Other / unclassifiable — 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 10 August 2026 and assigned it the closest of 31 fixed categories, at 87% 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 2 other registered channels. 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 (5 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 15 comparable posts for this entry, running 30 July 2026 to 7 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 3, the earliest publisher we hold is @alteogenIR — which is this entry. That is a statement about our reading window, not a claim of authorship.
Views per post sit far above this size band
15,100 average views per post against 9,788 subscribers — an engagement rate of 154.4%. Across the 32,857 registered channels in the same cohort — 3,163–9,999 subscribers — the middle half sit between 4.26% and 18.5%, with a median of 9.41%.
What this was computed from
Window
30 days (10 August 2026 – 9 September 2026)
Posts measured
14 of 14 published in the window (0 exact, 14 rounded by Telegram)
Views totalled
211,600
Mature posts only
154.4% over 14 posts read at least 24h after publication
When this is recorded. A channel is listed here only when its engagement rate sits at or above the 99th percentile of its cohort and is at least 3× away from that cohort’s median — above it — on both the all-readings figure and the mature-only figure. The percentile alone would be circular: a percentile cut puts the same share of every cohort in the tail whatever the data looks like. The distance from the median is what makes it a statement about this channel.
This is not a verdict, and the direction is not a quality signal. A low rate has many innocent causes — audiences that read in the Telegram app without opening the channel, a subscriber base built long before the current output, an audience in a different timezone from our reading. A high rate has innocent causes too: a post that travelled far beyond the channel’s own subscribers will do it. We publish the measurement and the distribution it sits in. The full cohort baselines are downloadable, so this comparison can be reproduced rather than trusted.
Recorded under the keys clone_source · err_high, last confirmed 9 September 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 2 other registered channels, 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.
12 measurements spanning 34 days, net +239. 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 9,513–9,824 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
8 Sept 2026, 17:16
9,788
+2
3 Sept 2026, 17:02
9,786
+74
31 Aug 2026, 01:56
9,712
+1
28 Aug 2026, 07:39
9,711
+10
24 Aug 2026, 23:13
9,701
+5
21 Aug 2026, 15:48
9,696
+78
18 Aug 2026, 21:55
9,618
+40
16 Aug 2026, 07:44
9,578
-8
12 Aug 2026, 12:53
9,586
+9
9 Aug 2026, 23:32
9,577
+26
6 Aug 2026, 15:50
9,551
+2
6 Aug 2026, 04:48
9,549
first reading
Engagement
36 posts held, back to 27 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 34 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
154.4%
avg views ÷ 9,788 subscribers
Avg views / post
15,100
14 posts measured
Reaction rate
0.664%
reactions ÷ views · ER floor
Posts in window
14
of 36 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 28 August 2026
Posts held
36 (27 July 2026 – 28 August 2026)
Views total
211,600
Reactions total
1,406
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
29 Aug 2026, 05:30 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.
Reaction mix
3,684 reactions across 34 posts, in 31 distinct kinds. The most used accounts for 48.4% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
1,784
48.4%
❤
1,324
35.9%
👎
355
9.64%
🤪
47
1.28%
👏
39
1.06%
🙏
31
0.841%
🍌
16
0.434%
🔥
16
0.434%
💯
9
0.244%
😭
9
0.244%
😁
7
0.19%
🥰
6
0.163%
😢
5
0.136%
🤷♂
5
0.136%
😱
4
0.109%
🤯
4
0.109%
💔
3
0.081%
😡
3
0.081%
🏆
2
0.054%
🤡
2
0.054%
11 further kinds
13
0.353%
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 36 of the 36 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 3,867 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 36 most recent posts we hold, published 27 July 2026 to 28 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.
💊알테오젠
: SC전환, 거스를 수 없는 흐름
[상상인증권 제약/바이오 이달미]
▶다수의 글로벌 빅파마 기술수출로 인증받은 기업
- 동사는 2019년 사노피 기술수출을 시작으로 2020년 머크, 2024년 다이치산쿄 등 대형 기술수출 이외에도 작은 기술수출이 연달아 이어지고 있음
- 그 중 머크의 키트루다는 SC제형이 2025년 9월에 미국 FDA 승인을 받아 현재 시판되고 있음
- 2024년 다이치산쿄의 ADC, 2025년 아스트라제네카의 이중항체 등 동사의 기술수출은 기존 항체 치료제에 국한되지 않고 다양한 모달리티로의 확장을 진행 중
- 글로벌 빅파마로의 기술수출로 레퍼런스가 쌓이면서 향후 추가 기술수출에 대한 기대감이 높은 상황
▶돈 벌기 시작
- 키트루다 QLEX는 2025년 9월 미국에서 승인을 받은 이후 9월말부터 바로 출시
…
[단독] 알테오젠의 피하주사(SC) 제형 변경 플랫폼이 적용된 ‘키트루다 큐렉스’가 일본에서 출시를 눈앞에 뒀습니다.
미국과 유럽에 이어 일본으로까지 상업화 지역이 확대되면, 알테오젠은 품목허가 마일스톤(단계별 기술료)뿐만 아니라 원료 공급과 판매 마일스톤을 통해 추가 매출을 확보할 것으로 전망됩니다.
일본 후생노동성은 24일 약사심의회 의약품제2부회를 열고, 다국적 제약사 MSD(미국 머크)의 키트루다 큐렉스의 제조·판매 승인을 권고했습니다. 키트루다 큐렉스는 ‘키트루다’의 주성분인 ‘펨브롤리주맙’과 알테오젠의 유전자재조합 인간 히알루로니다제 성분인 ‘베라히알루로니다제 알파’를 결합한 SC 제형입니다. 이 제품에는 정맥주사(IV)로 투여하던 고용량 항체의약품을 피부 아래로 빠르게 투여할 수 있도록 알테오젠의 하이브로자임 플랫폼인 ‘ALT…
안녕하세요. 당사의 파트너제품인 GSK의 Jemperli가 직장암 치료에 대해 미국 FDA로부터 priority review(우선 검토)를 받으며 적응증 확대에 나섰습니다.
• 미국 FDA는 이전에 치료받지 않은 2기 및 3기 dMMR/MSI-H 국소 진행성 직장암 환자에서 우선 검토를 위해 Jemperli(성분명: dostarlimab)에 대한 sBLA(추가 생물학적 제제 허가 신청)을 승인했습니다.
• 해당 sBLA는 미국 National Priority Voucher program(국가 우선 바우처 프로그램)을 통해 신속 심사를 받아 승인결정일을 2027년 2월로 단축했습니다.
자세한 내용은 아래 링크의 PR 참조해주시기 바랍니다.
https://www.gsk.com/en-gb/media/press-releases/jemperli-…
『알테오젠(196170.KQ) - 암 백신 또한 키트루다SC가 메인』
기업분석부 엄민용, 이호철 ☎️ 02-3772-1546
▶️ 신한생각: 암 백신 시대 도래, 가장 메인 폐암은 키트루다SC 병용
8월 19일 전세계 최초 mRNA 기반 모더나 암 백신 Intismeran(V940) + 키트루다 병용 흑색종(피부암) 임상 3상 성공. 모더나 +177%, MSD 역사적 신고가 달성. 암 백신 병용 임상 8개 중 매출액 비중 가장 높은 비소세포폐암은 지난 5월 키트루다SC와 병용 개시. 여전히 키트루다SC가 MSD의 핵심 전략임을 주목(매출 비중: 비소세포폐암 45% vs. 흑색종 10%)
▶️ 모더나와 MSD가 개척한 암 백신. 후발주자도 모두 키트루다가 메인
모더나 고형암 백신 V940, 키트루다 병용 흑색종 보조 3상(INTerpath-…
안녕하세요. 알테오젠이 바이오의약품 DS(원료의약품) 및 DP(완제의약품) 생산시설을 구축한다고 조금 전 공시했습니다.
▶️ 2028년 완공을 목표로 3년에 걸쳐 2,532억원을 투자할 계획이며, 이 바이오 plant에서는 당사의 ALT-B4(성분명: 베라히알루로니다제 알파), ALT-L9(아일리아 바이오시밀러) 등 제품을 생산할 예정입니다.
▶️ 당사는 지금까지 국내외 여러 CMO를 통해 제품을 생산해왔으나, 회사의 중장기 성장전략과 바이오의약품 사업 확대를 위해 자체 생산시설 구축을 결정했습니다.
공시는 하단 링크를 통해 확인해주시기 바랍니다.
https://dart.fss.or.kr/dsaf001/main.do?rcpNo=20260821900238
안녕하세요. 당사의 테르가제(재조합 인간 히알루로니다제 단독제품)가 올 상반기 작년 연간매출을 넘어서며 시장 점유율을 확대하고 있습니다.
테르가제는 부종 및 통증 완화 등에 사용되고 있으며 내년 국내에서 100억원 이상의 매출을 올릴 것으로 예상하고 있습니다. 아래 머니투데이의 기사 공유드립니다.
https://www.mt.co.kr/thebio/2026/08/20/2026082009495835973
안녕하세요. 당사의 파트너사 MSD(미국/캐나다 내 Merck)와 모더나가 공동으로 개발 중인 '키트루다' + mRNA 치료제 'intismeran'의 흑색종 임상 3상에서 긍정적인 결과를 발표했습니다.
이 병용 치료요법은 키트루다 단독요법과 비교해 주평가지표(primary endpoint)인 무재발 생존(RFS)과 주요 부평가지표(key secondary endpoint)였던 전이 무재발 생존(DMFS) 모두 hit 했습니다.
이번 결과는 이미 흑색종 절제 환자들에 보조요법으로 표준치료제로 자리잡은 키트루다 단독과 비교해 처음으로 개선된 효과를 나타낸 임상 3상이며, mRNA 항암치료제 중 처음으로 positive 결과를 낸 시험이라고 밝혔습니다.
MSD와 모더나는 이 병용요법을 흑색종, 비소세포폐암, 방광암, 신장세포암 등 다양한 암…
당사의 ALT-B4 기반 단독제품 '테르가제주(Tergase)'가 프리 갈리엥 코리아(Prix Galien Korea) 최우수 바이오제품 부문 최종후보에 선정되었습니다.
테르가제는 99% 이상의 고순도 재조합 인간 히알루로니다제로, 임상시험에서 항약물항체(ADA)가 확인되지 않았습니다. 또한 3개월 실온 보관이 가능해 의료 현장에서의 보관 및 사용 편의성을 높였습니다.
국내에서는 100개 이상의 병원 약사위원회 심의를 통과했으며, 47개 상급종합병원 가운데 30곳 이상에 제품을 공급하는 등 종합병원과 전문병원을 중심으로 채택이 확대되고 있습니다.
알테오젠은 자회사 알테오젠바이오로직스를 통해 테르가제의 국내외 마케팅과 사업화를 진행하고 있습니다. 해외시장 진출을 위한 임상시험 등을 준비하는 한편, 연구자 주도 임상시험을 통해 적용 진료과와…
안녕하세요. 키움증권에서 발간한 산업자료에 당사에 관련한 내용이 있어 공유드립니다.
▶️ 국내 제약·바이오 시장은 단순한 기대감을 넘어 실제 가치와 수익화(Cash Flow)를 증명해야 하는 신뢰의 국면에 진입
▶️ 키트루다 SC 상용화 성과: 알테오젠의 기술이 적용된 글로벌 블록버스터 면역항암제 '키트루다 SC'(미국 제품명: 키트루다 큐렉스)의 실제 출시 및 판매가 본격화되면서 마일스톤(단계별 기술료)과 실적 성장이 가시화되고 있음
▶️ 적용 범위(모달리티) 확대로 인한 기술이전: 기존 항체 치료제 중심에서 항체-약물 접합체(ADC), 핵산 치료제 등으로 플랫폼 적용 영역을 확대
▶️ 추가 계약 기대감: 다수의 물질이전계약(MTA)이 진행 중인 만큼 향후 추가적인 기술수출과 계약금·마일스톤 유입이 지속될 것으로 기대
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Showing the 12 most recent of 36 posts we hold for @alteogenIR. 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
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 2 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.
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.
“알테오젠 IR” (@alteogenIR), 9,788 subscribers as measured 8 September 2026. Telegram Register, tgregister.com/channel/alteogenIR.
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.
