Research Pulse — 2026-10-16 | Brown Biotech

# Research Pulse — 2026-10-16
_Generated 2026-10-16 07:30 KST · AI × Bioresearch Daily synthesis_

Brown Biotech daily research pulse — multi-front synthesis across CRISPR /
gene editing, AI drug discovery, protein AI, single-cell foundation models,
FDA/regulatory, and spatial transcriptomics. Sources: company filings,
peer-reviewed journals, FDA guidance, conference disclosures.

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### Post 1: 오늘의 전체 흐름

🧬 **AI × Bioresearch Daily — 10월 16일 (목요일) · Interfacing(연결) Day**

10/16(연결) → 10/15(규모화) → 10/14(평가) → 10/13(표준화) → 10/12(종합) → 10/11(문맥화) → 10/10(실현) → 10/09(임상 전환). Scaling(10/15)된 Framework들이 **각 분야主流 체계에서 서로 연결(Interfacing)되며 새로운 치료 개발 패러다임을 구현하는 단계로 진입하는 날** — Lushington et al.로 Prime editing first-in-human trial: pegRNA + Cas9 nickase + reverse transcriptase fusion + functional restoration + promising safety profile 확인(Molecular Therapy 2026), Feierman et al.로 FDA February 2026 plausible mechanism framework: personalized in vivo prime editing platform + 7 urea cycle disorders(UCDs) + "umbrella-of-umbrellas" clinical trial + formal FDA meeting(AJHG 2026), Huynh et al.로 AI agents drug discovery: LLM-based autonomous reasoning + literature synthesis + toxicity prediction + molecular design + AstraZeneca ChatInvent real-world deployment(Drug Discovery Today 2026), He et al.로 AstraZeneca ChatInvent: multi-agent architecture + molecular design + synthesis planning + proof-of-concept → scalable robust architecture + real-world pharmaceutical deployment(Drug Discovery Today 2026), Feldman et al.로 AlphaFold3 adversarial evaluation: 40% residue invariance + confidence metric unreliability + ESMFold greater mutational sensitivity + template memorization vs biophysical reasoning concerns(Computational Structural Biotechnology 2026), ElSheikh et al.로 Cell-JEPA single-cell: joint-embedding predictive architecture + 36% relative improvement over scGPT + dropout-robust features + perturbation modeling latent space(ArXiv 2026), Zhao et al.로 FDA oncology trials demographic disparities: Black EIR 0.25 + Hispanic EIR 0.51 + women EIR 0.92 + older adults EIR 0.67 + 372 pivotal trials 2018-2026(International Journal Equity Health 2026), Chen et al.로 MERFISH 2.0: 1,000 genes + frozen/FFPE + custom 100 genes + 815-gene IO panel + MERSCOPE Ultra( Cancer Research 2026)이 동시에 진행된다.

Interfacing은 Scaling의 다음 단계다. 오늘 6개 전선은 규모화된 Framework들이 **실제 임상-규제 연결·AI-제약 실제 배포·구조모델 한계-보완 연결·단일세포-perturbation 연결·피부과학-다양성 연결·공간해상도 확대 연결로 서로 인터페이싱되며 새로운 치료 개발 패러다임을 구현하는 날**이다.

*출처: [Lushington Prime Editing Molecular Therapy 2026](https://consensus.app/papers/details/79a173589b1d58598fd94fa334768881/), [Feierman FDA Plausible Mechanism AJHG 2026](https://consensus.app/papers/details/c10e0f99f20b517e8bb3b5ae4b666dcc/), [Huynh AI Agents Drug Discovery Today 2026](https://consensus.app/papers/details/d47b0cf47a4f5055a7175a6fe09bb6e4/), [He AstraZeneca ChatInvent Drug Discovery Today 2026](https://consensus.app/papers/details/4dc8cd75fd9d59a5943e992c0a05ae28/), [Feldman AlphaFold3 Adversarial Computational Structural Biotechnology 2026](https://consensus.app/papers/details/68f35194db525fb68a7bc8c10546001b/), [ElSheikh Cell-JEPA ArXiv 2026](https://consensus.app/papers/details/74432e657737569e9ae323e790db51ca/), [Zhao FDA Oncology Trials Demographic International Journal Equity Health 2026](https://consensus.app/papers/details/a64a53c623f057059a022ab23cfa1c4b/), [Chen MERFISH 2.0 Cancer Research 2026](https://consensus.app/papers/details/35d03bfda64e573d8373955505d0203d/)*

*#InterfacingDay #Lushington #MolecularTherapy #PrimeEditing #FirstInHuman #pegRNA #Cas9Nickase #FunctionalRestoration #SafetyProfile #Feierman #AJHG #FDA #PlausibleMechanism #PersonalizedPrimeEditing #7UCDs #UmbrellaOfUmbrellas #Huynh #DrugDiscoveryToday #AIAgents #AutonomousReasoning #LiteratureSynthesis #He #AstraZeneca #ChatInvent #MultiAgent #MolecularDesign #SynthesisPlanning #RealWorldDeployment #Feldman #ComputationalStructuralBiotechnology #AlphaFold3 #Adversarial #40PercentInvariance #ConfidenceMetric #ESMFold #TemplateMemorization #ElSheikh #CellJEPA #ArXiv #JointEmbedding #scGPT #DropoutRobust #PerturbationModeling #Zhao #InternationalJournal #EquityHealth #FDA #OncologyTrials #Demographics #Black #Hispanic #Women #OlderAdults #Chen #MERFISH20 #1000Genes #CustomGenes #MERSCOPE #Ultra #Interfacing #FrameworkConnection #BiomedicalAI #DailyReport*

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### Post 2: CRISPR & Gene Editing — Prime Editing First-in-Human × FDA Personalized Framework: Interfacing

🪄 **[CRISPR/Gene Editing] Lushington et al.로 Prime editing first-in-human trial: pegRNA + Cas9 nickase + reverse transcriptase fusion + functional restoration + promising safety profile(Molecular Therapy 2026) + Feierman et al.로 FDA February 2026 plausible mechanism framework: personalized in vivo prime editing platform + 7 urea cycle disorders(UCDs) + "umbrella-of-umbrellas" clinical trial + formal FDA meeting(AJHG 2026) + Ortega Moreno et al.로 CRISPR clinical translation global challenges: Latin America genomic infrastructure + delivery/targeting/off-target/immunogenicity/manufacturing/accessibility barriers(International Science Journal 2026): CRISPR Interfacing(연결) 동시 확인**

CRISPR gene therapy의 **prime editing first-in-human trial 실현 + FDA personalized framework 연결(Interfacing)**이 출처에서 동시에 확인된다[1][2][3].

**Prime Editing First-in-Human Trial: Functional Restoration + Safety Profile[1]**
Caleb Lushington et al.(Molecular Therapy 2026)의 review는 prime editing의 first-in-human trial 현황을 종합한다[1]. **pegRNA + Cas9 nickase + reverse transcriptase fusion**: prime editing의 핵심 메커니즘. **pegRNA**: prime editing guide RNA — desired repair template 암호화. **Cas9 nickase + engineered reverse transcriptase**: double-strand break 없이 정밀 편집 가능. **First-in-human study functional restoration 확인**: 기능적 회복 보고. **Promising safety profile to date**: 현재까지 안전한 프로파일. **Efficiency optimization**: Cas variant selection + RT engineering + pegRNA improvements. **Delivery advances**: nanoparticles + split viral systems로 preclinical disease models 적용 확대. 핵심: Prime editing — 이행의 first-in-human 실현(Interfacing).

**FDA February 2026 Plausible Mechanism Framework: Personalized Prime Editing Platform[2]**
Emily Feierman et al.(American Journal of Human Genetics 2026)의 연구는 2026년 2월 FDA의 새로운 plausible mechanism framework를 분석한다[2]. **February 2026 FDA draft guidance**: 개별화 치료제를 위한 프레임워크. **7 urea cycle disorders(UCDs) 대상**: customizable prime editing platform. **Liver-centered disorders**: 간 중심 질환. **"Umbrella-of-umbrellas" clinical trial**: 7개 UCDs를 모두 포함하는 임상 시험 설계. **Formal FDA meeting outcome**: 규제 협업 실현. **Expeditious FDA approvals**: ultra-rare diseases를 위한 신속 승인 경로. 핵심: FDA personalized prime editing — 이행의 규제-기술 연결(Interfacing).

**CRISPR Global Clinical Translation: Latin America + Challenges[3]**
Gabriel Ortega Moreno et al.(International Science Journal 2026)의 review는 CRISPR 임상 전환의 국제적 도전을 분석한다[3]. **Latin America genomic infrastructure**: 지역적 불균형. **Delivery and tissue targeting**: 전달 및 조직 표적화 과제. **Off-target and unintended on-target modifications**: 안전성 과제. **Immunogenicity**: 면역원성. **Manufacturing complexity**: 제조 복잡성. **Long-term safety**: 장기 안전성. **Regulatory requirements and accessibility**: 규제 및 접근성. **South-North disparity**: 글로벌 건강 불평등 심화. 핵심: CRISPR clinical translation — 이행의 글로벌 도전(Interfacing).

**10월 15일 Scaling(Şahin 1000+ trials + Casgevy approval + monitoring toolkit) vs 오늘의 차별점**: 10/15가 **Şahin Molecular Therapy**으로 1,000건 이상 trials + 51개국 규모화를, **Şahin Trends in Biotechnology 재확인**으로 Casgevy dual approval을, **Şahin Nature Reviews Genetics 재확인**으로 WGS + GUIDE-seq + CIRCLE-seq safety monitoring을 제시했다면, 오늘은 **Lushington Molecular Therapy**으로 **prime editing first-in-human functional restoration + promising safety profile**라는 새로운 임상 이행 단계(Interfacing)를, **Feierman AJHG**으로 **2026년 2월 FDA plausible mechanism framework + 7 UCDs personalized prime editing + umbrella-of-umbrellas trial**이라는 규제-기술 직접 연결(Interfacing)을, **Ortega Moreno International Science Journal**으로 **Latin America genomic infrastructure + global delivery/targeting/off-target/accessibility challenges**라는 글로벌 불평등 연결(Interfacing)을 동시에 제시한다. Scaling(10/15) → first-in-human prime editing + FDA personalized framework + global clinical translation challenges Interfacing(10/16).

실무 함의: 유전자 치료 개발팀에서 Feierman et al.의 FDA personalized prime editing framework를 ultra-rare disease strategy에 참조해야 한다.

*출처: [Lushington Prime Editing Molecular Therapy 2026](https://consensus.app/papers/details/79a173589b1d58598fd94fa334768881/), [Feierman FDA Plausible Mechanism Framework AJHG 2026](https://consensus.app/papers/details/c10e0f99f20b517e8bb3b5ae4b666dcc/), [Ortega Moreno CRISPR Clinical Translation International Science Journal 2026](https://consensus.app/papers/details/6ad15b7d7d215899a90d7d52f396bdac/)*

*#CRISPR #GeneEditing #Lushington #MolecularTherapy #PrimeEditing #FirstInHuman #FunctionalRestoration #SafetyProfile #pegRNA #Cas9Nickase #ReverseTranscriptase #Feierman #AJHG #FDA #PlausibleMechanism #PersonalizedPrimeEditing #7UCDs #UmbrellaOfUmbrellas #FormalFDA #UltraRare #OrtegaMoreno #InternationalScienceJournal #GlobalTranslation #LatinAmerica #GenomicInfrastructure #Delivery #OffTarget #Immunogenicity #Manufacturing #Accessibility #InterfacingDay #ClinicalTransition #RegulatoryConnection #BiomedicalAI #DailyReport*

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### Post 3: AI 신약 — AI Agents Drug Discovery × AstraZeneca ChatInvent Real-World Deployment: Interfacing

💊 **[AI 신약] Huynh et al.로 AI agents drug discovery: LLM-based autonomous reasoning + literature synthesis + toxicity prediction + molecular design + end-to-end decision-making(Drug Discovery Today 2026) + He et al.로 AstraZeneca ChatInvent: multi-agent architecture + molecular design + synthesis planning + proof-of-concept → scalable robust architecture + real-world pharmaceutical deployment(Drug Discovery Today 2026) + Seyhan et al.로 AI drug discovery review 재확인: scaffold-aware tools + 0건 완전 마케팅 승인 + AI clinical candidates 진입(Critical Reviews Oncology Hematology 2026): AI Drug Discovery Interfacing(연결) 동시 확인**

AI-Driven Drug Discovery의 **AI agents 개념→AstraZeneca 실제 배포 연결(Interfacing)**이 출처에서 동시에 확인된다[4][5][6].

**AI Agents Drug Discovery: Autonomous Reasoning + End-to-End Decision[4]**
Dinh-Long Huynh et al.(Drug Discovery Today 2026)의 review는 AI agents의 drug discovery 적용을 분석한다[4]. **LLM-based autonomous reasoning**: large language model 기반 자율 추론. **Literature synthesis**: 문헌 종합. **Toxicity prediction**: 독성 예측. **Molecular design**: 분자 설계. **Automated protocol generation**: 자동화된 프로토콜 생성. **End-to-end decision-making**: 종단간 의사결정. **Early implementations**: substantial gains in speed, reproducibility, scalability. **Challenges**: data heterogeneity + system reliability + privacy + benchmarking. 핵심: AI agents drug discovery — 개념에서 이행으로(Interfacing).

**AstraZeneca ChatInvent: Multi-Agent Real-World Deployment[5]**
Jiazhen He et al.(Drug Discovery Today 2026)의 연구는 AstraZeneca의 ChatInvent 실제 배포 사례를 제시한다[5]. **Multi-agent architecture**: 확장 가능한 다중 에이전트 구조. **Molecular design + synthesis planning**: 분자 설계 및 합성 계획. **Proof-of-concept → scalable robust architecture**: 개념증명에서 실제 배치로. **Real-world pharmaceutical deployment**: AstraZeneca 실제 제약 환경 구현. **Graphical user interface**: 사용자 인터페이스. **Extensible, robust, scalable**: 확장성 및 안정성 확보. **Lessons learnt + challenges persist**: 실제 운영 교훈 및 지속 과제. **Future of agentic systems in drug discovery**: 향후 전망. 핵심: AstraZeneca ChatInvent — AI agent 실제 제약 배치(Interfacing).

**AI Drug Discovery Review 재확인: 0건 완전 마케팅 승인 + AI Clinical Candidates 진입[6]**
(A. Seyhan et al. Critical Reviews in Oncology/Hematology 2026). scaffold-aware tools + 0건 완전 마케팅 승인 + AI-originated candidates clinical development 진입. 핵심: AI drug discovery — 이행의 임상 candidates(Interfacing).

**10월 15일 Scaling(Hills FDA-EMA 10 principles + Lenarczyk AI regulation + Mirakhori FDA framework) vs 오늘의 차별점**: 10/15가 **Hills AI Precision Oncology**으로 FDA-EMA Guiding Principles 10개 원칙 + transparency/reproducibility/oversight을, **Lenarczyk Journal Law Biosciences**으로 FDA flexible vs EMA structured risk-tiered comparison을, **Mirakhori Pharmaceuticals**으로 FDA AI/ML framework evolution을 제시했다면, 오늘은 **Huynh Drug Discovery Today**으로 **AI agents drug discovery — LLM-based autonomous reasoning + literature synthesis + toxicity prediction + molecular design + end-to-end decision-making**이라는 AI agent 개념(Interfacing)을, **He Drug Discovery Today**으로 **AstraZeneca ChatInvent multi-agent architecture + molecular design + synthesis planning + proof-of-concept → real-world pharmaceutical deployment**라는 실제 배치 연결(Interfacing)을, **Seyhan 재확인**으로 **0건 완전 마케팅 승인 + AI clinical candidates 진입**이라는 이행 현실(Interfacing)을 동시에 제시한다. Scaling(10/15) → AI agents 개념 + AstraZeneca 실제 배치 + clinical candidates 진입 Interfacing(10/16).

실무 함의: AI drug discovery 협업팀에서 He et al.의 ChatInvent deployment 사례를 agentic AI 시스템 도입 전략에 반영해야 한다.

*출처: [Huynh AI Agents Drug Discovery Drug Discovery Today 2026](https://consensus.app/papers/details/d47b0cf47a4f5055a7175a6fe09bb6e4/), [He AstraZeneca ChatInvent Drug Discovery Today 2026](https://consensus.app/papers/details/4dc8cd75fd9d59a5943e992c0a05ae28/), [Seyhan AI Drug Discovery Critical Reviews Oncology Hematology 2026](https://consensus.app/papers/details/0e6b685fd2be56fb8a78b64f1851a6/)*

*#AI #DrugDiscovery #Huynh #DrugDiscoveryToday #AIAgents #AutonomousReasoning #LLM #LiteratureSynthesis #ToxicityPrediction #MolecularDesign #EndToEnd #He #AstraZeneca #ChatInvent #MultiAgent #MolecularDesign #SynthesisPlanning #ProofOfConcept #RealWorldDeployment #Scalable #Robust #GraphicalUserInterface #Seyhan #CriticalReviews #OncologyHematology #ZeroApproval #AIClinicalCandidates #InterfacingDay #AgenticAI #PharmaceuticalDeployment #BiomedicalAI #DailyReport*

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### Post 4: 단백질 AI — AlphaFold3 Adversarial 40% Invariance × ProModEv Benchmarking: Interfacing

🔬 **[단백질 AI] Feldman et al.로 AlphaFold3 adversarial evaluation: 40% residue invariance + confidence metric unreliability 35% + ESMFold greater mutational sensitivity + template memorization vs biophysical reasoning(Computational Structural Biotechnology 2026) + Mahtha et al.로 AlphaFold2/3 vs ESMFold comparative: AF2/AF3 88% monomeric + 77% dimeric accuracy + ESMFold 76% monomeric + 41% dimeric + ProModEv web portal benchmarking(NAR Genomics Bioinformatics 2026) + Yin et al.로 AlphaFold3 performance 종합 재확인: AF1→AF2→AF3 evolution + pairwise→single-sequence + SBDD applications(Frontiers Molecular Biosciences 2026): 단백질 AI Interfacing(연결) 동시 확인**

단백질 AI의 **AlphaFold3 adversarial 한계 확인 + benchmarking 체계 연결(Interfacing)**이 출처에서 동시에 확인된다[7][8][9].

**AlphaFold3 Adversarial Evaluation: 40% Residue Invariance + Confidence Metric Failure[7]**
Jonathan Feldman et al.(Computational Structural Biotechnology 2026)의 연구는 AlphaFold3의 adversarial evaluation 결과를 제시한다[7]. **40% residue invariance**: 점변이나 결실에도 구조가 불변. **Confidence metric unreliability 35%**: confidence metric이 가장 정확한 구조를 선택하는 비율 35%. **ESMFold greater mutational sensitivity**: ESMFold가 더 높은突变 민감성 보임. **Template memorization vs biophysical reasoning**: AlphaFold가 생체물리적 추론보다 템플릿 암기 의존. **Mutation-effect interpretation 한계**: mutation-effect 해석에 직접적 함의. **Confidence-guided model selection 문제**: confidence 기반 모델 선택의 신뢰성 문제. **Sequence optimization workflows 영향**: 서열 최적화 워크플로우에 영향. 핵심: AlphaFold3 adversarial — 이행의 한계 인식 연결(Interfacing).

**AlphaFold2/3 vs ESMFold Comparative: 88% + 77% vs 76% + 41% + ProModEv[8]**
Sanjeet Kumar Mahtha et al.(NAR Genomics and Bioinformatics 2026)의 연구는 AlphaFold2/3 vs ESMFold의 예측 정확도를 비교한다[8]. **AlphaFold2/AF3 monomeric accuracy 88%**: monomeric 구조 예측 88% 정확. **AlphaFold2/AF3 dimeric accuracy 77%**: dimeric 구조 예측 77% 정확. **ESMFold monomeric accuracy 76%**: ESMFold monomeric 76%. **ESMFold dimeric accuracy 41%**: ESMFold dimeric 41% — 현저히 낮음. **X-ray and cryo-EM 기준 95%/83%**: 실험적 검증 시 더 높은 정확도. **ProModEv web portal**: interactive benchmarking portal 공개. ** PDB 1666 monomeric + 994 dimeric challenging targets**: 엄격한 기준의 challenging targets 평가. 핵심: AlphaFold2/3 vs ESMFold — 이행의 정확도 benchmarking(Interfacing).

**AlphaFold3 Performance 종합 재확인[9]**
(Tianxiang Yin et al. Frontiers Molecular Biosciences 2026). AF1→AF2→AF3 architecture evolution + pairwise→single-sequence + SBDD applications. 핵심: AlphaFold3 performance — architecture evolution 실현(Interfacing).

**10월 15일 Scaling(Kinde AF applications + Schnapka bAIes Bayesian IDP + Rennie millions structures) vs 오늘의 차별점**: 10/15가 **Kinde Results in Engineering**으로 AlphaFold disease/vaccine/design applications를, **Schnapka Nature Communications**으로 bAIes Bayesian AlphaFold2 IDP ensembles를, **Rennie Royal Society Interface**으로 millions protein structures applications를 제시했다면, 오늘은 **Feldman Computational Structural Biotechnology**으로 **AlphaFold3 adversarial evaluation — 40% residue invariance + confidence metric unreliability 35% + template memorization vs biophysical reasoning**라는 **AF3의 근본적 한계 인식 연결**(Interfacing)을, **Mahtha NAR Genomics Bioinformatics**으로 **AlphaFold2/AF3 88%/77% vs ESMFold 76%/41% accuracy + ProModEv web portal benchmarking**이라는 **정량적 정확도 비교 benchmarking 연결**(Interfacing)을, **Yin 재확인**으로 **AF1→AF2→AF3 architecture evolution**을(architecture 연결) 동시에 제시한다. Scaling(10/15) → AF3 adversarial limits + quantitative benchmarking + architecture evolution Interfacing(10/16).

실무 함의: SBDD 워크플로우팀에서 Feldman et al.의 adversarial evaluation 결과를 AlphaFold confidence metric 사용 기준에 반영해야 한다.

*출처: [Feldman AlphaFold3 Adversarial Computational Structural Biotechnology 2026](https://consensus.app/papers/details/68f35194db525fb68a7bc8c10546001b/), [Mahtha AlphaFold ESMFold Comparative NAR Genomics Bioinformatics 2026](https://consensus.app/papers/details/11a60df3938051cc87f7e295ef5e73f2/), [Yin AlphaFold3 Performance Frontiers Molecular Biosciences 2026](https://consensus.app/papers/details/880e72fce35255c4887811c76849c03a/)*

*#ProteinAI #AlphaFold #Feldman #ComputationalStructuralBiotechnology #AlphaFold3 #Adversarial #40PercentInvariance #ConfidenceMetric #Unreliable #ESMFold #MutationalSensitivity #TemplateMemorization #BiophysicalReasoning #Mahtha #NARGenoicsBioinformatics #AlphaFold2 #AlphaFold3 #ESMFold #88Percent #77Percent #76Percent #41Percent #ProModEv #Benchmarking #Yin #FrontiersMolecularBiosciences #AF1 #AF2 #AF3 #ArchitectureEvolution #SBDD #InterfacingDay #ConfidenceLimits #BenchmarkingConnection #BiomedicalAI #DailyReport*

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### Post 5: Single-Cell Multi-Omics Foundation Model — Cell-JEPA Dropout-Robust × scGPT Geometry Limits: Interfacing

🬬 **[Single-Cell Multi-Omics Foundation Model] ElSheikh et al.로 Cell-JEPA joint-embedding: mask count reconstruction → latent space prediction + 36% improvement over scGPT + dropout-robust features + perturbation modeling(ArXiv 2026) + Whalley et al.로 scGPT/Geneformer/scFoundation geometric outliers: ribosomal enrichment all 3 + mitochondrial enrichment Geneformer/scGPT only + gene-level agreement weak + outlier deletion no phenotypic impact(bioRxiv 2026) + Kendiukhov et al.로 scGPT/Geneformer topology: 141 hypotheses + 52 iterations + 0.80 CCA alignment + gene retrieval 72% + immune tissue signal robust + lung fragile(PLOS One 2026): foundation model Interfacing(연결) 동시 확인**

Single-Cell Foundation Model의 **Cell-JEPA 개선 + foundation model geometry 한계 연결(Interfacing)**이 출처에서 동시에 확인된다[10][11][12].

**Cell-JEPA: Joint-Embedding Predictive Architecture + 36% scGPT Improvement[10]**
Ali ElSheikh et al.(ArXiv 2026)의 연구는 Cell-JEPA를 제시한다[10]. **Joint-embedding predictive architecture**: mask count reconstruction에서 latent space prediction으로 전환. **Dropout-robust features**: dropout率为 90% 이상인 단일세포 데이터에서 robust feature 학습. **36% relative improvement over scGPT**: scGPT 대비 36% 상대적 개선. **AvgBIO 0.72 vs 0.53**: zero-shot transfer에서 cell-type clustering 0.72 vs scGPT 0.53. **Perturbation modeling**: latent space에서 perturbation modeling. **Complementary aspects**: representation learning과 perturbation modeling이 보완적. 핵심: Cell-JEPA — dropout-robust architecture 개선(Interfacing).

**scGPT/Geneformer/scFoundation Geometric Outliers: Weak Agreement + No Phenotypic Impact[11]**
James Whalley et al.(bioRxiv 2026)의 연구는 단일세포 foundation model의 gene embedding geometry를 분석한다[11]. **Ribosomal enrichment all 3 models**: ribosomal enrichment가 3개 모델 모두에서 공통. **Mitochondrial enrichment Geneformer/scGPT only**: mitochondrial enrichment는 Geneformer와 scGPT만. **scFoundation weaker/caller-dependent**: scFoundation의 ribosomal enrichment 더 약하고 caller 의존적. **Gene-level agreement weak**: 유전자 수준 동의 약함. **Outlier deletion no phenotypic impact**: outlier gene 제거가 세포 유형 주석에 영향 없음. **Model-specific geometry**: 모델 특이적 geometry. 핵심: scGPT/Geformer/scFoundation — 모델 간 불일치 + geometry 한계(Interfacing).

**scGPT/Geneformer Topology: 141 Hypotheses + 0.80 CCA Alignment + 72% Gene Retrieval[12]**
Ihor Kendiukhov et al.(PLOS One 2026)의 연구는 단일세포 foundation model의 topological/geometric structure를 체계적으로 분석한다[12]. **141 hypotheses across 52 iterations**: 141개 가설 52회 반복 체계적 검증. **Persistent homology significant 11/12 transformer layers**: immune tissue에서 robust signal. **CCA alignment scGPT-Geneformer 0.80**: 독립적 학습 모델 간 높은 정렬. **Gene retrieval accuracy 72%**: 72% 유전자 검색 정확도. **Lung/external-lung signals fragile**: 폐 및 외부 폐 조직에서 신호 취약. **Shared structure + precise gene placement disagreement**: 모델이 gene space의 "shape"은 공유하지만 정확한 유전자 배치에는 동의하지 않음. 핵심: scGPT/Geneformer topology — 이행의 구조 공유+배치 불일치(Interfacing).

**10월 15일 Scaling(Ji CAPTAIN 4M cells + Wang SCMBench FMs< DMs + Bahrami compositional) vs 오늘의 차별점**: 10/15가 **Ji Nature Communications**으로 CAPTAIN multimodal을, **Wang Molecular Therapy**으로 SCMBench FMs < DMs를, **Bahrami Cell Systems**으로 compositional modular design을 제시했다면, 오늘은 **ElSheikh ArXiv**으로 **Cell-JEPA joint-embedding → latent space prediction + 36% scGPT improvement + dropout-robust**라는 새로운 architecture 개선(Interfacing)을, **Whalley bioRxiv**으로 **scGPT/Geneformer/scFoundation geometric outliers — ribosomal all 3 + mitochondrial Geneformer/scGPT only + outlier deletion no phenotypic impact**라는 모델 간 불일치 한계(Interfacing)을, **Kendiukhov PLOS One**으로 **141 hypotheses 52 iterations + 0.80 CCA alignment + 72% gene retrieval + immune robust/lung fragile**라는 topological structure 체계적 검증(Interfacing)을 동시에 제시한다. Scaling(10/15) → Cell-JEPA architecture 개선 + model geometry 한계 + topological structure 검증 Interfacing(10/16).

실무 함의: 단일세포 연구팀에서 ElSheikh et al.의 Cell-JEPA approach를 dropout-robust feature learning에 적용해야 한다.

*출처: [ElSheikh Cell-JEPA ArXiv 2026](https://consensus.app/papers/details/74432e657737569e9ae323e790db51ca/), [Whalley Geometric Outliers bioRxiv 2026](https://consensus.app/papers/details/409b664d9f475668a4eca67488b59467/), [Kendiukhov scGPT Geneformer Topology PLOS One 2026](https://consensus.app/papers/details/e75fbb82883f5c78a30cb25c8f5c6954/)*

*#SingleCell #FoundationModel #ElSheikh #CellJEPA #ArXiv #JointEmbedding #LatentSpace #PerturbationModeling #DropoutRobust #36PercentImprovement #scGPT #Whalley #bioRxiv #GeometricOutliers #Ribosomal #Mitochondrial #Geneformer #scFoundation #Kendiukhov #PLOSOne #Topology #Geometry #141Hypotheses #52Iterations #CCA #072Percent #GeneRetrieval #ImmuneTissue #Lung #InterfacingDay #ArchitectureImprovement #GeometryLimits #BiomedicalAI #DailyReport*

---

### Post 6: FDA/규제 — FDA Personalized Prime Editing Framework × Oncology Trials Demographic Disparities: Interfacing

📋 **[FDA/규제] Feierman et al.로 FDA February 2026 plausible mechanism framework: personalized in vivo prime editing + 7 UCDs + umbrella-of-umbrellas clinical trial + formal FDA meeting(AJHG 2026) + Zhao et al.로 FDA oncology trials demographic disparities: 372 pivotal trials 2018-2026 + Black EIR 0.25 + Hispanic EIR 0.51 + women EIR 0.92 + older adults EIR 0.67 + modest increase women/time + no improvement Black-Hispanic(International Journal Equity Health 2026) + Roskoski et al.로 94 FDA-approved protein kinase inhibitors: 10 approved 2025 + MEK1/2 6개 + JAK/BTK 각 2개 + 80 oncology + 45 Lipinski violations(Pharmacological Research 2026): FDA/규제 Interfacing(연결) 동시 확인**

FDA 규제 영역의 **FDA personalized prime editing framework + oncology trials demographic disparities(Interfacing)**이 출처에서 동시에 확인된다[2][13][14].

**FDA Personalized Prime Editing Framework 재확인[2]**
(Emily Feierman et al. AJHG 2026 — Post 2에서 상세 기술). 7 UCDs + umbrella-of-umbrellas trial + formal FDA meeting. 핵심: FDA personalized prime editing — 규제-기술 직접 연결(Interfacing).

**FDA Oncology Trials Demographic Disparities: 372 Trials × Black EIR 0.25[13]**
Ming-Ye Zhao et al.(International Journal for Equity in Health 2026)의 연구는 FDA oncology drug approvals의 demographic 대표성을 분석한다[13]. **372 pivotal trials 2018-2026**: FDA oncology approvals 기초 임상 시험 372건. **Black participants EIR 0.25**: SEER incidence 대비 25%만 대표 — 심각한 저대표. **Hispanic EIR 0.51**: 51%만 대표. **Women EIR 0.92**: 92%. **Older adults ≥65 EIR 0.67**: 67%. **Modest increase women over time**: 시간 경과와 함께 여성 대표성 약간 증가. **No significant improvement Black or Hispanic**: 흑인/히스패닉 대표성 시간 경과 개선 없음. **Older adult representation declined slightly**: 노인 대표성 오히려 약간 감소. **Industry sponsorship correlation**: industry sponsor + accelerated approval + overall survival endpoint와 저대표 연관. 핵심: FDA oncology trials — 이행의 demographic 격차 지속(Interfacing).

**94 FDA-Approved Protein Kinase Inhibitors: 10 Approved 2025 + 45 Lipinski Violations[14]**
Robert Roskoski Jr.(Pharmacological Research 2026)의 review는 94개 FDA 승인 protein kinase inhibitors를 분석한다[14]. **94개 FDA approved protein kinase inhibitors**: 총 94개 승인. **10 approved in 2025**: 2025년 10개 신규 승인. **MEK1/2 dual specificity 6개**: MEK1/2 억제제 6개. **JAK family 2개 + BTK 2개**: JAK/BTK 각 2개. **Oncology ≈80개**: 80개 정도 종양학용. **Oral bioavailability 90개**: 90개 경구-bioavailable. **45 have Lipinski rule of five violations**: 45개가 Lipinski rule of five 위반. **Avutometinib/defactinib/delgocitinib/mirdametinib/remibrutinib/rilzabrutinib/sunvozertinib/taletrectinib/vimseltinib/zongertinib**: 2025년 신규 승인 10개. 핵심: Protein kinase inhibitors — 이행의 2025년 승인 현황(Interfacing).

**10월 15일 Scaling(Şahin 1000+ trials + Verma GCRC-F + Hills FDA-EMA 10 principles) vs 오늘의 차별점**: 10/15가 **Şahin Molecular Therapy**으로 1,000건 이상 trials + 51개국을, **Verma BioTech**으로 GCRC-F 4 pillars international convergence를, **Hills AI Precision Oncology**으로 FDA-EMA Guiding Principles 10개 원칙을 제시했다면, 오늘은 **Feierman AJHG**으로 **2026년 2월 FDA plausible mechanism framework + personalized prime editing + umbrella-of-umbrellas trial**이라는 규제-기술 직접 연결(Interfacing)을, **Zhao International Journal**으로 **372 trials demographic disparities — Black 0.25 + Hispanic 0.51 + women 0.92 + older adults 0.67 + 지속적 격차**라는 규제-건강 불평등 연결(Interfacing)을, **Roskoski Pharmacological Research**으로 **94 kinase inhibitors + 2025년 10개 신규 승인 + 45개 Lipinski 위반**이라는 약물 승인 현황 연결(Interfacing)을 동시에 제시한다. Scaling(10/15) → personalized prime editing framework + demographic disparities + kinase inhibitor approvals Interfacing(10/16).

실무 함의: Regulatory Affairs팀에서 Zhao et al.의 demographic disparity 결과를 diversity enrollment strategy에 반영해야 한다.

*출처: [Feierman FDA Plausible Mechanism Framework AJHG 2026](https://consensus.app/papers/details/c10e0f99f20b517e8bb3b5ae4b666dcc/), [Zhao FDA Oncology Trials Demographic International Journal Equity Health 2026](https://consensus.app/papers/details/a64a53c623f057059a022ab23cfa1c4b/), [Roskoski Protein Kinase Inhibitors Pharmacological Research 2026](https://consensus.app/papers/details/a1931950cc815d8a9136ea7cea0912f3/)*

*#FDA #GeneTherapy #Feierman #AJHG #PlausibleMechanism #PersonalizedPrimeEditing #7UCDs #UmbrellaOfUmbrellas #Zhao #InternationalJournal #EquityHealth #OncologyTrials #Demographics #Black #Hispanic #Women #OlderAdults #EIR #HealthDisparity #Roskoski #PharmacologicalResearch #KinaseInhibitors #94Approved #10Approved2025 #Lipinski #MEK #JAK #BTK #Avutometinib #Sunvozertinib #InterfacingDay #RegulatoryConnection #HealthEquity #BiomedicalAI #DailyReport*

---

### Post 7: Spatial Transcriptomics — MERFISH 2.0 1,000 Genes × Spatial Multi-Omics 30 Proteins: Interfacing

🗺️ **[Spatial Transcriptomics] Chen et al.로 MERFISH 2.0: 1,000 genes + frozen/FFPE + custom 100 genes + 815-gene IO panel + MERSCOPE Ultra + high sensitivity + customizable panels(Cancer Research 2026) + Wang et al.로 Spatial multi-omics: MERFISH 2.0 + 30 proteins + up to 1,000 RNA + MERSCOPE Ultra + user customization + tumor-immune interactions(Cancer Research 2026) + Li et al.로 Pan-cancer 56 LCPs + 13 niches 재확인: Niche_4(macrophage+tumor) poor prognosis + Niche_11(macrophage+immune) better survival(Cell Reports Medicine 2026): spatial transcriptomics Interfacing(연결) 동시 확인**

Spatial transcriptomics의 **MERFISH 2.0 1,000 genes × 30 proteins multi-omics 통합(Interfacing)**이 출처에서 동시에 확인된다[15][16][17].

**MERFISH 2.0: 1,000 Genes + Custom 100 Genes + Frozen/FFPE[15]**
Renchao Chen et al.(Cancer Research 2026)의 연구는 MERFISH 2.0의 성능을 제시한다[15]. **1,000 genes capability**: 최대 1,000개 유전자 동시 측정. **Frozen and FFPE tissue**: frozen 및 FFPE 조직 모두 지원. **Custom 100 genes**: 사용자 정의 100개 유전자 추가 가능. **815-gene immune-oncology panel**: 면역종양학 특화 815개 유전자 패널. **MERSCOPE Ultra platform**: Vizgen MERSCOPE Ultra 사용. **High sensitivity**: 높은 감도. **Customizable panels**: 맞춤형 패널 설계. **Streamlined workflow**: 간소화된 워크플로우. 핵심: MERFISH 2.0 — 1,000 genes + custom capability 확대(Interfacing).

**Spatial Multi-Omics: 30 Proteins + 1,000 RNA + MERSCOPE Ultra[16]**
Bin Wang et al.(Cancer Research 2026)의 연구는 MERFISH 2.0 기반 spatial multi-omics를 제시한다[16]. **Up to 30 proteins**: 최대 30개 단백질 동시 측정. **Up to 1,000 RNA transcripts**: 최대 1,000개 RNA 동시 측정. **MERSCOPE Ultra Platform**: Vizgen 플랫폼 기반. **User customization**: 완전한 사용자 정의. **Immune-oncology 815 IO protein signature**: 면역종양학 815개 IO 단백질 시그니처. **High detection sensitivity**: 높은 검출 감도. **Minimal background interference**: 최소 배경 간섭. **Subcellular resolution**: 아세포 수준 해상도. **Tumor-immune interactions**: 종양-면역 상호작용 특성화. **Cellular neighborhoods**: 세포 이웃 특성화. 핵심: Spatial multi-omics — 30 proteins × 1,000 RNA 통합(Interfacing).

**Pan-Cancer 56 LCPs + 13 Niches 재확인[17]**
(Jiarong Li et al. Cell Reports Medicine 2026). Niche_4(macrophage + tumor) poor prognosis + Niche_11(macrophage + immune) better survival. 핵심: Pan-cancer niche — 이행의 biomarker 체계(Interfacing).

**10월 15일 Scaling(Zhang spatial ecotypes cfDNA + Li Niche_4/11 + Nguyen TME workflows) vs 오늘의 차별점**: 10/15가 **Zhang Nature**으로 spatial ecotypes cfDNA melanoma 100명 validation을, **Li Cell Reports Medicine**으로 Niche_4/Niche_11를, **Nguyen Clinical & Translational Immunology**으로 TME standardized workflows를 제시했다면, 오늘은 **Chen Cancer Research**으로 **MERFISH 2.0 — 1,000 genes + frozen/FFPE + custom 100 genes + 815-gene IO panel**이라는 **해상도 확대+맞춤형 capability 확대**(Interfacing)를, **Wang Cancer Research**으로 **30 proteins × 1,000 RNA simultaneous detection + subcellular resolution + user customization**이라는 **spatial multi-omics 통합 확대**(Interfacing)를, **Li 재확인**으로 **Niche_4/11 pan-cancer differential**을(biomarker 연결) 동시에 제시한다. Scaling(10/15) → MERFISH 2.0 1,000 genes 확대 + 30 proteins multi-omics 통합 + Niche_4/11 biomarker Interfacing(10/16).

실무 함의: 면역종양학팀에서 Wang et al.의 spatial multi-omics approach를 tumor-immune interaction profiling protocol에 적용해야 한다.

*출처: [Chen MERFISH 2.0 Cancer Research 2026](https://consensus.app/papers/details/35d03bfda64e573d8373955505d0203d/), [Wang Spatial Multi-Omics Cancer Research 2026](https://consensus.app/papers/details/8c0528082d315cb88c48bc70507a4f96/), [Li Pan-Cancer Niche Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/)*

*#SpatialTranscriptomics #Chen #CancerResearch #MERFISH20 #1000Genes #CustomGenes #Frozen #FFPE #815Genes #IOPanel #MERSCOPE #Ultra #Wang #SpatialMultiOmics #30Proteins #1000RNA #SubcellularResolution #UserCustomization #TumorImmune #Interactions #CellularNeighborhoods #Li #PanCancer #56LCPs #13Niches #Niche4 #Niche11 #Macrophage #Tumor #Prognosis #ImmunotherapyResistance #InterfacingDay #ResolutionExpansion #MultiOmicsIntegration #BiomedicalAI #DailyReport*

---

### Post 8: 오늘의 요약 — Interfacing(연결) Day 핵심 정리

🧬 **오늘 6개 영역 핵심 요약:**

10/16(Interfacing)의 핵심: Scaling(10/15)된 Framework들이 **각 분야主流 체계에서 서로 연결(Interfacing)되며 새로운 치료 개발 패러다임을 구현하는 날**이었다.

**1. Prime Editing First-in-Human + FDA Personalized Plausible Mechanism Framework(Lushington Molecular Therapy 2026 + Feierman AJHG 2026)**: pegRNA + Cas9 nickase + RT fusion + functional restoration + promising safety profile; 2026년 2월 FDA plausible mechanism framework + 7 UCDs personalized prime editing + umbrella-of-umbrellas trial + formal FDA meeting — 규제-기술 직접 연결[1][2].

**2. AI Agents Drug Discovery + AstraZeneca ChatInvent Real-World Deployment(Huynh Drug Discovery Today 2026 + He Drug Discovery Today 2026)**: LLM-based autonomous reasoning + literature synthesis + toxicity prediction + molecular design + end-to-end decision-making; AstraZeneca multi-agent ChatInvent proof-of-concept → scalable robust real-world deployment — AI agent 개념-실제 배치 연결[4][5].

**3. AlphaFold3 Adversarial 40% Invariance + ProModEv Benchmarking(Feldman Computational Structural Biotechnology 2026 + Mahtha NAR Genomics Bioinformatics 2026)**: 40% residue invariance + confidence metric unreliability 35% + template memorization; AlphaFold2/AF3 88%/77% vs ESMFold 76%/41% + ProModEv web portal — AF3 한계-정량 benchmarking 연결[7][8].

**4. Cell-JEPA 36% Improvement + scGPT/Geformer Topology 0.80 CCA(ElSheikh ArXiv 2026 + Whalley bioRxiv 2026 + Kendiukhov PLOS One 2026)**: Cell-JEPA joint-embedding + latent space + dropout-robust 36% improvement; 141 hypotheses 52 iterations + CCA alignment 0.80 + gene retrieval 72% + immune robust/lung fragile — architecture 개선-geometry 한계 연결[10][11][12].

**5. FDA Personalized Framework + Oncology Trials Demographic Disparities 372 Trials(Feierman AJHG 2026 + Zhao International Journal Equity Health 2026 + Roskoski Pharmacological Research 2026)**: FDA 2026 February plausible mechanism + 7 UCDs umbrella-of-umbrellas; 372 trials Black EIR 0.25 + Hispanic EIR 0.51 + women EIR 0.92 + older adults EIR 0.67; 94 kinase inhibitors 10 approved 2025 + 45 Lipinski violations — 규제-건강 불평등-약물 승인 연결[2][13][14].

**6. MERFISH 2.0 1,000 Genes + Spatial Multi-Omics 30 Proteins × 1,000 RNA(Chen Cancer Research 2026 + Wang Cancer Research 2026 + Li Cell Reports Medicine 2026)**: MERFISH 2.0 1,000 genes + custom 100 genes + frozen/FFPE + 815-gene IO panel; 30 proteins + 1,000 RNA + subcellular resolution + user customization; Niche_4/11 pan-cancer differential — 해상도 확대-multi-omics 통합-biomarker 연결[15][16][17].

10월 17일 전망: Interfacing된 Framework들이 다음 단계에서 어떻게 실질적 치료 개발에 통합(Integrating)되는지가 핵심. 특히 FDA personalized prime editing framework의 실제 임상 trial 설계, AstraZeneca ChatInvent의 다른 제약사로의 확산, AlphaFold3 adversarial limits의 practical workflow 반영, demographic disparity 개선을 위한 enforceable requirements 도입이 관전이다.

Interfacing 다음은 **Integrating(통합)**이다. 오늘의 6개 전선이 제시하는 공통 질문: 연결(Interfacing)된 Framework들이 각 분야主流 체계에서 어떤 방식으로 통합(Integrating)되어 실질적 치료 개발에 기여하는가. 이 질문이 내일 이후 각 전선의 핵심 과제가 된다.

*출처: [Lushington Prime Editing Molecular Therapy 2026](https://consensus.app/papers/details/79a173589b1d58598fd94fa334768881/), [Feierman FDA Plausible Mechanism Framework AJHG 2026](https://consensus.app/papers/details/c10e0f99f20b517e8bb3b5ae4b666dcc/), [Huynh AI Agents Drug Discovery Drug Discovery Today 2026](https://consensus.app/papers/details/d47b0cf47a4f5055a7175a6fe09bb6e4/), [He AstraZeneca ChatInvent Drug Discovery Today 2026](https://consensus.app/papers/details/4dc8cd75fd9d59a5943e992c0a05ae28/), [Feldman AlphaFold3 Adversarial Computational Structural Biotechnology 2026](https://consensus.app/papers/details/68f35194db525fb68a7bc8c10546001b/), [Mahtha AlphaFold ESMFold Comparative NAR Genomics Bioinformatics 2026](https://consensus.app/papers/details/11a60df3938051cc87f7e295ef5e73f2/), [ElSheikh Cell-JEPA ArXiv 2026](https://consensus.app/papers/details/74432e657737569e9ae323e790db51ca/), [Whalley Geometric Outliers bioRxiv 2026](https://consensus.app/papers/details/409b664d9f475668a4eca67488b59467/), [Kendiukhov scGPT Geneformer Topology PLOS One 2026](https://consensus.app/papers/details/e75fbb82883f5c78a30cb25c8f5c6954/), [Zhao FDA Oncology Trials Demographic International Journal Equity Health 2026](https://consensus.app/papers/details/a64a53c623f057059a022ab23cfa1c4b/), [Roskoski Protein Kinase Inhibitors Pharmacological Research 2026](https://consensus.app/papers/details/a1931950cc815d8a9136ea7cea0912f3/), [Chen MERFISH 2.0 Cancer Research 2026](https://consensus.app/papers/details/35d03bfda64e573d8373955505d0203d/), [Wang Spatial Multi-Omics Cancer Research 2026](https://consensus.app/papers/details/8c0528082d315cb88c48bc70507a4f96/), [Li Pan-Cancer Niche Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/)*

*#InterfacingDay #Summary #Lushington #PrimeEditing #FirstInHuman #Feierman #FDA #PlausibleMechanism #PersonalizedPrimeEditing #7UCDs #Huynh #AIAgents #AutonomousReasoning #He #AstraZeneca #ChatInvent #MultiAgent #RealWorldDeployment #Feldman #AlphaFold3 #Adversarial #40PercentInvariance #ConfidenceMetric #Mahtha #AlphaFold2 #AF3 #ESMFold #88Percent #77Percent #ElSheikh #CellJEPA #JointEmbedding #36PercentImprovement #Whalley #GeometricOutliers #Kendiukhov #Topology #PLOSOne #CCA #072Percent #Zhao #Demographic #HealthDisparity #EIR #Roskoski #KinaseInhibitors #94Approved #10Approved2025 #Chen #MERFISH20 #1000Genes #Wang #SpatialMultiOmics #30Proteins #1000RNA #Li #Niche4 #Niche11 #Interfacing #FrameworkConnection #BiomedicalAI #DailyReport*

---

## Sources (1차 출처 16개 이상)

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[2] [Feierman E, et al. Implications of the FDA's New Plausible Mechanism Framework for the Development of a Personalized In Vivo Prime Editing Platform. American Journal of Human Genetics. 2026.](https://consensus.app/papers/details/c10e0f99f20b517e8bb3b5ae4b666dcc/)
[3] [Ortega Moreno GA, et al. CRISPR-Based Genome Editing in Contemporary Clinical Medicine: Therapeutic Translation, Global Challenges and the Future of Precision Medicine. International Science Journal. 2026.](https://consensus.app/papers/details/6ad15b7d7d215899a90d7d52f396bdac/)
[4] [Huynh DL, et al. AI agents in drug discovery: applications and case studies. Drug Discovery Today. 2026.](https://consensus.app/papers/details/d47b0cf47a4f5055a7175a6fe09bb6e4/)
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