: We assessed the efficacy of HFNC for treating patients with COVID-19 and risk factors for HFNC failure. METHODS: We searched PubMed, Embase, and the Cochrane Central Register of randomized controlled trials (RCTs) and observational studies of HFNC in patients with COVID-19 published in English from January 1st, 2020 to August 15th, 2021. The primary aim was to assess intubation, mortality, and failure rates in COVID-19 patients supported by HFNC. Secondary aims were to compare HFNC success and failure groups and to describe the risk factors for HFNC failure. RESULTS: A total of 25 studies fulfilled selection criteria and included 2851 patients. The intubation, mortality, and failure rates were 0.44 (95% confidence interval (CI): 0.38-0.51, I2 = 84%), 0.23 (95% CI: 0.19-0.29, I2 = 88%), and 0.47 (95% CI: 0.42-0.51, I2 = 56%), respectively. Compared to the success group, age, body mass index (BMI), Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation (APACHE) II score, D-dimer, lactate, heart rate, and respiratory rate were higher and PaO2, PaO2/FiO2, ROX index (the ratio of SpO2/FiO2 to respiratory rate), ROX index after the initiation of HFNC, and duration of HFNC were lower in the failure group (all Ps < 0.05). There were also more smokers and more comorbidities in the failure group (all Ps < 0.05). Pooled odds ratios (ORs) revealed that older age (OR: 1.04, 95% CI: 1.01-1.07, P = 0.02, I2 = 88%), a higher white blood cell (WBC) count (OR: 1.06, 95% CI: 1.01-1.12, P = 0.02, I2 = 0%), a higher heart rate (OR: 1.42, 95% CI: 1.15-1.76, P < 0.01, I2 = 0%), and a lower ROX index(OR: 0.61, 95% CI: 0.39-0.95, P = 0.03, I2 = 93%) after the initiation of HFNC were all significant risk factors for HFNC failure. CONCLUSIONS: HFNC is an effective way of providing respiratory support in the treatment of COVID-19 patients. Older age, a higher WBC count, a higher heart rate, and a lower ROX index after the initiation of HFNC are associated with an increased risk of HFNC failure.

Differential effects of macrophage subtypes on SARS-CoV-2 infection in a human pluripotent stem cell-derived model.

Dysfunctional immune responses contribute critically to the progression of Coronavirus Disease-2019 (COVID-19), with macrophages as one of the main cell types involved. It is urgent to understand the interactions among permissive cells, macrophages, and the SARS-CoV-2 virus, thereby offering important insights into effective therapeutic strategies. Here, we establish a lung and macrophage co-culture system derived from human pluripotent stem cells (hPSCs), modeling the host-pathogen interaction in SARS-CoV-2 infection. We find that both classically polarized macrophages (M1) and alternatively polarized macrophages (M2) have inhibitory effects on SARS-CoV-2 infection. However, M1 and non-activated (M0) macrophages, but not M2 macrophages, significantly up-regulate inflammatory factors upon viral infection. Moreover, M1 macrophages suppress the growth and enhance apoptosis of lung cells. Inhibition of viral entry using an ACE2 blocking antibody substantially enhances the activity of M2 macrophages. Our studies indicate differential immune response patterns in distinct macrophage phenotypes, which could lead to a range of COVID-19 disease severity.

Current Research Trends in Cytokine Storm: A Scientometric Study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is currently rampant worldwide, resulting in unpredictable harm to humans. High blood levels of cytokines and chemokines have been marked in patients with COVID-19 infection, leading to cytokine storm syndrome. Cytokine storms are violent inflammatory immune responses that reveal the devastating effect of immune dysregulation and the critical role of an effective host immune response. METHODS: Scientometric analysis summarizes the literature on cytokine storms in recent decades and provides a valuable and timely approach to tracking the development of new trends. This review summarizes the pathogenesis and treatment of diseases associated with cytokine storms comprehensively based on scientometric analysis. RESULTS: Field distribution, knowledge structure, and research topic evolution correlated with cytokine storms are revealed, and the occurrence, development, and treatment of disease relevant to cytokine storms are illustrated. CONCLUSION: Cytokine storms can be induced by pathogens and iatrogenic causes and can also occur in the context of autoimmune diseases and monogenic diseases as well. These reveal the multidisciplinary nature of cytokine storms and remind the complexity of the pathophysiological features, clinical presentation, and management. Overall, this scientometric study provides a macroscopic presentation and further direction for researchers who focus on cytokine storms.

How Healthy Aging and Contact With Children Are Associated With Satisfaction in Middle-Aged and Older Parents in China: A Mediation Analysis.

Objectives: This study aims to examine the mediation role of satisfaction with children on the association between contact with children (CCT) and healthy aging among middle-aged and older parents in China. Methods: Data from 9,575 parents over 45 years old were obtained from the 2018 China Health and Retirement Longitudinal Survey. A multinomial logistic regression model was applied to measure the association between contact, satisfaction, and healthy aging with potential confounders controlled. We used the Sobel-Goodman Mediation test to analyze the mediation role of satisfaction on the association between types of CCT and healthy aging. Results: Parents with contact with adult children had higher satisfaction with children [for contact weekly (satisfied/unsatisfied): relative risk ratio (RRR) = 2.44, CI = 1.92-3.10] and higher healthy aging [for contact weekly (Q5/Q1): RRR = 1.41, CI = 1.13-1.77]. Satisfaction was strongly related to healthy aging [for satisfied (Q5/Q1): RRR = 3.44, CI = 2.14-5.51], and mediated 19.05% of healthy aging for weekly contact (Sobel test z = 4.338; indirect role = 0.014, CI = 0.011-0.018; direct role = 0.061, CI = 0.029-0.094). Subgroup analysis further revealed that satisfaction with contact played a partial mediating role between monthly contact and healthy aging in female and rural groups. Conclusions: Monthly CCT is more appropriate for older parents. Satisfaction with children in older parents seems to act as a significant and partial mediator of the relationship between contact and healthy aging. The contribution of satisfaction to healthy aging could be important to be considered and promoted in women and rural older parents, independent of CCT.

Twelve-Month Systemic Consequences of Coronavirus Disease 2019 (COVID-19) in Patients Discharged From Hospital: A Prospective Cohort Study in Wuhan, China.

BACKGROUND: Follow-up study of coronavirus disease 2019 (COVID-19) survivors has rarely been reported. We aimed to investigate longitudinal changes in the characteristics of COVID-19 survivors after discharge. METHODS: A total of 594 COVID-19 survivors discharged from Tongji Hospital in Wuhan from February 10 to April 30, 2020 were included and followed up until May 17, 2021. Laboratory and radiological findings, pulmonary function tests, electrocardiogram, symptoms and signs were analyzed. RESULTS: 257 (51.2%) patients had at least one symptom at 3 months post-discharge, which decreased to 169 (40.0%) and 138 (28.4%) at 6-month and 12-month visit respectively. During follow-up period, insomnia, chest tightness, and fatigue were the most prevalent symptoms. Most laboratory parameters returned to normal, whereas increased incidence of abnormal liver and renal function and cardiovascular injury was evidenced after discharge. Fibrous stripes (213; 42.4%), pleural thickening and adhesions (188; 37.5%) and enlarged lymph nodes (120; 23.9%) were the most common radiographical findings at 3 months post-discharge. The abnormalities of pulmonary function included obstructive, restrictive, and mixed, which were 5.5%, 4.0%, 0.9% at 6 months post, and 1.9%, 4.7%, 0.2% at 12 months. Electrocardiogram abnormalities occurred in 256 (51.0%) patients at 3 months post-discharge, including arrhythmia, ST-T change and conduction block, which increased to 258 (61.1%) cases at 6-month visit and were maintained at high frequency (242;49.8%) at 12-month visit. CONCLUSIONS: Physiological, laboratory, radiological, or electrocardiogram abnormalities, particularly those related to renal, cardiovascular, and liver functions are common in patients who recovered from coronavirus disease 2019 (COVID-19) up to 12 months post-discharge.

Patient and Clinical Factors at Admission Affect the Levels of Neutralizing Antibodies Six Months after Recovering from COVID-19.

The rate of decline in the levels of neutralizing antibodies (NAbs) greatly varies among patients who recover from Coronavirus disease 2019 (COVID-19). However, little is known about factors associated with this phenomenon. The objective of this study is to investigate early factors at admission that can influence long-term NAb levels in patients who recovered from COVID-19. A total of 306 individuals who recovered from COVID-19 at the Tongji Hospital, Wuhan, China, were included in this study. The patients were classified into two groups with high (NAbhigh, n = 153) and low (NAblow, n = 153) levels of NAb, respectively based on the median NAb levels six months after discharge. The majority (300/306, 98.0%) of the COVID-19 convalescents had detected NAbs. The median NAb concentration was 63.1 (34.7, 108.9) AU/mL. Compared with the NAblow group, a larger proportion of the NAbhigh group received corticosteroids (38.8% vs. 22.4%, p = 0.002) and IVIG therapy (26.5% vs. 16.3%, p = 0.033), and presented with diabetes comorbidity (25.2% vs. 12.2%, p = 0.004); high blood urea (median (IQR): 4.8 (3.7, 6.1) vs. 3.9 (3.5, 5.4) mmol/L; p = 0.017); CRP (31.6 (4.0, 93.7) vs. 16.3 (2.7, 51.4) mg/L; p = 0.027); PCT (0.08 (0.05, 0.17) vs. 0.05 (0.03, 0.09) ng/mL; p = 0.001); SF (838.5 (378.2, 1533.4) vs. 478.5 (222.0, 1133.4) µg/L; p = 0.035); and fibrinogen (5.1 (3.8, 6.4) vs. 4.5 (3.5, 5.7) g/L; p = 0.014) levels, but low SpO2 levels (96.0 (92.0, 98.0) vs. 97.0 (94.0, 98.0)%; p = 0.009). The predictive model based on Gaussian mixture models, displayed an average accuracy of 0.7117 in one of the 8191 formulas, and ROC analysis showed an AUC value of 0.715 (0.657-0.772), and specificity and sensitivity were 72.5% and 67.3%, respectively. In conclusion, we found that several factors at admission can contribute to the high level of NAbs in patients after discharge, and constructed a predictive model for long-term NAb levels, which can provide guidance for clinical treatment and monitoring.

Evolutionary Dynamics of Indels in SARS-CoV-2 Spike Glycoprotein.

SARS-CoV-2, responsible for the current COVID-19 pandemic that claimed over 5.0 million lives, belongs to a class of enveloped viruses that undergo quick evolutionary adjustments under selection pressure. Numerous variants have emerged in SARS-CoV-2, posing a serious challenge to the global vaccination effort and COVID-19 management. The evolutionary dynamics of this virus are only beginning to be explored. In this work, we have analysed 1.79 million spike glycoprotein sequences of SARS-CoV-2 and found that the virus is fine-tuning the spike with numerous amino acid insertions and deletions (indels). Indels seem to have a selective advantage as the proportions of sequences with indels steadily increased over time, currently at over 89%, with similar trends across countries/variants. There were as many as 420 unique indel positions and 447 unique combinations of indels. Despite their high frequency, indels resulted in only minimal alteration of N-glycosylation sites, including both gain and loss. As indels and point mutations are positively correlated and sequences with indels have significantly more point mutations, they have implications in the evolutionary dynamics of the SARS-CoV-2 spike glycoprotein.

Responses of aquatic organisms downstream from WWTPs to disinfectants and their by-products during the COVID-19 pandemic, Wuhan.

The outbreak of COVID-19 has led to the large-scale usage of chlorinated disinfectants in cities. Disinfectants and disinfection by-products (DBPs) enter rivers through urban drainage and surface runoff. We investigated the variations in residual chlorine, DBPs, and different aquatic organisms in the Hanjiang, Fuhe, and Qinglinghe Rivers in Wuhan during the COVID-19 pandemic. The sampling sites were from the wastewater treatment plant outlets to the downstream drinking water treatment plant intakes. Total residual chlorine and DBPs (dichloromethane and trichloromethane) detected in the river water ranged from 0 to 0.84 mg/L and 0 to 0.034 mg/L, respectively. The residual chlorine and DBPs showed a gradual reduction pattern related to water flow, and the concentration at intakes did not exceed the Chinese drinking water source quality standards. Phytoplankton and zooplankton densities were not significantly correlated with residual chlorine and DBPs. The fluctuations in phytoplankton resource use efficiency (RUE) and zooplankton RUE in the Fuhe River, with the highest residual chlorine, and the Qinglinghe River with the highest DBPs, were higher than those in the Hanjiang River. For benthic macroinvertebrates, the number of functional feeding groups in the Hanjiang River was higher than that in the Fuhe and Qinglinghe Rivers. The water and sediment bacterial communities in the Hanjiang River differed significantly from those in the Fuhe and Qingling Rivers. The denitrification function involved in N metabolism was stronger in the Fuhe and Qinglinghe Rivers. Structural equation modelling revealed that residual chlorine and DBPs impacted the diversity of benthos through direct and indirect effects on plankton. Although large-scale chlorine-containing disinfectants use occurred during the investigation, it did not harm the density of the detected aquatic organisms in water sources. With the regular use of chlorinated disinfectants for indoor and outdoor environments in response to the SARS-CoV-2 globally, it is still necessary to study the long-term and accumulated responses of water ecosystems exposed to chlorine-containing disinfectants.

Research capacity of global health institutions in China: a gap analysis focusing on their collaboration with other low-income and middle-income countries.

INTRODUCTION: This paper presented qualitative and quantitative data collected on the research capacity of global health institutions in China and aimed to provide a landscaping review of the development of global health as a new discipline in the largest emerging economy of the world. METHODS: Mixed methods were used and they included a bibliometric analysis, a standardised survey and indepth interviews with top officials of 11 selected global health research and educational institutions in mainland China. RESULTS: The bibliometric analysis revealed that each institution had its own focus areas, some with a balanced focus among chronic illness, infectious disease and health systems, while others only focused on one of these areas. Interviews of key staff from each institution showed common themes: recognition that the current research capacity in global health is relatively weak, optimism towards the future, as well as an emphasis on mutual beneficial networking with other countries. Specific obstacles raised and the solutions applied by each institution were listed and discussed. CONCLUSION: Global health institutions in China are going through a transition from learning and following established protocols to taking a more leading role in setting up China's own footprint in this area. Gaps still remain, both in comparison with international institutions, as well as between the leading Chinese institutions and those that have just started. More investment needs to be made, from both public and private domains, to improve the overall capacity as well as the mutual learning and communication within the academic community in China.

Traditional Chinese medicine in COVID-19.

COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread across the globe, posing an enormous threat to public health and safety. Traditional Chinese medicine (TCM), in combination with Western medicine (WM), has made important and lasting contributions in the battle against COVID-19. In this review, updated clinical effects and potential mechanisms of TCM, presented in newly recognized three distinct phases of the disease, are summarized and discussed. By integrating the available clinical and preclinical evidence, the efficacies and underlying mechanisms of TCM on COVID-19, including the highly recommended three Chinese patent medicines and three Chinese medicine formulas, are described in a panorama. We hope that this comprehensive review not only provides a reference for health care professionals and the public to recognize the significant contributions of TCM for COVID-19, but also serves as an evidence-based in-depth summary and analysis to facilitate understanding the true scientific value of TCM.