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Background and Objectives: The relationship between histidine–tryptophan–ketoglutarate (HTK)-induced hyponatremia and brain injury in adult cardiac surgery patients is unclear. This study analyzed postoperative neurological outcomes after intraoperative HTK cardioplegia infusion. Materials and Methods: A prospective cohort study was conducted on 60 adult patients who underwent cardiac surgery with cardiopulmonary bypass. Of these patients, 13 and 47 received HTK infusion and conventional hyperkalemic cardioplegia, respectively. The patients' baseline characteristics, intraoperative data, brain injury markers, Mini-Mental State Examination (MMSE) scores, and quantitative electroencephalography (qEEG) data were collected. Electrolyte changes during cardiopulmonary bypass, the degree of hyponatremia, and any associated brain insults were evaluated. Results: The HTK group presented with acute hyponatremia during cardiopulmonary bypass, which was intraoperatively corrected through ultrafiltration and normal saline administration. Postoperative sodium levels were higher in the HTK group than in the conventional cardioplegia group. The change in neuron-specific enolase levels after cardiopulmonary bypass was significantly higher in the HTK group (p = 0.043). The changes showed no significant differences using case–control matching. qEEG analysis revealed a significant increase in relative delta power in the HTK group on postoperative day (POD) 7 (p = 0.018); however, no significant changes were noted on POD 60. The MMSE scores were not significantly different between the two groups on POD 7 and POD 60. Conclusions: HTK-induced acute hyponatremia and rapid correction with normal saline during adult cardiac surgeries were associated with a potential short-term but not long-term neurological impact. Further studies are required to determine the necessity of correction for HTK-induced hyponatremia. [ABSTRACT FROM AUTHOR]

(1) Background: The study investigated whether the ankle–brachial index (ABI) and pulse wave velocity (baPWV) could reflect the severity of small vessel disease (SVD) and large artery atherosclerosis (LAA). (2) Methods: A total of 956 consecutive patients diagnosed with ischemic stroke were prospectively enrolled from July 2016 to December 2017. SVD severity and LAA stenosis grades were evaluated via magnetic resonance imaging and carotid duplex ultrasonography. Correlation coefficients were calculated between the ABI/baPWV and measurement values. Multinomial logistic regression analysis was performed to determine predictive potential. (3) Results: Among the 820 patients included in the final analysis, the stenosis grade of extracranial and intracranial vessels was inversely correlated with the ABI (p < 0.001, respectively) and positively correlated with the baPWV (p < 0.001 and p = 0.004, respectively). Abnormal ABI, not baPWV, independently predicted the presence of moderate (adjusted odds ratio, aOR: 2.18, 95% CI: 1.31–3.63) to severe (aOR: 5.59, 95% CI: 2.21–14.13) extracranial vessel stenosis and intracranial vessel stenosis (aOR: 1.89, 95% CI: 1.15–3.11). Neither the ABI nor baPWV was independently associated with SVD severity. (4) Conclusions: ABI is better than baPWV in screening for and identifying the existence of cerebral large vessel disease, but neither test is a good predictor of cerebral SVD severity. [ABSTRACT FROM AUTHOR]

Background: The challenge of differentiating, at an early stage, Parkinson's disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson's disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson's disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson's disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson's disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application. [ABSTRACT FROM AUTHOR]