Evaluation of the Human IgG Antibody Response to Aedes albopictus Saliva as a New Specific Biomarker of Exposure to Vector Bites

Aedes-borne viruses like dengue and chikungunya are a major problem in Reunion Island. Assessing exposure to Aedes bites is crucial to estimating the risk of pathogen transmission. Currently, the exposure of populations to Aedes albopictus bites is mainly evaluated by entomological methods which are indirect and difficult to apply on a large scale. Recent findings suggest that evaluation of human antibody responses against arthropod salivary proteins could be useful in assessing exposure to mosquito bites. The results indicate that 88% of the studied population produce IgG to Ae. albopictus saliva antigens in Reunion Island and show that this biomarker can detect different levels of individual exposure. In addition, little cross-reactivity is observed with Aedes aegypti saliva, suggesting that this could be a specific marker for exposure to Aedes albopictus bites. Taken together, these results suggest that antibody responses to saliva could constitute a powerful immuno-epidemiological tool for evaluating exposure to Aedes albopictus and therefore the risk of arbovirus infection

A structurally distinct TGF-β mimic from an intestinal helminth parasite potently induces regulatory T cells.

Helminth parasites defy immune exclusion through sophisticated evasion mechanisms, including activation of host immunosuppressive regulatory T (Treg) cells. The mouse parasite Heligmosomoides polygyrus can expand the host Treg population by secreting products that activate TGF-β signalling, but the identity of the active molecule is unknown. Here we identify an H. polygyrus TGF-β mimic (Hp-TGM) that replicates the biological and functional properties of TGF-β, including binding to mammalian TGF-β receptors and inducing mouse and human Foxp3+ Treg cells. Hp-TGM has no homology with mammalian TGF-β or other members of the TGF-β family, but is a member of the complement control protein superfamily. Thus, our data indicate that through convergent evolution, the parasite has acquired a protein with cytokine-like function that is able to exploit an endogenous pathway of immunoregulation in the host

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability