Refine
Has Fulltext
- yes (4)
Is part of the Bibliography
- yes (4)
Document Type
- Journal article (4)
Language
- English (4)
Keywords
- CSF (1)
- Dark matter (1)
- Distress (1)
- High energy physics (1)
- High-energy jets (1)
- MRI criteria (1)
- MS (1)
- Medical students (1)
- Mindfulness (1)
- OCB (1)
Institute
High-energy jets recoiling against missing transverse energy (MET) are powerful probes of dark matter at the LHC. Searches based on large MET signatures require a precise control of the \({Z(ν\overline{ν})}+\) jet background in the signal region. This can be achieved by taking accurate data in control regions dominated by \(Z(ℓ^+ℓ^−)+\) jet, \(W(ℓν)+\) jet and \(γ+\) jet production, and extrapolating to the \({Z(ν\overline{ν})}+\) jet background by means of precise theoretical predictions. In this context, recent advances in perturbative calculations open the door to significant sensitivity improvements in dark matter searches. In this spirit, we present a combination of state-of-the-art calculations for all relevant \(V+\) jets processes, including throughout NNLO QCD corrections and NLO electroweak corrections supplemented by Sudakov logarithms at two loops. Predictions at parton level are provided together with detailed recommendations for their usage in experimental analyses based on the reweighting of Monte Carlo samples. Particular attention is devoted to the estimate of theoretical uncertainties in the framework of dark matter searches, where subtle aspects such as correlations across different \(V+\) jet processes play a key role. The anticipated theoretical uncertainty in the \({Z(ν\overline{ν})}+\) jet background is at the few percent level up to the TeV range.
Background
High prevalence rates of psychological distress in medical training and later professional life indicate a need for prevention. Different types of intervention were shown to have good effects, but little is known about the relative efficacy of different types of stress management interventions, and methodological limitations have been reported. In order to overcome some of these limitations, the present study aimed at evaluating the effect of a specifically developed mindfulness-based stress prevention training for medical students (MediMind) on measures of distress, coping and psychological morbidity.
Methods
We report on a prospective randomized controlled trial with three study conditions: experimental treatment (MediMind), standard treatment (Autogenic Training) and a control group without treatment. The sample consisted of medical or dental students in the second or eighth semester. They completed self-report questionnaires at baseline, after the training and at one year follow-up. Distress (Trier Inventory for the Assessment of Chronic Stress, TICS) was assessed as the primary outcome and coping (Brief COPE) as a co-primary outcome. Effects on the psychological morbidity (Brief Symptom Inventory, BSI) as a secondary outcome were expected one year after the trainings.
Results
Initially, N = 183 students were randomly allocated to the study groups. At one year follow-up N = 80 could be included into the per-protocol analysis: MediMind (n =31), Autogenic Training (n = 32) and control group (n = 17). A selective drop-out for students who suffered more often from psychological symptoms was detected (p = .020). MANCOVA’s on TICS and Brief COPE revealed no significant interaction effects. On the BSI, a significant overall interaction effect became apparent (p = .002, η2partial = .382), but post hoc analyses were not significant. Means of the Global Severity Index (BSI) indicated that MediMind may contribute to a decrease in psychological morbidity.
Conclusion
Due to the high and selective dropout rates, the results cannot be generalized and further research is necessary. Since the participation rate of the trainings was high, a need for further prevention programs is indicated. The study gives important suggestions on further implementation and evaluation of stress prevention in medical schools.
Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (n\(_{total}\) = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.
The majority of patients presenting with a first clinical symptom suggestive of multiple sclerosis (MS) do not fulfill the MRI criteria for dissemination in space and time according to the 2010 revision of the McDonald diagnostic criteria for MS and are thus classified as clinically isolated syndrome (CIS). To re-evaluate the utility of cerebrospinal fluid (CSF) analysis in the context of the revised McDonald criteria from 2010, we conducted a retrospective multicenter study aimed at determining the prevalence and predictive value of oligoclonal IgG bands (OCBs) in patients with CIS. Patients were recruited from ten specialized MS centers in Germany and Austria. We collected data from 406 patients; at disease onset, 44/406 (11 %) fulfilled the McDonald 2010 criteria for MS. Intrathecal IgG OCBs were detected in 310/362 (86 %) of CIS patients. Those patients were twice as likely to convert to MS according to McDonald 2010 criteria as OCB-negative individuals (hazard ratio = 2.1, p = 0.0014) and in a shorter time period of 25 months (95 % CI 21-34) compared to 47 months in OCB-negative individuals (95 % CI 36-85). In patients without brain lesions at first attack and presence of intrathecal OCBs (30/44), conversion rate to MS was 60 % (18/30), whereas it was only 21 % (3/14) in those without OCBs. Our data confirm that in patients with CIS the risk of conversion to MS substantially increases if OCBs are present at onset. CSF analysis definitely helps to evaluate the prognosis in patients who do not have MS according to the revised McDonald criteria.