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Channel

来一点医学科学前沿🤯🤯🥹🥹

@CNSmydream

On this record: Topic · Growth · Engagement · Reactions · Posts · Posts edited after publishing · Citations · Cite this entry

13,293subscribers

+1,207 since we began measuring on 6 August 2026

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

Register entry

Telegram ID-1002344769611
TypeChannel
Username@CNSmydream
CreatedBetween 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live18 September 2026
Measurements held34
Confirmed unchanged1 time, most recently 18 September 2026
On Telegramt.me/CNSmydream

Topic

Science — 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

12,08613,29312,689.56 August 2026 — 12,086 subscribers6 August 2026 — 12,145 subscribers7 August 2026 — 12,191 subscribers8 August 2026 — 12,232 subscribers9 August 2026 — 12,275 subscribers10 August 2026 — 12,323 subscribers12 August 2026 — 12,356 subscribers13 August 2026 — 12,403 subscribers14 August 2026 — 12,446 subscribers15 August 2026 — 12,490 subscribers16 August 2026 — 12,538 subscribers17 August 2026 — 12,571 subscribers18 August 2026 — 12,593 subscribers19 August 2026 — 12,601 subscribers21 August 2026 — 12,618 subscribers22 August 2026 — 12,646 subscribers24 August 2026 — 12,730 subscribers25 August 2026 — 12,786 subscribers26 August 2026 — 12,785 subscribers27 August 2026 — 12,781 subscribers28 August 2026 — 12,797 subscribers29 August 2026 — 12,809 subscribers30 August 2026 — 12,840 subscribers31 August 2026 — 12,890 subscribers1 September 2026 — 12,920 subscribers2 September 2026 — 12,940 subscribers3 September 2026 — 12,966 subscribers5 September 2026 — 13,015 subscribers8 September 2026 — 13,082 subscribers11 September 2026 — 13,154 subscribers13 September 2026 — 13,168 subscribers14 September 2026 — 13,218 subscribers16 September 2026 — 13,254 subscribers18 September 2026 — 13,293 subscribers6 August 202618 September 2026
34 measurements spanning 43 days, net +1,207. 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 11,905–13,474 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 34
Measured (UTC)SubscribersChange
18 Sept 2026, 17:1613,293+39
16 Sept 2026, 10:3913,254+36
14 Sept 2026, 17:1713,218+50
13 Sept 2026, 04:3913,168+14
11 Sept 2026, 07:4013,154+72
8 Sept 2026, 12:1913,082+67
5 Sept 2026, 04:3713,015+49
3 Sept 2026, 08:5312,966+26
2 Sept 2026, 00:3612,940+20
1 Sept 2026, 03:0412,920+30
31 Aug 2026, 00:3312,890+50
30 Aug 2026, 03:1312,840+31
29 Aug 2026, 03:2812,809+12
28 Aug 2026, 02:2512,797+16
27 Aug 2026, 00:4712,781-4
26 Aug 2026, 03:2512,785-1
25 Aug 2026, 04:4612,786+56
24 Aug 2026, 04:3412,730+84
22 Aug 2026, 10:4712,646+28
21 Aug 2026, 03:1512,618first reading

Engagement

84 posts held, back to 29 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 48 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
8.72%
avg views ÷ 13,293 subscribers
Avg views / post
1,160
21 posts measured
Reaction rate
0.489%
reactions ÷ views · ER floor
Posts in window
21
of 84 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 18 of 21 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 2 September 2026
Posts held84 (29 July 20262 September 2026)
Views total24,329
Reactions total102
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken2 Sept 2026, 20:50 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

376 reactions across 65 posts, in 22 distinct kinds. The most used accounts for 26.9% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
👍10126.9%
7519.9%
🔥379.84%
🌭318.24%
😁246.38%
🥰225.85%
😢164.26%
😇143.72%
🤔133.46%
👾61.60%
😨61.60%
👀51.33%
🤯51.33%
🍾30.798%
👌30.798%
😐30.798%
😭30.798%
🤬30.798%
👏20.532%
🤓20.532%
2 further kinds20.532%

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 69 of the 84 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 396 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 84 most recent posts we hold, published 29 July 2026 to 2 September 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

2 Sept 2026, 03:58 UTC683 views7 reactionsread 2 September 2026

年轻人上TikTok找心理建议,却分不清谁真靠谱 英国有超过55万儿童和青少年正在排队等待心理健康治疗,许多年轻人转而在TikTok上寻找建议。伯明翰大学一项新研究提醒,平台的设计本身可能正让年轻人难以分辨哪些心理建议可信。 研究通过对英格兰16至25岁年轻人的访谈和小组讨论,发现三大挑战:其一,年轻人更看重“共鸣感”而非“可靠性”,倾向于相信让自己觉得真实、有亲近感的创作者,而不是有专业资质的专家;其二,他们普遍把TikTok当作“有趣但掺假”的娱乐空间;其三,算法驱动的设计助长快速、被动刷屏,严肃的心理健康视频可能紧跟着无关娱乐内容,几乎没有反思和批判性评估的余地。许多人试图核实博主可信度,却只会看粉丝数和评论,而非查证其是否在医学委员会等专业机构注册。另一项配套研究分析了2.7万条视频,发现养生博主常推荐膳食补充剂来“治疗”心理问题。 研究者强调,年轻人并非被动的用户,他们有思考也常存怀疑,问题出在平台结构上。单纯提

😁6👍1

1 Sept 2026, 23:12 UTC741 views3 reactionsread 2 September 2026

心脏自我修复的“秘密武器”:毛细血管如何变身侧支血管? 心脏在缺血时,会尝试通过形成侧支血管来恢复血液供应。长期以来,人们认为这些侧支血管可能来自现有动脉的重新排列。 但一项新研究颠覆了这一认知,揭示了心脏自我修复的惊人机制。研究通过基因追踪技术发现,毛细血管内皮细胞(而非动脉内皮细胞)是形成新侧支血管的主要“建筑工人”。关键在于血管内皮生长因子(VEGF)的调控作用,它能通过调节HES1基因的表观遗传修饰(具体是通过YY1/SETD1A介导的H3K4三甲基化)来促进毛细血管向动脉样侧支的转化,这一过程对心脏修复至关重要。这一发现重新定义了冠状动脉侧支形成的细胞起源和分子机制,为理解心脏自我修复能力提供了新视角。 虽然研究在动物模型中取得了突破,但将其应用于人类缺血性心脏病治疗仍需更多研究,提醒我们“基因决定论”并非万能,环境与分子调控同样关键。 心脏的“备用通道”原来来自毛细血管变身?太神奇了🤯 来源:Science

1👍1🔥1

1 Sept 2026, 03:57 UTC976 views9 reactionsread 2 September 2026

睡眠的科学被重新改写:工业社会偷走的不是时长,而是节律 现代人总抱怨睡不够,还常把工业革命前的祖先想象成"日出而作、日落而息"的睡足八小时幸运儿。但一篇发表于《Brain Medicine》的综述整合了人类学、历史学与系统发生基因组学的最新证据后指出,这一假设可能从头就错了。 文章指出,当代狩猎采集部落(坦桑尼亚哈扎人、纳米比亚桑人、玻利维亚提斯曼人)每晚平均只睡约6.4至7.1小时,并不比工业社会更长,他们的关键差异在于睡眠与自然光暗周期高度同步、节律规律。历史上著名的"分段睡眠"理论也遭到重新审视:有学者在古罗马文献中几乎找不到两段式睡眠的证据。在微观层面,系统发生基因组学揭示了睡眠调控机制的古老起源,从单细胞绿藻到细菌都携带与动物睡眠调控基因同源的分子元件,而DEC2基因突变能让携带者每晚只睡6小时仍精神饱满,这种机制在多个物种中平行演化,说明"理想睡眠时长"因人而异,统一的8小时标准未必适合所有人。 这篇综述最大的

👍81

31 Aug 2026, 23:19 UTC999 views3 reactionsread 2 September 2026

不只是体重,腰围和腰臀比也能重新定义心血管风险 很多人可能觉得,体重指数(BMI)是判断肥胖和心血管风险的“标准尺”,但其实它只看体重和身高,忽略了身体脂肪的分布。研究发现,腰围(WC)和腰臀比(WHR)作为中心性肥胖的指标,能更精准地揭示心血管疾病(CVD)风险,甚至能重新分类传统BMI下的风险等级。 研究纳入25万多参与者,发现正常体重人群中,仍有5%腰围超标,18%腰臀比异常;超重人群中,高达39%腰围超标,40%腰臀比异常。关键数据是,对于正常体重或超重者,若腰围或腰臀比过高,心血管事件风险会显著增加15%到50%。比如,肥胖人群中,腰围过低可能风险不升反降,但女性肥胖者即使腰臀比正常,风险仍高于正常体重者。研究还指出,中心性肥胖指标能解释13%到49%的心血管风险,尤其在心衰和房颤中作用显著。 这意味着,仅靠BMI可能遗漏部分心血管风险。中心性肥胖指标为临床提供了更精细的风险评估工具,尤其对女性肥胖患者,不能仅看

😭3

31 Aug 2026, 00:21 UTC≈1,130 views2 reactionsread 2 September 2026

灵长类动物实验证实:新型HIV疫苗可诱导广泛中和抗体 HIV的快速变异和抗原多样性一直是开发有效疫苗的巨大挑战。传统疫苗难以诱导出能中和多种毒株的“广泛中和抗体”(bnAbs)。胚系靶向是一种创新策略,旨在通过精准靶向胚系B细胞,诱导出与已知有效bnAb结构相似的抗体。近日,一项发表在《自然》杂志上的研究在非转基因灵长类动物中测试了这种疫苗,结果显示,该疫苗成功诱导了能中和多种HIV临床毒株的bnAb类记忆B细胞和血清。这些抗体与人类bnAb在HIV包膜(Env)上的结合方式高度匹配,并在至少一半的动物中产生了具有保护潜力的血清活性。 研究团队发现,通过异源加强,B细胞能够从胚系状态成熟为具有高亲和力的bnAb。在44%的动物中,血清中检测到bnAb活性,其中一例的抗体滴度达到了预期可提供保护的水平。这一结果首次在非转基因动物中证明了胚系靶向方法的有效性,为HIV疫苗开发提供了关键证据。 尽管实验在部分动物中成功,但研究仍

2

30 Aug 2026, 10:18 UTC≈1,460 views2 reactionsread 2 September 2026

芬兰研究:常喝咖啡的男性睾酮水平更高,还与脂肪分布相关 咖啡是全球流行的饮品,不仅提神醒脑,还可能影响健康。一项发表在《欧洲营养杂志》上的研究,利用芬兰北方出生队列1966年的数据,调查了习惯性咖啡摄入与激素和代谢指标的关系,特别关注性别差异。 研究分析了2264名46岁参与者,发现男性中,随着咖啡摄入量增加(每天1-2杯到5杯以上),总睾酮水平显著上升(β=+0.29 nmol/L/杯),同时与SHBG(性激素结合球蛋白)正相关,与游离睾酮指数负相关。此外,高咖啡摄入组男性内脏脂肪减少,肌肉量增加。机制上,咖啡中的生物活性成分可能通过影响支链氨基酸等代谢物发挥作用,但具体化合物尚不明确。 研究提示,咖啡可能通过调节激素水平影响男性代谢健康,但这是横断面研究,无法证明因果关系。女性中激素关联性较弱,且研究样本中女性有部分因多囊卵巢综合征(PCOS)被排除。未来需长期干预研究确认具体作用机制。 男性喝咖啡还能顺便“补”点睾

🔥1😁1

30 Aug 2026, 05:08 UTC≈1,310 views5 reactionsread 2 September 2026

麻醉下的大脑仍能“学习”语言?海马体竟有超能力 我们总以为麻醉后意识完全丧失,大脑进入“休眠”状态。但一项新研究颠覆了这一认知。 科学家通过高密度神经电极记录麻醉患者海马体的神经活动,发现即使意识消失,海马体依然能处理复杂信息。当播放不同音调时,海马体神经元能区分“异常音”,且这种能力随时间增强,显示出可塑性。更令人惊讶的是,播放自然语言时,神经元还能捕捉语义和语法特征,甚至预测即将出现的词语。这意味着,海马体可能比我们想象的更“聪明”,在无意识状态下也能进行高级模式识别。 研究团队构建了生物 plausible 的神经网络模型,解释了这一现象:学习与模式识别是灵活音调区分的涌现属性。这表明,海马体作为与初级感官皮层相距甚远的结构,在无意识状态下也能执行复杂信息处理。不过,研究仍需更多样本和不同麻醉类型验证,但已为意识与高级认知的关系提供了新视角。 原来麻醉不是“关机”,只是“休眠模式”? 来源:Nature #麻醉

👍5

30 Aug 2026, 04:02 UTC≈1,170 viewsread 2 September 2026

#频道互推 #群组推荐 丰乳肥臀(这身材绝了) 金十数据 闪电资讯 无损音乐分享频道 拾用AI 百度网盘影音资源频道 VPSXB|主机|羊毛|资讯 折腾搞机频道New 图书杂志资源免费分享 异次元&里番动漫 电报频道&群组索引

29 Aug 2026, 23:50 UTC≈1,220 viewsread 2 September 2026

运动如何延缓卵巢衰老?关键机制揭晓:脂联素或成新靶点 对于女性来说,卵巢的健康直接关系到生育能力与激素平衡。随着年龄增长,卵巢功能会逐渐衰退,最终导致绝经。如何延缓这一过程,一直是医学界关注的焦点。 近日,一项发表在《自然·衰老》上的研究,为运动这一简单生活方式提供了新证据。研究团队通过分析英国生物银行152,435名参与者的数据,发现绝经后女性的运动水平普遍低于未绝经女性。动物实验进一步证实,运动能显著提高小鼠卵巢中的脂联素水平,而脂联素是一种与代谢和炎症相关的激素。有趣的是,当研究人员将脂联素的作用阻断后,运动延缓卵巢衰老的效果就消失了。此外,使用脂联素受体激动剂AdipoRon的小鼠,其生殖寿命也得到延长。这项研究为运动延缓卵巢衰老提供了分子机制支持,提示脂联素可能是一个重要的治疗靶点。 不过,目前的研究主要是横断面分析,无法完全确定因果关系,未来需要更多长期干预实验来验证。对于女性而言,保持适度运动可能不仅有益于整

29 Aug 2026, 11:10 UTC≈1,210 views4 reactionsread 2 September 2026

肿瘤外泌体也有“生物钟”?昼夜节律或影响靶向药效果 肿瘤细胞会释放外泌体,这些微小颗粒可能传递致癌信号,但它们是否也遵循昼夜节律?一项新研究揭示,肿瘤来源的外泌体(ctEVs)的分泌存在明显的昼夜波动,这可能影响靶向治疗的疗效。 研究人员开发了一种名为“ctEV-CLOCK”的新方法,通过代谢标记和时间分辨技术,精确追踪外泌体的生成。研究发现,ctEV的数量和蛋白质组成在一天中不同时间点有显著变化,例如某些蛋白质在特定时间点更活跃。关键发现是,将靶向治疗与ctEV蛋白的高峰期同步,能显著提高治疗效果。 这一发现为癌症时辰疗法提供了新思路,表明外泌体的昼夜节律是肿瘤生物学中的一个重要调节层。不过,研究目前主要在动物模型中进行,未来需要更多临床数据验证,且不同肿瘤类型可能存在差异。 肿瘤细胞也爱按时分泌“信号弹”?🕗 来源:Nature cell biology #肿瘤外泌体 #昼夜节律 #靶向治疗 #时辰疗法 🧬 频

4

29 Aug 2026, 11:00 UTC≈1,080 viewsread 2 September 2026

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29 Aug 2026, 04:09 UTC≈1,110 views5 reactionsread 2 September 2026

猴子也会"养宠物"?研究揭示跨物种社交行为广泛存在 动物世界里的跨物种互动并不稀奇, , 但你见过猴子给松鼠梳毛,或者背着野猪跑吗?一项发表于《Primates》的全球综述首次系统梳理了灵长类动物与其他物种之间的友好互动,发现这种行为远比人们想象中更普遍,甚至可能揭示人类养宠物的演化根源。 由牛津大学和西澳大学领衔的国际团队,从科学文献、媒体报道和全球37位灵长类学家的问卷中,汇总了427个跨物种社交案例,涉及88种灵长类动物与127种其他物种(包括哺乳类、鸟类、爬行类、两栖类甚至昆虫)。最常见的互动行为是玩耍(139例)和理毛(136例)。数据显示,雌性比雄性更倾向于进行跨物种亲和行为,幼年个体则更多参与玩耍互动。在明确方向性的案例中,66%由灵长类主动发起。值得注意的是,研究中还记录了26起"虐待性"互动,其中13起导致了对方死亡, , 说明这种友好行为有时会"好心办坏事"。 这项研究的意义在于,人类养宠物的行为, ,

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“来一点医学科学前沿🤯🤯🥹🥹” (@CNSmydream), 13,293 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/CNSmydream.

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