Rapid Determination of Dopamine and Its Metabolites During in vivo Cerebral Microdialysis by Routine High Performance Liquid Chromatography With Electrochemical Detection

To determine dopamine and its metabolites during in vivo cerebral microdialysis by routine high performance liquid chromatography with electrochemical detection. Methods Microdialysis probes were placed into the right striatum of Wistar rat brains and perfused with Ringer＇s solution at a rate of 1.5 pL/min. A reverse phase HPLC with electrochemistry was used to assay DA, DOPAC, and HVA after cerebral microdialysates were collected every 20 minutes from awake and freely moving rats. In order to identify the reliability of this method, its selectivity, linear range, precision and accuracy were tested and the contents of DA, DOPAC, and HVA in rat microdialysates were determined. Results The standard curve was in good linear at the concentration ranging from 74 nmol/L to 1.5 pmol/L for DOPAC （r^2= 0.9996）, from 66 nmol/L to 1.3 gmol/L for DA （r^2=l.0000） and from 69 nmol/L to 1.4 pmol/L for HVA （r^2=0.9992）. The recovery of DOPAC （0.30, 0.77, 1.49 gmol/L）, DA （0,26, 0.69, 1.32 gmol/L）, and HVA （0.27, 0.71, 1.37 gmol/L） was 82.00±1.70%, 104.00±4.00%, 98.70±3.10%; 92.30± 1.50%, 105.30±2.30%, 108.00±2.00%; 80.00±7.80%, 107.69±8.00%, and 108.66±3.10%, respectively at each concentration. Their intra-day RSD was 3.3%, 3.4%, and 2.5%, and inter-day RSD was 4.2%, 2.3%, and 5.6%, respectively. The mean extracellular concentrations of DOPAC, DA, and HVA in rat brain microdialysates were 10.7, 2.4, and 9.2 gmol/L （n=6）, respectively. Conclusion The findings of our study suggested that the simple, accurate and stable method can be applied to basic researches of diseases related to monoamines neurotransmitters by cerebral microdialysis in rats.

Future of neurocritical care:Integrating neurophysics,multimodal monitoring,and machine learning

Multimodal monitoring(MMM)in the intensive care unit(ICU)has become increasingly sophisticated with the integration of neurophysical principles.However,the challenge remains to select and interpret the most appropriate combination of neuromonitoring modalities to optimize patient outcomes.This manuscript reviewed current neuromonitoring tools,focusing on intracranial pressure,cerebral electrical activity,metabolism,and invasive and noninvasive autoregulation moni-toring.In addition,the integration of advanced machine learning and data science tools within the ICU were discussed.Invasive monitoring includes analysis of intracranial pressure waveforms,jugular venous oximetry,monitoring of brain tissue oxygenation,thermal diffusion flowmetry,electrocorticography,depth electroencephalography,and cerebral microdialysis.Noninvasive measures include transcranial Doppler,tympanic membrane displacement,near-infrared spectroscopy,optic nerve sheath diameter,positron emission tomography,and systemic hemodynamic monitoring including heart rate variability analysis.The neurophysical basis and clinical relevance of each method within the ICU setting were examined.Machine learning algorithms have shown promise by helping to analyze and interpret data in real time from continuous MMM tools,helping clinicians make more accurate and timely decisions.These algorithms can integrate diverse data streams to generate predictive models for patient outcomes and optimize treatment strategies.MMM,grounded in neurophysics,offers a more nuanced understanding of cerebral physiology and disease in the ICU.Although each modality has its strengths and limitations,its integrated use,especially in combination with machine learning algorithms,can offer invaluable information for individualized patient care.