Injection halves risk of chromosome error common in older human eggs 6%

By #author.fullName}0% Carissa Wong0%

7/9/2026, 11:00:58 AM

Keywords: Fertility, Pregnancy

BS Summary: This article contains 20 faulty reasoning types, including Unattributed Quote, Optimism Bias, and Ambiguity (Equivocation), with Appeal to Authority as the most egregious example at 10.2% saturation with 102 hits. Analysis detected 917 faulty-reasoning hits from 1,000 analyzed words, generating a BS Score of 22.3% and a BS Rank of 6% (20,598 of 21,886 articles). This article is better (less manipulative) than 94.10% of the article peer group.

Cells with a signal indicating the presence of too many chromosomes 
DEPT. 
OF CLINICAL CYTOGENETICS, ADDENBROOKES HOSPITAL/SCIENCE PHOTO LIBRARY 
Human eggs that contain too many or too few chromosomes can lead to miscarriage, IVF failure and conditions such as Down’s syndrome. 
Now, researchers have found that giving the eggs a single injection can substantially reduce the problem. 
The approach could eventually boost the chances of success for older women undergoing IVF. 
“It really seems like a big deal,” says Marcos Iuri Roos Kulmann at Nilo Frantz Reproductive Medicine in Porto Alegre, Brazil, who wasn’t involved in the new research. 
“To my knowledge, this is the first [therapy] to show such clinical potential for correcting this major cause of IVF failure.” 
During a process called meiosis, egg and sperm cells eject exactly half of their genetic material. 
This means that when egg and sperm combine during fertilisation, they form an embryo with a complete genome. 
Sometimes, however, a sperm or egg cell has slightly more or slightly less than the half genome it should contain. 
This is a condition known as aneuploidy. 
Lambs born via IVF using highly immature eggs in major breakthrough 
Aneuploidy affects about 10 to 25 per cent of eggs in the early 30s and becomes more common with age. 
“Already in the late 30s, more than 65 per cent of all eggs are aneuploid,” Agata Zielinska at Ovo Labs, a biotechnology company in Germany, told the audience at the European Society of Human Reproduction and Embryology conference in London on 6 July. 
Clinicians sometimes screen IVF embryos for aneuploidy when treating couples at greater risk of miscarriage or IVF failure. 
But for most couples , conditions caused by the genetic error  which include Down’s syndrome  are only detected via blood tests and ultrasound scans taken during the first trimester of pregnancy. 
Until now, there have been no ways to reduce the risk of aneuploidy occurring in the first place, says Zielinska. 
Now, Zielinska and her colleagues have found that the level of a protein called shugoshin-1 is substantially lower in older mouse and human eggs than in younger ones. 
Shugoshin-1 helps with a stage of meiosis in which two copies of each chromosome line up along the middle of an immature egg cell. 
The protein maintains the molecular glue that holds each pair together. 
Upon fertilisation, the two copies of the chromosomes separate and move to opposite sides of the cell. 
One end ultimately forms the mature egg cell, and the other end is discarded. 
But in older eggs, the glue holding the chromosome pairs together degrades, which can cause the two copies of each chromosome to separate before fertilisation. 
When this happens, the chromosomes spread unevenly throughout the cell  which means the resulting egg may be aneuploid. 
Sperm have been made magnetic to allow IVF inside the body 
To explore whether replenishing shugoshin-1 could prevent aneuploidy by helping to hold chromosome pairs together, the team collected 111 spare, immature eggs from more than 30 women aged between 22 and 43 who were banking eggs or undergoing IVF. 
The team injected the genetic code for shugoshin-1, in the form of mRNA, into one or more of each donor’s eggs, and left other eggs from the same donor untreated. 
A few hours later, chromosomes had prematurely separated in 53 per cent of the untreated eggs, whereas this figure was nearly half  29 per cent  in the treated ones. 
In eggs from nine donors who were aged over 35, aneuploidy rates were 65 per cent, on average, in untreated eggs. 
But in treated eggs, the average figure was just 44 per cent. 
This reduction wasn’t statistically significant, although this is probably because of the study’s small sample size, according to the researchers. 
Common IVF test misses some genetic abnormalities in embryos 
Human embryos formed with in vitro fertilisation can develop genetic abnormalities in the time between genetic testing and implantation  though this may not affect their viability 
Further experiments showed the approach could prevent aneuploidy in mouse eggs, which were then successfully fertilised to produce healthy offspring. 
No side effects were seen in the mouse or human studies. 
“We’ve achieved live births in mice, so, from that perspective, we’re confident that this approach is not interfering in the mouse model with any steps of embryo development, and it doesn’t interfere with pup health and pregnancy health,” Zielinska told the conference audience. 
The researchers are now working towards testing the effects of shugoshin-1 in people. 
This would involve tweaking standard IVF to use immature eggs rather than mature ones, but this change would be fairly easy to implement, says Zielinska. 
She hopes the therapy, which the team calls EmbryoProtect, will provide an affordable way to improve IVF for older women. 
“We anticipate that the treatment will cost a fraction of the cost of a full IVF cycle,” says Zielinska. 
“By meaningfully improving IVF success rates, especially for women over 35 where baseline success is low, we hope that fewer attempts will be needed to conceive.” 
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Article reasoning-pattern comparisonThis article: 2.0%#author.fullName}: 1.7%New Scientist: 1.8%Confirmation Bias2.0%This article: 0.0%#author.fullName}: 1.8%New Scientist: 1.0%Anchoring Bias0.0%This article: 0.9%#author.fullName}: 2.1%New Scientist: 2.1%Availability Heuristic0.9%This article: 0.0%#author.fullName}: 0.4%New Scientist: 0.5%Representativeness Heuristic0.0%This article: 2.0%#author.fullName}: 0.2%New Scientist: 0.2%Hindsight Bias2.0%This article: 8.0%#author.fullName}: 2.9%New Scientist: 2.6%Overconfidence Bias8.0%This article: 4.4%#author.fullName}: 4.3%New Scientist: 5.8%Framing Effect4.4%This article: 0.0%#author.fullName}: 0.2%New Scientist: 0.3%Loss Aversion0.0%This article: 0.0%#author.fullName}: 0.2%New Scientist: 0.2%Status Quo Bias0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Sunk Cost Effect0.0%This article: 8.5%#author.fullName}: 7.2%New Scientist: 6.6%Optimism Bias8.5%This article: 0.0%#author.fullName}: 1.6%New Scientist: 0.6%Pessimism Bias0.0%This article: 3.3%#author.fullName}: 3.9%New Scientist: 1.9%Negativity Bias3.3%This article: 0.0%#author.fullName}: 0.2%New Scientist: 0.2%Self-Serving Bias0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.1%Fundamental Attribution Error0.0%This article: 0.0%#author.fullName}: 0.4%New Scientist: 0.1%Actor-Observer Bias0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.6%In-Group Bias0.0%This article: 0.0%#author.fullName}: 0.3%New Scientist: 0.1%Out-Group Homogeneity Bias0.0%This article: 1.0%#author.fullName}: 1.0%New Scientist: 7.3%Halo Effect1.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Horn Effect0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Dunning-Kruger Effect0.0%This article: 0.9%#author.fullName}: 0.9%New Scientist: 0.4%Recency Bias0.9%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.3%Primacy Effect0.0%This article: 0.0%#author.fullName}: 0.3%New Scientist: 0.2%Blind-Spot Bias0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Ad Hominem0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Straw Man0.0%This article: 10.2%#author.fullName}: 8.4%New Scientist: 13.9%Appeal to Authority10.2%This article: 3.3%#author.fullName}: 0.8%New Scientist: 0.9%False Dilemma3.3%This article: 0.0%#author.fullName}: 1.6%New Scientist: 0.6%Slippery Slope0.0%This article: 0.0%#author.fullName}: 0.2%New Scientist: 0.1%Circular Reasoning0.0%This article: 5.3%#author.fullName}: 2.8%New Scientist: 3.2%Hasty Generalization5.3%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Red Herring0.0%This article: 0.0%#author.fullName}: 0.2%New Scientist: 0.7%Bandwagon0.0%This article: 1.2%#author.fullName}: 2.6%New Scientist: 5.1%Appeal to Emotion1.2%This article: 0.0%#author.fullName}: 0.7%New Scientist: 0.9%Begging the Question0.0%This article: 7.3%#author.fullName}: 2.3%New Scientist: 1.4%Post Hoc (False Cause)7.3%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Tu Quoque0.0%This article: 0.0%#author.fullName}: 0.2%New Scientist: 0.1%Burden of Proof0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.1%Appeal to Nature0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.1%Composition/Division0.0%This article: 0.0%#author.fullName}: 0.8%New Scientist: 0.8%Anecdotal0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%No True Scotsman0.0%This article: 8.5%#author.fullName}: 1.0%New Scientist: 1.0%Ambiguity (Equivocation)8.5%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Gambler’s Fallacy0.0%This article: 0.0%#author.fullName}: 0.1%New Scientist: 0.0%Middle Ground0.0%This article: 0.0%#author.fullName}: 0.1%New Scientist: 0.0%Personal Incredulity0.0%This article: 2.0%#author.fullName}: 0.2%New Scientist: 0.1%Special Pleading2.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Genetic Fallacy0.0%This article: 9.2%#author.fullName}: 1.2%New Scientist: 0.6%Unattributed Quote9.2%This article: 2.8%#author.fullName}: 0.5%New Scientist: 0.4%Quote-first Misdirection2.8%This article: 0.0%#author.fullName}: 2.1%New Scientist: 3.7%Biased Writer Voice0.0%This article: 2.6%#author.fullName}: 0.6%New Scientist: 1.9%Indoctrination2.6%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%#author.fullName}: 0.0%New Scientist: 0.0%Politically Right Leaning Bias0.0%This article: 8.3%#author.fullName}: 1.8%New Scientist: 20.0%Attempt to Sell a Product or S…8.3%

1000 words analyzed.

Speakers

2speakers23%attributed speech775writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageMarcos Iuri Roos Kulmann • 28 words • 100.0% coverageMarcos Iuri Roos Kulmann • 21 words • 100.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageAgata Zielinska • 43 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageAgata Zielinska • 20 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 39 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageAgata Zielinska • 43 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageAgata Zielinska • 25 words • 0.0% coverageWriter's voice • 20 words • 100.0% coverageAgata Zielinska • 19 words • 100.0% coverageAgata Zielinska • 26 words • 100.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 7 words • 100.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 11 words • 100.0% coverage
100%flagged-word coverage
49 attributed words22% of attributed speech41% writer coverage
0%50.0%100.0%Unattributed Quote+100.0 ptsWriter: 0.0%Marcos Iuri Roos Kulmann: 100.0%100.0%Quote-first Misdirection+57.1 ptsWriter: 0.0%Marcos Iuri Roos Kulmann: 57.1%57.1%Attempt to Sell a Product -4.9 ptsWriter: 4.9%Marcos Iuri Roos Kulmann: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.