Healthcare

One of the most damaging myths about sexual assault isn't just the violence itself.
It's the question that so often follows: "Why didn't she fight back?" The answer is simple. Because the brain isn't a courtroom. It's a survival machine. When faced with overwhelming terror, many people don't fight. They don't flee. They freeze. Their amygdala—the brain's alarm system—takes over, shutting down higher reasoning and triggering an automatic survival response. It isn't weakness. It isn't consent. It isn't a conscious choice. It is biology. Yet far too many victims have had their credibility questioned because people expected Hollywood heroics instead of understanding human neuroscience. Imagine surviving one of the worst moments of your life... Only to have your trauma dismissed because your brain did exactly what evolution designed it to do. It's time we stop asking,
"Why didn't you do more?" And start asking,
"What happened to you?" Believing survivors doesn't mean abandoning due process. It means abandoning ignorance. Science has explained this for decades. Now it's our turn to educate ourselves and challenge the myths that continue to silence survivors. Source: Apple Tree Yard (BBC) La Gouvernance au Féminin - Women in Governance #BelieveSurvivors #TraumaInformed #ViolenceAgainstWomen #EndVictimBlaming #GenderEquality #Neuroscience #StandWithWomen #Consent #Leadership #WomenInGovernance

🔬 Latest pharma industry updates 👇 🤝 Eli Lilly and Company and NVIDIA will build a 5-year AI research lab in the San Francisco Bay Area, investing $1B and using Nvidia’s “Vera Rubin” AI chips. 📈 Eikon Therapeutics filed for an IPO after raising about $1.1B privately, it would be among the largest pre-IPO war chests since Moderna (2018). 💔 US pharma companies cut 25,367 jobs in 2025, far above 2024’s 14,010. 💼 Merck is reportedly negotiating to buy Revolution Medicines for about $30B; talks could conclude this month but remain uncertain. 🧪 Fabentech won approval for the 1st global ricin antidote, using equine-derived polyclonal antibodies. 💔 Lyra Therapeutics halted development of LYR-210 for chronic rhinosinusitis, cut most staff, and is exploring strategic options. 📈 Abivax spiked then cooled after reports Eli Lilly and Company might bid up to €15B - France’s Economy Ministry said it received no foreign-investment authorization request and has no contact with Lilly. 🚫 Atara Biotherapeutics received an FDA Complete Response Letter for EBVALLO, saying the single-arm ALLELE trial is insufficient for accelerated approval. 💰 Kinaset Therapeutics raised $103M Series B to advance frevecitinib, an inhaled pan-JAK dry-powder therapy for severe uncontrolled asthma. 🧠 Insilico Medicine’s rentosertib is entering Phase 3 for idiopathic pulmonary fibrosis, potentially a first AI-origin drug to market. 🤝 Teva Pharmaceuticals and Royalty Pharma struck up to $500M financing for TEV-’408, an anti–IL-15 antibody for vitiligo. 🧠 Novartis licensed Alzheimer’s anti–beta-amyloid antibody programs from SciNeuro Pharmaceuticals for up to $1.7B.. 🧬 AbbVie will pay RemeGen $650M upfront (potentially >$5B total) for ex-China rights to a PD-1/VEGF bispecific antibody in Phase 1–2 solid-tumor trials, including lung and breast cancer. 🤝 AstraZeneca acquired Modella AI (price undisclosed) to expand generative/agentic AI capabilities for oncology R&D. 🧫 Caldera Therapeutics raised $112.5M (Series A/A1) for a Phase 1, aiming to raise efficacy ceilings in Crohn’s disease and ulcerative colitis. 👁️ Sanofi is reportedly preparing a higher bid for Ocular Therapeutix, Inc. after its board rejected an initial $16/share (~$2.2B) offer. 🛡️ The FDA and EMA jointly published “good practice” principles for using AI across the drug lifecycle. 🏛️ Novo Nordisk Foundation granted €736M to Denmark’s BioInnovation Institute (BII) through 2035, BII has incubated 130+ startups since 2018. 📊 American Cancer Society data show overall 5-year cancer survival reached 70%, up from ~50% around 1971. 🩸 A Nature's Medicines study validated a finger-prick blood test for Alzheimer’s with 86% accuracy in 337 participants. 🏷️ TrumpRx is expected to launch soon in the US, connecting patients to manufacturers offering discounted prices on their own sites. ---------- 📩 Get those updates directly via email (link in my profile)

You should not need to spend money to land a job. If lack of transportation to interviews, job-related trainings, or even your first few weeks on the job is preventing you from being able to consider local opportunities, Lyft has a program that will offer you free rides. If you're anxious about interviewing because you don't have professional clothing, Dress for Success Worldwide (they have many local chapters), Career Gear, or your local Buy Nothing group may be able to help. There are also many similar organizations focused on a specific community so do a search for options in your area. If you need some coaching or support in your job search, you can connect with Never Search Alone or Pay Forward Coaching for support navigating your search. The US Dept of Labor has a site called CareerOneStop.org that has links to resources for many scenarios - jobseekers with criminal conviction, those combatting ageism, entry level workers, etc. They link to jobs, training, scholarship opportunities, and more. If you don't have a computer or phone that can handle video interviews, or a quiet space to take interviews, your local library will let you use their computers for this purpose. If you need childcare for your job search, most YMCA of the USA locations offer some form of drop off care while you use the facilities, and they often have quiet spaces for you to work from as well. Your local Boys & Girls Clubs of America may be able to help, and many communities have organizations (like Worksource or Working Connections) which can offer childcare subsidies while looking for work, enrolled in education programs, or starting a new job. And all of these resources are free!!

Microsoft just released a 35-page report on medical AI - and it’s a reality check for healthcare. The paper, “The Illusion of Readiness”, tested six of the most popular models (OpenAI, Gemini, etc)… across six multimodal medical benchmarks. And the verdict? The models scored high on medical exams. But they’re not even close to being real-world ready. Here’s what the stress tests revealed: ▶ 1. Shortcut learning Models often answered correctly even when key information, like medical images, was removed. They weren’t reasoning - they were exploiting statistical shortcuts. That means benchmark wins may hide shallow understanding. ▶ 2. Fragile under small changes Making small tweaks caused big swings in predictions. This fragility shows how unreliable model reasoning becomes under stress. In visual substitution tests, accuracy dropped from 83% to 52% when images were swapped - exposing shallow visual–answer pairings. ▶ 3. Fabricated reasoning Models produced confident, step-by-step medical explanations - but many were medically unsound… or entirely fabricated. Convincing to the eye, dangerous in practice. And more importantly, healthcare isn’t a multiple-choice exam. It’s uncertainty, incomplete data, and high stakes. So Microsoft’s team calls for new standards: - Stress tests that expose fragility - Clinician-guided guidelines that profile benchmarks - Evaluation of robustness and trustworthiness - not just leaderboard scores The takeaway is simple: Medical AI may ace tests today. But until it proves reliable under stress, it’s not ready for the clinic. When do you think popular LLMs will be clinic-ready? #entrepreneurship #healthtech #AI

👁 Imagine losing your sight for 10 years… and then, the very first thing you do is recognize the faces of your loved ones again. That’s what happened to Jamal Furani, 78, thanks to a breakthrough in medical innovation: a fully synthetic cornea implant. No donor tissue. No immune rejection. A device that integrates directly with the eye’s own tissue. 💡 The deeper insight: The true revolution here isn’t only technological. It’s structural. Today, corneal blindness affects millions worldwide, but most can’t be treated because there simply aren’t enough donor corneas. A synthetic cornea changes the equation. It turns a scarce resource (donations) into a potentially unlimited one (innovation). And here’s what few realize: this implant doesn’t just restore vision. It restores autonomy, dignity, and human connection. Those are the “side effects” that make technology truly transformative. 👉 My take: The future of medicine won’t just be about “healing.” It will be about reinventing our organs — sometimes with solutions even better than the originals. If you could enhance or replace one organ with technology, which would you choose first? #Healthcare #Innovation #Biotech #FutureOfMedicine

This 3D-scanned ear not only restores people's face, but their life in hours. The Epithese Ear is redefining what’s possible in facial prosthetics. Every year, millions face the loss of an ear—not only with a huge physical impact, but also social burden. Now, a breakthrough in patient-specific prosthetics delivers what surgery alone couldn’t: Engineers and artisans built what medicine said couldn’t be done: → An ear sculpted with 0.1mm 3D scanning → 150 skin tones, seamlessly color-matched → Secure integration with bone-anchored titanium → Sculpted detail + texture, indistinguishable from life All from a studio that works more like a digital art lab than an operating room. The breakthrough? Immediate transformation—no waiting. From scan to new ear in less than 48 hours. Zero recovery, zero compromise. Why this changes everything: → Speed when it matters most A new ear, fit perfectly, before your next meeting—return to daily life with confidence restored. → Innovation, personalised → Real Impact In patient outcomes: -97% ear symmetry* -Zero rejection rate -Full social reintegration Every year, 1.5 million people require facial prosthetics: -60% report social challenges -Countless avoid public interactions This changes the narrative. Human's artistry using 3D Design tools—restoring more than appearance, rebuilding lives. These advances show a bigger shift: hyper-personalized, non-invasive medical solutions that merge rapid digital fabrication with hands-on craftsmanship. Follow me, Dr. Martha Boeckenfeld for more breakthroughs transforming people's life. ♻️ Repost to share these life-changing innovations. #MedTech #FacialProsthetics #HealthInnovation #FutureOfMedicine

India has 150 million+ people above the age 60 and there is a massive opportunity to keep them healthy & fit. But everyone’s focused on Gen Z and no one’s building for their parents. It’s a hard business but a big one. We’ve invested in two companies. Here’s why it’s tough and how one should crack it. Understand the reality first. 1. Elders don’t think of “health” as proactive. They’re conditioned to wait until something breaks before acting. You’re selling a solution to a problem they don’t know they have yet. 2. The 65-year-old needs it but their 35-year-old child pays for it. You're not selling to the elder. You’re selling to their guilt-driven kids in Gurgaon or US. The buyer ≠ the user. 3. Trust is everything and you don’t have it. Indian elders trust: Their doctor, astrologer & their neighbour Not apps. Not tech bros. Not AI. You can't growth hack trust. You earn it slowly, locally. 4. They don’t want new habits. They’ve had the same breakfast for 40 years. You’re not selling a product. You’re undoing decades of routine. 5. Distribution is hyperlocal. Elders don’t click Insta ads. They talk to the uncle in their colony. You scale building by building not by user cohorts. Yes, 150M+ elders. But it’s not one market. It’s a thousand tiny tribes. Different languages, cultures, food habits, family structures, and tech comfort levels. If it were easy, Tata or Reliance would’ve done it already. But it’s wide open now. The one who combines tech + trust + real care will win. So how do you crack it? 1. Think first principles & not trends Don’t build a “senior fitness app.” Ask: Why did they stop moving? What gives them joy? You’re selling independence, not health. 2. Design for peace, not features. One-click help, One daily routine, One trusted face. Great elder products feel like human care not software. 3. Human-first, tech-enable. Don’t replace the daughter. Support her. Train 100 amazing elder coaches. Build tools to help them scale. 4. Don't focus on CAC. Here, it’s about trust per acquisition. You’re not selling toothpaste. You’re asking to be let into their daily life. Start offline. Build trust then tech. 5. You’re in the business of habit change & not selling an app or a pill. Get them to walk 15 minutes a day. Add protein to breakfast. Laugh more. Sleep better. Small wins compound. Don’t build for scale first. Build for consistency. Be in the business of habit change. 6. This isn’t a hackable D2C play. It’s a decade-long trust business. Build for one community. Get to know 100 elders by name. Solve deep, boring problems with elegance. Everyone’s chasing the next billion youth users. But the hidden opportunity lies in serving the first 150 million elders. The elder care market in India isn’t just underserved. It’s misunderstood and needs long-term play. Founders who crack this will build generational companies.

Guide to Building an AI Agent 1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 Not all LLMs are equal. Pick one that: - Excels in reasoning benchmarks - Supports chain-of-thought (CoT) prompting - Delivers consistent responses 📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning. 2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰 Your agent needs a strategy: - Tool Use: Call tools when needed; otherwise, respond directly. - Basic Reflection: Generate, critique, and refine responses. - ReAct: Plan, execute, observe, and iterate. - Plan-then-Execute: Outline all steps first, then execute. 📌 Choosing the right approach improves reasoning & reliability. 3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Set operational rules: - How to handle unclear queries? (Ask clarifying questions) - When to use external tools? - Formatting rules? (Markdown, JSON, etc.) - Interaction style? 📌 Clear system prompts shape agent behavior. 4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 LLMs forget past interactions. Memory strategies: - Sliding Window: Retain recent turns, discard old ones. - Summarized Memory: Condense key points for recall. - Long-Term Memory: Store user preferences for personalization. 📌 Example: A financial AI recalls risk tolerance from past chats. 5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀 Extend capabilities with external tools: - Name: Clear, intuitive (e.g., "StockPriceRetriever") - Description: What does it do? - Schemas: Define input/output formats - Error Handling: How to manage failures? 📌 Example: A support AI retrieves order details via CRM API. 6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀 Narrowly defined agents perform better. Clarify: - Mission: (e.g., "I analyze datasets for insights.") - Key Tasks: (Summarizing, visualizing, analyzing) - Limitations: ("I don’t offer legal advice.") 📌 Example: A financial AI focuses on finance, not general knowledge. 7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀 Post-process responses for structure & accuracy: - Convert AI output to structured formats (JSON, tables) - Validate correctness before user delivery - Ensure correct tool execution 📌 Example: A financial AI converts extracted data into JSON. 8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) For complex workflows: - Info Sharing: What context is passed between agents? - Error Handling: What if one agent fails? - State Management: How to pause/resume tasks? 📌 Example: 1️⃣ One agent fetches data 2️⃣ Another summarizes 3️⃣ A third generates a report Master the fundamentals, experiment, and refine and.. now go build something amazing! Happy agenting! 🤖

BREAKING! The FDA just released this draft guidance, titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, that aims to provide industry and FDA staff with a Total Product Life Cycle (TPLC) approach for developing, validating, and maintaining AI-enabled medical devices. The guidance is important even in its draft stage in providing more detailed, AI-specific instructions on what regulators expect in marketing submissions; and how developers can control AI bias. What’s new in it? 1) It requests clear explanations of how and why AI is used within the device. 2) It requires sponsors to provide adequate instructions, warnings, and limitations so that users understand the model’s outputs and scope (e.g., whether further tests or clinical judgment are needed). 3) Encourages sponsors to follow standard risk-management procedures; and stresses that misunderstanding or incorrect interpretation of the AI’s output is a major risk factor. 4) Recommends analyzing performance across subgroups to detect potential AI bias (e.g., different performance in underrepresented demographics). 5) Recommends robust testing (e.g., sensitivity, specificity, AUC, PPV/NPV) on datasets that match the intended clinical conditions. 6) Recognizes that AI performance may drift (e.g., as clinical practice changes), therefore sponsors are advised to maintain ongoing monitoring, identify performance deterioration, and enact timely mitigations. 7) Discusses AI-specific security threats (e.g., data poisoning, model inversion/stealing, adversarial inputs) and encourages sponsors to adopt threat modeling and testing (fuzz testing, penetration testing). 8) And proposed for public-facing FDA summaries (e.g., 510(k) Summaries, De Novo decision summaries) to foster user trust and better understanding of the model’s capabilities and limits.

Right now, every CEO is wondering the same thing: “How can artificial intelligence help maximize our impact?”   Delivering on the promise of AI isn’t just good business, it has the potential to help us address some of society’s most pressing challenges. So today, I wanted to offer a closer look at how AI is helping us discover new medicines at Novartis.   The process of identifying a new drug, running patient clinical trials, and bringing it to market takes over a decade. Each new medicine costs on average $2 billion to develop, and we know nearly 9 in 10 of the treatments we work on will fail before they ever reach patients.   A major early step in that process is identifying individual targets in the body that we want to design a drug for. Once we identify that target, which most commonly is a protein, we look for molecules that might address the target’s underlying issue – ultimately those molecule structures form the basis for every successful treatment.   Unlocking the right protein and molecular structures is complex stuff – each step often takes years to get right and our scientists consider billions of potential chemical structures that might lead to effective and safe drug candidates.   AI offers us the chance to accelerate that process. Working with partners at Isomorphic Labs – including members of the Google DeepMind team that were awarded the Nobel Prize this year – we’re now able to do things like model how a protein folds and interacts with the molecules we design. AI models also make it possible for us to analyze different chemical structures simultaneously. It has the potential to add up to significant time savings for our drug development scientists and their work to predict what molecules might treat specific diseases better and faster.   We’re just at the beginning of what this technology can do. As we incorporate AI throughout Novartis’ work, I’m excited to see all the ways it helps us unlock the mysteries of human biology, so we can deliver better medicines that improve and extend patients’ lives.