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20 results on '"Chang, Michael L."'

1. Impact of the COVID-19 pandemic on pediatric faculty: a report from nine academic institutions

Author

O’Connor, Teresia M., Guaman, Milenka Cuevas, Randell, Kimberly A., Keenan, Heather T., Snowden, Jessica, Mack, Jennifer W., Camp, Elizabeth A., Perez, Oriana, Chang, Michael L., Myers, Angela L., Nigrovic, Lise E., O’Toole, Jennifer, Reed, Jennifer L., Reese, Jennifer, Rosenberg, Abby R., Slater, Anne C., Wootton, Susan H., Ziniel, Sonja I., Yost, H. Joseph, Murray, Kristy O., Shekerdemian, Lara, and Chumpitazi, Corrie E.

Published
2024
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4. Impact of the COVID-19 pandemic on pediatric faculty: a report from nine academic institutions.

Author

O'Connor, Teresia M., Guaman, Milenka Cuevas, Randell, Kimberly A., Keenan, Heather T., Snowden, Jessica, Mack, Jennifer W., Camp, Elizabeth A., Perez, Oriana, Chang, Michael L., Myers, Angela L., Nigrovic, Lise E., O'Toole, Jennifer, Reed, Jennifer L., Reese, Jennifer, Rosenberg, Abby R., Slater, Anne C., Wootton, Susan H., Ziniel, Sonja I., Yost, H. Joseph, and Murray, Kristy O.

Published
2024
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7. International Prevalence and Mechanisms of SARS-CoV-2 in Childhood Arterial Ischemic Stroke During the COVID-19 Pandemic

Author

Beslow, Lauren A., primary, Agner, Shannon C., additional, Santoro, Jonathan D., additional, Ram, Dipak, additional, Wilson, Jenny L., additional, Harrar, Dana, additional, Appavu, Brian, additional, Fraser, Stuart M., additional, Rossor, Thomas, additional, Torres, Marcela D., additional, Kossorotoff, Manoëlle, additional, Zuñiga Zambrano, Yenny C., additional, Hernández-Chávez, Marta, additional, Hassanein, Sahar M.A., additional, Zafeiriou, Dimitrios, additional, Dowling, Michael M., additional, Kopyta, Ilona, additional, Stence, Nicholas V., additional, Bernard, Timothy J., additional, Dlamini, Nomazulu, additional, Abd El-Hamid Rihan, Ahmed, additional, Mohammed, Maha, additional, Farid, Moustafa, additional, Guergues, Wessam S.S., additional, Babiker, Mohamed O.E., additional, Hochberg, Oded, additional, Saracco, Paola, additional, Main, Thomas, additional, Mallick, Andrew, additional, Kala, Selina, additional, Philbrook, Bryan L., additional, Reddy, Kartik, additional, Ichord, Rebecca N., additional, Shih, Evelyn K., additional, Pearson, Rachel P., additional, Rafay, Mubeen F., additional, Sharma, Mukta, additional, Allen Staat, Mary, additional, Vadivelu, Sudhakar, additional, Duarte, Marvid, additional, Whitworth, Mary Suzanne, additional, Parakh, Manish, additional, Meesters, Kevin, additional, Roud, Charles-Joris, additional, Leruez- Ville, Marianne, additional, Ganesan, Vijeya, additional, Lehman, Laura L., additional, Rivkin, Michael, additional, Hernandez, Zulma, additional, Balut, Fernanda, additional, Celeste Buompadre, Maria, additional, Gómez Naranjo, Heidy J., additional, Gonzalez Alvarez, Veronica, additional, Bakeer, Nihal, additional, Garrison, Stephanie, additional, Belcher, Christopher, additional, Miller, Lorie, additional, Whitmore, Maria, additional, Amico, Giulia, additional, Severino, Mariasavina, additional, Bertamino, Marta, additional, Signa, Sara, additional, Sun, Lisa R., additional, Felling, Ryan J., additional, Kashyape, Pawan, additional, Gerstl, Lucia, additional, Kovacevic, Gordana, additional, Basu, Anna, additional, Taha, Yusri, additional, Lo, Warren D., additional, Yau, Maggie L.Y., additional, Peake, Deirdre, additional, Stevenson, Kim, additional, Gwer, Samson, additional, Andrade, Andrea, additional, Amlie- Lefond, Catherine, additional, Lee-Eng, Jacqueline, additional, Lee, Sarah, additional, Mailo, Janette, additional, Hausman- Kedem, Moran, additional, Jaiswal, Kamna, additional, Brown, Kellie, additional, Stojanovski, Belinda, additional, Mackay, Mark T, additional, Carolina Vargas Nino, Adriana, additional, McGregor, Daune, additional, deVeber, Gabrielle, additional, Bhathal, Ishvinder, additional, Liza Pulcine, NP, additional, Moharir, Mahendra, additional, Bjornson, Bruce, additional, Kim, Danny H.C., additional, Kirton, Adam, additional, Floer, Amalia, additional, Fox, Christine K., additional, Smith- Anderson, Christiana, additional, Wilt, Kelly, additional, Thompson, Vivian, additional, Chang, Michael L., additional, Tan, Marilyn, additional, Jordan, Lori C, additional, Grefe, Annette E., additional, Guilliams, Kristin, additional, and Noetzel, Michael J., additional

Published
2022
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10. Extensive Mycobacterium abscessus Pneumonia in an Immunocompetent Infant with No Underlying Lung Pathology

Author

Alramadhan, Morouge M., Murphy, James R., and Chang, Michael L.

Subjects
Article Subject
Abstract

Pulmonary infection due to Mycobacterium abscessus occurs in patients with cystic fibrosis, but rarely in immunocompetent children without underlying lung pathology. Treatment is complicated by frequent resistance to many antibiotics. We present a case report of a 4-month-old female infant with 2 months of cough, difficulty feeding, and failure to thrive, with extensive culture-confirmed M. abscessus pulmonary infection without identified immunodeficiency or underlying lung pathology following multidisciplinary evaluation. We describe our complete evaluation including immunodeficiency evaluation incorporating whole-exome sequencing, describe our antibiotic selection and treatment duration given complicated susceptibility pattern of the M. abscessus isolate, and review literature for nontuberculous mycobacterial pulmonary disease in immunocompetent children. A complete multidisciplinary evaluation for underlying lung disease and primary and acquired immunodeficiency should be undertaken in pediatric patients with M. abscessus pneumonia. Confirming macrolide susceptibility through erm(41) gene evaluation is clinically important for isolates with complicated susceptibility pattern.

Published
2021
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11. Presumptive Dipylidium caninum Infection in a Toddler

Author

Chong, Hannah F, Al Hammoud, Roukaya, and Chang, Michael L

Subjects
Article Subject, fungi, parasitic diseases
Abstract

We report a female toddler who presented repetitively with a chief complaint of motile white worms seen in her diapers. Symptoms of perianal itching and visualization of visible motile worms persisted for 6 months despite being treated with multiple courses of albendazole causing a lot of frustration and distress to the caregivers. The characteristics of the worms by inspection along with the presence of 3 pet dogs are consistent with Dipylidium caninum.

Published
2020
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16. Respiratory Syncytial Virus (RSV) RNA loads in peripheral blood correlates with disease severity in mice

Author

Torres Juan, Gomez Ana M, Khokhar Shama, Bhoj Vijay G, Tagliabue Claudia, Chang Michael L, Kiener Peter A, Revell Paula A, Ramilo Octavio, and Mejias Asuncion

Subjects
Diseases of the respiratory system, RC705-779
Abstract

Abstract Background Respiratory Syncytial Virus (RSV) infection is usually restricted to the respiratory epithelium. Few studies have documented the presence of RSV in the systemic circulation, however there is no consistent information whether virus detection in the blood correlates with disease severity. Methods Balb/c mice were inoculated with live RSV, heat-inactivated RSV or medium. A subset of RSV-infected mice was treated with anti-RSV antibody 72 h post-inoculation. RSV RNA loads were measured by PCR in peripheral blood from day 1-21 post-inoculation and were correlated with upper and lower respiratory tract viral loads, the systemic cytokine response, lung inflammation and pulmonary function. Immunohistochemical staining was used to define the localization of RSV antigens in the respiratory tract and peripheral blood. Results RSV RNA loads were detected in peripheral blood from day 1 to 14 post-inoculation, peaked on day 5 and significantly correlated with nasal and lung RSV loads, airway obstruction, and blood CCL2 and CXCL1 expression. Treatment with anti-RSV antibody reduced blood RSV RNA loads and improved airway obstruction. Immunostaining identified RSV antigens in alveolar macrophages and peripheral blood monocytes. Conclusions RSV RNA was detected in peripheral blood upon infection with live RSV, followed a time-course parallel to viral loads assessed in the respiratory tract and was significantly correlated with RSV-induced airway disease.

Published
2010
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20. 2438. Using Artificial Neural Networks to Predict Intra-Abdominal Abscess Risk Post-Appendectomy.

Author

Khatib, Hassan S Al, Alramadhan, Morouge, Murphy, James, Tsao, KuoJen, and Chang, Michael L

Subjects
ARTIFICIAL neural networks, APPENDECTOMY, SURGICAL site infections, ABSCESSES, MACHINE learning, DEEP learning
Abstract

Background Applying Artificial Intelligence techniques to healthcare data are gaining momentum. Early identification of patients at risk of surgical site infections is a major clinical goal. Our objective for this study was to determine whether deep learning AI techniques could identify patients at risk of intra-abdominal abscess development post-appendectomy using clinical data for pediatric patients undergoing appendectomy. Methods A dataset of 1,574 patients classified by surgeons as negative (1,328) or positive (246) for Intra-Abdominal Abscess Post-Appendectomy for Appendicitis were selected from a database containing 6,127 patients less than 19 years-old who had appendectomy at our institution between 2009–2018. Demographic, clinical, and surgical information were extracted. 34 Independent variables were identified to be useful for the study. Using Random Forest methodology 12 variables with the highest influence on the outcome were selected for the final dataset. Data imputation (MICE algorithm) was used to replace missing data points. Two "Reproducible" Artificial Neural Networks with different architectures were developed to predict the risk of developing Intra-Abdominal Abscess Post- Appendectomy: Model (1) 12 Inputs, 3 hidden layers with 12 Neurons each, and 1 Output. Model (2) 12 Inputs, 2 hidden layers with 18 Neurons each, and 1 Output. Results For the 1,574 patients (80%-20% used as training and test sets), Model (1) achieved Accuracy of 89.84%, Sensitivity of ~ 70%, and Specificity of 93.61% on the test set while Model (2) achieved Accuracy of 84.13%, Sensitivity of 81.63%, and Specificity of 84.6%. The difference between the models is that in Model (2) we over sampled the minority class (using SMOTE algorithm) to balance both classes which helped the model to learn both classes without bias and improved sensitivity over Model (1). Conclusion Deep learning algorithms applied to enough clinical variables can identify patients with high probability for the risk of developing intra-abdominal abscess post-appendectomy. While further test sets are necessary to validate the models, Artificial Neural Networks can be an important addition to current post-surgical care guidelines to personalize and optimize care to reduce infections following appendectomy. Disclosures All authors: No reported disclosures. [ABSTRACT FROM AUTHOR]

Published
2019
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