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Objective To investigate the effect of MDM2 inhibitor RG-7388 on the proliferation, cell cycle, and apoptosis of diffuse large B-lymphoma (DLBCL) cells. Methods DLBCL cell strains SUDHL2 and HBL1 were treated with 2, 4, and 8 μmol/LRG7388, respectively. Cell proliferation was detected by CCK8 and EdU methods. Apoptosis was measured by Annexin V–FITC/PI double staining and Caspase 3/7-Glo enzyme activity methods. Cell cycle was assessed by flow cytometry. Changes in the expression of cell cycle and apoptosis-related proteins were determined by Western blot. Results The IC50 of RG7388 for inhibiting SUDHL2 and HBL1 cells were 3.36 and 3.76 μmol/L, respectively, and the inhibitory effect of RG7388 was dose dependent. The proportions of G1 phase and apoptotic cells in the SUDHL2 and HBL1 cells treated with different doses of RG7388 were significantly higher than those in the control group (all P

The article discusses the use of artificial intelligence (AI) and Internet of Things (IoT) technology in healthcare. It highlights how intelligent technology can assist clinicians in diagnosis and treatment, and how wearable devices with embedded algorithms can capture patient data. The article emphasizes the importance of sensor technology, body sensor networks, and AI techniques in patient monitoring and healthcare. It also presents several research studies that utilize AI and machine learning for various healthcare applications, such as blood glucose monitoring, tetanus severity detection, esophageal motility disorder diagnosis, and COVID-19 detection. The article concludes by discussing the challenges and future directions in implementing AI and IoT in healthcare. [Extracted from the article]

Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes – a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings (“virtual ward” and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources. [ABSTRACT FROM AUTHOR]