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19 results on '"Patel, Kashyap A."'

1. Development of a clinical calculator to aid the identification of MODY in pediatric patients at the time of diabetes diagnosis

Author

Shields, Beverley M., Carlsson, Annelie, Patel, Kashyap, Knupp, Julieanne, Kaur, Akaal, Johnston, Des, Colclough, Kevin, Larsson, Helena Elding, Forsander, Gun, Samuelsson, Ulf, Hattersley, Andrew, Ludvigsson, Johnny, Shields, Beverley M., Carlsson, Annelie, Patel, Kashyap, Knupp, Julieanne, Kaur, Akaal, Johnston, Des, Colclough, Kevin, Larsson, Helena Elding, Forsander, Gun, Samuelsson, Ulf, Hattersley, Andrew, and Ludvigsson, Johnny

Abstract

Maturity Onset Diabetes of the Young (MODY) is a young-onset, monogenic form of diabetes without needing insulin treatment. Diagnostic testing is expensive. To aid decisions on who to test, we aimed to develop a MODY probability calculator for paediatric cases at the time of diabetes diagnosis, when the existing "MODY calculator" cannot be used. Firth logistic regression models were developed on data from 3541 paediatric patients from the Swedish 'Better Diabetes Diagnosis' (BDD) population study (n = 46 (1.3%) MODY (HNF1A, HNF4A, GCK)). Model performance was compared to using islet autoantibody testing. HbA1c, parent with diabetes, and absence of polyuria were significant independent predictors of MODY. The model showed excellent discrimination (c-statistic = 0.963) and calibrated well (Brier score = 0.01). MODY probability > 1.3% (ie. above background prevalence) had similar performance to being negative for all 3 antibodies (positive predictive value (PPV) = 10% v 11% respectively i.e. similar to 1 in 10 positive test rate). Probability > 1.3% and negative for 3 islet autoantibodies narrowed down to 4% of the cohort, and detected 96% of MODY cases (PPV = 31%). This MODY calculator for paediatric patients at time of diabetes diagnosis will help target genetic testing to those most likely to benefit, to get the right diagnosis., Funding Agencies|Linkoeping University

Published
2024
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2. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L.T., Kreienkamp, Raymond J., Lim, Lee Ling, Männistö, Jonna M.E., Massey, Robert, Mclennan, Niamh Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K.W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S.F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J.F., Ma, Ronald C.W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., Franks, Paul W., Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L.T., Kreienkamp, Raymond J., Lim, Lee Ling, Männistö, Jonna M.E., Massey, Robert, Mclennan, Niamh Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K.W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S.F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J.F., Ma, Ronald C.W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., and Franks, Paul W.

Abstract

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Published
2023

3. DFSNet: A Steerable Neural Beamformer Invariant to Microphone Array Configuration for Real-Time, Low-Latency Speech Enhancement

Author

Kovalyov, Anton, Patel, Kashyap, Panahi, Issa, Kovalyov, Anton, Patel, Kashyap, and Panahi, Issa

Abstract

Invariance to microphone array configuration is a rare attribute in neural beamformers. Filter-and-sum (FS) methods in this class define the target signal with respect to a reference channel. However, this not only complicates formulation in reverberant conditions but also the network, which must have a mechanism to infer what the reference channel is. To address these issues, this study presents Delay Filter-and-Sum Network (DFSNet), a steerable neural beamformer invariant to microphone number and array geometry for causal speech enhancement. In DFSNet, acquired signals are first steered toward the speech source direction prior to the FS operation, which simplifies the task into the estimation of delay-and-summed reverberant clean speech. The proposed model is designed to incur low latency, distortion, and memory and computational burden, giving rise to high potential in hearing aid applications. Simulation results reveal comparable performance to noncausal state-of-the-art., Comment: 5 pages, 1 figure, 2 tables

Published
2023

4. Accurate Real-Time Estimation of 2-Dimensional Direction of Arrival using a 3-Microphone Array

Author

Kovalyov, Anton, Patel, Kashyap, Panahi, Issa, Kovalyov, Anton, Patel, Kashyap, and Panahi, Issa

Abstract

This paper presents a method for real-time estimation of 2-dimensional direction of arrival (2D-DOA) of one or more sound sources using a nonlinear array of three microphones. 2D-DOA is estimated employing frame-level time difference of arrival (TDOA) measurements. Unlike conventional methods, which infer location parameters from TDOAs using a theoretical model, we propose a more practical approach based on supervised learning. The proposed model employs nearest neighbor search (NNS) applied to a spherical Fibonacci lattice consisting of TDOA to 2D-DOA mappings learned directly in the field. Filtering and clustering post-processors are also introduced for improved source detection and localization robustness., Comment: 5 pages, 6 figures

Published
2023

5. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K, Tobias, Deirdre K, Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L, Bodhini, Dhanasekaran, Clark, Amy L, Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J, Duan, Daisy, Felton, Jamie L, Francis, Ellen C, Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M, Jacobsen, Laura M, Kahkoska, Anna R, Kettunen, Jarno LT, Kreienkamp, Raymond J, Lim, Lee-Ling, Männistö, Jonna ME, Massey, Robert, Mclennan, Niamh-Maire, Miller, Rachel G, Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N, Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J, Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R, Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha-ting, Thuesen, Anne Cathrine B, Tosur, Mustafa, Wallace, Amelia S, Wang, Caroline C, Wong, Jessie J, Yamamoto, Jennifer M, Young, Katherine, Amouyal, Chloé, Andersen, Mette K, Bonham, Maxine P, Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C, Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y, Deutsch, Aaron J, Dickens, Laura T, DiMeglio, Linda A, Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L, Gitelman, Stephen E, Goodarzi, Mark O, Grieger, Jessica A, Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M, Hoag, Benjamin, Johnson, Randi K, Jones, Angus G, Koivula, Robert W, Leong, Aaron, Leung, Gloria KW, Libman, Ingrid M, Liu, Kai, Long, S Alice, Lowe, William L, Morton, Robert W, Motala, Ayesha A, Onengut-Gumuscu, Suna, Pankow, James S, Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R, Powe, Camille E, Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Tobias, Deirdre K, Tobias, Deirdre K, Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L, Bodhini, Dhanasekaran, Clark, Amy L, Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J, Duan, Daisy, Felton, Jamie L, Francis, Ellen C, Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M, Jacobsen, Laura M, Kahkoska, Anna R, Kettunen, Jarno LT, Kreienkamp, Raymond J, Lim, Lee-Ling, Männistö, Jonna ME, Massey, Robert, Mclennan, Niamh-Maire, Miller, Rachel G, Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N, Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J, Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R, Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha-ting, Thuesen, Anne Cathrine B, Tosur, Mustafa, Wallace, Amelia S, Wang, Caroline C, Wong, Jessie J, Yamamoto, Jennifer M, Young, Katherine, Amouyal, Chloé, Andersen, Mette K, Bonham, Maxine P, Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C, Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y, Deutsch, Aaron J, Dickens, Laura T, DiMeglio, Linda A, Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L, Gitelman, Stephen E, Goodarzi, Mark O, Grieger, Jessica A, Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M, Hoag, Benjamin, Johnson, Randi K, Jones, Angus G, Koivula, Robert W, Leong, Aaron, Leung, Gloria KW, Libman, Ingrid M, Liu, Kai, Long, S Alice, Lowe, William L, Morton, Robert W, Motala, Ayesha A, Onengut-Gumuscu, Suna, Pankow, James S, Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R, Powe, Camille E, Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, and Ried-Larsen, Mathias

Abstract

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Published
2023

6. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L.T., Kreienkamp, Raymond J., Lim, Lee Ling, Männistö, Jonna M.E., Massey, Robert, Mclennan, Niamh Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K.W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S.F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J.F., Ma, Ronald C.W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., Franks, Paul W., Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L.T., Kreienkamp, Raymond J., Lim, Lee Ling, Männistö, Jonna M.E., Massey, Robert, Mclennan, Niamh Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K.W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S.F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J.F., Ma, Ronald C.W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., and Franks, Paul W.

Abstract

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Published
2023

7. Incretin hormone responses to carbohydrate and protein/fat are preserved in adults with sulfonylurea-treated KCNJ11 neonatal diabetes

Author

Bowman, Pamela, Patel, Kashyap A., McDonald, Timothy J., Holst, Jens J., Hartmann, Bolette, Leveridge, Maria, Shields, Beverley M., Hammersley, Suzie, Spaull, Steve R., Knight, Bridget A., Flanagan, Sarah E., Shepherd, Maggie H., Andrews, Rob C., Hattersley, Andrew T., Bowman, Pamela, Patel, Kashyap A., McDonald, Timothy J., Holst, Jens J., Hartmann, Bolette, Leveridge, Maria, Shields, Beverley M., Hammersley, Suzie, Spaull, Steve R., Knight, Bridget A., Flanagan, Sarah E., Shepherd, Maggie H., Andrews, Rob C., and Hattersley, Andrew T.

Abstract

The incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), are thought to be the main drivers of insulin secretion in individuals with sulfonylurea (SU)-treated KCNJ11 permanent neonatal diabetes. The aim of this study was to assess for the first time the incretin hormone response to carbohydrate and protein/fat in adults with sulfonylurea-treated KCNJ11 permanent neonatal diabetes compared with that of controls without diabetes. Participants were given a breakfast high in carbohydrate and an isocaloric breakfast high in protein/fat on two different mornings. Incremental area under the curve and total area under the curve (0-240 minutes) for total GLP-1 and GIP were compared between groups, using non-parametric statistical methods. Post-meal GLP-1 and GIP secretion were similar in cases and controls, suggesting this process is adenosine triphosphate-sensitive potassium channel-independent. Future research will investigate whether treatments targeting the incretin pathway are effective in individuals with KCNJ11 permanent neonatal diabetes who do not have good glycemic control on sulfonylurea alone.

Published
2023

8. Incretin hormone responses to carbohydrate and protein/fat are preserved in adults with sulfonylurea-treated KCNJ11 neonatal diabetes

Author

Bowman, Pamela, Patel, Kashyap A., McDonald, Timothy J., Holst, Jens J., Hartmann, Bolette, Leveridge, Maria, Shields, Beverley M., Hammersley, Suzie, Spaull, Steve R., Knight, Bridget A., Flanagan, Sarah E., Shepherd, Maggie H., Andrews, Rob C., Hattersley, Andrew T., Bowman, Pamela, Patel, Kashyap A., McDonald, Timothy J., Holst, Jens J., Hartmann, Bolette, Leveridge, Maria, Shields, Beverley M., Hammersley, Suzie, Spaull, Steve R., Knight, Bridget A., Flanagan, Sarah E., Shepherd, Maggie H., Andrews, Rob C., and Hattersley, Andrew T.

Abstract

The incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), are thought to be the main drivers of insulin secretion in individuals with sulfonylurea (SU)-treated KCNJ11 permanent neonatal diabetes. The aim of this study was to assess for the first time the incretin hormone response to carbohydrate and protein/fat in adults with sulfonylurea-treated KCNJ11 permanent neonatal diabetes compared with that of controls without diabetes. Participants were given a breakfast high in carbohydrate and an isocaloric breakfast high in protein/fat on two different mornings. Incremental area under the curve and total area under the curve (0-240 minutes) for total GLP-1 and GIP were compared between groups, using non-parametric statistical methods. Post-meal GLP-1 and GIP secretion were similar in cases and controls, suggesting this process is adenosine triphosphate-sensitive potassium channel-independent. Future research will investigate whether treatments targeting the incretin pathway are effective in individuals with KCNJ11 permanent neonatal diabetes who do not have good glycemic control on sulfonylurea alone.

Published
2023

9. Real-time Speech Enhancement and Separation with a Unified Deep Neural Network for Single/Dual Talker Scenarios

Author

Patel, Kashyap, Kovalyov, Anton, Panahi, Issa, Patel, Kashyap, Kovalyov, Anton, and Panahi, Issa

Abstract

This paper introduces a practical approach for leveraging a real-time deep learning model to alternate between speech enhancement and joint speech enhancement and separation depending on whether the input mixture contains one or two active speakers. Scale-invariant signal-to-distortion ratio (SI-SDR) has shown to be a highly effective training measure in time-domain speech separation. However, the SI-SDR metric is ill-defined for zero-energy target signals, which is a problem when training a speech separation model using utterances with varying numbers of talkers. Unlike existing solutions that focus on modifying the loss function to accommodate zero-energy target signals, the proposed approach circumvents this problem by training the model to extract speech on both its output channels regardless if the input is a single or dual-talker mixture. A lightweight speaker overlap detection (SOD) module is also introduced to differentiate between single and dual-talker segments in real-time. The proposed module takes advantage of the new formulation by operating directly on the separated masks, given by the separation model, instead of the original mixture, thus effectively simplifying the detection task. Experimental results show that the proposed training approach outperforms existing solutions, and the SOD module exhibits high accuracy., Comment: 6 Pages, Accepted at IEEE Asilomar

Published
2023

10. UX-NET: Filter-and-Process-based Improved U-Net for Real-time Time-domain Audio Separation

Author

Patel, Kashyap, Kovalyov, Anton, Panahi, Issa, Patel, Kashyap, Kovalyov, Anton, and Panahi, Issa

Abstract

This study presents UX-Net, a time-domain audio separation network (TasNet) based on a modified U-Net architecture. The proposed UX-Net works in real-time and handles either single or multi-microphone input. Inspired by the filter-and-process-based human auditory behavior, the proposed system introduces novel mixer and separation modules, which result in cost and memory efficient modeling of speech sources. The mixer module combines encoded input in a latent feature space and outputs a desired number of output streams. Then, in the separation module, a modified U-Net (UX) block is applied. The UX block first filters the encoded input at various resolutions followed by aggregating the filtered information and applying recurrent processing to estimate masks of separated sources. The letter 'X' in UX-Net is a name placeholder for the type of recurrent layer employed in the UX block. Empirical findings on the WSJ0-2mix benchmark dataset show that one of the UX-Net configurations outperforms the state-of-the-art Conv-TasNet system by 0.85 dB SI-SNR while using only 16% of the model parameters, 58% fewer computations, and maintaining low latency., Comment: Submitted to ICASSP 2023

Published
2022

11. YIPF5 mutations cause neonatal diabetes and microcephaly through endoplasmic reticulum stress.

Author

De Franco, Elisa, Lytrivi, Maria, Ibrahim, Hazem, Montaser, Hossam El-dien, Wakeling, Matthew MN, Fantuzzi, Federica, Patel, Kashyap A., Demarez, Céline, Cai, Ying, Igoillo Esteve, Mariana, Cosentino, Cristina, Lithovius, Väinö, Vihinen, Helena, Jokitalo, Eija, Laver, Thomas W, Johnson, Matthew B, Sawatani, Toshiaki, Shakeri, Hadis, Pachera, Nathalie, Haliloglu, Belma, Ozbek, Mehmet Nuri, Unal, Edip, Yıldırım, Ruken, Godbole, Tushar, Yildiz, Melek, Aydin, Banu, Bilheu, Angeline, Suzuki, Ikuo, Flanagan, Sarah E., Vanderhaeghen, Pierre, Senée, Valérie, Julier, Cécile, Marchetti, Piero, Eizirik, Decio L., Ellard, Sian, Saarimäki-Vire, Jonna, Otonkoski, Timo, Cnop, Miriam, Hattersley, Andrew T, De Franco, Elisa, Lytrivi, Maria, Ibrahim, Hazem, Montaser, Hossam El-dien, Wakeling, Matthew MN, Fantuzzi, Federica, Patel, Kashyap A., Demarez, Céline, Cai, Ying, Igoillo Esteve, Mariana, Cosentino, Cristina, Lithovius, Väinö, Vihinen, Helena, Jokitalo, Eija, Laver, Thomas W, Johnson, Matthew B, Sawatani, Toshiaki, Shakeri, Hadis, Pachera, Nathalie, Haliloglu, Belma, Ozbek, Mehmet Nuri, Unal, Edip, Yıldırım, Ruken, Godbole, Tushar, Yildiz, Melek, Aydin, Banu, Bilheu, Angeline, Suzuki, Ikuo, Flanagan, Sarah E., Vanderhaeghen, Pierre, Senée, Valérie, Julier, Cécile, Marchetti, Piero, Eizirik, Decio L., Ellard, Sian, Saarimäki-Vire, Jonna, Otonkoski, Timo, Cnop, Miriam, and Hattersley, Andrew T

Abstract

Neonatal diabetes is caused by single gene mutations reducing pancreatic β cell number or impairing β cell function. Understanding the genetic basis of rare diabetes subtypes highlights fundamental biological processes in β cells. We identified 6 patients from 5 families with homozygous mutations in the YIPF5 gene, which is involved in trafficking between the endoplasmic reticulum (ER) and the Golgi. All patients had neonatal/early-onset diabetes, severe microcephaly, and epilepsy. YIPF5 is expressed during human brain development, in adult brain and pancreatic islets. We used 3 human β cell models (YIPF5 silencing in EndoC-βH1 cells, YIPF5 knockout and mutation knockin in embryonic stem cells, and patient-derived induced pluripotent stem cells) to investigate the mechanism through which YIPF5 loss of function affects β cells. Loss of YIPF5 function in stem cell-derived islet cells resulted in proinsulin retention in the ER, marked ER stress, and β cell failure. Partial YIPF5 silencing in EndoC-βH1 cells and a patient mutation in stem cells increased the β cell sensitivity to ER stress-induced apoptosis. We report recessive YIPF5 mutations as the genetic cause of a congenital syndrome of microcephaly, epilepsy, and neonatal/early-onset diabetes, highlighting a critical role of YIPF5 in β cells and neurons. We believe this is the first report of mutations disrupting the ER-to-Golgi trafficking, resulting in diabetes., info:eu-repo/semantics/published

Published
2020

12. Heterozygous RFX6 protein truncating variants are associated with MODY with reduced penetrance

Author

Patel, Kashyap A., Kettunen, Jarno, Laakso, Markku, Stancakova, Alena, Laver, Thomas T.W., Colclough, Kevin, Johnson, Matthew B., Abramowicz, Marc, Groop, Leif, Miettinen, Päivi P.J., Shepherd, Maggie H., Flanagan, Sarah E., Ellard, Sian, Inagaki, Nobuya, Hattersley, Andrew T, Tuomi, Tiinamaija, Cnop, Miriam, Weedon, Michael N., Patel, Kashyap A., Kettunen, Jarno, Laakso, Markku, Stancakova, Alena, Laver, Thomas T.W., Colclough, Kevin, Johnson, Matthew B., Abramowicz, Marc, Groop, Leif, Miettinen, Päivi P.J., Shepherd, Maggie H., Flanagan, Sarah E., Ellard, Sian, Inagaki, Nobuya, Hattersley, Andrew T, Tuomi, Tiinamaija, Cnop, Miriam, and Weedon, Michael N.

Abstract

Finding new causes of monogenic diabetes helps understand glycaemic regulation in humans. To find novel genetic causes of maturity-onset diabetes of the young (MODY), we sequenced MODY cases with unknown aetiology and compared variant frequencies to large public databases. From 36 European patients, we identify two probands with novel RFX6 heterozygous nonsense variants. RFX6 protein truncating variants are enriched in the MODY discovery cohort compared to the European control population within ExAC (odds ratio = 131, P = 1 × 10-4). We find similar results in non-Finnish European (n = 348, odds ratio = 43, P = 5 × 10-5) and Finnish (n = 80, odds ratio = 22, P = 1 × 10-6) replication cohorts. RFX6 heterozygotes have reduced penetrance of diabetes compared to common HNF1A and HNF4A-MODY mutations (27, 70 and 55% at 25 years of age, respectively). The hyperglycaemia results from beta-cell dysfunction and is associated with lower fasting and stimulated gastric inhibitory polypeptide (GIP) levels. Our study demonstrates that heterozygous RFX6 protein truncating variants are associated with MODY with reduced penetrance., SCOPUS: ar.j, Miriam Cnop and Michael N. Weedon share last and corresponding authorship, info:eu-repo/semantics/published

Published
2017

13. The LKB1-salt-inducible kinase pathway functions as a key gluconeogenic suppressor in the liver.

Author

Patel, Kashyap, Foretz, Marc, Marion, Allison, Campbell, David G, Gourlay, Robert, Boudaba, Nadia, Tournier, Emilie, Titchenell, Paul, Peggie, Mark, Deak, Maria, Wan, Min, Kaestner, Klaus H, Göransson, Olga, Viollet, Benoit, Gray, Nathanael S, Birnbaum, Morris J, Sutherland, Calum, Sakamoto, Kei, Patel, Kashyap, Foretz, Marc, Marion, Allison, Campbell, David G, Gourlay, Robert, Boudaba, Nadia, Tournier, Emilie, Titchenell, Paul, Peggie, Mark, Deak, Maria, Wan, Min, Kaestner, Klaus H, Göransson, Olga, Viollet, Benoit, Gray, Nathanael S, Birnbaum, Morris J, Sutherland, Calum, and Sakamoto, Kei

Abstract

LKB1 is a master kinase that regulates metabolism and growth through adenosine monophosphate-activated protein kinase (AMPK) and 12 other closely related kinases. Liver-specific ablation of LKB1 causes increased glucose production in hepatocytes in vitro and hyperglycaemia in fasting mice in vivo. Here we report that the salt-inducible kinases (SIK1, 2 and 3), members of the AMPK-related kinase family, play a key role as gluconeogenic suppressors downstream of LKB1 in the liver. The selective SIK inhibitor HG-9-91-01 promotes dephosphorylation of transcriptional co-activators CRTC2/3 resulting in enhanced gluconeogenic gene expression and glucose production in hepatocytes, an effect that is abolished when an HG-9-91-01-insensitive mutant SIK is introduced or LKB1 is ablated. Although SIK2 was proposed as a key regulator of insulin-mediated suppression of gluconeogenesis, we provide genetic evidence that liver-specific ablation of SIK2 alone has no effect on gluconeogenesis and insulin does not modulate SIK2 phosphorylation or activity. Collectively, we demonstrate that the LKB1-SIK pathway functions as a key gluconeogenic gatekeeper in the liver.

Published
2014

14. A Time-to-Event Pharmacodynamic Model Describing Treatment Response in Patients with Pulmonary Tuberculosis Using Days to Positivity in Automated Liquid Mycobacterial Culture

Author

Chigutsa, Emmanuel, Patel, Kashyap, Denti, Paolo, Visser, Marianne, Maartens, Gary, Kirkpatrick, Carl M. J., McIlleron, Helen, Karlsson, Mats O., Chigutsa, Emmanuel, Patel, Kashyap, Denti, Paolo, Visser, Marianne, Maartens, Gary, Kirkpatrick, Carl M. J., McIlleron, Helen, and Karlsson, Mats O.

Abstract

Days to positivity in automated liquid mycobacterial culture have been shown to correlate with mycobacterial load and have been proposed as a useful biomarker for treatment responses in tuberculosis. However, there is currently no quantitative method or model to analyze the change in days to positivity with time on treatment. The objectives of this study were to describe the decline in numbers of mycobacteria in sputum collected once weekly for 8 weeks from patients on treatment for tuberculosis using days to positivity in liquid culture. One hundred forty-four patients with smear-positive pulmonary tuberculosis were recruited from a tuberculosis clinic in Cape Town, South Africa. A nonlinear mixed-effects repeated-time-to-event modeling approach was used to analyze the time-to-positivity data. A biexponential model described the decline in the estimated number of bacteria in patients' sputum samples, while a logistic model with a lag time described the growth of the bacteria in liquid culture. At baseline, the estimated number of rapidly killed bacteria is typically 41 times higher than that of those that are killed slowly. The time to kill half of the rapidly killed bacteria was about 1.8 days, while it was 39 days for slowly killed bacteria. Patients with lung cavitation had higher bacterial loads than patients without lung cavitation. The model successfully described the increase in days to positivity as treatment progressed, differentiating between bacteria that are killed rapidly and those that are killed slowly. Our model can be used to analyze similar data from studies testing new drug regimens.

Published
2013
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15. Cellular responses to the metal-binding properties of metformin

Author

Logie, Lisa, Harthill, Jean, Patel, Kashyap, Bacon, Sandra, Hamilton, David Lee, Macrae, Katherine, McDougall, Gordon, Wang, Huan-Huan, Xue, Lin, Jiang, Hua, Sakamoto, Kei, Prescott, Alan R, Rena, Graham, Logie, Lisa, Harthill, Jean, Patel, Kashyap, Bacon, Sandra, Hamilton, David Lee, Macrae, Katherine, McDougall, Gordon, Wang, Huan-Huan, Xue, Lin, Jiang, Hua, Sakamoto, Kei, Prescott, Alan R, and Rena, Graham

Published
2012

16. Tumor-derived interleukin-10 as a prognostic factor in stage III patients undergoing adjuvant treatment with an autologous melanoma cell vaccine.

Author

Mahipal, Amit, Terai, Mizue, Berd, David, Chervoneva, Inna, Patel, Kashyap, Mastrangelo, Michael, Sato, Takami, Mahipal, Amit, Terai, Mizue, Berd, David, Chervoneva, Inna, Patel, Kashyap, Mastrangelo, Michael, and Sato, Takami

Abstract

OBJECTIVES: Interleukin-10 (IL-10) downregulates T-cell-mediated immune responses. We studied the association between IL-10 production by freshly isolated melanoma cell suspensions in vitro and overall survival in patients undergoing adjuvant treatment with a vaccine prepared from the same autologous melanoma cells modified with a hapten, dinitrophenyl (DNP). METHODS: Forty-four patients with cutaneous melanoma (29 stage III and 15 stage IV) were prospectively evaluated. Tumor cells were extracted from metastatic deposits for production of DNP-modified autologous melanoma cell vaccine. Small aliquots of the melanoma cell suspensions were separated prior to vaccine processing and cultured overnight for IL-10 production. Based on a blind assessment of the distribution of IL-10 levels in the culture supernatants, a cutoff of 200 pg/ml was used to define high versus low IL-10 producers. Cox regression model was used for multivariate analysis. Overall survival was calculated using the Kaplan-Meier method, and survival curves were compared with the log-rank test. RESULTS: Out of 44 patients, 29 were low and 15 were high IL-10 producers. The median OS was significantly worse for high compared with low IL-10 producers (10.5 months vs. 42 months; P = 0.022). In stage III patients, the multivariate hazard ratio for high versus low IL-10 producers was 2.92 (95% CI, 1.04-8.20; P = 0.041). The corresponding hazard ratio in stage IV patients was 0.92 (95% CI, 1.04-8.20; P = 0.888). CONCLUSIONS: High IL-10 production in the tumor microenvironment could be a determinant of clinical outcomes in stage III melanoma patients receiving autologous melanoma cell vaccine.

Published
2011

17. Crisplant defects quantification and reduction at an amazon.com distribution center

Author

Roy Welsch and Stanley Gershwin., Leaders for Global Operations Program., Sloan School of Management., Massachusetts Institute of Technology. Dept. of Mechanical Engineering., Patel, Kashyap (Kashyap C.), Roy Welsch and Stanley Gershwin., Leaders for Global Operations Program., Sloan School of Management., Massachusetts Institute of Technology. Dept. of Mechanical Engineering., and Patel, Kashyap (Kashyap C.)

Abstract

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2010., Cataloged from PDF version of thesis., Includes bibliographical references (p. 59-60)., Crisplant is a tilt-tray sortation system used in Reno (RNO 1) fulfillment center (FC) to group items by customer orders. On average., crisplant processes about 80% of the total outbound volume through its multipart operation flow. Because of high volume and complex process flow, the majority of defects, in RNO 1 FC. are seen in crisplant costing distribution center (RNO 1) significantly in labor hours. This research paper identifies and quantifies the major defects in crisplant, and outlines the solutions to reduce the cost of handling these defects in RNO 1. The project work thoroughly assesses the entire RNO 1 crisplant operations (induct, sort, pack, SLAM, and problem solve) through four-phase approach: Understand the crisplant Process Flow, Develop a Data Collection Framework, Collect and Analyze Data, and Identify/Implement Data Driven Solutions. Lean principles and methodologies were used throughout the project work especially when identifying solutions. For example, opportunities that improved the packing process were identified based on a deep-dive analysis as a part of the Kaizen study. The project results demonstrated 50% reduction in cost of handling crisplant defects in RNO l. Furthermore, it highlighted opportunities for additional savings by identifying solutions that can also be implemented in other FCs (i.e. SDF 1, TUL 1) with similar operation as RNO 1., by Kashyap Patel., S.M., M.B.A.

Published
2010

18. Crisplant defects quantification and reduction at an amazon.com distribution center

Author

Roy Welsch and Stanley Gershwin., Leaders for Global Operations Program., Sloan School of Management., Massachusetts Institute of Technology. Dept. of Mechanical Engineering., Patel, Kashyap (Kashyap C.), Roy Welsch and Stanley Gershwin., Leaders for Global Operations Program., Sloan School of Management., Massachusetts Institute of Technology. Dept. of Mechanical Engineering., and Patel, Kashyap (Kashyap C.)

Abstract

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2010., Cataloged from PDF version of thesis., Includes bibliographical references (p. 59-60)., Crisplant is a tilt-tray sortation system used in Reno (RNO 1) fulfillment center (FC) to group items by customer orders. On average., crisplant processes about 80% of the total outbound volume through its multipart operation flow. Because of high volume and complex process flow, the majority of defects, in RNO 1 FC. are seen in crisplant costing distribution center (RNO 1) significantly in labor hours. This research paper identifies and quantifies the major defects in crisplant, and outlines the solutions to reduce the cost of handling these defects in RNO 1. The project work thoroughly assesses the entire RNO 1 crisplant operations (induct, sort, pack, SLAM, and problem solve) through four-phase approach: Understand the crisplant Process Flow, Develop a Data Collection Framework, Collect and Analyze Data, and Identify/Implement Data Driven Solutions. Lean principles and methodologies were used throughout the project work especially when identifying solutions. For example, opportunities that improved the packing process were identified based on a deep-dive analysis as a part of the Kaizen study. The project results demonstrated 50% reduction in cost of handling crisplant defects in RNO l. Furthermore, it highlighted opportunities for additional savings by identifying solutions that can also be implemented in other FCs (i.e. SDF 1, TUL 1) with similar operation as RNO 1., by Kashyap Patel., S.M., M.B.A.

Published
2010

19. Heterozygous RFX6 protein truncating variants cause Maturity-Onset Diabetes of the Young (MODY) with reduced penetrance

Author

Patel, Kashyap A., Laakso, Markku, Stancakova, Alena, Laver, Thomas W., Colclough, Kevin, Johnson, Matthew B., Abramowicz, Marc, Groop, Leif, Miettinen, Päivi P.J., Shepherd, Maggie H., Flanagan, Sarah E., Ellard, Sian, Inagaki, Nobuya, Hattersley, Andrew T, Tuomi, Tiinamaija, Cnop, Miriam, Weedon, Michael N., Patel, Kashyap A., Laakso, Markku, Stancakova, Alena, Laver, Thomas W., Colclough, Kevin, Johnson, Matthew B., Abramowicz, Marc, Groop, Leif, Miettinen, Päivi P.J., Shepherd, Maggie H., Flanagan, Sarah E., Ellard, Sian, Inagaki, Nobuya, Hattersley, Andrew T, Tuomi, Tiinamaija, Cnop, Miriam, and Weedon, Michael N.

Abstract

Finding new genetic causes of monogenic diabetes can help to understand development and function of the human pancreas. We aimed to find novel protein-truncating variants causing Maturity-Onset Diabetes of the Young (MODY), a subtype of monogenic diabetes. We used a combination of next-generation sequencing of MODY cases with unknown aetiology along with comparisons to the ExAC database to identify new MODY genes. In the discovery cohort of 36 European patients, we identified two probands with novel RFX6 heterozygous nonsense variants. RFX6 protein truncating variants were enriched in the MODY discovery cohort compared to the European control population within ExAC (odds ratio, OR=131, P=1x10-4). We found similar results in non-Finnish European (n=348, OR=43, P=5x10-5) and Finnish (n=80, OR=22, P=1x10-6) replication cohorts. The overall meta-analysis OR was 34 (P=1x10-16). RFX6 heterozygotes had reduced penetrance of diabetes compared to common HNF1A and HNF4A-MODY mutations (27%, 70% and 55% at 25 years of age, respectively). The hyperglycaemia resulted from beta-cell dysfunction and was associated with lower fasting and stimulated gastric inhibitory polypeptide (GIP) levels. Our study demonstrates that heterozygous RFX6 protein truncating variants are associated with MODY with reduced penetrance., info:eu-repo/semantics/nonPublished

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