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Many cell types migrate in response to naturally generated electric fields. Furthermore, it has been suggested that the external application of an electric field may be used to intervene in and optimize natural processes such as wound healing. Precise cell guidance suitable for such optimization may rely on predictive models of cell migration, which do not generalize. Here, we present a machine learning model that can forecast directedness of cell migration given a timeseries of previous directedness and electric field values. This model is trained using time series galvanotaxis data of mammalian cranial neural crest cells obtained through time-lapse microscopy of cells cultured at 37 °C in a galvanotaxis chamber at ambient pressure. Next, we show that our modeling approach can be used for a variety of cell types and experimental conditions with very limited training data using transfer learning methods. We adapt the model to predict cell behavior for keratocytes (room temperature, ~ 18-20 °C) and keratinocytes (37 °C) under similar experimental conditions with a small dataset (~ 2-5 cells). Finally, this model can be used to perform in silico studies by simulating cell migration lines under time-varying and unseen electric fields. We demonstrate this by simulating feedback control on cell migration using a proportional-integral-derivative (PID) controller. This data-driven approach provides predictive models of cell migration that may be suitable for designing electric field based cellular control mechanisms for applications in precision medicine such as wound healing.

Abstract Many cell types migrate in response to naturally generated electric fields. Furthermore, it has been suggested that the external application of an electric field may be used to intervene in and optimize natural processes such as wound healing. Precise cell guidance suitable for such optimization may rely on predictive models of cell migration, which do not generalize. Here, we present a machine learning model that can forecast directedness of cell migration given a timeseries of previous directedness and electric field values. This model is trained using time series galvanotaxis data of mammalian cranial neural crest cells obtained through time-lapse microscopy of cells cultured at 37 °C in a galvanotaxis chamber at ambient pressure. Next, we show that our modeling approach can be used for a variety of cell types and experimental conditions with very limited training data using transfer learning methods. We adapt the model to predict cell behavior for keratocytes (room temperature, ~ 18–20 °C) and keratinocytes (37 °C) under similar experimental conditions with a small dataset (~ 2–5 cells). Finally, this model can be used to perform in silico studies by simulating cell migration lines under time-varying and unseen electric fields. We demonstrate this by simulating feedback control on cell migration using a proportional–integral–derivative (PID) controller. This data-driven approach provides predictive models of cell migration that may be suitable for designing electric field based cellular control mechanisms for applications in precision medicine such as wound healing.

Patients who undergo orthotopic liver transplantation often sustain acute kidney injury(AKI). The toll-like receptor 4(TLR4)/Nuclear factor-кB(NF-кB) pathway plays a role in AKI. Dexmedetomidine(Dex) has been shown to attenuate AKI. The current study aimed to determine whether liver transplantation-induced AKI is associated with inflammatory response, and to assess the effects of dexmedetomidine pretreatment on kidneys in rats following orthotopic autologous liver transplantation(OALT). Seventy-seven adult male rats were randomized into 11 groups. Kidney tissue histopathology and levels of blood urea nitrogen(BUN) and serum creatinine(SCr) were evaluated. Levels of TLR4, NF-κB, tumor necrosis factor-α, and interleukin-1β levels were measured in kidney tissues. OALT resulted in significant kidney functional impairment and tissue injury. Pre-treatment with dexmedetomidine decreased BUN and SCr levels and reduced kidney pathological injury, TLR4 expression, translocation of NF-κB, and cytokine production. The effects of dexmedetomidine were reversed by pre-treatment with atipamezole and BRL44408, but not ARC239. These results were confirmed by using α2A-adrenergic receptor siRNA which reversed the protective effect of dexmedetomidine on attenuating NRK-52E cells injury induced by hypoxia reoxygenation. In conclusion, Dexmedetomidine-pretreatment attenuates OALT-induced AKI in rats which may be contributable to its inhibition of TLR4/MyD88/NF-κB pathway activation. The renoprotective effects are related to α2A-adrenergic receptor subtypes. [ABSTRACT FROM AUTHOR]