Automated Author ProfileMesri, Mehdi
Mesri, Mehdi
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.0 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset combines proteomic, metabolomic, genomic and transcriptomic measurements with imaging from the CPTAC-GBM and UPENN-GBM datasets on TCIA to uncover multi-scale regulatory interactions governing development and evolution of glioblastoma tumors. The dataset contains co-detection by indexing (CODEX) based multiplexed imaging for 18 samples from 12 patients. Hematoxylin and Eosin staining on adjacent sections are also included for pathological annotation. The CODEX images were segmented using the Mesmer pre-trained nuclei + membrane segmentation model in the Deepcell cell segmentation library.Matching clinical data and proteomic data (raw MS files and processed data files of global proteomics and PTMs) and metabolomic data from the same tumors can be accessed via the Proteomic Data Commons (PDC) (https://pdc.cancer.gov/ ). Genomic, transcriptomic, and multiome snRNA-seq data files can be accessed via Genomic Data Commons (GDC) (https://portal.gdc.cancer.gov/projects/CPTAC-3 ). We recommend using a sample ID manifest to ensure exploration of the complete dataset including metastatic samples. Multiome snATAC-seq data files can be accessed via the Cancer Data Service (CDS) (https://dataservice.datacommons.cancer.gov/#/program/CPTAC:%20Clinical%20Proteomic%20Tumor%20Analysis%20Consortium ) under accession phs001287.v17.p6.
Authors
- Liu, Jingxian ;
- Cao, Song ;
- Imbach, Kathleen J. ;
- Gritsenko, Marina A. ;
- Lih, Tung-Shing M. ;
- Kyle, Jennifer E. ;
- Yaron, Tomer M. ;
- Binder, Zev A. ;
- Li, Yize ;
- Strunilin, Ilya ;
- Wang, Yi-Ting ;
- Tsai, Chia-Feng ;
- Ma, Weiping ;
- Chen, Lijun ;
- Clark, Natalie M. ;
- Shinkle, Andrew ;
- Naser Al Deen, Nataly ;
- Caravan, Wagma ;
- Houston, Andrew ;
- Simin, Faria A. ;
- Wyczalkowski, Matthew A. ;
- Wang, Liang-Bo ;
- Storrs, Erik ;
- Chen, Siqi ;
- Illindala, Ritvik ;
- Li, Yuping D. ;
- Jayasinghe, Reyka G. ;
- Rykunov, Dmitry ;
- Cottingham, Sandra L. ;
- Chu, Rosalie K. ;
- Weitz, Karl K. ;
- Moore, Ronald J. ;
- Sagendorf, Tyler ;
- Petyuk, Vladislav A. ;
- Nestor, Michael ;
- Bramer, Lisa M. ;
- Stratton, Kelly G. ;
- Schepmoes, Athena A. ;
- Couvillion, Sneha P. ;
- Eder, Josie ;
- Kim, Young-Mo ;
- Gao, Yuqian ;
- Fillmore, Thomas L. ;
- Zhao, Rui ;
- Monroe, Matthew E. ;
- Southard-Smith, Austin N. ;
- Li, Yang E. ;
- Lu, Rita Jui-Hsien ;
- Johnson, Jared L. ;
- Wiznerowicz, Maciej ;
- Hostetter, Galen ;
- Newton, Chelsea J. ;
- Ketchum, Karen A. ;
- Thangudu, Ratna R. ;
- Barnholtz-Sloan, Jill S. ;
- Wang, Pei ;
- Fenyö, David ;
- An, Eunkyung ;
- Thiagarajan, Mathangi ;
- Robles, Ana I. ;
- Mani, D. R. ;
- Smith, Richard D. ;
- Porta-Pardo, Eduard ;
- Cantley, Lewis C. ;
- Iavarone, Antonio ;
- Chen, Feng ;
- Mesri, Mehdi ;
- Nasrallah, MacLean P. ;
- Zhang, Hui ;
- Resnick, Adam C. ;
- Chheda, Milan G. ;
- Rodland, Karin D. ;
- Liu, Tao ;
- Ding, Li
NOTE: there is no peer-reviewed publication associated with this data record.
This data record contains a single dataset, Statistics200.xls, in .xls file format.
The dataset contains information on 1142 patients suspected of having coronavirus. Data collection was performed using interviews, inserting information into the researcher-made questionnaires, and using the information in patients' medical records.
The dataset consists of 52 individual variables. Empty cells correspond to data that are not available for that patient. The variables are: age, gender, citizenship, taken to the hospital (1; no, 2; yes), section of hospital, contact coronadisease (0; no history of contact with COVID-19 patients, 1; history of contact with COVID-19 patients), sample for test (0; patient did not provide a sample for the PCR COVID test, 1; patient provided a sample for the PCR test), result PCR (0; negative for COVID-19, 1; positive for COVID-19, 3; test result is pending), The following symptoms were reported, for which 0 stands for absence of symptom, and 1 stands for presence of the symptom: fever, cough, myalgia or fatigue, shortness of breath, loss of consciousness, smell, taste, convulsions, headache, dizziness, limb paresis, limb plexus, chest pain, skin lesions, other signs, gastrointestinal, nausea, vomiting, diarrhea, anorexia), CT scan manifestation (0; CT scan results for COVID-19 are negative, 1; CT scan results for COVID-19 are positive), smoking, use of opium, patient has undergone intubation, rate of partial pressure of oxygen, Po2 (0; PO2 levels are greater than 93, 2; PO2 levels are less than 93), presence of underlying diseases (cancer, liver disease, hematologic disease, diabetes, HIV/AIDS, immune deficiency (acquired or congenital), pregnancy, heart disease, renal disease, dialysis status, asthma, Chronic obstructive pulmonary disease (COPD)), other chronic diseases, hypertension, condition when entering the hospital, death, duration of hospitalisation in days.
Study aims and methodology: The new Coronavirus disease (COVID-19) primarily targets the human respiratory system, and represents a public health emergency and global concern. The present study aimed to investigate the epidemiological and clinical characteristics of COVID-19, in Saveh city, of Iran, in 2020.The present study was descriptive-analytical research in which all 1142 patients suspected of having coronavirus, who visited Saveh Medical Centers from February 9 to April 1 7, 2020, were included in the study. Data collection was performed using interviews, inserting information into the researcher-made questionnaires, and using the information in patients' medical records. Data were analyzed by SPSS 21 using Chi-square, independent sample t tests, Fisher's Exact Test, and regression analysis.For more details on the methodology, please read the related article.
Authors
- Karimy, Mahmood ;
- Araban, Marzieh ;
- Mesri, Mehdi ;
- Armoon, Bahram ;
- Koohestani, Hamid Reza ;
- Azani, Hadi
NOTE: there is no peer-reviewed publication associated with this data record.
This data record contains a single dataset, Statistics200.xls, in .xls file format.
The dataset contains information on 1142 patients suspected of having coronavirus. Data collection was performed using interviews, inserting information into the researcher-made questionnaires, and using the information in patients' medical records.
The dataset consists of 52 individual variables. Empty cells correspond to data that are not available for that patient. The variables are: age, gender, citizenship, taken to the hospital (1; no, 2; yes), section of hospital, contact coronadisease (0; no history of contact with COVID-19 patients, 1; history of contact with COVID-19 patients), sample for test (0; patient did not provide a sample for the PCR COVID test, 1; patient provided a sample for the PCR test), result PCR (0; negative for COVID-19, 1; positive for COVID-19, 3; test result is pending), The following symptoms were reported, for which 0 stands for absence of symptom, and 1 stands for presence of the symptom: fever, cough, myalgia or fatigue, shortness of breath, loss of consciousness, smell, taste, convulsions, headache, dizziness, limb paresis, limb plexus, chest pain, skin lesions, other signs, gastrointestinal, nausea, vomiting, diarrhea, anorexia), CT scan manifestation (0; CT scan results for COVID-19 are negative, 1; CT scan results for COVID-19 are positive), smoking, use of opium, patient has undergone intubation, rate of partial pressure of oxygen, Po2 (0; PO2 levels are greater than 93, 2; PO2 levels are less than 93), presence of underlying diseases (cancer, liver disease, hematologic disease, diabetes, HIV/AIDS, immune deficiency (acquired or congenital), pregnancy, heart disease, renal disease, dialysis status, asthma, Chronic obstructive pulmonary disease (COPD)), other chronic diseases, hypertension, condition when entering the hospital, death, duration of hospitalisation in days.
Study aims and methodology: The new Coronavirus disease (COVID-19) primarily targets the human respiratory system, and represents a public health emergency and global concern. The present study aimed to investigate the epidemiological and clinical characteristics of COVID-19, in Saveh city, of Iran, in 2020.The present study was descriptive-analytical research in which all 1142 patients suspected of having coronavirus, who visited Saveh Medical Centers from February 9 to April 1 7, 2020, were included in the study. Data collection was performed using interviews, inserting information into the researcher-made questionnaires, and using the information in patients' medical records. Data were analyzed by SPSS 21 using Chi-square, independent sample t tests, Fisher's Exact Test, and regression analysis.For more details on the methodology, please read the related article.
Authors
- Karimy, Mahmood ;
- Araban, Marzieh ;
- Mesri, Mehdi ;
- Armoon, Bahram ;
- Koohestani, Hamid Reza ;
- Azani, Hadi