😎 Siz 2026-yil Tibbiyot talabalari safiga qoʼshildingizmi? Unda quydagi kanallar aynan siz uchun! 😎 Siz istagan tibbiyotning barcha yoʼnalishlariga oid Telegram kanallarga Koʼk yozuv ustiga bosish orqali qoʼshilib oling! https://t.me/addlist/7UHdEs7cVRliMzZi ↖️ Tibbiyot talabalariga yuborib qoʼyamiz 🛫

Channel
Apikal MED💊
@Apikal_med
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Cite this entry
9,460subscribers
-17 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
Register entry
| Telegram ID | -1002349538821 |
|---|---|
| Type | Channel |
| Username | @Apikal_med |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 8 September 2026 |
| Measurements held | 12 |
| Confirmed unchanged | 1 time, most recently 8 September 2026 |
| On Telegram | t.me/Apikal_med |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Sept 2026, 09:16 | 9,460 | -29 |
| 2 Sept 2026, 20:35 | 9,489 | +15 |
| 31 Aug 2026, 01:09 | 9,474 | +45 |
| 27 Aug 2026, 16:24 | 9,429 | -37 |
| 24 Aug 2026, 18:35 | 9,466 | +16 |
| 20 Aug 2026, 18:35 | 9,450 | -42 |
| 17 Aug 2026, 16:47 | 9,492 | +81 |
| 14 Aug 2026, 17:07 | 9,411 | -38 |
| 11 Aug 2026, 03:47 | 9,449 | -20 |
| 8 Aug 2026, 09:02 | 9,469 | -8 |
| 7 Aug 2026, 18:31 | 9,477 | no change |
| 7 Aug 2026, 18:19 | 9,477 | first reading |
Engagement
47 posts held, back to 31 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 36 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 10.3%
- avg views ÷ 9,460 subscribers
- Avg views / post
- 978
- 26 posts measured
- Reaction rate
- 0.575%
- reactions ÷ views · ER floor
- Posts in window
- 26
- of 47 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 25 of 26 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 29 August 2026 |
|---|---|
| Posts held | 47 (31 July 2026 – 29 August 2026) |
| Views total | 25,440 |
| Reactions total | 143 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 29 Aug 2026, 06:23 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
- 13s
- Average length
- 13s
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
334 reactions across 45 posts, in 14 distinct kinds. The most used accounts for 27.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 91 | 27.2% | |
| 🔥 | 82 | 24.6% | |
| 👍 | 77 | 23.1% | |
| 👏 | 38 | 11.4% | |
| 🤯 | 12 | 3.59% | |
| ⚡ | 11 | 3.29% | |
| 😭 | 11 | 3.29% | |
| 👌 | 2 | 0.599% | |
| 💊 | 2 | 0.599% | |
| 🤓 | 2 | 0.599% | |
| 🤔 | 2 | 0.599% | |
| 🤝 | 2 | 0.599% | |
| 😎 | 1 | 0.299% | |
| 🤩 | 1 | 0.299% |
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 46 of the 47 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 334 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 47 most recent posts we hold, published 31 July 2026 to 29 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.
Recent posts
📖 2026-yilning Top Tibbiy kanalllari ❗️ ↖️ @DrJasurbek_blog- Pediatr, Anesteziolog-reanimatolog Sizning Oilaviy Shifokoringiz! Tibbiyotni biz bilan oson oʼrganing! ↖️ @Qosimov_medic - UASH hamda Dmed qo'llanmalari, tibbiy protokollar va yosh ota-onalar uchun foydali malumotlar! ↖️ @Tursunboy_Ibrakhimov0203 -UASh va 103-Tez tibbiy yordam tayyorlov kanali ↖️ @Apikal_med - Fundamental xamda Klinik fanlar bo’yicha T…
❤3
QANDLI DIABET LABARATOR ANALIZLAR NORMASI. 🔬Shu ko'rsatkichlarni oshishi kuzatiladi... 🔝 Apikal Med || Tibbiy blog
🔥3❤2👍1👏1
🔰STEROID YALLIG’LANISHGA QARSHI DORI VOSITALARI. 🔝 Apikal Med || Tibbiy blog
👍4❤1🔥1🤔1
🔰BEKOR QILISH SINDROMI. (Синдром отмена) - dori yoki psixoaktiv moddani uzoq vaqt qabul qilgandan keyin uni birdan to‘xtatish yoki keskin kamaytirish natijasida paydo bo‘ladigan simptomlar majmuasidir. Bekor qilish belgilari Benzodiazepinlar (diazepam, alprazolam va boshqalar) xavotir, uyqusizlik, tremor, terlash, tutqanoq, deliriy Opioidlar (morfin, tramadol, fentanil va boshqalar) mushak og‘rig‘i, burun oqishi…
❤1👍1👏1🔥1
🔰 GASTRIT VA MEDA-ICHAK YARA KASALLIKLARI. 🔝 Apikal Med || Tibbiy blog
❤2👏2🔥2
Semavik( Semaglutid ) - 2- tip QDni davolovchi , ozdiruvchi preparat 🔝 Apikal Med || Tibbiy blog
🔥2❤1👍1👏1
O’RAB OLUVCHI TEMIRATKI ( опоясывающий лишай ) DAVOLASH TAKTIKASI: 1. Brilliant yashili ( zelyonka ) 2 mahal zararlangan sohaga 10 kun 2. Asiklovir maz 2 mahal zararlangan sohaga 10 kun 3. Tab. Asiklovir 400 1 tab x 2 mahal 5 kun to’q qoringa 4. Tab. Mellepsin 200 ( karbamazepin ) 1/2 tab x 2 mahal 10 kun 5. Tab. Nolpaza 40 mg 1 tab x 1 mahal naxorda 10 kun 6. NaCl 0.9 % - 100.0 ml + Sikloferon 2.0 ml v/i 5 …
❤3🔥3👏1
🔰O’RAB OLUVCHI TEKIRATKI 🔝 Apikal Med || Tibbiy blog
🔥2❤1👍1
🔰ANTIBAKTERIAL DORI VOSITALARINING TASNIFLANISHI. 🔝 Apikal Med || Tibbiy blog
❤3👍1🔥1
🔝 Apikal Med || Tibbiy blog
🔥4❤1
👉 Siz izlayotgan tibbiy kanal balki aynan shu bo'lishi mumkin... Telegram'da tibbiyotga oid kanallar ko'p. Ammo ularning barchasida ham amaliyotda foyda beradigan, dalillarga asoslangan ma'lumotlar topilavermaydi. @DrJasurbek_Blog — bu shunchaki kanal emas, balki shifokorlar, tibbiyot talabalari va ota-onalar uchun doimiy bilim manbai. 📚 Kanalda muntazam e'lon qilinadi: •Pediatriya bo'yicha zamonaviy diagnostika v…
❤2
Showing the 12 most recent of 47 posts we hold for @Apikal_med. 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
@Apikal_med edited 1 post 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
- 10 August 2026
- Most recent edit
- 10 August 2026
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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 15 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.
@doktor_ismoilov · 12413 postsXAVFLI XIRURGIYA ️⚕️
@xavflii_xirurgiyaa · 12,9233 posts🩸𝗭𝗮𝗺𝗼𝗻𝗮𝘃𝗶𝘆_𝗠𝗲𝗱𝗶𝗰𝗶𝗻a🩺
@Zamonaviy_Medicina · 1533 postsDorilar dunyosi
@dorilar_dunyosi · 8,4382 postsDr_Ergashev ||Tibbiy blog 🔬
@DrErgashevblog · 12,7512 postsMed_Lab | Rasmiy kanal ️️️️️️️️
@med_lab1 · 8,0102 postsPharmacology.uz
@Pharmacology_uz · 14,3892 postsAndrology_uz 🔞
@andrology_uz · 13,3221 postCardioscience❤️
@cardioscience · 7,3861 postDoctor Haus
@Doctor_Haus_1 · 7,8771 postBolalar salomatligi by Dr.Behruz
@doctor_pediatr_behruz · 5,0751 postDr Numonov || Tibbiy blog
@Dr_Numonov · 7,5381 postDr.Jasurbek | Pediatr, Anesteziolog-reanimatolog
@DrJasurbek_blog · 9,8791 postRadiologiya UZ
@radiologiya_uz · 9,8841 post🚨Tez yordam_103 va Terapiya 👨⚕
@Tez_tibbiyyordam_103 · 13,9581 post
Names
Channels on the register whose handles appear in this channel's posts.
@DrJasurbek_blog · 9,8794 posts✨Thera_MED🫧
@Thera_med_uz · 6,4733 postsDorilar dunyosi
@dorilar_dunyosi · 8,4382 postsIntensiv Ordinatura | MedXAcademy
@Intensiv_Ordinantura · 3,6412 postsMedX Academy
@MedXAcademiya · 3,3012 posts𝐂𝐀𝐌𝐔 𝕂𝕀𝕋𝕆𝔹
@Camu_kitob · 4,8591 postCardioscience❤️
@cardioscience · 7,3861 postDr.Jabborov
@Dc_Jabborov · 8,9931 postDoctor Haus
@Doctor_Haus_1 · 7,8771 postBolalar salomatligi by Dr.Behruz
@doctor_pediatr_behruz · 5,0751 postDr Numonov || Tibbiy blog
@Dr_Numonov · 7,5381 postDr_Ergashev ||Tibbiy blog 🔬
@DrErgashevblog · 12,7511 postGINEKO
@Gineko_Doctors · 6,6301 postGinekologiya va akusherstva🩺
@GINEKOLOGIYA_NEOMED · 6,1721 postGinekology.uz 👸 (Faqat ayollar uchun)
@ginekology_uz · 8,9081 postGineMed🩺
@GineMed_Iroda · 5,9651 postMed_Lab | Rasmiy kanal ️️️️️️️️
@med_lab1 · 8,0101 postPharmacology.uz
@Pharmacology_uz · 14,3891 postRadiologiya UZ
@radiologiya_uz · 9,8841 postSecond Aid
@SecondAid_asmi · 5,6511 post🚨Tez yordam_103 va Terapiya 👨⚕
@Tez_tibbiyyordam_103 · 13,9581 postTIBBIYOT OLAMI🥼⚕️💊
@tibbiyot_pro · 5,2781 postDr.Ibrakhimov | UASH | 103-T.T.Y. haqidagi kanal
@tursunboy_ibrakhimov0203 · 13,5331 postXAVFLI XIRURGIYA ️⚕️
@xavflii_xirurgiyaa · 12,9231 post
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 8 September 2026 — this entry's latest reading, not the date you are reading this.
“Apikal MED💊” (@Apikal_med), 9,460 subscribers as measured 8 September 2026. Telegram Register, tgregister.com/channel/Apikal_med.
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.