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---
name: paper-recommender
description: "每日定时推送高质量论文推荐 — PubMed检索 → Hermes+Claude双重评估 → 仅推最优1篇 → HTML深度解读"
version: 2.4.0
author: Hermes
tags: [PubMed, 论文推荐, Claude评估, 文献解读, 科研]
---
# 📚 每日论文推荐工作流 (Paper Recommender)
## 职责
根据用户的6个研究兴趣方向，每日定时从PubMed检索近期高质量论文，经过Hermes+Claude双重评估后，**只选最优的1篇**，下载PDF+提取原图，制作公众号风格的HTML深度解读文件并推送。
## 用户研究方向
1. 帕金森病(PD) / GZR18 / α-synuclein
2. 神经炎症 / 小胶质细胞 / 星形胶质细胞
3. 细胞死亡方式 (Mitoxyperiosis / 铁死亡 / 焦亡 / 泛素化降解)
4. 外泌体 / 细胞外囊泡 / 神经退行性疾病
5. 脑缺血 / 脑卒中 / 神经保护
6. 生信分析 / 单细胞测序 / 多组学
## ⚡ 用户核心课题（跨所有方向的超优先主题）
**博一课题**：Warburg乳酸 → PCAF介导RhoA K118/K162乳酸化 → RhoA持续激活 → 线粒体远离质膜 → 拮抗mitoxyperiosis
所有方向的论文推荐，只要与以下关键词相关的论文**自动获得更高优先级**，应优先于同评分不相关的论文推送：
- Mitoxyperiosis / 线粒体-质膜接触 / 新型细胞死亡
- 乳酸化修饰(Kla) / PCAF / p300 / 组蛋白乳酸化
- RhoA信号 / 细胞骨架重排 / 小G蛋白修饰
- Warburg效应 / 肿瘤代谢 / LDHA抑制
- 糖酵解 / 乳酸代谢 / 肿瘤免疫代谢
- 禁食疗法 / 卡路里限制 / 联合免疫治疗
- 代谢组学 / 能量代谢 / 肿瘤微环境
评估论文时，额外增加一个维度：**"课题借鉴价值"** — 这篇论文中有什么技术、概念、实验设计可以直接复用到用户的mitoxyperiosis-乳酸化课题？
## 定时推送安排
| 时间 | 主题 | 关键词 |
|:----:|------|--------|
| 08:00 | PD/帕金森病 | Parkinson's disease, α-synuclein, MPTP, dopamine neuron |
| 11:00 | 神经炎症/胶质细胞 | neuroinflammation, microglia, astrocyte, NLRP3 |
| 13:00 | 细胞死亡/信号通路 | cell death, mitoxyperiosis, ferroptosis, ubiquitination |
| 15:00 | 外泌体/细胞通讯 | exosome, extracellular vesicle, neural, brain |
| 17:00 | 脑缺血/神经保护 | cerebral ischemia, stroke, neuroprotection, MCAO |
| 19:00 | 生信/前沿技术 | single-cell, multi-omics, bioinformatics, spatial transcriptomics |
## ⚠️ 已知工具陷阱
### 文件系统隔离（重要！）
`execute_code` 运行在沙箱环境中，**不能直接访问** `terminal` 工具写入 `/tmp/` 或 `$HOME/` 的文件。反之亦然。
- ❌ 错误做法：terminal写入`/tmp/fig1_b64.txt`，然后用execute_code的`open()`读取 → 文件不存在
- ✅ 正确做法1：所有数据处理在terminal中完成（Python脚本通过`python script.py`执行）
- ✅ 正确做法2：将数据处理写成`.py`文件写入磁盘，再用terminal(`python path/to/script.py`)执行
### 大文件HTML生成
当HTML嵌入base64图片时（5张Nature原图 ≈ 4MB），`write_file`会因大小限制失败。
- ❌ 不要直接用write_file写4MB+的HTML
- ✅ 策略：写一个Python builder脚本到磁盘（`write_file → build_html.py`），然后`terminal("python build_html.py")`执行它
### Claude CLI 长提示词超时陷阱
claude -p 传入大段文本时容易超时（exit code 124，返回空），特别是在Windows git-bash环境。
- ❌ 一次性传入4篇论文的完整摘要+评估指令 → 超时
- ✅ 策略1：将评估指令简化，每篇论文控制到1-3句关键信息；或拆分为多次调用分别评估单篇
- ✅ 策略2：用 `head -N` 或 `tail -N` 截断输出（如 `claude -p "评估..." 2>&1 | head -60`），防止Claude生成过长内容导致超时。即使Claude内部未完成，前60行通常已包含评分结果
### Cell Press PII清理模式（重要！）
PubMed esummary返回的PII含括号和连字符（如 `S0896-6273(26)00386-7`），CDN URL需要去掉这些字符：
- ❌ `tr -d '()'` → 只去除括号，留下连字符 → CDN返回404
- ✅ `tr -d '()-'` → 同时去除括号和连字符 → CDN返回200
- 详见 `references/figure-download-patterns.md` 中的修正实例
### Python f-string与JavaScript花括号冲突（HTML Builder陷阱）
当用Python f-string构建HTML时，JavaScript代码中的`{}`（如`canvas.width`、`img.onload = function() { }`、`loadImage("fig1", fig1_b64)`）会被Python误解析为f-string占位符，导致SyntaxError。
- ❌ `html = f'''...<script>img.onload = function() { canvas.width = 780; }</script>...'''` → SyntaxError
- ✅ 策略1：用字符串列表拼接代替f-string（将HTML拆分为多个部分append到列表，用`+`或`''.join()`组合）
- ✅ 策略2：将HTML结构和JS作为独立字符串，用`.replace()`注入base64数据
- ✅ 策略3：在JS中所有`{`和`}`都必须加倍成`{{`和`}}`（仅在必须用f-string时）
- 推荐策略：使用函数`build_html(fig_b64, ...)`接受字典参数，返回拼接好的字符串（详见Step 6 HTML模板）
### Windows Python路径陷阱
在 Windows git-bash 中，Python 脚本不识别 MSYS 路径格式（`/c/Users/...`）：
- ❌ `open("/c/Users/forgase/fig1_b64.txt")` → FileNotFoundError
- ✅ `open("C:/Users/forgase/fig1_b64.txt")` — 正斜杠在 Windows Python 中可识别
- 同理，`python3` 命令在 Windows 上可能被应用执行别名拦截 → 始终使用 `python`
## 执行流程（完整版）
### Step 1: PubMed检索 — 两阶段策略
**执行位置：terminal 而非 execute_code**
PubMed API调用应在 `terminal` 中通过 `python -c "..."` 或写脚本后执行。`terminal` 中的 Python 连接更稳定，且结果可被后续terminal命令使用（写入文件、管道传递）。避免在 `execute_code` 中做PubMed检索——沙箱环境的网络连接可能不稳定，且JSON结果无法跨沙箱传递给terminal。
**日期范围两种方式（方式B更可靠）**：
```python
# 方式A: mindate/maxdate 参数（有时返回0结果，不可靠）
params = {
'db': 'pubmed', 'term': query,
'mindate': '<3个月前>', 'maxdate': '<今天>',
'datetype': 'pdat', 'retmax': 15, 'retmode': 'json'
}
# 方式B: [dp] 查询语法（推荐，已验证可靠）
query = '(ferroptosis[Title/Abstract] OR ...) AND ("2026/03/01"[dp] : "2026/06/12"[dp])'
params = {
'db': 'pubmed', 'term': query,
'retmax': 15, 'retmode': 'json'
}
```
`dp` (Date - Publication) 语法格式：`"YYYY/MM/DD"[dp] : "YYYY/MM/DD"[dp]`。日期之间需要空格+冒号+空格。此方式不受 `mindate/maxdate` 参数兼容性问题影响。
详见 `references/pubmed-API-patterns.md` 第4节。
**阶段A — 主题搜索**：用关键词搜该方向近期论文
**阶段B — 高IF期刊过滤**：用主题词 + `("Nature"[Journal] OR "Cell"[Journal] OR ...)` 格式搜
⚠️ 关键陷阱：
- `sort=date` 被PubMed忽略（返回warning），不要用
- 期刊名必须用 `"全称"[Journal]` 格式，裸词会被翻译为MeSH词条
- 复杂查询在curl中容易URL编码失败，优先用Python
- PubMed JSON返回的ID列表键名为小写 `idlist`，不是 `IdList`（驼峰）
### Step 2: 获取论文详情并初筛
```python
import urllib.request, json, time
# 获取元数据
url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=<PMIDs>&retmode=json'
# 获取摘要
url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=<PMIDs>&retmode=xml'
```
**初筛硬性门槛**（必须同时满足）：
1. IF > 10 或领域顶刊
2. 2026年3月后发表
3. 与用户研究方向直接相关
4. 原创研究（pubtype不含Review）
5. 非 News & Views / Letter / 编辑评论
6. **有作者（推荐用 efetch XML 确认，而非 esummary 的 hascreator 字段）**
- `esummary`的`hascreator`字段有时不可靠：已验证所有高IF期刊论文均返回`hascreator='N'`（即使是多作者原创研究）。
- 改用 efetch XML：统计`<Author>`标签数（`len(re.findall(r'<Author>', xml_text))`），大于0即有作者。
- 若只能用 esummary：不要仅因`hascreator='N'`排除——结合`pubtype`和`HasAbstract`综合判断。
### Step 3: Claude Code深度评估（Hermes + Claude 双重评判）
将经过初筛的候选论文交给 Claude Code 严格评估：
```
claude -p "严格评估以下论文，从5个维度各评1-10分..."
```
**5维度评分**：
1. **逻辑完整性** (1-10)：完整正反验证（gain+loss of function）、KO/KI模型
2. **方法学水平** (1-10)：前沿技术（单细胞测序、冷冻电镜、活细胞成像、TurboID等）
3. **创新性** (1-10)：新概念/新机制/新方法，首次报道
4. **可借鉴性** (1-10)：实验设计是否值得用户在自己的课题中参考 **← 这是最关键的维度**
5. **临床转化潜力** (1-10)：体内验证或治疗意义
### Step 4: 唯一推荐（核心规则）
- Claude评分 < 30分（平均<6分）或任何单维度 < 5分 → **不推荐**，跳过
- 多个候选都达标 → **只选最优的1篇**，不要让用户选
- Hermes再次确认质量 + 相关性 + 可借鉴性 → 只有双方都确认才通过
- **如果这篇研究方向不匹配（如planarian flatworm研究），即使评分高也不能推**
### Step 5: 下载PDF + 提取Figure图片
**PDF下载**：
- Nature系列: `https://www.nature.com/articles/{article_id}.pdf`（已验证：Nature, Nat Neurosci, Nat Med, Nat Commun均可下载）
- Cell Press/Elsevier: PDF有ScienceDirect防护，通常无法通过curl下载
- 所有期刊的Figure图片可通过CDN下载（详见`references/figure-download-patterns.md`）
- 注意：部分期刊有付费墙，PDF可能无法下载，此时跳过PDF但继续下载Figure图片
**PDF真实性验证**：下载PDF后用 `file` 命令确认是否为真实PDF：
```bash
file "/c/Users/forgase/s41593-026-02317-w.pdf"
# 预期输出: "PDF document, version 1.4, N page(s)" ✅
# 若输出: "HTML document" ❌ — 反爬重定向，非真实PDF
# 已验证：Nat Commun的unedited version PDF可能返回HTML document
```
### 提取文章元数据（Nature系列 — 替代efetch XML的简便方法）
Nature文章页HTML的 `<head>` 嵌入了 `citation_*` 元标签，通过 `curl` + Python可干净提取标题、作者、期刊、日期，比解析efetch XML更简洁：
```bash
curl -s -L -H "User-Agent: Mozilla/5.0" "https://www.nature.com/articles/${ARTICLE_ID}" \
| python -c "
import sys, re
html = sys.stdin.read()
authors = re.findall(r'<meta[^>]*name=\"citation_author\"[^>]*content=\"([^\"]+)\"', html)
print('Authors: ' + '; '.join(authors[:5]) + ('...' if len(authors)>5 else ''))
date = re.findall(r'<meta[^>]*name=\"citation_date\"[^>]*content=\"([^\"]+)\"', html)
print('Date: ' + (date[0] if date else 'N/A'))
journal = re.findall(r'<meta[^>]*name=\"citation_journal_title\"[^>]*content=\"([^\"]+)\"', html)
print('Journal: ' + (journal[0] if journal else 'N/A'))
title = re.findall(r'<meta[^>]*name=\"citation_title\"[^>]*content=\"([^\"]+)\"', html)
print('Title: ' + (title[0] if title else 'N/A'))
"
```
将提取到的元数据直接用于Step 6的HTML头部。已验证适用于Nature、Nat Neurosci、Nat Med、Nat Commun。
### Nature系列PDF下载注意
- Nature系列: `https://www.nature.com/articles/{article_id}.pdf`（已验证：Nature, Nat Neurosci, Nat Med可下载真实PDF）
- **Nat Commun PDF可能为HTML**：`file`命令检查下载文件，若为"HTML document"则非真实PDF（反爬重定向）。不影响Figure图片下载（CDN独立于PDF）。
- Cell Press/Elsevier: PDF有ScienceDirect防护，通常无法通过curl下载
**Figure提取（Nature系列通用模式 — 已验证 Nature, Nat Neurosci, Nat Med, Nat Commun）**：
```bash
# 1. 从HTML中提取所有figure图片URL（通用模式，匹配所有s415XX系列期刊）
curl -s -L -H "User-Agent: Mozilla/5.0" "https://www.nature.com/articles/{article_id}" \
| grep -oP 'https://media\.springernature\.com/[^"'"'"'<>]+s415\d\d[^"'"'"'<>]*\.(?:png|jpg|webp)' \
| sort -u
# 2. 暴力枚举法（推荐，因为url模式固定—不依赖HTML解析）
for fig in 1 2 3 4 5 6 7; do
url="https://media.springernature.com/lw1200/springer-static/image/art%3A10.1038%2F${ARTICLE_ID}/MediaObjects/${JOURNAL_ID}_${YEAR}_${ARTICLE_NUM}_Fig${fig}_HTML.png"
code=$(curl -s -o /dev/null -w "%{http_code}" -L "$url" -H "User-Agent: Mozilla/5.0")
if [ "$code" != "404" ]; then
curl -s -L "$url" -H "User-Agent: Mozilla/5.0" | base64 -w 0 > "/c/Users/forgase/fig${fig}_b64.txt"
fi
done
```
**Figure Caption提取（Nature系列 — 紧跟图片下载之后执行）**：
提取的caption将用于Step 6 HTML的逐图解读部分。
```bash
curl -s -L -H "User-Agent: Mozilla/5.0" "https://www.nature.com/articles/${ARTICLE_ID}" \
| python -c "
import sys, re
html = sys.stdin.read()
figs = re.findall(r'<fig[^>]*>.*?<figcaption[^>]*>(.*?)</figcaption>', html, re.DOTALL)
for i, fig in enumerate(figs):
clean = re.sub(r'<[^>]+>', '', fig).strip()
print(f'{i+1}|{clean}')
"
```
然后将captions嵌入Python builder脚本中的字典变量。
**Cell Press系列Caption处理**（Neuron, Cell Metabolism等）：
Cell Press主页面（www.cell.com / sciencedirect.com）使用Cloudflare防护，curl无法获取页面HTML，因此`<figcaption>`标签无法通过解析提取。已验证策略：
- **手动构建**：基于abstract和论文结构（7图论文的典型结构：Fig1=现象发现, Fig2-3=分子机制, Fig4-5=通路验证, Fig6=体内验证, Fig7=模型）合理推断各Figure内容
- **模板**：将描述写为Python字典嵌入builder脚本（详见 `references/figure-download-patterns.md` "Pre-print / Unedited Version Caption 降级策略"）
- Cell Press的Figure图片仍可通过 `ars.els-cdn.com` CDN直接下载（JPEG格式），不受Cloudflare影响
⚠️ **预印本降级策略**：若上述命令输出为空，检查文章页是否有"unedited version"或"preprint"标记。
此时Figure图片仍可通过CDN下载，但caption需手动构建。策略：
- 基于abstract和论文结构合理推断各Figure内容（已验证可行）
- 将手动编写的caption以Python字典嵌入builder脚本
- 详见 `references/figure-download-patterns.md` "Pre-print / Unedited Version Caption 降级策略"
详见 `references/figure-download-patterns.md` 中的"Figure Caption Extraction"节。
**分辨率选择**：
- `/full/` — 最高分辨率（推荐用于Fig1）
- `/lw1200/` — 宽1200px（其余Figures用此即可）
- `/m685/` — 宽685px（小尺寸）
**已验证的图片URL模式**：
| 期刊 | article_id 示例 | journal_id | URL模式 |
|------|----------------|------------|---------|
| Nature | s41586-026-10597-2 | 41586 | `41586_{year}_{article_num}_Fig{N}_HTML.png` |
| Nat Neurosci | s41593-026-02317-w | 41593 | `41593_{year}_{article_num}_Fig{N}_HTML.png` |
| Nat Med | s41591-026-04393-8 | 41591 | `41591_{year}_{article_num}_Fig{N}_HTML.png` |
| Nat Commun | s41467-026-73538-7 | 41467 | `41467_{year}_{article_num}_Fig{N}_HTML.png` |
- article_num 提取规则：从DOI中提取期刊ID后的倒数第二段（年份后的数字段），去前导零。如 `s41591-026-04393-8` → 编号段为 `04393` → 去前导零 → `4393`；`s41586-026-10480-0` → `10480`(无前导零即可直接使用) → `10480`；`s41593-026-02317-w` → `02317` → `2317`
- 参考 `references/figure-download-patterns.md` 获取各期刊的详细URL模式
### Step 6: 制作公众号风格HTML深度解读 — Python Builder模式
HTML文件要求：
1. 所有图片用base64 data URI嵌入，不使用file://协议
2. 仿公众号排版（简洁卡片风格）
3. 每条结果配"复现建议"（🔬 复现建议：...）
4. 包含论文原图 + 逐图解读 + 文章小结
5. 保存到桌面，文件命名规范：`{主题关键词}_文献解读.html`
**实现方法**（大文件HTML必须用此方式）：
由于HTML含多张base64图片（5-7张原图≈10MB），不能直接用`write_file`写入。
```python
# 1. 先读取base64数据（terminal工具写入的文件）
# ⚠️ 重要：Windows Python不识别 MSYS 路径 /c/Users/，必须用 C:/Users/ 格式
fig_b64 = {}
for i in range(1, 8):
path = f"C:/Users/forgase/fig{i}_b64.txt"
if os.path.exists(path):
with open(path, "r") as f:
fig_b64[f"fig{i}"] = f.read().strip()
# 2. 构建HTML字符串（用f-string嵌入base64）
html = f'''...<img src="data:image/png;base64,{fig_b64['fig1']}">...'''
# 3. 写入文件（使用正斜杠路径，Windows Python可识别）
with open("C:/Users/forgase/Desktop/主题_文献解读.html", "w", encoding="utf-8") as f:
f.write(html)
```
**⚠️ 重要：f-string与JavaScript花括号冲突**：当用Python f-string构建HTML时，JS代码中的花括号（如`img.onload = function() { }`）会与f-string语法冲突。推荐将HTML拆分为多个部分（parts列表），用字符串拼接代替f-string：
```python
# ✅ 推荐：字符串列表拼接，避免f-string花括号冲突
def build_html(fig_b64, captions):
parts = []
parts.append('<!DOCTYPE html>')
parts.append('<html>')
parts.append('<script>')
for i in range(1, 8):
parts.append(f'const fig{i}_b64 = "{fig_b64.get(f"fig{i}", "")}";')
parts.append('''
function loadImage(id, b64) {
var canvas = document.getElementById(id);
if (!canvas || !b64) return;
...
}
''')
parts.append('</script>')
return '\n'.join(parts)
html = build_html(fig_b64, captions)
```
详见 `references/html-template-structure.md` 获取完整模板框架。
**流程总结**（4步）：
1. terminal中`curl | base64 -w 0 > /c/Users/forgase/fig{N}_b64.txt` 下载所有图片（保存到用户目录，/tmp/在沙箱中不可用）
2. 用`write_file`将Python builder脚本写入磁盘（不含base64数据，仅模板）
3. terminal中执行该Python脚本（`python C:/Users/forgase/build_html.py` — 读取base64文件 + 构建完整HTML + 写入桌面）
4. 验证文件存在：`ls -la /c/Users/forgase/Desktop/`
**HTML模板风格指南**：
- 头部：渐变深蓝背景 + 白色文字 + 标签徽章
- 评分卡片：flex网格布局，5维度评分条
- 图文解读：Fig依次排列，每个配caption+复现建议
- 文章小结：渐变紫色卡片
- 参考 `references/html-template-structure.md` 获取模板框架
### Step 7: 微信推送（⚠️ 关键：必须附上HTML文件）
**格式要求**（最终消息末尾必须加上 `MEDIA:路径` 发送HTML文件）：
```
🔥 {时间} {主题} 论文推荐
🏆 标题...
📅 日期 | 📰 期刊
⭐ Claude评分：X/50
💡 推荐理由：...
MEDIA:C:\Users\forgase\Desktop\{文件名}.html
```
**如果当天没有好论文**：
```
📭 {时间} {主题}
今天这个方向没有找到特别适合你的论文，下次继续关注。
```
**⚠️ 不做这个动作等于白做**：最终推送消息的末尾必须包含 `MEDIA:C:\Users\forgase\Desktop\{文件名}.html` 这一行，这样微信才会把HTML文件当作附件发送给用户。只发文字说"已生成"用户收不到文件。
## 核心原则
- **只推1篇** —— 不要让用户从多个候选中选择
- **宁缺毋滥** —— 没有好论文就不推，不强塞
- **Hermes + Claude 双重把关** —— 都确认完美适合才推
- **不推荐综述**（除非极高价值）
- **不推荐与用户研究方向无关的论文**（即使评分高）
- **推送语言用中文**，但保留英文标题和期刊名
- **⚠️ 必须将HTML文件附在消息中一起发送** — 在最终消息末尾加上 `MEDIA:/path/to/file`，确保用户直接收到文件，不是只发文字
## 参考文件
- `references/pubmed-API-patterns.md` — PubMed E-utilities API详细笔记
- `references/figure-download-patterns.md` — Nature/Cell/Elsevier等期刊的图片URL提取模式
- `references/github-access-patterns.md` — 国内网络环境下载GitHub资源的优先级策略
The user has provided the following instruction alongside the skill invocation: [IMPORTANT: You are running as a scheduled cron job. DELIVERY: Your final response will be automatically delivered to the user — do NOT use send_message or try to deliver the output yourself. Just produce your report/output as your final response and the system handles the rest. SILENT: If there is genuinely nothing new to report, respond with exactly "[SILENT]" (nothing else) to suppress delivery. Never combine [SILENT] with content — either report your findings normally, or say [SILENT] and nothing more.]
你是Hermes，用户的科研助手。加载 paper-recommender skill。
**今日任务**：搜索生信分析/前沿技术方向论文，经过Hermes+Claude双重评判后**只推最优的1篇**，做成HTML深度解读并**必须在消息末尾用MEDIA:路径附上HTML文件**。
**主题关键词**：single-cell, multi-omics, bioinformatics, spatial transcriptomics, computational biology
**⚠️ 额外关注以下交叉领域（与用户博一课题相关，同评分时优先推送）**：
- 单细胞代谢组学 / 空间代谢组学在肿瘤研究中的应用
- 肿瘤代谢异质性的单细胞/空间分析
- 乳酸化修饰组学(Kla proteomics)新技术
- 代谢物-蛋白互作筛选平台
- 代谢组学+转录组学的多组学整合方法
- 基因依赖图谱/CRISPR筛选鉴定代谢脆弱性
日期范围：最近3个月内发表
期刊要求：IF > 10 或领域顶刊
遵循 paper-recommender skill 的完整6步流程。
**⚠️ 必须：最终微信消息末尾加上 `MEDIA:C:\Users\forgase\Desktop\{文件名}.html`**
论文推荐-19-生信技术 · Jun 13 19:09
2026-06-13 19:00 · 40 条 · deepseek-v4-flash
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"success": true, "name": "paper-recommender", "description": "每日定时推送高质量论文推荐 — PubMed检索 → Hermes+Claude双重评估 → 仅推最优1篇 → HTML深度解读", "tags": ["PubMed", "论文推荐", "Claude评估", "文献解读", "科研"], "related_skills": [], "content": "---\nname: paper-recommender\ndescription: \"每日定时推送高质量论文推荐 — PubMed检索 → Hermes+Claude双重评估 → 仅推最优1篇 → HTML深度解读\"\nversion: 2.4.0\nauthor: Hermes\ntags: [PubMed, 论文推荐, Claude评估, 文献解读, 科研]\n---\n\n# 📚 每日论文推荐工作流 (Paper Recommender)\n\n## 职责\n根据用户的6个研究兴趣方向，每日定时从PubMed检索近期高质量论文，经过Hermes+Claude双重评估后，**只选最优的1篇**，下载PDF+提取原图，制作公众号风格的HTML深度解读文件并推送。\n\n## 用户研究方向\n1. 帕金森病(PD) / GZR18 / α-synuclein\n2. 神经炎症 / 小胶质细胞 / 星形胶质细胞\n3. 细胞死亡方式 (Mitoxyperiosis / 铁死亡 / 焦亡 / 泛素化降解)\n4. 外泌体 / 细胞外囊泡 / 神经退行性疾病\n5. 脑缺血 / 脑卒中 / 神经保护\n6. 生信分析 / 单细胞测序 / 多组学\n\n## ⚡ 用户核心课题（跨所有方向的超优先主题）\n**博一课题**：Warburg乳酸 → PCAF介导RhoA K118/K162乳酸化 → RhoA持续激活 → 线粒体远离质膜 → 拮抗mitoxyperiosis\n\n所有方向的论文推荐，只要与以下关键词相关的论文**自动获得更高优先级**，应优先于同评分不相关的论文推送：\n- Mitoxyperiosis / 线粒体-质膜接触 / 新型细胞死亡\n- 乳酸化修饰(Kla) / PCAF / p300 / 组蛋白乳酸化\n- RhoA信号 / 细胞骨架重排 / 小G蛋白修饰\n- Warburg效应 / 肿瘤代谢 / LDHA抑制\n- 糖酵解 / 乳酸代谢 / 肿瘤免疫代谢\n- 禁食疗法 / 卡路里限制 / 联合免疫治疗\n- 代谢组学 / 能量代谢 / 肿瘤微环境\n\n评估论文时，额外增加一个维度：**\"课题借鉴价值\"** — 这篇论文中有什么技术、概念、实验设计可以直接复用到用户的mitoxyperiosis-乳酸化课题？\n\n## 定时推送安排\n\n| 时间 | 主题 | 关键词 |\n|:----:|------|--------|\n| 08:00 | PD/帕金森病 | Parkinson's disease, α-synuclein, MPTP, dopamine neuron |\n| 11:00 | 神经炎症/胶质细胞 | neuroinflammation, microglia, astrocyte, NLRP3 |\n| 13:00 | 细胞死亡/信号通路 | cell death, mitoxyperiosis, ferroptosis, ubiquitination |\n| 15:00 | 外泌体/细胞通讯 | exosome, extracellular vesicle, neural, brain |\n| 17:00 | 脑缺血/神经保护 | cerebral ischemia, stroke, neuroprotection, MCAO |\n| 19:00 | 生信/前沿技术 | single-cell, multi-omics, bioinformatics, spatial transcriptomics |\n\n## ⚠️ 已知工具陷阱\n\n### 文件系统隔离（重要！）\n`execute_code` 运行在沙箱环境中，**不能直接访问** `terminal` 工具写入 `/tmp/` 或 `$HOME/` 的文件。反之亦然。\n- ❌ 错误做法：terminal写入`/tmp/fig1_b64.txt`，然后用execute_code的`open()`读取 → 文件不存在\n- ✅ 正确做法1：所有数据处理在terminal中完成（Python脚本通过`python script.py`执行）\n- ✅ 正确做法2：将数据处理写成`.py`文件写入磁盘，再用terminal(`python path/to/script.py`)执行\n\n### 大文件HTML生成\n当HTML嵌入base64图片时（5张Nature原图 ≈ 4MB），`write_file`会因大小限制失败。\n- ❌ 不要直接用write_file写4MB+的HTML\n- ✅ 策略：写一个Python builder脚本到磁盘（`write_file → build_html.py`），然后`terminal(\"python build_html.py\")`执行它\n\n### Claude CLI 长提示词超时陷阱\nclaude -p 传入大段文本时容易超时（exit code 124，返回空），特别是在Windows git-bash环境。\n- ❌ 一次性传入4篇论文的完整摘要+评估指令 → 超时\n- ✅ 策略1：将评估指令简化，每篇论文控制到1-3句关键信息；或拆分为多次调用分别评估单篇\n- ✅ 策略2：用 `head -N` 或 `tail -N` 截断输出（如 `claude -p \"评估...\" 2>&1 | head -60`），防止Claude生成过长内容导致超时。即使Claude内部未完成，前60行通常已包含评分结果\n\n### Cell Press PII清理模式（重要！）\nPubMed esummary返回的PII含括号和连字符（如 `S0896-6273(26)00386-7`），CDN URL需要去掉这些字符：\n- ❌ `tr -d '()'` → 只去除括号，留下连字符 → CDN返回404\n- ✅ `tr -d '()-'` → 同时去除括号和连字符 → CDN返回200\n- 详见 `references/figure-download-patterns.md` 中的修正实例\n\n### Python f-string与JavaScript花括号冲突（HTML Builder陷阱）\n当用Python f-string构建HTML时，JavaScript代码中的`{}`（如`canvas.width`、`img.onload = function() { }`、`loadImage(\"fig1\", fig1_b64)`）会被Python误解析为f-string占位符，导致SyntaxError。\n- ❌ `html = f'''...<script>img.onload = function() { canvas.width = 780; }</script>...'''` → SyntaxError\n- ✅ 策略1：用字符串列表拼接代替f-string（将HTML拆分为多个部分append到列表，用`+`或`''.join()`组合）\n- ✅ 策略2：将HTML结构和JS作为独立字符串，用`.replace()`注入base64数据\n- ✅ 策略3：在JS中所有`{`和`}`都必须加倍成`{{`和`}}`（仅在必须用f-string时）\n- 推荐策略：使用函数`build_html(fig_b64, ...)`接受字典参数，返回拼接好的字符串（详见Step 6 HTML模板）\n\n### Windows Python路径陷阱\n在 Windows git-bash 中，Python 脚本不识别 MSYS 路径格式（`/c/Users/...`）：\n- ❌ `open(\"/c/Users/forgase/fig1_b64.txt\")` → FileNotFoundError\n- ✅ `open(\"C:/Users/forgase/fig1_b64.txt\")` — 正斜杠在 Windows Python 中可识别\n- 同理，`python3` 命令在 Windows 上可能被应用执行别名拦截 → 始终使用 `python`\n\n## 执行流程（完整版）\n\n### Step 1: PubMed检索 — 两阶段策略\n\n**执行位置：terminal 而非 execute_code**\nPubMed API调用应在 `terminal` 中通过 `python -c \"...\"` 或写脚本后执行。`terminal` 中的 Python 连接更稳定，且结果可被后续terminal命令使用（写入文件、管道传递）。避免在 `execute_code` 中做PubMed检索——沙箱环境的网络连接可能不稳定，且JSON结果无法跨沙箱传递给terminal。\n\n**日期范围两种方式（方式B更可靠）**：\n```python\n# 方式A: mindate/maxdate 参数（有时返回0结果，不可靠）\nparams = {\n 'db': 'pubmed', 'term': query,\n 'mindate': '<3个月前>', 'maxdate': '<今天>',\n 'datetype': 'pdat', 'retmax': 15, 'retmode': 'json'\n}\n\n# 方式B: [dp] 查询语法（推荐，已验证可靠）\nquery = '(ferroptosis[Title/Abstract] OR ...) AND (\"2026/03/01\"[dp] : \"2026/06/12\"[dp])'\nparams = {\n 'db': 'pubmed', 'term': query,\n 'retmax': 15, 'retmode': 'json'\n}\n```\n`dp` (Date - Publication) 语法格式：`\"YYYY/MM/DD\"[dp] : \"YYYY/MM/DD\"[dp]`。日期之间需要空格+冒号+空格。此方式不受 `mindate/maxdate` 参数兼容性问题影响。\n详见 `references/pubmed-API-patterns.md` 第4节。\n\n**阶段A — 主题搜索**：用关键词搜该方向近期论文\n**阶段B — 高IF期刊过滤**：用主题词 + `(\"Nature\"[Journal] OR \"Cell\"[Journal] OR ...)` 格式搜\n\n⚠️ 关键陷阱：\n- `sort=date` 被PubMed忽略（返回warning），不要用\n- 期刊名必须用 `\"全称\"[Journal]` 格式，裸词会被翻译为MeSH词条\n- 复杂查询在curl中容易URL编码失败，优先用Python\n- PubMed JSON返回的ID列表键名为小写 `idlist`，不是 `IdList`（驼峰）\n\n### Step 2: 获取论文详情并初筛\n\n```python\nimport urllib.request, json, time\n\n# 获取元数据\nurl = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=<PMIDs>&retmode=json'\n# 获取摘要\nurl = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=<PMIDs>&retmode=xml'\n```\n\n**初筛硬性门槛**（必须同时满足）：\n1. IF > 10 或领域顶刊\n2. 2026年3月后发表\n3. 与用户研究方向直接相关\n4. 原创研究（pubtype不含Review）\n5. 非 News & Views / Letter / 编辑评论\n6. **有作者（推荐用 efetch XML 确认，而非 esummary 的 hascreator 字段）**\n - `esummary`的`hascreator`字段有时不可靠：已验证所有高IF期刊论文均返回`hascreator='N'`（即使是多作者原创研究）。\n - 改用 efetch XML：统计`<Author>`标签数（`len(re.findall(r'<Author>', xml_text))`），大于0即有作者。\n - 若只能用 esummary：不要仅因`hascreator='N'`排除——结合`pubtype`和`HasAbstract`综合判断。\n\n### Step 3: Claude Code深度评估（Hermes + Claude 双重评判）\n\n将经过初筛的候选论文交给 Claude Code 严格评估：\n```\nclaude -p \"严格评估以下论文，从5个维度各评1-10分...\"\n```\n\n**5维度评分**：\n1. **逻辑完整性** (1-10)：完整正反验证（gain+loss of function）、KO/KI模型\n2. **方法学水平** (1-10)：前沿技术（单细胞测序、冷冻电镜、活细胞成像、TurboID等）\n3. **创新性** (1-10)：新概念/新机制/新方法，首次报道\n4. **可借鉴性** (1-10)：实验设计是否值得用户在自己的课题中参考 **← 这是最关键的维度**\n5. **临床转化潜力** (1-10)：体内验证或治疗意义\n\n### Step 4: 唯一推荐（核心规则）\n\n- Claude评分 < 30分（平均<6分）或任何单维度 < 5分 → **不推荐**，跳过\n- 多个候选都达标 → **只选最优的1篇**，不要让用户选\n- Hermes再次确认质量 + 相关性 + 可借鉴性 → 只有双方都确认才通过\n- **如果这篇研究方向不匹配（如planarian flatworm研究），即使评分高也不能推**\n\n### Step 5: 下载PDF + 提取Figure图片\n\n**PDF下载**：\n- Nature系列: `https://www.nature.com/articles/{article_id}.pdf`（已验证：Nature, Nat Neurosci, Nat Med, Nat Commun均可下载）\n- Cell Press/Elsevier: PDF有ScienceDirect防护，通常无法通过curl下载\n- 所有期刊的Figure图片可通过CDN下载（详见`references/figure-download-patterns.md`）\n- 注意：部分期刊有付费墙，PDF可能无法下载，此时跳过PDF但继续下载Figure图片\n\n**PDF真实性验证**：下载PDF后用 `file` 命令确认是否为真实PDF：\n```bash\nfile \"/c/Users/forgase/s41593-026-02317-w.pdf\"\n# 预期输出: \"PDF document, version 1.4, N page(s)\" ✅\n# 若输出: \"HTML document\" ❌ — 反爬重定向，非真实PDF\n# 已验证：Nat Commun的unedited version PDF可能返回HTML document\n```\n\n### 提取文章元数据（Nature系列 — 替代efetch XML的简便方法）\n\nNature文章页HTML的 `<head>` 嵌入了 `citation_*` 元标签，通过 `curl` + Python可干净提取标题、作者、期刊、日期，比解析efetch XML更简洁：\n\n```bash\ncurl -s -L -H \"User-Agent: Mozilla/5.0\" \"https://www.nature.com/articles/${ARTICLE_ID}\" \\\n | python -c \"\nimport sys, re\nhtml = sys.stdin.read()\nauthors = re.findall(r'<meta[^>]*name=\\\"citation_author\\\"[^>]*content=\\\"([^\\\"]+)\\\"', html)\nprint('Authors: ' + '; '.join(authors[:5]) + ('...' if len(authors)>5 else ''))\ndate = re.findall(r'<meta[^>]*name=\\\"citation_date\\\"[^>]*content=\\\"([^\\\"]+)\\\"', html)\nprint('Date: ' + (date[0] if date else 'N/A'))\njournal = re.findall(r'<meta[^>]*name=\\\"citation_journal_title\\\"[^>]*content=\\\"([^\\\"]+)\\\"', html)\nprint('Journal: ' + (journal[0] if journal else 'N/A'))\ntitle = re.findall(r'<meta[^>]*name=\\\"citation_title\\\"[^>]*content=\\\"([^\\\"]+)\\\"', html)\nprint('Title: ' + (title[0] if title else 'N/A'))\n\"\n```\n将提取到的元数据直接用于Step 6的HTML头部。已验证适用于Nature、Nat Neurosci、Nat Med、Nat Commun。\n\n### Nature系列PDF下载注意\n- Nature系列: `https://www.nature.com/articles/{article_id}.pdf`（已验证：Nature, Nat Neurosci, Nat Med可下载真实PDF）\n- **Nat Commun PDF可能为HTML**：`file`命令检查下载文件，若为\"HTML document\"则非真实PDF（反爬重定向）。不影响Figure图片下载（CDN独立于PDF）。\n- Cell Press/Elsevier: PDF有ScienceDirect防护，通常无法通过curl下载\n\n**Figure提取（Nature系列通用模式 — 已验证 Nature, Nat Neurosci, Nat Med, Nat Commun）**：\n```bash\n# 1. 从HTML中提取所有figure图片URL（通用模式，匹配所有s415XX系列期刊）\ncurl -s -L -H \"User-Agent: Mozilla/5.0\" \"https://www.nature.com/articles/{article_id}\" \\\n | grep -oP 'https://media\\.springernature\\.com/[^\"'\"'\"'<>]+s415\\d\\d[^\"'\"'\"'<>]*\\.(?:png|jpg|webp)' \\\n | sort -u\n\n# 2. 暴力枚举法（推荐，因为url模式固定—不依赖HTML解析）\nfor fig in 1 2 3 4 5 6 7; do\n url=\"https://media.springernature.com/lw1200/springer-static/image/art%3A10.1038%2F${ARTICLE_ID}/MediaObjects/${JOURNAL_ID}_${YEAR}_${ARTICLE_NUM}_Fig${fig}_HTML.png\"\n code=$(curl -s -o /dev/null -w \"%{http_code}\" -L \"$url\" -H \"User-Agent: Mozilla/5.0\")\n if [ \"$code\" != \"404\" ]; then\n curl -s -L \"$url\" -H \"User-Agent: Mozilla/5.0\" | base64 -w 0 > \"/c/Users/forgase/fig${fig}_b64.txt\"\n fi\ndone\n```\n\n**Figure Caption提取（Nature系列 — 紧跟图片下载之后执行）**：\n提取的caption将用于Step 6 HTML的逐图解读部分。\n```bash\ncurl -s -L -H \"User-Agent: Mozilla/5.0\" \"https://www.nature.com/articles/${ARTICLE_ID}\" \\\n | python -c \"\nimport sys, re\nhtml = sys.stdin.read()\nfigs = re.findall(r'<fig[^>]*>.*?<figcaption[^>]*>(.*?)</figcaption>', html, re.DOTALL)\nfor i, fig in enumerate(figs):\n clean = re.sub(r'<[^>]+>', '', fig).strip()\n print(f'{i+1}|{clean}')\n\"\n```\n然后将captions嵌入Python builder脚本中的字典变量。\n\n**Cell Press系列Caption处理**（Neuron, Cell Metabolism等）：\nCell Press主页面（www.cell.com / sciencedirect.com）使用Cloudflare防护，curl无法获取页面HTML，因此`<figcaption>`标签无法通过解析提取。已验证策略：\n- **手动构建**：基于abstract和论文结构（7图论文的典型结构：Fig1=现象发现, Fig2-3=分子机制, Fig4-5=通路验证, Fig6=体内验证, Fig7=模型）合理推断各Figure内容\n- **模板**：将描述写为Python字典嵌入builder脚本（详见 `references/figure-download-patterns.md` \"Pre-print / Unedited Version Caption 降级策略\"）\n- Cell Press的Figure图片仍可通过 `ars.els-cdn.com` CDN直接下载（JPEG格式），不受Cloudflare影响\n\n⚠️ **预印本降级策略**：若上述命令输出为空，检查文章页是否有\"unedited version\"或\"preprint\"标记。\n此时Figure图片仍可通过CDN下载，但caption需手动构建。策略：\n- 基于abstract和论文结构合理推断各Figure内容（已验证可行）\n- 将手动编写的caption以Python字典嵌入builder脚本\n- 详见 `references/figure-download-patterns.md` \"Pre-print / Unedited Version Caption 降级策略\"\n详见 `references/figure-download-patterns.md` 中的\"Figure Caption Extraction\"节。\n\n**分辨率选择**：\n- `/full/` — 最高分辨率（推荐用于Fig1）\n- `/lw1200/` — 宽1200px（其余Figures用此即可）\n- `/m685/` — 宽685px（小尺寸）\n\n**已验证的图片URL模式**：\n| 期刊 | article_id 示例 | journal_id | URL模式 |\n|------|----------------|------------|---------|\n| Nature | s41586-026-10597-2 | 41586 | `41586_{year}_{article_num}_Fig{N}_HTML.png` |\n| Nat Neurosci | s41593-026-02317-w | 41593 | `41593_{year}_{article_num}_Fig{N}_HTML.png` |\n| Nat Med | s41591-026-04393-8 | 41591 | `41591_{year}_{article_num}_Fig{N}_HTML.png` |\n| Nat Commun | s41467-026-73538-7 | 41467 | `41467_{year}_{article_num}_Fig{N}_HTML.png` |\n\n- article_num 提取规则：从DOI中提取期刊ID后的倒数第二段（年份后的数字段），去前导零。如 `s41591-026-04393-8` → 编号段为 `04393` → 去前导零 → `4393`；`s41586-026-10480-0` → `10480`(无前导零即可直接使用) → `10480`；`s41593-026-02317-w` → `02317` → `2317`\n- 参考 `references/figure-download-patterns.md` 获取各期刊的详细URL模式\n\n### Step 6: 制作公众号风格HTML深度解读 — Python Builder模式\n\nHTML文件要求：\n1. 所有图片用base64 data URI嵌入，不使用file://协议\n2. 仿公众号排版（简洁卡片风格）\n3. 每条结果配\"复现建议\"（🔬 复现建议：...）\n4. 包含论文原图 + 逐图解读 + 文章小结\n5. 保存到桌面，文件命名规范：`{主题关键词}_文献解读.html`\n\n**实现方法**（大文件HTML必须用此方式）：\n由于HTML含多张base64图片（5-7张原图≈10MB），不能直接用`write_file`写入。\n```python\n# 1. 先读取base64数据（terminal工具写入的文件）\n# ⚠️ 重要：Windows Python不识别 MSYS 路径 /c/Users/，必须用 C:/Users/ 格式\nfig_b64 = {}\nfor i in range(1, 8):\n path = f\"C:/Users/forgase/fig{i}_b64.txt\"\n if os.path.exists(path):\n with open(path, \"r\") as f:\n fig_b64[f\"fig{i}\"] = f.read().strip()\n\n# 2. 构建HTML字符串（用f-string嵌入base64）\nhtml = f'''...<img src=\"data:image/png;base64,{fig_b64['fig1']}\">...'''\n\n# 3. 写入文件（使用正斜杠路径，Windows Python可识别）\nwith open(\"C:/Users/forgase/Desktop/主题_文献解读.html\", \"w\", encoding=\"utf-8\") as f:\n f.write(html)\n```\n\n**⚠️ 重要：f-string与JavaScript花括号冲突**：当用Python f-string构建HTML时，JS代码中的花括号（如`img.onload = function() { }`）会与f-string语法冲突。推荐将HTML拆分为多个部分（parts列表），用字符串拼接代替f-string：\n```python\n# ✅ 推荐：字符串列表拼接，避免f-string花括号冲突\ndef build_html(fig_b64, captions):\n parts = []\n parts.append('<!DOCTYPE html>')\n parts.append('<html>')\n parts.append('<script>')\n for i in range(1, 8):\n parts.append(f'const fig{i}_b64 = \"{fig_b64.get(f\"fig{i}\", \"\")}\";')\n parts.append('''\nfunction loadImage(id, b64) {\n var canvas = document.getElementById(id);\n if (!canvas || !b64) return;\n ...\n}\n''')\n parts.append('</script>')\n return '\\n'.join(parts)\n\nhtml = build_html(fig_b64, captions)\n```\n详见 `references/html-template-structure.md` 获取完整模板框架。\n\n**流程总结**（4步）：\n1. terminal中`curl | base64 -w 0 > /c/Users/forgase/fig{N}_b64.txt` 下载所有图片（保存到用户目录，/tmp/在沙箱中不可用）\n2. 用`write_file`将Python builder脚本写入磁盘（不含base64数据，仅模板）\n3. terminal中执行该Python脚本（`python C:/Users/forgase/build_html.py` — 读取base64文件 + 构建完整HTML + 写入桌面）\n4. 验证文件存在：`ls -la /c/Users/forgase/Desktop/`\n\n**HTML模板风格指南**：\n- 头部：渐变深蓝背景 + 白色文字 + 标签徽章\n- 评分卡片：flex网格布局，5维度评分条\n- 图文解读：Fig依次排列，每个配caption+复现建议\n- 文章小结：渐变紫色卡片\n- 参考 `references/html-template-structure.md` 获取模板框架\n\n### Step 7: 微信推送（⚠️ 关键：必须附上HTML文件）\n\n**格式要求**（最终消息末尾必须加上 `MEDIA:路径` 发送HTML文件）：\n```\n🔥 {时间} {主题} 论文推荐\n\n🏆 标题...\n📅 日期 | 📰 期刊\n⭐ Claude评分：X/50\n💡 推荐理由：...\n\nMEDIA:C:\\Users\\forgase\\Desktop\\{文件名}.html\n```\n\n**如果当天没有好论文**：\n```\n📭 {时间} {主题}\n今天这个方向没有找到特别适合你的论文，下次继续关注。\n```\n\n**⚠️ 不做这个动作等于白做**：最终推送消息的末尾必须包含 `MEDIA:C:\\Users\\forgase\\Desktop\\{文件名}.html` 这一行，这样微信才会把HTML文件当作附件发送给用户。只发文字说\"已生成\"用户收不到文件。\n\n## 核心原则\n- **只推1篇** —— 不要让用户从多个候选中选择\n- **宁缺毋滥** —— 没有好论文就不推，不强塞\n- **Hermes + Claude 双重把关** —— 都确认完美适合才推\n- **不推荐综述**（除非极高价值）\n- **不推荐与用户研究方向无关的论文**（即使评分高）\n- **推送语言用中文**，但保留英文标题和期刊名\n- **⚠️ 必须将HTML文件附在消息中一起发送** — 在最终消息末尾加上 `MEDIA:/path/to/file`，确保用户直接收到文件，不是只发文字\n\n## 参考文件\n- `references/pubmed-API-patterns.md` — PubMed E-utilities API详细笔记\n- `references/figure-download-patterns.md` — Nature/Cell/Elsevier等期刊的图片URL提取模式\n- `references/github-access-patterns.md` — 国内网络环境下载GitHub资源的优先级策略\n", "path": "productivity\\paper-recommender\\SKILL.md", "skill_dir": "C:\\Users\\forgase\\AppData\\Local\\hermes\\skills\\productivity\\paper-recommender", "linked_files": {"references": ["references\\figure-download-patterns.md", "references\\github-access-patterns.md", "references\\html-template-structure.md", "references\\pubmed-API-patterns.md", "references\\user-project-context.md"]}, "usage_hint": "To view linked files, call skill_view(name, file_path) where file_path is e.g. 'references/api.md' or 'assets/config.yaml'", "required_environment_variables": [], "required_commands": [], "missing_required_environment_variables": [], "missing_credential_files": [], "missing_required_commands": [], "setup_needed": false, "setup_skipped": false, "readiness_status": "available"}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"todos": [{"id": "step1", "content": "PubMed检索 - 生信/前沿技术方向论文", "status": "in_progress"}, {"id": "step2", "content": "获取论文详情并初筛候选论文", "status": "pending"}, {"id": "step3", "content": "Claude Code深度评估候选论文", "status": "pending"}, {"id": "step4", "content": "唯一推荐确认", "status": "pending"}, {"id": "step5", "content": "下载PDF + 提取Figure图片", "status": "pending"}, {"id": "step6", "content": "制作HTML深度解读文件", "status": "pending"}, {"id": "step7", "content": "输出最终推送消息(含MEDIA路径)", "status": "pending"}], "summary": {"total": 7, "pending": 6, "in_progress": 1, "completed": 0, "cancelled": 0}}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"success": true, "name": "paper-recommender", "file": "references/pubmed-API-patterns.md", "content": "# PubMed E-utilities API 实战笔记\n\n## 搜索API (esearch)\n\n### 基础结构\n```\nbase: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi\nparams:\n db=pubmed\n term=<查询表达式>\n mindate/maxdate — 日期范围\n datetype=pdat (publication date)\n retmax=N — 最大返回数\n retmode=json\n```\n\n### 已验证的关键查询模式\n\n**1. 主题搜索 + 高影响因子期刊过滤**\n```python\nquery = 'Parkinson AND (\"Nat Neurosci\"[Journal] OR \"Neuron\"[Journal] OR \"Nat Med\"[Journal])'\n```\n- 期刊名必须用 `\"全称\"[Journal]` 格式\n- 不加 `[Journal]` 标签时，`Neuron` 被翻译为 MeSH 的 neurons（神经元）\n- `Brain` 被翻译为 MeSH 的 brain（大脑解剖结构）\n\n**2. 细胞死亡方向搜索（已验证）**\n```python\nquery = '(ferroptosis[Title/Abstract] OR pyroptosis[Title/Abstract] OR ' + \\\n 'necroptosis[Title/Abstract] OR PANoptosis[Title/Abstract] OR ' + \\\n 'cuproptosis[Title/Abstract] OR mitophagy[Title/Abstract]) ' + \\\n 'AND (Nature[Journal] OR \"Cell\"[Journal] OR \"Nat Cell Biol\"[Journal] OR ' + \\\n '\"Cell Death Differ\"[Journal] OR \"Nat Commun\"[Journal])'\n```\n这个模式成功返回了 Nature 的精胺/铁死亡论文。\n\n**3. 已测试的有效期刊标签**\n| 期刊 | 标签 |\n|------|------|\n| Nature | `\"Nature\"[Journal]` |\n| Nature Communications | `\"Nat Commun\"[Journal]` |\n| Nature Medicine | `\"Nat Med\"[Journal]` |\n| Neuron | `\"Neuron\"[Journal]` |\n| Nature Neuroscience | `\"Nat Neurosci\"[Journal]` |\n| Science Advances | `\"Sci Adv\"[Journal]` |\n| Cell | `\"Cell\"[Journal]` |\n| Cell Reports | `\"Cell Rep\"[Journal]` |\n| Cell Death & Differentiation | `\"Cell Death Differ\"[Journal]` |\n| EMBO Journal | `\"EMBO J\"[Journal]` |\n| Molecular Neurodegeneration | `\"Mol Neurodegener\"[Journal]` |\n| EBioMedicine | `\"EBioMedicine\"[Journal]` |\n| Acta Neuropathologica | `\"Acta Neuropathol\"[Journal]` |\n| Brain | `\"Brain\"[Journal]` |\n| Lancet Neurology | `\"Lancet Neurol\"[Journal]` |\n| Developmental Cell | `\"Dev Cell\"[Journal]` |\n| Nature Structural & Molecular Biology | `\"Nat Struct Mol Biol\"[Journal]` |\n| Cell Metabolism | `\"Cell Metab\"[Journal]` |\n| Molecular Cell | `\"Mol Cell\"[Journal]` |\n| Nature Cell Biology | `\"Nat Cell Biol\"[Journal]` |\n\n### 已验证无用的参数\n- `sort=date` — 返回 warning \"Unknown sort schema 'date' ignored\"，无效果\n\n## 获取详情API (esummary)\n\n```python\nurl = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=pubmed&id=<PMID列表>&retmode=json'\n```\n\n关键字段：\n- `pubtype` — 文章类型（含 \"Review\" 则为综述）\n- `source` — 期刊缩写名\n- `articleids[].idtype == 'doi'` — DOI\n- `attributes` — 包含 \"Has Abstract\" 则有摘要\n- `pubdate` — 发表日期\n- `title` — 标题\n\n⚠️ JSON的id列表键名是小写 `idlist`（如 `data['esearchresult']['idlist']`），不是驼峰的 `IdList`。\n\n## 获取摘要API (efetch)\n\n```python\n# XML模式（含结构化信息）\nurl = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=<PMIDs>&retmode=xml'\n\n# 文本模式（纯文本）\nurl = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=<PMIDs>&retmode=text&rettype=abstract'\n```\n\nXML模式返回结构化数据，适合用正则提取：\n- `<ArticleTitle>` → 标题（需要去除XML标签+解码HTML实体）\n- `<AbstractText>` → 摘要段落（可以有多段，Label属性区分BACKGROUND/METHODS/RESULTS/CONCLUSIONS）\n- `<ELocationID EIdType=\"doi\">` → DOI\n- `<Journal><Title>` → 期刊全名\n- `<PubDate><Year>` + `<Month>` → 发表日期\n\nXML转义字符需要手动替换：`α`=α, `β`=β, `γ`=γ, ` `=空格, `⁠`='', `&`=&, `<`=<, `>`=>\n\n## 获取文章元数据（XML完整模式）\n\n```python\nurl = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=<PMIDs>&retmode=xml'\n```\n此模式返回完整XML，包含：\n- 所有作者的 `<LastName>` 和 `<ForeName>`\n- 所有 `<Keyword>`\n- 完整 `<AbstractText>`（含多个Label段落）\n- `<PublicationTypeList>` 用于排除Review/Letter\n\n## 已知环境陷阱\n\n### 1. SSL连接失败（exit code 35）\n某些环境（git-bash/MSYS2）的 curl SSL 版本与 NCBI 服务器不兼容。\n- 修复1：`curl -k`（跳过证书验证）\n- 修复2：改用 Python 的 `urllib.request`（在本环境中工作可靠）\n- 修复3：更新 curl CA bundle\n\n### 2. URL编码问题\ncurl 命令行中包含 `()\"[]` 等特殊字符时容易解析失败（exit code 3）。\n- 修复：用 Python 的 `urllib.parse.urlencode(params)` 构建查询串\n\n### 3. `sort=date` 无效\nPubMed API 不支持 sort=date，搜索结果的顺序按默认相关性排序。\n\n### 4. `mindate`/`maxdate` + `datetype` 日期过滤可能返回0结果（2026-06发现）\n使用 `mindate` + `maxdate` + `datetype=pdat/ppub/edat` 参数组合对常见查询（如 `ferroptosis[Title/Abstract]`）返回 Count=0，不返回任何PMID。PubMed API的日期参数在此环境下不可靠。\n- **修复**：改用 `dp` (Date - Publication) 范围语法直接在查询串中限定日期：\n ```python\n query = 'ferroptosis[Title/Abstract] AND (\"2026/03/01\"[dp] : \"2026/06/12\"[dp])'\n params = {'db': 'pubmed', 'term': query, 'retmax': 20, 'retmode': 'json'}\n ```\n `dp` 语法格式：`\"YYYY/MM/DD\"[dp] : \"YYYY/MM/DD\"[dp]`（日期之间需加空格、冒号、空格）。此方式已验证可靠（2026-06-12），可与其他搜索条件自由组合（如 AND 期刊过滤）。\n\n### 5. execute_code 中 PubMed API 不稳定\n在 `execute_code` 沙箱中连续调用 PubMed API 时，部分请求返回空结果（即使同一查询在前一个调用中返回了完整的 count 和 idlist）。推测与沙箱环境的连接复用或速率限制有关。\n- 修复：将 PubMed API 调用移至 `terminal(\"python -c \\\"...\\\"\")` 中执行。terminal 中的 Python 连接更稳定，返回结果一致。\n- 如果结果仍然为空，在两次 API 调用之间添加 `time.sleep(0.5)` 避免速率限制。\n\n### 6. Cell/Elsevier 付费墙\nCell Press（www.cell.com、linkinghub.elsevier.com）对脚本访问严格限制，返回403/超时。Nature系列（www.nature.com）通常可正常访问。\n- 但 Cell Press 的 Figure 图片通过 `ars.els-cdn.com` CDN 分发，可直接通过 curl 下载（详见 figure-download-patterns.md）。\n- PII 中的括号和连字符需要清理：`tr -d '()-'`\n\n### 7. esummary hascreator 字段不可靠（2026-06发现）\n`esummary` 返回的 `hascreator` 字段对所有高IF期刊论文均显示 `'N'`，即使论文明确有多位作者。\n```json\n// 示例：Nat Med 2026 phase 1 clinical trial，真实作者8人，但：\n\"hascreator\": \"N\" // ❌ 错误\n```\n**对策**：改用 efetch XML 确认作者——统计 `<Author>` 标签数。不要因 `hascreator='N'` 排除论文。\n\n## 搜索策略（两阶段工作流）\n\n### 阶段A：主题搜索\n```python\n# 方式A: mindate/maxdate（可能不可靠）\nparams = {\n 'db': 'pubmed', 'term': query,\n 'mindate': '<3月前>', 'maxdate': '<今天>',\n 'datetype': 'pdat', 'retmax': 10, 'retmode': 'json'\n}\n# 方式B: [dp] 语法（推荐，已验证可靠）\nquery = 'Parkinson disease AND (\"2026/03/01\"[dp] : \"2026/06/12\"[dp])'\nparams = {\n 'db': 'pubmed', 'term': query,\n 'retmax': 10, 'retmode': 'json'\n}\n```\n目的：了解该时段总论文数，获取基础PMID列表。\n\n### 阶段B：高影响力期刊过滤\n```python\njournal_tags = '\"Nature\"[Journal] OR \"Cell\"[Journal] OR \"Nat Neurosci\"[Journal] OR ...'\nquery = f'({topic_keywords}) AND ({journal_tags}) AND (\"<3月前>\"[dp] : \"<今天>\"[dp])'\n```\n目的：直接命中IF>10的高质量论文。\n\n### 初筛要点\n1. `pubtype` 不含 \"Review\" → 原创研究\n2. `attributes` 含 \"Has Abstract\" → 有摘要\n3. 检查 DOI 是否存在\n4. 排除 \"No authors listed\" → 通常是 News & Views\n5. 从XML提取时注意HTML实体和Unicode转义符的处理\n\n## 评分标准（Claude评估用，5维度各1-10分）\n\n| 维度 | 评分要点 | 满分 |\n|------|---------|------|\n| 逻辑完整性 | gain+loss of function, KO/KI, 正反验证 | 10 |\n| 方法学水平 | 单细胞测序, 冷冻电镜, 活细胞成像, TurboID等 | 10 |\n| 创新性 | 新概念/新机制/新方法 | 10 |\n| 可借鉴性 | 实验设计可迁移到神经科学 **←最重要维度** | 10 |\n| 临床转化潜力 | 体内验证, 治疗靶点, 生物标志物 | 10 |\n\n总分 >= 30 且各维度 >= 5 → 候选。再经Hermes确认相关性后，只选最优1篇。", "file_type": ".md"}
tool · 2026-06-13 19:09
{"success": true, "name": "paper-recommender", "file": "references/figure-download-patterns.md", "content": "# 学术期刊Figure下载模式速查\n\n## Nature 系列\n**已验证模式（2026年6月）**\n\n### URL结构\n```\n文章页: https://www.nature.com/articles/{article_id}\nFigure URL: https://media.springernature.com/{size}/springer-static/image/\n art%3A10.1038%2F{article_id}/MediaObjects/\n {journal_id}_{year}_{article_num}_Fig{N}_HTML.png\n```\n\n### 关键参数\n- `{article_id}`: DOI中`/`后面的部分，如 `s41586-026-10597-2` 或 `s41593-026-02317-w`\n- `{journal_id}`: 期刊对应数字ID，如 Nature=41586, Nat Neurosci=41593\n- `{year}`: 4位年份\n- `{article_num}`: 文章编号（DOI中期刊ID后的数字部分，去连字符）\n- `{N}`: Figure编号（1-7）\n\n### 实例1 (Nature — 已验证 2026-06)\n```\narticle_id = s41586-026-10597-2\njournal_id = 41586\nyear = 2026\narticle_num = 10597\n\nFig1 全尺寸:\nhttps://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41586-026-10597-2/MediaObjects/41586_2026_10597_Fig1_HTML.png\n\nFig2 中等尺寸:\nhttps://media.springernature.com/lw1200/springer-static/image/art%3A10.1038%2Fs41586-026-10597-2/MediaObjects/41586_2026_10597_Fig2_HTML.png\n```\n\n### 实例2 (Nature Neuroscience — 已验证 2026-06)\n```\narticle_id = s41593-026-02317-w\njournal_id = 41593\nyear = 2026\narticle_num = 2317\n\nFig1 全尺寸:\nhttps://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41593-026-02317-w/MediaObjects/41593_2026_2317_Fig1_HTML.png\n\nFig4 中等尺寸:\nhttps://media.springernature.com/lw1200/springer-static/image/art%3A10.1038%2Fs41593-026-02317-w/MediaObjects/41593_2026_2317_Fig4_HTML.png\n```\n\n### 实例4 (Nature Neuroscience — 新验证 2026-06-12, PMID: 42151483)\n```\narticle_id = s41593-026-02267-3\njournal_id = 41593\nyear = 2026\narticle_num = 2267 # 从DOI \"02267\" 去前导零 → 2267\n\nFig1-Fig7 (lw1200): 全部 HTTP 200 ✅\nFig1-Fig3 (full): 全部 HTTP 200 ✅\nPDF: https://www.nature.com/articles/s41593-026-02267-3.pdf → HTTP 200, 26.7MB ✅\n```\n\n### 实例5 (Nature Communications — 新验证 2026-06-13, PMID: 42140967)\n```\narticle_id = s41467-026-73136-7\njournal_id = 41467\nyear = 2026\narticle_num = 73136 # 从DOI \"73136\" 无前导零 → 73136\n\nFig1 (full): HTTP 200 ✅ (1.5MB)\nFig2-Fig7 (lw1200): 全部 HTTP 200 ✅ (1.4-2.5MB each)\nPDF: https://www.nature.com/articles/s41467-026-73136-7.pdf → HTTP 200 ⚠️ 但返回的是HTML! file命令显示为\"HTML document\"，非真实PDF\n\n关键发现：该文章为\"unedited version\"（预印本版），HTML页面仅含Abstract，\n不含<figcaption>标签。Figure图片通过CDN可正常下载，但caption无法从HTML提取。\n```\n\n### 实例3 (Nature Medicine — 已验证 2026-06-11)\n```\narticle_id = s41591-026-04393-8\njournal_id = 41591\nyear = 2026\narticle_num = 4393\n\nFig1 全尺寸:\nhttps://media.springernature.com/full/springer-static/image/art%3A10.1038%2Fs41591-026-04393-8/MediaObjects/41591_2026_4393_Fig1_HTML.png\n\nFig2-Fig5 中等尺寸:\nhttps://media.springernature.com/lw1200/springer-static/image/art%3A10.1038%2Fs41591-026-04393-8/MediaObjects/41591_2026_4393_Fig2_HTML.png\n...\nhttps://media.springernature.com/lw1200/...41591_2026_4393_Fig5_HTML.png\n\narticle_num提取: DOI \"s41591-026-04393-8\" → 取s41591后的两段 \"026\"+\"04393\" → 截取最终段的数字部分去前导零 → 4393\n```\n\n### 快速提取命令（curl + grep）\n```bash\n# 1. 从HTML提取所有Figure URL（通用模式，匹配所有s415XX系列期刊）\ncurl -s -L -H \"User-Agent: Mozilla/5.0\" \\\n \"https://www.nature.com/articles/{article_id}\" \\\n | grep -oP 'https://media\\.springernature\\.com/[^\"'\"'\"'<>]+s415\\d\\d[^\"'\"'\"'<>]*\\.(?:png|jpg|webp)' \\\n | sort -u\n\n# 2. 下载某张Figure为base64（用于HTML嵌入）\nFIG_URL=\"https://media.springernature.com/lw1200/springer-static/image/art%3A10.1038%2F{article_id}/MediaObjects/{journal_id}_{year}_{article_num}_Fig{N}_HTML.png\"\ncurl -s \"$FIG_URL\" -H \"User-Agent: Mozilla/5.0\" | base64 -w 0 > /c/Users/<user>/fig{N}_b64.txt\n```\n\n### 分辨率控制\n| 前缀 | 尺寸 | 说明 |\n|------|------|------|\n| `/full/` | 全尺寸 | 最高分辨率，推荐用于Fig1。**注意：Nature系列通常在`/full/`下所有Figure都可用，非仅Fig1**（已验证：Nature s41586-026-10480-0, Fig1-Fig6全部200） |\n| `/lw1200/` | 宽1200px | 中等尺寸，适用于其余Figures。备选方案——若`/full/`偶有404，降级到此 |\n| `/m685/` | 宽685px | 小尺寸 |\n| `/w215h120/` | 215x120px | 缩略图 |\n\n### 已测试的期刊ID\n| 期刊 | ID | DOI前缀 | 验证状态 |\n|------|----|---------|---------|\n| Nature | 41586 | s41586 | ✅ 2026-06 (Fig1-Fig6 all HTTP 200 full/) |\n| **Nature Medicine** | **41591** | **s41591** | **✅ 2026-06** |\n| **Nature Neuroscience** | **41593** | **s41593** | **✅ 2026-06 (两篇独立验证)** |\n| Nature Communications | 41467 | s41467 | 🟢 已验证 |\n| Cell Death & Differentiation | 41418 | s41418 | 🟢 已验证 |\n| Scientific Reports | 41598 | s41598 | 🟢 已验证 |\n| Cell Discovery | 41420 | s41420 | 🟢 已验证 |\n\n## Figure Caption Extraction (Nature系列 — HTML解析)\n\n### 为什么需要提取caption\nStep 6的HTML深度解读需要为每张Figure配caption文字。直接从Nature文章页HTML中提取比手动编写更准确、更快速。\n\n### 方法：Python regex解析 `<figcaption>`\n\nNature系列文章将figure caption放在 `<figcaption>` 标签内，可通过Python regex可靠提取：\n\n```bash\ncurl -s -L -H \"User-Agent: Mozilla/5.0\" \"https://www.nature.com/articles/{article_id}\" \\\n | python -c \"\nimport sys, re\nhtml = sys.stdin.read()\nfigs = re.findall(r'<fig[^>]*>.*?<figcaption[^>]*>(.*?)</figcaption>', html, re.DOTALL)\nfor i, fig in enumerate(figs):\n clean = re.sub(r'<[^>]+>', '', fig).strip()\n print(f'Figure {i+1}: {clean}')\n print()\n\"\n```\n\n### 已验证（2026-06-12，Nat Neurosci s41593-026-02267-3）\n所有7张Figure的caption均正确提取，如：\n```\nFigure 1: Fig. 1: CODEX workflow for neuroscience applications.\nFigure 2: Fig. 2: Multicellular characterization of the Aβ plaque microenvironment at the proteomic level.\nFigure 3: Fig. 3: CODEX-CNS reveals specific cellular interactions in human AD brain.\n...\n```\n\n### 在Builder脚本中使用captions\n将提取到的captions以Python变量形式嵌入builder脚本：\n```python\ncaptions = {\n 1: \"CODEX workflow for neuroscience applications.\",\n 2: \"Multicellular characterization of the Aβ plaque microenvironment at the proteomic level.\",\n # ...\n}\n```\n\n### 已知注意事项\n- Nature系列的 `<figcaption>` 目前为纯文本加少量 `<sup>` 标签，清理 `<sup>` 后即可得到干净文本\n- Cell Press系列因Cloudflare防护，页面HTML不可直接解析→caption需从PDF或手动输入\n- 部分文章可能有Extended Data Figures（Fig S1, S2等），主文章通常只含Fig1-Fig7\n\n### Pre-print / Unedited Version Caption 降级策略\n**场景**：Nature Communications等期刊有时发布\"unedited version\"（未编辑预印本），HTML页面不含 `<figcaption>` 标签，regex提取返回空。\n\n**检测方法**：查看文章HTML中是否有 \"unedited version\" 或 \"preprint\" 字样。若无 `<figcaption>` 标签命中，且文章页Notice显示\"unedited version\"，则进入降级流程。\n\n**降级策略**（按优先级）：\n1. **手动构建**：基于abstract和论文结构合理推断各Figure内容（推荐，已验证可行）。将描述写为Python字典嵌入builder脚本。\n2. **PDF提取**：尝试 pdftotext 从PDF中提取captions。但⚠️预印本PDF有时也返回HTML（nat commun），此时跳过。\n3. **浏览器工具**：用browser工具导航到文章页 → Accept cookies → 滚动查看是否有JS渲染的figure captions。部分预印本也未渲染。\n\n**手动构建模板**（适用于机制类论文）：\n```python\ncaptions = {\n 1: \"Fig. 1: [主要发现，通常为表型]。[技术手段+关键结果1-2句]\",\n 2: \"Fig. 2: [机制研究，通常为通路验证]。[上游调控+下游效应]\",\n 3: \"Fig. 3: [分子机制细节]。[蛋白互作/修饰/定位数据]\",\n 4: \"Fig. 4: [细胞/体内功能验证]。[表型恢复/功能实验]\",\n 5: \"Fig. 5: [深入机制或转化研究]。[治疗/干预结果]\",\n 6: \"Fig. 6: [补充机制或模型验证]。[定量/统计支持]\",\n 7: \"Fig. 7: [整体工作模型示意图]。\"\n}\n```\n\n## Cell Press / Elsevier 系列（Cell Metabolism, Cell, Neuron, Cell Reports 等）\n**已验证模式（2026年6月）**\n\n### 关键发现：主页面有Cloudflare防护，但CDN图片可直接访问\nCell Press 主页面（www.cell.com）使用 Cloudflare 防护，curl/browser 直接访问被拦截。\n但图片通过 `ars.els-cdn.com` CDN 分发，**可以直接通过 curl 下载**。\n\n### URL结构（已成功用于 Cell Metabolism）\n```\nFigure N (大尺寸):\nhttps://ars.els-cdn.com/content/image/1-s2.0-{PII}-gr{N}_lrg.jpg\n\nFigure N (小尺寸):\nhttps://ars.els-cdn.com/content/image/1-s2.0-{PII}-gr{N}.jpg\n\nGraphical Abstract (大尺寸):\nhttps://ars.els-cdn.com/content/image/1-s2.0-{PII}-fx1_lrg.jpg\n```\n\n### 关键参数\n- `{PII}`: 文章的PII（Publisher Item Identifier），可从PubMed esummary结果中获取（idtype='pii'），或从DOI对应的ScienceDirect URL中提取\n- `{N}`: Figure编号（1-7），Cell Press系列使用 `gr1, gr2, ..., gr7` 格式（非 `Fig1`）\n- `fx1`: Graphical Abstract的固定标识\n\n### 实例（Neuron — 已验证 2026-06-12）\n```bash\nPMID: 42127911\nDOI: 10.1016/j.neuron.2026.04.033\nPII: S0896-6273(26)00328-4 # 含有括号和连字符，需全部去除\nURL-PII: S0896627326003284 # 去除括号和连字符后的实际URL用PII\n\n# Figure URL (CDN直接访问，无防护):\n# 核心：去除PII中的括号，替换 gr{N}_lrg 和 fx1_lrg\nFig1: https://ars.els-cdn.com/content/image/1-s2.0-S0896627326003284-gr1_lrg.jpg\nFig2: https://ars.els-cdn.com/content/image/1-s2.0-S0896627326003284-gr2_lrg.jpg\nFig3: https://ars.els-cdn.com/content/image/1-s2.0-S0896627326003284-gr3_lrg.jpg\nFig4: https://ars.els-cdn.com/content/image/1-s2.0-S0896627326003284-gr4_lrg.jpg\nFig5: https://ars.els-cdn.com/content/image/1-s2.0-S0896627326003284-gr5_lrg.jpg\nFig6: https://ars.els-cdn.com/content/image/1-s2.0-S0896627326003284-gr6_lrg.jpg\nFig7: https://ars.els-cdn.com/content/image/1-s2.0-S0896627326003284-gr7_lrg.jpg\nGraphical Abstract: https://ars.els-cdn.com/content/image/1-s2.0-S0896627326003284-fx1_lrg.jpg\n\n# PII括号去除规则\n# ⚠️ 必须同时去除括号和连字符：tr -d '()-'\n# \"S0896-6273(26)00328-4\" → tr -d '()-' → \"S0896627326003284\"\nPII_URL=$(echo \"$PII\" | tr -d '()-')\necho \"$PII_URL\" # → S0896627326003284\n\n```\n\n### 实例3（Neuron — 新验证 2026-06-13, PMID: 42263678）\n```bash\nPMID: 42263678\nDOI: 10.1016/j.neuron.2026.05.015\nPII: S0896-6273(26)00386-7\nURL-PII: S0896627326003867 # 经 tr -d '()-' 处理\n\n# gr1-gr7 _lrg.jpg 全部 HTTP 200 ✅\n# Fig1: 3.5MB, Fig4: 3.2MB, Fig7: 3.1MB (高清原图)\n# fx1 (Graphical Abstract): HTTP 404 ❌ (本文章无Graphical Abstract)\n\n# Cell Press页面有Cloudflare防护，无法提取figcaptions\n# -> 需要手动构建captions（基于abstract和论文结构推断）\n```\n```bash\nPMID: 42263678\nDOI: 10.1016/j.neuron.2026.05.015\nPII: S0896-6273(26)00386-7\nURL-PII: S0896627326003867 # 经 tr -d '()-' 处理\n\n# gr1-gr7 全部 HTTP 200 ✅ (含 _lrg 和无 _lrg 两种尺寸均可用)\ncurl -s \"https://ars.els-cdn.com/content/image/1-s2.0-S0896627326003867-gr1_lrg.jpg\" | base64 -w 0 > fig1_b64.txt\n# fx1: HTTP 404（非所有Cell Press文章都有Graphical Abstract）\n```\n\n### 实例（Cell Metabolism — 已验证 2026-06-11，需 tr -d '()-'）\n```bash\nPMID: 42049021\nDOI: 10.1016/j.cmet.2026.04.001\nPII: S1550-4131(26)00139-7 # 注意：PII可能不同于全文URL中的pii\n# ⚠️ 同样需要 tr -d '()-' 去除括号和连字符\n\n# ScienceDirect URL (有Cloudflare防护，curl会失败):\n# https://www.sciencedirect.com/science/article/pii/S1550413126001397\n\n# Figure URL (CDN直接访问，无防护):\nFig1: https://ars.els-cdn.com/content/image/1-s2.0-S1550413126001397-gr1_lrg.jpg\nFig2: https://ars.els-cdn.com/content/image/1-s2.0-S1550413126001397-gr2_lrg.jpg\nFig3: https://ars.els-cdn.com/content/image/1-s2.0-S1550413126001397-gr3_lrg.jpg\nFig4: https://ars.els-cdn.com/content/image/1-s2.0-S1550413126001397-gr4_lrg.jpg\nFig5: https://ars.els-cdn.com/content/image/1-s2.0-S1550413126001397-gr5_lrg.jpg\nFig6: https://ars.els-cdn.com/content/image/1-s2.0-S1550413126001397-gr6_lrg.jpg\nFig7: https://ars.els-cdn.com/content/image/1-s2.0-S1550413126001397-gr7_lrg.jpg\nGraphical Abstract: https://ars.els-cdn.com/content/image/1-s2.0-S1550413126001397-fx1_lrg.jpg\n```\n\n### 快速提取命令（修复版）\n```bash\n# 1. 从PubMed esummary中获取PII\n# PII字段: idtype='pii', value='S1550-4131(26)00139-7'\n# 或从DOI的ScienceDirect重定向URL中提取：/pii/{PII}\n\n# 2. ⚠️ 关键：清理PII中的括号和连字符，否则CDN URL会404\nPII_CLEAN=$(echo \"$PII\" | tr -d '()-')\n\n# 3. 暴力枚举法：从gr1试到gr7（推荐，因为url模式固定）\nfor fig in 1 2 3 4 5 6 7; do\n url=\"https://ars.els-cdn.com/content/image/1-s2.0-${PII_CLEAN}-gr${fig}_lrg.jpg\"\n code=$(curl -s -o /dev/null -w \"%{http_code}\" -L \"$url\" -H \"User-Agent: Mozilla/5.0\")\n echo \"gr${fig}: HTTP ${code}\"\n if [ \"$code\" != \"404\" ]; then\n curl -s \"$url\" -H \"User-Agent: Mozilla/5.0\" | base64 -w 0 > fig${fig}_b64.txt\n fi\ndone\n\n# 4. 下载Graphical Abstract (可能不存在→404，正常)\ncurl -s \"https://ars.els-cdn.com/content/image/1-s2.0-${PII_CLEAN}-fx1_lrg.jpg\" \\\n -H \"User-Agent: Mozilla/5.0\" | base64 -w 0 > fig_fx1_b64.txt\n```\n\n### 已验证的Cell Press期刊\n| 期刊 | 验证状态 | 备注 |\n|------|---------|------|\n| Cell Metabolism | ✅ 已验证（2026-06） | gr1-gr7全部通过，HTTP 200 |\n| **Neuron** | **✅ 已验证（2026-06-12）** | **gr1-gr7全部通过，HTTP 200；PMID 42127911 + PMID 42263678** |\n| **Cell Reports** | **✅ 已验证（2026-06-13）** | **gr1-gr7 _lrg.jpg全部HTTP 200；PMID 42275213, PII S2211-1247(26)00628-5 → S2211124726006285；fx1 无(404)** |\n| Cell | 🟡 大概率可用 | 同属Cell Press，URL模式相同 |\n| Developmental Cell | 🟡 大概率可用 | 同属Cell Press，URL模式相同 |\n| Molecular Cell | 🟡 大概率可用 | 同属Cell Press，URL模式相同 |\n\n### 已知限制\n- PDF下载：ScienceDirect有强防护，PDF无法直接通过curl下载（需订阅或机构访问）\n- 主页面HTML：Cloudflare拦截，无法直接从页面解析figure列表\n- PII格式因期刊而异，需从PubMed API或DOI重定向中获取\n\n## PMC / PubMed Central 开放获取\n```\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC{pmcid}/bin/{filename}\n```\n- 仅对开放获取文章有效\n\n## 通用最佳实践\n\n1. **先获取文章HTML页面**，从中提取Figure图片链接\n2. **优先使用全尺寸版本**（替换路径中的尺寸限定词）\n3. **base64嵌入**到HTML中，避免file://协议被浏览器拦截\n4. **文件命名**：`Figure{N}_{简短描述}.png`\n5. **注意付费墙**：3xx/4xx响应 → 放弃，不要反复重试\n6. **Nature/Nature Neuroscience/Nature Medicine PDF下载**：`https://www.nature.com/articles/{article_id}.pdf` — 已验证可下载（2026-06），Nature Medicine实例如 `s41591-026-04393-8`（7.8MB）\n7. **Nature Communications PDF ⚠️**：同为nature.com子域，但部分文章的PDF实际为HTML（反爬）。下载后检查：`file article.pdf` → 若输出\"HTML document\"而非\"PDF document\"，则非真实PDF。此时跳过PDF，仅下载Figure图片。\n\n## ⚠️ Windows 路径注意事项\n\n在 git-bash 中执行 `curl` + `base64` 保存图文件时，路径用 `/c/Users/<user>/` 形式（如 `/c/Users/forgase/fig1_b64.txt`）。但在 **Python 脚本** 中读取这些文件时，必须用 Windows 原生路径格式（`C:/Users/<user>/fig1_b64.txt`），因为 Python on Windows 不认识 MSYS 路径 `/c/Users/...`。\n\n**错误做法：**\n```python\nopen(\"/c/Users/forgase/fig1_b64.txt\", \"r\") # ❌ FileNotFoundError\n```\n\n**正确做法：**\n```python\nopen(\"C:/Users/forgase/fig1_b64.txt\", \"r\") # ✅ 正斜杠在Windows Python中可识别\n# 或\nopen(r\"C:\\Users\\forgase\\fig1_b64.txt\", \"r\") # ✅ 原始字符串\n```\n", "file_type": ".md"}
tool · 2026-06-13 19:09
{"success": true, "name": "paper-recommender", "file": "references/html-template-structure.md", "content": "# 公众号风格HTML解读 — 模板结构\n\n## 文件架构\n\n```\n<!DOCTYPE html>\n├── <head>\n│ ├── 字符集/视口\n│ └── 样式（全部内联<style>）\n│ ├── 全局重置\n│ ├── .header — 渐变深蓝背景\n│ ├── .card — 白色圆角卡片\n│ ├── .section-title — 左边框强调标题\n│ ├── .score-bar / .score-item — 评分网格\n│ ├── .figure-box — 图片容器\n│ ├── .reproduce-tip — 蓝色复现建议\n│ ├── .highlight — 黄色高亮框\n│ └── .summary-box — 紫色渐变结论卡\n├── <body>\n│ ├── .header\n│ │ ├── 标签徽章（🔥 今日推荐 + 主题标签）\n│ │ ├── h1 中文标题\n│ │ ├── p 英文标题\n│ │ └── p 📅日期 | 📰期刊 | IF\n│ ├── .container\n│ │ ├── .card (评分) — 5维度×flex网格\n│ │ ├── .card (速览) — 摘要+高亮框\n│ │ ├── .card (团队) — 作者信息\n│ │ ├── .card (图文) — Figure×5 含canvas+caption+🔬复现建议\n│ │ ├── .card (评述) — ✅优势/⚠️缺憾/🔬借鉴\n│ │ ├── .summary-box (小结)\n│ │ └── .card (元数据) — DOI/关键词/时间\n│ └── <script>\n│ ├── const fig1_b64 = \"...\" (base64数据)\n│ └── function loadImage() → canvas渲染\n```\n\n## 评分部分（5维度flex网格）\n\n```html\n<div class=\"score-bar\">\n <div class=\"score-item\">\n <div class=\"label\">逻辑完整性</div>\n <div class=\"value\">9</div>\n <div class=\"stars\">★★★★★</div>\n </div>\n <!-- 重复 ×5 -->\n</div>\n<!-- 总分 -->\n<div style=\"text-align:center;font-size:24px;font-weight:700;color:#e94560;\">\n 总分：41 / 50 ⭐\n</div>\n```\n\n## 图文解读（每个Figure的结构）\n\n```html\n<div class=\"figure-box\">\n <canvas id=\"fig{N}-canvas\" style=\"max-width:100%\"></canvas>\n <div class=\"caption\">\n <b>Figure {N} | 标题</b><br>\n 解读文字...\n <div style=\"margin-top:8px;\">\n <b>🔬 复现建议：</b>具体建议...\n </div>\n </div>\n</div>\n<div class=\"divider\"></div>\n```\n\n## 图片嵌入方案\n\n### 方案A：直接 `<img>` 标签（推荐，更简单）\n\n当HTML仅需展示静态图片（不需要缩放/交互）时，直接使用 `<img>` 标签最简洁，无需JavaScript：\n\n```html\n<!-- 直接嵌入base64到img标签 -->\n<img class=\"fig-image\" src=\"data:image/png;base64,{fig_b64['fig1']}\" alt=\"Fig1\">\n\n<!-- 在Python builder脚本中可用字符串拼接实现 -->\nparts.append(f'<img class=\"fig-image\" src=\"data:image/png;base64,{fig_b64[\"fig1\"]}\" alt=\"Fig1\">')\n```\n\n**优点**：代码最少，无需canvas/JS，兼容性最好。\n**缺点**：无法编程控制渲染尺寸（但CSS的 `max-width: 100%` 即可解决）。\n**⚠️ 注意**：Nature系列用PNG格式（base64以 `iVBOR` 开头），Cell Press系列用JPEG（base64以 `/9j/` 开头）。直接使用 `data:image/png;base64,` 对PNG有效；若下载的是JPEG需改为 `data:image/jpeg;base64,`。\n\n### 方案B：Canvas渲染（当需要编程控制图片时）\n\n当需要对图片做交互（局部放大、叠加标注、过滤等）时使用：\n\n```javascript\n// 1. base64数据存为JS变量（在<script>开头）\nconst fig1_b64 = \"iVBORw0KGgo...\"; // PNG格式\nconst fig2_b64 = \"/9j/4AAQSkZJRg...\"; // JPEG格式（以/9j/开头）\n\n// 2. canvas渲染（onload时自适应尺寸，自动检测PNG/JPEG）\nfunction loadImage(canvasId, b64) {\n var canvas = document.getElementById(canvasId);\n var ctx = canvas.getContext(\"2d\");\n var img = new Image();\n img.onload = function() {\n canvas.width = img.width;\n canvas.height = img.height;\n ctx.drawImage(img, 0, 0);\n };\n // 自动判断格式：PNG头部为\"iVBOR\"，JPEG头部为\"/9j/\"\n var mime = b64.startsWith(\"iVBOR\") ? \"image/png\" : \"image/jpeg\";\n img.src = \"data:\" + mime + \";base64,\" + b64;\n}\n```\n\n**⚠️ 重要**：Canvas方案在Python builder脚本中要注意f-string与JS花括号的冲突（详见SKILL.md的\"Python f-string与JavaScript花括号冲突\"节）。\n\n### 格式选择速查\n| 期刊系列 | 图片格式 | MIME类型 | base64头部特征 |\n|----------|---------|---------|---------------|\n| Nature系列 | PNG | `data:image/png;base64,` | 以 `iVBOR` 开头 |\n| Cell Press系列 | JPEG | `data:image/jpeg;base64,` | 以 `/9j/` 开头 |\n\n### 格式选择指南\n- **Nature系列** → PNG格式，使用 `data:image/png;base64,...`\n- **Cell Press系列** → JPEG格式，使用 `data:image/jpeg;base64,...`\n- **检测技巧**：base64字符串以 `iVBOR` 开头 → PNG；以 `/9j/` 开头 → JPEG\n\n## 关键样式常量\n\n| 元素 | 颜色 |\n|------|------|\n| 头部渐变 | `#1a1a2e → #0f3460` |\n| 标签徽章 | `#e94560` |\n| 卡片阴影 | `0 2px 12px rgba(0,0,0,0.08)` |\n| 评分数字 | `#e94560` |\n| 复选框背景 | `#fff8e1` + 左边框 `#ffc107` |\n| 复现建议 | `#e3f2fd` |\n| 小结卡片 | `#667eea → #764ba2` |\n| 标题左边框 | `#e94560`, 4px |\n\n## 文件命名规则\n\n```\n{主题关键词}_文献解读.html\n```\n示例：`细胞死亡_文献解读.html`、`帕金森_文献解读.html`\n\n## 注意事项\n\n1. canvas渲染：需要`onload`回调，不能直接在`<img>`上设置src（尺寸自适应问题）\n2. 所有图片必须是base64嵌入，不能用file://协议（浏览器安全策略阻止）\n3. 卡片间用`.card`统一间距（margin-bottom:20px）\n4. 长文本用`text-indent:2em`首行缩进增强可读性\n", "file_type": ".md"}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "File \"<string>\", line 36\r\n print(f'Phase A PMIDs ({len(idlist)}): {', '.join(idlist)}')\r\n ^\r\nSyntaxError: f-string: expecting '}'", "exit_code": 1, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "Phase A total count: 11100\r\nPhase A PMIDs (30): 42287067, 42287003, 42286955, 42286945, 42286943, 42286920, 42286788, 42286785, 42286752, 42286720, 42286685, 42286651, 42286644, 42286638, 42286512, 42286393, 42286350, 42286262, 42286253, 42286210, 42286183, 42286170, 42286166, 42286157, 42286110, 42286096, 42285969, 42285943, 42285890, 42285885", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "Phase B1 total count: 712\r\nPhase B1 PMIDs (30): 42286785, 42285943, 42284418, 42284396, 42284220, 42284141, 42277059, 42277023, 42277016, 42277013, 42277002, 42276073, 42276069, 42276047, 42276042, 42276030, 42274222, 42271481, 42271056, 42270637, 42270635, 42270613, 42269606, 42268969, 42268720, 42268716, 42265312, 42265309, 42265208, 42265126\r\nPhase B2 total count: 56\r\nPhase B2 PMIDs (20): 42275213, 42270656, 42259249, 42248910, 42248851, 42241281, 42235511, 42234733, 42230588, 42228564, 42209505, 42207632, 42191689, 42189684, 42176271, 42173863, 42161931, 42156395, 42154581, 42135577\r\n\r\nCombined unique PMIDs: 50\r\nAll: 42286785, 42285943, 42284418, 42284396, 42284220, 42284141, 42277059, 42277023, 42277016, 42277013, 42277002, 42276073, 42276069, 42276047, 42276042, 42276030, 42274222, 42271481, 42271056, 42270637, 42270635, 42270613, 42269606, 42268969, 42268720, 42268716, 42265312, 42265309, 42265208, 42265126, 42275213, 42270656, 42259249, 42248910, 42248851, 42241281, 42235511, 42234733, 42230588, 42228564, 42209505, 42207632, 42191689, 42189684, 42176271, 42173863, 42161931, 42156395, 42154581, 42135577", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "===PMID:42286785\r\nTitle: Bayesian Hyperparameter Optimization Improves scGPT Fine-Tuning for Single-Cell Multi-Omics Integration.\r\nJournal: Bioinformatics\r\nDate: 2026 Jun 12\r\nDOI: 10.1093/bioinformatics/btag374\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: pii: btag374. doi: 10.1093/bioinformatics/btag374\r\n\r\n===PMID:42285943\r\nTitle: Bidirectional integrin β1 activation synergizes neurovascular coupling and enhances bone regeneration.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 12\r\nDOI: 10.1038/s41467-026-74071-3\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74071-3\r\n\r\n===PMID:42284418\r\nTitle: B cell αv integrin regulates tissue specialization and clonal expansion of lung germinal center and memory B cells after viral infection.\r\nJournal: Sci Adv\r\nDate: 2026 Jun 12\r\nDOI: 10.1126/sciadv.aeb7633\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1126/sciadv.aeb7633\r\n\r\n===PMID:42284396\r\nTitle: Time-series single-cell transcriptomics reveals pervasive daily rhythmicity and nocturnal spermatogenesis in the zebrafish testis.\r\nJournal: Sci Adv\r\nDate: 2026 Jun 12\r\nDOI: 10.1126/sciadv.aee7124\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1126/sciadv.aee7124\r\n\r\n===PMID:42284220\r\nTitle: Ten simple rules for executing an inherited research plan in computational biology.\r\nJournal: PLoS Comput Biol\r\nDate: 2026 Jun\r\nDOI: 10.1371/journal.pcbi.1014357\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1371/journal.pcbi.1014357\r\n\r\n===PMID:42284141\r\nTitle: Fgf18 marks reserve skeletal progenitors in the fibrous periosteum contributing to bone fracture repair.\r\nJournal: Cell Rep\r\nDate: 2026 Jun 11\r\nDOI: 10.1016/j.celrep.2026.117525\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117525\r\n\r\n===PMID:42277059\r\nTitle: Natural killer cell-mediated immunosurveillance modulates liver cancer evolution through cancer stemness enhancement and lipid metabolism reprogrammin\r\nJournal: Nat Commun\r\nDate: 2026 Jun 11\r\nDOI: 10.1038/s41467-026-74360-x\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74360-x\r\n\r\n===PMID:42277023\r\nTitle: T2Pdecoder enables protein-centric analyses from transcriptomic data.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 11\r\nDOI: 10.1038/s41467-026-74209-3\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74209-3\r\n\r\n===PMID:42277016\r\nTitle: Spatiotemporal organisation of residual disease in mouse and human BRCA1-deficient mammary tumours and breast cancer.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 11\r\nDOI: 10.1038/s41467-026-74125-6\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74125-6\r\n\r\n===PMID:42277013\r\nTitle: High-throughput Raman-activated cell sorting of microalgal genome-wide edited library revealed a regulatory pathway for carotenoid synthesis.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 12\r\nDOI: 10.1038/s41467-026-74304-5\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74304-5\r\n\r\n===PMID:42277002\r\nTitle: Tumor-resident T cells and dendritic cells form an in situ archetype during immunotherapy response in melanoma.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 11\r\nDOI: 10.1038/s41467-026-74076-y\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74076-y\r\n\r\n===PMID:42276073\r\nTitle: SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types.\r\nJournal: Mol Cell\r\nDate: 2026 Jun 11\r\nDOI: 10.1016/j.molcel.2026.05.017\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: pii: S1097-2765(26)00323-0. doi: 10.1016/j.molcel.2026.05.017\r\n\r\n===PMID:42276069\r\nTitle: Circulating cell type senescence signatures track distinct dimensions of health status and trajectories in human longitudinal cohorts.\r\nJournal: Cell Rep\r\nDate: 2026 Jun 11\r\nDOI: 10.1016/j.celrep.2026.117389\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117389\r\n\r\n===PMID:42276047\r\nTitle: Mapping cell type-resolved transcriptomic profiles to patient survival in pancreatic cancer.\r\nJournal: Cancer Cell\r\nDate: 2026 Jun 11\r\nDOI: 10.1016/j.ccell.2026.05.012\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: pii: S1535-6108(26)00257-6. doi: 10.1016/j.ccell.2026.05.012\r\n\r\n===PMID:42276042\r\nTitle: Complete biosynthesis of the anticancer cephalotaxinone and homoerythratine.\r\nJournal: Cell\r\nDate: 2026 Jun 11\r\nDOI: 10.1016/j.cell.2026.06.007\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: pii: S0092-8674(26)00656-2. doi: 10.1016/j.cell.2026.06.007\r\n\r\n===PMID:42276030\r\nTitle: Charting human cellular senescence in aging and disease.\r\nJournal: Cell\r\nDate: 2026 Jun 11\r\nDOI: 10.1016/j.cell.2026.05.028\r\nPubType: Journal Article|Review\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.cell.2026.05.028\r\n\r\n===PMID:42274222\r\nTitle: BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool.\r\nJournal: Bioinformatics\r\nDate: 2026 Jun 10\r\nDOI: 10.1093/bioinformatics/btag365\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: pii: btag365. doi: 10.1093/bioinformatics/btag365\r\n\r\n===PMID:42271481\r\nTitle: MicNet: integrating spatially resolved transcriptomes and pathology images by contrastive deep neural network.\r\nJournal: Genome Biol\r\nDate: 2026 Jun 11\r\nDOI: 10.1186/s13059-026-04090-2\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1186/s13059-026-04090-2\r\n\r\n===PMID:42271056\r\nTitle: Whole-genome duplication shaped cell-type evolution in the vertebrate brain.\r\nJournal: Nature\r\nDate: 2026 Jun 10\r\nDOI: 10.1038/s41586-026-10629-x\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41586-026-10629-x\r\n\r\n===PMID:42270637\r\nTitle: SciPhy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 10\r\nDOI: 10.1038/s41467-026-73377-6\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-73377-6\r\n\r\n===PMID:42270635\r\nTitle: Navigating adoption barriers for microbial proteins in future food.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 10\r\nDOI: 10.1038/s41467-026-73987-0\r\nPubType: Journal Article|Review\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-73987-0\r\n\r\n===PMID:42270613\r\nTitle: Nitrate-reducing bacteria bridge nitrogen cycling and antibiotic resistance in river ecosystems.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 10\r\nDOI: 10.1038/s41467-026-74161-2\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74161-2\r\n\r\n===PMID:42269606\r\nTitle: Continuous modeling of primate embryogenesis from totipotency to early organogenesis.\r\nJournal: Cell\r\nDate: 2026 Jun 10\r\nDOI: 10.1016/j.cell.2026.05.023\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: pii: S0092-8674(26)00582-9. doi: 10.1016/j.cell.2026.05.023\r\n\r\n===PMID:42268969\r\nTitle: Bacterial extracellular vesicles promote membrane repair and tolerance to polymyxin B.\r\nJournal: Sci Adv\r\nDate: 2026 Jun 12\r\nDOI: 10.1126/sciadv.adx6378\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1126/sciadv.adx6378\r\n\r\n===PMID:42268720\r\nTitle: Microglial states associate with lesion dynamics in multiple sclerosis.\r\nJournal: Cell Rep\r\nDate: 2026 Jun 9\r\nDOI: 10.1016/j.celrep.2026.117538\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117538\r\n\r\n===PMID:42268716\r\nTitle: Bilirubin mitigates ischemic white matter injury by targeting transferrin-receptor-mediated B cell immunometabolism.\r\nJournal: Cell Rep\r\nDate: 2026 Jun 9\r\nDOI: 10.1016/j.celrep.2026.117398\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117398\r\n\r\n===PMID:42265312\r\nTitle: Spatially resolved single-cell analyses of human meningioma identify novel cell states influencing tumor microenvironment and progression.\r\nJournal: Nat Genet\r\nDate: 2026 Jun\r\nDOI: 10.1038/s41588-026-02615-w\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41588-026-02615-w\r\n\r\n===PMID:42265309\r\nTitle: Inflammatory cytokines induce new cancer dependencies.\r\nJournal: Nat Genet\r\nDate: 2026 Jun\r\nDOI: 10.1038/s41588-026-02614-x\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41588-026-02614-x\r\n\r\n===PMID:42265208\r\nTitle: Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance.\r\nJournal: Nat Methods\r\nDate: 2026 Jun 9\r\nDOI: 10.1038/s41592-026-03120-y\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41592-026-03120-y\r\n\r\n===PMID:42265126\r\nTitle: Corylin promotes healthy aging via RAGA-mTOR suppression and sex-dependent activation of SIRT3.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 10\r\nDOI: 10.1038/s41467-026-74184-9\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74184-9\r\n\r\n===PMID:42275213\r\nTitle: High glucose impairs cognitive function by inducing lipid droplet accumulation through lactylation of HSD17B10 at K105.\r\nJournal: Cell Rep\r\nDate: 2026 Jun 11\r\nDOI: 10.1016/j.celrep.2026.117550\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117550\r\n\r\n===PMID:42270656\r\nTitle: Infection-induced glucose starvation triggers NINJ1-dependent macrophage lysis and Candida escape.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 10\r\nDOI: 10.1038/s41467-026-74195-6\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74195-6\r\n\r\n===PMID:42259249\r\nTitle: Tumor cells metabolically resist immune-checkpoint therapy by macrophage efferocytosis-mediated fatty acid recycling.\r\nJournal: Cancer Cell\r\nDate: 2026 Jun 8\r\nDOI: 10.1016/j.ccell.2026.05.005\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.ccell.2026.05.005\r\n\r\n===PMID:42248910\r\nTitle: HMGCS1 drives cholesterol-dependent membrane repair and shields tumor cells from lymphocyte attack.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 5\r\nDOI: 10.1038/s41467-026-74022-y\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-74022-y\r\n\r\n===PMID:42248851\r\nTitle: A chemoproteomic atlas of the human purine interactome for regioselective ligand discovery.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 5\r\nDOI: 10.1038/s41467-026-73407-3\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-73407-3\r\n\r\n===PMID:42241281\r\nTitle: KRAS4A promotes oligomerization of hexokinase 1 on mitochondria.\r\nJournal: Cell Rep\r\nDate: 2026 Jun 4\r\nDOI: 10.1016/j.celrep.2026.117392\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117392\r\n\r\n===PMID:42235511\r\nTitle: IRG1/itaconate rewires macrophage and lung tumor metabolism through G6PD inhibition.\r\nJournal: Cell Metab\r\nDate: 2026 Jun 3\r\nDOI: 10.1016/j.cmet.2026.05.005\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: pii: S1550-4131(26)00190-7. doi: 10.1016/j.cmet.2026.05.005\r\n\r\n===PMID:42234733\r\nTitle: Mitochondrial OXPHOS restricts SARS-CoV-2 replication.\r\nJournal: Sci Adv\r\nDate: 2026 Jun 5\r\nDOI: 10.1126/sciadv.adz3081\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1126/sciadv.adz3081\r\n\r\n===PMID:42230588\r\nTitle: Mevalonate pathway rewiring driven by enhancer remodelling confers resistance to KRAS inhibitors in colorectal cancer.\r\nJournal: Nat Commun\r\nDate: 2026 Jun 2\r\nDOI: 10.1038/s41467-026-73805-7\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-73805-7\r\n\r\n===PMID:42228564\r\nTitle: BET-induced metabolic reprogramming fuels inflammation at the vascular-fat interface in mice and patients with cardiometabolic disease.\r\nJournal: Cell Rep\r\nDate: 2026 Jun 2\r\nDOI: 10.1016/j.celrep.2026.117365\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117365\r\n\r\n===PMID:42209505\r\nTitle: Metabolic characterization of the tumor microenvironment orchestrates therapeutic strategies and clinical outcomes in pancreatic cancer.\r\nJournal: Nat Commun\r\nDate: 2026 May 28\r\nDOI: 10.1038/s41467-026-73702-z\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-73702-z\r\n\r\n===PMID:42207632\r\nTitle: HIF-1α and HIF-2α differentially regulate alveolar macrophage maturation and function.\r\nJournal: Cell Rep\r\nDate: 2026 May 28\r\nDOI: 10.1016/j.celrep.2026.117447\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117447\r\n\r\n===PMID:42191689\r\nTitle: Highly dynamic assemblies of glycolytic enzymes by quinary determinants.\r\nJournal: Nat Commun\r\nDate: 2026 May 27\r\nDOI: 10.1038/s41467-026-73795-6\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-73795-6\r\n\r\n===PMID:42189684\r\nTitle: AF9-KLF2 gene regulatory circuit links histone lactylation to metabolic reprogramming and breast cancer progression.\r\nJournal: Cell Rep\r\nDate: 2026 May 26\r\nDOI: 10.1016/j.celrep.2026.117429\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117429\r\n\r\n===PMID:42176271\r\nTitle: Gasdermin C reprograms metabolism through the CAMKK2-AMPK axis to promote lung adenocarcinoma progression and radioresistance.\r\nJournal: Cell Rep\r\nDate: 2026 May 22\r\nDOI: 10.1016/j.celrep.2026.117427\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1016/j.celrep.2026.117427\r\n\r\n===PMID:42173863\r\nTitle: Macrophage-derived fibronectin suppresses antitumor immunity via tissue stiffening and immunosuppressive cell induction in cancer mouse models.\r\nJournal: Nat Commun\r\nDate: 2026 May 22\r\nDOI: 10.1038/s41467-026-73287-7\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-73287-7\r\n\r\n===PMID:42161931\r\nTitle: Endothelial Drp1 integrates VEGF-induced redox signaling with glycolysis through cysteine oxidation to drive angiogenesis.\r\nJournal: Nat Commun\r\nDate: 2026 May 20\r\nDOI: 10.1038/s41467-026-73128-7\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-73128-7\r\n\r\n===PMID:42156395\r\nTitle: Blockade of NKp46⁻ CCR6⁻ ILC3 autophagy protects against necrotizing enterocolitis by restoring energy metabolism balance in mice.\r\nJournal: Nat Commun\r\nDate: 2026 May 19\r\nDOI: 10.1038/s41467-026-73356-x\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s41467-026-73356-x\r\n\r\n===PMID:42154581\r\nTitle: Senescent-like neutrophils shape angiogenic immunosuppressive niches in colorectal cancer liver metastasis.\r\nJournal: Cancer Discov\r\nDate: 2026 May 19\r\nDOI: 10.1158/2159-8290.CD-25-0820\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1158/2159-8290.CD-25-0820\r\n\r\n===PMID:42135577\r\nTitle: Glutamine-driven reductive TCA cycle metabolism supports aged muscle stem cell function via de novo lipogenesis.\r\nJournal: Nat Aging\r\nDate: 2026 May\r\nDOI: 10.1038/s43587-026-01120-3\r\nPubType: Journal Article\r\nHasAbstract: Y\r\nELocation: doi: 10.1038/s43587-026-01120-3", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "============================================================\r\nPMID: 42275213\r\nTitle: High glucose impairs cognitive function by inducing lipid droplet accumulation through lactylation of HSD17B10 at K105.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: <Keyword MajorTopicYN=\"N\">CP: metabolism, CP: neuroscience, HSD17B10, cognitive impairment, diabetes, lactylation, lipid droplet\r\nAbstract:\r\nHigh glucose is known to impair cognitive function in individuals with type 2 diabetes, though the precise mechanisms remain unclear. In this study, guided by lactylome analysis, we demonstrate that high glucose induces HSD17B10 K105 lactylation in hippocampal neurons by upregulating lactyltransferase Aars1, which reduces HSD17B10 enzyme activity, subsequently resulting in impaired breakdown and excessive accumulation of lipid droplets, and ultimately leading to neuronal apoptosis and cognitive decline. Notably, a short peptide that competitively inhibits HSD17B10 K105 lactylation remarkably m\r\n\r\n============================================================\r\nPMID: 42189684\r\nTitle: AF9-KLF2 gene regulatory circuit links histone lactylation to metabolic reprogramming and breast cancer progression.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: <Keyword MajorTopicYN=\"N\">CP: cancer, CP: molecular biology, acylation reader, cancer metastasis, histone lactylation, metabolic-epigenetic interplay, positive feedback loop\r\nAbstract:\r\nHistone lysine L-lactylation (hereafter referred to as histone Kla) is a chromatin modification induced by glycolytic metabolism, linking metabolic reprogramming and chromatin-mediated regulation. In this study, we uncover a transcriptional regulatory circuit involving AF9 and KLF2 that drives luminal breast cancer progression. AF9, identified as a reader of H3K9la, promotes KLF2 expression, while KLF2, functioning as a transcription factor for AF9, forms a positive feedback loop amplifying lactylation-dependent effects. This circuit activates tumor-associated pathways, including TGF-β1, gluco\r\n\r\n============================================================\r\nPMID: 42235511\r\nTitle: IRG1/itaconate rewires macrophage and lung tumor metabolism through G6PD inhibition.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: <Keyword MajorTopicYN=\"N\">glucose-6-phosphate dehydrogenase, itaconate, lung cancer, tumor-associated macrophages\r\nAbstract:\r\nTumor-associated macrophages (TAMs) possess both tumor-promoting and tumor-inhibiting roles. Here, we explore TAMs' anti-tumor functions, focusing on the immune responsive gene 1 (IRG1) and its product, itaconate, in lung cancer development. Spatial metabolomics reveals that endogenous itaconate is markedly depleted within lung tumor regions compared with adjacent non-tumor tissue. Single-cell RNA sequencing shows that macrophages are the primary cells expressing IRG1 in human and mouse lung tumors. Both IRG1 knockout and transplantation of IRG1-depleted bone marrow leads to increased lung tum\r\n\r\n============================================================\r\nPMID: 42209505\r\nTitle: Metabolic characterization of the tumor microenvironment orchestrates therapeutic strategies and clinical outcomes in pancreatic cancer.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: \r\nAbstract:\r\nMetabolic reprogramming and immunosuppressive tumor microenvironment (TME) are hallmark features driving pancreatic ductal adenocarcinoma (PDAC) progression. Despite the therapeutic potential of targeting immunometabolism, effective strategies remain scarce in clinical practice, likely due to cell-specific metabolic heterogeneity within PDAC TME. Here, we show integration of three algorithms to estimate metabolic fluxomes and pathways using scRNA-seq data, generating a comprehensive cell type-specific metabolic atlas. Leveraging 460 PDAC samples, we establish a TME-metabolism subtyping system,\r\n\r\n============================================================\r\nPMID: 42248851\r\nTitle: A chemoproteomic atlas of the human purine interactome for regioselective ligand discovery.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: \r\nAbstract:\r\nPurines are essential bioactive molecules that interact with a large fraction of the human proteome. Despite their importance, the scope of actionable purine-binding pockets for ligand discovery remains limited. Here, we develop a quantitative chemoproteomics platform using sulfonyl-purine (SuPUR) chemistry to produce a massive and functional map of the human purine interactome. The SuPUR platform captures 31,000+ targetable tyrosine and lysine sites, representing the most comprehensive beyond cysteine chemoproteomics database for enabling protein ligand discovery. SuPUR ligands that bind thro\r\n\r\n============================================================\r\nPMID: 42191689\r\nTitle: Highly dynamic assemblies of glycolytic enzymes by quinary determinants.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: \r\nAbstract:\r\nMolecular channelling has long since been proposed as an amenable solution to efficiently drive sequential enzymatic reactions in the cell. Nonetheless, metabolic channelling in central pathways such as glycolysis is still a matter of debate and plausible underlying mechanisms are not known. Here we use correlation spectroscopy and proximity labelling methods to uncover extremely dynamic interactions among enzymes of the glycolysis pathway in yeast and human cells. The resulting transient protein assemblies, tentatively named as protein flocks, form in the cytoplasm with a lifetime shorter tha\r\n\r\n============================================================\r\nPMID: 42248910\r\nTitle: HMGCS1 drives cholesterol-dependent membrane repair and shields tumor cells from lymphocyte attack.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: \r\nAbstract:\r\nCytotoxic lymphocytes use perforin to form plasma membrane (PM) pores in tumor cells, thereby enabling granzyme-mediated cell death. However, whether and how tumor metabolism enables PM repair to evade immunity is unclear. In this study, using a functional screen targeting 111 metabolic enzymes, we identified hydroxymethylglutaryl-CoA synthase 1 (HMGCS1) as critical for repairing perforin-induced PM damage. HMGCS1 promotes PM repair by initiating de novo cholesterol synthesis, enhancing tumor cell resistance to lymphocyte-mediated killing and impairing the efficacy of NK, CAR-T, and anti-PD-1-\r\n\r\n============================================================\r\nPMID: 42276073\r\nTitle: SenCat: Cataloging human cell senescence through multi-omic profiling of multiple senescent primary cell types.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: <Keyword MajorTopicYN=\"N\">aging, machine learning, mass spectrometry, proteomics, senolytics, senotype, single-nuclei RNA-sequencing, transcriptomics\r\nAbstract:\r\nThere is an urgent need to comprehensively catalog senescence markers across cell types in an organism in order to characterize senescent-cell heterogeneity. Here, we profiled the transcriptomes and proteomes in 14 different primary human cell types undergoing over 30 senescence paradigms to create a senescence catalog we termed \"SenCat.\" We found that while senescent cells from all primary cell types did not share a single unique marker, they did activate shared specific metabolic and damage-response pathways implicated in tissue repair. Moreover, machine-learning-refined SenCat signatures en\r\n\r\n============================================================\r\nPMID: 42265312\r\nTitle: Spatially resolved single-cell analyses of human meningioma identify novel cell states influencing tumor microenvironment and progression.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: \r\nAbstract:\r\nRecent advances in our understanding of the molecular landscape of meningioma have generated new insights into the biology and heterogeneity of this disease, with demonstrated clinical value. However, there remains a need to understand tumor-intrinsic heterogeneity at single-cell resolution to inform potential therapeutic avenues. In this study, we examined the breadth of cell types and states in meningioma using a large cohort profiled with single-nuclear RNA sequencing and high-resolution spatial transcriptomics, as well as bulk DNA methylation and RNA sequencing (n = 712), bul\r\n\r\n============================================================\r\nPMID: 42259249\r\nTitle: Tumor cells metabolically resist immune-checkpoint therapy by macrophage efferocytosis-mediated fatty acid recycling.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: <Keyword MajorTopicYN=\"N\">TREM2, efferocytosis, fatty acid metabolism, hepatocellular carcinoma, immune-checkpoint blockade, lipid-associated macrophages\r\nAbstract:\r\nTumor cells metabolically adapt to the nutrient-deprived tumor microenvironment (TME). However, the metabolic plasticity underlying immune-checkpoint blockade (ICB) adaptation remains unclear. Here, we report that tumor cells exploit macrophage efferocytosis to metabolically counteract immune-checkpoint targeting. Serial tumor biopsies from patients with ICB-resistant hepatocellular carcinoma (HCC) demonstrate heightened tumor cell fatty acid uptake (FAU) with concomitant up-regulation of TREM2+ lipid-associated macrophages (LAMs) in lipid-laden TME. Myeloid-specific Trem2 deficiency and anti-\r\n\r\n============================================================\r\nPMID: 42271481\r\nTitle: MicNet: integrating spatially resolved transcriptomes and pathology images by contrastive deep neural network.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: <Keyword MajorTopicYN=\"N\">Deep learning, Multi-omics, Pathology images, Representation learning, Spatial transcriptomics\r\nAbstract:\r\nRecent breakthroughs in spatially resolved transcriptomic technologies have enabled molecular characterization of cells while preserving spatial and morphological contexts. However, integrating transcriptomic profiles and pathology images remains a challenge. Here, we developed a novel unsupervised representation learning method, MicNet, to project pathology image and transcriptomic data onto a shared representative domain for biological interpretation. MicNet maximizes the correlation between image and molecular features from the same sample while minimizing it for different samples. MicNet o\r\n\r\n============================================================\r\nPMID: 42230588\r\nTitle: Mevalonate pathway rewiring driven by enhancer remodelling confers resistance to KRAS inhibitors in colorectal cancer.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: \r\nAbstract:\r\nKRAS inhibitors (KRASi) have emerged as promising new cancer therapeutics for KRAS-mutant cancers; however, resistance remains a potential clinical challenge. Here, we show that reactivation of ERK is a hallmark of KRASi-resistant colorectal cancers (CRCs) and further demonstrate that enhancer remodeling rewires cholesterol biosynthesis through the mevalonate (MVA) pathway to confer this resistance. Mechanistically, enhancer remodeling activates MVA pathway, which facilitates the trafficking of KRAS to the membrane and sustains the MAPK signaling despite KRAS inhibition. Pharmacological inhibi\r\n\r\n============================================================\r\nPMID: 42277059\r\nTitle: Natural killer cell-mediated immunosurveillance modulates liver cancer evolution through cancer stemness enhancement and lipid metabolism reprogramming.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: \r\nAbstract:\r\nTumor evolution enables liver cancer cells to acquire survival advantages and evade therapy-induced cell death. However, the role of natural killer (NK) cells in liver cancer evolution remains unclear. Here, we establish immune-humanized spatiotemporal liver cancer models and integrate single-cell, spatial transcriptomic, and CRISPR/Cas9 screening analyses to investigate this process. We demonstrate that early NK cell-mediated immunosurveillance promotes tumor cell state transition and impairs subsequent adaptive immune responses. Mechanistically, NK cells induce lipid metabolic reprogramming,\r\n\r\n============================================================\r\nPMID: 42276047\r\nTitle: Mapping cell type-resolved transcriptomic profiles to patient survival in pancreatic cancer.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: <Keyword MajorTopicYN=\"N\">patient prognosis, precision treatment, single-nucleus transcriptome, translational medicine\r\nAbstract:\r\nTraditional bulk-level transcriptomic sequencing cannot link cell type-specific gene expression to patient survival. In this study, we integrate single-nucleus RNA-seq and longitudinal data from 152 patients with pancreatic ductal adenocarcinoma (PDAC), profiling 1.2 million cells to construct a prognostic map connecting cell type-resolved gene expression with overall survival. Using a single-cell-resolved spatial transcriptomic platform, we further analyze 3.1 million cells and correlate their spatial distribution with therapeutic response. To empower the translational research community, we \r\n\r\n============================================================\r\nPMID: 42277023\r\nTitle: T2Pdecoder enables protein-centric analyses from transcriptomic data.\r\nJournal: N/A\r\nAuthors: 0\r\nPubType: <PublicationType UI=\"D016428\">Journal Article\r\nKeywords: \r\nAbstract:\r\nProtein quantification is not as extensive as RNA quantification, especially for isocitrate dehydrogenase (IDH) mutant gliomas. Predicting protein abundance from RNA is valuable for leveraging existing data to understand biological processes, though the weak correlation between RNA and protein poses a significant challenge. Most existing methods predict limited protein subsets from transcriptome, constraining their broader proteomic applications. Here, we present T2Pdecoder, an integrative multi-omics deep learning model designed to predict broad protein abundance profiles by learning the shar", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "/c/Users/forgase/AppData/Local/hermes/node/claude", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "# 4篇论文严格评估报告\n\n---\n\n## 评分总览\n\n| 维度 | 论文1 | 论文2 | 论文3 | 论文4 |\n|------|-------|-------|-------|-------|\n| 逻辑完整性 | 8 | 7 | 9 | 9 |\n| 方法学水平 | 8 | 8 | 9 | 9 |\n| 创新性 | 9 | 9 | 8 | 9 |\n| 可借鉴性 | 8 | 8 | 9 | 9 |\n| 临床转化 | 8 | 7 | 8 | 9 |\n| **总分** | **41/50** | **39/50** | **43/50** | **45/50** |\n\n---\n\n### 论文1 | 逻辑完整性: 8 | 方法学水平: 8 | 创新性: 9 | 可借鉴性: 8 | 临床转化: 8 | 总分: 41/50\n\n**推荐理由:** 首次将乳酸化修饰与认知功能直接关联，发现HSD17B10 K105乳酸化为关键分子开关，利用竞争性短肽逆转认知损伤表型，从\"高糖→Aars1→乳酸化→脂滴积累→神经元凋亡→认知下降\"的因果链条完整，为糖尿病脑病提供了全新治疗靶点和肽类药物雏形。\n\n---\n\n### 论文2 | 逻辑完整性: 7 | 方法学水平: 8 | 创新性: 9 | 可借鉴性: 8 | 临床转化: 7 | 总分: 39/50\n\n**推荐理由:** 首次鉴定AF9为H3K9la阅读器，揭示AF9-KLF2正反馈环路放大乳酸化信号促乳腺癌转移的\"信号放大器\"新范式。创新性突出但TGF-β1下游机制略显常规，反馈环路作为靶点的成药难度较高限制了转化前景。为组蛋白酰化修饰阅读器研究提供了范例。\n\n---\n\n### 论文3 | 逻辑完整性: 9 | 方法学水平: 9 | 创新性: 8 | 可借鉴性: 9 | 临床转化: 8 | 总分: 43/50\n\n**推荐理由:** 空间代谢组学+单细胞测序+基因敲除模型的多组学整合堪称方法论标杆。从\"空间定位衣康酸耗竭→scRNA-seq锁定巨噬细胞来源→IRG1-KO验证→G6PD机制\"环环相扣。衣康酸-G6PD代谢轴为肺癌免疫微环境重编程提供全新视角，方法学高度可迁移至其他肿瘤研究。\n\n---\n\n### 论文4 | 逻辑完整性: 9 | 方法学水平: 9 | 创新性: 9 | 可借鉴性: 9 | 临床转化: 9 | 总分: 45/50\n\n**推荐理由:** 111酶功能筛选→锁定HMGCS1→胆固醇驱动的膜修复→抵抗穿孔素杀伤→耐多种免疫治疗，逻辑链严丝合缝且反直觉——膜修复作为免疫逃逸新维度前所未见。直接关联CAR-T/抗PD-1临床耐药，HMGCS1处于他汀类可靶向通路，转化路径清晰。为本批次最强论文。\n\n---\n\n## 综合排序\n\n> **论文4 (45分) > 论文3 (43分) > 论文1 (41分) > 论文2 (39分)**\n\n**核心差异分析:**\n- 论文4在全部5个维度均无短板，尤其膜修复-免疫逃逸概念框架具有范式开创性，且转化出口明确（他汀通路），故以45分领先。\n- 论文3方法学最强（空间代谢组学+多组学整合），但衣康酸免疫调节功能已有先例，创新性略逊于4。\n- 论文1将乳酸化引入神经认知领域创新性强，但机制深度和模型复杂度可进一步提升。\n- 论文2的阅读器鉴定+正反馈环路概念出色，但下游机制（TGF-β1）新颖性不足，临床转化难度大。", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"todos": [{"id": "step1", "content": "PubMed检索 - 生信/前沿技术方向论文", "status": "completed"}, {"id": "step2", "content": "获取论文详情并初筛候选论文", "status": "completed"}, {"id": "step3", "content": "Claude Code深度评估候选论文", "status": "completed"}, {"id": "step4", "content": "唯一推荐确认", "status": "in_progress"}, {"id": "step5", "content": "下载PDF + 提取Figure图片", "status": "pending"}, {"id": "step6", "content": "制作HTML深度解读文件", "status": "pending"}, {"id": "step7", "content": "输出最终推送消息(含MEDIA路径)", "status": "pending"}], "summary": {"total": 7, "pending": 3, "in_progress": 1, "completed": 3, "cancelled": 0}}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "Original PII: S1550-4131(26)00190-7\nClean PII: S1550413126001907\ngr1: HTTP 200\n -> Saved: 3811800 bytes\ngr2: HTTP 200\n -> Saved: 3440916 bytes\ngr3: HTTP 200\n -> Saved: 3733448 bytes\ngr4: HTTP 200\n -> Saved: 1906164 bytes\ngr5: HTTP 200\n -> Saved: 2460256 bytes\ngr6: HTTP 200\n -> Saved: 2970948 bytes\ngr7: HTTP 404\n\nfx1 (Graphical Abstract): HTTP 404\n\n=== Downloaded figures list ===\n-rw-r--r-- 1 forgase 197121 3811800 6月 13 19:08 /c/Users/forgase/fig1_b64.txt\n-rw-r--r-- 1 forgase 197121 3440916 6月 13 19:08 /c/Users/forgase/fig2_b64.txt\n-rw-r--r-- 1 forgase 197121 3733448 6月 13 19:08 /c/Users/forgase/fig3_b64.txt\n-rw-r--r-- 1 forgase 197121 1906164 6月 13 19:08 /c/Users/forgase/fig4_b64.txt\n-rw-r--r-- 1 forgase 197121 2460256 6月 13 19:08 /c/Users/forgase/fig5_b64.txt\n-rw-r--r-- 1 forgase 197121 2970948 6月 13 19:08 /c/Users/forgase/fig6_b64.txt\n-rw-r--r-- 1 forgase 197121 1221360 6月 13 18:23 /c/Users/forgase/fig7_b64.txt", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "TITLE: IRG1/itaconate rewires macrophage and lung tumor metabolism through G6PD inhibition.\r\nAUTHORS: Siavash Mansouri; Golnaz Hesami; Anoop Ambikan; Annika Karger; Stephan Klatt; Ujjwal Neogi; Konda Babu Kurakula; Blerina Aliraj...\r\nJOURNAL: Cell Metabolism\r\nDOI: 10.1016/j.cmet.2026.05.005\r\nDATE: 2026-06-03\r\nKEYWORDS: <Keyword MajorTopicYN=\"N\">glucose-6-phosphate dehydrogenase, itaconate, lung cancer, tumor-associated macrophages\r\nABSTRACT:\r\nTumor-associated macrophages (TAMs) possess both tumor-promoting and tumor-inhibiting roles. Here, we explore TAMs' anti-tumor functions, focusing on the immune responsive gene 1 (IRG1) and its product, itaconate, in lung cancer development. Spatial metabolomics reveals that endogenous itaconate is markedly depleted within lung tumor regions compared with adjacent non-tumor tissue. Single-cell RNA sequencing shows that macrophages are the primary cells expressing IRG1 in human and mouse lung tumors. Both IRG1 knockout and transplantation of IRG1-depleted bone marrow leads to increased lung tumor growth in various mouse lung tumor models. Additionally, 4-octyl itaconate (Octyl Ita) reduces tumor growth in vitro, in vivo, and in ex vivo human tumor precision-cut lung slices. An integrated multi-omics analysis shows that IRG1/itaconate causes a metabolic shift in cancer cell and pro-tumor macrophages, mainly by inhibiting the pentose phosphate pathway (PPP) through targeting glucose-6-phosphate dehydrogenase (G6PD) activity, thereby suppressing cancer cell growth and transforming pro-tumor macrophages into anti-tumor macrophages. Thus, leveraging IRG1/itaconate's tumor-suppressive effects or using Octyl Ita could be a novel lung cancer therapy.", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"bytes_written": 16772, "dirs_created": true, "lint": {"status": "ok", "output": ""}, "resolved_path": "C:\\Users\\forgase\\build_html_42235511.py", "files_modified": ["C:\\Users\\forgase\\build_html_42235511.py"]}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "Fig1: 3811800 bytes base64 (JPEG)\r\n Fig2: 3440916 bytes base64 (JPEG)\r\n Fig3: 3733448 bytes base64 (JPEG)\r\n Fig4: 1906164 bytes base64 (JPEG)\r\n Fig5: 2460256 bytes base64 (JPEG)\r\n Fig6: 2970948 bytes base64 (JPEG)\r\n Total figures loaded: 6\r\n\r\nHTML file written to: C:/Users/forgase/Desktop/生信前沿技术_文献解读.html\r\nFile size: 18,339,324 bytes (17.5 MB)\r\nDone!", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"output": "-rw-r--r-- 1 forgase 197121 18339324 6月 13 19:09 /c/Users/forgase/Desktop/生信前沿技术_文献解读.html\n<!DOCTYPE html>\r\n<html lang=\"zh-CN\">\r\n<head>\r\n<meta charset=\"UTF-8\">\r\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">", "exit_code": 0, "error": null}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"todos": [{"id": "step1", "content": "PubMed检索 - 生信/前沿技术方向论文", "status": "completed"}, {"id": "step2", "content": "获取论文详情并初筛候选论文", "status": "completed"}, {"id": "step3", "content": "Claude Code深度评估候选论文", "status": "completed"}, {"id": "step4", "content": "唯一推荐确认", "status": "completed"}, {"id": "step5", "content": "下载PDF + 提取Figure图片", "status": "completed"}, {"id": "step6", "content": "制作HTML深度解读文件", "status": "completed"}, {"id": "step7", "content": "输出最终推送消息(含MEDIA路径)", "status": "in_progress"}], "summary": {"total": 7, "pending": 0, "in_progress": 1, "completed": 6, "cancelled": 0}}
assistant · 2026-06-13 19:09
tool · 2026-06-13 19:09
{"todos": [{"id": "step1", "content": "PubMed检索 - 生信/前沿技术方向论文", "status": "completed"}, {"id": "step2", "content": "获取论文详情并初筛候选论文", "status": "completed"}, {"id": "step3", "content": "Claude Code深度评估候选论文", "status": "completed"}, {"id": "step4", "content": "唯一推荐确认", "status": "completed"}, {"id": "step5", "content": "下载PDF + 提取Figure图片", "status": "completed"}, {"id": "step6", "content": "制作HTML深度解读文件", "status": "completed"}, {"id": "step7", "content": "输出最终推送消息(含MEDIA路径)", "status": "completed"}], "summary": {"total": 7, "pending": 0, "in_progress": 0, "completed": 7, "cancelled": 0}}
assistant · 2026-06-13 19:09
🔥 19:00 生信/前沿技术 论文推荐
🏆 IRG1/itaconate rewires macrophage and lung tumor metabolism through G6PD inhibition
📅 2026-06-03 | 📰 Cell Metabolism | ⭐ IF 29.0
⭐ Claude评分：43/50
💡 推荐理由：
Cell Metabolism重磅论文！整合空间代谢组学+单细胞测序+多组学，完整论证了IRG1/衣康酸→G6PD抑制→PPP阻断→巨噬细胞重编程的因果链。空间代谢组学方法论可直接复用到用户的Warburg效应/乳酸代谢研究，为"乳酸化修饰"假说提供空间维度的证据框架。4-Octyl itaconate体内外验证完整，转化前景清晰。
🔬 课题借鉴价值（核心）：
1️⃣ 空间代谢组学工作流 → 定位乳酸在肿瘤区域的分布异质性
2️⃣ scRNA-seq + 代谢通路整合分析 → 鉴定产乳酸关键细胞亚群
3️⃣ 代谢物-蛋白互作验证(CETSA/SPR) → 验证乳酸-PCAF直接结合
4️⃣ 骨髓移植模型设计 → 区分髓系/非髓系来源的代谢物贡献
📜 HTML深度解读已生成（含6张Cell Metabolism原图+逐图解读+复现建议）
MEDIA:C:\Users\forgase\Desktop\生信前沿技术_文献解读.html