Pituitary apoplexy (PA) is a rare and potentially fatal clinical condition presenting acute headache, vomiting, visual impairment, ophthalmoplegia, altered mental state and possible panhypopituitarism. It mostly occurs in patients with haemorrhagic infarction of the pituitary gland due to a pre-existing macroadenoma. Although there are pathological and physiological conditions that may share similar imaging characteristics, both clinical and imaging features can guide the radiologist towards the correct diagnosis, especially using magnetic resonance imaging (MRI). In this review, we will describe the main clinical and epidemiological features of PA, illustrating CT and MRI findings and discussing the role of imaging in the differential diagnosis, prognosis and follow-up.Teaching points• Headache, ophtalmoplegia and visual impairment are frequent symptoms of pituitary apoplexy.• CT is often the first imaging tool in PA, showing areas of hyperdensity within the sellar region.• MRI could confirm haemorrhage within the pituitary gland and compression on the optic chiasm.• Frequent simulating conditions are aneurysms, Rathke cleft cysts, craniopharingioma and mucocele.• The role of imaging is still debated and needs more studies.

Customer demands for greater acceleration, performance, and vehicle range in pure EVs plus mandated requirements to further reduce emissions in HEVs increase the appeal for combined on-board energy storage systems and generators. This paper deals with a multiple input DC-DC power converter devoted to combine the power flow of multi-source on-board energy systems. The proposed energy storage arrangement includes fuel cell generator, ultracapacitor tank, and battery system. Possibility of optimization of the output capacitors bank in relation to the RMS value of the current ripple is investigate

More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.

Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.

Customer demands for greater acceleration, performance, and vehicle range in pure EVs plus mandated requirements to further reduce emissions in HEVs increase the appeal for combined on-board energy storage systems and generators. This paper deals with the control system of an original HEV propulsion system that includes fuel cell generator and storage energy system combining ultracapacitor tank and battery. The three on-board power sources supply the vehicle traction drive through a multi-input DC-to-DC power converter which provides the desired management of the power flows. In particular, in the proposed arrangement the ultracapacitor tank is used for leveling the battery load current during transients resulting from either acceleration or braking operation of the vehicle. The paper outlines the features of the control unit of the DC-to-DC power converter being used in the proposed propulsion system and describes the main characteristics of a 35 kW prototype developed to conduct laboratory experiments and validate the control strategy