13 research outputs found
Impact of Isotretinoin on Blood Lipids and Liver Enzymes: A Retrospective Cohort Study in Saudi Arabia
No full textAbdullah A Alrasheed,1,2 Khalid F Alsadhan,1,2 Nawaf F Alfawzan,2 Nasser M AbuDujain,2 Ali H Alnasser,3 Hisham Almousa3 1Family and Community Medicine Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 2Family and Community Medicine Department, King Saud University Medical City, Riyadh, Saudi Arabia; 3College of Medicine, King Saud University, Riyadh, Saudi ArabiaCorrespondence: Abdullah A Alrasheed, Tel +966114670836, Email [email protected]: Isotretinoin is an effective treatment for acne but can cause side effects such as changes in blood lipids and liver enzymes. Laboratory monitoring is essential during treatment, but there is variation in monitoring practices.Aim: This study aims to investigate the relationship between isotretinoin therapy and its effects on complete blood count in Saudi Arabia to improve patient outcomes.Methods: The study was a retrospective cohort study conducted at King Khalid University Hospital in Riyadh, Saudi Arabia, between January 2016 and December 2020. Following the inclusion and exclusion criteria, 515 patients were randomly selected for the study. The data was analyzed using SPSS, and descriptive statistics and paired samples t-tests were employed to analyze the data.Results: In this study, 515 patients were enrolled. Of these participants, 76.7% (n=395) were females and 23.3% (n=120) were males. The mean age of the study participants was 23.98± 7.4 years and ranged between 16 and 65 years. The mean dose of Isotretinoin administered was 27.65± 9.6 mg/day, with a range of 10– 60 mg/day. The mean BMI of the study participants was 24.3± 4.1 kg/m2, ranging from 14.3 to 44.8 kg/m2. Regarding the effect of Isotretinoin on laboratory measures, significant statistical differences were found in hemoglobin measurements (t=− 3.379, p=0.001), platelets (t=− 3.169, p=0.002), neutrophils (%) (t=3.107, p=0.002), total cholesterol (t=− 13.017, p=0.000), AST (t=− 6.353, p=0.000), ALT (t=− 4.352, p=0.000), HDL (t=2.446, p=0.015), and LDL (t=− 12.943, p=0.000). However, there were no significant statistical differences in the measurements of WBC, neutrophils (count), or triglycerides. In the Chi-square analysis and Fisher’s Exact test to identify the interaction between BMI, dose, and gender on abnormal lab results, significant interaction was found between participants’ BMI and abnormal HDL measurements (p=0.006). Furthermore, there were significant interactions between Isotretinoin dose (either less than 30 mg/day or 30 mg/day or more) and abnormal neutrophil count (p=0.04), abnormal HDL measurements (p=0.010), and abnormal triglycerides measurements (p=0.020). Moreover, a statistically significant interaction was found between participants’ gender and abnormal hemoglobin measurements (p=0.006), abnormal total cholesterol (p=0.016), abnormal AST measurements (p=0.001), abnormal ALT measurements (p=0.000), abnormal HDL measurements (p=0.000), and abnormal triglycerides measurements (p=0.007).Conclusion: In conclusion, the study found that isotretinoin therapy has significant effects on several laboratory measures, including hemoglobin, platelets, neutrophils, total cholesterol, AST, ALT, HDL, and LDL. The study also revealed significant interactions between BMI, dose, gender, and abnormal lab results.Keywords: Isotretinoin, acne treatment, laboratory monitoring, blood lipids, liver enzymes, Saudi Arabi
Self-perceived halitosis and related factors among adults residing in Riyadh, Saudi Arabia. A cross sectional study
No full textAbstractObjectivesThis cross-sectional observational study was conducted to determine the prevalence of self-perceived halitosis among adults in Riyadh, Saudi Arabia and to assess the relation of halitosis with some socio-demographic factors, oral habits and health practices.Materials and methodsA questionnaire was distributed to randomly selected subjects including senior high school students, college students and employees working in governmental offices. High schools and governmental offices were selected using systematic random sampling from each of the main five regions of Riyadh. The college students were selected from the major universities in Riyadh. One hundred questionnaires were randomly distributed in each of the 15 locations for males and 15 for females (5 schools, 5 universities and 5 governmental offices for each gender) giving a total of 3000 questionnaires.ResultsThe prevalence of self-perceived halitosis was 22.8% among the participants. The majority of the subjects with self-perceived halitosis experienced bad breath on waking up (83.5%). Nearly half of the sample with self-perceived halitosis was told by others that they had bad breath, 25.8% visited a doctor regarding that, 23.8% received treatment for their bad breath and 54.1% made trials to control their problem by using some aids. Self-perceived halitosis was found to be more prevalent among males compared to females (P<0.000), whereas, no statistically significant differences were found among the different age groups (P=0.317). A statistically significant relationship was found between self-perceived halitosis and times of mouth cleaning, use of tooth brush, use of tooth paste, tongue cleaning (P<0.000), and the use of dental floss (P=0.004). A statistically significant relationship was also found between self-perceived halitosis and shisha (P<0.000) and cigarette smoking (P=0.045).ConclusionThe prevalence of self-perceived halitosis among the population in Riyadh is within the range reported in other countries. Self-perceived halitosis is related to gender, inadequate oral hygiene practices and cigarettes and shisha smoking however, it is not related to age
Simultaneous Classification and Regression for Zakat Under-Reporting Detection
No full textTax revenue represents an essential budget source for most countries around the world. Accordingly, the modernization of relevant technological infrastructure has become a key factor of tax administration strategy for improving tax collection efficiency. In particular, the fiscal consolidation of the Kingdom of Saudi Arabia has been supported by considerable development in tax policy and administration, aimed at raising more taxes from non-oil activities. In fact, non-Saudi investors are liable for income tax in Saudi Arabia. On the other hand, Saudi citizen investors (and citizens of the GCC countries) are liable for Zakat, an Islamic assessment. Typically, taxpayers are in charge of preparing and accurately reporting their Zakat declaration. This allows tax authorities to overview and audit their business activities. However, despite administration efforts to increase taxpayer compliance, considerable revenue remains at under-reporting risk. In this paper, we introduce a novel intelligent approach to support tax authority efforts in detecting under-reporting among Zakat payer declarations. In particular, the proposed solution aims at improving detection accuracy and determining the fraud cases that correspond to a higher revenue at risk. Specifically, we formulate Zakat under-reporting detection as a supervised machine learning task through the design of a deep neural network that performs simultaneous classification and regression tasks. In particular, the proposed network contains an input layer, five hidden layers, and two output layers for classification and regression. Zakat declarations are mapped into the predefined “under-reporting” or “actual declaration” classes. Moreover, the revenue at risk caused by the predicted fraud cases is learned by the designed model. This allows the proposed approach to prioritize the auditing of specific Zakat payers based on the corresponding predicted revenue at risk. A real dataset including 51,919 Zakat declarations was used to validate and assess the designed model. Further, the Synthetic Minority Oversampling Technique (SMOTE) boosted the proposed model performance in terms of classification and prioritization
Discovering structure in Islamist postings using systemic nets
No full text© 2016 IEEE. Textual analytics based on representations of documents as bags of words has been extremely successful. However, analysis that requires deeper insight into language, into author properties, or into the contexts in which documents were created requires a richer representation. Systemic nets are one such representation. The jihadist groups AQAP, ISIS, and the Taliban have all produced English magazines designed to influence Western sympathizers. Using a model of jihadi language, we construct a systemic functional net for these magazines, and contrast the structures revealed by clustering using words versus clustering using the choices implicit in systemic functional nets. We then show that the systemic functional net derived from the magazines is consistent with the structure present in two Islamist forums, and therefore reveals two different mindsets, one that is political and another that is religious, that seem widely held within the relevant communities
SCARS-LOGISTIC: A novel variable selection approach for binary classification model to identify the significant determinants of sexually transmitted infections.
No full textVariable selection methods are very popular, especially in the field of big data with large predictors. These procedures improve the accuracy and performance of the model by eliminating irrelevant and redundant variables. The main contribution of this study is to couple a logit model with a novel variable selection approach, "Stability Competitive Adaptive Re-weighted Sampling" to address binary response. The efficiency of the proposed method is compared with the traditional logistic regression model based on eight model assessment criteria over real data from sexually transmitted infections in Indian men. Due to higher stability, the proposed method outperformed having a lower Akaike information criterion, and the Bayesian information criterion, as well as higher R-squared measures. The finally selected proposed model identified essential information regarding sexually transmitted infections in India for policymakers
SCARS-LOGISTIC: A novel variable selection approach for binary classification model to identify the significant determinants of sexually transmitted infections
No full textSkin Cancer Recognition Using Unified Deep Convolutional Neural Networks
No full textThe incidence of skin cancer is rising globally, posing a significant public health threat. An early and accurate diagnosis is crucial for patient prognoses. However, discriminating between malignant melanoma and benign lesions, such as nevi and keratoses, remains a challenging task due to their visual similarities. Image-based recognition systems offer a promising solution to aid dermatologists and potentially reduce unnecessary biopsies. This research investigated the performance of four unified convolutional neural networks, namely, YOLOv3, YOLOv4, YOLOv5, and YOLOv7, in classifying skin lesions. Each model was trained on a benchmark dataset, and the obtained performances were compared based on lesion localization, classification accuracy, and inference time. In particular, YOLOv7 achieved superior performance with an Intersection over Union (IoU) of 86.3%, a mean Average Precision (mAP) of 75.4%, an F1-measure of 80%, and an inference time of 0.32 s per image. These findings demonstrated the potential of YOLOv7 as a valuable tool for aiding dermatologists in early skin cancer diagnosis and potentially reducing unnecessary biopsies
Effect of textual enhancement and explicit rule presentation on the noticing and acquisition of L2 grammatical structures: a meta-analysis
No full text2011 Summer.Includes bibliographical references.This meta-analysis examines whether two instructional treatments would have a positive effect on grammar "noticing" and/or acquisition in an EFL/ESL context. The instructional treatments represent two examples of input enhancement: textual input enhancement and explicit rule presentation. In order to test the effect of these two treatments, I synthesized and analyzed the results of 45 study reports that addressed these two treatments and that were published between the years of 1980 and 2010. Then, I calculated the effect size of each individual study report and the average effect size of all the study reports using a fixed method meta-analysis approach. The average effect size of the textual enhancement treatment showed a low effect size (d = 0.30) while the average effect size of the explicit rule presentation treatment showed a high effect size (d = 0.93). The study offers some pedagogical implications for ESL/EFL teachers and some suggestions for future researchers
Deep learning-based dual optimization framework for accurate thyroid disease diagnosis using CNN architectures
No full textThyroid diseases, including hypothyroidism, hyperthyroidism, thyroid nodules, thyroiditis, and thyroid cancer, are among the most prevalent endocrine disorders, posing significant health risks, which need to be diagnosed and treated promptly. Traditional diagnostic approaches, reliant on manual interpretation of medical images, are time-consuming and prone to errors. This study introduces a novel deep learning framework utilizing advanced Convolutional Neural Networks (CNNs), specifically modified ResNet and InceptionV3 architectures, to improve the accuracy and efficiency of thyroid disease diagnosis. We present Dual-OptNet, a new hybrid deep learning architecture that effectively merges skip connections of ResNet with multi-scale feature extraction based on InceptionV3 for lung classification tasks. Dual-OptNet shows the most accurate and generalizability results in classifying the thyroid disease with an average and best classification accuracy of 97% from a dual-step optimized using Adam and SGD. Future work will focus on developing a real-time classification tool to demonstrate the potential utility of this model in a clinical context. Future work will also focus on enhancing the dataset to cover a wider range of uncommon thyroid cases, and incorporating explainable AI methods, so that the model decisions are more interpretable. Further research will also explore real-time ultrasound analysis and multi-modal data integration, such as combining medical images with patient history, to enhance diagnostic accuracy. Deploying the system in clinical environments will be key to validating its impact and scalability, ultimately contributing to more efficient and accurate healthcare solution
