257 件 ・ 関連度順
概要と使いどころ
This skill directs lifelong treatment of cardiovascular risk factors, osteoporosis, and psychiatric symptoms in all patients with Cushing's syndrome until biochemical resolution of hypercortisolism. Use when managing any CS patient for long‑term comorbidity control; triggers include persistent hypertension, dyslipidemia, low bone density, depression, anxiety, or cognitive changes in the setting of known or suspected CS.
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This skill recommends obtaining a postoperative pituitary MRI within 1–3 months after successful transsphenoidal surgery (TSS) to establish a new baseline for future recurrence assessment. Use when a Cushing’s syndrome patient has achieved biochemical remission post‑TSS and is entering routine follow‑up.
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This skill recommends against initiating treatments aimed at normalizing cortisol or its action when only borderline biochemical abnormalities of the hypothalamic-pituitary-adrenal (HPA) axis are present without specific signs of Cushing's syndrome. Use when encountering borderline urinary free cortisol, late-night salivary cortisol, or dexamethasone suppression test results without classic Cushingoid features; triggers include borderline UFC (1-2× ULN), equivocal late-night salivary cortisol, or nondiagnostic low-dose DST.
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Calculates the lowest effective glucocorticoid dose (dexamethasone or prednisone) to normalize blood pressure and serum potassium in glucocorticoid-remediable aldosteronism (GRA/FH-I) by titrating to biochemical and clinical targets. Indicated when initiating medical treatment for confirmed GRA, triggered by findings such as early-onset hypertension, family history of stroke before age 40, spontaneous hypokalemia, or suppressed plasma renin activity with elevated aldosterone.
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Recommends performing biochemical testing for HPA axis at least 18-24 hours after last hydrocortisone dose or longer for synthetic glucocorticoids. Use when scheduling HPA axis testing in glucocorticoid-treated patient needing assessment of adrenal reserve.
日本語の概要は準備中です。原文の説明を表示しています。
医学证据等级排序与编排层 skill。当用户请求"证据等级 / GRADE / 证据排序 / 医学证据评估 / evidence grading / quality of evidence / 系统评价排序 / 临床证据排序 / 证据金字塔"时触发。本 skill 不直接调用 NCBI/Europe PMC/ClinicalTrials API,而是编排 6 个底层原子 skill(pubmed-eutils / europepmc-search / clinical-trials-v2 / aact-bulk-trials / bioc-fulltext-fetch / pubtator-entity-search)的召回结果,按 ChatGPT 推荐的证据金字塔(指南 > Meta > RCT > Cohort > Case-Control > Case Report)做 GRADE 自动评级,自动剔除 editorial / letter / comment。优先使用 BioMCP MCP server 做 fan-out(genomoncology/biomcp),无 BioMCP 时走自建 slow-path 兜底。
日本語の概要は準備中です。原文の説明を表示しています。
NLM PubTator3 实体级关系挖掘原子 skill — 在 PubMed 文献里检索"疾病-药物-基因-化学品-突变-物种-细胞系"的标注与关系三元组。当用户问"BTK 与肺曲霉病的关联文献"、"BTK 抑制剂(MeSH D000077180)在哪些研究被讨论"、"标注这些 PMID 中提到的所有疾病/药物/基因实体"、"ibrutinib 的 MeSH/DrugBank ID"、"voriconazole 与 CYP2C19 药物-基因相互作用"、"BRAF V600E 突变 / rs113488022 检索"、"实体共现 / co-mention / 关系挖掘 / 实体规范化 / NER / annotation / entity normalization / disease-gene association / drug-gene interaction / chemical-disease relation" 时使用。无需 API key,跨平台稳定。**实体级 + 关系级**定位 — 不做综合文献检索(走 pubmed-eutils / europepmc-search)、不做全文段落抽取(走 bioc-fulltext-fetch)、不做证据等级排序(走 medical-evidence-grading)。
日本語の概要は準備中です。原文の説明を表示しています。
医学证据等级排序与编排层 skill - evidence grading orchestrator. 当用户请求"证据等级 / GRADE / 证据排序 / 医学证据评估 / 系统评价排序 / 临床证据排序 / 证据金字塔 / 给 PMID 打分 / Practice Guideline 优先 / Cochrane 优先 / 反向证据交叉验证 / evidence grading / quality of evidence / evidence pyramid / GRADE assessment / Cochrane review ranking / clinical evidence ranking"时,**调用本 skill 进行医学证据自动分级,自动选择最佳信息源**。本 skill 是编排层(L2)·不直接打 NCBI/Europe PMC/ClinicalTrials API,而是编排 6 个底层原子 skill(pubmed-eutils / europepmc-search / clinical-trials-v2 / aact-bulk-trials / bioc-fulltext-fetch / pubtator-entity-search)的召回结果,按证据金字塔(指南 > Meta > RCT > Cohort > Case-Control > Case Report)做 GRADE A/B/C/D 自动评级,自动剔除 editorial/letter/comment。优先使用 BioMCP MCP server 做 fan-out(genomoncology/biomcp);无 BioMCP 时走自建 slow-path 兜底。跨平台无锁(Claude Code / Codex / Gemini CLI)。
日本語の概要は準備中です。原文の説明を表示しています。
NLM PubTator3 实体级关系挖掘 — 检索"疾病-药物-基因-化学品-突变"在生物医学文献中的标注与关系。当用户问"哪些研究讨论了 BTK 抑制剂与肺曲霉病"、"基因 X 与疾病 Y 的关联"、"药物-基因相互作用"、"找含某突变的文献"、"PubTator / 实体标注 / NER / annotation / biomedical entity / 疾病基因关联 / 药物-基因 / 化学品-疾病 / 突变检索"时使用。无需 API key。**实体级 · 关系挖掘**定位 — 不做文献综合检索(用 pubmed-eutils / europepmc-search)、不做全文(用 BioC)、不做试验(用 ct-v2)。
日本語の概要は準備中です。原文の説明を表示しています。
Europe PMC RESTful Web Service 多源文献聚合检索原子 skill。**使用本 skill 进行医学文献广召回 / RAG 知识库构建 / 跨库聚合 / 预印本扫描 / 全文链接发现**。一次调用同时召回 PubMed (MED) + PMC + AGRICOLA + CABI + 预印本(bioRxiv / medRxiv / ChemRxiv / Research Square)+ NCBI Bookshelf + 专利,自动按 PMID/PMCID/DOI 三级去重,返回完整元数据(全文 OA 链接、citations、grants 资助信息)。**定位:广召回 · 多源 · RAG 友好**(与 pubmed-eutils 互补 — 后者做单源精准官方检索 + Clinical Queries;本 skill 做"撒网"式多源聚合)。触发词:Europe PMC / EuropePMC / 多源文献聚合 / 广召回 / RAG 召回 / RAG 知识库 / 预印本搜索 / preprint search / bioRxiv / medRxiv / ChemRxiv / DOI 检索 / DOI lookup / 全文链接 / full-text links / OA 全文 / PMCID / 灰色文献 / grey literature / 跨库去重 / 资助检索 / grant search / NIH funded / Wellcome funded / citations metadata。**不做**:NCBI E-utilities 直检(→ pubmed-eutils)、临床试验注册(→ clinical-trials-v2)、全文 XML 抽取(→ bioc-fulltext-fetch)、实体识别(→ pubtator-entity-search)、证据分级(→ medical-evidence-grading)、参考文献终态写入(→ evidence-appendix-sync)。
日本語の概要は準備中です。原文の説明を表示しています。
Europe PMC RESTful Web Service 多源文献聚合检索原子 skill。一次调用同时召回 PubMed + PMC + AGRICOLA + CABI + 预印本(bioRxiv / medRxiv / ChemRxiv 等)+ 灰色文献,自动按 PMID/PMCID/DOI 三级去重,返回完整元数据(含全文 OA 链接、citations metadata、grants 资助信息)。当任务出现 Europe PMC、多源文献聚合、广召回、RAG 检索、预印本搜索、bioRxiv、medRxiv、全文链接、DOI 检索、跨库去重、灰色文献、资助检索 等触发词时使用。**定位**:广召回 · 多源聚合 · RAG 友好(与 pubmed-eutils 互补——后者做精准官方源、Clinical Queries;本 skill 做"撒网"式广召回)。**不做**:NCBI E-utilities 直检(交给 pubmed-eutils)、临床试验(clinical-trials-v2)、全文 XML 抽取(bioc-fulltext-fetch)、实体识别(pubtator-entity-search)、证据分级(medical-evidence-grading)。
日本語の概要は準備中です。原文の説明を表示しています。
Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.
日本語の概要は準備中です。原文の説明を表示しています。
Audit required bioinformatics command-line tools against Lmod or Environment Modules on Linux HPC clusters, resolve executable and version requirements, and reproducibly deploy missing software through a site-approved EasyBuild, Spack, Bioconda/micromamba, Apptainer, binary, or source-build workflow. Use when Codex must inspect module availability, diagnose a missing or wrong-version bioinformatics tool, install scientific software on Slurm/PBS clusters, publish or validate modulefiles, or produce an auditable and rollback-ready cluster software deployment.
日本語の概要は準備中です。原文の説明を表示しています。
Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.
日本語の概要は準備中です。原文の説明を表示しています。
Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.
日本語の概要は準備中です。原文の説明を表示しています。
Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.
日本語の概要は準備中です。原文の説明を表示しています。
Audit required bioinformatics command-line tools against Lmod or Environment Modules on Linux HPC clusters, resolve executable and version requirements, and reproducibly deploy missing software through a site-approved EasyBuild, Spack, Bioconda/micromamba, Apptainer, binary, or source-build workflow. Use when Codex must inspect module availability, diagnose a missing or wrong-version bioinformatics tool, install scientific software on Slurm/PBS clusters, publish or validate modulefiles, or produce an auditable and rollback-ready cluster software deployment.
日本語の概要は準備中です。原文の説明を表示しています。