7 research outputs found
Jerusalem – an independent being – a portrait of the city in Mahmoud Shukair’s novel Jerusalem Stands Alone
No full textIn his novel Jerusalem Stands Alone, a Palestinian writer, Mahmoud Shukair, pays homage to his home city. Jerusalem is teeming with uninterrupted life, overwhelmed by the past’s facts and experiences. While observing the urban space, apart from the places of religious worship and historical monuments, the author notices the people. It is about them, for them, that Shukair dedicates 155 short stories – vignettes. Each story is a separate tale; through them, the author evokes the sights, sounds, and smells of ancient and modern Jerusalem.JOANNA WILDOWICZ – dr nauk humanistycznych; lektorka w Studium Praktycznej Nauki Języków Obcych na Wydziale Filologicznym Uniwersytetu w Białymstoku; absolwentka Nauczycielskiego Kolegium Języka Angielskiego na Uniwersytecie Warszawskim (Filia w Białymstoku); magisterium pod opieką prof. Cynthi Dominik na podstawie pracy: American values reflected in situation comedies (Ośrodek Studiów Amerykańskich, Uniwersytet Warszawski). Była
studentką stacjonarnych studiów doktoranckich w zakresie literaturoznawstwa na Wydziale Filologicznym UwB, gdzie obroniła rozprawę doktorską poświęconą twórczości Cormaca McCarthy’ego i jego roli w zachowanie ukształtowanych
postaw społecznych i kulturowych Ameryki. Autorka m.in. artykułu Strażnik pogranicza (2016). Współredaktorka tomu Żydzi wschodniej Polski, Seria VIII: Artyści żydowscy, Białystok 2020.Uniwersytet w BiałymstokuGoldhill S., Jerusalem, City of Longing, London 2008.Leśniewski S., Jerozolima 1099, Warszawa 2007.Montefiore S. S., Jerusalem, New York 2011.Shukair M., Jerusalem Stands Alone, New York 2018.9910
Protective effect of arabic gum against acetaminophen-induced hepatotoxicity in mice
No full textOverdose of acetaminophen, a widely used analgesic drug, can result in severe hepatotoxicity and is often fatal. This study was under-taken to examine the effects of arabic gum (AG), which is commonly used in processed foods, on acetaminophen-induced hepatotoxicity inmice. Mice were given arabic gum orally (100 g l−1) 5 days before a hepatotoxic dose of acetaminophen (500 mg kg−1) intraperitoneally.Arabic gum administration dramatically reduced acetaminophen-induced hepatotoxicity as evidenced by reduced serum alanine (ALT) andaspartate aminotransferase (AST) activities. Acetaminophen-induced hepatic lipid peroxidation was reduced significantly by arabic gumpretreatment. The protection offered by arabic gum does not appear to be caused by a decrease in the formation of toxic acetaminophenmetabolites, which consumes glutathione, because arabic gum did not alter acetaminophen-induced hepatic glutathione depletion. Ac-etaminophen increased nitric oxide synthesis as measured by serum nitrate plus nitrite at 4 and 6 h after administration and arabic gumpretreatment significantly reduced their formation. In conclusion, arabic gum is effective in protecting mice against acetaminophen-inducedhepatotoxicity. This protection may involve the reduction of oxidative stress.Corresponding Author:
Prof. Mahmoud N. Nagi, Professor, Department of Pharmacology, College of Pharmacy, P.O. Box 2457, Riyadh-11451, Saudi Arabia.
Email: [email protected]
Synergy of neuro-fuzzy controller and tuna swarm algorithm for maximizing the overall efficiency of PEM fuel cells stack including dynamic performance
No full textRecently, world endeavors are focused on promoting energy savings by operating both sources and loads at their maximum efficiency points. Thus, this paper presents a novel attempt to optimally determine the operating parameters of an isolated system comprising the proton exchange membrane fuel cells (PEMFCs) stack serving a variable load. A fitness function is adapted to maximize the PEMFCs stack’s efficiency using tuna swarm algorithm (TSA), subjected to set of inequality constraints. A well-known commercial type of PEMFCs stack namely Nedstack PS6 6 kW, are carefully studied over two TSA-based optimization scenarios. The first scenario aims at optimizing five operating parameters, while only two operating parameters are optimized in the second one. Numerical comparisons among the two scenarios are made. It’s worth indicating that the maximum absolute efficiency deviation between both scenarios is equal to 0.8064 at 100. Moreover, statistical tests are executed to appraise the performance of the TSA and others. At later stage, the TSA-based results are employed to train and learn an adaptive neuro-fuzzy controller for extracting the optimal operating parameters over wider range of loading conditions, while keeping the goal of maximum efficiency point in order. This allows predicting the optimal values of the operating parameters according to a certain load with a very low time burden, making it able to simulate the real-time load variations effectively and accurately. It can be reported here at low loading values as actual results for example, at 30% loading condition, the stack’s efficiency is improved from 16.27% to 63.47% at 60, from 17.24% to 64.24% at 80 and from 18.22% to 65.26% at 100. While, at load power of 40%, the FC’s efficiency is enhanced from 21.65% to 62.72% at 60, from 22.95% to 63.60% at 80 and from 24.25% to 64.76% at 100. It may be established that via this proposed synergy between TSA and neuro-fuzzy controller, the efficiency of PEMFCs can be maximized
Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights
No full textFor the sake of precise simulation, and proper controlling of the performance of the proton exchange membrane fuel cells (PEMFCs) generating systems, robust and neat mathematical modelling is crucially needed. Principally, the robustness and precision of modelling strategy depend on the accurate identification of PEMFC’s uncertain parameters. Hence, in the last decade, with the noteworthy computational development, plenty of meta-heuristic algorithms (MHAs) are applied to tackle such problem, which have attained very positive results. Thus, this review paper aims at announcing novel inclusive survey of the most up-to-date MHAs that are utilized for PEMFCs stack’s parameter identifications. More specifically, these MHAs are categorized into swarm-based, nature-based, physics-based and evolutionary-based. In which, more than 350 articles are allocated to attain the same goal and among them only 167 papers are addressed in this effort. Definitely, 15 swarm-based, 7 nature-based, 6 physics-based, 2 evolutionary-based and 4 others-based approaches are touched with comprehensive illustrations. Wherein, an overall summary is undertaken to methodically guide the reader to comprehend the main features of these algorithms. Therefore, the reader can systematically utilize these techniques to investigate PEMFCs’ parameter estimation. In addition, various categories of PEMFC’s models, several assessment criteria and many PEMFC commercial types are also thoroughly covered. In addition to that, 27 models are gathered and summarized in an attractive manner. Eventually, some insights and suggestions are presented in the conclusion for future research and for further room of improvements and investigations
Synergy of neuro-fuzzy controller and tuna swarm algorithm for maximizing the overall efficiency of PEM fuel cells stack including dynamic performance
No full textRecently, world endeavors are focused on promoting energy savings by operating both sources and loads at their maximum efficiency points. Thus, this paper presents a novel attempt to optimally determine the operating parameters of an isolated system comprising the proton exchange membrane fuel cells (PEMFCs) stack serving a variable load. A fitness function is adapted to maximize the PEMFCs stack’s efficiency using tuna swarm algorithm (TSA), subjected to set of inequality constraints. A well-known commercial type of PEMFCs stack namely Nedstack PS6 6 kW, is carefully studied over two TSA-based optimization scenarios. The first scenario aims at optimizing five operating parameters, while only two operating parameters are optimized in the second one. Numerical comparisons among the two scenarios are made. It’s worth indicating that the maximum absolute efficiency deviation between both scenarios is equal to 0.8064 at 100 °C. Moreover, statistical tests are executed to appraise the performance of the TSA and others. At later stage, the TSA-based results are employed to train and learn an adaptive neuro-fuzzy controller for extracting the optimal operating parameters over wider range of loading conditions, while keeping the goal of maximum efficiency point in order. This allows predicting the optimal values of the operating parameters according to a certain load with a very low time burden, making it able to simulate the real-time load variations effectively and accurately. It can be reported here at low loading values as actual results for example, at 30 % loading condition, the stack’s efficiency is improved from 16.27 % to 63.47 % at 60 °C, from 17.24 % to 64.24 % at 80 °C and from 18.22 % to 65.26 % at 100 °C. While, at load power of 40 %, the FC’s efficiency is enhanced from 21.65 % to 62.72 % at 60 °C, from 22.95 % to 63.60 % at 80 °C and from 24.25 % to 64.76 % at 100 °C. It may be established that via this proposed synergy between TSA and neuro-fuzzy controller, the efficiency of PEMFCs can be maximized
Characterising Acute and Chronic Care Needs: Insights From the Global Burden of Disease Study 2019
No full textMajeed, Azeem/0000-0002-2357-9858; Batiha, Prof. Abdul-Monim/0009-0003-8933-7637; Alvis-Guzman, Nelson/0000-0001-9458-864X; Stein, Dan/0000-0001-7218-7810; Silva, Luis Rodrigues Da/0000-0001-5264-3516; Alves Carneiro, Vera L./0000-0001-7830-0095; Fowobaje, Kayode/0000-0002-3995-160X; Douiri, Abdel/0000-0002-4354-4433; Olusanya, Bolajoko/0000-0002-3826-0583; Saif-Ur-Rahman, Km/0000-0001-8702-7094; Munblit, Daniel/0000-0001-9652-6856; Anwer, Razique/0000-0002-9223-1951; Park, Eun-Cheol/0000-0002-2306-5398; Wickramasinghe, Nuwan Darshana/0000-0001-6025-6022; Rashedi, Sina/0000-0003-0146-5611; Mohammed, Shafiu/0000-0001-5715-966X; Mossialos, Elias/0000-0001-8664-9297; Farooq, Umar/0000-0002-6421-9197; Poluru, Ramesh/0000-0002-7693-418X; Fekadu, Ginenus/0000-0002-4926-0685; Bhagavathula, Akshaya/0000-0002-0581-7808; Tanha, Amirabas/0009-0004-1729-6079; Rahman, Md. Obaidur/0000-0002-2219-3013; Iyamu, Ihoghosa/0000-0003-0271-9468; Skou, Soren T/0000-0003-4336-7059; Keskin, Cumali/0000-0003-3758-0654; Roever Mhs, Phd, Post-Doctorate, Mba (Data Science), Scientific Editor, Leonardo/0000-0002-7517-5548; Oliveira, Arao/0000-0001-6408-0634; /0000-0002-6657-2170; Hankey, Graeme/0000-0002-6044-7328; Cerin, Ester/0000-0002-7599-165X; Sachdev, Perminder/0000-0002-9595-3220; Hosseinzadeh, Mehdi/0000-0003-3040-1801; Christensen, Steffan/0000-0001-9068-0641; Bhaskar, Sonu/0000-0002-9783-3628; Tonelli, Marcello/0000-0002-0846-3187; Wang, Yanzhong/0000-0002-0768-1676; Papadopoulou, Paraskevi/0000-0002-8194-7495; Chong, Yuen Yu/0000-0001-5664-2051; Sun, Jing/0000-0002-0097-2438; Maulud, Sazan Qadir/0000-0002-2399-3055; Ward, Paul/0000-0002-5559-9714; Korzh, Oleksii/0000-0001-6838-4360; Murillo-Zamora, Efren/0000-0002-1118-498X; Sharma, Saurab/0000-0002-9817-5372; Moni, Mohammad Ali/0000-0003-0756-1006; Alam, Khurshid/0000-0002-7402-7519; , Nikolaos`/0000-0002-7269-2785; Guha, Avirup/0000-0003-0253-1174; Amin, Tarek Tawfik/0000-0003-2502-110X; Altirkawi, Khalid/0000-0002-7331-4196; Gazzelloni, Federica/0000-0002-4285-611X; Bulamu, Norma/0000-0003-4822-4858; Almalki, Mohammed J./0000-0001-9201-3164; Pepito, Veincent Christian/0000-0001-5391-3784; Kivimaki, Mika/0000-0002-4699-5627; Tufa, Derara Girma/0000-0002-4905-1189; Ezzikouri, Sayeh/0000-0002-3982-6163; Al-Raddadi, Rajaa/0000-0002-4188-0169; , Pawar/0000-0002-6157-2462; Andarge, Biniyam Demisse/0000-0002-6870-9193; Cerin, Ester/0000-0002-7599-165X; Mathioudakis, Alexander G/0000-0002-4675-9616; Krishan, Kewal/0000-0001-5321-0958; Tabish, Mohammad/0000-0001-8737-4088; Lim, David/0000-0002-2837-0973; Stephens, Jacqueline/0000-0002-7278-1374; Gorini, Giuseppe/0000-0002-1413-5948; Ntsekhe, Mpiko/0000-0002-0851-7675; Biswas, Atanu/0000-0001-5696-9839; Cederroth, Christopher R./0000-0001-7267-5136; Martinez Valle, Adolfo/0000-0001-6473-0262; Maude, Richard/0000-0002-5355-0562; Peng, Minijin/0000-0002-1350-4780; Goleij, Pouya/0000-0002-2213-497X; Algammal, Abdelazeem/0000-0001-5580-1559; M. Abdel-Azeem, Ahmed/0000-0003-2897-3966; Beran, David/0000-0001-7229-3920; Postma, Maarten/0000-0002-6306-3653; Adnani, Qorinah Estiningtyas Sakilah/0000-0002-4625-4861; Ks, Shaji/0000-0002-6029-9679; Anyasodor, Anayochukwu/0000-0003-1928-7657; Shin, Jae Il/0000-0003-2326-1820; Shiri, Rahman/0000-0002-9312-3100; Shivarov, Velizar/0000-0001-5362-7999; Hanif, Asif/0000-0002-2670-6402Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly. Chronic care manages long-term, progressive conditions, while acute care handles short-term ones. Here, authors show chronic conditions account for most of the global health burden, with 68% of DALYs and 93% of YLDs attributed to chronic care needs.Leona M. and Harry B. Helmsley Charitable Trust as part of the Addressing the Challenge and Constraints of Insulin Sources; Department of Science and Innovation at the National Research Foundation; Centre of Excellence in Epidemiological Modeling and Analysis, Stellenbosch University, South Africa; American University of Antigua; Shaqra University; National Institute for Health and Care Research (NIHR), Applied Research Collaboration (ARC); Department of Food Hygiene, Faculty of Veterinary Medicine, Amol University of Special Modern Technologies; University of Melbourne and Monash University; Department of Community Medicine, School of Medicine, International Medical University, Malaysia; International Centre for Casemix; Institute for Health Metrics and Evaluation (IHME) for the global health research initiatives at the University of Washington; Bernhard Nocht Institute of Tropical Medicine in Hamburg, Germany; Public Health Agency of Canada; Sir Seewoosagur Ramgoolam Medical College in Belle Rive, Mauritius; Heidelberg Institute of Global Health (HIGH), Faculty of Medicine, University Hospital, Heidelberg University in Heidelberg, Germany; Stellenbosch Institute of Advanced Study (STIAS), in Stellenbosch, South Africa; Kasturba Medical College, Mangalore and Manipal Academy of Higher Education, Manipal; Discipline of Public Health Medicine, University of KwaZulu-Natal in Durban, South Africa; CNPq [307329/2022-4]; University of Leicester Department of Population Health Sciences; Sociedad Argentina de Medicina; Global Alliance for Musculoskeletal Health; Italian Center of Precisione Medicine and Chronic Inflammation; United States Department of Veterans Affairs; Indian Institute of Technology Delhi (IIT Delhi); Ain Shams University, Faculty Of Medicine, Internal Medicine, and Adult Hematology Department in Cairo, Egypt; Institute of Health Sciences, Wallaga University; Department of Environmental Health Engineering of Isfahan University of Medical Sciences in Isfahan, Iran; Auckland University of Technology; UK National Institute of Health Research (NIHR) Academic Clinical Lectureship; Research Institute for Endocrine Sciences in Tehran, Iran; Asian Institute of Medicine, Science and Technology (AIMST University), Malaysia; Tsinghua University International Cooperation Special Project of Initiative Scientific Research Program; Department of Community Medicine at Kasturba Medical College, Mangalore; Manipal Academy of Higher Education in Manipal, India; Kornhauser Research Fellowship at The University of Sydney; Global Consortium for Public Health Research, Datta Meghe Institute of Higher Education and Research; Manipal Academy of Higher Education [K43 TW010716-05s1]; Sistema Nacional de Investigacion (SNI); Panama's Secretaria Nacional de Ciencia, Tecnologia e Innovacion (SENACYT); Taipei Medical University; Lung Foundation Australia; George Institute for Global Health and University of New SouthWales; Alexander von Humboldt (AvH) Foundation; National Institute for Health Research (NIHR) Biomedical Research Center at Guy's and St Thomas' National Health Service Foundation Trust, King's College London; NIHR [NIHR202769]; Italian Ministry of Health, Ricerca Corrente 2023; Ateneo de Manila University; International Center of Medical Sciences Research (ICMSR), Islamabad [44000]; I.M. Sechenov First Moscow State Medical University (Sechenov University); Nassau University Medical Center; Datta Meghe Institute of Higher Education and Research; Data Science Research Unit at Charles Sturt University; King Abdulaziz University (DSR) in Jeddah; King Abdulaziz City for Science & Technology (KACSAT) in Saudi Arabia, Science and Technology Development Fund (STDF); US-Egypt Science & Technology Joint Fund; Academy of Scientific Research and Technology (ASRT), in Egypt; Manipal College of Health Professions, Manipal Academy of Higher Education; International Center of Medical Sciences Research (ICMSR) in Islamabad, Pakistan; Ain Shams University; Egyptian Fulbright Mission Program; School of Computational and Integrative Sciences [SCIS]; Jawaharlal Nehru University in New Delhi, India; Institute for Advanced Studies in Basic Sciences (IASBS) Research Council; Kerala University of Health Sciences in Kerala, India; Jazan University; Department of Forensic Medicine and Toxicology, Kasturba Medical College, Mangalore, Manipal Academy Of Higher Education, Manipal, India; Health Data Research UK; International Graduate Research Scholarship, University of Tasmania; Bizzell Global, LLC; Scientific Research Unit at Shaqra University, Saudi Arabia; Medical Ethics and Law Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Institute for Health Metrics and Evaluation (IHME); Christian Medical College Vellore; Saveetha Institute of Medical and Technical Sciences and SRM Institute of Science and Technology; University of Agriculture, Faisalabad; Italian Ministry of Health [2023]; Public Health Sciences Division of Fred Hutchinson Cancer Center; Department of Basic Medical Sciences, Neyshabur University of Medical Sciences in Neyshabur, IranThis research was funded by The Leona M. and Harry B. Helmsley Charitable Trust as part of the Addressing the Challenge and Constraints of Insulin Sources and Supply (ACCISS) Study. Statements and conclusions presented in this report are those of the authors alone and do not necessarily reflect the views of the Helmsley Charitable Trust. All references and conclusions are intended for educational and informative purposes and do not constitute an endorsement or recommendation from the Helmsley Charitable Trust. O O Adetokunboh acknowledges support from the Department of Science and Innovation at the National Research Foundation, the Centre of Excellence in Epidemiological Modeling and Analysis, Stellenbosch University, South Africa. J M Acuna acknowledges support from American University of Antigua. A Ahmad acknowledges support from Shaqra University. K Ahmadi acknowledges support from the National Institute for Health and Care Research (NIHR), Applied Research Collaboration (ARC), Northwest London (the views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care). S Alian Samakkhah acknowledges support from the Department of Food Hygiene, Faculty of Veterinary Medicine, Amol University of Special Modern Technologies. S M Alif acknowledges support from The University of Melbourne and Monash University. S M Aljunid acknowledges support from the Department of Community Medicine, School of Medicine, International Medical University, Malaysia and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. S Almustanyir acknowledges support from the Institute for Health Metrics and Evaluation (IHME) for the global health research initiatives at the University of Washington. J H Amuasi acknowledges support from Bernhard Nocht Institute of Tropical Medicine in Hamburg, Germany. A Badawi acknowledges support from the Public Health Agency of Canada. I Banerjee acknowledges support from Sir Seewoosagur Ramgoolam Medical College in Belle Rive, Mauritius. S Barteit acknowledges support from the Heidelberg Institute of Global Health (HIGH), Faculty of Medicine, University Hospital, Heidelberg University in Heidelberg, Germany. H Benzian acknowledges support from Stellenbosch Institute of Advanced Study (STIAS), in Stellenbosch, South Africa. A N Bhat, J R Padubidi, A Shetty, B S K Shetty, and B Unnikrishnan acknowledge support from Kasturba Medical College, Mangalore and Manipal Academy of Higher Education, Manipal. M K Boachie acknowledges support from the Discipline of Public Health Medicine, University of KwaZulu-Natal in Durban, South Africa. L C Brant acknowledges support from CNPq (307329/2022-4). T Brugha acknowledges support from The University of Leicester Department of Population Health Sciences and the Leicestershire Partnership NHS Trust, United Kingdom. L A Camera acknowledges support from the Sociedad Argentina de Medicina. M Cross acknowledges support from the Global Alliance for Musculoskeletal Health. G Damiani acknowledges support from the Italian Center of Precisione Medicine and Chronic Inflammation. R P Dellavalle acknowledges support from the United States Department of Veterans Affairs, which in no way endorses the content of the manuscript. S Dey acknowledges support from the Indian Institute of Technology Delhi (IIT Delhi) for the Institute Chair Fellowship. G M T ElGohary acknowledges support from the Ain Shams University, Faculty Of Medicine, Internal Medicine, and Adult Hematology Department in Cairo, Egypt. WEtafa acknowledges support from the Institute of Health Sciences, Wallaga University. A Fatehizadeh acknowledges support from the Department of Environmental Health Engineering of Isfahan University of Medical Sciences in Isfahan, Iran. V L Feigin acknowledges support from Auckland University of Technology. J C Glasbey acknowledges support from a UK National Institute of Health Research (NIHR) Academic Clinical Lectureship. A Halimi acknowledges support from the Research Institute for Endocrine Sciences in Tehran, Iran. N E Ismail acknowledges institutional support from Asian Institute of Medicine, Science and Technology (AIMST University), Malaysia. J S Ji acknowledges support from Tsinghua University International Cooperation Special Project of Initiative Scientific Research Program. N Joseph acknowledges support from the Department of Community Medicine at Kasturba Medical College, Mangalore, and Manipal Academy of Higher Education in Manipal, India. H Kandel acknowledges support from the Kornhauser Research Fellowship at The University of Sydney. M N Khatib acknowledges support from the Global Consortium for Public Health Research, Datta Meghe Institute of Higher Education and Research. S L Koulmane Laxminarayana acknowledges support from Manipal Academy of Higher Education. M Kumar acknowledges support from K43 TW010716-05s1. I Landires acknowledges support from the Sistema Nacional de Investigacion (SNI) which is supported by Panama's Secretaria Nacional de Ciencia, Tecnologia e Innovacion (SENACYT). K Latief acknowledges support from Taipei Medical University during the conduct of this review. G Liu acknowledges support from the Lung Foundation Australia. M A Mahmoud acknowledges support from Taibah University to participate in this research project. P Maulik acknowledges support from the George Institute for Global Health and University of New SouthWales. S Mohammed acknowledges support from the Alexander von Humboldt (AvH) Foundation. M Molokhia acknowledges support from the National Institute for Health Research (NIHR) Biomedical Research Center at Guy's and St Thomas' National Health Service Foundation Trust, King's College London, and NIHR grant NIHR202769. A R Pathan acknowledges support from Author Gates Publications. P Pedersini acknowledges support from the Italian Ministry of Health, Ricerca Corrente 2023. V C Pepito acknowledges support from Ateneo de Manila University. Z Z Piracha acknowledges support from the International Center of Medical Sciences Research (ICMSR), Islamabad (44000) Pakistan. R V Polibin acknowledges support from the Department of Epidemiology and Evidence-Based Medicine, F. Erismann Institute of Public Health, I.M. Sechenov First Moscow State Medical University (Sechenov University). I Qattea acknowledges support from the Nassau University Medical Center. Z Quazi Syed acknowledges support from Datta Meghe Institute of Higher Education and Research. A Rahman acknowledges support from the Data Science Research Unit at Charles Sturt University. E M M Redwan acknowledges support from King Abdulaziz University (DSR) in Jeddah, and King Abdulaziz City for Science & Technology (KACSAT) in Saudi Arabia, Science and Technology Development Fund (STDF), and US-Egypt Science & Technology Joint Fund, and The Academy of Scientific Research and Technology (ASRT), in Egypt. B Reshmi acknowledges support from Manipal College of Health Professions, Manipal Academy of Higher Education. U Saeed acknowledges support from the International Center of Medical Sciences Research (ICMSR) in Islamabad, Pakistan. A M Samy acknowledges support from Ain Shams University and the Egyptian Fulbright Mission Program. R K Saroj acknowledges support from the School of Computational and Integrative Sciences (SC&IS), Jawaharlal Nehru University in New Delhi, India. HR Shahsavari acknowledges support from the Institute for Advanced Studies in Basic Sciences (IASBS) Research Council. K S Shaji acknowledges support from Kerala University of Health Sciences in Kerala, India. M Shanawaz acknowledges support from Jazan University. P H Shetty acknowledges support from the Department of Forensic Medicine and Toxicology, Kasturba Medical College, Mangalore, Manipal Academy Of Higher Education, Manipal, India. A Sheikh acknowledges support from Health Data Research UK. A Singh acknowledges support from the International Graduate Research Scholarship, University of Tasmania. D A Sleet acknowledges support from Bizzell Global, LLC. MTabish acknowledges support from the Scientific Research Unit at Shaqra University, Saudi Arabia. MTaheri acknowledges support from the Medical Ethics and Law Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. G W Tesema acknowledges support from the Institute for Health Metrics and Evaluation (IHME). Nih Thomas acknowledges support from the Christian Medical College Vellore. M R Tovani-Palone acknowledges support from the Saveetha Institute of Medical and Technical Sciences and SRM Institute of Science and Technology. Sai Ullah acknowledges support from the University of Agriculture, Faisalabad. J H Villafane acknowledges support and funding from the Italian Ministry of Health, Ricerca Corrente 2023. H Xiao acknowledges support from the Public Health Sciences Division of Fred Hutchinson Cancer Center. S Yaghoubi acknowledges support from the Department of Basic Medical Sciences, Neyshabur University of Medical Sciences in Neyshabur, Iran
