Understanding the Knowledge, Attitude and Behaviour (Practice) of Saudi Arabian Patients with Diabetes Type 2

This qualitative study explored knowledge, attitude and practices of male T2DM patients in rural and urban populations of Riyadh, Saudi Arabia. Participants included 40 male patients aged 35 to 65 years with T2DM and 20 health care providers from rural and urban areas. Using a Grounded Theory approach three models were developed to describe the influences of T2DM management for rural patients; for urban patients and the perspectives of health care providers on T2DM management

COVID-19 pandemic’s impact on eating habits in Saudi Arabia

Background: COVID-19 virus has been reported as a pandemic in March 2020 by the WHO. Having a balanced and healthy diet routine can help boost the immune system, which is essential in fighting viruses. Public Health officials enforced lockdown for residents resulting in dietary habits change to combat sudden changes. Design and Methods: A cross-sectional study was conducted through an online survey to describe the impact of the COVID-19 pandemic on the eating habits, quality and quantity of food intake among adults in Saudi Arabia. SPSS version 24 was used to analyze the data. Comparison between general dietary habits before and during COVID-19 for ordinal variables was performed by Wilcoxon Signed Rank test, while McNemar test was performed for nominal variables. The paired samples t-test was used to compare the total scores for food quality and quantity before and during COVID-19 periods.Results: 2706 adults residing in Riyadh completed the survey. The majority (85.6%) of the respondents reported eating home-cooked meals on a daily basis during COVID-19 as compared to 35.6% before (p<0.001). The mean score for the quality of food intake was slightly higher (p=0.002) before the COVID-19 period (16.46±2.84) as compared to the during period (16.39±2.79). The quantity of food mean score was higher (p<0.001) during the COVID-19 period (15.70±2.66) as compared to the before period (14.62±2.71).Conclusion: Dietary habits have changed significantly during the COVID-19 pandemic among Riyadh residents. Although some good habits increased, the quality and the quantity of the food was compromised. Public Health officials must focus on increased awareness on healthy eating during pandemics to avoid negative consequences. Future research is recommended to better understand the change in dietary habits during pandemics using a detailed food frequency questionnaire

Transorbital transnasal endoscopic combined approach to the anterior and middle skull base: a laboratory investigation

Orbital approaches provide significant trajectory to the skull base and are used with differently designed pathways. The aim of this study is to investigate the feasibility of a combined transorbital and transnasal approach to the anterior and middle cranial fossa. Cadaveric dissection of five silicon-injected heads was used. A total of 10 bilateral transorbital approaches and 5 extended endonasal approaches were performed. Identification of surgical landmarks, main anatomical structures, feasibility of a combined approach and reconstruction of the superior orbital defect were examined. Rod lens endoscope (with 0° and 45° lenses) and endoscopic instruments were used to complete the dissection. The transorbital approach showed good versatility and provides the surgeon with a direct route to the anterior and middle cranial fossa. The transorbital avascular plane showed no conflict with major nerves or vessels. Large exposure area from crista galli to the third ventricle was demonstrated with significant control of different neurovascular structures. A combined transorbital transnasal approach provides considerable value in terms of extent of exposure and free hand movement of the two surgeons, and allows better visualisation and control of the ventral skull base, thus overcoming the current surgical limits of a single approach. Combination of these two minimally invasive approaches should reduce overall morbidity. Clinical trials are needed to evaluate the virtual applications of this approach

Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma:a step towards virtual biopsy

Objectives: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour grading and staging in upper urothelial tract carcinoma patients and to compare these predictions with histopathological diagnosis used as reference standards.Methods: Protocol-based computed tomographic urogram data from 106 patients were obtained and visualised in 3D. Digital segmentation of the tumours was conducted by extracting textural radiomics features. They were further classified using 11 predictive models. The predicted grades and stages were compared to the histopathology of radical nephroureterectomy specimens.Results: Classifier models worked well in mining the radiomics data and delivered satisfactory predictive machine learning models. The multilayer panel showed 84% sensitivity and 93% specificity while predicting UTUC grades. The Logistic Regression model showed a sensitivity of 83% and a specificity of 76% while staging. Similarly, other classifier algorithms [e.g. Support Vector classifier (SVC)] provided a highly accurate prediction while grading UTUC compared to clinical features alone or ureteroscopic biopsy histopathology.Conclusion: Data mining tools could handle medical imaging datasets from small (<2 cm) tumours for UTUC. The radiomics-based machine learning algorithms provide a potential tool to model tumour grading and staging with implications for clinical practice and the upgradation of current paradigms in cancer diagnostics.Clinical Relevance: Machine learning based on radiomics features can predict upper tract urothelial cancer grading and staging with significant improvement over ureteroscopic histopathology. The study showcased the prowess of such emerging tools in the set objectives with implications towards virtual biopsy

Radiomics-Based Computed Tomography Urogram Approach for the Prediction of Survival and Recurrence in Upper Urinary Tract Urothelial Carcinoma

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with a poor prognosis. The accurate prediction of survival and recurrence in UTUC is crucial for effective risk stratification and guiding therapeutic decisions. Models combining radiomics and clinicopathological data features derived from computed tomographic urograms (CTUs) can be a way to predict survival and recurrence in UTUC. Thus, preoperative CTUs and clinical data were analyzed from 106 UTUC patients who underwent radical nephroureterectomy. Radiomics features were extracted from segmented tumors, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select the most relevant features. Multivariable Cox models combining radiomics features and clinical factors were developed to predict the survival and recurrence. Harrell’s concordance index (C-index) was applied to evaluate the performance and survival distribution analyses were assessed by a Kaplan–Meier analysis. The significant outcome predictors were identified by multivariable Cox models. The combined model achieved a superior predictive accuracy (C-index: 0.73) and higher recurrence prediction (C-index: 0.84). The Kaplan–Meier analysis showed significant differences in the survival (p < 0.0001) and recurrence (p < 0.002) probabilities for the combined datasets. The CTU-based radiomics models effectively predicted survival and recurrence in the UTUC patients, and enhanced the prognostic performance by combining radiomics features with clinical factors

Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma:a step towards virtual biopsy

Objectives: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour grading and staging in upper urothelial tract carcinoma patients and to compare these predictions with histopathological diagnosis used as reference standards.Methods: Protocol-based computed tomographic urogram data from 106 patients were obtained and visualised in 3D. Digital segmentation of the tumours was conducted by extracting textural radiomics features. They were further classified using 11 predictive models. The predicted grades and stages were compared to the histopathology of radical nephroureterectomy specimens.Results: Classifier models worked well in mining the radiomics data and delivered satisfactory predictive machine learning models. The multilayer panel showed 84% sensitivity and 93% specificity while predicting UTUC grades. The Logistic Regression model showed a sensitivity of 83% and a specificity of 76% while staging. Similarly, other classifier algorithms [e.g. Support Vector classifier (SVC)] provided a highly accurate prediction while grading UTUC compared to clinical features alone or ureteroscopic biopsy histopathology.Conclusion: Data mining tools could handle medical imaging datasets from small (<2 cm) tumours for UTUC. The radiomics-based machine learning algorithms provide a potential tool to model tumour grading and staging with implications for clinical practice and the upgradation of current paradigms in cancer diagnostics.Clinical Relevance: Machine learning based on radiomics features can predict upper tract urothelial cancer grading and staging with significant improvement over ureteroscopic histopathology. The study showcased the prowess of such emerging tools in the set objectives with implications towards virtual biopsy

Kuwaiti EFL Students’ Perceptions of the Effectiveness of the Remedial English Course 099 at the College of Technological Studies

The study aims to evaluate the English remedial course 099 taught in the College of Technological Studies (PAAET) as part of the English program which disseminates English Language Skills to EFL students studying at this college. This study is expected to provide sufficient information to policymakers and educators involved with this program at all levels, with the intention to help them evaluate this course and make useful decisions to improve English Language Teaching in order to combat the deficiency in the English language suffered by college students in Kuwait. A number of 155 students participated in a questionnaire of 15 statements divided into four areas: reading, grammar, writing, and speaking skills. The findings of the study showed that most EFL students benefited from the English course 099, and their language skills were improved. However, there were some drawbacks and weaknesses of the program in terms of learners’ assessments and follow up. The significance of the study arises from the fact that it would enable decision-makers and course evaluators to pinpoint the strengths and weaknesses of the course and hence find ways to improve it

Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma:Integrating Texture Features with Clinical Predictors

Background: Upper tract urothelial carcinoma (UTUC) presents significant challenges in prognostication due to its rarity and complex anatomy. This study introduces a novel approach integrating perirenal fat (PRF) radiomics with clinical factors to enhance prognostic accuracy in UTUC. Methods: The study retrospectively analyzed 103 UTUC patients who underwent radical nephroureterectomy. PRF radiomics features were extracted from preoperative CT scans using a semi-automated segmentation method. Three prognostic models were developed: clinical, radiomics, and combined. Model performance was assessed using concordance index (C-index), time-dependent Area Under the Curve (AUC), and integrated Brier score. Results: The combined model demonstrated superior performance (C-index: 0.784, 95% CI: 0.707–0.861) compared to the radiomics (0.759, 95% CI: 0.678–0.840) and clinical (0.653, 95% CI: 0.547–0.759) models. Time-dependent AUC analysis revealed the radiomics model’s particular strength in short-term prognosis (12-month AUC: 0.9281), while the combined model excelled in long-term predictions (60-month AUC: 0.8403). Key PRF radiomics features showed stronger prognostic value than traditional clinical factors. Conclusions: Integration of PRF radiomics with clinical data significantly improves prognostic accuracy in UTUC. This approach offers a more nuanced analysis of the tumor microenvironment, potentially capturing early signs of tumor invasion not visible through conventional imaging. The semi-automated PRF segmentation method presents advantages in reproducibility and ease of use, facilitating potential clinical implementation

Evaluating the Predictive Capability of Radiomics Features of Perirenal Fat in Enhanced CT Images for Staging and Grading of UTUC Tumours Using Machine Learning

Background: Upper tract urothelial carcinoma (UTUC) often presents with aggressive behaviour, demanding accurate preoperative assessment to guide management. Radiomics-based approaches have shown promise in extracting quantitative features from imaging, yet few studies have explored whether perirenal fat (PRF) radiomics can augment tumour-only models. Methods: A retrospective cohort of 103 UTUC patients undergoing radical nephroureterectomy was analysed. Tumour regions of interest (ROI) and concentric PRF expansions (10–30 mm) were segmented from computed tomography (CT) scans. Radiomic features were extracted using PyRadiomics, filtered by correlation and intraclass correlation coefficients, and integrated with clinical variables (e.g., age, BMI, multifocality). Multiple machine learning models, including MLPClassifier and CatBoost, were evaluated via repeated cross-validation. Performance was assessed using the area under the ROC curve (AUC), sensitivity, specificity, F1-score, and DeLong tests. Results: The best tumour grade model (AUC = 0.961) merged tumour-derived features with a 10 mm PRF margin, exceeding PRF-only (AUC = 0.900) and tumour-only (AUC = 0.934) approaches. However, the improvement over tumour-only was not always statistically significant. For stage prediction, combining tumour and 15 mm PRF features yielded the top AUC of 0.852, surpassing the tumour-alone model (AUC = 0.802) and outperforming PRF-only (AUC ≤ 0.778). PRF features provided an additional predictive value for both grade and stage models. Conclusions: Integrating PRF radiomics with tumour-based analyses enhances predictive accuracy for UTUC grade and stage, suggesting that the tumour microenvironment contains complementary imaging cues. These findings, pending external validation, support the potential for radiomics-driven risk stratification and personalised treatment planning in UTUC