To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
NeuroStar transcranial magnetic stimulation (TMS) is an effective acute treatment for patients with major depressive disorder (MDD). In order to further understand use of the NeuroStar in a clinical setting, Neuronetics has established a patient treatment and outcomes registry to collect and analyze utilization information on patients receiving treatment with the NeuroStar.
Individual NeuroStar providers are invited to participate in the registry and agree to provide their de-identified patient treatment data. The NeuroStar has an integrated electronic data management system (TrakStar) which allows for the data collection to be automated. The data collected for the registry include Demographic Elements (age, gender), Treatment Parameters, and Clinical Ratings. Clinical assessments are: Clinician Global Impression - Severity of Illness (CGI-S) and thePatient Health Questionnaire 9-item (PHQ-9). De-identified patient data is uploaded to Registry server; an independent statistical service then creates final data reports.
Over 500 patients have entered the NeuroStar Outcomes Registry since Sept 2016. Mean patient age: 48.0 (SD±16.0); 64% Female. Baseline PHQ-9, mean 18.8 (SD±5.0.) Response/Remission Rate, PHQ-9: 61%/33% CGI-S: 78%/59%.
For the initial 500 patients in the Outcomes Registry, approximately 2/3 patients achieve respond and 1/3 patients achieve remission with an acute course of NeuroStar. These treatment outcomes consistent with NeuroStar open-label study data (Carpenter, 2012). The TrakStar data management system makes large scale data collection feasible. The NeuroStarOutcomes Registry is ongoing, and expected to reach 6000 outpatients from more than 47 clinical sites in 36 months.
Bacterial cultures exposed to iron-doped apatite nanoparticles (IDANPs) prior to the introduction of antagonistic viruses experience up to 2.3 times the bacterial destruction observed in control cultures. Maximum antibacterial activity of these bacteria-specific viruses, or phage, occurs after bacterial cultures have been exposed to IDANPs for 1 hr prior to phage introduction, demonstrating that IDANP-assisted phage therapy would not be straight forward, but would instead require controlled time release of IDANPs and phage. These findings motivated the design of an electrospun nanofiber mesh treatment delivery system that allows burst release of IDANPs, followed by slow, consistent release of phage for treatment of topical bacterial infections. IDANPs resemble hydroxyapatite, a biocompatible mineral analogous to the inorganic constituent of mammalian bone, which has been approved by the Food and Drug Administration for many biomedical purposes. The composite nanofiber mesh was designed for IDANP-assisted phage therapy treatment of topical wounds and consists of a superficial, rapid release layer of polyethylene oxide (PEO) fibers doped with IDANPs, followed by inner, coaxial polycaprolactone / polyethylene glycol (PCL/PEG) blended polymer fiber layer for slower phage delivery. Our investigations have established that IDANP-doped PEO fibers are effective vehicles for dissemination of IDANPs for bacterial exposure and resultant increased bacterial death by phage. In this work, slower delivery of the phage behind IDANPs was accomplished using coaxial, electrospun fibers composed of PCL/PEG polymer blend.
OBJECTIVES/SPECIFIC AIMS: Recent evidence from resting-state fMRI studies have shown that brain network connectivity is altered in patients with neurodegenerative disorders. However, few studies have examined the complete connectivity patterns of these well-reported RSNs using a whole brain approach and how they compare between dementias. Here, we used advanced connectomic approaches to examine the connectivity of RSNs in Alzheimer disease (AD), Frontotemporal dementia (FTD), and age-matched control participants. METHODS/STUDY POPULATION: In total, 44 participants [27 controls (66.4±7.6 years), 13 AD (68.5.63±13.9 years), 4 FTD (59.575±12.2 years)] from an ongoing study at Indiana University School of Medicine were used. Resting-state fMRI data was processed using an in-house pipeline modeled after Power et al. (2014). Images were parcellated into 278 regions of interest (ROI) based on Shen et al. (2013). Connectivity between each ROI pair was described by Pearson correlation coefficient. Brain regions were grouped into 7 canonical RSNs as described by Yeo et al. (2015). Pearson correlation values were then averaged across pairs of ROIs in each network and averaged across individuals in each group. These values were used to determine relative expression of FC in each RSN (intranetwork) and create RSN profiles for each group. RESULTS/ANTICIPATED RESULTS: Our findings support previous literature which shows that limbic networks are disrupted in FTLD participants compared with AD and age-matched controls. In addition, interactions between different RSNs was also examined and a significant difference between controls and AD subjects was found between FP and DMN RSNs. Similarly, previous literature has reported a disruption between executive (frontoparietal) network and default mode network in AD compared with controls. DISCUSSION/SIGNIFICANCE OF IMPACT: Our approach allows us to create profiles that could help compare intranetwork FC in different neurodegenerative diseases. Future work with expanded samples will help us to draw more substantial conclusions regarding differences, if any, in the connectivity patterns between RSNs in various neurodegenerative diseases.