Bamberger Federführer. Die besten Texte aus drei Jahren Literaturwettbewerb an der Universität Bamberg (2009-2011)

Im Kreise des Redaktions-Teams der studentischen Hochschulgruppe Feki.de entstand im November 2008 die Idee, einen Literaturwettbewerb an der Universität Bamberg zu etablieren. Das Projekt sollte es Studierenden aller Fachrichtungen ermöglichen, literarisch tätig zu werden und sich mit ihren Texten dem Urteil einer Jury bzw. der Leser zu stellen. Insgesamt drei Mal wurde der Wettbewerb durchgeführt: 2009 wurde die beste Kurzgeschichte zum Thema „Um 20 Uhr am Gabelmoo“ gesucht, im Jahr darauf lautete das Thema schlicht „fertig. Der letzte Wettbewerb stand schließlich unter dem Motto „Heimat“. Die Auswahl der Siegertexte übernahm eine Jury, bestehend aus Vertretern verschiedener Hochschulgruppen – Feki.de, Rezensöhnchen und Ottfried –, Prof. Dr. Andrea Bartl, Inhaberin der Professur für Neuere deutsche Literaturwissenschaft, dem Autor und Kritiker Rolf Bernhard Essig sowie wechselnd den Autoren Nora Gomringer, Kurt Kreiler und Nefvel Cumart. Daneben konnten die Feki.de-Leser über den jeweiligen Publikumspreisträger abstimmen. Neben den Studierenden der Universität Bamberg waren ab dem zweiten Jahr auch Teilnehmer der Schreibwerkstatt der JVA Ebrach eingeladen, ihre Texte einzu­senden. Im Vergleich mit den studentischen Texten boten die Beiträge der Gefangenen einen interessanten Perspektivwechsel für alle Teil­nehmer und Leser. Insgesamt 24 Kurzgeschichten aus den Jahren 2009 bis 2011 haben wir in diesem Band zusammengestellt. Neben den jeweiligen Siegertexten finden sich in der Anthologie auch die Beiträge der JVA-Bewohner zum Thema „fertig“, sowie ausgewählte Texte Studierender, die die Podestplätze nur knapp verfehlten

Clinical determinants and neural correlates of presbyphagia in community-dwelling older adults

Background“Presbyphagia” refers to characteristic age-related changes in the complex neuromuscular swallowing mechanism. It has been hypothesized that cumulative impairments in multiple domains affect functional reserve of swallowing with age, but the multifactorial etiology and postulated compensatory strategies of the brain are incompletely understood. This study investigates presbyphagia and its neural correlates, focusing on the clinical determinants associated with adaptive neuroplasticity.Materials and methods64 subjects over 70 years of age free of typical diseases explaining dysphagia received comprehensive workup including flexible endoscopic evaluation of swallowing (FEES), magnetoencephalography (MEG) during swallowing and pharyngeal stimulation, volumetry of swallowing muscles, laboratory analyzes, and assessment of hand-grip-strength, nutritional status, frailty, olfaction, cognition and mental health. Neural MEG activation was compared between participants with and without presbyphagia in FEES, and associated clinical influencing factors were analyzed. Presbyphagia was defined as the presence of oropharyngeal swallowing alterations e.g., penetration, aspiration, pharyngeal residue pooling or premature bolus spillage into the piriform sinus and/or laryngeal vestibule.Results32 of 64 participants showed swallowing alterations, mainly characterized by pharyngeal residue, whereas the airway was rarely compromised. In the MEG analysis, participants with presbyphagia activated an increased cortical sensorimotor network during swallowing. As major clinical determinant, participants with swallowing alterations exhibited reduced pharyngeal sensation. Presbyphagia was an independent predictor of a reduced nutritional status in a linear regression model.ConclusionsSwallowing alterations frequently occur in otherwise healthy older adults and are associated with decreased nutritional status. Increased sensorimotor cortical activation may constitute a compensation attempt to uphold swallowing function due to sensory decline. Further studies are needed to clarify whether the swallowing alterations observed can be considered physiological per se or whether the concept of presbyphagia may need to be extended to a theory with a continuous transition between presbyphagia and dysphagia

More Space, Less Noise—New-generation Low-Field Magnetic Resonance Imaging Systems Can Improve Patient Comfort: A Prospective 0.55T–1.5T-Scanner Comparison

Objectives: The objectives of this study were to assess patient comfort when imaged on a newly introduced 0.55T low-field magnetic resonance (MR) scanner system with a wider bore opening compared to a conventional 1.5T MR scanner system. Materials and Methods: In this prospective study, fifty patients (mean age: 66.2 ± 17.0 years, 22 females, 28 males) underwent subsequent magnetic resonance imaging (MRI) examinations with matched imaging protocols at 0.55T (MAGNETOM FreeMax, Siemens Healthineers; Erlangen, Germany) and 1.5T (MAGNETOM Avanto Fit, Siemens Healthineers; Erlangen, Germany) on the same day. MRI performed between 05/2021 and 07/2021 was included for analysis. The 0.55T MRI system had a bore opening of 80 cm, while the bore diameter of the 1.5T scanner system was 60 cm. Four patient groups were defined by imaged body regions: (1) cranial or cervical spine MRI using a head/neck coil (n = 27), (2) lumbar or thoracic spine MRI using only the in-table spine coils (n = 10), (3) hip MRI using a large flex coil (n = 8) and (4) upper- or lower-extremity MRI using small flex coils (n = 5). Following the MRI examinations, patients evaluated (1) sense of space, (2) noise level, (3) comfort, (4) coil comfort and (5) overall examination impression on a 5-point Likert-scale (range: 1= “much worse” to 5 = “much better”) using a questionnaire. Maximum noise levels of all performed imaging studies were measured in decibels (dB) by a sound level meter placed in the bore center. Results: Sense of space was perceived to be “better” or “much better” by 84% of patients for imaging examinations performed on the 0.55T MRI scanner system (mean score: 4.34 ± 0.75). Additionally, 84% of patients rated noise levels as “better” or “much better” when imaged on the low-field scanner system (mean score: 3.90 ± 0.61). Overall sensation during the imaging examination at 0.55T was rated as “better” or “much better” by 78% of patients (mean score: 3.96 ± 0.70). Quantitative assessment showed significantly reduced maximum noise levels for all 0.55T MRI studies, regardless of body region compared to 1.5T, i.e., brain MRI (83.8 ± 3.6 dB vs. 89.3 ± 5.4 dB; p = 0.04), spine MRI (83.7 ± 3.7 dB vs. 89.4 ± 2.6 dB; p = 0.004) and hip MRI (86.3 ± 5.0 dB vs. 89.1 ± 1.4 dB; p = 0.04). Conclusions: Patients perceived 0.55T new-generation low-field MRI to be more comfortable than conventional 1.5T MRI, given its larger bore opening and reduced noise levels during image acquisition. Therefore, new concepts regarding bore design and noise level reduction of MR scanner systems may help to reduce patient anxiety and improve well-being when undergoing MR imaging

Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage

Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health–economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers. Material and Methods: A total of 7421 computed tomography (CT) datasets between January 2007–July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and p = 0.705 and p = 0.423). Conclusions: In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies

Prospective Assessment of Cerebral Microbleeds with Low-Field Magnetic Resonance Imaging (0.55 Tesla MRI)

Purpose: Accurate detection of cerebral microbleeds (CMBs) on susceptibility-weighted (SWI) magnetic resonance imaging (MRI) is crucial for the characterization of many neurological diseases. Low-field MRI offers greater access at lower costs and lower infrastructural requirements, but also reduced susceptibility artifacts. We therefore evaluated the diagnostic performance for the detection of CMBs of a whole-body low-field MRI in a prospective cohort of suspected stroke patients compared to an established 1.5 T MRI. Methods: A prospective scanner comparison was performed including 27 patients, of whom 3 patients were excluded because the time interval was >1 h between acquisition of the 1.5 T and 0.55 T MRI. All SWI sequences were assessed for the presence, number, and localization of CMBs by two neuroradiologists and additionally underwent a Likert rating with respect to image impression, resolution, noise, contrast, and diagnostic quality. Results: A total of 24 patients with a mean age of 74 years were included (11 female). Both readers detected the same number and localization of microbleeds in all 24 datasets (sensitivity and specificity 100%; interreader reliability ϰ = 1), with CMBs only being observed in 12 patients. Likert ratings of the sequences at both field strengths regarding overall image quality and diagnostic quality did not reveal significant differences between the 0.55 T and 1.5 T sequences (p = 0.942; p = 0.672). For resolution and contrast, the 0.55 T sequences were even significantly superior (p < 0.0001; p < 0.0003), whereas the 1.5 T sequences were significantly superior (p < 0.0001) regarding noise. Conclusion: Low-field MRI at 0.55 T may have similar accuracy as 1.5 T scanners for the detection of microbleeds and thus may have great potential as a resource-efficient alternative in the near future

Potential of Stroke Imaging Using a New Prototype of Low-Field MRI: A Prospective Direct 0.55 T/1.5 T Scanner Comparison

Objectives: Ischemic stroke is a leading cause of mortality and acquired disability worldwide and thus plays an enormous health-economic role. Imaging of choice is computed-tomographic (CT) or magnetic resonance imaging (MRI), especially diffusion-weighted (DW) sequences. However, MR imaging is associated with high costs and therefore has a limited availability leading to low-field-MRI techniques increasingly coming into focus. Thus, the aim of our study was to assess the potential of stroke imaging with low-field MRI. Material and Methods: A scanner comparison was performed including 27 patients (17 stroke cohort, 10 control group). For each patient, a brain scan was performed first with a 1.5T scanner and afterwards with a 0.55T scanner. Scan protocols were as identical as possible and optimized. Data analysis was performed in three steps: All DWI/ADC (apparent diffusion coefficient) and FLAIR (fluid attenuated inversion recovery) sequences underwent Likert rating with respect to image impression, resolution, noise, contrast, and diagnostic quality and were evaluated by two radiologists regarding number and localization of DWI and FLAIR lesions in a blinded fashion. Then segmentation of lesion volumes was performed by two other radiologists on DWI/ADC and FLAIR. Results: DWI/ADC lesions could be diagnosed with the same reliability by the most experienced reader in the 0.55T and 1.5T sequences (specificity 100% and sensitivity 92.9%, respectively). False positive findings did not occur. Detection of number/location of FLAIR lesions was mostly equivalent between 0.55T and 1.5T sequences. No significant difference (p = 0.789–0.104) for FLAIR resolution and contrast was observed regarding Likert scaling. For DWI/ADC noise, the 0.55T sequences were significantly superior (p < 0.026). Otherwise, the 1.5T sequences were significantly superior (p < 0.029). There was no significant difference in infarct volume and volume of infarct demarcation between the 0.55T and 1.5T sequences, when detectable. Conclusions: Low-field MRI stroke imaging at 0.55T may not be inferior to scanners with higher field strengths and thus has great potential as a low-cost alternative in future stroke diagnostics. However, there are limitations in the detection of very small infarcts. Further technical developments with follow-up studies must show whether this problem can be solved

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value <0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity, P value <0.05). Machine learning-based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC