#Predictive Biomarkers

[creativeera]

Cancer Biomarkers Market is Trending by Increasing Personalized Care

Cancer biomarkers are biological molecules found in blood, tissues, or other body fluids whose presence indicates normal or abnormal processes, or conditions of concern regarding health. They are used in patient diagnosis, staging, treatment selection, monitoring of cancer progression or recurrence. Cancer biomarkers help in early cancer detection and assessing the likelihood of cancer recurrence after treatment. They play an important role in cancer risk assessment, screening, diagnosis, prognosis, and predicting treatment response for a variety of cancers. With increasing technological advancements, more personalized and targeted treatment options are emerging. This is fueling the demand for cancer biomarkers to help physicians detect cancer in early stages, determine the best treatment for each patient, monitor the effectiveness of treatment, and check for signs of recurrence.

The Global Cancer Biomarkers Market is estimated to be valued at US$ 25.60 billion in 2024 and is expected to exhibit a CAGR of 12% over the forecast period 2024 to 2031. Key Takeaways Key players operating in the Cancer Biomarkers are Schlumberger Limited, Rockwell Automation Inc., SIS-TECH Solutions LP, Emerson Electric Company, HIMA Paul Hildebrandt GmbH, Honeywell International Inc., Siemens AG, Yokogawa Electric Corporation, Schneider Electric SE, and ABB Ltd. The increasing prevalence of cancer globally has boosted the usage of cancer biomarkers. Rising demand for non-invasive diagnostic techniques along with increasing funding for cancer research are fueling the market growth. Growing awareness regarding the benefits of early detection of cancer is further driving the demand for cancer biomarkers. The growing Cancer Biomarkers Market demand for personalized medicine is also propelling the demand for cancer biomarkers. Personalized medicine focuses on classifying individuals based on their susceptibility and likely response to particular treatment. This allows clinicians to choose the most safe and effective treatment for each patient. Many companies are increasingly investing in biomarker research and development to introduce innovative cancer diagnostics and targeted therapies. The increasing global incidence of cancer has encouraged market players to expand their geographical presence. Emerging countries in Asia Pacific and Latin America offer lucrative opportunities for players due to growing healthcare investments, favorable government policies, and rising patient disposable incomes in these regions. Players are also focusing on partnerships, mergers, acquisitions, and collaborations with research institutes and biotechs to strengthen their product portfolios and geographical footprints. Market Key Trends Next-generation sequencing (NGS) has emerged as a key trend in the global cancer biomarkers market. NGS helps to discover and validate novel biomarkers by generating huge amounts of DNA sequence data from tumor and normal samples. It allows comprehensive genomic profiling of tumors to guide treatment decisions. NGS enables the analysis of multiple biomarkers simultaneously compared to traditional techniques. This allows physicians to obtain a complete molecular profile of the tumor specific to each patient for precision diagnosis and treatment selection.

Porter’s Analysis Threat of new entrants: High capital requirements and strong intellectual property rights protections limit new entrants in this competitive market.

Bargaining power of buyers: Large pharmaceutical companies have significant bargaining power over biotech companies developing novel biomarkers, putting pricing pressure.

Bargaining power of suppliers: Suppliers of analytical instruments and clinical testing kits have some bargaining power as they provide core tools and technologies needed by most companies in this space.

Threat of new substitutes: Biomarkers able to better diagnose, monitor, or predict therapeutic responses could emerge as substitutes over time.

Competitive rivalry: Intense competition exists among large pharmaceutical companies and smaller biotech firms to develop and commercialize novel cancer biomarker diagnostic tests and services. Geographical Regions North America currently accounts for the largest share of the global cancer biomarkers market, in terms of value, owing to the high adoption of advanced cancer diagnostic techniques and presence of leading biomarker testing companies in the region. The Asia Pacific market is expected to grow at the fastest rate during the forecast period, due to growing awareness regarding early cancer detection, increasing healthcare expenditure, and expanding base of pharma & biotech companies in China, India, and other Asia Pacific countries.

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Vaagisha brings over three years of expertise as a content editor in the market research domain. Originally a creative writer, she discovered her passion for editing, combining her flair for writing with a meticulous eye for detail. Her ability to craft and refine compelling content makes her an invaluable asset in delivering polished and engaging write-ups.

(LinkedIn: https://www.linkedin.com/in/vaagisha-singh-8080b91)

[jcmarchi]

Beyond the Hype: Unveiling the Real Impact of Generative AI in Drug Discovery

New Post has been published on https://thedigitalinsider.com/beyond-the-hype-unveiling-the-real-impact-of-generative-ai-in-drug-discovery/

Beyond the Hype: Unveiling the Real Impact of Generative AI in Drug Discovery

Since Insilico Medicine developed a drug for idiopathic pulmonary fibrosis (IPF) using generative AI, there’s been a growing excitement about how this technology could change drug discovery. Traditional methods are slow and expensive, so the idea that AI could speed things up has caught the attention of the pharmaceutical industry. Startups are emerging, looking to make processes like predicting molecular structures and simulating biological systems more efficient. McKinsey Global Institute estimates that generative AI could add $60 billion to $110 billion annually to the sector. But while there’s a lot of enthusiasm, significant challenges remain. From technical limitations to data quality and ethical concerns, it’s clear that the journey ahead is still full of obstacles. This article takes a closer look at the balance between the excitement and the reality of generative AI in drug discovery.

The Hype Surrounding Generative AI in Drug Discovery

Generative AI has captivated the imagination of the pharmaceutical industry with its potential to drastically accelerate the traditionally slow and expensive drug discovery process. These AI platforms can simulate thousands of molecular combinations, predict their efficacy, and even anticipate adverse effects long before clinical trials begin. Some industry experts predict that drugs that once took a decade to develop will be created in a matter of years, or even months with the help of generative AI.

Startups and established companies are capitalizing on the potential of generative AI for drug discovery. Partnerships between pharmaceutical giants and AI startups have fueled dealmaking, with companies like Exscientia, Insilico Medicine, and BenevolentAI securing multi-million-dollar collaborations. The allure of AI-driven drug discovery lies in its promise of creating novel therapies faster and cheaper, providing a solution to one of the industry’s biggest challenges: the high cost and long timelines of bringing new drugs to market.

Early Successes

Generative AI is not just a hypothetical tool; it has already demonstrated its ability to deliver results. In 2020, Exscientia developed a drug candidate for obsessive-compulsive disorder, which entered clinical trials less than 12 months after the program started — a timeline far shorter than the industry standard. Insilico Medicine has made headlines for discovering novel compounds for fibrosis using AI-generated models, further showcasing the practical potential of AI in drug discovery.

Beyond developing individual drugs, AI is being employed to address other bottlenecks in the pharmaceutical pipeline. For instance, companies are using generative AI to optimize drug formulations and design, predict patient responses to specific treatments, and discover biomarkers for diseases that were previously difficult to target. These early applications indicate that AI can certainly help solve long-standing challenges in drug discovery.

Is Generative AI Overhyped?

Amid the excitement, there is growing skepticism regarding how much of generative AI’s hype is grounded versus inflated expectations. While success stories grab headlines, many AI-based drug discovery projects have failed to translate their early promise into real-world clinical results. The pharmaceutical industry is notoriously slow-moving, and translating computational predictions into effective, market-ready drugs remains a daunting task.

Critics point out that the complexity of biological systems far exceeds what current AI models can fully comprehend. Drug discovery involves understanding an array of intricate molecular interactions, biological pathways, and patient-specific factors. While generative AI is excellent at data-driven prediction, it struggles to navigate the uncertainties and nuances that arise in human biology. In some cases, the drugs AI helps discover may not pass regulatory scrutiny, or they may fail in the later stages of clinical trials — something we’ve seen before with traditional drug development methods.

Another challenge is the data itself. AI algorithms depend on massive datasets for training, and while the pharmaceutical industry has plenty of data, it’s often noisy, incomplete, or biased. Generative AI systems require high-quality, diverse data to make accurate predictions, and this need has exposed a gap in the industry’s data infrastructure. Moreover, when AI systems rely too heavily on historical data, they run the risk of reinforcing existing biases rather than innovating with truly novel solutions.

Why the Breakthrough Isn’t Easy

While generative AI shows promise, the process of transforming an AI-generated idea into a viable therapeutic solution is a challenging task. AI can predict potential drug candidates but validating those candidates through preclinical and clinical trials is where the real challenge begins.

One major hurdle is the ‘black box’ nature of AI algorithms. In traditional drug discovery, researchers can trace each step of the development process and understand why a particular drug is likely to be effective. In contrast, generative AI models often produce outcomes without offering insights into how they arrived at those predictions. This opacity creates trust issues, as regulators, healthcare professionals, and even scientists find it difficult to fully rely on AI-generated solutions without understanding the underlying mechanisms.

Moreover, the infrastructure required to integrate AI into drug discovery is still developing. AI companies are working with pharmaceutical giants, but their collaboration often reveals mismatched expectations. Pharma companies, known for their cautious, heavily regulated approach, are often reluctant to adopt AI tools at a pace that startup AI companies expect. For generative AI to reach its full potential, both parties need to align on data-sharing agreements, regulatory frameworks, and operational workflows.

The Real Impact of Generative AI

Generative AI has undeniably introduced a paradigm shift in the pharmaceutical industry, but its real impact lies in complementing, not replacing, traditional methods. AI can generate insights, predict potential outcomes, and optimize processes, but human expertise and clinical testing are still crucial for developing new drugs.

For now, generative AI’s most immediate value comes from optimizing the research process. It excels in narrowing down the vast pool of molecular candidates, allowing researchers to focus their attention on the most promising compounds. By saving time and resources during the early stages of discovery, AI enables pharmaceutical companies to pursue novel avenues that may have otherwise been deemed too costly or risky.

In the long term, the true potential of AI in drug discovery will likely depend on advancements in explainable AI, data infrastructure, and industry-wide collaboration. If AI models can become more transparent, making their decision-making processes clearer to regulators and researchers, it could lead to a broader adoption of AI across the pharmaceutical industry. Additionally, as data quality improves and companies develop more robust data-sharing practices, AI systems will become better equipped to make groundbreaking discoveries.

The Bottom Line

Generative AI has captured the imagination of scientists, investors, and pharmaceutical executives, and for good reason. It has the potential to transform how drugs are discovered, reducing both time and cost while delivering innovative therapies to patients. While the technology has demonstrated its value in the early phases of drug discovery, it is not yet prepared to transform the entire process.

The true impact of generative AI in drug discovery will unfold over the coming years as the technology evolves. However, this progress depends on overcoming challenges related to data quality, model transparency, and collaboration within the pharmaceutical ecosystem. Generative AI is undoubtedly a powerful tool, but its true value depends on how it’s applied. Although the current hype may be exaggerated, its potential is genuine — and we are only at the beginning of discovering what it can accomplish.

[scichores]

Fascinating Role of Genomics in Drug Discovery and Development

This article dives deep into the significance of genomics in drug discovery and development, highlighting well-known genomic-based drug development services that are driving the future of pharmaceutical therapies. #genomics #drugdiscovery

A scientist using a whole genome DNA sequencer, in order to determine the “DNA fingerprint” of a specific bacterium. Original image sourced from US Government department: Public Health Image Library, Centers for Disease Control and Prevention. Under US law this image is copyright free, please credit the government department whenever you can”. by Centers for Disease Control and Prevention is…

[mindblowingscience]

Scientists have figured out a non-invasive way to determine if a transplanted organ is failing to take in a patient – no matter if it's a kidney, liver, lung, or heart. It's the first time that biomarkers of dysfunction have matched across multiple types of transplanted organs, and it hints at the possibility of a blood test that can diagnose early rejection in all transplant scenarios – a tool that doesn't yet exist. If more research is done, the newly identified biomarkers could even be used to differentiate between various types of organ rejection, including immune issues, inadequate blood supply, or maladaptive repairs.

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[seeingivy]

elevated heart rates 

levi ackerman x f!reader

levi’s a mind reader and you’re a love expert 

content: grad student levi, brain researchers, nile being a weirdo freak (sorry yall), mentions of drinking, levi is shirtless at one point, reader has claustrophobia 

an: started my big girl brain research fellowship today. hence - brain jargon and GRAD STUDENT LEVI

- 

The room is small - the nineteen of you cramming into the small space of the conference room. You’re located directly at the front, sitting next to your advisor, Dot Pyxis. A leading expert in the field, one of the first neuroscientists you had met at a conference when you were a freshman in college. 

You saw it - the way his eyes lighted up, the way he was stumbling over his words because he was so excited to explain what he did everyday that you wanted that. To be that excited about something. And here you were, sitting next to him about to make it happen. 

You moved to Marley two months ago for this very moment. Your first day at the Brain Consortium - one of the best neuroscience research labs in the country, led by Pyxis himself. He was going to co-advise your thesis, guide you into becoming an expert in the field. Unlike any other, this lab was barely limited to one field, instead equipped with researchers from many different departments, the projects, the papers entirely interdisciplinary. 

There was no other place like it. You can feel your hands shaking as you hand over your hard drive, your presentation loaded on to it. Pyxis had explained it all - there were weekly lab meetings where everyone came together, presenting their research. Everyone gave feedback, asked questions to help further expand and build on the projects. 

And it was your turn. On your very first day, you were expected to explain. What you were going to research, what you were going to contribute, what you were excited about. 

It’s fucking nerve wracking. Pyxis stands up, giving you one last shoulder squeeze, before introducing everyone in the lab to you. He points everyone out - the other assistant professors, post-doctoral researchers, and the other PhD students. 

“Hange Zoe, Erwin Smith, Levi Ackerman, Petra Ral, and Nile Dok. The other PhD students. I want the five of you to give her a tour of the lab after.” 

They all nod, a few of them giving you encouraging smiles as you start. Pyxis turns to you, taking your seat at the table as you take the pointer in your hands, starting your presentation. 

“Right. Um, I’m F/N L/N. It’s nice to meet you all. I, um, completed my undergraduate studies at Shiganshina University. I got a b-bachelors in applied neuroscience and computational biology. I’ll be presenting my thesis project pr-proposal.” 

You hate this shit. You’re stuttering over your words and they’re all staring back, completely uninterested in your work. The PhD students in front of you aren’t even taking you seriously - the girl with glasses nearly stumbling off her chair from sliding around on it and the guy with dark black, grey steely eyes more interested in his cup of fucking tea than what you were talking about. 

“Right, so. My project aims to study interoceptive signals - like heartbeat, respiration cycles, blood pressure - and use them to predict and decode intentions. These small biomarkers, entirely unconscious to us, are consistent during decision making, unbeknownst to us. We can exploit that - to understand higher level cognition.” 

You’ve got their attention - you can tell. This is always the easy part, drawing them in - the woman from before stopped sliding on her chair, instead leaning forward with her eyes shining at your slides, the guy with the tea momentarily flickering his eyes up to the screen. 

“You can use it to predict how people act, how they feel. Especially for something like heart rate, which is what I want to focus on, you can understand so many things - anxiety, stress, companionship, sexual attraction, romance.” 

You see one of the PhD students murmur under his breath, interrupting you in your stead. Nile, they said his name was. 

“So you want to be a…love expert?” 

The entire room laughs, giving you smiles as you continue on. You give him a smile, responding. 

“I guess you could say that.” 

You continue on - highlighting how the brain regulates these signals, what equipment you’ll be using to record all of it. 

They clap when you’re done. Success. 

 - 

You feel fully settled into the lab, a few months later. You’ve decorated your tiny cubicle, directly in the middle with the other PhD students, with a few knick knacks - a picture of you and your best friend, a tiny little green figurine your parents gifted you, and a rack for your headphones. 

You’re located in the section with the other PhD students, who are…interesting. 

On the first day, they lead you to take the cubicle directly next to Hange, which you realized was a bad idea. Because they set you up. Hange’s a biochemist - doing research on the brain tissue at the molecular level, trying to understand how glioblastomas progress. Meaning - they’re always playing with chemicals at their desk, sometimes too lazy to walk over to the lab, which leads to some interesting smells and…smokes in your area. 

They never get in trouble, because Erwin and Petra always come to save the day. They’re both leading policy experts, studying volition and decision making in hopes to use in applications to the law and judicial systems. Figuring out why criminals commit crimes, using it for to serve justice. They cover up the evidence, distract Pyxis and Shadis, and talk their way out of it on Hange’s behalf. 

And that leaves Nile, who isn’t particularly your favorite. He’s a bit hard to get along with, not exactly personable per say. He’s researching microdosing and addiction - trying to figure out how we can manipulate medicines or drugs into being more or less addictive. 

You almost forgot about him. Levi, who's currently leading you to the MRI room on the other side of the building. Definitely the most intriguing of all of your colleagues - using transcranial brain stimulation to decode intentions. In less jargony terms, he read minds. 

He puts the decisions made on the tests into algorithms, correcting it until the machines can predict the decisions being made perfectly - that can be applied to anyone, not just singular participants. He’s coding human thought into machines. And doing it successfully. 

Levi’s quiet, perplexing, and intelligent. An enigma. He’s stood out to you, more than anyone else, for the simple reason that he’s the only one who doesn’t want to talk to you. Hange invites you out for drinks, Petra introduced you to her boyfriend, Erwin bought you a birthday present even though you didn’t tell anyone it was your birthday, and Nile asked you on a date (which you obviously declined). 

But Levi doesn’t care. You don’t either, but it does intrigue you at times. Why he’s so quiet, so closed off, what he’s always doing on his laptop, who he texts on his breaks. This was the first time you were alone with him - getting roped into participating in his newest study. 

“Newbie has to do it.” 

“Do what, Hange?” 

“Levi likes to experiment on all of our brains. You’ve never done it and he needs someone, so we’re volunteering you.” 

Hange and Erwin pull you up by the wrists, all but pushing you out of the conference room into Levi’s cubicle, where you almost trip and fall over him. He looks up - already deeply uninterested with the three of you standing in his space - as he removes his hands from his keyboard. 

“What, brats?” 

“I’m not participating. She is. Take her away!” 

He looks between the three of you, clearly unamused with how nonchalant Hange was being about the whole thing, as they knocked over Levi’s stack of books on the floom. They nearly shake his entire frame in their hands as they thanked him profusely for not making them participate. 

Erwin picks up the stack of books - somehow shuffling them all out of order as Levi gets even more frustrated - shooing the two of them out of his space. After successfully removing them, you and Levi walk towards the MRI room, all the way across the building, in silence. 

When you get there, he taps his hand on the platform, signaling for you to sit on it. You obediently follow, still not uttering an entire word. You watch him mill around the room - pressing switches, using the intercom to communicate with the operator, turning the lights off. 

“Wearing any metal?” 

“My necklace. I’ll take it off.” 

You reach up, awkwardly fumbling with the clasp as he watches you, his hands pressed to his sides as he waits. You’re not sure what it is - how sweaty your hands are, the way he’s looking at you, awkwardly waiting for you to finish - but you can’t get the clasp off, your hold shaking behind your hair. 

“I can help you.” 

You meekly nod, getting off the platform. Before you can, he reaches forward, his slender hands gathering your hair before placing them across the side to your shoulder. You feel his knuckles against your nape, quickly unlatching the necklace and fixing your hair back into place. 

“I’ll hold it for you.” 

You get back onto the platform, lying flat, as Levi uses the intercom to signal to Armin, one of the undergraduate students who worked in the MRI building. You can feel the platform sliding you into the tube and you suddenly feel it. 

Your claustrophobia. Every horrible thought you can imagine is running through your head as the machine starts whirring, your heart pounding in your chest. An earthquake - the machine would crush you. The magnets can be too fast, the machine malfunctioning while you’re stuck inside it. There could be a fire and you would be left here, everyone leaving you and locking you out of the room. 

“You okay?” 

“Y-yeah, Armin. Sorry. I get a bit claustrophobic, that’s all.” 

“Okay, take your time. Try to stay still so we can get better pictures.” 

You nod, trying to still your breaths as the machine whirrs on again. You can feel your nails digging into your palms, as you try to calm down, the panic still sitting in your chest. You feel a hand circle around your ankle, squeezing twice, as the machine keeps going. 

“You okay, Newbie?” 

“Yeah, Levi. I’m okay.” 

“I’m here. Get out if you’re uncomfortable. I’ll just drag Shitty Glasses by the scalp to do it instead of you.” 

You laugh, his hold still firm on your ankle. You try to focus on it - the fine print on the machine, your back against the platform, his fingers on your skin as the machine keeps going, your panic still writhing in your chest. The MRI finishes - Levi giving you one last squeeze before the platform slides out and you nearly jump out of the machine. 

You and Levi walk back to the main lab, in silence. When you get there, Levi gives Hange’s ponytail one big yank before settling back into his cubicle, giving you a soft smile before you return to yours. 

-   

It’s Levi’s turn to present for the lab meeting. The lab is going to Hizuru for Sigtuna, one of the largest neuroscience conferences to date. The PhD students are all presenting posters, except Levi who was invited to give a talk. 

You had been helping Levi as of late - working with him to identify the sulcuses and the lobes on all of Levi’s MRIs. He had no experience in magnetic resonance imaging whatsoever - something you had spent years learning during undergrad. So the two of you had worked out a system - you helped him with identifying the images and helped you troubleshoot your code for your tasks whenever you needed it (which was often). 

You spent a lot of time together - even if it wasn’t direct. You’d sit in silence in the main conference room, working for hours. He’d bring you a cup of coffee and you would pick up dinner, talking through ideas as you finished off your projects. 

You had helped him write the grant for the talk instead of the poster, helping him with all the physiological portions. He taught you how to do all the analysis for yours - the two of you often the one’s leaving the lab latest, Levi walking you to your car in the dark before walking off to his own. 

You were friends. Project partners. 

He gives you one last look before starting the presentation and you shoot him a thumbs up under the table, which he returns with a smile. He’s explaining - using your brain and Hange’s as the sample templates to explain what he was doing - what parts of the brain he has to use for his machine learning. 

“This is Newbie’s and this is Hange’s brain. In theory, each part of the brain is slightly bigger, depending on what parts of your brain you exercise more. For example, Hange is involved in more motor-dexterity - running all their projects by hand. This part of the sulcus is more developed, bigger because of it, compared to Newbie.” 

Nile nudges you on the side, whispering something about how he can give you something to do with your hands if you needed it. You roll your eyes, awkwardly shuffling farther as you refocus on what Levi was saying. 

“This part of the brain is more developed for Newbie, the Brodmann areas - associated with critical thinking, higher level cognition, decision making. Good thing I didn’t use your brain, Dok. We wouldn’t even be able to catch it on the image if we used yours.” 

The entire room laughs - Nile sulking in his chair as Levi continues. You don’t miss the look he gives you afterwards, his eyes uncharacteristically soft when he meets yours, as he continues the presentation. 

When he finishes, Pyxis goes over the room assignments, mentioning that there were three rooms for all the PhD students - meaning a few of you would have to pair up. You turn your neck to look at Petra, who's already nodding and agreeing with Hange that they would room together. You deflate, watching Erwin and Levi pair up. Which leaves you next to Nile, who's all but too excited to be your partner. 

He slings his arm around your shoulder, saying that you guys can share the bed if it gets cold at night, which leaves you shooting dangerous looks at Hange. Levi catches on first, immediately dragging Erwin over to where the two of you were standing. 

“Dok. Erwin is going to room with you.” 

“Says who?” 

“Says me. Don’t argue with me today, I’m already sick of you.” 

Levi grabs you by the wrist, dragging you towards the other side of the room as he rambles on. 

“What a fucking idiot. First he interrupts me during my talk and then starts saying perverted shit like that. Someone’s going to smack him upside the head one day and I surely hope for my sake it’s me.” 

You wrap your arms around his neck, squeezing him twice before letting go. 

“Thank you for that - I was literally going to vomit if I had to room with him.” 

“Well, I told you before. I’m here if you’re uncomfortable.” 

You nod, the two of you walking into the conference room to make edits to your presentation. 

 - 

You and Levi come back to your hotel room after the conference, positively plastered. He’d all but given his talk perfectly and your poster won an award at the end - which meant you and Levi were celebrating well into the night. 

You had your arms slung around each other, your weight uneven, as you both slide back into the hotel room, falling onto the singular bed in the room. You and Levi were greeted with the unpleasant sight earlier in the day - you and Levi both insisting that you would be the ones to sleep on the couch. 

You’re both lying face up on the bed - your cheeks flushed, your chests heaving up and down, the only sound in the room being your shaky breaths. Your hands are still locked together, your brain fuzzy from the events of the night. 

You and Levi amble up after a few minutes, both attempting to change into your pajamas and go to bed. You ogle Levi as he takes his shirt off, watching from the side of the mirror. He catches you, walking closer to you. He still reeks of beer, still shaking on his feet. 

He leans over, pressing his forehead against yours as you hold onto his arms, grounding your fingers into his biceps. He’s still not wearing a shirt, his bare chest on display. You fight the urge to stare at him full on. 

“You’re smart, Y/N.” 

“You’re smart too, Levi.” 

“Did you pay attention during my talk?” 

“Y-yes. You code the information, like a puzzle, to figure out what people’s intentions are.” 

“Hm. You be me. I’ll give you the information and you figure it out, okay?” 

You nod, barely understanding what he was getting at as you lean into him. You can feel the buzz dying down, the tiredness setting into your bones. 

“I’m not a mind reader like you, Levi.” 

“You’ll get this one. You’re my smart girl.” 

He reaches down, securing his hands around your waist as he pulls you closer to him. Your hands and frame are pressed against his chest, his skin cold to the touch.

“You caught my eye on the first day, with your perfectly pressed hair and that stupid black skirt.” 

You can feel your breath catch in your throat, the sound not leaving your throat.

“You take the cubicle two feet down from mine and I can’t help but watch you - reorganize your desk, get up to get water, scribble things on the whiteboard.” 

You can feel his heartbeat get faster against your hear, his grip on your waist tightening. You’re suddenly too aware of what’s happening - Levi, PhD Levi, is shirtless, hugging you in a hotel room. The lights are dim, there’s only one bed, and he’s holding you. 

“I don’t work with other people at the lab, but when you ask, I do. I leave the lab way past the required time, willingly spending more time in a room with that idiot Nile in it just because you’re in it too.”

“Levi.” 

“I’m not done.”

“It drives me crazy, every time Nile talks to you, touches you, looks at you. I want to sock him in the face - because he’s not nearly good enough for you. Not that anyone could be, but for some idiot like that to think he stands a chance is next level infuriating.”

He releases his hands from your face, lifting his hands to cup your face. His touch his soft, his thumb caressing the burning skin on your cheeks as his eyes meet yours.

“I think about you all the time. When I wake up, when I go to sleep, when I eat my breakfast. When I’m not with you, I just want to be around you. And when I’m around you, I want to be with you.” 

He leans forward, pressing a soft kiss to your forehead. His lips are pillowy soft, his breath tickling the edges of your forehead. 

“What does it mean? Figure out my intentions, smart girl.” 

You can feel your entire body burning, your head still spinning - from the alcohol, Levi’s touch, his words ringing in your ears. 

“You…like me.” 

“That’s a fact. Not an intention.” 

“You…want to kiss me?” 

He smiles, leaning forward to press his lips to yours. The kiss is warm, the taste of the beer still hanging on his lips. You can feel his hands moving, carding through your hair as you reach up to press your hand against his shoulders. He kisses you for a long time - your body burning at the entire sensation. He breaks apart, still smiling against your lips. 

“Smart girl.” 

“Do you…remember my research, Levi? From the first day?” 

“I’ve memorized every single thing you’ve ever said to me.” 

You can feel your cheeks flushing, Levi’s hands returning to squish the sides of your face. You grab one of his hands, opening up his fingers and placing it flat against your chest. You move his hand around, until you’re sure he can feel your heart - which is pounding in your chest. 

“Heart rate can give away a great deal. The biomarker can help you understand a lot of different emotions. Figure out which one I’m feeling, Levi.” 

He leans forward, pressing soft kisses all over your face as he starts asking. 

“Anxiety?” - a soft kiss, right on top of your head. 

“No.” 

“Stress?” - a light kiss, right on your closed eyelids. 

“No, Levi.” 

“Companionship.” - a sweet kiss, right on your lips. 

“Yes. But that’s not the one I was looking for.” 

You watch a smirk spread across his face as he leans down, spreading soft kisses all along your neck. He murmurs against your neck, a hint of teasing in his voice. 

“Sexual attraction?” 

“Levi. Quit being a tease.” 

“Shut up, brat.” 

“No. You missed one, Levi.” 

“What was it?” 

“Love. A heartbeat can give away a great deal - can even be used to indicate and understand romantic feelings.” 

He press his hand against your chest again, your heart still hammering. 

“It’s fast. What does that mean?” 

“That I love you.” 

You see a big smile spread across his face, reaching all the way up to his eyes. You see him now and you think it’s the best he’s ever looked - messy black hair, pink cheeks, squinted eyes. He reaches down, opening your fingers and placing them against his bare chest. You can feel his heart hammering in his chest. 

“Fast.” 

“Yeah. Means I love you too, smart girl.” 

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[covid-safer-hotties]

Also preserved on our archive

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In our latest roundtable series, experts discussed Long COVID prevalence, underreporting, accurate diagnosis, and emphasized that Long COVID serves as an umbrella term.

This content was originally published by our sister publication, ContagionLive.

Since the start of the COVID-19 pandemic, the persistence or appearance of neurologic symptoms after clearance of SARS-CoV-2 infection has become a serious health challenge for patients and clinicians worldwide. The effects of postacute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, can be debilitating and persist for months after infection. Some of these symptoms can include fatigue, neuropsychiatric sequelae, sleep disturbances, sensorimotor symptoms, cognitive impairment/brain fog, hypoguesia/hyposmia, hearing loss, and ocular symptoms.

As emphasized by the research and experts in the field, currently there are no specific tests for the diagnosis of Long COVID, and clinical features such as laboratory findings and biomarkers may not specifically relate to the condition. It is important to develop and validate biomarkers for the prediction, diagnosis, and prognosis of Long COVID and its response to therapeutics. Regardless of age or preexisting health conditions, Long COVID can affect anyone, highlighting that this condition is not restricted to any specific demographic and does not discriminate, even against the healthiest individuals.

Recently, we conducted a Long COVID roundtable in collaboration with ContagionLive® to continue our roundtable video series where we delve into important clinical neurological disease topics with a comprehensive discussion with clinicians in the field. In this first episode, clinicians discussed the prevalence of Long COVID, its underestimation because of subclinical cases and recruitment challenges, and stressed the importance of thorough history-taking for accurate diagnosis, especially regarding its overlap with Myalgic Encephalomyelitis/chronic fatigue syndrome (ME/CFS).

Our panel of clinicians include:

Ravindra Ganesh, MD, MBBS, FACP, Dip ABOM, general medicine doctor at the Mayo Clinic and leader of their Long COVID clinic. Svetlana Blitshteyn, MD, FAAN, clinical associate professor of neurology at the University at Buffalo Jacobs School of Medicine and Biomedical Sciences, director of the Dysautonomia Clinic. Monica Verduzco-Gutierrez, MD, physical medicine and rehabilitation physician, professor, and chair of rehabilitation medicine at UT Health, leader of the Long COVID clinic. Transcript edited for clarity.

Verduzco-Gutierrez: “Long COVID can be something that we know is very prevalent, but sometimes it is an invisible illness. People might not think that it's that much of an issue, but can be for sure. One thing that we see from numbers, if we look at household pulse survey, is probably somewhere close to one in five people in the United States have had to deal with Long COVID. Maybe they don't actively have it, and then we know that other studies have showed that one in 10 people who get COVID can have Long COVID and develop something related to Long COVID. So that number is significantly high, if you think of the number of people who've had infections, and the number of Americans, I think it was one in seven at one point, may have been dealing with Long COVID.”

Ganesh: “That number is probably higher when you consider demo to people with subclinical, Long COVID, who've had covid but haven't been quite as sharp afterwards, or haven't been quite 100%, and that number of people doesn't get reported, but you talk to patients, and they'll tell you they just haven't been quite right after having had COVID.”

Verduzco-Gutierrez: “One of the things I hear from researchers is that is it hard to recruit patients who've had COVID and completely return to normal. People say, "I have a little bit of brain fog," or "I have not been able to exercise just as much as I used to before." But maybe studies showing that the diagnoses given, maybe using the ICD 10 code of Long COVID is very low compared to what numbers of people that have it."

Blitshteyn: "It's important to establish that there was a COVID infection, some patients don't even realize. They presented to me with new onsets of neurologic symptoms or conditions. It's important to ask, “How did this start?” “Did you have COVID before the new onset of symptoms?” Sometimes it's a revelation to the patient that, “Oh, yeah, I had COVID.” The numbers are underestimated, because the patients may not realize that the prior COVID infection is related to the new onset of symptoms that they're having, and the infection may have been very mild and hardly visible, but we know the consequences may be significantly bigger than the original infection."

“The first thing I do is obtain a very thorough history of symptomatology, onset, preexisting conditions, comorbid conditions, medications, and how these symptoms affect the patient currently. Because I specialize in autonomic disorders, I end up getting the sickest patients. Therefore, I have a referral bias, and my patients tend to be quite sick, and by the time they see me, they have already seen their primary care physician, their cardiologist, their gastroenterologists allergists and other specialists. I obtain a history of medications, social history, (which means they're able to continue their employment). Many times, they are unable to engage in prior activities. Many of my patients are unemployed or unable to attend school, and there is a history of exercise intolerance. The patients identify that they were able to go to the gym or run or bike and now they can’t do it either, they're severely impaired, or they can do barely a walk around the block. Following that they get a family history; is there a family history for cardiovascular, neurologic and autoimmune disorders?”

“We obtain a thorough medication list, including vitamins and supplements, and then proceed with a review of the patient's history. Oftentimes, when assessing from head to toe, we recognize that many patients with Long COVID experience a significant symptom burden that spans multiple systems and organs. Symptoms can range from neuropsychiatric manifestations to muscular and skeletal issues, as well as allergic reactions and gastrointestinal disturbances, including sleep disturbances.”

“And then after that, we go straight to physical exam, which always includes a 10-minute stand test, which is the consensus guidance from multiple societies and organizations recommend because it's very important to establish whether there is evidence of orthostatic intolerance, including postural tachycardia or orthostatic hypotension. At least 70% of patients with Long COVID have autonomic dysfunction, so we try to assess for that, and then we proceed with physical exam includes neurologic examination.”

“Special attention is paid to neuromuscular exam, including all the sensation’s reflexes. Often there is a comorbid small fiber neuropathy. It's not uncommon for our patients with Long COVID to have reduced sensation of temperature and the pinprick distally and sometimes approximately in Apache distribution. There is a cardiovascular exam and skin exam because patients may have rashes. We must do an exam for joint hypermobility, because we know that people with joint hypermobility as a sign may be at risk for Long COVID.”

Verduzco-Gutierrez: “I would definitely say I still always want to see how, and document how their Long COVID symptoms impact their function and their quality of life. So especially, if they were working before, if they can't work; can they get out of bed? Can they spend time with their family? Are they having post exertional malaise? That's something in the history that I want to hear about, because we know that's something patients with Long COVID definitely have, other than just regular fatigue, and especially if you think someone that may eventually need disability, it's important that we document functionality as much as possible.”

Verduzco-Gutierrez: “Some patients, their manifestation of Long COVID is a picture of ME/CFS as well, because they have the criteria for that, because a lot of the criteria also are some of very similar to Long COVID symptoms, because they have post exertional malaise, they have unrefreshing sleep, some of them have autonomic dysfunction, cognitive deficits as well. Each of those individual things can be part of a Long COVID picture too. I won't say you can only have Long COVID, you can only have ME/CFS, depends on the history they may have both. They may have one, maybe they didn't have a SARScoV2 infection. ME/CFS has been around for decades and can happen from other infections or other things as well. It's going back to listening to the patient, listening to their history, seeing what the symptoms are and what criteria they meet.

Blitshteyn: “We have a number of studies that showed that at least 50% of patients with Long COVID qualify for the diagnosis of ME/CFS. It's certainly a subtype that we all need to learn how to assess and manage to the best of our abilities. Now, I think it's important to say that in addition to this phenotype, we must think about post-COVID conditions, because Long COVID is an umbrella term. We have to make sure that we allow cardiovascular manifestations, hyper coagulase, diabetes, metabolic abnormalities, hyperlipidemia, hypertension, that can often begin after COVID infection. Once we go through that process, and we arrive at something like, tests look normal, but the patient is still sick. We have to go with autonomic dysfunction and ME/CFS type of phenotype, and there we have to do our best to assess and also manage the symptoms. Because there are no FDA-approved therapists for Long COVID, but we certainly have a number of pharmacologic and non-pharmacologic measures to treat patients with autonomic disorders as well as ME/CFS.”

Ganesh: “Taking care of ME/CFS before Long COVID, we knew even then that about 70% of all cases of ME/CFS were related to an infection of sorts, and we have seen it with different infections, most commonly EBV (Epstein-Barr Virus), we were seeing it with chronic Lyme, reported mold exposure, and after Zika. We have seen after a bunch of different other infections before this. When I diagnose a patient with Long COVID, but what kind of Long COVID? Long covid with an ME/CFS phenotype plus autonomic dysfunction with GI predominance, you have to drill it down because can you devise a therapeutic regimen that may help your patient.”

Verduzco-Gutierrez: “One thing we know about, patients after COVID and with Long COVID, is that we've seen also those patients have viral reactivation. In bringing up EBV, then we know that sometimes after COVID, some patients have reactivation of EBV or other types of viruses, and then there's the issues that can come with that.”

[pandemic-info]

Researchers Develop Algorithm To Predict Whether A Person Gets Long Covid

Researchers Develop Algorithm To Predict Whether A Person Gets Long Covid

https://www.forbes.com/sites/willskipworth/2023/09/25/researchers-develop-algorithm-to-predict-whether-a-person-gets-long-covid/?sh=442ada729259

[creativeera]

Cancer Biomarkers Market Poised to Exhibit a CAGR of 7.3% by 2031

Cancer biomarkers are substances whose presence is indicative of some biological condition, processes, or pathology. They can be used for cancer diagnosis or checking effectiveness of treatment. Being non-invasive procedures, demand for cancer biomarkers is growing rapidly. They aid in early detection of cancer during screening programs and reduce cost of cancer treatment. Global cancer biomarkers market is estimated to be valued at USD 25.60 Bn in 2024 and is expected to reach USD 59.01 Bn by 2031, exhibiting a compound annual growth rate (CAGR) of 12.7% from 2024 to 2031.

Key Takeaways Key players operating in the Cancer Biomarkers market are Schlumberger Limited, Rockwell Automation Inc., SIS-TECH Solutions LP, Emerson Electric Company, HIMA Paul Hildebrandt GmbH, Honeywell International Inc., Siemens AG, Yokogawa Electric Corporation, Schneider Electric SE, and ABB Ltd. They are investing heavily in biomarker detection methods and panels targeting unmet clinical needs. Rising incidence of cancer across the world is driving for Cancer Biomarkers Market Demand. Biomarkers help in cancer screening and detecting disease at early stages. This improves treatment outcomes and survival rates significantly. Initiatives by governments and cancer councils to spread cancer awareness are also boosting the market. Global expansion strategies adopted by leading players are expected to support market growth during the forecast period. They are expanding their footprint in emerging markets of Asia Pacific, Latin America, and Middle East & Africa to tap the high growth opportunities. This will increase access to advanced cancer diagnostic solutions. Market Key Trends The use of artificial intelligence and machine learning algorithms to discover novel biomarkers from large datasets is a key trend in the market. It helps accelerate the process of biomarker identification. Genomic and proteomic biomarkers are also gaining traction for their role in cancer detection as well as tracking cancer progression and drug response. Development of personalized diagnostics based on multi-omics approaches and liquid biopsy tests are some other trends expected to shape the market.

Porter’s Analysis Threat of new entrants: The cancer biomarkers market requires huge capital investments in R&D for developing novel biomarkers and testing kits which makes the entry difficult for new players. Bargaining power of buyers: Buyers have moderate bargaining power in this market as there are many players offering similar cancer biomarker testing services. Bargaining power of suppliers: Suppliers have low bargaining power due to availability of alternative raw material suppliers in the market. Threat of new substitutes: Substitutes have low threat as there are limited substitutes available for cancer biomarker tests. Competitive rivalry: The market is highly competitive due to presence of many global as well as regional players. Geographical Regions North America region accounts for the largest share of the cancer biomarkers market in terms of value due to presence of major players, rising healthcare expenditure and increasing prevalence of cancer in the region. Asia Pacific is expected to grow at the fastest CAGR during the forecast period owing to increasing awareness regarding cancer, improving healthcare infrastructure and rising access to diagnostic services in emerging economies of China and India in this region.

Get more insights on Cancer Biomarkers Market

About Author:

Money Singh is a seasoned content writer with over four years of experience in the market research sector. Her expertise spans various industries, including food and beverages, biotechnology, chemical and materials, defense and aerospace, consumer goods, etc. (https://www.linkedin.com/in/money-singh-590844163)

[circumlocutive]

Undergrad research blast from the past. Here I am in 2020 assembling a micro fluidic flow cell with a gold electrode block. I think I took this video for myself so I knew what to clip to what. This was when I worked with electrochemical sensors, transducing signals via impedance spectroscopy.

A lot of electrochemical techniques rely on measuring voltages or currents, but in this lab we looked at impedance- which is a fancy combination of regular resistance (like the same one from ohms law) and the imaginary portion of the resistance that arises from the alternating current we supply.

I would functionalize different groups on the gold working electrode by exposing the surface to a solution of thiolated biomarker capture groups. Thiols love to form self-assembled mono layers over gold, so anything tagged with thiol ends up sticking. [Aside: Apparently after I left the group they moved away from gold thiol interactions because they weren't strong enough to modify the electrode surface in a stable and predictable way, especially if we were flowing the solution over the surface (which we wanted to do for various automation reasons)]. The capture groups we used were various modified cyclodextrins- little sugar cups with hydrophobic pockets inside and a hydrophilic exterior. Cyclodextrins are the basis of febreeze- a cyclodextrin spray that captures odor molecules in that hydrophobic pocket so they can't interact with receptors in your nose. We focused on capturing hydrophobic things in our little pocket because many different hydrophobic biomarkers are relevant to many different diseases, but a lot of sensors struggle to interact with them in the aqueous environment of bodily fluids.

My work was two fold:

1) setting up an automated system for greater reproducibility and less human labor. I had to figure out how to get my computer, the potentiostat (which controls the alternating current put in, and reads the working electrode response), the microfluidic pump, and the actuator that switched between samples to all talk to each other so I could set up my solutions, automatically flow the thiol solution for an appropriate time and flow rate to modify the surface, then automatically flow a bio fluid sample (or rather in the beginning, pure samples of specific isolated biomarkers, tho their tendency to aggregate in aqueous solution may have changed the way they would interact with the sensor from how they would in a native environment, stabilized in blood or urine) over the electrode and cue the potentiostat for multiple measurements, and then flow cleaning solutions to clean out the tubings and renew the electrode. This involved transistor level logic (pain) and working with the potentiostat company to interact with their proprietary software language (pain) and so much dicking around with the physical components.

2) coming up with new cyclodextrin variants to test, and optimizing the parameters for surface functionalization. What concentrations and times and flow rates to use? How do different groups around the edge of the cyclodextrin affect the ability to capture distinct classes of neurotransmitters? I wasn't working with specific sensors, I was trying to get cross reactivity for the purpose of constructing nonspecific sensor arrays (less akin to antibody/antigen binding of ELISAs and more like the nonspecific combinatorial assaying you do with receptors in your tongue or nose to identify "taste profiles" or "smell profiles"), so I wanted diverse responses to diverse assortments of molecules.

Idk where I'm going with this. Mostly reminiscing. I don't miss the math or programming or the physical experience of being at the bench (I find chemistry more "fun") but I liked the ultimate goal more. I think cross reactive sensor arrays and principle component analysis could really change how we do biosample testing, and could potentially be useful for defining biochemical subtypes of subjectively defined mental illnesses.... I think that could (maybe, possibly, if things all work and are sufficiently capturing relevant variance in biochemistry from blood or piss or sweat or what have you) be a more useful way to diagnose mental illness and correlate to possible responses to medications than phenotypic analysis/interviews/questionnaires/trial and error pill prescribing.

[cbirt]

A Bioinformatics Roadmap for Drug Prioritization from Cancer Genomics Data

Cancer is a complicated disease brought on by the interaction of various informational layers. Tumor origin, the appearance of genomic and transcriptomic variations, or interactions with the microenvironment are all factors that contribute to the difficulty of treating tumors. With reference to the types of tumor heterogeneity and the accessible data from next-generation sequencing, the current methods for selecting a therapy are presented in this article. The study elucidates the potential integration of bioinformatics in precision oncology.

Cancer is an evolving dynamic disease that becomes more diverse as the disease progresses. One of the core reasons why medication doesn’t work and patients relapse has been identified as its heterogeneity. A burgeoning area called precision oncology aims to create personalized cancer treatments for every cancer patient using data from epidemiological, clinical, and omics sources. Targeted therapies are considered an anchorage of precision oncology. The Food and Drug Administration (FDA) has authorized 214 predictive biomarkers by the year 2022, up from 39 in 2013, as a result of ongoing attempts to identify novel predictive biomarkers of anticancer drug effectiveness. BRAF V600E inhibitors in melanoma patients and imatinib to target BCR-ABL translocations in chronic myeloid leukemia are common examples of targeted therapies.

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[bpod-bpod]

More the Better

Images of immunofluorescence highlighting signature molecules and traditional histology methods from the same tumour section generate biomarkers highly predictive of cancer progression rate

Read the published research paper here

Image from work by Jia-Ren Lin, Yu-An Chen and Daniel Campton, and colleagues

Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA

Image originally published with a Creative Commons Attribution 4.0 International (CC BY 4.0)

Published in Nature Cancer, June 2023

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[drforambhuta]

Targeted Therapy:

Precision or targeted therapies encompass medications engineered to disrupt specific molecules implicated in the progression of cancer. In contrast to conventional chemotherapy's broad impact on fast-dividing cells, precision therapies selectively target cancer cells while preserving healthy tissue integrity. These drugs aim at various molecular pathways involved in cancer development, including signaling cascades, angiogenesis, and DNA repair mechanisms.

An illustrative example of precision therapy is the application of tyrosine kinase inhibitors (TKIs) in treating specific cancer types like non-small cell lung cancer (NSCLC) and chronic myeloid leukemia (CML). TKIs hinder the activity of particular tyrosine kinases, crucial enzymes in cancer-promoting cell signaling pathways. By obstructing these kinases, TKIs effectively inhibit tumor growth and extend patient survival.

Likewise, monoclonal antibodies represent another form of precision therapy, binding to specific proteins on cancer cell surfaces, initiating immune-mediated tumor destruction. These antibodies can also be combined with cytotoxic agents or radioactive isotopes to heighten their anti-cancer properties.

Personalized Chemotherapy:

While precision therapies are central to personalized medicine, tailored chemotherapy remains vital in cancer treatment. Tailored chemotherapy involves customizing traditional cytotoxic drugs to suit the unique characteristics of each patient's tumor. This may involve adjusting drug doses, combining different agents, or selecting chemotherapy regimens based on tumor biology and patient-specific factors.

One approach to tailored chemotherapy utilizes predictive biomarkers to identify patients likely to respond positively to specific chemotherapy drugs. For example, certain mutations in the BRCA genes are associated with increased sensitivity to platinum-based chemotherapy in breast and ovarian cancers. By identifying these biomarkers, oncologists can identify patients who will benefit most from a particular chemotherapy regimen while minimizing potential toxicity for others.

Furthermore, progress in pharmacogenomics, which explores how genetic variations affect drug response, has provided insights into individual differences in drug metabolism and toxicity. By analyzing patients' genetic profiles, oncologists can predict their likelihood of experiencing adverse effects or poor response to chemotherapy drugs, enabling personalized dose adjustments and treatment optimization.

Early cancer detection and management is important for an improved success rate in cancer treatment. You can undergo regular health checkups to get diagnosed for cancer at an early-stage. There are many good hospitals in Mumbai that offer health checkup packages for cancer screening, such as a full body health checkup at Saifee Hospital Mumbai, which is one of the best hospitals in the country.

[mindblowingscience]

In a study in the journal Science Advances, the researchers validated the accuracy of the blood test that identifies key biomarkers of osteoarthritis. They showed that it predicted development of the disease, as well as its progression, which was demonstrated in their earlier work. The research advances the utility of a blood test that would be superior to current diagnostic tools that often don’t identify the disease until it has caused structural damage to the joint.

Continue Reading.

[janelongevityintime]

How AI models are transforming evidence-based predictions

AI is everywhere now. Every sector is taking the help of AI to make predictions. Through Machine Learning we can make computer to think like a human and do as a human. By combining various models there are new innovative ideas that are being produced. We can make new drugs and new way of treatments for patients. This way of treatment is giving right predictions with 95-100% of accuracy at most of the cases. This is now highly used in increasing the Longevity. Finding out the One can take care of their health at any conditions. Regular fitness is checked out. Alerts for the upcoming chances of dangerous diseases is predicted out correctly with AI models. It is not possible to make personalized healthcare for everyone at all the times. That’s why we need this new form of Healthcare Innovation to create a change. These models will maintain separate dataset analysis for each patient. With linear regression we can make the comparison with inputs and outputs and AI models will make the necessary predictions to make it to the output. The status of the patient is always traced correctly and always follows the health trends. People who want to make their bodies to reduce aging and go back to their fit form from any stage can opt for the AI predictive models. With deadliest viruses and life taking climate conditions, every second makes a count. In emergency conditions where we need to make the correct prediction within less time AI models will help a lot. These health predictions are the most helpful in the case of obesity, stroke, various types of cancer and HIV. Diseases which show immediate reaction on our bodies with very less symptoms to show are predicted with various measured biomarkers. These AI models can use various hundreds of markers to make the prediction right. Human of any age can start working on longevity and plan for further ages. The number of centenarians is increasing in the world. The biggest reason for this is increasing in technology. Because we are able to make the correct prediction regarding the health, we can tackle most dangerous diseases.