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Article: An Underlooked Cause of Periodic Fever (PFAPA) in an Adult Patient with No Response to Tonsillectomy.

Alam, Fiaz / Hammoudeh, Mohammed

2018 Volume 2018, Page(s) 6580835

Abstract: Periodic fever with aphthous stomatitis, pharyngitis, and adenitis (PFAPA) is an autoinflammatory disease manifested as recurrent febrile episodes associated with one of the following cardinal features: aphthous ulceration, pharyngitis, and cervical ... ...

Abstract	Periodic fever with aphthous stomatitis, pharyngitis, and adenitis (PFAPA) is an autoinflammatory disease manifested as recurrent febrile episodes associated with one of the following cardinal features: aphthous ulceration, pharyngitis, and cervical adenitis. It was initially described in children and thought to be a disease of pediatric age group. Few adult cases were also reported in the literature. We describe the case of a 39-year-old female affected by PFAPA who presented with a history of febrile episodes associated with aphthous ulceration, stomatitis, and tonsillitis for 4 years. The febrile episodes occurred at a regular interval of 4 weeks and resolved within 5 days. The patient underwent tonsillectomy without any significant improvement. The patient responded only to a single high dose of steroid during the attack. Although PFAPA was initially thought to be a disease of pediatric age group, it should be considered in patients with recurrent febrile illness in all age groups.
Language	English
Publishing date	2018-05-02
Publishing country	United States
Document type	Case Reports
ZDB-ID	2666708-3
ISSN	2090-6897 ; 2090-6889
ISSN (online)	2090-6897
ISSN	2090-6889
DOI	10.1155/2018/6580835
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach.

Mudassir, Mohammed / Bennbaia, Shada / Unal, Devrim / Hammoudeh, Mohammad

Neural computing & applications

2020 , Page(s) 1–15

Abstract: Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, ... ...

Abstract	Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit non-stationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh-ninetieth-day forecast. For daily price forecast, the error percentage is as low as 1.44%, while it varies from 2.88 to 4.10% for horizons of seven to ninety days. These results indicate that the presented models outperform the existing models in the literature.
Keywords	covid19
Language	English
Publishing date	2020-07-04
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1480526-1
ISSN	1433-3058 ; 0941-0643
ISSN (online)	1433-3058
ISSN	0941-0643
DOI	10.1007/s00521-020-05129-6
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Article ; Online: Towards robust autonomous driving systems through adversarial test set generation.

Unal, Devrim / Catak, Ferhat Ozgur / Houkan, Mohammad Talal / Mudassir, Mohammed / Hammoudeh, Mohammad

ISA transactions

2022 Volume 132, Page(s) 69–79

Abstract: Correct environmental perception of objects on the road is vital for the safety of autonomous driving. Making appropriate decisions by the autonomous driving algorithm could be hindered by data perturbations and more recently, by adversarial attacks. We ... ...

Abstract	Correct environmental perception of objects on the road is vital for the safety of autonomous driving. Making appropriate decisions by the autonomous driving algorithm could be hindered by data perturbations and more recently, by adversarial attacks. We propose an adversarial test input generation approach based on uncertainty to make the machine learning (ML) model more robust against data perturbations and adversarial attacks. Adversarial attacks and uncertain inputs can affect the ML model's performance, which can have severe consequences such as the misclassification of objects on the road by autonomous vehicles, leading to incorrect decision-making. We show that we can obtain more robust ML models for autonomous driving by making a dataset that includes highly-uncertain adversarial test inputs during the re-training phase. We demonstrate an improvement in the accuracy of the robust model by more than 12%, with a notable drop in the uncertainty of the decisions returned by the model. We believe our approach will assist in further developing risk-aware autonomous systems.
Language	English
Publishing date	2022-11-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2012746-7
ISSN	1879-2022 ; 0019-0578
ISSN (online)	1879-2022
ISSN	0019-0578
DOI	10.1016/j.isatra.2022.11.007
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Article ; Online: Sex differences in clinical features, utilization of oral anticoagulant agents, and 1-year outcome in Middle Eastern patients with atrial fibrillation.

Alrabadi, Nasr / Al-Nusair, Mohammed / Haddad, Razan / Alghizzawi, Basheer S / Al-Nusair, Nashmi / Alhaj Mohammad, Saleh / Shteiwi, Saif M / El-Zubi, Maryam K / Marie, Zaid / Alnsour, Ayham / Jarrah, Mohamad / Alzoubi, Karem H / Hammoudeh, Ayman

Current medical research and opinion

2024 , Page(s) 1–8

Abstract: Objective: Studies have revealed that sex can predict differences in multiple aspects of atrial fibrillation (AF). These differences are underreported in the Middle East. This study aims to describe sex-specific differences in risk factors, ... ...

Abstract	Objective: Studies have revealed that sex can predict differences in multiple aspects of atrial fibrillation (AF). These differences are underreported in the Middle East. This study aims to describe sex-specific differences in risk factors, symptomatology, management, and outcomes in Middle Eastern patients with AF. Methods: The JoFib (Jordan-Atrial-Fibrillation) study is an observational, prospective, multicenter, nationwide registry in AF. Comparisons were made between female and male patients using Pearson chi-square and Mann-Whitney U tests. Multivariable regression models were constructed to investigate whether the female sex was predictive of any AF-related outcomes (all-cause death, cardiovascular death, ischemic stroke or systemic embolism [IS/SE], major bleeding, and clinically relevant non-major bleeding). Results: Of 2,020 patients with AF, 54% ( Conclusion: Females with AF are more symptomatic, yet they are treated less with rhythm control. Despite higher risk, females have similar risk-adjusted all-cause cardiovascular death and stroke rates compared to males. Future studies should explore how treatments and interventions can influence quality-of-life and cardiovascular outcomes in females with AF.
Language	English
Publishing date	2024-04-02
Publishing country	England
Document type	Journal Article
ZDB-ID	80296-7
ISSN	1473-4877 ; 0300-7995
ISSN (online)	1473-4877
ISSN	0300-7995
DOI	10.1080/03007995.2024.2332440
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Article ; Online: Evaluation of Clinical, Echocardiographic, and Therapeutic Characteristics, and Prognostic Outcomes of Coexisting Heart Failure among Patients with Atrial Fibrillation: The Jordan Atrial Fibrillation (JoFib) Study.

Alrabadi, Nasr / Al-Nusair, Mohammed / El-Zubi, Farah K / Tashtoush, Mais / Alzoubi, Osama / Khamis, Sa'ed / Masadeh, Majd M / Alzoubi, Karem H / Al-Hiari, Mohammed / Hammoudeh, Ayman

Current vascular pharmacology

2023 Volume 22, Issue 1, Page(s) 58–66

Abstract: Background: Atrial fibrillation (AF) is the most commonly encountered cardiac arrhythmia in clinical practice. Heart failure (HF) can occur concurrently with AF.: Aim: We compared different demographic, clinical, and echocardiographic characteristics ...

Abstract	Background: Atrial fibrillation (AF) is the most commonly encountered cardiac arrhythmia in clinical practice. Heart failure (HF) can occur concurrently with AF. Aim: We compared different demographic, clinical, and echocardiographic characteristics between patients with AF+HF and patients with AF only. Furthermore, we explored whether concurrent HF independently predicts several outcomes (all-cause mortality, cardiovascular mortality, ischemic stroke/systemic embolism (IS/SE), major bleeding, and clinically relevant non-major bleeding (CRNMB)). Materials and methods: Comparisons between the AF+HF and the AF-only group were carried out. Multivariable Cox proportional hazard models were constructed for each outcome to assess whether HF was predictive of any of them while controlling for possible confounding factors. Results: A total of 2020 patients were included in this study: 481 had AF+HF; 1539 had AF only. AF+HF patients were older, more commonly males, and had a higher prevalence of diabetes mellitus, dyslipidemia, coronary artery disease, and chronic kidney disease (p≤0.05). Furthermore, AF+HF patients more commonly had pulmonary hypertension and low ejection fraction (p≤0.001). Finally, HF was independently predictive of all-cause mortality (adjusted HR 2.17, 95% CI (1.66-2.85) and cardiovascular mortality (adjusted HR 2.37, 95% CI (1.68-3.36). Conclusion: Coexisting AF+HF was associated with a more labile and higher-risk population among Jordanian patients. Furthermore, coexisting HF independently predicted higher all-cause mortality and cardiovascular mortality. Efforts should be made to efficiently identify such cases early and treat them aggressively.
MeSH term(s)	Male ; Humans ; Atrial Fibrillation/diagnostic imaging ; Atrial Fibrillation/epidemiology ; Prognosis ; Jordan ; Stroke Volume ; Heart Failure/diagnostic imaging ; Heart Failure/epidemiology ; Risk Factors ; Echocardiography
Language	English
Publishing date	2023-11-30
Publishing country	United Arab Emirates
Document type	Journal Article
ZDB-ID	2192362-0
ISSN	1875-6212 ; 1570-1611
ISSN (online)	1875-6212
ISSN	1570-1611
DOI	10.2174/0115701611260211231115094716
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Article ; Online: Anxiety symptoms among children visiting a pediatric emergency center in Qatar during the COVID-19 pandemic: A cross-sectional study.

Khan, Abdul Waheed / Khan, Yasser Saeed / Khoodoruth, Mohamed Adil Shah / Dehwari, Asieh / Hammoudeh, Samer / Al-Amri, Mohammed H A Nasser / Alabdulla, Majid

Bulletin of the Menninger Clinic

2022 Volume 86, Issue 4, Page(s) 283–299

Abstract: Although the evidence base regarding the impact of the COVID-19 pandemic on children has been growing, descriptions of their experiences remain scarce. In this cross-sectional study, the authors used the Child-Reported Spence Children's Anxiety Scale to ... ...

Abstract	Although the evidence base regarding the impact of the COVID-19 pandemic on children has been growing, descriptions of their experiences remain scarce. In this cross-sectional study, the authors used the Child-Reported Spence Children's Anxiety Scale to collect data from 91 children visiting a pediatric emergency center in Qatar during the pandemic. Around 25% of the children reported elevated levels of overall anxiety. Obsessive-compulsive symptoms were the most common, with 59.3% of children reporting elevated symptoms. The mean score of physical injury fears was significantly affected by gender, with females having higher scores. Overall rates for elevated anxiety symptoms were similar in natives and expatriate children. The findings suggest that the effects of the pandemic on children may depend on several vulnerability factors, including developmental age and gender. This study highlights the need to plan multidisciplinary strategies to enhance children's access to mental health services during and after the current health crisis.
MeSH term(s)	Female ; Child ; Humans ; COVID-19 ; Pandemics ; Cross-Sectional Studies ; Qatar/epidemiology ; Anxiety/epidemiology
Language	English
Publishing date	2022-12-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	390416-7
ISSN	1943-2828 ; 0025-9284
ISSN (online)	1943-2828
ISSN	0025-9284
DOI	10.1521/bumc.2022.86.4.283
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Article ; Online: Secure Bluetooth Communication in Smart Healthcare Systems: A Novel Community Dataset and Intrusion Detection System.

Zubair, Mohammed / Ghubaish, Ali / Unal, Devrim / Al-Ali, Abdulla / Reimann, Thomas / Alinier, Guillaume / Hammoudeh, Mohammad / Qadir, Junaid

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 21

Abstract: Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to ... ...

Abstract	Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the
MeSH term(s)	Artificial Intelligence ; Internet of Things ; Delivery of Health Care ; Communication
Language	English
Publishing date	2022-10-28
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22218280
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Article: Oral Manifestations of Systemic Lupus Erythematosus Patients in Qatar: A Pilot Study.

Hammoudeh, Mohammed / Al-Momani, Ahmed / Sarakbi, Husam / Chandra, Prem / Hammoudeh, Samer

International journal of rheumatology

2018 Volume 2018, Page(s) 6052326

Abstract: Objective: The purpose of this pilot study was to assess the prevalence of oral manifestations among systemic lupus erythematosus (SLE) patients in Qatar, in order to warrant future studies that would investigate each one of these manifestations with ... ...

Abstract	Objective: The purpose of this pilot study was to assess the prevalence of oral manifestations among systemic lupus erythematosus (SLE) patients in Qatar, in order to warrant future studies that would investigate each one of these manifestations with detail and further scrutiny. Methods: Study procedures took place between November 2014 and April 2016. All patients visiting the outpatient rheumatology clinics at Hamad General Hospital, Doha, Qatar, were asked to join. The American College of Rheumatology (ACR) 1997 criteria of SLE were used. The patients were examined initially by a rheumatologist and were later scheduled for an appointment with a dentist at the same institution. A total of 77 patients were recruited for the study. Results: Prevalence rates for the different oral manifestations ranged from 2.4% for soft palate ulcers, cheilitis, and oral candida to 88.1% for the presence of cavitation. Gingivitis, periodontal disease, cavities, and missing teeth were observed in more than 50% of the sample. The prevalence of periodontal disease and missing teeth was higher among those with an SLE duration > 8 years. On the contrary, the prevalence of gingivitis and cavities was higher among those with an SLE duration ≤ 8 years. Conclusion: This study found high rates of gingivitis, periodontal disease, cavities, and missing teeth among SLE patients in Qatar. It is recommended that healthcare providers of such patients monitor the presence of any oral manifestations in order to arrange for early treatment and prevention efforts. Future prospective longitudinal studies with adequate sample size and power are needed in order to ascertain any causation factors or common etiology pathways.
Language	English
Publishing date	2018-04-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	2503284-7
ISSN	1687-9279 ; 1687-9260
ISSN (online)	1687-9279
ISSN	1687-9260
DOI	10.1155/2018/6052326
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Article: Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach

Mudassir, Mohammed / Bennbaia, Shada / Unal, Devrim / Hammoudeh, Mohammad

Neural Comput Appl

Abstract	Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit non-stationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh-ninetieth-day forecast. For daily price forecast, the error percentage is as low as 1.44%, while it varies from 2.88 to 4.10% for horizons of seven to ninety days. These results indicate that the presented models outperform the existing models in the literature.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #632436
Database	COVID19

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Article: Recurrent postpartum episodic rheumatoid arthritis.

Hammoudeh, Mohammed

Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases

2006 Volume 12, Issue 4, Page(s) 196–198

Abstract: It is well known that rheumatoid arthritis improves in the majority of patients during pregnancy and that aggravation of disease symptoms occurs within the first 6 months postpartum. We report a female patient who had 4 postpartum episodes of transient ... ...

Abstract	It is well known that rheumatoid arthritis improves in the majority of patients during pregnancy and that aggravation of disease symptoms occurs within the first 6 months postpartum. We report a female patient who had 4 postpartum episodes of transient polyarthritis typical of rheumatoid arthritis with positive serum rheumatoid factor and anti-CCP antibodies. All bouts resolved within 4 to 10 weeks after onset without any symptoms in between the episodes.
MeSH term(s)	Adult ; Arthritis, Rheumatoid/diagnosis ; Arthritis, Rheumatoid/drug therapy ; Diagnosis, Differential ; Female ; Follow-Up Studies ; Glucocorticoids/therapeutic use ; Humans ; Methylprednisolone/therapeutic use ; Postpartum Period ; Recurrence
Chemical Substances	Glucocorticoids ; Methylprednisolone (X4W7ZR7023)
Language	English
Publishing date	2006-08
Publishing country	United States
Document type	Case Reports ; Journal Article
ZDB-ID	1283266-2
ISSN	1076-1608
ISSN	1076-1608
DOI	10.1097/01.rhu.0000231463.79643.b1
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