A first estimation of the impact of public health actions against COVID-19 in Veneto (Italy)

Veneto is one of the first Italian regions where the COVID-19 outbreak started spreading. Containment measures were approved soon thereafter. The present study aims at providing a first look at the impact of the containment measures on the outbreak progression in the Veneto region, Italy

Bayesian Machine Learning Techniques for revealing complex interactions among genetic and clinical factors in association with extra-intestinal Manifestations in IBD patients

The objective of the study is to assess the predictive performance of three different techniques as classifiers for extra-intestinal manifestations in 152 patients with Crohn's disease. Na\uefve Bayes, Bayesian Additive Regression Trees and Bayesian Networks implemented using a Greedy Thick Thinning algorithm for learning dependencies among variables and EM algorithm for learning conditional probabilities associated to each variable are taken into account. Three sets of variables were considered: (i) disease characteristics: presentation, behavior and location (ii) risk factors: age, gender, smoke and familiarity and (iii) genetic polymorphisms of the NOD2, CD14, TNFA, IL12B, and IL1RN genes, whose involvement in Crohn's disease is known or suspected. Extra-intestinal manifestations occurred in 75 patients. Bayesian Networks achieved accuracy of 82% when considering only clinical factors and 89% when considering also genetic information, outperforming the other techniques. CD14 has a small predicting capability. Adding TNFA, IL12B to the 3020insC NOD2 variant improved the accuracy

Sternal foramina : anatomy and clinical significance

Vengono presentati casi di forami sternali multipli e viene discussa l'importanza della conoscenza da parte dei clinici di questa anomalia

Osteologic topography of the trochlear spine and fovea as landmarks to locate the superior oblique trochlea

The position of the superior oblique tendon, attached to the orbital roof by a cartilaginous trochlea, is marked by osteologic landmarks like the trochlear spine and/or fovea, approximately located at the superomedial angle of the orbit. Aim of the study is to place the trochlea within the orbit with a series of measurements to give the surgeon detailed references of the trochlea location. For this purpose, we undertook the study of a collection of dry skulls of known sex and age to investigate bony landmarks. Measurements were taken to assess the position of the trochlear spine/fovea on a frontal plane employing a system of vertical and horizontal lines passing through known bony reference points. Measurements were also recorded between the trochlear spine/fovea and the orbital opening on one side and the anterior rim of the optic canal on the other side. The distances of the trochlear spine/fovea from the lines passing along the supraorbital notch and the frontozygomatic suture were respectively 8.5 ± 2.3 mm and 5.7 ± 1.5 mm. The distances of the trochlear fovea/spine from the anterior orbital opening and from the anterior rim of the optic foramen were respectively 4.2 ± 0.11 and 37.5 ± 3.1 mm. Only the distance from the optic canal showed sex-related differences. In conclusion, to avoid unwanted injuries of the trochlea of the superior oblique in surgery of the superomedial angle of the orbit, the surgeon should be aware of its precise position

Using Social Networks to Estimate the Number of COVID-19 Cases: The Incident (Hidden COVID-19 Cases Network Estimation) Study Protocol

Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate

Weak surveillance and policy attention to cancer in global health: the example of Mozambique

Cancer is an emerging public health problem in sub-Saharan Africa due to population growth, ageing and westernisation of lifestyles. The increasing burden of cancer calls for urgent policy attention to develop cancer prevention and control programmes. Cancer surveillance is an essential prerequisite. Only one in five low-income and middle-income countries have the necessary data to drive policy and reduce the cancer burden. In this piece, we use data from Mozambique over a 50-year period to illustrate cancer epidemiological trends in low-income and middle-income countries to hypothesise potential circumstances and factors that could explain changes in cancer burden and to discuss surveillance weaknesses and potential improvements. Like many low-income and middle-income countries, Mozambique faces the dual challenge of a still high morbidity and mortality due to infectious diseases in rural areas and increased incidence of cancers associated with westernisation of lifestyles in urban areas, as well as a rise of cancers related to the HIV epidemic. An increase in cancer burden and changes in the cancer profile should be expected in coming years. The Mozambican healthcare and health-information systems, like in many other low-income and middle-income countries, are not prepared to face this epidemiological transition, which deserves increasing policy attention