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21 results on '"Diaz, Damaris"'

1. Human Eosinophils Express a Distinct Gene Expression Program in Response to IL-3 Compared with Common β-Chain Cytokines IL-5 and GM-CSF

Author

Nelson, Ryan K, Brickner, Howard, Panwar, Bharat, Ramírez-Suástegui, Ciro, Herrera-de la Mata, Sara, Liu, Neiman, Diaz, Damaris, Alexander, Laura E Crotty, Ay, Ferhat, Vijayanand, Pandurangan, Seumois, Grégory, and Akuthota, Praveen

Subjects
Biotechnology, Genetics, Lung, Clinical Research, Asthma, 2.1 Biological and endogenous factors, Aetiology, Cytokines, Down-Regulation, Eosinophils, Gene Expression, Granulocyte-Macrophage Colony-Stimulating Factor, Humans, Interleukin-3, Interleukin-5, Signal Transduction, Up-Regulation, Immunology
Abstract

Despite recent advances in asthma management with anti-IL-5 therapies, many patients have eosinophilic asthma that remains poorly controlled. IL-3 shares a common β subunit receptor with both IL-5 and GM-CSF but, through α-subunit-specific properties, uniquely influences eosinophil biology and may serve as a potential therapeutic target. We aimed to globally characterize the transcriptomic profiles of GM-CSF, IL-3, and IL-5 stimulation on human circulating eosinophils and identify differences in gene expression using advanced statistical modeling. Human eosinophils were isolated from the peripheral blood of healthy volunteers and stimulated with either GM-CSF, IL-3, or IL-5 for 48 h. RNA was then extracted and bulk sequencing performed. DESeq analysis identified differentially expressed genes and weighted gene coexpression network analysis independently defined modules of genes that are highly coexpressed. GM-CSF, IL-3, and IL-5 commonly upregulated 252 genes and downregulated 553 genes, producing a proinflammatory and survival phenotype that was predominantly mediated through TWEAK signaling. IL-3 stimulation yielded the most numbers of differentially expressed genes that were also highly coexpressed (n = 119). These genes were enriched in pathways involving JAK/STAT signaling. GM-CSF and IL-5 stimulation demonstrated redundancy in eosinophil gene expression. In conclusion, IL-3 produces a distinct eosinophil gene expression program among the β-chain receptor cytokines. IL-3-upregulated genes may provide a foundation for research into therapeutics for patients with eosinophilic asthma who do not respond to anti-IL-5 therapies.

Published
2019

2. Youth and Parent Appraisals of Participation in a Study of Spontaneous and Induced Pediatric Clinical Pain.

Author

Hawley, Kara, Huang, Jeannie S, Goodwin, Matthew, Diaz, Damaris, de Sa, Virginia R, Birnie, Kathryn A, Chambers, Christine T, and Craig, Kenneth D

Subjects
Ethics, acceptability, adolescents, children, experimental pain, induced pain, Pain Research, Chronic Pain, Pediatric, ethics, Psychology, Applied Ethics
Abstract

The current study examined youths' and their parents' perceptions concerning participation in an investigation of spontaneous and induced pain during recovery from laparoscopic appendectomy. Youth (age range 5-17 years) and their parents independently completed surveys about their study participation. On a 0 (very negative) -to-10 (very positive) scale, both parents 9.4(1.3) [mean(SD)] and youth 7.9(2.4) rated their experience as positive. Among youth, experience ratings did not differ by pain severity and survey responses did not differ by age. Most youth (83%) reported they would tell another youth to participate. Ethical issues regarding instigation of pain in youth for research purposes are examined.

Published
2019

3. Automated Pain Assessment using Electrodermal Activity Data and Machine Learning

Author

Susam, Busra T, Akcakaya, Murat, Nezamfar, Hooman, Diaz, Damaris, Xu, Xiaojing, de Sa, Virginia R, Craig, Kenneth D, Huang, Jeannie S, and Goodwin, Matthew S

Subjects
Information and Computing Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Neurosciences, Pain Research, Chronic Pain, Algorithms, Galvanic Skin Response, Humans, Machine Learning, Pain, Pain Measurement, Sensitivity and Specificity
Abstract

Objective pain assessment is required for appropriate pain management in the clinical setting. However, clinical gold standard pain assessment is based on subjective methods. Automated pain detection from physiological data may provide important objective information to better standardize pain assessment. Specifically, electrodermal activity (EDA) can identify features of stress and anxiety induced by varying pain levels. However, notable variability in EDA measurement exists and research to date has demonstrated sensitivity but lack of specificity in pain assessment. In this paper, we use timescale decomposition (TSD) to extract salient features from EDA signals to identify an accurate and automated EDA pain detection algorithm to sensitively and specifically distinguish pain from no-pain conditions.

Published
2018

4. Automated Pain Detection in Facial Videos of Children using Human-Assisted Transfer Learning.

Author

Xu, Xiaojing, Craig, Kenneth D, Diaz, Damaris, Goodwin, Matthew S, Akcakaya, Murat, Susam, Büşra Tuğçe, Huang, Jeannie S, and de Sa, Virginia R

Subjects
FACS, automated pain detection, facial action units, transfer learning, Chronic Pain, Pain Research
Abstract

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity provides sensitive and specific information about pain, and computer vision algorithms have been developed to automatically detect Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). Our prior work utilized information from computer vision, i.e., automatically detected facial AUs, to develop classifiers to distinguish between pain and no-pain conditions. However, application of pain/no-pain classifiers based on automated AU codings across different environmental domains results in diminished performance. In contrast, classifiers based on manually coded AUs demonstrate reduced environmentally-based variability in performance. In this paper, we train a machine learning model to recognize pain using AUs coded by a computer vision system embedded in a software package called iMotions. We also study the relationship between iMotions (automatically) and human (manually) coded AUs. We find that AUs coded automatically are different from those coded by a human trained in the FACS system, and that the human coder is less sensitive to environmental changes. To improve classification performance in the current work, we applied transfer learning by training another machine learning model to map automated AU codings to a subspace of manual AU codings to enable more robust pain recognition performance when only automatically coded AUs are available for the test data. With this transfer learning method, we improved the Area Under the ROC Curve (AUC) on independent data from new participants in our target domain from 0.67 to 0.72.

Published
2018

5. Towards Automated Pain Detection in Children Using Facial and Electrodermal Activity

Author

Xu, Xiaojing, Susam, Büşra Tuğçe, Nezamfar, Hooman, Diaz, Damaris, Craig, Kenneth D., Goodwin, Matthew S., Akcakaya, Murat, Huang, Jeannie S., de Sa, Virginia R., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Koch, Fernando, editor, Koster, Andrew, editor, Riaño, David, editor, Montagna, Sara, editor, Schumacher, Michael, editor, ten Teije, Annette, editor, Guttmann, Christian, editor, Reichert, Manfred, editor, Bichindaritz, Isabelle, editor, Herrero, Pau, editor, Lenz, Richard, editor, López, Beatriz, editor, Marling, Cindy, editor, Martin, Clare, editor, Montani, Stefania, editor, and Wiratunga, Nirmalie, editor

Published
2019
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6. Automated Pain Detection in Facial Videos of Children Using Human-Assisted Transfer Learning

Author

Xu, Xiaojing, Craig, Kenneth D., Diaz, Damaris, Goodwin, Matthew S., Akcakaya, Murat, Susam, Büşra Tuğçe, Huang, Jeannie S., de Sa, Virginia R., Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Koch, Fernando, editor, Koster, Andrew, editor, Riaño, David, editor, Montagna, Sara, editor, Schumacher, Michael, editor, ten Teije, Annette, editor, Guttmann, Christian, editor, Reichert, Manfred, editor, Bichindaritz, Isabelle, editor, Herrero, Pau, editor, Lenz, Richard, editor, López, Beatriz, editor, Marling, Cindy, editor, Martin, Clare, editor, Montani, Stefania, editor, and Wiratunga, Nirmalie, editor

Published
2019
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9. The Precision Interventions for Severe and/or Exacerbation-Prone (PrecISE) Asthma Network: An overview of Network organization, procedures, and interventions

Author

Georas, Steve N., primary, Wright, Rosalind J., additional, Ivanova, Anastasia, additional, Israel, Elliot, additional, LaVange, Lisa M., additional, Akuthota, Praveen, additional, Carr, Tara F., additional, Denlinger, Loren C., additional, Fajt, Merritt L., additional, Kumar, Rajesh, additional, O’Neal, Wanda K., additional, Phipatanakul, Wanda, additional, Szefler, Stanley J., additional, Aronica, Mark A., additional, Bacharier, Leonard B., additional, Burbank, Allison J., additional, Castro, Mario, additional, Crotty Alexander, Laura, additional, Bamdad, Julie, additional, Cardet, Juan Carlos, additional, Comhair, Suzy A.A., additional, Covar, Ronina A., additional, DiMango, Emily A., additional, Erwin, Kim, additional, Erzurum, Serpil C., additional, Fahy, John V., additional, Gaffin, Jonathan M., additional, Gaston, Benjamin, additional, Gerald, Lynn B., additional, Hoffman, Eric A., additional, Holguin, Fernando, additional, Jackson, Daniel J., additional, James, John, additional, Jarjour, Nizar N., additional, Kenyon, Nicholas J., additional, Khatri, Sumita, additional, Kirwan, John P., additional, Kraft, Monica, additional, Krishnan, Jerry A., additional, Liu, Andrew H., additional, Liu, Mark C., additional, Marquis, M. Alison, additional, Martinez, Fernando, additional, Mey, Jacob, additional, Moore, Wendy C., additional, Moy, James N., additional, Ortega, Victor E., additional, Peden, David B., additional, Pennington, Emily, additional, Peters, Michael C., additional, Ross, Kristie, additional, Sanchez, Maria, additional, Smith, Lewis J., additional, Sorkness, Ronald L., additional, Wechsler, Michael E., additional, Wenzel, Sally E., additional, White, Steven R., additional, Zein, Joe, additional, Zeki, Amir A., additional, Noel, Patricia, additional, Billheimer, Dean, additional, Bleecker, Eugene R., additional, Branch, Emily, additional, Conway, Michelle, additional, Daines, Cori, additional, Deaton, Isaac, additional, Evans, Alexandria, additional, Field, Paige, additional, Francisco, Dave, additional, Hastie, Annette T., additional, Hmieleski, Bob, additional, Krings, Jeffrey O., additional, Liu, Yanqin, additional, Merchen, Janell L., additional, Meyers, Deborah A., additional, Narendran, Nirushan, additional, Peters, Stephen P., additional, Pippins, Anna, additional, Rank, Matthew A., additional, Schunk, Ronald, additional, Skeps, Raymond, additional, Wright, Benjamin, additional, Banzon, Tina M., additional, Bartnikas, Lisa M., additional, Baxi, Sachin N., additional, Betapudi, Vishwanath, additional, Brick, Isabelle, additional, Brockway, Conor, additional, Casale, Thomas B., additional, Castillo-Ruano, Kathleen, additional, Cinelli, Maria Angeles, additional, Crestani, Elena, additional, Cunningham, Amparito, additional, Day-Lewis, Megan, additional, Diaz-Cabrera, Natalie, additional, DiMango, Angela, additional, Esty, Brittany, additional, Fandozzi, Eva, additional, Fernandez, Jesse, additional, Fitzpatrick, Elizabeth, additional, Forth, Victoria E., additional, Gentile, Katarina, additional, Gubernick, David, additional, Gueye-Ndiaye, Seyni, additional, Gunnlaagsson, Sigfus, additional, Hauptmann, Marissa, additional, Hudey, Stephanie N., additional, Imanirad, Donya S., additional, Kaage, Tiffani, additional, Kolinsky, Nicholas, additional, LaBere, Brenna, additional, Lai, Peggy Sue, additional, Le, Meghan, additional, Ledford, Dennis K., additional, Lockey, Richard, additional, Louisias, Margee, additional, Macginnitie, Andrew J., additional, Maciag, Michelle C., additional, O’Neill, Allison, additional, Pepper, Amber N., additional, Permaul, Perdita, additional, Pugh, Mya, additional, Queheillalt, Dianna, additional, Saroya, Tarnjot, additional, Sheehan, William, additional, Smith, Catherine, additional, Socolovsky, Carmela, additional, Treffeisen, Else, additional, Trippa, Lorenzo, additional, Tulchinsky, Abigail, additional, Yee, Christina, additional, Carter, Tina, additional, Fu, Jun, additional, Garcia, Vanessa, additional, Hixon, Jenny, additional, Jackson, Carly, additional, Ji, Yuan, additional, Kalhan, Ravi, additional, Kaur, Opinderjit, additional, Li, Grace, additional, Makhija, Melanie M., additional, Maleckar, Spring, additional, Naureckas, Edward T., additional, Peters, Anju T., additional, Press, Valerie, additional, Qureshi, Mehreen, additional, Reyfman, Paul A., additional, Rosenberg, Sharon R., additional, Ryba, Dominika, additional, Sheng, Jianrong, additional, Xu, Ben, additional, Alam, Rafeul, additional, Anderson, Darci, additional, Belimezova, Sonya, additional, Bitzan, Jennifer, additional, Chupp, Geoffrey, additional, Clark, Brian J., additional, Cohn, Lauren, additional, Cruse, Margaret Hope, additional, Estrom, Jean, additional, Freid, Leah, additional, Villalobos, Jose Gomez, additional, Grant, Nicole, additional, Guntur, Vamsi P., additional, Holm, Carole, additional, Kolakowski, Christena, additional, Manka, Laurie A., additional, Miyazawa, Naomi, additional, Pak, Juno, additional, Pruitt, Diana M., additional, Sharma, Sunita, additional, Stevens, Allen D., additional, Thomas, Kisori, additional, Tippin, Brooke, additional, Valente, Karissa, additional, Wainscoat, Cynthia L., additional, White, Michael P., additional, Winnica, Daniel, additional, Ye, Shuyu, additional, Zeitlin, Pamela L., additional, Bach, Julia, additional, Brownell, Joshua, additional, Castro, Lauren, additional, DeLisa, Julie, additional, Fain, Sean B., additional, Fichtinger, Paul S., additional, Floerke, Heather, additional, Gern, James E., additional, Goswamy, Vinay, additional, Grogan, Jenelle, additional, Hasse, Wendy, additional, Kelley, Rick L., additional, Klaus, Danika, additional, LaBedz, Stephanie, additional, Lowell, Paige, additional, Maddox, Andrew, additional, Mathur, Sameer K., additional, McIntyre, Amanda, additional, Norwick, Lourdes M., additional, Nyenhuis, Sharmilee M., additional, O’Brien, Matthew J., additional, Palas, Tina, additional, Pappalardo, Andrea A., additional, Potter, Mark, additional, Ramratnam, Sima K., additional, Rosenberg, Daniel L., additional, Schauberger, Eric M., additional, Schiebler, Mark L., additional, Schraml, Angela, additional, Taki, Mohamed, additional, Tattersall, Matthew C., additional, Torres, Jissell, additional, Wollet, Lori, additional, Abi-Saleh, Simon, additional, Bendy, Lisa, additional, Borish, Larry, additional, Chmiel, James F., additional, Dix, Aska, additional, France, Lisa, additional, Gammell, Rebecca, additional, Gluvna, Adam, additional, Hirth, Brittany, additional, Hu, Bo, additional, Hyser, Elise, additional, Kloepfer, Kirsten M., additional, Koo, Michelle, additional, Krupp, Nadia L., additional, Labadia, Monica, additional, Lawrence, Joy, additional, Logan, Laurie, additional, Marko, Angela, additional, Matuska, Brittany, additional, Murphy, Deborah, additional, Owensby, Rachel, additional, Roesch, Erica A., additional, Sanders, Don B., additional, Sharp, Jackie, additional, Teague, W. Gerald, additional, Veri, Laura, additional, Shifflett, Kristin Wavell, additional, Camiolo, Matt, additional, Collins, Sarah, additional, Demas, Jessa, additional, Elvin, Courtney, additional, Gauthier, Marc C., additional, Ilnicki, Melissa, additional, Ingram, Jenn, additional, Lane, Lisa, additional, Nouraie, Seyed Mehdi, additional, Trudeau, John B., additional, Zhang, Michael, additional, Barry, Jeffrey, additional, Brickner, Howard, additional, Celso, Janelle, additional, Cernelc-Kohan, Matejka, additional, Diaz, Damaris, additional, Du, Ashley, additional, Jain, Sonia, additional, Liu, Neiman, additional, Nazir, Yusife, additional, Ryu, Julie, additional, Vijayanand, Pandurangan, additional, Almario, Rogelio, additional, Baum, Ariana, additional, Brown, Kellen, additional, Chan, Marilynn H., additional, Gale, Barbara, additional, Haczku, Angela, additional, Harper, Richart W., additional, Heromin, Raymond, additional, Kivler, Celeste, additional, Kuhn, Brooks T., additional, Ly, Ngoc P., additional, McCourt, Paula, additional, Orain, Xavier, additional, Plough, Audrey, additional, Ramirez, Karla, additional, Roberts, Ellese, additional, Schivo, Michael, additional, Singapuri, Amisha, additional, Tham, Tina, additional, Tompkins, Daniel, additional, Twitmyer, Patricia Michelle, additional, Vi, Jade, additional, Atha, Jarron, additional, Bedard, Jennifer, additional, Boomer, Jonathan S., additional, Chung, Andrew, additional, Curtis, Vanessa, additional, Hall, Chase S., additional, Hart, Emily, additional, Jackson, Fatima, additional, Kemp, Pamela, additional, Maxwell, Sharli, additional, Messplay, Maggie, additional, Ramirez, Crystal, additional, Thompson, Brynne, additional, Britt, Ashley, additional, Bryan, Hope, additional, Gotman, Nathan M., additional, Jiang, Yue, additional, Kosorok, Michael R., additional, Mauger, David T., additional, Meekins, Kelsey, additional, Mollenhauer, Jeanette K., additional, Moody, Sarah, additional, Ritz, Cheyanne, additional, Schwartz, Stefanie, additional, Thomlinson, Chalmer, additional, and Wilson, Nicole, additional

Published
2022
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13. Discovery of novel triazolobenzazepinones as γ-secretase modulators with central Aβ42 lowering in rodents and rhesus monkeys

Author

Fischer, Christian, Zultanski, Susan L., Zhou, Hua, Methot, Joey L., Shah, Sanjiv, Hayashi, Ikuo, Hughes, Bethany L., Moxham, Christopher M., Bays, Nathan W., Smotrov, Nadya, Hill, Armetta D., Pan, Bo-Sheng, Wu, Zhenhua, Moy, Lily Y., Tanga, Flobert, Kenific, Candia, Cruz, Jonathan C., Walker, Deborah, Bouthillette, Melanie, Nikov, George N., Deshmukh, Sujal V., Jeliazkova-Mecheva, Valentina V., Diaz, Damaris, Michener, Maria S., Cook, Jacquelynn J., Munoz, Benito, and Shearman, Mark S.

Published
2015
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14. Potent benzoazepinone γ-secretase modulators with improved bioavailability

Author

Methot, Joey L., Fischer, Christian, Li, Chaomin, Rivkin, Alexey, Ahearn, Sean P., Brown, William Colby, Kattar, Sam, Kelley, Elizabeth, Mampreian, Dawn M., Schell, Adam, Rosenau, Andrew, Zhou, Hua, Ball, Richard, Deshmukh, Sujal V., Jeliazkova-Mecheva, Valentina V., Diaz, Damaris, Moy, Lily Y., Kenific, Candia M., Moxham, Chris, Shah, Sanjiv, Nuthall, Hugh, Szewczak, Alexander A., Hill, Armetta, Hughes, Bethany, Smotrov, Nadya, Munoz, Benito, Miller, Thomas A., and Shearman, Mark S.

Published
2015
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15. Discovery of LX2761, a Sodium-Dependent Glucose Cotransporter 1 (SGLT1) Inhibitor Restricted to the Intestinal Lumen, for the Treatment of Diabetes

Author

Goodwin, Nicole C., primary, Ding, Zhi-Ming, additional, Harrison, Bryce A., additional, Strobel, Eric D., additional, Harris, Angela L., additional, Smith, Melinda, additional, Thompson, Andrea Y., additional, Xiong, Wendy, additional, Mseeh, Faika, additional, Bruce, Debra J., additional, Diaz, Damaris, additional, Gopinathan, Suma, additional, Li, Ling, additional, O’Neill, Emily, additional, Thiel, Mary, additional, Wilson, Alan G. E., additional, Carson, Kenneth G., additional, Powell, David R., additional, and Rawlins, David B., additional

Published
2017
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17. LP ‐925219 maximizes urinary glucose excretion in mice by inhibiting both renal SGLT 1 and SGLT 2

Author

Powell, David R., primary, Smith, Melinda G., additional, Doree, Deon D., additional, Harris, Angela L., additional, Xiong, Wendy W., additional, Mseeh, Faika, additional, Wilson, Alan, additional, Gopinathan, Suma, additional, Diaz, Damaris, additional, Goodwin, Nicole C., additional, Harrison, Bryce, additional, Strobel, Eric, additional, Rawlins, David B., additional, Carson, Ken, additional, Zambrowicz, Brian, additional, and Ding, Zhi‐Ming, additional

Published
2015
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18. Automated Pain Assessment using Electrodermal Activity Data and Machine Learning.

Author

Susam BT, Akcakaya M, Nezamfar H, Diaz D, Xu X, de Sa VR, Craig KD, Huang JS, and Goodwin MS

Subjects
Algorithms, Galvanic Skin Response, Humans, Pain, Sensitivity and Specificity, Machine Learning, Pain Measurement
Abstract

Objective pain assessment is required for appropriate pain management in the clinical setting. However, clinical gold standard pain assessment is based on subjective methods. Automated pain detection from physiological data may provide important objective information to better standardize pain assessment. Specifically, electrodermal activity (EDA) can identify features of stress and anxiety induced by varying pain levels. However, notable variability in EDA measurement exists and research to date has demonstrated sensitivity but lack of specificity in pain assessment. In this paper, we use timescale decomposition (TSD) to extract salient features from EDA signals to identify an accurate and automated EDA pain detection algorithm to sensitively and specifically distinguish pain from no-pain conditions.

Published
2018
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19. Automated Pain Detection in Facial Videos of Children using Human-Assisted Transfer Learning.

Author

Xu X, Craig KD, Diaz D, Goodwin MS, Akcakaya M, Susam BT, Huang JS, and de Sa VR

Abstract

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity provides sensitive and specific information about pain, and computer vision algorithms have been developed to automatically detect Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). Our prior work utilized information from computer vision, i.e., automatically detected facial AUs, to develop classifiers to distinguish between pain and no-pain conditions. However, application of pain/no-pain classifiers based on automated AU codings across different environmental domains results in diminished performance. In contrast, classifiers based on manually coded AUs demonstrate reduced environmentally-based variability in performance. In this paper, we train a machine learning model to recognize pain using AUs coded by a computer vision system embedded in a software package called iMotions. We also study the relationship between iMotions (automatically) and human (manually) coded AUs. We find that AUs coded automatically are different from those coded by a human trained in the FACS system, and that the human coder is less sensitive to environmental changes. To improve classification performance in the current work, we applied transfer learning by training another machine learning model to map automated AU codings to a subspace of manual AU codings to enable more robust pain recognition performance when only automatically coded AUs are available for the test data. With this transfer learning method, we improved the Area Under the ROC Curve (AUC) on independent data from new participants in our target domain from 0.67 to 0.72.

Published
2018

20. Towards Automated Pain Detection in Children using Facial and Electrodermal Activity.

Author

Xu X, Susam BT, Nezamfar H, Diaz D, Craig KD, Goodwin MS, Akcakaya M, Huang JS, and Virginia RS

Abstract

Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity and electro- dermal activity (EDA) provide rich information about pain, and both have been used in automated pain detection. In this paper, we discuss preliminary steps towards fusing models trained on video and EDA features respectively. We compare fusion models using original video features and those using transferred video features which are less sensitive to environmental changes. We demonstrate the benefit of the fusion and the transferred video features with a special test case involving domain adaptation and improved performance relative to using EDA and video features alone.

Published
2018

21. LP-925219 maximizes urinary glucose excretion in mice by inhibiting both renal SGLT1 and SGLT2.

Author

Powell DR, Smith MG, Doree DD, Harris AL, Xiong WW, Mseeh F, Wilson A, Gopinathan S, Diaz D, Goodwin NC, Harrison B, Strobel E, Rawlins DB, Carson K, Zambrowicz B, and Ding ZM

Abstract

Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a new class of oral anti-diabetic agents that improve glycemic control by inhibiting SGLT2-mediated renal glucose reabsorption. Currently available agents increase urinary glucose excretion (UGE) to <50% of maximal values because they do not inhibit SGLT1, which reabsorbs >50% of filtered glucose when SGLT2 is completely inhibited. This led us to test whether LP-925219, a small molecule dual SGLT1/SGLT2 inhibitor, increases UGE to maximal values in wild-type (WT) mice. We first tested LP-925219 inhibition of glucose transport by HEK293 cells expressing SGLT1 or SGLT2, and then characterized LP-925219 pharmacokinetics. We found that LP-925219 was a potent inhibitor of mouse SGLT1 (IC50 = 22.6 nmol/L) and SGLT2 (IC50 = 0.5 nmol/L), and that a 10 mg/kg oral dose was bioavailable (87%) with a long half-life (7 h). We next delivered LP-925219 by oral gavage to WT, SGLT1 knockout (KO), SGLT2 KO, and SGLT1/SGLT2 double KO (DKO) mice and measured their 24-h UGE. We found that, in vehicle-treated mice, DKO UGE was maximal and SGLT2 KO, SGLT1 KO, and WT UGEs were 30%, 2%, and 0.2% of maximal, respectively; we also found that LP-925219 dosed at 60 mg/kg twice daily increased UGE of SGLT1 KO, SGLT2 KO, and WT mice to DKO UGE levels. These findings show that orally available dual SGLT1/SGLT2 inhibitors can maximize 24-h UGE in mammals, and suggest that such agents merit further evaluation for their potential, in diabetic patients, to achieve better glycemic control than is achieved using selective SGLT2 inhibitors.

Published
2015
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