Insomnia complaints correlated with higher risk of cognitive impairment in older adults following stroke: a National Representative Comparison Study.

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. A total of 447 older adults with a mean age of 74.63 years (50.1% men) were included. Self-reported insomnia and stroke occurrence were determined using a questionnaire. Cognitive impairment was assessed using the Mini-Mental State Examination. We used multivariate logistic regression to analyze the association between insomnia complaints and cognitive impairment. Participants were categorized into four groups: those with stroke and insomnia (58), those with stroke without insomnia (91), those without stroke with insomnia (116), and those without stroke or insomnia (182). The prevalence of insomnia complaints was 38.9%, and the frequency of poststroke cognitive impairment was 50.3%. After controlling for potential confounders, participants with stroke (with or without insomnia) had a significantly higher risk of cognitive impairment than those without stroke or insomnia (adjusted odds ratios: 4.16 and 2.91, 95% confidence intervals: 1.91-9.07 and 1.56-5.43, respectively). Stroke with or without insomnia complaints was associated with a higher risk of cognitive impairment relative to older adults without stroke or insomnia. The risk of cognitive impairment was the highest among participants with both stroke and insomnia.

Pan­cancer analysis identified ARHGAP23 as a potential biomarker for pancreatic adenocarcinoma.

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. Through these data combined with a variety of biological information analysis methods, the potential role of ARHGAP23 as a carcinogenic gene was explored in the present study. The present analysis revealed that ARHGAP23 expressed abnormalities in >10 tumors, which was associated with differences in prognosis. Furthermore, the findings of the present study indicated that ARHGAP23 is associated with DNA methylation and multiple immune cell infiltrations in these tumors. ARHGAP23 expression was related to clinical prognosis, DNA methylation and immune infiltration. These findings support the potential of ARHGAP23 as a prognostic biomarker and a molecular target for cancer treatment.

Global prevalence of post-COVID-19 sleep disturbances in adults at different follow-up time points: A systematic review and meta-analysis.

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. Employing multilevel mixed-effects meta-analyses, we estimated the overall pooled prevalence of post-COVID sleep disturbances being 28.98% (25.73-32.34), with the highest prevalence reported in Europe and the lowest in Southeast Asia. Poor sleep quality was the most prevalent definition of sleep disturbances, followed by excessive daytime sleepiness, insomnia, sleep apnea. Prevalence estimates were notably higher when measured with Epworth sleepiness scale, or Pittsburgh sleep quality index compared to symptom questionnaires, self-reports, or personal interviews. Female sex (Odds ratio, OR = 1.59, 1.38-1.83) and severe/critical acute COVID-19 (OR = 1.36, 1.09-1.69) emerged as substantial risk factors. Our review underscore the persistent prevalence of sleep disturbances among COVID-19 survivors, and the importance of factors such as geography, definition, measures of sleep disorders, sex, and severity of acute COVID-19 infection. These findings highlight the urgent need for further investigation into the underlying molecular mechanisms driving these sleep disturbances to develop effective therapeutic strategies.

Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine-learning and bag-of-features framework.

Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is effective in treating OSA, but adherence to it is often inadequate. A promising solution is to detect sleep apnea events in advance, and to adjust the pressure accordingly, which could improve the long-term use of CPAP treatment. The use of CPAP titration data may reflect a similar response of patients to therapy at home. Our study aimed to develop a machine-learning algorithm using retrospective electrocardiogram (ECG) data and CPAP titration to forecast sleep apnea events before they happen. We employed a support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), and linear discriminative analysis (LDA) to detect sleep apnea events 30-90 s in advance. Preprocessed 30 s segments were time-frequency transformed to spectrograms using continuous wavelet transform, followed by feature generation using the bag-of-features technique. Specific frequency bands of 0.5-50 Hz, 0.8-10 Hz, and 8-50 Hz were also extracted to detect the most detected band. Our results indicated that SVM outperformed KNN, LDA, and DT across frequency bands and leading time segments. The 8-50 Hz frequency band gave the best accuracy of 98.2%, and a F1-score of 0.93. Segments 60 s before sleep events seemed to exhibit better performance than other pre-OSA segments. Our findings demonstrate the feasibility of detecting sleep apnea events in advance using only a single-lead ECG signal at CPAP titration, making our proposed framework a novel and promising approach to managing obstructive sleep apnea at home.

Pharmaceutical approaches for COVID-19: An update on current therapeutic opportunities.

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. Various natural compounds such as sarsapogenin, lycorine, biscoclaurine, vitamin B12, glycyrrhizic acid, riboflavin, resveratrol and kaempferol, various vaccines and drugs such as AZD1222, mRNA-1273, BNT162b2, Sputnik V, and remdesivir, lopinavir, favipiravir, darunavir, oseltamivir, and umifenovir, resp., have been discussed comprehensively. We attempted to provide exhaustive information regarding the various prospective therapeutic approaches available in order to assist researchers and physicians in treating COVID-19 patients.

Plasmon-activated water as a therapeutic strategy in Alzheimer's disease by altering gut microbiota.

Gut microbiota (GM) are involved in the pathophysiology of Alzheimer's disease (AD) and might correlate to the machinery of the gut-brain axis. Alteration of the GM profiles becomes a potential therapy strategy in AD. Here, we found that plasmon-activated water (PAW) therapy altered GM profile and reduced AD symptoms in APPswe/PS1dE9 transgenic mice (AD mice). GM profile showed the difference between AD and WT mice. PAW therapy in AD mice altered GM profile and fecal microbiota transplantation (FMT) reproduced GM profile in AD mice. PAW therapy and FMT in AD mice reduced cognitive decline and amyloid accumulation by novel object recognition (NOR) test and amyloid PET imaging. Immunofluorescent staining and western blot analysis of ß-amyloid (Aß) and phosphorylated (p)-tau in the brain of AD mice were reduced in PAW therapy and FMT. The inflammatory markers, interleukin (IL)-6, IL-1ß, and tumor necrosis factor (TNF)-α and pro-inflammatory indicator of arginase-1/CD86 ratio were also reduced. Furthermore, immunohistochemistry (IHC) analysis of occludin and claudin-5 in the intestine and AXL in the brain were increased to correlate with the abundant GM in PAW therapy and FMT. Our results showed the machinery of gut-brain axis, and PAW might be a potential therapeutic strategy in AD.

Understanding Alkaline Hydrogen Oxidation Reaction on PdNiRuIrRh High-Entropy-Alloy by Machine Learning Potential.

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 3.25 mA µg-1 for alkaline hydrogen oxidation reaction (HOR), which is 8-fold enhancement compared to that of commercial Pt/C. Through machine learning potential-based Monte Carlo simulation, we reveal that the dominant Pd-Pd-Ni/Pd-Pd-Pd bonding environments and Ni/Ru oxophilic sites on HEA surface are beneficial to the optimized adsorption/desorption of *H and enhanced *OH adsorption, contributing to the excellent HOR activity and stability. This work provides significant insights into atomic structure and catalytic mechanism for HEA and offers novel prospects for developing advanced HOR electrocatalysts.

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events.

Objectives: Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods: We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results: The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions: The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

Psychometric properties of Indonesian version of sleep condition indicator for screening poststroke insomnia.

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. In the first stage, the English version of the SCI was translated into the ISCI using standard procedures. The psychometric properties of the ISCI were tested in the second stage. Internal consistency and test-retest reliability of ISCI were used to evaluate reliability. A confirmatory factor analysis (CFA) was performed to test construct validity. To test concurrent and convergent validity, the Indonesian version of the insomnia severity index (ISI-INA), generalized anxiety disorder questionnaire (IGAD-7), and patient health questionnaire (IPHQ-9) were used. A receiver operating characteristic (ROC) analysis was conducted to calculate the optimal cutoff score of the ISCI on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria for insomnia. RESULTS: A total of 160 adults with a diagnosis of stroke for more than 3 months were included (median age of 58.5 years, 31% met the DSM-5 criteria for insomnia). The ISCI had a satisfactory Cronbach's alpha of 0.89 and test-retest reliability of 0.78. The CFA revealed that the ISCI exhibited a satisfactory model fit and was associated with the ISI-INA, IGAD-7, and IPHQ-9 (r = -0.81, -0.32, and -0.52, respectively; all P < .001). The ROC test revealed that the optimal cutoff point of ≤23 yielded the highest sensitivity (94%) and specificity (97%). CONCLUSION: The study results revealed that the 8-item ISCI is a reliable and valid screening tool for detecting insomnia symptoms according to the DSM-5 criteria in the chronic period after stroke.

Continuous Positive Airway Pressure Reduces Plasma Neurochemical Levels in Patients with OSA: A Pilot Study.

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 (n = 10) and CPAP (n = 20) groups. Next, we examined their in-lab sleep data (polysomnography and CPAP titration), sleep-related questionnaire outcomes, and neurochemical biomarker levels at baseline and the 3-month follow-up. The paired t-test and Wilcoxon signed-rank test were used to examine changes. Analysis of covariance (ANCOVA) was performed to increase the robustness of outcomes. The Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were significantly decreased in the CPAP group. The mean levels of total tau (T-Tau), amyloid-beta-42 (Aß42), and the product of the two (Aß42 × T-Tau) increased considerably in the control group (ΔT-Tau: 2.31 pg/mL; ΔAß42: 0.58 pg/mL; ΔAß42 × T-Tau: 48.73 pg2/mL2), whereas the mean levels of T-Tau and the product of T-Tau and Aß42 decreased considerably in the CPAP group (ΔT-Tau: -2.22 pg/mL; ΔAß42 × T-Tau: -44.35 pg2/mL2). The results of ANCOVA with adjustment for age, sex, body mass index, baseline measurements, and apnea-hypopnea index demonstrated significant differences in neurochemical biomarker levels between the CPAP and control groups. The findings indicate that CPAP may reduce neurochemical biomarker levels by alleviating OSA symptoms.

Post-Stroke Insomnia Increased the Risk of Cognitive Impairments: A Hospital-Based Retrospective Cohort Study.

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. PARTICIPANTS: Stroke participants who had a stroke and were 20 years and older. METHODS: This multicenter hospital-based retrospective cohort study with a 13-year follow-up period (2004-2017). The diagnosis of stroke, insomnia, and cognitive impairment was based on the International Classification of Diseases. The study participants who experienced a stroke were divided into two cohorts: those who also had insomnia and those who did not have insomnia. A Cox proportional-hazards regression model was used. RESULTS: A total of 1,775 patients with a mean age of 67.6 years were included. Of these patients, 146 and 75 patients were diagnosed with insomnia and cognitive impairment during the follow-up period, respectively. The cumulative incidence of cognitive impairment in the stroke with insomnia cohort was significantly lower than that in the stroke without insomnia cohort (log-rank test, P < .001). The adjusted hazard ratio and 95% confidence interval (CI) of the stroke with insomnia cohort indicated a higher risk of cognitive impairment compared with the stroke without insomnia cohort (adjusted hazard ratio: 2.38; 95% CI: 1.41-4.03). CONCLUSIONS: Patients who had a stroke and were diagnosed with insomnia exhibited a substantial increased risk of cognitive impairment over time.

The effect of silica exposure on the risk of lung cancer: A meta-analysis.

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. The International Agency for Research on Cancer (IARC) classified silica as a human lung carcinogen. The opinion of lung cancer is a question that has been addressed in this review. Three electronic databases, including MEDLINE, Scopus, and Web of Science, were used to search for relevant literature from 2000 to 2022. To evaluate the relationship between exposure to silica and developing lung cancer, we performed a meta-analysis using the random-effects model. For each study, the overall odds ratio (OR), relative risk (RR) with 95% confidence intervals (CI), and p values were calculated. An extensive database search resulted in the selection of 20 (case-control and nested case-control studies were selected) out of 527 studies. Among the 20 selected studies, 7 studies showed a significant association between silica exposure and an increased risk of lung cancer. Further analysis showed that among the selected studies, six studies showed a significant correlation between combined exposure to silica and smoking with an increased risk of lung cancer. The data from the present study showed that smoking habits increased the impact of silica exposure on the initiation of lung carcinogenesis in exposed workers.

Ambient relative humidity-dependent obstructive sleep apnea severity in cold season: A case-control study.

BACKGROUND: The objective of this study was to examine associations of daily averages and daily variations in ambient relative humidity (RH), temperature, and PM2.5 on the obstructive sleep apnea (OSA) severity. METHODS: A case-control study was conducted to retrospectively recruit 8628 subjects in a sleep center between January 2015 and December 2021, including 1307 control (apnea-hypopnea index (AHI) < 5 events/h), 3661 mild-to-moderate OSA (AHI of 5-30 events/h), and 3597 severe OSA subjects (AHI > 30 events/h). A logistic regression was used to examine the odds ratio (OR) of outcome variables (daily mean or difference in RH, temperature, and PM2.5 for 1, 7, and 30 days) with OSA severity (by the groups). Two-factor logistic regression models were conducted to examine the OR of RH with the daily mean or difference in temperature or PM2.5 with OSA severity. An exposure-response relationship analysis was conducted to examine the outcome variables with OSA severity in all, cold and warm seasons. RESULTS: We observed associations of mean PM2.5 and RH with respective increases of 0.04-0.08 and 0.01-0.03 events/h for the AHI in OSA patients. An increase in the daily difference of 1 % RH increased the AHI by 0.02-0.03 events/h in OSA patients. A daily PM2.5 decrease of 1 µg/m3 reduced the AHI by 0.03 events/h, whereas a daily decrease in the RH of 1 % reduced the AHI by 0.03-0.04 events/h. The two-factor model confirmed the most robust associations of ambient RH with AHI in OSA patients. The exposure-response relationship in temperature and RH showed obviously seasonal patterns with OSA severity. CONCLUSION: Short-term ambient variations in RH and PM2.5 were associated with changes in the AHI in OSA patients, especially RH in cold season. Reducing exposure to high ambient RH and PM2.5 levels may have protective effects on the AHI in OSA patients.

Associations between risk of Alzheimer's disease and obstructive sleep apnea, intermittent hypoxia, and arousal responses: A pilot study.

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. Methods: Patients with suspected OSA were recruited and underwent in-lab polysomnography (PSG). Subsequently, blood samples were collected from participants. Patients' plasma levels of total tau (T-Tau) and amyloid beta-peptide 42 (Aß42) were measured using an ultrasensitive immunomagnetic reduction assay. Next, the participants were categorized into low- and high-risk groups on the basis of the computed product (Aß42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared. Results: We included 36 patients in this study, of whom 18 and 18 were assigned to the low- and high-risk groups, respectively. The average apnea-hypopnea index (AHI), apnea, hypopnea index [during rapid eye movement (REM) and non-REM (NREM) sleep], and oxygen desaturation index (≥3%, ODI-3%) values of the high-risk group were significantly higher than those of the low-risk group. Similarly, the mean arousal index and respiratory arousal index (R-ArI) of the high-risk group were significantly higher than those of the low-risk group. Sleep-disordered breathing indices, oxygen desaturation, and arousal responses were significantly associated with an increased risk of AD. Positive associations were observed among the AHI, ODI-3%, R-ArI, and computed product. Conclusions: Recurrent sleep-disordered breathing, intermittent hypoxia, and arousal responses, including those occurring during the NREM stage, were associated with AD risk. However, a longitudinal study should be conducted to investigate the causal relationships among these factors.

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.

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. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Air pollution associated with cognitive decline by the mediating effects of sleep cycle disruption and changes in brain structure in adults.

The effects of air pollution on sleep and dementia remain unclear. The objective of this study was to investigate the effects of air pollution on cognitive function as mediated by the sleep cycle. A cross-sectional study design was conducted to recruit 4866 subjects on which PSG had been performed. Fifty of them were further given a cognitive function evaluation by the MMSE and CASI as well as brain images by CT and MRI. Associations of 1-year air pollution parameters with sleep parameters, cognitive function, and brain structure were examined. We observed that O3 was associated with a decrease in arousal, an increase in the N1 stage, and a decrease in the N2 stage of sleep. NO2 was associated with an increase in the N1 stage, a decrease in the N2 stage, and an increase in REM. PM2.5 was associated with a decrease in the N1 stage, increases in the N2 and N3 stages, and a decrease in REM. The N1 and N2 stages were associated with cognitive decline, but REM was associated with an increase in cognitive function. The N1 stage was a mediator of the effects of PM2.5 on the concentration domain of the MMSE. O3 was associated with an increase in the pars orbitalis volume of the left brain. NO2 was associated with increases in the rostral middle frontal volume, supramarginal gyrus volume, and transverse temporal volume of the left brain, and the pars opercularis volume of the right brain. PM2.5 was associated with increases in the pars triangularis volume of the left brain and the fusiform thickness of the right brain. In conclusion, we observed that air pollution was associated with cognitive decline by mediating effects on the sleep cycle with changes in the brain structure in controlling executive, learning, and language functions in adults.

The impacts of ambient relative humidity and temperature on supine position-related obstructive sleep apnea in adults.

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. Associations of 1-night polysomnography with ambient RH and temperature in 1-day, 7-day, 1-month, 6-month, and 1-year averages were examined using linear regression models and a mediation analysis. RH increase was associated with snoring index decrease and apnea/hypopnea index (AHI) increase. Temperature increase was associated with decreases in sleep efficiency and the AHI, and increases in the wake time after sleep onset and snoring index. RH increase was inversely associated with non-rapid eye movement (NREM) sleep stage I (N1), III (N3), and rapid eye movement (REM) sleep, but positively associated with the NREM sleep stage II (N2) stage. Temperature increase was associated with N1, N2, and N3 sleep. An increase in RH was associated with an increase in the arousal index and a decrease in the < 95% arterial oxygen saturation (SaO2) among total, REM, and NREM sleep, whereas a temperature increase was associated with a decrease in the arousal index and an increase in < 95% SaO2 among total, REM, and NREM sleep. An increase in RH was associated with increases in the time spent in a supine posture and the supine AHI. An increase in temperature was associated with decreases in the supine posture, supine AHI, and non-supine AHI. The N3 sleep stage was an important mediator in increasing the supine AHI with a long-term increase in RH. But the N1 and N2 sleep stages mediated a decrease in the supine AHI with an increase in RH. In conclusion, ambient RH and temperature were associated with alterations in sleep parameters in adults, which were mediated by the sleep cycle. An understanding of outdoor environments has important implications for diagnostic classifications in the supine dominance of OSA in adults.

Association between cyclic variation in the heart rate index and biomarkers of neurodegenerative diseases in obstructive sleep apnea syndrome: A pilot study.

PURPOSE: Obstructive sleep apnea syndrome (OSAS) has mostly been examined using in-laboratory polysomnography (Lab-PSG), which may overestimate severity. This study compared sleep parameters in different environments and investigated the association between the plasma levels of neurochemical biomarkers and sleep parameters. METHODS: Thirty Taiwanese participants underwent Lab-PSG while wearing a single-lead electrocardiogram patch. Participants' blood samples were obtained in the morning immediately after the recording. Participants wore the patch for the subsequent three nights at home. Sleep disorder indices were calculated, including the apnea-hypopnea index (AHI), chest effort index, and cyclic variation of heart rate index (CVHRI). The 23 eligible participants' derived data were divided into the normal-to-moderate (N-M) group and the severe group according to American Association of Sleep Medicine (AASM) guidelines (Lab-PSG) and the recommendations of a previous study (Rooti Rx). Spearman's correlation was used to examine the correlations between sleep parameters and neurochemical biomarker levels. RESULTS: The mean T-Tau protein level was positively correlated with the home-based CVHRI (r = 0.53, p < 0.05), whereas no significant correlation was noted between hospital-based CVHRI and the mean T-tau protein level (r = 0.25, p = 0.25). The home-based data revealed that the mean T-Tau protein level in the severe group was significantly higher than that in the N-M group (severe group: 24.75 ± 6.16 pg/mL, N-M group: 19.65 ± 3.90 pg/mL; p < 0.05). Furthermore, the mean in-hospital CVHRI was higher than the mean at-home values (12.16 ± 13.66 events/h). CONCLUSION: Severe OSAS patients classified by home-based CVHRI demonstrated the higher T-Tau protein level, and CVHRI varied in different sleep environments.

Comparative efficacy of digital cognitive behavioral therapy for insomnia: A systematic review and network meta-analysis.

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. Primary outcomes were self-reported total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), and insomnia symptoms; these were measured using sleep diaries or valid questionnaires. A random-effects network meta-analysis in a frequentist framework was used. Fifty-four randomized controlled trials comprising 11,815 participants were included. Compared with usual care, web-based CBTi with a therapist demonstrated significantly longer TST (mean difference [MD]: 23.19 min, 95% confidence interval [CI]: 18.98-27.39 min), shorter SOL (MD: -18.76 min, 95% CI -24.20 to -13.31 min), lower WASO (MD: -31.40 min, 95% CI: -36.26 to -26.55 min), and greater SE (MD: 10.37%, 95% CI: 8.08%-12.65%). The surface under the cumulative ranking curve indicates that web-based CBTi with therapists is most likely to be ranked the highest among all treatments, and thus, this network meta-analysis suggests that such a treatment is the optimal intervention for improving sleep duration and SE as well as the reductions in SOL and WASO. PROSPERO REGISTRATION NUMBER: CRD42020171134.

Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile.

(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.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.