Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 11 August 2026 and assigned it the closest of 31 fixed categories, at 100% 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.
Observations
These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.
Content that also appears on other registered channels
Posts published here appear word for word on 1 other registered channel. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.
Matching posts — open both and compare (1 of the pairs behind the counts below)
Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.
What this cannot establish
MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.
Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. None of the 0 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.
“Published first” means first in this corpus. We hold 17 comparable posts for this entry, running 20 December 2025 to 5 August 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.
The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.
Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).
Across the whole group of 2, the earliest publisher we hold is @ResearchShip. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_mutual, last confirmed 7 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.
Also posting the same content
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.
14 measurements spanning 42 days, net +481. 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 8,359–8,992 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
16 Sept 2026, 20:19
8,919
+2
12 Sept 2026, 20:39
8,917
+9
9 Sept 2026, 02:41
8,908
+6
4 Sept 2026, 05:38
8,902
+54
31 Aug 2026, 11:13
8,848
+69
28 Aug 2026, 11:17
8,779
+61
25 Aug 2026, 13:58
8,718
+89
22 Aug 2026, 15:26
8,629
+135
19 Aug 2026, 03:23
8,494
+62
16 Aug 2026, 06:28
8,432
-1
13 Aug 2026, 01:14
8,433
-10
9 Aug 2026, 20:21
8,443
+7
7 Aug 2026, 03:30
8,436
-2
6 Aug 2026, 04:06
8,438
first reading
Engagement
43 posts held, back to 20 December 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 25 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
10.9%
avg views ÷ 8,919 subscribers
Avg views / post
973
8 posts measured
Reaction rate
0.393%
reactions ÷ views · ER floor
Posts in window
9
of 43 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 7 of 8 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 27 August 2026
Posts held
43 (20 December 2025 – 27 August 2026)
Views total
7,782
Reactions total
27
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
28 Aug 2026, 16:17 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
7m 34s
Average length
50s
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
313 reactions across 40 posts, in 15 distinct kinds. The most used accounts for 64.2% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
201
64.2%
🕊
21
6.71%
🔥
20
6.39%
🙏
17
5.43%
👍
16
5.11%
👌
15
4.79%
👏
12
3.83%
🏆
2
0.639%
🤝
2
0.639%
🤯
2
0.639%
⚡
1
0.319%
❤🔥
1
0.319%
💔
1
0.319%
🤓
1
0.319%
🤗
1
0.319%
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 40 of the 43 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 313 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 43 most recent posts we hold, published 20 December 2025 to 27 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.
🗺️ نقشهی راه آناتومی ستون فقرات در نمای Sagittal MRI
🧠 تفاوت یک رادیولوژیست معمولی با یک متخصص، در «جزئیات آناتومیک» است. دیدنِ یک دیسک ساده کافی نیست؛ شما باید بتوانید لایههای زیر را در نمای Sagittal به سرعت بازخوانی کنید:
✅ ستون قدامی: از مهرهها تا رباط ALL
✅ کانال مرکزی: از Conus Medullaris تا ریشههای Cauda Equina
✅ ستون خلفی: از رباطهای فوقشوکی تا چربی اپیدورال _ کلید تشخیص بسیاری از ضایعات!
🎯 نکته طلا…
⌛️آخرین ساعتهای ثبتنام…..
🔚و آخرین بلیتهای پیشتازان و ثبتنام زودهنگام 🚨
📚 ثبتنام دوره جامع (منبع مسابقه):
🔗https://mohit.online/course/hwt3pl
⚠️فرصت خرید دوره و رقابت نورولمپیاد با تخفیف ویژه (در حال اتمام!):
🔗https://mohit.online/event/d5083b
📱 @neurophile
‼️ یادآوری
امروز، 1405/06/05 ساعت 20:30 جلسه اول دوره نورورادیولوژی با تدریس دکتر سلطانی شروع خواهد شد
لطفا اگر اسکرین شات ثبتنامیتون رو برای پشتیبانی نفرستاید سریعا بفرستید تا در کانال اختصاصی دوره جوین بشید.
میتونید در آخریم ساعات قبل از شروع دوره از لینک های زیر دوره و رقابت نورەلمپیاد رو ریجستر کنید 😊🤝
📚 ثبتنام دوره جامع (منبع مسابقه):
🔗https://mohit.online/course/hwt3pl
⚠️فرصت خرید با تخفیف ویژه (در حال …
🧠 نقشه راه آناتومی مغز در مقاطع Axial MRI؛ چقدر مسلطید؟
تا زمانی که لندمارکهای کلیدی را با یک نگاه نشناسید، تعیین محل دقیق ایسکمی، تومور یا ضایعات دمیلینهکننده غیرممکن خواهد بود. ❌
🔍 لندمارکهای حیاتی (از بالا به پایین):
📍 قشر و شکنجهای فرونتال
📍 خط وسط و هستههای قاعدهای
📍 سیستم لیمبیک و ساقه مغز
📍 پل و بصلالنخاع
🎯 نکته طلایی:
دیدن یک ضایعه کار سادهای است؛ هنر واقعی، لوکالیزاسیون دقیق و تطبیق آن با علائم …
🧠 الفبای خواندن سیتی اسکن مغز (Brain CT) در یک نگاه!
سیتی اسکن، خط اول تشخیص در اورژانس و تروما است. آیا میتوانید در کمتر از یک دقیقه، علائم اولیه ایسکمی مثل Insular Ribbon Sign را تشخیص دهید؟ ⏱️
🔍 نقشه راه ساختارهای کلیدی در نمای Axial:
📍 لوبهای مغزی:
• Frontal: بخش قدامی
• Temporal: بخش لترال
• Parietal & Occipital: بخش خلفی
📍 هستههای عمقی و کپسول داخلی:
• Caudate: مجاورت شاخ قدامی بطن
• Lentiform Nuc…
🤖🧠 هوش مصنوعی مرزهای نوروایمیجینگ رو بازنویسی میکنه؛ شما آمادهاید؟
متا از مدل جدید TRIBE v2 رونمایی کرده که با تحلیل بیش از ۱۰۰۰ ساعت داده تصویربرداری مغزی، پاسخ نورونی به محرک ها رو دقیقتر از خطاهای fMRI پیشبینی میکنه.
پیامش برای جامعه پزشکی واضحه:
🔹 نورورادیولوژی داره داده محورترین شاخه علوم اعصاب میشه.
🔹 قبل از استفاده از AI، تسلط بر آناتومی عملکردی و فیزیولوژی تصویربرداری ضروریه.
🔹 هوش مصنوعی قراره دید بالینی پ…
🧠 وقتی هوش مصنوعی وارد دنیای تصویربرداری پزشکی میشه...
یه مسابقهی ۷۷,۰۰۰ دلاری برای تشخیص ناهنجاریهای زانو روی تصاویر MRI راه افتاده! هدف اینه که مدلهای هوش مصنوعی بتونن آسیبهای زانو رو روی MRI تشخیص بدن، دقیقاً همون کاری که یه رادیولوژیست هر روز انجام میده.
این موضوع نشون میده که آیندهی تصویربرداری پزشکی داره سریعتر از همیشه به سمت هوش مصنوعی و تحلیل داده حرکت میکنه. اما نکتهی مهم اینه که پشت هر مدل هوشمن…
🧠 تصویربرداری از مغز فقط گرفتن عکس نیست؛ هنر خواندن و تفسیر دادههای خامِ خروجی از اسکنرهاست.
🧩 تو آزمایشگاههای دانشگاه میشیگان، پروژهای به نام «مغز اندی» در حال متحول کردن روش آموزش نوروایمیجینگ به صورت متنباز و آزاده 🆓🌐.
دکتر اندرو جان به جای حبس کردن دانش تو پشت درهای بستهی مقالات پولی 🚫💸، یه اکوسیستم آموزشی ساخته که مختص کسانیه که میخوان واقعاً دست به کد و تحلیل بشن 💻🧪.
اگه هدفتون تسلط عملی 🎯 روی ابزارهای ق…
📜 معرفی مدرسین دوره نورورادیولوژی
👨🏻⚕دکتر مصطفی الماسی
نورولوژیست
استادیار نورولوژی دانشگاه علوم پزشکی ایران
🧠 Neurodegenerative Diseases & Cognitive Neuroscience
در این بخش از دوره نورورادیولوژی، دکتر مصطفی الماسی به تدریس مبحث بیماری آلزایمر و مارکرهای تصویربرداری آن میپردازند.
🔹 Alzheimer’s Imaging Markers
🔹 نقش تصویربرداری در تشخیص و ارزیابی آلزایمر
🔹 ارتباط یافتههای تصویربرداری با عملکرد شناختی
📚 نگاهی تخ…
🧠 چالش سریع: محل دقیق ایسکمی کجاست؟ ⏱️
در نگاه اول، این اسکن ممکن است کاملاً متقارن و طبیعی به نظر برسه؛ اما در اورژانسهای واسکولار، یک اشتباه کوچک در اسکرول کردن میتونه به معنای از دست دادن بیمار باشه.
باید به دنبال چی باشید؟
🔹 Hyperdense MCA Sign: حضور ترومبوز در شریان مغزی میانی.
🔹 Sulcal Effacement: از بین رفتن تقارن و پاکشدگی شیارهای مغزی ناشی از ادم.
و کلی نکته دیگه...
تفسیر دقیق، “تجربه” نیست؛ بلکه نتی…
❤3👌1🙏1
Showing the 12 most recent of 43 posts we hold for @neurophile. 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
@neurophile 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
19 August 2026
Most recent edit
24 August 2026
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 46 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
0
we ourselves saw one of these resolve, at some point
Never seen alive
1
vacant every time we have ever looked
@neurophile 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.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@dr_meducation2026 named in 5 posts, 8 August 2026 – 19 August 2026
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 #12 of 94 here — alongside 93 others — read 8 September 2026
دکتر آذرخش مکری @DrAzarakhshMokri · 201,418 Telegram ranks this channel #17 of 78 here — alongside 77 others — read 19 August 2026
بانک PDF پزشکی📚 @PDFbankdaro · 32,184 Telegram ranks this channel #26 of 94 here — alongside 93 others — read 5 September 2026
Dr Sargolzaee podcast @drsargolzaeipodcast · 23,848 Telegram ranks this channel #27 of 93 here — alongside 92 others — read 17 September 2026
دکتر آذرخش مکری | آرشیو @Dr_Azarakhsh_Mokri_Public_Files · 22,111 Telegram ranks this channel #32 of 90 here — alongside 89 others — read 21 September 2026
علوم پایه پزشکی و دندانپزشکی @oloompaye · 22,759 Telegram ranks this channel #35 of 93 here — alongside 92 others — read 20 September 2026
نسخهخوانی و داروشناسی @med_prescription_educ · 26,705 Telegram ranks this channel #37 of 93 here — alongside 92 others — read 12 September 2026
کنکور ارشد و دکتری روانشناسی (آکادمی روانتاج 👑) @RavanTajj · 22,019 Telegram ranks this channel #47 of 95 here — alongside 94 others — read 22 September 2026
کارگاههای روانشناسی رایگان @voicepsycology · 30,380 Telegram ranks this channel #52 of 89 here — alongside 88 others — read 7 September 2026
شبکه نخبگان ایران @IranElitesNet · 27,756 Telegram ranks this channel #62 of 100 here — alongside 99 others — read 10 September 2026
🧬Medical_Science_Apply✈ @Medical_Science_Apply · 28,846 Telegram ranks this channel #71 of 95 here — alongside 94 others — read 9 September 2026
کانال اتحاد زیستشناسان ایران @UIBiologists · 29,855 Telegram ranks this channel #75 of 96 here — alongside 95 others — read 8 September 2026
دانلود کتاب و جزوه | کتابخانه علوم پزشکی @med_science · 31,347 Telegram ranks this channel #76 of 96 here — alongside 95 others — read 6 September 2026
مدرسه پزشکی | امیرمحمد قربانی @schoolofmedicine_ir · 26,480 Telegram ranks this channel #84 of 93 here — alongside 92 others — read 12 September 2026
This channel appears in 14 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 16 September 2026 — this
entry's latest reading, not the date you are reading this.
“Neurophile | نوروفیل” (@neurophile), 8,919 subscribers as measured 16 September 2026. Telegram Register, tgregister.com/channel/neurophile.
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.