Prevalence of thyroid disorders among the diabetic population in Arar, Saudi Arabia.

OBJECTIVES: To study the prevalence of thyroid disorders (TDs) among the diabetic population in Arar, Saudi Arabia. METHODS: A cross-sectional design study carried out in Arar, northern province of Saudi Arabia, from October 2023 to January 2024. A structured questionnaire was used to collect the data. From the diabetic population aged over 18 years old. RESULTS: A total of 501 participants were enrolled. Most fall within the 20-35 age range, comprising 36.5% of the sample. Vitamin D deficiency appears to be the most prevalent comorbid condition. Following closely behind is vitamin B12 deficiency; hypertension and high blood lipids also show notable prevalence rates, affecting 10.5-22.1% of the population. In terms of diabetes, 42.8% of the population has been diagnosed with the condition. Among those with diabetes, the majority (67.6%) have been diagnosed with the second type, while 32.4% have the first type. There is an association between diabetes and TDs, with 51.3% of participants reporting this. CONCLUSION: The findings indicate that the adults in Arar, Saudi Arabia, lack some knowledge of TDs and their relationship to diabetes.

Nigella sativa, active principle thymoquinone, alleviates palmitate-induced cytotoxicity, inflammation and bioenergetic disruptions in macrophages: An invitro study model.

Thymoquinone (TQ) is one of the main phytochemical bioactive ingredients in Nigella sativa, with reported immunity-boosting properties. The current study evaluated the anti-inflammatory effect of TQ against inflammation brought on by free fatty acid Palmitate (PA) using macrophages raw 264.7 cell line. Data revealed that TQ significantly improved the viability of basal and PA stimulated Macrophages at concentrations of 50 and 100 µg/mL. Also, TQ significantly reduced nitric oxide and triglyceride levels in PA-stimulated macrophages at concentrations of 50 and 100 µg/mL. The pro-inflammatory cytokines studies revealed that PA significantly increased the release of the cytokines TNF-α, MHGB-1, IL-1ß, and IL-6. TQ at concentrations 25, 50, and 100 µg/ml significantly decreases the release of the studied cytokines in PA-stimulated macrophages to variable extents with parallel inhibition to their corresponding gene expression. Bioenergetic assays showed that PA significantly decreased cellular ATP, mitochondrial complexes I and III activities and mitochondrial membrane potential with a subsequent significant increase in lactate production. At the same time, TQ can alleviate the effect of PA on macrophages' bioenergetics parameters to variable extent based on TQ concentration. To conclude, TQ could mitigate palmitate-induced inflammation and cytotoxicity in macrophages by improving macrophage viability and controlling cytokine release with improved PA-induced bioenergetics disruption.

The Evaluation of Artificial Intelligence Technology for the Differentiation of Fresh Human Blood Cells From Other Species' Blood in the Investigation of Crime Scenes.

OBJECTIVES: The current study used the deep machine learning approach to differentiate human blood specimens from cow, goat, and chicken blood stains based on cell morphology. METHODS: A total of 1,955 known Giemsa-stained digitized images were acquired from the blood of humans, cows, goats, and chickens. To train the deep learning models, the well-known VGG16, Resnet18, and Resnet34 algorithms were used. Based on the image analysis, confusion matrices were generated. RESULTS: Findings showed that the F1 score for the chicken, cow, goat, and human classes were all equal to 1.0 for each of the three algorithms. The Matthews correlation coefficient (MCC) was 1 for chickens, cows, and humans in all three algorithms, while the MCC score was 0.989 for goats by ResNet18, and it was 0.994 for both ResNet34 and VGG16 algorithms. The three algorithms showed 100% sensitivity, specificity, and positive and negative predictive values for the human, cow, and chicken cells. For the goat cells, the data showed 100% sensitivity and negative predictive values with specificity and positive predictive values ranging from 98.5% to 99.6%. CONCLUSION: These data showed the importance of deep learning as a potential tool for the differentiation of the species of origin of fresh crime scene blood stains.