Heterozygous Knockout Effect on Pancreatic and Body Weight in F1 Population Using Collaborative Cross Lines.

, a critical tumor suppressor gene, plays a significant role in pancreatic biology and tumorigenesis. Genetic background and sex are known to influence phenotypic outcomes, but their impact on pancreatic weight in -deficient mice remains unclear. This study investigates the impact of deficiency on pancreatic weight in first-generation (F1) mice from diverse collaborative cross (CC) lines, focusing on the influence of genetic background and sex.

Upstream regulation of microRNA-9 through a complex cellular machinery during neurogenesis.

While microRNAs (miRs) like miR-9 are crucial for neurogenesis and neuronal differentiation, their regulatory mechanisms are not well understood. miR-9 is highly expressed in the brain and plays a significant role in neurogenesis. Using the Collaborative Cross resource, we identified significant quantitative trait loci (QTL) through genetic analyses.

Host Genetics Background Affects Intestinal Cancer Development Associated with High-Fat Diet-Induced Obesity and Type 2 Diabetes.

Background: Obesity and type 2 diabetes (T2D) promote inflammation, increasing the risk of colorectal cancer (CRC). High-fat diet (HFD)-induced obesity is key to these diseases through biological mechanisms. This study examined the impact of genetic background on the multimorbidity of intestinal cancer, T2D, and inflammation due to HFD-induced obesity.

The collaborative cross mouse for studying the effect of host genetic background on memory impairments due to obesity and diabetes.

Background: Over the past few decades, a threefold increase in obesity and type 2 diabetes (T2D) has placed a heavy burden on the health-care system and society. Previous studies have shown correlations between obesity, T2D, and neurodegenerative diseases, including dementia. It is imperative to further understand the relationship between obesity, T2D, and cognitive deficits.

Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models.

Background: The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients' health and well-being. Therefore, making it easier and more accurate to understand and diagnose patients with skeletal malocclusions is necessary.