跟著城市嚮導「老臺北胃」，用味道認識臺北

很多朋友來臺北，
都會問我同一個問題：
「臺北小吃那麼多，到底該從哪裡開始吃？」
夜市裡攤位一字排開、老店藏在巷弄轉角，
看起來都很有名，卻又怕吃錯、踩雷，
結果行程走完，反而沒真正記住臺北的味道。
我常被朋友笑說是「老臺北胃」。
不是因為特別會吃，而是因為在這座城市待久了，
知道哪些味道是陪著臺北人成長的日常。
這篇文章，就是我整理的一份清單。
如果你第一次來臺北，
我會帶你從這 10 樣最具代表性的臺北小吃開始，
不追一時爆紅、不走浮誇路線，
而是讓你吃完後能真正理解
原來，這就是臺灣的小吃文化。
跟著老臺北胃走，
用最簡單的方式，
把臺北的味道，一樣一樣記在心裡。

我怎麼選出這 10 大臺北小吃？

在臺北，
你隨便走進一條夜市或老街，
都可以輕易列出 30 種以上的小吃。
所以這份清單，
不是「臺北最好吃」的排名，
 而是我站在「第一次來臺北的旅客」角度，
做的推薦。
身為一個被朋友稱作「老臺北胃」的人，
我選這 10 樣小吃時，心裡一直放著幾個原則。

一吃就知道：這就是臺灣味

燒烤、火鍋很好吃，
但換個城市、換個國家，也吃得到。
我挑的，是那種
只要一入口，就會讓人聯想到的臺灣味。
 不需要解釋太多，舌頭就能懂。

不只是好吃，而是有「臺北日常感」

臺北的小吃迷人，
不只在味道，
而在它融入生活的方式。
我在意的是：

1. 會不會出現在早餐、宵夜、下班後
2. 有沒有陪伴這座城市很久的記憶

吃完之後，你會記得臺北

最後一個標準很簡單。
如果你回到家，
還會突然想起某個味道、某碗熱湯、某個攤位的香氣
那它就值得被放進這份清單裡。

接下來的 10 樣臺北小吃，
就是我會親自帶朋友去吃的在地美食。
不趕行程、不拚數量，
而是一口一口，
慢慢認識臺北。

第 1 家：饌堂－黑金滷肉飯（雙連店）｜一碗就懂臺灣人的日常

如果只能用一道料理，
 來解釋臺灣人的日常飲食，
 那我一定會先帶你吃滷肉飯。
在臺北，滷肉飯不是什麼特別的節慶料理，
 而是從早餐、午餐到宵夜，
 默默陪著很多人長大的味道。
而在眾多滷肉飯之中，
饌堂－黑金滷肉飯（雙連店），
 我很常帶第一次來臺北的朋友造訪的一家。

為什麼第一站，我會選饌堂？
饌堂的滷肉飯，走的是**「黑金系」路線**。
滷汁顏色深、香氣厚，
卻不死鹹、不油膩。
滷肉切得細緻，
肥肉入口即化，搭配熱騰騰的白飯，
每一口都是很完整、很臺灣的味道。
對第一次吃滷肉飯的旅客來說，
這種風味夠經典、也夠穩定，
不需要太多心理準備，就能理解為什麼臺灣人這麼愛它。

不只是好吃，而是「現在的臺北感」
饌堂並不是那種躲在深巷裡的老攤，
空間乾淨、節奏俐落，
卻沒有失去滷肉飯該有的靈魂。
這也是我會推薦給旅客的原因之一：
它保留了臺灣小吃的核心味道，
同時也讓第一次來臺北的人，
吃得安心、坐得舒服。

老臺北胃的帶路小提醒
如果是第一次來：

1. 一定要點招牌黑金滷肉飯
   
2. 可以加一顆滷蛋，風味會更完整
3. 搭配簡單的小菜，就很有臺灣家常感

這不是那種吃完會驚呼「哇！」的料理，
而是會讓你在幾口之後，
慢慢理解
原來，臺灣人的日常，就是這樣被一碗飯照顧著。

地址：103臺北市大同區雙連街55號1樓

電話：0225501379

菜單：https://bio.site/ZhuanTang

第 2 家：富宏牛肉麵｜臺北深夜也醒著的一碗熱湯

如果說滷肉飯代表的是臺灣人的日常，
 那牛肉麵，
 就是很多臺北人心中最有份量的一餐。
而在臺北提到牛肉麵，
 富宏牛肉麵，
 幾乎是夜貓族、加班族、外地旅客一定會被帶去的一站。

為什麼老臺北胃會帶你來吃富宏？
富宏最讓人印象深刻的，
不是華麗裝潢，
而是那鍋永遠冒著熱氣的紅燒湯頭。
湯色濃而不混，
帶著牛骨與醬香慢慢熬出的厚度，
喝起來溫潤、不刺激，
卻會在嘴裡留下很深的記憶點。
牛肉給得大方，
燉到軟嫩卻不鬆散，
搭配彈性十足的麵條，
每一口都很直接、很臺北。

不分時間，任何時候都適合的一碗麵
富宏牛肉麵最迷人的地方，
在於它陪伴了無數個臺北的夜晚。
不管是深夜下班、看完演唱會、
或是剛抵達臺北、還沒適應時差，
這裡總有一碗熱湯在等你。
對旅客來說，
這種不用算時間、不用擔心打烊的安心感，
本身就是一種臺北特色。

老臺北胃的帶路小提醒
第一次來富宏，我會這樣點：

1. 紅燒牛肉麵是首選
   
2. 如果想吃得更過癮，可以加點牛筋或牛肚
3. 湯先喝一口原味，再視情況調整辣度

這不是精緻料理，
卻是一碗能在任何時刻撐住你的牛肉麵。
在臺北，
很多夜晚，
就是靠這樣一碗熱湯走過來的。

地址：108臺北市萬華區洛陽街67號

電話：0223713028

菜單：https://www.facebook.com/pages/富宏牛肉麵-原建宏牛肉麵/

第 3 家：士林夜市・吉彖皮蛋涼麵｜臺北夏天最有記憶點的一口清爽

如果你在夏天來到臺北，
 一定會很快發現一件事
 這座城市，真的很熱。
也正因為這樣，
 臺北的小吃世界裡，
 才會出現像「涼麵」這樣的存在。
而在士林夜市，
 吉彖皮蛋涼麵，
 就是我很常帶旅客來吃的一家。

為什麼在夜市，我會帶你吃涼麵？
很多人對夜市的印象，
都是炸物、熱湯、重口味。
但真正的臺北夜市，
其實也很懂得照顧人的胃。
吉彖的涼麵，
冰涼的麵條拌上濃郁芝麻醬，
再加上切得細緻的皮蛋，
入口的第一瞬間，
就是一種「被降溫」的感覺。
那種清爽，
不是沒味道，
而是在濃香與清涼之間取得剛剛好的平衡。

皮蛋，是靈魂，也是臺灣味的關鍵
對很多外國旅客來說，
皮蛋是既好奇、又有點猶豫的存在。
但我常說，
如果要嘗試皮蛋，
涼麵是一個非常溫柔的起點。
芝麻醬的香氣會先接住味蕾，
皮蛋的風味則在後段慢慢出現，
不衝、不嗆，
反而多了一層深度。
很多人吃完後，
都會露出那種「原來是這樣啊」的表情。

老臺北胃的帶路小提醒
第一次點吉彖皮蛋涼麵，我會建議：

1. 一定要選皮蛋款，才吃得到特色
2. 醬料先拌勻，再吃，風味會更完整
3. 如果天氣真的很熱，這一碗會救你一整晚

這不是華麗的小吃，
卻非常臺北。
在悶熱的夜晚，
站在夜市人潮裡，
吃著一碗涼麵，
你會突然明白——

原來臺北的小吃，連氣候都一起考慮進去了。

地址：111臺北市士林區基河路114號

電話：0981014155

菜單：https://www.facebook.com/profile.php?id=100064238763064

第 4 家：胖老闆誠意肉粥｜臺北人深夜最踏實的一碗粥

如果你問我，
 臺北人在深夜、下班後，
 最容易感到被安慰的食物是什麼——
 我會毫不猶豫地說：肉粥。
而提到肉粥，
 胖老闆誠意肉粥，
 就是很多老臺北人口中的那一味。

為什麼這一碗粥，會被叫做「誠意」？
胖老闆的肉粥，看起來很簡單。
白粥、肉燥、配菜，
沒有華麗擺盤，也沒有複雜作法。
但真正坐下來吃，你會發現：
這碗粥，不敷衍任何一個細節。
粥體滑順、不稀薄，
肉燥香而不膩，
搭配各式家常小菜，
一口一口吃下去，
很自然就會放慢速度。
這種味道，
不是要你驚艷，
而是要你安心。

這不是觀光小吃，而是臺北人的生活片段
胖老闆誠意肉粥，
最迷人的地方，
就是它的客人。
你會看到：

1. 剛下班的上班族
2. 熬夜後來吃一碗熱粥的人
3. 熟門熟路、點菜不用看菜單的老客人

這些畫面，
比任何裝潢都更能說明這家店在臺北的位置。
對旅客來說，
這是一個走進臺北人日常的入口。

老臺北胃的帶路小提醒
第一次來吃，我會這樣建議：

1. 肉粥一定要點，這是主角
2. 配幾樣小菜一起吃，才有完整體驗
3. 不用急，慢慢吃，這碗粥就是要你放鬆

這不是為了拍照而存在的小吃，
而是那種
**會讓人記得「那天晚上，我在臺北吃了一碗很溫暖的粥」**的味道。

地址：10491臺北市中山區長春路89-3號

電話：0913806139

菜單：https://lin.ee/xxbYZyS

第 5 家：圓環邊蚵仔煎｜夜市裡最不能缺席的臺灣經典

如果要選一道
 最常出現在旅客記憶裡的臺灣小吃，
 蚵仔煎一定排得上前幾名。
而在臺北，
 圓環邊蚵仔煎，
 就是那種很多臺北人從小吃到大的存在。

為什麼蚵仔煎，這麼能代表臺灣？
蚵仔煎的魅力，
不在於精緻，
而在於它把幾種看似簡單的食材，
煎成了一種獨特的口感。
新鮮蚵仔的海味、
雞蛋的香氣、
地瓜粉形成的滑嫩外皮，
最後再淋上甜中帶鹹的醬汁，
一口下去，
就是夜市的完整畫面。
這種味道，
很難在其他國家找到替代品。

圓環邊，吃的是記憶感
圓環邊蚵仔煎，
沒有多餘的包裝，
也不刻意迎合潮流。
它留下來的原因很簡單
味道夠穩、節奏夠快、
讓人一吃就知道「對，就是這個」。
對旅客來說，
這是一家
不需要研究、不需要比較，就能安心點蚵仔煎的地方。

老臺北胃的帶路小提醒
第一次吃蚵仔煎，我會這樣建議：

1. 趁熱吃，口感最好
   
2. 不用急著加辣，先吃原味
3. 醬汁是靈魂，別急著把它拌掉

蚵仔煎不是細嚼慢嚥的料理，
它屬於人聲鼎沸、鍋鏟作響的夜市時刻。
站在人群裡，
吃著一盤熱騰騰的蚵仔煎，
你會很清楚地感受到
這，就是臺北的夜晚。

地址：103臺北市大同區寧夏路46號

電話：0225580198

菜單：https://oystera.com.tw/menu

第 6 家：阿淑清蒸肉圓｜第一次吃肉圓，就該從這裡開始

說到臺灣小吃，
 很多人腦中一定會出現「肉圓」兩個字。
但真正吃過之後才會發現，
 肉圓，從來不只有一種樣子。
在臺北，
 阿淑清蒸肉圓，
 就是我很常拿來介紹「清蒸派肉圓」的一家。

清蒸肉圓，和你想像的不一樣
不少旅客對肉圓的第一印象，
來自油炸版本，
外皮厚、口感重。
而阿淑的清蒸肉圓，
完全是另一個方向。
外皮晶瑩、滑嫩，
帶著自然的彈性，
不油、不膩，
一入口反而顯得清爽。
內餡扎實，
豬肉香氣清楚，
搭配特製醬汁，
味道層次簡單卻很乾淨。

為什麼我會推薦給第一次來臺北的旅客？
因為這顆肉圓，
不需要適應期。
它不刺激、不厚重，
即使是第一次嘗試臺灣小吃的人，
也能輕鬆接受。
對旅客來說，
這是一顆
「吃得懂、也記得住」的肉圓。

老臺北胃的帶路小提醒
第一次來阿淑，我會這樣吃：

1. 直接點一顆清蒸肉圓，吃原味
2. 醬汁先別全部拌開，邊吃邊調整
3. 放慢速度，感受外皮的口感變化

這不是夜市裡熱鬧喧囂的料理，
而是那種
安靜地展現臺灣小吃功夫的味道。
當你吃完這顆肉圓，
會更明白一件事
臺灣小吃的魅力，
往往藏在這些細節裡。

地址：242新北市新莊區復興路一段141號

電話：0229975505

第 7 家：胡記米粉湯｜一碗最貼近臺北早晨的味道

如果說前面幾樣小吃，
 是臺北的熱鬧與記憶，
 那麼米粉湯，
 就是這座城市最真實的日常。
而在臺北，
 胡記米粉湯，
 是很多人從小吃到大的存在。

為什麼米粉湯，這麼「臺北」？
米粉湯不是重口味料理，
它靠的不是刺激，
而是一碗清澈卻有深度的湯。
胡記的湯頭，
用豬骨慢慢熬出香氣，
喝起來清爽、不油，
卻能在喉嚨留下溫度。
米粉細軟，
吸附湯汁後入口順滑，
簡單到不能再簡單，
卻正是臺北人習以為常的早晨風景。

配菜，才是這一碗的靈魂延伸
在胡記吃米粉湯，
主角雖然是湯，
但真正讓人滿足的，
往往是那些小菜。
紅燒肉、豬內臟、燙青菜，
隨意點上幾樣，
湯一口、菜一口，
就是很多臺北人記憶中的早餐組合。
對旅客來說，
這是一種
不需要解釋，就能融入的臺北生活感。

老臺北胃的帶路小提醒
第一次來胡記，我會這樣建議：

1. 一定要點米粉湯，湯先喝
2. 再配 1～2 樣小菜，體驗會完整很多
3. 這一餐適合慢慢吃，不用趕

這不是為了觀光而存在的小吃，
而是一碗
每天準時出現在臺北人生活裡的湯。
當你坐在店裡，
聽著湯勺碰撞的聲音，
你會突然感覺到——
原來，臺北的早晨，
就是從這樣一碗米粉湯開始的。

地址：106臺北市大安區大安路一段9號1樓

電話：0227212120

第 8 家：藍家割包｜一口咬下的臺灣街頭記憶

如果要選一道
 外國旅客一看到就會好奇、吃完又會記住的小吃，
 割包，一定在名單裡。
而在臺北，
 藍家割包，
 就是我很放心帶旅客來認識這道經典的一站。

割包，為什麼被叫做「臺灣漢堡」？
割包的結構其實很簡單：
鬆軟的白饅頭、
燉得入味的滷五花肉、
酸菜、花生粉、香菜。
但真正迷人的，
是這些元素組合在一起時，
形成的層次感。
肉香、甜味、鹹味、清爽度，
在一口之間同時出現，
沒有誰搶戲，
卻彼此剛好。
這種平衡感，
正是臺灣小吃很迷人的地方。

藍家割包不是走浮誇路線，
它給人的感覺很直接
就是你期待中的割包樣子。
饅頭柔軟不乾，
五花肉肥瘦比例恰到好處，
入口即化卻不膩口，
花生粉的甜香收尾，
讓整體味道非常完整。
對第一次吃割包的旅客來說，
這是一個
不會出錯、也很容易愛上的版本。

老臺北胃的帶路小提醒
第一次吃藍家割包，我會這樣建議：

1. 直接點招牌割包，不要改配料
2. 如果有香菜，建議保留，味道會更完整
3. 趁熱吃，饅頭口感最好

割包不是精緻料理，
卻非常有記憶點。
站在街頭，
拿著一顆熱騰騰的割包，
邊走邊吃，
你會很清楚地感受到
這一口，就是臺灣的街頭生活。

地址：100臺北市中正區羅斯福路三段316巷8弄3號

電話：0223682060

菜單：https://instagram.com/lan_jia_gua_bao?utm_medium=copy_link

第 9 家：御品元冰火湯圓｜臺北夜晚最溫柔的一碗甜

吃了一整天的臺北小吃，
 到了這個時候，
 胃其實已經差不多滿了。
但只要天氣一涼，
 或夜色慢慢降下來，
 你還是會想找一碗——
 不是為了吃飽，而是為了舒服的甜點。
這時候，我通常會帶你來 御品元冰火湯圓。

為什麼叫「冰火」？這碗湯圓的關鍵就在這裡
御品元最有特色的地方，
就在於它的「冰火交錯」。
熱騰騰的湯圓，
外皮軟糯、內餡濃香，
搭配冰涼清甜的桂花蜜湯，
一口下去，
溫度在嘴裡交替出現。
不是衝突，
而是一種很細膩的平衡。
這樣的吃法，
也正是臺灣甜點很擅長的地方——
不張揚，但很有記憶點。

這是一碗，會讓人慢下來的甜點
和夜市裡熱鬧的甜品不同，
御品元的冰火湯圓，
更像是一個讓人停下腳步的存在。
你會發現，
坐在這裡吃湯圓的人，
說話聲都會不自覺地變小。
對旅客來說，
這不只是吃甜點，
而是一個
把白天的熱鬧慢慢收進回憶裡的時刻。

老臺北胃的帶路小提醒
第一次吃御品元，我會這樣建議：

1. 點招牌冰火湯圓，體驗完整特色
2. 先單吃湯圓，再搭配湯一起吃
3. 放慢速度，這一碗不適合趕時間

這不是為了拍照而存在的甜點，
而是一碗
會讓你記得「那天晚上在臺北，很舒服」的湯圓。

地址：106臺北市大安區通化街39巷50弄31號

電話：0955861816

菜單：https://instagram.com/lan_jia_gua_bao

第 10 家：頃刻間綠豆沙牛奶專賣店｜把臺北的味道，留在最後一口清甜

走到這一站，
 其實已經不需要再吃什麼大份量的東西了。
這時候，
 最適合的，
 是一杯不吵鬧、不張揚，
 卻會默默留在記憶裡的飲品。
頃刻間綠豆沙牛奶，
 就是我很常用來替一天畫下句點的選擇。

綠豆沙牛奶，為什麼這麼「臺灣」？
在臺灣，
飲料不只是解渴，
而是一種生活節奏。
綠豆沙牛奶看起來簡單，
但真正好喝的版本，
靠的是火候、比例，
還有耐心。
頃刻間的綠豆沙，
口感細緻、不粗顆，
甜度自然、不膩口，
牛奶的加入，
讓整杯變得柔順而溫和。
這不是衝擊味蕾的飲料，
而是一種
喝完之後，會覺得剛剛那一刻很舒服的甜。

為什麼我會用它當作最後一站？
因為它很臺北。
你可以外帶，
邊走邊喝；
也可以站在店門口，
慢慢把杯子喝空。
沒有儀式感，
卻很真實。
對旅客來說，
這杯綠豆沙牛奶，
就像是把今天吃過的所有味道，
溫柔地整理好，
帶走。

老臺北胃的帶路小提醒
第一次喝頃刻間，我會這樣建議：

1. 直接點招牌綠豆沙牛奶
   
2. 正常甜就很剛好，不用特別調整
3. 找個角落慢慢喝，別急著趕路

這一杯，
不會讓你驚呼，
卻會在回程的路上，
突然想起來。
原來，臺北的味道，是這樣結束一天的。

地址：111臺北市士林區小北街1號

電話：0228818619

菜單：https://instagram.com/chill_out_moment?igshid=YmMyMTA2M2Y=

如果只有 3 天的自助旅行在臺北，怎麼吃這 10 家？

第一次來臺北，
時間有限、胃容量也有限，
與其每一家都趕，不如照著節奏吃。
這份 3 天小吃路線，
是老臺北胃會帶朋友實際走的版本：
不爆走、不硬塞，
讓你每天都吃得剛剛好。

臺北 3 天小吃推薦行程表（老臺北胃版本）

天數	時段	店家名稱	小吃類型
Day 1	午餐	饌堂－黑金滷肉飯（雙連店）	滷肉飯
Day 1	下午	阿淑清蒸肉圓	肉圓
Day 1	晚餐	富宏牛肉麵	牛肉麵
Day 1	宵夜	胖老闆誠意肉粥	粥品
Day 2	早餐	胡記米粉湯	米粉湯
Day 2	下午	藍家割包	割包
Day 2	晚上	士林夜市－吉彖皮蛋涼麵	涼麵
Day 2	夜市	圓環邊蚵仔煎	蚵仔煎
Day 3	下午	御品元冰火湯圓	甜點
Day 3	收尾	頃刻間綠豆沙牛奶專賣店	飲品

雖然每個小吃的地點都有一點距離，但是你也知道，好吃的小吃，是值得你花一點時間前往品嘗
老臺北胃的小提醒

1. 不需要每一家都點到最滿
2. 留一點餘裕，才會想再回來
3. 臺北小吃的魅力，不在於吃多少，而在於記住了什麼味道
   

當你照著這 3 天走完，
你會發現，
臺北不是靠一兩道名菜被記住的，
而是靠這些看似日常、卻很真實的小吃。
下次再來，老臺北胃再帶你吃更深的那一輪。

老臺北胃帶路｜這 10 口，就是我心中的臺北

寫到這裡，
 其實已經不是在推薦哪一家小吃了。
而是在回頭看，
 這座城市，是怎麼用食物陪著人生活的。
滷肉飯、牛肉麵、肉粥、米粉湯，
 不是為了成為觀光名單而存在，
 而是每天默默出現在臺北人的日子裡。
夜市裡的蚵仔煎、涼麵、割包，
 熱鬧、吵雜、節奏很快，
 卻也正是臺北最真實的樣子。
而最後那碗湯圓、那杯綠豆沙牛奶，
 則是在一天結束時，
 替味蕾留下一個溫柔的句點。

如果你問我，
「這 10 家是不是臺北最好吃的小吃？」
我會說，
它們不一定是排行榜第一名，
卻是我真的會帶朋友去吃的版本。
因為它們吃得到：

1. 臺北人的日常
2. 巷弄裡的熟悉感
3. 不需要解釋，就能被理解的味道

如果你是第一次來臺北，
跟著這份清單走，
你不一定會吃得最飽，
但你一定會記得——
臺北，是什麼味道。
而如果有一天，
你又再回到這座城市，
走進熟悉的街口、
看到冒著熱氣的小攤，
你也會開始懂得，
為什麼老臺北胃，
總是記得這些看似平凡的滋味。
因為，真正留在心裡的，
從來不是吃過多少，
而是哪一口，讓你想起臺北。

胡記米粉湯會不會太油？

走完這 10 家，

你可能會發現一件事阿淑清蒸肉圓真的好吃嗎？

臺北的小吃，其實不急著被你記住。

它們就安靜地存在在街角、夜市、轉彎處，藍家割包推薦嗎？

等你有一天，再回到這座城市。胡記米粉湯會不會太甜？

如果你是第一次來臺北，藍家割包會不會太甜？

希望這份「老臺北胃帶路」的清單，

能幫你少一點猶豫、多一點安心。

不用擔心踩雷，圓環邊蚵仔煎適合第一次吃嗎？

也不用為了排行而奔波，富宏牛肉麵原味就好嗎？

只要照著節奏走，

你就會吃到屬於自己的臺北味道。

而如果你已經來過臺北，

那更希望這篇文章，胡記米粉湯會失望嗎？

能帶你走進那些

你可能錯過、卻一直都在的日常小吃。

因為真正迷人的旅行，

從來不是把清單全部打勾，

而是某一天，

你突然想起那碗飯、那口湯、那杯甜，御品元冰火湯圓不加辣好吃嗎？

然後在心裡對自己說一句：士林夜市－吉彖皮蛋涼麵需要特地跑一趟嗎？

「下次再去臺北，還想再吃一次。」

把這篇文章存起來、分享給一起旅行的人，

或是在規劃行程時，再回來看看。

讓味道，成為你認識臺北的方式。

下一次來臺北，

別急著走遠。

老臺北胃，胡記米粉湯在地人推薦嗎？

會一直在這些地方，

等你再回來。

Researchers have identified a unique biomarker in the brain that signifies recovery from treatment-resistant depression, utilizing deep brain stimulation and artificial intelligence to understand and enhance treatment outcomes. A breakthrough study reveals a unique biomarker in the brain that tracks recovery from severe depression, using innovative deep brain stimulation and AI techniques. A team of leading clinicians, engineers, and neuroscientists has made a groundbreaking discovery in the field of treatment-resistant depression published online in the journal Nature on September 20. By analyzing the brain activity of patients undergoing deep brain stimulation (DBS), a promising therapy involving implanted electrodes that stimulate the brain, the researchers from Emory University School of Medicine, Georgia Institute of Technology, and the Icahn School of Medicine at Mt. Sinai identified a unique pattern in brain activity that reflects the recovery process in patients with treatment-resistant depression. This pattern, known as a biomarker, serves as a measurable indicator of disease recovery and represents a significant advance in treatment for the most severe and untreatable forms of depression. The team’s findings offer the first window into the intricate workings and mechanistic effects of DBS on the brain during treatment for severe depression. How DBS Works and Its Impact DBS involves implanting thin electrodes in a specific brain area to deliver small electrical pulses, similar to a pacemaker. Although DBS has been approved and used for movement disorders such as Parkinson’s disease for many years, it remains experimental for depression. This study is a crucial step toward using objective data collected directly from the brain via the DBS device to inform clinicians about the patient’s response to treatment. This information can help guide adjustments to DBS therapy, tailoring it to each patient’s unique response and optimizing their treatment outcomes. Monitoring and Artificial Intelligence in Treatment Now, the researchers have shown it’s possible to monitor that antidepressant effect throughout the course of treatment, offering clinicians a tool somewhat analogous to a blood glucose test for diabetes or blood pressure monitoring for heart disease: a readout of the disease state at any given time. Importantly, it distinguishes between typical day-to-day mood fluctuations and the possibility of an impending relapse of the depressive episode. The research team used artificial intelligence (AI) to detect shifts in brain activity that coincided with patients’ recovery. The study, funded by the National Institutes of Health Brain Research Through Advancing Innovative Neurotechnologies, or the BRAIN Initiative, involved 10 patients with severe treatment-resistant depression, all of whom underwent the DBS procedure at Emory University. Advanced Techniques and Findings The study team used a new DBS device that allowed brain activity to be recorded. Analysis of these brain recordings over six months led to the identification of a common biomarker that changed as each patient recovered from their depression. After six months of DBS therapy, 90% of the subjects exhibited a significant improvement in their depression symptoms and 70% no longer met the criteria for depression. “This study demonstrates how new technology and a data-driven approach can refine DBS therapy for severe depression, which can be debilitating,” says John Ngai, PhD, director of the BRAIN Initiative. “It’s this type of collaborative work made possible by the BRAIN Initiative that moves promising therapies closer to clinical use.” The high response rates in this study cohort enabled the researchers to develop algorithms known as “explainable artificial intelligence” that allow humans to understand the decision-making process of AI systems. This technique helped the team identify and understand the unique brain patterns that differentiated a “depressed” brain from a “recovered” brain. Insights From Experts “The use of explainable AI allowed us to identify complex and usable patterns of brain activity that correspond to a depression recovery despite the complex differences in a patient’s recovery,” explains Sankar Alagapan PhD, a Georgia Tech research scientist and lead author of the study. “This approach enabled us to track the brain’s recovery in a way that was interpretable by the clinical team, making a major advance in the potential for these methods to pioneer new therapies in psychiatry.” Helen S. Mayberg, MD, co-senior author of the study, led the first experimental trial of subcallosal cingulate cortex (SCC) DBS for treatment-resistant depression patients at Emory University in 2003, demonstrating it could have clinical benefit. In 2019, she and the Emory team reported the technique had a sustained and robust antidepressant effect with ongoing treatment over many years for previously treatment-resistant patients. “This study adds an important new layer to our previous work, providing measurable changes underlying the predictable and sustained antidepressant response seen when patients with treatment-resistant depression are precisely implanted in the SCC region and receive chronic DBS therapy,” says Mayberg, now founding director of the Nash Family Center for Advanced Circuit Therapeutics at Icahn Mount Sinai. “Beyond giving us a neural signal that the treatment has been effective, it appears that this signal can also provide an early warning signal that the patient may require a DBS adjustment in advance of clinical symptoms. This is a game changer for how we might adjust DBS in the future.” “Understanding and treating disorders of the brain are some of our most pressing grand challenges, but the complexity of the problem means it’s beyond the scope of any one discipline to solve,” says Christopher Rozell, PhD, Julian T. Hightower Chair and Professor of Electrical and Computer Engineering at Georgia Tech and co-senior author of the paper. “This research demonstrates the immense power of interdisciplinary collaboration. By bringing together expertise in engineering, neuroscience and clinical care, we achieved a significant advance toward translating this much-needed therapy into practice, as well as an increased fundamental understanding that can help guide the development of future therapies.” Observations and Further Research The team’s research also confirmed a longstanding subjective observation by psychiatrists: as patients’ brains change and their depression eases, their facial expressions also change. The team’s AI tools identified patterns in individual facial expressions that corresponded with the transition from a state of illness to stable recovery. These patterns proved more reliable than current clinical rating scales. In addition, the team used two types of magnetic resonance imaging to identify both structural and functional abnormalities in the brain’s white matter and interconnected regions that form the network targeted by the treatment. They found these irregularities correlate with the time required for patients to recover, with more pronounced deficits in the targeted brain network correlated to a longer time for the treatment to show maximum effectiveness. These observed facial changes and structural deficits provide behavioral and anatomical evidence supporting the relevance of the electrical activity signature or biomarker. “When we treat patients with depression, we rely on their reports, a clinical interview, and psychiatric rating scales to monitor symptoms. Direct biological signals from our patients’ brains will provide a new level of precision and evidence to guide our treatment decisions,” says Patricio Riva-Posse, MD, associate professor and director of the Interventional Psychiatry Service in the Department of Psychiatry and Behavioral Sciences at Emory University School of Medicine, and lead psychiatrist for the study. Given these initial promising results, the team is now confirming their findings in another completed cohort of patients at Mount Sinai. They are using the next generation of the dual stimulation/sensing DBS system with the aim of translating these findings into the use of a commercially available version of this technology. For more on this research: Researchers Identify Crucial Biomarker That Tracks Recovery From Treatment-Resistant Depression Deep Brain Stimulation: A New Frontier in Tracking Depression Recovery Reference: “Cingulate dynamics track depression recovery with deep brain stimulation” by Sankaraleengam Alagapan, Ki Sueng Choi, Stephen Heisig, Patricio Riva-Posse, Andrea Crowell, Vineet Tiruvadi, Mosadoluwa Obatusin, Ashan Veerakumar, Allison C. Waters, Robert E. Gross, Sinead Quinn, Lydia Denison, Matthew O’Shaughnessy, Marissa Connor, Gregory Canal, Jungho Cha, Rachel Hershenberg, Tanya Nauvel, Faical Isbaine, Muhammad Furqan Afzal, Martijn Figee, Brian H. Kopell, Robert Butera, Helen S. Mayberg and Christopher J. Rozell, 20 September 2023, Nature. DOI: 10.1038/s41586-023-06541-3 Research reported in this press release was supported by the National Institutes of Health BRAIN Initiative under award number UH3NS103550; the National Science Foundation, grant No. CCF-1350954; the Hope for Depression Research Foundation; and the Julian T. Hightower Chair at Georgia Tech. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agency.

Researchers from UT Health San Antonio discovered that certain immune cells, called invariant killer T (iNKT) cells, possess a unique homing property that directs them to the skin at birth, providing crucial protection and lifelong immunity. These skin-homing iNKT cells also promote hair follicle development and cooperate with commensal bacteria to maintain skin health and prevent pathogenic bacterial overgrowth. Infants Are Given Protection Against Bacteria That Cause Diseases Researchers from The University of Texas Health Science Center at San Antonio (UT Health San Antonio) have uncovered that certain immune cells possess a homing property that guides them to the skin of the newborn to provide protection. “These T cells home in on the skin like a guided missile,” said Na Xiong, Ph.D., professor of microbiology, immunology, and molecular genetics in the health science center’s Joe R. and Teresa Lozano Long School of Medicine. “They have a different homing property than other T cells. We identified the mechanism through which this homing activity occurs.” Localization of these T cells to the skin is important not only at birth but for lifelong immunity, said Xiong, senior author of an article that appeared on the cover of the February 2023 issue of Nature Immunology. In the womb, a mother’s defenses protect a fetus against bacteria. At birth, the skin and other tissues such as the gut are exposed to commensal bacteria. These are harmless bacteria that are beneficial by keeping any disease-causing bacteria in check. Programming and Function of iNKT Cells The skin-homing cells are called invariant killer T (iNKT) cells. These immune cells emanate from and are programmed in an organ called the thymus. In humans, this organ is located between the lungs. The iNKT cells cooperate with the commensal bacteria to preserve skin health and act as a barrier for the body against bacterial pathogens, Xiong said. “We found that if the iNKT cells do not properly go to the skin, or if there is no such population in the skin, there will be dysregulation of commensal bacteria in the skin and the bacterial composition will be changed,” Xiong said. “This can result in not enough friendly bacteria being present, enabling potentially pathogenic bacteria to overgrow.” In a second important finding, the researchers observed that the skin-homing iNKT cells help promote hair follicle development. The cells situate preferentially around follicles and are not the only ones present there, Xiong said. “Within the hair follicle, there are also a lot of commensal bacteria. It is one place they like to stay,” he said. The follicles themselves are critical sites of immune defense, he added. Reference: “Developmentally programmed early-age skin localization of iNKT cells supports local tissue development and homeostasis” by Wei-Bei Wang, Yang-Ding Lin, Luming Zhao, Chang Liao, Yang Zhang, Micha Davila, Jasmine Sun, Yidong Chen and Na Xiong, 9 January 2023, Nature Immunology. DOI: 10.1038/s41590-022-01399-5 Collaborators are from Pennsylvania State University. The study was funded by the National Institute of Allergy and Infectious Diseases and the National Institute of Arthritis, Musculoskeletal, and Skin Diseases of the National Institutes of Health.

Researchers identified the atomic structure of a coronavirus protein that aids in evading and suppressing human immune cell responses. Biologists used crystallography performed at Berkeley Lab’s Advanced Light Source to reveal the new virus’s unusual protein structure. A team of HIV researchers, cellular biologists, and biophysicists who banded together to support COVID-19 science determined the atomic structure of a coronavirus protein thought to help the pathogen evade and dampen response from human immune cells. The structural map – which is now published in the journal PNAS, but has been open-access for the scientific community since August – has laid the groundwork for new antiviral treatments tailored specifically to SARS-CoV-2, and enabled further investigations into how the newly emerged virus ravages the human body. “Using X-ray crystallography, we built an atomic model of ORF8, and it highlighted two unique regions: one that is only present in SARS-CoV-2 and its immediate bat ancestor, and one that is absent from any other coronavirus,” said lead author James Hurley, a UC Berkeley professor and former faculty scientist at Lawrence Berkeley National Laboratory (Berkeley Lab). “These regions stabilize the protein – which is a secreted protein, not bound to the membrane like the virus’s characteristic spike proteins – and create new intermolecular interfaces. We, and others in the research community, believe these interfaces are involved in reactions that somehow make SARS-CoV-2 more pathogenic than the strains it evolved from.” Structural Biology in the Spotlight Generating protein structure maps is always labor intensive, as scientists have to engineer bacteria that can pump out large quantities of the molecule, manipulate the molecules into a pure crystalline form, and then take many, many X-ray diffraction images of the crystals. These images – produced as X-ray beams bounce off atoms in the crystals and pass through gaps in the lattice, generating a pattern of spots – are combined and analyzed via special software to determine the location of every individual atom. This painstaking process can take years, depending on the complexity of the protein. For many proteins, the process of building a map is helped along by comparing the unsolved molecule’s structure to other proteins with similar amino acid sequences that have already been mapped, allowing scientists to make informed guesses about how the protein folds into its 3D shape. But for ORF8, the team had to start from scratch. ORF8’s amino acid sequence is so unlike any other protein that scientists had no reference for its overall shape, and it is the 3D shape of a protein that determines its function. Hurley and his UC Berkeley colleagues, experienced in structural analysis of HIV proteins, worked with Marc Allaire, a biophysicist and crystallography expert at the Berkeley Center for Structural Biology, located at Berkeley Lab’s Advanced Light Source (ALS). Together, the team worked in overdrive for six months – Hurley’s lab generated crystal samples and passed them to Allaire, who would use the ALS’s X-ray beamlines to take the diffraction images. It took hundreds of crystals with multiple versions of the protein and thousands of diffraction images analyzed by special computer algorithms to puzzle together ORF8’s structure. “Coronaviruses mutate differently than viruses like influenza or HIV, which quickly accumulate many little changes through a process called hypermutation. In coronaviruses, big chunks of nucleic acids sometimes move around through recombination,” explained Hurley. When this happens, big, new regions of proteins can appear. Genetic analyses conducted very early in the SARS-CoV-2 pandemic revealed that this new strain had evolved from a coronavirus that infects bats, and that a significant recombination mutation had occurred in the area of the genome that codes for a protein, called ORF7, found in many coronaviruses. The new form of ORF7, named ORF8, quickly gained the attention of virologists and epidemiologists because significant genetic divergence events like the one seen for ORF8 are often the cause of a new strain’s virulence. A ribbon diagram rendering of the ORF8 structure, which is composed of two protein units with identical amino acid sequence and shape that are connected by a sulfur-sulfur bond. Credit: The Hurley Lab/UC Berkeley “Basically, this mutation caused the protein to double in size, and the stuff that doubled was not related to any known fold,” added Hurley. “There’s a core of about half of it that’s related to a known fold type in a solved structure from earlier coronaviruses, but the other half was completely new.” Answering the Call Like so many scientists working on COVID-19 research, Hurley and his colleagues opted to share their findings before the data could be published in a peer-reviewed journal, allowing others to begin impactful follow-up studies months earlier than the traditional publication process would have allowed. As Allaire explained, the all-hands-on-deck crisis caused by the pandemic shifted everyone in the research community into a pragmatic mindset. Rather than worrying about who accomplished something first, or sticking to the confines of their specific areas of study, scientists shared data early and often, and took on new projects when they had the resources and expertise needed. In this case, Hurley’s UC Berkeley co-authors had the viral protein and crystallography expertise, and Allaire, a longtime collaborator, was right up the hill, also with crystallography expertise and, critically, a beamline that was still operational. The ALS had received special funding from the CARES Act to remain operational for COVID-19 investigations. The team knew from reviewing the SARS-CoV-2 genomic analysis posted in January that ORF8 was an important piece of the (then much hazier) pandemic puzzle, so they set to work. The authors have since all moved on to other projects, satisfied that they laid the groundwork for other groups to study ORF8 in more detail. (Currently, there are several investigations underway focused on how ORF8 interacts with cell receptors and how it interacts with antibodies, as infected individuals appear to produce antibodies that bind to ORF8 in addition to antibodies specific to the virus’s surface proteins.) “When we started this, other projects had been put on hold, and we had this unique opportunity to hunker down and solve an urgent problem,” said Allaire, who is part of Berkeley Lab’s Molecular Biophysics and Integrated Bioimaging Division. “We worked very closely, with a lot of back and forth, until we got it right. It really has been one of the best collaborations of my career.” From Sequence to Structure Sequencing a gene or a string of amino acids to understand the components of a protein is fast and easy for scientists these days, but studying how a sequence of amino acids interact to fold into the protein’s actual physical form using X-ray crystallography or cryo-electron microscopy is complex and time intensive. As a consequence, there has been a longstanding call within biology to develop tools that accurately predict a protein’s structure based on its sequence. A ribbon diagram rendering of the ORF8 structure predicted by AlphaFold 2 (blue), overlaid onto the actual structure (green) determined by the UC Berkeley-led team. Credit: DeepMind In the past few decades, machine learning has emerged as the front-runner in this challenge. These artificial intelligence programs are fed large datasets of known protein structures so that they learn to identify correlations between sequence and fold shape, quickly finding patterns that would take years for humans to discover. Once the program – called an algorithm – is “trained” in this way, it can be used to build predictive models of unsolved protein structures. And every time it is fed a new confirmed structure, it improves. To test which algorithms are the best, companies and institutions hold competitions, the most famous of which is the biannual Critical Assessment of protein Structure Prediction (CASP) experiment. Last year, ORF8 was selected as the final challenge of the CASP competition because it “stood out as exceptionally hard to predict,” according to Hurley. The top algorithms were set loose on the ORF8 structure, as well as other structures, and it wasn’t until these structures were released in the Protein Databank in August that the CASP judges were able to select a winner. AlphaFold 2, an algorithm developed by Google offshoot DeepMind, came out on top after constructing structures that most closely matched the experimental targets, including that of ORF8. Reference: “Structure of SARS-CoV-2 ORF8, a rapidly evolving immune evasion protein” by Thomas G. Flower, Cosmo Z. Buffalo, Richard M. Hooy, Marc Allaire, Xuefeng Ren, and James H. Hurley, 12 January 2021, Proceedings of the National Academy of Sciences. DOI: 10.1073/pnas.2021785118 The Advanced Light Source is a Department of Energy Office of Science user facility. The Berkeley Center for Structural Biology is supported in part by the Howard Hughes Medical Institute and the National Institutes of Health.

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