Objectives In reconstructive surgery, flap monitoring is crucial for early identification of perfusion problems. Using hyperspectral imaging (HSI), this clinical study aimed to develop a non-invasive, objective approach for perfusion monitoring of free and pedicled flaps. Material and methods HSI of 22 free (FF) and 8 pedicled flaps (PF) in 30 patients was recorded over time. Parameters assessed were tissue oxygenation/superficial perfusion (0–1 mm) (StO2 (0–100%)), near-infrared perfusion/deep perfusion (0–4 mm) (NIR (0–100)), distribution of haemoglobin (THI (0–100)), and water (TWI (0–100)). Measurements up to 72 h were correlated to clinical assessment. Results Directly after flap inset, mean StO2 was significantly higher in FF (70.3 ± 13.6%) compared with PF 56.2 ± 14.2% (p = 0.05), whereas NIR, THI, and TWI were similar (NIR_p = 0.82, THI_p = 0.97, TWI_p = 0.27). After 24 h, StO2, NIR, THI, and TWI did not differ between FF and PF. After 48 h, StO2, NIR, and TWI did not differ between FF and PF whereas THI was significantly increased in FF compared with PF(p = 0.001). In three FF, perfusion decreased clinically and in HSI, 36(1), 40(2), 5(3), and 61(3) h after flap inset which was followed by prompt intervention. Conclusions StO2 < 40%, NIR < 25/100, and THI < 40/100 indicated arterial occlusion, whereas venous problems revealed an increase of THI. In comparison with FF, perfusion parameters of PF were decreased after flap transfer but remained similar to FF later on. Clinical relevance HSI provides objective and non-invasive perfusion monitoring after flap transplantation in accordance to the clinical situation. With HSI, signs of deterioration can be detected hours before clinical diagnosis.

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Since supportive therapeutic options for BP-ONJ are lacking, GGOH might be a promising substitute for BP-ONJ prevention and therapy.

Objectives: The purpose of this study was to refine a commercial car driving simulation for occupational research. As the effects of ethanol on driving behavior are well established, we choose alcohol as a test compound to investigate the performance of subjects during simulation. Materials and Methods: We programmed a night driving scenario consisting of monotonous highway and a rural road on a Foerst F10-P driving simulator. Twenty healthy men, 19-30 years, participated in a pilot study. Subjects were screened for simulator sickness, followed by training on the simulator one hour in total. Experiments were performed in the morning on a separate day. Participants were randomized into either an alcoholized or a control group. All subjects drove two courses consisting of highway and rural road and were sober for the first course. During a 1 h break the ethanol group drank an alcoholic beverage to yield 0.06% blood alcohol concentration (BAC). Generalized linear mixed models were used to analyze the influence of alcohol on driving performance. Among others, independent variables were Simulator Sickness Questionnaire scores and subjective sleepiness. Results: Subjects did not experience simulator sickness during the experiments. Mean BAC before the second test drive was 0.065% in the mildly intoxicated group. There was no clear-cut difference in the number of crashes between 2 groups. BAC of 0.1% would deteriorate mean braking reaction time by 237 ms (SE = 112, p < 0.05). Ethanol slightly impaired the tracking in the righthand curves (p = 0.058). Braking reaction time improved by 86 ms (SE = 36, p < 0.05) for the second test drive, indicating a learning effect. Conclusions: In sum, a clear ethanol effect was observed in the driving simulation. This simulation seems suitable for occupational research and produces little simulator sickness. Controlling for possible learning effects is recommended in driving simulation studies.