Hydrocortisone

我们的机体与外部世界接触的部位都被称之为屏障组织（barrier tissues），比如皮肤、眼睛表面、口腔内部、肠道内部和尿道等，它们依赖于一类称为先天性淋巴样细胞（ILCs）的守卫者来维护安全。当机体面临威胁时，就会刺激名为细胞因子的特殊蛋白来进一步激活宿主机体的免疫系统，并帮助控制肠道微生物群落。 然而，随着年龄的增长或特定健康状况的影响，这些细胞数量减少或遭到破坏。目前研究表明，ILCs在骨髓中产生并经血液循环输送，但其如何被激活、定向迁移到需要补充的位置以恢复耗损的细胞库，仍是科学探索中的谜团。 近日，一篇发表在国际杂志Cell Reports上题为“Circadian-clock-controlled endocrine and cytokine signals regulate multipotential innate lymphoid cell progenitors in the bone marrow”的研究报告中，来自密歇根大学医学院等机构的科学家们通过研究发现，这种迁移或许是由昼夜节律钟所控制。 研究团队聚焦于小鼠模型，观察其夜间活跃与日间休息期间ILCs的迁移如何受到生物钟的指挥。对于小鼠和人类而言，一种称之为皮质醇（cortisol）的压力激素会在机体觉醒状态下达到峰值，而皮质醇的减少、褪黑激素（melatonin）和腺苷的增加会在小鼠休息期间增多。 机体的昼夜节律钟或会驱动重要免疫细胞的释放 图片来源：Cell Reports (2024). DOI:10.1016/j.celrep.2024.114200 研究揭示，ILCs的前体细胞（ILCPs）富含时钟基因，这些基因使它们在一天中的特定时刻变得活跃。具体而言，在活动期，皮质醇会增加ILCPs上名为CXCR4的化学趋化（chemoattractant）受体的激活，并促使其留在骨髓内部。在休息的时候，细胞因子白介素-18和名为RORα的受体就会激活ILCPs上的S1PR1受体，从而诱发其从骨髓释放到血液中。 Kim教授解释，这一机制似乎是机体为了高效运作而设定的节奏：在动物醒着的12小时里积累ILCPs，然后在睡眠时分将其释放，确保肠道屏障组织中ILCs的数量稳定，以应对日常挑战。 研究者强调，干扰正常的昼夜节律会导致ILCs数量下降，增加感染风险并干扰微生物群的平衡。认识到ILCs在生物钟调控下的动员机制，不仅对理解干细胞行为有重要意义，也对细胞疗法等领域具有潜在的应用价值。综上，本文研究结果揭示了昼夜节律信号在骨髓ILCPs的稳态外排过程中所发挥的重要作用。 参考文献： Qingyang Liu，Shams Tabrez，Patrick Niekamp, et al. Circadian-clock-controlled endocrine and cytokine signals regulate multipotential innate lymphoid cell progenitors in the bone marrow, Cell Reports (2024). DOI:10.1016/j.celrep.2024.114200 本文仅用于学术分享，转载请注明出处。若有侵权，请联系微信：bioonSir 删除或修改！

Hospitals, health systems and other healthcare organizations are flocking to generative AI, standing up a wide array of projects and use cases to see where the emerging technology can best help them find clinical and financial ROI. Most are thankfully being cautious to make sure not to go too far, too fast. But as AI technology gets ever more powerful, it's worth asking a common question: Could artificial intelligence ever replace doctors? The answer to this question almost always is no. AI will help doctors , not replace them, say most experts. Or, as Mayo Clinic platform President Dr. John Halamka has said, " If your doctor could be replaced by AI, your doctor should be replaced by AI ." But Dr. Bruce Darrow, interim chief digital and information officer and chief medical information officer at Mount Sinai Health System in New York, takes a more nuanced view. In some cases, he says, where the clinical accuracy of doctors and AI are nearly the same, some clinical care in the future could indeed migrate over to AI. In this ninth installment of our Top Voices in AI series, Darrow has plenty to say about AI overall (today's part one) and at his own health system (tomorrow's part two). Q. Why do you think artificial intelligence is having such a big moment in healthcare today? A. A lot of it has to do with this "A-ha" moment that happened when ChatGPT was first made available to the general public. AI has been around for a while, especially the predictive models. But the generative models really caught people's attention. Especially when they could use it not in the setting of, "Well, I'm going to the doctor, and the doctor is going to have some Ninja Logic behind the scenes." But they could use it at home, and they could say, "Tell me about my cholesterol medication." It would spit out all of this information about cholesterol medication. It would summarize things for them, and it would really create information in a way that was really tangible to a lot of people. I think since then, we've had a lot of interest in AI. Also, in my experience provider organizations are always looking for something that makes them attractive to patients. So, saying something along the lines of, "We use AI to make sure you get the best possible care, that we make the most accurate diagnosis," becomes potentially a differentiator for an organization. Q. When AI and healthcare come to mind, some people think AI will replace doctors. Will that ever happen? And why or why not? A. When I talk to people about artificial intelligence , I've heard some suggest we should instead talk about augmented intelligence because a lot of the AI use cases these days are not to replace the doctor, but specifically to make the doctor more successful, more accurate, faster, to make their job easier. In many cases, it's not a replacement. But I think the question of replacing the doctor, in some cases, may be just a matter of how far in advance you're looking. Today, most AI applications are not to replace the doctor, but to make the doctor better at his or her job. For example, if you have a radiologist who uses AI tools to do the same job they always do, to look at radiology images and point out areas of disease or lack of disease, to be able to make a diagnosis, that job still exists. But the AI may make that job easier, faster, may help them prioritize out of a list of 50 different head CTs to read, which one to read first because there's the greatest impact potentially for a time-sensitive diagnosis. These are things that don't replace the doctor. Having said that, over time, I could potentially see ways in which a lot of the functions of doctors could potentially be replaced by AI, especially in areas of pattern recognition. Let's say you had a rash on your hand, and I gave you two options. I say, "I can make you an appointment with a dermatologist." You'll probably need to wait five to 10 days to get that appointment. By the time you get there, maybe your rash is better, maybe it's worse, maybe it's gone. But the doctor will look at it and they will probably give you a correct diagnosis and treatment plan with, say, 95% accuracy. Let's say I say, instead of waiting those five to 10 days to see the dermatologist, I can give you an app that you could download on your phone and you take a picture of that rash on your hand, and it would give you information with a certain amount of accuracy. Then you could potentially treat that with over-the-counter hydrocortisone, or you could leave it alone and it would resolve on its own, or where you would know this is something that really would benefit from medical attention and the intervention of a doctor. If I told you the accuracy of that was on par with what a doctor would tell you in five to 10 days, say 95% or even better accuracy, it would be no contest. Every patient would choose the app. Even if you had accuracy that was approaching what a doctor would be able to, maybe even 85% or 90% accuracy, there may be an instinct to go there. I think the longer we look over time, as we look five years, 10 years, 20 years into the future, and some of these pattern recognition and predictive models get better, you will see some aspects of clinical care migrate in the direction away from doctors being the first line of approach. But I think doctors will still be in the picture for a long time to come. BONUS CONTENT: Click here to watch a video of this interview in which Dr. Bruce Darrow also discusses patient-facing artificial intelligence, like chatbots, and where he sees this form of AI heading. Editor's Note: This is the ninth in a series of features on top voices in health IT discussing the use of artificial intelligence in healthcare. To read the first feature, on Dr. John Halamka at the Mayo Clinic, click here . To read the second interview, with Dr. Aalpen Patel at Geisinger, click here . To read the third, with Helen Waters of Meditech, click here . To read the fourth, with Sumit Rana of Epic, click here . To read the fifth, with Dr. Rebecca G. Mishuris of Mass General Brigham, click here . To read the sixth, with Dr. Melek Somai of the Froedtert & Medical College of Wisconsin Health Network, click here . To read the seventh, with Dr. Brian Hasselfeld of Johns Hopkins Medicine, click here . And to read the eighth, with Craig Kwiatkowski, senior vice president and CIO at Cedars-Sinai, click here . The HIMSS AI in Healthcare Forum is scheduled to take place September 5-6 in Boston. Learn more and register . Follow Bill's HIT coverage on LinkedIn: Bill Siwicki Email him: bsiwicki@himss.org Healthcare IT News is a HIMSS Media publication.