Recognising the importance and impact of Imaging Scientists: Global guidelines for establishing career paths within core facilities.

In the dynamic landscape of scientific research, imaging core facilities are vital hubs propelling collaboration and innovation at the technology development and dissemination frontier. Here, we present a collaborative effort led by Global BioImaging (GBI), introducing international recommendations geared towards elevating the careers of Imaging Scientists in core facilities. Despite the critical role of Imaging Scientists in modern research ecosystems, challenges persist in recognising their value, aligning performance metrics and providing avenues for career progression and job security. The challenges encompass a mismatch between classic academic career paths and service-oriented roles, resulting in a lack of understanding regarding the value and impact of Imaging Scientists and core facilities and how to evaluate them properly. They further include challenges around sustainability, dedicated training opportunities and the recruitment and retention of talent. Structured across these interrelated sections, the recommendations within this publication aim to propose globally applicable solutions to navigate these challenges. These recommendations apply equally to colleagues working in other core facilities and research institutions through which access to technologies is facilitated and supported. This publication emphasises the pivotal role of Imaging Scientists in advancing research programs and presents a blueprint for fostering their career progression within institutions all around the world.

Ocular Ischemic Syndrome and the Role of Carotid Artery Revascularization.

BACKGROUND: Ocular ischemic syndrome (OIS) is a rare presentation of atherosclerotic carotid artery stenosis that can result in permanent visual loss. This severely disabling syndrome remains under diagnosed and undertreated due to lack of awareness; especially since it requires expedited multidisciplinary care. The relevance of early diagnosis and treatment is increasing due to an increasing prevalence of cerebrovascular disease. METHODS: The long-term visual and cerebrovascular outcomes following intervention for nonarteritic OIS, remain poorly described and were the objective of this concise review. We conducted a PubMed search to include all English language publications (cohort studies and case reports) between 2002 and 2023. RESULTS: A total of 33 studies (479 patients) report the outcomes of treatment of OIS with carotid endarterectomy (CEA, 304 patients, 19 studies), and carotid artery stenting (CAS, 175 patients, 14 studies). Visual outcomes were improved or did not worsen in 447 patients (93.3%). No periprocedural stroke was reported. Worsening visual symptoms were rare (35 patients, 7.3%); they occurred in the immediate postoperative period secondary to ocular hypoperfusion (3 patients) and in the late postoperative period due to progression of systemic atherosclerotic disease. Symptomatic recurrence due to recurrent stenosis after CEA was reported in 1 patient (0.21%); this was managed successfully with CAS. None of these studies report the results of transcarotid artery revascularization, the long-term operative outcome or stroke rate. CONCLUSIONS: OIS remains to be an underdiagnosed condition. Early diagnosis and prompt treatment are crucial in reversal or stabilization of OIS symptoms. An expedited multidisciplinary approach between vascular surgery and ophthalmology services is necessary to facilitate timely treatment and optimize outcome. If diagnosed early, both CEA and CAS have been associated with visual improvement and prevention of progressive visual loss.

The 'Fault' Lies in the Choroid: Peripapillary Intrachoroidal Cavitation Presenting with Progressive Vision Loss.

A 55-year-old male was referred to the Neuro-ophthalmology clinic due to gradual onset, progressive vision loss. On fundus examination a subtle yellow-orange peripapillary lesion was detected in the left eye. Optical coherence tomography with radial scanning illustrated retinal nerve fibre layer thinning as well as an area of intrachoroidal cavitation that corresponded to the lesion. Visual field testing showed a left inferior arcuate defect. Magnetic resonance imaging of the brain and orbit, and laboratory testing was unremarkable. Clinical examination, imaging, and testing were consistent with peripapillary intrachoroidal cavitation (PICC). Follow-up with serial visual field testing showed mild progression of the field defect. While PICC is not well understood in the literature, studies have reported associated risk factors including pathological myopia, older age, increased ocular axial length, chorioretinal atrophy, and vascular abnormalities. Importantly, glaucoma-like visual field defects as well as structural changes have been noticed in a high proportion of patients with PICC. While these alterations are evident, the pathogenic relationship between them is yet to be uncovered. Treatment with anti-glaucoma medications has been suggested, however, the evidence remains scarce for its true benefits. Care providers must be aware of the presentation of a yellow-orange peripapillary lesion with an associated visual field defect to accurately diagnose and manage this condition.

Experimental study of pre- and postnatal caffeine exposure and its observable effects on selected neurotransmitters and behavioural attributes at puberty : Caffeine exposure and its observable effects on selected neurotranmitters and behaviour.

Caffeine is globally consumed as a stimulant in beverages. It is also ingested in purified forms as power and tablets. Concerns have been raised about the potential consequences of intrauterine and early life caffeine exposure on brain health. This study modeled caffeine exposure during pregnancy and early postanal life until puberty, and the potential consequences. Caffeine powder was dissolved in distilled water. Thirty-two (n = 32) pregnant mice (Mus musculus) (dams) were divided into four groups- A, B, C and D. Group A animals served as a control, receiving placebo. Caffeine doses in mg/kg body weight were administered as follows: Group B, 10 mg/kg; Group C, 50 mg/kg; Group D, 120 mg/kg. Prenatal caffeine exposure [phase I] lasted throughout pregnancy. Half the number of offspring (pups) were sacrificed at birth; the rest were recruited into phase II and the experiment continued till day 35, marking puberty. Brain samples were processed following sacrifice. γ-aminobutyric acid (GABA), acetylcholine (ACh), and serotonin (5Ht) neurotransmitters were assayed in homogenates to evaluate functional neurochemistry. Anxiety and memory as neurobehavioural attributes were observed using the elevated plus and Barnes' mazes respectively. Continuous caffeine exposure produced positive effects on short and long-term memory parameters; the pattern interestingly was irregular and appeared more effective with the lowest experimental dose. Anxiety test results showed no attributable significant aberrations. Caffeine exposure persistently altered the neurochemistry of selected neurotransmitters including ACh and 5Ht, including when exposure lasted only during pregnancy. ACh significantly increased in group BC+ to 0.3475µgg-1 relative to control's 0.2508µgg-1; pre-and continuous postnatal exposure in Group B increased 5Ht to 0.2203 µgg-1 and 0.2213 µgg-1 respectively relative to control's 0.1863 µgg-1. From the current investigation, caffeine exposure in pregnancy had persistent effects on brain functional attributes including neurotransmitters activities, memory and anxiety. Caffeine in moderate doses affected memory positively but produced negative effects at the higher dosage including increased anxiety tendencies.

Synthesis of zinc-carboxylate metal-organic frameworks for the removal of emerging drug contaminant (amodiaquine) from aqueous solution.

We herein report the removal of amodiaquine, an emerging drug contaminant from aqueous solution using [Zn2(fum)2(bpy)] and [Zn4O(bdc)3] (fum=fumaric acid; bpy=4,4-bipyridine; bdc=benzene-1,4-dicarboxylate) metal-organic frameworks (MOFs) as adsorbents. The adsorbents were characterized by elemental analysis, Fourier transform infrared (FT-IR) spectroscopy, and powder X-ray diffraction (PXRD). Adsorption process for both adsorbents were found to follow the pseudo-first-order kinetics, and the adsorption equilibrium data fitted best into the Freundlich isotherm with the R2 values of 0.973 and 0.993 obtained for [Zn2(fum)2(bpy)] and [Zn4O(bdc)3] respectively. The maximum adsorption capacities foramodiaquine in this study were found to be 0.478 and 47.62mg/g on the [Zn2(fum)2(bpy)] and [Zn4O(bdc)3] MOFs respectively, and were obtained at pH of 4.3 for both adsorbents. FT-IR spectroscopy analysis of the MOFs after the adsorption process showed the presence of the drug. The results of the study showed that the prepared MOFs could be used for the removal of amodiaquine from wastewater.

Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression.

The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R²) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.

Evaluations of groundwater contamination by leachates around Olusosun open dumpsite in Lagos metropolis, southwest Nigeria.

A major cause of groundwater pollution in urban areas is contamination by leachates emanating from municipal solid waste dumpsites. The study evaluated the quality of water of selected boreholes and wells around Olusosun open solid waste dumpsite in Lagos metropolis, using water quality index (WQI) rating and geospatial techniques. Water samples were randomly collected from fifteen boreholes and five wells downslopes of the dumpsite. The samples were analysed for the physico-chemical parameters and heavy metals. Factor Analysis was employed to analyse the information content of the water quality indicators to determine their appropriateness for indexing. The spatial distribution of the WQIs determined using Inverse Distance Weighting (IDW) interpolation procedure. Results showed that the waters were generally acidic with 85% of pH values below the range suggested by WHO for drinking water. All samples contained concentration of dissolved oxygen (DO) in quantities higher than the recommended limit of 2.0 mg/l (average = 4.97 mg/l), while 40% of the samples contained concentration of K above the recommended limit. The concentration of heavy metals was generally low. The major cations (Mg, Na, and K) were highly positively correlated, and were positively correlated with pH, TA, TAL, TH and Cl. Negative correlations were observed between TDS, NO3(-) and PO4(3-); NO3(-) and Ag; and DO with the heavy metals. Eighteen parameters consisting of pH, EC, TDS, TA, TAL, TH, Cl, NO3(-), PO4(3-), Mg, Na, K, Zn, Mn, Fe, Cd, Ag and Pb were found to be the main indicators of groundwater pollution caused by landfill leachate percolation. Evaluation of the WQIs indicated that 35% of the water samples were unsuitable for consumption, while 15%, 15% and 35% were in the good, very good and excellent categorises, respectively. The degree of suitability of the borehole and well waters was closely related to proximity to the dumpsite. It is imperative that appropriate remediation strategies are adopted to forestall further contamination of the groundwater by leachates in the area.

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia.

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.

Peroral Exposure to Cannabis Sativa Ethanol Extract Caused Neuronal Degeneration and Astrogliosis in Wistar Rats' Prefrontal Cortex.

Background: Despite widespread concerns about its possible side effects, notably on the prefrontal cortex (PFC), which mediates cognitive processes, the use of Cannabis sativa as a medicinal and recreational drug is expanding exponentially. This study evaluated possible behavioral alterations, neurotransmitter levels, histological, and immunohistochemical changes in the PFC of Wistar rats exposed to Cannabis sativa. Purpose: To evaluate the effect of graded doses of Cannabis sativa on the PFC using behavioural, histological, and immunohistochemical approaches. Methods: Twenty-eight juvenile male Wistar rats weighing between 70 g and 100 g were procured and assigned into groups A-D (n = 7 each). Group A served as control which received distilled water only as a placebo; rats in groups B, C, and D which were the treatment groups were orally exposed to graded doses of Cannabis sativa (10 mg/kg, 50 mg/kg, and 100 mg/kg, respectively). Rats in all experimental groups were exposed to Cannabis sativa for 21 days, followed by behavioral tests using the open field test for locomotor, anxiety, and exploratory activities, while the Y-maze test was for spatial memory assessment. Rats for biochemical analysis were cervically dislocated and rats for tissue processing were intracardially perfused following neurobehavioral tests. Sequel to sacrifice, brain tissues were excised and prefrontal cortices were obtained for the neurotransmitter (glutamate, acetylcholine, and dopamine) and enzymatic assay (Cytochrome C oxidase (CcO) and Glucose 6- Phosphate Dehydrogenase-G-6-PDH). Brain tissues were fixed in 10% Neutral Buffered Formalin (NBF) for histological demonstration of the PFC cytoarchitecture using H&E and glial fibrillary acidic protein (GFAP) for astrocyte evaluation. Results: Glutamate and dopamine levels were significantly increased (F = 24.44, P = .0132) in groups D, and B, C, and D, respectively, compared to control; likewise, the activities of CcO and G-6-PDH were also significantly elevated (F = 96.28, P = .0001) (F = 167.5, P = .0001) in groups C and D compared to the control. Cannabis sativa impaired locomotor activity and spatial memory in B and D and D, respectively. All Cannabis sativa exposed groups demonstrated evidence of neurodegeneration in the exposed groups; GFAP immunoexpression was evident in all groups with a marked increase in group D. Conclusion: Cannabis sativa altered neurotransmitter levels, energy metabolism, locomotor, and exploratory activity, and spatial working memory, with neuronal degeneration as well as reactive astrogliosis in the PFC.

Anatomy Education in Nigeria: A Study of Program Curricula Toward Advancing Training and Improving Program Outcomes.

Background Anatomy education in this context refers to the training of anatomists particularly in the university or college setting with an emphasis on equipping them with skills to be biomedical researchers and scientists, educators, and providers of applied or allied health services. There has been a recurring call to carefully evaluate and scrutinize biomedical science programs in Nigerian universities. This study considered the anatomy curriculum in representative Nigerian institutions with an emphasis on their philosophy, program design, program objectives, and program contents among other considerations. Materials and methods Structured and validated questionnaires, electronic, were administered to collect quantitative and qualitative data from heads of the anatomy department in representative institutions. Head of anatomy departments in 11 representative institutions returned their properly completed questionnaires, representing over 60% return rate of the target representative institutions. Quantitative data sets were analyzed and presented as tables, charts, and figures. Qualitative data in the form of free responses were analyzed and presented based on themes. Results Degree programs, including bachelor's, master's, and doctorate degrees, are currently offered in respondents' universities. The curricula are generally robust in scope and depth of content as they address all the main domains of anatomy or anatomical sciences, especially gross anatomy, histology, embryology, neuroscience, and physical anthropology in many instances. The average duration for the bachelor's program (BSc) is 4 years, master's 2 years, and PhD (Doctor of Philosophy) 3-5 years. Analysis of the main methods of training indicated that the programs include significant coursework at every level as well as the main research project leading to the presentation of a dissertation or thesis. We also identified gaps in training, with emphasis on transferable skills, which must be addressed in line with modern realities in basic medical sciences. Conclusion  We consider it a necessity to equip graduates at all levels of training with competencies that are directly and clearly aligned with the roles that graduates of the program should play in workplaces. We, therefore, recommend that curricula be reviewed to emphasize competencies in scientific investigations, transferable skills, and science education. Specific cutting-edge skills and research methods should be included in alignment with overall program objectives and deliverables.

Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach.

The rapid spread of the Covid-19 outbreak led many countries to enforce precautionary measures such as complete lockdowns. These lifestyle-altering measures caused a significant increase in anxiety levels globally. For that reason, decision-makers are in dire need of methods to prevent potential public mental crises. Machine learning has shown its effectiveness in the early prediction of several diseases. Therefore, this study aims to classify two-class and three-class anxiety problems early by utilizing a dataset collected during the Covid-19 pandemic in Saudi Arabia. The data was collected from 3017 participants from all regions of the Kingdom via an online survey containing questions to identify factors influencing anxiety levels, followed by questions from the GAD-7, a screening tool for Generalized Anxiety Disorders. The prediction models were built using the Support Vector Machine classifier for its robust outcomes in medical-related data and the J48 Decision Tree for its interpretability and comprehensibility. Experimental results demonstrated promising results for the early classification of two-class and three-class anxiety problems. As for comparing Support Vector Machine and J48, the Support Vector Machine classifier outperformed the J48 Decision Tree by attaining a classification accuracy of 100%, precision of 1.0, recall of 1.0, and f-measure of 1.0 using 10 features.

Sentiment analysis for cruises in Saudi Arabia on social media platforms using machine learning algorithms.

Social media has great importance in the community for discussing many events and sharing them with others. The primary goal of this research is to study the quality of the sentiment analysis (SA) of impressions about Saudi cruises, as a first event, by creating datasets from three selected social media platforms (Instagram, Snapchat, and Twitter). The outcome of this study will help in understanding opinions of passengers and viewers about their first Saudi cruise experiences by analyzing their feelings from social media posts. After cleaning, this experiment contains 1200 samples. The data was classified into positive or negative classes using the choice of machine learning algorithms, such as multilayer perceptron (MLP), naive bayes (NB), random forest (RF), support vector machine (SVM), and voting. The results show the highest classification accuracy for the RF algorithm, as it achieved 100% accuracy with over-sampled data from Snapchat using both test options. The algorithms were compared among the three different datasets. All algorithms achieved a high level of accuracy. Hence, the results show that 80% of the sentiments were positive while 20% were negative.

Preemptive Diagnosis of Alzheimer's Disease in the Eastern Province of Saudi Arabia Using Computational Intelligence Techniques.

Alzheimer's Disease (AD) is a silent disease that causes the brain cells to die progressively, influencing consciousness, behavior, planning ability, and language to name a few. AD increases exponentially with aging, where it doubles every 5-6 years, causing profound implications, such as swallowing difficulties and losing the ability to speak before death. According to the Ministry of Health in Saudi Arabia, AD patients will triple by 2060 to reach 14 million patients worldwide. The rapid rise of patients is caused by the silent progress of the disease, leading to late diagnosis as the symptoms will not be distinguished from normal aging affect. Moreover, with the current medical capabilities, it is impossible to confirm AD with 100% certainty via specific medical examinations. The literature review revealed that most recent publications used images to diagnose AD, which is insufficient for local hospitals with limited imaging capabilities. Other studies that used clinical and demographical data failed to achieve adequate results. Consequently, this study aims to preemptively predict AD in Saudi Arabia by employing machine learning (ML) techniques. The dataset was acquired from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia, containing standard clinical tests for 152 patients. Four ML algorithms, namely, support vector machine (SVM), k-nearest neighbors (k-NN), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost), were employed to preemptively diagnose the disease. The empirical results demonstrated the robustness of SVM in the pre-emptive diagnosis of AD with accuracy, precision, recall, and area under the receiver operating characteristics (AUROC) of 95.56%, 94.70%, 97.78%, and 0.97, respectively, with 13 features after applying the sequential forward feature selection technique. This model can assist the medical staff in controlling the progression of the disease at low costs.

A Novel Ensemble-Based Technique for the Preemptive Diagnosis of Rheumatoid Arthritis Disease in the Eastern Province of Saudi Arabia Using Clinical Data.

Rheumatoid arthritis (RA) is a chronic inflammatory disease caused by numerous genetic and environmental factors leading to musculoskeletal system pain. RA may damage other tissues and organs, causing complications that severely reduce patients' quality of life. According to the World Health Organization (WHO), over 1.71 billion individuals worldwide had musculoskeletal problems in 2021. Rheumatologists face challenges in the early detection of RA since its symptoms are similar to other illnesses, and there is no definitive test to diagnose the disease. Accordingly, it is preferable to profit from the power of computational intelligence techniques that can identify hidden patterns to diagnose RA early. Although multiple studies were conducted to diagnose RA early, they showed unsatisfactory performance, with the highest accuracy of 87.5% using imaging data. Yet, imaging data requires diagnostic tools that are challenging to collect and examine and are more costly. Recent studies indicated that neither a blood test nor a physical finding could early confirm the diagnosis. Therefore, this study proposes a novel ensemble technique for the preemptive prediction of RA and investigates the possibility of diagnosing the disease using clinical data before the symptoms appear. Two datasets were obtained from King Fahad University Hospital (KFUH), Dammam, Saudi Arabia, including 446 patients, with 251 positive cases of RA and 195 negative cases of RA. Two experiments were conducted where the former was developed without upsampling the dataset, and the latter was carried out using an upsampled dataset. Multiple machine learning (ML) algorithms were utilized to assemble the novel voting ensemble, including support vector machine (SVM), logistic regression (LR), and adaptive boosting (Adaboost). The results indicated that clinical laboratory tests fed to the proposed voting ensemble technique could accurately diagnose RA preemptively with an accuracy, recall, and precision of 94.03%, 96.00%, and 93.51%, respectively, with 30 clinical features when utilizing the original data and sequential forward feature selection (SFFS) technique. It is concluded that deploying the proposed model in local hospitals can contribute to introducing a method that aids medical specialists in preemptively diagnosing RA and stopping or delaying the course using clinical laboratory tests.

Observable Protective Activities of Quercetin on Aluminum Chloride-Induced Testicular Toxicity in Adult Male Wistar Rat.

BACKGROUND: Aluminum chloride (AlCl3 ) present in many manufactured consumable is considered as a toxic element. AIM: Our study evaluates the toxic effects induced by AlCl3 on the testes as well as the therapeutic tendency of Quercetin (QUE) agent as an antioxidant. SETTING AND DESIGN: In the department of Anatomy of Medical School. METHODS AND MATERIALS: Thirty-two male Wistar rats weighing approximately 170 ± 10 g were assigned into four groups with eight each, fed with rat chow and water ad-libitum. Group A served as control and was given distilled water throughout; Group B was given only QUE (200 mg/kg body weight) for 21 days; Group C was given only AlCl3 (300 mg/kg body weight) for 14 days; and Group D was given AlCl3 (300 mg/kg body weight) for 14 days followed with QUE (200 mg/kg body weight) for 21 days. Substance administrations were done orally. STATISTICAL ANALYSIS: One-way analysis of variance was used to analyze the data, in GraphPad Prism 6.0 being the statistical software. RESULTS: AlCl3 significantly reduced the relative organ (testes) weight, correlating the decrease in sperm count, sperm motility and sperm viability. Furthermore, there was a decrease in luteinizing hormone with an increase in follicle-stimulating hormone which accounted for a significant reduction in testosterone level that plays a great role in spermatogenesis, following AlCl3 treatment. The cytoarchitecture of the testes showed degenerative changes in the seminiferous tubules and leydin cells, nitric oxide synthases immunoreactivity was intense in the seminiferous epithelium of rat in Group C. CONCLUSION: These suggest that QUE antioxidant property could reverse the decrease in sperm status, hormonal effects, and functional deficit induced by aluminum chloride on the testes of Wistar rats.

Early diagnosis of thyroid cancer diseases using computational intelligence techniques: A case study of a Saudi Arabian dataset.

In recent times, researchers have noticed that chronic diseases have become more common. In the Kingdom of Saudi Arabia, the number of patients with thyroid cancer (TC) has become a concern, necessitating a proactive system that can help cut down the incidence of this disease, where the system can assist in early interventions to prevent or cure the disease. In this paper, we introduce our work developing machine learning-based tools that can serve as early warning systems by detecting TC at very early stages (pre-symptomatic stage). In addition, we aimed at obtaining the greatest possible accuracy while using fewer features. It must be noted that while there have been past efforts to use machine learning in predicting TC, this is the first attempt using a Saudi Arabian dataset as well as targeting diagnosis in the pre-symptomatic stage (pre-emptive diagnosis). The techniques used in this work include random forest (RF), artificial neural network (ANN), support vector machine (SVM), and naïve Bayes (NB), each of which was selected for their unique capabilities. The highest accuracy rate obtained was 90.91% with the RF technique, while SVM, ANN, and NB achieved 84.09%, 88.64%, and 81.82% accuracy, respectively. These levels were obtained by using only seven features out of an available 15. Considering the pattern of the obtained results, it is clear that the RF technique is better and, hence, recommended for this specific problem.

Comorbidities and Risk Factors for Severe Outcomes in COVID-19 Patients in Saudi Arabia: A Retrospective Cohort Study.

PURPOSE: The first novel coronavirus disease-19 (COVID-19) case in the Kingdom of Saudi Arabia (KSA) was reported in Qatif in March 2020 with continual increase in infection and mortality rates since then. In this study, we aim to determine risk factors which effect severity and mortality rates in a cohort of hospitalized COVID-19 patients in KSA. METHOD: We reviewed medical records of hospitalized patients with confirmed COVID-19 positive results via reverse-transcriptase-polymerase-chain-reaction (RT-PCR) tests at Prince Mohammed Bin Abdulaziz Hospital, Riyadh between May and August 2020. Data were obtained for patient's demography, body mass index (BMI), and comorbidities. Additional data on patients that required intensive care unit (ICU) admission and clinical outcomes were recorded and analyzed with Python Pandas. RESULTS: A total of 565 COVID-19 positive patients were inducted in the study out of which, 63 (11.1%) patients died while 101 (17.9%) patients required ICU admission. Disease incidences were significantly higher in males and non-Saudi nationals. Patients with cardiovascular, respiratory, and renal diseases displayed significantly higher association with ICU admissions (p<0.001) while mortality rates were significantly higher in COVID-19 patients with cardiovascular, respiratory, renal and neurological diseases. Univariate cox proportional hazards regression model showed that COVID-19 positive patients requiring ICU admission [Hazard's ratio, HR=4.2 95% confidence interval, CI 2.5-7.2); p<0.001] with preexisting cardiovascular [HR=4.1 (CI 2.5-6.7); p<0.001] or respiratory [HR=4.0 (CI 2.0-8.1); p=0.010] diseases were at significantly higher risk for mortality among the positive patients. There were no significant differences in mortality rates or ICU admissions among males and females, and across different age groups, BMIs and nationalities. Hospitalized patients with cardiovascular comorbidity had the highest risk of death (HR=2.9, CI 1.7-5.0; p=0.020). CONCLUSION: Independent risk factors for critical outcomes among COVID-19 in KSA include cardiovascular, respiratory and renal comorbidities.

Perceptions of the COVID-19 vaccine and willingness to receive vaccination among health workers in Nigeria.

OBJECTIVE: The study aimed to examine health workers' perceptions of the coronavirus disease 2019 (COVID-19) vaccine in Nigeria and their willingness to receive the vaccine when it becomes available. METHODS: This multi-center cross-sectional study used non-probability convenience sampling to enroll 1,470 hospital workers aged 18 and above from 4 specialized hospitals. A structured and validated self-administered questionnaire was used for data collection. Data entry and analysis were conducted using IBM SPSS ver. 22.0. RESULTS: The mean age of respondents was 40±6 years. Only 53.5% of the health workers had positive perceptions of the COVID-19 vaccine, and only slightly more than half (55.5%) were willing to receive vaccination. Predictors of willingness to receive the COVID-19 vaccine included having a positive perception of the vaccine (adjusted odds ratio [AOR], 4.55; 95% confidence interval [CI], 3.50-5.69), perceiving a risk of contracting COVID-19 (AOR, 1.50; 95% CI, 1.25-3.98), having received tertiary education (AOR, 3.50; 95% CI, 1.40-6.86), and being a clinical health worker (AOR, 1.25; 95% CI, 1.01-1.68). CONCLUSION: Perceptions of the COVID-19 vaccine and willingness to receive the vaccine were sub-optimal among this group. Educational interventions to improve health workers' perceptions and attitudes toward the COVID-19 vaccine are needed.

Garcinia kola Attenuates MDMA-Induced Neuroinflammation in the CA1 Region of the Hippocampus in the Rat Model.

BACKGROUND: Garcinia kola (GK) has been experimentally tested for its potential usefulness against oxidative stress-related disorders in a number of body tissues, as well as a number of pathogenic and parasitic diseases. Studies investigating GK extracts' usefulness in combating nervous tissue toxicity, neuroinflammatory disorders, and neuronal degeneration are still inadequate and not yet conclusive. PURPOSE: To evaluate the effects of 3,4-methylenedioxymethamphetamine (MDMA)-induced neuroinflammation on the pyramidal neurons and astrocytes of the cornu ammonis 1 (CA1) region of the hippocampus and the role of GK extract (GKE) in attenuating the effects in the rat model. METHODS: The study was carried out by using 60 healthy adult male Wistar rats, which were randomly assigned into six groups, A, B, C, D, E, and F (n = 10)-A (control), B (100 mg/kg body weight of GKE only), C (200 mg/kg body weight of GKE only), D (20 mg/kg body weight of MDMA only), E (100 mg/kg body weight of GKE and 20 mg/kg body weight of MDMA), and F (200 mg/kg body weight of GKE and 20 mg/kg body weight of MDMA). Treatment was given for 21 days. Following 24 hours after the last administration, five rats in each group were anesthetized with diether and perfused intracardially, and the brains were excised and fixed in 10 percent neutral buffered formalin for the histological hematoxylin and eosin (H&E) and immunohistochemical glial fibrillary acidic proteins (GFAP). A thin-slice coronal section of the brain was obtained at the level of the optic chiasma and processed via the paraffin-embedding method. Also, the remaining five brains were used to assess neurotransmitter levels (serotonin and dopamine) and cytochrome c-oxidase. The statistical analysis was done using a one-way analysis of variance (ANOVA). RESULTS: A significant reduction (P < .05) in body weight was observed in the group that was administered with MDMA when compared with the control and the rest of the treated groups. Dopamine and serotonin levels were significantly decreased (P < .05) in the MDMA-only group when compared with the control and the rest of the treated groups. The control group and groups B, C, and F showed intact pyramidal neurons, unlike group D, which showed vacuolated and degenerating neurons. The expressions of vacuolation and degeneration in group D were less than those in group E, which received a low dose of GKE and MDMA.Hippocampal astrocytic expressions were significantly higher (P > .05) in the MDMA-only group when compared with the control and other groups. CONCLUSION: GKE has significant neuroprotective potential against MDMA-induced toxicity in brain tissue. This is evident in its prevention of MDMA-induced oxidative stress, pyramidal neuronal vacuolation, dispersion, and reactive astrogliosis in the CA1 region of the hippocampus. Our findings are dose-dependent, with 200 mg/kg of the extract being novel. We, however, recommend further study into the mechanism of action of GKE, on how it attenuates the astrocytic reaction caused by MDMA exposure.