Objektiv strukturierte Bewertung einer Hautnaht am Modell : ist die direkte Beobachtung der video-basierten Beobachtung überlegen? ; Poster

Poster Einleitung: OSCEs werden immer häufiger in der Ausbildung von Studierenden eingesetzt. Die Einführung eines OSCEs im fach Chirurgie ist in Planung. Durch die große Anzahl von Studierenden pro Semester oder Studienjahr (400 Studierende in Frankfurt) ist die Durchführung einer OSCE Prüfung mit großem personellen Aufwand verbunden. Vor allem während der Prüfung müssen eine Vielzahl von Chirurgen simultan zu Prüfungszwecken zur Verfügung stehen. Ziel der Studie war es, zu überprüfen, ob eine video-basierte Bewertung einer „Nahtstation“ zu einem späteren Zeitpunkt zu gleichen Ergebnissen in der Bewertung der Leistung der Studierenden führt. Methode: 33 Studierende führten unter standardisierten Bedingungen eine Hautnaht an einem Modell durchzuführen. Die Studierenden wurden während der Prozedur von zwei prüfenden Chirurgen und zwei Studierenden im PJ (praktischen Jahr) beobachtet und anhand einer objektiv strukturierten Checkliste bewertet (Prozessevaluation). Die Prozedur wurde gleichzeitig auf Video aufgezeichnet und zu einem späteren Zeitpunkt zwei weiteren Chirurgen und zwei weiteren Studierenden im PJ zur Bewertung gezeigt. Ergebnisse: Der Vergleich zwischen "live“-prüfenden und "video“-prüfenden Chirurgen zeigt eine signifikant hohe Korrelation (r=0,87; p<0,01) und eine hohe Übereinstimmung (88,2%) in der Bewertung. Ebenso zeigen die prüfenden PJler eine signifikant hohe Korrelation (r=0,84; p<0,01). Die Übereinstimmung ist bei den PJlern mit (82.4%) etwas niedriger als bei den beteiligten Chirurgen. Zusammenfassung: Mit dieser Studie konnte zeigt werden, daß es bei der Beurteilung der Performance von Studierenden bei einer Hautnaht am Modell unter Anwendung von objektiv strukturierten Checklisten möglich ist, eine direkte Beobachtung der Studierenden durch eine video-basierte Beobachtung zu ersetzen. Eine "Nahtstation“ in einem OSCE kann somit während der Prüfungszeit ohne Prüfer auskommen und im Anschluß bewertet werden

Careggi Smart Hospital: A mobile app for patients, citizens and healthcare staff

This paper presents a mobile app called “Careggi Smart Hospital” which has been developed for the Careggi Polyclinic in Florence. The application is designed for Android smartphones and tablets and it is freely downloadable from the Google Play Store. It provides various useful tools to the hospital's users such as personnel and structures finding, way-finding and the possibility to access personal medical records collected on regional electronic health record

Adsorption of Rhodamine B from Wastewater on the Arsenic- Hyperaccumulator Pteris Vittata Waste Roots

The Pteris vittata fern, which is a perennial plant known for hyper-accumulating Arsenic, can be grown in hydroponic cultures and is often used for phytoremediation of contaminated water. To reduce the cost of disposing As-contaminated biomass, this study examined the potential of using waste roots from Pteris vittata as a new and inexpensive bio-adsorbent for removing Rhodamine B (RB) dye, which is commonly used in industrial applications. Batch tests were performed at 25°C in order to observe both the rate and the equilibrium conditions of the system. The isotherm showed a typical Langmuir behavior exhibiting a maximum adsorption capacity of 42.7 mg/g. Kinetics tests were conducted at different solid-liquid ratios and fitted by a mathematical model. The maximum likelihood method was employed to estimate the effective diffusivity of RB in the solid which resulted 4.48 10-9 cm2/min. This study lays the groundwork for future investigations into the use of this material in continuous systems to determine its feasibility for application in industrial apparatus

Fractal dimension of cerebral white matter : A consistent feature for prediction of the cognitive performance in patients with small vessel disease and mild cognitive impairment

Patients with cerebral small vessel disease (SVD) frequently show decline in cognitive performance. However, neuroimaging in SVD patients discloses a wide range of brain lesions and alterations so that it is often difficult to understand which of these changes are the most relevant for cognitive decline. It has also become evident that visually-rated alterations do not fully explain the neuroimaging correlates of cognitive decline in SVD. Fractal dimension (FD), a unitless feature of structural complexity that can be computed from high-resolution T1-weighted images, has been recently applied to the neuroimaging evaluation of the human brain. Indeed, white matter (WM) and cortical gray matter (GM) exhibit an inherent structural complexity that can be measured through the FD. In our study, we included 64 patients (mean age \ub1 standard deviation, 74.6 \ub1 6.9, education 7.9 \ub1 4.2 years, 53% males) with SVD and mild cognitive impairment (MCI), and a control group of 24 healthy subjects (mean age \ub1 standard deviation, 72.3 \ub1 4.4 years, 50% males). With the aim of assessing whether the FD values of cerebral WM (WM FD) and cortical GM (GM FD) could be valuable structural predictors of cognitive performance in patients with SVD and MCI, we employed a machine learning strategy based on LASSO (least absolute shrinkage and selection operator) regression applied on a set of standard and advanced neuroimaging features in a nested cross-validation (CV) loop. This approach was aimed at 1) choosing the best predictive models, able to reliably predict the individual neuropsychological scores sensitive to attention and executive dysfunctions (prominent features of subcortical vascular cognitive impairment) and 2) identifying a features ranking according to their importance in the model through the assessment of the out-of-sample error. For each neuropsychological test, using 1000 repetitions of LASSO regression and 5000 random permutations, we found that the statistically significant models were those for the Montreal Cognitive Assessment scores (p-value =.039), Symbol Digit Modalities Test scores (p-value =.039), and Trail Making Test Part A scores (p-value =.025). Significant prediction of these scores was obtained using different sets of neuroimaging features in which the WM FD was the most frequently selected feature. In conclusion, we showed that a machine learning approach could be useful in SVD research field using standard and advanced neuroimaging features. Our study results raise the possibility that FD may represent a consistent feature in predicting cognitive decline in SVD that can complement standard imaging

Tissue-specific patterns of allelically-skewed DNA methylation

While DNA methylation is usually thought to be symmetrical across both alleles, there are some notable exceptions. Genomic imprinting and X chromosome inactivation are two well-studied sources of allele-specific methylation (ASM), but recent research has indicated a more complex pattern in which genotypic variation can be associated with allelically-skewed DNA methylation in cis. Given the known heterogeneity of DNA methylation across tissues and cell types we explored inter- and intra-individual variation in ASM across several regions of the human brain and whole blood from multiple individuals. Consistent with previous studies, we find widespread ASM with >4% of the ~220,000 loci interrogated showing evidence of allelically-skewed DNA methylation. We identify ASM flanking known imprinted regions, and show that ASM sites are enriched in DNase I hypersensitivity sites and often located in an extended genomic context of intermediate DNA methylation. We also detect examples of genotype-driven ASM, some of which are also tissue-specific. These findings contribute to our understanding about the nature of differential DNA methylation across tissues and have important implications for genetic studies of complex disease. As a resource to the community, ASM patterns across each of the tissues studied are available in a searchable online database: http://epigenetics.essex.ac.uk/ASMBrainBlood

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS