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 20 August 2026 and assigned it the closest of 31 fixed categories, at 98% 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 the other topics.
Growth
62 measurements spanning 93 days, net -1,234. 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 62,809–64,413 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 62
Measured (UTC)
Subscribers
Change
7 Nov 2026, 01:00
62,994
-8
6 Nov 2026, 01:16
63,002
-10
5 Nov 2026, 02:30
63,012
-10
4 Nov 2026, 01:07
63,022
-7
3 Nov 2026, 00:44
63,029
-9
2 Nov 2026, 03:36
63,038
-16
1 Nov 2026, 01:48
63,054
-6
31 Oct 2026, 02:11
63,060
-18
30 Oct 2026, 02:56
63,078
-16
29 Oct 2026, 03:40
63,094
-16
28 Oct 2026, 02:38
63,110
-12
27 Oct 2026, 00:00
63,122
+6
25 Oct 2026, 20:28
63,116
-13
24 Oct 2026, 22:10
63,129
-15
23 Oct 2026, 22:14
63,144
-16
22 Oct 2026, 21:55
63,160
-13
21 Oct 2026, 17:45
63,173
+3
20 Oct 2026, 15:21
63,170
-8
19 Oct 2026, 14:36
63,178
-23
18 Oct 2026, 12:20
63,201
first reading
Engagement
31 posts held, back to 15 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 187 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
2.51%
avg views ÷ 62,994 subscribers
Avg views / post
1,580
6 posts measured
Reaction rate
0.865%
reactions ÷ views · ER floor
Posts in window
6
of 31 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 5 of 6 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 28 October 2026
Posts held
31 (15 July 2026 – 28 October 2026)
Views total
9,470
Reactions total
70
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
6 Nov 2026, 21:21 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
23m 30s
Average length
5m 53s
Measured directly from 4 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
362 reactions across 27 posts, in 14 distinct kinds. The most used accounts for 35.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
128
35.4%
🔥
105
29.0%
👍
65
18.0%
👏
27
7.46%
🤝
9
2.49%
🤩
6
1.66%
🆒
4
1.10%
😁
4
1.10%
😍
4
1.10%
😱
3
0.829%
🙏
3
0.829%
💋
2
0.552%
💘
1
0.276%
😐
1
0.276%
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 27 of the 31 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 362 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 31 most recent posts we hold, published 15 July 2026 to 28 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.
Recent posts
Telegram’s own ‘edited’ marker: 2 of 6 posts held from the last 30 days (33.3%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
🤔 Нутрициология кажется перспективной… но с чего начать?
В интернете — хаос:
♥️ одни обещают быстрый доход
♥️ другие пугают сложностью
♥️ третьи продают «секретные методики»
В итоге человек годами думает и так и не делает шаг.
На нашем интенсиве «Путь в нутрициологию: как выйти на новый уровень знаний о здоровье» мы покажем реальную картину без прикрас:
♥️как устроена индустрия здоровья сегодня
♥️ кто востребован …
💚💚💚💚 Биохакинг: что это на самом деле и как не навредить себе, экспериментируя 🤔
🎙️Этот подкаст — мой честный разбор того, что скрывается за модным словом «биохакинг». Без розовых очков и без запугивания.
Я, Станислав Шереметьев, занимаюсь биохакингом более 10 лет и пробовал на себе разные протоколы — включая те, которые сегодня сам не рекомендует никому.
🎧Что вы узнаете из подкаста:
♥️Чем биохакинг на самом деле…
Друзья, хочу поделиться с вами одной историей и попросить о помощи 🙏
В нашем сообществе мы много говорим о здоровье, о профилактике, о том, как важно вовремя заботиться о себе и близких. Но иногда жизнь ставит перед нами испытания, против которых не работает ни правильное питание, ни образ жизни. И тогда на помощь приходят люди.
Я хочу рассказать вам о Павле Борисовиче Чугунове — отце моей хорошей знакомой Дарьи Вя…
Почему подруга видит эффект от коллагена, а я нет? Продолжение разбора ♥️
В прошлом посте мы разобрались, что бульон и пептиды — разные инструменты. И я оставил одну важную мысль на потом: эффект коллагена сильно зависит от того, как работает ваш ЖКТ и ферментная система. Чей-то кишечник справится с желатином из холодца, чей-то — нет.
И вот тут самое интересное. Эта разница — не случайность. Она во многом заложена …
Костный бульон или коллагеновые пептиды? Разбираемся, что реально в кастрюле 🍲
Меня часто спрашивают: зачем покупать коллаген, если можно сварить холодец или бульон на костях — там же тот же коллаген? Я полез в исследования — ответ оказался интереснее, чем «да» или «нет».
Что на самом деле есть в бульоне ♥️
Когда кости варятся несколько часов, коллаген частично распадается, и в бульон уходят свободные аминокислоты…
Не ещё один курс. Профессия, которая уже ждёт вас 🔥
Друзья, мы открываем набор на 49-й поток курса «Интегративная нутрициология и системы немедикаментозного оздоровления».
Это не просто курс. Это годовая программа, которая готовит специалистов, умеющих работать с первопричинами, а не с симптомами.
Что вас ждёт:
✅ Поддержка кураторов 12 часов в день, 6 дней в неделю — на протяжении всего обучения
✅ Развитие клини…
Друзья, 22–23 октября я буду в Алматы! 🇰🇿
Знаю, что среди вас много подписчиков из Казахстана. И у нас есть отличная возможность встретиться лично на большой конференции NUTRICON Central Asia.
Я выступлю с презентацией обновлений сервиса Micra — умного трекинга питания и параметров здоровья. Будет интересно всем, кто следит за технологиями в нутрициологии.
Если вы живёте в Казахстане или можете прилететь в Алматы …
Showing the 12 most recent of 31 posts we hold for @nsa_academy. 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 recorded as deleted after publishing
We recorded 4 posts on @nsa_academy as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
First seen missing
20 August 2026
Most recent seen missing
20 August 2026
Median time before seen missing
3 days
Seen missing in the last 30 days
0
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 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.
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.
НСОИМ (всё о микробиоте человека) @nsoim_news · 4,202#96
Dr.Shefova / Доктор Шефова @dr_shefova · 69,320#97
Read from Telegram’s recommendation API, most recently 21 August 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.
МАН Нутрициология @man_academyofnutrition · 32,739 Telegram ranks this channel #6 of 94 here — alongside 93 others — read 4 September 2026
Клуб МИИН @miin_ankz · 51,645 Telegram ranks this channel #6 of 97 here — alongside 96 others — read 25 August 2026
MOLODAY @molodayproduct · 147,219 Telegram ranks this channel #8 of 93 here — alongside 92 others — read 12 August 2026
Гастроэнтеролог Ольга Помойнецкая @olga_pomoynetskaya · 248,871 Telegram ranks this channel #8 of 92 here — alongside 91 others — read 10 August 2026
Олеся Ревеко | Нутрициолог @olesya_reveko1 · 42,341 Telegram ranks this channel #9 of 88 here — alongside 87 others — read 28 August 2026
PRO питание и БАДы | Диляра Лебедева гастроэнтеролог @stomach_lebedeva · 63,138 Telegram ranks this channel #11 of 92 here — alongside 91 others — read 21 August 2026
УПДН | Университет диетологии и питания @shkola_dietologov · 20,245 Telegram ranks this channel #12 of 81 here — alongside 80 others — read 27 September 2026
Школа Здоровья врача Ольги Помойнецкой @health_club_drPomoinetskaya · 21,817 Telegram ranks this channel #14 of 84 here — alongside 83 others — read 21 September 2026
Николай Панасюк | Нутрициология | GetFit @nikolay_panasiuk · 36,526 Telegram ranks this channel #14 of 90 here — alongside 89 others — read 1 September 2026
Академия EDPRO: Нутрициология @academy_nutrition_edpro · 20,033 Telegram ranks this channel #15 of 90 here — alongside 89 others — read 28 September 2026
Панасюк Николай @getfit_nutrition · 69,668 Telegram ranks this channel #15 of 96 here — alongside 95 others — read 20 August 2026
🌱🥑 Детокс - канал DR.SHEFOVA @shefova_detox · 31,125 Telegram ranks this channel #16 of 71 here — alongside 70 others — read 6 September 2026
Эндокринолог ❤️ Ольга Павлова 💊 Диабет @dia_dietolog_olga_pavlova · 58,637 Telegram ranks this channel #16 of 91 here — alongside 90 others — read 22 August 2026
ЛЕКАРКА2 @lekarka2 · 27,321 Telegram ranks this channel #17 of 64 here — alongside 63 others — read 10 September 2026
Катя Медушкина | Похудение @katya_medushkina_fit · 39,605 Telegram ranks this channel #19 of 85 here — alongside 84 others — read 30 August 2026
PRO гормоны и БАДы | Диляра Лебедева эндокринолог @lebedeva_dilyara · 133,837 Telegram ranks this channel #20 of 90 here — alongside 89 others — read 13 August 2026
МИФИМ ИНСТИТУТ НУТРИЦИОЛОГИИ @zubareva_onlinee · 20,084 Telegram ranks this channel #21 of 89 here — alongside 88 others — read 27 September 2026
Дадали В.А. Школа здоровья @putkzdoroviyu · 34,155 Telegram ranks this channel #21 of 89 here — alongside 88 others — read 6 September 2026
⚕️ Дмитрий Семирядов️️ / Превентивный врач, нутрициолог / dsemiryadov @dsemiryadov_official · 75,985 Telegram ranks this channel #23 of 85 here — alongside 84 others — read 19 August 2026
Велнес медиа by MOLODAY🧬 @MolodayHealth · 27,713 Telegram ranks this channel #24 of 89 here — alongside 88 others — read 17 September 2026
Здоровье с Екатериной Новиковой @katerina_dietolog · 52,232 Telegram ranks this channel #24 of 94 here — alongside 93 others — read 25 August 2026
Здравомыслие в еде @zdravomislievede · 31,878 Telegram ranks this channel #26 of 97 here — alongside 96 others — read 6 September 2026
Ирина Шиманская Holistica @tibetdoc · 78,920 Telegram ranks this channel #27 of 96 here — alongside 95 others — read 18 August 2026
Доктор Зубарева / Doctor Zubareva @doctor_zubareva · 400,246 Telegram ranks this channel #27 of 85 here — alongside 84 others — read 10 August 2026
This channel appears in 44 seed channels' Telegram-generated recommendation lists in total, of which the 24 where it ranks highest are shown above. 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 7 November 2026 — this
entry's latest reading, not the date you are reading this.
“Академия Нутрициологии” (@nsa_academy), 62,994 subscribers as measured 7 November 2026. Telegram Register, tgregister.com/channel/nsa_academy.
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.