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7. Overview on Epidemiology and Management of Myxedema Coma or Crisis.

Author

Hussain, Mohammed Salah, Alqurashi, Waad Mohammed, Tunsi, Mohammed Hazem, Almarzooq, Abdulhakim Yousef, Almohsen, Danyah Saleh, MAL, ABDULRAHMAN ABDULLAH, Alismail, Ahmed Jawad M., Al-Shahrani, Ali Madhaf Nasser, Alyami, Saeed Saleh, Mohamed, Ghaidaa Sulaiman, NAJMI, ATIAH ALI, Alqarni, Abdullah Khaled, Al Dira, Jaber Ali, Alotaibi, Abdullah Fehaid, and Elsherif, Abdelhalim A.

Subjects
COMA, THYROID crisis, EPIDEMIOLOGY, THYROID hormones, HOSPITAL laboratories, THYROTROPIN receptors
Abstract

The most severe type of hypothyroidism is known as myxedema coma, which can quickly lead to death if not identified and treated aggressively. The condition known as hypothyroidism is easily detected and treated. But if ignored, it might eventually progress to myxedema coma, the most serious form of hypothyroidism. Since the majority of patients do not initially appear in a coma, the term "myxedema coma" is usually regarded as misleading. Lethargy usually progresses to stupor, which then becomes a coma with hypothermia and respiratory failure. only clinical criteria are used for diagnosis because thyroid hormone assays are unable to distinguish between simple hyperthyroidism and thyroid storm. Apart from essential medical interventions, the treatment focuses on preventing thyroid hormone production and secretion as antithyroid medications, and preventing the peripheral effects of thyroid hormone as β-blocker, glucocorticoids. The diagnosis of thyroid-stimulating hormone (TSH) in the blood is the same as that of simple hypothyroidism. As soon as a diagnosis is obtained, treatment should start right away. The majority of hospital and commercial laboratories can turnaround a TSH test in a matter of hours. [ABSTRACT FROM AUTHOR]

Published
2024

8. Old World cutaneous leishmaniasis treatment response varies depending on parasite species, geographical location and development of secondary infection

Author

Al-Salem, Waleed S., Solórzano, Carla, Weedall, Gareth D., Dyer, Naomi A., Kelly-Hope, Louise, Casas-Sánchez, Aitor, Alraey, Yasser, Alyamani, Essam J., Halliday, Alice, Balghonaim, Salah M., Alsohibany, Khalid S., Alzeyadi, Zeyad, Alzahrani, Mohamed H., Al-Shahrani, Ali M., Assiri, Abdullah M., Memish, Ziad, and Acosta-Serrano, Álvaro

Published
2019
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13. Machine Learning-Enabled Smart Industrial Automation Systems Using Internet of Things.

Author

Al Shahrani, Ali M., Alomar, Madani Abdu, Alqahtani, Khaled N., Basingab, Mohammed Salem, Sharma, Bhisham, and Rizwan, Ali

Subjects
*INDUSTRIAL robots, *COMPUTERS, *INDUSTRIALISM, *INTERNET of things, *ARTIFICIAL neural networks
Abstract

Industrial automation uses robotics and software to operate equipment and procedures across industries. Many applications integrate IoT, machine learning, and other technologies to provide smart features that improve the user experience. The use of such technology offers businesses and people tremendous assistance in successfully achieving commercial and noncommercial requirements. Organizations are expected to automate industrial processes owing to the significant risk management and inefficiency of conventional processes. Hence, we developed an elaborative stepwise stacked artificial neural network (ESSANN) algorithm to greatly improve automation industries in controlling and monitoring the industrial environment. Initially, an industrial dataset provided by KLEEMANN Greece was used. The collected data were then preprocessed. Principal component analysis (PCA) was used to extract features, and feature selection was based on least absolute shrinkage and selection operator (LASSO). Subsequently, the ESSANN approach is proposed to improve automation industries. The performance of the proposed algorithm was also examined and compared with that of existing algorithms. The key factors compared with existing technologies are delay, network bandwidth, scalability, computation time, packet loss, operational cost, accuracy, precision, recall, and mean absolute error (MAE). Compared to traditional algorithms for industrial automation, our proposed techniques achieved high results, such as a delay of approximately 52%, network bandwidth accomplished at 97%, scalability attained at 96%, computation time acquired at 59 s, packet loss achieved at a minimum level of approximately 53%, an operational cost of approximately 59%, accuracy of 98%, precision of 98.95%, recall of 95.02%, and MAE of 80%. By analyzing the results, it can be seen that the proposed system was effectively implemented. [ABSTRACT FROM AUTHOR]

Published
2023
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14. A Clinical Tool to Identify Candidates for Stress-First Myocardial Perfusion Imaging

Author

Rouhani, Soroush, primary, Al Shahrani, Ali, additional, Hossain, Alomgir, additional, Yam, Yeung, additional, Wells, R. Glenn, additional, deKemp, Robert A., additional, Beanlands, Rob S., additional, Ruddy, Terrence D., additional, Di Carli, Marcelo F., additional, Merhige, Michael E., additional, Williams, Brent A., additional, Veledar, Emir, additional, Berman, Daniel S., additional, Dorbala, Sharmila, additional, and Chow, Benjamin J.W., additional

Published
2020
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18. Isolation and genetic characterization of Rift Valley fever virus from Aedes vexans arabiensis, Kingdom of Saudi Arabia. (Dispatches)

Author

Miller, Barry R., Godsey, Marvin S., Crabtree, Mary B., Savage, Harry M., Al-Mazrao, Yagob, Al-Jeffri, Mohammed H., Abdoon, Abdel-Mohsin M., Al-Seghayer, Suleiman M., Al-Shahrani, Ali M., and Ksiazek, Thomas G.

Subjects
Mosquitoes -- Health aspects, Rift Valley fever -- Genetic aspects, Communicable diseases -- Genetic aspects
Abstract

An outbreak of Rift Valley fever in the Kingdom of Saudi Arabia and Yemen in 2000 was the first recognized occurrence of the illness outside of Africa and Madagascar. An [...]

Published
2002

23. Detection of high levels of anti-α-galactosyl antibodies in sera of patients with Old World cutaneous leishmaniasis: a possible tool for diagnosis and biomarker for cure in an elimination setting

Author

AL-SALEM, WALEED S., primary, FERREIRA, DANIELA M., additional, DYER, NAOMI A., additional, ALYAMANI, ESSAM J., additional, BALGHONAIM, SALAH M., additional, AL-MEHNA, AHMED Y., additional, AL-ZUBIANY, SALEEM, additional, IBRAHIM, EL-KEIR, additional, AL SHAHRANI, ALI M., additional, ALKHUAILED, HAMED, additional, ALDAHAN, MOHAMMED A., additional, AL JARALLH, ABDULAZIZ M., additional, ABDELHADY, SAMER S., additional, AL-ZAHRANI, MOHAMMED H., additional, ALMEIDA, IGOR C., additional, and ACOSTA-SERRANO, ALVARO, additional

Published
2014
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24. MOESM2 of Old World cutaneous leishmaniasis treatment response varies depending on parasite species, geographical location and development of secondary infection

Author

Al-Salem, Waleed, Solórzano, Carla, Weedall, Gareth, Dyer, Naomi, Kelly-Hope, Louise, Casas-Sánchez, Aitor, Alraey, Yasser, Alyamani, Essam, Halliday, Alice, Balghonaim, Salah, Alsohibany, Khalid, Alzeyadi, Zeyad, Alzahrani, Mohamed, Al-Shahrani, Ali, Assiri, Abdullah, Memish, Ziad, and Acosta-Serrano, Álvaro

Subjects
parasitic diseases, 3. Good health
Abstract

Additional file 2: Figure S1. Scheme representing the current leishmaniasis treatment policy in KSA. Figure S2. Leishmania spp. identification in Central Region by PCR-RFLP analysis of parasite ITS1 region. Lane Lt: L. tropica positive control; Lane Lm: L. major positive control; Lanes 1–10: different examples of Leishmania isolates from Rass, Dwadmi and Muzahmyyah. Figure S3. Leishmania spp identification in Al Madinah Province by PCR-RFLP analysis of parasite ITS1 region. Lanes 1–4: different examples of Leishmania isolates from Aljadaida and Sulailah, Al Madinah Province; Lanes 1 and 2: L. tropica samples; Lanes 3 and 4: L. major samples; Lane Lm: L. major positive control; Lane Lt: L. tropica positive control. Figure S4. Leishmania spp. identification in Al Ahsa Region by PCR-RFLP analysis of parasite ITS1 region. Lanes 1–13: different examples of Leishmania isolates from Al Ahsa Region; Lane Lt: L. tropica positive control; Lane Lm: L. major positive control. Figure S5. Leishmania spp identification in Asir Region by PCR-RFLP analysis of parasite ITS1 region. Lanes 1–5: different examples of Leishmania isolates from Asir Province. Figure S6. Distribution of Leishmania species (a) and patient response to anti-leishmanial treatment (b) within the Eastern region of Saudi Arabia. The map was created using software ArcGIS 10 (ESRI, Redlands, CA). Figure S7. Distribution of Leishmania species (a) and patient response to anti-leishmanial treatment (b) within the Northwest region of Saudi Arabia. The map was created using software ArcGIS 10 (ESRI, Redlands, CA). Figure S8. Distribution of Leishmania species (a) and patient response to anti-leishmanial treatment (b) within the southwest region of Saudi Arabia. The map was created using software ArcGIS 10 (ESRI, Redlands, CA).

25. MOESM2 of Old World cutaneous leishmaniasis treatment response varies depending on parasite species, geographical location and development of secondary infection

Author

Al-Salem, Waleed, Solórzano, Carla, Weedall, Gareth, Dyer, Naomi, Kelly-Hope, Louise, Casas-Sánchez, Aitor, Alraey, Yasser, Alyamani, Essam, Halliday, Alice, Balghonaim, Salah, Alsohibany, Khalid, Alzeyadi, Zeyad, Alzahrani, Mohamed, Al-Shahrani, Ali, Assiri, Abdullah, Memish, Ziad, and Acosta-Serrano, Álvaro

Subjects
parasitic diseases, 3. Good health
Abstract

Additional file 2: Figure S1. Scheme representing the current leishmaniasis treatment policy in KSA. Figure S2. Leishmania spp. identification in Central Region by PCR-RFLP analysis of parasite ITS1 region. Lane Lt: L. tropica positive control; Lane Lm: L. major positive control; Lanes 1–10: different examples of Leishmania isolates from Rass, Dwadmi and Muzahmyyah. Figure S3. Leishmania spp identification in Al Madinah Province by PCR-RFLP analysis of parasite ITS1 region. Lanes 1–4: different examples of Leishmania isolates from Aljadaida and Sulailah, Al Madinah Province; Lanes 1 and 2: L. tropica samples; Lanes 3 and 4: L. major samples; Lane Lm: L. major positive control; Lane Lt: L. tropica positive control. Figure S4. Leishmania spp. identification in Al Ahsa Region by PCR-RFLP analysis of parasite ITS1 region. Lanes 1–13: different examples of Leishmania isolates from Al Ahsa Region; Lane Lt: L. tropica positive control; Lane Lm: L. major positive control. Figure S5. Leishmania spp identification in Asir Region by PCR-RFLP analysis of parasite ITS1 region. Lanes 1–5: different examples of Leishmania isolates from Asir Province. Figure S6. Distribution of Leishmania species (a) and patient response to anti-leishmanial treatment (b) within the Eastern region of Saudi Arabia. The map was created using software ArcGIS 10 (ESRI, Redlands, CA). Figure S7. Distribution of Leishmania species (a) and patient response to anti-leishmanial treatment (b) within the Northwest region of Saudi Arabia. The map was created using software ArcGIS 10 (ESRI, Redlands, CA). Figure S8. Distribution of Leishmania species (a) and patient response to anti-leishmanial treatment (b) within the southwest region of Saudi Arabia. The map was created using software ArcGIS 10 (ESRI, Redlands, CA).

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