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한빛사논문
한빛사논문
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Nat. Mater., Feb 24 2025, | https://doi.org/10.1038/s41563-024-02115-4
On-patient medical record and mRNA therapeutics using intradermal microneedles
Jooli Han 1,2,11, Maria Kanelli 1,11, Yang Liu 3, John L. Daristotle 1, Apurva Pardeshi 1, Timothy A. Forster 1, Ari Karchin 4, Brandon Folk 4, Lukas Murmann 3, Lisa H. Tostanoski 5, Sebastian E. Carrasco 6, Shahad K. Alsaiari 1, Erika Yan Wang 1, Khanh Tran 1, Linzixuan Zhang 7, Behnaz Eshaghi 1, Lauren Levy 1, Sydney Pyon 1, Charles Sloane 1, Stacey Qiaohui Lin 1, Alicia Lau 1, Collin F. Perkinson 8, Moungi G. Bawendi 8, Dan H. Barouch 5,9,10, Frédo Durand 3, Robert Langer 1,7,* & Ana Jaklenec 1,*
1Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
2Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.
3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
4Global Health Labs, Bellevue, WA, USA.
5Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
6Laboratory of Comparative Pathology, Weill Cornell Medicine, Memorial Sloan Kettering Cancer Center, Rockefeller University, New York, NY, USA.
7Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
8Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
9Harvard Medical School, Boston, MA, USA.
10The Ragon Institute of Mass General Brigham, MIT, and Harvard, Cambridge, MA, USA.
11These authors contributed equally: Jooli Han, Maria Kanelli.
2Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.
3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
4Global Health Labs, Bellevue, WA, USA.
5Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
6Laboratory of Comparative Pathology, Weill Cornell Medicine, Memorial Sloan Kettering Cancer Center, Rockefeller University, New York, NY, USA.
7Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
8Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
9Harvard Medical School, Boston, MA, USA.
10The Ragon Institute of Mass General Brigham, MIT, and Harvard, Cambridge, MA, USA.
11These authors contributed equally: Jooli Han, Maria Kanelli.
*Corresponding authors: correspondence to Robert Langer or Ana Jaklenec
Abstract
Medical interventions often require timed series of doses, thus necessitating accurate medical record-keeping. In many global settings, these records are unreliable or unavailable at the point of care, leading to less effective treatments or disease prevention. Here we present an invisible-to-the-naked-eye on-patient medical record-keeping technology that accurately stores medical information in the patient skin as part of microneedles that are used for intradermal therapeutics. We optimize the microneedle design for both a reliable delivery of messenger RNA (mRNA) therapeutics and the near-infrared fluorescent microparticles that encode the on-patient medical record-keeping. Deep learning-based image processing enables encoding and decoding of the information with excellent temporal and spatial robustness. Long-term studies in a swine model demonstrate the safety, efficacy and reliability of this approach for the co-delivery of on-patient medical record-keeping and the mRNA vaccine encoding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This technology could help healthcare workers make informed decisions in circumstances where reliable record-keeping is unavailable, thus contributing to global healthcare equity.
논문정보
- Research article
- 2025년 02월 (BRIC 등록일 2025-03-03)
- 국내+국외 연구진
- 바이오·의료융합 > 다학제 융합/복합
관련 보도자료
환자 부착형 mRNA 백신 전달 및 의료정보 기록 마이크로니들 패치
시간이 경과함에 따라 인체 내 퀀텀닷 발광 효율 감소 및 다른 외부적 요인으로 인한 정보 출력 오류를 방지하기 위해 오류 정정 코드(error correcting code)와 딥 러닝(deep learning) 기술을 접목하여 정확한 정보 입력 및 출력 시스템을 구현함. 65만 개의 합성 패턴 이미지를 훈련 세트로 사용하여 이미지 정정 및 인식 네트워크 모델을 훈련시키고, 이를 통해 21개의 마이크로니들 패치를 3마리의 돼지에 in vivo 투여했을 시 3개월 동안 100%의 성공률로 패턴을 인식하고 출력하는 것을 확인함. 마지막으로, 퀀텀닷과 SARS-CoV-2 mRNA 백신을 마이크로니들 패치에 co-loading 하여 쥐에 동시 투여했을 시 근육주사로 접종되는 기존 COVID-19 백신과 동일한 효과를 나타내는 것을 확인함.
- Bio통신원
- 2025. 03. 04
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