Dataset Information

PiRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties.

ABSTRACT: Real-time transformation was important for the practical implementation of impedance flow cytometry. The major obstacle was the time-consuming step of translating raw data to cellular intrinsic electrical properties (e.g., specific membrane capacitance C_sm and cytoplasm conductivity σ_cyto). Although optimization strategies such as neural network-aided strategies were recently reported to provide an impressive boost to the translation process, simultaneously achieving high speed, accuracy, and generalization capability is still challenging. To this end, we proposed a fast parallel physical fitting solver that could characterize single cells' C_sm and σ_cyto within 0.62 ms/cell without any data preacquisition or pretraining requirements. We achieved the 27000-fold acceleration without loss of accuracy compared with the traditional solver. Based on the solver, we implemented physics-informed real-time impedance flow cytometry (piRT-IFC), which was able to characterize up to 100,902 cells' C_sm and σ_cyto within 50 min in a real-time manner. Compared to the fully connected neural network (FCNN) predictor, the proposed real-time solver showed comparable processing speed but higher accuracy. Furthermore, we used a neutrophil degranulation cell model to represent tasks to test unfamiliar samples without data for pretraining. After being treated with cytochalasin B and N-Formyl-Met-Leu-Phe, HL-60 cells underwent dynamic degranulation processes, and we characterized cell's C_sm and σ_cyto using piRT-IFC. Compared to the results from our solver, accuracy loss was observed in the results predicted by the FCNN, revealing the advantages of high speed, accuracy, and generalizability of the proposed piRT-IFC.

SUBMITTER: Luan X

PROVIDER: S-EPMC10250341 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties.

Luan Xiaofeng X Liu Pengbin P Huang Di D Zhao Haiping H Li Yuang Y Sun Sheng S Zhang Wenchang W Zhang Lingqian L Li Mingxiao M Zhi Tian T Zhao Yang Y Huang Chengjun C

Microsystems & nanoengineering 20230608

Real-time transformation was important for the practical implementation of impedance flow cytometry. The major obstacle was the time-consuming step of translating raw data to cellular intrinsic electrical properties (e.g., specific membrane capacitance C<sub>sm</sub> and cytoplasm conductivity σ<sub>cyto</sub>). Although optimization strategies such as neural network-aided strategies were recently reported to provide an impressive boost to the translation process, simultaneously achieving high s ...[more]

PMID: 37303829

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Project description:Study objectivesReal-time monitoring of upper airway collapse during sleep could be instrumental for studies in biomechanics of obstructive sleep apnea (OSA) and selecting individualized treatment modalities. Although some imaging techniques are used under sedated sleep, none are available during the entire natural sleep process. We hypothesized that electrical impedance tomography (EIT) can be used for noninvasive continuous imaging of the upper airway during natural sleep and quantifying upper airway collapse in terms of its size.MethodsAfter determining surface landmarks to attach the electrodes for monitoring the retroglossal airway, EIT was conducted in 10 healthy participants. As a feasibility test of EIT in detecting upper airway collapse, transient airway closure was induced by the swallowing maneuver. These EIT images were confirmed by simultaneous magnetic resonance imaging (MRI) scans. Subsequently, EIT scans were conducted in 7 healthy participants and 10 patients with OSA under nonsedated sleep to determine whether it could identify upper airway narrowing or collapse. Respiratory events were identified by concurrent polysomnography (PSG).ResultsSwallowing-induced airway closure was identified successfully in all 10 participants on simultaneous EIT and MRI scans. Sizes and positions of the upper airway closures in reconstructed EIT images were well correlated with those in magnetic resonance images. Obstructive hypopnea and apnea were detected successfully by EIT in 10 patients with OSA, and no significant changes in EIT data were observed in 7 healthy participants during concurrent EIT and PSG tests. Additionally, conductivity changes in the airway were greater during obstructive apnea than during hypopnea (64.3% versus 26.3%, respectively; P < .001) compared with those during baseline respiration.ConclusionsEIT could be a useful real-time monitoring device for detecting upper airway narrowing or collapse during natural sleep in patients with OSA. Currently, changes in the upper airway size can be estimated with good accuracy, but shape estimation needs future improvements in the EIT image quality.

Dataset Information

PiRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties.

Publications

piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties.

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