Stability of SARS-CoV-2 spike antigens against mutations

Modern health care needs preventive vaccines and therapeutic treatments with stability against pathogen mutations to cope with current and future viral infections. At the beginning of the COVID-19 pandemic, our analytic and predictive tool identified a set of eight short SARS-CoV-2 S-spike protein epitopes that had the potential to persistently avoid mutation. Here a combination of genetic, Systems Biology and protein structure analyses confirm the stability of our identified epitopes against viral mutations. Remarkably, this research spans the whole period of the pandemic, during which 93.9% of the eight peptides remained invariable in the globally predominant 43 circulating variants, including Omicron. Likewise, the selected epitopes are conserved in 97% of all 1,514 known SARS-CoV-2 lineages. Finally, experimental analyses performed with these short peptides showed their specific immunoreactivity. This work opens a new perspective on the design of next-generation vaccines and antibody therapies that will remain reliable against future pathogen mutations. HighlightsO_LIOur novel method predicts SARS-CoV-2 epitopes that are stable against future mutations C_LIO_LIGenetic analyses (performed 2 years after epitopes were identified) validate the stability of the identified peptides C_LIO_LIThese epitopes remained invariable in 97% of all 1,514 known SARS-CoV-2 lineages C_LIO_LI93.9% of such peptides were conserved in the 43 variants of most interest, including Omicron C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=166 SRC="FIGDIR/small/22280980v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@a9cb10org.highwire.dtl.DTLVardef@152a16corg.highwire.dtl.DTLVardef@1e3cea5org.highwire.dtl.DTLVardef@113ec06_HPS_FORMAT_FIGEXP M_FIG C_FIG

Machine learning uncovers blood test patterns subphenotypes at hospital admission discerning increased 30-day ICU mortality rates in COVID-19 elderly patients

BackgroundElderly patients with COVID-19 are among the most numerous populations being admitted in the ICU due to its high mortality rate and high comorbidity incidence. An early severity risk stratification at hospital admission could help optimize ICU usage towards those more vulnerable and critically ill patients. MethodsOf 503 Spanish patients aged>64 years admitted in the ICU between 26 Feb and 02 Nov 2020 in two Spanish hospitals, we included 193 quality-controlled patients. The subphenotyping combined PCA and t-SNE dimensionality reduction methods to maximize non-linear correlation and reduce noise among age and full blood count tests (FBC) at hospital admission, followed by hierarchical clustering. FindingsWe identified five subphenotypes (Eld-ICU-COV19 clusters) with heterogeneous FBC patterns associated to significantly disparate 30-day ICU mortality rates ranging from 2% in a healthy cluster to 44% in a severe cluster, along three moderate clusters. InterpretationsTo our knowledge, this is the first study using age and FBC at hospital admission to early stratify the risk of death in ICU at 30 days in elderly patients. Our results provide guidance to comprehend the phenotypic classification and disparate severity patterns among elderly ICU patients with COVID-19, based only on age and FBC, that have the potential to establish target groups for early risk stratification or early triage systems to provide personalized treatments or aid the decision-making during resource allocation process for each target Eld-ICU-COV19 cluster, especially in those circumstances with resource scarcity problem. FundingFONDO SUPERA COVID-19 by CRUE-Santander Bank grant SUBCOVERWD-19. O_TEXTBOXResearch in contextEvidence before this study We searched on PubMed and Google Scholar using the search terms "COVID-19", "SARS-CoV2", "phenotypes" for research published between 2020 to 2022, with no language restriction, to detect any published study identifying and characterizing phenotypes among ICU COVID-19 patients. A previous COVID-19 phenotyping study found three phenotypes from hospitalized patients associated with significantly disparate 30-day mortality rates (ranging from 2{middle dot}5 to 60{middle dot}7%). However, it seems to become harder to find phenotypes with discriminative mortality rates among ICU COVID-19 patients. For example, we found one study that uncovered two phenotypes from 39 ICU COVID-19 patients based on biomarkers with 39% and 63% mortality rates, but such difference was not statistically significant. We also found another study with more success that uncovered two ICU COVID-19 phenotypes using two different trajectories with somehow disparate 28-day mortality rates of 27% versus 37% (Ventilatory ratio trajectories) and of 25% versus 39% (mechanical power trajectories). Added value of this study To our knowledge, this is the first study that uses age and laboratory results at hospital admission (i.e., before ICU admission) in elderly patients to early stratify, prior ICU admission, the risk of death in ICU at 30 days. We classified 193 patients with COVID-19, based on age and ten Full Blood Count (FBC) tests, into five subphenotypes (one healthy, three moderate, and one severe) that showed significantly disparate 30-day ICU mortality rates from 2% to 44%. Implications of all the available evidence Identifying, from elderly ICU patients with COVID-19 (Eld-ICU-COV19), subphenotypes could spur further investigation to analyze the potential differences in their underlying disease mechanisms, acquire better phenotypical understanding among Eld-ICU-COV19 toward better decision-making in distributing the limited resources (including both logistic and medical) as well as shedding light on tailoring personalized treatment for each specific target subgroup in future medical research and clinical trial. C_TEXTBOX

Prognostic models for locally advanced cervical cancer: external validation of the published models / 부인종양

OBJECTIVE: To externally validate the prognostic models for predicting the time-dependent outcome in patients with locally advanced cervical cancer (LACC) who were treated with concurrent chemoradiotherapy in an independent cohort. METHODS: A historical cohort of 297 women with LACC who were treated with radical concurrent chemoradiotherapy from 1999 to 2014 at the 12 de Octubre University Hospital (H12O), Madrid, Spain. The external validity of prognostic models was quantified regarding discrimination, calibration, measures of overall performance, and decision curve analyses. RESULTS: The review identified 8 studies containing 13 prognostic models. Different (International Federation of Gynecology and Obstetrics [FIGO] stages, parametrium involvement, hydronephrosis, location of positive nodes, and race) but related cohorts with validation cohort (5-year overall survival [OS]=70%; 5-year disease-free survival [DFS]=64%; average age of 50; and over 79% squamous cell) were evaluated. The following models exhibited good external validity in terms of discrimination and calibration but limited clinical utility: the OS model at 3 year from Kidd et al.'s study (area under the receiver operating characteristic curve [AUROC]=0.69; threshold of clinical utility [TCU] between 36% and 50%), the models of DFS at 1 year from Kidd et al.'s study (AUROC=0.64; TCU between 24% and 32%) and 2 years from Rose et al.'s study (AUROC=0.70; TCU between 19% and 58%) and the distant recurrence model at 5 years from Kang et al.'s study (AUROC=0.67; TCU between 12% and 36%). CONCLUSION: The external validation revealed the statistical and clinical usefulness of 4 prognostic models published in the literature.