The association between serum biomarkers and disease outcome in influenza A(H1N1)pdm09 virus infection: results of two international observational cohort studies

BACKGROUND Prospective studies establishing the temporal relationship between the degree of inflammation and human influenza disease progression are scarce. To assess predictors of disease progression among patients with influenza A(H1N1)pdm09 infection, 25 inflammatory biomarkers measured at enrollment were analyzed in two international observational cohort studies. METHODS Among patients with RT-PCR-confirmed influenza A(H1N1)pdm09 virus infection, odds ratios (ORs) estimated by logistic regression were used to summarize the associations of biomarkers measured at enrollment with worsened disease outcome or death after 14 days of follow-up for those seeking outpatient care (FLU 002) or after 60 days for those hospitalized with influenza complications (FLU 003). Biomarkers that were significantly associated with progression in both studies (p<0.05) or only in one (p<0.002 after Bonferroni correction) were identified. RESULTS In FLU 002 28/528 (5.3%) outpatients had influenza A(H1N1)pdm09 virus infection that progressed to a study endpoint of complications, hospitalization or death, whereas in FLU 003 28/170 (16.5%) inpatients enrolled from the general ward and 21/39 (53.8%) inpatients enrolled directly from the ICU experienced disease progression. Higher levels of 12 of the 25 markers were significantly associated with subsequent disease progression. Of these, 7 markers (IL-6, CD163, IL-10, LBP, IL-2, MCP-1, and IP-10), all with ORs for the 3(rd) versus 1(st) tertile of 2.5 or greater, were significant (p<0.05) in both outpatients and inpatients. In contrast, five markers (sICAM-1, IL-8, TNF-α, D-dimer, and sVCAM-1), all with ORs for the 3(rd) versus 1(st) tertile greater than 3.2, were significantly (p≤.002) associated with disease progression among hospitalized patients only. CONCLUSIONS In patients presenting with varying severities of influenza A(H1N1)pdm09 virus infection, a baseline elevation in several biomarkers associated with inflammation, coagulation, or immune function strongly predicted a higher risk of disease progression. It is conceivable that interventions designed to abrogate these baseline elevations might affect disease outcome

The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists

The DAVID gene functional classification tool uses a novel fuzzy clustering algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules

A randomised trial of subcutaneous intermittent interleukin-2 without antiretroviral therapy in HIV-infected patients: the UK-Vanguard Study

Objective: The objective of the trial was to evaluate in a pilot setting the safety and efficacy of interleukin-2 (IL-2) therapy when used without concomitant antiretroviral therapy as a treatment for HIV infection. Design and Setting: This was a multicentre randomised three-arm trial conducted between September 1998 and March 2001 at three clinical centres in the United Kingdom. Participants: Participants were 36 antiretroviral treatment naive HIV-1-infected patients with baseline CD4 T lymphocyte counts of at least 350 cells/mm(3). Interventions: Participants were randomly assigned to receive IL-2 at 15 million international units (MIU) per day ( 12 participants) or 9 MIU/day ( 12 participants) or no treatment ( 12 participants). IL-2 was administered by twice-daily subcutaneous injections for five consecutive days every 8 wk. Outcome Measures: Primary outcome was the change from baseline CD4 T lymphocyte count at 24 wk. Safety and plasma HIV RNA levels were also monitored every 4 wk through 24 wk. The two IL-2 dose groups were combined for the primary analysis. Results: Area under curve (AUC) for change in the mean CD4 T lymphocyte count through 24 wk was 129 cells/mm(3) for those assigned IL-2 ( both dose groups combined) and 13 cells/mm(3) for control participants (95% CI for difference, 51.3 - 181.2 cells/mm(3); p = 0.0009). Compared to the control group, significant increases in CD4 cell count were observed for both IL-2 dose groups: 104.2/mm(3) ( p = 0.008) and 128.4 cells/mm(3) ( p = 0.002) for the 4.5 and 7.5 MIU dose groups, respectively. There were no significant differences between the IL-2 (0.13 log(10) copies/ ml) and control (0.09 log(10) copies/ml) groups for AUC of change in plasma HIV RNA over the 24-wk period of follow- up ( 95% CI for difference, - 0.17 to 0.26; p = 0.70). Grade 4 and dose-limiting side effects were in keeping with those previously reported for IL-2 therapy. Conclusions: In participants with HIV infection and baseline CD4 T lymphocyte counts of at least 350 cells/mm(3), intermittent subcutaneous IL-2 without concomitant antiretroviral therapy was well tolerated and produced significant increases in CD4 T lymphocyte counts and did not adversely affect plasma HIV RNA levels

Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

Background: Selecting the optimal combination of HIV drugs for an individual in resourcelimited settings is challenging because of the limited availability of drugs and genotyping.Objective: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype, using cases from the Phidisa cohort in South Africa.Methods: Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load, details of failing and previous antiretroviral drugs, drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative’s (RDI’s) models used these data to predict the probability of a viral load &lt; 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs.Results: The models achieved accuracy (area under the receiver–operator  characteristic curve) of 0.72 when predicting response to therapy, which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic.Conclusion: The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI’s models have the potential to optimise treatment selection and reduce virological failure in different patient populations, without the use of a genotype

Living with the COVID-19 pandemic: act now with the tools we have.

Fil: Bedford, Juliet. Anthrologica, Oxfordshire; Reino Unido.Fil: Enria, Delia. ANLIS Dr.C.G.Malbrán. Instituto Nacional de Enfermedades Virales Humanas; Argentina.Fil: Giesecke, Johan. Karolinska Institute, Stockholm; Suecia.Fil: Heymann, David L. Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine; Reino Unido.Fil: Ihekweazu, Chikwe. Nigeria Centre for Disease Control; Nigeria.Fil: Kobinger, Gary. Infectious Disease Research Centre, Université Laval, Faculty of Medicine; Canada.Fil: Lane, H Clifford. National Institute of Allergy and Infectious Diseases; Estados Unidos.Fil: Memish, Ziad A. J W Lee Center for Global Medicine, SNU College of Medicine, Department of Internal Medicine, Seoul National University Hospital; Corea del Sur.Fil: Oh, Myoung-Don. J W Lee Center for Global Medicine, SNU College of Medicine, Department of Internal Medicine, Seoul National University Hospital; Corea del Sur.Fil: Sall, Amadou Alpha. Institut Pasteur de Dakar; Senegal.Fil: Ungchusak, Kumnuan. Ministry of Health, Department of Diseases Control; Tailandia.Fil: Wieler, Lothar H. Robert Koch Institute; Alemania.The responses of countries to the COVID-19 pandemic have been disparate.1, 2 Many countries are reopening workplaces, schools, and social gatherings and striving to adapt their economies and resume international travel. Other countries are attempting to suppress transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by again restricting businesses, industries, and schools while hoping for future COVID-19 vaccines or treatments. The Strategic and Technical Advisory Group for Infectious Hazards (STAG-IH), the independent advisory group to the WHO Health Emergencies Programme, has reviewed information from countries around the world and has concluded that the most sound approach on the basis of current understanding is to deploy long-term strategies with a focus on preventing amplification of transmission, protecting those most at risk of severe illness, and supporting research to better understand the virus, the disease, and people's responses to them