Other / unclassifiable — 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 52% 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
35 measurements spanning 44 days, net +476. 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 29,308–29,926 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 35
Measured (UTC)
Subscribers
Change
19 Sept 2026, 11:57
29,855
+6
17 Sept 2026, 03:39
29,849
+9
15 Sept 2026, 07:15
29,840
+9
13 Sept 2026, 16:19
29,831
+18
11 Sept 2026, 23:39
29,813
+14
9 Sept 2026, 13:01
29,799
+24
6 Sept 2026, 02:55
29,775
+11
4 Sept 2026, 01:57
29,764
+11
2 Sept 2026, 15:49
29,753
+7
1 Sept 2026, 11:46
29,746
+38
31 Aug 2026, 12:53
29,708
-2
30 Aug 2026, 13:46
29,710
+6
29 Aug 2026, 16:55
29,704
+4
28 Aug 2026, 20:13
29,700
+15
27 Aug 2026, 22:34
29,685
+16
26 Aug 2026, 22:25
29,669
+29
25 Aug 2026, 19:33
29,640
+13
24 Aug 2026, 19:04
29,627
+11
23 Aug 2026, 06:14
29,616
-5
21 Aug 2026, 18:25
29,621
first reading
Engagement
137 posts held, back to 1 August 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 65 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
12.5%
avg views ÷ 29,855 subscribers
Avg views / post
3,720
79 posts measured
Reaction rate
0.952%
reactions ÷ views · ER floor
Posts in window
82
of 137 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.
What these figures were computed from
Window
Rolling 30 days · latest post in window 19 September 2026
Posts held
137 (1 August 2026 – 19 September 2026)
Views total
293,708
Reactions total
2,795
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
19 Sept 2026, 17:29 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
Photos
≈4,440
Videos
≈716
Links
≈3,250
Lifetime counters from Telegram’s own channel header, read 19 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Video runtime
16m 14s
Average length
2m 02s
Measured directly from 8 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
4,919 reactions across 127 posts, in 18 distinct kinds. The most used accounts for 80.3% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
3,949
80.3%
❤🔥
203
4.13%
👍
142
2.89%
🔥
138
2.81%
🤔
95
1.93%
👏
84
1.71%
👌
67
1.36%
🤩
46
0.935%
💯
42
0.854%
👀
37
0.752%
🕊
32
0.651%
🥰
19
0.386%
🏆
17
0.346%
🎉
16
0.325%
🙏
11
0.224%
⚡
10
0.203%
💊
10
0.203%
🤝
1
0.02%
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 130 of the 137 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 5,001 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 137 most recent posts we hold, published 1 August 2026 to 19 September 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.
🧠 اگر میتوانستیم تمام ارتباطات مغز یک مگس را ببینیم، چه چیزی پیدا میکردیم؟
🪰 مگس سرکه یکی از جانداران مدل مهم در پژوهشهای زیستی است و مغز کوچک آن، شبکهای پیچیده از نورونها و ارتباطات عصبی دارد.
🔬 برای بررسی این شبکه، مغز به برشهای بسیار نازک تقسیم و از هر برش تصویربرداری میشود. سپس میلیونها تصویر دوبعدی با کمک روشهای محاسباتی و هوش مصنوعی کنار هم قرار میگیرند تا ساختار سهبعدی نورونها و ارتباطاتشان بازسا…
👀 وقتی یک سلول را از چند زاویه میبینیم!
🔗 تا اینجا با ژنومیکس، ترنسکریپتومیکس، پروتئومیکس و متابولومیکس آشنا شدیم؛ اما همه این حوزهها در یک خانواده بزرگتر به نام اُمیکس (Omics) قرار میگیرند.
🔬 اُمیکس به مجموعهای از رویکردها گفته میشود که بهجای بررسی یک یا چند مولکول بهصورت جداگانه، مجموعه بزرگی از اجزای یک سیستم زیستی را بهصورت گسترده و سیستماتیک بررسی میکنند.
🧪 این رویکردها در پژوهشهای زیستی کمک میکنن…
🧬 فردریک سنگر؛ دانشمندی که خواندن DNA را متحول کرد!
📖 اوایل دهه ۱۹۷۰، دانشمندان میدانستند DNA حامل اطلاعات ژنتیکی است؛ اما هنوز روش سادهای برای تعیین دقیق ترتیب A، T، C و G نداشتند.
🔍 سنگر برای حل این مسئله، سراغ فرایند ساخت DNA رفت. او در کنار نوکلئوتیدهای معمولی، دیدئوکسینوکلئوتیدها (ddNTPs) را وارد واکنش کرد؛ مولکولهایی که با قرارگرفتن در رشته DNA، ادامه ساخت آن را متوقف میکردند.
🧬 در نتیجه، مجموعهای از ق…
🧬 اپیژنومیکس Epigenomics
🧬 اپیژنومیکس به مطالعه تغییرات اپیژنتیکی در سراسر ژنوم میپردازد؛ تغییراتی که میتوانند بر فعالیت ژنها اثر بگذارند، بدون اینکه توالی DNA تغییر کند.
🔬 برای مطالعه اپیژنوم، دادههایی درباره متیلاسیون DNA، تغییرات هیستونی و وضعیت کروماتین به دست میآید. بیوانفورماتیک با پردازش و تحلیل این دادهها، الگوهای اپیژنتیکی را شناسایی و مقایسه میکند و به بررسی ارتباط آنها با فعالیت ژنها کمک می…
🥼 آزمایشگاهی که خودش روپوش به تن دارد!
⚗️ تا امروز آزمایشگاه جایی بود که پژوهشگر در آن آزمایش انجام میداد؛ اما حالا خود آزمایشگاه هم میتواند در فرایند کشف علمی نقش داشته باشد.
🔬 آزمایشگاههای خودران (SDLs) با ترکیب هوش مصنوعی، رباتیک و فناوریهای نوین، میتوانند آزمایشها را طراحی و اجرا کنند و نتایج را تحلیل کنند.
🧬 مثلا در یک پروژه مهندسی پروتئین، SDLs میتواند بر اساس نتایج آزمایشهای قبلی، آزمایش بعدی را پیش…
🦠 متاژنومیکس Metagenomics
🧬 متاژنومیکس به مطالعه و تحلیل مجموعه DNA موجود در یک جامعه میکروبی میپردازد؛ بدون اینکه لازم باشد هر میکروارگانیسم بهصورت جداگانه کشت و بررسی شود. با این روش میتوان ترکیب میکروبی یک نمونه و همچنین ژنها و قابلیتهای عملکردی موجود در آن جامعه را بررسی کرد.
🔬 یکی از روشهای مهم در متاژنومیکس، Shotgun Metagenomics است؛ در این روش DNA موجود در نمونه بهصورت گسترده توالییابی و سپس با ابزار…
📽️ نام فیلم: کلونی (Colony) -2026
🎬 ژانر: اکشن ـ علمیتخیلی
💯 امتیاز: ۶.۶ / ۱۰
🎞️ در یک کنفرانس بزرگ بیوتکنولوژی، انتشار ناگهانی ویروسی ناشناخته و بهشدت جهشپذیر، همهچیز را به کابوسی مرگبار تبدیل میکند. مقامات برای جلوگیری از گسترش آلودگی، کل مجموعه را قرنطینه میکنند؛ اما برای گروهی از افراد حاضر در آنجا، راه فراری باقی نمیماند.
🔖 خلاصه فیلم:
پروفسور سهجونگ و گروه کوچکی از بازماندگان، در حالی که در محاصره ویر…
🤖 نسل جدید کشف دارو با هوش مصنوعی
🔬 در این ویدئو، با نقش هوش مصنوعی و روشهای محاسباتی پیشرفته در نسل جدید فرایند Drug Discovery آشنا میشویم.
⚗️ هدف چیست؟
کاهش زمان و هزینه جستوجوی ترکیبات دارویی و افزایش احتمال شناسایی کاندیداهای موفق در مراحل اولیه توسعه دارو.
🧫 هوش مصنوعی میتواند فرایند کشف دارو را از یک جستوجوی گسترده و پرهزینه به فرایندی دادهمحور، هدفمند و هوشمندانهتر تبدیل کند.
💊 آینده Drug Discovery …
🦠 روز جهانی میکروارگانیسمها را به همه زیستشناسان، بهویژه میکروبیولوژیستها، تبریک میگوییم.
👩🔬 در این پست، تصاویر ۱۰ نفر از برجستهترین دانشمندانی را آوردهایم که هرکدام نقشی مهم در شناخت و پیشرفت علم میکروبیولوژی داشتهاند.
🎁 اگر بتوانید نام ۵ نفر از این دانشمندان را درست حدس بزنید، در قرعهکشی شرکت داده خواهید شد.
📥 مشاهده پست اینستاگرام
اینستاگرام | تلگرام | لینکدین | بله | درباره ما
┏━━━━━━
🆔 @UIBiologis…
🎗️درخشش جناب پروفسور امیرعلی حمیدیه، استاد دانشگاه علوم پزشکی تهران، در عرصه بینالمللی؛ دریافت جایزه سازمان جهانی بهداشت (WHO) در حوزه سرطان
پروفسور امیرعلی حمیدیه، متخصص خون و سرطان کودکان و پیوند سلولهای بنیادی، به عنوان دریافتکننده جایزه سازمان جهانی بهداشت در منطقه مدیترانه شرقی برای کنترل سرطان انتخاب شد.
این جایزه از سوی سازمان جهانی بهداشت (WHO) به پاس دستاوردهای برجسته در زمینه پیشگیری، کنترل و پژوهشهای…
⚗ متابولومیکس Metabolomics
🔬 متابولومیکس به مطالعه و تحلیل مجموعه مولکولهای کوچک متابولیکی موجود در یک سلول، بافت یا نمونه زیستی در یک زمان و شرایط مشخص میپردازد. با بررسی متابولوم میتوان تغییرات متابولیکی سلول و پاسخ آن به شرایط مختلف را بررسی کرد.
💻 در تحلیل دادههای متابولومیکس، بیوانفورماتیسینها میتوانند:
🔹 دادههای خام متابولومیکس را پردازش و کیفیت آنها را بررسی کنند
🔹 متابولیتهای موجود در نمونه را شنا…
🫀 قلب نوزاد؛ تحت فرمان سیگنالهای سلولی
🧫 بیشتر سلولهای بدن آنتنهایی دارند که اطلاعات محیط اطراف را دریافت میکنند. اگر این آنتنها در دوران جنینی مشکل پیدا کنند، ساخت قلب مختل شده و ناهنجاری مادرزادی رخ میدهد؛ مشکلی که سالانه ۲ تا ۲.۵ میلیون نوزاد را درگیر میکند.
🧪 در این آنتنهای میکروسکوپی، پروتئینهای خاصی مانند یک مرکز فرماندهی عمل کرده و زمان تبدیل سلولهای بنیادی به سلولهای قلبی را مدیریت میکنند. محققا…
❤19👀2🔥2
Showing the 12 most recent of 137 posts we hold for @UIBiologists. 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
@UIBiologists edited 4 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
15 August 2026
Most recent edit
31 August 2026
Polls
The poll we hold for this entry, as Telegram rendered it when we read the post. A poll’s figures keep moving after that, so each one is dated.
⁉️بزرگترین دغدغهات برای ورود به صنعت بیوتکنولوژی چیه؟
1️⃣شناخت مسیرهای شغلی38%
2️⃣مهارتهای موردنیاز45%
3️⃣انتخاب مسیر مناسب36%
4️⃣ورود از دانشگاه به صنعت54%
The shares total 173%, above 100: this poll accepts more than one answer per voter. No per-option vote count is published, so the number of voters who chose each option is not derivable and is not shown.
Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.
The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.
Read from the 137 most recent posts we hold, published 1 August 2026 to 19 September 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republished by 92 registered channels. The 48 listed are the ones that have forwarded the most posts; the rest are counted here but not each listed.
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 117 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.
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.
Read from Telegram’s recommendation API, most recently 8 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.
موسسه بین المللی علوم نوبنیاد @iins_ir · 36,399 Telegram ranks this channel #10 of 94 here — alongside 93 others — read 8 September 2026
لبتل نیازمندی آزمایشگاههای ایران @LabTel · 28,648 Telegram ranks this channel #77 of 85 here — alongside 84 others — read 9 September 2026
زبان عمومی ارشد و دکتری @RMS_English · 52,475 Telegram ranks this channel #83 of 84 here — alongside 83 others — read 24 August 2026
دانشجو یار | کاد گروپ @kadgroup_telegram · 24,264 Telegram ranks this channel #87 of 89 here — alongside 88 others — read 16 September 2026
This channel appears in 4 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 19 September 2026 — this
entry's latest reading, not the date you are reading this.
“کانال اتحاد زیستشناسان ایران” (@UIBiologists), 29,855 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/UIBiologists.
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.