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Article ; Online: Perception of the Impact of Artificial Intelligence in the Decision-Making Processes of Public Healthcare Professionals.

Saleh Ibrahim, Yousif / Khalid Al-Azzawi, Waleed / Hamad Mohamad, A Abdullah / Nouri Hassan, Ahmed / Meraf, Zelalem

Journal of environmental and public health

2022 Volume 2022, Page(s) 8028275

Abstract: Technologies are increasingly independent and play important roles in society. Artificial intelligence (AI) is a branch of science that can improve various environments and processes. The health sector stands out among these contexts, especially ... ...

Abstract	Technologies are increasingly independent and play important roles in society. Artificial intelligence (AI) is a branch of science that can improve various environments and processes. The health sector stands out among these contexts, especially ophthalmology and dentistry. Studies evaluating the impact of using these technologies in these contexts are still developing. There are still few studies that assess how AI can impact the decision-making process of health professionals and how it can improve the quality of care provided to these professionals. In this sense, this study aims to evaluate the perception of the impact of AI on the decision-making process of health professionals and the quality of patient care from the perspective of ophthalmologists and dentists. The methodological strategy used was the application of an online questionnaire with eighteen professionals in these areas. Based on the respondents' opinions, we sought to assess how these decision-making processes are affected by the use of technologies and how they impact the quality of patient care. As a result, it was observed that AI has become essential and a facilitator of the diagnostic processes. However, it presents some challenges related to cost, accessibility, AI
MeSH term(s)	Artificial Intelligence ; Delivery of Health Care ; Health Personnel ; Humans ; Ophthalmology ; Perception
Language	English
Publishing date	2022-07-13
Publishing country	United States
Document type	Journal Article ; Retracted Publication
ZDB-ID	2526611-1
ISSN	1687-9813 ; 1687-9813
ISSN (online)	1687-9813
ISSN	1687-9813
DOI	10.1155/2022/8028275
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Article ; Online: Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care.

Akram Abdulrazzaq, Ammar / Sulaiman Hamid, Sana / Al-Douri, Asaad T / Hamad Mohamad, A A / Selvi, D / Mohamed Ibrahim, Abdelrahman

publication RETRACTED

Journal of environmental and public health

2022 Volume 2022, Page(s) 8670534

Abstract: Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour extends to the lower part of the colon (rectum), the cancer may be colorectal. Medical imaging is the ... ...

Abstract	Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour extends to the lower part of the colon (rectum), the cancer may be colorectal. Medical imaging is the denomination of methods used to create visual representations of the human body for clinical analysis, such as diagnosing, monitoring, and treating medical conditions. In this research, a computational proposal is presented to aid the diagnosis of colon cancer, which consists of using hyperspectral images obtained from slides with biopsy samples of colon tissue in paraffin, characterizing pixels so that, afterwards, imaging techniques can be applied. Using computer graphics augmenting conventional histological deep learning architecture, it can classify pixels in hyperspectral images as cancerous, inflammatory, or healthy. It is possible to find connections between histochemical characteristics and the absorbance of tissue under various conditions using infrared photons at various frequencies in hyperspectral imaging (HSI). Deep learning techniques were used to construct and implement a predictor to detect anomalies, as well as to develop a computer interface to assist pathologists in the diagnosis of colon cancer. An infrared absorbance spectrum of each of the pixels used in the developed classifier resulted in an accuracy level of 94% for these three classes.
MeSH term(s)	Colonic Neoplasms/diagnostic imaging ; Deep Learning ; Delivery of Health Care ; Humans ; Hyperspectral Imaging
Language	English
Publishing date	2022-05-31
Publishing country	United States
Document type	Journal Article ; Retracted Publication
ZDB-ID	2526611-1
ISSN	1687-9813 ; 1687-9813
ISSN (online)	1687-9813
ISSN	1687-9813
DOI	10.1155/2022/8670534
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Article: Antimicrobial activity of nature-inspired molecules against multidrug-resistant bacteria.

Hamad, Mohamad / Al-Marzooq, Farah / Srinivasulu, Vunnam / Sulaiman, Ashna / Menon, Varsha / Ramadan, Wafaa S / El-Awady, Raafat / Al-Tel, Taleb H

Frontiers in microbiology

2024 Volume 14, Page(s) 1336856

Abstract: Multidrug-resistant bacterial infections present a serious challenge to global health. In addition to the spread of antibiotic resistance, some bacteria can form persister cells which are tolerant to most antibiotics and can lead to treatment failure or ... ...

Abstract	Multidrug-resistant bacterial infections present a serious challenge to global health. In addition to the spread of antibiotic resistance, some bacteria can form persister cells which are tolerant to most antibiotics and can lead to treatment failure or relapse. In the present work, we report the discovery of a new class of small molecules with potent antimicrobial activity against Gram-positive bacteria and moderate activity against Gram-negative drug-resistant bacterial pathogens. The lead compound SIMR 2404 had a minimal inhibitory concentration (MIC) of 2 μg/mL against methicillin-resistant
Language	English
Publishing date	2024-01-22
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587354-4
ISSN	1664-302X
ISSN	1664-302X
DOI	10.3389/fmicb.2023.1336856
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Article ; Online: Simulation of Vehicular Network Use in Emergency Situations and Security Applications on a Pakistan Highway

Asaad T. Al-Douri / Noor Mohammed Kadhim / A. A. Hamad Mohamad / Melese Abeyie

Security and Communication Networks, Vol

2022 Volume 2022

Abstract: VANETs (vehicular ad hoc networks), which are revolutionary techniques to enhance road safety, can be used to broadcast information about dangerous traffic conditions or accidents. However, distributing important information for driver safety and well- ... ...

Abstract	VANETs (vehicular ad hoc networks), which are revolutionary techniques to enhance road safety, can be used to broadcast information about dangerous traffic conditions or accidents. However, distributing important information for driver safety and well-being has strict time and reliability requirements. This is because messages must be received by all cars involved in a potentially dangerous scenario for proper precautions to be taken to avoid the problem from materializing or intensifying. Because of the deterioration in conventional wireless communication system performance, ensuring that such requirements are met is a serious concern. To validate the concept before the actual installation of such systems and their absorption into the vehicle sector, it is therefore critical to employ simulation methodologies that are both reliable and thorough. This piece consists of large-scale, realistic security simulation research of an emergency situation based on actual road traffic data acquired on a Pakistan route. The study’s findings are detailed in the following paragraphs. Aspects such as the incorporation of fixed communication units along a stretch of roadway and the performance of the vehicular network notifying all vehicles engaged in the various accident scenarios modeled on the same stretch of highway were evaluated. Both of these characteristics were designed to increase safety and security applications. After doing the investigation, it was observed that when fixed communication units are incorporated into the network infrastructure, there is a shorter delay in receiving the accident notification. This was the conclusion made after reviewing the findings. Drivers of vehicles located closer to the accident site will be able to respond in a timely and safe manner as a result of this improvement in network performance, and drivers security of vehicles located further away will have the option of exiting the highway to avoid potential congestion caused by increased road traffic.
Keywords	Technology (General) ; T1-995 ; Science (General) ; Q1-390
Subject code	380
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi-Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Study of the Unidimensionality of the Subjective Measurement Scale of Schizophrenia Coping Oral Health Profile and Index: SCOOHPI.

Hamad, Mohamad / Rude, Nathalie / Mesbah, Mounir / Siu-Paredes, Francesca / Denis, Frederic

Behavioral sciences (Basel, Switzerland)

2022 Volume 12, Issue 11

Abstract: Background: The Schizophrenia Coping Oral Health Profile and Index (SCOOHPI) scale studies the coping strategies of schizophrenic patients with regard to oral health. The structural validity of this scale is studied has been studied using factor ... ...

Abstract	Background: The Schizophrenia Coping Oral Health Profile and Index (SCOOHPI) scale studies the coping strategies of schizophrenic patients with regard to oral health. The structural validity of this scale is studied has been studied using factor analyses. In this article, we study the unidimensionality of the SCOOHPI scale to use it as an index. Methods: We studied the internal consistency of the items of the SCOOHPI scale. Then, we studied the construct validity. The unidimensionality of the SCOOHPI scale was studied by the partial credit model. Results: The data used in this study come from five hospitals, and the total number of individuals participating in this study is 96, of which 72% are men and 59% are smokers. The SCOOHPI scale has good internal consistency (α = 0.84). The validity of divergence was checked by the absence of correlation between the SCOOHPI scale and the GOHAI (General Oral Health Assessment Index) scale. The unidimensionality of the SCOOHPI scale with data smoothing was demonstrated by the partial credit model. Conclusion: In this study, we completed the study of the psychometric validation of the SCOOHPI. The SCOOHPI scale can then contribute to improving evaluation of the coping strategies of schizophrenic patients with regard to oral health.
Language	English
Publishing date	2022-11-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2651997-5
ISSN	2076-328X
ISSN	2076-328X
DOI	10.3390/bs12110442
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Article ; Online: Evolutionary relevance of metabolite production in relation to marine sponge bacteria symbiont.

Hamoda, Alshaimaa M / Hamdy, Rania / Fayed, Bahgat / Abouleish, Mohamed / Sulaiman, Ashna / Hamad, Mohamad / Soliman, Sameh S M

Applied microbiology and biotechnology

2023 Volume 107, Issue 16, Page(s) 5225–5240

Abstract: Sponges are habitats for a diverse community of microorganisms. Sponges provide shelter, whereas microbes provide a complementary defensive mechanism. Here, a symbiotic bacterium, identified as Bacillus spp., was isolated from a marine sponge following ... ...

Abstract	Sponges are habitats for a diverse community of microorganisms. Sponges provide shelter, whereas microbes provide a complementary defensive mechanism. Here, a symbiotic bacterium, identified as Bacillus spp., was isolated from a marine sponge following culture enrichment. Fermentation-assisted metabolomics using thin-layer chromatography (TLC) and gas chromatography-mass spectrometry (GC-MS) indicated that marine simulated nutrition and temperature was the optimum in metabolite production represented by the highest number of metabolites and the diverse chemical classes when compared to other culture media. Following large-scale culture in potato dextrose broth (PDB) and dereplication, compound M1 was isolated and identified as octadecyl-1-(2',6'-di-tert-butyl-1'-hydroxyphenyl) propionate. M1, at screening concentrations up to 10 mg/ml, showed no activity against prokaryotic bacteria including Staphylococcus aureus and Escherichia coli, while 1 mg/ml of M1 was sufficient to cause a significant killing effect on eukaryotic cells including Candida albicans, Candida auris, and Rhizopus delemar fungi and different mammalian cells. M1 exhibited MIC
MeSH term(s)	Animals ; Porifera ; Bacteria/metabolism ; Antifungal Agents/chemistry ; Biological Evolution ; Bacillus ; Candida albicans ; Mammals
Chemical Substances	Antifungal Agents
Language	English
Publishing date	2023-06-26
Publishing country	Germany
Document type	Journal Article
ZDB-ID	392453-1
ISSN	1432-0614 ; 0171-1741 ; 0175-7598
ISSN (online)	1432-0614
ISSN	0171-1741 ; 0175-7598
DOI	10.1007/s00253-023-12649-3
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Article ; Online: GDF15

Mohammad, Mohammad G / Saeed, Rania / Mohammed, Abdul Khader / Khalique, Anila / Hamad, Mohamad / El-Huneidi, Waseem / Hamad, Mawieh / Taneera, Jalal

Experimental biology and medicine (Maywood, N.J.)

2023 Volume 248, Issue 4, Page(s) 339–349

Abstract: Mounting evidence points to a link between growth differentiation factor-15 ( ...

Abstract	Mounting evidence points to a link between growth differentiation factor-15 (
MeSH term(s)	Humans ; Insulin Secretion ; Glucose Transporter Type 1/metabolism ; Islets of Langerhans/metabolism ; Insulin-Secreting Cells/metabolism ; Insulin/metabolism ; Glucose/metabolism ; Growth Differentiation Factor 15/genetics ; Growth Differentiation Factor 15/metabolism ; Alpha-Ketoglutarate-Dependent Dioxygenase FTO/metabolism
Chemical Substances	Glucose Transporter Type 1 ; Insulin ; Glucose (IY9XDZ35W2) ; GDF15 protein, human ; Growth Differentiation Factor 15 ; FTO protein, human (EC 1.14.11.33) ; Alpha-Ketoglutarate-Dependent Dioxygenase FTO (EC 1.14.11.33)
Language	English
Publishing date	2023-02-05
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	4015-0
ISSN	1535-3699 ; 1525-1373 ; 0037-9727
ISSN (online)	1535-3699 ; 1525-1373
ISSN	0037-9727
DOI	10.1177/15353702221146552
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Article ; Online: Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care

Ammar Akram Abdulrazzaq / Sana Sulaiman Hamid / Asaad T. Al-Douri / A. A. Hamad Mohamad / D. Selvi / Abdelrahman Mohamed Ibrahim

Journal of Environmental and Public Health, Vol

2022 Volume 2022

Abstract	Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour extends to the lower part of the colon (rectum), the cancer may be colorectal. Medical imaging is the denomination of methods used to create visual representations of the human body for clinical analysis, such as diagnosing, monitoring, and treating medical conditions. In this research, a computational proposal is presented to aid the diagnosis of colon cancer, which consists of using hyperspectral images obtained from slides with biopsy samples of colon tissue in paraffin, characterizing pixels so that, afterwards, imaging techniques can be applied. Using computer graphics augmenting conventional histological deep learning architecture, it can classify pixels in hyperspectral images as cancerous, inflammatory, or healthy. It is possible to find connections between histochemical characteristics and the absorbance of tissue under various conditions using infrared photons at various frequencies in hyperspectral imaging (HSI). Deep learning techniques were used to construct and implement a predictor to detect anomalies, as well as to develop a computer interface to assist pathologists in the diagnosis of colon cancer. An infrared absorbance spectrum of each of the pixels used in the developed classifier resulted in an accuracy level of 94% for these three classes.
Keywords	Public aspects of medicine ; RA1-1270
Subject code	610
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Early Detection of Autism Spectrum Disorders (ASD) with the Help of Data Mining Tools

Ammar Akram Abdulrazzaq / Sana Sulaiman Hamid / Asaad T. Al-Douri / A. A. Hamad Mohamad / Abdelrahman Mohamed Ibrahim

BioMed Research International, Vol

2022 Volume 2022

Abstract: Autism is a disorder of neurobiological origin that originates a different course in the development of verbal and nonverbal communication, social interactions, the flexibility of behavior, and interests. The results obtained offer relevant information ... ...

Abstract	Autism is a disorder of neurobiological origin that originates a different course in the development of verbal and nonverbal communication, social interactions, the flexibility of behavior, and interests. The results obtained offer relevant information to reflect on the practices currently used in assessing the development of children and the detection of ASD and suggest the need to strengthen the training of health professionals in aspects such as psychology and developmental disorders. This study, based on genuine and current facts, used data from 292 children with an autism spectrum disorder. The input dataset has 20 characteristics, and the output dataset has one attribute. The output property indicates whether or not a certain person has autism. The research study first and foremost performed data pretreatment activities such as filling in missing data gaps in the data collection, digitizing categorical data, and normalizing. The features were then clustered using k-means and x-means clustering methods, then artificial neural networks and a linguistic strong neurofuzzy classifier were used to classify them. The outcomes of each strategy were examined, and their respective performances were compared.
Keywords	Medicine ; R
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automatic Segmentation of Calcification Areas in Digital Breast Images

Ammar Akram Abdulrazzaq / Yasser Muhammed / Asaad T. Al-Douri / A. A. Hamad Mohamad / Abdelrahman Mohamed Ibrahim

BioMed Research International, Vol

2022 Volume 2022

Abstract: In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction ... ...

Abstract	In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mammography can be made more effective in diagnosing breast abnormalities and aiding in the early diagnosis of breast cancer, hence increasing its overall effectiveness. The methodology, which includes some concepts of digital imaging and machine learning techniques, will be described in the following section after a review of the literature on breast cancer (categories, prevention involving the environment and lifestyle, diagnosis, and tracking of the disease) has been completed (neural networks and random forests). It was possible to achieve these results by working with an image collection that previously had questionable regions (per the given technique). Fiji software extracted problematic candidate regions from mammography images, which were subsequently subjected to further examination. To categorize the results of the picture segmentation, they were sorted into three groups, which were as follows: random forest and neural networks both generated promising results in the segmentation of suspicious parts that were emphasized in the highlight of the image, and this was true for both algorithms. Detection of contours of the regions was carried out, indicating that cuts of these segmented sections may be created. Later on, automatic categorization of the targets can be carried out using a learning algorithm, as illustrated in the experiment.
Keywords	Medicine ; R
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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