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Recent wildfire events (e.g. Mediterranean region, USA, and Australia) showed that this hazard poses a serious threat for wildland-urban interface (WUI) areas around the globe. Furthermore, recent events in regions where wildfire does not constitute a frequent hazard (e.g. Siberia, Scandinavia) indicated that the spatial pattern of wildfire risk might have significantly changed. To prepare for upcoming extreme events, it is critical for decision-makers to have a thorough understanding of the vulnerability of the built environment to wildfire. Building quality and design standards are important not only because building loss is costly but also because robust buildings may offer shelter when evacuation is not possible. However, studies aiming at the analysis of wildfire vulnerability for the built environment are limited. This paper presents an innovative solution for the vulnerability assessment to wildfires, making use of an all-relevant feature selection algorithm established on statistical relationships to develop a physical vulnerability index for buildings subject to wildfire. Data from a recent and systematically documented wildfire event in Greece (Mati, 2018) are used to select and weight the relevant indicators using a permutation-based automated feature selection based on random forests. Building characteristics including the structural type, the roof type, material and shape, the inclination of the ground, the surrounding vegetation, the material of the shutters and the ground covering were selected and formed into the index. The index may be used in other places in Europe and beyond, especially where no empirical data are available supporting decision-making and risk reduction of an emerging hazard amplified by climate change.

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[90Y]DOTA-DPhe1-Tyr3-octreotide ([90Y]-SMT487) has been suggested as a promising radiotherapeutic agent for somatostatin receptor-expressing tumours. In order to quantify the in vivo parameters of this compound and the radiation doses delivered to healthy organs, the analogue [86Y]DOTA-DPhe1-Tyr3-octreotide was synthesised and its uptake measured in baboons using positron emission tomography (PET). [86Y]DOTA-DPhe1-Tyr3-octreotide was administered at two different peptide concentrations, namely 2 and 100 microg peptide per m2 body surface. The latter concentration corresponded to a radiotherapeutic dose. In a third protocol [86Y]DOTA-DPhe1-Tyr3-octreotide was injected in conjunction with a simultaneous infusion of an amino acid solution that was high in l-lysine in order to lower the renal uptake of radioyttrium. Quantitative whole-body PET scans were recorded to measure the uptake kinetics for kidneys, liver, lung and bone. The individual absolute uptake kinetics were used to calculate the radiation doses for [90Y]DOTA-DPhe1-Tyr3-octreotide according to the MIRD recommendations extrapolated to a 70-kg human. The highest radiation dose was received by the kidneys, with 2.1-3.3 mGy per MBq [90Y]DOTA-DPhe1-Tyr3-octreotide injected. For the 100 microg/m2 SMT487 protocol with amino acid co-infusion this dose was about 20%-40% lower than for the other two treatment protocols. The liver and the red bone marrow received doses ranging from 0.32 to 0.53 mGy and 0.03 to 0.07 mGy per MBq [90Y]DOTA-DPhe1-Tyr3-octreotide, respectively. The average effective dose equivalent amounted to 0. 23-0.32 mSv/MBq. The comparatively low estimated radiation doses to normal organs support the initiation of clinical phase I trials with [90Y]DOTA-DPhe1-Tyr3-octreotide in patients with somatostatin receptor-expressing tumours.

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Using the German data of the European referral study from primary to secondary care this paper attempts to answer the following questions: which are the specialties GPs refer to, and which referrals were initiated by the patients. According to the study's methodology the gatekeeper function of the GPs or the patient initiative should be described by distinguishing between direct and indirect referrals. A direct referral is defined by a face to face encounter between patient and GP, while an indirect referral requires no such contact. For direct referral the GPs recorded the influence of the patient on their referral decision. In 71 practices (only the western part of the Federal Republic of Germany) 2077 direct and 1665 indirect referrals were documented. Every second direct referral was influenced by the patients. Women had a greater influence than men. An analysis by specialty showed that most of the direct referrals were directed to orthopaedics (14%), surgery (12%) and internal medicine (10%) and most of the indirect referrals to ophthalmology (25%), gynaecology (20%) and dermatology (8%) (64% of all indirect referrals were given to women). These results could be taken as an example how patients take over the responsibility of managing their complaints and illnesses by themselves in a health system with free access to specialist care. The role of the German GP as a gatekeeper and co-ordinator in the health care system turned out to be very limited. In consequence this might result in future problems such as a lack of co-operation, deficiencies in the continuity of medical treatment, over-medication unnecessary parallel investigations and additional costs.

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An inexpensive therapeutic concept compared to the dantrolene-therapy to counter the malignant hyperthermia (MH) is to be discussed, using a case-study from swine-anaesthesia. Hyperventilating the animals (with O2), administering metamizol, beta-blocker, bicarbonate and sufficiently cooling the patient can, if the symptoms are recognized early enough, arrest the hypermetabolic cascade in its track. All the animals that were treated according to this scheme survived the (MH) crisis without any lasting post operative damage.

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A new approach for the assessment of gait patterns is presented. The use of neural network techniques for decision making in gait analysis is for some purposes more effective than biomechanical methods or conventional statistics. To demonstrate this, a neural network was trained to distinguish 'healthy' from 'pathological' gait. The algorithm presented here can be used for several purposes because it learns from examples of diagnosed gait patterns without having any built-in model of gait.

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