The Notch family consists of four highly conserved transmembrane receptors, the release of the active intracellular domain requires the enzymatic activity of gamma secretase. Notch is involved in embryonic development and in many physiological processes of normal cells where it regulates growth, apoptosis and differentiation. Notch 1, a member of the Notch family, is implicated in many types of cancer, including breast cancer (especially triple negative breast cancer), leukemias, brain tumors, and many others. Notch 1 is tightly connected to many signaling pathways that are therapeutically involved in tumorigenesis. Together, they impact apoptosis, proliferation, chemosensitivity, immune-response and the population of cancer stem cells. Notch 1 inhibition can be achieved through various and diverse methods, among the most common are the gamma secretase inhibitors which produce a pan-Notch inhibition, or the use of Notch 1 siRNA or Notch 1 monoclonal antibodies (mAb) which produce a more specific blockade. Downregulation of Notch 1 can be used alone or in combination with chemotherapy where a synergistic effect and a decrease in chemoresistance can be achieved. Targeting Notch1 in cancers that harbor high expression levels of Notch 1 offers an addition to therapeutic strategies recruited for managing cancer. Considering available evidence, Notch 1 offers a legitimate target that might be incorporated in future strategies for combating cancer. In this review, the possible clinical applications of Notch 1 inhibition and the obstacles that hinder its clinical application are discussed. Significance Statement Notch 1 plays an important role in different types of cancer. Numerous approaches of Notch 1 inhibition possess potential benefits in the management of various clinical aspects of cancer. The application of different Notch 1 inhibition modalities faces many challenges.

Background: The emergence of Chat-Generative Pre-trained Transformer (ChatGPT) by OpenAI has revolutionized AI technology, demonstrating significant potential in healthcare and pharmaceutical education, yet its real-world applicability in clinical training warrants further investigation. Methods: A cross-sectional study was conducted between April and May 2023 to assess PharmD students’ perceptions, concerns, and experiences regarding the integration of ChatGPT into clinical pharmacy education. The study utilized a convenient sampling method through online platforms and involved a questionnaire with sections on demographics, perceived benefits, concerns, and experience with ChatGPT. Statistical analysis was performed using SPSS, including descriptive and inferential analyses. Results: The findings of the study involving 211 PharmD students revealed that the majority of participants were male (77.3%), and had prior experience with artificial intelligence (68.2%). Over two-thirds were aware of ChatGPT. Most students (n= 139, 65.9%) perceived potential benefits in using ChatGPT for various clinical tasks, with concerns including over-reliance, accuracy, and ethical considerations. Adoption of ChatGPT in clinical training varied, with some students not using it at all, while others utilized it for tasks like evaluating drug-drug interactions and developing care plans. Previous users tended to have higher perceived benefits and lower concerns, but the differences were not statistically significant. Conclusion: Utilizing ChatGPT in clinical training offers opportunities, but students’ lack of trust in it for clinical decisions highlights the need for collaborative human-ChatGPT decision-making. It should complement healthcare professionals’ expertise and be used strategically to compensate for human limitations. Further research is essential to optimize ChatGPT’s effective integration.

Adequate glycemic control in children with type 1 diabetes reduces the risk of future complications. Identifying factors affecting haemoglobin A1c (HbA1c) is crucial to management of metabolic control. We aimed to identify possible socioeconomic predictors of poor metabolic control this patient group in Jordan, a developing country with limited resources. Medical charts of children with type 1 diabetes attending the pediatric endocrine clinics in two major diabetes centers were reviewed. HbA1c ≥7.5% (58 mmol/mol) was considered to reflect poor metabolic control. Logistic regression analysis was performed to identify predictors of poor glycemic control. The association between socioeconomic characteristics and metabolic control was evaluated using multiple correspondence analysis (MCA). Two hundred and fifty-nine children were enrolled in the study. One fifth of the patients (20.5%) achieved HbA1c <7.5%. Patients with dietary non-compliance [odds ratio (OR): 3.533, confidence interval (CI): 1.803 - 6.926; p<0.001], and those who were overweight (OR: 3.869, CI: 1.218 - 12.294; p=0.022) were more likely to have poor metabolic control. Children whose mothers had a bachelor’s degree or higher were less likely to have poor metabolic control compared to children whose mothers had only elementary education (OR: 0.241, CI: 0.079 - 0.734; p=0.012). MCA revealed an association between low socioeconomic status and poor metabolic control. Children with deceased mothers had significantly higher HbA1c of 10.6±1.86% compared to an average of 8.7±1.45% for the rest of participants (p=0.005). Low socioeconomic status, lower levels of maternal education and maternal death were associated with poor metabolic control. Identifying children with these risk factors might play an important role in optimizing metabolic control and provide better diabetes care.