About

Lijing Jiang is an assistant professor in the Department of History of Science and Technology at Johns Hopkins University. Her research focuses on the history of modern life sciences, particularly how epistemic and cultural interpretations and material manipulations of organisms or objects have shaped processes of knowledge production in biology and biotechnology. She began her inquiries with a PhD dissertation on the history of cell death and aging research and is currently working on a monograph about how ornamental fish functioned as model organisms in twentieth-century East Asia and North America. Her first book project, The Entangled Model Fish, provides a transnational history of how goldfish, medaka, and zebrafish became model organisms in the life sciences, revealing the influence of national concerns, cultural traditions, and international politics on their development and use. She is also working on a second book, Fish Nations, which explores the history of modern aquaculture in Asia within social and environmental contexts. Jiang's scholarly interests include the history of biology, biomedicine, biotechnology, and environmental history, with a focus on China, Japan, and Asia. Her work emphasizes transnational and comparative perspectives, and she has studied how cultural imaginations shape laboratory practices and scientific narratives. Jiang's academic background includes training at the Center for Biology and Society at Arizona State University, and postdoctoral fellowships at Princeton University, Nanyang Technological University, and Yale University. She has taught courses related to the history of modern science, biology, medicine, science and technology studies, and environmental history, bringing a global perspective to her teaching.

Research topics

• Artificial Intelligence
• Computer Science
• Speech recognition
• Embedded system
• Neuroscience

Selected publications

• Mechanism study on lactic acid promoting intervertebral disc degeneration by regulating senescence and DNA damage of cartilage endplate stem cells

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-25

  articleOpen access

  Abstract Intervertebral disc degeneration, a leading cause of low back pain with incompletely elucidated molecular mechanisms, was studied via integrated in vivo/vitro approaches. This study first reveals that lactic acid accelerates intervertebral disc degeneration by inducing cartilage endplate stem cells senescence and DNA damage, thereby activating the P16/P21/P53-centered senescence pathway. In a rat tail vertebra puncture-induced intervertebral disc degeneration model, degenerated discs exhibited increased lactic acid levels, narrowed intervertebral spaces, and disrupted nucleus pulposus structure ( P <0.05). In vitro, 0/2/6/10 mM lactic acid dose-dependently suppressed cartilage endplate stem cells viability (10 mM group: 15.7% of the control), elevated intracellular reactive oxygen species (ROS, 2.8-fold relative to the control), induced G0 cell cycle arrest (10 mM group: 85.63%), reduced EdU-positive cells (8.62%), and increased β-galactosidase-positive cells (10 mM group: 33.06%) and γ-H2AX foci (all P <0.01).Molecularly, lactic acid significantly upregulated P16 (2.1-fold), P21 (3.1-fold), P53 (2.4-fold), and γ-H2AX (1.8-fold). In vivo intervertebral disc injection confirmed a positive correlation between lactic acid concentration and intervertebral disc degeneration severity. This study clarifies lactic acid’s role in intervertebral disc degeneration via the “oxidative stress–cell cycle arrest–cellular senescence” axis, advancing understanding of intervertebral disc degeneration pathogenesis and providing a basis for targeted therapies against lactic acid metabolism.

  PublisherOA PDFDOI

• Highly selective and reversible adsorption of trace gaseous ammonia by a stable nanoporous carboxylic acid-functionalized silica aerogel

  Chemical Engineering Journal · 2025-04-28 · 5 citations

  article
  PublisherDOI

• A Biomimetic Peptide Functions as Specific Extracellular Matrix for Quiescence of Stem Cells against Intervertebral Disc Degeneration

  Small · 2023-07-09 · 5 citations

  article

  Maintaining quiescence of stem cells is a potential way to decrease cell nutrition demand for restoring the organization. Herein, a biomimetic peptide to maintain quiescence of stem cells through C-X-C motif chemokine ligand 8 (CXCL8)-C-X-C motif chemokine receptor 1 (CXCR1) pathway against intervertebral disc degeneration (IVDD) is developed. First, it is confirmed that quiescence can be induced via inhibiting phosphatidylinositol 3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) pathway in nucleus pulposus stem cells (NPSCs). Meanwhile, it is well known that CXCR1, a chemokine receptor, can be targeted by CXCL8, resulting in cell proliferation via activating PI3K/Akt/mTOR pathway. Second, a biomimetic peptide (OAFF) that can bind to CXCR1 and form fibrous networks on NPSCs, mimicking extracellular matrix formation is developed. The multivalent effect and long-term binding to CXCR1 on NPSCs of OAFF fibers offer forcefully competitive inhibition with natural CXCL8, which induces NPSCs quiescence and ultimately overcomes obstacle in intradiscal injection therapy. In rat caudal disc puncture model, OAFF nanofibers still maintain at 5 weeks after operation and inhibit degeneration process of intervertebral disc in terms of histopathology and imageology. In situ fibrillogenesis of biomimetic peptide on NPSCs provides promising stem cells for intradiscal injection therapy against IVDD.

  PublisherDOI

• Long-term evolution of LR-2, LR-3 and LR-4 observations in HBV-related cirrhosis based on LI-RADS v2018 using gadoxetic acid-enhanced MRI

  Abdominal Radiology · 2023-09-23

  article
  PublisherDOI

• Ivermectin inhibits tumor metastasis by regulating the Wnt/β-catenin/integrin β1/FAK signaling pathway.

  PubMed · 2022-01-01 · 9 citations

  articleOpen access1st authorCorresponding

  . Mechanistically, IVM suppressed the expressions of the migration-related proteins via inhibiting the activation of Wnt/β-catenin/integrin β1/FAK and the downstream signaling cascades. Our findings indicated that IVM was capable of suppressing tumor metastasis, which provided the rationale on exploring the potential clinical application of IVM in the prevention and treatment of cancer metastasis.

  PublisherOA PDF

• A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response

  Frontiers in Human Neuroscience · 2022-02-03 · 11 citations

  articleOpen access1st author

  Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and "BCI illiteracy." To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8-2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects' feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.

  PublisherDOI

• A user-friendly SSVEP-based BCI using imperceptible phase-coded flickers at 60Hz

  China Communications · 2022-02-01 · 30 citations

  article1st authorCorresponding

  A brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) was developed by four-class phase-coded stimuli. SSVEPs elicited by flickers at 60Hz, which is higher than the critical fusion frequency (CFF), were compared with those at 15Hz and 30Hz. SSVEP components in electroencephalogram (EEG) were detected using task related component analysis (TRCA) method. Offline analysis with 17 subjects indicated that the highest information transfer rate (ITR) was 29.80±4.65bpm with 0.5s data length for 60Hz and the classification accuracy was 70.07±4.15%. The online BCI system reached an averaged classification accuracy of 87.75±3.50% at 60Hz with 4s, resulting in an ITR of 16.73±1.63bpm. In particular, the maximum ITR for a subject was 80bpm with 0.5s at 60Hz. Although the BCI performance of 60Hz was lower than that of 15Hz and 30Hz, the results of the behavioral test indicated that, with no perception of flicker, the BCI system with 60Hz was more comfortable to use than 15Hz and 30Hz. Correlation analysis revealed that SSVEP with higher signal-to-noise ratio (SNR) corresponded to better classification performance and the improvement in comfortableness was accompanied by a decrease in performance. This study demonstrates the feasibility and potential of a user-friendly SSVEP-based BCI using imperceptible flickers.

  PublisherDOI

• An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces

  Figshare · 2021-01-01

  datasetOpen access

  The brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenges to the practical application. This study presents an open dataset collected with a wearable SSVEP-based BCI system that compared wet and dry electrodes comprehensively with continuous recording of multiple sessions. The dataset consists of 8-channel SSVEP data from 102 healthy subjects while they performed a cue-guided target selecting task with a 12-target SSVEP-based BCI. For each subject, wet and dry electrodes were used to record 10 consecutive blocks respectively in an overall duration of around two hours. The dataset can be used to evaluate the performance of wet and dry electrodes in SSVEP-based BCIs. The dataset also provide sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.<br>

  PublisherDOI

• An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces

  Figshare · 2021-01-01

  datasetOpen access

  The brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenges to the practical application. This study presents an open dataset collected with a wearable SSVEP-based BCI system that compared wet and dry electrodes comprehensively with continuous recording of multiple sessions. The dataset consists of 8-channel SSVEP data from 102 healthy subjects while they performed a cue-guided target selecting task with a 12-target SSVEP-based BCI. For each subject, wet and dry electrodes were used to record 10 consecutive blocks respectively in an overall duration of around two hours. The dataset can be used to evaluate the performance of wet and dry electrodes in SSVEP-based BCIs. The dataset also provide sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.<br>

  PublisherDOI

• An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces

  Sensors · 2021 · 55 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.

  PublisherOA PDFDOI

Frequent coauthors

• Kristine Glunde

  Johns Hopkins University

  151 shared

• Zaver M. Bhujwalla

  Johns Hopkins University

  86 shared

• Weihua Pei

  University of Chinese Academy of Sciences

  63 shared

• Menglin Cheng

  Beijing Friendship Hospital

  56 shared

• Yijun Wang

  Tsinghua University

  55 shared

• Tiffany R. Greenwood

  Johns Hopkins Medicine

  51 shared

• Venu Raman

  Johns Hopkins Medicine

  45 shared

• Asif Rizwan

  Government College University, Faisalabad

  43 shared