Davenportschultz2354
Posterolateral lumbar fusion (PLF) is a commonly performed surgical procedure for the treatment of pathological conditions of the lumbosacral spine. In the present study, we evaluated an autologous bone graft substitute (ABGS) containing rhBMP6 in autologous blood coagulum (ABC) and synthetic ceramics used as compression resistant matrix (CRM) in the rabbit PLF model. In the pilot PLF rabbit experiment, we tested four different CRMs (BCP 500-1700 μm, BCP 1700-2500 μm and two different TCP in the form of slabs) which were selected based on achieving uniform ABC distribution. Next, ABGS implants composed of 2.5 mL ABC with 0.5 g ceramic particles (TCP or BCP (TCP/HA 80/20) of particle size 500-1700 μm) and 125 μg rhBMP6 (added to blood or lyophilized on ceramics) were placed bilaterally between transverse processes of the lumbar vertebrae (L5-L6) following exposition and decortication in 12 New Zealand White Rabbits observed for 7 weeks following surgery. Spinal fusion outcome was analysed by μCT, palpatory segmental mobility testing and selected specimens were either tested biomechanically (three-point bending test) and/or processed histologically. The total fusion success rate was 90.9% by both μCT analyses and by palpatory segmental mobility testing. The volume of newly formed bone between experimental groups with TCP or BCP ceramics and the different method of rhBMP6 application was comparable. The newly formed bone and ceramic particles integrated with the transverse processes on histological sections resulting in superior biomechanical properties. The results were retrospectively found superior to allograft devitalized mineralized bone as a CRM as reported previously in rabbit PLF. Overall, this novel ABGS containing rhBMP6, ABC and the specific 500-1700 μm synthetic ceramic particles supported new bone formation for the first time and successfully promoted posterolateral lumbar fusion in rabbits.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disability with altered connectivity in brain networks.
In this study, brain connections in Resting-state functional Magnetic Resonance Imaging (Rs-fMRI) of ASD and Typical Developing (TD) are analyzed by partial and full correlation methods such as Gaussian Graphical Least Absolute Shrinkage and Selection Operator (GLASSO), Max-Det Matrix Completion (MDMC), and Pearson Correlation Co-Efficient (PCCE). We investigated Functional Connectivity (FC) of ASD and TD brain from 238 functionally defined regions of interest. Furthermore, we constructed a series of feature sets by applying conditional random forests and conditional permutation importance. We built classifier models by Random Forest (RF), Oblique RF (ORF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) for each feature set. FC features are ranked based on p-value and we analyzed the top 20 FC features.
We achieved a single-trial test accuracy of 72.5 %, though MDMC-SVM and PCCE-CNN pipelines. Further, PCCE-CNN pipeline gives better average test accuracy (70.31 %) and area under the curve (0.73) compared to other pipelines. We found that top-20 PCCE based FC features are from networks such as Dorsal Attention (DA), Cingulo-Opercular Task Control (COTC), somatosensory motor hand and subcortical. In addition, among top 20 PCCE features, many FC links are found between COTC and DA (4 connections) which helped to discriminate the ASD and TD.
The generalized classifier models built in our study for highly heterogeneous participants perform better than previous studies with similar data sets and diagnostic groups.
The generalized classifier models built in our study for highly heterogeneous participants perform better than previous studies with similar data sets and diagnostic groups.
The processing of brain signals for Motor imagery (MI) classification to have better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional methods like Artificial neural network (ANN), Linear discernment analysis (LDA), K-Nearest Neighbor (KNN), Support vector machine (SVM), etc. have made significant progress in terms of classification accuracy, deep transfer learning-based systems have shown the potential to outperform them. BCI can play a vital role in enabling communication with the external world for persons with motor disabilities.
Deep learning has been a success in many fields. However, for Electroencephalogram (EEG) signals, relatively minimal work has been carried out using deep learning. This paper proposes a combination of Continuous Wavelet Transform (CWT) along with deep learning-based transfer learning to solve the problem. CWT transforms one dimensional EEG signals into two-dimensional time-frequency-amplitude representation enabling us to exploit available deep networks through transfer learning.
The effectiveness of the proposed approach is evaluated in this study using an openly available BCI competition data-set. The results of the approach have been compared to earlier works on the same dataset, and a promising validation accuracy of 95.71% is achieved in our investigation.
Our approach has shown significant improvement over other studies, which is 5.71% improvement over earlier reported algorithm (Tabar and Halici, 2017) using the same dataset. Results show the validity of the proposed Deep Transfer-Learning based technique as a state of the art technique for MI classification in BCI.
Our approach has shown significant improvement over other studies, which is 5.71% improvement over earlier reported algorithm (Tabar and Halici, 2017) using the same dataset. Results show the validity of the proposed Deep Transfer-Learning based technique as a state of the art technique for MI classification in BCI.It is thought that the hippocampal neurogenesis is an important mediator of the antidepressant effect of electroconvulsive therapy (ECT). However, most previous studies failed to demonstrate the relationship between the increase in the hippocampal volume and the antidepressant effect. We reinvestigated this relationship by looking at distinct hippocampal subregions and applying repeated measures correlation. Using a 3 Tesla MRI-scanner, we scanned 22 severely depressed in-patients at three time points before the ECT series, after the series, and at six-month follow-up. The depression severity was assessed by the 17-item Hamilton Rating Scale for Depression (HAMD-17). The hippocampus was segmented into subregions using Freesurfer software. The dentate gyrus (DG) was the primary region of interest (ROI), due to the role of this region in neurogenesis. The other major hippocampal subregions were the secondary ROIs (n = 20). The general linear mixed model and the repeated measures correlation were used for statistical analyses. Immediately after the ECT series, a significant volume increase was present in the right DG (Cohen's d = 1.7) and the left DG (Cohen's d = 1.5), as well as 15 out of 20 secondary ROIs. The clinical improvement, i.e., the decrease in HAMD-17 score, was correlated to the increase in the right DG volume (rrm = -0.77, df = 20, p less then .001), and the left DG volume (rrm = -0.75, df = 20, p less then .001). Similar correlations were observed in 14 out of 20 secondary ROIs. LDK378 ic50 Thus, ECT induces an increase not only in the volume of the DG, but also in the volume of other major hippocampal subregions. The volumetric increases may reflect a neurobiological process that may be related to the ECT's antidepressant effect. Further investigation of the relationship between hippocampal subregions and the antidepressant effect is warranted. A statistical approach taking the repeated measurements into account should be preferred in the analyses.In December 2019, the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) infection was reported. In only few weeks it has caused a global pandemic, with mortality reaching 3.4%, mostly due to a severe pneumonia. However, the impact of SARS-CoV-2 virus on the central nervous system (CNS) and mental health outcomes remains unclear. Previous studies have demonstrated the presence of other types of coronaviruses in the brain, especially in the brainstem. There is evidence that the novel coronavirus can penetrate CNS through the olfactory or circulatory route as well as it can have an indirect impact on the brain by causing cytokine storm. There are also first reports of neurological signs in patients infected by the SARS-Cov-2. They show that COVID-19 patients have neurologic manifestations like acute cerebrovascular disease, conscious disturbance, taste and olfactory disturbances. In addition, there are studies showing that certain psychopathological symptoms might appear in infected patients, including those related to mood and psychotic disorders as well as post-traumatic stress disorder. link2 Accumulating evidence also indicates that the pandemic might have a great impact on mental health from the global perspective, with medical workers being particularly vulnerable. In this article, we provide a review of studies investigating the impact of the SARS-CoV-2 on the CNS and mental health outcomes. We describe neurobiology of the virus, highlighting the relevance to mental disorders. Furthermore, this article summarizes the impact of the SARS-CoV-2 from the public health perspective. link3 Finally, we present a critical appraisal of evidence and indicate future directions for studies in this field.
Message framing can be leveraged to motivate adult smokers to quit, but its value for parents in pediatric settings is unknown. Understanding parents' preferences for smoking cessation messages may help clinicians tailor interventions to increase quitting.
We conducted a discrete choice experiment in which parent smokers of pediatric patients rated the relative importance of 26 messages designed to increase smoking cessation treatment. Messages varied on who the message featured (child, parent, and family), whether the message was gain- or loss-framed (emphasizing benefits of engaging or costs of failing to engage in treatment), and the specific outcome included (eg, general health, cancer, respiratory illnesses, and financial impact). Participants included 180 parent smokers at 4 pediatric primary care sites. We used latent class analysis of message ratings to identify groups of parents with similar preferences. Multinomial logistic regression described child and parent characteristics associated with group membership.
We identified 3 groups of parents with similar preferences for messages Group 1 prioritized the impact of smoking on the child (n = 92, 51%), Group 2 favored gain-framed messages (n = 63, 35%), and Group 3 preferred messages emphasizing the financial impact of smoking (n = 25, 14%). Parents in Group 2 were more likely to have limited health literacy and have a child over age 6 and with asthma, compared to Group 1.
We identified 3 groups of parent smokers with different message preferences. This work may inform testing of tailored smoking cessation messages to different parent groups, a form of behavioral phenotyping supporting motivational precision medicine.
We identified 3 groups of parent smokers with different message preferences. This work may inform testing of tailored smoking cessation messages to different parent groups, a form of behavioral phenotyping supporting motivational precision medicine.
