Neuropsychiatric Symptoms Predict Faster Cognitive Decline in Dementia Collaborative Care Than Antipsychotic Use.

Background: To compare short-term cognitive outcomes among groups with and without neuropsychiatric symptoms (NPSs) or antipsychotic prescription and to determine which disease status or treatment modality is associated with relatively faster cognitive decline. Methods: We retrospectively analyzed a prospective cohort of patients diagnosed with dementia and mild cognitive impairment. All participants were evaluated using the Cognitive Abilities Screening Instrument (CASI) during their initial clinical assessments and at the annual follow-up. The dependent variable was annual delta CASI. Multivariate linear regression analysis was used to assess the degree of association between NPS, antipsychotic use, and cognitive decline after adjusting for confounding factors. Neuropsychiatric symptoms were examined individually to determine their predictive value for cognitive decline. Results: A total of 407 (N = 407) patients were included in the study. NPSs, rather than antipsychotic use, led to faster cognitive decline. A higher baseline NPI total score predicted a significantly faster decline in CASI scores (1-year delta CASI = -0.22, 95% CI = -0.38~ -0.05, p = 0.010). Specific items (delusions, agitation, depression, anxiety, euphoria, and apathy) in the NPS significantly increased cognitive decline. Conclusion: Certain neuropsychiatric symptoms, rather than antipsychotic use, lead to faster cognitive decline in a dementia collaborative care model. Checking for and providing appropriate interventions for NPS in people with dementia and their caregivers are highlighted.

Mixed-size spot scanning with a compact large momentum acceptance superconducting (LMA-SC) gantry beamline for proton therapy.

OBJECTIVE: Lowering treatment costs and improving treatment quality are two primary goals for next-generation proton therapy (PT) facilities. This work will design a compact large momentum acceptance superconducting (LMA-SC) gantry beamline to reduce the footprint and expense of the PT facilities, with a novel mixed-size spot scanning method to improve the sparing of organs at risk (OAR). Approach: For the LMA-SC gantry beamline, the movable energy slit is placed in the middle of the last achromatic bending section, and the beam momentum spread of delivered spots can be easily changed during the treatment. Simultaneously, changing the collimator size can provide spots with various lateral spot sizes. Based on the provided large-size and small-size spot models, the treatment planning with mixed spot scanning is optimized: the interior of the target is irradiated with large-size spots (to cover the uniform-dose interior efficiently), while the peripheral of the target is irradiated with small-size spots (to shape the sharp dose falloff at the peripheral accurately). Main results: The treatment plan with mixed-size spot scanning was evaluated and compared with small and large-size spot scanning for thirteen clinical prostate cases. The mixed-size spot plan had superior target dose homogeneities, better protection of OAR, and better plan robustness than the large-size spot plan. Compared to the small-size spot plan, the mixed-size spot plan had comparable plan quality, better plan robustness, and reduced plan delivery time from 65.9 to 40.0 s. Significance: The compact LMA-SC gantry beamline is proposed with mixed-size spot scanning, with demonstrated footprint reduction and improved plan quality compared to the conventional spot scanning method.

Neural Network Design for Impedance Modeling of Power Electronic Systems Based on Latent Features.

Data-driven approaches are promising to address the modeling issues of modern power electronics-based power systems, due to the black-box feature. Frequency-domain analysis has been applied to address the emerging small-signal oscillation issues caused by converter control interactions. However, the frequency-domain model of a power electronic system is linearized around a specific operating condition. It thus requires measurement or identification of frequency-domain models repeatedly at many operating points (OPs) due to the wide operation range of the power systems, which brings significant computation and data burden. This article addresses this challenge by developing a deep learning approach using multilayer feedforward neural networks (FNNs) to train the frequency-domain impedance model of power electronic systems that is continuous of OP. Distinguished from the prior neural network designs relying on trial-and-error and sufficient data size, this article proposes to design the FNN based on latent features of power electronic systems, i.e., the number of system poles and zeros. To further investigate the impacts of data quantity and quality, learning procedures from a small dataset are developed, and K-medoids clustering based on dynamic time warping is used to reveal insights into multivariable sensitivity, which helps improve the data quality. The proposed approaches for the FNN design and learning have been proven simple, effective, and optimal based on case studies on a power electronic converter, and future prospects in its industrial applications are also discussed.

Improving delivery efficiency using spots and energy layers reduction algorithms based on a large momentum acceptance beamline.

BACKGROUND: Intensity-modulated proton therapy (IMPT) is a well-known delivery method of proton therapy. Besides higher plan quality, reducing the delivery time is also essential to IMPT plans. It can enhance patient comfort, reduce treatment costs, and improve delivery efficiency. From the perspective of treatment efficacy, it contributes to mitigating the intra-fractional motions and improving the accuracy of radiotherapy, especially for moving tumors. PURPOSE: However, there is a tradeoff problem between the plan quality and delivery time. We consider the potential of a large momentum acceptance (LMA) beamline and apply the spots and energy layers reduction method to reduce the delivery time. METHODS: The delivery time for each field consists of the energy layer switching time, spot traveling time, and dose delivery time. The larger momentum spread and higher intensity beam offered by the LMA beamline contribute to reducing the total delivery time compared to the conventional beamline. In addition to the dose fidelity term, an L1 and logarithm items were added to the objective function to increase the sparsity of the low-weighted spots and energy layers. After that, the low-weighted spots and layers were iteratively excluded in the reduced plan, which reduced the energy layer switching time and spot traveling time. We used the standard, reduced, and LMA-reduced plans to validate the proposed method and tested it on prostate and nasopharyngeal cases. Then, we compared and evaluated the plan quality, treatment time, and plan robustness against delivery uncertainty. RESULTS: Compared with the standard plans, the number of spots in the LMA-reduced plans was on average reduced by 13 400 (95.6%) for prostate cases and by 48 300 (80.7%) for nasopharyngeal cases and the number of energy layers was on average reduced by 49 (61.3%) for prostate cases and by 97 (50.5%) for nasopharyngeal cases. And, the delivery time of the LMA-reduced plans was shortened from 34.5 to 8.6 s for prostate cases and from 163.8 to 53.6 s for nasopharyngeal cases. The LMA-reduced plans had comparable robustness to the spot monitor unit (MU) error compared with the standard plans, but the LMA-reduced plans became more sensitive to spot position uncertainty. CONCLUSION: The delivery efficiency can be significantly improved using the LMA beamline and spots and energy layers reduction strategies. The method is promising to improve the efficiency of motion mitigation strategies for treating moving tumors.

A Novel Load Balancing Scheme for Satellite IoT Networks Based on Spatial-Temporal Distribution of Users and Advanced Genetic Algorithms.

Satellite IoT networks (S-IoT-N), which have been a hot issue regarding the next generation of communication, are quite important for the coming era of digital twins and the metaverse because of their performance in sensing and monitoring anywhere, anytime, and anyway, in more dimensions. However, this will cause communication links to face greater traffic loads. Satellite internet networks (SIN) are considered the most possible evolution road, possessing characteristics of many satellites, such as low earth orbit (LEO), the Ku/Ka frequency, and a high data rate. Existing research on load balancing schemes for satellite networks cannot solve the problems of low efficiency under conditions of extremely non-uniform distribution of users (DoU) and dynamic density variances. Therefore, this paper proposes a novel load balancing scheme of adjacent beams for S-IoT-N based on the modeling of spatial-temporal DoU and advanced GA. In our scheme, the PDF of the DoU in the direction of movement of the SSP's trajectory was modeled first, which provided a multi-directional constraint for the non-uniform distribution of users in S-IoT-N. Fully considering the prior periodicity of satellite movement and the similarity of DoU in different areas, we proposed an adaptive inheritance iteration to optimize the crossover factor and mutation factor for GA for the first time. Based on the proposed improved GA, we obtained the optimal scheme of load balancing under the conditions of the adaptation from the local balancing scheme to global balancing, and a selection of Ser-Beams to access. Finally, the simulations show that the proposed method can improve the average throughput by 3% under specific conditions and improve processing efficiency by 30% on average.

Feasibility study of fast intensity-modulated proton therapy dose prediction method using deep neural networks for prostate cancer.

PURPOSE: Compared to the pencil-beam algorithm, the Monte-Carlo (MC) algorithm is more accurate for dose calculation but time-consuming in proton therapy. To solve this problem, this study uses deep learning to provide fast 3D dose prediction for prostate cancer patients treated with intensity-modulated proton therapy (IMPT). METHODS: A novel recurrent U-net (RU-net) architecture was trained to predict the 3D dose distribution. Doses, CT images, and beam spot information from IMPT plans were used to train the RU-net with a five-fold cross-validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the peak-monitor unit (MU) model, this work develops the multi-MU model that adopted more comprehensive inputs and was trained with a combinational loss function. The dose difference between the prediction dose and Monte Carlo (MC) dose was evaluated with gamma analysis, dice similarity coefficient (DSC), and dose-volume histogram (DVH) metrics. The MC dropout was also added to the network to quantify the uncertainty of the model. RESULTS: Compared to the peak-MU model, the multi-MU model led to smaller mean absolute errors (3.03% vs. 2.05%, p = 0.005), higher gamma-passing rate (2 mm, 3%: 97.42% vs. 93.69%, p = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (clinical target volume (CTV) D98% : 3.03% vs. 6.08%, p = 0.017; in Bladder V30: 3.08% vs. 5.28%, p = 0.028; and in Bladder V20: 3.02% vs. 4.42%, p = 0.017). Considering more prior knowledge, the multi-MU model had better-predicted accuracy with a prediction time of less than half a second for each fold. The mean uncertainty value of the multi-MU model is 0.46%, with a dropout rate of 10%. CONCLUSION: This method was a nearly real-time IMPT dose prediction algorithm with accuracy comparable to the pencil beam (PB) analytical algorithms used in prostate cancer. This RU-net might be used in plan robustness optimization and robustness evaluation in the future.

Prediction of Institutionalization for Patients With Dementia in Taiwan According to Condition at Entry to Dementia Collaborative Care.

This study aimed to examine the institutionalization rate in patients with dementia in Taiwan, identify the predictors of institutionalization, and conduct a mediation analysis of caregiver burden between neuropsychiatric symptoms and institutionalization. We analyzed data from a retrospective cohort registered in dementia collaborative care (N = 518). The analyses applied univariate and multivariate Cox proportional hazard regression with Firth's penalized likelihood to assess the relationship between each predictor at entry and institutionalization for survival analysis. Thirty (5.8%) patients were censored due to institutionalization after a median follow-up of one-and-a-half years. Neuropsychiatric symptoms, loss of walking ability, and living alone predicted institutionalization. Caregiver burden may partially mediate the effects of neuropsychiatric symptoms and institutionalization. High caregiver burden due to presence of neuropsychiatric symptoms may partially contribute to institutionalization among people living with dementia in Taiwan. However, proper management of neuropsychiatric symptoms and caregiver empowerment may ameliorate institutionalization risk.

Acute Hepatitis Due to Agomelatine Use in Elderly Women with Depression: Case Series.

Although agomelatine may be associated with an increased risk of hepatotoxicity, the incidence rate of acute hepatitis seemed divergent between clinical trials and daily practice. Whether aging or gender is a risk factor in developing hepatotoxicity due to agomelatine is not clear. We present 3 older female cases with acute hepatitis occurring due to highly probable idiosyncratic drug-induced liver injury caused by agomelatine. From these cases, regular surveillance on liver function in the older women taking antidepressants would be of benefits.

Using the Apriori Algorithm to Explore Caregivers' Depression by the Combination of the Patients with Dementia and Their Caregivers.

PURPOSE: The purpose of this study was to identify the caring scenarios that result in severe depression in caregivers caring for dementia patients. PATIENTS AND METHODS: A cross-sectional study with 1111 dementia patients and their caregivers in Taiwan from October 2015 to January 2020 was conducted. Gender, age, type of dementia, clinical dementia rating, walking ability, mood symptoms, behavioral symptoms, and psychological symptoms were the variables from the dementia patients. Age, relation to the patient, employment, type of primary care, frequency of care, mood symptoms, and the score from the Center for Epidemiologic Studies Depression Scale were the variables from the caregivers. A comprehensive viewpoint of both dementia patients and their caregivers was evaluated by the Apriori algorithm to find the attributes resulting in different caregiving depressions. RESULTS: Forty-seven rules were found with 18 rules of mild depressive symptomatology, 17 rules of moderate depressive symptomatology, and 12 rules of severe depressive symptomatology. A total of 7 general rules were summarized to be the severe depressive symptomatology. The results showed that an unemployed or retired caregiver with the mood symptoms such as helplessness, anger, emotional liability, or anxiety who took care of AD patients or AD patients with a moderate severity would have severe depression. Increased care frequencies (≥6 days per week) and multiple mood problems from caregivers result in severe depression. The composition of adult children, patients' aggression, and caregivers' helplessness as well as the combinations of male patients aged 75-84 years with the caregiver's mood of helplessness or nervousness and hopelessness were highly associated with severe depression. CONCLUSION: For those caring for AD patients, severe depression was associated with the combination of different parameters to constitute each of these seven scenarios. Unlike previous studies which often evaluated one or two variables related to caregiver's depression, this study provided a more comprehensive viewpoint that enabled the collaborative team to efficiently identify and manage different scenarios by summarizing the rules of caregivers with severe depression from a systematic viewpoint.

Severity and prevalence of behavioral and psychological symptoms among patients of different dementia stages in Taiwan

Abstract Background To better understand the trends of behavioral and psychological symptoms of dementia (BPSD) over the disease progression is important to provide psychoeducation for dementia caregivers. Objective This study examined the severity and occurrence of BPSD across the various degrees of the disease. Methods This study was a cross-sectional design. Patients (N = 276) who had dementia from July 2001 to October 2008 were surveyed and assessed for dementia stage, using the clinical dementia rating scale (CDR). BPSD was evaluated using the Neuropsychiatric Inventory (NPI). We examined the differences between the severities and occurrence of the individual's BPSD among various CDR stages with the Kruskal-Wallis test and Chi-square test. Results Delusion (p = 0.01), agitation/aggression (p = 0.033), apathy/indifference (p = 0.009), aberrant motor behavior (p < 0.001), nighttime behavior disturbances (p < 0.001), and eating abnormalities (p = 0.001) were significantly different among stages of dementia. The severity of BPSD became exacerbated over the course of the disease, and was highest in moderate (CDR = 2) or severe (CDR = 3) dementia. The occurrence of BPSD was highest when the CDR equaled 2 (97.5%). Discussion The association of global (or certain) BPSD, across different stages of dementia, is a non-linear relationship. These findings suggest the importance of taking into account clinical dementia stage for managing BPSD.

Association between caregiver depression and individual behavioral and psychological symptoms of dementia in Taiwanese patients.

INTRODUCTION: The aim of this study was to investigate caregiver depression associated with neuropsychiatric symptoms in Taiwanese people. METHODS: A cross-sectional design was used in this study. Two hundred seventy-six pairs of patients with dementia and their caregivers who visited the memory clinic of a general hospital from July 2001 to October 2008 were recruited. Caregiver depression was evaluated with the Center for Epidemiologic Studies Depression Scale (CES-D); the behavioral and psychological symptoms of dementia were evaluated using the Neuropsychiatric Inventory. Demographic data of the patients and caregivers, including cognitive functions and clinical dementia ratings, were collected. In addition to descriptive statistics, we examined the relationship between each parameter and caregiver depression using Pearson correlation, independent t-test, or analysis of variance. RESULTS: The results showed a statistically significant positive correlation between the total Neuropsychiatric Inventory score and CES-D score (r = 0.345, P < 0.001) in the bivariate analyses. For individual behavioral and psychological symptoms of dementia, agitation/aggression, anxiety, nighttime behavior disturbances, irritability/lability, and hallucinations were the five leading symptoms significantly associated with caregiver depression (CES-D). DISCUSSION: Carefully managing these symptoms is likely to reduce depression in dementia caregivers.

Evaluation of a critical appraisal program for clinical nurses: a controlled before-and-after study.

BACKGROUND: Evidence-based practice is important to.clinical health care professionals. Clinical nurses can make informed decisions by applying the best evidence to their care. However, standardized curricula on evidence-based medicine are lacking in nursing education programs. This study evaluated a critical appraisal education program and assessed its value in increasing nurses' knowledge of critical appraisal and confidence in their critical appraisal skills. METHODS: A controlled before-and-after study design was used. The education program integrated lectures,practice, and group discussion. A questionnaire was used to evaluate participants' knowledge and confidence in critical appraisal pre- and postintervention. RESULTS: Participants' knowledge and confidence in critical appraisal improved significantly postintervention(both p < .001 ). CONCLUSION: A 1-day, small group discussion education program can effectively improve nurses' knowledge and confidence in critical appraisal. Educators and administrators may replicate this education program to improve the quality of nursing care

Caregiver burden associated with behavioral and psychological symptoms of dementia (BPSD) in Taiwanese elderly.

The purpose of this study was to investigate caregiver burden associated with BPSD in Taiwanese people. The study had a cross-sectional design. Eighty-eight patients with dementia and 88 caregivers who visited the memory clinic of a medical center from January 2007 to December 2007 were recruited. The BPSD were assessed using the neuropsychiatric inventory (NPI); caregiver burden was evaluated using the NPI caregiver distress scale (NPI-D). Demographic data on the patients and caregivers along with patients' cognitive functions and clinical dementia ratings were collected. In addition to descriptive statistics, we analyzed the relationship between each parameter and caregiver burden using binary correlation. The results showed a statistically significant positive correlation between the total NPI-D score and the total NPI score (r=0.898, p<0.001). For individual BPSD, delusions had the highest mean NPI-D score, followed by agitation/aggression, anxiety, irritability/lability, and dysphoria/depression. The symptom frequency of anxiety, delusions, and agitation/aggression showed a statistically significant positive correlation with caregiver's NPI-D score. These findings suggest that improvement of treatments for delusions, agitation/aggression, anxiety, irritability/lability, and dysphoria/depression among dementia patients may reduce caregiver burden.

High dosage of aripiprazole-induced dysphagia.

BACKGROUND: Dysphagia as a main manifestation of extrapyramidal symptoms is an uncommon adverse effect of second-generation antipsychotics. METHOD: We present a 54-year-old drug-naïve patient with schizophrenia, who developed dysphagia with aripiprazole 30 mg daily treatment. RESULTS: This is the first case report on aripiprazole-induced dysphagia. We discuss the risk factors that led to dysphagia in this case. CONCLUSION: Aripiprazole-induced dysphagia is rare, and it is important to be aware that it does occur with high-dosage treatment.

Closed-loop seizure control on epileptic rat models.

In this paper numerous alternative treatments in addition to pharmacological therapy are proposed for their use in epileptic patients. Epileptic animal models can play a crucial role in the performance evaluation of new therapeutic techniques. The objective of this research is to first develop various epileptic rat models; second, develop a portable wireless closed-loop seizure controller including on-line seizure detection and real-time electrical stimulation for seizure elimination; and third, apply the developed seizure controller to the animal models to perform on-line seizure elimination. The closed-loop seizure controller was applied to three Long-Evans rats with spontaneous spike-wave discharges (non-convulsive) and three Long-Evans rats with epileptiform activities induced by pentylenetetrazol (PTZ) injection (convulsive) for evaluation. The seizure detection accuracy is greater than 92% (up to 99%), and averaged seizure detection latency is less than 0.6 s for both spontaneous non-convulsive and PTZ-induced convulsive seizures. The average false stimulation rate is 3.1%. Near 30% of PTZ-induced convulsive seizures need more than two times of 0.5 s electrical stimulation for suppression and 90% of the non-convulsive seizures can be suppressed by only one 0.5 s electrical stimulation.

A closed-loop brain computer interface for real-time seizure detection and control.

The worldwide prevalence of epilepsy is approximately 1%, and 25% of epilepsy patients cannot be treated sufficiently by available therapies. Brain stimulation with closed-loop seizure control has recently been proposed as an innovative and effective alternative. In this paper, a portable closed-loop brain computer interface for seizure control was developed and shown with several aspects of advantages, including high seizure detection rate (92-99% during wake-sleep states), low false detection rate (1.2-2.5%), and small size. The seizure detection and electrical stimulation latency was not greater than 0.6 s after seizure onset. A wireless communication feature also provided flexibility for subjects freeing from the hassle of wires. Experimental data from freely moving rats supported the functional possibility of a real-time closed-loop seizure controller.

Plasma homocysteine levels and major depressive disorders in Alzheimer disease.

BACKGROUND: The main aim of this study was to examine the symptomatology of major depression in Alzheimer disease (AD) and its relationship with plasma homocysteine level. METHODS: Eighty-three patients with AD were enrolled for clinical assessments and examination of fasting plasma homocysteine. Diagnosis of major depression was made, and the severity of the depression was assessed. RESULTS: The moderate dementia patients presented with more common behavioral disturbances related to major depression than mild dementia patients. Major depression in patients with moderate AD was associated with higher plasma homocysteine levels. Furthermore, a high plasma homocysteine level was positively associated with behavioral disturbance among study participants with major depression. CONCLUSION: More behavioral disturbance associated with major depression occurred as the dementia progressed. Patients with a higher level of plasma homocysteine presented with a higher behavioral disturbance symptomatology. This finding may account for the relationship between elevated homocysteine levels and depression only in patients with moderate AD.

Effects of amisulpride on the cognitive function of patients with schizophrenia who switched from risperidone.

Objectives. The aims of this 13-week study were to examine the efficacy and safety of amisulpride, and effects on cognitive function in patients with schizophrenia after they switched from risperidone. Methods. Twenty-three patients with schizophrenia whose antipsychotic was switched from risperidone to amisulpride were recruited. The efficacy, safety, and cognitive function were assessed. Results. Significant improvements were noted in the PANSS, CGI-S, and MADRS. The prolactin level, but not any of the remaining laboratory variables, increased significantly. The cognitive function improved significantly, particularly in memory subtests. Conclusions. Switching antipsychotic from risperidone to amisulpride in schizophrenia might have significantly improved not only the efficacy, but also various domains of cognitive function. However, hyperprolactinemia existed and was sometimes even worse.

[An experience promoting evidence-based nursing at a medical center in central Taiwan].

In the 21st Century, evidence-based nursing (EBN) may provide a mechanism by which nurses can manage the explosion in published information in the literature, introduce new techniques, effectively control healthcare costs, and increase attention on nursing quality and health outcomes. The objective of this paper is to share the experience implementing evidence-based nursing. We had applied strategies to implement evidence-based nursing care in our hospital since 2002. Over a more than three-year period (January 2002 to October 2005), we held 46 basic course sessions (around 1,840 attendees) and 58 advanced course sessions (around 500 attendees). There are 445 clinical questions with answered reports, Most questions related to foley care, central vein care and peripheral intravenous injection. Our nursing department revised three guidelines on nursing care skills related to suction and foley and nasal-gastric feeding to meet local conditions. Based on our subjective experience, promotion and implementation of EBN is feasible and helpful to update knowledge and decrease variance in clinical nursing care. However, further outcome research is needed in the future in order to obtain more objective evidence.