Health & wellness — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 11 September 2026 and assigned it the closest of 31 fixed categories, at 76% 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.
Growth
25 measurements spanning 35 days, net +126. 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 10,752–10,932 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 25
Measured (UTC)
Subscribers
Change
11 Sept 2026, 05:21
10,899
-7
8 Sept 2026, 10:16
10,906
-3
5 Sept 2026, 06:57
10,909
-2
1 Sept 2026, 08:15
10,911
+1
31 Aug 2026, 08:08
10,910
+5
30 Aug 2026, 09:08
10,905
+7
29 Aug 2026, 08:38
10,898
+6
28 Aug 2026, 07:08
10,892
+1
27 Aug 2026, 08:12
10,891
+6
26 Aug 2026, 07:24
10,885
+15
25 Aug 2026, 05:13
10,870
+8
22 Aug 2026, 09:24
10,862
+8
20 Aug 2026, 20:28
10,854
+8
19 Aug 2026, 21:37
10,846
+20
18 Aug 2026, 18:13
10,826
+4
17 Aug 2026, 16:18
10,822
-3
16 Aug 2026, 06:57
10,825
+14
14 Aug 2026, 16:28
10,811
+7
13 Aug 2026, 04:25
10,804
+5
12 Aug 2026, 02:57
10,799
first reading
Engagement
26 posts held, back to 22 June 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 42 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
18.2%
avg views ÷ 10,899 subscribers
Avg views / post
1,980
6 posts measured
Reaction rate
1.28%
reactions ÷ views · ER floor
Posts in window
6
of 26 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 29 August 2026
Posts held
26 (22 June 2026 – 29 August 2026)
Views total
11,908
Reactions total
152
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
3 Sept 2026, 02:32 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
22s
Average length
22s
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
704 reactions across 25 posts, in 6 distinct kinds. The most used accounts for 75.1% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
529
75.1%
🔥
57
8.10%
👏
45
6.39%
💯
34
4.83%
😁
33
4.69%
👌
6
0.852%
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 25 of the 26 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 704 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 26 most recent posts we hold, published 22 June 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.
📘 نظرة طبية
بين يديك كتاب فريد يجمع بين سحر القصة ودقة الطب!
بقلم الدكتور والمخترع باقر نزار السوداني، يأخذك هذا الكتاب في رحلة مشوقة داخل عالم الأمراض، حيث تُروى الحالات بأسلوب قصصي ممتع، مع شرح مبسّط لأعراضها، طرق علاجها، وأساليب الوقاية منها — بلغة عربية سلسلة وبأسس علمية معتمدة من اللجنة العلمية في كلية الطب.
📚 مثالي لكل من:
✅ طلاب الطب
✅ المثقفين والمهتمين بالصحة
✅ من يبحث عن المعرفة بأسلوب سهل وشيق
اطلب نسختك…
نسخة MartinLab Pro
راح يكون بيها ربط بين حاسبات المختبر والاستقبال
ربط جهاز ال CBC
نظام دردشة بين المختبر والاستقبال حتى لا يضطر ابو الاستقبال يروح ويجي ولا يضطر ابو المختبر يطلع
راح اعلمهم عالكسل🌚
......
@abdiq96
مارتن كروك، صاحب كتابه الشهير بالكيمياء السريرية
هذا الشخص صاحب فضل جبير كون كتابه من اكثر الكتب اللي فادتني بمجال التحليلات المختبرية وفتحتلي افاق واسعة لفهم التحليلات والتشخيص والمختبر بشكل عام.
فمن سويت التطبيق مال إدارة المختبرات ما ترددت ابد انه اسميه بإسمه
فلهذا نعلن لكم بشكل رسمي أطلاق تطبيق ادارة المختبرات الطبية #مارتن_لاب MartinLab
مع صور لبعض الميزات اللي بيه اضافة لاشكال التقارير.
تتوفر ايضا نسخة تجريبي…
السلام عليكم ورحمة الله
الحمدلله تم إكمال الملف، وقد جمعت فيه معلومات قناة فقه المختبر ووضعتها في هذا الملف، علما المعلومات ليس كلها 100% من هذه القناة، بل هناك بعض المعلومات ايضاً قد جمعتها واخذتها من أشخاص موثوقين في هذا المجال، تقريبا 96% من معلومات هذا الملف هي من قناة فقه المختبر
ايضا هناك بعض المعلومات قد تجدونها مكررة، لكن مكررة بأسلوب يختلف كل منهما عن الآخر، حتى من لا يفهم الأول، يفهم الثاني
صباح الخير
ينبض قلبك يوميا حوالي 100 الف نبضة لتبقى على قيد الحياة وتمارس حياتك بشكل طبيعي.
اما طول عمرك فقلبك ينبض مليارات المرات.
وانت زعلان تكول محد وياي
لا حبيبي گلبك وياك.
......
@abdiq96
اذا رحت لمختبر مضبوط وخوش مختبر وسويت تحليل
Anti-TPO
وطلع موجب
وبعدها مثلا صار عندك كسل بالدرقية وبديت رحلة العلاج، واكيد تبقى متابع نتائج الغدة بشكل دوري لهنا تمام؟
ال Anti-TPO هذا لا تعيده، تسويه فقط مرة واحدة في العمر واذا طلع موجب خلص هاي هية لا تعيده مرة اخرى لان يبقى مدى الحياة وماكو اي علاج يوقفه وما راح تفيدك الاعادة بأي شي بس مصرف وقلق زايد.
لذا تحليل Anti-TPO يتم عمله لمرة واحدة في العمر فقط.
......
@abdiq…
اي احد تشوفه عنده سمنة مركزية ( منطقة البطن ) وافخاذ وارجل نحيفة فهاي سمنة هرمونية بسبب ارتفاع الكورتزول
السبب لان الكورتزول يسوي شغلتين:
- زيادة خزن الدهون
- زيادة هدم العضلات
وشكل الجسم يكون شلون الصورة:
......
@abdiq96
اكيد اعدكم احد بالعائلة طالب اعدادي او طالب متوسط؟
واكيد الاغلبية مشكلتهم اللغة الإنكليزية
هاي القناة مال اخوي الجبير ا. يونس
مدرس انكلش دزوها الهم وبيها تنزل شغلات تفيدهم ان شاء الله
وانتو هم اشتركوا بيها هم الا ينزل شي يفيدكم بالانكلش
https://t.me/younisenglish157890
......
@abdiq96
❤9
Signed Abdalruhman Muhammad
Showing the 12 most recent of 26 posts we hold for @fiqhalmokhtaber. 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.
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.
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.
health professions🧠 @helath · 39,327 Telegram ranks this channel #45 of 90 here — alongside 89 others — read 31 August 2026
مع أستاذ ضياء نحو الدراسات العليا @diaeabdulzahr · 61,252 Telegram ranks this channel #45 of 89 here — alongside 88 others — read 22 August 2026
This channel appears in 2 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 11 September 2026 — this
entry's latest reading, not the date you are reading this.
“فقه المختبر 🔬” (@fiqhalmokhtaber), 10,899 subscribers as measured 11 September 2026. Telegram Register, tgregister.com/channel/fiqhalmokhtaber.
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.
