Telegram RegisterThe public register of Telegram
Telegram profile photo for Sina Nouri | Clinical Data Insights

Channel

Sina Nouri | Clinical Data Insights

@sina_nouri_data

On this record: Topic · Growth · Engagement · Reactions · Posts · Cite this entry

366subscribers

+0 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1003953433916
TypeChannel
Username@sina_nouri_data
CreatedBetween 1 April 2026 and 17 June 2026 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live11 October 2026
Measurements held7
Confirmed unchanged1 time, most recently 11 October 2026
On Telegramt.me/sina_nouri_data

Topic

Education — 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 22 September 2026 and assigned it the closest of 31 fixed categories, at 97% 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

361366363.57 August 2026 — 366 subscribers8 August 2026 — 366 subscribers22 August 2026 — 363 subscribers29 August 2026 — 365 subscribers16 September 2026 — 361 subscribers1 October 2026 — 362 subscribers11 October 2026 — 366 subscribers7 August 202611 October 2026
7 measurements spanning 65 days. 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 360–367 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
11 Oct 2026, 21:16366+4
1 Oct 2026, 20:56362+1
16 Sept 2026, 04:21361-4
29 Aug 2026, 02:35365+2
22 Aug 2026, 20:09363-3
8 Aug 2026, 06:31366no change
7 Aug 2026, 19:41366first reading

Engagement

20 posts held, back to 17 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 1 page of Telegram’s post history, 20 posts per page.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 3 August 2026. An engagement rate over an empty window would be a number about nothing.

Reaction mix

95 reactions across 15 posts, in 5 distinct kinds. The most used accounts for 84.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤8084.2%
👏1010.5%
👍33.16%
🔥11.05%
😍11.05%

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 15 of the 20 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 95 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 20 most recent posts we hold, published 17 June 2026 to 3 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

3 Aug 2026, 19:25 UTC76 views5 reactionsread 8 August 2026
File

Practical Computing for Biologists HADDAC 2011 کتاب دوم که برای مطالعه توصیه میشه، این کتاب هست که تا حدی آموزش اصول و پایه‌ی لینوکس رو هم داره. #Book Telegram: @ssina_n Channel: https://t.me/sina_nouri_data Phone: 09363922565

❤5

26 Jul 2026, 18:06 UTC176 views9 reactionsread 8 August 2026
File

Essential Bioinformatics Jin Xiong 2006 به عنوان اولین کتاب، این‌ کتاب رفرنس مناسبی برای مطالعه و شروع بیوانفورماتیک است. با اینکه قدیمی است ولی دید اصولی و درستی ایجاد میکند. #Book Telegram: @ssina_n Channel: https://t.me/sina_nouri_data Phone: 09363922565

❤9

25 Jul 2026, 07:41 UTC192 views14 reactionsread 8 August 2026

با سلام و احترام بنا به خواسته همراهان گرامی و فرهیخته ازین به بعد سعی میکنم کتاب ها و مقالات مفیدی که در زمینه بیوانفورماتیک خودم مطالعه کردم و احساس میکنم مفید هست رو در کانال قرار میدم. باعث افتخار من هست اگر به نکته‌ای برخوردین که براتون جالب و مهم یا چالشی بود در کامنت پست یا PV خودم بیان کنید تا در موردش گفتگو کنیم و مباحث جدید یاد بگیریم. ممنون از همراهی شما پژوهشگران فرهیخته. سینا نوری -----------------…

❤12👍1👏1

21 Jul 2026, 18:53 UTC205 views4 reactionsread 8 August 2026

Did you know the correlation between mRNA and protein levels in a single cell is often 𝑅^2<0.4? Yet, many computational biologists and R&D teams still celebrate a beautiful UMAP showing high mRNA expression as a definitive biological discovery. Here is a harsh reality for translational bioinformatics: A transcript is not a phenotype. Due to post-transcriptional regulations, mRNA half-lives, and translation effici…

❤4

17 Jul 2026, 18:17 UTC254 views5 reactionsread 8 August 2026

ادغام بیش‌از‌حد (Over-integration) در Single-cell: آیا برای داشتن یک UMAP زیبا، بیولوژی بیماری را پاک می‌کنیم؟ یکی از بزرگترین تله‌ها در تحلیل داده‌های Single-cell RNA-seq، وسواس بیش از حد برای حذف کامل Batch Effect است. ما ساعت‌ها وقت می‌گذاریم تا با الگوریتم‌هایی مثل Harmony یا CCA در Seurat، کلاسترهای گروه کنترل و بیمار را دقیقاً روی هم بیندازیم. اما به چه قیمتی؟ در بیماری‌های خودایمنی پیچیده مثل MS یا در ریزمحی…

❤4👏1

17 Jul 2026, 18:17 UTC187 views4 reactionsread 8 August 2026

The Trap of Over-Integration in scRNA-seq: Erasing Disease Biology for a “Perfect” UMAP. One of the most dangerous pitfalls in Single-cell RNA-seq analysis is the obsession with perfectly eliminating batch effects. We often spend hours fine-tuning algorithms like Harmony or Seurat’s CCA to force perfect overlapping of patient and healthy control clusters. But at what cost? In complex microenvironments—such as the t…

👏4

12 Jul 2026, 19:16 UTC238 views4 reactionsread 8 August 2026

https://www.linkedin.com/posts/sina-nouri-bioinfo_singlecell-bioinformatics-immunology-share-7482149853066760192-dmar/?utm_source=share&utm_medium=member_android&rcm=ACoAADPCB_8BW0nilzE2_q-voOBRmJSduE46n2w

❤4

12 Jul 2026, 19:15 UTC195 views8 reactionsread 8 August 2026

آیا می‌دانستید هدف نهایی در درمان سرطان، دقیقا همان چیزی است که در بیماری ام‌اس (MS) از آن فرار می‌کنیم؟ 🧬 پدیده “خستگی سلول‌های T” (T-cell Exhaustion) یک شمشیر دولبه در ایمونولوژی است. در سرطان ما می‌خواهیم این خستگی را رفع کنیم تا تومور نابود شود، اما در MS آرزو داریم سلول‌های مهاجم خسته شوند تا به سیستم عصبی حمله نکنند! تحلیل داده‌های Single-cell RNA-seq به ما اجازه می‌دهد این مسیرهای مشترک را با دقت تک‌سلولی رم…

❤6👍1😍1

6 Jul 2026, 19:01 UTC252 views5 reactionsread 8 August 2026

فراتر از یک سلول: چرا برای درمان MS باید شبکه‌ی ارتباطی سلول‌ها را هدف قرار دهیم؟ در پاتوژنز بیماری‌های اتوایمیون مانند مولتیپل اسکلروزیس (MS)، سلول‌ها به تنهایی عمل نمی‌کنند. التهاب، دمیلیناسیون و حتی فرآیند ترمیم، حاصل یک دیالوگ پیچیده و پیوسته در ریزمحیط سیستم عصبی مرکزی است. با وجود پیشرفت‌ها در توسعه درمان‌های اصلاح‌کننده بیماری (DMTs)، ما همچنان در کنترل پیشرفت بیماری (Progressive MS) چالش‌های اساسی داریم. یک…

❤5

Showing the 12 most recent of 20 posts we hold for @sina_nouri_data. 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.

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 October 2026 — this entry's latest reading, not the date you are reading this.

“Sina Nouri | Clinical Data Insights” (@sina_nouri_data), 366 subscribers as measured 11 October 2026. Telegram Register, tgregister.com/channel/sina_nouri_data.

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.