OBJECTIVES/GOALS: A growing number of older adults in the United States have multiple social determinants of health (SDoH) that are barriers to effective medical care. We used generalizable machine learning methods to identify and visualize subtypes based on participant-reported SDoH profiles, and their association with delayed medical care (self-reported yes/no). METHODS/STUDY POPULATION: Data. All participants aged >=65 in All of Us with complete data on 18 SDoH self-reported variables, selected through consensus by 2 experienced health services researchers, and guided by Andersen’s behavioral model. Covariates included demographics, and the outcome was delayed medical care . Cases (n=4090) consisted of participants with at least one of the 18 SDoH variables, and controls (n=7414) consisted of participants with none of them. Method. (1) Used bipartite network analysis and modularity maximization to identify participant-SDoH biclusters, and visualize them through ExplodeLayout. (2) Used multivariable logistic regression (adjusted for demographics and corrected through Bonferroni) to measure the odds ratio (OR) of each participant bicluster to the outcome, compared with the controls. RESULTS/ANTICIPATED RESULTS: The analysis identified 7 SDoH subtypes (https://postimg.cc/Vd7Pg4xZ) with statistically significant modularity compared with 100 random permutations of the data (All of Us=.51, Random Mean=.38, z=20, P DISCUSSION/SIGNIFICANCE: The results identified 7 distinct subtypes based on SDoH profiles and their risk for delayed medical care, highlighting the importance of addressing specific combinations of barriers, with affordability having the highest risk. Furthermore, the analytical methods used are generalizable and have been made publicly available on CRAN and All of Us.

OBJECTIVES/GOALS: Approximately 10% of COVID-19 patients experience multiple symptoms weeks and months after the acute phase of infection. Our goal was to use advanced machine learning methods to identify PASC phenotypes based on their symptom profiles, and their association with critical adverse outcomes, with the goal of designing future targeted interventions. METHODS/STUDY POPULATION: Data. All COVID-19 outpatients from 12 University of Minnesota hospitals and 60 clinics. Independent variables consisted of 20 CDC-defined PASC symptoms extracted from clinical notes using NLP. Covariates included demographics, and outcomes included New Psychological Diagnostic Evaluation, and Number of PASC Hospital Visits (>=5). Cases (n=3235) consisted of patients with at least one symptom, and controls (n=3034) consisted of patients with no symptoms. Method. (1) Used bipartite network analysis and modularity maximization to identify patient-symptom biclusters. (2) Used multivariable logistic regression (adjusted for demographics and corrected through Bonferroni) to measure the odds ratio of each patient bicluster to adverse outcomes, compared to controls, and to each of the other biclusters. RESULTS/ANTICIPATED RESULTS: The analysis identified 6 PASC phenotypes (http://www.skbhavnani.com/DIVA/Images/Fig-1-PASC-Network.jpg), which was statistically significant compared to 1000 random permutations of the data (PASC=.31, Random Median=.27, z=11, P<.01). Three of the clusters (Cluster-1, Cluster-4, and Cluster-5 encircled with ovals in Fig. 1) contained CNS-related symptoms, which had statistically significant risk for one or both of the adverse outcomes. For example, Cluster-1 with critical CNS symptoms (depression, insomnia, anxiety, brain-fog/difficulty-thinking), had a significantly higher OR compared to the controls for New Psychological Diagnostic Evaluation (OR=6.6, CI=4.9-9.1, P-corr<.001), in addition to having a significantly higher ORs for the same outcome compared to all the other clusters. DISCUSSION/SIGNIFICANCE: The results identified distinct PASC phenotypes based on symptom profiles, with three of them related to CNS symptoms, each of which had significantly higher risk for specific adverse outcomes compared to controls. We will test whether these phenotypes replicate in the N3C data, and explore their translation into triage and treatment strategies.

Clozapine is the least likely anti-psychotic to induce extrapyramidal symptoms (EPS). We present a surprising case of a woman schizophrenic patient treated with clozapine suffering from EPS. Single photon emission computed tomography (SPECT) revealed a low density of presynaptic dopamine transporters in our patient's brain. A comorbid diagnosis of Parkinson's disease in schizophrenia was confirmed in this way. This helped us to find a proper therapeutic strategy for our patient.

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

Extended abstract of a paper presented at MC 2007, 33rd DGE Conference in Saarbrücken, Germany, September 2 – September 7, 2007

Extended abstract of a paper presented at Microscopy and Microanalysis 2006 in Chicago, Illinois, USA, July 30 – August 3, 2005

Extended abstract of a paper presented at Microscopy and Microanalysis 2005 in Honolulu, Hawaii, USA, July 31--August 4, 2005

Extended abstract of a paper presented at Microscopy and Microanalysis 2005 in Honolulu, Hawaii, USA, July 31--August 4, 2005

Extended abstract of a paper presented at the Pre-Meeting Congress: Materials Research in an Aberration-Free Environment, at Microscopy and Microanalysis 2004 in Savannah, Georgia, USA, July 31 and August 1, 2004.

With the availability of resolution boosting and delocalization minimizing techniques, for example, spherical aberration correction and exit-plane wave function reconstruction, high-resolution transmission electron microscopy is drawing to a breakthrough with respect to the atomic-scale imaging of common semiconductor materials. In the present study, we apply a combination of these two state-of-the-art techniques to investigate lattice defects in GaAs-based heterostructures at atomic resolution. Focusing on the direct imaging of stacking faults as well as the core structure of edge and partial dislocations, the practical capabilities of both techniques are illustrated. For the first time, we apply the technique of bright-atom contrast imaging at negative spherical aberration together with an appropriate overfocus setting for the investigation of lattice defects in a semiconductor material. For these purposes, the elastic displacements associated with lattice defects in GaAs viewed along the [110] zone axis are measured from experimental images using reciprocal space strain map algorithms. Moreover, we demonstrate the benefits of the retrieval of the exit-plane wave function not only for the elimination of residual imaging artefacts but also for the proper on-line alignment of specimens during operation of the electron microscope—a basic prerequisite to obtain a fair agreement between simulated images and experimental micrographs.

La clozapina es el antipsicótico con menos probabilidad de inducir síntomas extrapiramidales (SEP). Presentamos un caso sorprendente de una paciente esquizofrénica tratada con clozapina que sufrió SEP. La tomografía computarizada por emisión de fotón simple (SPECT) reveló una densidad baja de transportadores de dopamina presinápticos en el cerebro de nuestra paciente. De esta manera, se confirmó un diagnóstic comórbido de enfermedad de Parkinson en la esquizofrenia. Esto nos ayudó a encontrar una estrategia terapéutica apropiada para nuestra paciente.