Electroencephalogram-Driven Machine-Learning Scenario for Assessing Impulse Control Disorder Comorbidity in Parkinson's Disease Using a Low-Cost, Custom LEGO-Like Headset.

Patients with Parkinson's disease (PD) may develop cognitive symptoms of impulse control disorders (ICDs) when chronically treated with dopamine agonist (DA) therapy for motor deficits. Motor and cognitive comorbidities critically increase the disability and mortality of the affected patients. This study proposes an electroencephalogram (EEG)-driven machine-learning scenario to automatically assess ICD comorbidity in PD. We employed a classic Go/NoGo task to appraise the capacity of cognitive and motoric inhibition with a low-cost, custom LEGO-like headset to record task-relevant EEG activity. Further, we optimized a support vector machine (SVM) and support vector regression (SVR) pipeline to learn discriminative EEG spectral signatures for the detection of ICD comorbidity and the estimation of ICD severity, respectively. With a dataset of 21 subjects with typical PD, 9 subjects with PD and ICD comorbidity (ICD), and 25 healthy controls (HC), the study results showed that the SVM pipeline differentiated subjects with ICD from subjects with PD with an accuracy of 66.3% and returned an around-chance accuracy of 53.3% for the classification of PD versus HC subjects without the comorbidity concern. Furthermore, the SVR pipeline yielded significantly higher severity scores for the ICD group than for the PD group and resembled the ICD vs. PD distinction according to the clinical questionnaire scores, which was barely replicated by random guessing. Without a commercial, high-precision EEG product, our demonstration may facilitate deploying a wearable computer-aided diagnosis system to assess the risk of DA-triggered cognitive comorbidity in patients with PD in their daily environment.

SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations.

High throughput nuclear segmentation and classification of whole slide images (WSIs) is crucial to biological analysis, clinical diagnosis and precision medicine. With the advances of CNN algorithms and the continuously growing datasets, considerable progress has been made in nuclear segmentation and classification. However, few works consider how to reasonably deal with nuclear heterogeneity in the following two aspects: imbalanced data distribution and diversified morphology characteristics. The minority classes might be dominated by the majority classes due to the imbalanced data distribution and the diversified morphology characteristics may lead to fragile segmentation results. In this study, a cost-Sensitive MultI-task LEarning (SMILE) framework is conducted to tackle the data heterogeneity problem. Based on the most popular multi-task learning backbone in nuclei segmentation and classification, we propose a multi-task correlation attention (MTCA) to perform feature interaction of multiple high relevant tasks to learn better feature representation. A cost-sensitive learning strategy is proposed to solve the imbalanced data distribution by increasing the penalization for the error classification of the minority classes. Furthermore, we propose a novel post-processing step based on the coarse-to-fine marker-controlled watershed scheme to alleviate fragile segmentation when nuclei are with large size and unclear contour. Extensive experiments show that the proposed method achieves state-of-the-art performances on CoNSeP and MoNuSAC 2020 datasets. The code is available at: https://github.com/panxipeng/nuclear_segandcls.

Simulations for double dividend of carbon tax and improved energy efficiency in the transportation industry.

The Chinese visional goal of achieving the "carbon peak" and "carbon neutrality" puts forward higher requirements for low-carbon development in the transportation industry. Seeking appropriate mitigation strategies to develop low-carbon transportation has been an important part of low-carbon economic development. This study develops a CGE model to analyze the impact of carbon-tax implementation on the transportation industry. It designs four carbon tax-recycling scenarios and simulates for double dividend of carbon tax policy. Then, it designs three scenarios including improved energy efficiency and a carbon tax to explore appropriate mitigation strategies combination. The carbon tax will reduce carbon emissions but it will also reduce sectoral outputs. However, carbon tax recycling can alleviate the negative impact on sectoral outputs, meanwhile achieving reducing carbon emissions. The energy rebound effect brought by improved energy efficiency will greatly reduce the carbon emissions reduction effect, but the carbon tax can promote the awareness of emission reduction of consumers and inhibit the energy rebound effect in the transportation industry. Therefore, at the same time of improved energy efficiency, carbon tax policies should be timely formulated to better promote the sustainable development of the varied transport sectors.

Coupling Coordination and Dynamic Response Analysis of New-Type Urbanization, Urban Infrastructure and Urban Environment-A Case Study of the Jiaodong Economic Circle.

The process of urban development in China is that cities retain independent administrative divisions, realize inter-regional urban integration, and then establish metropolitan areas; this process has high requirements for the coordinated development of cities. China's cities still need to receive approximately 300 million people in the future, and these urban populations are mainly planned in the emerging metropolitan area. In order to clarify the problems and development of the direction of China's emerging megalopolis in the process of building a framework for carrying urban population, and to provide reference for China's urbanization process, the entropy weight method-coupling coordination degree model-panel vector autoregression model (EWM-CCDM-PVAR) is constructed to measure the development level, coupling coordination degree and dynamic response relationship of NTU-UI-UE in the Jiaodong Economic Circle. First, the development level of new-type urbanization and urban infrastructure in the Jiaodong Economic Circle has been steadily improved, but regional differences have been expanding year by year. The urban environment of most cities in the Jiaodong Economic Circle is deteriorating. Second, the CCD level of NTU-UI-UE in the Jiaodong Economic Circle is on the rise, but the CCD level of NTU-UI-UE in Weifang and Yantai is only a Moderate-coupling coordination type, and the development within the region is uneven. Third, both NTU-UI and UI-UE in the Jiaodong Economic Circle have established a dynamic response relationship of mutual promotion, but NTU-UE has a dynamic response relationship of mutual inhibition. Fourth, despite different problems, the Jiaodong Economic Circle has made certain achievements in the process of regional integration and has initially formed a metropolitan circle pattern with Qingdao as its core, Weifang as its west center, Yantai and Weihai as its north center and Rizhao as its south center.

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

BACKGROUND: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death. METHODS: DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N.ß=.ß80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N.ß=.ß805; D2, N.ß=.ß1917; D3, N.ß=.ß169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity. FINDINGS: The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93...0.96] on the independent validation cohort (N.ß=.ß49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR.ß=.ß1.50, 95% CI [1.20...1.88], P.ß<.ß.001). INTERPRETATION: The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N.ß=.ß2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment. FUNDING: For a full list of funding bodies, please see the Acknowledgements.

Effectiveness of Different Methods for Baroreflex Sensitivity Assessment in Determining the Severity of Cardiovascular Autonomic Neuropathy in Patients With Parkinson's Disease.

Background: Autonomic disorders are an important non-motor feature of Parkinson's disease (PD). Baroreflex sensitivity (BRS) is often used as an indicator of cardiovascular autonomic function, and it is clinically significant. Several different methods of BRS assessment have been described. We evaluated and compared the efficiency of several methods of BRS assessment for additional insight into the underlying physiology and the determination of its severity in patients with PD. Materials and Methods: Eighty-five patients with PD underwent cardiovascular autonomic testing. The Composite Autonomic Scoring Scale (CASS) was used to grade the severity of autonomic impairment and to define the presence of cardiovascular autonomic neuropathy (CAN). BRS was assessed using the Valsalva maneuver (BRS_VM). In addition, spontaneous BRS was computed using the sequence method and the spectral method. Results and Conclusion: There was considerable agreement between the different methods of BRS assessment. Nevertheless, BRS_VM exhibited a higher degree of correlation with cardiovascular autonomic function than spontaneous BRS indexes obtained by the sequence or spectral method. BRS_VM, rather than spontaneous BRS, also had a predictive value for the presence of CAN to the diagnostic criteria by CASS in patients with PD.

Improving the Process of Shared Decision-Making by Integrating Online Structured Information and Self-Assessment Tools.

The integration of face-to-face communication and online processes to provide access to information and self-assessment tools may improve shared decision-making (SDM) processes. We aimed to assess the effectiveness of implementing an online SDM process with topics and content developed through a participatory design approach. We analyzed the triggered and completed SDM cases with responses from participants at a medical center in Taiwan. Data were retrieved from the Research Electronic Data Capture (REDCap) database of the hospital for analysis. Each team developed web-based patient decision aids (PDA) with empirical evidence in a multi-digitized manner, allowing patients to scan QR codes on a leaflet using their mobile phones and then read the PDA content online. From July 2019 to December 2020, 48 web-based SDM topics were implemented in the 24 clinical departments of this hospital. The results showed that using the REDCap system improved SDM efficiency and quality. Implementing an online SDM process integrated with face-to-face communication enhanced the practice and effectiveness of SDM, possibly through the flexibility of accessing information, self-assessment, and feedback evaluation.

Assessment of High Risk for Alzheimer's Disease Using Plasma Biomarkers in Subjects with Normal Cognition in Taiwan: A Preliminary Study.

BACKGROUND: In Alzheimer's disease (AD), cognitive impairment begins 10-15 years later than neurodegeneration in the brain. Plasma biomarkers are promising candidates for assessing neurodegeneration in people with normal cognition. It has been reported that subjects with the concentration of plasma amyloid-ß 1-42×total tau protein higher than 455 pg2/ml2 are assessed as having a high risk of amnesic mild impairment or AD, denoted as high risk of AD (HRAD). OBJECTIVE: The prevalence of high-risk for dementia in cognitively normal controls is explored by assaying plasma biomarkers. METHODS: 422 subjects with normal cognition were enrolled around Taiwan. Plasma Aß1-40, Aß1-42, and T-Tau levels were assayed using immunomagnetic reduction to assess the risk of dementia. RESULTS: The results showed that 4.6% of young adults (age: 20-44 years), 8.5% of middle-aged adults (age: 45-64 years), and 7.3% of elderly adults (age: 65-90 years) had HRAD. The percentage of individuals with HRAD dramatically increased in middle-aged and elderly adults compared to young adults. CONCLUSION: The percentage of HRAD in cognitively normal subjects are approximately 10%, which reveals that the potentially public-health problem of AD in normal population. Although the subject having abnormal levels of Aß or tau is not definitely going on to develop cognitive declines or AD, the risk of suffering cognitive impairment in future is relatively high. Suitable managements are suggested for these high-risk cognitively normal population. Worth noting, attention should be paid to preventing cognitive impairment due to AD, not only in elderly adults but also middle-aged adults.

Multiplexed Molecular Imaging Strategy Integrated with RNA Sequencing in the Assessment of the Therapeutic Effect of Wharton's Jelly Mesenchymal Stem Cell-Derived Extracellular Vesicles for Osteoporosis.

INTRODUCTION: Osteoporosis is a result of an imbalance in bone remodeling. Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) have been considered as a potentially promising treatment for osteoporosis. However, the therapeutic effect, genetic alterations, and in vivo behavior of exogenous EVs for osteoporosis in mice models remain poorly understood. METHODS: A multiplexed molecular imaging strategy was constructed by micro-positron emission tomography (µPET)/computed tomography (CT), µCT, and optical imaging modality which reflected the osteoblastic activity, microstructure, and in vivo behavior of EVs, respectively. RNA sequencing was used to analyze the cargo of EVs, and the bone tissues of ovariectomized (OVX) mice post EV treatment. RESULTS: The result of [18F]NaF µPET showed an increase in osteoblastic activity in the distal femur of EV-treated mice, and the bone structural parameters derived from µCT were also improved. In terms of in vivo behavior of exogenous EVs, fluorescent dye-labeled EVs could target the distal femur of mice, whereas the uptakes of bone tissues were not significantly different between OVX mice and healthy mice. RNA sequencing demonstrated upregulation of ECM-related genes, which might associate with the PI3K/AKT signaling pathway, in line with the results of microRNA analysis showing that mir-21, mir-29, mir-221, and let-7a were enriched in Wharton's jelly-MSC-EVs and correlated to the BMP and PI3K/AKT signaling pathways. CONCLUSION: The therapeutic effect of exogenous WJ-MSC-EVs in the treatment of osteoporosis was successfully assessed by a multiplexed molecular imaging strategy. The RNA sequencing demonstrated the possible molecular targets in the regulation of bone remodeling. The results highlight the novelty of diagnostic and therapeutic strategies of EV-based treatment for osteoporosis.

A nationwide cohort investigation on pay-for-performance and major adverse limb events in patients with diabetes.

A retrospective cohort study was conducted using claims data from Taiwan's National Health Insurance program to assess the effect of diabetic pay-for-performance (P4P) program on major adverse limb events (MALE) and major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). This study included patients with T2DM who had completed or not completed a 1-year P4P program from 2002 to 2013. Propensity-score matching was used to balance the baseline characteristics between groups. The Cox proportional-hazard model and Fine and Gray subdistribution hazard model were used to examine the association between P4P and the risks of MALE, MACE, systemic thromboembolism (ST), heart failure (HF) hospitalization, and all-cause mortality. Patients who underwent the P4P program had a significantly decreased incidence of MALE (2.0% vs. 2.6%, subdistribution hazard ratio [SHR] 0.73, 95% CI 0.71-0.76). Regarding the individual components, the P4P group demonstrated lower risks for foot ulcer (1.1% vs 1.3%, SHR 0.80, 95% CI 0.77-0.84), gangrene (0.57% vs 0.93%, SHR 0.59, 95% CI 0.56-0.63), percutaneous transluminal angioplasty (0.61% vs 0.79%, SHR 0.72, 95% CI 0.68-0.77), and amputation (0.46% vs 0.75%, SHR 0.58, 95% CI 0.55-0.62). In addition, the risks of MACE, ST, HF hospitalization, and all-cause mortality were remarkably lower in the P4P group. The P4P program might significantly reduce critical events of MALE, MACE, ST, HF, and mortality in the diabetic population.

Objective assessment of impulse control disorder in patients with Parkinson's disease using a low-cost LEGO-like EEG headset: a feasibility study.

BACKGROUND: Patients with Parkinson's disease (PD) can develop impulse control disorders (ICDs) while undergoing a pharmacological treatment for motor control dysfunctions with a dopamine agonist (DA). Conventional clinical interviews or questionnaires can be biased and may not accurately diagnose at the early stage. A wearable electroencephalogram (EEG)-sensing headset paired with an examination procedure can be a potential user-friendly method to explore ICD-related signatures that can detect its early signs and progression by reflecting brain activity. METHODS: A stereotypical Go/NoGo test that targets impulse inhibition was performed on 59 individuals, including healthy controls, patients with PD, and patients with PD diagnosed by ICDs. We conducted two Go/NoGo sessions before and after the DA-pharmacological treatment for the PD and ICD groups. A low-cost LEGO-like EEG headset was used to record concurrent EEG signals. Then, we used the event-related potential (ERP) analytical framework to explore ICD-related EEG abnormalities after DA treatment. RESULTS: After the DA treatment, only the ICD-diagnosed PD patients made more behavioral errors and tended to exhibit the deterioration for the NoGo N2 and P3 peak amplitudes at fronto-central electrodes in contrast to the HC and PD groups. Particularly, the extent of the diminished NoGo-N2 amplitude was prone to be modulated by the ICD scores at Fz with marginal statistical significance (r = - 0.34, p = 0.07). CONCLUSIONS: The low-cost LEGO-like EEG headset successfully captured ERP waveforms and objectively assessed ICD in patients with PD undergoing DA treatment. This objective neuro-evidence could provide complementary information to conventional clinical scales used to diagnose ICD adverse effects.

Efficacy and safety assessment of severe COVID-19 patients with Chinese medicine: A retrospective case series study at early stage of the COVID-19 epidemic in Wuhan, China.

ETHNOPHARMACOLOGICAL RELEVANCE: The coronavirus disease 2019 (COVID-19) has formed a global pandemic since late 2019. Benefitting from the application experience of Chinese Medicine (CM) for influenza and SARS, CM has been used to save patients at the early stage of COVID-19 outbreak in China. AIM OF THE STUDY: In order to evaluate the efficacy and safety of CM, and compare with Western Medicine (WM) for COVID-19, we conducted a retrospective case series study based on the patients in Wuhan Jinyintan Hospital, Wuhan, China. METHODS: The inclusion and exclusion criteria of data extraction were set for this retrospective study. All patients who were admitted by the Wuhan Jinyintan Hospital between January 17th and February 25th 2020 were considered. In addition, patients enrolled met the severe defined by the guidelines released by the National Health Commission of the People's Republic of China. In these cases included in the study, CM or WM treatment was selected according to the wishes of the patients at the beginning of hospitalization. The patients in CM group were treated with Huashi Baidu granule (137 g po, bid) combined with the injections of Xiyanping (100 mg iv, bid), Xuebijing (100 ml iv, bid) and Shenmai (60 ml iv, qd) according to the syndrome of epidemic toxin blocking the lung in the theory of Traditional Chinese Medicine. The WM group received antiviral therapy (including abidor capsule 0.2 g po, tid; Lopinavir-Ritonavir tablets, 500 mg po, bid), antibiotics (such as cefoperazone 2 g iv, bid; moxifloxacin hydrochloride tablets, 0.4 g po, qd) or corticosteroid therapy (such as methylprednisolone succinate sodium 40 mg iv, qd; prednisone, 30 mg po, qd). In addition, patients in both groups received routine supportive treatment, including oxygen inhalation, symptomatic therapy, and/or human intravenous immunoglobulin, and/or serum albumin, and treatment for underlying diseases. The clinical outcomes were evaluated based on changes related with clinical manifestations, computer tomography (CT) scan images, and laboratory examinations before and after the treatment. RESULTS: 55 severe COVID-19 patients, with 23 in CM group and 32 in WM group, were included for analyzed. There was no case of death, being transferred to ICU, or receiving invasive mechanical ventilation in two groups during hospitalization. The median time of SARS-CoV-2 RNA clearance in CM and WM group were 12 days and 15.5 days respectively, the ratio of nucleic acid negative conversion of CM group at different follow-up time points was significantly higher than that of WM group (HR: 2.281, P = 0.018). Further, the chest CT imaging showed more widely lung lesion opacity absorbed in the CM group. The high sensitivity C-reactive protein and serum ferritin decreased significantly in the CM group (P<0.05). There was no significant difference in adverse events in terms of liver function and renal function between the two groups. CONCLUSION: Based on this retrospective analysis from Wuhan Jinyintan Hospital, CM has better effects in SARS-CoV-2 RNA clearance, promoting lung lesion opacity absorbed and reducing inflammation in severe COVID-19 patients, which is effective and safe therapy for treating severe COVID-19 and reducing mortality.

Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability.

AIM: To evaluate the potential of radiomics-based ultra-widefield fluorescein angiography (UWFA)-derived imaging biomarkers in retinal vascular disease for predicting therapeutic durability of intravitreal aflibercept injection (IAI). METHODS: The Peripheral and Macular Retinal Vascular Perfusion and Leakage Dynamics in Diabetic Macular Edema and Retinal Venous Occlusions During Intravitreal Aflibercept Injection (IAI) Treatment for Retinal Edema (PERMEATE) study prospectively evaluated quantitative UWFA dynamics in diabetic macular oedema or macular oedema secondary to retinal vascular occlusion. 27 treatment-naïve eyes were treated with 2 mg IAI q4 weeks for the first 6 months, and then administered q8 weeks. Morphological and graph-based attributes were used to model the spatial distribution of leakage areas, while tortuosity measures were used to model the vessel network disorder. Eyes were grouped based on functional tolerance of the first 8-week treatment interval challenge. 'Non-rebounders' (N=15) maintained/improved best-corrected visual acuity (BCVA) following the 8-week challenge. 'Rebounders' (N=12) exhibited worsened BVCA. The image biomarkers were used with a machine learning classifier to preliminarily evaluate their ability to predict BCVA stability. RESULTS: Two new UWFA image-derived biomarkers were identified and extracted. The cross-validated area under the receiver operating characteristic curve (AUC) was 0.77±0.14 using baseline leakage distribution features and 0.73±0.10 for the UWFA baseline tortuosity measures. Additionally, the change in vascular tortuosity between month 4 and baseline yielded an AUC of 0.73±0.08. Three baseline clinical features of letter score, macular volume and central subfield thickness yielded a corresponding AUC of 0.42±0.09. CONCLUSIONS: Two computer-extracted UWFA radiomics-based descriptors were identified as potential biomarkers for predicting treatment durability and tolerance of longer treatment intervals. Conventional treatment parameters were not significantly different between these same groups.

A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex.

Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.

Assessing Neural Compensation With Visuospatial Working Memory Load Using Near-Infrared Imaging.

Alzheimer's disease is characterized by the progressive deterioration of cognitive abilities particularly working memory while mild cognitive impairment (MCI) represents its prodrome. It is generally believed that neural compensation is intact in MCI but absent in Alzheimer's disease. This study investigated the effects of increasing task load as a means to induce neural compensation through a novel visual working memory (VSWM) task using functional near-infrared spectroscopy (fNIRS). The bilateral prefrontal cortex (PFC) was explored due to its relevance in VSWM and neural compensation. A total of 31 healthy controls (HC), 12 patients with MCI and 18 patients with mild Alzheimer's disease (mAD) were recruited. Although all groups showed sensitivity in terms of behavioral performance (i.e. score) towards increasing task load (level 1 to 3), only in MCI load effect on cortical response (as measured by fNIRS) was significant. At lower task load, bilateral PFC activation did not differ between MCI and HC. Neural compensation in the form of hyperactivation was only noticeable in MCI with a moderate task load. Lack of hyperactivation in mAD, coupled with significantly poorer task performance across task loads, suggested the inability to compensate due to a greater degree of neurodegeneration. Our findings provided an insight into the interaction of cognitive load theory and neural compensatory mechanisms. The experiment results demonstrated the feasibility of inducing neural compensation with the proposed VSWM task at the right amount of cognitive load. This may provide a promising avenue to develop an effective cognitive training and rehabilitation for dementia population.

Neuropsychiatric symptoms in Parkinson's disease: association with caregiver distress and disease severity.

OBJECTIVES: The condition of caregivers is important to the quality of care received by people with Parkinson's disease (PD), especially at the late disease stages. This study addresses the distress placed on caregivers by participants' neuropsychiatric symptoms at different stages of PD in Taiwan. METHODS: This prospective study enrolled 108 people with PD. All participants were examined with the Unified Parkinson's Disease Rating Scale (UPDRS), Neuropsychiatric Inventory (NPI), Mini-Mental State Examination (MMSE), Cognitive Abilities Screening Instrument (CASI), and Clinical Dementia Rating (CDR) scale. Caregiver distress was measured using the Neuropsychiatric Inventory Caregiver Distress Scale (NPI-D). Statistical analysis was used to explore the PD-related factors that contribute to caregiver distress. RESULTS: The mean follow-up interval in the 108 PD participants were 24.0 ± 10.2 months with no participant lost to follow-up due to death. NPI-distress (the sum of NPI caregiver distress scale across the 12 domains of the NPI) was positively correlated with NPI-sum (the total score across the 12 domains of the NPI) (r = 0.787, p < 0.001), CDR (r = 0.403, p < 0.001), UPRDS (r = 0.276, p = 0.004), and disease duration (r = 0.246, p = 0.002), but negatively correlated with CASI (r = -0.237, p = 0.043) and MMSE (r = -0.281, p < 0.001). Multiple linear regression analysis showed that only NPI-sum and disease duration were independently correlated with NPI-distress. CONCLUSION: The disease duration and NPI-sum are independent predictors of caregiver distress in Taiwanese populations with PD. Early detection and reduction of neuropsychiatric symptoms in people with PD can help decrease caregiver distress.

Recycled gypsum board acted as a mineral swelling agent for improving thermal conductivity characteristics in manufacturing of green lightweight building brick.

Lightweight building bricks manufactured from non-hazardous residues incorporating mineral foaming agents have been successfully developed over past two decades. Very little information is available on recycling and reutilization of construction and demolition waste used as the pore foaming agent in manufacturing lightweight brick. In this research, the mineral swelling agent was gypsum board recycled from construction and demolition waste. The mineral swelling agent effect on the characteristics of green lightweight building materials sintered from drinking water purification (DWP) sludge was investigated. Green lightweight building materials were contained up to 50% (wt%) mineral swelling agent and fired at a temperature ranged between 950 °C and 1050 °C. The experimental results indicated that lightweight building materials have successfully sintered between 1000 °C and 1020 °C and added up to 40% (wt%) recycled gypsum board. The sintered building materials have the characteristics of relatively high compressive strength, low bulk density, and thermal conductivity that were in compliance with relevant Taiwan criteria for application in lightweight building materials. To further consider the eco-friendly and environmental safety of lightweight building materials, the recycled gypsum board can act as a good mineral swelling agent, but can also enhance the chemical stabilization and reduce the metals leachability of lightweight materials based on acidic neutralization capacity (ANC) analysis results. To estimate the carbon dioxide emission in manufacturing and transportation of lightweight materials that sintered this experimental conditions, the estimated carbon dioxide reduction rates were approximately 28.6% and 16.7%, respectively, as a result of the energy saving. Experimental results have confirmed that the feasibility of recycled gypsum board used as a swelling agent and good potential for construction works in green lightweight building materials.

Toxicoproteomic assessment of liver responses to acute pyrrolizidine alkaloid intoxication in rats.

A toxicoproteomic study was performed on liver of rats treated with retrorsine (RTS), a representative hepatotoxic pyrrolizidine alkaloid at a toxic dose (140 mg/kg) known to cause severe acute hepatotoxicity. By comparing current data with our previous findings in mild liver lesions of rats treated with a lower dose of RTS, seven proteins and three toxicity pathways of vascular endothelial cell death, which was further verified by observed sinusoidal endothelial cell losses, were found uniquely associated with retrorsine-induced hepatotoxicity. This toxicoproteomic study of acute pyrrolizidine alkaloid intoxication lays a foundation for future investigation to delineate molecular mechanisms of pyrrolizidine alkaloid-induced hepatotoxicity.

Protective preparation: a process central to family caregivers of persons with mild cognitive impairment.

ABSTRACTBackground:To develop a theoretical model explaining the longitudinal changes in the caregiving process for family caregivers of persons with mild cognitive impairment (MCI) in Taiwan. METHODS: A longitudinal, grounded theory approach using in-depth face-to-face interviews and an open-ended interview guide. We conducted 42 interviews over a two-year period; each participant was interviewed at least once every six months. All participants were interviewed in their home. The participants total of 13 family caregivers of persons with MCI. RESULTS: One core theme emerged: "protective preparation." This reflected the family caregiving process of preparation for a further decline in cognitive function, and protection from the impact of low self-esteem, accidents, and symptoms of comorbidities for the family member with MCI. Protective preparation contained three components: ambivalent normalization, vigilant preparation, and protective management. CONCLUSIONS: Interventions to help family caregivers manage the changes in persons with MCI can reduce caregiver burden. Our findings could provide a knowledge base for use by healthcare providers to develop and implement strategies to reduce caregiver burden for family caregivers of persons with MCI.

Noninvasive measurement of radiopharmaceutical time-activity data using external thermoluminescent dosimeters (TLDs).

In this study, we present a new method for estimating the time-activity data using serial timely measurements of thermoluminescent dosimeters (TLDs). The approach is based on the combination of the measurement of surface dose using TLD and Monte Carlo (MC) simulation to estimate the radiopharmaceutical time-activity data. It involves four steps: (1) identify the source organs and outline their contours in computed tomography images; (2) compute the S values on the body surface for each source organ using a MC code; (3) obtain a serial measurement of the dose with numerous TLDs placed on the body surface; (4) solve the dose-activity equation to generate organ cumulative activity for each period of measurement. The activity of each organ at the time of measurement is simply the cumulative activity divided by the timespan between measurements. The usefulness of this method was studied using a MC simulation based on an Oak Ridge National Laboratory mathematical phantom with 18F-FDG filled in six source organs. Numerous TLDs were placed on different locations of the surface and were repeatedly read and replaced. The time-activity curves (TACs) of all organs were successfully reconstructed. Experiments on a physical phantom were also performed. Preliminary results indicate that it is an effective, robust, and simple method for assessing the TAC. The proposed method holds great potential for a range of applications in areas such as targeted radionuclide therapy, pharmaceutical research, and patient-specific dose estimation.