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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: Hypomimia is a clinical feature of Parkinson's disease (PD). Based on the embodied simulation theory, the impairment of facial mimicry may worsen facial emotion recognition; however, the empirical results are inconclusive. Objective: We aimed to explore the worsening of emotion recognition by hypomimia. We further explored the relationship between the hypomimia, emotion recognition, and social functioning. Methods: A total of 114 participants were recruited. The patients with PD and normal controls (NCs) were matched for demographic characteristics. All the participants completed the Mini-Mental State Examination and the Chinese Multi-modalities Emotion Recognition Test. In addition to the above tests, the patients were assessed with the Movement Disorder Society-Unified Parkinson's Disease Rating Scale and Parkinson's Disease Social Functioning Scale (PDSFS). Results: Patients with PD with hypomimia had worse recognition of disgust than NCs (p = 0.018). The severity of hypomimia was predictive of the recognition of disgust (β= –0.275, p = 0.028). Facial emotion recognition was predictive of the PDSFS score of PD patients (β= 0.433, p = 0.001). We also found that recognizing disgust could mediate the relationship between hypomimia and the PDSFS score (β= 0.264, p = 0.045). Conclusion: Patients with hypomimia had the worst disgust facial recognition. Hypomimia may affect the social function of PD patients, which is related to recognizing the expression of disgust. Emotion recognition training may improve the social function of patients with PD. [ABSTRACT FROM AUTHOR]

Background: Antiplatelet use on the risk of intracerebral hemorrhage (ICH) in patients with Alzheimer's disease (AD) has not yet been completely elucidated.Objective: This large epidemiologic study aims to estimate the risk of ICH in AD patients treated with antiplatelet therapy (APT).Methods: Using data from Taiwan's National Health Insurance Research Database, ICH risk in APT-treated AD patients with a validated diagnosis (N = 824) was determined. AD without APT and non-AD with and without APT comparison cohorts were selected. To adjust for confounders and competing risk of death, inverse probability of treatment weighting using propensity scores and competing risks regression (CRR) were applied. Cox proportional hazards regression analysis estimated ICH risk in all cohorts comparing with non-AD without APT.Results: Among the 824 AD patients with APT, 79.6% were prescribed aspirin. ICH incidence rates in the AD (with/without APT) and non-AD (with/without APT) cohorts were 2.88/2.70 and 2.24/1.20 per 1,000 person-years, respectively. Overall, AD with (adjusted hazards ratio (aHR), 2.29; 95% CI, 1.19-4.38) and without (aHR, 1.97; 95% CI, 1.08-3.61) APT and non-AD with APT (aHR, 1.80; 95% CI, 1.34-2.42) were at a higher risk and had elevated subdistribution HR obtained from CRR than non-AD without APT controls. However, the risk was comparable between the AD cohorts with and without APT (HR, 1.16; 95% CI, 0.51-2.66).Conclusions: Our study indicated both the APT and non-APT users in AD population yielded higher ICH risks. However, whether APT use potentiate the risk of ICH in AD patients may warrant further evaluation. [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]

Background: Parkinson's disease (PD) is a neurodegenerative condition characterized by movement disorders and probable cognitive impairment. Exercise plays an important role in PD management, and recent studies have reported improvement in motor symptoms and cognitive function following aerobic and Tai Chi Chuan exercise., Aim: To explore the different effects of Tai Chi Chuan and aerobic exercise on the clinical motor status and neurocognitive performance of patients with early-stage PD., Design: A randomized controlled trial., Setting: Parkinson's Disease Center at Kaohsiung Chang Gung Memorial Hospital and National Cheng Kung University Hospital., Population: Patients with idiopathic PD., Methods: Fifty-six patients with PD were recruited and divided into three groups: aerobic exercise (AE, N.=14), Tai Chi Chuan exercise (TE, N.=16), and control (CG, N.=13). Before and after a 12-week intervention period, we used unified Parkinson's disease rating scale Part III (UPDRS-III) scores and neuropsychological (e.g., accuracy rates [ARs] and reaction times [RTs]) and neurophysiological (e.g., event-related potential [ERP] N2 and P3 latencies and amplitudes) parameters to respectively assess the patients' clinical motor symptoms and neurocognitive performance when performing a working memory (WM) task., Results: Compared to baseline, UPDRS-III scores were significantly lower in the AE and TE groups after the intervention period, whereas those for the CG group were higher. In terms of the neurocognitive parameters, when performing the WM task after the intervention period, the AE group exhibited significantly faster RTs and larger ERP P3 amplitudes, the TE group exhibited an improvement only in ERP P3 amplitude, and the CG group exhibited a significantly reduced ERP P3 amplitude. However, neither the TE nor the AE group exhibited improved ARs and ERP N2 performance., Conclusions: The present study supported the distinct effectiveness of Tai Chi Chuan and aerobic exercise for improving motor symptoms and providing neurocognitive benefits in PD patients., Clinical Rehabilitation Impact: These results have important implications regarding the use of these exercise interventions for managing PD, particularly in the early stages.

Background: Social functioning is crucial for daily living and is an essential indicator of dementia in patients with Parkinson's disease. The pattern of social functioning in patients with Parkinson's disease without dementia (i.e. those who are cognitively intact or have mild cognitive impairment (PD-MCI)) and its determinants are unclear., Aims: In exploring the heterogeneity of social functioning among patients with Parkinson's disease-associated dementia, we determined the optimal cut-off score of the Parkinson's Disease Social Functioning Scale (PDSFS) for patients with PD-MCI, and the variables influencing patients' social functioning., Method: A total of 302 participants underwent the Mini-Mental State Examination (MMSE) and PDSFS; 120 patients with Parkinson's disease completed the measurements (MMSE, Activities of Daily Living Scale and Neuropsychiatric Inventory). Group comparisons, receiver operating characteristic curves, Spearman correlation and multiple and hierarchical regression analyses were conducted., Results: The PD-MCI group scored the lowest on the PDSFS ( F = 10.10, P < 0.001). The PDSFS cut-off score was 53 (area under the curve 0.700, sensitivity 0.800, specificity 0.534). The MMSE ( β = 0.293, P = 0.002), Activities of Daily Living Scale ( β = 0.189, P = 0.028) and Neuropsychiatric Inventory ( β = -0.216, P = 0.005) scores predicted the PDSFS score. Further, there was an interaction effect between the Activities of Daily Living Scale and Neuropsychiatric Inventory scores on the PDSFS score ( β = 0.305, P < 0.001)., Conclusions: We determined a PDSFS cut-off score for detecting PD-MCI and found that patients with PD-MCI have social dysfunction. Future research should focus on the effects of neuropsychiatry symptoms and activities of daily living on social functioning, and tailor the intervention programme for patients with Parkinson's disease.

Background: The commonly used screening tests for Parkinson's disease (PD) are the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE), both of which only focus on cognitive function. A composite assessment that considers both cognitive and social dysfunction in PD would be helpful in detecting mild cognitive impairment (MCI) and PD dementia (PDD)., Objective: We aimed to simplify the commonly used tools and combine cognitive and social functioning tests to detect early MCI and PDD., Materials and Methods: A total of 166 participants (84 PD patients and 82 healthy) were recruited who completed the MMSE, MoCA, PD social functioning scale (PDSFS), clock drawing test, activities of daily living, comprehensive neuropsychological assessment (e.g., executive, attention, language, memory, and visuospatial functions), and movement disorder society (MDS)-unified PD rating scale. According to the MDS diagnostic criteria, the patients were grouped into PD-nonMCI, PD-MCI, or PDD., Results: To detect PD-MCI, the optimal cut-off scores for the simplified MoCA and the combined test were 9 and 35. The discrimination values measured by the area under the receiver operating characteristic curve (AUC) of the two tests were 0.767 ( p < 0.001) and 0.790 ( p < 0.001). When the simplified MoCA was 7 or the combined test 30, the patients would be classified as having PDD. The AUCs of the two tests were 0.846 ( p < 0.001) and 0.794 ( p = 0.003)., Conclusion: We suggest considering both cognitive and social functions when detecting PD-MCI and PDD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yu, Tan, Su, Chien, Sung, Lin, Lee and Yu.)

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.