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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 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  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
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.