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Various forms of cognitive behavioral therapy for insomnia (CBT-i) have been developed to improve its scalability and accessibility for insomnia management in young people, but the efficacy of digitally-delivered cognitive behavioral therapy for insomnia (dCBT-i) remains uncertain. This study systematically reviewed and evaluated the effectiveness of dCBT-i among young individuals with insomnia. We conducted comprehensive searches using four electronic databases (PubMed, Cochrane Library, PsycINFO, and Embase; until October 2021) and examined eligible records. The search strategy comprised the following three main concepts: (1) participants were adolescents or active college students; (2) dCBT-I was employed; (3) standardized tools were used for outcome measurement. Four randomized controlled trials qualified for meta-analysis. A significant improvement in self-reported sleep quality with a medium-to-large effect size after treatment (Hedges's g = −0.58~−0.80) was noted. However, a limited effect was detected regarding objective sleep quality improvement (total sleep time and sleep efficiency measured using actigraphy). These preliminary findings from the meta-analysis suggest that dCBT-i is a moderately effective treatment in managing insomnia in younger age groups, and CBT-i delivered through the web or a mobile application is an acceptable approach for promoting sleep health in young people. [ABSTRACT FROM AUTHOR]

Reviewing learned knowledge is critical in the learning process. Testing the learning content instead of restudying, which is known as the testing effect, has been demonstrated to be an effective review strategy. However, education research recommends that instructors generate practice tests, but this burdens teachers and may also hinder teaching quality. To resolve this issue, the current study applied a modern artificial intelligence technique (BERT) to automate the generation of tests and evaluate the testing effect through e-books in a university lecture (N = 74). The last 5 minutes of each course session were utilized to review the taught content by having students either answer cloze item questions or restudy the summary of the core concepts covered in the lecture. A reading comprehension pretest was conducted before the experiment to ensure that the differences in prior knowledge were nonsignificant between groups, and a posttest was performed to examine the effectiveness of testing. In addition, we evaluated students' reading skills and reading engagement through their ability to identify key concepts and their interaction with e-books, respectively. A positive effect was observed for students who engaged in cloze item practice before the end of each class. The results indicated that the repeated testing group exhibited significantly better reading skills and engaged more with e-books than the restudying group did. More importantly, compared with only restudying the key concepts, answering the cloze items questions significantly improved students' reading comprehension. Our results suggest that machinegenerated cloze testing may benefit learning in higher education. [ABSTRACT FROM AUTHOR]

Precision education is a new challenge in leveraging artificial intelligence, machine learning, and learning analytics to enhance teaching quality and learning performance. To facilitate precision education, text marking skills can be used to determine students' learning process. Text marking is an essential learning skill in reading. In this study, we proposed a model that leverages the state-of-the-art text summarization technique, Bidirectional Encoder Representations from Transformers (BERT), to calculate the marking score for 130 graduate students enrolled in an accounting course. Then, we applied learning analytics to analyze the correlation between their marking scores and learning performance. We measured students' self-regulated learning (SRL) and clustered them into four groups based on their marking scores and marking frequencies to examine whether differences in reading skills and text marking influence students' learning performance and awareness of self-regulation. Consistent with past research, our results did not indicate a strong relationship between marking scores and learning performance. However, high-skill readers who use more marking strategies perform better in learning performance, task strategies, and time management than high-skill readers who use fewer marking strategies. Furthermore, high-skill readers who actively employ marking strategies also achieve superior scores of environment structure, and task strategies in SRL than low-skill readers who are inactive in marking. The findings of this research provide evidence supporting the importance of monitoring and training students' text marking skill and facilitating precision education. [ABSTRACT FROM AUTHOR]

The article focuses on canon of plays attributed to the Elizabethan playwright Thomas Kyd consists of "The Spanish Tragedy", "Soliman and Perseda", and "Cornelia". Topics include quantitative studies of Kyd's vocabulary, expanding Kyd canon with an information categorization method that considers rank-order frequency of all the words in the texts, and author's style in the lexical domain and possibly a deeper level of stylistic differences related to the meaning of words.

The intrinsic composition and functional relevance of resting-state blood oxygen level–dependent signals are fundamental in research using functional magnetic resonance imaging (fMRI). Using the Hilbert–Huang Transform to estimate high-resolution time-frequency spectra, we investigated the instantaneous frequency and amplitude modulation of resting-state fMRI signals, as well as their functional relevance in a large normal-aging cohort ( n = 420, age = 21–89 years). We evaluated the cognitive function of each participant and recorded respiratory signals during fMRI scans. The results showed that the Hilbert–Huang Transform effectively categorized resting-state fMRI power spectra into high (0.087–0.2 Hz), low (0.045–0.087 Hz), and very-low (≤0.045 Hz) frequency bands. The high-frequency power was associated with respiratory activity, and the low-frequency power was associated with cognitive function. Furthermore, within the cognition-related low-frequency band (0.045–0.087 Hz), we discovered that aging was associated with the increased frequency modulation and reduced amplitude modulation of the resting-state fMRI signal. These aging-related changes in frequency and amplitude modulation of resting-state fMRI signals were unaccounted for by the loss of gray matter volume and were consistently identified in the default mode and salience network. These findings indicate that resting-state fMRI signal modulations are dynamic during the normal aging process. In summary, our results refined the functionally related blood oxygen level–dependent frequency band in a considerably narrow band at a low-frequency range (0.045–0.087 Hz) and challenged the current method of resting-fMRI preprocessing by using low-frequency filters with a relatively wide range below 0.1 Hz. [ABSTRACT FROM AUTHOR]

White matter hyperintensities (WMHs) are prevalent in the older adult and are often accompanied by cognitive decline and an increased risk of dementia. However, the roles of WMHs in the periventricular white matter and deep white matter regions in the aging process remain controversial. This study aimed to investigate the WMH burden across the adult lifespan and determine the interrelationships among age, WMH, and cognition. The present study included 312 healthy individuals aged 21–89 years who received structural magnetic resonance imaging and cognitive assessments. Periventricular WMH (PVWMH) and deep WMH (DWMH) volumes were computed and fitted using different regression models to evaluate the trajectory of WMH changes across the lifespan. Our findings support that the changes in WMH volume in the healthy population follow a nonlinear pattern with age, especially in PVWMH. With 2-mediator mediation analysis, we further suggest that the effect of age on the cognitive performance is mediated only by PVWMH. Conclusively, the increased PVWMH, but not DWMH, plays a major role in predicting cognitive aging in healthy adults. [ABSTRACT FROM AUTHOR]

Schizophrenia has been primarily associated with dopamine dysfunction, and treatments have been developed that target the dopamine pathway in the central nervous system. However, accumulating evidence has shown that the core pathophysiology of schizophrenia might involve dysfunction in dopaminergic, glutamatergic, serotonergic, and gamma-aminobutyric acid (GABA) signaling, which may lead to aberrant functioning of interneurons that manifest as cognitive, behavioral, and social dysfunction through altered functioning of a broad range of macro- and microcircuits. The interactions between neurotransmitters can be modeled as nodes and edges by using graph theory, and oxidative balance, immune, and glutamatergic systems may represent multiple nodes interlocking at a central hub; imbalance within any of these nodes might affect the entire system. Therefore, this review attempts to address novel treatment targets beyond the dopamine hypothesis, including glutamate, serotonin, acetylcholine, GABA, and inflammatory cytokines. Furthermore, we outline that these treatment targets can be possibly integrated with novel treatment strategies aimed at different symptoms or phases of the illness. We anticipate that reversing anomalous activity in these novel treatment targets or combinations between these strategies might be beneficial in the treatment of schizophrenia. [ABSTRACT FROM AUTHOR]

To improve students' learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from transformers and generative pretrained transformer-2, to automatically generate Python programming remedial materials; subsequently, learning performance prediction models constructed using various machine learning methods are used to determine students' learning type, enabling the automatic generation of personalized remedial materials. The participants in this study were 78 students from a university in northern Taiwan enrolled in an 8-week Python course. Students in the experimental (n = 36) and control (n = 42) groups engaged in the same programming learning activities during the first 5 weeks of the course, and they completed a pretest of programming knowledge in Week 6. For the review activity in Week 7, the 36 students in the experimental group received personalized intervention, whereas those in the control group received traditional class tutoring. We examined the effect of the self-regulated learning and personalized intervention on the learning performance of students. Compared with the traditional class tutoring, the personalized intervention review activity not only helped students obtain higher learning performance but also prompted greater improvements in the following learning strategies: rehearsal, critical thinking, metacognitive self-regulation, effort regulation, and peer learning. We also observed that students' rehearsal and help-seeking learning strategies indirectly affected learning performance through students' note-taking in the provided e-book. [ABSTRACT FROM AUTHOR]

Background: The adaptability of the human brain to the constantly changing environment is reduced in patients with psychotic disorders, leading to impaired cognitive functions. Brain signal complexity, which may reflect adaptability, can be readily quantified via resting-state functional magnetic resonance imaging (fMRI) signals. We hypothesized that resting-state brain signal complexity is altered in psychotic disorders, and is correlated with cognitive impairment.Methods: We assessed 156 healthy controls (HC) and 330 probands, including 125 patients with psychotic bipolar disorder (BP), 107 patients with schizophrenia (SZ), 98 patients with schizoaffective disorder (SAD) and 230 of their unaffected first-degree relatives (76 BPR, 79 SADR, and 75 SZR) from four sites of the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium. Using multi-scale entropy analysis, we determined whether patients and/or relatives had pathologic differences in complexity of resting-state fMRI signals toward regularity (reduced entropy in all time scales), or toward uncorrelated randomness (increased entropy in fine time scales that decays as the time scale increases) and how these complexity differences might be associated with cognitive impairment.Results: Compared to HC subjects, proband groups showed either decreased complexity toward regularity or toward randomness. SZ probands showed decreased complexity toward regular signal in hypothalamus, and BP probands in left inferior occipital, right precentral and left superior parietal regions, whereas no brain region with decreased complexity toward regularity was found in SAD probands. All proband groups showed significantly increased brain signal randomness in dorsal and ventral prefrontal cortex (PFC), and unaffected relatives showed no complexity differences in PFC regions. SZ had the largest area of involvement in both dorsal and ventral PFC. BP and SAD probands shared increased brain signal randomness in ventral medial PFC, BP and SZ probands shared increased brain signal randomness in ventral lateral PFC, whereas SAD and SZ probands shared increased brain signal randomness in dorsal medial PFC. Only SZ showed increased brain signal randomness in dorsal lateral PFC. The increased brain signal randomness in dorsal or ventral PFC was weakly associated with reduced cognitive performance in psychotic probands.Conclusion: These observations support the loss of brain complexity hypothesis in psychotic probands. Furthermore, we found significant differences as well as overlaps of pathologic brain signal complexity between psychotic probands by DSM diagnoses, thus suggesting a biological approach to categorizing psychosis based on functional neuroimaging data. [ABSTRACT FROM AUTHOR]

We apply a statistical method, information-based energy, to quantify informative symbolic sequences. To apply this method to literary texts, it is assumed that different words with different occurrence frequencies are at different energy levels, and that the energy-occurrence frequency distribution obeys a Boltzmann distribution. The temperature within the Boltzmann distribution can be an indicator for the author’s writing capacity as the repertory of thoughts. The relative temperature of a text is obtained by comparing the energy-occurrence frequency distributions of words collected from one text versus from all texts of the same author. Combining the relative temperature with the Shannon entropy as the text complexity, the information-based energy of the text is defined and can be viewed as a quantitative evaluation of an author’s writing performance. We demonstrate the method by analyzing two authors, Shakespeare in English and Jin Yong in Chinese, and find that their well-known works are associated with higher information-based energies. This method can be used to measure the creativity level of a writer’s work in linguistics, and can also quantify symbolic sequences in different systems. [ABSTRACT FROM AUTHOR]

Pelvic inflammatory disease (PID) a common infection in women that is associated with significant morbidity and is a major cause of infertility. A clear temporal causal relationship between PID and psychiatric disorders has not been well established. We used a nationwide population-based retrospective cohort study to explore the relationship between PID and the subsequent development of psychiatric disorders. We identified subjects who were newly diagnosed with PID between 1 January 2000 and 31 December 2002 in the Taiwan National Health Insurance Research Database. A comparison cohort was constructed for patients without PID. A total of 21 930 PID and 21 930 matched control patients were observed until diagnosed with psychiatric disorders, or until death, withdrawal from the NHI system, or until 31 December 2009. Adjusted hazard ratio (HR) of bipolar disorder, depressive disorder, anxiety disorder and sleep disorder in subjects with PID were significantly higher (HR: 2.671, 2.173, 2.006 and 2.251, respectively) than that of the controls during the follow-up. PID may increase the risk of subsequent newly diagnosed bipolar disorder, depressive disorder, anxiety disorder and sleep disorder, which will impair life quality. Our findings highlight that clinicians should pay particular attention to psychiatric comorbidities in PID patients. [ABSTRACT FROM AUTHOR]

Heart rate variability (HRV) reflects the healthiness of autonomic nervous system, which is associated with exercise capacity. We therefore investigated whether HRV could predict the exercise capacity in the adults with cardiac syndrome X (CSX). A total of 238 subjects (57±12 years, 67.8% men), who were diagnosed as CSX by the positive exercise stress test and nearly normal coronary angiogram were enrolled. Power spectrum from the 24-hour recording of heart rate was analyzed in frequency domain using total power (TP) and spectral components of the very low frequency (VLF), low frequency (LF) and high frequency (HF) ranges. Among the study population, 129 subjects with impaired exercise capacity during the treadmill test had significantly lower HRV indices than those with preserved exercise capacity (≥90% of the age predicted maximal heart rate). After accounting for age, sex, and baseline SBP and heart rate, VLF (odds ratio per 1SD and 95% CI: 2.02, 1.19–3.42), LF (1.67, 1.10–2.55), and TP (1.82, 1.17–2.83) remained significantly associated with preserved exercise capacity. In addition, increased HRV indices were also associated with increased exercise duration, rate-pressure product, and heart rate recovery, independent of age, body mass index, and baseline SBP and heart rate. In subgroup analysis, HRV indices demonstrated similar predictive values related to exercise capacity across various subpopulations, especially in the young. In patients with CSX, HRV was independently associated with exercise capacity, especially in young subjects. The healthiness of autonomic nervous system may have a role in modulating the exercise capacity in patients with CSX. [ABSTRACT FROM AUTHOR]

Schizophrenia is characterized by heterogeneous pathophysiology. Using multiscale entropy (MSE) analysis, which enables capturing complex dynamics of time series, we characterized MSE patterns of blood-oxygen-level-dependent (BOLD) signals across different time scales and determined whether BOLD activity in patients with schizophrenia exhibits increased complexity (increased entropy in all time scales), decreased complexity toward regularity (decreased entropy in all time scales), or decreased complexity toward uncorrelated randomness (high entropy in short time scales followed by decayed entropy as the time scale increases). We recruited 105 patients with schizophrenia with an age of onset between 18 and 35 years and 210 age- and sex-matched healthy volunteers. Results showed that MSE of BOLD signals in patients with schizophrenia exhibited two routes of decreased BOLD complexity toward either regular or random patterns. Reduced BOLD complexity toward regular patterns was observed in the cerebellum and temporal, middle, and superior frontal regions, and reduced BOLD complexity toward randomness was observed extensively in the inferior frontal, occipital, and postcentral cortices as well as in the insula and middle cingulum. Furthermore, we determined that the two types of complexity change were associated differently with psychopathology; specifically, the regular type of BOLD complexity change was associated with positive symptoms of schizophrenia, whereas the randomness type of BOLD complexity was associated with negative symptoms of the illness. These results collectively suggested that resting-state dynamics in schizophrenia exhibit two routes of pathologic change toward regular or random patterns, which contribute to the differences in syndrome domains of psychosis in patients with schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Researches to date on the association between headache and weather have yielded inconsistent results. Only a limited number of studies have examined the clinical significance of self-reported weather sensitivity. This study aimed to identify the difference in the association of headache with temperature between migraine patients with and without temperature sensitivity. Methods: 66 migraine patients (75.8% female; mean age 43.3 ± 12.9 years) provided their 1-year headache diaries from 2007 to a headache clinic in Taipei, Taiwan. 34 patients (51.5%) reported sensitivity to temperature change but 32 (48.5%) did not. Time series of daily headache incidence was modeled and stratified by temperature sensitivity. Empirical mode decomposition was used to identify temporal weather patterns that were correlated to headache incidence, and regression analysis was used to examine the amount of variance in headache incidence that could be explained by temperature in different seasons. Results: Among all migraine patients, temperature change accounted for 16.5% of variance in headache incidence in winter and 9.6% in summer. In winter, the explained variance increased to 29.2% among patients with temperature sensitivity, but was not significant among those without temperature sensitivity. Overall, temperature change explained 27.0% of the variance of the mild headache incidence but only 4.8% of the incidence of moderate to severe headache during winter. Conclusions: This diary-based study provides evidence to link the perception of temperature sensitivity and headache incidence in migraine patients. Those who reported temperature sensitivity are more likely to have headache increase during the winter, particular for mild headaches. [ABSTRACT FROM AUTHOR]

The apolipoprotein E (APOE) gene is associated with structural and functional brain changes. We have used multiscale entropy (MSE) analysis to detect changes in the complexity of resting blood oxygen level-dependent (BOLD) signals associated with aging and cognitive function. In this study, we further hypothesized that the APOE genotype may affect the complexity of spontaneous BOLD activity in younger and older adults, and such altered complexity may be associated with certain changes in functional connectivity. We conducted a resting-state functional magnetic resonance imaging experiment in a cohort of 100 younger adults (aged 20-39 years; mean 27.2 ± 4.3 years; male/female: 53/47) and 112 older adults (aged 60-79 years; mean 68.4 ± 6.5 years; male/female: 54/58), and applied voxelwise MSE analysis to assess the main effect of APOE genotype on resting-state BOLD complexity and connectivity. Although the main effect of APOE genotype on BOLD complexity was not observed in younger group, we observed that older APOE ɛ4 allele carriers had significant reductions in BOLD complexity in precuneus and posterior cingulate regions, relative to noncarriers. We also observed that reduced BOLD complexity in precuneus and posterior cingulate regions was associated with increased functional connectivity to the superior and inferior frontal gyrus in the older group. These results support the compensatory recruitment hypothesis in older APOE ɛ4 carriers, and confer the impact of the APOE genotype on the temporal dynamics of brain activity in older adults. Hum Brain Mapp 35:3238-3248, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: Schizophrenia has been associated with autonomic dysregulation and increased cardiovascular co-morbidity. We hypothesised that autonomic dysregulation in patients with schizophrenia is associated with psychopathology and metabolic profiles. In this study, we aimed to evaluate psychopathology, comprehensive metabolic profiles and cardiac autonomic function using heart-rate variability (HRV) analysis in patients with schizophrenia. A total of 94 patients with schizophrenia and 51 healthy controls were recruited. Each patient underwent a physical examination, laboratory tests and rating scale evaluation, and all subjects underwent a 1-h electrocardiogram monitoring. Analysis of variance was used to compare demographic and HRV variables between control and patient groups. We applied multiple regression analysis with backward selection to examine the association between HRV indices and demographic, metabolic and psychopathology profiles. A decreased HRV was found in patient groups, compared to controls. Reduced vagal-related and complexity domain of HRV indices in patient groups were correlated with increased body mass indices, diastolic pressure, triglycerides, high- and low-density lipoprotein and severity of psychosis mainly in the negative symptom domain. This study provides evidence that altered autonomic function is associated with both psychopathology and metabolic profiles in patients with schizophrenia. These findings may warrant future research in using HRV as objective markers to monitor cardiovascular health and the severity of psychosis in patients with schizophrenia. [Copyright &y& Elsevier]

Abstract: This study assessed the utility of multiscale entropy (MSE), a complexity analysis of biological signals, to identify changes in dynamics of surface electroencephalogram (EEG) in patients with Alzheimer's disease (AD) that was correlated to cognitive and behavioral dysfunction. A total of 108 AD patients were recruited and their digital EEG recordings were analyzed using MSE methods. We investigate the appropriate parameters and time scale factors for MSE calculation from EEG signals. We then assessed the within-subject consistency of MSE measures in different EEG epochs and correlations of MSE measures to cognitive and neuropsychiatric symptoms of AD patients. Increased severity of AD was associated with decreased MSE complexity as measured by short-time scales, and with increased MSE complexity as measured by long-time scales. MSE complexity in EEGs of the temporal and occipitoparietal electrodes correlated significantly with cognitive function. MSE complexity of EEGs in various brain areas was also correlated to subdomains of neuropsychiatric symptoms. MSE analysis revealed abnormal EEG complexity across short- and long-time scales that were correlated to cognitive and neuropsychiatric assessments. The MSE-based EEG complexity analysis may provide a simple and cost-effective method to quantify the severity of cognitive and neuropsychiatric symptoms in AD patients. [Copyright &y& Elsevier]

Background: Autonomic imbalance may play an important role in the pathogenesis of reversible cerebral vasoconstriction syndromes (RCVS). This study aimed to assess the autonomic function by analyzing heart rate variability (HRV) in patients with RCVS. Methods: Patients with RCVS and age- and gender-matched controls were consecutively recruited. All patients (both ictal and remission stage) and controls underwent 24-hour ambulatory electrocardiographic (ECG) recordings. HRV measures covering time and frequency domains were used to assess autonomic functioning. Results: Thirty-nine patients with RCVS and 39 controls completed the study. Compared to the controls, RCVS patients during the ictal stage showed reductions in parasympathetic-related indices, including the root mean square of difference of consecutive interbeat intervals (RMSSD) (22.1 ± 7.0 vs. 35.2 ± 14.2, p < 0.001), the percentage of adjacent intervals that varied by more than 50 ms (pNN50) (3.7 ± 3.4 vs. 10.6 ± 8.1, p < 0.001), and high-frequency power (HF) (5.82 ± 0.73 vs. 6.77 ± 0.74; p < 0.001), and increased low-frequency/high-frequency (LF/HF) ratio (index of sympathovagal balance) (3.38 ± 1.32 vs. 2.48 ± 1.07; p =0.001). These HRV indices improved partially but remained significantly different from controls during remission. Conclusions: Decreased parasympathetic modulations and accentuated sympathetic activity might be a biological trait in patients with RCVS. [ABSTRACT FROM AUTHOR]

Abstract: A defining but elusive feature of the human brain is its astonishing complexity. This complexity arises from the interaction of numerous neuronal circuits that operate over a wide range of temporal and spatial scales, enabling the brain to adapt to the constantly changing environment and to perform various amazing mental functions. In mentally ill patients, such adaptability is often impaired, leading to either ordered or random patterns of behavior. Quantification and classification of these abnormal human behaviors exhibited during mental illness is one of the major challenges of contemporary psychiatric medicine. In the past few decades, attempts have been made to apply concepts adopted from complexity science to better understand complex human behavior. Although considerable effort has been devoted to studying the abnormal dynamic processes involved in mental illness, unfortunately, the primary features of complexity science are typically presented in a form suitable for mathematicians, physicists, and engineers; thus, they are difficult for practicing psychiatrists or neuroscientists to comprehend. Therefore, this paper introduces recent applications of methods derived from complexity science for examining mental illness. We propose that mental illness is loss of brain complexity and the complexity of mental illness can be studied under a general framework by quantifying the order and randomness of dynamic macroscopic human behavior and microscopic neuronal activity. Additionally, substantial effort is required to identify the link between macroscopic behaviors and microscopic changes in the neuronal dynamics within the brain. [Copyright &y& Elsevier]

This study aims to develop and validate the use of machine learning-based prediction models to select individualized pharmacological treatment for patients with depressive disorder. This study used data from Taiwan's National Health Insurance Research Database. Patients with incident depressive disorders were included in this study. The study outcome was treatment failure, which was defined as psychiatric hospitalization, self-harm hospitalization, emergency visits, or treatment change. Prediction models based on the Super Learner ensemble were trained separately for the initial and the next-step treatments if the previous treatments failed. An individualized treatment strategy was developed for selecting the drug with the lowest probability of treatment failure for each patient as the model-selected regimen. We emulated clinical trials to estimate the effectiveness of individualized treatments. The area under the curve of the prediction model using Super Learner was 0.627 and 0.751 for the initial treatment and the next-step treatment, respectively. Model-selected regimens were associated with reduced treatment failure rates, with a 0.84-fold (95% confidence interval (CI) 0.82–0.86) decrease for the initial treatment and a 0.82-fold (95% CI 0.80–0.83) decrease for the next-step. In emulation of clinical trials, the model-selected regimen was associated with a reduced treatment failure rate. [ABSTRACT FROM AUTHOR]

Bipolar disorder seasonality has been documented previously, though information on the effect of demographic and clinical variables on seasonal patterns is scant. This study examined effects of age, sex, index admission, and predominant polarity on bipolar disorder seasonality in a nationwide population. An inpatient cohort admitted to hospital exclusively for mental illness was derived from the Taiwan National Health Insurance Research Database for 2002-2007. The authors identified 9619 inpatients with bipolar disorder, who had generated 15 078 acute admission records. An empirical mode decomposition method was used to identify seasonal oscillations in bipolar admission data, and regression and cross-correlation analyses were used to quantify the degree and timing of bipolar admission seasonality. Results for seasonality timing found that manic or mixed episodes peak in spring or summer, and depressive episodes peak in winter. Analysis for degree of seasonality revealed that (1) the polarity of patients' index admission predicted the seasonality of relapse admissions; (2) seasonality was significant in female admissions for depressive episodes and in male admissions for manic episodes; (3) young adults displayed a higher degree of seasonality for acute admissions than middle-aged adults; and (4) patients with predominantly depressive admissions displayed a higher degree of seasonality than patients with predominantly manic admissions. Demographic and clinical variables were found to affect the seasonality of acute admissions for bipolar disorders. These findings highlight the need for research on identification and management of seasonal features in bipolar patients. (Author correspondence: ) [ABSTRACT FROM AUTHOR]

Abstract: The nonlinear properties of spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals remain unexplored. We test the hypothesis that complexity of BOLD activity is reduced with aging and is correlated with cognitive performance in the elderly. A total of 99 normal older and 56 younger male subjects were included. Cognitive function was assessed using Cognitive Abilities Screening Instrument and Wechsler Digit Span Task. We employed a complexity measure, multiscale entropy (MSE) analysis, and investigated appropriate parameters for MSE calculation from relatively short BOLD signals. We then compared the complexity of BOLD signals between the younger and older groups, and examined the correlation between cognitive test scores and complexity of BOLD signals in various brain regions. Compared with the younger group, older subjects had the most significant reductions in MSE of BOLD signals in posterior cingulate gyrus and hippocampal cortex. For older subjects, MSE of BOLD signals from default mode network areas, including hippocampal cortex, cingulate cortex, superior and middle frontal gyrus, and middle temporal gyrus, were found to be positively correlated with major cognitive functions, such as attention, orientation, short-term memory, mental manipulation, and language. MSE from subcortical regions, such as amygdala and putamen, were found to be positively correlated with abstract thinking and list-generating fluency, respectively. Our findings confirmed the hypothesis that complexity of BOLD activity was correlated with aging and cognitive performance based on MSE analysis, and may provide insights on how dynamics of spontaneous brain activity relates to aging and cognitive function in specific brain regions. [Copyright &y& Elsevier]

Abstract: Methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism is involved in folate and homocysteine metabolism, and has been associated with geriatric disorders, including dementia and late-life depression. The present work aimed to investigate the effect of MTHFR C677T polymorphism on the presence of depression and loneliness in cognitively normal male subjects. A total of 323 cognitively normal male subjects were included in this study (mean age=80.6; SD=5.3). Depression was assessed by the Geriatric Depression Scale-Short Form (GDS-SF) and loneliness by UCLA loneliness scales. Analysis of variance (ANOVA) was used to test the between MTHFR genotype difference in depression and loneliness. Multiple regression was used to test the effect of MTHFR polymorphism on the loneliness, controlling for age, education, cognitive function, and depression. ANOVA showed a significant between-genotype difference in loneliness scores (P =0.015), and post hoc comparisons showed that subjects with C/C genotype had significantly higher loneliness ratings, compared to those with C/T or T/T genotype. Regression analysis indicated that the effect of MTHFR polymorphism on loneliness was independent of age, education, cognitive function, and depression. Our findings suggest that MTHFR C677T polymorphism may be linked more to loneliness than depression in the cognitively normal elderly males, and may be implicated in the pathophysiology of late-life depression in relation to MTHFR genes. [Copyright &y& Elsevier]

Abstract: Background: Although Internet has become an important source for affected people seeking suicide information, the connection between Internet searches for suicide information and suicidal death remains largely unknown. This study aims to evaluate the association between suicide and Internet searches trends for 37 suicide-related terms representing major known risks of suicide. Methods: This study analyzes suicide death data in Taipei City, Taiwan and corresponding local Internet search trend data provided by Google Insights for Search during the period from January 2004 to December 2009. The investigation uses cross correlation analysis to estimate the temporal relationship between suicide and Internet search trends and multiple linear regression analysis to identify significant factors associated with suicide from a pool of search trend data that either coincides or precedes the suicide death. Results: Results show that a set of suicide-related search terms, the trends of which either temporally coincided or preceded trends of suicide data, were associated with suicide death. These search factors varied among different suicide samples. Searches for “major depression” and “divorce” accounted for, at most, 30.2% of the variance in suicide data. When considering only leading suicide trends, searches for “divorce” and the pro-suicide term “complete guide of suicide,” accounted for 22.7% of variance in suicide data. Conclusions: Appropriate filtering and detection of potentially harmful source in keyword-driven search results by search engine providers may be a reasonable strategy to reduce suicide deaths. [Copyright &y& Elsevier]

Abstract: Background: Depression is known to be associated with altered cardiovascular variability and increased cardiovascular comorbidity, yet it is unknown whether altered cardiac autonomic function in depression is associated with insomnia, a common symptom comorbid with depression. This study aimed to investigate the long-term diurnal profile of autonomic function as measured by heart rate variability (HRV) in both major depression and primary insomnia patients. Method: A total of 52 non-medicated patients with major depression, 47 non-medicated patients with primary insomnia, and 88 matched controls without insomnia were recruited. Each subject was assessed by means of sleep and mood questionnaires and underwent twenty-four-hour ambulatory electrocardiogram monitoring. Standard HRV analysis and a well-validated complexity measure, multiscale entropy, were applied to comprehensively assess the diurnal profiles of autonomic function and physiologic complexity in our study sample. Results: Compared with the controls, the patients with major depression and those with primary insomnia exhibited significant reductions in parasympathetic-related HRV indices, and this association was mainly driven by the presence of poor sleep. Both groups of patients also exhibited significant reductions in physiologic complexity during the sleep period as compared with the healthy controls. Alterations in HRV indices were correlated with perceived sleep questionnaire scores but not with depression scales. Conclusions: Our findings suggest a pivotal role of sleep disturbance in regulating cardiovascular variability in major depression and primary insomnia patients. These findings could highlight the importance of treating insomnia as an independent disease rather than a symptom. [Copyright &y& Elsevier]

Background: Genetic polymorphisms in the gene encoding the β-adrenergic receptors (β-AR) have a pivotal role in the functions of the autonomic nervous system. Using heart rate variability (HRV) as an indicator of autonomic function, we present a bottom-up genotype-phenotype analysis to investigate the association between β-AR gene polymorphisms and heart rate dynamics. Methods: A total of 221 healthy Han Chinese adults (59 males and 162 females, aged 33.6±10.8 years, range 19 to 63 years) were recruited and genotyped for three common β-AR polymorphisms: β1-AR Ser49Gly, β2-AR Arg16Gly and β2-AR Gln27Glu. Each subject underwent two hours of electrocardiogram monitoring at rest. We applied an information-based similarity (IBS) index to measure the pairwise dissimilarity of heart rate dynamics among study subjects. Results: With the aid of agglomerative hierarchical cluster analysis, we categorized subjects into major clusters, which were found to have significantly different distributions of β2-AR Arg16Gly genotype. Furthermore, the non-randomness index, a nonlinear HRV measure derived from the IBS method, was significantly lower in Arg16 homozygotes than in Gly16 carriers. The non-randomness index was negatively correlated with parasympathetic-related HRV variables and positively correlated with those HRV indices reflecting a sympathovagal shift toward sympathetic activity. Conclusions: We demonstrate a bottom-up categorization approach combining the IBS method and hierarchical cluster analysis to detect subgroups of subjects with HRV phenotypes associated with β-AR polymorphisms. Our results provide evidence that β2-AR polymorphisms are significantly associated with the acceleration/deceleration pattern of heart rate oscillation, reflecting the underlying mode of autonomic nervous system control. [ABSTRACT FROM AUTHOR]

Abstract: Background: Research has implicated environmental risk factors, such as meteorological variables, in suicide. However, studies have not investigated air pollution, known to induce acute medical conditions and increase mortality, in suicide. This study comprehensively assesses the temporal relationship between suicide and air pollution, weather, and unemployment variables in Taipei City from January 1 1991 to December 31 2008. Methods: This research used the empirical mode decomposition (EMD) method to de-trend the suicide data into a set of intrinsic oscillations, called intrinsic mode functions (IMFs). Multiple linear regression analysis with forward stepwise method was used to identify significant predictors of suicide from a pool of air pollution, weather, and unemployment data, and to quantify the temporal association between decomposed suicide IMFs with these predictors at different time scales. Results: Findings of this study predicted a classic seasonal pattern of increased suicide occurring in early summer by increased air particulates and decreased barometric pressure, in which the latter was in accordance with increased temperature during the corresponding time. Gaseous air pollutants, such as sulfur dioxide and ozone, were found to increase the risk of suicide at longer time scales. Decreased sunshine duration and sunspot activity predicted the increased suicide. After controlling for the unemployment factor, environmental risks predicted 33.7% of variance in the suicide data. Conclusions: Using EMD analysis, this study found time-scale dependent associations between suicide and air pollution, weather and unemployment data. Contributing environmental risks may vary in different geographic regions and in different populations. [Copyright &y& Elsevier]

Sleep disruption is an important aspect of major depressive disorder but lacks an objective and inexpensive means of assessment. We evaluated the utility of electrocardiogram (ECG)-based cardiopulmonary coupling analysis to quantify physiologic sleep stability in patients with major depression. Relative to controls, unmedicated depressed patients had a reduction in high-frequency coupling, an index of stable sleep, an increase in low-frequency coupling, an index of unstable sleep, and an increase in very-low-frequency coupling, an index of wakefulness/REM sleep. The medicated depressed group showed a restoration of stable sleep to a level comparable with that of the control group. ECG-based cardiopulmonary coupling analysis may provide a simple, cost-efficient point-of-care method to quantify sleep quality/stability and to objectively evaluate the severity of insomnia in patients with major depression. [ABSTRACT FROM AUTHOR]

Background: Patients frequently report that weather changes trigger headache or worsen existing headache symptoms. Recently, the method of empirical mode decomposition (EMD) has been used to delineate temporal relationships in certain diseases, and we applied this technique to identify intrinsic weather components associated with headache incidence data derived from a large-scale epidemiological survey of headache in the Greater Taipei area. Methodology/Principal Findings: The study sample consisted of 52 randomly selected headache patients. The weather time-series parameters were detrended by the EMD method into a set of embedded oscillatory components, i.e. intrinsic mode functions (IMFs). Multiple linear regression models with forward stepwise methods were used to analyze the temporal associations between weather and headaches. We found no associations between the raw time series of weather variables and headache incidence. For decomposed intrinsic weather IMFs, temperature, sunshine duration, humidity, pressure, and maximal wind speed were associated with headache incidence during the cold period, whereas only maximal wind speed was associated during the warm period. In analyses examining all significant weather variables, IMFs derived from temperature and sunshine duration data accounted for up to 33.3% of the variance in headache incidence during the cold period. The association of headache incidence and weather IMFs in the cold period coincided with the cold fronts. Conclusions/Significance: Using EMD analysis, we found a significant association between headache and intrinsic weather components, which was not detected by direct comparisons of raw weather data. Contributing weather parameters may vary in different geographic regions and different seasons. [ABSTRACT FROM AUTHOR]

Background: Seasonal depression has generated considerable clinical interest in recent years. Despite a common belief that people in higher latitudes are more vulnerable to low mood during the winter, it has never been demonstrated that human's moods are subject to seasonal change on a global scale. The aim of this study was to investigate large-scale seasonal patterns of depression using Internet search query data as a signature and proxy of human affect. Methodology/Principal Findings: Our study was based on a publicly available search engine database, Google Insights for Search, which provides time series data of weekly search trends from January 1, 2004 to June 30, 2009. We applied an empirical mode decomposition method to isolate seasonal components of health-related search trends of depression in 54 geographic areas worldwide. We identified a seasonal trend of depression that was opposite between the northern and southern hemispheres; this trend was significantly correlated with seasonal oscillations of temperature (USA: r =20.872, p,0.001; Australia: r =20.656, p<0.001). Based on analyses of search trends over 54 geological locations worldwide, we found that the degree of correlation between searching for depression and temperature was latitude-dependent (northern hemisphere: r =20.686; p<0.001; southern hemisphere: r = 0.871; p,0.0001). Conclusions/Significance: Our findings indicate that Internet searches for depression from people in higher latitudes are more vulnerable to seasonal change, whereas this phenomenon is obscured in tropical areas. This phenomenon exists universally across countries, regardless of language. This study provides novel, Internet-based evidence for the epidemiology of seasonal depression. [ABSTRACT FROM AUTHOR]

We recently proposed a novel approach to categorize information carried by symbolic sequences based on their usage of repetitive patterns. A simple quantitative index to measure the dissimilarity between two symbolic sequences can be defined. This information dissimilarity index, defined by our formula, is closely related to the Shannon entropy and rank order of the repetitive patterns in the symbolic sequences. Here we discuss the underlying statistical physics assumptions of this dissimilarity index. We use human cardiac interbeat interval time series and DNA sequences as examples to illustrate the applicability of this generic approach to real-world problems. [ABSTRACT FROM AUTHOR]

Background: This study evaluated the effects of stroke on regulation of cerebral blood flow in response to fluctuations in systemic blood pressure (BP). The autoregulatory dynamics are difficult to assess because of the nonstationarity and nonlinearity of the component signals. Methods: We studied 15 normotensive, 20 hypertensive and 15 minor stroke subjects (48.0 ± 1.3 years). BP and blood flow velocities (BFV) from middle cerebral arteries (MCA) were measured during the Valsalva maneuver (VM) using transcranial Doppler ultrasound. Results: A new technique, multimodal pressure-flow analysis (MMPF), was implemented to analyze these short, nonstationary signals. MMPF analysis decomposes complex BP and BFV signals into multiple empirical modes, representing their instantaneous frequency-amplitude modulation. The empirical mode corresponding to the VM BP profile was used to construct the continuous phase diagram and to identify the minimum and maximum values from the residual BP (BPR) and BFV (BFVR) signals. The BP-BFV phase shift was calculated as the difference between the phase corresponding to the BPR and BFVR minimum (maximum) values. BP-BFV phase shifts were significantly different between groups. In the normotensive group, the BFVR minimum and maximum preceded the BPR minimum and maximum, respectively, leading to large positive values of BP-BFV shifts. Conclusion: In the stroke and hypertensive groups, the resulting BP-BFV phase shift was significantly smaller compared to the normotensive group. A standard auto-regulation index did not differentiate the groups. The MMPF method enables evaluation of autoregulatory dynamics based on instantaneous BP-BFV phase analysis. Regulation of BP-BFV dynamics is altered with hypertension and after stroke, rendering blood flow dependent on blood pressure. [ABSTRACT FROM AUTHOR]

Scientific analysis of the linguistic styles of different authors has generated considerable interest. We present a generic approach to measuring the similarity of two symbolic sequences that requires minimal background knowledge about a given human language. Our analysis is based on word rank order–frequency statistics and phylogenetic tree construction. We demonstrate the applicability of this method to historic authorship questions related to the classic Chinese novel “The Dream of the Red Chamber,” to the plays of William Shakespeare, and to the Federalist papers. This method may also provide a simple approach to other large databases based on their information content. [Copyright &y& Elsevier]

Background: People may experience tinnitus after sudden hearing impairment. The details of the relationship between tinnitus improvement and hearing recovery are still unclear. Personality traits may play a role in the modulation of tinnitus sensation. We investigated the moderating effect of personality traits on pretreatment and posttreatment tinnitus sensation in patients with idiopathic sudden sensorineural hearing loss (ISSHL). Methods: This prospective longitudinal study enrolled 33 patients diagnosed with unilateral ISSHL and acute tinnitus in 2018-2019 at one institute. Clinical data were collected before and after treatment, including results of pure-tone audiometry (PTA), the Clinical Tinnitus Questionnaire (CTQ), the Tinnitus Handicap Inventory (THI), tinnitus loudness and annoyance (Visual Analog Scale; VAS), the Hospital Anxiety and Depression Scale (HADS), and the Big Five Inventory (BFI)-44. Results: Eighteen men and 15 women with an average age of 48.3 ± 15.8 years were enrolled. The pretreatment hearing threshold and THI score were 58.0 ± 27.4 dB HL and 42.9 ± 26.2 (range: 0-88), respectively; the posttreatment values were 39.8 ± 22.4 dB HL and 20.7 ± 22.5 (range: 0-64), respectively. There were significant differences between pretreatment and posttreatment hearing thresholds as well as THI, VAS, and HADS scores (p < 0.001) but not BFI-44 results. Neuroticism and openness had significant positive and negative correlations, respectively, with acute tinnitus stress before treatment (r = 0.561, p = 0.001; r = −0.359, p = 0.040). After 3 months of follow-up, all patients were analyzed separately by dividing them into recovery (n = 16) and nonrecovery groups (n = 14) according to their hearing improvement status by Siegel's criteria. Neuroticism showed a significant moderating effect on acute tinnitus sensation in the ISSHL recovery group (p < 0.001) but not in the nonrecovery group (p = 0.106). Conclusion: Neuroticism and openness may affect acute tinnitus stress in ISSHL patients. Practitioners should consider personality traits when managing ISSHL patients with bothersome tinnitus. [ABSTRACT FROM AUTHOR]

Background: Fractures are a great health issue associated with morbidity, quality of life, life span, and health care expenditure. Fractures are correlated with cardiovascular disease, type 2 diabetes mellitus, cerebrovascular disease, and some psychiatric disorders. However, representative national data are few, and longitudinal cohort studies on the association between schizophrenia and the subsequent fracture risk are scant. We designed a nationwide population-based cohort study to investigate the association of schizophrenia with hip, vertebral, and wrist fractures over a 10-year follow-up. Methods: Data of patients with schizophrenia (International Classification of Diseases, Ninth Revision, Clinical Modification code 295) and matched over January 2000–December 2009) were extracted from Taiwan National Health Insurance Research Database. A Cox proportional-hazards regression model was constructed to calculate hazard ratios (HRs) for fractures between the schizophrenia and control cohorts. Results: Of 2028 people with schizophrenia (mean age: 36.3 years, 49.4% female), 89 (4.4%) reported newly diagnosed fractures—significantly higher than the proportion in the control population (257, 3.2%; P = 0.007). The incidences of hip (1.2%, P = 0.009) and vertebral (2.6%, P = 0.011) fractures were significantly higher in the schizophrenia cohort than in the control cohort. In Cox regression analysis, hip (adjusted HR: 1.78, 95% confidence interval [CI]: 1.08–2.93) and vertebral (adjusted HR: 1.40, 95% CI: 1.01–1.95) fracture risks were significantly higher in patients with schizophrenia. Furthermore, a sex-based subgroup analysis revealed that the risk of hip fracture remained significantly higher in female patients with schizophrenia (HR: 2.68, 95% CI: 1.32–5.44) than in female controls. On the other hand, there was no significant interaction between effects of sex and schizophrenia on the risk of fractures. Conclusions: Over a 10-year follow-up, hip and vertebral fracture risks were higher in the people with schizophrenia than in the controls. The risk of fractures in patients with schizophrenia does not differ between female and male. [ABSTRACT FROM AUTHOR]

Background: The progressive neurodegenerative disorder Parkinson disease (PD) is well-established as the second most common neurodegenerative disease. Associations between the sequential risk of PD and gout have been addressed in other studies, but findings have been inconclusive. Accordingly, we executed the present study with the purpose of assessing PD risk in patients with gout.Methods: From Taiwan's National Health Insurance Research Database, we identified the data of patients newly diagnosed as having gout between January 1, 2000 and December 1, 2000. A cohort of patients without gout, matched for sex and age, was constructed for comparison. Hazard ratios (HRs) and the incidence rate of subsequent PD were calculated for both cohorts and separately for male and female groups. The gout and comparison cohorts consisted of 7900 patients each.Results: The HR for PD was not significantly higher in the gout cohort compared with the control cohort (HR 1.01, 95% confidence interval [CI], 0.93-1.31, P = .268), even after adjustment for age, urbanization, monthly income, sex, and comorbidities. We did not observe gender differences in the gout-PD association (male: HR 1.01, 95% CI, 0.88-1.36, P = .400; female: HR 1.11, 95% CI, 0.84-1.46, P = .466).Conclusions: Our study identified that there was no protective effect of gout for the risk of PD in the Taiwanese population. [ABSTRACT FROM AUTHOR]

In this paper, we first apply ensemble Empirical Mode Decomposition (EEMD) to analyze the change rate of GDP time series for ten major countries. Each signal is decomposed into four modes with different scales and a residual trend. The variance contribution and averaged period cycle for each mode is then calculated. Results show that economic growth fluctuations for most of the countries mainly fluctuate at a short period between 3–5 years. Then the causal decomposition proposed by Yang et al. is utilized to detect the mutual causation among different countries. The novelty of this method is that the causal interaction is identified in instantaneous phase coherence at a specific time scale. The strengths and directions of causal relationship for each mode among countries differ from each other, which mirrors the fact that the world economic activities are fluctuated and changeable over varying periods. However, no visible causation for the fourth mode (long-term cycle) among countries is characterized, meaning that the development of economy over a long period for most countries depends on its own conditions. • We apply ensemble Empirical Mode Decomposition to analyze the change rate of GDP series for ten countries. • Results show that economic growth fluctuations mainly fluctuate at a short period between 3-5 years. • We utilize causal decomposition to the change rate of GDP time series. • We find that the strengths and directions of causal relationship for each mode among countries differ from each other. • The results mirror the fact that the world economic activities are fluctuated and changeable over varying periods. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented that discusses the renal function and cognitive performance of male dementia patients.

A letter to the editor is presented in response to the article "Association Between Heart Rate Variability and Cognitive function in Elderly Community-Dwelling Men Without Dementia: A Preliminary Report."

Major depressive disorder (MDD), which is a leading psychiatric illness across the world, severely affects quality of life and causes an increased incidence of suicide. Evidence from animal as well as clinical studies have indicated that increased peripheral or central cytokine interleukin-6 (IL-6) levels play an important role in stress reaction and depressive disorder, especially physical disorders comorbid with depression. Increased release of IL-6 in MDD has been found to be a factor associated with MDD prognosis and therapeutic response, and may affect a wide range of depressive symptomatology. However, study results of the IL6 genetic effects in MDD are controversial. Increased IL-6 activity may cause depression through activation of hypothalamic-pituitary-adrenal axis or influence of the neurotransmitter metabolism. The important role of neuroinflammation in MDD pathogenesis has created a new perspective that the combining of blood IL-6 and other depression-related cytokine levels may help to classify MDD biological subtypes, which may allow physicians to identify the optimal treatment for MDD patients. To modulate the IL-6 activity by IL-6-related agents, current antidepressive agents, herb medication, pre-/probiotics or non-pharmacological interventions may hold great promise for the MDD patients with inflammatory features. [ABSTRACT FROM AUTHOR]

Purpose: Accurate predictions of postoperative pain intensity are necessary for customizing analgesia plans. Insomnia is a risk factor for severe postoperative pain. Moreover, heart rate variability (HRV) can provide information on the sympathetic–parasympathetic balance in response to noxious stimuli. We developed a prediction model that uses the insomnia severity index (ISI), HRV, and other demographic factors to predict the odds of higher postoperative pain. Methods: We recruited gynecological surgery patients classified as American Society of Anesthesiologists class 1–3. An ISI questionnaire was completed 1 day before surgery. HRV was calculated offline using intraoperative electrocardiogram data. Pain severity at the postanesthesia care unit (PACU) was assessed with the 0–10 numerical rating scale (NRS). The primary outcome was the model's predictive ability for moderate‐to‐severe postoperative pain. The secondary outcome was the relationship between individual risk factors and opioid consumption in the PACU. Results: Our study enrolled 169 women. Higher ISI scores (p = 0.001), higher parasympathetic activity (rMSSD, pNN50, HF; p < 0.001, p < 0.001, p < 0.001), loss of fractal dynamics (SD2, alpha 1; p = 0.012, p = 0.039) in HRV analysis before the end of surgery were associated with higher NRS scores, while laparoscopic surgery (p = 0.031) was associated with lower NRS scores. We constructed a multiple logistic model (area under the curve = 0.852) to predict higher NRS scores at PACU arrival. The five selected predictors were age (OR: 0.94; p = 0.020), ISI score (OR: 1.14; p = 0.002), surgery type (laparoscopic or open; OR: 0.12; p < 0.001), total power (OR: 2.02; p < 0.001), and alpha 1 (OR: 0.03; p < 0.001). Conclusion: We employed a multiple logistic regression model to determine the likelihood of moderate‐to‐severe postoperative pain upon arrival at the PACU. Physicians could personalize analgesic regimens based on a deeper comprehension of the factors that contribute to postoperative pain. [ABSTRACT FROM AUTHOR]

Aim: No previous studies, to our knowledge, have investigated the association between psychiatrist density and suicide, accounting for individual‐ and area‐level characteristics. Methods: We investigated all suicide cases in 2007‐2017 identified from the national cause‐of‐death data files, with each suicide case matched to 10 controls by age and sex and each suicide case/control assigned to one of the 355 townships across Taiwan. Our primary outcome was the odds ratio (OR) of suicide and its 95% confidence interval (CI) estimated via multilevel models, which included both individual‐ and area‐level characteristics. Townships with no psychiatrists were compared with the quartiles of townships with psychiatrists (density per 100,000 population): quartile 1 (Q1) (0.01–3.02); quartile 2 (Q2) (3.02–7.20); quartile 3 (Q3) (7.20–13.82); and quartile 4 (Q4) (>13.82). Results: A total of 40,930 suicide cases and 409,300 age‐ and sex‐matched controls were included. We found that increased psychiatrist density was associated with decreased suicide risk (Q1: adjusted OR [aOR], 0.95 [95% CI, 0.90–1.01]; Q2: aOR, 0.90 [95% CI, 0.85–0.96]; Q3: aOR, 0.89 [95% CI, 0.83–0.94]; Q4: aOR, 0.89 [95% CI, 0.83–0.95]) after adjusting for individual‐level characteristics (employment state, monthly income, physical comorbidities, and the diagnosis of psychiatric disorders) and area socioeconomic characteristics. Conclusions: The psychiatrist density–suicide association suggests an effect of increased availability of psychiatric services on preventing suicide. Suicide prevention strategies could usefully focus on enhancing local access to psychiatric services. [ABSTRACT FROM AUTHOR]

Background: The cysteine-altering variants in NOTCH3 have been suggested to be associated with stroke, dementia, and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), where aberrant blood pressure levels represent the characteristics of these diseases. We aimed to assess whether the cysteine-altering p.Arg544Cys (p.R544C; rs201118034) variant and common single nucleotide variants (SNVs) in NOTCH3 could contribute to systolic and diastolic blood pressure and related phenotypes in the Taiwan Biobank. Methods: We employed a discovery sample of 68,925 individuals, an independent replication sample of 45,676 individuals, and a combined/total sample of 114,601 individuals; all from the Taiwan Biobank. Blood pressure, such as systolic and diastolic blood pressure, was measured for all participants. Association was evaluated using a general linear model, where results were considered statistically significant if the P value < 0.05 divided by the number of independent tests per model. Results: From our analysis, we identified and replicated three novel candidates for blood pressure that have not previously been reported: the cysteine-altering p.R544C variant for systolic blood pressure, the common SNV rs11669950 for diastolic blood pressure, and the common SNV rs4808235 for diastolic blood pressure. We also generalized two previously identified SNVs (i.e., rs10418305 and rs7408868) in NOTCH3 for blood pressure in European and non-Taiwanese East Asian populations to the Taiwanese population. Moreover, the participants with NOTCH3 p.R544C had an increased stroke frequency (P < 1.0 × 10–5) and a higher dementia frequency (P = 2.0 × 10–4) compared with the whole Taiwan Biobank population in the combined/total sample. Conclusion: NOTCH3 is a strong candidate for a role in stroke, dementia, and CADASIL, which has previously been linked to blood pressure changes. While our preliminary study suggests that NOTCH3 p.R544C may influence blood pressure, stroke, and dementia in the Taiwan Biobank, replication in a well-powered external sample is required. This study also underlines considerable prospects of detecting novel genetic biomarkers in underrepresented worldwide populations. [ABSTRACT FROM AUTHOR]

The heritability levels of two traits for diabetes diagnosis, serum fasting glucose (FG) and glycated hemoglobin (HbA1c), were estimated to be 51–62%. Studies have shown that cigarette smoking is a modifiable risk factor for diabetes. It is important to uncover whether smoking may modify the genetic risk of diabetes. This study included unrelated Taiwan Biobank subjects in a discovery cohort (TWB1) of 25,460 subjects and a replication cohort (TWB2) of 58,774 subjects. Genetic risk score (GRS) of each TWB2 subject was calculated with weights retrieved from the TWB1 analyses. We then assessed the significance of GRS-smoking interactions on FG, HbA1c, and diabetes while adjusting for covariates. A total of five smoking measurements were investigated, including active smoking status, pack-years, years as a smoker, packs smoked per day, and hours as a passive smoker per week. Except for passive smoking, all smoking measurements were associated with FG, HbA1c, and diabetes (P < 0.0033) and were associated with an exacerbation of the genetic risk of FG and HbA1c (PInteraction < 0.0033). For example, each 1 SD increase in GRS is associated with a 1.68% higher FG in subjects consuming one more pack of cigarettes per day (PInteraction = 1.9 × 10–7). Smoking cessation is especially important for people who are more genetically predisposed to diabetes. [ABSTRACT FROM AUTHOR]

The heritability levels of two traits for diabetes diagnosis, serum fasting glucose (FG) and glycated hemoglobin (HbA1c), were estimated to be 51-62%. Studies have shown that cigarette smoking is a modifiable risk factor for diabetes. It is important to uncover whether smoking may modify the genetic risk of diabetes. This study included unrelated Taiwan Biobank subjects in a discovery cohort (TWB1) of 25,460 subjects and a replication cohort (TWB2) of 58,774 subjects. Genetic risk score (GRS) of each TWB2 subject was calculated with weights retrieved from the TWB1 analyses. We then assessed the significance of GRS-smoking interactions on FG, HbA1c, and diabetes while adjusting for covariates. A total of five smoking measurements were investigated, including active smoking status, pack-years, years as a smoker, packs smoked per day, and hours as a passive smoker per week. Except for passive smoking, all smoking measurements were associated with FG, HbA1c, and diabetes (P < 0.0033) and were associated with an exacerbation of the genetic risk of FG and HbA1c (PInteraction < 0.0033). For example, each 1 SD increase in GRS is associated with a 1.68% higher FG in subjects consuming one more pack of cigarettes per day (PInteraction = 1.9 × 10-7). Smoking cessation is especially important for people who are more genetically predisposed to diabetes. [ABSTRACT FROM AUTHOR]

Background: Youth suicide rates have increased markedly in some countries. This study aimed to estimate the population-attributable risk of psychiatric disorders associated with suicide among Taiwanese youth aged 10–24 years. Methods: Data were obtained from the National Death Registry and National Health Insurance (NHI) claims database between 2007 and 2019. Youth who died by suicide were included, and comparisons, 1:10 matched by age and sex, were randomly selected from the Registry for NHI beneficiaries. We used multivariable logistic regression to estimate suicide odds ratios for psychiatric disorders. The population-attributable fractions (PAF) were calculated for each psychiatric disorder. Results: A total of 2345 youth suicide and 23 450 comparisons were included. Overall, 44.8% of suicides had a psychiatric disorder, while only 7.9% of the comparisons had a psychiatric disorder. The combined PAF for all psychiatric disorders was 55.9%. The top three psychiatric conditions of the largest PAFs were major depressive disorder, dysthymia, and sleep disorder. In the analysis stratified by sex, the combined PAF was 45.5% for males and 69.2% for females. The PAF among young adults aged 20–24 years (57.0%) was higher than among adolescents aged 10–19 years (48.0%). Conclusions: Our findings of high PAF from major depressive disorder, dysthymia, and sleep disorder to youth suicides suggest that youth suicide prevention that focuses on detecting and treating mental illness may usefully target these disorders. [ABSTRACT FROM AUTHOR]

Occurrence of amyloid-β (Aβ) aggregation in brain begins before the clinical onset of Alzheimer's disease (AD), as preclinical AD. Studies have reported that sleep problems and autonomic dysfunction associate closely with AD. However, whether they, especially the interaction between sleep and autonomic function, play critical roles in preclinical AD are unclear. Therefore, we investigated how sleep patterns and autonomic regulation at different sleep–wake stages changed and whether they were related to cognitive performance in pathogenesis of AD mice. Polysomnographic recordings in freely-moving APP/PS1 and wild-type (WT) littermates were collected to study sleep patterns and autonomic function at 4 (early disease stage) and 8 months of age (advanced disease stage), cognitive tasks including novel object recognition and Morris water maze were performed, and Aβ levels in brain were measured. APP/PS1 mice at early stage of AD pathology with Aβ aggregation but without significant differences in cognitive performance had frequent sleep–wake transitions, lower sleep-related delta power percentage, lower overall autonomic activity, and lower parasympathetic activity mainly during sleep compared with WT mice. The same phenomenon was observed in advanced-stage APP/PS1 mice with significant cognitive deficits. In mice at both disease stages, sleep-related delta power percentage correlated positively with memory performance. At early stage, memory performance correlated positively with sympathetic activity during wakefulness; at advanced stage, memory performance correlated positively with parasympathetic activity during both wakefulness and sleep. In conclusion, sleep quality and distinction between wake- and sleep-related autonomic function may be biomarkers for early AD detection. [ABSTRACT FROM AUTHOR]

Major depressive disorder places a great burden on healthcare resources worldwide. Antidepressants are the first-line treatment for major depressive disorder, but if patients don't respond adequately, brain stimulation therapy may be needed as second-line treatment. Digital phenotyping in patients with major depressive disorder will aid in the timely prediction of treatment effectiveness. This study explored electroencephalographic (EEG) signatures that diversify depression treatment responsivity, including antidepressant administration or brain stimulation therapy. Resting-state, pre-treatment EEG sequences from depressive patients who received fluoxetine treatment (n = 55; 26 remitters and 29 poor responders) or electroconvulsive therapy (ECT, n = 58; 36 remitters and 22 nonremitters) were recorded on 19 channels. Twenty-nine EEG segments were obtained from each patient per recording electrode. Power spectral analysis was conducted for feature extraction and showed the highest predictive accuracy for fluoxetine or ECT outcomes. Both occurred with beta-band oscillations within right-side frontal-central (F1-score = 0.9437) or prefrontal areas of the brain (F1-score = 0.9416), respectively. Significantly higher beta-band power was observed among patients who lacked adequate treatment response than the remitters, specifically at 19.2 Hz or 24.5 Hz for fluoxetine administration or ECT outcome, respectively. Our findings indicated that pre-treatment, right-side cortical hyperactivation is associated with poor outcomes of antidepressant-based or ECT-based treatment in major depression. Whether depression treatment response rates can be improved by reducing the high-frequency EEG power in corresponding areas of the brain to provide a protective effect against depression recurrence warrants further study. [ABSTRACT FROM AUTHOR]

Abstract Sleep electroencephalographic (EEG) provides the opportunity to study sleep scientifically. Slow wave activity (SWA), presenting EEG spectral power in the low-frequency range, has proven to be a useful parameter in sleep medicine. Drawing inspiration from the adaptive and noise-assist features of symbolic dynamics , we introduced a symbolic analogue of SWA as EEG signal was generally considered as non-linear and non-stationary. Moreover, we investigated whether the proposed metrics can capture patterns that characterize and differentiate different sleep stages, and whether EEG dynamical features during the wake to sleep transition after light-off share a correlation with the overall sleep quality during the whole night. Single-channel EEGs derived from the polysomnography (PSG) of 111 healthy adults in the Sleep Heart Health Study were analyzed retrospectively. Every 30-second epoch of EEG data was transformed into a symbolic sequence using equiprobable symbolization and then the percentage of constant word (P CW) was calculated. The results revealed that the proposed metric, P CW , exhibits a correlation with wake/sleep stages over the night. More importantly, average P CW in short sections (15–60 min) at the beginning of the night shows a correlation with various indices of sleep quality for the entire night, suggesting P CW as a potential indicator for the requirement for an early sleep intervention. In conclusion, the results validate the use of symbolic dynamics in automatic sleep scoring and evaluation, and might further expand the application of SWA measurement to the early intervention of sleep disorders. Highlights • Symbolic dynamics of EEG may assist the continuous measurement of sleep depth. • Average P CW of EEGs may be used as an indicator for timely sleep quantification. • Results support the use of nonlinear EEG analysis in automatic sleep evaluation. [ABSTRACT FROM AUTHOR]