3 measurements spanning 9 days, net +51. 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 36–103 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
12 Sept 2026, 10:18
95
+41
3 Sept 2026, 22:59
54
+10
2 Sept 2026, 22:46
44
first reading
Engagement
18 posts held, back to 7 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 1 page of Telegram’s post history, 20 posts per page.
ERR · 30 days
75.7%
avg views ÷ 95 subscribers
Avg views / post
71.9
10 posts measured
Reaction rate
4.45%
reactions ÷ views · ER floor
Posts in window
11
of 18 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 2 September 2026
Posts held
18 (7 July 2026 – 2 September 2026)
Views total
719
Reactions total
32
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
2 Sept 2026, 22:46 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
2m 30s
Average length
1m 15s
Measured directly from 2 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
50 reactions across 15 posts, in 2 distinct kinds. The most used accounts for 72.0% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
36
72.0%
🔥
14
28.0%
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 15 of the 18 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 50 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 18 most recent posts we hold, published 7 July 2026 to 2 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.
رویداد HBC2026 فراتر از یه سمیناره !
علاوه بر ۳ روز محتوای علمی فوقالعاده و به روز فرصت داری تا :
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عضو کامیونیتی HBC2026 باشی !
✅ ارتباط با جامعهی علمی فعال
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✅ دسترسی به شبکهای از پژوهشگران و متخصصان
📜 امتیاز دریافت گواهی معتبر حضور در سمینار پیشرفته HBC
🔗 برای ثبتنام، اینجا کلیک کن
———…
🧠 What is a Black Box Model?
An AI model that takes input and delivers output—
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🔥 Here's the real challenge...
Can we trust black box predictions in genomics?
How do we handle errors from missing data?
And most importantly, how do we make these models interpretable?
___
مدل جعبهسیاه یعنی چی؟
یه مدل هوش مصنوعی که …
✔️ Prof. Marcus Kaiser | پروفسور مارکِس کایزر
🔗 Professor of Neuroinformatics at the University of Nottingham
🔅 Academic & Industrial Background:
🔹 Co-director of the Centre for Neurotechnology, Neuromodulation, and Neurotherapeutics
🔹 Chair of Neuroinformatics UK
🔹 Chair of the Neuroinformatics SIG of the British Neuroscience Association (BNA)
🔹 Fellow of the Royal Society of Biology (FRSB)
🔹 Member of the Medica…
✔️ Prof. Heidi Rehm | پروفسور هایدی رم
🔗 Chief Genomics Officer at Massachusetts General Hospital & Professor of Pathology at Harvard Medical School
🔅 Academic & Industrial Background:
🔹 Professor of Pathology, Harvard Medical School
🔹 Chief Genomics Officer, Massachusetts General Hospital (MGH)
🔹 Institute Member at the Broad Institute of MIT and Harvard
🎓 Educational Background:
▪️ Ph.D. in Genetics, Harvard U…
🎤 Ready to meet the best?
The 3rd HBC Advanced Seminar brings together 10 international speakers from the world's most prestigious universities, ready to introduce you to the latest research in healthcare, bioinformatics, and computational biology🔥
📆 But before the event...
Would you like to get to know each of these speakers better and see what exciting topics they'll be sharing with us?
📢 Today, around 6 PM (I…
🚀HBC 2026
📣 شاخه دانشجویی انجمن جهانی زیستشناسی محاسباتی در ایران، با همکاری SpentaGen برگزار میکند:
.
سومین سمینار پیشرفتهی (HBC2026) HBC
Healthcare, Bioinformatics, and Computational Biology
.
🌍 با حضور پژوهشگران و اساتید بینالمللی در حوزههای سلامت، بیوانفورماتیک و زیستشناسی محاسباتی🧬👨🏻💻
.
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💻 3 روز برنامهی علمی »» بهصورت آنلاین
📃 همراه با ارائه گواهی شرکت در رویداد
🗓…
HBC 2026 🚀
Three days 📅
One shared language: science.
.
📣 The Iranian Student Branch of the International Society for Computational Biology (ISCB-RSG-Iran), in collaboration with SpentaGen, presents:
.
🔠 The 3rd HBC Advanced Seminar (HBC2026)
Healthcare, Bioinformatics, and Computational Biology
.
🌍 Featuring international researchers and professors across healthcare, bioinformatics, and computational biology 🧬👨🏻💻
…
🧬ساخت پروتئین عملیاتی با AI!
.
دانشمندان دانشگاه واشنگتن مدل هوش مصنوعی MP4 رو ساختن که مستقیماً از پرامپتهای متنی، پروتئینهای جدید و فعال طراحی میکنه — بدون نیاز به هیچ ساختار شناختهشدهای.
✅ تأیید آزمایشگاهی: بیان، پایدار، فعال و حتی دارای ساختاری کاملاً جدید که در طبیعت دیده نشده!
.
این روش هنوز در ابتدای راهه، ولی دریچهای تازه به دنیای برنامهنویسی مولکولی باز کرده.
.
📄 مطالعه بیشتر و دسترسی به منبع خبر در …
Proteomics – The Functional Blueprint of Cells
Proteomics is the comprehensive study of all proteins expressed in a cell, tissue, or organism at a given time. Unlike the stable genome, the proteome is highly dynamic and responsive to environmental cues, disease states, and therapeutic interventions.
It encompasses two main approaches:
🔍 Qualitative: Which proteins are present? (e.g., detecting EGFR and p53 in canc…
پروتئومیکس به مطالعهی مجموعهی کامل پروتئینهای یک سلول یا بافت در یک بازهی زمانی مشخص میپردازد. برخلاف ژنوم ثابت، پروتئوم پویا و وابسته به شرایط محیطی، بیماری و درمان است.
.
این حوزه دو رویکرد اصلی دارد:
🔍 کیفی: کدام پروتئینها وجود دارند؟
(مثلاً شناسایی پروتئینهای EGFR و p53 در سرطان)
.
📊 کمی: چه مقدار از هر پروتئین وجود دارد؟
(مقایسهی شرایط سالم در برابر بیمار، یا قبل و بعد از درمان)
.
⚙️ ابزار اصلی: طیفسنج…
✨ سی و هفتمین #ژورنال_کلاب شاخه دانشجویی انجمن جهانی زیستشناسی محاسباتی در ایران
📌موضوع:
ساختارهای مولکولی بهمثابه ابر نقاط: زبانی نو برای زیستشناسی ساختاری
Molecular Structures as Point Clouds: A New Language for Structural Biology
👨🏻💻 سخنران: دکتر محمد طاهری
🗣️ دکتر محمد طاهری، دانش آموخته رشته بیوانفورماتیک از دانشگاه تهران است. حوزه تحقیقاتی او مدلسازی ریاضی سیستمهای پیچیده زیستی و توسعه الگوریتمهای مح…
Showing the 12 most recent of 18 posts we hold for @spentagen. 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.
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.
Shares as published, totalling 0%. No per-option vote count is published by Telegram, so none is 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 18 most recent posts we hold, published 7 July 2026 to 2 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
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 1 registered channel — 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.
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.
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 12 September 2026 — this
entry's latest reading, not the date you are reading this.
“SpentaGen” (@spentagen), 95 subscribers as measured 12 September 2026. Telegram Register, tgregister.com/channel/spentagen.
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.