ion; further research to evaluate its influence on tissue regeneration is necessary due to low methodological quality of the animal studies.

High-quality in vitro evidence suggests that EDTA-treated dentine positively influences TGF-β release, cell migration, attachment and differentiation; further research to evaluate its influence on tissue regeneration is necessary due to low methodological quality of the animal studies.Fluent conversation requires temporal organization between conversational exchanges. By performing a systematic review and Bayesian multi-level meta-analysis, we map the trajectory of infants’ turn-taking abilities over the course of early development (0 to 70 months). We synthesize the evidence from 26 studies (78 estimates from 429 unique infants, of which at least 152 are female) reporting response latencies in infant-adult dyadic interactions. The data were collected between 1975 and 2019, exclusively in North America and Europe. Infants took on average circa 1 s to respond, and the evidence of changes in response over time was inconclusive. Infants’ response latencies are related to those of their adult conversational partners an increase of 1 s in adult response latency (e.g., 400 to 1400 ms) would be related to an increase of over 1 s in infant response latency (from 600 to 1857 ms). These results highlight the dynamic reciprocity involved in the temporal organization of turn-taking. Based on these results, we provide recommendations for future avenues of enquiry studies should analyze how turn-by-turn exchanges develop on a longitudinal timescale, with rich assessment of infants’ linguistic and social development.

Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination.

A three dimensional algorithm that combined multi-instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID-19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID-19 and 183 CAP cases) and relatively small datasets (17 COVID-19MTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training.

The 3DMTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training.

This study was undertaken to calculate epilepsy-related direct, indirect, and total costs in adult patients with active epilepsy (ongoing unprovoked seizures) in Germany and to analyze cost components and dynamics compared to previous studies from 2003, 2008, and 2013. This analysis was part of the Epi2020study.

Direct and indirect costs related to epilepsy were calculated with a multicenter survey using an established and validated questionnaire with a bottom-up design and human capital approach over a 3-month period in late 2020. Epilepsy-specific costs in the German health care sector from 2003, 2008, and 2013 were corrected for inflation to allow for a valid comparison.

Data on the disease-specific costs for 253 patients in 2020 were analyzed. The mean total costs were calculated at €5551 (±€5805, median = €2611, range = €274-€21667) per 3months, comprising mean direct costs of €1861 (±€1905, median = €1276, range = €327-€13158) and mean indirect costs of €3690 (±€5298, median = €0, range = €0-€1192The present study shows that disease-related costs in adult patients with active epilepsy increased from 2013 to 2020. As direct costs have remained constant, this increase is attributable to an increase in indirect costs. These findings highlight the impact of productivity loss caused by early retirement, unemployment, working time reduction, and seizure-related days off.

Current concepts highlight the neurological and psychological heterogeneity of functional/dissociative seizures (FDS). However, it remains uncertain whether it is possible to distinguish between a limited number of subtypes of FDS disorders. We aimed to identify profiles of distinct FDS subtypes by cluster analysis of a multidimensional dataset without any a priori hypothesis.

We conducted an exploratory, prospective multicenter study of 169 patients with FDS. We collected biographical, trauma (childhood and adulthood traumatic experiences), semiological (seizure characteristics), and psychopathological data (psychiatric comorbidities, dissociation, and alexithymia) through psychiatric interviews and standardized scales. Clusters were identified by the Partitioning Around Medoids method. The similarity of patients was computed using Gower distance. The clusters were compared using analysis of variance, chi-squared, or Fisher exact tests.

Three patient clusters were identified in this exploratory, hypothypothesis, the nature of the trauma history emerged as the most important differentiator between three common FDS disorder subtypes. This subdifferentiation of FDS disorders may facilitate the development of more specific therapeutic programs for each patient profile.

We present a framework for robust automated treatment planning using machine learning, comprising scenario-specific dose prediction and robust dosemimicking.

The scenario dose prediction pipeline is divided into the prediction of nominal dose from input image and the prediction of scenario dose from nominal dose, each using a deep learning model with U-net architecture. By using a specially developed dose-volume histogram-based loss function, the predicted scenario doses are ensured sufficient target coverage despite the possibility of the training data being non-robust. Deliverable plans may then be created by solving a robust dose mimicking problem with the predictions as scenario-specific reference doses.

Numerical experiments are performed using a data set of 52 intensity-modulated proton therapy plans for prostate patients. We show that the predicted scenario doses resemble their respective ground truth well, in particular while having target coverage comparable to that of the nominal scenario. The deliverable plans produced by the subsequent robust dose mimicking were showed to be robust against the same scenario set considered forprediction.

We demonstrate the feasibility and merits of the proposed methodology for incorporating robustness into automated treatment planningalgorithms.

We demonstrate the feasibility and merits of the proposed methodology for incorporating robustness into automated treatment planning algorithms.

To clarify whether treatment with systemic corticosteroids at a certain dose was associated with better outcomes in patients with epiglottitis requiring airway management (tracheotomy or airway intubation).

This was a retrospective cohort study on patients hospitalized for epiglottitis requiring airway management from a nationwide inpatient database (between July 2010 and March 2019). Patients treated with systemic corticosteroids equivalent to methylprednisolone ≥40 mg/d within 2 days of admission and patients who were not treated with corticosteroids within 2 days of admission were compared after inverse probability of treatment weighting using covariate balancing propensity score. The primary outcome was all-cause 30-day in-hospital mortality, and secondary outcomes included all-cause 7-day in-hospital mortality, length of hospital stay, and total medical cost.

There were 1986 and 1771 patients in the corticosteroid and control groups, respectively. A total of 72 of 3757 (1.9%) patients died within 30 days of admission, including 17 of 1986 (0.9%) patients in the corticosteroid group and 55 of 1771 (3.1%) in the control group (weighted odds ratio, 0.28 [95% confidence interval, 0.11-0.70]; weighted risk difference, -2.2% [-3.2% to -1.3%]). Treatment with corticosteroids was associated with lower total medical costs (weighted median, $6,187 vs. $6,587; weighted difference, $-1,123 [-2,238 to -8]) but not all-cause 7-day in-hospital mortality (weighted odds ratio, 0.63 [0.22-1.82]; weighted risk difference, -0.3% [-0.9 to 0.2]) and length of hospital stay (weighted median, 13 vs. 13 days; weighted difference, -0.2 days [-2.1 to 1.8]).

Systemic corticosteroids may be beneficial to patients with epiglottitis requiring airway management.

3 Laryngoscope, 2022.

3 Laryngoscope, 2022.

The management of patients with atrial fibrillation (AF) and malignancy is challenging given the paucity of evidence supporting their appropriate clinical management.

To evaluate the outcomes of patients with active or prior malignancy in a contemporary cohort of European AF patients.

Patients enrolled in the EURObservational Research Programme in AF General Long-Term Registry were categorized into 3 categories No Malignancy (NoMal), Prior Malignancy (PriorMal) and Active Malignancy (ActiveMal). The primary outcomes were all-cause death and the composite outcome MACE.

A total of 10 383 patients were analysed. Of these, 9597 (92.4%) were NoMal patients, 577 (5.6%) PriorMal and 209 (2%) ActiveMal. Lack of any antithrombotic treatment was more prevalent in ActiveMal patients (12.4%) as compared to other groups (5.0% vs 6.3% for PriorMal and NoMal, p<.001). After a median follow-up of 730days, there were 982 (9.5%) deaths and 950 (9.7%) MACE events. ActiveMal was independently associated with a higher risk for all-cause death (HR 2.90, 95% CI 2.23-3.76) and MACE (HR 1.54, 95% CI 1.03-2.31), as well as any haemorrhagic events and major bleeding (OR 2.42, 95% CI 1.49-3.91 and OR 4.18, 95% CI 2.49-7.01, respectively). Use of oral anticoagulants was not significantly associated with a higher risk for all-cause death or bleeding in ActiveMal patients.

In a large contemporary cohort of AF patients, active malignancy was independently associated with all-cause death, MACE and haemorrhagic events. Use of anticoagulants was not associated with a higher risk of all-cause death in patients with active malignancies.

In a large contemporary cohort of AF patients, active malignancy was independently associated with all-cause death, MACE and haemorrhagic events. Use of anticoagulants was not associated with a higher risk of all-cause death in patients with active malignancies.