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757 results on '"Shaikh, Mohammad"'

151. Development of Herbal Milk Shake Dip Bags from Milk Powder, Tulsi Powder and Turmeric Powder

Author

Ameer, Shaikh Mohammad and Chawla, Varsha

Subjects
food and beverages, Herbal milk shake
Abstract

Food industries have rather high demand for the products that meet the consumer’s demand for a healthy life style for which functional food fortified with the plant ingredients plays an important role. Tulsi, turmeric and cinnamon are such important medicinal plants whose medicinal usage has been reported in the Indian and British Pharmacopoeias and in the Ayurvedic system of medicine and day to day life of human beings, also having various health benefits including immunomodulatory, anti-inflammatory, antiarthritis, and anti-bacterial, antioxidant, anti-diabetic and anti-tumor. The present study was anticipated to develop ready to mix and make herbal milk shake bags by using natural herbs such as tulsi, turmeric and brown sugar with milk powder. The herbal shake was developed by adding different level of milk powder (T1) 10 %, (T2) 20 %, (T3) 30 %, while control sample was prepared without addition of milk powder (T0). Sensory attributes, physic-chemical constituents and microbiological analysis were analyzed from prepared product and data was collected from that were tabulated.

Published
2022

153. Exaggerating and retraining factors leading to migraine incidence in Pakistan

Author

Sadaf Ahmed, Shamoon Noushad, Rida Nasir, Huma Khalid, and Shaikh Mohammad Tahir

Subjects
migraine, triggers, exaggerating, stress, headache, Psychology, BF1-990, Neurophysiology and neuropsychology, QP351-495
Abstract

Migraine is a neurological disorder with primary episodes of headache pain affecting populations worldwide. Almost people of all ages and family history are able to develop this pattern of throbbing and unilateral pain with visual disturbance to physical sensations. The key triggers that are most likely to initiate this headache includes psychosocial, economical, environmental or physical stress, hormonal disturbances, dietary variations, high sensitivity to light, smell and sounds.185 subjects both male and females of different ages from 16-60 years were interviewed to investigate the key triggering reasons for migraine attacks. Meanwhile they were also inquired by any restraining factors that help them to get instant or long-lasting relief. Evaluation was done on the basis of ICHD-II criteria. Moreover the family history, socio-economic status and demographic data were also noted. Our results gave a clear view that emotional and mental stress was the key trigger that can exaggerate the feeling of headache with physical strain as the other main burden. Secondly exposure to immense sunlight and bright lights was frequently reported. Other minor initiators observed were lack of sleep, dietary changes, skipped meals, menstruation, and particular smells like fragrance or unpleasant odors. While excessive sleep and medications were noted as chief controls this can restrain headache pain incidence. In this study, emotional stresses were significant in female. Psychological stress is reported to be a major contributor to headaches. The attacks may be relief by proper medication and sleep. Stress and sunlight trigger migraine head pain in majority that can be cure by proper medicine and sleep.

Published
2014
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154. Cu(I)‐Catalyzed Microwave‐Assisted Multicomponent Reaction Towards Synthesis of Diverse Fluorescent Quinazolino[4,3‐b]quinazolin‐8‐ones and Their Photophysical Study

Author

Pawar, Gaurav, primary, Ghouse, Shaikh Mohammad, additional, Joshi, Swanand Vinayak, additional, Rana, Preeti, additional, Kar, Swayamsiddha, additional, Sarma, P. Mahesh, additional, Dannarm, Srinivas Reddy, additional, Sonti, Rajesh, additional, and Nanduri, Srinivas, additional

Published
2022
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163. PI3K/Akt/mTOR Pathways Inhibitors with Potential Prospects in Non-Small-Cell Lung Cancer

Author

Alharbi, Khalid Saad, primary, Shaikh, Mohammad Arshad Javed, additional, Almalki, Waleed Hassan, additional, Kazmi, Imran, additional, Al-Abbasi, Fahad A., additional, Alzarea, Sami I., additional, Imam, Syed Sarim, additional, Alshehri, Sultan, additional, Ghoneim, Mohammed M., additional, Singh, Sachin Kumar, additional, Chellappan, Dinesh Kumar, additional, Oliver, Brian G., additional, Dua, Kamal, additional, and Gupta, Gaurav, additional

Published
2022
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164. Dying with dignity

Author

Shaikh Mohammad Kais

Subjects
Dignity, media_common.quotation_subject, Environmental ethics, Sociology, media_common
Published
2021
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165. LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation

Author

Shaikh, Mohammad Abuzar, Ji, Zhanghexuan, Moukheiber, Dana, Shen, Yan, Srihari, Sargur, and Gao, Mingchen

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computation and Language (cs.CL)
Abstract

Pre-training visual and textual representations from large-scale image-text pairs is becoming a standard approach for many downstream vision-language tasks. The transformer-based models learn inter and intra-modal attention through a list of self-supervised learning tasks. This paper proposes LAViTeR, a novel architecture for visual and textual representation learning. The main module, Visual Textual Alignment (VTA) will be assisted by two auxiliary tasks, GAN-based image synthesis and Image Captioning. We also propose a new evaluation metric measuring the similarity between the learnt visual and textual embedding. The experimental results on two public datasets, CUB and MS-COCO, demonstrate superior visual and textual representation alignment in the joint feature embedding space, 14 pages, 10 Figures, 5 Tables

Published
2021

166. Effect of Nigella sativa on general health and immune system in young healthy volunteers; a randomized, placebo-controlled, double-blinded clinical trial

Author

Salem, Ayad, primary, Bamosa, Abdullah, additional, Alam, Mohammed, additional, Alshuraim, Saleh, additional, Alyalak, Hamad, additional, Alagga, Abdulrahman, additional, Tarabzouni, Faisal, additional, Alisa, Omar, additional, Sabit, Hussein, additional, Mohsin, Ashfaq, additional, Shaikh, Mohammad, additional, Farea, Abdulaziz, additional, Alshammari, Thamer, additional, and Obeid, Obeid, additional

Published
2021
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168. Diagnosis and Management of Diffuse Large B Cell Lymphoma in Resource Constraint Settings: Society of Medical Oncology Pakistan, Pakistan Society of Haematology and Pakistan Society of Clinical Oncology Joint Clinical Practice Guideline

Author

Iftikhar, Raheel, primary, Ayaz Mir, Muhammad, additional, Moosajee, Munira, additional, Rasheed, Kamran, additional, Imam Bukhari, Syed Waqas, additional, Nadeem Abbasi, Ahmed, additional, Shamsi, Tahir Sultan, additional, Khan, Maryam, additional, Ahmed, Parvez, additional, ud Din, Hafeez, additional, Chaudhry, Qamar un Nisa, additional, Ahmad, Imran N, additional, Ali, Natasha, additional, Umair, Muhammad, additional, Bangash, Mussavir, additional, Ahmad, Usman, additional, Sattar, Wasim, additional, Zargham, Anum, additional, Shafi, Azhar, additional, Shamshad, Ghassan U., additional, Rizvi, Qurratulain, additional, Usman Shaikh, Mohammad, additional, Muhammad Irfan, Syed, additional, Zaidi, Uzma, additional, Naqi, Naeem, additional, Mahmood, Humera, additional, Hussain, Asghar, additional, Ijaz Masood, Ahmed, additional, Siddiqui, Neelam, additional, Masood, Misbah, additional, Faheem, Mohammad, additional, Adil, Salman Naseem, additional, and Aziz, Zeba, additional

Published
2021
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170. Socio-demographic Characteristics of Women with Reproductive Tract Infection Attending a Military Hospital

Author

Julfikkar Alam, Shaikh Mohammad Edi Ameen, Quazi Md Rashid Un Nabi, Abm Belayet Hossain, and Sheikh Shahidul Islam

Subjects
business.industry, Socio demographics, Reproductive tract, Medicine, business, Demography
Abstract

Introduction: Reproductive tract infections (RTIs) are being increasingly recognized as a serious global health problem with impact on individual women and men, their families and communities. RTIs are a group of disease that cause infection of the genital tract and include both those that are sexually transmitted and those that are not. RTIs are a significant cause of morbidity and mortality in both men and women, especially in women of reproductive age. Objectives: To determine the socio-demographic characteristics among women with RTIs attending at gynecology outpatient department, CMH, Dhaka. Materials and Methods: This descriptive cross-sectional study was carried out with a view to find out the socio-demographic characteristics of women with RTIs and their common clinical presentations. The study was conducted among women of reproductive age group of Bangladesh Armed Forces attending at Gynecology OPD, CMH Dhaka, for the period of 1st march to 30th June 2011. A total of 128 women (114 married and 14 were unmarried) were interviewed with the help of structured question and checklist. Data was collected through clinical diagnostic approach made by gynecologists. Results: It was found that highest percentage of respondents (32.8%) were in the age group of 30-34 years, mean age at marriage was 17.79 years. The highest percentage of respondents (46.5%) was married at the age range of 16-18 years. Among the respondents, 89.1% were married and 10.9% were unmarried. The women with higher education level had lower occurrence of RTIs than women with lower education. In relation to status of spouse of married women, RTIs were significantly more among the wife of other ranks. Practice of personal hygiene during menstruation showed safe positive impact on RTIs. It was found that 50% respondents were presenting with vaginal discharge (Leucorrhoea). Most of the causes were vaginitis (38.3%). Conclusion: Study findings indicate that high prevalence of RTIs among women is due to the influence of less educational status, low income and also husband's educational background and employment status. JAFMC Bangladesh. Vol 15, No 2 (December) 2019: 218-220

Published
2020
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171. Performance evaluation of B-doped graphene prepared via two different methods in symmetric supercapacitor using various electrolytes

Author

Subramanian Balaji, P. Anandhaganesh, Shaikh Mohammad Tauquir, Marappan Sathish, and M. Karnan

Subjects
Materials science, Analytical chemistry, General Physics and Astronomy, chemistry.chemical_element, 02 engineering and technology, Electrolyte, 010402 general chemistry, 01 natural sciences, law.invention, Boric acid, chemistry.chemical_compound, law, Boron, Supercapacitor, Aqueous solution, Graphene, Surfaces and Interfaces, General Chemistry, 021001 nanoscience & nanotechnology, Condensed Matter Physics, Supercritical fluid, 0104 chemical sciences, Surfaces, Coatings and Films, chemistry, Ionic liquid, 0210 nano-technology
Abstract

Here, the boron-doped graphene was produced through both supercritical fluid processing as well as hydrothermal route by means of boric acid as a boron source. The availability of boron containing functional groups, quantity of B-doping as well as chemical nature were evaluated using FT-IR, EDAX and XPS analysis. The B-doped graphene synthesized via SCF processing showed 8.9 atomic % of boron while the conventional hydrothermal method showed 9.5 atomic % of boron doping. In 20% KOH solution at 1 A/g, the former displayed an enriched specific capacitance of 286 F/g and revealed a greater specific capacitance retention of 96% at 20 A/g over 10,000 cycles. To assess the performance of device, the complete cell performance was investigated in two different aqueous electrolytes. An aqueous solution of 1 M NaClO4 showed a 4 fold higher energy density of 21.2 Wh/kg compared to 5.3 Wh/kg in 20% KOH solution. Among the three different ionic liquid electrolytes, the capacitive performance is maximum in1-ethyl-3-methyl imidazolium tetrafluoroborate (EMIMBF4) and accomplished a specific capacitance of 138 F/g along with an energy density of 43.1 Wh/kg.

Published
2019
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174. Evaluation of Critical Limit of Sulphur in Soils for Wheat (Triticum aestivum L.) and Mustard (Brassica napus L.)

Author

Mohammad Jahiruddin, Shaikh Mohammad Bokhtiar, Abdus Satter, Mahmud Hossain, Zakaria M. Solaiman, Baktear Hossain, Mohammad Golam Kibria, Rubina Yesmin, and Anwarul Abedin

Subjects
0106 biological sciences, Soil test, S content, Geography, Planning and Development, Brassica, TJ807-830, Management, Monitoring, Policy and Law, engineering.material, TD194-195, 01 natural sciences, Renewable energy sources, Crop, Human fertilization, Nutrient, wheat, GE1-350, Dry matter, S uptake, Environmental effects of industries and plants, biology, Renewable Energy, Sustainability and the Environment, 04 agricultural and veterinary sciences, biology.organism_classification, Environmental sciences, Agronomy, Soil water, critical limit, 040103 agronomy & agriculture, engineering, MUSTARD, 0401 agriculture, forestry, and fisheries, Fertilizer, 010606 plant biology & botany
Abstract

The conception of critical limit (CL) of a nutrient element distinguishes its deficiency from sufficiency, which could advise fertilizer application. A pot culture experiment was conducted during Rabi season (2019–2020) to study the CL of sulphur (S) in soil and plant. A total of 20 soil samples collected from intensive cropping areas of three agroecological zones (AEZs) of Bangladesh were used in the experiment. The 0.15% CaCl2 extractable S (expressed as available S) contents of the test soils ranged from 6.84 mg/kg to 38 mg/kg. Wheat (Triticum aestivum L.) cv. BARI gom 30 and mustard (Brassica napus L.) cv. BINA sorisha 9 were used as test crops in this study. There were two rates of S application to soil-0 and 15 mg/kg for wheat and 0 and 18 mg/kg for mustard from gypsum (CaSO4.2H2O). Each S treatment was replicated thrice. Dry matter yield, S content and S uptake by the crops increased with added S. For mustard, the CL of soil S was estimated to be 14 mg/kg by graphical procedure and 11 mg/kg in statistical method while plant tissue concentration showed the CL of 0.35% in both methods. The CL of soil S for wheat was found to be 14 mg/kg and 11 mg/kg in graphical and statistical methods, respectively, and the CL of plant tissue concentration was recorded as 0.14%, in both methods. It is expected that mustard and wheat crops would respond to S fertilization in soils containing S at or below the CL. The results would be useful for predicting crop (wheat and mustard) response to S fertilizer and developing efficient S fertilizer management to promote sustainable crop production.

Published
2021
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175. Hypomethylating agents+venetoclax induction therapy in acute myeloid leukemia unfit for intensive chemotherapy - novel avenues for lesser venetoclax duration and patients with baseline infections from a developing country

Author

Mirgh, Sumeet, Sharma, Archana, Shaikh, Mohammad Rizwan Mohammad Anwar, Kadian, Kirti, Agrawal, Narendra, Khushoo, Vishvdeep, Mehta, Pallavi, Ahmed, Rayaz, and Bhurani, Dinesh

Subjects
Original Article
Abstract

Both elderly acute myeloid leukemia (AML) patients and those with baseline infections, when treated with intensive chemotherapy, are associated with high induction mortality. We report 24 patients (16-newly-diagnosed, 8-relapsed/refractory) with AML deemed unfit for intensive chemotherapy (by virtue of age >60 years, ECOG-PS 3-4, or those with non-resolving infections at baseline), treated with azacytidine-venetoclax combination as induction chemotherapy. Median follow-up of the study group was 8 months. The overall complete remission (CR)+CR with incomplete count recovery (CRi) rate was 58.3%. 1-year progression-free survival and overall survival of the whole cohort was 44.4% and 55.8%, respectively. On subgroup analysis, newly-diagnosed AML (p=0.05), intermediate-risk cytogenetics (p=0.007), and HMA-naïve (p=0.05) patients had a significantly better outcome. AML patients with baseline infections (versus without infections) treated with azacytidine-venetoclax induction, have lesser induction mortality (compared with historic intensive chemotherapy) with equivalent response rates. A detailed analysis amongst cohorts with different venetoclax durations revealed that, shorter duration (

Published
2021

176. Report on RCC and Prestressed Pipes

Author

Shivraj Jangam, Harshad Kengale, Patil, Harish, Shaikh Mohammad Aarif Khaildahmed, and S.G.Pawar

Subjects
Prestressed concrete, PCCP, high compressive strength
Abstract

Prestressed concrete pipe has been mostly used in water-transmission line. In this paper we present the full-scale of our studies of PCCP and the evaluation of delimitation behavior of the structure. Recent attempt has been made to enable the PCCP structure suitable for the conventional cut and cover method. In this way, the disturbance to structures, construction time, and the cost can be decreased., {"references":["Alavinasab, A., Jha, R., & Ahmadi, G. (2011). Damage identification based on modal analysis of prestressed concrete pipes. In Pipelines 2011: A Sound Conduit for Sharing Solutions (pp. 12-23).","AWWA C301-07 (2007). Prestressed Concrete Cylinder Pipe, Steel Cylinder Pipe. American Water Works Association, Denver, CO.","Kola, R., & Sinha, S. K. (2010). Predictability and Preventability Indices for PCCP Water Mains. In Pipelines 2010: Climbing New Peaks to Infrastructure Reliability: Renew, Rehab, and Reinvest (pp. 730- 742).","Piratla, K. R., & Ariaratnam, S. T. (2011). Criticality analysis of water distribution pipelines. Journal of Pipeline Systems Engineering and Practice, 2(3), 91-101."]}

Published
2021
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177. ENHANCEMENT OF BANDWIDTH AND BEAM FORMING ANTENNA ARRAYS IN 5G CELLULAR COMMUNICATION NETWORKS.

Author

Shaikh, Mohammad Shahnawaz, Jayadurga, R., Jadhav, Jagannath, and Ahmad, Shadab

Subjects
DIRECTIONAL antennas, TELECOMMUNICATION systems, RADIO antennas, RADIO waves, ANTENNAS (Electronics), ANTENNA arrays
Abstract

In general, an antenna is an interface that transmits signal data and receives incoming signal data. The radio waves received through this interface help to do the necessary things for the transmitter and receiver circuit systems used there. Also a radio transmitter antenna transmits different waves generated from the current generated at its tip to different areas. In this paper, the functions of increasing its bandwidth by making changes in some dimensions of the antenna are proposed. The oscillating current used in its transmitter area increases its vibration waves. This increases the amount of airwaves generated there and the number of data transmitted through it. So its bandwidth is more likely to be high. Furthermore these functions generate varying magnetic fields so that the time taken by the cross-sectional magnetic fields of the antenna varies. [ABSTRACT FROM AUTHOR]

Published
2022
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178. IMPROVING THE REQUIREMENTS BASED BANDWIDTH ALLOCATION IN 5G POINT TO POINT NETWORKS.

Author

Jadhav, Jagannath, Sajitha, L. P., Haripriya, M. P., and Shaikh, Mohammad Shahnawaz

Subjects
BANDWIDTH allocation, 5G networks, PUBLIC administration, MUNICIPAL services, TELECOMMUNICATION
Abstract

In General, the 5G is a fifth-generation technology that works at speeds of 4G to 100 times the network speed. The main objective of introducing modern information technologies is to facilitate and facilitate access to public services. The use of new technologies is a key factor in improving the overall structure of public administration and increasing its efficiency. In addition, it is important to improve the infrastructure of all types of communications. The telecom operators are often willing to invest more in infrastructure development. In this paper, a new model was proposed to enhance the bandwidth allocation and utilization. It is a requirements-based network that allows the users to increase the speed of wireless networks. This proposed method also increases the amount of data that can be transmitted over wireless networks. [ABSTRACT FROM AUTHOR]

Published
2022
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182. Disruption of the biological activity of protease‐activated receptors2/4 in adults rather than children in SARS CoV‐2 virus‐mediated mortality in COVID‐19 infection

Author

Singh, Yogendra, primary, Fuloria, Neeraj Kumar, additional, Fuloria, Shivkanya, additional, Subramaniyan, Vetriselvan, additional, Almalki, Waleed Hassan, additional, Gupta, Gaurav, additional, Shaikh, Mohammad Arshad Javed, additional, Singh, Mahaveer, additional, Al‐Abbasi, Fahad A., additional, and Kazmi, Imran, additional

Published
2021
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184. Computer Aided Generic Disease Prediction System developed using a Multiple Model Machine Learning Technique

Author

Bilal N Shaikh Mohammad and Shreyal Kulaye

Subjects
Vocabulary, Interactive programming, Computer science, business.industry, media_common.quotation_subject, Supervised learning, Decision tree, Machine learning, computer.software_genre, Domain (software engineering), Naive Bayes classifier, Information seeking behavior, Artificial intelligence, User interface, business, computer, media_common
Abstract

Health Information needs are changing the information seeking behavior drastically and can be observed around the globe. Many challenges are faced by people when looking online for health information around diseases, different treatments and diagnosis. People face many problems in understanding the heterogeneous medical vocabulary. People are confused because a huge amount of medical information is available on various mediums. If a prediction system can be made for laymen, doctors and the medicine industry using Review Mining will be very useful and save a lot of time. The idea behind the Disease Prediction System is to adapt to cope up with the special requirements of the health domain. In the present paper we have explained how we have developed a prediction model which is trained using four machine learning algorithms in the same model and achieved accuracy greater than 94%. User interface will help to connect to a machine learning model which gets easier for lay public and connection of machine learning model to user interface will be useful for making interactive programming interfaces with higher functionality.

Published
2021
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185. Random Forest Regressor Machine Learning Model Developed for Mental Health Prediction Based on Mhi-5, Phq-9 and Bdi Scale

Author

Kulsum Siddiqui and Bilal N Shaikh Mohammad

Subjects
Hyperparameter, Computer science, business.industry, Bayesian optimization, Machine learning, computer.software_genre, Mental health, Field (computer science), Random forest, Classifier (linguistics), Health care, Hyperparameter optimization, Artificial intelligence, business, computer
Abstract

Mental health is the new hot area of study, research and experimentation in the current decade scenario from both medical science perspective and Technology domain. As there is observed a sudden spike and familiarity in Mental health related cases and issues, this invites a variety of research in the field. There exist multiple procedures to diagnose mental disorders but no one method has gained momentum to diagnose and treat mental health issues with remarkable accuracy. Since the symptoms and factors influencing mental health problems vary a lot, human tendency seems to be less compactible and hence there exists need to introduce Artificial intelligence-based systems to assist psychiatrists and monitor on micro level basis. The research focuses Mental Health Data collected through online forms consisting of 3 Questionnaires(MHI-5,BDI,PHQ-9) consisting of 26 questions about various factors influencing mental disorders, Each Questionnaire is used to train an individual model using random forest regressor, random forest classifiers followed by Hyper parameter Optimization techniques namely Grid Search CV, Randomized Search CV, Bayesian Optimization -Automate Hyperparameter Tuning (Hyperopt),Optuna- Automate Hyperparameter Tuning, Genetic Algorithms (TPOT) Classifier - for the purpose to optimize the model result. MHI-5 based model gives accuracy of 95.83% using Bayesian Optimization, whereas PHQ-9 with 82.61% using Optuna and BDI model with 83.33 using Bayesian Optimization, Randomize Search Cv, Grid Search Cv each.

Published
2021
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186. A Proposed Model for Smart Public Distribution System of Ration Commodities to Indian Citizens using an IoT based Design

Author

Bilal N Shaikh Mohammad and Amrutha Nair

Subjects
Public distribution system, Government, Commerce, business.industry, Corruption, Fair value, media_common.quotation_subject, Distribution (economics), Subsidy, Smart card, Internet of Things, business, media_common
Abstract

The paper deals with the methods adopted by India to eradicate the malpractices that take place while Ration Distribution. The responsibility of providing food and non-food items to the poor at subsidized rates is of the Government of India under the Ministry of Consumer Affairs, Food and Public Distribution. Food grains, such as wheat, rice, sugar and fuels like kerosene are the goods provided through a network of fair price shops also known as ration shops established in several states across the India. The main motive of such scheme is to ensure that the citizens of India get ample food material whatever their financial condition. But still the goal is not achieved due to problems like ration hijacking, delivery of false ration, corruption, etc. and these troubles have been resolved by the developed solution.

Published
2021
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187. A Deep Learning Based Dual Model Developed for Automated Detection of Glioma-brain Tumor

Author

AasimBaig Mirza and Bilal N Shaikh Mohammad

Subjects
Computer science, Dual model, business.industry, Deep learning, education, Brain tumor, Pattern recognition, medicine.disease, Class imbalance, ComputingMethodologies_PATTERNRECOGNITION, Glioma, Fully automatic, medicine, Brain segmentation, Segmentation, Artificial intelligence, business
Abstract

Advances in machine learning (ML) and artificial intelligence (AI) are truly groundbreaking. We propose a fully automatic way to classify whether individuals contain lower-grade gliomas (brain tumors) using a deep-learning segmentation algorithm in this research paper. There are two deep-learning models in our paper. The first model detects whether the brain contains a tumor. The second model generalizes the tumor's location. In recent years, deep learning for automated brain segmentation has progressed to the level of success of a professional radiologist. Because of the class imbalance, we used the Tversky loss function. Experimental results show a better performance of the proposed model compared to the existing deep-learning models.

Published
2021
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188. COVID-19 infection in adult patients with hematological malignancies:a European Hematology Association Survey (EPICOVIDEHA)

Author

Pagano, Livio, Salmanton-García, Jon, Marchesi, Francesco, Busca, Alessandro, Corradini, Paolo, Hoenigl, Martin, Klimko, Nikolai, Koehler, Philipp, Pagliuca, Antonio, Passamonti, Francesco, Verga, Luisa, Víšek, Benjamin, Ilhan, Osman, Nadali, Gianpaolo, Weinbergerová, Barbora, Córdoba-Mascuñano, Raúl, Marchetti, Monia, Collins, Graham P., Farina, Francesca, Cattaneo, Chiara, Cabirta, Alba, Gomes-Silva, Maria, Itri, Federico, van Doesum, Jaap, Ledoux, Marie-Pierre, Čerňan, Martin, Jakšić, Ozren, Duarte, Rafael F., Magliano, Gabriele, Omrani, Ali S., Fracchiolla, Nicola S., Kulasekararaj, Austin, Valković, Toni, Poulsen, Christian Bjørn, Machado, Marina, Glenthøj, Andreas, Stoma, Igor, Ráčil, Zdeněk, Piukovics, Klára, Navrátil, Milan, Emarah, Ziad, Sili, Uluhan, Maertens, Johan, Blennow, Ola, Bergantim, Rui, García-Vidal, Carolina, Prezioso, Lucia, Guidetti, Anna, del Principe, Maria Ilaria, Popova, Marina, de Jonge, Nick, Ormazabal-Vélez, Irati, Fernández, Noemí, Falces-Romero, Iker, Cuccaro, Annarosa, Meers, Stef, Buquicchio, Caterina, Antić, Darko, Al-Khabori, Murtadha, García-Sanz, Ramón, Biernat, Monika M., Tisi, Maria Chiara, Sal, Ertan, Rahimli, Laman, Čolović, Natasa, Schönlein, Martin, Calbacho, Maria, Tascini, Carlo, Miranda-Castillo, Carolina, Khanna, Nina, Méndez, Gustavo-Adolfo, Petzer, Verena, Novák, Jan, Besson, Caroline, Duléry, Rémy, Lamure, Sylvain, Nucci, Marcio, Zambrotta, Giovanni, Žák, Pavel, Seval, Guldane Cengiz, Bonuomo, Valentina, Mayer, Jiří, López-García, Alberto, Sacchi, Maria Vittoria, Booth, Stephen, Ciceri, Fabio, Oberti, Margherita, Salvini, Marco, Izuzquiza, Macarena, Nunes-Rodrigues, Raquel, Ammatuna, Emanuele, Obr, Aleš, Herbrecht, Raoul, Núñez-Martín-Buitrago, Lucía, Mancini, Valentina, Shwaylia, Hawraa, Sciumè, Mariarita, Essame, Jenna, Nygaard, Marietta, Batinić, Josip, Gonzaga, Yung, Regalado-Artamendi, Isabel, Karlsson, Linda Katharina, Shapetska, Maryia, Hanakova, Michaela, El-Ashwah, Shaimaa, Borbényi, Zita, Çolak, Gökçe Melis, Nordlander, Anna, Dragonetti, Giulia, Maraglino, Alessio Maria Edoardo, Rinaldi, Amelia, De Ramón-Sánchez, Cristina, Cornely, Oliver A., Finizio, Olimpia, Fazzi, Rita, Sapienza, Giuseppe, Chauchet, Adrien, Van Praet, Jens, Prattes, Juergen, Dargenio, Michelina, Rossi, Cédric, Shirinova, Ayten, Malak, Sandra, Tafuri, Agostino, Ommen, Hans-Beier, Bologna, Serge, Khedr, Reham Abdelaziz, Choquet, Sylvain, Joly, Bertrand, Ceesay, M. Mansour, Philippe, Laure, Kho, Chi Shan, Desole, Maximilian, Tsirigotis, Panagiotis, Otašević, Vladimir, Borducchi, Davimar M. M., Antoniadou, Anastasia, Gaziev, Javid, Almaslamani, Muna A., García-Poutón, Nicole, Paterno, Giovangiacinto, Torres-López, Andrea, Tarantini, Giuseppe, Mellinghoff, Sibylle, Gräfe, Stefanie, Börschel, Niklas, Passweg, Jakob, Merelli, Maria, Barać, Aleksandra, Wolf, Dominik, Shaikh, Mohammad Usman, Thiéblemont, Catherine, Bernard, Sophie, Funke, Vaneuza Araújo Moreira, Daguindau, Etienne, Khostelidi, Sofya, Nucci, Fabio Moore, Martín-González, Juan-Alberto, Landau, Marianne, Soussain, Carole, Laureana, Cécile, Lacombe, Karine, Kohn, Milena, Aliyeva, Gunay, Piedimonte, Monica, Fouquet, Guillemette, Rêgo, Mayara, Hoell-Neugebauer, Baerbel, Cartron, Guillaume, Pinto, Fernando, Alburquerque, Ana Munhoz, Passos, Juliana, Yilmaz, Asu Fergun, Redondo-Izal, Ana-Margarita, Altuntaş, Fevzi, Heath, Christopher, Kolditz, Martin, Schalk, Enrico, Guolo, Fabio, Karthaus, Meinolf, Della Pepa, Roberta, Vinh, Donald, Noël, Nicolas, Deau Fischer, Bénédicte, Drenou, Bernard, Mitra, Maria Enza, Meletiadis, Joseph, Bilgin, Yavuz M., Jindra, Pavel, Espigado, Ildefonso, Drgoňa, Ľuboš, Serris, Alexandra, Di Blasi, Roberta, Ali, Natasha, EPICOVIDEHA working group, [missing], Pagano, Livio, Salmanton-Garcia, Jon, Marchesi, Francesco, Busca, Alessandro, Corradini, Paolo, Hoenigl, Martin, Klimko, Nikolai, Koehler, Philipp, Pagliuca, Antonio, Passamonti, Francesco, Verga, Luisa, Visek, Benjamin, Ilhan, Osman, Nadali, Gianpaolo, Weinbergerova, Barbora, Cordoba-Mascunano, Raul, Marchetti, Monia, Collins, Graham P., Farina, Francesca, Cattaneo, Chiara, Cabirta, Alba, Gomes-Silva, Maria, Itri, Federico, van Doesum, Jaap, Ledoux, Marie-Pierre, Cernan, Martin, Jaksic, Ozren, Duarte, Rafael F., Magliano, Gabriele, Omrani, Ali S., Fracchiolla, Nicola S., Kulasekararaj, Austin, Valkovic, Toni, Poulsen, Christian Bjorn, Machado, Marina, Glenthoj, Andreas, Stoma, Igor, Racil, Zdenek, Piukovics, Klara, Navratil, Milan, Emarah, Ziad, Sili, Uluhan, Maertens, Johan, Blennow, Ola, Bergantim, Rui, Garcia-Vidal, Carolina, Prezioso, Lucia, Guidetti, Anna, del Principe, Maria Ilaria, Popova, Marina, de Jonge, Nick, Ormazabal-Velez, Irati, Fernandez, Noemi, Falces-Romero, Iker, Cuccaro, Annarosa, Meers, Stef, Buquicchio, Caterina, Antic, Darko, Al-Khabori, Murtadha, Garcia-Sanz, Ramon, Biernat, Monika M., Tisi, Maria Chiara, Sal, Ertan, Rahimli, Laman, Colovic, Natasa, Schonlein, Martin, Calbacho, Maria, Tascini, Carlo, Miranda-Castillo, Carolina, Khanna, Nina, Mendez, Gustavo-Adolfo, Petzer, Verena, Novak, Jan, Besson, Caroline, Dulery, Remy, Lamure, Sylvain, Nucci, Marcio, Zambrotta, Giovanni, Zak, Pavel, Seval, Guldane Cengiz, Bonuomo, Valentina, Mayer, Jiri, Lopez-Garcia, Alberto, Sacchi, Maria Vittoria, Booth, Stephen, Ciceri, Fabio, Oberti, Margherita, Salvini, Marco, Izuzquiza, Macarena, Nunes-Rodrigues, Raquel, Ammatuna, Emanuele, Obr, Ales, Herbrecht, Raoul, Nunez-Martin-Buitrago, Lucia, Mancini, Valentina, Shwaylia, Hawraa, Sciume, Mariarita, Essame, Jenna, Nygaard, Marietta, Batinic, Josip, Gonzaga, Yung, Regalado-Artamendi, Isabel, Karlsson, Linda Katharina, Shapetska, Maryia, Hanakova, Michaela, El-Ashwah, Shaimaa, Borbenyi, Zita, Colak, Gokce Melis, Nordlander, Anna, Dragonetti, Giulia, Maraglino, Alessio Maria Edoardo, Rinaldi, Amelia, De Ramon-Sanchez, Cristina, Cornely, Oliver A., Pagano, L., Salmanton-Garcia, J., Marchesi, F., Busca, A., Corradini, P., Hoenigl, M., Klimko, N., Koehler, P., Pagliuca, A., Passamonti, F., Verga, L., Visek, B., Ilhan, O., Nadali, G., Weinbergerova, B., Cordoba-Mascunano, R., Marchetti, M., Collins, G. P., Farina, F., Cattaneo, C., Cabirta, A., Gomes-Silva, M., Itri, F., van Doesum, J., Ledoux, M. -P., Cernan, M., Jaksic, O., Duarte, R. F., Magliano, G., Omrani, A. S., Fracchiolla, N. S., Kulasekararaj, A., Valkovic, T., Poulsen, C. B., Machado, M., Glenthoj, A., Stoma, I., Racil, Z., Piukovics, K., Navratil, M., Emarah, Z., Sili, U., Maertens, J., Blennow, O., Bergantim, R., Garcia-Vidal, C., Prezioso, L., Guidetti, A., del Principe, M. I., Popova, M., de Jonge, N., Ormazabal-Velez, I., Fernandez, N., Falces-Romero, I., Cuccaro, A., Meers, S., Buquicchio, C., Antic, D., Al-Khabori, M., Garcia-Sanz, R., Biernat, M. M., Tisi, M. C., Sal, E., Rahimli, L., Colovic, N., Schonlein, M., Calbacho, M., Tascini, C., Miranda-Castillo, C., Khanna, N., Mendez, G. -A., Petzer, V., Novak, J., Besson, C., Dulery, R., Lamure, S., Nucci, M., Zambrotta, G., Zak, P., Seval, G. C., Bonuomo, V., Mayer, J., Lopez-Garcia, A., Sacchi, M. V., Booth, S., Ciceri, F., Oberti, M., Salvini, M., Izuzquiza, M., Nunes-Rodrigues, R., Ammatuna, E., Obr, A., Herbrecht, R., Nunez-Martin-Buitrago, L., Mancini, V., Shwaylia, H., Sciume, M., Essame, J., Nygaard, M., Batinic, J., Gonzaga, Y., Regalado-Artamendi, I., Karlsson, L. K., Shapetska, M., Hanakova, M., El-Ashwah, S., Borbenyi, Z., Colak, G. M., Nordlander, A., Dragonetti, G., Maraglino, A. M. E., Rinaldi, A., De Ramon-Sanchez, C., Cornely, O. A., Finizio, O., Fazzi, R., Sapienza, G., Chauchet, A., Van Praet, J., Prattes, J., Dargenio, M., Rossi, C., Shirinova, A., Malak, S., Tafuri, A., Ommen, H. -B., Bologna, S., Khedr, R. A., Choquet, S., Joly, B., Ceesay, M. M., Philippe, L., Kho, C. S., Desole, M., Tsirigotis, P., Otasevic, V., Borducchi, D. M. M., Antoniadou, A., Gaziev, J., Almaslamani, M. A., Garcia-Pouton, N., Paterno, G., Torres-Lopez, A., Tarantini, G., Mellinghoff, S., Grafe, S., Borschel, N., Passweg, J., Merelli, M., Barac, A., Wolf, D., Shaikh, M. U., Thieblemont, C., Bernard, S., Funke, V. A. M., Daguindau, E., Khostelidi, S., Nucci, F. M., Martin-Gonzalez, J. -A., Landau, M., Soussain, C., Laureana, C., Lacombe, K., Kohn, M., Aliyeva, G., Piedimonte, M., Fouquet, G., Rego, M., Hoell-Neugebauer, B., Cartron, G., Pinto, F., Alburquerque, A. M., Passos, J., Yilmaz, A. F., Redondo-Izal, A. -M., Altuntas, F., Heath, C., Kolditz, M., Schalk, E., Guolo, F., Karthaus, M., Della Pepa, R., Vinh, D., Noel, N., Deau Fischer, B., Drenou, B., Mitra, M. E., Meletiadis, J., Bilgin, Y. M., Jindra, P., Espigado, I., Drgona, L., Serris, A., Di Blasi, R., Ali, N., Stem Cell Aging Leukemia and Lymphoma (SALL), Salvy-Córdoba, Nathalie, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università cattolica del Sacro Cuore = Catholic University of the Sacred Heart [Roma] (Unicatt), University Hospital of Cologne [Cologne], Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, IFO - Istituto Nazionale Tumori Regina Elena [Roma] (IRE), Città della Salute e della Scienza University-Hospital, IRCCS Istituto Nazionale dei Tumori [Milano], University of California [San Diego] (UC San Diego), University of California (UC), Medical University of Graz, Odessa National I.I.Mechnikov University, Faculty of Medicine [Cologne], University Hospital of Cologne [Cologne]-University of Cologne, King's College Hospital (KCH), Universitá degli Studi dell’Insubria = University of Insubria [Varese] (Uninsubria), Dipartimento di Medicina e Chirurgia = School of Medicine and Surgery [Monza], Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), Faculty of Medicine in Hradec Kralove [Republique Tchèque], Charles University [Prague] (CU), Ankara University School of Medicine [Turkey], Azienda Ospedaliera Universitaria Integrata of Verona, Masaryk University [Brno] (MUNI), Fundación Jiménez Díaz, Fundacion Jimenez Diaz [Madrid] (FJD), Ospedale SS Antonio e Biagio e Cesare Arrigo, Churchill Hospital Oxford Centre for Haematology, IRCCS San Raffaele Scientific Institute [Milan, Italie], ASST Spedali Civili of Brescia, Vall d'Hebron Institute of Oncology [Barcelone] (VHIO), Vall d'Hebron University Hospital [Barcelona], Universitat Autònoma de Barcelona (UAB), Instituto Português de Oncologia de Lisboa Francisco Gentil, Ospedale San Luigi Gonzaga, University Medical Center Groningen [Groningen] (UMCG), Institut de Cancérologie de Strasbourg Europe (ICANS), Palacky University Olomouc, Zagreb School of Medicine [Zagreb, Croatia] (Dubrava University Hospital), University of Zagreb, Hospital Universitario Puerta de Hierro-Majadahonda [Madrid, Spain], ASST Great Metropolitan Niguarda / ASST Grande Ospedale Metropolitano Niguarda [Milan, Italia], Hamad Medical Corporation [Doha, Qatar], Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, University of Rijeka, Croatian Cooperative Group for Hematological Diseases (CROHEM), Zealand University Hospital [Roskilde, Denmark], Hospital General Universitario 'Gregorio Marañón' [Madrid], Department of Clinical Microbiology [Rigshospitalet], Rigshospitalet [Copenhagen], Copenhagen University Hospital-Copenhagen University Hospital, Homieĺ State Medical University (GSMU), Institute of Hematology and Blood Transfusion [Prague, Czech Republic], University of Szeged [Szeged], University Hospital Ostrava, Mansoura University [Egypt], Marmara University [Kadıköy - İstanbul], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Karolinska University Hospital [Stockholm], Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto = University of Porto, Instituto de Patologia e Imunologia Molecular da Universidade do Porto (IPATIMUP), Hospital de São João [Porto], Faculdade de Medicina da Universidade do Porto (FMUP), Clinic Barcelona Hospital Universitari, Department of Public Health and Cell Biology, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy, Pavlov First Saint Petersburg State Medical University [St. Petersburg], Vrije Universiteit Medical Centre (VUMC), Vrije Universiteit Amsterdam [Amsterdam] (VU), Complejo Hospitalario de Navarra, Hospital Universitario Marqués de Valdecilla [Santander], La Paz University Hospital, Azienda Usl Toscana centro [Firenze], AZ Klina, Clinical Center of Serbia (KCS), University of Belgrade [Belgrade], Sultan Qaboos University Hospital, Partenaires INRAE, Hospital Universitario de Salamanca, Servicio de Haematologia, Centro de Investigación del Cáncer-IBMCC (USAL-CSIC), University of Wrocław [Poland] (UWr), San Bortolo Hospital, Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), Hospital Universitario 12 de Octubre [Madrid], Azienda Sanitaria Universitaria Friuli Centrale (ASU FC), Universidad Rey Juan Carlos [Madrid] (URJC), University of Basel (Unibas), Innsbruck Medical University = Medizinische Universität Innsbruck (IMU), University Hospital Kralovské Vinohrady, Centre Hospitalier de Versailles André Mignot (CHV), Centre de Recherche Saint-Antoine (CRSA), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Département Hématologie biologique [CHRU Montpellier], Pôle Biologie-Pathologie [CHRU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Universidade Federal do Estado do Rio de Janeiro (UNIRIO), San Gerardo Hospital of Monza, Oxford NIHR Biomedical Research Centre, IRCCS Ospedale San Raffaele [Milan, Italy], Assi Sette Llaghi Varese, Instituto Português de Oncologia do Porto / Portuguese Oncology Institute of Porto (IPO Porto), University Hospital Olomouc [Czech Republic], ASST Grande Ospedale Metropolitano Niguarda, University Hospital Centre Zagreb, Instituto Nacional do Câncer, Copenhagen University Hospital, Republican Scientific and Practical Center (RSPC) for organ and Tissue Transplantation, Minsk, Republican Scientific and Practical Center (RSPC) for Organ and Transplantation, German Centre for Infection Research (DZIF), Gilead Sciences, Pagano, Livio [0000-0001-8287-928X], Salmanton-García, Jon [0000-0002-6766-8297], Marchesi, Francesco [0000-0001-6353-2272], Busca, Alessandro [0000-0001-5361-5613], Corradini, Paolo [0000-0002-9186-1353], Hoenigl, Martin [0000-0002-1653-2824], Klimko, Nikolay [0000-0001-6095-7531], Koehler, Philipp [0000-0002-7386-7495], Pagliuca, Antonio [0000-0003-2519-0333], Passamonti, Francesco [0000-0001-8068-5289], Verga, Luisa [0000-0003-1142-8435], Víšek, Benjamin [0000-0001-8268-452X], Ilhan, Osman [0000-0003-1665-372X], Weinbergerová, Barbora [0000-0001-6460-2471], Córdoba, Raúl [0000-0002-7654-8836], Marchetti, Monia [0000-0001-7615-0572], Farina, Francesca [0000-0002-5124-6970], Cattaneo, Chiara [0000-0003-0031-3237], Cabirta, Alba [0000-0001-7198-8894], Gomes-Silva, Maria [0000-0002-6993-2450], Itri, Federico [0000-0002-3532-5281], Doesum, Jaap van [0000-0003-0214-3219], Ledoux, Marie-Pierre [0000-0002-3261-3616], Čerňan, Martin [0000-0003-2345-1229], Jakšić, Ozren [0000-0003-4026-285X], Magliano, Gabriel [0000-0002-9129-1530], Omrani, Ali S. [0000-0001-5309-6358], Fracchiolla, Nicola S. [0000-0002-8982-8079], Kulasekararaj, Austin G. [0000-0003-3180-3570], Valković, Toni [0000-0001-6083-8815], Poulsen, Christian Bjørn [0000-0001-9785-1378], Machado, Marina [0000-0002-8370-2248], Glenthøj, Andrea [0000-0003-2082-0738], Stoma, Igor [0000-0003-0483-7329], Ráčil, Zdeněk [0000-0003-3511-4596], Piukovics, Klára [0000-0003-4480-3131], Emarah, Ziad [0000-0003-0622-2598], Sili, Uluhan [0000-0002-9939-9298], Maertens, Johan [0000-0003-4257-5980], Bergantim, Rui [0000-0002-7811-9509], García-Vidal, Carolina [0000-0002-8915-0683], Prezioso, Lucia [0000-0003-1660-4960], Principe, Maria Ilaria del [0000-0002-3958-0669], Popova, Marina [0000-0001-8536-5495], Jonge, Nick de [0000-0002-9901-0887], Ormazabal-Vélez, Irati [0000-0003-1141-5546], Falces-Romero, Iker [0000-0001-5888-7706], Cuccaro, Annarosa [0000-0002-0237-1839], Meers, Stef [0000-0003-1754-2175], Buquicchio, Caterina [0000-0002-3683-5953], Antić, Darko [0000-0002-2608-1342], Al-Khabori, Murtadha [0000-0002-2937-8838], García-Sanz, Ramón [0000-0003-4120-2787], Biernat, Monika [0000-0003-3161-3398], Tisi, Maria Chiara [0000-0001-8231-6700], Sal, Ertan [0000-0003-2761-2675], Rahimli, Laman [0000-0003-2266-445X], Schönlein, Martin [0000-0002-1010-0975], Calbacho, María [0000-0001-8106-4863], Tascini, Carlo [0000-0001-9625-6024], Miranda-Castillo, Carolina [0000-0001-8763-9576], Khanna, Nina [0000-0002-2642-419X], Méndez, Gustavo-Adolfo [0000-0003-0514-7004], Petzer, Verena [0000-0002-9205-1440], Besson, Caroline [0000-0003-4364-7173], Duléry, Rémy [0000-0002-5024-1713], Lamure, Sylvain [0000-0001-5980-305X], Nucci, Marcio [0000-0003-4867-0014], Zambrotta, Giovanni [0000-0002-8612-2994], Žák, Pavel [0000-0003-4465-5343], Cengiz Seval, Guldane [0000-0001-9433-2054], Bonuomo, Valentina [0000-0001-6491-8337], Mayer, Jiří [0000-0003-0567-9887], López-García, Alberto [0000-0002-5354-5261], Sacchi, Maria Vittoria [0000-0001-8133-3357], Booth, Stephen [0000-0003-2687-0234], Ciceri, Fabio [0000-0003-0873-0123], Nunes-Rodrigues, Raquel [0000-0002-8347-4281], Ammatuna, Emanuele [0000-0001-8247-4901], Obr, Aleš [0000-0002-6758-3074], Herbrecht, Raoul [0000-0002-9381-4876], Shwaylia, Hawraa [0000-0002-4098-6092], Sciumè, Mariarita [0000-0001-7958-4966], Essame, Jenna [0000-0003-0926-5577], Batinić, Josip [0000-0001-5595-9911], Gonzaga, Yung [0000-0003-1416-2118], Regalado-Artamendi, Isabel [0000-0002-9673-9015], Karlsson, Linda Katharina [0000-0003-3317-7550], Shapetska, Maryia [0000-0002-1223-9161], El-Ashwah, Shaimaa [0000-0003-2210-1534], Çolak, Gökçe Melis [0000-0002-7662-7454], Dragonetti, Giulia [0000-0003-1775-6333], Rinaldi, Amelia [0000-0002-8211-5076], Ramón, Cristina de [0000-0002-8167-6410], Cornely, Oliver A. [0000-0001-9599-3137], Institut Català de la Salut, [Pagano L] Hematology, Fondazione Policlinico Universitario Agostino Gemelli - IRCCS – Università Cattolica del Sacro Cuore, Rome, Italy. Università Cattolica del Sacro Cuore, Rome, Italy. [Salmanton-García J] Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, Excellence Center for Medical Mycology (ECMM), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany. Cologne Excellence Cluster On Cellular Stress Responses in Aging Associated Diseases (CECAD), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany. [Marchesi F] Hematology and Stem Cell Transplant Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy. [Busca A] Stem Cell Transplant Center, AOU Citta’ Della Salute E Della Scienza, Turin, Italy. [Corradini P] University of Milan and Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan, Italy. [Hoenigl M] Division of Infectious Diseases and Global Public Health, Department of Medicine, University of California San Diego, San Diego, CA, USA. Clinical and Translational Fungal Working Group, University of California San Diego, La Jolla, CA, USA. Section of Infectious Diseases and Tropical Medicine, Department of Internal Medicine, Medical University of Graz, Graz, Austria. [Cabirta A, Izuzquiza M] Servei d’Hematologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Experimental Hematology, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain, Vall d'Hebron Barcelona Hospital Campus, Hematology, Salmanton-García, Jon, Klimko, Nikolay, Víšek, Benjamin, Weinbergerová, Barbora, Córdoba, Raúl, Doesum, Jaap van, Čerňan, Martin, Jakšić, Ozren, Magliano, Gabriel, Kulasekararaj, Austin G., Valković, Toni, Poulsen, Christian Bjørn, Glenthøj, Andrea, Ráčil, Zdeněk, Piukovics, Klára, García-Vidal, Carolina, Principe, Maria Ilaria del, Jonge, Nick de, Ormazabal-Vélez, Irati, Antić, Darko, García-Sanz, Ramón, Biernat, Monika, Schönlein, Martin, Calbacho, María, Méndez, Gustavo-Adolfo, Duléry, Rémy, Žák, Pavel, Cengiz Seval, Guldane, Mayer, Jiří, López-García, Alberto, Obr, Aleš, Sciumè, Mariarita, Batinić, Josip, Çolak, Gökçe Melis, Ramón, Cristina de, and Universidad de Sevilla. Departamento de Medicina

Subjects
[SDV.MHEP.HEM] Life Sciences [q-bio]/Human health and pathology/Hematology, Male, Cancer Research, MESH: Registries, Epidemiology, MESH: Hospitalization, Hematological malignancies, Otros calificadores::Otros calificadores::/complicaciones [Otros calificadores], MESH: Aged, 80 and over, MESH: Risk Factors, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Risk Factors, Malalties - Factors de risc, Risk of mortality, Medicine and Health Sciences, virosis::infecciones por virus ARN::infecciones por Nidovirales::infecciones por Coronaviridae::infecciones por Coronavirus [ENFERMEDADES], 80 and over, Medicine, MESH: COVID-19, Registries, Sang - Malalties - Complicacions, RC254-282, Cause of death, BIOMEDICINA I ZDRAVSTVO. Kliničke medicinske znanosti. Interna medicina, MESH: Aged, Aged, 80 and over, Hematology, MESH: Middle Aged, Mortality rate, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Myeloid leukemia, Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections [DISEASES], [SDV.MHEP.HEM]Life Sciences [q-bio]/Human health and pathology/Hematology, Middle Aged, CANCER, Europe, Hospitalization, Intensive Care Units, Oncology, MESH: Young Adult, Hematologic Neoplasms, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Female, BIOMEDICINA I ZDRAVSTVO. Kliničke medicinske znanosti. Infektologija, Life Sciences & Biomedicine, Adult, medicine.medical_specialty, [SDV.CAN]Life Sciences [q-bio]/Cancer, COVID-19, EHA, Pandemic, Aged, Humans, SARS-CoV-2, Young Adult, técnicas de investigación::métodos epidemiológicos::estadística como asunto::probabilidad::riesgo::factores de riesgo [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], [SDV.CAN] Life Sciences [q-bio]/Cancer, Intensive care, Internal medicine, Diseases of the blood and blood-forming organs, MESH: SARS-CoV-2, neoplasias::neoplasias por localización::neoplasias hematológicas [ENFERMEDADES], Molecular Biology, BIOMEDICINE AND HEALTHCARE. Clinical Medical Sciences. Internal Medicine, pandemic, hematological malignancies, epidemiology, MESH: Humans, Science & Technology, business.industry, Myelodysplastic syndromes, Research, MESH: Adult, Neoplasms::Neoplasms by Site::Hematologic Neoplasms [DISEASES], medicine.disease, BIOMEDICINE AND HEALTHCARE. Clinical Medical Sciences. Infectology, Settore MED/15, MESH: Male, Settore MED/15 - MALATTIE DEL SANGUE, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, COVID-19 (Malaltia) - Diagnòstic, MESH: Intensive Care Units, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Probability::Risk::Risk Factors [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], MESH: Europe, RC633-647.5, business, MESH: Female, Other subheadings::Other subheadings::/complications [Other subheadings], MESH: Hematologic Neoplasms
Abstract

Background Patients with hematological malignancies (HM) are at high risk of mortality from SARS-CoV-2 disease 2019 (COVID-19). A better understanding of risk factors for adverse outcomes may improve clinical management in these patients. We therefore studied baseline characteristics of HM patients developing COVID-19 and analyzed predictors of mortality. Methods The survey was supported by the Scientific Working Group Infection in Hematology of the European Hematology Association (EHA). Eligible for the analysis were adult patients with HM and laboratory-confirmed COVID-19 observed between March and December 2020. Results The study sample includes 3801 cases, represented by lymphoproliferative (mainly non-Hodgkin lymphoma n = 1084, myeloma n = 684 and chronic lymphoid leukemia n = 474) and myeloproliferative malignancies (mainly acute myeloid leukemia n = 497 and myelodysplastic syndromes n = 279). Severe/critical COVID-19 was observed in 63.8% of patients (n = 2425). Overall, 2778 (73.1%) of the patients were hospitalized, 689 (18.1%) of whom were admitted to intensive care units (ICUs). Overall, 1185 patients (31.2%) died. The primary cause of death was COVID-19 in 688 patients (58.1%), HM in 173 patients (14.6%), and a combination of both COVID-19 and progressing HM in 155 patients (13.1%). Highest mortality was observed in acute myeloid leukemia (199/497, 40%) and myelodysplastic syndromes (118/279, 42.3%). The mortality rate significantly decreased between the first COVID-19 wave (March–May 2020) and the second wave (October–December 2020) (581/1427, 40.7% vs. 439/1773, 24.8%, p value, EPICOVIDEHA has received funds from Optics COMMITTM (COVID-19 Unmet Medical Needs and Associated Research Extension) COVID-19 RFP program by GILEAD Science, United States (Project 2020-8223).

Published
2021
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194. Caffeic Acid Phenethyl Ester reduces Pro Inflammatory Cytokines in Moderate Swimming Test in Growing Rats Model

Author

Al-Hariri,Mohammed, Alsunni,Ahmed, Shaikh,Mohammad Habeeb, Gamal Eldin,Tharwat, Al Ghamdi,Kholoud, Alharbi,Abdulelah Fawzi, Alhawaj,Hussain, Chathoth,Shahanas, Al-Hariri,Mohammed, Alsunni,Ahmed, Shaikh,Mohammad Habeeb, Gamal Eldin,Tharwat, Al Ghamdi,Kholoud, Alharbi,Abdulelah Fawzi, Alhawaj,Hussain, and Chathoth,Shahanas

Abstract

Mohammed Al-Hariri,1 Ahmed Alsunni,1 Mohammad Habeeb Shaikh,1 Tharwat Gamal Eldin,1 Kholoud Al Ghamdi,1 Abdulelah Fawzi Alharbi,2 Hussain Alhawaj,3 Shahanas Chathoth4 1Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; 2Department of Internal Medicine, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; 3Department of Environmental Health, Institute for Research and Medical Consultations, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabi; 4Department of Biochemistry, College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaCorrespondence: Mohammed Al-HaririDepartment of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 2114-31451, Dammam, Saudi ArabiaTel +966 50-727-5028Email mtalhariri@iau.edu.saBackground: Caffeic acid phenethyl ester (CAPE) is a naturally occurring polyphenolic concentrated in propolis of honeybee hives. CAPE has been shown various physiological and pharmacologic properties. The aim of the present study was to investigate the effects of CAPE on proinflammatory markers in growing rats by performing the moderate swimming test.Methods: A total number of 21 male Wistar albino rats were separated into three groups (n = 7): sedentary: negative control group; exercise: positive control group received vehicle orally and exercise + CAPE: CAPE treated group: treated with CAPE (20 mg/kg) orally 30 min before exercise, for 5 days. The animals were left free to swim in the tank, 20 minutes/day for 5 days. At 24 hours after finishing the experiment, rats were euthanised and blood was collected to analyze the level of serum interleukin IL-6 and tumor necrosis factor–α (TNF-α).Results: Growing rats subjected to the moderate swimming test and in those treated with CAPE showed a lower level of TNF-α compared to the negative control. More interestingly, the one-way ANOVA data demonstrated a decreased level o

Published
2021

199. The Anti-Cancer Peptide, PNC-27, Induces Tumor Cell Necrosis of a Poorly Differentiated Non-Solid Tissue Human Leukemia Cell Line that Depends on Expression of HDM-2 in the Plasma Membrane of these Cells

Author

Davitt, Katlin, Babcock, Blake D., Fenelus, Maly, Poon, Chi Kong, Sarkar, Abhishek, Trivigno, Vincent, Zolkind, Paul A., Matthew, Sheena M., Grinʼkina, Natalia, Orynbayeva, Zulfiya, Shaikh, Mohammad F., Adler, Victor, Michl, Josef, Sarafraz-Yazdi, Ehsan, Pincus, Matthew R., and Bowne, Wilbur B.

Published
2014

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