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@yuki223343: တစ်ယောက်မှအကျင့်မကောင်းပါ🎀💗
𝗦❣
Open In TikTok:
Region: MM
Tuesday 22 September 2026 05:32:46 GMT
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Comments
စောက်ပျင်း :
သူလေးကိုချစ်လို့🤭
2026-09-22 06:44:19
42
💬?? :
သူလေးစိုမိုးနေလို့🫶🫶
2026-10-11 05:41:51
1
爱•|call me 🍒ဦးခန့်💥 :
အရှေကကောင်မလေးကိုချစ်မိနေပီ🤭🤭🤭🤭
2026-09-23 02:32:05
6
💥𝕄𝕒 𝔼𝕚💥 :
အမွှာလားမမ ချစ်စရာလေး🥰🥰🥰🥰🥰
2026-09-22 13:26:49
10
မောင်မောင်ဝင်း. 🇲🇲💈💇✂️👌 :
2026-09-24 06:59:18
2
🌷🌼ချာတိပ်🌼🌷 :
အမဲလေနဲ့တစ်ယောက်ချစ်စရာလေး
2026-09-22 05:44:16
5
ဖိုးသိန်းဆိုတာငါဘဲဘက်ဆုံ :
သူ့ကိုဘဲမြင်တယ်
2026-09-23 05:30:57
5
🚬အလတ်ကောင်💣😎(ရွှေဘို) :
အဲ့ရှမ်းမလေး acc ပေးချင်ဘူးလား😘🥰
2026-09-25 13:52:49
3
👺ဂန္ဓာမာ..အိုလီဗာ💨👺!! :
OMG 😳
2026-09-22 13:41:41
3
💐🤬DawKay😡💐 :
😘😘
2026-09-23 14:45:15
2
雪瑶 :
ချစ်စရာလေ တွေအကုန်လုံးက🥰🥰🥰
2026-09-26 12:56:39
3
မောင်ယိုကီ :
2026-09-22 10:32:45
3
Marina(မာရီယာ)🐒 :
လှတယ်မကကတို့က
2026-09-22 15:17:00
3
@'😍 K😊 :
ချောလိုက်တာ 🥰🥰🥰🥰
2026-09-23 00:30:51
3
Thae Thae :
rpချစ်လေးတို့🥰🥰😘😘😘
2026-10-01 11:53:01
2
Meena Acy :
❤️❤️❤️❤️❤️
2026-09-24 01:57:22
1
ကိုကိုသဲငယ်🥰🥰🥰🥰🥰🥰🥰🥰🥰 :
2026-09-22 14:52:24
2
မောင်မောင်ဝင်း. 🇲🇲💈💇✂️👌 :
ချစ်တယ်ဗျ💔💔💔
2026-09-24 14:43:53
3
✔𝐌𝐠 𝐆𝐲𝐢 𝐄𝐝𝐢𝐭〖ᴷⁱⁿᴳ🇲🇲 :
မိန်းမလိုချင်တယ်.......
2026-09-27 03:30:13
3
🤬ဒေါ်ဖူး🤬(fan,-2,)💐 :
သူလို့ကို ချစ်မီနေပီ ယို့.....🥰
2026-10-12 09:47:08
2
💢ဒေါ်နှင်း😩 :
အချစ်ခံချင်တယ် 😩😩😩
2026-09-27 15:44:04
2
Divya✨ :
Mayalu Tay cele pl harr
2026-09-22 10:06:34
3
NGA THAN🍁 :
အနက္ေရာင္ေလးကခ်စ္စရားေလ
2026-09-22 13:38:26
4
👑🗯꧁༒☬👹 T👹☬༒꧂🗯👑 :
2026-09-22 06:24:42
3
To see more videos from user @yuki223343, please go to the Tikwm homepage.
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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.
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