The AAAS will have the Emerging Researchers National (ERN) Conference in Science, Technology, Engineering and Mathematics (STEM) in Washington DC next month.
https://emerging-researchers.org/
Very interesting review paper!!! This paper gives an overview of the available chemical tools for Intramembrane Proteases (IMPs), including activity-based probes, affinity-based probes, and synthetic substrates. They also discussed how these have been used to increase our structural and functional understanding of this fascinating group of enzymes, and how they might be applied to address future questions and challenges.
What are the advantages of using fluorophore or biotin-coupled activity-based probes (ABPs) for detecting MALT1 activity in research and clinical settings?
The research focuses on designing and synthesizing chemical probes for studying protease function, specifically targeting caspase-3 with probes based on its substrate specificity, incorporating an uncleavable peptide bond mimic and a photocrosslinker. While these probes successfully inhibited caspase-3 activity, they did not covalently bind to the protease. Upon photoactivation, the probes were found to generate peptide aldehydes that inhibited caspase-3, suggesting a potential application in photopharmacology for creating precise, location-specific protease inhibitors.
The study investigates a next-generation chemical degrader of BET proteins, dBET6, demonstrating its significant effects on reducing BET protein functions in glioblastoma (GBM) cells, including decreased proliferation and tumorigenic abilities. dBET6 shows superior antiproliferative effects compared to conventional BET bromodomain inhibitors (BBIs) and overcomes resistance to BBIs, highlighting the importance of BET proteins in GBM and the therapeutic potential of targeting BET protein degradation.
The study utilized Random Forest, XGBoost, and Adaboost machine learning methods to predict the elastic modulus of titanium triply periodic minimal surface structures, using a dataset from elastic finite element analysis with large lattice-cells. Among these methods, Adaboost showed the best performance with an R2 of 0.959 and the lowest mean square error (MSE) of 0.532, demonstrating that machine learning can significantly extend the finite element analysis results to a theoretically infinite range with improved computational efficiency.
This article introduces a novel large-scale machine learning classifier for biomedical data, utilizing functional networks based on propensity score and Newton Raphson-maximum-likelihood optimizations to tackle challenges like high dimensionality and imbalanced distributions. It demonstrates the classifier's superior performance over traditional machine learning and statistical approaches through use-cases in cancer chemotherapy design, inpatient admission for cancer diagnosis, severe asthma exacerbation in children, and mixture models simulation studies. The new classifier not only outperforms existing models in terms of efficiency and reliability but also offers potential for future applications in next-generation sequencing data analysis on high-performance computing platforms.
This is a special issue with several articles covering AI, machine learning, deep learning. The variety of topics covered by the articles in this Special Issue is yet another proof of enormous opportunities for biomedical engineers and scientists familiar with artificial intelligence, machine, and deep learning.
The study investigates the impact of melatonin on fuel utilization during exercise by administering melatonin or a placebo to eight physical education students before a 45-minute run at 60% of their maximum aerobic speed. Results showed that post-exercise cortisol levels increased significantly in both conditions, indicating stress from exercise. However, glucose levels only rose significantly in the placebo group, suggesting melatonin might reduce glucose utilization during exercise. Triglyceride levels significantly increased post-exercise in the melatonin group, indicating enhanced lipid utilization. Thus, acute melatonin administration before endurance exercise could shift energy expenditure towards greater fat burning, altering the balance between glucose and lipid use.
Interesting review article. This review suggests that the redistribution of carbon and nitrogen to muscles and lungs for fuel replenishment and cell regeneration, rather than direct fat oxidation, plays a key role in exercise-induced fat loss. The timing of meals to coincide with periods of high muscle reconstruction demand could further enhance this effect, reducing abdominal fat while promoting muscle mass and repair.
This study identifies inosine as a regulator of adipose tissue thermogenesis. Moreover, targeting ENT1 or increasing extracellular inosine may represent novel anti-obesity strategies.
Very interesting perspective. The study by Huang et al. discovered that lipolysis activates the DNA repair enzyme p53, leading to the release of extracellular vesicles (AdEVs) and free proteins like fatty acid binding protein 4 (FABP4) from adipocytes. This process is suppressed when p53 is inhibited. The research also highlights a role for the mammalian target of rapamycin (mTOR) pathway in this process, suggesting that p53 and mTOR are important regulators of AdEV release. These findings link lipolysis, p53 activation, and the release of AdEVs, offering insights into potential therapeutic targets for metabolic diseases like obesity.
This research elucidates the molecular interaction between human dynein and the lissencephaly 1 protein (LIS1), crucial for understanding mechanisms underlying diseases like type-1 lissencephaly. By analyzing cryo-EM structures of the dynein-LIS1 complex, the study highlights differences in dynein-LIS1 interactions between yeast and humans, offering insights into the role of LIS1 in dynein activation and providing a framework for investigating disease-related mutations within this complex.
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