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Medical Lab Technologists™ | Official

@Medical_Lab_Technicians

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Polls · Handles named that no longer answer · Telegram's recommendations · Cite this entry

12,717subscribers

-220 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1001332192346
TypeChannel
Username@Medical_Lab_Technicians
Description🧪🤖 MEDICAL LABORATORY TECHNOLOGISTS™ | OFFICIAL AI COMMUNITY 🔬 AI-Powered MLT Learning & Practice 📚 Daily MCQs • Answers • Explanations 🩸 Hematology • 🧬 Biochemistry 🦠 Microbiology • 🩸 Blood Banking 🤖 AI Learning Assistant & Smart Quiz Practice
CreatedBetween 1 March 2018 and 31 July 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live4 November 2026
Measurements held57
Confirmed unchanged1 time, most recently 4 November 2026
On Telegramt.me/Medical_Lab_Technicians

Topic

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 61% 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

12,71712,94312,8307 August 2026 — 12,937 subscribers7 August 2026 — 12,937 subscribers8 August 2026 — 12,934 subscribers9 August 2026 — 12,937 subscribers10 August 2026 — 12,930 subscribers12 August 2026 — 12,923 subscribers13 August 2026 — 12,929 subscribers14 August 2026 — 12,939 subscribers15 August 2026 — 12,935 subscribers17 August 2026 — 12,936 subscribers18 August 2026 — 12,930 subscribers19 August 2026 — 12,935 subscribers20 August 2026 — 12,940 subscribers21 August 2026 — 12,943 subscribers24 August 2026 — 12,932 subscribers25 August 2026 — 12,925 subscribers26 August 2026 — 12,927 subscribers26 August 2026 — 12,929 subscribers28 August 2026 — 12,924 subscribers29 August 2026 — 12,925 subscribers30 August 2026 — 12,919 subscribers31 August 2026 — 12,907 subscribers1 September 2026 — 12,906 subscribers2 September 2026 — 12,905 subscribers3 September 2026 — 12,906 subscribers5 September 2026 — 12,899 subscribers8 September 2026 — 12,909 subscribers11 September 2026 — 12,894 subscribers13 September 2026 — 12,892 subscribers14 September 2026 — 12,886 subscribers16 September 2026 — 12,880 subscribers19 September 2026 — 12,879 subscribers26 September 2026 — 12,859 subscribers4 October 2026 — 12,853 subscribers9 October 2026 — 12,850 subscribers13 October 2026 — 12,848 subscribers15 October 2026 — 12,839 subscribers16 October 2026 — 12,843 subscribers17 October 2026 — 12,838 subscribers18 October 2026 — 12,827 subscribers19 October 2026 — 12,818 subscribers20 October 2026 — 12,817 subscribers22 October 2026 — 12,810 subscribers23 October 2026 — 12,795 subscribers24 October 2026 — 12,790 subscribers24 October 2026 — 12,784 subscribers25 October 2026 — 12,780 subscribers26 October 2026 — 12,775 subscribers28 October 2026 — 12,771 subscribers29 October 2026 — 12,759 subscribers30 October 2026 — 12,752 subscribers31 October 2026 — 12,747 subscribers1 November 2026 — 12,740 subscribers2 November 2026 — 12,733 subscribers3 November 2026 — 12,727 subscribers3 November 2026 — 12,726 subscribers4 November 2026 — 12,717 subscribers7 August 20264 November 2026
57 measurements spanning 89 days, net -220. 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 12,683–12,977 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 57
Measured (UTC)SubscribersChange
4 Nov 2026, 23:5012,717-9
3 Nov 2026, 23:0912,726-1
3 Nov 2026, 00:4512,727-6
2 Nov 2026, 02:3012,733-7
1 Nov 2026, 02:4712,740-7
31 Oct 2026, 05:3812,747-5
30 Oct 2026, 03:1712,752-7
29 Oct 2026, 05:4012,759-12
28 Oct 2026, 00:4712,771-4
26 Oct 2026, 23:0812,775-5
25 Oct 2026, 21:1612,780-4
24 Oct 2026, 23:3812,784-6
24 Oct 2026, 01:0812,790-5
23 Oct 2026, 01:4912,795-15
22 Oct 2026, 02:1112,810-7
20 Oct 2026, 17:4812,817-1
19 Oct 2026, 14:5612,818-9
18 Oct 2026, 17:5612,827-11
17 Oct 2026, 17:5712,838-5
16 Oct 2026, 20:4912,843first reading

Engagement

42 posts held, back to 29 March 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 53 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
2.30%
avg views ÷ 12,717 subscribers
Avg views / post
292
20 posts measured
Reaction rate
0.253%
reactions ÷ views · ER floor
Posts in window
20
of 42 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 2 of 20 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 29 October 2026
Posts held42 (29 March 2026 – 29 October 2026)
Views total5,843
Reactions total1
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken5 Nov 2026, 18:51 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

Photos
568
Videos
10
Links
61

Lifetime counters from Telegram’s own channel header, read 5 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Reaction mix

117 reactions across 18 posts, in 2 distinct kinds. The most used accounts for 95.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤11295.7%
👎54.27%

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 19 of the 42 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 117 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 42 most recent posts we hold, published 29 March 2026 to 29 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: 0 of 20 posts held from the last 30 days (0%) 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).

29 Oct 2026, 10:43 UTC840 viewsread 5 November 2026
Poll

🩺 Q992: [Hematology & Hemostasis Case (Series #5, Case #10)] Which anticoagulant is the gold standard for routine Prothrombin Time (PT) and APTT testing?

  1. A) K2-EDTA10%
  2. B) Lithium Heparin8%
  3. C) Sodium Citrate (3.2%, 0.109 M)80%
  4. D) Potassium Oxalate3%

Shares as published, totalling 101%. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC808 viewsread 5 November 2026
Poll

🩺 Q991: [Hematology & Hemostasis Case (Series #5, Case #9)] In automated hematology analyzers, the Coulter impedance principle detects cells by measuring:

  1. A) Light scatter intensity at 90 degrees15%
  2. B) Electrical resistance change as non-conductive cells pass an aperture64%
  3. C) Fluorescence emission after nucleic acid staining19%
  4. D) Radiofrequency conductivity absorption2%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC679 viewsread 5 November 2026
Poll

🩺 Q990: [Hematology & Hemostasis Case (Series #4, Case #8)] Which anticoagulant is the gold standard for routine Prothrombin Time (PT) and APTT testing?

  1. A) K2-EDTA2%
  2. B) Lithium Heparin2%
  3. C) Sodium Citrate (3.2%, 0.109 M)95%
  4. D) Potassium Oxalate0%

Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC648 viewsread 5 November 2026
Poll

🩺 Q989: [Hematology & Hemostasis Case (Series #4, Case #7)] In automated hematology analyzers, the Coulter impedance principle detects cells by measuring:

  1. A) Light scatter intensity at 90 degrees2%
  2. B) Electrical resistance change as non-conductive cells pass an aperture90%
  3. C) Fluorescence emission after nucleic acid staining7%
  4. D) Radiofrequency conductivity absorption0%

Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC304 viewsread 5 November 2026
Poll

🩺 Q988: [Hematology & Hemostasis Case] In automated hematology analyzers, the Coulter impedance principle detects cells by measuring:

  1. A) Light scatter intensity at 90 degrees0%
  2. B) Electrical resistance change as non-conductive cells pass an aperture93%
  3. C) Fluorescence emission after nucleic acid staining6%
  4. D) Radiofrequency conductivity absorption1%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC272 viewsread 5 November 2026
Poll

🩺 Q987: [Histopathology & Cytology Case (Series #5, Case #10)] In Papanicolaou (Pap) staining of cervical smears, EA-50 (or EA-65) counterstains:

  1. A) Cytoplasm of superficial squamous cells, intermediate cells, and cilia58%
  2. B) Nuclear chromatin and nucleoli deep blue-purple32%
  3. C) Keratin exclusively orange9%
  4. D) Mucin and glycogen droplets brown1%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC246 viewsread 5 November 2026
Poll

🩺 Q986: [Histopathology & Cytology Case (Series #5, Case #9)] What is the recommended fixative-to-tissue volume ratio for optimal 10% Neutral Buffered Formalin fixation?

  1. At least 10:1 to 20:160%
  2. B) 1:1 equal volume26%
  3. C) 2:1 slight excess12%
  4. D) 50:1 ultra high dilution2%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC230 views0 reactionsread 5 November 2026
Poll

🩺 Q985: [Histopathology & Cytology Case (Series #4, Case #8)] In Papanicolaou (Pap) staining of cervical smears, EA-50 (or EA-65) counterstains:

  1. A) Cytoplasm of superficial squamous cells, intermediate cells, and cilia89%
  2. B) Nuclear chromatin and nucleoli deep blue-purple7%
  3. C) Keratin exclusively orange3%
  4. D) Mucin and glycogen droplets brown1%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC202 viewsread 5 November 2026
Poll

🩺 Q984: [Histopathology & Cytology Case (Series #4, Case #7)] What is the recommended fixative-to-tissue volume ratio for optimal 10% Neutral Buffered Formalin fixation?

  1. At least 10:1 to 20:191%
  2. B) 1:1 equal volume4%
  3. C) 2:1 slight excess4%
  4. D) 50:1 ultra high dilution0%

Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC182 viewsread 5 November 2026
Poll

🩺 Q983: [Histopathology & Cytology Case (Series #3, Case #6)] In Papanicolaou (Pap) staining of cervical smears, EA-50 (or EA-65) counterstains:

  1. A) Cytoplasm of superficial squamous cells, intermediate cells, and cilia88%
  2. B) Nuclear chromatin and nucleoli deep blue-purple10%
  3. C) Keratin exclusively orange2%
  4. D) Mucin and glycogen droplets brown0%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC167 viewsread 5 November 2026
Poll

🩺 Q982: [Histopathology & Cytology Case (Series #3, Case #5)] What is the recommended fixative-to-tissue volume ratio for optimal 10% Neutral Buffered Formalin fixation?

  1. At least 10:1 to 20:186%
  2. B) 1:1 equal volume10%
  3. C) 2:1 slight excess5%
  4. D) 50:1 ultra high dilution0%

Shares as published, totalling 101%. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

29 Oct 2026, 10:43 UTC159 viewsread 5 November 2026
Poll

🩺 Q981: [Histopathology & Cytology Case (Series #2, Case #4)] In Papanicolaou (Pap) staining of cervical smears, EA-50 (or EA-65) counterstains:

  1. A) Cytoplasm of superficial squamous cells, intermediate cells, and cilia93%
  2. B) Nuclear chromatin and nucleoli deep blue-purple5%
  3. C) Keratin exclusively orange2%
  4. D) Mucin and glycogen droplets brown0%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Signed MedLab Bot AI

Showing the 12 most recent of 42 posts we hold for @Medical_Lab_Technicians. 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.

Polls

The 6 most recent of 20 polls we hold for this entry, as Telegram rendered them when we read the post. A poll’s figures keep moving after that, so each one is dated.

29 Oct 2026, 10:43 UTCAnonymous Quiz277 voters

🩺 Q992: [Hematology & Hemostasis Case (Series #5, Case #10)] Which anticoagulant is the gold standard for routine Prothrombin Time (PT) and APTT testing?

  1. A) K2-EDTA10%
  2. B) Lithium Heparin8%
  3. C) Sodium Citrate (3.2%, 0.109 M)80%
  4. D) Potassium Oxalate3%

Shares as published, totalling 101%. No per-option vote count is published by Telegram, so none is shown.

29 Oct 2026, 10:43 UTCAnonymous Quiz176 voters

🩺 Q991: [Hematology & Hemostasis Case (Series #5, Case #9)] In automated hematology analyzers, the Coulter impedance principle detects cells by measuring:

  1. A) Light scatter intensity at 90 degrees15%
  2. B) Electrical resistance change as non-conductive cells pass an aperture64%
  3. C) Fluorescence emission after nucleic acid staining19%
  4. D) Radiofrequency conductivity absorption2%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

29 Oct 2026, 10:43 UTCAnonymous Quiz162 voters

🩺 Q990: [Hematology & Hemostasis Case (Series #4, Case #8)] Which anticoagulant is the gold standard for routine Prothrombin Time (PT) and APTT testing?

  1. A) K2-EDTA2%
  2. B) Lithium Heparin2%
  3. C) Sodium Citrate (3.2%, 0.109 M)95%
  4. D) Potassium Oxalate0%

Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is shown.

29 Oct 2026, 10:43 UTCAnonymous Quiz123 voters

🩺 Q989: [Hematology & Hemostasis Case (Series #4, Case #7)] In automated hematology analyzers, the Coulter impedance principle detects cells by measuring:

  1. A) Light scatter intensity at 90 degrees2%
  2. B) Electrical resistance change as non-conductive cells pass an aperture90%
  3. C) Fluorescence emission after nucleic acid staining7%
  4. D) Radiofrequency conductivity absorption0%

Shares as published, totalling 99%. No per-option vote count is published by Telegram, so none is shown.

29 Oct 2026, 10:43 UTCAnonymous Quiz104 voters

🩺 Q988: [Hematology & Hemostasis Case] In automated hematology analyzers, the Coulter impedance principle detects cells by measuring:

  1. A) Light scatter intensity at 90 degrees0%
  2. B) Electrical resistance change as non-conductive cells pass an aperture93%
  3. C) Fluorescence emission after nucleic acid staining6%
  4. D) Radiofrequency conductivity absorption1%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

29 Oct 2026, 10:43 UTCAnonymous Quiz103 voters

🩺 Q987: [Histopathology & Cytology Case (Series #5, Case #10)] In Papanicolaou (Pap) staining of cervical smears, EA-50 (or EA-65) counterstains:

  1. A) Cytoplasm of superficial squamous cells, intermediate cells, and cilia58%
  2. B) Nuclear chromatin and nucleoli deep blue-purple32%
  3. C) Keratin exclusively orange9%
  4. D) Mucin and glycogen droplets brown1%

Shares as published. No per-option vote count is published by Telegram, so none is shown.

Percentages only — there are no per-option vote counts here, because Telegram publishes none. The public post preview gives each option’s share and a single voter total, and nothing else. Multiplying one by the other would produce a per-option tally that looks measured and is not: the shares are rounded to whole numbers before we ever see them. We print what was published and leave the column that does not exist empty.

The shares need not add up to 100. Rounding alone puts many polls at 99 or 101. A poll that allows more than one answer per voter runs well past 100 by design, and several here do. The bars are drawn against a fixed 100% track at each option’s own percentage rather than normalised to the total, so a poll that exceeds it shows that it does instead of being quietly rescaled.

Read from the 42 most recent posts we hold, published 29 March 2026 to 29 October 2026. Telegram labels each poll by kind — an anonymous poll, a quiz, a closed set of final results — and that label is reproduced rather than paraphrased.

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.

MEDICAL LABORATORY TECHNICIAN
@medical147 · 18,822
Telegram ranks this channel #2 of 65 here — alongside 64 others — read 5 October 2026
MLT Education Point
@Mlteducationpoint · 43,656
Telegram ranks this channel #22 of 73 here — alongside 72 others — read 28 August 2026
Testpaperlive Paramedical Classes
@tplparamedicalclasses · 21,929
Telegram ranks this channel #27 of 74 here — alongside 73 others — read 5 October 2026
English Lovers
@EnglishLoversofficial · 130,904
Telegram ranks this channel #66 of 68 here — alongside 67 others — read 13 August 2026

This channel appears in 4 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 4 November 2026 — this entry's latest reading, not the date you are reading this.

“Medical Lab Technologists™ | Official” (@Medical_Lab_Technicians), 12,717 subscribers as measured 4 November 2026. Telegram Register, tgregister.com/channel/Medical_Lab_Technicians.

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.