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Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
4.2 KiB
4.2 KiB
id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack
| id | title | category | status | canonical_id | aliases | duplicate_of | source_trust_level | confidence_score | verification_status | tags | raw_sources | last_reinforced | github_commit | tech_stack | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| wiki-2026-0508-nutritional-biochemistry | Nutritional Biochemistry | 10_Wiki/Topics | verified | self |
|
none | A | 0.9 | applied |
|
2026-05-10 | pending |
|
Nutritional Biochemistry
매 한 줄
"매 nutrient 는 metabolic substrate + cofactor + signal". Nutritional biochemistry 는 macronutrient 와 micronutrient 가 cellular metabolism, gene expression, signaling 에 어떻게 작용하는지 연구. 2026 perspective 에서 personalized nutrition + microbiome interaction + metabolomics 가 frontier.
매 핵심
매 macronutrient pathways
- Carbohydrate: glycolysis → pyruvate → acetyl-CoA → TCA → ETC. 4 kcal/g.
- Lipid: β-oxidation → acetyl-CoA → TCA. 9 kcal/g. Membrane lipids, eicosanoids.
- Protein: amino acid → transamination → urea / gluconeogenesis. 4 kcal/g.
매 micronutrient roles
- B-vitamins: coenzymes (NAD, FAD, CoA, THF, PLP).
- Fat-soluble (ADEK): signaling (retinoic acid, calcitriol).
- Minerals: cofactors (Mg-ATP, Zn-fingers, Fe-heme), electrolytes.
매 응용
- Sports nutrition / supplement design.
- Disease management (diabetes, NAFLD).
- Personalized diet (genotype + microbiome).
- Public health policy.
💻 패턴
Basal metabolic rate (Mifflin-St Jeor)
def bmr(weight_kg, height_cm, age_y, sex="M"):
base = 10*weight_kg + 6.25*height_cm - 5*age_y
return base + 5 if sex == "M" else base - 161
def tdee(bmr_val, activity="moderate"):
factors = {"sedentary": 1.2, "light": 1.375,
"moderate": 1.55, "active": 1.725}
return bmr_val * factors[activity]
Macro split optimization
def macro_split(tdee, goal="maintain", body_kg=70):
protein_g = body_kg * (1.6 if goal == "cut" else 1.2)
p_cal = protein_g * 4
fat_cal = tdee * 0.25
carb_cal = tdee - p_cal - fat_cal
return {"protein_g": protein_g,
"fat_g": fat_cal / 9,
"carb_g": carb_cal / 4}
Glycemic load
def glycemic_load(food: dict) -> float:
return food["gi"] * food["carb_g"] / 100
# Low <10, medium 11-19, high 20+
TCA cycle ATP yield
def atp_per_glucose():
glycolysis = 2
nadh_glyc = 2 * 2.5
pyruvate_to_acetyl = 2 * 2.5
tca_per_acetyl = (3*2.5) + 1.5 + 1
tca_total = 2 * tca_per_acetyl
return glycolysis + nadh_glyc + pyruvate_to_acetyl + tca_total
# ≈ 30 ATP / glucose (modern stoichiometry)
Nitrogen balance
def n_balance(protein_intake_g, urea_n_excreted_g, fecal_skin_g=4):
n_in = protein_intake_g / 6.25
n_out = urea_n_excreted_g + fecal_skin_g
return n_in - n_out
Vitamin D activation cascade
def vit_d_activation():
return [
"7-dehydrocholesterol",
"cholecalciferol (D3, skin UVB)",
"25-OH-D3 (liver, CYP2R1)",
"1,25-(OH)2-D3 (kidney, CYP27B1, calcitriol)",
"VDR-RXR transcription factor",
]
매 결정 기준
| 상황 | Approach |
|---|---|
| Weight loss | Caloric deficit + protein-priority |
| Performance | Periodized carb + creatine |
| T2D management | Low GL + Mediterranean |
| Deficiency screen | Serum 25-OH-D, B12, ferritin, Mg |
기본값: TDEE 기반 + protein floor + micronutrient screen.
🔗 Graph
🤖 LLM 활용
언제: meal plan template, nutrient interaction summary, label decoding. 언제 X: clinical diagnosis / Rx — RD / MD 필수.
❌ 안티패턴
- Calorie-only thinking: hormone / micronutrient 무시.
- Single-nutrient hype: antioxidant / superfood 일반화 — context-free.
- Ignoring bioavailability: total intake ≠ absorbed.
- Population stat → individual: personal genetics / microbiome 무시.
🧪 검증 / 중복
- Verified (Lehninger 8e, Modern Nutrition in Health and Disease 11e, USDA DRI 2024).
- 신뢰도 A.
🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — BMR/macro/TCA/Vit-D 패턴 |