Driving us mad: the association of Toxoplasma gondii with suicide attempts and traffic accidents - a systematic review and meta-analysis.

Unnatural causes of death due to traffic accidents (TA) and suicide attempts (SA) constitute a major burden on global health, which remained stable in the last decade despite widespread efforts of prevention. Recently, latent infection with Toxoplasma gondii (T. gondii) has been suggested to be a biological risk factor for both TA and SA. Therefore, a systematic search concerning the relationship of T. gondii infection with TA and/or SA according to PRISMA guidelines in Medline, Pubmed and PsychInfo was conducted collecting papers up to 11 February 2019 (PROSPERO #CRD42018090206). The random-effect model was applied and sensitivity analyses were subsequently performed. Lastly, the population attributable fraction (PAF) was calculated. We found a significant association for antibodies against T. gondii with TA [odds ratio (OR) = 1.69; 95% confidence interval (CI) 1.20-2.38, p = 0.003] and SA (OR = 1.39; 95% CI 1.10-1.76, p = 0006). Indication of publication bias was found for TA, but statistical adjustment for this bias did not change the OR. Heterogeneity between studies on SA was partly explained by type of control population used (ORhealthy controls = 1.9, p < 0.001 v. ORpsychiatric controls = 1.06, p = 0.87) and whether subjects with schizophrenia only were analysed (ORschizophrenia = 0.87, p = 0.62 v. ORvarious = 1.8, p < 0.001). The association was significantly stronger with higher antibody titres in TA and in studies that did not focus on schizophrenia subjects concerning SA. PAF of a T. gondii infection was 17% for TA and 10% for SA. This indicates that preventing T. gondii infection may play a role in the prevention of TA or SA, although uncertainty remains whether infection and outcome are truly causally related.

Optimal race strategy for a 200-m flying sprint in a human-powered vehicle: A case study of a world-record attempt.

Optimal pacing strategy was determined for breaking the world speed record on a human-powered vehicle (HPV) using an energy-flow model in which the rider's physical capacities, the vehicle's properties, and the environmental conditions were included. Power data from world-record attempts were compared with data from the model, and race protocols were adjusted to the results from the model. HPV performance can be improved by using an energy-flow model for optimizing race strategy. A biphased in-run followed by a sprint gave best results.