Rationale. Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic interstitial lung disease, with high mortality. Currently, the aetiology and the pathology of IPF are poorly understood, with both innate and adaptive responses previously being implicated in the disease pathogenesis. Heat shock proteins (Hsp) and antibodies to Hsp in patients with IPF have been suggested as therapeutic targets and prognostic biomarkers, respectively. We aimed to study the relationship between the expression of Hsp72 and anti-Hsp72 antibodies in the BAL fluid and serum Aw disease progression in patients with IPF.Methods. A novel indirect ELISA to measure anti-Hsp72 IgG was developed and together with commercially available ELISAs used to detect Hsp72 IgG, Hsp72 IgGAM, and Hsp72 antigen, in the serum and BALf of a cohort of IPF (n=107) and other interstitial lung disease (ILD) patients (n=66). Immunohistochemistry was used to detect Hsp72 in lung tissue. The cytokine expression from monocyte-derived macrophages was measured by ELISA.Results. Anti-Hsp72 IgG was detectable in the serum and BALf of IPF (n=107) and other ILDs (n=66). Total immunoglobulin concentrations in the BALf showed an excessive adaptive response in IPF compared to other ILDs and healthy controls (p=0.026). Immunohistochemistry detection of C4d and Hsp72 showed that these antibodies may be targeting high expressing Hsp72 type II alveolar epithelial cells. However, detection of anti-Hsp72 antibodies in the BALf revealed that increasing concentrations were associated with improved patient survival (adjusted HR 0.62, 95% CI 0.45-0.85;p=0.003).In vitroexperiments demonstrate that anti-Hsp72 complexes stimulate macrophages to secrete CXCL8 and CCL18.Conclusion. Our results indicate that intrapulmonary anti-Hsp72 antibodies are associated with improved outcomes in IPF. These may represent natural autoantibodies, and anti-Hsp72 IgM and IgA may provide a beneficial role in disease pathogenesis, though the mechanism of action for this has yet to be determined.
Over 200 million malaria cases globally lead to half-million deaths annually. The development of malaria prevalence prediction systems to support malaria care pathways has been hindered by lack of data, a tendency towards universal “monolithic” models (one-size-fits-all-regions) and a focus on long lead time predictions. Current systems do not provide short-term local predictions at an accuracy suitable for deployment in clinical practice. Here we show a data-driven approach that reliably produces one-month-ahead prevalence prediction within a densely populated all-year-round malaria metropolis of over 3.5 million inhabitants situated in Nigeria which has one of the largest global burdens of P. falciparum malaria. We estimate one-month-ahead prevalence in a unique 22-years prospective regional dataset of > 9 × 104 participants attending our healthcare services. Our system agrees with both magnitude and direction of the prediction on validation data achieving MAE ≤ 6 × 10–2, MSE ≤ 7 × 10–3, PCC (median 0.63, IQR 0.3) and with more than 80% of estimates within a (+ 0.1 to − 0.05) error-tolerance range which is clinically relevant for decision-support in our holoendemic setting. Our data-driven approach could facilitate healthcare systems to harness their own data to support local malaria care pathways.
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