Evaluating Retinal Function in Age-Related Maculopathy with the ERG Photostress Test

PURPOSE. To evaluate the diagnostic potential of the electroretinogram (ERG) photostress test and the focal cone ERG in age-related maculopathy (ARM). METHODS. The cohort comprised 31 patients with ARM and 27 age-matched control subjects. The ERG photostress test was used to monitor cone adaptation after intense light adaptation. Focal 41- and 5-Hz cone ERGs were recorded monocularly (central 20°) to assess steady state retinal function. Univariate analysis identified electrophysiological parameters that differed between groups, and receiver operating characteristic (ROC) curves were constructed to assess their diagnostic potential. Logistic regression analysis determined the diagnostic potential of a model incorporating several independent predictors of ARM. RESULTS. The rate of recovery of the ERG photostress test was reduced (recovery was slower) in subjects with ARM. The parameter exhibited good diagnostic potential (P = 0.002, area under ROC curve = 0.74). The implicit times of the 5-Hz (a-wave, P = 0.002; b-wave, P < 0.001) and the 41-Hz (P < 0.001) focal cone ERGs were increased, and the 41-Hz focal cone ERG amplitude (P = 0.003) and focal to full-field amplitude ratio (P = 0.001) were reduced in the ARM group. Logistic regression analysis identified three independent predictors of ARM, including the rate of recovery of the ERG photostress test. CONCLUSIONS. Early ARM has a marked effect on the kinetics of cone adaptation. The clinical application of the ERG photostress test increases the sensitivity and specificity of a model for the diagnosis of ARM. Improved assessment of the functional integrity of the central retina will facilitate early diagnosis and evaluation of therapeutic interventions

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Assessing Change in Social Support During Late Life

The purpose of this study is to evaluate change in 14 measures of social support with data provided by a nationwide longitudinal study of older adults. The findings reveal that fairly substantial change took place during the three-year follow-up period. More important, the data indicate that change is not uniform or systematic across the entire study sample. Instead, there appears to be considerable individual-level change taking place. The implications of these findings for the development of conceptual models as well as support-based interventions are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68866/2/10.1177_0164027599214002.pd

a cluster analysis of PARADIGM registry data

Patient-specific phenotyping of coronary atherosclerosis would facilitate personalized risk assessment and preventive treatment. We explored whether unsupervised cluster analysis can categorize patients with coronary atherosclerosis according to their plaque composition, and determined how these differing plaque composition profiles impact plaque progression. Patients with coronary atherosclerotic plaque (n = 947; median age, 62 years; 59% male) were enrolled from a prospective multi-national registry of consecutive patients who underwent serial coronary computed tomography angiography (median inter-scan duration, 3.3 years). K-means clustering applied to the percent volume of each plaque component and identified 4 clusters of patients with distinct plaque composition. Cluster 1 (n = 52), which comprised mainly fibro-fatty plaque with a significant necrotic core (median, 55.7% and 16.0% of the total plaque volume, respectively), showed the least total plaque volume (PV) progression (+ 23.3 mm3), with necrotic core and fibro-fatty PV regression (− 5.7 mm3 and − 5.6 mm3, respectively). Cluster 2 (n = 219), which contained largely fibro-fatty (39.2%) and fibrous plaque (46.8%), showed fibro-fatty PV regression (− 2.4 mm3). Cluster 3 (n = 376), which comprised mostly fibrous (62.7%) and calcified plaque (23.6%), showed increasingly prominent calcified PV progression (+ 21.4 mm3). Cluster 4 (n = 300), which comprised mostly calcified plaque (58.7%), demonstrated the greatest total PV increase (+ 50.7mm3), predominantly increasing in calcified PV (+ 35.9 mm3). Multivariable analysis showed higher risk for plaque progression in Clusters 3 and 4, and higher risk for adverse cardiac events in Clusters 2, 3, and 4 compared to that in Cluster 1. Unsupervised clustering algorithms may uniquely characterize patient phenotypes with varied atherosclerotic plaque profiles, yielding distinct patterns of progressive disease and outcome.publishersversionpublishe

Tissue specific characteristics of cells isolated from human and rat tendons and ligaments

<p>Abstract</p> <p>Background</p> <p>Tendon and ligament injuries are common and costly in terms of surgery and rehabilitation. This might be improved by using tissue engineered constructs to accelerate the repair process; a method used successfully for skin wound healing and cartilage repair. Progress in this field has however been limited; possibly due to an over-simplistic choice of donor cell. For tissue engineering purposes it is often assumed that all tendon and ligament cells are similar despite their differing roles and biomechanics. To clarify this, we have characterised cells from various tendons and ligaments of human and rat origin in terms of proliferation, response to dexamethasone and cell surface marker expression.</p> <p>Methods</p> <p>Cells isolated from tendons by collagenase digestion were plated out in DMEM containing 10% fetal calf serum, penicillin/streptomycin and ultraglutamine. Cell number and collagen accumulation were by determined methylene blue and Sirius red staining respectively. Expression of cell surface markers was established by flow cytometry.</p> <p>Results</p> <p>In the CFU-f assay, human PT-derived cells produced more and bigger colonies suggesting the presence of more progenitor cells with a higher proliferative capacity. Dexamethasone had no effect on colony number in ACL or PT cells but 10 nM dexamethasone increased colony size in ACL cultures whereas higher concentrations decreased colony size in both ACL and PT cultures. In secondary subcultures, dexamethasone had no significant effect on PT cultures whereas a stimulation was seen at low concentrations in the ACL cultures and an inhibition at higher concentrations. Collagen accumulation was inhibited with increasing doses in both ACL and PT cultures. This differential response was also seen in rat-derived cells with similar differences being seen between Achilles, Patellar and tail tendon cells. Cell surface marker expression was also source dependent; CD90 was expressed at higher levels by PT cells and in both humans and rats whereas D7fib was expressed at lower levels by PT cells in humans.</p> <p>Conclusion</p> <p>These data show that tendon & ligament cells from different sources possess intrinsic differences in terms of their growth, dexamethasone responsiveness and cell surface marker expression. This suggests that for tissue engineering purposes the cell source must be carefully considered to maximise their efficacy.</p

ATG5 is essential for ATG8-dependent autophagy and mitochondrial homeostasis in Leishmania major

Macroautophagy has been shown to be important for the cellular remodelling required for Leishmania differentiation. We now demonstrate that L. major contains a functional ATG12-ATG5 conjugation system, which is required for ATG8-dependent autophagosome formation. Nascent autophagosomes were found commonly associated with the mitochondrion. L. major mutants lacking ATG5 (Δatg5) were viable as promastigotes but were unable to form autophagosomes, had morphological abnormalities including a much reduced flagellum, were less able to differentiate and had greatly reduced virulence to macrophages and mice. Analyses of the lipid metabolome of Δatg5 revealed marked elevation of phosphatidylethanolamines (PE) in comparison to wild type parasites. The Δatg5 mutants also had increased mitochondrial mass but reduced mitochondrial membrane potential and higher levels of reactive oxygen species. These findings indicate that the lack of ATG5 and autophagy leads to perturbation of the phospholipid balance in the mitochondrion, possibly through ablation of membrane use and conjugation of mitochondrial PE to ATG8 for autophagosome biogenesis, resulting in a dysfunctional mitochondrion with impaired oxidative ability and energy generation. The overall result of this is reduced virulence

Report of the 2021 U.S. Community Study on the Future of Particle Physics (Snowmass 2021) Summary Chapter

The 2021-22 High-Energy Physics Community Planning Exercise (a.k.a. ``Snowmass 2021'') was organized by the Division of Particles and Fields of the American Physical Society. Snowmass 2021 was a scientific study that provided an opportunity for the entire U.S. particle physics community, along with its international partners, to identify the most important scientific questions in High Energy Physics for the following decade, with an eye to the decade after that, and the experiments, facilities, infrastructure, and R&D needed to pursue them. This Snowmass summary report synthesizes the lessons learned and the main conclusions of the Community Planning Exercise as a whole and presents a community-informed synopsis of U.S. particle physics at the beginning of 2023. This document, along with the Snowmass reports from the various subfields, will provide input to the 2023 Particle Physics Project Prioritization Panel (P5) subpanel of the U.S. High-Energy Physics Advisory Panel (HEPAP), and will help to guide and inform the activity of the U.S. particle physics community during the next decade and beyond.Comment: 75 pages, 3 figures, 2 tables. This is the first chapter and summary of the full report of the Snowmass 2021 Workshop. This version fixes an important omission from Table 2, adds two references that were not available at the time of the original version, fixes a minor few typos, and adds a small amount of material to section 1.1.

Study protocol for the Innovative Support for Patients with SARS-COV-2 Infections Registry (INSPIRE): A longitudinal study of the medium and long-term sequelae of SARS-CoV-2 infection

Background: Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection. Methods: INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses. Results: Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing. Conclusions: This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection