Five-year survival outcomes in oropharyngeal squamous cell carcinoma following transoral laser microsurgery.

Objective: To determine the 5-year survival outcomes of patients with oropharyngeal cancer treated with transoral laser microsurgery at our institution. Methods: A prospective longitudinal cohort study of all cases of oropharyngeal squamous cell cancer or clinically unknown primaries diagnosed at our institution between September 1, 2014, to December 31, 2019, treated with primary transoral laser microsurgery were analyzed. Patients with a previous history of head and neck radiation were excluded from analysis. Kaplan-Meier survival curves were used to estimate 5-year overall survival, disease-specific survival, local control, and recurrence free survival rates in oropharyngeal squamous cell carcinoma. Results: Of 142 patients identified, 135 met criteria and were included in the survival analysis. Five-year local control rates in p16 positive and negative disease were 99.2% and 100%, respectively, with one locoregional failure in the p16 positive cohort. Five-year overall survival, disease-specific survival, and recurrence free survival in p16 positive disease were 91%, 95.2%, and 87% respectively (n = 124). Five-year overall survival, disease-specific survival, and recurrence free survival in p16 negative disease were 39.8%, 58.3%, and 60%, respectively (n = 11). The permanent gastrostomy tube rate was 1.5% and zero patients received a tracheostomy at the time of surgery. One patient (0.74%) required a return to the OR for a post-operative pharyngeal bleed. Conclusion: Transoral laser microsurgery is a safe primary treatment option for oropharyngeal squamous cell carcinoma with high 5-year survival outcomes, notably in p16 positive disease. More randomized trials are needed to compare survival outcomes and associated morbidity in transoral laser microsurgery compared to treatment with primary chemoradiation. Level of Evidence: 3.

Enhancing global preparedness during an ongoing pandemic from partial and noisy data

AO_SCPLOWBSTRACTC_SCPLOWAs the coronavirus disease 2019 (COVID-19) spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75 and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Country-level epidemic intelligence is not enough to contrast the pandemic of respiratory pathogens such as SARS-CoV-2 and a scalable integrated approach, i.e. pandemic intelligence, is required to enhance global preparedness.

Lineage replacement and evolution captured by the United Kingdom Covid Infection Survey

The Office for National Statistics COVID-19 Infection Survey is a large household-based surveillance study based in the United Kingdom. Here, we report on the epidemiological and evolutionary dynamics of SARS-CoV-2 determined by analysing sequenced samples collected up until 13th November 2021. We observed four distinct sweeps or partial-sweeps, by lineages B.1.177, B.1.1.7/Alpha, B.1.617.2/Delta, and finally AY.4.2, a sublineage of B.1.617.2, with each sweeping lineage having a distinct growth advantage compared to their predecessors. Evolution was characterised by steady rates of evolution and increasing diversity within lineages, but with step increases in divergence associated with each sweeping major lineage, leading to a faster overall rate of evolution and fluctuating levels of diversity. These observations highlight the value of viral sequencing integrated into community surveillance studies to monitor the viral epidemiology and evolution of SARS-CoV-2, and potentially other pathogens, particularly as routine PCR testing is phased out or in settings where large-scale sequencing is not feasible.

Viral infection and Transmission in a large well-traced outbreak caused by the Delta SARS-CoV-2 variant

We report the first local transmission of the SARS-CoV-2 Delta variant in mainland China. All 167 infections could be traced back to the first index case. Daily sequential PCR testing of the quarantined subjects indicated that the viral loads of Delta infections, when they first become PCR+, were on average [~]1000 times greater compared to A/B lineage infections during initial epidemic wave in China in early 2020, suggesting potentially faster viral replication and greater infectiousness of Delta during early infection. We performed high-quality sequencing on samples from 126 individuals. Reliable epidemiological data meant that, for 111 transmission events, the donor and recipient cases were known. The estimated transmission bottleneck size was 1-3 virions with most minor intra-host single nucleotide variants (iSNVs) failing to transmit to the recipients. However, transmission heterogeneity of SARS-CoV-2 was also observed. The transmission of minor iSNVs resulted in at least 4 of the 30 substitutions identified in the outbreak, highlighting the contribution of intra-host variants to population level viral diversity during rapid spread. Disease control activities, such as the frequency of population testing, quarantine during pre-symptomatic infection, and level of virus genomic surveillance should be adjusted in order to account for the increasing prevalence of the Delta variant worldwide.

Ten months of temporal variation in the clinical journey of hospitalised patients with COVID-19: an observational cohort

BackgroundThere is potentially considerable variation in the nature and duration of the care provided to hospitalised patients during an ongoing infectious disease epidemic or pandemic. Improvements in care and clinician confidence may shorten the time spent as an inpatient, or the need for admission to an intensive care unit (ICU) or high density unit (HDU), while novel treatment modalities may reduce the time course of illness. On the other hand, limited resources at times of high demand may lead to rationing of resources, with less beneficial consequences. Despite little evidence on how the values of such variables change over the course of a crisis (such as the current COVID-19 pandemic), they may nevertheless be used as proxies for disease severity, outcome measures for clinical trials, and to inform planning and logistics. We hypothesise that variation of this kind has been present over the first year of the pandemic. Methods and FindingsWe investigate such time trends in an extremely large international cohort of 142,540 patients with symptom onset of, or hospital admission for, COVID-19 during 2020. The variables investigated are time from symptom onset to hospital admission, probability of ICU/HDU admission, time from hospital admission to ICU/HDU admission, case fatality ratio (CFR) and total length of hospital stay. Time from hospital symptom onset to hospital admission showed a rapid decline during the first months of the pandemic followed by peaks during August/September and December. ICU/HDU admission was more frequent from June to August, while there were only modest time trends in time from hospital admission to ICU/HDU. The CFR was lowest from June to August, a trend mostly driven by patients with no ICU/HDU admission. Raw numbers for overall hospital stay showed little overall variation over the time period, but further examination reveals a clear decline in time to discharge for ICU/HDU survivors. The main limitations are that these are predominantly severe COVID-19 cases, and that there are temporal, spatial and demographic biases present in an observational study of this kind. ConclusionsOur results establish that variables of these kinds have limitations when used as outcome measures in a rapidly-evolving situation.

A hybrid in silico approach reveals novel inhibitors of multiple SARS-CoV-2 variants

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytophatic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (~16% of predicted hits) active compounds (Efficacy > 30%, IC50 [≤] 15 M). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further analysis identified allosteric binders to host receptor angiotensin-converting enzyme 2, which were able to inhibit the entry of pseudoparticles bearing spike protein of wild type SARS-CoV-2 as well as South African B.1.351 and UK B.1.1.7 variants.

Non-covalent TMPRSS2 inhibitors identified from virtual screening

The SARS-CoV-2 pandemic has prompted researchers to pivot their efforts to finding antiviral compounds and vaccines. In this study, we focused on the human host cell transmembrane protease serine 2 (TMPRSS2), which plays an important role in the viral life cycle by cleaving the spike protein to initiate membrane fusion. TMPRSS2 is an attractive target and has received attention for the development of drugs against SARS and MERS. Starting with comparative structural modeling and binding model analysis, we developed an efficient pharmacophore-based approach and applied a large-scale in silico database screening for small molecule inhibitors against TMPRSS2. The hits were evaluated in the TMPRSS2 biochemical assay and the SARS-CoV-2 pseudotyped particle (PP) entry assay. A number of novel inhibitors were identified, providing starting points for further development of drug candidates for the treatment of COVID-19.

OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: an agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing. It can simulate a population of 1 million people in seconds per day allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 is its Python interface, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19 epidemic.

Drug Repurposing Screen for Compounds Inhibiting the Cytopathic Effect of SARS-CoV-2

Drug repurposing is a rapid approach to identifying therapeutics for the treatment of emerging infectious diseases such as COVID-19. To address the urgent need for treatment options, we carried out a quantitative high-throughput screen using a SARS-CoV-2 cytopathic assay with a compound collection of 8,810 approved and investigational drugs, mechanism-based bioactive compounds, and natural products. Three hundred and nineteen compounds with anti-SARS-CoV-2 activities were identified and confirmed, including 91 approved drug and 49 investigational drugs. Among these confirmed compounds, the anti-SARS-CoV-2 activities of 230 compounds, including 38 approved drugs, have not been previously reported. Chlorprothixene, methotrimeprazine, and piperacetazine were the three most potent FDA approved drugs with anti-SARS-CoV-2 activities. These three compounds have not been previously reported to have anti-SARS-CoV-2 activities, although their antiviral activities against SARS-CoV and Ebola virus have been reported. These results demonstrate that this comprehensive data set of drug repurposing screen for SARS-CoV-2 is useful for drug repurposing efforts including design of new drug combinations for clinical trials.

ISARIC COVID-19 Clinical Data Report: 8 June 2020

ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global participation has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report, our 17th report, is a part of a series published over the past 2 years. Data have been entered for 800,459 individuals from 1701 partner institutions and networks across 60 countries. The comprehensive analyses detailed in this report includes hospitalised individuals of all ages for whom data collection occurred between 30 January 2020 and up to and including 5 January 2022, AND who have laboratory-confirmed SARS-COV-2 infection or clinically diagnosed COVID-19. For the 699,014 cases who meet eligibility criteria for this report, selected findings include: O_LImedian age of 58 years, with an approximately equal (50/50) male:female sex distribution C_LIO_LI29% of the cohort are at least 70 years of age, whereas 4% are 0-19 years of age C_LIO_LIthe most common symptom combination in this hospitalised cohort is shortness of breath, cough, and history of fever, which has remained constant over time C_LIO_LIthe five most common symptoms at admission were shortness of breath, cough, history of fever, fatigue/malaise, and altered consciousness/confusion, which is unchanged from the previous reports C_LIO_LIage-associated differences in symptoms are evident, including the frequency of altered consciousness increasing with age, and fever, respiratory and constitutional symptoms being present mostly in those 40 years and above C_LIO_LI16% of patients with relevant data available were admitted at some point during their illness into an intensive care unit (ICU), which is slightly lower than previously reported (19%) C_LIO_LIantibiotic agents were used in 35% of patients for whom relevant data are available (669,630), a significant reduction from our previous reports (80%) which reflects a shifting proportion of data contributed by different institutions; in ICU/HDU admitted patients with data available (50,560), 91% received antibiotics C_LIO_LIuse of corticosteroids was reported in 24% of all patients for whom data were available (677,012); in ICU/HDU admitted patients with data available (50,646), 69% received corticosteroids C_LIO_LIoutcomes are known for 632,518 patients and the overall estimated case fatality ratio (CFR) is 23.9% (95%CI 23.8-24.1), rising to 37.1% (95%CI 36.8-37.4) for patients who were admitted to ICU/HDU, demonstrating worse outcomes in those with the most severe disease C_LI To access previous versions of ISARIC COVID-19 Clinical Data Report please use the link below: https://isaric.org/research/covid-19-clinical-research-resources/evidence-reports/

Massive-scale biological activity-based modeling identifies novel antiviral leads against SARS-CoV-2

The recent global pandemic caused by the new coronavirus SARS-CoV-2 presents an urgent need for new therapeutic candidates. While the importance of traditional in silico approaches such as QSAR in such efforts in unquestionable, these models fundamentally rely on structural similarity to infer biological activity and are thus prone to becoming trapped in the very nearby chemical spaces of already known ligands. For novel and unprecedented threats such as COVID-19 much faster and efficient paradigms must be devised to accelerate the identification of new chemical classes for rapid drug development. Here we report the development of a new biological activity-based modeling (BABM) approach that builds on the hypothesis that compounds with similar activity patterns tend to share similar targets or mechanisms of action. In BABM, compound activity profiles established on massive scale across multiple assays are used as signatures to predict compound activity in a new assay or against a new target. We first trained and validated this approach by identifying new antiviral lead candidates for Zika and Ebola based on data from ~0.5 million compounds screened against ~2,000 assays. BABM models were then applied to predict ~300 compounds not previously reported to have activity for SARS-CoV-2, which were then tested in a live virus assay with high (>30%) hit rates. The most potent compounds showed antiviral activities in the nanomolar range. These potent confirmed compounds have the potential to be further developed in novel chemical space into new anti-SARS-CoV-2 therapies. These results demonstrate unprecedented ability using BABM to predict novel structures as chemical leads significantly beyond traditional methods, and its application in rapid drug discovery response in a global public health crisis.

Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro

COVID-19 is undoubtedly the most impactful viral disease of the current century, afflicting millions worldwide. As yet, there is not an approved vaccine, as well as limited options from existing drugs for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Overall, we identified 16 synergistic and 8 antagonistic combinations, 4 of which were both synergistic and antagonistic in a dose-dependent manner. Among the 16 synergistic cases, combinations of nitazoxanide with three other compounds (remdesivir, amodiaquine and umifenovir) were the most notable, all exhibiting significant synergy against SARS-CoV-2. The combination of nitazoxanide, an FDA-approved drug, and remdesivir, FDA emergency use authorization for the treatment of COVID-19, demonstrate a strong synergistic interaction. Notably, the combination of remdesivir and hydroxychloroquine demonstrated strong antagonism. Overall, our results emphasize the importance of both drug repurposing and preclinical testing of drug combinations for potential therapeutic use against SARS-CoV-2 infections.

Nitrofurantoin-associated Acute Pulmonary Toxicity Mimicking Severe Sepsis with Significantly Elevated Procalcitonin.

Nitrofurantoin is a commonly used treatment for urinary tract infections with a risk for pulmonary toxicity. We report a case of a 48-year-old woman on a prophylactic regimen of nitrofurantoin who exhibited classic signs of bacterial sepsis including elevated procalcitonin (PCL) and C-reactive protein (CRP) levels two days post-nephrolithotripsy. The microbial analysis did not reveal an infectious cause for the initial symptoms and, subsequently, the patient developed a dry cough, fever, chills, and transient hypoxemia requiring supplemental oxygen. Pulmonary imaging revealed significant abnormal features inconsistent with the patient's symptoms which indicated an inflammatory/immune reaction to nitrofurantoin. Treatment discontinuation improved the patient's symptoms and reduced PCL and CRP levels to within normal limits. A high index of suspicion for nitrofurantoin-associated pulmonary toxicity is warranted for patients on a regimen of nitrofurantoin who exhibit severe pulmonary symptoms and elevated PCL and CRP levels with no corresponding infection.