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A programmable bioresorbable electrochemical microneedle sensor array for perioperative monitoring of organ health

Nature Biomedical Engineering (2026)Cite this article

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Abstract

Comprehensive and continuous assessment of organ physiology and biochemistry, beyond the capabilities of conventional monitoring tools, can enable timely interventions for perioperative complications such as organ ischaemia and transplant rejection. Here we present an integrated bioresorbable system that enables multiplexed, real-time and spatially mapped electrochemical monitoring of deep organs throughout the surgical course. Using a 3D printing-based, photolithography-free fabrication process, the system features a flexible, 3D programmed, individually addressable microneedle sensor array with backward-facing barbs for conformal and stable organ interfacing and 3D parenchymal probing. Electrochemical functionalization of microneedle tips enable concurrent monitoring and spatial mapping of key biochemical markers, such as electrolytes, metabolites and oxygenation, in deep organs for at least 7 days. An electrically programmable self-destruction mechanism offers controllability over the degradation process, eliminating the need for device retrieval. Demonstrations in clinically relevant complications such as kidney ischaemia and gut disorders in animal models highlight the broad applications of this device in intra- and postoperative monitoring, advancing perioperative care and critical care medicine.

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Fig. 1: Design concepts and system features.
Fig. 2: Photographs and micrographs of the 3D printing-based, photolithography-free fabrication process.
Fig. 3: In vitro and in vivo characterizations of the implant.
Fig. 4: Characterizations of the microneedle sensors.
Fig. 5: Monitoring of acute and chronic kidney ischaemia in rats.
Fig. 6: Biocompatibility study of the implant.

Data availability

All data supporting the findings of this study are presented in the Article and its Supplementary Information. Source data are provided with this paper.

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Acknowledgements

We acknowledge the startup funding to W.O. from the Thayer School of Engineering at Dartmouth College. This work was also supported by the National Institute of General Medical Sciences (NIGMS) under award number R35GM159840 (W.O.). The authors further acknowledge the following Shared Resources facilities at the Dartmouth Cancer Center: Irradiation, Pre-clinical Imaging and Microscopy Resource (IPIMSR, RRID:SCR_025077), Pathology Shared Resource (PSR, RRID:SCR_023479), and Trace Element Analysis Shared Resource (TEASR, RRID:SCR_009777), supported by the NCI Cancer Center Support Grant (5P30CA023108-41). The Dartmouth Biomedical National Elemental Imaging Resource (BNEIR), part of TEASR, is additionally supported by NIGMS under award R24GM141194 and by the NIH Shared Instrumentation Grant S10OD032352.

Author information

Authors and Affiliations

  1. Thayer School of Engineering, Dartmouth College, Hanover, NH, USA

    Xiangling Li, Shibo Liu, Gen Li, Mingwei Zhou, Jaehyeon Ryu, Matthew Morales, Hui Fang & Wei Ouyang

  2. School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang, Guangdong, People’s Republic of China

    Jingshan Mo

  3. National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China

    Cheng Yang

Authors
  1. Xiangling Li
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  2. Shibo Liu
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  4. Cheng Yang
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Contributions

X.L. and W.O. conceived the ideas and designed the research. X.L. developed the sensors. X.L., G.L., M.Z., J.R. and M.M. manufactured and tested the sensors. S.L. designed and manufactured the electronics. J.M. and C.Y. performed the finite element simulation. X.L. performed the sensor characterizations and animal experiments. W.O. and H.F. supervised the research. X.L. and W.O. wrote the manuscript. All authors reviewed and commented on the manuscript.

Corresponding author

Correspondence to Wei Ouyang.

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Competing interests

The authors declare no competing interests.

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Nature Biomedical Engineering thanks Joshua Rainbow and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Additional data on design concepts and system features.

a, Bioresorption processes of materials. b, Schematic illustration of the rolling process of the e-suture. c, Definition of rolling parameters. d, E-suture diameter v.s. total width of electrical interconnects at different substrate thickness. e, Micrograph of the e-suture and a standard size #4 suture. f, Cross-sectional micrograph of the e-suture. g, Photograph of a completed 6×6 device. h, Photograph of a completed 6×6 device on the palm. i, Stability of a 3×3 device in PBS at 37 °C. j, The electrical resistance of individual electrical interconnects of the e-suture (1-9#) in PBS at 37 °C.

Source data

Extended Data Fig. 2 Mechanical characterizations of the barbed microneedle array.

a, Schematic illustration of the fabrication process of barbed microneedles. b-d, Numerical simulation of the tissue retention characteristics of microneedles with 0 (b), 1 (c), and 2 (d) rows of barbs. e, Photographs of weight-holding tests of microneedles with 0, 1, 2, and 3 rows of barbs using 10-gram weights. f, Simulated maximum pull-out force of barbed microneedles. g, Maximum pull-out force of barbed microneedles measured by the weight-holding test. The data are presented as mean ± s.d. (n = 3 independent experiments). h, Resistance force experienced by a bare microneedle during an insertion test in a rat kidney. i, Photographs of the insertion test of a bare microneedle. j, Resistance force experienced by a barbed microneedle during an insertion test in a rat kidney. k, Photographs of the insertion test of a barbed microneedle.

Source data

Extended Data Fig. 3 Response and reversibility of microneedle electrochemical sensors.

a, Na+ sensor. b, pH sensor. c, Lactic acid sensor. d, Uric acid sensor.

Source data

Extended Data Fig. 4 Reproducibility of microneedle electrochemical sensors (n = 3 independent sensors).

a, K+ sensor. b, Na+ sensor. c, pH sensor. d, Glucose sensor. e, Lactic acid sensor. f, Uric acid sensor. g, Oxygen sensor. All the data are presented as mean ± s.d.

Source data

Extended Data Fig. 5 Validation of microneedle electrochemical sensors against standard methods.

a-b, Error grid analysis (a) and relative errors to reference values (b) of the K+ sensor. The data are presented as mean ± s.d. (n = 3 independent sensors). c-d, Error grid analysis (c) and relative errors to reference values (d) of the Na+ sensor. The data are presented as mean ± s.d. (n = 3 independent sensors). e-f, Error grid analysis (e) and relative errors to reference values (f) of the pH sensor. Region A corresponds to those values within <20% deviation from the reference results, which could inform reliable decisions. Region B shows inaccurate values with 20%-50% deviation from the reference results. Region C reflects inaccurate values with 50%-80% deviation. Region D shows inaccurate values indicating a potential failure to detect target chemicals. The data are presented as mean ± s.d. (n = 3 independent sensors). g-h, Clarke’s error grid analysis (g) and relative errors to reference values (h) of the glucose sensor. Region A corresponds to those values within 20% deviation from the reference glucose values. Region B shows inaccurate values with >20% deviation from the reference glucose values but would not lead to inappropriate diabetes treatment. Region C reflects inaccurate values leading to unnecessary diabetes treatment. Region D shows inaccurate values indicating a potential failure to detect hypoglycemia or hyperglycemia. Region E corresponds to those inaccurate values that would confuse treatment of hypoglycemia for hyperglycemia and vice versa. The data are presented as mean ± s.d. (n = 3 independent sensors). i-j, Error grid analysis (i) and relative errors to reference values (j) of the lactic acid sensor. The data are presented as mean ± s.d. (n = 3 independent sensors). k-l, Error grid analysis (k) and relative errors to reference values (l) of the uric acid sensor. The definitions of the regions are the same as those in a-f. The data are presented as mean ± s.d. (n = 3 independent sensors).

Source data

Extended Data Fig. 6 Monitoring of gut disorders in rats.

a, Schematic illustration of gut monitoring using the device. Created in BioRender. Ouyang, W. (2025) https://BioRender.com/21omdf7. b, Photograph of the SMART encircling the small intestine of a rat. c, Concurrent monitoring of glucose, Na+, K+, and pH in the lumen of the intestine. d, EMGs of the intestine upon injection of PBS, glucose, and capsaicin.

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, Figs. 1–19 and Table 1.

Reporting Summary

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Source Data Extended Data Fig. 1

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Source Data Extended Data Fig. 5

Statistical source data.

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Li, X., Liu, S., Mo, J. et al. A programmable bioresorbable electrochemical microneedle sensor array for perioperative monitoring of organ health. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-025-01609-z

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