When I read stories that appear throughout the book, the room becomes pin-drop silent. Not uncomfortable, but attentive. People lean forward. Some close their eyes. Others quietly wipe away tears. Even after reading these stories again and again, my own eyes still mist. These are not reactions to theory or argument. They are responses to a painful reality many recognize.

What becomes clear in those rooms is that the frustration is not isolated to one role or perspective. Patients speak about waiting and uncertainty. Clinicians describe exhaustion and moral strain. Innovators and policymakers wrestle with systems that move more slowly than the problems they are trying to solve. The details differ, but the throughline is the same: people want care that recognizes their presence and treats them as more than a process to be managed. When that recognition happens, the tone of the conversation changes.

Since its listing, the book has spent several consecutive weeks on Amazon’s Top New Releases list. That matters in a conventional sense. Still, rankings, whether in print or digital format, do not explain what happens when people hear their own experience reflected back to them with clarity and respect. Stories do that work. Many are weary of facts and figures deployed to justify positions rather than illuminate lived reality.

Human experience carries a different kind of truth. It does not compete with data, but it precedes it. When experience is named accurately, people do not feel persuaded. They feel recognized. That recognition opens space for reflection, dialogue, and ultimately for change.

A Question That Changes the Room

I finished a book talk and signing with The Marfan Foundation, and the impact lingers beyond the formal program. During the signing, people ask thoughtful, personal questions. I often ask permission to respond by reading a short passage from the book. Then I listen to stories of courage, love, and endurance that surface naturally and without prompting.

Parents speak about children. Siblings talk about one another. Families describe navigating medical uncertainty and emotional trauma over years, sometimes decades. Individuals share how they discover the strength they did not know they possessed, and how they learn to share that strength with others walking similar paths. These are not stories of abstraction. They are lived, detailed, and deeply human.

The Marfan Foundation is one of the patient and professional communities reflected in the book, and in the room, the reason is unmistakable. Physicians are spoken of by first name – Alan, Duke, Kim and Reed – not title. They are described not as distant experts, but as people who show up consistently and with care. These stories remind everyone present that even in the most complex conditions, care is sustained by relationships as much as by scientific excellence.

Between Two Meetings, on a Moving Train

As I board a Brightline train for the next meeting, the contrast stays with me in a quiet, persistent way. I am traveling from a gathering centered on shared human experience to SCOPE Summit 2026, a global convening focused on clinical trials and research infrastructure. The agenda centers on development planning, protocol optimization, patient-centric trial design, site engagement and recruitment, generative AI, and the technologies that move science from hypothesis to evidence.

One meeting is rooted in lived journeys, where science is received as hope amid uncertainty. The other is grounded in structure and precision, where science is designed, measured, and scaled. Both spaces matter deeply, and both are essential to progress. Clinical research is where rigor lives and where uncertainty is reduced in ways that allow care to advance responsibly.

Yet the transition between these two gatherings and two cities reveals something essential. People do not leave their humanity at the door of the operating room or the halls of science. They carry it with them into protocols, endpoints, enrollment decisions and trial participation. Patients do not experience trials as abstractions. They experience them as acts of trust layered onto already complex lives.

When Structure Forgets Experience

Too often, human experience is treated as something to be accounted for after systems are built, rather than as a foundation for their design. Trials are optimized for efficiency and compliance, yet struggle when recruitment falters, participation drops, or trust erodes. These outcomes are not solely technical failures. They are relational failures.

Patient-centric trial design is not a feature added late in development. It is a mindset that shapes questions, assumptions, and priorities from the start. Site engagement is not a procedural step, but a relationship built over time. Technology reduces burden only when shaped by empathy, context, and understanding.

Rare disease communities such as The Marfan Foundation understand this instinctively. When systems fall short, patients and families organize, advocate, and collaborate more intentionally. In doing so, they model what the broader system aspires to scale: trust, continuity, shared language, and partnership. People do not fragment their lives the way systems fragment care.

When Experience Finally Counts

At SCOPE, this question becomes practical rather than theoretical. I moderate a fireside chat with StuffThatWorks executives Yael Elish and newly appointed CEO Julie Ross, exploring what happens when patient experience is treated not as a marginal input but as the foundation of artificial intelligence itself. Billions of dollars are invested in pre-clinical discovery, yet clinical trials remain a costly bottleneck, often stretching beyond seven years before therapies reach patients.

One story from the book captures why this matters. A woman living with a chronic autoimmune condition follows treatment guidelines faithfully yet struggles with side effects that force her to stop therapy repeatedly. Her medical record reflects non-adherence, not struggle. It is only when she joins a patient-driven community where thousands share lived experience that patterns emerge her clinicians have never seen.

Within weeks, she learns how others adjust dosing, manage side effects, and balance treatment with daily life. When these experiences are aggregated and analyzed, they do not contradict clinical science. They complete it. What once looks like noise becomes a signal when the human story is allowed to remain intact.

This is why patient-derived models matter. Real-world evidence is not simply post-market surveillance. It is the accumulated story of how people actually live with disease, navigate treatment, and make trade-offs that controlled environments rarely capture. These data are not neutral artifacts. They are lives rendered into patterns with meaning.

Restoring What Was Lost

What I witness in quiet rooms, at signing tables, and in conversations that follow readings is not resistance to science. I see the same truth as a fireside chat moderator, alongside people dedicated to bridging patient voice, data, and science in ways that honor those it seeks to serve. What emerges, again and again, is a longing for connection.

People are not asking to be spared complexity, nor do they believe science belongs only in a sterile laboratory. They are asking not to be erased by it. They want science that recognizes them even as it advances, and systems that remember who they are designed to serve.

This is where Why People Matter ultimately resides. Healing does not begin when systems are optimized or when data moves faster. It starts when relationships are restored and when people feel recognized within the structures meant to help them. Science advances when trust is present, and trust grows when listening is treated not as an accessory but as a foundation.

If there is a path forward, it is not found by choosing between humanity and innovation. It is found by refusing to separate them. Data matters because people do. And when science remembers that progress becomes worthy of the lives it touches.

The post Why Healing Still Begins with Relationship appeared first on Medika Life.

Artificial Intelligence (AI) is rapidly reshaping public health — from enhancing disease surveillance and diagnostics to easing workforce burdens — but it also raises complex risks and ethical questions. In Europe and globally, public health leaders are grappling with how best to harness AI’s revolutionary potential while managing its pitfalls. After decades of experience, many recognise that AI is not a magic fix for health challenges; its value depends on thoughtful integration into health systems. This article provides an in-depth review of the current relationship between AI and public health. It examines the opportunities it offers, real-world innovations already underway, practical implementation challenges, and the risks and governance frameworks that must guide responsible use. All discussions equally consider European contexts (including emerging EU regulations) and broader global health perspectives.

TL;DR Summary

• AI’s growing role in health: Artificial intelligence is increasingly used to augment public health efforts — from automating administrative tasks to advanced disease surveillance and diagnostics — offering new ways to improve efficiency and reach.
• Tangible benefits observed: Early deployments show promising results. AI tools have reduced clinicians’ paperwork burden, flagged outbreaks days before traditional systems, and enhanced diagnosis in low-resource settings (e.g. catching 15% more TB cases via X-ray analysis).
• Innovations across sectors: NGOs, governments, and companies are all investing in AI for health. For example, PATH and others use AI in field programmes, the NHS has dozens of AI pilots improving care delivery, and pharma companies leverage AI to speed up drug and vaccine development.
• Practical hurdles remain: Successful implementation requires robust data infrastructure, interoperability, and high-quality data. Many health systems must modernise IT systems and address data silos and quality issues before AI can perform optimally.
• Human factors are critical: Integrating AI into workflows and gaining staff acceptance are significant challenges. Training health workers, providing explainable outputs, and maintaining human oversight are essential to building trust in AI-assisted care.
• Key risks to manage: AI in public health brings serious risks — privacy breaches, algorithmic bias harming disadvantaged groups, opaque “black box” decisions undermining trust, and AI-generated misinformation spreading false health advice. Over-reliance on AI without safeguards can also be dangerous.
• Ethics and governance frameworks: Clear principles and regulations are emerging to guide responsible AI use. WHO’s six ethical principles (e.g. transparency, equity, accountability) set value-based guardrails, while the EU’s AI Act will enforce strict requirements on high-risk health AI (mandating transparency, risk management, and human oversight).
• Collaboration and capacity-building: Effectively advancing AI in public health will require interdisciplinary collaboration (health experts with technologists), investment in workforce AI literacy, and inclusive approaches that involve LMICs and marginalised groups so benefits are shared widely.
• Continuous evaluation and adaptation: To ensure AI delivers on its promise, public health authorities must continually monitor outcomes, audit algorithms for bias or errors, and be ready to adjust or suspend systems if problems arise. Adaptive governance and ongoing community feedback are vital for safe, effective AI integration.
• Seizing the opportunity responsibly: When guided by ethical principles and strong oversight, AI can greatly strengthen public health, easing workforce burdens, expanding outreach, and providing data-driven insights. The next few years are crucial for implementing the policies, education, and trust-building measures that will allow AI to be a force for health equity and innovation rather than a source of new disparities or dangers.

Opportunities: Transforming Public Health with AI

AI is being deployed to alleviate several longstanding public health challenges. One significant opportunity is reducing clinician burnout and workforce shortages by automating routine tasks. For example, a 2024 survey found that 57% of physicians believe automating administrative burdens is the top opportunity for AI to ease workloads amid staff shortages. Machine learning systems can transcribe medical notes, pull up patient records, and handle scheduling or prescription refills — freeing clinicians to spend more time on patient care. Many doctors see such automation as a key to improving work efficiency and reducing stress, suggesting AI could help mitigate the healthcare burnout epidemic.

AI also offers powerful tools for disease surveillance and epidemic intelligence. Algorithms can continuously scan vast data sources — news reports, social media, travel data — to spot early signs of outbreaks far faster than traditional methods. Notably, the HealthMap and BlueDot platforms (which use natural language processing and machine learning) flagged the COVID-19 outbreak days before official alerts. By sifting through informal signals and anomalies, AI-driven systems can provide precious early warnings of emerging health threats. BlueDot’s AI surveillance tools have dramatically sped up outbreak detection, reducing manual scanning time by nearly 90% in some cases. Such early alerts enable public health agencies to mobilise quicker responses and potentially contain outbreaks before they spread.

Another area of opportunity is improving diagnostics and clinical decision support, especially in resource-constrained settings. AI image recognition has shown great promise in interpreting medical images like X-rays and retinal scans. For example, AI-based chest X-ray tools for tuberculosis (TB) are being used to help screen patients in low-resource areas that lack radiologists. A recent programme in India led by PATH found that an AI tool (qXR) boosted TB case detection by ~15.8% — identifying cases that human readers missed. Many countries are now utilising AI-assisted chest X-ray screening for TB, which can lead to earlier diagnosis and treatment in underserved communities. Beyond imaging, AI-powered diagnostic apps and chatbots can guide patients through symptom checks or flag high-risk cases for follow-up, expanding access to essential healthcare advice where clinicians are scarce.

Crucially, AI is also being enlisted to address climate-related health threats and environmental impacts on health. Public health researchers increasingly pair AI with climate data to predict disease patterns under changing environmental conditions. For instance, machine learning models can correlate weather patterns (temperature, rainfall) and even animal health data with disease outbreaks to anticipate risks in specific locations. By analysing such data, AI-driven predictive analytics can serve as early warning systems — forecasting surges in vector-borne diseases like malaria following heavy rains or heat-related illness during extreme heatwaves. This capability is ever more critical as climate change intensifies health hazards. AI can help public health officials prepare for climate-sensitive disease outbreaks, allocate resources proactively, and develop adaptation strategies to protect vulnerable populations.

Real-world Applications and Innovations

AI in public health is not just theoretical — numerous real-world initiatives by NGOs, governments, and private companies have already demonstrated its potential. Global health nonprofits and international agencies have been early adopters of AI to support their missions. For example, the Bill & Melinda Gates Foundation has invested heavily in AI-driven global health projects. In 2023, it awarded grants to nearly 50 pilot projects exploring AI solutions for health and development challenges — these range from AI-augmented diagnostic tools to data systems for disease surveillance in low-income settings.

One Gates-backed innovation is AI-assisted ultrasound: in 2020, a $44 million grant was given to develop an AI-guided portable ultrasound to improve lung disease diagnosis in low-resource countries (e.g. detecting pneumonia). Likewise, PATH and other NGOs are integrating AI into field programmes — as seen in the TB screening project, where an AI tool significantly increased case finding while illuminating practical deployment hurdles. These efforts by NGOs underscore AI’s promise to close gaps in healthcare access and quality for underserved populations.

Governments and public health agencies are also launching AI initiatives. In Europe, national health systems pilot AI to improve services and efficiency. For instance, the UK’s National Health Service (NHS) created an NHS AI Lab to fund and evaluate AI innovations in care delivery. By 2025, the NHS had over 80 AI projects live, targeting everything from optimising nurse rostering and predicting hospital bed occupancy to speeding up radiology workflows.

One NHS program provided £100+ million in awards to develop AI for earlier cancer detection, resource management, and patient safety improvements. The NHS AI Lab’s “Skunkworks” team has run short-term projects that yielded practical tools — e.g. an algorithm to streamline the placement of nurses across wards and a natural language processing engine to search health records more efficiently. Meanwhile, European public health agencies are leveraging AI for epidemiology; the European Centre for Disease Prevention and Control (ECDC) has incorporated systems like BlueDot’s AI to enhance epidemic intelligence, including monitoring outbreaks during events such as the 2020 Olympics. These government-led efforts illustrate growing public sector commitment to deploying AI for health system strengthening and emergency preparedness.

The private sector, particularly in healthcare and pharmaceuticals, is likewise driving innovation at the intersection of AI and public health. Pharmaceutical companies now routinely use AI in drug discovery and development. For example, Novartis recently struck a wide-ranging partnership (worth up to $1 billion) to use a generative AI platform for designing new protein-based therapies — aiming to accelerate the search for novel disease treatments. GSK has also embraced AI to speed up R&D: its CEO noted that AI modelling helped cut two years off an RSV vaccine trial by predicting where outbreaks would occur and optimising trial site selection. This led to the faster development of the world’s first RSV vaccine, an essential public health breakthrough.

Beyond pharma, medical technology firms are integrating AI into devices, from smart wearables that flag irregular heart rhythms to imaging systems where AI assists in analysing scans for early signs of cancer. Startups and tech companies are introducing AI-driven health apps and chatbots (such as symptom checkers and mental health conversational agents), which some health services in Europe are trialling for patient triage and support. These real-world examples underscore that AI is already deeply enmeshed in the health ecosystem — from global disease surveillance networks to hospital wards and R&D labs — delivering innovations that could improve population health outcomes.

Practicalities and Implementation Challenges

While the potential is immense, implementing AI in public health is a pragmatic challenge. Infrastructure and data interoperability are foundational hurdles. Effective AI requires robust digital infrastructure — high-quality data streams, electronic health records, and cloud computing capacity — which many health systems lack, especially in low-resource settings. Data needed for public health AI often reside in silos or incompatible formats across hospitals, labs, and agencies. Poor interoperability means AI tools struggle to aggregate and interpret information from disparate sources. Bridging these gaps will require significant investment in health information systems, common data standards, and connectivity. Encouragingly, current AI technology can assist in standardising and mapping messy health datasets to make them more usable. Nonetheless, without reliable infrastructure and data-sharing frameworks, even the best AI algorithms cannot deliver consistent results across a public health network.

A related challenge is data quality and representativeness. AI models are only as good as the data they learn from, and health data can be incomplete, biased, or unrepresentative of specific populations. Studies highlight issues like variability in how data are recorded, large amounts of unstructured text, missing information, and coverage bias (e.g. most training data coming from high-income populations).

These factors can undermine an AI system’s accuracy and value to end users. Developing good AI for health requires carefully cleaning and curating data to reflect clinical reality. For instance, algorithms trained only on European hospital data may perform poorly in rural African communities. Implementers must thus invest effort in data preparation and continuously monitor model outputs for anomalies. Establishing metadata standards, common terminologies, and data quality metrics can facilitate better AI development. Additionally, clarity on data ownership and governance is needed: questions about who “owns” health data (patients, providers, governments?) affect how data can be integrated for AI. Resolving these issues through policies and trust frameworks is key to unlocking data for public health AI while respecting privacy and rights.

Another practical consideration is integrating AI tools into healthcare workflows and gaining workforce acceptance. Introducing AI decision-support systems or automation in clinics requires adapting processes and training staff. Health workers may be understandably cautious — some lack familiarity with AI, worry about accuracy, or fear being displaced. Clear protocols are needed if an AI system’s recommendation conflicts with clinical judgment. Early experience shows that human-AI collaboration works best when AI is framed as an assistive tool rather than a professional replacement. Building trust among the workforce involves providing explainable outputs and demonstrating reliability in pilot phases. It also means training clinicians in basic AI concepts and ensuring they feel confident interpreting AI outputs.

Successful deployments (like the PATH TB screening program) emphasise that significant workflow integration and training efforts are required. In that program, implementers had to solve issues of installing the software in clinics, securing internet connectivity for the AI, and ensuring staff could effectively use the AI results within their screening workflow. Without such groundwork, even a high-performing algorithm might sit on the shelf unused. Thus, the human element is crucial: public health organisations must engage and educate their workforce, adjusting roles and processes so that AI enhances rather than disrupts care delivery. Over time, as clinicians see AI reducing drudgery (e.g. auto-filling forms) and improving outcomes, their acceptance tends to grow. Indeed, physician enthusiasm for health AI has been rising year-on-year. Patience and iterative refinement are needed to blend AI smoothly into the complex fabric of health systems.

Risks and Concerns of AI in Public Health

Despite the optimism, it is vital to acknowledge the risks and potential harms associated with AI in public health. Data privacy and security tops the list of concerns. AI systems often require large datasets of patient information, raising the stakes for protecting sensitive personal health data. Any breach or misuse of such data can erode public trust and violate individuals’ rights. There is also the risk of “function creep”, where data collected for health purposes might be used in other ways (for example, a COVID-19 contact tracing app’s data later being used for law enforcement — a scenario that drew criticism in some countries). Moreover, complex AI models could inadvertently leak private details — for instance, a model might be reverse-engineered to reveal records it was trained on. Ensuring robust cybersecurity and strict data governance is therefore paramount. Many call for comprehensive privacy safeguards and compliance with regulations like Europe’s GDPR whenever AI handles health data. Techniques such as anonymisation or synthetic data can help, but they are not foolproof (even de-identified data can sometimes be unidentified).

The bottom line: without public confidence that AI will maintain confidentiality and data security, its benefits will be lost. Public health agencies must be transparent about what data are used and how to obtain informed consent where appropriate and implement state-of-the-art security measures to prevent breaches. Privacy isn’t just a legal box to tick — it’s fundamental to preserving the trust on which public health interventions depend.

Another significant risk is algorithmic bias and the exacerbation of health inequalities. AI systems can unintentionally perpetuate or even worsen disparities if their design is not carefully managed. This was starkly illustrated by a widely used healthcare risk algorithm in the United States that was found to be racially biased. The algorithm helped determine access to extra care programs and used healthcare cost as a proxy for need. This choice systematically underestimated the needs of Black patients (who often had lower healthcare expenditures due to access barriers). As a result, many high-risk Black patients were less likely to be flagged for additional care, denying them the resources they needed. This example shows how bias in data or design can translate into inequitable outcomes: the AI effectively discriminates against a vulnerable group. Similar issues could arise in public health if an AI model is trained on predominantly male patients under-detect conditions in women or if disease surveillance AI better covers wealthier communities with more data. AI could widen gaps if not addressed, with marginalised populations benefiting the least or even being harmed.

Equity must be a central design principle to counter this: datasets should be diverse and inclusive, algorithms should be tested for bias, and bias mitigation strategies (like reweighing data or algorithmic fairness adjustments) should be applied. The WHO explicitly highlights inclusiveness and equity as core ethical principles for AI, ensuring that AI tools work for all segments of society regardless of race, gender, income, or other characteristics. Ultimately, careful governance and auditing of AI systems are needed to avoid encoding systemic biases into digital form and instead use AI to reduce health inequities (for example, by targeting interventions to underserved areas).

A further concern is the lack of transparency (“black box” issue) and its impact on trust and safety. Many AI models, especially deep learning networks, operate as complex black boxes — they do not explain their reasoning in human-understandable terms. In healthcare, this opacity is problematic. Clinicians and public health decision-makers are wary of acting based on a recommendation they don’t understand, particularly if an AI’s advice contradicts intuition or standard practice. Unexplainable AI can also undermine accountability: if an AI makes a harmful mistake, it may be unclear why it happened or who is responsible. This lack of transparency feeds directly into trust issues among professionals and the public. If people perceive AI as a mysterious, untrustworthy “magic wand” imposed on health decisions, they may reject its use. There have been cautionary tales: an AI system deployed in hospitals to predict which COVID-19 patients would need ICU care was later found to underperform because it hadn’t been adequately validated. Clinicians grew sceptical of its risk scores.

To prevent such scenarios, experts call for explainable and interpretable AI in health — algorithms that can provide reasons for their predictions or use transparent, logical rules where possible. At a minimum, users should have access to information about how an AI was developed and its known limitations. Regulatory frameworks like the EU AI Act are likely to mandate a degree of transparency for high-risk AI (including many medical applications) precisely to bolster trust and enable oversight. Building more explainability into AI models remains a technical challenge, but one that is essential for aligning with the principles of transparency and accountability in healthcare.

In the age of ChatGPT and generative AI, misinformation and “AI hallucinations” have emerged as new public health risks. Advanced chatbots can produce remarkably human-like answers to questions — but they do not guarantee factual accuracy. They can hallucinate false information, confidently output incorrect medical advice, nonexistent statistics, or even fake health news. The potential for harm is considerable if the public uses such tools for health information. There is concern that AI chatbots could magnify the health misinformation problem exponentially — for instance, by generating convincing anti-vaccine narratives or spurious cures, which then spread on social media.

In recent years, public health agencies have struggled to combat misinformation (for example, false claims about vaccines or COVID-19 treatments that undermine uptake). The rise of AI-driven content generators and deepfakes only fuels this fire. Misinformation undermines public trust and can lead people to reject proven interventions in favour of dangerous alternatives. Tackling this will require new strategies — such as watermarking AI-generated content, strengthening content moderation, and improving digital health literacy so the public can better discern credible information. On the flip side, public health communicators might also leverage AI to fight misinformation (for example, using AI to detect false rumours early or personalise accurate health messages). Regardless, the advent of easy, AI-generated disinformation is a serious risk factor that the global health community cannot ignore.

Finally, there is the risk of over-reliance and systemic dependency on AI. If health systems come to depend on AI for critical functions without adequate safeguards, any failures in the technology could have severe consequences. For example, an AI model might perform well in normal conditions but fail to generalise during an unexpected scenario. If everyone has come to rely on its output, they may miss the warning signs until too late. Moreover, heavy reliance on automation might erode human skills over time (a phenomenon observed in other industries). In healthcare, this raises concerns about “deskilling” — clinicians might lose practice in specific tasks (like reading x-rays or making complex diagnoses) if those are always handled by AI, leaving them less prepared to step in when needed.

Over-reliance can also dull vigilance: users might stop double-checking results if an algorithm usually works well so that an undetected error could propagate. The key is to maintain a human-in-the-loop approach: AI should support, not replace, human expertise. Mechanisms for human review of AI outputs and fallback plans in case of system outages are essential.

Additionally, performing regular audits and updates of AI models can prevent performance from degrading unnoticed. In summary, while AI can increase efficiency, public health systems must guard against blindly relying on algorithms. A balanced approach that values human judgment and institutional memory, alongside AI’s computational power, will be safest in the long run.

Ethical and Regulatory Frameworks

Addressing the above risks requires robust ethical guidelines and regulatory oversight for AI in health. Globally, there is growing consensus on core ethical principles that should govern AI development and use in public health. The World Health Organization’s landmark 2021 report laid out six guiding principles for ethical AI in health: (1) Protect human autonomy — humans should remain in control of health decisions, with informed consent and respect for privacy; (2) Promote human well-being and safety — AI must be safe, effective, and designed to improve health outcomes; (3) Ensure transparency, explainability and intelligibility — stakeholders should have sufficient information about how AI systems work and decisions should be traceable; (4) Foster responsibility and accountability — developers and users are accountable for AI behaviour, and mechanisms for redress must exist; (5) Ensure inclusiveness and equity — AI should benefit all groups, enhancing fairness and not amplifying disparities; and (6) Promote AI that is responsive and sustainable — meaning AI should be adaptable, monitored, and designed for long-term societal benefit.

These principles, while high-level, provide a value framework to guide everything from design choices (e.g. using diverse training data to ensure equity) to deployment (e.g. always keeping a human in the loop to protect autonomy). Public health organisations are increasingly adopting such ethical frameworks. For instance, the WHO urges that AI deployments be accompanied by community engagement, training for health workers, and continuous evaluation to ensure technologies remain aligned with the public interest. The ethos is straightforward: AI must be people-centred and uphold human rights. Ethics committees or advisory boards can help oversee AI projects, reviewing them for compliance with these principles before they scale up.

On the regulatory front, governments are now moving to establish formal rules for AI in healthcare. The European Union’s AI Act is a pioneering example of comprehensive regulation. Passed in 2024, the EU AI Act takes a risk-based approach, classifying AI systems by risk level and imposing requirements accordingly. Health-related AI is generally deemed “high-risk” under this law, given its potential impact on people’s lives and rights. High-risk AI systems (including most AI used for medical diagnostics, decision support, or resource allocation in health) will face strict obligations. These include rigorous standards for transparency, risk management, and human oversight. For instance, developers of a clinical AI tool must implement a quality management system, ensure their model is trained on appropriate data, and provide documentation detailing the AI’s function and limitations. They must also conduct risk assessments and put in place human oversight measures to prevent automation bias. Notably, the EU AI Act doesn’t just apply to creators of AI — it also holds deployers (such as hospitals or public health agencies) accountable for the safe use of AI.

Health providers must monitor AI system performance, keep logs, and retain ultimate responsibility for decisions (clinicians must have the authority to override AI recommendations if needed). These provisions aim to ensure that human accountability and patient safety remain paramount even as AI becomes embedded in care delivery. Additionally, the Act has a broad reach: any AI system impacting people in Europe must comply, even if developed elsewhere. This could set an effective global benchmark as companies worldwide adjust their practices to meet the EU’s requirements.

Other jurisdictions are also crafting guidelines. The United States, through the FDA, has been evolving its regulatory approach for AI/ML-based medical devices, focusing on premarket evaluation and the idea of “continuously learning” algorithms needing ongoing monitoring. International bodies like the WHO have issued guidance and urged governance innovation, suggesting that governments update regulations to cover AI, establish certification processes, and possibly create registries of approved AI health products. We also see emerging governance models such as algorithmic impact assessments (to evaluate a health AI system’s potential societal impact before deployment) and independent reviewers’ bias audits. In some health systems, procurement of AI now requires meeting ethical checklists or obtaining approval from institutional review boards, similar to new medical interventions.

These steps are part of building a “responsible innovation” culture around AI, encouraging experimentation and advancement, but within guardrails that protect individuals and communities. Multi-stakeholder collaboration is key here — regulators, technologists, health professionals, and patient representatives need to work together to define safe and effective AI in practice and update those definitions as the technology evolves. As one example, the NHS AI Lab in the UK partnered with regulators to create a sandbox for AI developers, guiding them on navigating regulatory pathways and using synthetic data for testing. Such efforts show that with thoughtful governance, innovation and safety can advance hand in hand.

Future Directions and Recommendations

To fully realise AI’s promise in public health while minimising its downsides, several changes and strategic efforts are needed going forward:

• Investing in data and digital infrastructure: Health systems, especially in low- and middle-income countries, need support to build the data foundations for AI. This means digitising health records, improving data quality, and ensuring platform interoperability. Governments and global donors should prioritise funding for health information systems and broadband connectivity as part of public health capacity building. Better data infrastructure not only enables AI — it strengthens health systems overall. Innovative approaches like federated learning (where AI models train on distributed data without moving it) could be scaled to allow resource-constrained regions to benefit from AI insights without breaching privacy. The goal is to create a world where data flows securely and efficiently to wherever it can improve health outcomes.
• Strengthening workforce capacity and AI literacy: As AI becomes a standard tool, public health and healthcare workers must be equipped to use and oversee it. Training programmes are needed to raise AI literacy among the health workforce, including understanding AI’s capabilities and limitations. This may involve updating medical and public health curricula to cover data science basics. Additionally, new specialist roles (such as clinical AI safety officers or epidemiologists with AI expertise) could be developed to bridge the gap between tech and health domains. Frontline staff should be engaged in co-designing AI solutions so that tools are user-friendly and address actual pain points. When health workers understand and trust AI, they can become champions for its adoption and serve as critical watchdogs who notice when something isn’t right. Fostering a culture of continuous human oversight and feedback will ensure that AI remains a servant to health professionals, not a black box dictator.
• Ensuring inclusivity and equity in AI advancement: The global health community must actively work to prevent a digital divide in AI. Much cutting-edge AI development is concentrated in wealthier countries and tech companies. Deliberate efforts are needed to include researchers and perspectives from low- and middle-income countries in AI design so that solutions address diverse needs. This could consist of research funding earmarked for LMIC-led AI projects, technology transfer programs, and south-south collaboration on AI for health. Moreover, data from underrepresented populations should be collected (with consent and protection) to improve algorithms’ relevance in those settings. By democratising AI knowledge and resources, we can avoid a scenario where only certain countries or communities benefit from AI while others are left behind or subject to unchecked harm. Equity considerations should also extend to gender, age, and other demographics — for instance, ensuring women and minority groups are included in AI development teams and that tools serve users of different languages and literacy levels. An inclusive approach will make AI tools fairer and enlarge the talent pool working on creative AI solutions for entrenched public health challenges.
• Fostering collaboration between public health and technology sectors: Effective AI in public health sits at the intersection of epidemiology, medicine, data science, and engineering. No single sector can do it alone. We need stronger partnerships: governments linking with academia and tech firms, NGOs working with startups, and international agencies convening multi-sector consortia for global health AI initiatives. Such collaboration can accelerate innovation and ensure that public health priorities guide technological development (and vice versa, that technologists are aware of on-the-ground needs). For example, a partnership between a national health ministry and AI researchers might focus on building an early warning system for malaria outbreaks, combining epidemiological expertise with cutting-edge modelling. A pharmaceutical company could also collaborate with global health organisations to use AI in vaccine R&D for diseases of poverty. These cross-sector collaborations should be underpinned by fair agreements (e.g. around data sharing or intellectual property) so that all parties benefit and trust is maintained. The complexity of health + AI demands breaking down silos. International forums and networks can play a role here, enabling countries to share best practices and lessons learned (e.g. how one country successfully regulated an AI symptom-checker or how another trained health workers on AI). Since pathogens do not respect borders, a collaborative global approach to AI-enhanced public health security is in everyone’s interest.
• Adaptive governance and continuous evaluation: As AI tools roll out, it is critical to monitor their real-world impact and be ready to adjust course. Public health authorities should implement mechanisms to continuously evaluate AI interventions — collecting data on their accuracy, outcomes, and any unintended effects. Are the predictions helping improve disease control? Is a triage algorithm safely directing patients to the right level of care? This requires establishing key performance indicators and perhaps creating independent evaluation units. When problems are identified (such as an AI starting to drift in accuracy due to changes in data), there should be processes to update or pull back the tool until fixes are in place. Regulation must also remain adaptive; rigid rules could stifle innovation or become outdated as technology advances. One idea is regulatory sandboxes where new AI solutions can be tested under supervision, allowing regulators to learn and guidelines to evolve. Governance models should be proactive yet flexible, emphasising learning and iteration. Importantly, communities and civil society should have a voice in evaluating AI in public health — their feedback on whether these tools are culturally acceptable, understandable, and improving services is invaluable. Responsible AI is not a one-time certification but an ongoing commitment to quality and ethics throughout the technology’s lifecycle.

Looking ahead, it is clear that AI will play an expanding role in public health — whether in combating the next pandemic, extending healthcare to remote villages via smart apps, or analysing big data to pinpoint disease drivers. The revolution is already underway, but its trajectory depends on our current choices. With enlightened leadership, adequate safeguards, and inclusive collaboration, AI could usher in significant public health gains — from more efficient health systems to healthier communities worldwide. However, if we ignore the risks — allowing unchecked use, widening inequities, or losing the human touch in care — the potential benefits could unravel, and public trust could be irrevocably lost. The coming years are thus pivotal. Armed with decades of hard-won experience, public health professionals have a key role in steering this journey. By insisting on evidence, equity, transparency, and community engagement, they can ensure that the AI revolution in health truly becomes a boon and not a threat. The opportunity is immense, but so is the responsibility to guide AI’s integration into public health thoughtfully and ethically.

The post AI in Public Health: Revolution, Risk and Opportunity appeared first on Medika Life.

Recurrent respiratory papillomatosis (RRP) is just one of those unexpected HPV-driven conditions. RRP is not a sexually transmitted disease and patients are not contagious. An estimated 15,000 to 20,000 people in the United States and more than 125,000 globally have RRP. This burdensome disease takes several forms and impacts people’s upper or lower respiratory tracts or presents as recurrent lesions on the vocal cords or adjacent tissues that require endless corrective surgeries. The treatment often results in permanent damage to a person’s voice.

RRP falls into two demographic subtypes: juvenile-onset (even toddlers) RRP and adult-onset RRP. Each presents unique medical management and lifestyle difficulties, and in addressing these challenges, patient advocacy—raising awareness and building a supportive community—is critically important.

Since it has no Food and Drug Administration (FDA)- approved treatment or cure, patients and scientists devote energy and resources to ensuring people with RRP have access to information. They are in the loop about clinical possibilities for this rare disease. No cure doesn’t mean there is no action!

Beyond the physical challenges of dealing with the disease – and the missed life events and career detours resulting from repeated surgeries, patients also face significant and demoralizing administrative challenges, such as battling payers to cover care using drugs not indicated by the Food and Drug Administration (FDA) for RRP or deemed “not sufficiently proven.”

The lack of treatment does not mean the RRP community is without hope. They are resilient and courageous and are making meaningful connections through the patient advocacy efforts of the Recurrent Respiratory Papillomatosis Foundation. They are reaching and inspiring researchers at the National Institutes of Health to pursue breakthrough research and oversee clinical trials. They also connect with scientists advancing possible therapies at discovery and clinical-stage biopharmaceutical companies like Precigen and encourage them to move forward by enrolling in clinical trials.

Collaboration Accelerates Change

When people unite, their presence creates energy. The Recurrent Respiratory Papillomatosis Foundation, biotech company Precigen, the National Cancer Institute (NCI), and RRP patients and their caregivers met on June 11th at the National Press Club for the Inaugural International Recurrent Respiratory Papillomatosis Awareness Day. This was an inflection point for those who follow the rare disease category.

The gathering wasn’t about hype or baseless optimism; it was a meeting that brought people together, prepared and ready to roll up their sleeves and get to work. It was a day that reaffirmed a commitment to transparency and a truthful assessment of the current situation and path forward.

Virginia Senator Mark Warner, chair of the powerful Senate Intelligence Committee, which oversees cybersecurity efforts that are key to healthcare and innovation data protection, kicked off RRP Awareness Day by expressing his support for people with rare diseases and his desire to help RRP patients find their voice. Senator Warner stated his desire to advance research and innovation and ensure access to care, an expression of determination that reflected his long-standing record on behalf of people seeking treatment options and improved outcomes.   

RRP Foundation President Kim McClellan also spoke as an advocate for the RRP community and as a patient. “We are here to raise awareness about RRP and bring together critical stakeholders in a dialogue on important aspects impacting individuals living with RRP,” she said. “We invite and encourage anyone living with RRP, either as a patient, family member or caregiver, to join us in spreading the word about RRP and participate in clinical trials and advocacy efforts.”

The date of this groundbreaking gathering has special meaning for the RRP community. June 11th (6/11) corresponds to HPV variants 6 and 11 associated with RRP. As the date symbolizes, the gathering united people with the disease, their family members, congressional leaders, and researchers from government agencies and corporate partners in a community united in a common cause.

The opportunity to share and hear multiple perspectives enriched discussions and underscored the importance of taking a comprehensive approach to tackling this condition. Panels of experts and patients sharing personal stories about their journeys gave attendees an unmatched opportunity to delve into the intricacies and impacts of RRP.

Helen Sabzevari, PhD, President and CEO of Precigen, expressed that she and her company were “proud to join forces with the RRP Foundation to establish the first global RRP Awareness Day to bring visibility to the many challenges experienced by RRP patients and to help forge connections among patients, clinicians and government officials.”

A former NCI team leader, Dr. Sabzevari’s commitment to RRP awareness and patient well-being as an animating principle is a model biopharma company C-Suite executives would be wise to emulate. For her and her Precigen colleagues, patients are the focal point of every decision, action, and investment.

RRP Awareness Day was an inspiring platform for discussing struggle, stigma, and science. Lunch was optional, but tissues were required as attendees in the filled-to-capacity room listened to a patient panel on how RRP impacts people and their families. They learned how some individuals living with RRP have needed hundreds of surgeries over the years, beginning when they were toddlers or young children in primary school.

Culture Drives Clinical Performance

Therapeutic innovations are needed to ensure that future generations living with RRP have options reviewed and indicated by the FDA for treating this viral condition.

During the event, a panel of representatives from advocacy and research reflected on how their collaborative approach centering around patients – from the design of clinical trials to allocating resources that have enabled patients to participate in those trials – has been vital in accelerating the R&D process toward identifying and developing viable treatments. The panel included James Gulley, MD., PhD., NIH, Senior Investigator, Center for Immuno-Oncology, Acting Co-Director, National Cancer Institute/Center for Cancer Research; Scott M. Norberg, DO., NIH, Associate Research Physician, Center for Immuno-Oncology; Helen Sabzevari, PhD, CEO, Precigen; and Kim McClellan, President, RRP Foundation.

Dr. Gulley, who is part of the NCI team and has been instrumental in advancing research on RRP and its connection to HPV, emphasized the pressing need for innovative therapies. In his panel comments, Dr. Gulley highlighted the importance of collaborative research efforts to explore potential immunotherapeutic approaches that could offer new hope for patients suffering from this debilitating condition.

No Disagreement – Harmony

Panelists Gulley, Norberg, and Sabzevari applauded the patient community, acknowledging the courage of their readiness to volunteer to participate in clinical trials to speed possible therapeutics forward. It was a reassuring presence and a reminder that public-private collaborations, particularly for rare diseases, do more than spark hope; they spur action. The patient-panel takeaways were: (1) Connect with the RRP Foundation, (2) Support ongoing clinical trial efforts, (3) Prevention through HPV vaccination is key.

While there is still no FDA-approved treatment to manage RRP, this community remains resilient and upbeat, inspiring everyone facing the challenge of rare conditions. The RRP Foundation, Precigen, and NCI are on the same page—science is essential. People living with RRP can remain hopeful that this collaboration will continue until actions result in better options for this patient community.

The post HPV Urban Legends – From Contagion to Symptoms to Risks to Prevention – There Are More Rare Concerns that Deserve Our Attention appeared first on Medika Life.

Human brain organoids (HBOs) are made in a lab from human stem cells and look and work like parts of the brain. Since it is clearly impossible to study living humans, scientists have been using animal models and cultured neuronal cells to discern how diseases work. However, these methods still have important differences with real brains, such as how they are organized in three dimensions and differences between species, making it hard to study how the higher brain works. These problems can be addressed in a new way with HBO.

In addition to stem cells, researchers have discovered a new source of research material, one that had never been considered in the past; amniotic fluid. During pregnancy, a baby sheds cells into the amniotic fluid surrounding and protecting it.

As a baby grows inside the womb, these cells mix with the amniotic fluid. Now, scientists have shown that they can use those cells to make organoids, which are three-dimensional structures that look and work like human organs. In one case, the organoids were the kidneys, small intestines, and lungs. Organoids might help physicians learn more about how fetal organs are growing, which could lead to earlier detection of birth defects like spina bifida.

It is not the first time organoids have been made from baby cells. Other groups have grown them from baby tissue that was thrown away. But this group is one of the first to make organoids from cells taken from amniotic fluid.

The idea is innovative, and organoids made from fetal fluid have shown it to work. But there is still room for improvement in how you describe the cells that are there.

For many years, scientists have known that fetus cells are in the amniotic fluid. With amniocentesis, a needle is used to take a sample of the fluid. This lets physicians discern conditions like Down syndrome and sickle-cell disease before the baby is born. At least 95% of these cells the baby is shedding are dead.

Organoids made from baby cells have been made before. Other groups have grown them from leftover fetal tissue. On the other hand, one group is the first to make organoids from cells taken from amniotic fluid. It does not affect the baby.

Most people have heard of mini-brains, which are groups of neurons meant to fire in a way similar to how cells fire in a real brain, but not quite. There have been heated arguments about whether these tiny blobs could ever be aware, feel pain, or think, and whether they should even be called “mini-brains” because they differ from a fully developed human brain.

In another area, thyroid disease, a breakthrough may benefit those who suffer from thyroid disease. One study group working on thyroid cells has successfully used stem cells to make tiny thyroids that can be transplanted into mice, but not humans yet.

Hypothyroidism, or an underactive thyroid, affects about 5% of people and can cause tiredness, aches and pains, weight gain, and sadness. It can also change the way children’s brains grow. And people who have it often have to take a treatment every day to replace their hormones. This team’s efforts resulted in getting mice to make thyroid hormones again, which opened the door for humans. About which therapies will work with cancer, there is hope there, too, derived from studies of organoids.

Patient-derived organoids (PDOs) are new and strong pre-clinical models. However, it is still not clear how well they can predict how patients will do in the clinic. What effects will certain treatments have on a patient’s cancer? Organoids made from patients did help predict how well treatment would work for metastatic gastrointestinal cancers.

Researchers also use organoids to study how the host and bacteria interact. Adding immune system parts to infected organoids would be the next step in this direction. A few methods using triple co-cultures have been created, most of which try to replicate harmful illnesses with viruses or bacteria. In all these important research findings, one consideration must be in the mix; ethics with animal brain organoids and simple organoids in dishes.

Until now, these human brain organoids have only been found in test tubes. The most advanced ones are about the size of a pea and pulse with the electrical activity that makes real brains work. In a way that is similar to brains, they make new neurons. They also build the six layers of the human cortex, which is where thinking, speaking, making decisions, and other complex cognitive processes happen.

Many experts in the field do not think an organoid in a dish can think, but we need to talk about this. While we are discovering new ways to combat illness and developmental issues through the use of these organoids, we are also faced with ethical issues all along the way. Each of these issues must be addressed in a way that will benefit everyone and not inhibit the growth of science in its quest for health.

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The future is often unpredictable and presents many challenges. It seems that 2024 we will face even more surprises, making it a year full of ups and downs. I always believe that resisting the day’s developments is unwise and stems from a lack of awareness.

 My suggested approach is “adaptation,” which derives from intelligence and can bring us joy, happiness, and peace. As someone interested in studying the relationship between technology and well-being, I believe researchers should investigate human beings in an integrated manner, not solely focusing on the cognitive dimension.

 Equipping individuals with a happier life in the modern world requires collaboration between technology experts, researchers, and those in the humanities and social sciences. To build a globe that I refer to as “happy modernity” and address the flaws and shortcomings of the previous year, I suggest paying attention to these paths.

Approaches and suggestions

1-The first suggested path is to study children and teenagers in educational centers. They are expected to be prepared for modern society, so it is essential to consider their feelings, attitudes, motivations, and interactions in the educational environment, especially regarding artificial intelligence and ChatGPT.

My proposed research focus does not solely involve cognitive outputs and learning the content of courses but also includes their interactions and emotional responses to learning through technology. Therefore, variables such as adaptability, satisfaction, inner pleasure, self-confidence, happiness, empathy, and other emotional factors should be considered. While discussing research methods in this regard is beyond the scope of this article, developers can collaborate with other experts, including psychologists, to turn these ideas into research and practical applications. Furthermore, fundamental theories of psychology can be utilized to formulate research proposals.

2-Another suggestion is to examine thinking, meta-cognitive, and other intellectual skills. Why is this solution inevitable (in my opinion)? The answer is that thoughtfulness and accuracy in thinking are closely related to happiness, peace, and mental health, and correct thinking reduces the waste of human mental energy.

Furthermore, right-thinking skills have a deeper meaning in the era of artificial intelligence and the height of modernity; everyone must think correctly, recognize how to /respond to challenges arising from technology, and solve problems. In the current era, to raise the potential of people’s thinking power and skills in the face of prevalent challenges, engaging in the study and training of thinking skills (critical thinking, problem-solving, metacognition, etc.) seems more necessary than any other moment in human history.

3-The third suggested approach involves analyzing job classes and categorizing them based on the extent of utilizing artificial intelligence tools.  The significance of understanding the usage of tools like GPTs across different business classes cannot be overstated. This inquiry is crucial due to the direct connection between job satisfaction, working conditions, and overall life satisfaction and health. 

If society’s well-being is a priority for executives, governments, and developers, a detailed investigation into job happiness becomes imperative. Factors such as job category, individuals’ personality traits, the types of AI tools employed, and the percentage of their usage warrant thorough exploration through longitudinal and experimental studies. This approach benefits future prospects and facilitates a more in-depth analysis of current issues. Moreover, it is essential not to overlook comparative studies and the influence of sociological and cultural factors. 

Given the transformative impact of artificial intelligence on various industries and its potential to redefine job roles, examining and prioritizing mental health becomes inevitable.

Towards the pursuit of HAPPY MODERNITY

2024, it will be subject to qualitative and quantitative changes in artificial intelligence. However, the key to driving positive developments in human society is not the discussion about the existence or removal of this technology from human life, as it surrounds various aspects of individual and social life.

Therefore, a more prudent approach would be how to manage it to build a better, more successful, more compatible, peaceful, and happier society and world. In particular, it seems that education and healthcare will be dominated by synthetics and JPTs more than any other aspect in the new year. Therefore, researchers, governments, professionals, experts, and even ordinary people must invest in and prioritize these two areas.

In this article, given the constraints of my writing capacity and the limited scope, I have introduced research-based solutions from among the treasures I have in mind. Furthermore, I have additional solutions in mind that I can offer. Moreover, these solutions can be presented and implemented through various research projects conducted by research institutes and universities.

As I have said many times, implementing these approaches necessitates the collaboration and agreement of diverse groups of professionals beyond just developers and AI creators. If the national program head considers all these factors, we can look forward to a year that embodies “happy modernity.”

The post Pursuing Happy Modernity: AI and Human Exploration in New Research-Based Tech appeared first on Medika Life.

Inserting Genes into CAR T Cells

Chimeric Antigen Receptor T cell (CAR T) therapy genetically alters a patient’s T cells to recognize cancer cells and subsequently kill them. This engineered recognition relies on hybrid T cell receptors with antibody components to detect antigens, or biological tags, found on the surface of cancer cells (see Figure 1).

Researchers typically incorporate hybrid receptor genes into a CAR T cell via viral gene insertion. Despite its regard as a staple in cell therapy, retroviral gene transfer comes with several drawbacks. Viral vector manufacturing is expensive and time-consuming. The method lacks precision and could potentially allow an unwanted gene entry. Perhaps most limiting, it cannot be personalized to detect uncommon antigens. For this reason, all approved CAR T therapies in circulation target blood cancers that share a common antigen (usually CD19 or BCMA) rather than solid tumors, which greatly vary in antigen presentation. Standardizing a new means to insert genes would improve the accessibility, efficiency and usage of CAR T therapy.

Innovating with CRISPR Gene Editing 

In their Phase I clinical trial, the researchers at PACT Pharma and the University of California, Los Angeles explore the possibility of a different type of CAR T therapy—one that creates a hybrid receptor with CRISPR gene editing. With CRISPR, the team selectively removed native T cell receptor genes and replaced them with new, cancer-fighting alternatives.

The researchers began by searching and isolating a novel T cell receptor from the patient’s own immune system. First, they screened the patients by sequencing DNA from healthy blood samples and tumor biopsies; this step identified mutations which the tumor cells share but cannot be found in normal tissue. Algorithms then predicted which antigens would be present on the tumor.

Next, the team copied the antigens and mixed them with different versions of HLA, a type of molecule needed to present antigens to T cells. This process revealed specific T cells which could react to this particular combination of antigen-HLA. Researchers copied up to three of the highly personalized receptor genes to be integrated into the T cells using CRISPR/Cas9.

Figure 2 illustrates the subsequent process. The CRISPR/Cas9 interface knocked out two T cell receptor genes, TRCα and TRCβ (see Figure 3), and replaced them with three new receptor genes in a single step—decidedly more efficient than sourcing and cultivating retroviruses for gene transfer, as is currently standard in CAR T therapy.

The researchers multiplied the T cells to great numbers. Finally, the patients underwent lymphodepletion chemotherapy before receiving up to three doses of their personalized CRISPR/CAR T cell infusion.

The post Teaming Up Two Biotech Winners to Fight Cancer: CRISPR and CAR T appeared first on Medika Life.

CAR T therapy, a “living drug,” traditionally involves isolation and purification of T cells outside the body. The cells are then modified with a synthetic receptor and then re-infused into the body for treatment of cancers. Researchers have now successfully demonstrated that T cells can be modified in vivo by mRNA technology, bypassing the need for extraction, chemotherapy and re-infusion. Although this method proves effective in treating mice with scarred hearts, considering fibrosis contributes to over 800,000 deaths worldwide, the study contains great potential for human treatment.

A Damaged Heart 

The heart, flexible yet strong, circulates blood through the body by pumping blood through its chambers. Aging and injury tamper with this function, creating scarred and thickened tissue called fibrosis. Although fibrosis occurs normally when healing, a highly fibrotic heart loses its elasticity; the stiffened tissues and interrupted electrical signaling prevent proper contractions of the heart (see Figure 1). Cardiac fibrosis is highly associated with heart disease and heart failure.

Cardiac fibrosis has no “cure-all” treatment. Early detection improves prognosis, but options dwindle as damage progresses irreversibly. People with advanced cardiac fibrosis may take drugs which antagonize overstimulation of the heart or might even require heart valve replacement.

How CAR T Cells Work

In their study, Rurik et al. explore a new method to directly counter cardiac fibrosis. This method builds upon the basics of CAR T: the use of T cells with a synthetically engineered receptor to target and kill specific cells.

CAR T is approved to treat people with certain lymphomas, leukemias, and multiple myeloma. Figure 2 illustrates this process. In these cases, the desired T cells are extracted from the patient’s body. Synthetic mRNA is inserted into the cell with a retrovirus, a virus commonly used in gene therapy to permanently change other cells’ genomes. The altered and expanded cells are then infused back into the body after preparatory chemotherapy. These T cells target either CD19 or BCMA, two antigens found on malignant B cells.

The benefit of inserting genetic information with a retrovirus lies in its permanence. The CAR T cells can expand and persist in the body for a long time after infusion, continually fighting the cancerous cells they encounter. However, this is of no benefit to researchers hoping to fight cardiac fibrosis. If T cells continuously target fibrotic cells, they would impair normal healing processes and potentially induce autoimmunity. Rurik et al. employ an elegant solution which shortens the CAR T cells’ active duty, thereby circumventing the extraction process altogether.

 New CAR T Cell Design 

The team adapted mRNA delivery technology seen in current COVID-19 vaccines and applied it to basic Chimeric Antigen Receptor design. The mRNA does not integrate into the T cell genome, allowing for temporary transcription of the mRNA and transient expression of the new receptor.

CD5 Lipid Nanoparticles (LNP) 

The authors adopted a strategy to introduce the chimeric receptor to T cells in the body rather than extracting and purifying them outside the body. To accomplish this aim, they first synthesized mRNA that encodes a receptor against fibroblast activation protein (FAP), a protein expressed on activated fibroblasts responsible for fibrosis. They purified the mRNA and packaged the engineered mRNA into standard lipid nanoparticles (LNP).

The team then decorated the lipid nanoparticle surface with CD5 targeting antibodies to direct lipid uptake. The integration of CD5 antibodies allowed the lipid nanoparticles to target antigen CD5 naturally expressed by T cells once injected into the body; the CAR T cells are made after a single shot.

Chimeric Antigen Receptor 

The chimeric antigen receptor contains a single chain variable fragment (scFv) derived from fibroblast activation protein monoclonal antibodies; this recognition domain enables the CAR T cell to target cells which express fibroblast activation protein. The CAR design also includes CD28 and CD3z signaling domains in the cytoplasm. All three components are mouse-specific. Not illustrated in Figure 3 is an added small peptide which prevents immune suppression.

Genetic Integration In Vivo

The team found that lipid nanoparticles could successfully deliver the mRNA package to T cells, as seen in Figure 4. The killer T cell absorbs the lipid nanoparticle by endocytosis. The lipid particle then degrades and the synthetic mRNA releases into the cell. Finally, the cellular machinery reads the genetic instruction and briefly produces the receptor against fibroblast activation protein. This is possible with both animal and human T cell cultures.

Transitory CAR Expression 

Unlike traditional CAR T cells that carry a chimeric receptor encoded by DNA inserted into the genome, these CD5+ T cells carry mRNA only transiently. The mRNA is not integrated into the cell’s genome and remains stuck in the T cell cytoplasm before degrading. This is ideal; fibroblast activation protein receptors must be expressed briefly as longer expression may harm other tissues.

 Results 

The research team assessed the efficacy of the CAR T cells in different conditions. When they treated the cells in tissue culture, more than 80% of T cells expressed the chimeric antigen receptor and could effectively kill target cells with fibroblast activation protein.

The team then tested this model on mice with cardiac fibrosis. The mice received medication to injure the heart and induce scarring. After one week, the team administered the lipid-mRNA injection. Consistent CAR expression was noted 48 hours after injection, and disappeared after one week.

The results were impressive. The function of the heart’s largest chamber improved, in some cases returning to uninjured levels. Similarly, the amount of blood filling the heart normalized to safe volumes. The therapy notably reduced the thickness of the heart. Finally, although the mass of the largest chamber did not normalize, it trended towards improvement.

One caveat in lipid-CAR T cell delivery is that some cells, perivascular fibroblasts, do not express fibroblast activation protein. In consequence, these cells were not impacted by CAR T cells and some fibrosis persisted. No overly toxic side effects were noted.

Trogocytosis

A key observation of effective CAR T therapy is the ability of the modified T cells to take small bites of the target cell—a phenomenon known as trogocytosis. Deriving “trogo” from the Greek word “to bite,” trogocytosis entails one cell nibbling another and, in the process, transferring the surface molecules from one to the other. The researchers found evidence of CAR T cells “nibbling” the activated fibroblasts and retaining the stolen antigens (illustrated in Figure 5), suggesting that the T cells successfully adopted the chimeric antigen receptors in vivo.

Future Implications

CAR T therapy revolutionized cancer treatment with its efficacy and innovation. Combining mRNA technology to this therapy creates a temporary version of this “living drug” that does not sacrifice on quality. The therapy is well tailored to heal mice with damaged and scarred hearts, and widens the possibilities to treat other non-cancerous human ailments. If translated to clinical settings, transient CAR T therapy may be less expensive and more readily available than its traditional counterpart

The post CAR T-mRNA Therapy For Cardiac Fibrosis: A New Way Forward appeared first on Medika Life.

Perhaps all we heard from the title and the take away is “I want it now.” When it comes to science and public health, that’s a mega problem. When do I want it? Now!

Clinical Trials Usually Take Years.  Enter Operation Warp Speed

How long do or should clinical trials take – well, it depends – but often years. Science takes time. It demands rigor and objectivity. It’s not a “now” pursuit. It’s why so many potential medicines fail to advance through clinical stages to our medicine chests as physicians and patients work diligently to evaluate their safety, effectiveness and long-term risks in observational studies. 

For example, the research into drugs to reduce life-threatening high cholesterol spans decades.  Many think of the incredible drugs now available as generic, which fueled continued research. Few think of the game-changing Framingham Heart Study or the groundbreaking and Nobel Prize-level work of scientists Drs. Michael Brown and Joseph Goldstein. There is beauty to science.  In the famed Academy Award-winning movie The Agony and the Ecstasy with Charlton Heston, as Michelangelo and Rex Harrison playing Pope Julius II, Heston’s character is constantly asked by Harrison, when will the Sistine Chapel ceiling be completed.  The artist replies: “When it’s done.”  That’s science!

It’s possible that COVID threw science – the purity of the art of discovery to improve humanity’s lot – out with the peer-review bathwater.  Everyone is at fault in some way. Government agencies, elected officials, public health champions, media, and, yes, the public are all part of the now movement.  We all wanted a biomedical elixir to ward off the virus NOW! We have been conditioned to get what we want quickly.  We order online at Grub Hub or Amazon, and within hours – a day tops – a vehicle pulls up to our doorstep.  NOW!

Now, how about COVID? We expected salvation at warp speed.  Companies no longer wait to share data at peer-review forums or in top-notch journals.  When the public cries out, we send out a press release. We expect answers from pharma, the White House and CDC immediately. We moved to evaluate, approve and move to rally people to access the COVID vaccines a mere 13 months after trial initiation. The mRNA vaccine became the first FDA-approved COVID-19 vaccine on August 23, 2021.  That’s the equivalent of now when it comes to drug development.

It Takes Years to Develop a Vaccine – NOW?

In comparison, the usual vaccine development timeline is five to 10 years and sometimes longer to determine if a product is safe and efficacious in clinical trials, completes the required regulatory approval processes, and a manufacturer has a sufficient quantity of vaccine doses for public access.  COVID broke the previous record of four years set by the development of a mumps vaccine in the 1960s.

But there are reasons we were able to go fast. The infectious disease community is collaborative. There are previous models of engagement, We have technologies that enable us to screen options that didn’t exist in the 1960s. It’s impossible to compare apples to apples or oranges. The times have changed. Science can move faster; however, objectivity and peer review remain musts.

Don’t point the finger of blame at any one institution or segment of the process.  Everyone created and bought into this urge for now!  We were frightened for our survival, mental health and economies.  The White House was responding to public pressure.  Events changed rapidly, and so did the news flow.  Media leaped into the fray to bring out their wagons of consulting experts aboard, with varied opinions to keep eyes glued to screens. Researchers slept at lab benches to sustain the world – to ward off the – then-deadly pandemic. 

We cannot forget that while we criticize the scientific process and unknown long-term effects of these vaccines, the “power of now” drives decisions and actions.  We cannot forget that the ERs were filling up, and people were dying at the start of the pandemic.  We were scared, and fear ignited non-reflective action. Countless public health challenges were pressing – addiction, poverty, isolation and more. We needed a response. Sometimes the process is imperfect. Let’s not forget to evaluate how all this impacted science and apply the learnings in the future.

Tech as a Scientific Accelerator

Technology has become the gas pedal for science.  AI, AR, machine learning, and big data are all variations of the same concept, but technology does enable scientists to move rapidly. The urgency to offer hope tips the hat to companies being permitted to update the public via news releases and later share detailed information in a peer-review setting. Industry scientists yearn to help sustain lives. Everyone had good intentions. However, we need to find better balance and return to a culture that encourages objective reflection and third-party (even uncensored) pushback,

The Power of Now was geared to get us to think beyond the moment.  To consider who we are and our purpose in the world.  However, like most things, we commercialize good ideas. COVID left too many casualties – most important among them precious people and, yes – scientific exchange.  Now is a competitive advantage – often a first-to-market must.  However, science is a reflective task accelerated by technology. 

Let’s Open the Door to the Power of Options

The Power of Now has given way to the Power of Options, a concept shared by David Noble and Carol Kauffman in the recent issue of HBR. Scientists remain societal leaders.  Scientists are curious and explorers.  We must encourage scientists to create their life-saving magic in coordination with the checks and balances of their peer-review culture.

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CAR T is an effective treatment for some hard to treat cancers. This “living drug” is made by extracting killer T cells from the body, manipulating them to target cancer cells, multiplying the newly engineered cells and infusing them back into the body. Development over the last forty years has evolved the precision, efficiency and safety of this technology. Arguably the best example is the treatment of  B cell cancers.

B cells to B cell cancers

CUSABIO Link Added

Figure 1 illustrates the development of antibody cells. B cell maturation begins with stem cells in the bone marrow and is completed with the antibody producing plasma B cells.

Typically, threats to the body leave trails of foreign antigen which can be followed. B cells detect these antigens and proliferate to eliminate pathogens, but these numbers quickly subside. This is done by design. The body regulates this process to ensure the bloodstream is not flooded with too many antibodies to prevent normal function. However, this system can go awry at any point. B cell precursors, intermediate cells or plasma cells can mutate and grow uncontrollably, causing damage to the body rather than shielding from it. When this happens, the immune system weakens and B cell cancers result.

LYMPHOMA CANADA

B cell lymphomas originate from the lymphatic system organs, vessels and tissues, such as the lymph nodes or the spleen. In contrast, leukemias circulate in the bone marrow and blood instead of the lymph organs. Although multiple myeloma is also a cancer of the bone marrow, it entails the abnormal growth of plasma B cells in particular.

Treating B cell cancers

Chemotherapy and radiation most successfully reduce the size and quantity of B cell tumors. Partial remission is very achievable, but complete remission—the total absence of cancer— is much more difficult to attain. For many, the cancer may temporarily recede for months or years after treatment before recurring. And when the cancer recurs, it can be resistant to treatment.

CAR T cell therapy addresses this problem by transforming patient immune cells into an anti-cancer drug. Cells are taken from the body and modified to detect the tumor cells. CAR T cells are fitted with a fusion protein (scFV, Figure 3) made from antigen-recognizing regions of antibodies. This component is typically engineered to target CD19, a B cell antigen known for its role in B cell signaling. This protein is found in B cells of all stages and is present on the surface of many B cell cancers. CD19 is not found on hematopoietic stem cells—those which have yet to mature and gain purpose; as a result, the therapy is less likely to target non-cancerous immune cells, an ideal quality in a therapeutic target.

FIGURE 3: CD19 is an antigen expressed on cancer cells. CAR T cells are fitted with an antigen recognition domain, a single chain variable fragment (scFV), to target the CD19 on the surface of these cancer cells. Once the antigen domain binds to the cancer cell, the CAR T cell can induce apoptosis to eliminate the tumor cell.

BRITTEN, OLIVER, ET AL. 2019 Link Added

Once the CAR T cell binds to CD19 on the tumor cell, several signals are released from the endodomain that trigger cell death of the tumor cell through apoptosis. The co-stimulatory molecules found in the interior of the CAR T cell allow it to multiply and persist in the body.

Normal T cells from the body lack the precision of this antigen recognizing protein and usually require specific proteins—major histocompatibility complexes—to present the antigen and facilitate similar binding. CAR T cells forgo these steps, producing superior hybrid molecules which combine antibody detection with T cell signal transduction. This synthetic engineering defines the chimeric nature of Chimeric Antigen Receptor T cells.

Why CAR T therapy?

As of publication, CAR T is only considered after standard cancer treatments have run their course. Why, then, do people turn to CAR T therapy if it is only considered after several other lines of treatment?

For those who have B cancers which are unresponsive to alternative anti-cancer treatments, CAR T can deliver lasting remission and extend life expectancy by several years—sometimes without additional treatment.

NOVARTIS

For example, one study revealed that 44% of young patients with acute lymphoblastic leukemia (ALL) live at least five years without relapse after CAR T therapy. This is especially remarkable given how difficult it can be to treat the condition and the less than 10% five-year survival rate. Approved CAR T therapies also exist for patients with diffuse large B cell lymphoma (DLCL), follicular lymphoma, mantle cell lymphoma and multiple myeloma.

ACCELERATING CANCER IMMUNOTHERAPY RESEARCH

There is a caveat—it is possible to experience relapse after CAR T therapy. One contributing factor is CD19 antigen escape, a type of CAR T resistance. As illustrated in Figure 5, patients with antigen escape develop cancer cells which no longer express CD19 and thus escape recognition by CAR T cells. So while CD19 targeting has proven effective, this phenomena highlights the need to find alternative antigen targets to improve the drug’s efficiency.

One possible solution is dual targeting CAR T cells. By engineering T cells which detect more than one antigen on cancer cells, the therapy has a greater chance of attacking tumor-only cells and overcoming antigen escape. Current contenders include dual targeting of antigens CD19 and CD22, as well as CD19 and CD20.

Summary 

CAR T shines best in solving what other therapies cannot. When other lines of cancer treatments such as chemotherapy or radiation cause relapse, CAR T therapy often provides a more lasting remission. There’s promise for these engineered T cells to become even more effective in the future with the advent of dual-targeting CAR T cells. And while none of the six FDA approved CAR T therapies are currently used as first-line treatment, developments are underway to establish this innovative technology as a primary line of defense. This is a major step forward for treating B cell cancers, and we can anticipate more to come.

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Age-related macular degeneration (AMD), or macular degeneration, is a common eye disorder. AMD results from the macula’s deterioration, a small area in the middle of the retina at the back of the eye.

Historically, clinical trials showed that supplements could slow the progression of age-related macular degeneration. Now comes a new study confirming this observation, albeit with a newer supplementation formulation.

We begin with macular degeneration basics before turning to positive new results.

What is age-related macular degeneration?

Macular degeneration is a common eye disorder. Deterioration of the macula, a small spot in the back of the eye, is the cause.

Macular degeneration can result in central vision loss; you may have trouble seeing what is in front of you as you look forward. At the end of this short video, you will see the visual field of someone with age-related macular degeneration:

As you can see, macular degeneration doesn’t affect your peripheral vision.

Macular degeneration is common: Approximately 11 million Americans have the disease. Age-related macular degeneration is the leading cause of visual loss.

There are two types of macular degeneration:

• Dry macular degeneration (affects 85 to 90 percent of those with macular degeneration). Occurs because of small yellow deposits (drusen) that develop under the macula.
• Wet macular degeneration results from abnormal blood vessels developing under the retina and macula.

Here are some risk factors for age-related macular degeneration:

• A diet rich in saturated fat (for example, meat, cheese, and butter) raises risk. On the other hand, fruits, vegetables, and nuts can reduce risk.
• Being overweight may be a risk factor
• Cigarettes
• Age over 50 years
• A family history of age-related macular degeneration.
• High blood pressure
• Heart disease
• High blood pressure or high cholesterol
• Race. Whites have the highest risk, followed by Chinese and Hispanic people and African-Americans with the least risk. Whites are also more likely to go blind from age-related macular degeneration than African-Americans.
• Ultraviolet ray exposure (for example, from sunlight)

Age-related macular degeneration treatment

Unfortunately, treatment for age-related macular degeneration cannot restore vision. On the other hand, intervention may slow vision loss.

Dry AMD

There is no good treatment for dry age-related macular degeneration. However, management tools include choosing larger print books (larger screen font sizes), using a magnifying device, or optimizing lighting.

Wet AMD

There are management strategies that may slow the rate of progression of age-related macular degeneration. I won’t go into too much detail here, but refer you to this Medicalnewstoday reference:Age-related macular degeneration (AMD): Symptoms and treatmentMacular degeneration affects the retina, a layer at the back of the eyeball. This layer contains light-sensitive cells…www.medicalnewstoday.com.

Macular degeneration risk reduction

While prevention is not assured, you may lower your macular degeneration risk by eating well, getting physical activity, not smoking, and getting regular eye tasks.

You may also lower your risk of age-related macular degeneration by wearing sunglasses that block ultraviolet (UV) rays to protect your eyes.

Some research points to increasing omega3 polyunsaturated fatty acids, especially docosahexaenoic acid and eicosapentaenoic acid, possibly dropping the risk of an early subtype of age-related macular degeneration.

Let’s look at new evidence suggesting that dietary supplements can lower the risk of macular degeneration progression.

Supplements and age-related macular degeneration

According to the Age-Related Eye Disease Studies (AREDS and AREDS2), supplements can slow the progression of age-related macular degeneration.

The AREDS2 formulation provided an additional one-quarter (26 percent) drop in the risk of age-related macular degeneration while not increasing lung cancer risk.

The AREDS1 supplement included beta-carotene. After scientists discovered an association between beta-carotene and lung cancer risk, the AREDS2 formulation substituted the antioxidants lutein and zeaxanthin for beta-carotene.

The original AREDS study showed that a dietary supplement formulation (500 mg vitamin C, 400 international units vitamin E, 2 mg copper, 80 mg zinc, and 15 mg beta-carotene) slowed AMD progression from moderate to late disease.

Alas, two concurrent studies also revealed that those who smoked and took beta-carotene had a significantly higher risk of lung cancer than expected.

For the AREDS2 clinical trial, researchers compared the beta-carotene formulation to one with the antioxidants lutein (10 milligrams) and zeaxanthin (2 milligrams) instead. Only those who never smoked (or had quit) could use the beta-carotene formulation.

Here are the results of the five-year AREDS2 study:

The AREDS2 formulation provided an additional one-quarter (26 percent) drop in the risk of age-related macular degeneration while not increasing lung cancer risk.

After the five-year study ended, researchers offered all participants the AREDS2 formulation.

Symptoms

Let’s end with some symptoms of macular degeneration. Dry macular degeneration symptoms usually develop gradually and without pain. Symptoms may include:

• Visual distortions (for example, straight lines appearing bent)
• Diminished central vision in one or both eyes
• The need for brighter light when reading or doing close-up work
• Increased challenges adapting to low light, for example, when entering a dimly lit restaurant
• Increased blurriness of printed words
• Decreased brightness or intensity of colors
• Challenges recognizing faces
• A well-defined blurry spot or a blind spot in your visual field

Let’s end with some risk-reduction tips from the good folks at WebMD:

• Check your vision daily by looking at an Amsler grid — a pattern of straight lines like a checkerboard. It can help you spot changes in your vision.
• Don’t smoke, eat a balanced diet with leafy green vegetables, and protect your eyes with sunglasses that block harmful ultraviolet (UV) rays.
• Supplements with antioxidants plus zinc may lower your risk of age-related macular degeneration.
• If you’re over 65, your vision exams should include testing for age-related macular degeneration.

Healthline adds two more:

• Maintain a healthy-for-you weight
• Exercise consistently, as much as you can

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