Assessment of protein–protein interfaces in cryo-EM derived assemblies

Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein–protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein–protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.

Density of Patient-Sharing Networks: Impact on the Value of Parkinson Care

Background: Optimal care for Parkinson’s disease (PD) requires coordination and collaboration between providers within a complex care network. Individual patients have personalised networks of their own providers, creating a unique informal network of providers who treat (‘share’) the same patient. These ‘patient-sharing networks’ differ in density, ie, the number of identical patients they share. Denser patient-sharing networks might reflect better care provision, since providers who share many patients might have made efforts to improve their mutual care delivery. We evaluated whether the density of these patient-sharing networks affects patient outcomes and costs. Methods: We analysed medical claims data from all PD patients in the Netherlands between 2012 and 2016. We focused on seven professional disciplines that are commonly involved in Parkinson care. We calculated for each patient the density score: the average number of patients that each patient’s providers shared. Density scores could range from 1.00 (which might reflect poor collaboration) to 83.00 (which might reflect better collaboration). This score was also calculated at the hospital level by averaging the scores for all patients belonging to a specific hospital. Using logistic and linear regression analyses we estimated the relationship between density scores and health outcomes, healthcare utilization, and healthcare costs. Results: The average density score varied considerably (average 6.7, SD 8.2). Adjusted for confounders, higher density scores were associated with a lower risk of PD-related complications (odds ratio [OR]: 0.901; P<.001) and with lower healthcare costs (coefficients: -0.018, P=.005). Higher density scores were associated with more frequent involvement of neurologists (coefficient 0.068), physiotherapists (coefficient 0.052) and occupational therapists (coefficient 0.048) (P values all <.001). Conclusion: Patient sharing networks showed large variations in density, which appears unwanted as denser networks are associated with better outcomes and lower costs.

HIGH-RESOLUTION CT FEATURES OF THE COVID-19 INFECTION, INITIALAND FOLLOW-UP CHANGES AMONG DIFFERENT CLINICAL TYPES

Background: To discuss the high resolution computed tomography (HRCT) manifestations of corona virus disease 2019 (COVID-19) patients among different clinical types on initial and follow-up CT. Methods: Seventy COVID-19 patients admitted to the Affiliated Hospital of SP Medical Collage Bikaner were enrolled. All patients underwent initial and follow-up chest HRCT. The main CT features and semi-quantitative score which represent disease severity among different clinical types were evaluated. Result: On initial CT, the main abnormalities observed in common and severe cases respectively were pure ground glass opacities (GGOs) and patchy consolidation surrounded by GGOs. Critical cases had multiple consolidation surrounded by wide range of GGOs distributed in the whole lung fields. The CT severity score and density score in mild (up to 8 and 5), moderate (>8 to 15 and 9) and severe (>15 and 12) cases were increased by gradient. On follow-up CT, mild and moderate types manifested as decreasing density of lesion, absorbed consolidation and GGOs. Severe cases showed progression of the disease. The extent and progression scores in mild and moderate patients were significantly decreased, while the range score of patients with severe disease reached the highest points, accompanied with an increase in the density score. Conclusion: CT scanning can accurately assess the severity of COVID-19, and help to monitor disease transformation during follow-up among different clinical conditions.

CT manifestations of the coronavirus disease 2019 in patients outside Wuhan: with a history of exposure to Wuhan vs. with second-generation infection

Objective: To explore discrepancy in CT manifestations of coronavirus disease 2019 (COVID-19) in patients outside Wuhan between cases with a history of exposure to Wuhan and with the second-generation infection.Methods: Twenty-two patients with confirmed COVID-19 from two hospitals in Nanchong outside Wuhan were enrolled. All patients underwent initial and follow-up computed tomography after admission, and were divided into two groups. Group A and B were composed of 15 patients with a history of exposure to Wuhan and 7 with the second-generation infection in Nanchong, respectively. Initial CT features including extent score and density score between groups were statistically compared.Results: All patients in group A had abnormal CT findings while 3 of 7 patients in group B had. Patients with abnormal CT findings were more frequent in group A than in group B (P < 0.05). On initial CT, pure ground glass opacity (GGO), and GGO with consolidation and/or other abnormalities were found in 20% (3/15) and 80% (12/15) patients in group A, respectively, while 1 (14.3%), 2 (28.6%) and 4 (57.1%) had pure GGO, GGO with focal consolidation, and normal CT appearances in Group B, respectively. Patients with extent and density scores of ≥5 were more frequent in group A than in group B (Ps < 0.01). Additionally, 3 of 4 (75%) patients with normal initial CT findings had focal pure GGO lesions on follow-up CT.Conclusion: The COVID-19 in patients with a history of exposure to Wuhan can be severer than with the second-generation infection on CT.

Abstract P103: Tobacco-21 Policy Implementation In A Texas Community: Utilizing Interorganizational Network Analysis To Evaluate Tobacco Coalition Dynamics

Introduction: In El Paso County, TX, a local tobacco coalition is working in partnership with the American Heart Association’s (AHA) Heart Racial and Ethnic Approaches to Community Health (REACH) program to focus efforts on aligning local activities with the new, statewide Tobacco-21 policy. As coalitions have been on the forefront of spearheading policy implementation efforts, understanding and quantifying coalition dynamics is necessary to increase collaboration and leverage resources. The purpose of this study is to highlight the findings of a baseline Interorganizational Network (ION) analysis of a tobacco coalition in El Paso County, TX in order to determine current levels of collaboration between organizations and strength of relationships. Hypothesis: We assessed the hypothesis that by using ION analysis, we can characterize relationships between organizations, identify organizations that are working in silos, and leverage ties between organizations to implement new, tobacco control initiatives that support the Tobacco-21 policy. Methods: Evaluators from Texas A&M University worked in conjunction with the AHA to conduct an ION survey that assessed information sharing and joint planning of organizations involved in the tobacco coalition (n=18). Using UCINET© network analysis software, density and centrality scores at the network-level were calculated. Network diagrams were then developed to depict relationships among partners using Gephi© visualization software. Results: Network collaboration related to information sharing had a network density score of 0.340 (SD=0.474) while joint planning had a network density score of 0.297 (SD=0.457). The centralization index for information sharing was 0.055 and 0.0729 for joint planning with the coalition as the most central for both domains. Conclusion: This study shows how determination of organizational relationships within a coalition can be leveraged for strategic planning. Density measures were useful to understand the connectedness of the network, and centrality measures at the network-level were helpful in determining network structure. In conclusion, results from this study informed program implementers on how to better foster collaboration among organizations. Subsequent iterations of the survey will allow for documentation of changes in the strength of relationships over the project period.

Abstract P355: Development and Validation of a Nutrient Density Score That Includes Foods Groups

Introduction: There has been considerable work performed on nutrient profiling to assess the nutritional contribution of a food to a healthy dietary pattern. Most profiling approaches have focused on nutrients to limit and nutrients to encourage. A few profiling approaches have also included certain food groups in the profiling algorithm. Objectives: The objective of this study was to develop a nutrient density score, based on the Nutrient Rich Food Index (NRF) 6.3, that includes food groups and validate the score against a gold-standard marker of diet quality, the Healthy Eating Index (HEI) 2015. Methods: Stepwise regression was used to develop a nutrient density score based on the day 1 total dietary intake of the U.S. population 2 years and older (excluding pregnant and lactating women) from the National Health and Nutrition Examination Survey (NHANES) 2011-2016 (n=23,743). Intake of food groups was taken from the Food Patterns Equivalent Database (FPED) 2011-2016. Sixteen nutrients (as a percent of the Daily Value) as well as five food groups (as a percentage of recommended intake in 2015-2020 Dietary Guidelines) were considered in the score. Results: When only the 16 nutrients were included in the score, 66% of the variability in the HEI 2015 could be accounted for (R 2 = 0.66). When only the five food groups were considered, the maximum R 2 with the HEI 2015 was 0.50. However, when both nutrients and foods groups were considered, the model explained 74% of the variability in the HEI 2015 (Table). The increase in the R 2 begins to plateau after the inclusion of 10 elements: 3 nutrients to encourage (fiber, potassium and unsaturated fat), 4 food groups (dairy, fruit, whole grains, and nuts and seeds) and 3 nutrients to limit (added sugar, saturated fat, sodium). Conclusion: A nutrient density score that includes both nutrients and foods groups best predicts diet quality as measured by the HEI 2015.

Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark

Background Screening mammography works better in fatty than in dense breast tissue. Computerized assessment of parenchymal texture is a non-subjective method to obtain a refined description of breast tissue, potentially valuable in addition to breast density scoring for the identification of women in need of supplementary imaging. We studied the sensitivity of screening mammography by a combination of radiologist-assessed Breast Imaging Reporting and Data System (BI-RADS) density score and computer-assessed parenchymal texture marker, mammography texture resemblance (MTR), in a population-based screening program. Methods Breast density was coded according to the fourth edition of the BI-RADS density code, and MTR marker was divided into quartiles from 1 to 4. Screening data were followed up for the identification of screen-detected and interval cancers. We calculated sensitivity and specificity with 95% confidence intervals (CI) by BI-RADS density score, MTR marker, and combination hereof. Results Density and texture were strongly correlated, but the combination led to the identification of subgroups with different sensitivity. Sensitivity was high, about 80%, in women with BI-RADS density score 1 and MTR markers 1 or 2. Sensitivity was low, 67%, in women with BI-RADS density score 2 and MTR marker 4. For women with BI-RADS density scores 3 and 4, the already low sensitivity was further decreased for women with MTR marker 4. Specificity was 97–99% in all subgroups. Conclusion Our study showed that women with low density constituted a heterogenous group. Classifying women for extra imaging based on density only might be a too crude approach. Screening sensitivity was systematically high in women with fatty and homogenous breast tissue.