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Fulltext Inhibitory effects of Ephedra alte on IL-6, hybrid TLR4, TNF-α, IL-1β, and extracted TLR4 receptors: in silico molecular docking

Salman HA, Yaakop AS, Aladaileh S, Mustafa M, Gharaibeh M, Kahar UM

Heliyon, 2023 Jan;9(1):e12730.
PMID: 36685394 DOI: 10.1016/j.heliyon.2022.e12730

Inflammation is a physiological reaction of the immune system required to remove the presence of pathogenic germs. Many herbal-derived extracts and phytoconstituents show anti-inflammatory effects. Among these natural phytoconstituents is Ephedra alte (E. alte), which shows pepsin enzyme inhibitory, antibacterial, and antioxidant activities. In this work, molecular docking study is conducted on five major human anti-inflammatory cytokines receptors (IL-6, hybrid TLR4, TNF-α, IL-1β, and extracted TLR4) to explore the molecular recognition process and complex ligand-receptor interactions of E. alte phytoconstituents. Human TLR4 receptor has been computationally extracted, for the first time, from the hybrid TLR4 human and VLRB inshore hagfish. Among E. alte phytoconstituents, only β-Sitosterol and Androstan-3-one have better LBE (Lowest Binding Energy) scores with inhibition constant (K i) values than those of other tested compounds. The β-Sitosterol and Androstan-3-one results indicate that these compounds could be efficient inhibitors of inflammation and reduce the oxidative stress by interfering with the activity of the five studied proteins.
Fulltext Impact of the COVID-19 pandemic on imaging case volumes in King Abdullah University Hospitals (KAUH)

Gharaibeh M, El-Obeid E, Khasawneh R, Karrar M, Salman M, Farah A, et al.

Front Med (Lausanne), 2023;10:1103083.
PMID: 36844230 DOI: 10.3389/fmed.2023.1103083

OBJECTIVE: COVID-19 has an increased burden on the delivery of services because the measures taken by the governments forced hospitals to cancel most of their elective procedures and led to the shutting down of outpatient clinics. This study aimed to evaluate the impact COVID-19 pandemic on the volume of radiology exams based on patient service locations and imaging modality in the North of Jordan.
METHODS: The imaging case volumes that were performed at the King Abdullah University Hospital (KAUH), Jordan, from 1 January 2020 to 8 May 2020, were retrospectively collected and compared to those from 1 January 2019 to 28 May 2019, to determine the impact of the pandemic of COVID-19 on the volume of radiological examinations. The 2020 study period was chosen to cover the peak of COVID-19 cases and to record the effects on imaging case volumes.
RESULTS: A total of 46,194 imaging case volumes were performed at our tertiary center in 2020 compared to 65,441 imaging cases in 2019. Overall, the imaging case volume in 2020 decreased by 29.4% relative to the same period in 2019. The imaging case volumes decreased for all imaging modalities relative to 2019. The number of nuclear images showed the highest decline (41.0%) in 2020, followed by the number of ultrasounds (33.2%). Interventional radiology was the least affected imaging modality by this decline, with about a 22.9% decline.
CONCLUSION: The number of imaging case volumes decreased significantly during the COVID-19 pandemic and its associated lockdown. The outpatient service location was the most affected by this decline. Effective strategies must be adopted to avoid the aforementioned effect on the healthcare system in future pandemics.
Fulltext The ability of digital breast tomosynthesis to reduce additional examinations in older women

Gharaibeh M, Alfwares AA, Elobeid E, Khasawneh R, Rousan L, El-Heis M, et al.

Front Med (Lausanne), 2023;10:1276434.
PMID: 38076239 DOI: 10.3389/fmed.2023.1276434

AIMS: To assess the diagnostic performance of digital breast tomosynthesis (DBT) in older women across varying breast densities and to compare its effectiveness for cancer detection with 2D mammography and ultrasound (U/S) for different breast density categories. Furthermore, our study aimed to predict the potential reduction in unnecessary additional examinations among older women due to DBT.
METHODS: This study encompassed a cohort of 224 older women. Each participant underwent both 2D mammography and digital breast tomosynthesis examinations. Supplementary views were conducted when necessary, including spot compression and magnification, ultrasound, and recommended biopsies. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated for 2D mammography, DBT, and ultrasound. The impact of DBT on diminishing the need for supplementary imaging procedures was predicted through binary logistic regression.
RESULTS: In dense breast tissue, DBT exhibited notably heightened sensitivity and NPV for lesion detection compared to non-dense breasts (61.9% vs. 49.3%, p 0.05) between DBT and the four dependent variables.
CONCLUSION: Our findings indicate that among older women, DBT does not significantly decrease the requirement for further medical examinations.
Fulltext Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

Alzu'bi D, Abdullah M, Hmeidi I, AlAzab R, Gharaibeh M, El-Heis M, et al.

J Healthc Eng, 2022;2022:3861161.
PMID: 37323471 DOI: 10.1155/2022/3861161

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life.