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@wusa_9: The Naresh Bhatt murder trial has resumed with the court seeing more footage from police interactions with Naresh on Aug. 9, 2024, discussing medication Mamta took.

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Tuesday 13 October 2026 17:06:03 GMT
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soflobaby786
soflobaby786 :
Absolutely outrageous 🤦🏾‍♂️
2026-10-13 18:06:25
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andrea_pardohenriquez80
🇺🇸🇨🇺❤️🦋Andrea🦋❤️🇺🇸🇨🇺 :
Wow that room he cleaned up
2026-10-13 18:44:13
1
gonzo_great
Gonzo the Great :
2026-10-14 08:33:55
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Female data isn't noisy. Researchers were just measuring it wrong. In this conversation, Dr. Jennifer Garrison breaks down a fundamental flaw in how female biology has been studied — and why it's been hiding in plain sight. For years, researchers have used chronological age to organize and interpret data from female subjects. The problem? Chronological age doesn't account for the enormous variability that exists between women. When you use age of natural menopause as the reference point instead, something remarkable happens. The noise disappears. Sharp, clear biological signatures emerge from data that was previously dismissed as too variable to be useful. And here's what makes this even more striking: researchers were able to go back into large-scale historical datasets that already existed — datasets that weren't originally designed to capture this information — and clean them up simply by changing how the data was organized. The data was never the problem. The framework was. Tune into the full conversation, out on all major streaming platforms.
Female data isn't noisy. Researchers were just measuring it wrong. In this conversation, Dr. Jennifer Garrison breaks down a fundamental flaw in how female biology has been studied — and why it's been hiding in plain sight. For years, researchers have used chronological age to organize and interpret data from female subjects. The problem? Chronological age doesn't account for the enormous variability that exists between women. When you use age of natural menopause as the reference point instead, something remarkable happens. The noise disappears. Sharp, clear biological signatures emerge from data that was previously dismissed as too variable to be useful. And here's what makes this even more striking: researchers were able to go back into large-scale historical datasets that already existed — datasets that weren't originally designed to capture this information — and clean them up simply by changing how the data was organized. The data was never the problem. The framework was. Tune into the full conversation, out on all major streaming platforms.
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