Publications by authors named "Dean Wu"

Background And Purpose: Post-stroke cognitive impairment (PSCI) is highly prevalent in modern society. However, there is limited study implying an accurate and explainable machine learning model to predict PSCI. The aim of this study is to develop and validate a web-based artificial intelligence (AI) tool for predicting PSCI.

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Although associations among insomnia, cognitive impairment, and stroke have been demonstrated, whether insomnia increases the risk of cognitive impairment after stroke remains unclear. The aim of this study was to examine whether insomnia complaints moderated the association between stroke and cognitive impairment in older adults. This study was a secondary data analysis that used data from the National Health Interview Survey 2009.

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Rho GTPASE-activating protein 23 (ARHGAP23) is known to activate RHO-GTPase and has an important role in the infiltration and metastasis of tumors. Although previous studies suggested its involvement in certain human cancers, its role in pan-cancer remains unclear. In the present study, the expression, prognosis and potential functions of ARHGAP23 in pan-cancer were evaluated through various public databases such as Human Protein Atlas, Tumor IMmune Estimation Resource, Gene Set Co-Expression Analysis, Gene Expression Profiling Interactive Analysis, cBio Cancer Genomics Portal, Tumor-Immune System Interactions Database (TISIDB) and others.

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Our systematic review and meta-analysis estimated the prevalence of post-COVID sleep disturbances in adult population. We systematically searched relevant studies from four databases that reported post-COVID sleep disturbances prevalence with a mean or median follow-up duration of ≥28 days. We identified 153 eligible papers, with a total COVID-19 population of 252437.

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Article Synopsis
  • * This study developed a machine-learning algorithm leveraging electrocardiogram (ECG) data to predict sleep apnea events 30-90 seconds in advance, using techniques like SVM, KNN, decision trees, and LDA.
  • * Results showed that SVM outperformed other methods, achieving 98.2% accuracy, particularly in the 8-50 Hz frequency band, indicating that monitoring ECG during CPAP titration can effectively forecast apnea events and improve at-home management of OSA.
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SARS-CoV-2, a newly discovered coronavirus, has been linked to the COVID-19 pandemic and is currently an important public health issue. Despite all the work done to date around the world, there is still no viable treatment for COVID-19. This study examined the most recent evidence on the efficacy and safety of several therapeutic options available including natural substances, synthetic drugs and vaccines in the treatment of COVID-19.

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High-entropy alloy (HEA) catalysts have been widely studied in electrocatalysis. However, identifying atomic structure of HEA with complex atomic arrangement is challenging, which seriously hinders the fundamental understanding of catalytic mechanism. Here, we report a HEA-PdNiRuIrRh catalyst with remarkable mass activity of 3.

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Obstructive sleep apnea (OSA) is a risk factor for neurodegenerative diseases. This study determined whether continuous positive airway pressure (CPAP), which can alleviate OSA symptoms, can reduce neurochemical biomarker levels. Thirty patients with OSA and normal cognitive function were recruited and divided into the control ( = 10) and CPAP ( = 20) groups.

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Article Synopsis
  • * Researchers analyzed data from 3,529 patients in Taiwan, employing six machine learning techniques, including random forest, and determined feature importance through Shapley values to identify factors influencing OSA risk.
  • * The random forest model achieved the highest accuracy, with 79.32% for moderate-to-severe OSA and 74.37% for severe OSA, highlighting snoring events and visceral fat as key screening features.
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Background: No study has examined the psychometric properties of the sleep condition indicator (SCI) for screening poststroke insomnia in the Indonesian population. We aimed to develop the Indonesian version of the sleep condition indicator (ISCI) and to examine its psychometric properties for screening adult patients in late sub-acute and chronic periods after stroke.

Methods: This was a cross-sectional study with two stages.

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Lung cancer is an incurable disease with an increased mortality rate caused by the inhalation of dust-containing crystalline silica particles. Silica exposure is one of the most important occupational hazards in the world. Whether the association between silica exposure and lung cancer is because of the fibrotic process or to the effect of respirable silica itself is unclear.

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Objectives/background: Insomnia is a common sleep complaint among patients who had a stroke and has been recognized as an independent risk factor for cognitive impairment. However, the relationship between poststroke insomnia and cognitive impairment over time is under-researched. Therefore, we examined the association between poststroke insomnia and the risk of cognitive impairment.

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Objectives: Obstructive sleep apnea (OSA) may increase the risk of Alzheimer's disease (AD). However, potential associations among sleep-disordered breathing, hypoxia, and OSA-induced arousal responses should be investigated. This study determined differences in sleep parameters and investigated the relationship between such parameters and the risk of AD.

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  • The study aimed to investigate how daily averages and fluctuations in relative humidity (RH), temperature, and particulate matter (PM) impact the severity of obstructive sleep apnea (OSA).
  • Researchers analyzed data from 8,628 subjects over six years, categorizing them based on their apnea-hypopnea index (AHI) scores to determine the relationship between environmental factors and OSA severity.
  • Findings revealed that higher levels of PM and RH could increase AHI events in OSA patients, especially during colder seasons, suggesting that minimizing exposure to high humidity and PM could help mitigate OSA severity.
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Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA.

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  • A study aimed to determine how air pollution affects cognitive function through sleep disruption by examining data from 4,866 participants who underwent polysomnography (PSG) tests.
  • *The findings indicated that different air pollutants (O, NO, PM) influenced various sleep stages, with N1 and N2 stages linked to cognitive decline, while REM sleep was connected to better cognitive function.
  • *Ultimately, the research concluded that air pollution may lead to cognitive decline by altering sleep patterns and affecting brain structures crucial for executive functions, learning, and language.
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Obstructive sleep apnea (OSA) is associated with seasonal variations. The objective of this study was to examine associations of ambient relative humidity (RH) and temperature on sleep parameters. We conducted a cross-sectional study by retrospectively recruiting 5204 adults from a sleep center in Taipei, Taiwan.

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  • This study explores how obstructive sleep apnea syndrome (OSAS) symptoms differ between laboratory settings and home environments, highlighting the potential overestimation of severity using in-lab tests.
  • Thirty participants were monitored using both lab-based polysomnography and home-based electrocardiogram patches, with blood samples analyzed for neurochemical biomarkers.
  • Results showed a significant correlation between T-Tau protein levels and home-based heart rate variations (CVHRI), suggesting that severe OSAS patients have higher T-Tau levels, and indicating that sleep parameters fluctuate depending on the environment.
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  • - The study investigates how insomnia disorder (ID) and obstructive sleep apnea (OSA) can have overlapping symptoms, making it challenging to differentiate between them using standard diagnostic methods like polysomnography.
  • - Researchers developed new machine learning models to classify patients into three groups: ID, low-ArTH OSA, and high-ArTH OSA, utilizing sleep data and body measurements from participants who primarily reported insomnia.
  • - The random forest (RF) machine learning approach showed the best accuracy rates of 77.53% and 80.06% for distinguishing between these sleep disorders using either only oximetry data or a combination of oximetry and anthropometric data.
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The comparative efficacy of various approaches of digital cognitive behavioral therapy for insomnia (CBTi) is still unclear. This network meta-analysis explored the comparative efficacy of digital CBTi approaches in adults with insomnia. Four electronic databases were searched from inception to June 27, 2020.

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(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.

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Obstructive sleep apnoea (OSA) is a global health concern, and polysomnography (PSG) is the gold standard for assessing OSA severity. However, the sleep parameters of home-based and in-laboratory PSG vary because of environmental factors, and the magnitude of these discrepancies remains unclear. We enrolled 125 Taiwanese patients who underwent PSG while wearing a single-lead electrocardiogram patch (RootiRx).

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Article Synopsis
  • This study analyzed the relationship between sleep disorders and the risk of mild cognitive impairment (MCI) in participants complaining of memory issues and sleep problems.
  • It found that participants with MCI had significantly higher levels of spontaneous arousal during non-rapid eye movement sleep compared to those without cognitive impairment.
  • The results suggest that frequent arousal and respiratory events during sleep may contribute to cognitive decline, but further research is needed to establish a causal link.
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Pneumonia is one of the most frequent disorder induced by S. aureus infection and accounts for 13.3% of the all the infections caused by staphylococcus.

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