Citations
Mayo Clinic
76 citation(s) found for "Nagy, A".
COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts
Suri, Jasjit S. , Agarwal, Sushant , Carriero, Alessandro , Paschè, Alessio , Danna, Pietro , Columbu, Marta, Saba, Luca , Višković, Klaudija , Mehmedović, Armin , Agarwal, Samriddhi, Gupta, Lakshya, Faa, Gavino , Singh, Inder M. , Turk, Monika, Chadha, Paramjit S., Johri, Amer M. , Khanna, Narendra N. , Laird, John R. , Pareek, Gyan , Miner, Martin , Sobel, David , Balestrieri, Antonella , Sfikakis, Petros P. , Tsoulfas, George, Protogerou, Athanase D. , Misra, Durga Prasanna , Agarwal, Vikas , Teji, Jagjit S. , Al-Maini, Mustafa , Dhanjil, Surinder, Rathore, Vijay , Fatemi, Mostafa , Krishnan, Pudukode R., Nagy, F. , Ruzsa, Zoltán , Naidu, Subbaram , Paraskevas, Kosmas I. , Danna, Pietro S C , Tsoulfas, George , Protogerou, Athanasios , Fatemi, Mostafa , Alizad, Azra , Ferenc, Nagy , Ruzsa, Zoltan , Paraskevas, Kosmas I , Misra, Durga Prasanna , Agarwal, Vikas , Faa, Gavino , Mehmedović, Armin , Agarwal, Sushant , Suri, Jasjit S. , Saba, Luca , Mavrogeni, Sophie , Kitas, George D. , Nicolaides, Andrew , Sharma, Aditya , Alizad, Azra , Gupta, Archna , Kalra, Mannudeep K.
Diagnostics | December 15, 2021 | Volume: 11 | Issue: 12 | Pages: 2367
10.3390/diagnostics11122367
| PMID 34943603
(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.
Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography
Suri, Jasjit S. , Agarwal, Sushant , Elavarthi, Pranav, Ketireddy, Vedmanvitha, Columbu, Marta, Saba, Luca , Gupta, Suneet Kumar , Faa, Gavino , Singh, Inder M. , Turk, Monika, Chadha, Paramjit S., Johri, Amer M. , Khanna, Narendra N. , Višković, Klaudija , Laird, John R. , Pareek, Gyan , Miner, Martin , Sobel, David , Balestrieri, Antonella , Sfikakis, Petros P. , Tsoulfas, George, Protogerou, Athanase D. , Misra, Durga Prasanna , Agarwal, Vikas , Teji, Jagjit S. , Al-Maini, Mustafa , Dhanjil, Surinder, Rathore, Vijay , Fatemi, Mostafa , Krishnan, Pudukode, Nagy, F. , Naidu, Subbaram, Gupta, Suneet K , Faa, Gavino , Tsoulfas, George , Protogerou, Athanasios , Agarwal, Vikas , Fatemi, Mostafa , Alizad, Azra , Ferenc, Nagy , Naidu, Subbaram , Misra, Durga Prasanna , Agarwal, Sushant , Suri, Jasjit S. , Pathak, Rajesh, Saba, Luca , Mavrogeni, Sophie , Kitas, George D. , Nicolaides, Andrew , Sharma, Aditya , Alizad, Azra , Krishnan, Pudukode R., Ruzsa, Zoltán , Gupta, Archna , Kalra, Mannudeep K.
Diagnostics | November 1, 2021 | Volume: 11 | Issue: 11 | Pages: 2025
10.3390/diagnostics11112025
| PMID 34829372
Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.
Progressive decline of function in renal allografts with normal 1‐year biopsies: Gene expression studies fail to identify a classifier
Park, Walter D. , Kim, Dean Y. , Mai, Martin L., Reddy, Kunam S. , Gonwa, Thomas A., Ryan, Margaret , Hernandez, Loren P. Herrera , Geiger, Xochiquetzal J., Túrkevi-Nagy, Sándor , Cornell, Lynn D. , Kremers, Walter K. , Gonwa, Thomas, Ryan, Margaret S, Herrera Hernandez, Loren P, Smith, Byron , Stegall, Mark D , Park, Walter , Cornell, Lynn , Kremers, Walter K , Mai, Martin L., Smith, Maxwell L. , Smith, Byron H. , Stegall, Mark D.
Clinical Transplantation | October 30, 2021 | Volume: 35 | Issue: 12 | Pages: No page available
10.1111/ctr.14456
| PMID 34717009
AbstractHistologic findings on 1‐year biopsies such as inflammation with fibrosis and transplant glomerulopathy predict renal allograft loss by 5 years. However, almost half of the patients with graft loss have a 1‐year biopsy that is either normal or has only interstitial fibrosis. The goal of this study was to determine if there was a gene expression profile in these relatively normal 1‐year biopsies that predicted subsequent decline in renal function. Using transcriptome microarrays we measured intragraft mRNA levels in a retrospective Discovery cohort (170 patients with a normal/minimal fibrosis 1‐year biopsy, 54 with progressive decline in function/graft loss and 116 with stable function) and developed a nested 10‐fold cross‐validated gene classifier that predicted progressive decline in renal function (positive predictive value = 38 ± 34%%; negative predictive value = 73 ± 30%, c‐statistic = .59). In a prospective, multicenter Validation cohort (270 patients with Normal/Interstitial Fibrosis [IF]), the classifier had a 20% positive predictive value, 85% negative predictive value and .58 c‐statistic. Importantly, the majority of patients with graft loss in the prospective study had 1‐year biopsies scored as Normal or IF. We conclude predicting graft loss in many renal allograft recipients (i.e., those with a relatively normal 1‐year biopsy and eGFR > 40) remains difficult.
COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models
Suri, Jasjit S. , Agarwal, Sushant , Ketireddy, Vedmanvitha, Columbu, Marta, Saba, Luca , Gupta, Suneet Kumar , Singh, Inder M. , Turk, Monika, Chadha, Paramjit S., Johri, Amer M. , Khanna, Narendra N. , Višković, Klaudija , Laird, John R. , Pareek, Gyan , Miner, Martin , Sobel, David , Balestrieri, Antonella , Sfikakis, Petros P. , Tsoulfas, George, Protogerou, Athanase D. , Misra, Durga Prasanna , Agarwal, Vikas , Teji, Jagjit S. , Al-Maini, Mustafa , Dhanjil, Surinder, Nicolaides, Andrew , Rathore, Vijay , Fatemi, Mostafa , Krishnan, Pudukode R., Frence, Nagy, Naidu, Subbaram, Nagy, F. , Tsoulfas, George , Protogerou, Athanasios , Misra, Durga Prasanna , Agarwal, Vikas , Nicolaides, Andrew , Alizad, Azra , Fatemi, Mostafa , Ferenc, Nagy , Agarwal, Sushant , Suri, Jasjit S. , Pathak, Rajesh, Faa, Gavino , Mavrogeni, Sophie , Kitas, George D. , Sharma, Aditya , Alizad, Azra , Frence, Nagy, Ruzsa, Zoltán , Gupta, Archna , Kalra, Mannudeep K.
Diagnostics | August 4, 2021 | Volume: 11 | Issue: 8 | Pages: 1405
10.3390/diagnostics11081405
| PMID 34441340
Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.
Ipsilateral and Simultaneous Comparison of Responses from Acceleromyography- and Electromyography-based Neuromuscular Monitors
Nemes, Réka, Nagy, György M., Hampton, David R., Gray, Martyn, Brull, Sorin J. , Brull, Sorin J , Lengyel, Szabolcs, Nagy, György, Renew, J. Ross , TASSONYI, Edömer , Fülesdi, Béla
Anesthesiology | July 30, 2021 | Volume: 135 | Issue: 4 | Pages: 597-611
10.1097/aln.0000000000003896
| PMID 34329371
Background The paucity of easy-to-use, reliable objective neuromuscular monitors is an obstacle to universal adoption of routine neuromuscular monitoring. Electromyography (EMG) has been proposed as the optimal neuromuscular monitoring technology since it addresses several acceleromyography limitations. This clinical study compared simultaneous neuromuscular responses recorded from induction of neuromuscular block until recovery using the acceleromyography-based TOF-Watch SX and EMG-based TetraGraph. Methods Fifty consenting patients participated. The acceleromyography and EMG devices analyzed simultaneous contractions (acceleromyography) and muscle action potentials (EMG) from the adductor pollicis muscle by synchronization via fiber optic cable link. Bland–Altman analysis described the agreement between devices during distinct phases of neuromuscular block. The primary endpoint was agreement of acceleromyography- and EMG-derived normalized train-of-four ratios greater than or equal to 80%. Secondary endpoints were agreement in the recovery train-of-four ratio range less than 80% and agreement of baseline train-of-four ratios between the devices. Results Acceleromyography showed normalized train-of-four ratio greater than or equal to 80% earlier than EMG. When acceleromyography showed train-of-four ratio greater than or equal to 80% (n = 2,929), the bias was 1.3 toward acceleromyography (limits of agreement, –14.0 to 16.6). When EMG showed train-of-four ratio greater than or equal to 80% (n = 2,284), the bias was –0.5 toward EMG (–14.7 to 13.6). In the acceleromyography range train-of-four ratio less than 80% (n = 2,802), the bias was 2.1 (–16.1 to 20.2), and in the EMG range train-of-four ratio less than 80% (n = 3,447), it was 2.6 (–14.4 to 19.6). Baseline train-of-four ratios were higher and more variable with acceleromyography than with EMG. Conclusions Bias was lower than in previous studies. Limits of agreement were wider than expected because acceleromyography readings varied more than EMG both at baseline and during recovery. The EMG-based monitor had higher precision and greater repeatability than acceleromyography. This difference between monitors was even greater when EMG data were compared to raw (nonnormalized) acceleromyography measurements. The EMG monitor is a better indicator of adequate recovery from neuromuscular block and readiness for safe tracheal extubation than the acceleromyography monitor. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New
Genomic complexity is associated with epigenetic regulator mutations and poor prognosis in diffuse large B-cell lymphoma
You, Hua , Xu‐Monette, Zijun Y. , Li, Wei , Nunns, Harry , Nagy, Máté , Bhagat, Govind , Fang, Xiaosheng , Zhu, Feng , Visco, Carlo , Tzankov, Alexandar , Dybkær, Karen , Chiu, April , Tam, Wayne , Zu, Youli , Hagemeister, Fredrick B., Huh, Jooryung , Ferreri, Andrés J.M. , Møller, Michael , Parsons, Benjamin M. , Au, Qingyan , Xu, Bing , Albitar, Maher , Young, Ken H. , Parsons, Benjamin M , Young, Ken H , Møller, Michael Boe , Tzankov, Alexandar , Dybkaer, Karen , Bhagat, Govind , Zu, Youli , Xu-Monette, Zijun Y , Hsi, Eric D. , Ponzoni, Maurilio , Krieken, J. Han van , Piris, Miguel Á. , Winter, Jane N. , Li, Yong , Fang, Xiaosheng
OncoImmunology | July 20, 2021 | Volume: 10 | Issue: 1 | Pages: No page available
Local impedance‐guided radiofrequency ablation with standard and high power: Results of a preclinical investigation
Osei, Kofi , Sulkin, Matthew S. , Hamann, Jason J., Hughes, Christopher C.W. , Shuros, Allan, Nagy, Tamás , Kapa, Suraj , Meyers, Jason D., Kapa, Suraj , Hughes, Christopher
Journal of Cardiovascular Electrophysiology | July 5, 2021 | Volume: 32 | Issue: 8 | Pages: 2060-2068
10.1111/jce.15135
| PMID 34223691
AbstractBackgroundLocal impedance (LI) drop measured with microfidelity electrodes embedded in the tip of an ablation catheter accurately reflects tissue heating during radiofrequency (RF) ablation. Previous studies found 15–30 Ω LI drops created successful lesions, while more than 40 Ω drops were associated with steam pops. The objective of this study was to evaluate the safety and efficacy of LI‐guided ablation using standard (30 W) and high‐power (50 W) in a preclinical model.MethodsRF lesions were created in explanted swine hearts (n = 6) to assess the feasibility of LI‐guided ablation by targeting 10, 20, or 30 Ω (n = 20/group) drops. Subsequently, LI‐guided ablation was evaluated in a chronic animal model (n = 8 Canines, 25–29 kg, 30/50 W). During the index procedure point‐by‐point intercaval line ablation and left inferior pulmonary vein (PV) isolation were performed. RF duration was at the operators’ discretion but discontinued early if a 15–30 Ω drop was achieved. Operators attempted to avoid LI drops of more than 40 Ω. At 1‐month, durable conduction block was evaluated with electroanatomic mapping followed by necropsy and histopathology.ResultsIn explanted tissue, terminating ablation at 10, 20, or 30 Ω LI drops created statistically larger lesions (p < .05; 1.8 [1.6–2.4] mm, 3.3 [3.0–3.7] mm; 4.9 [4.3–5.5] mm). LI‐guided high‐power ablation in vivo significantly reduced RF duration per application compared to standard‐power (p < .05; intercaval: 8.9 ± 5.2 vs. 18.1 ± 11.0 s, PV: 9.6 ± 5.4 vs. 23.2 ± 10.3 s). LI drops of 15–40 Ω were more readily achievable for high‐power (90.1%, 318/353) than standard‐power (71.7%, 243/339). All intercaval lines and PV isolations were durable (16/16) at 1‐month. Necropsy revealed no major collateral injury to the pericardium, phrenic nerve, esophagus, or lungs. There was no pericardial effusion, stroke, tamponade, or PV stenosis. Vagal nerve injury was found in two 30 W animals after using 19.7 ± 13.9 and 19.5 ± 11.8 s RF applications.ConclusionLI‐guided ablation was found to be safe and efficacious in a chronic animal model. High‐power ablation more readily achieved more than 15 Ω drops, reduced RF duration compared with standard‐power, and had no major RF collateral injury.
Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition) 1
Klionsky, Daniel J., Abdel‐Aziz, Amal Kamal , Abdellatif, Mahmoud , Abdoli, Asghar , Abel, Steffen , Abeliovich, Hagai , Abildgaard, Marie H. , Abudu, Yakubu Princely , Acevedo‐Arozena, Abraham , Adamopoulos, Iannis E. , Adeli, Khosrow , Adolph, Timon E. , Adornetto, Annagrazia , Aflaki, Elma, Agam, Galila , Aggarwal, Bharat B. , Agnello, Maria, Agostinis, Patrizia , Agrewala, Javed N. , Agrotis, Alexander , Aguilar, Patricia V., Ahmad, S. Tariq, Ahumada-Castro, Ulises , Aits, Sonja , Aizawa, Shu, Akkoç, Yunus , Akoumianaki, Tonia, Akpinar, Hafize Aysin , Al‐Abd, Ahmed M. , Al-Akra, Lina, Gharaibeh, Abeer , Alaoui‐Jamali, Moulay A. , Alberti, Simon , Alessandri, Cristiano , Alonso, Alicia , Altan‐Bonnet, Nihal , Álvarez, Élida, Alves, Sara , Costa, Cristine Alvès da , Alzaharna, Mazen M. , Amadio, Marialaura , Amantini, Consuelo , Amaral, Cristina , Ambrosio, Susanna , Amer, Amal O. , Ammanathan, Veena , An, Zhenyi, Andrabi, Shaida A. , Andres, Allen M. , Ann, David K. , Anozie, Uche C. , Ansari, Mohammad Y. , Antas, Pedro , Antebi, Adam , Antón, Zuriñe , Anwar, Tahira , Apétoh, Lionel , Araki, Toshiyuki , Araki, Yasuhiro , Araya, Jun , Arévalo, María‐Ángeles , Argüelles, Sandro , Arias, Esperanza , Arikkath, Jyothi , Ariosa, Aileen, Aroca, Ángeles , Arroyo, Daniela S., Arsov, Ivica, Artero, Rubén , Asaro, Dalia Maria Lucia, Ashur‐Fabian, Osnat , Atanasov, Atanas G. , Au, Alicia K. , Auberger, Patrick , Auner, Holger W. , Aurelian, Laure , Avagliano, Laura , Ávalos, Yenniffer , Aveleira, Célia A. , Aydın, Yücel , Ayyadevara, Srinivas , Azzopardi, Maria , Baba, Misuzu, Backer, Jonathan , Bae, Soo Han , Baehrecke, Eric H. , Bagetta, Giacinto , Bagniewska‐Zadworna, Agnieszka , Bai, Hua , Bai, Xiyuan , Bairagi, Nandadulal , Baksi, Shounak , Balbi, Teresa , Ballester, María , Balazadeh, Salma , Balzan, Rena , Bandopadhyay, Rina , Banerjee, Sulagna , Bánréti, Ágnes , Baracca, Alessandra , Barbati, Cristiana , Bargiela, Ariadna , Barilà, Daniela , Barlow, Peter G. , Barmada, Sami J. , Barreiro, Esther , Bártek, Jiří , Bartel, Bonnie , Bartolomé, Alberto , Barve, Gaurav , Bassham, Diane C. , Baulieu, E.E. , Baumgarner, Bradley L. , Bayry, Jagadeesh , Beale, Rupert , Beau, Isabelle , Beaumatin, Florian , Bechara, Luiz Roberto Grassmann , Beck, George R. , Beers, Michael F. , Begun, Jakob , Behrends, Christian , Bejarano, Eloy , Bel, Shai , Belaïd, Amine , Belgareh‐Touzé, Naïma, Bellarosa, Cristina , Belleudi, Francesca , Belló-Pérez, Melissa , Beltran, Jackeline Soares de Oliveira , Beltran, Sebastián, Benbrook, Doris M. , Bendorius, Mykolas , Benítez, Bruno A. , Benito‐Cuesta, Irene , Bensalem, Julien , Berchtold, Martin W. , Berezowska, Sabina , Bergamaschi, Daniele , Berliocchi, Laura , Berlioz‐Torrent, Clarisse , Bernard, Amélie , Besirli, Cagri G. , Besteiro, Sébastien , Betin, Virginie M.S., Beyaert, Rudi , Bezbradica, Jelena S. , Bhaskar, Kiran , Bhatia‐Kiššová, Ingrid, Bhattacharya, Sujoy , Bhuiyan, Md. Shenuarin , Bi, Xiaolin , Biden, Trevor J. , Bijian, Krikor , Billes, Viktor , Binart, Nadine , Bincoletto, Cláudia , Blanco, Gonzalo , Blas‐García, Ana , Błasiak, Janusz , Blomgran, Robert , Blum, Janice S. , Boada-Romero, Emilio, Boban, Mirta , Boeuf, Philippe , Boland, Barry , Bomont, Pascale , Bonaldo, Paolo , Bonam, Srinivasa Reddy , Bonfili, Laura , Bootman, Martin D. , Bordi, Matteo , Borner, Christoph , Bornhäuser, Beat , Bose, Santanu , Botas, Juan , Boulanger, Chantal M. , Bourgeois, Benjamin , Bourke, Nollaig M. , Bousquet, Guilhem , Boya, Patricia , Bozhkov, Peter V. , Bozi, Luiz H. M. , Bozkurt, Tolga O. , Pedro, José Manuel Bravo‐San , Brest, Patrick , Bringer, Marie-Agnès , Briones‐Herrera, Alfredo , Broaddus, V. Courtney, Brodersen, Peter , Brodsky, Jeffrey L. , Bronson, Paola G. , Bronstein, Jeff M. , Brown, Rhoderick E. , Brum, Patrı́cia C. , Bruno, Daniele , Bryson‐Richardson, Robert J. , Bucci, Cecilia , Buchrieser, Carmen , Bueno, Marta , Buchan, J. Ross , Buckingham, Erin M., Budak, Hikmet , Budini, Mauricio , Bultynck, Geert , Burada, Florin , Burgoyne, Joseph R. , Burón, María I. , Bustos, Victor , Büttner, Sabrina , Byrd, Aaron , Cabas, Isabel , Cabrera, Sandra , Cadwell, Ken , Cai, Jingjing , Cai, Lu , Cai, Qian , Calbet, José A. L. , Caldwell, Guy A. , Call, Jarrod A. , Calvani, Riccardo , Calvo, Ana Cristina , Calvo‐Rubio, Miguel , Câmara, Niels Olsen Saraiva , Camonis, Jacques , Camougrand, Nadine , Campanella, Michelangelo , Campbell, Edward M. , Campos, Juliane C. , Camuzard, Olivier , Cancino, Jorge , Caniggia, Isabella , Canonico, Barbara , Cantı́, Carles , Cao, Bin , Caramés, B. , Cardenal‐Muñoz, Elena , Cárdenas, César , Cárdenas, Luis , Cardoso, Sandra M. , Carew, Jennifer S. , Carle, Georges F. , Carleton, Gillian , Carloni, Silvia , Carneiro, Letícia A. M. , Carosi, Julian M. , Carra, Serena , Carrier, Alice , Carter, A. Brent, Carvalho, Andreia Neves , Casanova, Magali , Casas, Josefina , Cassioli, Chiara , Castillo, Karen , Castillo‐Lluva, Sonia , Castoldi, Francesca , Castori, Marco , Castro, Ariel F. , Castro‐Hernández, Javier , Cavadas, Cláudia , Cavaliere, Federica, Cavallini, Gabriella , Cavinato, Maria , Rica, Paula Cebollada , Cechowska‐Pasko, Marzanna , Cerqueira, João , Cerutti, Janete M. , Çetintaş, Vildan Bozok , Cetrullo, Silvia , Chagin, Andrei S. , Chai, Chee‐Yin , Chakrabarti, Gopal , Chakraborty, Tapas , Chakraborty, Trinad , Chami, Mounia , Chamilos, Georgios , Chan, David W. , Chan, Edmond Y. W., Chan, Ho Yin Edwin , Chan, Helen H., Chan, Hung , Chan, Matthew T.V. , Chan, Yau Sang , Chang, Hao‐Chun , Chang, Kai , Chao, Jie , Charlet‐Berguerand, Nicolas , Chatterjee, Samrat , Chaube, Shail K. , Chaudhary, Anu , Chauhan, Santosh , Chaum, Edward , Checler, Frédéric , Cheetham, Michael E. , Chen, Chang‐Shi , Chen, Jian‐Fu , Chen, Liam L., Chen, Mingliang , Chen, Mu‐Kuan , Chen, Ning , Chen, Wei , Chen, Xinming , Chen, Xiongwen , Chen, Yan , Chen, Ye‐Guang , Chen, Yingyu , Chen, Yongqiang , Chen, Zhefan Stephen , Chen, Zhihua , Cheng, Xiu‐Tang , Cheng, Yiyun , Cheng, Zhiyong , Cheong, Jit Kong , Cherry, Sara , Cheung, Chi F., Cheung, Chun Hei Antonio , Cheung, King‐Ho , Chevet, Éric , Chi, Richard J., Chiaradonna, Ferdinando , Chiariello, M , Chica, Nathalia , Chiocca, Susanna , Chiong, Mario , Chiou, Shih‐Hwa , Chiramel, Abhilash I., Chiurchiù, Valerio , Cho, Dong-Hyung, Choe, Seong‐Kyu , Choi, Augustine M.K. , Choi, Mary E. , Choudhury, Kamalika Roy , Chow, Norman , Chua, Jason P. , Chua, John Jia En , Chung, Hyewon , Chung, Kin Pan , Chung, So‐Hyang , Chung, Yuen-Li, Ciechomska, Iwona A. , Cinque, Laura , Çırak, Sebahattin , Clarke, Robert , Clementi, Emilio , Coccia, Eliana M. , Codogno, Patrice , Colasanti, Tania , Colasuonno, Fiorella , Colbert, Robert A. , Colell, Anna , Coll, Núria S. , Collins, Mark O. , Colombo, Marı́a I. , Colón‐Ramos, Daniel A. , Cominetti, Márcia R. , Consiglio, Antonella , Conte, A. , Contu, Viorica Raluca , Cookson, Mark , Coombs, Kevin M. , Costa, Maria do Carmo , Costantino, Sarah , Costelli, Paola , Criollo, Alfredo , Crippa, V. , Cristofani, Riccardo , Csizmadia, Tamás , Cui, Bing , Cui, Yixian , Culetto, Emmanuel , Cumino, Andrea C. , Cybulsky, Andrey V. , Czaja, Mark J. , Czuczwar, Stanisław J. , D’Adamo, Stefania , D’Amelio, Marcello , D’Orazi, Gabriella , Silva, James Almada da , Dafsari, Hormos Salimi , Daglia, Maria , Dai, Xiaoxia , Dai, Yun , Dai, Yuyuan , Col, Jessica Dal , Dalhaimer, Paul , Valle, Luisa Dalla , Dallenga, Tobias , Dalmasso, Guillaume , Dando, Ilaria , Das, Hiranmoy , Dasari, Santosh K. , Dash, Srikanta, Dauphinee, Adrian N. , Davies, Jeffrey S. , Dávila, Valeria A. , Davis, Roger J. , Naidu, Sharadha Dayalan , Bosscher, Karolien De , Leonibus, Chiara De , Barbosa, Mayara Garcia de Mattos , Meyer, Guido R.Y. De , Palma, Clara De , Virgilio, Claudio De , Zio, Daniela De , Decuypere, Jean-Paul , Deehan, Mark , Deflorian, Gianluca , Dehay, Benjamin , Delaney, Joe R. , Delorme‐Axford, Elizabeth , Demers, Nicholas D. , Deng, Hongbin , Dengjel, Jörn , Dent, Paul, DePamphilis, Melvin L., Descoteaux, Albert , Devis, Laura , Devkota, Sushil , Devuyst, Olivier , Dewson, Grant , Dharmasivam, Mahendiran , Domenico, Fabio Di , Fazio, Pietro Di , Fonzo, Alessio Di , Guardo, Giovanni Di , Guglielmo, Gianni M. Di , Leo, Luca Di , Malta, Chiara Di , Nardo, Alessia Di, Rienzo, Martina Di , Diao, Jiajie , Diaz-Araya, Guillermo, Díaz‐Laviada, Inés , Dickinson, Jared M. , Diederich, Marc , Dieudé, Mélanie , Ding, Shiping , Ding, Wen-Xing, Dinić, Jelena , Dinić, Miroslav , Dinkova‐Kostova, Albena T. , Distler, Jörg H. W. , Đokić, Jelena , Emre, Serap , Dong, Xiaonan , Dong, Zhiwu , Dorn, Gerald W. , Dou, Juan, Dowaidar, Moataz , Drucker, Liat , Du, Ailian , Du, Guangwei , Du, Hai‐Ning , Toit, André du , Duan, Shao-Bin , Duan, Xiaoqiong , Duarte, Sónia , Dupont, Nicolas , Durán, Raúl V. , Dyshlovoy, Sergey A. , Ebrahimi‐Fakhari, Darius , Eckhart, Leopold , Edelstein, Charles L. , Efferth, Thomas , Eid, Nabil , Eisenberg, Tobias , Eissa, N. Tony, Ejarque, Miriam , Andaloussi, Abdeljabar El, El‐Hage, Nazira , Eleuteri, Anna Maria , El‐Shafey, Eman S. , Elgendy, Mohamed , Elizalde, María Mercedes , Elsässer, Hans‐Peter , Elsherbiny, Eslam S. , Emre, N. C. Tolga , Eng, Christina H. , Engedal, Nikolai , Engelsen, Agnete S. T. , Enserink, Jorrit M. , Escalante, Ricardo , Esclatine, Audrey , Eskelinen, Eeva‐Liisa , Espert, Lucile , Eusebio, Makandjou-Ola , Facchiano, Antonio , Facchiano, Francesco , Fadeel, Bengt , Fader, Claudio M., Faesen, Alex C. , Fairlie, W. 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Fielding , Henkel, Vanessa, Herb, Marc , Herman-Antosiewicz, Anna , Hernández, Agustín , Hernández, Carlos , Hernández-Díaz, Sergio , Hernández–Gea, Virginia , Herpin, Amaury , Herreros, Judit , Hervás, Javier H. , Hesselson, Daniel , Hetz, Claudio , Higuchi, Yujiro , Hilfiker, Sabine , Hill, Joseph A., Ho, Emmanuel A. , Ho, Idy H. T. , Ho, Philip Wing‐Lok , Ho, Shu‐Leong , Ho, Wan Yun, Hoet, Peter , Hofius, Daniel , Höhn, Annika , Hong, Yi-Ren Hong Chang-Won, Hooper, Lora V. , Hsin, I‐Lun , Hu, Bing , Hu, Dong , Hu, Wei , Hu, Zhuowei , Hua, Jinlian, Huan, Chongmin , Huang, Chuanshu , Huang, Chunling , Huang, Haishan , Huang, Michael , Huang, Shan , Huang, Tianzhi , Huang, Xing , Huang, Yuxiang , Hubert, Virginie , Hübner, Christian A. , Hughes, Stephanie M. , Hughes, William E. , Hummer, Gerhard , Hurley, James H. , Hussain, Sabah N. 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Razaul , Kato, Masaru , Kauppinen, Anu , Kaushal, Gur P. , Kawasaki, Kiyoshi, Kazan, Kemal , Keber, Ursula , Keller, Kate E. , Kenific, Candia M. , Kepp, Oliver , Kern, Andreas , Keulers, Tom G. , Khalfin, Boris , Khalil, Hany , Khan, Shahid Y. , Khandelwal, Vinoth Kumar Megraj, Khandia, Rekha , Kho, Widuri , Khobrekar, Noopur V. , Khuansuwan, Sataree , Khundadze, Mukhran , Killackey, Samuel A. , Kim, Dasol , Kim, Deok Ryong, Kim, Do‐Hyung , Kim, Dong‐Eun , Kim, Eun‐Kyoung , Kim, Hak-Rim, Kim, Hee-Sik , Kim, Hyung-Ryong, Kim, Jeong Hun , Kim, Joungmok , Kim, Seong‐Jun , Kimball, Scot R., Kimchi, Adi , Kimmelman, Alec , Kimura, Tomonori , King, Matthew, Kinsey, Conan G. , Kirshenbaum, Lorrie A. , Kishi, Shuji , Kitamoto, Katsuhiko , Kitaoka, Yasushi , Kitazato, Kaio, Kitsis, Richard N. , Kjærulff, Ole , Klein, Peter S. , Klopstock, Thomas , Klucken, Jochen , Knævelsrud, Helene , Ko, Fred , Kobayashi, Satoru, Koch, Ina , Koch, Jan Christoph , Kögel, Donat , Koh, Young Ho , Kocatürk, Nur Mehpare , Komatsu, Masaaki, König, Jeannette , Kono, Toru, Korkmaz, Gözde , Korsnes, Mónica Suárez , Koskela, Ali , Kota, Janaiah , Kotake, Yaichiro , Kotler, Monica L., Kou, Yanjun , Koukourakis, Michael I., Koustas, Evangelos , Kovács, Tibor , Kovács, Tibor, Kraft, Claudine , Krainc, Dimitri, Krämer, Helmut, Krasnodembskaya, Anna , Kretz‐Remy, Carole , Kroemer, Guido, Kuenen, Sabine , Kuerschner, Lars , Kukar, Thomas , Kumar, Ajay , Kumar, Deepak, Kumar, Dhiraj, Kumar, Sharad , Kume, Shinji , Kumsta, Caroline , Kundu, Chanakya Nath , Kundu, Mondira, Kunnumakkara, Ajaikumar B. , Kutlu, Özlem , Kwak, SeongAe , Kwon, Yong Tae , Spada, Albert La, Labonté, Patrick , Ladoire, Sylvain , Laface, Ilaria, Lafont, Frank , Lagace, Diane C. , Lahiri, Vikramjit , Lai, Zhibing, Laird, Angela S. , Lakkaraju, Aparna , Lan, Sheng‐Hui , Landajuela, Ane , Lane, Darius J.R. , Lange, Carsten , Langer, Rupert , Lapaquette, Pierre, Laporte, Jocelyn , Lastres-Becker, Isabel, Lau, Wilson Chun Yu , Laurie, Gordon W. , Le, Weidong, Stunff, Hervé Le , Leary, Alexandre Y , Lebrun, Jean-Jacques, Leck, Lionel , Lee, Changwook , Lee, Chung‐Pei , Lee, Da-Hye , Lee, Edward B., Lee, Erinna F. , Lee, Gyun Min, Lee, He-Jin, Lee, Heung Kyu , Lee, Jason S. , Lee, Jin-A, Lee, Joo‐Yong , Lee, Michael , Lee, Min Goo , Lee, Min Jae, Lee, Myung‐Shik , Lee, Sangyoon , Lee, Stella Y. , Lee, Sung, Lee, Won Hee , Lee, Yong‐ho , Lee, Youngil , Lei, Yu L., Lei, Yuchen , Leikin, Sergey , Lemus, Leticia , Leng, Shuilong, Lenoir, Olivia , Lenz, Guido , Lenz, Heinz Josef, Lenzi, Paola, León, Yolanda , Leschczyk, Christoph, Leskelä, Stina, Letellier, Elisabeth , Leung, Chi-Ting, Leung, Po Sing , Leventhal, Jeremy S. , Levine, Beth, Lewis, Patrick A. , Ley, Klaus , Li, Bin , Li, Da‐Qiang , Li, Jian‐Ming , Li, Jiong , Li, Liwu , Li, Mei , Li, Ming , Li, Mingchuan , Li, Pin‐Lan, Li, Ming‐Qing , Li, Qing , Li, Sheng , Li, Wei , Li, Wenming , Li, Xue , Li, Yi-Ping, Li, Yuan , Li, Zhiqiang, Li, Zhiyong , Li, Zhiyuan , Lian, Jiqin, Liang, Chengyu, Liang, Qiangrong, Liang, Weicheng , Liang, Yongheng, Liang, YongTian , Liao, Guanghong , Liao, Lujian , Liao, Yung-Feng, Librizzi, Mariangela, Lie, Pearl P.Y. , Lim, Hyunjung J., Lima, Thania Rios Rossi , Limana, Federica , Lin, Chao , Lin, Chih-Wen, Lin, Dar‐Shong , Lin, Fu‐Cheng , Lin, Jiandie D. , Lin, Kurt M., Lin, Kwang‐Huei , Lin, Qiong, Lin, Shaofeng , Lin, Su-Ju, Lin, Wenyu , Lin, Yao‐Xin , Lin, Yee‐Shin, Linden, Rafael, Lindner, Paula , Lipinski, Marta M. , Lipovšek, Saška, Lisiak, Natalia , Liton, Paloma B. , Liu, Chao , Liu, Ching‐Hsuan , Liu, Fang , Liu, Hua-feng , Liu, Huifang , Liu, Jia , Liu, Jing , Liu, Leyuan, Liu, Wei , Liu, Wende , Liu, Xiao-Hong, Liu, Xu , Liu, Yanfen , Liu, Yang, Liu, Yang, Liu, Yueyang , Liu, Yule , Livingston, J. 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H. , Ma, Ning-Fang, Ma, Quanhong , Ma, Xinliang , Ma, Yueyun , Ma, Zhenyi , MacDougald, Ormond A. , MacIntosh, Gustavo C. , Macleod, Kay F. , Maday, Sandra , Madeo, Frank , Madesh, Muniswamy , Madl, Tobias , Madrigal‐Matute, Julio , Maeda, Akiko, Maejima, Yasuhiro , Magariños, Marta , Mahavadi, Poornima , Maiani, Emiliano , Maiese, Kenneth , Maiti, Panchanan , Maiuri, Maria Chiara , Major, Michael Ben, Makareeva, Elena , Malik, Fayaz , Mallilankaraman, Karthik , Maloyan, Alina , Mammadova, Najiba, Man, Gene Chi Wai , Manai, Federico , Mancias, Joseph D. , Mandelkow, Eva‐Maria , Manfredi, Angelo A. , Manshian, Bella B. , Manzano, Raquel , Marchese, Chiara , Marchetti, Sandrine , Marcucci, F, Mardente, Stefania , Mareninova, Olga A. , Mari, Muriel , Marinelli, Sara , Mariño, Guillermo , Mariotto, Sofia , Martin, Alexandre , Martin, Katie R. , Martin, Sara L. , Martín‐Segura, Adrián , Martin-Burriel, Inmaculada, Martín-Rincón, Marcos , Martín‐Sanz, Paloma , Martinez, Aitor , Martı́nez, Ana , Martinez-Lopez, Nuria, Martínez‐Vicente, Marta , Martins, Daniel Oliveira , Martins, Joilson O. , Martins, Waleska K. , Martins-Marques, Tania, Marzetti, Emanuele , Masaldan, Shashank , Masclaux-Daubresse, Celine, Mashek, Douglas G. , Massa, Valentina , Massieu, Lourdes , Masyuk, Anatoliy I. , Masyuk, Tetyana V., Matheu, Ander , Matoba, Satoaki , Matsuzaki, Sachiko , Mattar, Pamela , Mattè, Alessandro , Mauriz, José L., Mauthe, Mario , Maycotte, Paola , Mayer, Johanna , Mazzoni, Cristina , Mazzulli, Joseph R. , McCarty, Nami , McGill, Mitchell R. , McKenna, Sharon L. , McLoughlin, Fionn , McNiven, Mark A., McWilliams, Thomas G. , Medeiros, Tania , Medina, Diego L., Megeney, Lynn A. , Megyeri, Klára , Mehrpour, Maryam , Mehta, Jawahar L. , Meijer, Alfred J., Mejlvang, Jakob , Meléndez, Alicia , Melk, Annette, Memişoğlu, Gönen , Mendes, A.F. , Meng, Delong , Meng, Fei, Mercier, A., Mergny, Jean‐Louis , Merkley, Seth D. , Meske, Volker, Mestre, Ana Cecilia, Metur, Shree Padma, Meyer, C., Meyer, Hemmo , Mi, Wenyi , Mialet‐Perez, Jeanne , Miao, Jun‐Ying, Micale, Lucia , Miki, Yasuo, Milan, Enrico , Milczarek, Małgorzata , Miller, Samuel I. , Millward, Steven W. , Mirzaei, Hamed, Mirzaei, Hamid Reza , Mirzaei, Mehdi , Mishra, Amit , Mishra, Nandita , Mishra, Paras Kumar, Misirkić, Maja , Misasi, Roberta, Misra, Amit , Misso, Gabriella , Mitchell, Claire H. , Mitou, Géraldine, Miura, Tetsuji , Miyazaki, Makoto, Miyazaki, Mitsunori , Miyazaki, Taiga , Miyazawa, Keisuke, Mizushima, Noboru , Mogensen, Trine H. , Mohammadinejad, Reza , Mohamud, Yasir , Mohanty, Abhishek , Möhlmann, Torsten , Moley, Kelle H. , Möller, Andreas, Mollereau, Bertrand , Mollinedo, Faustino, Montagna, Costanza , Monteiro, Mervyn J. , Montella, Andrea , Montes, L. Ruth , Montico, Barbara , Mony, Vinod K., Compagnoni, Giacomo Monzio , Moore, Michael N. , Moosavi, Mohammad Amin , Morales‐Álamo, David , Moratalla, Rosario , Moreira, Paula I. , Morelli, Elena , Moreno-Blas, Daniel , Morgan, Alwena H. , Morin, Fabrice , Morishita, Hideaki , Moriyama, Mariko , Moriyasu, Yuji, Morleo, Manuela , Morselli, Eugenia , Moruno-Manchon, Jose F. , Moscat, Jorge , Motori, Elisa , Moura, Andréa Felinto , Mrakovcic, Maria, Muciño-Hernández, Gabriel, Mukherjee, Anupam , Mukhopadhyay, Subhadip , Levy, Jean M. Mulcahy , Mulero, Victoriano, Muller, Sylviane, Münch, Christian , Muñoz-Cánoves, Pura, Muñoz‐Galdeano, Teresa , Murakawa, Tomokazu , Murphy, Brona M. , Murthy, Aditya, Myöhänen, Timo T. , Mysorekar, Indira U. , Mytych, Jennifer , Nabavi, Seyed Mohammad , Nagy, Péter , Nahimana, Aimable , Nakagawa, Ichiro, Nakamura, Ken , Nandi, Shyam Sundar , Nanjundan, Meera, Nanni, Monica , Napolitano, Gennaro , Narita, Masashi, Nassif, Melissa , Nathan, Ilana , Natsumeda, Manabu , Naudé, Ryno J., Naumann, Christin , Naveiras, Olaia , Navid, Fatemeh , Nawrocki, Steffan T., Nazarko, Taras Y. , Nazio, Francesca , Negoita, Florentina , Neill, Thomas , Neisch, Amanda L. , Netea, Mihai G. , Neubert, Patrick , Neumann, Dietbert , Neutzner, Albert , Newton, Phillip T. , Ney, Paul A., Ng, Charlene Cheuk Wing, Ng, T.B., Nguyen, Hang Thi Thu , Nguyen, Long T. , Ni, Hong-Min, Cheallaigh, Clíona Ní , Ni, Zhenhong , Nicolao, M. Celeste, Nicoli, Francesco , Nieto‐Díaz, Manuel , Niranjan, Rituraj , Nishimune, Hiroshi , Niso-Santano, Mireia, Nixon, Ralph A. , Nobili, Annalisa , Noda, Takeshi , Nogueira-Recalde, Uxía , Nombela, Iván , Novak, Ivana, Novoa, Beatriz , Nukina, Nobuyuki , Nussbaum-Krammer, Carmen, Nylandsted, Jesper , O’Donovan, Tracey R. , O’Leary, Seónadh M. , O’Rourke, Eyleen J. , O’Sullivan, Mary P. , O’Sullivan, Timothy E., Oddo, Salvatore , Oehme, Ina, Ogawa, Michinaga , Ogier‐Denis, Éric , Ögmundsdóttir, Margrét H. , Öğretmen, Besim , Oh, Goo Taeg , Oh, Seon‐Hee , Oh, Young J. , Ohama, Takashi, Ohashi, Yohei , Ohmuraya, Masaki, Oikonomou, Vasileios , Ojha, Rani , Okazawa, Hitoshi , Oku, M., Oliván, Sara , Oliveira, Jorge M.A. , Ollmann, Michael, Olzmann, James A. , Omari, Shakib , Önal, Gizem, Ondrej, Martin , Ong, Sang‐Bing , Onnis, Anna , Orellana, Juan , Orellana‐Muñoz, Sara , Ortega-Villaizán, María del Mar , Ortiz‐González, Xilma R. , Osiewacz, Heinz D. , Osman, Abdel‐Hamid K. , Otsu, Kinya, Ott, Christiane , Ou, Jing-hsiung James, Outeiro, Tiago F., Öztürk, Melek , Pagès, Gilles, Pahari, Susanta , Pajares, Marta, Pal, Rituraj, Paladino, Simona , Palmieri, Michela , Palmisano, Giuseppe , Pan, Qingjun , Pan, Xin , Panasyuk, Ganna , Pandey, Rahul , Pandey, Udai B., Pandya, Vrajesh , Paneni, Francesco , Pang, Shirley Yin-Yu, Papademetrio, Daniela L. , Papinski, Daniel, Papp, Diana , Park, Hwan Tae , Park, Ji-Man, Park, Jong‐In , Park, Junsoo, Park, Sang-Youel, Pasquier, Adrien , Pasquier, Benoit , Passos, João F. , Pastore, Nunzia , Patschan, Daniel , Pattingre, Sophie , Pedraza‐Alva, Gustavo , Pedraza-Chaverri, Jose, Pedrozo, Zully , Pei, Gang , Pei, Jianming , Peled-Zehavi, Hadas, Pellegrini, Joaquín Miguel , Pelletier, Joffrey , Peñalva, Miguel A., Peng, Di , Peng, Ying , Pennuto, Maria , Pentimalli, Francesca , Pereira, Cláudia MF, Pereira, Gustavo J.S. , Pereira, Lílian Cristina , Almeida, Luís Pereira de , Perera, Nirma D. , Pérez-Lara, Ángel, Pérez‐Oliva, Ana B. , Perl, András , Pestell, Richard G. , Petrache, I., Petrovski, Goran , Pfirrmann, Thorsten , Pfister, Astrid S., Pi, Huifeng , Pickrell, Alicia M. , Picot, Sandy, Pierantoni, Giovanna Maria , Pierre, Philippe , Pierrefite‐Carle, Valérie , Pierzynowska, Karolina , Pietruczuk, Mirosława , Pimentel-Muiños, Felipe X., Pinar, Mario , Pinheiro, Roberta Olmo , Pinkas‐Kramarski, Ronit , Pircs, Karolina , Piya, Sujan , Pizzo, Paola , Platta, Harald W. , Plomann, Markus , Pocock, Roger , Pöggeler, Stefanie , Pohl, Christian, Poirot, Marc , Poletti, Angelo , Ponpuak, Marisa , Popelka, Hana , Popova, Bilyana , Porta, Helena, Alcón, Soledad Porte , Portilla-Fernández, Eliana , Potts, Malia B. , Powers, Ted, Prahlad, Veena , Prajsnar, Tomasz K. , Praticò, Domenico , Prencipe, Rosaria, Priault, Muriel , Proikas‐Cezanne, Tassula , Puertollano, Rosa , Puglielli, Luigi, Pulinilkunnil, Thomas , Puri, Deepika , Puri, Rajat, Puyal, Julien , Qi, Yongmei , Quadrilatero, Joe , Quarleri, Jorge, Raben, Nina, Rabinowich, Hannah, Ragona, Debora, Rahmati, Marveh , Raia, Valeria, Raimundo, Nuno , Rajasekaran, Namakkal-Soorappan, Rao, Sriganesh Ramachandra , Rami, Abdelhaq, Ramírez‐Pardo, Ignacio , Ramsden, D. , Randow, Felix , Ranieri, Danilo , Rao, Hai , Rao, Rekha , Rathore, Sumit , Ratnayaka, J. Arjuna , Ratovitski, Edward A., Ravanan, Palaniyandi, Ravegnini, Gloria , Ray, Swapan K. , Rebecca, Vito, Reggiori, Fulvio , Reigada, David , Reiling, Jan H., Rein, Theo , Reipert, Siegfried , Rekha, Rokeya Sultana , Ren, Hongmei, Ren, Jun , Ren, Weichao , Renault, Tristan, Renga, Giorgia , Reue, Karen, Rewitz, Kim , Ramos, Bruna Ribeiro de Andrade , Riazuddin, S. Amer, Ribeiro‐Rodrigues, Teresa , Riccio, Victoria , Richardson, Des R. , Risbud, Makarand V. , Risueño, Ruth M. , Ritis, Konstantinos, Rizza, Salvatore , Rizzuto, Rosario , Roberts, Scott J. , Roberts, Luke D., Robinson, Katherine J. , Rocchi, Stéphane , Rodrigues, Tiago , Silva, Vagner Ramon Rodrigues , Rodriguez-Barrueco, Ruth, Rodríguez‐Henche, Nieves , Rogers, Robert S. , Rogov, Vladimir V., Rojo, Ana I. , Romanello, Vanina, Romani, Luigina , Romano, Alessandra , Romano, Patricia Silvia , Romeo-Guitart, David, Romero, Luís C. , Romero, Montserrat , Roney, Joseph C. , Rongo, Christopher , Rosenfeldt, Mathias T. , Rosenstiel, Philip , Rosenwald, Anne , Roth, Steven, Rouschop, Kasper M.A. , Roussel, Benoit D. , Roux, Sophie , Rovere–Querini, Patrizia, Roy, Ajit , Rozières, Aurore , Ruano, Diego , Rubtsova, Maria P. , Ruckdeschel, Klaus, Ruckenstuhl, Christoph , Rudolf, Emil, Rudolf, Rüdiger, Ruggieri, Alessandra , Ruparelia, Avnika A. , Rusmini, P. , Russell, Ryan R., Russo, Gian Luigi , Russo, Maria , Russo, Rossella , Ryabaya, O. O. , Sabater-Arcis, María , Sacher, M, Sachse, Carsten , Sadhu, Abhishek , Säftig, Paul , Sahebkar, Amirhossein, Şahin, Mustafa , Şahin, Özgür , Saito, Nayuta , Saito, Tsunenori , Sakai, Yasuyoshi , Sakamaki, Jun-Ichi , Saksela, Kalle , Salazar, Gloria , Salazar-Degracia, Anna, Salekdeh, Ghasem Hosseini , Saluja, Ashok K. , Sampaio‐Marques, Belém , Sánchez, Marı́a C. , Sánchez-Alcázar, José A., Sanchez‐Vera, Victoria , Sancho‐Shimizu, Vanessa , Santambrogio, Laura , Santana, Magda M., Santoni, G, Sanz, Alberto , Saran, Shweta , Sardiello, Marco, Sargeant, Timothy J. , Sarin, Apurva , Sarkar, Chinmoy, Sarkar, Sovan , Sarrias, Maria-Rosa, Sarkar, Surajit , Sarmah, Dipanka Tanu, Sarparanta, J. , Sathyanarayan, Aishwarya , Ranganayaki, S. , Scaglione, K. Matthew , Scatozza, Francesca , Schafer, Zachary T. , Schaible, Ulrich E., Schapira, Anthony H.V. , Scharl, Michael , Schein, Catherine H. , Schiaffino, Maria Vittoria , Schiappacassi, Mónica , Schindl, Rainer , Schlattner, Uwe , Schmidt, Oliver, Schmitt, Roland , Schmidt, Stephen D., Schmitz, Ingo , Schmukler, Eran, Schneider, Anja , Schneider, Bianca E. , Schober, Romana , Schoijet, Alejandra C. , Schramm, M., Schröder, Bernd , Schuh, Kai , Schüller, Christoph , Schulze, Ryan J. , Schürmanns, Lea , Schwamborn, Jens C. , Schwarten, Melanie , Scott, Melanie J. , Scotto, Kathleen W., Scrima, Andrea , Scrivo, Aurora , Sebastián, David , Sebti, Salwa , Sedej, Simon , Segev, Nava , Seglen, Per O., Selleck, Scott B. , Selsby, Joshua, Sendtner, Michael , Şentürk, Şerif , Seranova, Elena, Sergi, Consolato M., Serra-Moreno, Ruth , Sesaki, Hiromi , Settembre, Carmine, Setty, Subba Rao Gangi , Sgarbi, Gianluca , Sha, Ou , Shacka, John J., Shah, Javeed A. , Shang, Dantong, Shao, Feng, Sharbati, Soroush , Sharkey, Lisa M. , Sharma, Dipali , Sharma, Gaurav, Sharma, Kulbhushan , Sharma, Pawan , Sharma, Surendra, Shen, Han‐Ming , Shen, Hongtao , Shen, Jiangang , Shen, Ming , Shen, Weili , Shen, Zheni, Sheng, Rui , Sheng, Zu-Hang, Shi, Jianjian , Shi, Ying–Hong , Shiba‐Fukushima, Kahori, Shieh, Jeng‐Jer , Shimada, Yohta , Shimizu, Shigeomi, Shimozawa, Makoto , Shintani, Takahiro , Shoemaker, Christopher J. , Shojaei, Shahla, Shoji, Ikuo , Shravage, Bhupendra V. , Shridhar, Viji, Shu, Chih‐Wen , Shu, Hong-Bing, Shui, Ke, Shukla, Arvind Kumar , Shutt, Timothy E. , Sica, Valentina , Siddiqui, Aleem , Sierra-Torre, Virginia, Signorelli, Santiago , Silva, Bruno Jorge de Andrade , Silva, Johnatas D. , Silva-Pavez, Eduardo , Silvente‐Poirot, Sandrine , Simmonds, Rachel E. , Singh, Anurag K. , Singh, Rajat Kumar , Singh, Shivendra V. , Singh, Shrawan Kumar , Singh, Sudha , Singh, Sunaina , Singh, Surinder Pal , Sinha, Debasish, Sinha, Rohit A. , Sinha, Sangita, Sirko, Agnieszka , Sivridis, Efthimios L., Skendros, Panagiotis , Sohn, Eun Jung , Sok, Sophia P. 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David , Sze, Stephen Cho Wing , Szewczyk, Nathaniel J. , Tabęcka-Łonczynska, Anna, Tabolacci, Claudio , Taegtmeyer, Heinrich, Tagaya, Mitsuo , Tai, Haoran , Tait, Stephen W. G., Takahashi, Yoshinori , Tam, Chit , Tam, Shing Yau , Tampellini, Davide , Tamura, Atsushi , Tan, Eng-King, Tan, Ya‐Qin , Tanaka, Masaki , Tang, Daolin , Tang, Jingfeng , Tang, Tie-Shan , Tao, Zhipeng , Taouis, Mohammed , Tavernarakis, Nektarios, Taylor, Allen, Teixeira, Natércia , Tekirdağ, Kumsal Ayşe, Tencomnao, Tewin , Tenreiro, Sandra , Tharaux, Pierre‐Louis , Thedieck, Kathrin , Thekkinghat, Arvind A. , Thinwa, Josephine , Thirumalaikumar, Venkatesh P. , Thomes, Paul G. , Thorburn, Andrew , Thum, Thomas, Thumm, Michael , Tian, Ling, Tichý, Aleš , Timmerman, Vincent , Titorenko, Vladimir I. , Toivonen, Janne M. , Tomaipitinca, Luana , Tomar, Dhanendra , Tomás‐Zapico, Cristina , Tomić, Sergej , Tong, Chao, Tong, Xin , Torgersen, Maria L., Torii, Satoru , Torres-López, Liliana , Torriglia, Alicia , Towers, Christina G. , Towns, Roberto, Tran, Quynh‐Giao , Travassos, Leonardo H. , Tremel, Shirley , Trougakos, Ioannis P., Tsao, Betty P. , Tse, Tak Fu, Tsvetkov, Andrey S. , Tumbarello, David A. , Tyler, Jessica K., Tyutereva, Elena V. , Uchiyama, Yasuo , Ugun‐Klusek, Aslihan , Uhlig, Holm H. , Ułamek-Kozioł, Marzena, Ulasov, Ilya V. , Umekawa, Midori, Ungermann, Christian , Urbe, Sylvie, Uribe-Carretero, Elisabet , Uversky, Vladimir N. , Vaccaro, María I. , Vahsen, Björn F. , Vakifahmetoglu-Norberg, Helin, Valdor, Rut , Valente, Maria João , Valko, Ayelén , Vallée, Richard, Valverde, Ángela M. , Berghe, Greet Van den, Veen, Stijn van der , Kaer, Luc Van , Loosdregt, Jorg van , Wijk, Sjoerd J. L. van , Vandenberghe, Wim , Vanhorebeek, Ilse , Vannier-Santos, Marcos A., Vannini, Nicola , Vanrell, M. Cristina, Vantaggiato, Chiara , Varela‐Nieto, Isabel , Varga, Máté , Vasconcelos, M. Helena , Vats, Somya , Vega-Rubín-de-Celis, Silvia , Velasco, Guillermo , Velázquez, Ariadna P., Vellai, Tibor , Vellenga, Edo , Verdier, Mireille , Verginis, Panayotis , Vergne, Isabelle , Verkade, Paul , Verma, Manish , Verstreken, Patrik , Vervliet, Tim , Vervoorts, Jörg , Víctor, Víctor M., Vidoni, Chiara , Vieira, Otília V. , Vierstra, Richard D., Viganò, Sonia , Vihinen, Helena , Vijayan, Vinoy, Vila, Miquel , Villalba, José M., Villalobo, Antonio , Villarejo‐Zori, Beatriz , Vincent, Olivier , Vindis, Cécile , Viret, Christophe , Vocadlo, David J. , Voitsekhovskaja, Olga V. , Volonté, Cinzia , Volta, Mattia , Vomero, Marta , Haefen, Clarissa von, Vooijs, Marc , Voos, Wolfgang , Vučićević, Ljubica , Waguri, Satoshi , Waite, Kenrick A., Wakatsuki, Shuji , Walker, David W., Walker, Mark J. , Walter, Jochen, Wandosell, Francisco , Wang, Bo , Wang, Chao-Yung, Wang, Chen , Wang, Chenran, Wang, Chenwei , Wang, Cun-Yu, Wang, Fengming, Wang, Han, Wang, Hexiang , Wang, Hong‐Gang , Wang, Jianrong, Wang, Jigang , Wang, Jun-Dong , Wang, Kui , Wang, Lianrong, Wang, Liming , Wang, Maggie Haitian, Wang, Meiqing , Wang, Nanbu , Wang, Pengwei , Wang, Ping , Wang, Ping, Wang, Q., Wang, Qiong , Wang, Wentao , Wang, Xinnan, Wang, Xuejun , Wang, Yan, Wang, Yanchang , Wang, Yanzhuang , Wang, Yihua , Wang, Yu , Wang, Yuqi , Wang, Zhe, Wang, Zhenyu , Wang, Zhouguang , Watanabe, Eizo, Watchon, Maxinne , Wawrzyńska, Anna , Weaver, Timothy E., Węgrzyn, Grzegorz, Wehman, Ann M. , Wei, Huafeng , Wei, Taotao , Wei, Yongjie, Weiergräber, Oliver H. , Weihl, Conrad C., Weindl, Günther , Weiskirchen, Ralf , Wen, Runxia H. , Werner, Antonia, Weykopf, Beatrice , Whitton, J. 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N. , Bei, Roberto , Behl, Christian , Bello‐Morales, Raquel , Beltrán, Sebastián , Bergami, Matteo , Bergmann, Andreas , Berthoux, Lionel , Bhattacharya, Resham , Bhattacharyya, Shalmoli , Bhutia, Sujit Kumar , Bi, Lanrong , Birgisdottir, Åsa Birna , Bjørkøy, Geir , Blomgren, Klas , Boada-Romero, Emilio , Boesze‐Battaglia, Kathleen , Bonifacino, Juan S. , Boone, Brian A. , Borthakur, Gautam , Bosch, Jürgen , Bose, Santanu , Botana, Luis Miguel , Boulton, Michael E. , Bourdenx, Mathieu , Brackney, Doug E. , Brandts, Christian , Braun, Ralf J. , Braus, Gerhard H. , Bravo, Roberto , Brody, Steven L. , Brown, Carolyn Nicole , Brumell, John Hunter , Brunetti‐Pierri, Nicola , Buitrago‐Molina, Laura Elisa , Buraschi, Simone , Buch, Shilpa J. , Butturini, Elena , Cairó, Montserrat , Caldwell, Kim A. , Campbell‐Valois, François‐Xavier , Campello, Silvia , Campesi, Ilaria , Almeida, Danilo Cândido de , Canesi, Laura , Caraglia, Michele , Carchman, Evie H. , Carmona‐Gutiérrez, Didac , Carnevali, Oliana , Carrier, Lucie , Carroll, Bernadette , Carter, A. Brent , Casas, Caty , Castillo, Eliseo F. , Castro‐Caldas, Margarida , Castro‐Obregón, Susana , Catz, Sergio , Cayuela, María Luisa , Cecarini, Valentina , Cecconi, Francesco , Cenci, Simone , Ceperuelo‐Mallafré, Victòria , Cervia, Davide , Chae, Han–Jung , Chakrabarti, Oishee , Chan, Edmond Y. 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G.
Autophagy | March 24, 2021 | Volume: 17 | Issue: 1 | Pages: 1-382
Thoracoabdominalis aortakirekesztés során fellépő elektrofiziológiai változások a gerincvelőben
Dzsinich, Csaba, Nagy, Gabriella , Nagy, Klaudia Vivien , Gloviczki, Péter
Magyar Sebészet (Hungarian Journal of Surgery) | December 26, 2020 | Volume: 73 | Issue: 4 | Pages: 160-166
10.1556/1046.73.2020.4.5
| PMID 33310917
Összefoglaló. A thoracoabdominalis aortakirekesztés okozta gerincvelő ischemia súlyos neurológiai következményeit számos klinikai és kísérleti tanulmány bizonyítja. E nehezen kiszámítható, súlyos szövődmény megelőzésének érdekében régi törekvés megfelelő intra- és posztoperatív monitorizálás kifejlesztése, ami előre jelzi a gerincvelő-funkció romlását, illetve a kialakuló celluláris károsodást. A legelterjedtebb, a klinikai gyakorlatban széles körben alkalmazott megoldás a gerincvelői kiváltott motoros potenciál (MEP) folyamatos ellenőrzése. Ritkábban alkalmazott – bár ígéretes – eljárás a biokémiai változások nyomon követése, ami a sejtszintű károsodás markereit használja fel az ischemia okozta változások felismerésére. Korábbi dolgozatunkban kutyákon végzett kísérleteink azon eredményeit ismertettük, amelyekben a 60 perces thoracoabdominalis aortakirekesztés okozta neurológiai változások és a perfúzió adatainak összefüggéseit tárgyaltuk. Jelen tanulmányunkban a gerincvelői motoros (MEP) és szenzoros (SEP) kiváltott potenciálok változásait vizsgáljuk a neurológiai végállapot vonatkozásában. Megállapítottuk, hogy SEP változásai a neurológiai károsodás mértékével értékelhető összefüggést nem mutatnak. A MEP-amplitúdó és -latencia értékei biztonsággal jelzik a fenyegető gerincvelő ischemiát. A neurológiai deficit mélységét (Tarlov 2,1,0) a MEP-értékek változásai numerikusan nem értékelhetően követik. Summary. Severe neurological complications of the thoracoabdominal aortic clamping were published in numerous clinical and experimental studies. These hardly predictable, devastating consequences demanded to develop a monitoring system which might detect impending level of spinal cord ischemia in time – in order to introduce or enhance protective procedures and prevent permanent neurological deficit. The most widely used monitoring in clinical practice is the continuous surveillance of the motor evoked potentials (MEP) during and after thoracoabdominal aortic clamping. Much less used, but promising opportunity is to control the metabolic changes and cellular integrity utilizing specific markers like liquor lactate and neuron specific enolase (NSE) etc. In our earlier study we published data of our canine experiment related to coherencies between neurological outcome and specific perfusion of the spinal cord during and after one hour thoracoabdominal aortic clamping. In the present paper we investigate the behavior of motor evoked (MEP) and sensory evoked (SEP) potentials related to neurological changes. We conclude the behavior of SEP values hardly correlate with the neurologic outcome, meanwhile decrease of MEP amplitude provides reliable signal for developing spinal cord ischemia. We could not confirm a numeric correlation of these data and the level of the final neurologic outcome.
A sebészi gerincvelő ischemia és reperfusio hatása a specifikus átáramlásra és a neurológiai következményekre
Dzsinich, Csaba, Nagy, Gabriella , Nagy, Klaudia Vivien , Gloviczki, Péter
Magyar Sebészet (Hungarian Journal of Surgery) | December 26, 2020 | Volume: 73 | Issue: 4 | Pages: 153-159
10.1556/1046.73.2020.4.4
| PMID 33310918
Összefoglaló. A thoracoabdominalis aorta kirekesztése a gerincvelő keringésének csökkenését okozza. Az ischemia klinikailag paraparesis, paraplegia formájában jelenik meg. Ez elsősorban nyitott műtétek során jelent aligha kiszámítható szövődményt, de a modern endovascularis technikák sem oldották meg teljes mértékben ennek biztonságos kivédését – bár arányát jelentősen mérsékelték. A javuló eredmények számos tényezőre vezethetők vissza, mint a keringés kirekesztési idejének csökkentése, a gerincvelő-funkció és metabolismus ellenőrzése. Mesterséges keringésjavító direkt és indirekt eljárások, liquor drainage, hűtés mellett a collateralis keringés javítását szolgáló prekondicionáló módszerek kerültek bevezetésre. Kísérletes munkánkban 25–30 kg testsúlyú kutyákon vizsgáltuk – más paraméterek mellett – a distalis perfusio növelésének, a liquornyomás csökkentésének, illetve ezek kombinációjának protektív hatását egyórás thoracoabdominalis aortakirekesztés során. Dolgozatunkban a kísérleti állataink neurológiai végállapotát a keringési paraméterek és szöveti perfusio és a reperfusio változásainak összefüggésein keresztül tárgyaljuk. Megállapítottuk, hogy distalis gerincvelő reperfusiós hyperaemiája szorosan összefügg a neurológiai károsodás mértékével. Summary. Clamping of the thoracoabdominal aorta reduces perfusion of the spinal cord significantly, which clinically may present as paraparesis or paraplegia – devastating and unpredictable complications of open thoracoabdominal aortic surgery. Introduction of monitoring of evoked potentials and/or biochemical markers, methods increasing distal arterial pressure, indirect procedures enhancing residual flow (like liquor drainage), drugs, and use of hypothermia contributed to achieve better outcome. Preconditioning of spinal cord circulation is also a promising method. New endovascular techniques for thoracoabdominal aortic aneurysms and dissections reduced surgical trauma significantly. Despite all these progressions spinal cord ischemic damage is still a significant risk. To address this problem we carried out an experimental work using a canine model focusing on the protective effect of distal arterial perfusion, spinal fluid drainage, and their combination in a one hour setting of thoracoabdominal aortic clamping. In this paper we publish our data of circulatory and specific perfusion parameters of the spinal cord during and after declamping in correlation of final neurologic outcome.