Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 99% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
Growth
50 measurements spanning 80 days, net +756. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 22,171–23,198 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 50
Measured (UTC)
Subscribers
Change
25 Oct 2026, 08:25
23,056
-1
24 Oct 2026, 10:44
23,057
-20
23 Oct 2026, 10:40
23,077
-2
22 Oct 2026, 09:45
23,079
+10
21 Oct 2026, 05:00
23,069
-1
20 Oct 2026, 03:37
23,070
+80
19 Oct 2026, 04:06
22,990
+18
18 Oct 2026, 02:39
22,972
-3
17 Oct 2026, 05:47
22,975
-8
16 Oct 2026, 06:18
22,983
-3
15 Oct 2026, 03:37
22,986
+5
14 Oct 2026, 02:56
22,981
-5
11 Oct 2026, 18:38
22,986
+52
7 Oct 2026, 04:58
22,934
+119
2 Oct 2026, 09:38
22,815
+181
18 Sept 2026, 18:19
22,634
-5
16 Sept 2026, 07:37
22,639
+5
14 Sept 2026, 13:41
22,634
-7
13 Sept 2026, 01:40
22,641
-1
11 Sept 2026, 06:01
22,642
first reading
Engagement
190 posts held, back to 24 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 96 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
3.93%
avg views ÷ 23,056 subscribers
Avg views / post
906
66 posts measured
Reaction rate
0.733%
reactions ÷ views · ER floor
Posts in window
67
of 190 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 47 of 66 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 21 October 2026
Posts held
190 (24 July 2026 – 21 October 2026)
Views total
59,826
Reactions total
399
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
24 Oct 2026, 06:48 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
5m 00s
Average length
33s
Measured directly from 9 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
1,149 reactions across 134 posts, in 9 distinct kinds. The most used accounts for 63.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
728
63.4%
👍
234
20.4%
😢
53
4.61%
🔥
41
3.57%
👏
35
3.05%
🙏
34
2.96%
👌
11
0.957%
😍
7
0.609%
🤝
6
0.522%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 137 of the 190 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 1,149 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 190 most recent posts we hold, published 24 July 2026 to 21 October 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
📍 محل برگزاری: تهران، خیابان ولیعصر، خیابان قبادیان غربی، پلاک ۵۳، دانشکده فناوریهای نوین پزشکی
💰 هزینه شرکت در کارگاه: 🔸 هزینه آزاد: ۲ میلیون و ۵۰۰ هزار تومان 🔸 هزینه ویژه دانشجویان با تخفیف: ۱ میلیون و ۵۰۰ هزار تومان
🔗 لینک ثبتنام و پرداخت:
https://atm.sbmu.ac.ir/pay
📩 ارسال رسید پرداخت:
[email protected]
✨ فرصتی برای یادگیری اصولی و کسب تجربه عملی در زمینه کشت سلولی، آشنایی با تکنیکهای رایج آزمایشگاهی…
🧫🔬 کارگاه تئوری و عملی کشت سلولی
📣 کمیته تحقیقات و فناوری دانشجویی دانشکده فناوریهای نوین پزشکی دانشگاه علوم پزشکی شهید بهشتی، با همکاری شرکت ارژنگ تجهیز آزما برگزار میکند:
🧪 کارگاه تخصصی کشت سلولی
👨🏻🔬 مدرس: دکتر مهنوش عباسزاده دیباور دکتری هماتولوژی آزمایشگاهی و بانک خون و عضو هیئت علمی دانشگاه علوم پزشکی شهید بهشتی
📚 سرفصلهای کارگاه: 🔹 آموزش تئوری و عملی روشهای دفریز، پاساژ، فریز و ذخیرهسازی سلولی 🔹 آشنایی ب…
⭕️داروی آسم در درمان التهاب مری نیز امیدوارکننده است
⏺شرکتهای امجن و استرازنکا اعلام کردند که داروی تزپلومَب (tezepelumab) با نام تجاری تِزسپایر (Tezspire) در یک مطالعه بالینی، علائم ازوفاژیت ائوزینوفیلیک، نوعی بیماری التهابی مزمن مری، را کاهش داده و در ۶۹ درصد بیماران به بهبود بیماری منجر شده است
⏺این دارو با مهار پروتئین TSLP، مسیر التهابی دخیل در آسم و ازوفاژیت ائوزینوفیلیک را هدف قرار میدهد
⏺پس از ۲۴ هفته، ب…
_چه ازدواج کنی و چه نه، ممکنه پشیمون بشی.
_چه درس بخونی و چه نه، ممکنه پشیمون بشی.
_چه بچهدار بشی و چه نه، ممکنه پشیمون بشی.
👈 چه مهاجرت کنی و چه نه، ممکنه پشیمون بشی.
👈 چه رابطهت رو ادامه بدی و چه نه، ممکنه پشیمون بشی.
👈 چه شغلت رو عوض کنی و چه نه، ممکنه پشیمون بشی.
🔺 میدونی چرا گاهی حتی بعد از تصمیمگیری هم احساس پشیمونی میکنیم؟
چون ذهنمون مدام سناریوهای جایگزین میسازه و میپرسه: «چی میشد اگه راه دیگهای رو …
یادآوری امروزت:
تو مسابقهی گاوبازی، کسی نفر اول میشه که قشنگترین جاخالی رو بده، نه کسی که با گاوها درگیر بشه!
پس بنابراین، با گاوهای «همسایه»، با گاوهای «فامیل» و با گاوهای «همکار» درگیر نشید؛ جاخالی بدید بره!
یادآوری امروزت:
ماشین خوب، ظاهر زیبا و ثروت زیاد یه روزی عادی میشه. تنها چیزی که هیچوقت عادی نمیشه، ادبه؛ ادب یعنی هنگام عصبانیت بیاحترامی نکنی، هنگام پولداری فروتن و متواضع باشی، به حریم خصوصی آدمها احترام بذاری، با هیچکسی زیاد شوخی نکنی، با صدای خیلی بلند حرف نزنی، آدمها رو زیاد سؤالپیچ نکنی، قبل از حرف زدن فکر کنی، تو زندگی بقیه دخالت نکنی، پشت سر بقیه حرف نزنی و کسی رو قضاوت نکنی.
🔮 با #آکادمیک #ساینس ه…
در مورد یادگیری و تمرین سیتوژنتیک و شناسایی کروموزوم ها ازم پرسیده بودید
اینو خودم نوشتم
شاید برای شروع کمک کننده باشه
بقیه اش با تمرین و تکرار
@medical_human_genetics
🧬🚀 وقتی الگوریتمها بخشهای پنهان ژنوم را آشکار میکنند؛ معرفی HitSV، رویکردی نوین برای شناسایی تغییرات ساختاری DNA
🔬 مقالهای تازه منتشرشده در مجله معتبر Nature Biotechnology در ۱۵ اکتبر ۲۰۲۶، به یکی از چالشهای مهم ژنومیکس مدرن پرداخته است: چگونه میتوان تغییرات ساختاری ژنوم را با دقت بالا، در فناوریهای مختلف توالییابی شناسایی کرد؟
📌 عنوان مقاله: _Structural variant detection across sequencing technologies_
👨…
شاید یکی از مهمترین پیامهای این مطالعه این باشد که بخشی از تنوع ژنتیکی پنهان، نه به دلیل نبود داده، بلکه به دلیل محدودیت روشهای تحلیل از دید ما دور مانده است. 🧬🔍
━━━━━━━━━━━━━━
📚 منبع اصلی و دسترسی به مقاله:
Li, G., Liu, Y. et al. (2026). _Structural variant detection across sequencing technologies_. Nature Biotechnology.
🔗 https://doi.org/10.1038/s41587-026-03332-9
💻 کد ابزار HitSV: https://github.com/hitbc/
بر اساس نتایج گزارششده در مقاله:
📈 بهبود عملکرد در دادههای Long-read
در بنچمارک نمونه انسانی HG002، امتیاز F1 مربوط به ژنوتیپها برای HitSV به حدود ۸۵٪ در دادههای PacBio HiFi با پوشش ۳۰ برابر و ۸۸٫۴۸٪ در دادههای Oxford Nanopore با پوشش ۳۰ برابر رسید.
نویسندگان، بسته به فناوری و ابزار مقایسهشده، بهبود نسبی ۱۵ تا ۷۲ درصدی را در معیار F1 ژنوتیپ نسبت به روشهای برتر مقایسهشده گزارش کردند.
📈 بهبود شناسایی در داد…
Structural variant detection across sequencing technologies
Gaoyang Li , Yadong Liu …Yadong Wang
Nature Biotechnology (2026)
Showing the 12 most recent of 190 posts we hold for @academic_science. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Posts edited after publishing
@academic_science edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
13 October 2026
Most recent edit
13 October 2026
Forward network
Republishes
Channels on the register whose posts this channel has forwarded.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 85 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it. The 24 listed below are the most frequent namers; the rest are counted above but not each listed.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Handles this channel named that no longer answer
Dead references
1
handles named in this channel’s posts, vacant today
Evidenced gone
1
we ourselves saw one of these resolve, at some point
Never seen alive
0
vacant every time we have ever looked
@academic_science named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Evidenced gone
We ourselves saw each of these resolve to a real page at some point before it went vacant — a genuine, evidenced change, not an inference from absence.
@biio_test named in 3 posts, 8 August 2026 – 12 August 2026 · confirmed gone 4 October 2026 (two independent sightings — see how we confirm a dead handle)evidenced
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Ужасающий @terrifier_nsk · 247 (as read 20 September 2026)#78
Read from Telegram’s recommendation API, most recently 20 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
🧬Medical_Science_Apply✈ @Medical_Science_Apply · 28,487 Telegram ranks this channel #31 of 95 here — alongside 94 others — read 9 September 2026
🎓آموزش تحقیق دانشگاهی🎓 @Research_Q1 · 34,298 Telegram ranks this channel #38 of 89 here — alongside 88 others — read 4 September 2026
اخبار دانشجویی @Daneshjou_news · 19,132 Telegram ranks this channel #61 of 71 here — alongside 70 others — read 2 October 2026
This channel appears in 3 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 25 October 2026 — this
entry's latest reading, not the date you are reading this.
“Academic_science” (@academic_science), 23,056 subscribers as measured 25 October 2026. Telegram Register, tgregister.com/channel/academic_science.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.