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Background. Glioma is the most common fatal malignant tumor of the CNS. Early detection of glioma grades based on diffusion-weighted imaging (DWI) properties is considered one of the most recent noninvasive promising tools in the assessment of glioma grade and could be helpful in monitoring patient prognosis and response to therapy. Aim. This study aimed to investigate the accuracy of DWI at both standard and high b values (b = 1000 s/mm2 and b = 3000 s/mm2) to distinguish high-grade glioma (HGG) from low-grade glioma (LGG) in clinical practice based on histopathological results. Materials and Methods. Twenty-three patients with glioma had DWI at l.5 T MR using two different b values (b = 1000 s/mm2 and b = 3000 s/mm2) at Al-Shifa Medical Complex after obtaining ethical and administrative approvals, and data were collected from March 2019 to March 2020. Minimum, maximum, and mean of apparent diffusion coefficient (ADC) values were measured through drawing region of interest (ROI) on a solid part at ADC maps. Data were analyzed by using the MedCalc analysis program, version 19.0.4, receiver operating characteristic (ROC) curve analysis was done, and optimal cutoff values for grading gliomas were determined. Sensitivity and specificity were also calculated. Results. The obtained results showed the ADCmean, ADCratio, ADCmax, and ADCmin were performed to differentiate between LGG and HGG at both standard and high b values. Moreover, ADC values were inversely proportional to glioma grade, and these differences are more obvious at high b value. Minimum ADC values using standard b value were 1.13 ± 0.17 × 10−3 mm2/s, 0.89 ± 0.85 × 10−3 mm2/s, and 0.82 ± 0.17 × 10−3 mm2/s for grades II, III, and IV, respectively. Concerning high b value, ADCmin values were 0.76 ± 0.07 × 10−3 mm2/s, 0.61 ± 0.01 × 10−3 mm2/s, and 0.48 ± 0.07 × 10−3 mm2/s for grades II, III, and IV, respectively. ADC values were inversely correlated with results of glioma grades, and the correlation was stronger at ADC3000 (r = −0.722, P≤0.001). The ADC3000 achieved the highest diagnostic accuracy with an area under the curve (AUC) of 0.618, 100% sensitivity, 85.7% specificity, and 85.7% accuracy for glioma grading at a cutoff point of ≤0.618 × 10−3 mm2/s. The high b value showed stronger agreement with histopathology compared with standard b value results (k = 0.89 and 0.79), respectively. Conclusion. The ADC values decrease with an increase in tumor cellularity. Meanwhile, high b value provides better tissue contrast by reflecting more tissue diffusivity. Therefore, ADC-derived parameters at high b value are more useful in the grading of glioma than those obtained at standard b value. They might be a better surrogate imaging sequence in the preoperative evaluation of gliomas.

COVID-19 is a global pandemic viral infection that has affected millions worldwide. Limited data is available on the effect of COVID-19 on hematological parameters in Saudi Arabia. This study is aimed at examining the role of hematological parameters among COVID-19 patients admitted to King Khalid Hospital in Najran, Saudi Arabia. This is a retrospective, hospital-based study of 514 cases who were recruited during August to October 2020. 257 COVID-19 patients formed the study group, and a further 257 negative subjects formed the control group. Anemia was significantly elevated in positive subjects over controls (respectively, 64.2% and 35.8%), with patients 2.5 times more likely to be anemic ( p < 0.01 ). Thrombocytopenia was higher in patients over controls (respectively, 62% and 38%), with patients ~1.7 times more likely to be thrombocytopenic ( p < 0.01 ). Moreover, leukopenia was significantly higher in patients over controls (respectively, 71% and 29%), with positive subjects ~2.6 times more likely to be leukopenic. Our study results indicate that mild anemia associated with leukopenia may have diagnostic value for COVID-19. Careful assessment of hematological parameters, at baseline and throughout the disease path, will assist physicians in formulating personalized approaches to treatment and promptly offer intensive care to those in greater need.

The increase of security threats and hacking the computer networks are one of the most dangerous issues should treat in these days. Intrusion Detection Systems (IDSs), are the most appropriate methods to prevent and detect the attacks of networks and computer systems. This study presents several techniques to discover network anomalies using data mining tasks, Machine learning technology and dependence of artificial intelligence techniques. In this research, the smart hybrid model was developed to explore any penetrations inside the network. The model divides into two basic stages. The first stage includes the Genetic Algorithm (GA) in selecting the characteristics with depends on a process of extracting, Discretize And dimensionality reduction through Proportional K-Interval Discretization (PKID) and Fisher Linear Discriminant Analysis (FLDA) on respectively. At the end of the first stage combining Naïve Bayes classifier (NB) and Decision Table (DT) using NSL-KDD data set divided into two separate groups for training and testing. The second stage completely depends on the first stage outputs (predicted class) and reclassified with multilayer perceptrons using Deep Learning4J (DL) and the use of algorithm Stochastic Gradient Descent (SGD). In order to improve the performance in terms of the accuracy in classification of penetrations, raising the average of discovering and reducing the false alarms. The comparison of the proposed model and conventional models show the superiority of the proposed model and the previous conventional hybrid models. The result of the proposed model is 99.9325 of classification accuracy, the rate of detection is 99.9738 and 0.00093 of false alarms

In health care facilities, pharmaceutical waste is generally discharged down the drain or sent to landfill. Poor knowledge about their potential downstream impacts may be a primary factor for improper disposal behavior. The objective of this study was to determine the impact of an intervention program on knowledge and practice of health care staff regarding pharmaceutical waste management. The study was designed as a pre/posttest intervention study. Total sample size was 530 in the pre-intervention phase, and then a subsample of 69 individuals was selected for the intervention and the post-intervention phases. Paired-sample t test was used to assess the difference between pretest and follow-up test results. A statistically significant improvement in knowledge and practice was achieved (P < 0.001). Poor knowledge and poor practice levels (scores <50%) were found to improve to satisfactory levels (scores ≥75%). Therefore, educational programs could be considered as an effective tool for changing health care staff practice in pharmaceutical waste management.Implications: In health care facilities, pharmaceutical waste is generally discharged down the drain or sent to landfill. A lack of knowledge about the potential impacts of this type of waste may be a leading factor in improper disposal behavior. Following an educational program, statistically significant improvement in knowledge and practice of health care staff as regards to pharmaceutical waste management (PWM) was achieved. It is thus recommended that authorities implement training-of-trainers (TOT) programs to educate health care staff on PWM and organize refreshment workshops regularly.PAPER HISTORY