Effectiveness of ultrasonography in the diagnosis of temporomandibular joint disorders: A systematic review and meta-analysis.

BACKGROUND: Temporomandibular disorders (TMDs) pose diagnostic challenges, and selecting appropriate imaging modalities is crucial for accurate assessment. This study aimed to compare the diagnostic accuracy and efficacy of ultrasonography (US) and magnetic resonance imaging (MRI) in identifying TMDs. METHODS: A comprehensive meta-analysis was conducted, including studies that compared US and MRI for TMJ disorder assessments. Fixed-effects models were utilized to calculate pooled odds ratios (ORs) and relative risks (RRs) with 95% confidence intervals (CIs). Heterogeneity was assessed using the chi-squared test and I2 statistic. Newcastle-Ottawa scale was used to assess the methodological quality of the studies included. RESULTS: Six studies were included, involving a total of 281 participants. The meta-analysis demonstrated that MRI was statistically somewhat better than US in identifying TMJ disorders. The summary OR was 0.64 (95% CI: 0.46-0.90), and the summary RR was 0.80 (95% CI: 0.68-0.95). Heterogeneity among the studies was low (χ2 = 2.73, df = 5, p = .74; I2 = 0%). Demographic variables revealed variations in sample size, gender ratio and mean age across the studies. CONCLUSION: This meta-analysis provides evidence that MRI may be more effective than US in diagnosing TMDs. However, the study is limited by the small number of included studies and variations in demographic variables and study designs. Future research with larger samples and standardised protocols is essential to confirm and strengthen these findings. Understanding the diagnostic accuracy of MRI and US for TMJ disorders will aid clinicians in making informed decisions for effective TMJ disorder assessments and patient management.

Arterial diameter ratio as a reliable predictor for upper limb steal syndrome in patients with arteriovenous fistula for hemodialysis.

OBJECTIVES: The aim of this study is to assess the association between the anastomosis diameter enlargement and steal syndrome incidence in patients with upper limb arteriovenous fistula using ratios as reliable predictors. MATERIAL AND METHODS: An analytical cross-sectional prospective study was conducted. A total of 49 patients with AVF hemodialysis access were recruited. Twenty-four participants with positive steal syndrome and 25 control were enrolled in the study. Anastomosis diameter, anastomosis diameter ratio, and volume flow ratio were measured ultrasonographically by two expert vascular sonographers. These clinical parameters were recorded and analyzed to assess the difference and association. Patient risk factors and steal syndrome association were emphasized. RESULTS: The study analysis indicates a strong association in the anastomosis diameter and anastomosis diameter ratio between steal and non-steal patients with a p-value ≤0.05. Additionally, there was a significant increase in the volume flow ratio in the patients with steal syndrome compared to the control group (p-value ≤0.05). There was a strong relationship between steal syndrome and the presence of peripheral arterial disease (73.9%, p = 0.001). The ICC index of absolute agreement between the two observers was ICC= 0.99 (95% CI 0.99-0.99, n = 10), indicating excellent agreement between observers. CONCLUSION: Anastomosis diameter and volume flow ratio strongly suggest that steal syndrome is associated with the increased diameter of anastomosis. Patients with a≥1.05 anastomosis diameter ratio have a greater risk of developing steal syndrome than those with an anastomosis ratio of ≤0.8. In addition, patients with a volume flow ratio ≥0.98 have an increased risk of developing steal syndrome than those with a volume flow ratio of ≤0.75.

Feedforward Artificial Neural Network-Based Model for Predicting the Removal of Phenolic Compounds from Water by Using Deep Eutectic Solvent-Functionalized CNTs.

In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10-5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99.

Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent.

Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.

A clean approach for functionalized carbon nanotubes by deep eutectic solvents and their performance in the adsorption of methyl orange from aqueous solution.

In this study two deep eutectic solvents (DESs) were prepared using ethylene glycol (EG) and two different ammonium-based salts. The potential of these DESs as novel agents for CNTs functionalization was examined by performing a comprehensive characterization study to identify the changes developing after the functionalization process. The impact of DESs was obvious by increasing the surface area of CNTs to reach 197.8 (m2/g), and by adding new functional groups to CNTs surface without causing any damage to the unique structure of CNTs. Moreover, CNTs functionalized with DESs were applied as new adsorbents for the removal of methyl orange (MO) from water. The adsorption conditions were optimized using RSM-CCD experimental design. The kinetics and the equilibrium adsorption data were analyzed using different kinetic and isotherm models. According to the regression results, adsorption kinetics data were well described by pseudo-second order model, whereas adsorption isotherm data were best represented by Langmuir isotherm model. The highest recorded maximum adsorption capacity (qmax) value was found to be 310.2 mg/g.

The formation of hybrid carbon nanomaterial by chemical vapor deposition: an efficient adsorbent for enhanced removal of methylene blue from aqueous solution.

In this study, carbon species were grown on the surface of Ni-impregnated powder activated carbon to form a novel hybrid carbon nanomaterial by chemical vapor deposition. The carbon nanomaterial was obtained by the precipitation of the methane elemental carbon atoms on the surface of the Ni catalyst. The physiochemical properties of the hybrid material were characterized to illustrate the successful growth of carbon species on the carbon substrate. The response surface methodology was used for the evaluation of adsorption parameters effect such as pH, adsorbent dose and contact time on the percentage removal of MB dye from aqueous solution. The optimum conditions were found to be pH = 11, adsorbent dose = 15 mg and contact time of 120 min. The material we prepared showed excellent removal efficiency of 96% for initial MB concentration of 50 mg/L. The adsorption of MB was described accurately by the pseudo-second-order model with R2 of 0.998 and qe of 163.93 (mg/g). The adsorption system showed the best agreement with Langmuir model with R2 of 0.989 and maximum adsorption capacity (Qm) of 250 mg/g.

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Profiling of microRNAs by next-generation sequencing: Potential biomarkers for diffuse large B-cell lymphoma.

Background: Lymphoma ranks fifth in prevalence among common cancer types worldwide. This lymphatic system cancer arises from T or B cells. Diffuse large B cell lymphomas (DLBCLs) are associated with most non-Hodgkin lymphomas. Non-coding microRNAs (miRNAs) greatly affect gene expression. A single miRNA can target numerous genes, thus largely influencing gene expression networks. MiRNAs can act as oncogenes or tumor suppressors in controlling DLBCL progression. This study investigated the roles of miRNAs in patients with DLBCL through next-generation sequencing, which was found to be sensitive, accurate, and robust. Methods: The study involved seven patients with DLBCLs and three controls at a hematology-oncology clinic. MiRNA was extracted from existing formalin-fixed, paraffin-embedded (FFPE) tissue specimens. Illumina next-generation sequencing was used to sequence samples for miRNA profiling. Results: Samples from patients showed expression of various hsa-mir miRNAs (1248, 3607, 21, 142, 1244, 182, 6516, 766, 1291, 4449, and 181a), whereas those from healthy individuals showed expression of hsa-mir 1248, 3607, 21, 142, and 877. Hsa-mir-877-3p is known to target multiple genes, and miRNAs such as hsa-mir-877-3p, hsa-mir-1291, and hsa-mir-181a-5p interact primarily with target genes. Conclusions: MiRNA profiling in FFPE tissues from patients with DLBCL suggested that miRNA levels can distinguish patients with DLBCL from controls, and therefore may provide prognostic or diagnostic biomarkers for DLBCL. Altered genes and miRNAs may also be potential therapeutic targets.

Development and characterization of three novel mouse monoclonal antibodies targeting spike protein S1 subunit of Middle East respiratory syndrome corona virus.

BACKGROUND: Middle East Respiratory Syndrome Coronavirus is a highly pathogenic virus that poses a significant threat to public health. OBJECTIVE: The purpose of this study is to develop and characterize novel mouse monoclonal antibodies targeting the spike protein S1 subunit of the Middle East Respiratory Syndrome Corona Virus (MERS-CoV). METHODS: In this study, three mouse monoclonal antibodies (mAbs) against MERS-CoV were generated and characterized using hybridoma technology. The mAbs were evaluated for their reactivity and neutralization activity. The mAbs were generated through hybridoma technology by the fusion of myeloma cells and spleen cells from MERS-CoV-S1 immunized mice. The resulting hybridomas were screened for antibody production using enzyme-linked immunosorbent assays (ELISA). RESULTS: ELISA results demonstrated that all three mAbs exhibited strong reactivity against the MERS-CoV S1-antigen. Similarly, dot-ELISA revealed their ability to specifically recognize viral components, indicating their potential for diagnostic applications. Under non-denaturing conditions, Western blot showed the mAbs to have robust reactivity against a specific band at 116 KDa, corresponding to a putative MERS-CoV S1-antigen. However, no reactive bands were observed under denaturing conditions, suggesting that the antibodies recognize conformational epitopes. The neutralization assay showed no in vitro reactivity against MERS-CoV. CONCLUSION: This study successfully generated three mouse monoclonal antibodies against MERS-CoV using hybridoma technology. The antibodies exhibited strong reactivity against MERS-CoV antigens using ELISA and dot ELISA assays. Taken together, these findings highlight the significance of these mAbs for potential use as valuable tools for MERS-CoV research and diagnosis (community and field-based surveillance and viral antigen detection).

Development of a T-ARMS-PCR Assay for Detecting Genetic Polymorphism in the Catalase (rs7943316) Gene in the Iraqi Population with Breast Cancer.

Numerous investigations have demonstrated that oxidative stress is markedly increased in breast cancer patients compared to their healthy counterparts. Catalase (CAT), a crucial antioxidant enzyme, plays a pivotal role in safeguarding cells against oxidative damage initiated by reactive oxygen species (ROS). The CAT (rs7943316) gene encodes catalase, and certain genetic variations in this gene have been observed to modify catalase activity and levels. Such changes can lead to an altered response to oxidative stress, potentially increasing the risk of breast cancer. In light of this, a novel tetra-primer amplification-refractory mutation system (T-ARMS)-PCR assay was developed to investigate the possible correlation between the CAT (rs7943316) gene polymorphism and the development of breast cancer in patients. This method employs a one-step PCR, which is faster, more cost-effective, and more precise than existing techniques. Sanger sequencing was performed to validate the accuracy of our findings. The T-ARMS-PCR assay revealed a significant association between the A/T allele of the CAT (rs7943316) gene and breast cancer. Specifically, individuals with the TT genotype had a higher risk of developing breast cancer than those with the AA genotype. The T allele frequency was greater among breast cancer patients than in the control group, and genotype frequencies were consistent with the principles of the Hardy-Weinberg Equilibrium. This study is the first to showcase a rapid, cost-effective, and high-throughput method for detecting the SNP in the CAT (rs7943316) gene. This method has the potential to be employed in large-scale clinical trials.

Detection and diagnosis of posterior circulation calcification: An interobserver agreement study.

Background: Posterior cerebral ischemic stroke and its underlying causes can be easily misdiagnosed in routine practice. Therefore, more than a third of positive cases can be easily missed during routine CT image reporting unless expert neuroimaging radiologists carefully report it. Objective: To assess the inter-rater agreement level between senior residents and general radiologists and a specialized expert neuroradiologist when detecting and diagnosing posterior cerebral circulation calcification. Methods: This was a cross-sectional observational study. A total of fifteen senior radiology residents (SRRs) and fifteen general radiologists (GRs) at four different hospitals in Riyadh, Saudi Arabia, were included in the study. A total of four CT-scanned brain cases with the presence of posterior circulation calcification (PCC) with different degrees of severity and one brain case with negative PCC were selected. These cases were predefined by expert neuroradiologists at our center. The cases were uploaded into the picture archiving and communication systems (PACS) at four different centers as outsider cases. These cases were then randomly assigned to the participating SRRs and GRs for reporting. All radiologists were blinded to the findings of the cases. Inter-observer agreement was assessed using the weighted kappa coefficient of agreement (k) between the two groups. Results: The cerebral calcification misdiagnosis rate for the SRRs and GRs was > 93% for most of the positive cases. There was 1) poor inter-observer agreement between the SRRs and GRs for the detection of severe posterior cerebral calcification(PCC) in a negative stroke case (agreement for misdiagnosis, k = 0.93; correct diagnosis, k = 0.00), 2) poor inter-observer agreement for mild PCC in a negative stroke case (agreement for misdiagnosis, k = 0.93; correct diagnosis, k = 0.00), 3) moderate PCC in a positive posterior stroke case (agreement for misdiagnosis, k = 0.92; correct diagnosis, k = 0.00), and 4) poor interobserver agreement for severe PCC in a positive posterior cerebral stroke case (agreement for misdiagnosis, k = 0.846; correct diagnosis, k = 0.00). There was excellent agreement between the SRRs and GRs when reporting negative cases of PCC and stroke. Conclusion: Our study concluded that most of the SRRs and GRs missed the diagnosis of posterior cerebral calcification in the presented cases.

Metabolomics: A New Era in the Diagnosis or Prognosis of B-Cell Non-Hodgkin's Lymphoma.

A wide range of histological as well as clinical properties are exhibited by B-cell non-Hodgkin's lymphomas. These properties could make the diagnostics process complicated. The diagnosis of lymphomas at an initial stage is essential because early remedial actions taken against destructive subtypes are commonly deliberated as successful and restorative. Therefore, better protective action is needed to improve the condition of those patients who are extensively affected by cancer when diagnosed for the first time. The development of new and efficient methods for early detection of cancer has become crucial nowadays. Biomarkers are urgently needed for diagnosing B-cell non-Hodgkin's lymphoma and assessing the severity of the disease and its prognosis. New possibilities are now open for diagnosing cancer with the help of metabolomics. The study of all the metabolites synthesised in the human body is called "metabolomics." A patient's phenotype is directly linked with metabolomics, which can help in providing some clinically beneficial biomarkers and is applied in the diagnostics of B-cell non-Hodgkin's lymphoma. In cancer research, it can analyse the cancerous metabolome to identify the metabolic biomarkers. This review provides an understanding of B-cell non-Hodgkin's lymphoma metabolism and its applications in medical diagnostics. A description of the workflow based on metabolomics is also provided, along with the benefits and drawbacks of various techniques. The use of predictive metabolic biomarkers for the diagnosis and prognosis of B-cell non-Hodgkin's lymphoma is also explored. Thus, we can say that abnormalities related to metabolic processes can occur in a vast range of B-cell non-Hodgkin's lymphomas. The metabolic biomarkers could only be discovered and identified as innovative therapeutic objects if we explored and researched them. In the near future, the innovations involving metabolomics could prove fruitful for predicting outcomes and bringing out novel remedial approaches.

Prevalence of elastofibroma dorsi found incidentally upon chest computed tomography scan: A tertiary care center experience.

OBJECTIVES: To determine the prevalence and demographic data regarding incidentally encountered elastofibroma dorsi (EFD) in the Saudi population-based on chest CT (computed tomography) scans. METHODS: This retrospective study was carried out on 4,435 chest CT examinations that were collected between January 2014 and December 2016 in Riyadh, Saudi Arabia. One musculoskeletal radiologist who was unaware of the patients' clinical presentations or reasons for CT study reviewed the 4,435 chest CTs prospectively, searching for soft tissue tumors in the subscapular region. RESULTS: Elastofibroma dorsi was incidentally confirmed via CT scan in 36 of the 4,435 patients, including 8 males and 28 females, with a mean age of 76.6 years, yielding a prevalence of 0.8%. Elastofibroma dorsi was more common in females, with a 3.3-fold higher prevalence (77.8%) as compared to males (22.2%); however, this difference was not statistically significant (p>0.05). Twenty-five (69.44%) patients had unilateral EFD and 11 (30.56%) had bilateral EFD. Right-sided EFD was encountered in 60% of females and 50% of males. Furthermore, there was no statistically significant correlation between the size of EFD and the patient's gender or age (p>0.05). CONCLUSION: Elastofibroma dorsi is a rare but uncommon incidental finding on routine chest CT examination encountered in 0.8% of elderly Saudi patients.

Job Satisfaction of Radiographers in Saudi Arabia.

PURPOSE: To examine the various facets of job satisfaction among radiographers in Saudi Arabia and compare the influential contributing factors locally and internationally. METHODS: A cross-sectional survey was conducted in June and July 2020 with an electronic questionnaire distributed to radiographers in Saudi Arabia. The validated Job Satisfaction Survey was used to measure overall job satisfaction and specific satisfaction regarding pay, promotions, supervision, benefits, contingent rewards, operating conditions, coworkers, nature of work, and communication (total Job Satisfaction Survey score can range from 36 to 216). Additional personal and work-related characteristics were collected as explanatory variables. Data were analyzed with descriptive and inferential statistics, including 1-way analysis of variance, independent sample t test, and Spearman correlation. RESULTS: A total of 412 radiographers in Saudi Arabia completed the survey. The total job satisfaction score was 140.0 (95% confidence interval [CI], 138.7-141.2), showing that 6 respondents (1.5%) were dissatisfied with their job, while the remaining majority were neutral (63%) or satisfied (36%). The radiographers were satisfied with the nature of work, contingent rewards, and coworkers. However, they were less satisfied with promotions, pay, operating conditions, and benefits. The only significant variable in overall job satisfaction was the working sector (P = .03), with radiographers working at Ministry of Health hospitals who were more satisfied than were their colleagues who worked at other types of facilities. DISCUSSION: The radiographers in Saudi Arabia had higher overall job satisfaction than had other allied health science professionals in Saudi Arabia. The differences in job satisfaction subscales between radiographers from different countries suggest that their job satisfaction likely is related to culture and socioeconomics. Job satisfaction improvement programs should be tailored for each radiographer's community. CONCLUSION: Radiographers in Saudi Arabia expressed moderate to high job satisfaction, which is relatively higher than their international peers and national colleagues in physical therapy and nursing. These job satisfaction results call for governmental policymakers to focus their efforts on improving radiographer promotion schemes and career development.

The incidence of deltoid tear among patients with full-thickness rotator cuff tear.

Backgroun: Full-thickness rotator cuff tear is common in the older population. The incidence of traumatic deltoid tears post-surgery is well addressed. However, non-traumatic spontaneous injury is not well recognized despite a few case reports and previous studies. The aim of the study is to determine the incidence and association of deltoid tear among patients with non-traumatic full-thickness rotator cuff tear using shoulder magnetic resonance imaging. Methods: A retrospective cross-sectional study was conducted of 271 shoulders magnetic resonance imaging examinations with full-thickness rotator cuff tear between 2012 and 2022. The analyzed variables were full-thickness rotator cuff tear size, tear grading (small, medium, large, and massive), muscle fatty degeneration, and deltoid tear. Acromio-humeral interval was also recorded and analyzed on the anteroposterior projection of shoulder radiographs. Results: The incidence of deltoid tear was 7% (19 cases), encountered in eleven females (6.4%) and eight males (8%) with a mean age of 65 years. Deltoid tears were located on the right side in fifteen patients (9.4%) and on the left side in four patients (3.6%). The Man-Whitney U test indicated a significant association between deltoid tears and full-thickness rotator cuff tear, P < 0.001. The deltoid tear was more notably associated with large and massive full-thickness rotator cuff tear (16.7% and 42.3%, respectively), P < 0.001. Acromio-humeral interval showed a significant difference between the deltoid and non-deltoid cases, P = 0.045. Conclusion: The incidence and association of deltoid tears with full-thickness rotator cuff tear with no prior surgical intervention or traumatic insults were considered significant, with a positive impact of large and massive tear size and association of muscle fatty degeneration. This association is statistically significant and should be adequately evaluated by the radiologist.

Metabolic Biomarkers in B-Cell Lymphomas for Early Diagnosis and Prediction, as Well as Their Influence on Prognosis and Treatment.

B-cell lymphomas exhibit a vast variety of clinical and histological characteristics that might complicate the diagnosis. Timely diagnosis is crucial, as treatments for aggressive subtypes are considered successful and frequently curative, whereas indolent B-cell lymphomas are incurable and often need several therapies. The purpose of this review is to explore the current advancements achieved in B-cell lymphomas metabolism and how these indicators help to early detect metabolic changes in B-cell lymphomas and the use of predictive biological markers in refractory or relapsed disease. Since the year 1920, the Warburg effect has been known as an integral part of metabolic reprogramming. Compared to normal cells, cancerous cells require more glucose. These cancer cells undergo aerobic glycolysis instead of oxidative phosphorylation to metabolize glucose and form lactate as an end product. With the help of these metabolic alterations, a novel biomass is generated by the formation of various precursors. An aggressive metabolic phenotype is an aerobic glycolysis that has the advantage of producing high-rate ATP and preparing the biomass for the amino acid, as well as fatty acid, synthesis needed for a rapid proliferation of cells, while aerobic glycolysis is commonly thought to be the dominant metabolism in cancer cells. Later on, many metabolic biomarkers, such as increased levels of lactate dehydrogenase (LDH), plasma lactate, and deficiency of thiamine in B-cell lymphoma patients, were discovered. Various kinds of molecules can be used as biomarkers, such as genes, proteins, or hormones, because they all refer to body health. Here, we focus only on significant metabolic biomarkers in B-cell lymphomas. In conclusion, many metabolic biomarkers have been shown to have clinical validity, but many others have not been subjected to extensive testing to demonstrate their clinical usefulness in B-cell lymphoma. Furthermore, they play an essential role in the discovery of new therapeutic targets.

Assessment of breast dose and cancer risk for young females during CT chest and abdomen examinations.

CT scanning deliver much higher radiation doses than planar radiological procedures, which puts patients to high risks. This study measures and evaluates patient doses during chest and abdomen computed tomography procedures. Particular attention is given to measuring the dose to the equivalent breast (mSv) and to estimate the associated risks of breast cancer to young female patients (15-35 years). Data was obtained from standard examinations from three hospitals. The measured values of CT dose indexes, CTDI (mGy) as well as exposure-related parameters were used for assessment. Breast and effective doses were extrapolated using a software. The results showed remarkable variations of the mean organ equivalent doses for similar CT examinations in the studied hospitals. This could be attributed to the variation in CT scanning imaging technique, and clinical indications. The average effective dose to the chest was 7.9 mSv (2.3-47.0 mSv) and for the abdomen the mean dose was 6.6 mSv, ranging from (3.3-27 mSv). The breast received equivalent doses from chest and abdomen procedures as follows: 10.2 (1.6-33 mSv) and 10.1(2.3-19 mS) Sv respectively. Each procedure yielded high risks of breast cancer for young females. Implementation of accurate referral criteria is recommended to avoid unnecessary breast radiation exposure.

Assessment of Patient Knowledge Level Towards MRI Safety Before the Scanning in Saudi Arabia.

Purpose: Magnetic resonance imaging (MRI) is an important diagnostic method in modern clinical medicine. Patients' knowledge about MRI is of utmost importance for optimizing the workflow, safety, and patient comfort and saving valuable time for the MRI department. This study investigates patient knowledge levels regarding MRI safety before an MRI examination. Patients and Methods: A cross-sectional survey was completed by 200 patients who required MRI. Recruitment occurred over eight weeks in governmental and private hospitals in Saudi Arabia; both hospitals and participants were selected randomly. The questionnaire was given to the patients prior to their MRI scans. Descriptive statistics and chi-square tests were performed to identify relationships between knowledge of MRI safety and selected demographic groupings (age, gender, education level, income level). It also evaluates their source of information and assesses the patient's previous experience regarding MRI scans. . Results: The study sample consisted of 200 patients from 230 surveys distributed in Saudi Arabia, and the response rate was ~ 87%. Depending on the P-values of the chi-square statistical test to find the relationship between socio-demographic factors and patient' knowledge level, the results showed a significant association (p-value=0.006) between age and gender and the level of knowledge relating to MRI safety issues. A similar significance (p-value=0.042) is observed between the knowledge level of patients and their educational level and annual income. In addition, a highly significant association (p-value<0.001) is found between education level or previous experience and whether people seek information about MRI safety. Conclusion: The study findings suggest that a complex combination of factors affects patient knowledge regarding MRI safety before an MRI examination. Hence, the hospital and radiological department must provide the patient with accurate information about MRI. .

Data-driven models for atmospheric air temperature forecasting at a continental climate region.

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels' U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models' efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.

A literature perspective on the pharmacological applications of yohimbine.

Introduction: Phytochemicals have garnered much attention because they are useful in managing several human diseases. Yohimbine is one such phytochemical with significant pharmacological potential and could be exploited for research by medicinal chemists. It is an indole alkaloid obtained from various natural/synthetic sources.Aims and Results: The research on yohimbine started early, and its use as a stimulant and aphrodisiac by humans has been reported for a long time. The pharmacological activity of yohimbine is mediated by the combined action of the central and peripheral nervous systems. It selectively blocks the pre and postsynaptic α2-adrenergic receptors and has a moderate affinity for α1 and α2 subtypes. Yohimbine also binds to other behaviourally relevant monoaminergic receptors in the following order: α-2 NE > 5HT-1A>, 5HT-1B > 1-D > D3 > D2 receptors.Conclusion: The current review highlights some significant findings that contribute to developing yohimbine-based drugs. It also highlights the therapeutic potential of yohimbine against selected human diseases. However, further research is recommended on the pharmacokinetics, molecular mechanisms, and drug safety requirements using well-designed randomized clinical trials to produce yohimbine as a pharmaceutical agent for human use.Key MessagesYohimbine is a natural indole alkaloid with significant pharmacological potential.Humans have used it as a stimulant and aphrodisiac from a relatively early time.It blocks the pre- and postsynaptic α2-adrenergic receptors that could be exploited for managing erectile dysfunction, myocardial dysfunction, inflammatory disorders, and cancer.