Administration of corticosteroids in patients with severe influenza pneumonia is associated with increased ICU mortality, and these agents should not be used as co-adjuvant therapy.

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HIV-1 elite controllers (EC) maintain undetectable viral load (VL) in the absence of antiretroviral treatment. However, these subjects have heterogeneous clinical outcomes including a proportion loosing HIV-1 control over time. In this work we compared, in a longitudinal design, transient EC, analyzed before and after the loss of virological control, versus persistent EC. The aim was to identify factors leading to the loss of natural virological control of HIV-1-infection with a longitudinal retrospective study design. Gag-specific T-cell response was assessed by intracellular poly-cytokine production quantified by flow cytometry. Viral diversity and sequence-dating were performed in proviral DNA by PCR amplification at limiting dilution in and genes. The expression profile of 70 serum cytokines and chemokines was assessed by multiplex immunoassays. We identified transient EC as subjects with low Gag-specific T-cell polyfunctionality, high viral diversity and high proinflammatory cytokines levels before the loss of control. Gag-specific T-cell polyfunctionality was inversely associated with viral diversity in transient controllers before the loss of control (r=-0.8;=0.02). RANTES was a potential biomarker of transient control. This study identified, virological and immunological factors including inflammatory biomarkers associated with two different phenotypes within EC. These results may allow a more accurate definition of EC, which could help in a better clinical management of these individuals and in the development of future curative approaches. There is a rare group of HIV-infected patients who have the extraordinary capacity to maintain undetectable viral load levels in the absence of antiretroviral treatment, the so called HIV-1 elite controllers (EC). However, there is a proportion within these subjects that eventually loses this capability. In this work we found differences in virological and immune factors including soluble inflammatory biomarkers between subjects with persistent control of viral replication and EC that will loss the virological control. The identification of these factors could be a key point for a right medical care of those EC who are going to lose the natural control of viral replication, and for the design of future immunotherapeutic strategies using as a model the natural persistent control of HIV-infection.

Background The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 ICUs in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results The database included a total of 2022 patients (mean age 64 [IQR 5–71] years, 1423 (70.4%) male, median APACHE II score (13 [IQR 10–17]) and SOFA score (5 [IQR 3–7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A (mild) phenotype (537; 26.7%) included older age (< 65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623, 30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C (severe) phenotype was the most common (857; 42.5%) and was characterized by the interplay of older age (> 65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications. Conclusion The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

One of the main challenges in nuclear magnetic resonance (NMR) metabolomics is to obtain valuable metabolic information from large datasets of raw NMR spectra in a high throughput, automatic, and reproducible way. To date, established software packages used to match and quantify metabolites in NMR spectra remain mostly manually operated, leading to low resolution results and subject to inconsistencies not attributable to the NMR technique itself. Here, we introduce a new software package, called Dolphin, able to automatically quantify a set of target metabolites in multiple sample measurements using an approach based on 1D and 2D NMR techniques to overcome the inherent limitations of 1D (1)H-NMR spectra in metabolomics. Dolphin takes advantage of the 2D J-resolved NMR spectroscopy signal dispersion to avoid inconsistencies in signal position detection, enhancing the reliability and confidence in metabolite matching. Furthermore, in order to improve accuracy in quantification, Dolphin uses 2D NMR spectra to obtain additional information on all neighboring signals surrounding the target metabolite. We have compared the targeted profiling results of Dolphin, recorded from standard biological mixtures, with those of two well established approaches in NMR metabolomics. Overall, Dolphin produced more accurate results with the added advantage of being a fully automated and high throughput processing package.

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Objectives:Poor immunological recovery in treated HIV-infected patients is associated with greater morbidity and mortality. To date, predictive biomarkers of this incomplete immune reconstitution have not been established. We aimed to identify a baseline metabolomic signature associated with a poor immunological recovery after antiretroviral therapy (ART) to envisage the underlying mechanistic pathways that influence the treatment response.Design:This was a multicentre, prospective cohort study in ART-naive and a pre-ART low nadir (<200 cells/μl) HIV-infected patients (n = 64).Methods:We obtained clinical data and metabolomic profiles for each individual, in which low molecular weight metabolites, lipids and lipoproteins (including particle concentrations and sizes) were measured by NMR spectroscopy. Immunological recovery was defined as reaching CD4+ T-cell count at least 250 cells/μl after 36 months of virologically successful ART. We used univariate comparisons, Random Forest test and receiver-operating characteristic curves to identify and evaluate the predictive factors of immunological recovery after treatment.Results:HIV-infected patients with a baseline metabolic pattern characterized by high levels of large high density lipoprotein (HDL) particles, HDL cholesterol and larger sizes of low density lipoprotein particles had a better immunological recovery after treatment. Conversely, patients with high ratios of non-HDL lipoprotein particles did not experience this full recovery. Medium very-low-density lipoprotein particles and glucose increased the classification power of the multivariate model despite not showing any significant differences between the two groups.Conclusion:In HIV-infected patients, a baseline healthier metabolomic profile is related to a better response to ART where the lipoprotein profile, mainly large HDL particles, may play a key role.

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