Thermus Thermophilus Extract Does Not Increase Hair Density By 96.88%, Despite Dermatology Times’ Claims.

Article Summary

Can Thermus Thermophilus Extract (TTFE) really boost hair density by 96.88%? That’s what a recent Dermatology Times article claimed. But a closer look at the original study tells a very different story. In this article, we unpack the misleading interpretation of TTFE data, why it matters even when it comes from trusted sources, and how misinformation like this spreads through media, marketing, and even AI systems. We also highlight why scientific literacy and source-checking are more important than ever in the age of LLMs and rapid content creation.

Full Article

In a recent article published by Dermatology Times, a headline claim caught our attention: that Thermus Thermophilus Ferment Extract (TTFE) increased hair density by 96.88%.^[1]Bosslett, M. (2025). Thermos Thermophilus Fermentation Extract Can Treat Androgenic Alopecia. Dermatology Times. Available at: … Continue reading

For anyone following advances in cosmetic dermatology, these numbers sound groundbreaking…almost too good to be true. 

As it turns out, they are.

After checking out the primary research cited in the article, we found a significant discrepancy. The original scientific publication does not report a 96.88% increase in hair density. Instead, it states that 96.88% of study participants demonstrated some increase in hair density.^[2]Hu, Y., Wei, Z., Wu, X., Peng, M., Sun, G., Liu, Q., Liu, Q., Zhang, S.Y. (2025). Efficacy of Thermus thermophilus Fermentation Extract in Promoting Hair Growth and Improving Scalp Health in … Continue reading  

That’s an incredibly important difference. The distinction between an individual showing any degree of improvement and a universal 96.88% increase in hair density across the board is massive, and it alters the implication of the data entirely. 

This isn’t just a nitpick over numbers either. It’s a textbook example of why critical reading and verification of scientific claims matter, especially when reported secondhand through news or industry outlets.

Source Credibility: It’s Not Always What It Seems

Dermatology Times is generally seen as a reputable source in the field. That’s what makes this kind of misrepresentation especially concerning. When trusted publications get it wrong, intentionally or not, their errors don’t stay contained. They often cascade across social media, blog posts, product marketing, and even into conversations with patients and consumers.

Even more concerning is the growing trend of these misrepresentations being fed into large language models (LLMs), which are increasingly used to draft content, summarize literature, and even power patient-facing AI chatbots. A mistake published by one reputable site can propagate quickly and silently through an entire ecosystem of content. And unless someone goes back to the original research, the error persists, unquestioned and amplified. 

AI and the Amplification of Inaccuracy

We recently released a video titled “Can A.I. Cure Hair Loss?” in which we discussed how even credible websites can unintentionally (or in some cases intentionally) misrepresent scientific findings, and how those misrepresentations become embedded in AI systems trained on web content.

Language models, while powerful, often operate without a true understanding of source hierarchy or context. If a high-ranking article contains an error, that information can be treated as fact during the training or generation process. The AI doesn’t discern whether a number came from a peer-reviewed journal or a poorly paraphrased blog; it just sees frequently repeated data and assumes it’s accurate.

This creates a feedback loop:

1. A respected outlet publishes a flawed interpretation.
2. The article is picked up or paraphrased by other sites and influencers.
3. AI tools trained or fine-tuned on public data scrape these articles.
4. The AI outputs this information again, reinforcing the misconception.
5. Users, assuming the AI is drawing from trustworthy sources, believe the claim.

In the case of TTFE, a single misread statistic could lead to widespread misinformation about the effectiveness of an ingredient, influencing product development, marketing strategies, and even clinical recommendations.

Why Scientific Literacy Still Matters

This situation highlights an uncomfortable but necessary truth: even reputable sources are fallible. That’s not necessarily due to malice or laziness, but it can be damaging all the same.  Scientific literacy and, more specifically, source literacy, are essential for this. 

We can make sure we can maintain this by:

• Checking the original studies when possible.
• Noting the difference between relative and absolute claims.
• Being cautious of statistics that lack context or citations.
• Understanding the limitations and structure of the research.

It’s equally important for those disseminating information to cross-verify claims before repeating them. This isn’t just about credibility, it’s about maintaining trust in an industry that already contends with skepticism and pseudo-science.

To be clear, we are not accusing Dermatology Times of deliberately misleading readers or of using AI to generate or publish the article in question. But this incident underscores the broader issue: errors can and do happen even at high levels, and when they do, their ripple effects are hard to contain. 

That’s why we believe it’s worth talking about this openly and constructively. It’s not just about holding publications accountable, it’s about all of us taking responsibility for the accuracy and integrity of the information we consume and share.

Final Thoughts

The science behind ingredients like TTFE is worth discussing, and we encourage further study into its potential for hair growth and skin protection. But these conversations must be rooted in accurate, well-contextualized data, not inflated statistics of misunderstood summaries. In an era where AI-generated content is becoming more common, the responsibility to ensure scientific accuracy is higher than ever. We can no longer rely solely on the reputation of a publication or the polish of a press release. We need to dig deeper, question assumptions, and value transparency over hype.

After all, if 96.88% of readers walked away with a fundamentally incorrect understanding of the data, what does that say about how we’re communicating science?