Mammalian hibernators undergo major behavioural, physiological and biochemical changes to survive hypothermia, ischaemia-reperfusion and finite fuel reserves during days or weeks of continuous torpor. During hibernation, the 13-lined ground squirrel (Ictidomys tridecemlineatus) undergoes a global suppression of energetically expensive processes such as transcription and translation, while selectively upregulating certain genes/proteins to mitigate torpor-related damage. Antioxidant defenses are critical for preventing damage caused by reactive oxygen species (ROS) during torpor and arousal, and Nrf2 is a critical regulator of these antioxidant genes. This study analysed the relative protein expression levels of Nrf2, KEAP1, small Mafs (MafF, MafK and MafG) and catalase and the regulation of Nrf2 transcription factors by post-translational modifications (PTMs) and protein-protein interactions with a negative regulator (KEAP1) during hibernation. It was found that a significant increase in MafK during late torpor predicated an increase in relative Nrf2 and catalase levels seen in arousal. Additionally, Nrf2-KEAP1 protein-protein interactions and Nrf2 PTMs, including serine phosphorylation and lysine acetylation, were responsive to cycles of torpor-arousal with peak responses occurring during arousal. These peaks seen during arousal correspond to a surge in oxygen consumption, which causes increased ROS production. Thus, these regulatory mechanisms could be important during hibernation because they provide mechanisms for mitigating the deleterious effects of oxidative stress by modifying Nrf2 expression and function in an energetically inexpensive manner.Alzheimer’s disease (AD) is a common dementia that is currently incurable. The existing treatments can only moderately relieve the symptoms of AD to slow down its progress. How to achieve effective neural regeneration to ameliorate cognitive impairments is a major challenge for current AD treatment. Here, the therapeutic potential of a nanoformulation-mediated neural stem cell (NSC) therapy capable of simultaneous Aβ clearance and neural regeneration is investigated in a murine model. Genetically engineered NSCs capable of stably and continuously expressing neprilysin (NEP) are developed to enhance Aβ degradation and NSC survival in the brain. A PBAE-PLGA-Ag2 S-RA-siSOX9 (PPAR-siSOX9) nanoformulation with high gene/drug deliverability is synthesized to overcome AD microenvironment-associated adverse effects and to promote neuronal differentiation of the NEP-expressing NSCs. For achieving accurate stereotactic transplantation, Ag2 S quantum-dot-based fluorescence imaging is used to guide NSC transplantation in real time. selleckchem This strategy shows numerous benefits, including efficient and long-lasting Aβ degradation, improved neural regeneration, and accurate cell transplantation. It is shown that a single administration of this therapy achieves long-term efficacy (6 months) with respect to memory reversal and improvement of learning deficits.Multiple Sclerosis (MS) is a demyelinating disease affecting the central nervous system, with no curative medicine available. The use of herbal drugs and dietary supplements is increasing among people with MS (PwMS), raising a need for knowledge about potential interactions between conventional MS medicine and herbal drugs/dietary supplements. This systematic review provides information about the safety of simultaneous use of conventional MS-drugs and herbal drugs frequently used by PwMS. The study included 14 selected disease-modifying treatments and drugs frequently used for symptom-alleviation. A total of 129 published papers found via PubMed and Web of Science were reviewed according to defined inclusion- and exclusion criteria. Findings suggested that daily recommended doses of Panax ginseng and Ginkgo biloba should not be exceeded, and herbal preparations differing from standardized products should be avoided, especially when combined with anticoagulants or substrates of certain cytochrome P450 isoforms. Further studies are required regarding ginseng’s ability to increase aspirin bioavailability. Combinations between chronic cannabis use and selective serotonin reuptake inhibitors or non-steroidal antiinflammatory drugs should be carefully monitored, whereas no significant evidence for drug-interactions between conventional MS-drugs and ginger, cranberry, vitamin D, fatty acids, turmeric, probiotics or glucosamine was found.The future of gastrointestinal bleeding will include the integration of machine learning algorithms to enhance clinician risk assessment and decision making. Machine learning algorithms have shown promise in outperforming existing clinical risk scores for both upper and lower gastrointestinal bleeding but have not been validated in any prospective clinical trials. The adoption of electronic health records provides an exciting opportunity to deploy risk prediction tools in real time and also to expand the data available to train predictive models. Machine learning algorithms can be used to identify patients with acute gastrointestinal bleeding using data extracted from the electronic health record. This can lead to an automated process to find patients with symptoms of acute gastrointestinal bleeding so that risk prediction tools can be then triggered to consistently provide decision support to the physician. Neural network models can be used to provide continuous risk predictions for patients who are at higher risk, which can be used to guide triage of patients to appropriate levels of care. Finally, the future will likely include neural network-based analysis of endoscopic stigmata of bleeding to help guide best practices for hemostasis during the endoscopic procedure. Machine learning will enhance the delivery of care at every level for patients with acute gastrointestinal bleeding through identifying very low risk patients for outpatient management, triaging high risk patients for higher levels of care, and guiding optimal intervention during endoscopy.The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.