This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
3 measurements spanning 5 days, net +1. 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 1,498–1,499 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
11 Aug 2026, 15:51
1,499
+1
6 Aug 2026, 07:45
1,498
no change
6 Aug 2026, 05:19
1,498
first reading
Engagement
23 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 2 pagesof Telegram’s post history, 20 posts per page.
ERR · 30 days
29.9%
avg views ÷ 1,499 subscribers
Avg views / post
448
23 posts measured
Reaction rate
1.61%
reactions ÷ views · ER floor
Posts in window
23
of 23 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 22 of 23 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 7 August 2026
Posts held
23 (24 July 2026 – 7 August 2026)
Views total
10,295
Reactions total
156
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
7 Aug 2026, 18:13 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
45s
Average length
45s
Measured directly from 1 video 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
147 reactions across 20 posts, in 1 kind.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
147
100.0%
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 22 of the 23 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 156reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 23 most recent posts we hold, published 24 July 2026 to 7 August 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.
🎙✨ پویش | قسمت هشتم
تا به حال برایتان پیش آمده مقالهای علمی را با حوصله بخوانید، اما در پایان ندانید دقیقاً پیام اصلی آن چه بوده یا چقدر میتوان به نتایجش اعتماد کرد؟
📖 خواندن مقاله، فقط از اول تا آخر ورق زدن صفحات نیست؛ بلکه مهارتی است که به شما کمک میکند پژوهشها را درست تحلیل کنید، نقاط قوت و ضعفشان را بشناسید و از یافتههای علمی در مسیر یادگیری و پژوهش بهره ببرید.
✨ در این قسمت از «پویش»، قدمبهقدم یاد میگ…
📖 علوم پایه فقط یک آزمون نیست؛ اولین چالش جدی دوران پزشکیه و دندان پزشکیه!
اگر هنوز نمیدونی از کجا شروع کنی، چه منابعی بخونی یا چطور برنامهریزی کنی، این پنل دقیقاً برای توئه.
🎯 قراره از تجربه دانشجوهایی بشنوی که همین مسیر رو با موفقیت پشت سر گذاشتن و با رتبههای برتر و استریت از آزمون علوم پایه عبور کردن.
تو این پنل یاد میگیری: ✅ چطور هوشمندانه درس بخونی، نه فقط بیشتر. ✅ چه منابعی واقعاً ارزش وقت گذاشتن دارن. ✅…
📝چهارشنبه ها با پژوهش!
💡عنوان پادکست:
چرا دانشجویان پزشکی با وجود دسترسی به همه منابع، کمتر مطالعه عمیق میکنند؟
#چهارشنبه_ها_با_پژوهش
#پادکست #پژوهش
🔎〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️
🆔Research_SCMED_Mubabol
🆔SCMED_BMU
#گزارش_تصویری
✅ برگزاری جلسه CBL با موضوع «آپاندیسیت» با تدریس دکتر محمدرضا ولیپور
در این جلسه، دانشجویان با رویکرد تشخیصی و تشخیصهای افتراقی آپاندیسیت، بر پایه بررسی کیسهای بالینی، آشنا شدند و نحوه برخورد اصولی با بیماران مبتلا به درد حاد شکم مورد بحث و بررسی قرار گرفت.
📍 ۱۴ مرداد ۱۴۰۵
🆔 @SCMED_BMU
🆔 @mubabol_mentoring
🔴 پنجمین اپیزود پادکست «نکسوس» 🎙️✨
🔴 آیا روزی خواهد رسید که هوش مصنوعی بتواند جای پزشکان را بگیرد؟ یا همیشه چیزی فراتر از دانش و الگوریتمها، پزشکی را انسانی نگه میدارد؟
🔴 این بار در بخش پژوهش و هوش مصنوعی پادکست «نکسوس»، به یکی از داغترین پرسشهای دنیای امروز میپردازیم؛ اینکه هوش مصنوعی تا کجا میتواند در تشخیص، درمان و تصمیمگیریهای پزشکی پیش برود و مرز تواناییهای آن کجاست.
🔴 در این اپیزود، با نگاهی علمی و …
○صلَّی اللهُ عَلَیکَ یَا اَبَاعَبدِاللهِ الحُسَین○
نه اشکها آرام میگیرند،
نه دلها از داغ حسین(ع) رها میشوند...
اربعین آمد تا دوباره یادمان بیاورد که عشق به حسین(ع)، پایان ندارد و تا ابد در دلها زنده است.🖤
~ فرارسیدن اربعین حسینی تسلیت باد🥀
🆔 @SCMED_BMU
🆔 instagram
📸 گزارش تصویری | نشست هماهنگی بوتکمپ نوآوری در سلامت 🚀🩺
با همکاری کمیتههای تحقیقات و کمیته دانشجویی توسعه آموزش پزشکی بابل (SCMED) و با نظارت گروه توسعه فناوری سلامت دانشگاه علوم پزشکی بابل، نشست هماهنگی و برنامهریزی بوتکمپ فناوری سلامت و کافه فناوری (Tech Club) برگزار شد. 🤝
💡 در این نشست، راهاندازی کمیته دانشجویی توسعه نوآوری سلامت بهعنوان زیرمجموعهای از مرکز توسعه فناوری سلامت مورد بررسی قرار گرفت و اهداف،…
جلسات اول و دوم | مبانی پایتون
بوتکمپ تخصصی هوش مصنوعی در سلامت
📅 چهارشنبه و پنجشنبه ، ۱۴ و ۱۵ مرداد
🕗 ساعت ۲۰ تا ۲۲
💻 بهصورت مجازی، در بستر Google Meet
🎥 جلسات ضبط میشوند و در دسترس شرکتکنندگان قرار میگیرند
اولین گام از مسیر یادگیری بوتکمپ رو با هم شروع میکنیم: مبانی پایتون، پایه و اساس همهچیز تا رسیدن به هوش مصنوعی و ساخت محصول هوشمند پزشکی.
📌 اطلاعات بیشتر:
🔗 برای ثبت نام کلیک کنید.
📢ایدی تلگرام و بله و…
😅 همهی ما توی دوران کارآموزی، حداقل یه خاطره داریم که با یادآوریش یا از خنده ریسه میریم، یا هنوزم دلمون میخواد زمین دهن باز کنه و قورتمون بده!
اون اشتباه بامزه، سوتی فراموشنشدنی یا لحظهی پراسترسی که هیچوقت از یادتون نمیره چی بوده؟ 🤭
✍️ برای چاپ در نشریه پویش، خاطرات تلخوشیرین دوران کارآموزیتون رو برای آیدی @Ftm_Fathi14 بفرستید.
شاید بزرگترین درسهای دوران تحصیل دقیقاً از همون اشتباههای کوچیک شروع شده باشن…
🎙✨ پویش | قسمت هفتم
هر دانشجویی اگر فرصتی پیدا کند تا به ترمیکِ خودش برگردد، احتمالاً یک جمله برای گفتن دارد؛ جملهای که از دل تجربه، شکست، موفقیت، استرسها و روزهای شیرین و سخت دانشگاه بیرون آمده است.
✨ در این قسمت از «پویش»، پای صحبت دانشجویان و فارغالتحصیلان رشتههای مختلف علوم پزشکی نشستهایم؛ از رادیولوژی و رادیوتراپی تا مامایی، دندانپزشکی، بهداشت، گفتاردرمانی، پزشکی هستهای و اتاق عمل. روایتهایی صمیمی از …
🎙️✨ دیگه لازم نیست ویسهای طولانی رو بشنوی و تایپ کنی!
اگه شما هم از اون دسته آدمهایی هستید که پیامهای صوتی طولانی رو ترجیح میدن بهصورت متنی بخونن، این معرفی مخصوص شماست 👇
🤖 ابزار امروز: Voicy | @voicybot
یک ربات تلگرامی رایگان و کاربردی که پیامهای صوتی، فایلهای صوتی و حتی ویدیو رو در کسری از ثانیه به متن تبدیل میکنه. کافیه اضافهاش کنید تا زیر هر ویس، متنش رو ببینید 🪄
🌟 چرا این ربات؟
🔹 رایگان و بدون نیاز …
❤8
Showing the 12 most recent of 23 posts we hold for @SCMED_BMU. 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.
Citation-graph rank
Citation-graph rank — 15,494 of 1,169,250entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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 20 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.
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 11 August 2026 — this
entry's latest reading, not the date you are reading this.
“SCMED_mubabol” (@SCMED_BMU), 1,499 subscribers as measured 11 August 2026. Telegram Register, tgregister.com/channel/SCMED_BMU.
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.