TUCSON, Ariz. – October 14, 2026 – Siensmetrica, Inc., developer of the AI-powered Tessa research evaluation platform, today announced that it has changed its corporate name to Tessa AI, Inc. The change, effective October 7, 2026, brings the company’s name in line with the platform that its customers, partners, and the broader research community already know it by.

Tessa takes its name from T.E.S. (Transparency, Explainability, and Significance), the framework at the core of the company’s technology. Since its public launch in May 2026, Tessa has helped researchers, clinicians, reviewers, and biopharma professionals evaluate the trustworthiness of biomedical research, analyzing more than 200 data points per paper and showing the evidence behind every score.

From measuring research to supporting its entire life

The original name, Siensmetrica, was a coined word meaning roughly “knowledge measurement.” It described where the company began: a system for scoring the quality of published research. Scoring remains central to what the company does, but it is now one part of a much larger mission.

Tessa AI is building a suite of research analysis, discovery, and data science tools on the Tessa platform and the research data that powers it. Together, they are designed to support a study across its entire life: helping authors strengthen a manuscript before it is submitted, helping research teams survey an entire field of literature in a fraction of the time it takes today, helping the public tell whether a news story or podcast accurately reflects the research behind it, and evaluating work published years or even decades earlier. Company Chief Science Officer Marvin J. Slepian, MD, JD, describes this arc as the “natural history of scientific research.” All of the new tools will carry the Tessa name.

“For a while now, our customers, partners, and even our own team have simply called us Tessa,” said Marc Toth, CEO of Tessa AI. “Siensmetrica described where we started. Tessa describes where we’re going. We’re building tools that will be part of research from the moment a question is first asked to long after the answer is published, and it made sense for the company to carry the name people already associate with that work.”

A system ready for change

The way science is produced, reviewed, and judged has struggled to keep pace with the volume of research now being published. Peer review is stretched thin and, in some cases, gamed. Citation counts can be padded. Prestige metrics such as journal rank and author reputation often stand in for an actual evaluation of the work, and retractions and reproducibility failures continue to erode public trust in science.

Tessa AI’s leadership team, made up of serial technology innovators who have built and disrupted businesses before, believes the research enterprise is overdue for the same kind of change. The company’s recent decision to remove author reputation from the TScore, so that papers are judged on their methods and evidence rather than the names on the title page, offers an early look at that approach.

“Every study has a natural history: an idea, a design, an experiment, a publication, and a long afterlife of citation, replication, and sometimes correction,” said Dr. Slepian. “Today, most of the tools that touch that history were built for a slower world. Our goal is to bring transparency and rigor to every stage of it, so that good science can be recognized for what it is, wherever it comes from.”

The first of the new Tessa products is expected to launch in the coming weeks, with more to follow.

Visitors to siensmetrica.com will be redirected to the new site, and company email addresses are moving to the @tessaai.co domain.

About Tessa AI, Inc.

Tessa AI, Inc. (formerly Siensmetrica, Inc.) develops Tessa, an AI-powered platform that evaluates the trustworthiness of biomedical and healthcare research. Built on the T.E.S. (Transparency, Explainability, and Significance) framework, Tessa uses coordinated, specialized AI agents to assess a paper’s methodology, citations, reporting completeness, and risk of bias, and shows the evidence behind every score. The company is building a family of Tessa tools to support scientific research across its full lifecycle. Learn more at www.tessaai.co.

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Every year, hundreds of people in the United States die waiting for a heart transplant. Demand keeps climbing, driven largely by an aging population: the number of U.S. heart recipients aged 65 and older rose 127% between 2010 and 2021. The supply of donor hearts has not kept pace.

Part of the problem is that a matching donor is hard to find. Another part is age. There is no official upper age limit for heart donors, but in practice donors under 45 are recommended, and few transplant programs accept hearts from donors older than 50. As the population ages, a growing share of potential donor hearts falls outside that window.

A new study from researchers at Brigham and Women’s Hospital and Harvard Medical School, with collaborators at Altos Labs and Charité in Berlin, asks a question that could change how we think about that limit: does a transplanted heart keep the biological age of its donor, or does it gradually take on the biological age of the person who receives it?

To find out, the team transplanted hearts between young, middle-aged and old mice, then measured each heart’s “biological age” four to six months later using epigenetic clocks, which estimate age from chemical tags on DNA (DNA methylation), along with gene activity profiling. They then looked for the same pattern in humans, using archived heart biopsies from 11 transplant patients and one-year functional data from hundreds of heart recipients at their hospital.

Their answer: the heart appears to adopt the biological age of its new body.

One important note before going further: this study is a preprint. It was posted publicly on bioRxiv but has not yet been through formal peer review and journal publication, a process that commonly takes months and sometimes years.

That is exactly why we chose it for this spotlight. More on that in the Tessa section below.

What Makes This Study Noteworthy

• Old hearts in young bodies looked younger. In mice, older hearts transplanted into younger recipients measured biologically younger than their donors’ age on every epigenetic clock the researchers used. The reverse was also true: young hearts placed in older recipients aged faster than they otherwise would have.
• It wasn’t just one measurement. Beyond the age clocks, the researchers looked at more than 260,000 individual DNA sites across the genome. In old hearts placed in young recipients, the pattern of change ran in the opposite direction of normal aging. In other words, the aging signature didn’t just stall; it appeared to reverse.
• The effect was a one-way street. The recipient’s own heart, liver and blood were largely unaffected by the transplanted heart. The body changed the graft, but the graft did not noticeably change the body. That matters for any future allocation strategy, because it suggests a younger recipient would not be “aged” by receiving an older heart.
• The cell’s power plants may be involved. Genes tied to mitochondria, the structures that produce a cell’s energy, showed some of the largest shifts: turned down in young hearts placed in old recipients, and turned up in old hearts placed in young recipients. The authors present this as a starting point for future research, not a proven mechanism.
• The same pattern appeared in people. In the 11 human transplant patients studied, the biological age of the transplanted heart tracked the age of the recipient rather than the donor, including in cases where the donor was up to 24 years older than the recipient.
• Recipient age, not donor age, tracked with heart function. In a separate analysis of hundreds of patients one year after transplant, the recipient’s age was linked to measures of exercise capacity (functional capacity and peak oxygen uptake, or VO2 max), even after accounting for the donor’s age.

Limitations & Cautions

This is promising early work, not a change to clinical practice. Several cautions are worth keeping in mind:

• “Biologically younger” is not the same as “performs better.” The study measured molecular age markers. It did not show that older donor hearts placed in younger recipients lead to equal or better long-term survival. Other research has linked older donor hearts to higher mortality after transplant, and the authors say this question still needs to be tested directly.
• The mouse model is a simplified version of human transplant. The mice were genetically identical, so there was no organ rejection, and the transplanted heart was added alongside the animal’s own heart rather than replacing it.
• The human molecular evidence is small. The biopsy analysis included just 11 patients, using older preserved tissue samples that are not ideal for this kind of DNA testing.
• The functional data shows an association, not cause and effect. Recipient age was linked to some (not all) measures of heart function at one year. Longer-term outcomes, such as chronic vessel disease in the transplanted heart, were not studied.
• Peer review is still ahead. As a preprint, the methods and conclusions have not yet been vetted through formal review.

A new study published in Nature Communications looked at how the body handles inflammation—the immune system’s natural response to injury or infection. Researchers tested a drug compound (GSK2256294) on healthy volunteers who were given a mild, controlled skin challenge to see how their bodies naturally resolved the issue.

The team wanted to see if blocking a specific protein in the body could change how key white blood cells behave during recovery. They discovered that the drug increased a specific natural lipid (a type of fat molecule), which stopped certain inflammatory immune cells from maturing and spreading.

When we ran the ScienceDaily article covering this research (“Scientists discover a hidden switch that shuts down inflammation”) through our new Tessa Newslink beta, Tessa flagged that the reporting overstated the findings. Out of seven claims made by the news media piece, only two were fully supported by the paper, three were overstated, and two were not covered by the study at all.

Noteworthy Findings

While headlines framed this discovery as a “hidden switch that shuts down inflammation,” the empirical human data paints a much more nuanced picture:

• No Effect on Primary Signs of Inflammation: sEH inhibition had zero effect on local heat, redness, or swelling. It only accelerated the resolution of pain.
• Specific Immune Cell Tuning: Instead of shutting down the entire inflammatory process, the mechanism specifically alters monocyte subset differentiation (reducing circulating and tissue intermediate monocytes) and subsequently lowers tissue CD4 T-cell presence.
• Controlled Experimental Scope: The human trial was conducted on healthy volunteers in a non-randomized, non-placebo-controlled model of acute skin inflammation. Broad claims of a universal “off-switch” for chronic inflammatory diseases outrun the current experimental evidence.

Tessa Overall Score

TScore: 76 / 100 Green

While the ScienceDaily article overstated the findings of the paper, the paper itself was very good. A green score reflects solid, trustworthy research with just a few areas that fall short of exemplary reporting. Those are described in detail below.

A graphical representation of Tessa's analysis of Epoxy-oxylipins direct monocyte fate in inflammatory resolution in humans.

• Theoretical vs. Experimental: 90 (Strongly empirical, human in vivo interventional study)
• Weak to Rigorous: 52 (Solid primary monocyte/lipid endpoints, but weakened by non-randomized design, lack of placebo, and small sample sizes for secondary downstream T-cell assays)
• Known to Novel: 80 (High conceptual novelty in linking 12,13-EpOME and p38 MAPK directly to human monocyte fate)
• Journal Score: 100 (Nature Communications)
• Media Score: 37 (Reflects moderate interest by mainstream media coverage relative to the paper's actual data)

Tessa Analysis Breakdown

Research Question
In humans undergoing an acute inflammatory response, do CYP-derived epoxy-oxylipins and pharmacologic sEH inhibition causally direct monocyte differentiation during resolution?

Core Contribution
The study provides in vivo human proof that sEH inhibition elevates 12,13-EpOME, which downregulates p38 MAPK signaling. This specific pathway prevents classical monocytes from maturing into intermediate monocytes, reducing intermediate monocyte accumulation in both blood and inflamed skin without broadly shutting down clinical inflammation.

Key Methodological Strengths & Limitations

• Strengths: Strong translational design using human biospecimens (serial blood, blister exudate, skin biopsies); robust pharmacodynamic confirmation via mass spectrometry lipidomics; mechanistic validation using parallel in vitro assays and in vivo p38 inhibition (losmapimod).
• Limitations: Non-randomized trial design without a placebo control arm; small sample sizes ($n \approx 8\text12$) for secondary immunological subsets; reporting gaps in formal trial registration and pre-specified primary endpoints.

Read Tessa's full summary analysis of this paper, including the author and institution relationship map, citation timeline, and figure gallery, at https://www.tessapp.ai/report/41545341

Most health headlines chase something dramatic — a miracle pill, a breakthrough drug, an extreme new workout. Today’s Research Spotlight is the opposite. It’s a small, quiet study that never made headlines, yet points to one of the simplest, cheapest tools available for managing blood sugar after a meal: climbing a flight of stairs for about a minute.

Researchers at San Diego State University asked a simple question: can a single minute of light stair-stepping, done in the evening after eating, blunt the rise in blood sugar that follows a meal? Thirty healthy, non-diabetic adults (43% men, average age 29) took part in a randomized, crossover-controlled trial, meaning each person served as their own comparison. Every participant visited the lab twice in the evening (no earlier than 5 p.m., at least 5 hours after their last meal), and on both visits drank a 75-gram glucose solution, the same drink used in a standard diabetes screening test.

On one visit, participants simply sat for two hours afterward. On the other, they climbed up and down a flight of stairs for one minute, at whatever pace felt comfortable, timed to begin 57 minutes after the glucose drink, right around when blood sugar was expected to peak. Blood sugar was measured repeatedly over the following two hours with a standard fingerstick glucometer.

Noteworthy Findings

A few things make this study worth a second look:

• A single minute mattered. Sixty minutes after the glucose drink, blood sugar had risen an average of 2.5 mmol/L (about 45 mg/dL) in the stair-stepping condition, versus 4.3 mmol/L (about 77 mg/dL) with sitting alone — roughly a 40% smaller rise (P < 0.001).
• The evening made the effect bigger, not smaller. Earlier studies from the same research group found that a one-minute stair break lowers post-meal blood sugar in the morning, after an overnight fast. This study moved the test to the evening, when the body typically handles sugar less efficiently and glucose responses run higher, and found an even larger benefit than the morning studies did. That matters because most people eat their biggest meal at dinner, not breakfast.
• The timing of the benefit lined up with the timing of the exercise. The gap between the two conditions was clear at 60 and 70 minutes, narrowed by 80 minutes, and had essentially disappeared by 90 and 120 minutes. Total glucose exposure over the full two hours (measured as area under the curve) was also modestly lower with stair-stepping. The pattern suggests the timing of a movement break, not just that it happened, shapes how much it helps.
• It didn’t feel like exercise. Participants’ heart rates reached 61% of their estimated maximum during the stair-climbing (moderate effort), yet they rated it just 1.9 out of 10 on a standard perceived-exertion scale, or “very light.” People appear to consistently underestimate how much a quick set of stairs actually does, which may make it an easier habit to stick with than something that feels like a workout.
• It’s a “sleeping beauty.” For a finding with real, practical relevance to how millions of people manage blood sugar day to day, this study has attracted very little public attention — which is exactly why we chose it for this spotlight.

Tessa Overall Score

TScore: 77 / 100 Green

A green score reflects solid, trustworthy research with a few areas that fall short of exemplary reporting — described in detail below. Notably, this paper’s Media Score of just 35/100 is what earns it “sleeping beauty” status: Tessa’s Media Score tracks how much a topic is being discussed in the news and on social media, and this one has seen very little coverage relative to how useful its finding is.

Tessa’s Graphical Analysis of the paper

Tessa Analysis Breakdown

Tessa evaluates every paper across three main pillars — Transparency, Explainability and Significance (T.E.S.) — broken into five weighted sub-scores:

Tessa’s Deep Dive also checked the paper’s reporting against two established evaluation frameworks used for randomized trials:

Strengths

• A genuine randomized, crossover trial — each participant acted as their own control, which is one of the strongest designs available for a same-day, single-session effect like this.
• An objective outcome (repeated fingerstick glucose readings, averaged in line with ISO reproducibility standards), not a self-reported one.
• Zero dropouts — all 30 participants completed both visits, so missing data isn’t a concern.
• Published in Experimental Physiology, the official journal of The Physiological Society, reflected in a perfect 100/100 Journal Score.
• Tessa’s citation check verified 40 of the paper’s 42 references (95%) as real, existing publications, and rated 90% of citations in context as good-to-excellent for relevance.

Weaknesses

• No trial registration or published protocol, so it isn’t possible to confirm the analysis plan was locked in before the results were seen.
• The paper doesn’t describe how participants were randomly assigned to visit order or whether that assignment was concealed from staff.
• No blinding was used or described — largely unavoidable, since participants obviously know whether they’re climbing stairs or sitting, but still a limitation of the design.
• The sample was small, young and healthy (30 adults, average age 29). It’s not yet known whether the same one-minute effect holds for older adults or people with prediabetes or diabetes.
• Blood sugar was measured with a fingerstick glucometer rather than a laboratory blood draw or continuous glucose monitor.

Read Tessa’s full summary analysis of this paper, including the author and institution relationship map, citation timeline, and figure gallery,  at www.tessapp.ai/report/39705060.

Hat Tip to @WilliamWallace for bringing this to our attention.

You might have caught our recent announcement on the latest updates to Tessa, our AI-powered medical research analysis platform. Buried in the changelog is one decision that’s going to ruffle some feathers in academia: we completely removed the Author Score from our core trustworthiness rating (TScore) and redistributed its weight across our direct methodological metrics.

Here’s why we pulled the trigger, where author analytics live now instead, and why the traditional scientific hierarchy needs a reality check.

The Famous-Actor Fallacy

When we first built Tessa’s scoring logic across 200+ distinct data points, we included legacy benchmarks like Journal Impact Factor and author h-index. Why? Because that’s historically how the scientific community has evaluated credibility. If a paper came out of a high-profile lab with a heavily cited principal investigator, people assumed the work was solid.

Then we ran the numbers.

As Tessa processed thousands of papers across biomedical and healthcare literature, the data told a different story: an author’s h-index tracks publishing volume and career longevity, not the quality of any one paper.

Think of it like a Hollywood star. Two Oscars on the mantle didn’t stop your favorite actor from headlining last summer’s box-office disaster. A great track record is no guarantee the next project isn’t a dud.

In science, leaning on author prestige creates a dangerous halo effect: flawed methodology gets a free pass because a famous name sits on the title block. Good research has to stand on its own merits, so we stripped the author bias out. Now Tessa scores papers strictly on what actually matters: transparency, explainability, and significance — T.E.S.

Author History Isn’t Gone, It’s Just Not Grading the Paper

Removing the Author Score from the TScore doesn’t mean author history is irrelevant. Context still matters. It just shouldn’t be allowed to corrupt the objective evaluation of a single study.

So we built the Author Report: a dedicated dashboard, one click away from any author name in a Tessa Summary, with:

• A complete, interactive list of their published work
• Direct integration with their Google Scholar profile
• A standalone Author Score, built on i10-index principles (publications with 10+ citations)

The New Author Report

Full transparency: this standalone Author Score is still a work in progress. Like the h-index it’s modeled on, it doesn’t fully capture early-career researchers doing brilliant work, and it doesn’t properly weight the rare, seminal paper that racks up thousands of citations over time. We’re actively tuning the algorithm to better reflect real long-term contributions in future releases.

The difference is where this data lives. It’s context in a separate room, not a thumb on the scale of whether today’s paper is actually any good.

Science Over Status: What We’re Coming for Next

We expect pushback from academic traditionalists. The system has run on institutional prestige and author reputation for centuries, and disrupting that incentive structure is going to make some people uncomfortable.

That’s fine. We’re not here to protect academic egos, we’re here to fix how science gets validated. Good research is good research, no matter whose name is on it.

While we’ve got your attention: there’s another legacy prestige metric still sitting in our algorithm, and it’s next on the chopping block: the Journal Score.

The scientific community has long operated on the unproven assumption that a paper in a top-tier journal is inherently better than one in an open-access repository or a niche journal. As Tessa tears through thousands of new papers every week, we’re collecting the data to test that assumption directly.

Are top journals actually publishing better science, or are they just gatekeeping? How many high-rigor “sleeping beauties” are getting ignored simply because they didn’t land at a prestigious publication?

We have the data. Stay tuned.

 

Coffee and health have a complicated relationship. Depending on the study (or the headline) it can seem like coffee is either a health food or something we should be avoiding.

This study takes a more detailed look.

Researchers analyzed data from 2,264 46-year-old participants in the Northern Finland Birth Cohort 1966 to examine how habitual coffee consumption relates to body composition, metabolic markers, and sex hormones.

Rather than relying solely on self-reported health outcomes, the researchers combined several objective measurements, including proton nuclear magnetic resonance (NMR) metabolomics, liquid chromatography–tandem mass spectrometry (LC-MS/MS) testosterone measurements, oral glucose tolerance testing, and bioimpedance body composition analysis.

The result is a detailed picture of the biological differences associated with habitual coffee consumption—but not necessarily an explanation for why those differences exist.

What Makes This Study Interesting?

A few findings stand out.

Leaner body composition, despite similar BMI. Participants who reported drinking more coffee tended to have lower total and visceral body fat and higher skeletal muscle mass. Yet their BMI was essentially unchanged.

That is an interesting distinction because BMI alone doesn’t capture where body mass comes from.

A surprisingly complicated testosterone picture. Among men, higher coffee intake was associated with higher total testosterone and higher SHBG. At the same time, free testosterone and Free Androgen Index (FAI) were lower.

In other words, the relationship between coffee and testosterone isn’t as simple as “more coffee = more testosterone.”

Women did not show the same increase in total testosterone, although higher coffee intake was also associated with higher SHBG and lower FAI.

Lower circulating BCAAs. Higher habitual coffee intake was associated with lower circulating levels of the branched-chain amino acids (BCAAs) isoleucine, leucine, and valine in both men and women. These metabolites are frequently associated with insulin resistance and metabolic dysfunction.

Taken together, the findings suggest that habitual coffee consumption is associated with several measurable differences in metabolic and hormonal profiles.

But association is where the story needs to stop… for now.

The Tessa Overall Score

TScore: 66 / 100 ( Yellow) — Moderate Trustworthiness / Hypothesis-Generating

Tessa’s analysis places the study in an important middle ground: the underlying measurements are strong, the reporting is thorough, and the findings are biologically interesting. But the study design doesn’t allow us to conclude that coffee caused any of these changes.

Theoretical vs. Experimental: 60 The study is a cross-sectional observational analysis based on primary physical and biological measurements. It does not include an intervention or causal validation.

Weak to Rigorous: 62 The study benefits from objective measurements using techniques such as LC-MS/MS and NMR spectroscopy, along with multiple-testing corrections for the metabolomics analysis. However, coffee intake was self-reported, cup volumes weren’t defined, and potentially important confounding factors remain.

Known to Novel: 60 Coffee, metabolism, and hormones have been studied extensively. The novelty here comes from bringing detailed androgen measurements, BCAA metabolomics, and body composition together in a single sex-stratified cohort.

Tessa Quality & Rigor Analysis

Tessa’s deeper analysis reveals a useful distinction between how well a study is reported and how confidently its conclusions can be interpreted.

Citation Integrity: 96% Tessa verified 96% of the study’s references, with an average citation relevance score of 7.3/10 and no unverified or uncited references detected.

Reporting Completeness: 100% The study fully met the STROBE reporting framework for observational research, including participant flow, variable definitions, and statistical reporting.

Risk of Bias: High

Tessa identified several important limitations:

• Confounding: The analysis does not fully account for factors such as overall dietary patterns, sleep, stress, or female menstrual and contraceptive variables.
• Participant selection: Restricting the analysis to participants who consumed only coffee or neither coffee nor tea, along with complete-case analysis, reduced the final sample from the broader cohort to 2,264 participants.
• Exposure measurement: Participants reported cups per day, but the study did not establish precise cup volumes, roast types, or additions such as milk and sugar.
• Outcome measurement: This is where the study is strongest. Standardized clinical protocols and objective laboratory assays reduce the risk of measurement bias.

What Tessa Adds to the Conversation

This is exactly the kind of study where a headline can be more certain than the evidence.

The data show that habitual coffee consumption tracks with differences in body composition, metabolic markers, and hormone profiles.

They do not show that drinking coffee produces those differences.

People who drink more coffee may differ from people who drink less coffee in other ways that weren’t completely captured by the analysis. And because the study looks at participants at a single point in time, it cannot establish which factor came first.

That’s why Tessa classifies the findings as hypothesis-generating rather than causal.

The interesting question isn’t simply whether coffee is “good” or “bad.”

It’s what these biological associations might tell researchers about the relationship between coffee consumption, metabolism, body composition, and hormones—and what future studies would need to do to determine whether those relationships are actually causal.

See the Summary Tessa Report here: https://www.tessapp.ai/report/42461444

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If you follow health influencers or longevity experts online, you’ve likely heard about Nattokinase, an enzyme derived from fermented soybeans.

It’s frequently hyped as an inexpensive miracle supplement that can single-handedly melt away up to 95% of arterial plaque, offering a cheap alternative to expensive statins or invasive surgical procedures.

Influencers routinely point to scientific literature to back up these bold claims. But does the published research actually say what they claim it does?

To separate online hype from scientific reality, we ran a Tessa analysis on one of the primary systematic reviews frequently cited to validate nattokinase: “Nattokinase Supplementation and Cardiovascular Risk Factors: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.“

Here is what Tessa revealed when we put this paper under the microscope.

What the Paper Actually Showed

Despite social media claims that nattokinase clears plaque and dramatically lowers cholesterol, this meta-analysis of randomized controlled trials (RCTs) found something very different:

• No Plaque Busting or Lipid Lowering: Across the pooled trial data, nattokinase showed no consistent lipid-lowering effect. In fact, lower total doses of nattokinase were actually associated with a slight increase in total cholesterol and LDL (“bad”) cholesterol, and a slight decrease in HDL (“good”) cholesterol.
• Modest Blood Pressure Reduction: The paper did confirm a small, statistically significant drop in blood pressure (roughly 3.4 mmHg systolic and 2.3 mmHg diastolic). While helpful, this modest drop is a far cry from replacing standard medical treatments.
• A Slight Increase in Blood Glucose: The analysis noted a small but statistically significant increase in fasting blood glucose among participants taking nattokinase.

The Tessa Overall Score

TScore: 66 / 100 ( Yellow – Caution)

A graphical illustration of Tessa’s analysis of the paper

Tessa Analysis Results

Tessa Sub-Scores

The Tessa Analysis Breakdown

While the authors followed standard PRISMA reporting protocols (achieving an 88% completeness score), Tessa Pro’s deep-dive evaluation highlighted major structural vulnerabilities in the underlying evidence:

• Methodological Quality (AMSTAR 2): Critically Low Confidence — Although the authors conducted duplicate screening and used standard risk-of-bias tools, the review suffered from critical methodological gaps. Key issues included an incomplete search strategy (excluding trial registries and grey literature), failure to provide a detailed list of excluded studies with justifications, and no reporting on the funding sources of the primary trials analyzed.
• Certainty of Evidence (GRADE): Very Low Certainty — When evaluated using the GRADE framework across 8 primary outcomes, the certainty of evidence dropped to Very Low for most findings, including LDL, HDL, blood pressure, and adverse event reporting. The primary driver for this rating was the small number of overall participants (only 6 RCTs totaling 546 subjects) combined with unexplained heterogeneity (variability) between trial results.
• Citation Integrity: Tessa’s citation engine verified 95% of the paper’s references with an average relevance score of 7.1/10, showing solid context alignment without evidence of aggressive citation padding.

The Bottom Line: While the systematic review itself was conducted in good faith, the actual primary data simply does not support the massive health claims circulating on social media.

See for yourself: Read the full, objective Tessa analysis report here (includes links to the study): https://www.tessapp.ai/report/39076715

If you’re interested in similar studies, check out the Tessa analysis of these:

• Cohort Study (1,062 people): (65 / 100 ( Yellow) — Effective management of atherosclerosis progress and hyperlipidemia with nattokinase: A clinical study with 1,062 participants. (Frontiers in cardiovascular medicine)
• Narrative Review: (50 / 100 ( Yellow) — Research Progress of Nattokinase in Reducing Blood Lipid. (Nutrients)