# Research Pulse — 2026-10-13 _Generated 2026-10-13 09:16 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. --- ### Post 1: 오늘의 전체 흐름 🧬 **AI × Bioresearch Daily — 10월 13일 (화요일) · Standardizing(표준화) Day** 10/13(표준화) → 10/12(종합) → 10/11(문맥화) → 10/10(실현) → 10/09(임상 전환) → 10/08(성숙) → 10/07(산업화) → 10/06(산업화). Synthesizing(10/12)된 기술들이 **각 분야主流 체계에서 표준화(Standardizing) Framework를 제시하는 단계로 진입하는 날** — Lummerstorfer et al.로 Non-Viral CRISPR carriers: 136건 임상 trials 중 36건 in vivo non-viral delivery + 2025년 12월 기준 'hit-and-run' transient delivery 우위 재確認(Drug Delivery 2026), Martin et al.로 in vivo genome editing delivery 종합: CRISPR-Cas9/base/prime/RNA/epigenome editor 전체 포괄 + organ-specific 전략 + COST GenE-HumDi network 4개 권고사항(International Journal of Biological Sciences 2026), Fay Lin et al.로 Pharma AI 2026 Deals: Iscientific/Recursion/Exscientia/Inn四erb/Insilico Medicine-big pharma 다수 계약 체결 + AI platform bets 강화(GEN Biotechnology 2026), Nagamatsu et al.로 Acerola-derived exosome-like nanoparticles( AELNs) + GLP2 peptide + intranasal CRISPR-Cas9 RNP in vivo brain delivery 성공 + C9orf72 ALS/FTD 게놈 편집 확인(Molecular Therapy. Nucleic Acids 2026), Truong et al.로 LNP payload distribution: single-particle CICS 분석으로 co-encapsulated 50.7-60.4% + mRNA copies 9.8 vs 8.0 + indel 55.4% vs 36.3% 상관관계 입증(Molecular Therapy 2026), Verma et al.로 Global CGT Regulatory Convergence Framework(GCRC-F): FDA/EMA/PMDA + BRIC-M 8개 관할권 4개 convergence gaps + 4 pillars 제안(BioTech 2026), Ciganek et al.로 LNP immune cell engineering: selective organ targeting + ligand conjugation + sequence-level translational control + PEG dilemma(Materials Today Bio 2026), Nguyen et al.로 Spatial omics clinical TME: AI/ML cross-platform integration + standardized workflows + precision cancer medicine 적용(Clinical & Translational Immunology 2026)이 동시에 진행된다. Standardizing은 Synthesizing의 다음 단계다. 오늘 6개 전선은 종합된 기술들이 **각 분야主流 체계에서 표준화 Framework를 제시하며, 규제-기술 협력이 국제적 차원에서 구체화되는 날**이다. *출처: [Lummerstorfer Non-Viral CRISPR Carriers Drug Delivery 2026](https://consensus.app/papers/details/747d35e9e5bc5ca9a9854998c7acd326/), [Martin In Vivo Genome Editing Delivery International Journal of Biological Sciences 2026](https://consensus.app/papers/details/5d58355f4ce053dc80e42bf7ba924045/), [Fay Lin Pharma AI 2026 Deals GEN Biotechnology 2026](https://consensus.app/papers/details/bc6e9ea3ec2e5025af707131d2fa0ca7/), [Nagamatsu Acerola Exosome CNS CRISPR Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/c6f1f573312a567cb720861b6f9b7e55/), [Truong LNP Payload Distribution Molecular Therapy 2026](https://consensus.app/papers/details/31a186a2826b5b9f9eb8c87ed71a040c/), [Verma Global CGT Regulatory Convergence BioTech 2026](https://consensus.app/papers/details/105ceebe08e754aeabb177c733ca9ba1/), [Ciganek LNP Immune Cell Engineering Materials Today Bio 2026](https://consensus.app/papers/details/48353b932e435fcc94a00ac93469e299/), [Nguyen Spatial Omics TME Clinical Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/)* *#StandardizingDay #Lummerstorfer #DrugDelivery #NonViral #CRISPR #136Trials #36InVivo #HitAndRun #TransientDelivery #Martin #InternationalJournal #BiologicalSciences #InVivo #Delivery #BaseEditing #PrimeEditing #RNAEditing #EpigenomeEditing #COST #GenEHumDi #OrganSpecific #FayLin #GENBiotechnology #PharmaAI #2026Deals #Iscientific #Recursion #Exscientia #InsilicoMedicine #Nagamatsu #Acerola #AELNs #Exosome #GLP2 #CNS #Intranasal #C9orf72 #ALS #FTD #Truong #MolecularTherapy #LNPs #PayloadDistribution #CICS #CoEncapsulated #Indels #Verma #BioTech #RegulatoryConvergence #GCRCF #FDA #EMA #PMDA #BRICM #Ciganek #MaterialsTodayBio #LNP #ImmuneCell #SelectiveOrganTargeting #PEG #Nguyen #ClinicalTranslationalImmunology #SpatialOmics #TME #AIML #Standardizing #FrameworkIntegration #BiomedicalAI #DailyReport* --- ### Post 2: CRISPR & Gene Editing — Non-Viral In Vivo 36건 Trials × CNS Delivery 표준화: Standardizing 🪄 **[CRISPR/Gene Editing] Lummerstorfer et al.로 Non-Viral CRISPR carriers: 136건 임상 trials 중 36건 in vivo non-viral delivery + 2025년 12월 기준 'hit-and-run' transient delivery 우위 재확인(Drug Delivery 2026) + Martin et al.로 in vivo genome editing delivery 종합: CRISPR-Cas9/base/prime/RNA/epigenome editor 전체 포괄 + organ-specific 전략 + COST GenE-HumDi network 권고사항(International Journal of Biological Sciences 2026) + Nagamatsu et al.로 Acerola-derived exosome-like nanoparticles(AELNs) + GLP2 peptide + intranasal CRISPR-Cas9 RNP in vivo brain delivery + C9orf72 ALS/FTD 게놈 편집 확인(Molecular Therapy. Nucleic Acids 2026): CRISPR Standardizing(표준화) 동시 확인** CRISPR gene therapy의 **non-viral in vivo 36건 trials 표준화 + CNS delivery 체계화(Standardizing)**가 출처에서 동시에 확인된다[1][2][3]. **Non-Viral CRISPR Carriers: 136건 Trials 중 36건 In Vivo[1]** M. Lummerstorfer et al.(Drug Delivery 2026)의 review는 CRISPR non-viral delivery 임상 시험 현황을 종합한다[1]. **2025년 12월 기준 136건 CRISPR trials 진행 중**. **그 중 36건이 in vivo delivery 기반** — non-viral vectors로의 명확한 전환 확인. **In vivo trials의 clear shift towards non-viral vectors**: AAV 중심에서 LNPs 등 non-viral로 이동. **'Hit-and-run' transient delivery 우위 재확인**: 게놈 편집의 영구적 특성상 prolonged expression 불필요 + transient delivery가 off-target 위험 감소. **Clinical trial landscape 체계적 survey**: in vivo CRISPR therapies 특화. **Delivery strategies 정리**: 현재 임상 적용 중인 전략 종합. 핵심: Non-viral CRISPR carriers — 136건 중 36건 in vivo non-viral trials + hit-and-run transient delivery 우위 재확인(Standardizing). **In Vivo Genome Editing Delivery 종합: 全 Editor Type 포괄[2]** L. Martin et al.(International Journal of Biological Sciences 2026)의 review는 COST Action GenE-HumDi network 소속으로 in vivo genome editing delivery를 종합한다[2]. **CRISPR systems 전체 포괄**: conventional nucleases + base editing + prime editing + RNA targeting + epigenetic modulation. **Organ-specific strategies**: anatomical, cellular, immunological constraints별 맞춤 전략. **Preclinical + clinical milestones**: 현재 이행 상태 정리. **Delivery modalities 심층 검토**: efficacy, safety, scalability 결정 요인. **Key remaining barriers**: durable, tissue-selective, broadly deployable CRISPR therapeutics을 위한 과제. **GenE-HumDi network 권고사항**: 4개 주요 권고사항 제시. 핵심: In vivo genome editing delivery 종합 — 全 editor type 포괄 + organ-specific 전략 + GenE-HumDi 권고사항(Standardizing). **Acerola Exosome-Like Nanovesicles: Intranasal CNS Delivery 성공[3]** Yui Nagamatsu et al.(Molecular Therapy. Nucleic Acids 2026)의 연구는 Acerola 유래 exosome-like nanoparticles(AELNs)로的中枢신경계 CRISPR delivery를 성공시킨 사례를 제시한다[3]. **C9orf72 표적**: ALS와 frontotemporal dementia(FTD)의 가장 흔한 유전 원인. **AELNs**: acerola 유래 exosome-like nanoparticles — 기존 human exosomes의 한계를 극복 가능. **GLP2 peptide conjugation**: GLP2-receptor-expressing neurons 표적화. **Intranasal administration**: 비침습적 brain delivery. **In vivo genome editing 확인**: C9orf72突变 편집 성공. ** neurodegenerative diseases 치료 가능성**: 비침습적 CRISPR CNS delivery의 실현 가능성 입증. 핵심: Acerola AELNs + intranasal CNS delivery — CRISPR 뇌送达 표준화 가능성(Standardizing). **10월 12일 Synthesizing(Leonard 10년 evolution + Magdy on/off-target + Wang LNPs 93%) vs 오늘의 차별점**: 10/12가 **Leonard Seminars in Hematology**으로 SCD gene therapy 10년 evolution 4세대 기술을, **Magdy Trends in Biotechnology**으로 3세대 치료제 on/off-target activities를, **Wang Frontiers Bioengineering**으로 LNPs 93% protein knockdown을 제시했다면, 오늘은 **Lummerstorfer Drug Delivery**으로 **136건 trials 중 36건 in vivo non-viral — non-viral vectors로의 명확한 전환 현실**을(표준화), **Martin International Journal**으로 **base/prime/RNA/epigenome 전체 editor type 포괄 + organ-specific strategies + GenE-HumDi 4개 권고사항**을(가이드라인 표준화), **Nagamatsu Molecular Therapy**으로 **AELNs intranasal CNS delivery의 비침습적 CRISPR brain delivery 실현 가능성**을(CNS 표준화 가능성) 동시에 제시한다. Synthesizing(10/12) → non-viral in vivo 36건 trials 표준화 + 全editor 포괄 + CNS delivery 체계화 Standardizing(10/13). 실무 함의: 유전자 치료 개발팀에서 Lummerstorfer et al.의 non-viral trials 현황을 참조하여 in vivo delivery strategy 표준화에 활용해야 한다. *출처: [Lummerstorfer Non-Viral CRISPR Carriers Drug Delivery 2026](https://consensus.app/papers/details/747d35e9e5bc5ca9a9854998c7acd326/), [Martin In Vivo Genome Editing Delivery International Journal of Biological Sciences 2026](https://consensus.app/papers/details/5d58355f4ce053dc80e42bf7ba924045/), [Nagamatsu Acerola Exosome CNS CRISPR Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/c6f1f573312a567cb720861b6f9b7e55/), [Yang Genome Editing Delivery MedComm 2026](https://consensus.app/papers/details/87a151ebbb6759e48db9d0a028ff4597/)* *#CRISPR #GeneEditing #Lummerstorfer #DrugDelivery #NonViral #136Trials #36InVivo #HitAndRun #TransientDelivery #Martin #InternationalJournal #BiologicalSciences #InVivo #BaseEditing #PrimeEditing #RNAEditing #EpigenomeEditing #OrganSpecific #COST #GenEHumDi #Delivery #Nagamatsu #MolecularTherapy #NucleicAcids #Acerola #AELNs #Exosome #GLP2 #CNS #Intranasal #BrainDelivery #C9orf72 #ALS #FTD #GenomeEditing #NonInvasive #StandardizingDay #NonViralShift #HitAndRun #CNSDelivery #BiomedicalAI #DailyReport* --- ### Post 3: AI 신약 — Pharma AI 2026 Deals × Big Pharma Platform Bets 표준화: Standardizing 💊 **[AI 신약] Fay Lin et al.로 Pharma AI 2026 Deals: Iscientific/Recursion/Exscientia/Insilico Medicine-big pharma 다수 계약 체결 + AI platform bets 강화(GEN Biotechnology 2026) + Zahlan et al.로 AI startup 3,807개 분석: drug discovery 2위 투자 유치 + 5-tier AI complexity framework(NPJ Digital Medicine 2026) + Ciganek et al.로 LNP immune cell engineering: selective organ targeting + ligand conjugation + PEG dilemma(Materials Today Bio 2026): AI Drug Discovery Standardizing(표준화) 동시 확인** Pharma AI 영역의 **2026년 빅파마 AI 플랫폼 계약 집중 + 투자 프레임워크 표준화(Standardizing)**가 출처에서 동시에 확인된다[4][5][6]. **Pharma AI 2026 Deals: 다수 계약 체결 + Platform Bets 강화[4]** Fay Lin et al.(GEN Biotechnology 2026)의 기사는 2026년 pharma AI 계약 체결 현황을 보도한다[4]. **Iscientific, Recursion, Exscientia, Insilico Medicine-big pharma 다수 계약 체결**: 2026년 AI-drug discovery 분야 활발한 계약 활동. **AI platform bets 강화**: 빅파마들의 AI 플랫폼에 대한 전략적 투자 확대. **Pharma bets big on AI platforms**: 업계 전체의 AI 전환 가속화 확인. **Industry consolidation in AI drug discovery**: 주요 빅파마들의 AI biotech와의 제휴 강화. 핵심: Pharma AI 2026 Deals — 빅파마 AI 플랫폼投注 표준화(Standardizing). **AI Startup Investment: Drug Discovery 2위 + 5-tier Framework[5]** Ahmed Zahlan et al.(NPJ Digital Medicine 2026)의 연구는 3,807개 AI health startups를 분석한다[5]. **3,807개 AI health startups 분석**: 2010-2024년 설립 companies. **Drug discovery 2위 투자 유치**: clinical decision support 다음 2번째 largest investment area. **5-tier AI systems complexity framework**: medical domain, AI systems level, funding, geography, team composition별 분류. **Higher-complexity deep learning systems**: drug discovery가 해당. **Clinical decision support, drug discovery, diagnostics**: nearly two-thirds of AI investments 집중. **Mental health, public health, rehabilitation**: 상대적 저투자 영역. **High-income countries 집중**: 스타트업 지리적 편중. **Technical/business-oriented teams**: 임상 전문가 제한. 핵심: AI startup investment — drug discovery 2위 + 5-tier framework으로 투자 표준화(Standardizing). **LNP Immune Cell Engineering: Delivery 표준화[6]** Ivan Ciganek et al.(Materials Today Bio 2026)의 review는 LNP 기반 CRISPR immune cell engineering을 종합한다[6]. **Selective organ targeting**: 특정 장기/세포 표적화 기술. **Ligand conjugation**: 표적화 리간드 결합. **Sequence-level translational control**: 염기서열 수준 발현 조절. **PEG dilemma**: PEG Immunogenicity 문제. **Endosomal escape 한계**: 현재 주요 도전 과제. **Extraviration targeting 한계**: 비간장 표적화 어려움. **Immune trade-offs**: 면역 원성 문제. 핵심: LNP immune cell engineering — selective targeting + ligand conjugation 표준화(Standardizing). **10월 12일 Synthesizing(Cai preclinical pipeline + Seyhan molecular + Mao algorithmic) vs 오늘의 차별점**: 10/12가 **Cai Pharmaceuticals**으로 preclinical pipeline comprehensive review를, **Seyhan Critical Reviews**으로 AI molecule generation을, **Mao Frontiers Pharmacology**으로 algorithmic foundations를 제시했다면, 오늘은 **Fay Lin GEN Biotechnology**으로 **Iscientific/Recursion/Exscientia/Insilico Medicine-big pharma 2026년 다수 계약 체결**이라는 pharma AI investment 표준화(Standardizing)를, **Zahlan NPJ Digital Medicine**으로 **3,807개 startups 분석 + drug discovery 2위 투자 + 5-tier AI complexity framework**라는 투자 프레임워크 표준화(Standardizing)를, **Ciganek Materials Today Bio**으로 **selective organ targeting + ligand conjugation + sequence-level translational control + PEG dilemma**라는 delivery 기술 표준화(Standardizing)를 동시에 제시한다. Synthesizing(10/12) → pharma AI 2026 deals 표준화 + 투자 framework 표준화 + LNP engineering 표준화 Standardizing(10/13). 실무 함의: AI drug discovery 협업팀에서 Fay Lin et al.의 2026년 pharma AI deals 현황을 참조하여 partnership strategy를 수립해야 한다. *출처: [Fay Lin Pharma AI 2026 Deals GEN Biotechnology 2026](https://consensus.app/papers/details/bc6e9ea3ec2e5025af707131d2fa0ca7/), [Zahlan AI Startup Investment NPJ Digital Medicine 2026](https://consensus.app/papers/details/9f772fe9c53c575bb93a6d4f3e730280/), [Ciganek LNP Immune Cell Engineering Materials Today Bio 2026](https://consensus.app/papers/details/48353b932e435fcc94a00ac93469e299/), [Seyhan AI Drug Discovery Critical Reviews Oncology Hematology 2026](https://consensus.app/papers/details/0e6b685fd2be56fb8a814115873a73d5/)* *#AI #DrugDiscovery #FayLin #GENBiotechnology #2026Deals #Iscientific #Recursion #Exscientia #InsilicoMedicine #BigPharma #PlatformBets #Zahlan #NPJDigitalMedicine #3807Startups #DrugDiscovery #Investment #5TierFramework #AICComplexity #Ciganek #MaterialsTodayBio #LNP #ImmuneCell #SelectiveOrganTargeting #LigandConjugation #PEG #EndosomalEscape #StandardizingDay #PharmaAI #InvestmentFramework #DeliveryStandardization #BiomedicalAI #DailyReport* --- ### Post 4: 단백질 AI — AlphaFold Peptide/Peptide-Receptor + SBDD Data Gap 표준화: Standardizing 🔬 **[단백질 AI] Zhang et al.로 AlphaFold peptide structure prediction: AF2/AF3/ipTM score conformational dynamics 한계 + MD/free energy/ensemble 보충 필요성(Biotechnology Advances 2026) + Singh et al.로 AlphaFold protein-ligand data gap: hierarchical framework + frontier model dataset 필요성(Current Opinion Structural Biology 2026) + Truong et al.로 LNP payload distribution CICS single-particle analysis: co-encapsulated 50.7-60.4% + mRNA copies 9.8 vs 8.0 + indel 55.4% vs 36.3% 상관관계(Molecular Therapy 2026): 단백질 AI Standardizing(표준화) 동시 확인** 단백질 AI의 **AlphaFold peptide prediction 한계 체계화 + SBDD data gap framework + LNP payload distribution 표준화(Standardizing)**가 출처에서 동시에 확인된다[7][8][9]. **AlphaFold Peptide Prediction: Conformational Dynamics 한계 + MD 보충 필요성[7]** Buke Zhang et al.(Biotechnology Advances 2026)의 review를 재확인한다[7]. **AF2/AF3/ipTM score**: monomeric peptide + multi-chain complexes high accuracy. **Conformational dynamics 한계**: bioactive peptides의 functional conformations low-probability states로 static predictions 놓침. **MD/free energy calculations/ensemble sampling 보충 필요성**: complementary approaches 필요. 핵심: AlphaFold peptide — 한계 체계화 + MD 보충 표준화(Standardizing). **AlphaFold Protein-Ligand Data Gap: Hierarchical Framework[8]** Sukrit Singh et al.(Current Opinion in Structural Biology 2026)을 재확인한다[8]. **Hierarchical framework**: SBDD 정확도 boundary. **Frontier model dataset 필요성**: model development를 위한 체계적 데이터셋 필요. **Current models 한계**: real-world drug design tasks 반영 부족. 핵심: Protein-ligand data gap — hierarchical framework로 표준화(Standardizing). **LNP Payload Distribution: CICS Single-Particle Analysis[9]** L. B. Truong et al.(Molecular Therapy 2026)의 연구는 CICS(cylindrical illumination confocal spectroscopy)로 LNP의 payload distribution을 분석한다[9]. **4개 subpopulations**: co-encapsulated(50.7-60.4%) + gRNA only(30.0-36.5%) + mRNA only(2.0-3.4%) + empty(4.2-13.8%). **Particle-to-particle variability**: 각 class별 RNA copy number 차이. **Formulation과 mixing processes影响**: payload distribution 영향. **Therapeutic performance correlation**: mice에서 확인. **Higher cargo loads = 1.5-fold higher editing**: mRNA copies 9.8 vs 8.0 + gRNA copies 25.4 vs 20.3. **Indel rates**: 55.4% vs 36.3%. **Payload distribution가 gene-editing potency 결정 요인**: potency quality attributes 상관관계. **Single-particle CICS가 rational design 도구**: multi-component nucleic acid delivery systems 최적화. 핵심: LNP payload distribution — CICS single-particle 분석으로 quality standards 표준화(Standardizing). **10월 12일 Synthesizing(Zhang AlphaFold peptide + Singh data gap + Chakraborty AF3) vs 오늘의 차별점**: 10/12가 **Zhang Biotechnology Advances**으로 AlphaFold peptide conformational dynamics 한계를, **Singh Current Opinion**으로 protein-ligand data gap hierarchical framework를, **Chakraborty Nature Biotechnology**으로 AF3 evolution을 제시했다면, 오늘은 **Zhang/Singh 재확인**으로 **한계 framework 체계화**를(표준화), **Truong Molecular Therapy**으로 **CICS single-particle analysis로 co-encapsulated 50.7-60.4% + indel 55.4% vs 36.3% 상관관계**라는 새로운 payload distribution quality standards(Standardizing)를 추가 제시한다. Synthesizing(10/12) → 한계 framework 체계화 + CICS payload distribution 표준화 Standardizing(10/13). 실무 함의: SBDD 워크플로우팀에서 Truong et al.의 CICS payload distribution 분석을 LNP quality control 표준화에 참조해야 한다. *출처: [Zhang AlphaFold Peptide Structure Biotechnology Advances 2026](https://consensus.app/papers/details/948caefacacc54fcb7706db4d8660a93/), [Singh AlphaFold Protein-Ligand Data Gap Current Opinion Structural Biology 2026](https://consensus.app/papers/details/81a3128ae1615cfeb1bd6544d3ad2f1b/), [Truong LNP Payload Distribution Molecular Therapy 2026](https://consensus.app/papers/details/31a186a2826b5b9f9eb8c87ed71a040c/), [Chakraborty AlphaFold3 Nature Biotechnology 2026](https://consensus.app/papers/details/34db0d4f32255b8696144613f6182df8/)* *#ProteinAI #AlphaFold #Zhang #BiotechnologyAdvances #PeptideStructure #ConformationalDynamics #MD #FreeEnergy #Ensemble #Singh #CurrentOpinion #ProteinLigand #DataGap #HierarchicalFramework #FrontierModel #Truong #MolecularTherapy #LNPs #PayloadDistribution #CICS #SingleParticle #CoEncapsulated #Indels #mRNA #gRNA #QualityControl #StandardizingDay #SBDD #FrameworkStandardization #BiomedicalAI #DailyReport* --- ### Post 5: Single-Cell Multi-Omics Foundation Model — CAPTAIN Benchmarking + FMs< DMs 표준화: Standardizing 🬬 **[Single-Cell Multi-Omics Foundation Model] Ji et al.로 CAPTAIN multimodal: 4M+ cells + 382 proteins + protein imputation/expansion/cell type annotation(Nature Communications 2026) + Wang et al.로 SCMBench 23 methods: FMs < DMs in multi-omics integration + lightweight adaptation(Molecular Therapy 2026) + Bahrami et al.로 compositional foundation models: modality-specific→compositional modular design + agentic virtual cell models(Cell Systems 2026): foundation model Standardizing(표준화) 동시 확인** Single-Cell Foundation Model의 **CAPTAIN multimodal + FMs< DMs benchmarking 현실 + compositional modular 표준화(Standardizing)**가 출처에서 동시에 확인된다[10][11][12]. **CAPTAIN: Multimodal Transcriptome + Protein[10]** Bo-Ya Ji et al.(Nature Communications 2026)을 재확인한다[10]. **4M+ cells + 382 proteins**: concurrently measured. **Cross-modality dependencies modeling**: unified multimodal representations. **Protein imputation/expansion**: transcriptomes에서 단백질 예측/보강. **Cell type annotation**: 세포 유형 주석 개선. **Batch harmonization**: 배치 효과 보정. **COVID-19 immune interaction patterns**: 단백질-유전체 통합으로才发现. 핵심: CAPTAIN multimodal — 실험실 표준 데이터셋(Standardizing). **SCMBench: FMs < DMs + Lightweight Adaptation[11]** Yixuan Wang et al.(Nature Communications 2026)을 재확인한다[11]. **23개 방법 평가**: integration accuracy, biomarker detection, trajectory inference, batch effect correction. **FMs < DMs**: foundation models가 domain-specific models보다 낮은 성능. **Lightweight adaptation**: FM 성능 향상 전략. 핵심: SCMBench — benchmarking 표준 체계(Standardizing). **Compositional Foundation Models: Modularity + Agentic Virtual Cells[12]** Mojtaba Bahrami et al.(Cell Systems 2026)을 재확인한다[12]. **Modality-specific → compositional modular design**: chromatin + protein + spatial + imaging + textual unified. **Attention strategies**: transformer-based approaches. **Agentic virtual cell models**: 궁극적 목표. 핵심: Compositional modular design — 설계 표준(Standardizing). **10월 12일 Synthesizing(Bahrami modular + Acera-Mateos kidney + Ji CAPTAIN) vs 오늘의 차별점**: 10/12가 **Bahrami Cell Systems**으로 modality-specific→compositional modular design을, **Acera-Mateos Genome Biology**으로 kidney atlas를, **Ji Nature Communications**으로 CAPTAIN을 제시했다면, 오늘은 **Ji/Wang/Bahrami 모두 재확인**으로 **CAPTAIN multimodal (4M+ cells, 382 proteins) 실험실 표준 + SCMBench 23 methods FMs< DMs benchmarking 체계 + compositional modular design 설계 표준**을(모두 Standardizing 관점) 동시에 제시한다. Synthesizing(10/12) → CAPTAIN 표준 dataset + SCMBench benchmarking 체계 + compositional modular 설계 표준 Standardizing(10/13). 실무 함의: 단일세포 연구팀에서 Wang et al.의 SCMBench 결과를 foundation model selection criteria에 반영해야 한다. *출처: [Ji CAPTAIN Multimodal Foundation Nature Communications 2026](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/), [Wang SCMBench Nature Communications 2026](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/), [Bahrami Compositional Foundation Models Cell Systems 2026](https://consensus.app/papers/details/dc130e77f84657098171472264ad3d6f/), [Acera-Mateos Kidney Multimodal Atlas Genome Biology 2026](https://consensus.app/papers/details/3e310c088a7f538a9cc7e8498109cde7/)* *#SingleCell #FoundationModel #Ji #CAPTAIN #NatureCommunications #Multimodal #4MillionCells #382Proteins #Imputation #Expansion #CellTypeAnnotation #BatchHarmonization #Wang #SCMBench #23Methods #FMsLessThanDMs #LightweightAdaptation #Benchmarking #Bahrami #CellSystems #Compositional #Modular #Agentic #VirtualCell #ModalitySpecific #StandardizingDay #DataStandard #BenchmarkStandard #DesignStandard #BiomedicalAI #DailyReport* --- ### Post 6: FDA/규제 — Global CGT Regulatory Convergence × GCRC-F 4 Pillars 표준화: Standardizing 📋 **[FDA/규제] Verma et al.로 Global CGT Regulatory Convergence Framework(GCRC-F): FDA/EMA/PMDA + BRIC-M 8개 관할권 4개 convergence gaps + 4 pillars 제안(BioTech 2026) + Oo et al.로 FDA 44개 CGT approvals platform-aligned AI/ML analytics 재확인(J Clinical Pharmacology 2026) + Verma et al.로 GCRC-F 4 pillars: Unified CMC + Data Harmonization + Adaptive Approval + Manufacturing Standardization(BioTech 2026): FDA/규제 Standardizing(표준화) 동시 확인** FDA 규제 영역의 **Global CGT Regulatory Convergence Framework + 4 pillars 제안(Standardizing)**이 출처에서 동시에 확인된다[13][14][15]. **Global CGT Regulatory Convergence Framework: 8개 관할권 + 4 Convergence Gaps[13]** Nagendra Verma et al.(BioTech 2026)의 review는 CGT 규제 통합 프레임워크를 제시한다[13]. **8개 관할권**: FDA(US) + EMA(EU) + PMDA(Japan) + Brazil + Russia + India + China + Mexico(BRIC-M). **4개 convergence gaps 재발견**: (1) unstandardized potency assay validation, (2) inconsistent post-change comparability expectations, (3) uneven ICH guideline implementation, (4) divergent evidentiary thresholds for small-population trials. **Convergence readiness vari**: China + Brazil은 ICH benchmarked CMC guidance vs Mexico + Russia + India는 reference reliance. **Global CGT Regulatory Convergence Framework(GCRC-F) 제안**: 4 pillars architecture. 핵심: Global CGT regulatory convergence — 8개 관할권 4개 gaps + GCRC-F 제안(Standardizing). **GCRC-F 4 Pillars Architecture[15]** (Verma et al. BioTech 2026). **Pillar 1 — Unified CMC Standards**: 통일된 화학·제조·관리 표준. **Pillar 2 — Data Harmonization Layer**: 장기 추적 관찰 + 실세계 증거 데이터 통합. **Pillar 3 — Adaptive Approval Layer**: 가속화 designation cross-agency 연결. **Pillar 4 — Manufacturing Standardization Layer**: 기존 규제 precedent 기반 + regulatory-science maturity tiered participation. **현실적 제안**: regulators 또는 industry stakeholders 평가 전 제안 단계. 핵심: GCRC-F 4 pillars — 제조·데이터·승인 표준화(Standardizing). **FDA 44개 CGT Approvals Platform-Aligned AI/ML Analytics 재확인[14]** C. Oo et al.(J Clinical Pharmacology 2026)을 재확인한다[14]. **44개 CGT 승인(2025년 5월 기준)**. **Platform-aligned risk-based approach**: plausible mechanism framework + CMC flexibility + advanced manufacturing. **AI/ML analytics convergence**: dose selection + safety evaluation + durability prediction. 핵심: FDA 44개 approvals + AI/ML convergence — 이행 현실(Standardizing). **10월 12일 Synthesizing(Leonard 10년 evolution + Oo 44 CGT + Chakraborty Casgevy) vs 오늘의 차별점**: 10/12가 **Leonard Seminars in Hematology**으로 SCD gene therapy 10년 evolution을, **Oo J Clinical Pharmacology**으로 44개 CGT approvals를, **Chakraborty Molecular Therapy**으로 Casgevy dual approval을 제시했다면, 오늘은 **Verma BioTech**으로 **FDA/EMA/PMDA + BRIC-M 8개 관할권 4개 convergence gaps + GCRC-F 4 pillars 제안**이라는 국제적 규제 표준화 프레임워크(Standardizing)를, **Oo 재확인**으로 **44개 approvals + platform-aligned AI/ML analytics** 이행 현실(Standardizing)을 동시에 제시한다. Synthesizing(10/12) → international regulatory convergence framework + 4 pillars 표준화 Standardizing(10/13). 실무 함의: Regulatory Affairs팀에서 Verma et al.의 GCRC-F 4 pillars를 international regulatory strategy 수립에 참조해야 한다. *출처: [Verma Global CGT Regulatory Convergence BioTech 2026](https://consensus.app/papers/details/105ceebe08e754aeabb177c733ca9ba1/), [Oo FDA CGT Approvals J Clinical Pharmacology 2026](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/), [Chakraborty CRISPR Therapeutics Approval Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/68148f0062ec554ba4af1996dd3718cb/), [Iglesias-López CGT Quality Regulatory Science 2026](https://consensus.app/papers/details/49b52f00e1d45031af864f85aeb08f46/)* *#FDA #GeneTherapy #Verma #BioTech #RegulatoryConvergence #GCRCF #FDA #EMA #PMDA #BRICM #8Jurisdictions #4ConvergenceGaps #PotencyAssay #CMC #ICH #SmallPopulation #UnifiedCMC #DataHarmonization #AdaptiveApproval #ManufacturingStandardization #Oo #JClinicalPharmacology #44CGTApprovals #PlatformAligned #AIAnalytics #DoseSelection #SafetyEvaluation #DurabilityPrediction #StandardizingDay #InternationalFramework #4Pillars #RegulatoryStandardization #BiomedicalAI #DailyReport* --- ### Post 7: Spatial Transcriptomics — Spatial Ecotypes × TME Standardization × AI Integration: Standardizing 🗺️ **[Spatial Transcriptomics] Zhang et al.로 spatial ecotypes: 9개 conserved SEs + cfDNA deep learning + melanoma near 100명 validation(Nature 2026) + Nguyen et al.로 spatial omics clinical TME: AI/ML cross-platform integration + standardized workflows + precision cancer medicine(Clinical & Translational Immunology 2026) + Li et al.로 pan-cancer 56 LCPs + 13 niches + Niche_4 poor prognosis + Niche_11 better survival(Cell Reports Medicine 2026): spatial transcriptomics Standardizing(표준화) 동시 확인** Spatial transcriptomics의 **spatial ecotypes framework + TME standardization + AI integration(Standardizing)**이 출처에서 동시에 확인된다[16][17][18]. **Spatial Ecotypes 9 SEs + cfDNA Deep Learning 재확인[16]** Wubing Zhang et al.(Nature 2026)을 재확인한다[16]. **9개 conserved SEs**: 10M+ cells에서 확인. **cfDNA deep learning**: liquid biopsy로 TME profiling. **Melanoma near 100명 validation**: immunotherapy response 예측. 핵심: Spatial ecotypes — biomarker 표준 framework(Standardizing). **Spatial Omics Clinical TME: AI/ML Integration + Standardized Workflows[17]** Hao Nguyen et al.(Clinical & Translational Immunology 2026)의 review는 spatial omics의 임상 적용을 종합한다[17]. **AI/ML cross-platform integration**: ST + SP 통합. **Standardized workflows 필요성**: precision cancer medicine을 위한 표준화. **Cross-platform integration gaps**: major challenges. **Data standardisation**: 필요성. **Computational scalability**: high-plex single-cell datasets를 위한 확장성. **Explainable AI**: 해석 가능성 중요성. **Precision cancer medicine 적용**: ultimate goal. 핵심: Spatial omics clinical TME — AI/ML integration + standardized workflows 표준화(Standardizing). **Pan-Cancer 56 LCPs + 13 Niches 재확인[18]** Jiarong Li et al.(Cell Reports Medicine 2026)을 재확인한다[18]. **Niche_4(macrophage + tumor cell)**: poor prognosis + immunotherapy resistance. **Niche_11(macrophage + immune cell)**: better survival + treatment response. 핵심: Pan-cancer niche survival — biomarker 표준 체계(Standardizing). **10월 12일 Synthesizing(Zhang spatial ecotypes + Li niche + Jeong GraphTME) vs 오늘의 차별점**: 10/12가 **Zhang Nature**으로 9개 SEs + cfDNA deep learning를, **Li Cell Reports Medicine**으로 Niche_4/11를, **Jeong NPJ Systems Biology**으로 GraphTME F1 0.83을 제시했다면, 오늘은 **Nguyen Clinical & Translational Immunology**으로 **AI/ML cross-platform integration + standardized workflows + precision cancer medicine 적용**이라는 임상 표준화 프레임워크(Standardizing)를 추가 제시한다. Synthesizing(10/12) → spatial ecotypes framework + TME standardized workflows + AI integration Standardizing(10/13). 실무 함의: 면역종양학팀에서 Nguyen et al.의 standardized workflows 권고사항을 spatial omics clinical integration protocol에 반영해야 한다. *출처: [Zhang Spatial Ecotypes Nature 2026](https://consensus.app/papers/details/b1d6ae0b47be530d8298e2eb72eff187/), [Nguyen Spatial Omics TME Clinical Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/), [Li Pan-Cancer Niche Cell Reports Medicine 2026](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/), [Jeong GraphTME NPJ Systems Biology 2026](https://consensus.app/papers/details/fb21e8a3a3cc50eba50bc796e658fc7a/)* *#SpatialTranscriptomics #Zhang #SpatialEcotypes #Nature #9SEs #cfDNA #DeepLearning #Immunotherapy #Melanoma #Nguyen #ClinicalTranslationalImmunology #SpatialOmics #TME #AIML #CrossPlatform #StandardizedWorkflows #PrecisionMedicine #Li #PanCancer #56LCPs #13Niches #Niche4 #Niche11 #Macrophage #Tumor #Prognosis #Jeong #GraphTME #F1Score #MERFISH #StandardizingDay #BiomarkerStandard #WorkflowStandard #ClinicalIntegration #BiomedicalAI #DailyReport* --- ### Post 8: 오늘의 요약 — Standardizing(표준화) Day 핵심 정리 🧬 **오늘 6개 영역 핵심 요약:** 10/13(Standardizing)의 핵심: Synthesizing(10/12)된 기술들이 **각 분야主流 체계에서 표준화 Framework를 제시하며, 규제-기술 협력이 국제적 차원에서 구체화되는 날**이었다. **1. Non-Viral In Vivo CRISPR 36건 Trials + CNS Delivery 체계화(Lummerstorfer Drug Delivery 2026 + Martin International Journal of Biological Sciences 2026 + Nagamatsu Molecular Therapy. Nucleic Acids 2026)**: 136건 trials 중 36건 in vivo non-viral delivery 전환 확인 + hit-and-run transient delivery 우위 재확인; 全editor type(base/prime/RNA/epigenome) organ-specific strategies + GenE-HumDi 4개 권고사항; Acerola AELNs + GLP2 + intranasal CNS delivery C9orf72 편집 성공 — 표준화[1][2][3]. **2. Pharma AI 2026 Deals × Investment Framework Standardizing(Fay Lin GEN Biotechnology 2026 + Zahlan NPJ Digital Medicine 2026 + Ciganek Materials Today Bio 2026)**: Iscientific/Recursion/Exscientia/Insilico Medicine-big pharma 다수 계약 체결 + AI platform bets 강화; 3,807개 AI startups 분석 drug discovery 2위 투자 + 5-tier AI complexity framework; LNP selective organ targeting + ligand conjugation + sequence-level translational control + PEG dilemma — 표준화[4][5][6]. **3. AlphaFold Peptide 한계 Framework + CICS LNP Payload Distribution 표준화(Zhang Biotechnology Advances 2026 + Singh Current Opinion Structural Biology 2026 + Truong Molecular Therapy 2026)**: AF2/AF3 conformational dynamics 한계 + MD/free energy/ensemble 보충 필요성 체계화; hierarchical framework로 SBDD data gap 표준화; CICS single-particle로 co-encapsulated 50.7-60.4% + indel 55.4% vs 36.3% 상관관계 입증 — quality standards 표준화[7][8][9]. **4. CAPTAIN Multimodal + SCMBench FMs< DMs Benchmarking 체계(Bahrami Cell Systems 2026 + Ji Nature Communications 2026 + Wang Nature Communications 2026)**: CAPTAIN 4M+ cells + 382 proteins 실험실 표준 dataset; SCMBench 23 methods FMs < DMs + lightweight adaptation benchmarking 체계; compositional modular design 설계 표준 — 데이터/벤치마크 표준화[10][11][12]. **5. Global CGT Regulatory Convergence Framework GCRC-F 4 Pillars(Verma BioTech 2026 + Oo J Clinical Pharmacology 2026)**: FDA/EMA/PMDA + BRIC-M 8개 관할권 4개 convergence gaps + GCRC-F 4 pillars(Unified CMC + Data Harmonization + Adaptive Approval + Manufacturing Standardization); 44개 CGT approvals + platform-aligned AI/ML analytics — 국제 규제 표준화[13][14][15]. **6. Spatial Ecotypes + TME Standardized Workflows + AI Integration(Nguyen Clinical & Translational Immunology 2026 + Zhang Nature 2026 + Li Cell Reports Medicine 2026)**: spatial ecotypes 9 SEs + cfDNA deep learning melanoma near 100명 validation; AI/ML cross-platform integration + standardized workflows + precision cancer medicine; Niche_4(poor prognosis) + Niche_11(better survival) — biomarker + 워크플로우 표준화[16][17][18]. 10월 14일 전망: Standardizing된 Framework들이 각 분야主流 체계에서 어떻게 실현되는지가 핵심. 특히 GCRC-F 4 pillars의 실현 가능성, CICS LNP quality standards의 임상 적용, non-viral in vivo 36건 trials의后续 데이터가 관전이다. Standardizing 다음은 **Scaling(규모화)**이다. 오늘의 6개 전선이 제시하는 공통 질문: 표준화된 Framework들이 어떻게 규모화(Scale)되어 각 분야主流 체계에서 광범위 적용될 수 있는가. 이 질문이 내일 이후 각 전선의 핵심 과제가 된다. *출처: [Lummerstorfer Non-Viral CRISPR Carriers Drug Delivery 2026](https://consensus.app/papers/details/747d35e9e5bc5ca9a9854998c7acd326/), [Martin In Vivo Genome Editing Delivery International Journal of Biological Sciences 2026](https://consensus.app/papers/details/5d58355f4ce053dc80e42bf7ba924045/), [Fay Lin Pharma AI 2026 Deals GEN Biotechnology 2026](https://consensus.app/papers/details/bc6e9ea3ec2e5025af707131d2fa0ca7/), [Nagamatsu Acerola Exosome CNS CRISPR Molecular Therapy Nucleic Acids 2026](https://consensus.app/papers/details/c6f1f573312a567cb720861b6f9b7e55/), [Truong LNP Payload Distribution Molecular Therapy 2026](https://consensus.app/papers/details/31a186a2826b5b9f9eb8c87ed71a040c/), [Verma Global CGT Regulatory Convergence BioTech 2026](https://consensus.app/papers/details/105ceebe08e754aeabb177c733ca9ba1/), [Ciganek LNP Immune Cell Engineering Materials Today Bio 2026](https://consensus.app/papers/details/48353b932e435fcc94a00ac93469e299/), [Nguyen Spatial Omics TME Clinical Translational Immunology 2026](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/), [Zhang Spatial Ecotypes Nature 2026](https://consensus.app/papers/details/b1d6ae0b47be530d8298e2eb72eff187/), [Zhang AlphaFold Peptide Biotechnology Advances 2026](https://consensus.app/papers/details/948caefacacc54fcb7706db4d8660a93/), [Singh AlphaFold Data Gap Current Opinion Structural Biology 2026](https://consensus.app/papers/details/81a3128ae1615cfeb1bd6544d3ad2f1b/)* *#StandardizingDay #Summary #Lummerstorfer #136Trials #36InVivo #NonViral #Martin #GenEHumDi #BaseEditing #PrimeEditing #RNAEditing #EpigenomeEditing #FayLin #PharmaAI #2026Deals #Iscientific #Recursion #Exscientia #InsilicoMedicine #Zahlan #3807Startups #DrugDiscovery #5TierFramework #Nagamatsu #Acerola #AELNs #GLP2 #CNS #C9orf72 #ALS #FTD #Truong #CICS #CoEncapsulated #Indels #Payload #Verma #GCRCF #RegulatoryConvergence #FDA #EMA #PMDA #BRICM #4Pillars #CMC #DataHarmonization #AdaptiveApproval #Manufacturing #Ciganek #LNP #SelectiveOrganTargeting #LigandConjugation #PEG #Nguyen #SpatialOmics #TME #AIML #Workflows #Zhang #SpatialEcotypes #cfDNA #Melanoma #Li #Niche4 #Niche11 #Standardizing #FrameworkStandardization #BiomedicalAI #DailyReport* --- ## Sources (1차 출처 18개 이상) [1] [Lummerstorfer M, et al. Non-Viral CRISPR carriers: transient delivery with lasting effects. Drug Delivery. 2026.](https://consensus.app/papers/details/747d35e9e5bc5ca9a9854998c7acd326/) [2] [Martin L, et al. In vivo delivery strategies for therapeutic CRISPR genome editing. International Journal of Biological Sciences. 2026.](https://consensus.app/papers/details/5d58355f4ce053dc80e42bf7ba924045/) [3] [Nagamatsu Y, et al. Exosome-like nanovesicles from acerola for CRISPR-Cas9 ribonucleoprotein delivery to the central nervous system. Molecular Therapy. Nucleic Acids. 2026.](https://consensus.app/papers/details/c6f1f573312a567cb720861b6f9b7e55/) [4] [Fay Lin, et al. Pharma Bets Big on AI Platforms with Flurry of 2026 Deals. GEN Biotechnology. 2026.](https://consensus.app/papers/details/bc6e9ea3ec2e5025af707131d2fa0ca7/) [5] [Zahlan A, et al. Mapping AI startup investment and innovation in healthcare using a five-tier AI systems complexity framework. NPJ Digital Medicine. 2026.](https://consensus.app/papers/details/9f772fe9c53c575bb93a6d4f3e730280/) [6] [Ciganek I, et al. Delivering the future of immunotherapy: A state-of-the-art review of gene editing in immune cells with lipid nanoparticles. Materials Today Bio. 2026.](https://consensus.app/papers/details/48353b932e435fcc94a00ac93469e299/) [7] [Zhang B, et al. AlphaFold-based peptide structure prediction: opportunities, limitations, and future directions. Biotechnology Advances. 2026.](https://consensus.app/papers/details/948caefacacc54fcb7706db4d8660a93/) [8] [Singh S, et al. More protein-ligand data is needed for AlphaFold-like models to enable drug discovery. Current Opinion in Structural Biology. 2026.](https://consensus.app/papers/details/81a3128ae1615cfeb1bd6544d3ad2f1b/) [9] [Truong LB, et al. Messenger RNA and guide RNA distributions in lipid nanoparticles impact gene-editing efficiency in vivo. Molecular Therapy. 2026.](https://consensus.app/papers/details/31a186a2826b5b9f9eb8c87ed71a040c/) [10] [Ji BY, et al. CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein. Nature Communications. 2026.](https://consensus.app/papers/details/40361e55d4815c1dae3d5f6a95d4468a/) [11] [Wang Y, et al. SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration. Nature Communications. 2026.](https://consensus.app/papers/details/24b273d01c1f508c9a20a5a981ac2e3c/) [12] [Bahrami M, et al. From modality-specific to compositional foundation models for cell biology. Cell Systems. 2026.](https://consensus.app/papers/details/dc130e77f84657098171472264ad3d6f/) [13] [Verma N, et al. Regulatory Convergence in Cell and Gene Therapy: Harmonizing Quality, CMC, and Approval Pathways Across the FDA, EMA, PMDA, and Emerging Markets. BioTech. 2026.](https://consensus.app/papers/details/105ceebe08e754aeabb177c733ca9ba1/) [14] [Oo C, et al. FDA Gene Therapy Approvals (1998–2025): Current Status, Regulatory Evolution, and Future Directions. J Clinical Pharmacology. 2026.](https://consensus.app/papers/details/181658e93c825ea581a0664845a98d12/) [15] [Chakraborty C, et al. CRISPR-Cas9-mediated therapeutics: current clinical trials and therapy approval landscape. Molecular Therapy. Nucleic Acids. 2026.](https://consensus.app/papers/details/68148f0062ec554ba4af1996dd3718cb/) [16] [Zhang W, et al. Non-invasive profiling of the tumour microenvironment with spatial ecotypes. Nature. 2026.](https://consensus.app/papers/details/b1d6ae0b47be530d8298e2eb72eff187/) [17] [Nguyen H, et al. Spatial omics for profiling the dynamic tumor microenvironment. Clinical & Translational Immunology. 2026.](https://consensus.app/papers/details/1b906e5abaaf5e789aaabde458127265/) [18] [Li J, et al. Pan-cancer analysis of spatial transcriptomics reveals heterogeneous tumor spatial microenvironment. Cell Reports Medicine. 2026.](https://consensus.app/papers/details/f420ab985e1257e18352cac9e0c425cf/)
Research Pulse·2026-10-13
Research Pulse — 2026-10-13
28 journals × 7 topics · fibrosis · OXPHOS · ferroptosis · sarcopenia · senescence
Raw data + scoring: Daily Tech Digest (separate feed)
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