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304 results on '"Mall, Raghvendra"'

4. A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer

Author

Mason, Mike, Lapuente-Santana, Óscar, Halkola, Anni S., Wang, Wenyu, Mall, Raghvendra, Xiao, Xu, Kaufman, Jacob, Fu, Jingxin, Pfeil, Jacob, Banerjee, Jineta, Chung, Verena, Chang, Han, Chasalow, Scott D., Lin, Hung Ying, Chai, Rongrong, Yu, Thomas, Finotello, Francesca, Mirtti, Tuomas, Mäyränpää, Mikko I., Bao, Jie, Verschuren, Emmy W., Ahmed, Eiman I., Ceccarelli, Michele, Miller, Lance D., Monaco, Gianni, Hendrickx, Wouter R. L., Sherif, Shimaa, Yang, Lin, Tang, Ming, Gu, Shengqing Stan, Zhang, Wubing, Zhang, Yi, Zeng, Zexian, Das Sahu, Avinash, Liu, Yang, Yang, Wenxian, Bedognetti, Davide, Tang, Jing, Eduati, Federica, Laajala, Teemu D., Geese, William J., Guinney, Justin, Szustakowski, Joseph D., Vincent, Benjamin G., and Carbone, David P.

Published
2024
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5. An integrated tumor, immune and microbiome atlas of colon cancer.

Author

Roelands, Jessica, Kuppen, Peter, Ahmed, Eiman, Mall, Raghvendra, Masoodi, Tariq, Singh, Parul, Monaco, Gianni, Raynaud, Christophe, de Miranda, Noel, Ferraro, Luigi, Carneiro-Lobo, Tatiana, Syed, Najeeb, Rawat, Arun, Awad, Amany, Decock, Julie, Mifsud, William, Miller, Lance, Sherif, Shimaa, Mohamed, Mahmoud, Rinchai, Darawan, Van den Eynde, Marc, Sayaman, Rosalyn, Ziv, Elad, Bertucci, Francois, Petkar, Mahir, Lorenz, Stephan, Mathew, Lisa, Wang, Kun, Murugesan, Selvasankar, Chaussabel, Damien, Vahrmeijer, Alexander, Wang, Ena, Ceccarelli, Anna, Fakhro, Khalid, Zoppoli, Gabriele, Ballestrero, Alberto, Tollenaar, Rob, Marincola, Francesco, Galon, Jérôme, Khodor, Souhaila, Ceccarelli, Michele, Hendrickx, Wouter, and Bedognetti, Davide

Subjects
Humans, Cohort Studies, Biomarkers, Tumor, Colonic Neoplasms, Transcriptome, Tumor Microenvironment
Abstract

The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.

Published
2023

7. Permutation-Invariant Subgraph Discovery

Author

Mall, Raghvendra, Parambath, Shameem A., Yufei, Han, Yu, Ting, and Chawla, Sanjay

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We introduce Permutation and Structured Perturbation Inference (PSPI), a new problem formulation that abstracts many graph matching tasks that arise in systems biology. PSPI can be viewed as a robust formulation of the permutation inference or graph matching, where the objective is to find a permutation between two graphs under the assumption that a set of edges may have undergone a perturbation due to an underlying cause. For example, suppose there are two gene regulatory networks X and Y from a diseased and normal tissue respectively. Then, the PSPI problem can be used to detect if there has been a structural change between the two networks which can serve as a signature of the disease. Besides the new problem formulation, we propose an ADMM algorithm (STEPD) to solve a relaxed version of the PSPI problem. An extensive case study on comparative gene regulatory networks (GRNs) is used to demonstrate that STEPD is able to accurately infer structured perturbations and thus provides a tool for computational biologists to identify novel prognostic signatures. A spectral analysis confirms that STEPD can recover small clique-like perturbations making it a useful tool for detecting permutation-invariant changes in graphs., Comment: 8 pages, 4 Figures, 2 Tables

Published
2021

12. Ancestry-associated transcriptomic profiles of breast cancer in patients of African, Arab, and European ancestry

Author

Roelands, Jessica, Mall, Raghvendra, Almeer, Hossam, Thomas, Remy, Mohamed, Mahmoud G, Bedri, Shahinaz, Al-Bader, Salha Bujassoum, Junejo, Kulsoom, Ziv, Elad, Sayaman, Rosalyn W, Kuppen, Peter JK, Bedognetti, Davide, Hendrickx, Wouter, and Decock, Julie

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Genetics, Human Genome, Clinical Research, Cancer, Breast Cancer, Good Health and Well Being, Clinical sciences, Oncology and carcinogenesis, Epidemiology
Abstract

Breast cancer largely dominates the global cancer burden statistics; however, there are striking disparities in mortality rates across countries. While socioeconomic factors contribute to population-based differences in mortality, they do not fully explain disparity among women of African ancestry (AA) and Arab ancestry (ArA) compared to women of European ancestry (EA). In this study, we sought to identify molecular differences that could provide insight into the biology of ancestry-associated disparities in clinical outcomes. We applied a unique approach that combines the use of curated survival data from The Cancer Genome Atlas (TCGA) Pan-Cancer clinical data resource, improved single-nucleotide polymorphism-based inferred ancestry assignment, and a novel breast cancer subtype classification to interrogate the TCGA and a local Arab breast cancer dataset. We observed an enrichment of BasalMyo tumors in AA patients (38 vs 16.5% in EA, p = 1.30E - 10), associated with a significant worse overall (hazard ratio (HR) = 2.39, p = 0.02) and disease-specific survival (HR = 2.57, p = 0.03). Gene set enrichment analysis of BasalMyo AA and EA samples revealed differences in the abundance of T-regulatory and T-helper type 2 cells, and enrichment of cancer-related pathways with prognostic implications (AA: PI3K-Akt-mTOR and ErbB signaling; EA: EGF, estrogen-dependent and DNA repair signaling). Strikingly, AMPK signaling was associated with opposing prognostic connotation (AA: 10-year HR = 2.79, EA: 10-year HR = 0.34). Analysis of ArA patients suggests enrichment of BasalMyo tumors with a trend for differential enrichment of T-regulatory cells and AMPK signaling. Together, our findings suggest that the disparity in the clinical outcome of AA breast cancer patients is likely related to differences in cancer-related and microenvironmental features.

Published
2021

18. Kernel Spectral Clustering and applications

Author

Langone, Rocco, Mall, Raghvendra, Alzate, Carlos, and Suykens, Johan A. K.

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane in a high dimensional space induced by a kernel. In addition, the multi-way clustering can be obtained by combining a set of binary decision functions via an Error Correcting Output Codes (ECOC) encoding scheme. Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. In the validation stage model selection is performed to obtain tuning parameters, like the number of clusters present in the data. This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics. Once a KSC model is trained on a small subset of the entire data, it is able to generalize well to unseen test points. Beyond the basic formulation, sparse KSC algorithms based on the Incomplete Cholesky Decomposition (ICD) and $L_0$, $L_1, L_0 + L_1$, Group Lasso regularization are reviewed. In that respect, we show how it is possible to handle large scale data. Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. Finally, real-world applications such as image segmentation, power load time-series clustering, document clustering and big data learning are considered., Comment: chapter contribution to the book "Unsupervised Learning Algorithms"

Published
2015

23. ISaaC: Identifying Structural Relations in Biological Data with Copula-Based Kernel Dependency Measures

Author

Al Meer, Hossam, Mall, Raghvendra, Ullah, Ehsan, Megrez, Nasreddine, Bensmail, Halima, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Rojas, Ignacio, editor, and Ortuño, Francisco, editor

Published
2018
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25. Politics on YouTube: Detecting Online Group Polarization Based on News Videos' Comments.

Author

Mall, Raghvendra, Nagpal, Mridul, Salminen, Joni, Almerekhi, Hind, Soon-gyo Jung, and Jansen, Bernard J.

Subjects
*SOCIAL media, *MACHINE learning, *ORGANIZATION management, *INFORMATION retrieval
Abstract

Technology-mediated group toxicity polarization is a major socio-technological issue of our time. For better large-scale monitoring of polarization among social media news content, we quantify the toxicity of news video comments using a Toxicity Polarization Score. For polarizing news videos, our premise is that the comments' toxicity approximates either an "M" or "U" shaped distribution--that is, there is unevenly balanced toxicity among the comments. We evaluate our premises through a case study using a dataset of ~180,000 YouTube comments on ~3,700 real news videos from an international online news organization. Toward polarization-mitigating information systems, we build a predictive machine learning model to score the toxicity polarization of news content even when its comments are disabled or not available, as it is a current trend among news publishers to disable comments. Findings imply that the most engaging news content is also often the most polarizing, which we associate with increasing research on clickbait content and the detrimental effect of attention-based metrics on the health of online social media communities, especially news communities. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Discovering Cluster Dynamics Using Kernel Spectral Methods

Author

Langone, Rocco, Mall, Raghvendra, Vandewalle, Joos, Suykens, Johan A. K., Abarbanel, Henry, Series editor, Braha, Dan, Series editor, Érdi, Péter, Series editor, Friston, Karl, Series editor, Haken, Hermann, Series editor, Jirsa, Viktor, Series editor, Kacprzyk, Janusz, Series editor, Kaneko, Kunihiko, Series editor, Kelso, Scott, Series editor, Kirkilionis, Markus, Series editor, Kurths, Jürgen, Series editor, Nowak, Andrzej, Series editor, Qudrat-Ullah, Hassan, Series editor, Reichl, Linda, Series editor, Schuster, Peter, Series editor, Schweitzer, Frank, Series editor, sornette, didier, Series editor, Thurner, Stefan, Series editor, Lü, Jinhu, editor, Yu, Xinghuo, editor, Chen, Guanrong, editor, and Yu, Wenwu, editor

Published
2016
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32. MATE-Pred: Multimodal Attention-based TCR-Epitope interaction Predictor

Author

Goffinet, Etienne, Mall, Raghvendra, Singh, Ankita, Kaushik, Rahul, Castiglione, Filippo, Goffinet, Etienne, Mall, Raghvendra, Singh, Ankita, Kaushik, Rahul, and Castiglione, Filippo

Abstract

An accurate binding affinity prediction between T-cell receptors and epitopes contributes decisively to develop successful immunotherapy strategies. Some state-of-the-art computational methods implement deep learning techniques by integrating evolutionary features to convert the amino acid residues of cell receptors and epitope sequences into numerical values, while some other methods employ pre-trained language models to summarize the embedding vectors at the amino acid residue level to obtain sequence-wise representations. Here, we propose a highly reliable novel method, MATE-Pred, that performs multi-modal attention-based prediction of T-cell receptors and epitopes binding affinity. The MATE-Pred is compared and benchmarked with other deep learning models that leverage multi-modal representations of T-cell receptors and epitopes. In the proposed method, the textual representation of proteins is embedded with a pre-trained bi-directional encoder model and combined with two additional modalities: a) a comprehensive set of selected physicochemical properties; b) predicted contact maps that estimate the 3D distances between amino acid residues in the sequences. The MATE-Pred demonstrates the potential of multi-modal model in achieving state-of-the-art performance (+8.4\% MCC, +5.5\% AUC compared to baselines) and efficiently capturing contextual, physicochemical, and structural information from amino acid residues. The performance of MATE-Pred projects its potential application in various drug discovery regimes., Comment: Patent pending: U.S. Provisional Application No. 63/603,952

Published
2023

33. A metabolic function of FGFR3-TACC3 gene fusions in cancer

Author

Frattini, Vronique, Pagnotta, Stefano M., Tala, Fan, Jerry J., Russo, Marco V., Lee, Sang Bae, Garofano, Luciano, Zhang, Jing, Shi, Peiguo, Lewis, Genevieve, Sanson, Heloise, Frederick, Vanessa, Castano, Angelica M., Cerulo, Luigi, Rolland, Delphine C. M., Mall, Raghvendra, Mokhtari, Karima, Elenitoba-Johnson, Kojo S. J., Sanson, Marc, Huang, Xi, Ceccarelli, Michele, Lasorella, Anna, and Iavarone, Antonio

Subjects
Metabolism -- Genetic aspects -- Health aspects, Fibroblast growth factor receptors -- Health aspects -- Genetic aspects, Cancer -- Genetic aspects, Gene fusion -- Health aspects -- Genetic aspects, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Vronique Frattini [1]; Stefano M. Pagnotta [1, 2]; Tala [1]; Jerry J. Fan [3, 4]; Marco V. Russo [1]; Sang Bae Lee [1]; Luciano Garofano [1, 2, 5]; Jing [...]

Published
2018
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34. Supplementary Methods from Fasting-Mimicking Diet Is Safe and Reshapes Metabolism and Antitumor Immunity in Patients with Cancer

Author

Vernieri, Claudio, primary, Fucà, Giovanni, primary, Ligorio, Francesca, primary, Huber, Veronica, primary, Vingiani, Andrea, primary, Iannelli, Fabio, primary, Raimondi, Alessandra, primary, Rinchai, Darawan, primary, Frigè, Gianmaria, primary, Belfiore, Antonino, primary, Lalli, Luca, primary, Chiodoni, Claudia, primary, Cancila, Valeria, primary, Zanardi, Federica, primary, Ajazi, Arta, primary, Cortellino, Salvatore, primary, Vallacchi, Viviana, primary, Squarcina, Paola, primary, Cova, Agata, primary, Pesce, Samantha, primary, Frati, Paola, primary, Mall, Raghvendra, primary, Corsetto, Paola Antonia, primary, Rizzo, Angela Maria, primary, Ferraris, Cristina, primary, Folli, Secondo, primary, Garassino, Marina Chiara, primary, Capri, Giuseppe, primary, Bianchi, Giulia, primary, Colombo, Mario Paolo, primary, Minucci, Saverio, primary, Foiani, Marco, primary, Longo, Valter Daniel, primary, Apolone, Giovanni, primary, Torri, Valter, primary, Pruneri, Giancarlo, primary, Bedognetti, Davide, primary, Rivoltini, Licia, primary, and de Braud, Filippo, primary

Published
2023
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35. Supplementary Tables S1-S6, S17-S18 from Fasting-Mimicking Diet Is Safe and Reshapes Metabolism and Antitumor Immunity in Patients with Cancer

Author

Vernieri, Claudio, primary, Fucà, Giovanni, primary, Ligorio, Francesca, primary, Huber, Veronica, primary, Vingiani, Andrea, primary, Iannelli, Fabio, primary, Raimondi, Alessandra, primary, Rinchai, Darawan, primary, Frigè, Gianmaria, primary, Belfiore, Antonino, primary, Lalli, Luca, primary, Chiodoni, Claudia, primary, Cancila, Valeria, primary, Zanardi, Federica, primary, Ajazi, Arta, primary, Cortellino, Salvatore, primary, Vallacchi, Viviana, primary, Squarcina, Paola, primary, Cova, Agata, primary, Pesce, Samantha, primary, Frati, Paola, primary, Mall, Raghvendra, primary, Corsetto, Paola Antonia, primary, Rizzo, Angela Maria, primary, Ferraris, Cristina, primary, Folli, Secondo, primary, Garassino, Marina Chiara, primary, Capri, Giuseppe, primary, Bianchi, Giulia, primary, Colombo, Mario Paolo, primary, Minucci, Saverio, primary, Foiani, Marco, primary, Longo, Valter Daniel, primary, Apolone, Giovanni, primary, Torri, Valter, primary, Pruneri, Giancarlo, primary, Bedognetti, Davide, primary, Rivoltini, Licia, primary, and de Braud, Filippo, primary

Published
2023
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36. SupplementaryTables S7-S16 from Fasting-Mimicking Diet Is Safe and Reshapes Metabolism and Antitumor Immunity in Patients with Cancer

Author

Vernieri, Claudio, primary, Fucà, Giovanni, primary, Ligorio, Francesca, primary, Huber, Veronica, primary, Vingiani, Andrea, primary, Iannelli, Fabio, primary, Raimondi, Alessandra, primary, Rinchai, Darawan, primary, Frigè, Gianmaria, primary, Belfiore, Antonino, primary, Lalli, Luca, primary, Chiodoni, Claudia, primary, Cancila, Valeria, primary, Zanardi, Federica, primary, Ajazi, Arta, primary, Cortellino, Salvatore, primary, Vallacchi, Viviana, primary, Squarcina, Paola, primary, Cova, Agata, primary, Pesce, Samantha, primary, Frati, Paola, primary, Mall, Raghvendra, primary, Corsetto, Paola Antonia, primary, Rizzo, Angela Maria, primary, Ferraris, Cristina, primary, Folli, Secondo, primary, Garassino, Marina Chiara, primary, Capri, Giuseppe, primary, Bianchi, Giulia, primary, Colombo, Mario Paolo, primary, Minucci, Saverio, primary, Foiani, Marco, primary, Longo, Valter Daniel, primary, Apolone, Giovanni, primary, Torri, Valter, primary, Pruneri, Giancarlo, primary, Bedognetti, Davide, primary, Rivoltini, Licia, primary, and de Braud, Filippo, primary

Published
2023
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37. Supplementary Figures from Fasting-Mimicking Diet Is Safe and Reshapes Metabolism and Antitumor Immunity in Patients with Cancer

Author

Vernieri, Claudio, primary, Fucà, Giovanni, primary, Ligorio, Francesca, primary, Huber, Veronica, primary, Vingiani, Andrea, primary, Iannelli, Fabio, primary, Raimondi, Alessandra, primary, Rinchai, Darawan, primary, Frigè, Gianmaria, primary, Belfiore, Antonino, primary, Lalli, Luca, primary, Chiodoni, Claudia, primary, Cancila, Valeria, primary, Zanardi, Federica, primary, Ajazi, Arta, primary, Cortellino, Salvatore, primary, Vallacchi, Viviana, primary, Squarcina, Paola, primary, Cova, Agata, primary, Pesce, Samantha, primary, Frati, Paola, primary, Mall, Raghvendra, primary, Corsetto, Paola Antonia, primary, Rizzo, Angela Maria, primary, Ferraris, Cristina, primary, Folli, Secondo, primary, Garassino, Marina Chiara, primary, Capri, Giuseppe, primary, Bianchi, Giulia, primary, Colombo, Mario Paolo, primary, Minucci, Saverio, primary, Foiani, Marco, primary, Longo, Valter Daniel, primary, Apolone, Giovanni, primary, Torri, Valter, primary, Pruneri, Giancarlo, primary, Bedognetti, Davide, primary, Rivoltini, Licia, primary, and de Braud, Filippo, primary

Published
2023
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38. Data from Fasting-Mimicking Diet Is Safe and Reshapes Metabolism and Antitumor Immunity in Patients with Cancer

Author

Vernieri, Claudio, primary, Fucà, Giovanni, primary, Ligorio, Francesca, primary, Huber, Veronica, primary, Vingiani, Andrea, primary, Iannelli, Fabio, primary, Raimondi, Alessandra, primary, Rinchai, Darawan, primary, Frigè, Gianmaria, primary, Belfiore, Antonino, primary, Lalli, Luca, primary, Chiodoni, Claudia, primary, Cancila, Valeria, primary, Zanardi, Federica, primary, Ajazi, Arta, primary, Cortellino, Salvatore, primary, Vallacchi, Viviana, primary, Squarcina, Paola, primary, Cova, Agata, primary, Pesce, Samantha, primary, Frati, Paola, primary, Mall, Raghvendra, primary, Corsetto, Paola Antonia, primary, Rizzo, Angela Maria, primary, Ferraris, Cristina, primary, Folli, Secondo, primary, Garassino, Marina Chiara, primary, Capri, Giuseppe, primary, Bianchi, Giulia, primary, Colombo, Mario Paolo, primary, Minucci, Saverio, primary, Foiani, Marco, primary, Longo, Valter Daniel, primary, Apolone, Giovanni, primary, Torri, Valter, primary, Pruneri, Giancarlo, primary, Bedognetti, Davide, primary, Rivoltini, Licia, primary, and de Braud, Filippo, primary

Published
2023
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39. Additional file 1 of An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study

Author

Mir, Fayaz Ahmad, Mall, Raghvendra, Ullah, Ehsan, Iskandarani, Ahmad, Cyprian, Farhan, Samra, Tareq A., Alkasem, Meis, Abdalhakam, Ibrahem, Farooq, Faisal, Taheri, Shahrad, and Abou-Samra, Abdul-Badi

Abstract

Additional file 1: Figure S1. Flowchart highlighting different number of MiRNAs, PCGs, Metabolites analyzed in our dataset and steps taken to identify key MiRNAs, PCGs, Metabolites involved in significantly enriched metabolic pathways between OBM vs OBO. Figure S2. Correlation between clinical traits and enriched metabolic pathways. Here each block represents correlation between the enrichment profile of a metabolic pathway and a clinical characteristic based on their values across the 39 participant profiles. Here we only considered those pathways which were significantly enriched and those clinical traits which were highlighted in Fig. 3A. Figure S3. Gene expression of 25 identified genes (differentially expressed in the samples) across multiple tissue types in the GTEx portal. ( https://gtexportal.org/ ). The expression of these genes is higher across multiple tissue types and not biased towards blood tissue from which samples were collected.

Published
2023
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40. A multiomic approach to examine the molecular signatures differentiating people with obesity alone from those with obesity and metabolic complications

Author

Mir, Fayaz, primary, Mall, Raghvendra, additional, Ullah, Ehsan, additional, Iskandarani, Ahmad, additional, Cyprian, Farhan, additional, Samra, Tareq A., additional, Alkasem, Meis, additional, Abdalhakam, Ibrahem, additional, Farooq, Faisal, additional, Taheri, Shahrad, additional, and Abou-Samra, Abdul-Badi, additional

Published
2023
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41. A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

Author

Mason, Mike, primary, Lapuente-Santana, Óscar, additional, Halkola, Anni S., additional, Wang, Wenyu, additional, Mall, Raghvendra, additional, Xiao, Xu, additional, Kaufman, Jacob, additional, Fu, Jingxin, additional, Pfeil, Jacob, additional, Banerjee, Jineta, additional, Chung, Verena, additional, Chang, Han, additional, Chasalow, Scott D., additional, Lin, Hung Ying, additional, Chai, Rongrong, additional, Yu, Thomas, additional, Finotello, Francesca, additional, Mirtti, Tuomas, additional, Mäyränpää, Mikko I., additional, Bao, Jie, additional, Verschuren, Emmy W., additional, Ahmed, Eiman I., additional, Ceccarelli, Michele, additional, Miller, Lance D., additional, Monaco, Gianni, additional, Hendrickx, Wouter R.L., additional, Sherif, Shimaa, additional, Yang, Lin, additional, Tang, Ming, additional, Gu, Shengqing Stan, additional, Zhang, Wubing, additional, Zhang, Yi, additional, Zeng, Zexian, additional, Sahu, Avinash Das, additional, Liu, Yang, additional, Yang, Wenxian, additional, Bedognetti, Davide, additional, Tang, Jing, additional, Eduati, Federica, additional, Laajala, Teemu D., additional, Geese, William J., additional, Guinney, Justin, additional, Szustakowski, Joseph D., additional, Carbone, David P., additional, and Vincent, Benjamin G., additional

Published
2022
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49. ZBP1-dependent inflammatory cell death, PANoptosis, and cytokine storm disrupt IFN therapeutic efficacy during coronavirus infection

Author

Karki, Rajendra, primary, Lee, SangJoon, additional, Mall, Raghvendra, additional, Pandian, Nagakannan, additional, Wang, Yaqiu, additional, Sharma, Bhesh Raj, additional, Malireddi, RK Subbarao, additional, Yang, Dong, additional, Trifkovic, Sanja, additional, Steele, Jacob A., additional, Connelly, Jon P., additional, Vishwanath, Gella, additional, Sasikala, Mitnala, additional, Reddy, Duvvur Nageshwar, additional, Vogel, Peter, additional, Pruett-Miller, Shondra M., additional, Webby, Richard, additional, Jonsson, Colleen Beth, additional, and Kanneganti, Thirumala-Devi, additional

Published
2022
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50. Characteristic MicroRNAs Linked to Dysregulated Metabolic Pathways in Qatari Adult Subjects With Obesity and Metabolic Syndrome

Author

Mir, Fayaz Ahmad, primary, Mall, Raghvendra, additional, Iskandarani, Ahmad, additional, Ullah, Ehsan, additional, Samra, Tareq A., additional, Cyprian, Farhan, additional, Parray, Aijaz, additional, Alkasem, Meis, additional, Abdalhakam, Ibrahem, additional, Farooq, Faisal, additional, and Abou-Samra, Abdul-Badi, additional

Published
2022
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