Channel
@CNSmydream
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
12,490subscribers
+404 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1002344769611 |
|---|---|
| Type | Channel |
| Username | @CNSmydream |
| Created | Between 1 September 2024 and 31 March 2025— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 15 August 2026 |
| Measurements held | 10 |
| Confirmed unchanged | 1 time, most recently 15 August 2026 |
| On Telegram | t.me/CNSmydream |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 15 Aug 2026, 19:35 | 12,490 | +44 |
| 14 Aug 2026, 12:16 | 12,446 | +43 |
| 13 Aug 2026, 04:25 | 12,403 | +47 |
| 12 Aug 2026, 00:53 | 12,356 | +33 |
| 10 Aug 2026, 23:37 | 12,323 | +48 |
| 9 Aug 2026, 21:11 | 12,275 | +43 |
| 8 Aug 2026, 20:52 | 12,232 | +41 |
| 7 Aug 2026, 22:33 | 12,191 | +46 |
| 6 Aug 2026, 22:20 | 12,145 | +59 |
| 6 Aug 2026, 06:47 | 12,086 | first reading |
45 posts held, back to 29 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 18 pagesof Telegram’s post history, 20 posts per page.
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 35 of 45 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 15 August 2026 |
|---|---|
| Posts held | 45 (29 July 2026 – 15 August 2026) |
| Views total | 66,239 |
| Reactions total | 187 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 15 Aug 2026, 12:53 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.
167 reactions across 31 posts, in 16 distinct kinds. The most used accounts for 19.8% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 33 | 19.8% | |
| 🌭 | 31 | 18.6% | |
| ❤ | 25 | 15.0% | |
| 🔥 | 24 | 14.4% | |
| 😁 | 14 | 8.38% | |
| 😢 | 9 | 5.39% | |
| 👾 | 6 | 3.59% | |
| 😨 | 6 | 3.59% | |
| 🤯 | 5 | 2.99% | |
| 🍾 | 3 | 1.80% | |
| 👌 | 3 | 1.80% | |
| 🤓 | 2 | 1.20% | |
| 🤬 | 2 | 1.20% | |
| 🥰 | 2 | 1.20% | |
| 🎃 | 1 | 0.599% | |
| 🤔 | 1 | 0.599% |
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 35 of the 45 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 187reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 45 most recent posts we hold, published 29 July 2026 to 15 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.
AI 首次设计出完整病毒基因组：16 个新噬菌体成功杀灭大肠杆菌 如果有一天，AI 能像写代码一样"写"出生命，会怎样？这不再是科幻：斯坦福大学团队近日宣布，首次用生成式 AI 设计出完整、可在实验室中自我复制的全新病毒基因组，成果登上《科学》杂志。 团队使用的 Evo1、Evo2 基因组语言模型，原理类似 ChatGPT，只不过预测的不是文字，而是生命的语言：DNA 序列。模型以约 5400 个碱基对的噬菌体 ΦX174 为模板，学习了病毒、细菌、植物和人类的基因序列规律后，生成全新完整基因组。研究人员选出最看好的 302 个设计加以合成，其中 16 个被证实能感染并杀死大肠杆菌。这些"人造病毒"只攻击细菌，对人体无害。 这项突破被视为"非常重大的转折点"：未来或能用 AI 快速设计噬菌体，对付日益泛滥的耐药菌感染。不过约翰霍普金斯大学专家在同期评论中警告，该技术带来"紧迫的生物安全与安保问题"。团队也已主动设防：训练数…
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新研究揭示：一个关键基因的罕见变异或与更健康的代谢指标相关 我们常听说代谢健康与体重、血脂、血糖息息相关，而遗传因素在其中扮演重要角色。近日，一项发表在《自然》杂志上的研究，通过分析超过100万人的基因数据，发现了一个与代谢指标改善密切相关的基因变异。 研究人员在103万参与者的外显子测序分析中，识别出59个与能量代谢相关的基因，其中FNIP1基因的罕见截断变异（频率约0.01%）尤为突出。这种变异与较低的甘油三酯与高密度脂蛋白胆固醇比值（TG:HDL）、较少的肝脏脂肪、更好的血糖控制以及约60%降低的心血管代谢疾病风险相关。机制上，FNIP1编码的蛋白抑制能量消耗和线粒体代谢，在人类肝细胞中敲低FNIP1能促进脂肪分解，而在小鼠模型中，联合敲低相关基因可抵抗高脂饮食导致的体重增加和脂肪肝。 该发现为理解人类能量代谢的遗传基础提供了新视角，并提示FNIP1通路可能成为治疗代谢疾病的新靶点。不过，由于该罕见变异在人群中频率很…
压力让大脑里的“天线”变短？新发现或为抗焦虑提供新思路 我们常觉得压力会让人焦虑、易怒，但大脑里究竟发生了什么？一项新研究指出，压力可能通过改变大脑中一种名为“初级纤毛”的细胞结构，影响情绪反应。杏仁核作为情绪和压力响应的核心区域，其中的星形胶质细胞，这些“大脑的维护工”，其初级纤毛在压力下会缩短，进而影响行为。 研究团队发现，压力会导致杏仁核星形胶质细胞的初级纤毛变短，相关分子表达也发生改变。他们通过化学遗传学方法，靶向激活了星形胶质细胞上的S1PR1受体，成功恢复了纤毛长度，并纠正了分子异常，最终改善了小鼠的压力相关行为。有趣的是，这些与纤毛相关的基因在人类杏仁核星形胶质细胞中也有表达，且在焦虑等疾病中可能被干扰。 这一发现为抗焦虑等压力相关疾病提供了新思路——通过恢复星形胶质细胞初级纤毛的功能，可能调节情绪行为。不过，研究目前主要在动物模型中进行，人类相关机制仍需更多研究来验证，且具体治疗策略还需进一步探索。 压力…
跑步还是举铁？研究发现：两个都练，延寿效果最好 想长寿，运动到底该怎么做？跑步、游泳这类有氧运动练心肺，举铁、深蹲这类力量训练练肌肉，很多人二选一，还有人干脆懒得动。到底哪种更“保命”，一直缺大规模证据。 《英国运动医学杂志》2022年发表的研究追踪了近10万名平均71.3岁的成年人，中位随访9年。结果很直观：只做有氧运动（如跑步）的人，全因死亡风险比完全不运动者低32%；只做抗阻训练（如举铁）的人只低9%；而有氧+抗阻“双修”的人，风险大幅降低41%。也就是说，两类运动护的是不同身体系统：有氧强心肺、抗阻保肌肉和代谢，叠加起来效果接近“1+1>2”，甚至比单练翻倍还多。 别急着只挑效果大的练。研究是观察性研究，只能说明关联而非因果，且样本是老年人群。但对普通人最实在的启示是：不用纠结选边，每周既有几次快走慢跑，再穿插两回力量训练，性价比最高。 健身房练腿的痛，换来的是多活几年的甜，这笔账不亏💪 📖British Jo…
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母乳中的“免疫蛋白”如何通过微生物组保护婴儿？新研究揭示OPN的“抗感染密码” 母乳喂养被广泛认为能降低婴儿患严重下呼吸道感染（sLRI）的风险，这是导致婴儿死亡的主要原因之一。然而，其背后的保护机制一直是个谜。 最近一项发表在《细胞》期刊上的研究，首次揭示了母乳中一种名为骨桥蛋白（OPN）的关键作用。研究团队发现，缺乏母乳OPN的新生小鼠更容易患上病毒或细菌引起的严重下呼吸道感染。这是因为OPN的缺失导致它们肝脏和肺部发育中的树突状细胞（DC）造血过程被破坏。有趣的是，通过口服补充OPN，可以显著减轻病情。进一步分析显示，补充OPN后，小鼠肠道中的乳杆菌科（Lactobacillaceae）细菌数量增加，同时血清中一种名为3-苯乳酸（PLA）的代谢物水平也升高。PLA是一种PPARγ受体激动剂，能激活肺上皮细胞。这种激活通过释放趋化因子CCL25，招募淋巴髓系前体细胞，并诱导一个支持性的肺微环境，从而恢复DC造血。 这项…
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喜欢甜味的人，可能更爱低风险、即时回报？甜味偏好与经济决策的隐藏联系 很多人喜欢甜食，觉得甜味能带来快乐。但一项新研究提出，喜欢甜味的人，可能在经济决策上更倾向于低风险、即时回报。这听起来有点反直觉，因为通常我们以为追求即时满足的人会更喜欢甜？不过研究要探究甜味偏好与奖励选择之间的联系。 研究通过两个实验，首先用“坎波-波列沃甜味喜好测试”将参与者分为甜味爱好者和不喜欢者。在彩票选择任务中，甜味爱好者更倾向于低风险选项，而在气球风险任务中则无显著差异。在延迟折扣任务中，他们更偏好即时的小奖励，这主要与对奖励即时性的敏感有关，不过风险规避也解释了部分关联。 研究结果表明，甜味偏好与冲动性（延迟折扣）的关联，可能并非源于对即时奖励的强烈渴望，而是因为甜味爱好者更倾向于低风险选择，这种风险规避影响了他们的奖励选择。不过研究样本量有限，且不同任务中结果不一致，未来需要更多研究验证这一机制。 甜食控可能更谨慎？这甜味偏好还藏着风险…
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化疗“打晕”的癌细胞，竟靠喝果糖偷偷跑路？新研究揭开卵巢癌复发之谜 化疗明明在杀癌细胞，为什么卵巢癌还是容易复发？因为有些癌细胞没被杀死，只是被打“睡着”了。更意外的是，它们睡着后没闲着，反而在偷偷帮同伴“越狱”。 这项发表于《自然·衰老》的研究发现，被打“睡着”的癌细胞会释放大量果糖，果糖会干扰细胞膜的胆固醇合成。胆固醇好比细胞间的“胶水”，胶水少了，癌细胞彼此抓不牢，就容易从肿瘤上脱落，顺着血液流到别处重新安家，这就是转移。实验中，给小鼠喂高果糖饮食，肿瘤播散明显加速；反过来，阻断癌细胞利用果糖的能力，转移数量就明显减少。 这个发现为“化疗后为何仍高复发”提供了新解释，也提示限制果糖摄入或许能提高治疗效果。不过先别急着戒水果：研究针对的主要是饮料、甜食里的添加糖，天然水果富含纤维、果糖浓度低，正常吃完全没问题。 癌细胞：化疗都打不醒我，一杯果汁倒可能让我“跑得更快”🍹 📖Nature Aging 📃The chem…
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高剂量维生素D3辅助治疗结直肠癌？临床试验显示效果不显著 很多人认为维生素D可能有助于预防或治疗癌症，特别是结直肠癌，因为维生素D与免疫调节有关。最近一项大型临床试验却带来了令人失望的结果，研究探讨将高剂量维生素D3加入标准化疗方案中，是否能改善转移性结直肠癌患者的预后。 这项名为SOLARIS的随机临床试验招募了455名此前未接受过治疗的转移性结直肠癌患者，将他们随机分为两组：一组接受高剂量维生素D3（每天8000 IU，前14天为负荷剂量），另一组接受标准剂量（每天400 IU）。结果显示，高剂量组的无进展生存期中位数为11.8个月，而标准剂量组为10.3个月，但统计学上无显著差异（单侧log-rank检验P=0.25）。此外，两组的客观缓解率、总生存期及严重副作用的发生率均无显著区别。 研究结论表明，对于转移性结直肠癌患者，在标准化疗（如FOLFOX6或FOLFIRI联合贝伐珠单抗）基础上添加高剂量维生素D3，并未能…
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中年心肺功能强，老年更健康？新研究揭示健康长寿的秘诀 很多人关心如何才能健康地活到老年，心肺功能在其中扮演什么角色？一项新研究为这个问题提供了重要线索。研究跟踪了超过2.4万名在65岁前保持健康的人，通过跑步测试评估他们的心肺功能，并追踪他们之后的生活。结果显示，中年时心肺功能更强的人，老年时更健康的时间更长，患慢性病的数量更少，寿命也更长。具体来说，心肺功能高的人比低的人健康寿命平均长2%，患慢性病数量减少9%，寿命延长3%。无论男女，这种趋势都一致存在，且不受年龄、吸烟或体重等因素影响。 研究通过多变量模型分析，发现高心肺功能者各种慢性病（如心血管疾病、癌症等）的发病时间平均晚1.5年。这表明，保持良好的心肺功能可能通过延缓疾病 onset 来促进健康衰老。不过，研究是观察性的，无法确定是心肺功能导致健康长寿，还是两者共同受其他因素（如生活方式）影响。 运动真能让老年少生病？看来中年就该多跑跑步了🏃♂️ 来源：Jo…
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AI“查错官”上岗：审论文、查手册，还揪出了错75年的沸点数据 化学家鉴定物质、设计蒸馏工艺，常要查参考手册里的沸点值。但如果这些被奉为“权威”的数字，本身一直就是错的呢？ 浙江实验室的理论化学家Pios在用AI预测分子沸点时，发现结果总与一本有75年历史的参考数据库“打架”。他起初以为是模型出错，人工核对原始文献后才确认：错的是数据库，其中既有论文笔误，也有百年前的错误测量值。更系统的测试显示，AI代理评估了2026年ICML口头报告的168篇论文，92篇可评估的论文中仅34篇能复现至少五分之二的核心结论，复现率超80%的只有8篇；另一项研究则发现，NeurIPS论文中的客观错误数从2021年的3.8个升至2025年的5.9个，涨幅达55%。 这表明AI能以人类难以企及的速度和规模扫描文献、修补“科学地基”。但研究者也提醒：AI查错员同样会犯错，结论必须经人工复核，而“创新性”“重要性”这类主观判断，还是该留给人类。 …
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化疗后癌细胞竟会“变软”脱落？果糖或成促转移元凶 化疗是癌症治疗的利器，但有时也会带来意想不到的副作用。研究发现，化疗诱导的细胞衰老会释放一种“衰老相关分泌表型”（SASP），其中包含多种因子。这些因子可能影响周围癌细胞的行为，甚至促进转移。对于卵巢癌等高复发癌症，理解SASP的作用至关重要。 研究团队发现，顺铂诱导的SASP中，果糖是一种关键代谢成分。它通过抑制NAD+-SIRT-SREBP轴，导致细胞膜胆固醇减少，使癌细胞更容易从原发肿瘤脱落。机制上，复合物I（呼吸链中的关键酶）是驱动这一过程的核心，而高果糖饮食在动物模型中确实增加了癌细胞转移。这揭示了SASP如何通过代谢重编程，以旁分泌方式促进转移。 这一发现解释了为何某些化疗后癌症复发率高，因为SASP可能反而助长了转移。不过，研究主要基于卵巢癌模型，其他癌症是否适用仍需验证。同时，高果糖饮食的影响也提示，饮食因素可能与癌症治疗效果相关，但具体机制和临床应用还需更…
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Showing the 12 most recent of 45 posts we hold for @CNSmydream. 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.
Citation-graph rank — 173,777 of 1,481,217entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
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.
Names
Channels on the register whose handles appear in this channel's posts.
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.
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 15 August 2026 — this entry's latest reading, not the date you are reading this.
“来一点医学科学前沿🤯🤯🥹🥹” (@CNSmydream), 12,490 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/CNSmydream.
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.