What\u27s in it for me? Perspectives from Community Participants in an Inter-professional Service Learning Program

Purpose: This study assessed Interdisciplinary Family Health (IFH) Program participants‘ perceived health outcomes associated with program participation at the University of Florida. Background: Service-learning has emerged as a dynamic way in which students derive practical skills to address the needs of their community. Research has probed student perspectives but has seldom explored community feedback. The Interdisciplinary Family Health Program (IFH) is a mandatory Interprofessional service learning experience designed to foster collaborative teamwork across first year health professions students at UF. Students are assigned in interprofessional teams of four to improve a local volunteer family’s health over the course of four visits in one academic year. Description of Research: Data from twenty-one semi-structured telephone interviews were audio-recorded and transcribed to assess participants’ perceived health outcomes using a grounded theory approach. Emergent themes were conceptualized via selective coding and peer-reviewing. Results: All program participants reported positive health outcomes within a social support construct. Responses fell into four social support domains within a greater framework of bridging student-participant generations: informational support, emotional support, companionship support, and tangible support. Trends in social support domains observed were associated with participant SES. Participants with lower SES levels reported greater needs for health information and access, whereas participants with higher SES levels desired building social relationships with students. Conclusions: Tailoring IPE training to address specific social support domains and SES associations is an opportunity for enhanced participant experiences and perceived health outcomes. Educational planning can utilize social support domain-SES association findings as a guide for students to attune their efforts at improving the overall health outcomes of their target population. Learning Objectives and Related Conference Objectives: Participants will be able to describe qualitative methods of evaluating perceived patient health outcomes in assessing the effectiveness of the Interdisciplinary Family Health Program (Conference Objective 1). Participants will be able to communicate research findings regarding social support domain-SES approaches for enhanced educational programming and perceived patient health outcomes (Conference Objective 3)

Atomically precise Au144(SR)60 nanoclusters (R = Et, Pr) are capped by 12 distinct ligand types of 5-fold equivalence and display gigantic diastereotopic effects

For two decades, Au144(SR)60 has been one of the most studied and used thiolate (SR) protected gold nanoclusters. In many ways, however, it proved to be a challenging and elusive case, also because of the difficulties in solving its structure by single-crystal X-ray crystallography. We used very short thiols and could prepare Au144(SC2H5)60 and Au144(SC3H7)60 in a very pure form, which was confirmed by UV-vis absorption spectroscopy and very regular electrochemistry patterns. Inductively coupled plasma and electrospray ionization mass spectrometries gave definite proof of the Au144(SR)60 stoichiometry. Highresolution 1D and 2D NMR spectroscopy in the solution phase provided the result of assessing the presence of 12 ligand types in exactly the same amount (5-fold equivalence). Equally important, we found that the two protons belonging to each methylene group along the thiolate chain are diastereotopic. For the a-CH2 protons, the diastereotopic effect can be indeed gigantic, as it reaches chemical-shift differences of 2.9 ppm. DFT calculations provided insights into the relationship between structure and NMR results. In particular, the 12 ligand types and corresponding diastereotopic effects may be explained by considering the presence of C\u2013H/S hydrogen bonds. These results thus provide fundamental insights into the structure of the thiolate layer capping this long-studied gold nanocluster

Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI Methods and Outcome Measures

The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 years multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimer\u27s disease, mild cognitive impairment, Parkinson\u27s disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. The purpose of this paper is to serve as a reference and conceptual overview of the comprehensive neuroimaging pipeline used to generate regional brain tissue volumes and neurovascular marker data that will be made publicly available online

Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs

Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural network (CNN) to segment the ICV and ventricles from both single and multi-contrast MRI data. Our models were trained on a large dataset from two multi-site studies (N = 528 subjects for ICV, N = 501 for ventricular segmentation) consisting of older adults with varying degrees of cerebrovascular lesions and atrophy, which pose significant challenges for most segmentation approaches. The models were tested on 238 participants, including subjects with vascular cognitive impairment and high white matter hyperintensity burden. Two of the three test sets came from studies not used in the training dataset. We assessed our algorithms relative to four state-of-the-art ICV extraction methods (MONSTR, BET, Deep Extraction, FreeSurfer, DeepMedic), as well as two ventricular segmentation tools (FreeSurfer, DeepMedic). Our multi-contrast models outperformed other methods across many of the evaluation metrics, with average Dice coefficients of 0.98 and 0.96 for ICV and ventricular segmentation respectively. Both models were also the most time efficient, segmenting the structures in orders of magnitude faster than some of the other available methods. Our networks showed an increased accuracy with the use of a conditional random field (CRF) as a post-processing step. We further validated both segmentation models, highlighting their robustness to images with lower resolution and signal-to-noise ratio, compared to tested techniques. The pipeline and models are available at: https://icvmapp3r.readthedocs.io and https://ventmapp3r.readthedocs.io to enable further investigation of the roles of ICV and ventricles in relation to normal aging and neurodegeneration in large multi-site studies

Consistency and accuracy of diagnostic cancer codes generated by automated registration: comparison with manual registration

BACKGROUND: Automated procedures are increasingly used in cancer registration, and it is important that the data produced are systematically checked for consistency and accuracy. We evaluated an automated procedure for cancer registration adopted by the Lombardy Cancer Registry in 1997, comparing automatically-generated diagnostic codes with those produced manually over one year (1997). METHODS: The automatically generated cancer cases were produced by Open Registry algorithms. For manual registration, trained staff consulted clinical records, pathology reports and death certificates. The social security code, present and checked in both databases in all cases, was used to match the files in the automatic and manual databases. The cancer cases generated by the two methods were compared by manual revision. RESULTS: The automated procedure generated 5027 cases: 2959 (59%) were accepted automatically and 2068 (41%) were flagged for manual checking. Among the cases accepted automatically, discrepancies in data items (surname, first name, sex and date of birth) constituted 8.5% of cases, and discrepancies in the first three digits of the ICD-9 code constituted 1.6%. Among flagged cases, cancers of female genital tract, hematopoietic system, metastatic and ill-defined sites, and oropharynx predominated. The usual reasons were use of specific vs. generic codes, presence of multiple primaries, and use of extranodal vs. nodal codes for lymphomas. The percentage of automatically accepted cases ranged from 83% for breast and thyroid cancers to 13% for metastatic and ill-defined cancer sites. CONCLUSION: Since 59% of cases were accepted automatically and contained relatively few, mostly trivial discrepancies, the automatic procedure is efficient for routine case generation effectively cutting the workload required for routine case checking by this amount. Among cases not accepted automatically, discrepancies were mainly due to variations in coding practice

The gp38 Adhesins of the T4 Superfamily: A Complex Modular Determinant of the Phage’s Host Specificity

The tail fiber adhesins are the primary determinants of host range in the T4-type bacteriophages. Among the indispensable virion components, the sequences of the long tail fiber genes and their associated adhesins are among the most variable. The predominant form of the adhesin in the T4-type phages is not even the version of the gene encoded by T4, the archetype of the superfamily, but rather a small unrelated protein (gp38) encoded by closely related phages such as T2 and T6. This gp38 adhesin has a modular design: its N-terminal attachment domain binds at the tip of the tail fiber, whereas the C-terminal specificity domain determines its host receptor affinity. This specificity domain has a series of four hypervariable segments (HVSs) that are separated by a set of highly conserved glycine-rich motifs (GRMs) that apparently form the domain’s conserved structural core. The role of gp38’s various components was examined by a comparative analysis of a large series of gp38 adhesins from T-even superfamily phages with differing host specificities. A deletion analysis revealed that the individual HVSs and GRMs are essential to the T6 adhesin’s function and suggests that these different components all act in synergy to mediate adsorption. The evolutionary advantages of the modular design of the adhesin involving both conserved structural elements and multiple independent and easily interchanged specificity determinants are discussed

Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative

Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.Rationale: We evaluated K-means clustering to classify amyloid brain PETs as positive or negative. Patients and Methods: Sixty-six participants (31 men, 35 women; age range, 52–81 years) were recruited through a multicenter observational study: 19 cognitively normal, 25 mild cognitive impairment, and 22 demen- tia (11 Alzheimer disease, 3 subcortical vascular cognitive impairment, and 8 Parkinson–Lewy Body spectrum disorder). As part of the neurocognitive and imaging evaluation, each participant had an 18F-flutemetamol (Vizamyl, GE Healthcare) brain PET. All studies were processed using Cortex ID soft- ware (General Electric Company, Boston, MA) to calculate SUV ratios in 19 regions of interest and clinically interpreted by 2 dual-certified radiologists/ nuclear medicine physicians, using MIM software (MIM Software Inc, Cleveland, OH), blinded to the quantitative analysis, with final interpreta- tion based on consensus. K-means clustering was retrospectively used to classify the studies from the quantitative data. Results: Based on clinical interpretation, 46 brain PETs were negative and 20 were positive for amyloid deposition. Of 19 cognitively normal partici- pants, 1 (5%) had a positive 18F-flutemetamol brain PET. Of 25 participants with mild cognitive impairment, 9 (36%) had a positive 18F-flutemetamol brain PET. Of 22 participants with dementia, 10 (45%) had a positive 18F-flutemetamol brain PET; 7 of 11 participants with Alzheimer disease (64%), 1 of 3 participants with vascular cognitive impairment (33%), and 2 of 8 participants with Parkinson–Lewy Body spectrum disorder (25%) had a positive 18F-flutemetamol brain PET. Using clinical interpretation as the criterion standard, K-means clustering (K = 2) gave sensitivity of 95%, specificity of 98%, and accuracy of 97%. Conclusions: K-means clustering may be a powerful algorithm for classifying amyloid brain PET.This is a multisite project of the Toronto Dementia Research Alli- ance (www.tdra.utoronto.ca) partly funded by Brain Canada, The Edward Foundation, the Canadian Institutes of Health Research (FDN 159910), the LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, and the Dr Sandra Black Cen- tre for Brain Resilience and Recovery. M.F. received support from the Saul A. Silverman Family Foundation as a Canada Interna- tional Scientific Exchange Program and the Morris Kerzner Memo- rial Fund. We gratefully acknowledge GE Healthcare and the CAMH Brain Health Imaging Centre for manufacturing and sup- plying the ligand. We are also grateful to GE Healthcare for provid- ing the software to calculate the brain region of interest SUV ratios. The study protocol, Brain Eye Amyloid Memory study (BEAM), is registered at https://clinicaltrials.gov/ct2/show/NCT02524405? term=beam+sandra+black&rank=1