Radiotherapy during the COVID-19: a review about management and treatment strategies

BackgroundThe administration of radiotherapy should be encouraged despite the emergency of COVID-19; therefore, our aim is to analyze management and therapeutic interventions to be implemented in a Radiotherapy department to allow patients to continue their treatment and health professionals to continue their work safely.Materials and methodsA Pubmed search was performed, in which all articles specific to Radiotherapy and COVID-19 were included. Those articles that were too specific about the COVID-19, surgery and chemiotherapy, were excluded.Results315 articles were selected, of which 35 were about therapeutic strategies and 25 about management strategies. In the first category, 5 articles were about how radiotherapy could be a weapon to be used for COVID-19 positive patients with important lung problems. While 30 articles described priorities and new treatment plans for oncology patients who have to undergo radiotherapy during the pandemic. In the second category, almost all the articles explained how triage can be a preventive and monitoring way against COVID-19 in an operating unit with many patients and professionals, and other articles developed a telemedicine system, too, which allows patients to make scheduled visits without coming to the hospital and also for the staff, who can work remotely. In addition, 5 articles concerning psychological aspects of both patients and health care providers were included.ConclusionThis document can be used as a summary in the coming months/years, during the recovery phase from COVID-19 pandemic outbreak and as a starting point to be used in case of further pandemic break-out.     

Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis

PurposeTo assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images.MethodsFour different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (ΔQMs) were calculated between RS and other segmentations.ResultsHighest QS and lower ΔQMs values were associated to the CNN algorithm. However, only 45% CNN segmentations were judged to need no or only minimal corrections, and in only 17 cases (31%), automatic segmentations provided RS without manual corrections. Median values of the DI for the four algorithms ranged from 0.993 to 0.904. Significant differences for all QMs calculated between automatic segmentations and RS were found both when data were pooled together and stratified according to QS, indicating a relationship between qualitative and quantitative measurements. The most unstable QM was the histogram 90th percentile, with median ΔQMs values ranging from 10HU and 158HU between different algorithms.ConclusionsNone of tested algorithms provided fully reliable segmentation. Segmentation accuracy impacts differently on different quantitative metrics, and each of them should be individually evaluated according to the purpose of subsequent analyses.     

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.     

Tailored treatment strategies for cancer patients during COVID-19 pandemic

The global pandemic of respiratory disease caused by the novel human coronavirus (SARS-CoV-2) has caused indefinite global distress, uncertainty, and disturbance. This pandemic has had direct and indirect impacts for the healthcare systems across the world, but certain subgroups of patients have been particularly affected. Among these groups are patients with cancer, who as a result of their immunosuppressed status either from the disease itself or as a consequence of treatment, are at increased risk of severe COVID-19 infection and complications. The pandemic has also led to limited resources as medical services have been primarily directed to emergency care. In this context, physicians and healthcare providers have had to balance the importance of continuing treatment of cancer patients with the risk of virus infection.In this review, we outline the treatment strategies for cancer patients during this pandemic, focusing on tailored treatment in this challenging situation of varying risks and benefits.     

Robust prediction of mortality of COVID-19 patients based on quantitative,operator-independent,lung CT densitometry

PurposeTo train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry.MethodsTwo-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. “Aerated” (AV), “consolidated” (CV) and “intermediate” (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n = 166) and validation (n = 85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model’s performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model’s performances were tested in the validation group.ResultsForty-three patients died (25/18 in training/validation). CT_model included AVmax (i.e. maximum AV between lungs), CV and CV/AE, while CLIN_model included random glycemia, C-reactive protein and biological drugs (protective). Goodness-of-fit and discrimination were similar (H&L:0.70 vs 0.80; AUC:0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L:0.91; AUC:0.89, 95%CI:0.82–0.93). All models showed good calibration (R²:0.77–0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R²:0–70-0.81) and discrimination for CT_model/COMB_model (AUC:0.72/0.76), while CLIN_model performed worse (AUC:0.64).ConclusionsFew automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration.     

Evaluation of COVID-19 vaccination strategies with a delayed second dose

Two of the Coronavirus Disease 2019 (COVID-19) vaccines currently approved in the United States require 2 doses, administered 3 to 4 weeks apart. Constraints in vaccine supply and distribution capacity, together with a deadly wave of COVID-19 from November 2020 to January 2021 and the emergence of highly contagious Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants, sparked a policy debate on whether to vaccinate more individuals with the first dose of available vaccines and delay the second dose or to continue with the recommended 2-dose series as tested in clinical trials. We developed an agent-based model of COVID-19 transmission to compare the impact of these 2 vaccination strategies, while varying the temporal waning of vaccine efficacy following the first dose and the level of preexisting immunity in the population. Our results show that for Moderna vaccines, a delay of at least 9 weeks could maximize vaccination program effectiveness and avert at least an additional 17.3 (95% credible interval [CrI]: 7.8–29.7) infections, 0.69 (95% CrI: 0.52–0.97) hospitalizations, and 0.34 (95% CrI: 0.25–0.44) deaths per 10,000 population compared to the recommended 4-week interval between the 2 doses. Pfizer-BioNTech vaccines also averted an additional 0.60 (95% CrI: 0.37–0.89) hospitalizations and 0.32 (95% CrI: 0.23–0.45) deaths per 10,000 population in a 9-week delayed second dose (DSD) strategy compared to the 3-week recommended schedule between doses. However, there was no clear advantage of delaying the second dose with Pfizer-BioNTech vaccines in reducing infections, unless the efficacy of the first dose did not wane over time. Our findings underscore the importance of quantifying the characteristics and durability of vaccine-induced protection after the first dose in order to determine the optimal time interval between the 2 doses.

There are two widely used COVID-19 vaccination strategies; administering the two doses three to four weeks apart or delaying the administration of the second dose. A modelling study calibrated to COVID-19 spread and vaccination in the US shows that delaying the second dose can maximize the benefits of vaccination programs under certain conditions.     

Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies

Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a “stay at home” order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. We find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Our work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.     

新型冠状病毒肺炎细胞治疗的关键策略和研究进展

新型冠状病毒肺炎病毒感染性强,感染后的重型、危重型患者病死率高,尚无特效治疗方法。间充质干细胞具有免疫调节和组织修复功能,一方面可以通过分泌抑炎因子减少炎性因子表达,降低细胞因子风暴和急性呼吸窘迫综合征发生的风险,从而降低重症患者的死亡率;另一方面间充质干细胞可分泌营养因子且具有多向分化能力,能修复肺部组织损伤,阻止肺部纤维化进程并使其恢复,从而治疗病毒感染肺炎后引起的难治性肺损伤相关疾病。此外自然杀伤细胞等也可在病毒感染性疾病预防、减少轻症患者向重型患者转化等方面发挥作用。本文总结并分析了细胞治疗新型冠状病毒肺炎最新研究进展。     

COVID-19 prevention and control strategies: learning from the Macau model

Background: Macau is a densely populated international tourist city. Compared to most tensely populated countries/territories, the prevalence and mortality of COVID-19 in Macau are lower. The experiences in Macau could be helpful for other areas to combat the COVID-19 pandemic. This article introduced the endeavours and achievements of Macau in combatting the COVID-19 pandemic.Method: Both qualitative and quantitative analysis methods were used to explore the work, measures, and achievements of Macau in dealing with the COVID-19 pandemic.Results: The results revealed that Macau has provided undifferentiated mask purchase reservation services, COVID-19 vaccination services to all residents and non-residents in Macau along with delivering multilingual services, in Chinese, English and Portuguese, to different groups of the population. To facilitate the travels of people, business and trades between Macau and mainland China, the Macau government launched the Macau Health Code System, which uses the health status declaration, residence history declaration, contact history declaration of the declarant to match various relevant backend databases within the health authority and provide a risk-related colour code operations. The Macau Health Code System connects to the Chinese mainland''s own propriety health code system seamlessly, whilst effectively protecting the privacy of the residents. Macau has also developed the COVID-19 Vaccination Appointment system, the Nucleic Acid Test Appointment system, the Port and Entry/Exit Quarantine system, the medical and other supporting systems.Conclusion: The efforts in Macau have achieved remarkable results in COVID-19 prevention and control, effectively safeguarding the lives and health of the people and manifesting the core principle of “serving the public”. The measures used are sustainable and can serve as an important reference for other countries/regions.     

Adaptive and optimized COVID-19 vaccination strategies across geographical regions and age groups

We evaluate the efficiency of various heuristic strategies for allocating vaccines against COVID-19 and compare them to strategies found using optimal control theory. Our approach is based on a mathematical model which tracks the spread of disease among different age groups and across different geographical regions, and we introduce a method to combine age-specific contact data to geographical movement data. As a case study, we model the epidemic in the population of mainland Finland utilizing mobility data from a major telecom operator. Our approach allows to determine which geographical regions and age groups should be targeted first in order to minimize the number of deaths. In the scenarios that we test, we find that distributing vaccines demographically and in an age-descending order is not optimal for minimizing deaths and the burden of disease. Instead, more lives could be saved by using strategies which emphasize high-incidence regions and distribute vaccines in parallel to multiple age groups. The level of emphasis that high-incidence regions should be given depends on the overall transmission rate in the population. This observation highlights the importance of updating the vaccination strategy when the effective reproduction number changes due to the general contact patterns changing and new virus variants entering.     

A critical evaluation of glucocorticoids in the management of severe COVID-19

The viral infection by SARS-CoV-2 has irrevocably altered the life of the majority of human beings, challenging national health systems worldwide, and pushing researchers to rapidly find adequate preventive and treatment strategies. No therapies have been shown effective with the exception of dexamethasone, a glucocorticoid that was recently proved to be the first life-saving drug in this disease. Remarkably, around 20 % of infected people develop a severe form of COVID-19, giving rise to respiratory and multi-organ failures requiring subintensive and intensive care interventions. This phenomenon is due to an excessive immune response that damages pulmonary alveoli, leading to a cytokine and chemokine storm with systemic effects. Indeed glucocorticoids’ role in regulating this immune response is controversial, and they have been used in clinical practice in a variety of countries, even without a previous clear consensus on their evidence-based benefit.     

Diagnosis-wide analysis of COVID-19 complications: an exposure-crossover study

BACKGROUND:Many studies reporting coronavirus disease 2019 (COVID-19) complications have involved case series or small cohorts that could not establish a causal association with COVID-19 or provide risk estimates in different care settings. We sought to study all possible complications of COVID-19 to confirm previously reported complications and to identify potential complications not yet known.METHODS:Using United States health claims data, we compared the frequency of all International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis codes occurring before and after the onset of the COVID-19 pandemic in an exposure-crossover design. We included patients who received a diagnosis of COVID-19 between Mar. 1, 2020, and Apr. 30, 2020, and computed risk estimates and odds ratios (ORs) of association with COVID-19 for every ICD-10-CM diagnosis code.RESULTS:Among 70 288 patients with COVID-19, 69 of 1724 analyzed ICD-10-CM diagnosis codes were significantly associated with COVID-19. Disorders showing both strong association with COVID-19 and high absolute risk included viral pneumonia (OR 177.63, 95% confidence interval [CI] 147.19–214.37, absolute risk 27.6%), respiratory failure (OR 11.36, 95% CI 10.74–12.02, absolute risk 22.6%), acute kidney failure (OR 3.50, 95% CI 3.34–3.68, absolute risk 11.8%) and sepsis (OR 4.23, 95% CI 4.01–4.46, absolute risk 10.4%). Disorders showing strong associations with COVID-19 but low absolute risk included myocarditis (OR 8.17, 95% CI 3.58–18.62, absolute risk 0.1%), disseminated intravascular coagulation (OR 11.83, 95% CI 5.26–26.62, absolute risk 0.1%) and pneumothorax (OR 3.38, 95% CI 2.68–4.26, absolute risk 0.4%).INTERPRETATION:We confirmed and provided risk estimates for numerous complications of COVID-19. These results may guide prognosis, treatment decisions and patient counselling.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel strain of coronavirus that has been identified as the cause of the coronavirus disease 2019 (COVID-19) pandemic. As of Nov. 20, 2020, more than 50 million people have received a diagnosis of COVID-19 globally.¹ The clinical spectrum of disease is wide and can range from symptoms typical of the common cold to respiratory failure and death.² Most patients have mild symptoms and can be managed as outpatients, but as many as 20% have a severe form of the disease requiring admission to hospital, commonly presenting with hypoxia secondary to pneumonia.³Studies also show that COVID-19 is associated with a wide variety of nonrespiratory sequelae, including endothelial, thrombotic, cardiac, inflammatory, neurologic and other complications. ^4–9 Whether these associations are causal is not well established, as many of these findings originate from case reports, which are prone to publication bias and cannot provide risk estimates, or from cohort studies that often do not provide relative risk estimates.An alternative strategy for identifying potential complications of COVID-19 is studying all possible complications as captured in International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10 CM) diagnosis codes, which allows for the discovery of unreported complications and can confirm previously identified ones. The objective of our study was to analyze all diagnoses associated with COVID-19, to identify those that could be complications of the disease and to present both the absolute risk and relative odds of any complications identified.     

Clinical progress in MSC-based therapies for the management of severe COVID-19

Considering the high impact that severe Coronavirus disease 2019 (COVID-19) cases still pose on public health and their complex pharmacological management, the search for new therapeutic alternatives is essential. Mesenchymal stromal cells (MSCs) could be promising candidates as they present important immunomodulatory and anti-inflammatory properties that can combat the acute severe respiratory distress syndrome (ARDS) and the cytokine storm occurring in COVID-19, two processes that are mainly driven by an immunological misbalance. In this review, we provide a comprehensive overview of the intricate inflammatory process derived from the immune dysregulation that occurs in COVID-19, discussing the potential that the cytokines and growth factors that constitute the MSC-derived secretome present to treat the disease. Moreover, we revise the latest clinical progress made in the field, discussing the most important findings of the clinical trials conducted to date, which follow 2 different approaches: MSC-based cell therapy or the administration of the secretome by itself, as a cell-free therapy.     

A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: Application to COVID-19 patients

PurposeQuantitative metrics in lung computed tomography (CT) images have been widely used, often without a clear connection with physiology. This work proposes a patient-independent model for the estimation of well-aerated volume of lungs in CT images (WAVE).MethodsA Gaussian fit, with mean (Mu.f) and width (Sigma.f) values, was applied to the lower CT histogram data points of the lung to provide the estimation of the well-aerated lung volume (WAVE.f). Independence from CT reconstruction parameters and respiratory cycle was analysed using healthy lung CT images and 4DCT acquisitions. The Gaussian metrics and first order radiomic features calculated for a third cohort of COVID-19 patients were compared with those relative to healthy lungs. Each lung was further segmented in 24 subregions and a new biomarker derived from Gaussian fit parameter Mu.f was proposed to represent the local density changes.ResultsWAVE.f resulted independent from the respiratory motion in 80% of the cases. Differences of 1%, 2% and up to 14% resulted comparing a moderate iterative strength and FBP algorithm, 1 and 3 mm of slice thickness and different reconstruction kernel. Healthy subjects were significantly different from COVID-19 patients for all the metrics calculated. Graphical representation of the local biomarker provides spatial and quantitative information in a single 2D picture.ConclusionsUnlike other metrics based on fixed histogram thresholds, this model is able to consider the inter- and intra-subject variability. In addition, it defines a local biomarker to quantify the severity of the disease, independently of the observer.     

COVID-19-related coagulopathy: A review of pathophysiology and pharmaceutical management

December 2019 will never be forgotten in the history of medicine when an outbreak of pneumonia of unknown etiology in Wuhan, China sooner or later prompted the World Health Organization to issue a public health warning emergency. This is not the first nor will it be the last time that a member of β-coronaviruses (CoVs) is waging a full-scale war against human health. Notwithstanding the fact that pneumonia is the primary symptom of the novel coronavirus (2019nCoV; designated as SARS-CoV-2), the emergence of severe disease mainly due to the injury of nonpulmonary organs at the shadow of coagulopathy leaves no choice, in some cases, rather than a dreadful death. Multiple casual factors such as inflammation, endothelial dysfunction, platelet and complement activation, renin-angiotensin-aldosterone system derangement, and hypoxemia play a major role in the pathogenesis of coagulopathy in coronavirus disease 2019 (COVID-19) patients. Due to the undeniable role of coagulation dysfunction in the initiation of several complications, assessment of coagulation parameters and the platelet count would be beneficial in early diagnosis and also timely prediction of disease severity. Although low-molecular-weight heparin is considered as the first-line of treatment in COVID-19-associated coagulopathy, several possible therapeutic options have also been proposed for better management of the disease. In conclusion, this review would help us to gain insight into the pathogenesis, clinical manifestation, and laboratory findings associated with COVID-19 coagulopathy and would summarize management strategies to alleviate coagulopathy-related complications.     

Immunology of COVID-19

The immune response to SARS-CoV-2 reveals a delicate balance between protective effects and harmful pathological reactions and can possibly explain the highly variable disease manifestations in subjects infected with this novel coronavirus. A better understanding of the anti-viral immune response is not only critical for vaccine development but might also provide targets for pharmaceutical and immunological treatment options. Recent research literature on immune aspects of COVID-19 is summarized in this review with an outlook how bats have evolved to live with these viral infections.     

SARS-CoV-2 variants,immune escape,COVID-19 vaccine,and therapeutic strategies

Science China Life Sciences -     

The NLRP3 inflammasome and COVID-19: Activation,pathogenesis and therapeutic strategies

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a wide spectrum of clinical presentations, ranging from asymptomatic cases to severe pneumonia or even death. In severe COVID-19 cases, an increased level of proinflammatory cytokines has been observed in the bloodstream, forming the so-called “cytokine storm”. Generally, nucleotide-binding oligomerization domain-like receptor containing pyrin domain 3 (NLRP3) inflammasome activation intensely induces cytokine production as an inflammatory response to viral infection. Therefore, the NLRP3 inflammasome can be a potential target for the treatment of COVID-19. Hence, this review first introduces the canonical NLRP3 inflammasome activation pathway. Second, we review the cellular/molecular mechanisms of NLRP3 inflammasome activation by SARS-CoV-2 infection (e.g., viroporins, ion flux and the complement cascade). Furthermore, we describe the involvement of the NLRP3 inflammasome in the pathogenesis of COVID-19 (e.g., cytokine storm, respiratory manifestations, cardiovascular comorbidity and neurological symptoms). Finally, we also propose several promising inhibitors targeting the NLRP3 inflammasome, cytokine products and neutrophils to provide novel therapeutic strategies for COVID-19.