Faculty of Communication, Arts and Sciences

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24-epibrasinolide modulates the vase life of lisianthus cut flowers by modulating acc oxidase enzyme activity and physiological responses
(MDPI AG, 2021-05) Darvish, Mohammad; Shirzad, Habib; Asghari, Mohammadreza; Noruzi, Parviz; Alirezalu, Abolfazl; Pateiro, Mirian; Takshe, Aseel A.; Lorenzo, José Manuel
Ethylene is the most important factor playing roles in senescence and deterioration of harvested crops including cut flowers. Brassinosteroids (BRs), as natural phytohormones, have been reported to differently modulate ethylene production and related senescence processes in different crops. This study was carried out to determine the effects of different levels of 24-epibrassinolide (EBL) on ACC oxidase enzyme activity, the final enzyme in ethylene biosynthesis pathway, vase life, and senescence rate in lisianthus cut flowers. Harvested flowers were treated with EBL (at 0, 3, 6, and 9 µmol/L) and kept at 25◦C for 15 days. The ACC oxidase activity, water absorption, malondialdehyde (MDA) production and vase solution absorption rates, chlorophyll and anthocyanin contents, and the vase life of the flowers were evaluated during and at the end of storage. EBL at 3 µmol/L significantly (p ≤ 0.01) enhanced the flower vase life by decreasing the ACC oxidase activity, MDA production and senescence rates, and enhancing chlorophyll and anthocyanin biosynthesis and accumulation, relative water content, and vase solution absorption rates. By increasing the concentration, EBL negatively affected the flower vase life and postharvest quality probably via enhancing the ACC oxidase enzyme activity and subsequent ethylene production. EBL at 6 and 9 µmol/L and in a concentration dependent manner, enhanced the ACC oxidase activity and MDA production rate and decreased chlorophyll and anthocyanin accumulation and water absorption rate. The results indicate that the effects of brassinosteroids on ethylene production and physiology of lisianthus cut flowers is highly dose dependent. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
A Predictive Model to Diagnose Psychophysiological Insomnia with Daytime Hyperarousal and Nighttime Micro–Macro-Structures Correlation
(Springer, 2023) Ghermezian, Ali; Nami, Mohammad; Shalbaf, Reza; Khosrowabadi, Reza; Nasehi, Mohammad
A review of the environmental implications of the COVID-19 pandemic in the United Arab Emirates
(Elsevier B.V., 2022-08) Alalawi, Shaikha; Issa, Sahar T.; Takshe, Aseel A.; ElBarazi, Iffat
This paper reviews the environmental implications associated with the COVID-19 pandemic at the individual and community levels in the UAE. The positive effects emanating from the pandemic include improved air quality and reduced contamination of public spaces with pollutants. On the other hand, far-reaching negative effects include poor disposal of medical plastic waste and facemasks and the rise in unhygienic health practices amongst residents of UAE. The long-term ecological implications of the pandemic are still not well understood. The findings shed the light on the importance of addressing the consequences of the COVID-19 pandemic through preventative policies and strategies for better environmental health and readiness for future crises. Future research could assess the long-term environmental conse-quences of the pandemic on the UAE. © 2022
A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease
(Springer Science and Business Media Deutschland GmbH, 2023-12) Arya, Akhilesh Deep; Verma, Sourabh Singh; Chakarabarti, Prasun; Chakrabarti, Tulika; Elngar, Ahmed A.; Kamali, Ali-Mohammad; Nami, Mohammad
Alzheimer’s disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer’s disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer’s disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers. © 2023, The Author(s).
Analysis of the United Arab Emirates' contribution to the sustainable development goals with a focus on global health and climate change
(Emerald Publishing, 2023) Alkhaldi, Mohammed; Moonesar, Immanuel Azaad; Issa, Sahar T.; Ghach, Wissam; Okasha, Ahmad; Albada, Marina; Chelli, Sabrina; Takshe, Aseel A.
Purpose: The world is confronted by various current development challenges, including global health security and climate change. The rapid growth of these challenges warned all nations regardless of their development or geographical position. As an emerging international power, the United Arab Emirates (UAE) was among these nations and is viewed as a proactive key actor. Design/methodology/approach: This review was conducted as a thematic synthesis from 27 studies, reports and publications along with authors' insights. Using MS Word and Excel programs, three stages of data exploration, extraction and synthesis and analysis were applied. Data gathering, analysis and thematization and compilation. Findings: The UAE is giving significant attention to global health and climate change. Over the past 20 years, multipolicies, strategies and bodies were developed to lead the national, regional and global SDGs. Global health and climate change became the most two notable priorities on the government agenda and its strategic thinking is that both priorities can no longer be overlooked. Nationally, the UAE has made significant economic, scientific, social and health growth. Building a resilient and world-class healthcare system was one of six national priorities of the achieved UAE National Agenda 2021. Globally, UAE has proved its global health leadership by ensuring lasting and collective multilateral partnerships and collaborations that led to remarkable achievements in global health and climate change. Examples on the global scale: partnership with the World Health Organization (WHO) to target billions of people of the world's population and ensure they get Universal Healthcare Coverage (UHC) without financial hardship, the partnership between UAE and Bill and Melinda Gates Foundation to establish the Global Institute for Disease Elimination (GLIDE) to fight diseases and put an end to polio. Additionally, the state's role in the COVID-19 global efforts such as vaccine development, supply chain and distribution targeted low- and middle-income countries (LMIC). The UAE has shown a constant commitment to climate change mitigation and building a sustainable ecosystem by hosting global organizations, leading initiatives, supporting countries and is now organizing the 28th Conference of the Parties (COP28) this year. Great opportunities can be exploited to promote the country's contributions through further investment in cooperation, research and technology for better knowledge, sound policies, and innovative solutions for all regional and global health and climate change challenges. Originality/value: This review is a fresh evidence-synthesizing attempt to document the role of the UAE. This role is well placed to play an additional major role with all partners to address these pressing challenges by boosting its role, especially in the Middle East region and advancing a new regional-oriented revolutionary expanded developmental plan that centered on low-resource countries empowerment, multilateralism, intersectionality and lasting collaborations. © 2023, Mohammed Alkhaldi, Immanuel Azaad Moonesar, Sahar T. Issa, Wissam Ghach, Ahmad Okasha, Marina Albada, Sabrina Chelli and Aseel A. Takshe.
Analysis on severe fever with thrombocytopenia syndrome bunyavirus infection combined with atrial fibrillation under digital model detection
(Elsevier B.V., 2021) Tao, Lin; Yi, Yinping; Shan, Yu; Yu, Dan; Zhang, Jing; Qu, Yongsheng; Qin, Qingzhu; Pei, Yongju; Zhang, Hongmei; Chen, Xiongbiao; Kaddouri, Meriem; Omar, Khairi Mohamed
To heighten the diagnostic efficiency, in this study, the algebraic reconstruction technology (ART)-based echocardiography (ECG) was used to analyze severe fever with thrombocytopenia syndrome bunyavirus (SFTSV) complicated by atrial fibrillation. From Jan. 2015 to Dec. 2019, 200 elderly patients with SFTSV infection and hypertrophic cardiomyopathy (HCM) admitted to our hospital were selected as the observation group, and 20 healthy volunteers were in the control group. Then 200 patients were randomly divided into two groups with 100 people in each group. One group received routine clinical observation after surgery, and the other group received artificial intelligence atrial fibrillation monitoring. ECG displayed the left ventricular ejection fraction (LVEF), left ventricular mass index (LVMI), left ventricular end diastolic volume index (EDVI), and end systolic volume index (ESVI) of patients. The accuracy and satisfaction of different methods in observation were recorded. The risk factors of postoperative atrial fibrillation in elderly patients with HCM were evaluated, and changes in their white blood cell levels were detected. The results showed that, there was a significant difference in ECG between normal people and patients after surgery. Also, differences were noted in accuracy and satisfaction of the two methods in observation group (P < 0.05). The atrial fibrillation group and the non-atrial fibrillation group showed notable differences in smoking history and age (P < 0.05); the white blood cell content of the atrial fibrillation group was 8.64 × 109, and that in non-atrial fibrillation group was 3.25 × 109. The content of ST-2 in postoperative atrial fibrillation group was 21.3 g/mL, and the content of Gal-3 was 9.57 g/mL, while the content of ST-2 in non-atrial fibrillation group was 17.24 g/mL, and the content of Gal-3 was 5.21 g/mL. There was no significant difference in LVEF, LVMI, EDVI, ESVI between postoperative atrial fibrillation and the non-atrial fibrillation group. In summary, ECG can effectively detect HCM, and the digital model demonstrated superb capabilities in monitoring atrial fibrillation after cardiac sympathetic block. The atrial fibrillation group showed a higher level of white blood cells after surgery and was more likely to develop SFTSV infection. Measures should be taken to prevent infection. © 2021 The Authors
Antifungal activity of some indigenous lactic acid bacteria isolated from soft wheat
(Journal of Pure and Applied Microbiology, 2018) Djaaboub, Serra; Abdallah, Moussaoui; Meddah, Boumedien; Makhloufi, Souad; Gouri, Saif; El Khatib, Rami
The objective of this study was to find an alternative to chemical control of pathogenic fungi in wheat, using microorganisms that are safe and that can be isolated from the same biotopes of the pathogens. Lactic acid bacteria isolated from soft wheat grains were screened for their antifungal activity against Fusarium graminearum Schwab, Aspergillus flavus Link and Aspergillus parasiticus Speare, using two techniques (overlay and co-culture) on De Man, Rogosa, and Sharpe agar. The overlay method showed that out of forty-six lactic acid bacteria, five isolates showed an inhibition of radial growth range from 1% to 73.89%. According to the co-culture method, the most efficient biological agent for wheat mold growth isolate was LAB001 with an average rate of inhibition of 31.18% against A. flavus, 42.26% against A. parasiticus and 55.53% against F. graminearum. Lactic acid bacteria LAB001 was identified as Enterococcus faecium with 99.6% of similarity. E. faecium LAB001 can be considered as promising isolate for the biocontrol of pathogenic molds in small grain cereals. © 2018 Journal of Pure and Applied Microbiology. All rights reserved.
Application of machine learning risk prediction mathematical model in the diagnosis of Escherichia coli infection in patients with septic shock by cardiovascular color doppler ultrasound
(Elsevier B.V., 2021-07) Shen, Hualiang; Hu, Yinfeng; Liu, Xiatian; Jiang, Zhenzhen; Ye, Hongwei; Takshe, Aseel A.; Al Dulaimi, Saeed Hameed Kurdi
this study was to explore the diagnosis of septic shock patients with Escherichia coli (E. coli) infection based on cardiovascular color Doppler ultrasound (CCDUS) images under the machine learning risk prediction mathematical model (risk prediction model). 120 septic shock patients with Escherichia coli (E. coli) infection, admitted to xxx hospital were selected as research subjects, and they were randomly divided into experimental group and control group, including 76 males and 44 females, with an average age of (45.47 ± 11.35) years old. The prediction model, random forest mathematical model (RF model), and feature combination were trained and applied in the CCDUS. The error rate, F1-score, and area under the curve (AUC) were compared. It was found that the prediction effect of the risk prediction model was better (P < 0.05). The receiver operating characteristic curve (ROC) was drawn based on the risk prediction model, and it was found that the AUC was 0.924, and the best cutoff value was 0.247. The consistency test between the predicted death result and the actual result showed that Kappa = 0.824, which was higher than 0.75. The pathogenic microorganisms of the patients were mainly Gram-positive bacteria (GPB) in 32 cases (53.33%). There were 19 cases whose pathogenic bacteria was E. coli, and 11 cases (57.9%) of which were acquired in the intensive care unit (ICU). The patient mortality rate was 41.67%. Finally, the acute physiology and chronic health II (APACH II) score and D-dimer of the patients were substituted into the Logistic regression model. The effect of the risk prediction model was better than the RF model and feature combination; the measurement results based on the risk prediction model had good consistency; the D-dimer and APACH II score were independent factors for death of the septic shock. © 2021 The Author(s)
Application of PET/CT image under convolutional neural network model in postoperative pneumonia virus infection monitoring of patients with non-small cell lung cancer
(Elsevier B.V., 2021-07) Wei, Jing; Zhu, Ronghua; Zhang, Huai; Li, Pingwei; Okasha, Ahmad; Muttar, Ahmed K.H.
It was to study the adoption of positron emission computed tomography (PET-CT) based on the convolutional neural networks (CNN) model in the monitoring of postoperative pneumonia virus infection in patients with non-small cell lung cancer (NSCLC). 120 patients with NSCLC were set as the research object. CNN model was constructed and applied to PET-CT images to identify lesions and screen tumor markers for detection. Then, the patients were randomly divided into group A (CT), group B (PET-CT), group C (PET-CT based on artificial neural network model), and group D (PET-CT diagnosis based on CNN model), 30 cases in each group, and infection surveillance was conducted. The result showed that the accuracy (Acc), sensitivity (Sen), and specificity (Spe) of PET-CT image recognition based on the CNN model were 99.31%, 100%, and 98.31%, respectively. The proportion of serum neutrophils, white blood cell count, and PCT content in group D three days after operation were significantly lower than those in groups B, C, and A (P < 0.05). The proportions of patients with surgical wound infection and lung infection in group D were 6.54% and 15.38% respectively, which were significantly lower than those in groups B, C, and A (P < 0.05). The complication rates of patients in groups A, B, C, and D were 32.4%, 30.2%, 28.75, and 8.7%, respectively. The complication rate of patients in group D was significantly lower than that of the other three groups (P < 0.05). In short, PET-CT images based on the CNN model had high accuracy, sensitivity, and specificity in monitoring postoperative pneumonia virus infection in NSCLC patients. Applying it to the patient's virus infection monitoring can effectively prevent the patient's lung and surgical wound infection and improve the patient's postoperative recovery effect. © 2021 The Author(s)
Arcaboard: An Overview of the SElectromagnetic HoverBoard
(Institute of Electrical and Electronics Engineers Inc., 2023) Al-Madi, Nagham; Al-Madi, Mohammad; Alzyadat, Wael; Mariah, Khulood Abu; Al-Khateeb, Ahmed; Al-Madi, Fanan
Assessing climate change indicators in the United Arab Emirates
(Inderscience Publishers, 2022) Kamkar, Fatma; Khawatmi, Layla; Arif, Aysha; Hamed, Hamed; Issa, Sahar T.; El Khatib, Rami; Takshe, Aseel A.; Karkain, Rashed M.
Anthropogenic and natural activities have led to a global phenomenon known as climate change, which evidence shows, is worsening. Climatic changes can sometimes be observed at a regional level by assessing certain indicators such as temperature, precipitation, and humidity. This study aims to explore climate indicators in the United Arab Emirates that play a possible role in climate change. To gain insight into the longer-term changes, we looked at regional changes by analysing trends of mean monthly temperature, maximum monthly temperature, minimum monthly temperature, mean monthly humidity, and rainfall quantities per year over the period of 2003 to 2019. The trend analysis showed an increasing trend in the maximum monthly temperature, minimum monthly temperature, mean monthly temperature, and mean monthly humidity, but a decreasing trend in rainfall quantities, which may suggest climate change at a regional level. Copyright © 2022 Inderscience Enterprises Ltd.
Assessing the current state of university-based business incubators in Canada
(SAGE Publications Ltd, 2023-06) Yasin, Naveed; Majid Gilani, Sayed Abdul
This paper explores the current state of university-based business incubators (UBIs) in Canada by utilizing both secondary and primary data obtained through desk-based secondary research and semi-structured interviews with UBI managers, academics, and support staff. These data informed the development of nine cases of UBIs in Canada. The data were collected from VoIP (Voice-Over-Internet-Protocol) based semi-structured interviews with 32 participants during the COVID-19 pandemic (March 2021–February 2022), from which 9 cases were developed during the pandemic. The key themes derived from the findings were the development of communication skills, curriculum development, extra-curricular activities, industry engagement, innovation, research skills and strategic thinking. The originality of this study lies in its identification of the current state of UBI activities as well as its assessment of the broad range of activities and provisions among Canadian UBIs. The empirical development of showcasing these initiatives is also novel for the efficacy of UBIs concerning institutional and managerial decision-making and operational planning. There are implications for academics, senior management in higher education, entrepreneurs, policymakers and other stakeholders in the entrepreneurship ecosystem. © The Author(s) 2022.
Assessing the enterprising tendencies of Arab female undergraduate engineering students in the Sultanate of Oman
(SAGE Publications Ltd, 2020-03) Yasin, Naveed; Khansari, Zeinab; Sharif, Taimur
This study assesses the enterprising characteristics of first-year undergraduate Omani female chemical engineering students in Muscat, Oman. Pre and post surveys were conducted with 27 respondents from an entrepreneurship boot camp module mandated by the Oman Ministry of Higher Education. The variables, measured on a 10-point Likert scale, included need for achievement, need for autonomy, creativity, risk-taking, and locus of control. Statistical analysis was performed on the integrated data to measure the impact of student learning using a t-test approach and comparing mean averages. This was followed by qualitative semi-structured interviews that were examined using thematic analysis. The comparison of students’ enterprise tendencies before and after the module indicates minor to moderate improvements in their entrepreneurial abilities and their understanding of entrepreneurial behavior. The most noticeable impact was on students’ risk-taking abilities, followed by their creativity, need for achievement, need for control, and, lastly, their need for autonomy. The findings illustrate that students perceived entrepreneurship positively but were concerned about the scheduling of the module and its integration into their core program of study. Students may have benefited further from a module of extended duration as opposed to the block delivery “boot camp” mode of learning. Due to the limited number of participants and the focus on female students only, the results of the study cannot be generalized. However, the article presents an initial exploration of and offers insights into enterprising characteristics among an empirically underexplored demographic and nonbusiness group. © The Author(s) 2020.
Awareness and preparedness of human monkeypox outbreak among university student: Time to worry or one to ignore?
(Elsevier Ltd, 2022-10) Jairoun, Ammar Abdulrahman; Al-Hemyari, Sabaa Saleh; Abdulla, Naseem Mohammed; El-Dahiyat, Faris; Shahwan, Moyad; Hassan, Nageeb; Jairoun, Obaida; Alyousef, Nuha Ghazi; Sharif, Safia; Jaber, Ammar Ali Saleh
Background: The growing number of human monkeypox cases worldwide illustrates the importance of early detection, prevention, management and quick action from healthcare authorities. The WHO confirmed a hundred of Monkeypox cases worldwide and disclosed Monkdypox as a worldwide emergency situation Objectives: To assess the knowledge about human monkeypox’ source, signs/symptoms, transmission, prevention and treatment among Al Ain university students in the UAE. Methods: This descriptive cross-sectional study aimed to assess Al Ain University students’ knowledge of Human Monkeypox. A validated questionnaire was distributed to students between lectures. The respondents’ knowledge of human Monkeypox was assessed by 21 questions that examined the participants’ knowledge of Monkeypox as follows: 5 items examined knowledge of the source, definition, and incubation time; 2items assessed the mechanism of transmission of human Monkeypox, 7 items assessed the signs and symptoms; 7 items assessed the preventative measures; and 6 items assessed the treatment modalities. A multivariate logistic regression model was used to identify the factors influencing respondents’ knowledge of human Monkeypox among university students. Results: A total of five hundred and fifty-eight (558) students participated in the study. The average knowledge score was 70.1%, with a 95% confidence interval (CI) of 68.9 − 71.3. Of the total participants, 111 (19.9%) had poor knowledge about human Monkeypox, 320 (57.3%) had moderate knowledge, and 127 (22.8%) had good knowledge. The results of the statistical modelling showed that Old age (OR 0.681; 95% CI 1.005–1.016), female gender (OR 1.26; 95% CI 0.813 –0.961), participants from medical colleges (OR 1.22; 95% CI 1.13 –1.32) having a history of human chickenpox infection (OR 2.6; 95% CI 2.3–2.9) and receiving information on human Monkeypox during education (OR 1.14; 95% CI 1.05–1.2) were strong determinants for good knowledge about human Monkeypox. Conclusion: knowledge of Monkeypox among the participants is relatively low, particularly regarding the epidemiology, symptoms and treatments. Therefore, increasing knowledge of Monkeypox will be key to enhancing the capacity to respond to human monkeypox cases and to relay pertinent data to a disease surveillance system. © 2022 The Author(s)
Beyond Us: Building Collective Wellbeing
(Springer International Publishing, 2022-01-01) Lambert, Louise; Warren, Meg A.; Waldrop, Rachael J.; Subaiei, Sabrina Al; Eubanks, Alecia; Fisher, Jen L.
Big data-based grey forecast mathematical model to evaluate the effect of Escherichia coli infection on patients with lupus nephritis
(Elsevier B.V., 2021-07) Fan, Maoxiao; Gu, Shuaishuai; Jin, Yansheng; Ding, Lan; Ghonaem, Eman; Arbab, Ahmed Mohamed Hamad
The grey predictive mathematical model based on big data was used for analysis on the effect of Escherichia coli infection on patients with lupus nephritis (LN) in this study. Then, 156 patients diagnosed with LN infections by Wuzhong People's Hospital's information system (HIS) from October 30, 2017 to October 30, 2019 were selected as the experimental group, and 89 patients without LN infections were selected as the control group. Besides, the grey theory mathematical model was applied to process the integrated data, and feature analysis was employed to screen out disease-related bio-markers for the diagnosis of LN. The two groups were compared for affected organs, treatment, laboratory indicators, pathogenic bacteria, and recovery status. Multivariate logistic regression was used to analyze the related factors of patients with infections. The results showed that the specificity, sensitivity, and accuracy of the big data diagnosis based on the grey theory mathematical model were 78.9%, 87.6%, and 92.1, respectively; hormones, c-reactive protein, procalcitonin, and the daily antibiotic dose were positively correlated with concurrent infections (P < 0.05); 38 cases of Gram-negative bacteria were screened out, accounting for the largest proportion (37.18%); the effective rate of the experimental group was obviously lower than that of the control group (P < 0.05), suggesting that C-reactive protein (CRP), procalcitonin (PCT), antibiotics, daily dose of hormones, and serum albumin were independent risk factors for LN infection. In conclusion, the grey predictive mathematical model based on big data had high specificity, sensitivity, and accuracy in diagnosing the occurrence of infection in patients with LN; LN infection was mainly respiratory infection, and gram-negative bacteria were the main pathogen. Patients with LN infections showed higher serum creatinine, 24-hour urine protein quantification, CRP, and PCT, and lower serum albumin and recovery effect versus those without LN infections. © 2021 The Author(s)
Bioethics: A look at animal testing in medicine and cosmetics in the UK
(Tehran University of Medical Sciences, 2019) Kabene, Stefane; Baadel, Said
Using animals for cosmetics and medical tests has contributed towards a debate based on conflicting interests. Despite the efforts in justifying the value of animals in conducting analyses, this study seeks to elaborate whether or not it is rational to use animals as test subjects in medical and cosmetics fields. The value of animal life is at the core of the emotional conflicts that arise when animals become experimental subjects in medical and cosmetics fields. The aim of this study is to determine if there are ethical differences in the use of animal testing in medicine versus cosmetics. The research, through review and content analysis of the existing literature, compares and provides the outcomes of using animals in medical and cosmetics tests by examining studies conducted in the UK. The findings of this research indicated that animal testing is considered acceptable in the medical field only if there are no other alternatives, but is completely unacceptable in the cosmetics field. The study also provides recommendations in the form of alternatives that protect animals from cruelty and may benefit the different stakeholders and the society at large. © 2019 Medical Ethics and History of Medicine Research Center, Tehran University of Medical Sciences. All rights reserved.
Boosting Student Wellbeing Despite a Pandemic: Positive Psychology Interventions and the Impact of Sleep in the United Arab Emirates
(Springer Nature, 2022-12) Lambert L.; Joshanloo M.; Marquez J.M.; Cody B.; Arora T.; Warren M.; Aguilar L.; Samways M.; Teasel S.
Branding cancer research institutions through social media platforms
(Bastas, 2023-04) Medina-Aguerrebere, Pablo; Medina, Eva; Gonzalez-Pacanowski, Toni
Cancer research institutions resort to social media platforms to reinforce their relations with stakeholders and promote their brand. Nevertheless, they face several challenges: strict legal frameworks, patients’ new demands, and the development health technology. This paper aims to analyze how cancer research institutions manage social media platforms, as well as their corporate websites, for branding purposes. To do that, we conducted a literature review about cancer hospitals’ corporate communication strategies on these platforms; and then, we resorted to 48 indicators to evaluate how the top 100 cancer research institutions in the world managed their corporate websites, as well as their corporate profiles on Facebook, Twitter, and YouTube, for promoting their brand. We concluded that these organizations should use social media platforms to explain their brand architecture, develop a corporate website based on a public health approach, and describe their social engagements in a clearer way. Finally, we recommended three managerial initiatives for these organizations: creating an in-house communication department employing experts in communication and public health, conducting an intellectual reflection about the company’s brand genealogy, and integrating oncologists and nurses in the company’s corporate communication initiatives carried out on social media platforms. © 2023 by authors.
Branding hospitals on social media through health professionals
(Equinox Publishing Ltd, 2019) Aguerrebere, Pablo Medina
Hospitals are facing a constantly changing context in which patients are becoming more demanding, public health education initiatives are regarded as increasingly important and hospital business models have to take account of constantly developing medical technologies. In order to better interact with internal and external stakeholders, hospitals try to reinforce their corporate communication strategies as well as their brand reputation by means that include using social media platforms. This literature review paper aims to better understand why health professionals have the potential to play a key role in hospitals’ branding initiatives through social media. First, I report the findings from studies of concepts related to corporate communication, branding and the connection between social media and personal branding; and, second, I propose a communication model – what I call the PMA branding model – to help hospitals build a brand reputation based on health professionals’ participation in corporate communication initiatives led by hospitals on social media. The paper concludes by showing that the PMA branding model consists of organisational tools based on a rigorous methodology that will help health professionals participate in branding initiatives led by the hospital through these platforms. Copyright © 2020 Equinox Publishing Ltd.

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