Zoonotic occupational diseases in forestry workers: Lyme borreliosis, tularemia and leptospirosis in Europe

INTRODUCTION: Forestry workers and other people who come into close contact with wild animals, such as hunters, natural science researchers, game managers or mushroom/berry pickers, are at risk of contracting bacterial, parasitological or viral zoonotic diseases. Synthetic data on the incidence and prevalence of zoonotic diseases in both animals and humans in European forests do not exist. It is therefore difficult to promote appropriate preventive measures among workers or people who come into direct or indirect contact with forest animals. OBJECTIVES: The objectives of this review are to synthesise existing knowledge on the prevalence of the three predominant bacterial zoonotic diseases in Europe, i.e. Lyme borreliosis, tularemia and leptospirosis, in order to draw up recommendations for occupational or public health. METHODS: 88 papers published between 1995-2013 (33 on Lyme borreliosis, 30 on tularemia and 25 on leptospirosis) were analyzed. CONCLUSIONS: The prevalences of these three zoonotic diseases are not negligible and information targeting the public is needed. Moreover, the results highlight the lack of standardised surveys among different European countries. It was also noted that epidemiological data on leptospirosis are very scarce.

Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.