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5. Outcomes of thoracic endovascular aortic repair in patients with concomitant blunt thoracic aortic injury and traumatic brain injury from the Aortic Trauma Foundation global registry

Author

Saqib, Naveed, Charlton-Ouw, Kristofer, Afifi, Rana, McNutt, Michelle, Al-Rustum, Zain, Shrestha, Binod, Quiroga, Elina, Starnes, Ben, Gilani, Rami, Turay, David, Luo-Owen, Xian, Bee, Tiffany, Moyer, Suzanne, DuBose, Joe, Shutze, William, Dockery, William, Petrey, Laura, Phelps, Timothy N., Fox, Chuck, Moore, Ernest, Cralley, Alexis, Teixeira, Pedro, Leede, Emily, Buchanan, Frank, Ramos, Emilio, Ngoue, Marielle, Fox, Nicole, Shea, Lisa, Zielinski, Martin, Fischmann, Marianna Martini, Inaba, Kenji, Khor, Desmond, Magee, Gregory, Sheahan, Malachi, Unruh, Marie, Parry, Neil, Dubois, Luc, Berne, John, Puente, Ivan, Gomez, Mario F., Mederos, Dalier R., Bini, John, Herzing, Karen, Hardman, Claire, Schanzer, Andres, Aiello, Francesco, Arous, Edward, Arous, Elias, Jones, Douglas, Judelson, Dejah, Messina, Louis, Nguyen, Tammy, Simons, Jessica, Steppacher, Robert, Rezende-Neto, Joao, Haan, James, Lightwine, Kelly, Dunn, Julie, Smoot, Brittany, Horer, Tal, McGreevy, David, Riambau, Vincent, Mestres, Gaspar, Yugueros, Xavier, Passman, Marc, Beck, Adam W., Patterson, Mark, Pearce, Ben, Spangler, Emily, McFarland, Graeme, Sutzko, Danielle, Smeds, Matt, Zakhary, Emad, Williams, Michael, Wittgen, Catherine, Vogel, Todd, Eagleton, Matt, Azizzadeh, Ali, Gewertz, Bruce, Barmparas, Galinos, Arbabi, Cassra, DuBose, Joseph, Kundi, Rishi, Morrison, Jonathan, Rossi, Peter, Pacini, Davide, Botta, Luca, Amodio, Ciro, Rimoldi, Pierantonio, D'Alessio, Ilenia, Compagnoni, Nicola Monzio, Aftab, Muhammad, Al-Musawi, Mohammed, Reece, T. Brett, Pal, Jay D., Jacobs, Donald, Malgor, Rafael D., Zarkowsky, Devin, Rajani, Ravi, Benarroch-Gampel, Jaime, Ramos, Christopher R., Schermerhorn, Marc, Wyers, Mark, Hamdan, Allen, Stangenberg, Lars, Lee, Andy, Davies, Mark, Jayakumar, Lalithapriya (Priya), Sideman, Matthew J., Mitromaras, Christopher, Miserlis, Dimitrios, Brahmbhatt, Reshma, Darling, Ralph, Caliste, Xzabia, Chang, Benjamin B., Hnath, Jeffrey C., Kreienberg, Paul B., Kryszuk, Alexander, Laser, Adriana, Roddy, Sean P., Saltzberg, Stephanie, Shah, Melissa, Warner, Courtney, Yeh, Chin-Chin, Reva, Viktor, Zhigalo, Viktor, Krasikov, Alexander V., Trimarchi, Santi, Domanin, Maurizio, Babrowski, Trissa, Milner, Ross, Pocivavsek, Luka, Skelly, Christopher, Malka, Kimberly, Nolan, Brian, D’Oria, Mario, Lepidi, Sandro, Arbabi, Cassra N., Starnes, Benjamin W., and Miller, Charles

Published
2022
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6. Outcomes and practice patterns of medical management of blunt thoracic aortic injury from the Aortic Trauma Foundation global registry

Author

Saqib, Naveed, Charlton-Ouw, Kristofer, Afifi, Rana, McNutt, Michelle, Al-Rustum, Zain, Shrestha, Binod, Dipasupil, Edmundo, Quiroga, Elina, Starnes, Ben, Gilani, Rami, Turay, David, Luo-Owen, Xian, Bee, Tiffany, Moyer, Suzanne, DuBose, Joe, Shutze, William, Dockery, William, Petrey, Laura, Phelps, Timothy N., Fox, Chuck, Moore, Ernest, Cralley, Alexis, Teixeira, Pedro, Leede, Emily, Buchanan, Frank, Ramos, Emilio, Ngoue, Marielle, Fox, Nicole, Shea, Lisa, Zielinski, Martin, Fischmann, Marianna Martini, Inaba, Kenji, Khor, Desmond, Magee, Gregory, Sheahan, Malachi, Unruh, Marie, Parry, Neil, Dubois, Luc, Berne, John, Puente, Ivan, Gomez, Mario F., Mederos, Dalier R., Bini, John, Herzing, Karen, Hardman, Claire, Schanzer, Andres, Aiello, Francesco, Arous, Edward, Arous, Elias, Jones, Douglas, Judelson, Dejah, Messina, Louis, Nguyen, Tammy, Simons, Jessica, Steppacher, Robert, Rezende-Neto, Joao, Haan, James, Lightwine, Kelly, Dunn, Julie, Smoot, Brittany, Horer, Tal, McGreevy, David, Riambau, Vincent, Mestres, Gaspar, Yugueros, Xavier, Passman, Marc, Beck, Adam W., Patterson, Mark, Pearce, Ben, Spangler, Emily, McFarland, Graeme, Sutzko, Danielle, Smeds, Matt, Zakhary, Emad, Williams, Michael, Wittgen, Catherine, Vogel, Todd, Eagleton, Matt, Azizzadeh, Ali, Gewertz, Bruce, Barmparas, Galinos, Arbabi, Cassra, DuBose, Joseph, Kundi, Rishi, Morrison, Jonathan, Rossi, Peter, Pacini, Davide, Botta, Luca, Amodio, Ciro, Rimoldi, Pierantonio, D'Alessio, Ilenia, Compagnoni, Nicola Monzio, Aftab, Muhammad, Al-Musawi, Mohammed, Reece, T. Brett, Pal, Jay D., Jacobs, Donald, Malgor, Rafael D., Zarkowsky, Devin, Rajani, Ravi, Benarroch-Gampel, Jaime, Ramos, Christopher R., Schermerhorn, Marc, Wyers, Mark, Hamdan, Allen, Stangenberg, Lars, Lee, Andy, Davies, Mark, Jayakumar, Lalithapriya (Priya), Sideman, Matthew J., Mitromaras, Christopher, Miserlis, Dimitrios, Brahmbhatt, Reshma, Darling, Ralph, Caliste, Xzabia, Chang, Benjamin B., Hnath, Jeffrey C., Kreienberg, Paul B., Kryszuk, Alexander, Laser, Adriana, Roddy, Sean P., Saltzberg, Stephanie, Shah, Melissa, Warner, Courtney, Yeh, Chin-Chin, Reva, Viktor, Zhigalo, Viktor, Krasikov, Alexander V., Trimarchi, Santi, Domanin, Maurizio, Babrowski, Trissa, Milner, Ross, Pocivavsek, Luka, Skelly, Christopher, Malka, Kimberly, Nolan, Brian, D’Oria, Mario, Lepidi, Sandro, Arbabi, Cassra N., Starnes, Benjamin W., and Miller, Charles

Published
2022
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8. Documenting the Recovery of Vascular Services in European Centres Following the Initial COVID-19 Pandemic Peak: Results from a Multicentre Collaborative Study

Author

Ruffino, Maria Antonella, Chan, Sharon, Coughlin, Patrick, Awopetu, Ayoola, Stather, Philip, Lane, Tristan, Theodosiou, Dimitrios, Ahmed, Mohamed Abozeid, Vasudevan, Thodur, Ibrahim, Mohammed, Al Maadany, Faraj, Eljareh, Mohamed, Alkhafeefi, Fatimah Saad, Coscas, Raphael, Ünal, Ertekin Utku, Pulli, Raffaele, Zacà, Sergio, Angiletta, Domenico, Kotsis, Thomas, Moawad, Magdy, Tozzi, Matteo, Patelis, Nikolaos, Lazaris, Andreas M., Chuen, Jason, Croo, Alexander, Tsolaki, Elpiniki, Zenunaj, Gladiol, Kamal, Dhafer, Tolba, Mahmoud MH., Maresch, Martin, Khetarpaul, Vipul, Mills, Joseph, Gangwani, Gaurav, Elahwal, Mohamed, Khalil, Rana, Azab, Mohammed A., Mahomed, Anver, Whiston, Richard, Contractor, Ummul, Esposito, Davide, Pratesi, Carlo, Giacomelli, Elena, Troncoso, Martín Veras, Elkouri, Stephane, Johansson, Flavia Gentile, Dodos, Ilias, Benezit, Marie, Vidoedo, José, Rocha-Neves, João, Pereira-Neves, António Henrique, Dias-Neto, Marina Felicidade, Campos Jácome, Ana Filipa, Loureiro, Luis, Silva, Ivone, Garza-Herrera, Rodrigo, Canata, Victor, Bezard, Charlotte, Bowser, Kathryn, Tobar, Jorge Felipe, Vera, Carlos Gomez, Parra, Carolina Salinas, Lopez, Eugenia, Serra, Yvis Gadelha, Varela, Juan, Rubio, Vanessa, Victoria, Gerardo, Johnson, Adam, O’Banion, Leigh Ann, Makar, Ragai, Tantawy, Tamer Ghatwary, Storck, Martin, Jongkind, Vincent, falah, Orwa, McBride, Olivia, Isik, Arda, Papaioannou, Athanasios, Ocke Reis, Paulo Eduardo, Bracale, Umberto Marcello, Atkins, Ellie, Tinelli, Giovanni, Scott, Emma, Wales, Lucy, Sivaharan, Ashwin, Priona, Georgia, Nesbitt, Craig, Grainger, Tabitha, Shelmerdine, Lauren, Chong, Patrick, Bajwa, Adnan, Arwynck, Luke, Hadjievangelou, Nancy, Elbasty, Ahmed, Rubio, Oscar, Ricardo, Michael, Ulloa, Jorge H., Tarazona, Marcos, Pabon, Manuel, Pitoulias, Georgios, Corless, Kevin, Ioannidis, Orestis, Friedrich, Oliver, Van Herzeele, Isabelle, Vijaynagar, Badri, Cohnert, Tina, Bell, Rachel, Moore, Hayley, Saha, Prakash, Gifford, Edward, Laine, Matti, Barkat, Adel, Karkos, Christos, Binti Safri, Lenny Suryani, Buitron, Gabriel, Del Castillo, Javier, Carrera, Paul, Salinas, Nilson, Biagioni, Rodrigo Bruno, Benites, Sergio, Mafla, César Andrés, Pian, Putera Mas, Albino, Pereira, Serrano, Ernesto, Marin, Andres, González, Marco, Foreroga, Marsha, Russo, Alejandro, Reyes, Andrés, Guglielmone, Daniel, Grillo, Lorena, Flumignan, Ronald, Palones, Francisco Gomez, Silveira, Pierre Galvagni, Ramely, Rosnelifaizur Bin, Edeiken, Sara, Chetter, Ian, Green, Lucy, Sudarsanam, Abhilash, Lyons, Oliver, Lemmon, Gary, Neville, Richard, Castelli, Mariano, Hinojosa, Carlos A., Carvajal, Rubén Rodríguez, Rivera, Aksim, Wong, Peng, Drudi, Laura, Perkins, Jeremy, Sieunarine, Kishore, Attia, Doaa, Atef, Mahmoud, Eftychios, Lostoridis, Weaver, Fred, Ren, Leong Chuo, Alomari, Mohannad, Jamjoom, Reda, Aljarrah, Qusai, Abbas, Ayman, Alomran, Faris, Kumar, Ambrish, Altoijri, Abdulmajeed, ElSanhoury, Kareem T., Alhumaid, Ahmed, Fekry, Tamer, Sekhar, Raghuram, Theodoridis, Panagiotis, Panagiotis, Theodoridis, Roditis, Konstantinos, Tsiantoula, Paraskevi, Antoniou, Afroditi, Soler, Raphael, Hasemaki, Natasha, Baili, Efstratia, Mpaili, Eustratia, Huasen, Bella, Wallace, Tom, Duncan, Andrew, Metcalfe, Matthew, Mannoia, Kristyn, Bechara, Carlos F., Tsilimparis, Nikolaos, Aranson, Nathan, Riding, David, Palena, Mariano, McDonnell, Ciarán, Mouawad, Nicolas J., Banegas, Shonda, Rossi, Peter, Oshodi, Taohid, Diaz, Rodney, Afifi, Rana, Dindyal, Shiva, Thapar, Ankur, Kordzadeh, Ali, Pullas, Gonzalo, Lin, Stephanie, Davies, Chris, Darvall, Katy, Kodama, Akio, Gooneratne, Thushan, Gunawansa, Nalaka, Munoz, Alberto, Jie, Ng Jun, Bradley, Nicholas, Al-Jundi, Wissam, Meyer, Felicity, Lee, Cheong, Malina, Martin, Renton, Sophie, Lui, Dennis, Batchelder, Andrew, Oszkinis, Grzegorz, Freyrie, Antonio, Giordano, Jacopo, Saratzis, Nikolaos, Tigkiropoulos, Konstantinos, Kyriakos, Stavridis, Popov, Guriy, Cheema, Muhammad Usman, Lapolla, Pierfrancesco, Ling Patricia, Yih Chun, Ennab, Raed, Ullery, Brant W., Pasenidou, Ketino, Tam, Jacky, Sidel, Gabriel, Jayaprakash, Vivek Vardhan, Bennett, Lisa, Hardy, Simon, Davies, Emma, Baker, Sara, Wijesinghe, Lasantha, Tam, Adam, McCune, Ken, Chana, Manik, Lowe, Chris, Goh, Aaron, Powezka, Katarzyna, Kyrou, Ioanna, Altaf, Nishath, Harkin, Denis, Travers, Hannah, Cragg, James, sharif, Atif, Akhtar, Tasleem, Chávez, José Antonio, Ordonez, Claudia, Mazzurco, Martin, Choke, Edward, Asghar, Imran, Summerour, Virginia, Dunlop, Paul, Morley, Rachel, Hardy, Thomas, Bevis, Paul, Cuff, Robert, Stavroulakis, Konstantinos, Beropoulis, Efthymios, Argyriou, Angeliki, Loftus, Ian, Azhar, Bilal, Sheth, Sharvil, Usai, Marco Virgilio, Choudhry, Asad, Nicole, Kira, Boyle, Emily, Joyce, Doireann, Abdelaty Hassan, Mohammed Hassan, Saltiel, Alberto, Frahm-Jensen, Gert, Antoniou, George, Elhadi, Muhammed, Kimyaghalam, Ali, Malgor, Rafael, O'Banion, Leigh Ann, Telve, Diego, Isaak, Andrej, Schmidli, Jürg, McKevitt, Kevin, Siddiqui, Tam, Asciutto, Giuseppe, Floros, Nikolaos, Papadopoulos, George, Kafetzakis, Alexandros, Koutsias, Stylianos G., Nana, Petroula, Giannoukas, Athanasios, Kakkos, Stavros, Moulakakis, Konstantinos G., Shafique, Natasha, Jawien, Arkadiusz, Popplewell, Matthew, Imray, Chris, Abayasekara, Kumar, Rowlands, Timothy, Kuhan, Ganesh, Rajagopalan, Sriram, Jaipersad, Anthony, Sadia, Uzma, Kobe, Isaac, Mittapalli, Devender, Enemosah, Ibrahim, Behrendt, Christian-Alexander, Beck, Adam, Almudhafer, Muayyad, Ancetti, Stefano, Jacobs, Donald, Jayakumar, Priya, Malekpour, Fatemeh, Shalhub, Sherene, Keldiyorov, Boboyor, Simon, Meryl, Khashram, Manar, Rich, Nicole, Shepherd, Amanda, Meecham, Lewis, Doherty, Daniel, and Benson, Ruth A.

Published
2022
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13. Methods, design, and initial results of an angiographic core lab from VOYAGER-PAD.

Author

Rogers, R Kevin, Herold, Joerg, Govsyeyev, Nicholas, Iezzi, Roberto, Morrison, Justin, Hogan, Shea E, Nehler, Mark, Bricker, Rory, Andring, Brice, Bergmark, Brian, Cavender, Matt, Malgor, Emily, Jacobs, Donald, Young, Michael N, Capell, Warren, Yčas, Joseph W, Anand, Sonia S, Berkowitz, Scott D, Debus, E Sebastian, and Haskell, Lloyd P

Subjects
ANGIOGRAPHY, CORONARY disease, THERAPEUTIC complications, TREATMENT effectiveness, PERIPHERAL vascular diseases
Abstract

Background: Anatomy is critical in risk stratification and therapeutic decision making in coronary disease. The relationship between anatomy and outcomes is not well described in PAD. We sought to develop an angiographic core lab within the VOYAGER-PAD trial. The current report describes the methods of creating this core lab, its study population, and baseline anatomic variables. Methods: Patients undergoing lower-extremity revascularization for symptomatic PAD were randomized in VOYAGER-PAD. The median follow up was 2.25 years. Events were adjudicated by a blinded Clinical Endpoint Committee. Angiograms were collected from study participants; those with available angiograms formed this core lab cohort. Angiograms were scored for anatomic and flow characteristics by trained reviewers blinded to treatment. Ten percent of angiograms were evaluated independently by two reviewers; inter-rater agreement was assessed. Clinical characteristics and the treatment effect of rivaroxaban were compared between the core lab cohort and noncore lab participants. Anatomic data by segment were analyzed. Results: Of 6564 participants randomized in VOYAGER-PAD, catheter-based angiograms from 1666 patients were obtained for this core lab. Anatomic and flow characteristics were collected across 16 anatomic segments by 15 reviewers. Concordance between reviewers for anatomic and flow variables across segments was 90.5% (24,417/26,968). Clinical characteristics were similar between patients in the core lab and those not included. The effect of rivaroxaban on the primary efficacy and safety outcomes was also similar. Conclusions: The VOYAGER-PAD angiographic core lab provides an opportunity to correlate PAD anatomy with independently adjudicated outcomes and provide insights into therapy for PAD. (ClinicalTrials.gov Identifier: NCT02504216) [ABSTRACT FROM AUTHOR]

Published
2024
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21. Probability Density Estimation through Nonparametric Adaptive Partitioning and Stitching.

Author

Merino, Zach D., Farmer, Jenny, and Jacobs, Donald J.

Subjects
CUMULATIVE distribution function, NONPARAMETRIC estimation, MAXIMUM entropy method, PROBABILITY density function, PROBABILITY theory, TREE branches
Abstract

We present a novel nonparametric adaptive partitioning and stitching (NAPS) algorithm to estimate a probability density function (PDF) of a single variable. Sampled data is partitioned into blocks using a branching tree algorithm that minimizes deviations from a uniform density within blocks of various sample sizes arranged in a staggered format. The block sizes are constructed to balance the load in parallel computing as the PDF for each block is independently estimated using the nonparametric maximum entropy method (NMEM) previously developed for automated high throughput analysis. Once all block PDFs are calculated, they are stitched together to provide a smooth estimate throughout the sample range. Each stitch is an averaging process over weight factors based on the estimated cumulative distribution function (CDF) and a complementary CDF that characterize how data from flanking blocks overlap. Benchmarks on synthetic data show that our PDF estimates are fast and accurate for sample sizes ranging from 2 9 to 2 27 , across a diverse set of distributions that account for single and multi-modal distributions with heavy tails or singularities. We also generate estimates by replacing NMEM with kernel density estimation (KDE) within blocks. Our results indicate that NAPS(NMEM) is the best-performing method overall, while NAPS(KDE) improves estimates near boundaries compared to standard KDE. [ABSTRACT FROM AUTHOR]

Published
2023
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22. Ab initio gene prediction for protein-coding regions.

Author

Baker, Lonnie, David, Charles, and Jacobs, Donald J

Subjects
GENOMICS, PHYLOGENY, DNA sequencing, AB initio quantum chemistry methods, COMPUTATIONAL biology
Abstract

Motivation Ab initio gene prediction in nonmodel organisms is a difficult task. While many ab initio methods have been developed, their average accuracy over long segments of a genome, and especially when assessed over a wide range of species, generally yields results with sensitivity and specificity levels in the low 60% range. A common weakness of most methods is the tendency to learn patterns that are species-specific to varying degrees. The need exists for methods to extract genetic features that can distinguish coding and noncoding regions that are not sensitive to specific organism characteristics. Results A new method based on a neural network (NN) that uses a collection of sensors to create input features is presented. It is shown that accurate predictions are achieved even when trained on organisms that are significantly different phylogenetically than test organisms. A consensus prediction algorithm for a CoDing Sequence (CDS) is subsequently applied to the first nucleotide level of NN predictions that boosts accuracy through a data-driven procedure that optimizes a CDS/non-CDS threshold. An aggregate accuracy benchmark at the nucleotide level shows that this new approach performs better than existing ab initio methods, while requiring significantly less training data. Availability and implementation https://github.com/BioMolecularPhysicsGroup-UNCC/MachineLearning. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Quasar Identification Using Multivariate Probability Density Estimated from Nonparametric Conditional Probabilities.

Author

Farmer, Jenny, Allen, Eve, and Jacobs, Donald J.

Subjects
MAXIMUM entropy method, NONPARAMETRIC estimation, QUASARS, CONDITIONAL probability, ORDER statistics, PROBABILITY theory, DENSITY
Abstract

Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE). A novel density estimator recently developed by Farmer and Jacobs offers an alternative high-throughput automated approach to univariate nonparametric density estimation based on maximum entropy and order statistics, improving accuracy over univariate KDE. This article presents an extension of the single variable case to multiple variables. The univariate estimator is used to recursively calculate a product array of one-dimensional conditional probabilities. In combination with interpolation methods, a complete joint probability density estimate is generated for multiple variables. Good accuracy and speed performance in synthetic data are demonstrated by a numerical study using known distributions over a range of sample sizes from 100 to 10 6 for two to six variables. Performance in terms of speed and accuracy is compared to KDE. The multivariate density estimate developed here tends to perform better as the number of samples and/or variables increases. As an example application, measurements are analyzed over five filters of photometric data from the Sloan Digital Sky Survey Data Release 17. The multivariate estimation is used to form the basis for a binary classifier that distinguishes quasars from galaxies and stars with up to 94% accuracy. [ABSTRACT FROM AUTHOR]

Published
2023
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32. Protein Function Analysis through Machine Learning.

Author

Avery, Chris, Patterson, John, Grear, Tyler, Frater, Theodore, and Jacobs, Donald J.

Subjects
MACHINE learning, PROTEIN analysis, PROTEIN structure prediction, COMPUTATIONAL biology, DRUG discovery, ALLOSTERIC regulation, PROTEIN-protein interactions
Abstract

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics. [ABSTRACT FROM AUTHOR]

Published
2022
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40. PDFEstimator: An R Package for Density Estimation and Analysis.

Author

Farmer, Jenny and Jacobs, Donald

Subjects
*DENSITY, *DATA distribution
Abstract

This article presents PDFEstimator, an R package for nonparametric probability density estimation and analysis, as both a practical enhancement and alternative to kernel-based estimators. PDFEstimator creates fast, highly accurate, data-driven probability density estimates for continuous random data through an intuitive interface. Excellent results are obtained for a diverse set of data distributions ranging from 10 to 106 samples when invoked with default parameter definitions in the absence of user directives. Additionally, the package contains methods for assessing the quality of any estimate, including robust plotting functions for detailed visualization and trouble-shooting. Usage of PDFEstimator is illustrated through a variety of examples, including comparisons to several kernel density methods. [ABSTRACT FROM AUTHOR]

Published
2022
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41. Functional Dynamics of Substrate Recognition in TEM Beta-Lactamase.

Author

Avery, Chris, Baker, Lonnie, and Jacobs, Donald J.

Subjects
BETA lactam antibiotics, MOLECULAR dynamics, BETA lactamases, CEFTAZIDIME, LIGAND binding (Biochemistry), ALLOSTERIC regulation, BETA-lactamase inhibitors, CEFOTAXIME
Abstract

The beta-lactamase enzyme provides effective resistance to beta-lactam antibiotics due to substrate recognition controlled by point mutations. Recently, extended-spectrum and inhibitor-resistant mutants have become a global health problem. Here, the functional dynamics that control substrate recognition in TEM beta-lactamase are investigated using all-atom molecular dynamics simulations. Comparisons are made between wild-type TEM-1 and TEM-2 and the extended-spectrum mutants TEM-10 and TEM-52, both in apo form and in complex with four different antibiotics (ampicillin, amoxicillin, cefotaxime and ceftazidime). Dynamic allostery is predicted based on a quasi-harmonic normal mode analysis using a perturbation scan. An allosteric mechanism known to inhibit enzymatic function in TEM beta-lactamase is identified, along with other allosteric binding targets. Mechanisms for substrate recognition are elucidated using multivariate comparative analysis of molecular dynamics trajectories to identify changes in dynamics resulting from point mutations and ligand binding, and the conserved dynamics, which are functionally important, are extracted as well. The results suggest that the H10-H11 loop (residues 214-221) is a secondary anchor for larger extended spectrum ligands, while the H9-H10 loop (residues 194-202) is distal from the active site and stabilizes the protein against structural changes. These secondary non-catalytically-active loops offer attractive targets for novel noncompetitive inhibitors of TEM beta-lactamase. [ABSTRACT FROM AUTHOR]

Published
2022
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42. Iliac Artery Endoconduits Should be the Preferred Adjunctive Access Procedure to Facilitate Complex Endovascular Aortic Aneurysm Repair.

Author

Siada, Sammy, Malgor, Emily A., Al-Musawi, Mohammed, Giannopoulos, Stefanos, Jacobs, Donald L., and Malgor, Rafael D.

Subjects
PATIENT aftercare, LENGTH of stay in hospitals, ILIAC artery, REHABILITATION centers, AORTIC aneurysms, TREATMENT effectiveness, NURSING care facilities, DESCRIPTIVE statistics, ENDOVASCULAR surgery, DISCHARGE planning, EVALUATION
Abstract

Background: Iliac artery anatomy can have a dramatic impact on the success of endovascular complex aortic aneurysm (CAA) procedures as endograft delivery systems need to be advanced and manipulated through these access vessels. The aim of this study was to evaluate the outcomes of iliac artery conduits with emphasizes on open vs endovascular conduits performed to facilitate CAA endovascular repair. Methods: All patients who had open or endovascular iliac conduits prior to endovascular CAA repair to treat thoracoabdominal, juxtarenal, or suprarenal aneurysms at the University of Colorado Hospital from January 2009 through January 2019 were included. Patients who presented with symptomatic or ruptured aortic aneurysms were excluded. Outcomes of interest included postoperative complications and mortality in patients undergoing iliac conduits. Results: Twenty-seven patients with a total of 42 conduits were included in the study. The majority of patients (N = 15, 56%) were female and the average age was 72 ± 9 years. The calculated VQI cardiac index was.6% (range,.3%–.8%). Eighteen (43%) endovascular and 24 (57%) open iliac conduits were performed during the study period. Thirty (71%) conduits were performed in a staged fashion, while 12 (29%) were performed at the same time as endovascular CAA repair. The mean time between conduit and definitive aneurysm repair surgery was 130 ± 68 days in the endovascular and 107 ± 79 days in the open groups (P =.87). No aneurysm rupture occurred during the staging period in either group. The median follow-up for the entire cohort was 18 ± 22 months. The median length of hospital stay for patients undergoing endovascular and open ICs was 6 (ranging, 1–28 days) and 7 days (ranging, 3–18 days), respectively. Patients undergoing open conduits had significantly more complications than those undergoing endovascular conduit (endoconduit) creation. A total of 4 (15%) patients died within 30 days after aneurysm repair. Out of 23 survivors, 18 (78%) patients were discharged home, 4 (18%) patients were discharged to a skilled nursing facility, and 1 (4%) patient was discharged to an acute rehabilitation facility. No mortality difference based on type of conduit was found. Conclusions: Overall complication rate associated with creation of open iliac artery conduits is not negligible. Endoconduits, which carry less morbidity than open conduits, are preferred as a first-line adjunctive access procedure to facilitate complex endovascular aortic aneurysm repair. [ABSTRACT FROM AUTHOR]

Published
2022
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