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Introduction: Pulse harmonic analysis is a quantitative and objective methodology within traditional Chinese medicine (TCM) used to evaluate pulse characteristics. However, interpreting pulse wave data is challenging due to its inherent complexity. This study aims to provide a comprehensive review and comparison of existing human pulse wave harmonic analysis methods to elucidate their patterns and characteristics. Methods: A systematic review of clinical research reports published from 1990 to 2021 was conducted, focusing on variations in harmonic characteristics across different medical conditions and physiological states. Keyword searches included terms related to analysis methods (e.g., ''Pulse Spectrum,'' ''harmonic analysis,'' ''harmonic index'') and measured indicators (e.g., ''vascular response,'' ''PPG,'' ''Photoplethysmography,'' ''aortic,'' ''arterial,'' ''blood pressure''). Supplementary research using PubMed's Mesh terms specifically targeted ''Pulse wave analysis'' within the methods and statistical analysis domain. Articles were filtered based on predefined criteria, including human participants and research related to pulse pressure or vascular volume changes. Conference papers, animal studies, and irrelevant research were excluded, with literature evaluation scales selected based on the retrieved research reports. Results: Initially, 6487 research reports were identified, and after screening, 50 reports were included in the review. The analysis revealed that low-frequency harmonics increase following vigorous activity or sympathetic excitation but decrease during rest or parasympathetic excitation. Cardiovascular patients exhibited elevated first harmonics associated with the liver meridian, while diabetes patients displayed weakened third harmonics related to the spleen meridian. Liver dysfunction was linked to changes in the first harmonic, and cancer patients showed signs of liver and kidney yin deficiency in the first and second harmonics. These findings underscore the potential of harmonic analysis for TCM disease diagnosis and organ assessment. Moreover, individuals with conditions such as liver dysfunction, cancer, and gynecological disorders displayed distinct intensity patterns across harmonics one through ten compared to healthy controls, albeit with some variations. Heterogeneity in these studies mainly stemmed from differences in measurement methods and study populations. Additionally, research suggested that factors like blood circulation and cognitive activity influenced harmonic intensity. Conclusions: In summary, this report consolidates prior research on pulse wave harmonics analysis, revealing unique patterns associated with various physiological conditions. Despite limitations, such as limited sample sizes in previous studies, the observed associations between physiological states and harmonics hold promise for potential clinical applications. This study lays a solid foundation for future applications of arterial wave harmonics analysis, promoting wider adoption of this analytical approach.

A new approach to treating vascular blockages has been developed to overcome the limitations of current thrombolytic therapies. This approach involves biosafety and multimodal plasma-derived theranostic platelet vesicle incorporating iron oxide constructed nano-propellers platformed technology that possesses fluorescent and magnetic features and manifold thrombus targeting modes. The platform is capable of being guided and visualized remotely to specifically target thrombi, and it can be activated using near-infrared phototherapy along with an actuated magnet for magnetotherapy. In a murine model of thrombus lesion, this proposed multimodal approach showed an approximately 80 % reduction in thrombus residues. Moreover, the new strategy not only improves thrombolysis but also boosts the rate of lysis, making it a promising candidate for time-sensitive thrombolytic therapy.

Background Air pollution may alter body water distribution, it may also be linked to low-arousal-threshold obstructive sleep apnoea (low-ArTH OSA). Here, we explored the mediation effects of air pollution on body water distribution and low-ArTH OSA manifestations.Methods In this retrospective study, we obtained sleep centre data from healthy participants and patients with low-ArTH OSA (N=1924) in northern Taiwan. Air pollutant exposure at different time intervals (1, 3, 6 and 12 months) was estimated using the nearest station estimation method, and government air-quality data were also obtained. Regression models were used to assess the associations of estimated exposure, sleep disorder indices and body water distribution with the risk of low-ArTH OSA. Mediation analysis was performed to explore the relationships between air pollution, body water distribution and sleep disorder indices.Results First, exposure to particulate matter (PM) with a diameter of ≤10 µm (PM10) for 1 and 3 months and exposure to PM with a diameter of ≤2.5 µm (PM2.5) for 3 months were significantly associated with the Apnoea–Hypopnoea Index (AHI), Oxygen Desaturation Index (ODI), Arousal Index (ArI) and intracellular-to-extracellular water ratio (I-E water ratio). Significant associations were observed between the risk of low-ArTH OSA and 1- month exposure to PM10 (OR 1.42, 95% CI 1.09 to 1.84), PM2.5 (OR 1.33, 95% CI 1.02 to 1.74) and ozone (OR 1.27, 95% CI 1.01 to 1.6). I-E water ratio alternation caused by 1-month exposure to PM10 and 3-month exposure to PM2.5 and PM10 had partial mediation effects on AHI and ODI.Conclusion Air pollution can directly increase sleep disorder indices (AHI, ODI and ArI) and alter body water distribution, thus mediating the risk of low-ArTH OSA.

Objective Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters—namely heart rate variability, oxygen saturation, and body profiles—to predict arousal occurrence. Methods Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing datasets. Eight machine learning approaches were employed. The model with the highest accuracy, area under the receiver operating characteristic curve, and area under the precision recall curve values in the training/validation dataset was applied to the testing dataset and to determine feature importance. Results InceptionTime, which exhibited superior performance in predicting sleep arousal in the training dataset, was used to classify the testing dataset and explore feature importance. In the testing dataset, InceptionTime achieved an accuracy of 76.21%, an area under the receiver operating characteristic curve of 84.33%, and an area under the precision recall curve of 86.28%. The standard deviations of time intervals between successive normal heartbeats and the square roots of the means of the squares of successive differences between normal heartbeats were predominant predictors of arousal occurrence. Conclusions The established models can be considered for screening sleep arousal occurrence or integrated in wearable devices for home-based sleep examination.

IntroductionAge-specific risk factors may delay posttraumatic functional recovery; complex interactions exist between these factors. In this study, we investigated the prediction ability of machine learning models for posttraumatic (6 months) functional recovery in middle-aged and older patients on the basis of their preexisting health conditions.MethodsData obtained from injured patients aged ≥45 years were divided into training–validation (n = 368) and test (n = 159) data sets. The input features were the sociodemographic characteristics and baseline health conditions of the patients. The output feature was functional status 6 months after injury; this was assessed using the Barthel Index (BI). On the basis of their BI scores, the patients were categorized into functionally independent (BI >60) and functionally dependent (BI ≤60) groups. The permutation feature importance method was used for feature selection. Six algorithms were validated through cross-validation with hyperparameter optimization. The algorithms exhibiting satisfactory performance were subjected to bagging to construct stacking, voting, and dynamic ensemble selection models. The best model was evaluated on the test data set. Partial dependence (PD) and individual conditional expectation (ICE) plots were created.ResultsIn total, nineteen of twenty-seven features were selected. Logistic regression, linear discrimination analysis, and Gaussian Naive Bayes algorithms exhibited satisfactory performances and were, therefore, used to construct ensemble models. The k-Nearest Oracle Elimination model outperformed the other models when evaluated on the training–validation data set (sensitivity: 0.732, 95% CI: 0.702–0.761; specificity: 0.813, 95% CI: 0.805–0.822); it exhibited compatible performance on the test data set (sensitivity: 0.779, 95% CI: 0.559–0.950; specificity: 0.859, 95% CI: 0.799–0.912). The PD and ICE plots showed consistent patterns with practical tendencies.ConclusionPreexisting health conditions can predict long-term functional outcomes in injured middle-aged and older patients, thus predicting prognosis and facilitating clinical decision-making.

BackgroundExposure to air pollution may be a risk factor for obstructive sleep apnea (OSA) because air pollution may alter body water distribution and aggravate OSA manifestations.ObjectivesThis study aimed to investigate the mediating effects of air pollution on the exacerbation of OSA severity through body water distribution.MethodsThis retrospective study analyzed body composition and polysomnographic data collected from a sleep center in Northern Taiwan. Air pollution exposure was estimated using an adjusted nearest method, registered residential addresses, and data from the databases of government air quality motioning stations. Next, regression models were employed to determine the associations between estimated air pollution exposure levels (exposure for 1, 3, 6, and 12 months), OSA manifestations (sleep-disordered breathing indices and respiratory event duration), and body fluid parameters (total body water and body water distribution). The association between air pollution and OSA risk was determined.ResultsSignificant associations between OSA manifestations and short-term (1 month) exposure to PM2.5 and PM10 were identified. Similarly, significant associations were identified among total body water and body water distribution (intracellular-to-extracellular body water distribution), short-term (1 month) exposure to PM2.5 and PM10, and medium-term (3 months) exposure to PM10. Body water distribution might be a mediator that aggravates OSA manifestations, and short-term exposure to PM2.5 and PM10 may be a risk factor for OSA.ConclusionBecause exposure to PM2.5 and PM10 may be a risk factor for OSA that exacerbates OSA manifestations and exposure to particulate pollutants may affect OSA manifestations or alter body water distribution to affect OSA manifestations, mitigating exposure to particulate pollutants may improve OSA manifestations and reduce the risk of OSA. Furthermore, this study elucidated the potential mechanisms underlying the relationship between air pollution, body fluid parameters, and OSA severity.

Background: Mobile phones, laptops, and computers have become an indispensable part of our lives in recent years. Workers may have an incorrect posture when using a computer for a prolonged period of time. Using these products with an incorrect posture can lead to neck pain. However, there are limited data on postures in real-life situations. Methods: In this study, we used a common camera to record images of subjects carrying out three different tasks (a typing task, a gaming task, and a video-watching task) on a computer. Different artificial intelligence (AI)-based pose estimation approaches were applied to analyze the head’s yaw, pitch, and roll and coordinate information of the eyes, nose, neck, and shoulders in the images. We used machine learning models such as random forest, XGBoost, logistic regression, and ensemble learning to build a model to predict whether a subject had neck pain by analyzing their posture when using the computer. Results: After feature selection and adjustment of the predictive models, nested cross-validation was applied to evaluate the models and fine-tune the hyperparameters. Finally, the ensemble learning approach was utilized to construct a model via bagging, which achieved a performance with 87% accuracy, 92% precision, 80.3% recall, 95.5% specificity, and an AUROC of 0.878. Conclusions: We developed a predictive model for the identification of non-specific neck pain using 2D video images without the need for costly devices, advanced environment settings, or extra sensors. This method could provide an effective way for clinically evaluating poor posture during real-world computer usage scenarios.

Abstract Introduction Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct prediction models for the functional outcomes of robotic neurorehabilitation in adult patients. Methods and materials Data of 139 patients who underwent Lokomat training at Taipei Medical University Hospital were retrospectively collected. After screening for data completeness, records of 91 adult patients with acute or chronic neurological disorders were included in this study. Patient characteristics and quantitative data from Lokomat were incorporated as features to construct prediction models to explore early responses and factors associated with patient recovery. Results Experimental results using the random forest algorithm achieved the best area under the receiver operating characteristic curve of 0.9813 with data extracted from all sessions. Body weight (BW) support played a key role in influencing the progress of functional ambulation categories. The analysis identified negative correlations of BW support, guidance force, and days required to complete 12 Lokomat sessions with the occurrence of progress, while a positive correlation was observed with regard to speed. Conclusions We developed a predictive model for ambulatory outcomes based on patient characteristics and quantitative data on impairment reduction with early-stage robotic neurorehabilitation. RAGT is a customized approach for patients with different conditions to regain walking ability. To obtain a more-precise and clearer predictive model, collecting more RAGT training parameters and analyzing them for each individual disorder is a possible approach to help clinicians achieve a better understanding of the most efficient RAGT parameters for different patients. Trial registration: Retrospectively registered.

ObjectivesObstructive 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.MethodsPatients 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 (Aβ42) 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 (Aβ42 × T-Tau, the cutoff for AD risk). PSG parameters were analyzed and compared.ResultsWe 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.ConclusionsRecurrent 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.

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E–I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E–I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA.

Abstract Spasticity measured by manual tests, such as modified Ashworth scale (MAS), may not sufficiently reflect mobility function in stroke survivors. This study aims to identify additional ambulatory information provided by the pendulum test. Clinical assessments including Brünnstrom recovery stage, manual muscle test, MAS, Tinetti test (TT), Timed up and go test, 10-m walk test (10-MWT), and Barthel index were applied to 40 ambulant chronic stroke patients. The pendular parameters, first swing excursion (FSE) and relaxation index (RI), were extracted by an electrogoniometer. The correlations among these variables were analyzed by the Spearman and Pearson partial correlation tests. After controlling the factor of motor recovery (Brünnstrom recovery stage), the MAS of paretic knee extensor was negatively correlated with the gait score of TT (r = − 0.355, p = 0.027), while the FSE revealed positive correlations to the balance score of TT (r = 0.378, p = 0.018). RI were associated with the comfortable speed of 10-MWT (r = 0.367, p = 0.022). These results suggest a decrease of knee extensor spasticity links to a better gait and balance in chronic stroke patients. The pendular parameters can provide additional ambulatory information, as complementary to the MAS. The pendulum test can be a potential tool for patient selection and outcome assessment after spasticity treatments in chronic stroke population.

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 (Aβ42), and the product of the two (Aβ42 × T-Tau) increased considerably in the control group (ΔT-Tau: 2.31 pg/mL; ΔAβ42: 0.58 pg/mL; ΔAβ42 × T-Tau: 48.73 pg2/mL2), whereas the mean levels of T-Tau and the product of T-Tau and Aβ42 decreased considerably in the CPAP group (ΔT-Tau: −2.22 pg/mL; ΔAβ42 × 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.

Abstract Background We designed a novel ankle foot orthosis (AFO), namely, ideal training AFO (IT-AFO), with motion feedback on the hemiparetic lower limb to improve ambulation in individuals with stroke-related hemiplegia. We, therefore sought to compare the kinematic parameters of gait between IT-AFO with and without dynamic control and conventional anterior-type AFO or no AFO. Methods Gait parameters were measured using the RehaWatch® system in seven individuals with hemiplegia (mean 51.14 years). The parameters were compared across four conditions: no AFO, conventional anterior AFO, IT-AFO without dynamic control, and IT-AFO with dynamic control, with three trials of a 10-m walk test for each. Results The dorsiflexion angle increased during the swing phase when the IT-AFO was worn, and it was larger with dynamic control. These data can confirm drop foot improvement; however, the difference between the parameters with- and without-AFO control conditions was not significant in the swing phase. The IT-AFO with or without dynamic control enhanced the loading response to a greater extent between the hemiparetic and unaffected lower limbs than conventional AFO or no AFO. The duration of the stance phase on the hemiparetic lower limb was also longer when using IT-AFO with and without dynamic control than that when using conventional AFO, which improved asymmetry. User comfort and satisfaction was greater with IT-AFO than with the other conditions. Conclusions The IT-AFO with dynamic control improved gait pattern and weight shifting to the hemiparetic lower limb, reducing gait asymmetry. The difference with and without dynamic control of IT-AFO is not statistically significant, and it is limited by sample size. However, this study shows the potential of IT-AFO in applying positive motion feedback with gait training. Trial registration Taipei Medical University-Joint Institutional Review Board. N201510010 . Registered 12 February 2015. http://ohr.tmu.edu.tw/main.php .

Abstract Fibromyalgia syndrome (FM) is a multifactorial disorder whose pathogenesis and diagnosis are poorly understood. This study investigated differential serum proteome profiles in patients with FM and healthy pain-free controls and explored the association between serum proteome and clinical profiles in patients with FM. Twenty patients with FM (according to the American College of Rheumatology criteria, 2010) and 20 healthy pain-free controls were recruited for optimized quantitative serum proteomics analysis. The levels of pain, pressure pain threshold, sleep, anxiety, depression, and functional status were evaluated for patients with FM. We identified 22 proteins differentially expressed in FM when compared with healthy pain-free controls and propose a panel of methyltransferase-like 18 (METTL18), immunoglobulin lambda variable 3–25 (IGLV3–25), interleukin-1 receptor accessory protein (IL1RAP), and IGHV1OR21-1 for differentiating FM from controls by using a decision tree model (accuracy: 0.97). In addition, we noted several proteins involved in coagulation and inflammation pathways with distinct expression patterns in patients with FM. Novel proteins were also observed to be correlated with the levels of pain, depression, and dysautonomia in patients with FM. We suggest that upregulated inflammation can play a major role in the pathomechanism of FM. The differentially expressed proteins identified may serve as useful biomarkers for diagnosis and evaluation of FM in the future.

Abnormal resting-state functional connectivity (rs-FC) and brain structure have emerged as pathological hallmarks of fibromyalgia (FM). This study investigated and compared the accuracy of network rs-FC and brain structural features in identifying FM with a machine learning (ML) approach. Twenty-six FM patients and thirty healthy controls were recruited. Clinical presentation was measured by questionnaires. After MRI acquisitions, network rs-FC z-score and network-based gray matter volume matrices were exacted and preprocessed. The performance of feature selection and classification methods was measured. Correlation analyses between predictive features in final models and clinical data were performed. The combination of the recursive feature elimination (RFE) selection method and support vector machine (rs-FC data) or logistic regression (structural data), after permutation importance feature selection, showed high performance in distinguishing FM patients from pain-free controls, in which the rs-FC ML model outperformed the structural ML model (accuracy: 0.91 vs. 0.86, AUC: 0.93 vs. 0.88). The combined rs-FC and structural ML model showed the best performance (accuracy: 0.95, AUC: 0.95). Additionally, several rs-FC features in the final ML model correlated with FM’s clinical data. In conclusion, ML models based on rs-FC and brain structural MRI features could effectively differentiate FM patients from pain-free subjects.

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.

Objectives: This study aimed to investigate the safety and efficacy of high-definition transcranial alternating current stimulation (HD-tACS) to the left primary motor cortex (M1) in the treatment of fibromyalgia (FM) patients. Methods: In this randomized, double-blind, sham-controlled clinical trial, patients with FM were recruited in a teaching hospital. Thirty-eight patients were randomized to active HD-tACS (n = 19) or sham stimulation (n = 19). Active stimulation included a daily session of 20-min stimulation of 1 mA HD-tACS over the left M1 for ten sessions in two weeks. The primary outcome was the change in pain intensity and quality of life, assessed using the numeric rating scale (NRS) and the fibromyalgia impact questionnaire (FIQ) at baseline and after two weeks of treatment. Secondary outcomes included other core symptoms of FM (psychological distress, sleep quality, hyperalgesia measured by pressure pain threshold) and changes in biomarkers’ total Tau and Aβ1-42. All analyses were based on intention-to-treat for a significance level of p < 0.05. Results: Of the 38 randomized patients, 35 completed the study. After two weeks, HD-tACS induced a significant reduction in FIQ score post-treatment. However, there were no significant differences in NRS and FIQ scores compared to sham stimulation. Most adverse events were mild in severity. Nevertheless, one patient receiving HD-tACS attempted suicide during the trial. Conclusions: These results suggest that HD-tACS may effectively reduce pain, psychological distress, and symptom impacts in FM patients. However, we found no significant differences between the two groups. Future studies investigating HD-tACS in FM are warranted.

We propose the deep hierarchical network (DHN) for the quantitative analysis of facial palsy. Facial palsy, also known as Bell’s palsy, is the most common type of facial nerve palsy that results in the loss of muscle control in the affected facial regions. Typical symptoms include facial deformity and facial expression dysfunction. To the best of our best knowledge, all approaches for the automatic detection of facial palsy consider hand-crafted features. This paper reports the first deep-learning-based approach developed for the real-time quantitative analysis of facial palsy. The proposed DHN consists of three component networks: the first detects the subject’s face, the second detects the facial landmarks and line segments on the detected face, and the third detects the local palsy regions. The first component network is built on the YOLO2 detector. The second component network is developed on a fused network architecture that incorporates a line segment learning network for locating the facial landmarks and line segments. The third component network is developed on an object detection network with the line-segment-embedded input that combines the landmarked region and the line segments detected by the second component network. The novelties of this research include: 1) the modification of a state-of-the-art edge detector for extracting the facial line segments; 2) the embedding of the line segment learning for the detection of facial landmarks and local palsy regions; 3) the quantitative description of the facial palsy syndrome intensity; and 4) the release of the first clinically labeled database, the YouTube Facial Palsy (YFP) database. The making of the YFP database solves the issue that previous methods were all evaluated on proprietary databases, making the comparison of different methods extremely difficult. The YFP database includes 32 videos of 21 patients collected from YouTube and labeled by clinic specialists. To enhance the robustness against facial expression variations, we include the CK+ facial expression database in the training. We show that the proposed DHN not only just detects the local palsy regions but also captures the intensity of the facial palsy syndrome over time, enabling the quantitative description of the syndrome. The experiments show that the proposed approach offers an accurate and efficient real-time solution for facial palsy analysis.

Abstract Background Effects of air pollution on neurotoxicity and behavioral alterations have been reported. The objective of this study was to investigate the pathophysiology caused by particulate matter (PM) in the brain. We examined the effects of traffic-related particulate matter with an aerodynamic diameter of

Introduction Stroke can lead to life-long disability and constitutes a huge financial burden on the family and society. Stroke survivors with cognitive impairment often experience considerable challenges in the process of recovery and returning to society. Interventions that effectively help individuals resume essential daily activities and return to active participation in their communities are lacking. This study examines the efficacy of a newly-developed intervention programme, the Optimising Participation after Stroke through Strategy-training (OPASS) programme, for improving community participation among stroke survivors with cognitive impairment.Methods and analysis A single-blind, parallel-group randomised controlled trial with allocation concealment and assessor blinding will be implemented to assess the efficacy of the OPASS programme. An expected 210 adults with cognitive impairment following stroke will be randomly assigned to either the experimental intervention (OPASS) group or the attention control group. In addition to their usual rehabilitation, both groups will receive 45 min sessions, twice weekly for a total of 12–15 sessions. The primary outcome is change in participation performance, which will be measured using the participation measure—three domains, four dimensions scale. Additional measures include the Activity Measure for Post-Acute Care generic outpatient short forms, Montreal Cognitive Assessment, Stroop Test, Trail Making Test and General Self-Efficacy Scale. These scales will be administered at baseline, post-intervention, 3-month follow-up, 6-month follow-up and 12-month follow-up. Their results will be analysed using multiple linear regression models and mixed-effects regression models. Further assessment of feasibility and acceptability of the intervention will be conducted through structured interviews with participants, caregivers and therapists. These interviews will be transcribed and thematically analysed.Ethics and dissemination Ethics approval was obtained from the Ethics Committee of Taipei Medical University (approval number: N201804055). The findings will be disseminated through presentations at scientific conferences and through publication in peer-reviewed journals.Trial registration number NCT03792061; pre-results.

Neurodegenerative diseases, such as dementia, Parkinson's disease, and parkinsonism, are due to the gradual and progressive loss of neural cells, leading to nervous system dysfunction. Increasing epidemiological and toxicological evidence has demonstrated the possible association between neurological diseases and particulate air pollution. Chronic exposure to particulate matter (PM) of < 2.5 μm in aerodynamic size (PM2.5) is related to reductions in white matter and gray matter in brains of older women. Alterations of the structural integrity of the brain were reported for particulate air pollution-induced neurological disorders. Clinically, intraneuronal accumulation of tau proteins is considered to be an important hallmark of the development of neurodegenerative diseases. In Alzheimer's disease brains, for example, hyperphosphorylated levels of tau are around 3–4 times higher than levels in normal adult brains. Tau overexpression in neuroblastoma cells can lead to tau aggregations and the appearance of smaller proteolytic fragments. Degradative mechanisms, such as autophagy that remove tau from cells are considered essential functions for maintaining the brain's health. Notably, increasing numbers of reports have indicated that autophagy dysfunction occurs due to particulate air pollution in vitro and in vivo. Dysfunction of autophagy can lead to tau accumulation in the brain. We reviewed the effects of particulate air pollution on neurological diseases and the underlying mechanisms (i.e., tau and autophagy). Further toxicological evidence is required to fill in the gaps between epidemiological and clinical observations.

Energy expenditure through metabolic equivalent (MET) prediction during resistance exercises in humans can be modeled by using cardiorespiratory parameters. In this study, we aimed to predict MET during six moderate-intensity resistance training sessions consisting of three different exercises. Eleven participants were recruited into two groups; an untrained (n = 5; with no resistance training experience) and a trained group (n = 6; with 2 months resistance training experience). Each participant completed six training sessions separated with a rest interval of 1–2 days. While wearing a mask for indirect calorimetric measurements using Cortex Metalyzer 3B, each participant performed training sessions consisting of three types of dumbbell exercises: shoulder press, deadlift, and squat. The metabolic equivalents (METs), respiratory exchange ratio (RER), heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), blood lactate (BL), and Borg rate of perceived exertion (RPE) were measured. The MET was predicted using generalized estimating equations (GEE) for repeated measure data collected during exercise and rest periods. It was observed that during exercise period, RER, HR, SBP, and BL for the training group (QIC = 187, 95% CI = −0.012~0.915, p = 0.000*~0.033*) while RER, HR, SBP, DBP, and RPE (QIC = 48, 95% CI = −0.024~0.422, p = 0.000*~0.002*) during resting period for untrained group significantly predicted MET for moderate-intensity interval resistance training. It is concluded that the cardiorespiratory variables are significantly related to MET. During exercise, RER and HR significantly predicted MET for both groups along with additional parameters of SBP and BL for the training group. While during the resting period, RER, HR, SBP, DBP, and RPE related significantly for untrained and BL for training group respectively.

Energy expenditure during weight training exercises produces great fitness and health benefits for humans, but few studies have investigated energy expenditure directly during weight training. Therefore, in this study, we aimed to determine energy costs during three training sessions consisting of three different exercises. Ten participants were randomly allocated into two groups: an untrained (n = 5, with no weight training experience) and a trained group (n = 5, with some weight training experience). Each participant completed three training sessions on separate days. While wearing a mask for indirect calorimetric measurements, each participant participated in training sessions conducted with three dumbbell exercises: the bent-over row, deadlift, and lunge. Metabolic equivalents (METs), energy expenditure (EE), respiratory exchange ratio (RER), heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and rate of perceived exertion (RPE) were measured. The total energy cost was calculated from the oxygen consumption (VO2) during each exercise. Our results showed that the METs of a single training session were 3.3 for the untrained group and 3.4 for the trained group, while the sum of the EE was 683–688 and 779–840 kcal, respectively. The physiological parameters, such as heart rate (p = 0.001 *) for the lunge and rate of perceived exertion (p = 0.005 *) for the bent-over row, changed significantly in both groups. It was concluded that the exercise protocol of this study involved a moderate intensity of 2.4–3.9 METs. The energy cost of each training exercise was between 179 and 291 kcal.

Mechanical properties are crucial parameters for scaffold design for bone tissue engineering; therefore, it is important to understand the definitions of the mechanical properties of bones and relevant analysis methods, such that tissue engineers can use this information to properly design the mechanical properties of scaffolds for bone tissue engineering. The main purpose of this article is to provide a review and practical guide to understand and analyze the mechanical properties of compact bone that can be defined and extracted from the stress–strain curve measured using uniaxial tensile test until failure. The typical stress–strain curve of compact bone measured using uniaxial tensile test until failure is a bilinear, monotonically increasing curve. The associated mechanical properties can be obtained by analyzing this bilinear stress–strain curve. In this article, a computer programming code for analyzing the bilinear stress–strain curve of compact bone for quantifying the associated mechanical properties is provided, such that the readers can use this computer code to perform the analysis directly. In addition to being applied to compact bone, the information provided by this article can also be applied to quantify the mechanical properties of any material having a bilinear stress–strain curve, such as a whole bone, some metals and biomaterials. The information provided by this article can be applied by tissue engineers, such that they can have a reference to properly design the mechanical properties of scaffolds for bone tissue engineering. The information can also be applied by researchers in biomechanics and orthopedics to compare the mechanical properties of bones in different physiological or pathological conditions.

Failure to deliver the standard stroke care is suspected to be a potential reason for disproportionately high mortality among patients with co-morbid bipolar disorder (BD). Few studies have explored adverse outcomes and medical care costs concurrently (as a proxy for care intensity) among patients with BD admitted for stroke. Data for this nationwide population-based study were extracted from the Taiwan National Health Insurance Research Database, on 580 patients with BD hospitalized for stroke (the study group) and a comparison group consisting of randomly selected 1740 stroke patients without BD matched by propensity scores. Conditional logistic regression was used to estimate odds ratios (OR) for adverse in-hospital outcomes between study group and comparison group. We found that stroke patients with BD had significantly lower in-hospital mortality (3.28% vs. 5.63%), acute respiratory failure (2.59% vs. 5.57%), and use of mechanical ventilation (6.55% vs. 10.23%) than the comparison group. After adjusting for geographical location, urbanization level, monthly income, hypertension, diabetes, hyperlipidemia, and coronary heart disease, the odds of in-hospital mortality, acute respiratory failure, and use of mechanical ventilation in the BD group were 0.56 (95% CI: 0.34-0.92), 0.46 (95% CI: 0.26-0.80), and 0.63 (95% CI: 0.44-0.91), respectively. No differences were found in hospitalization costs and the length of hospital stay. With comparable hospitalization costs and length of hospital stay, we concluded that stroke patients with BD had lower in-hospital mortality and serious adverse events compared to stroke patients without BD.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Whether statins and an angiotensin-converting enzyme inhibitors (ACEIs) / angiotensin receptor blockors (ARBs) are associated with reduced risks of infection events is still inconclusive. This study aimed to explore the risk of hospitalization for pneumonia among patients who had received treatment with ACEIs/ARBs and/or statins using a population-based dataset. This study included 19,281 patients as cases who were hospitalized for pneumonia and 19,281 controls. We used a logistic regression to compute the odds ratio (OR) and 95% confidence interval (CI) for having previously used statins or an ACEI/ ARB between patients who were hospitalized for pneumonia treatment and controls. We found there were significant associations between hospitalization for pneumonia and statin-only users (p

Fourteen-day rehospitalization with new traumatic spinal cord injury (tSCI) diagnosis is used as an indicator for the diagnostic quality of the first hospitalization. In this nationwide population-based cohort study, we identified risk factors for this indicator.We conducted a nested case-control study by using the data of patients who received a first hospitalization for trauma between 2001 and 2011. The data were retrieved from Taiwan's National Health Insurance Research Database. Variables including demographic and trauma characteristics were compared between patients diagnosed with tSCI at the first hospitalization and those receiving a 14-day rehospitalization with new tSCI diagnosis.Of the 23 617 tSCI patients, 997 had 14-day rehospitalization with new tSCI diagnosis (incidence rate, 4.22%). The risk of 14-day rehospitalization with new tSCI diagnosis was significantly lower in patients with severe (injury severity score [ISS] = 16-24; odds ratio [OR], 0.17; 95% confidence interval [CI], 0.13-0.21) and profound (ISS > 24; OR, 0.11; 95% CI, 0.07-0.18) injuries. Interhospital transfer (OR, 8.20; 95% CI, 6.48-10.38) was a significant risk factor, along with injuries at the thoracic (OR, 1.62; 95% CI, 1.21-2.18), lumbar (OR, 1.30; 95% CI, 1.02-1.65), and multiple (OR, 3.23; 95% CI, 1.86-5.61) levels. Brain (OR, 2.82), chest (OR, 2.99), and abdominal (OR, 2.74) injuries were also identified as risk factors. In addition, the risk was higher in patients treated at the orthopedic department (OR, 2.26; 95% CI, 1.78-2.87) and those of other surgical disciplines (OR, 1.89; 95% CI, 1.57-2.28) than in those treated at the neurosurgery department.Delayed tSCI diagnoses are not uncommon, particularly among trauma patients with ISSs < 16 or those who are transferred from lower-level hospitals. Further validation and implementation of evidence-based decision rules is essential for improving the diagnostic quality of traumatic thoracolumbar SCI.

Some of the prior literature investigated the potential association between rheumatoid arthritis (RA) and Alzheimer's disease (AD) because these two diseases may share similar inflammatory mechanisms. Nevertheless, to date, findings of the previous literature are still controversial, and some methodological limitations were observed in those studies. The aim of this case-control study was to investigate the relationship between prior RA and AD using a large population-based dataset. This study used the Taiwan Longitudinal Health Insurance Database 2005. We included 2271 patients with AD who had received prescriptions for acetylcholinesterase inhibitors (AChEIs) as cases and 6813 patients without AD as controls in this study. In addition, we performed a conditional logistic regression to examine the odds ratio (OR) and 95% confidence interval (CI) for prior RA between cases and controls. The study found that 330 (3.63%) of the total sampled patients had an RA diagnosis before the index date. Additionally, prior RA was found in 60 (2.64%) cases and in 270 (3.96%) controls. The conditional logistic regression analysis showed that the crude OR of prior RA for cases was 0.66 (95% confidence interval (CI): 0.49~0.87) compared to controls. After adjusting for patients' geographic location, urbanization level, and comorbidities, the adjusted OR of prior RA for patients with AD was 0.73 (95% CI: 0.55~0.98) compared to those without AD. We concluded that there was an inverse association between prior RA and AD even after adjusting for potential confounders.

Recurrent falls not only have risk factors different from those of single falls but also have less favorable outcomes. The aim of our study was to determine the association between the injury characteristics of a first fall and the likelihood of recurrent injurious falls in a cohort of hospitalized patients.We designed a nationwide retrospective cohort study and selected hospitalized patients who had injurious falls between 2001 and 2010. Cox proportional hazards models were used to estimate the hazard ratios (HRs) of recurrent injurious falls requiring hospitalization in the following year on the basis of the patients' demographic characteristics, comorbidities, and the characteristics of injuries from the first injurious fall requiring hospitalization.Among the 504 512 patients hospitalized for injurious falls, 19 442 experienced recurrent injurious falls requiring hospitalization. The 1-year incidence of recurrent injurious falls requiring hospitalization was 3.85%. The incidence density was the highest within the 3-month period after the first injurious fall. The risk of recurrent injurious falls among patients aged 40 to 64, 65 to 74, and ≥ 75 years increased progressively (HR: 2.11, 95% confidence interval [CI]: 1.90-2.34; HR: 2.80, 95% CI: 2.51-3.11; and HR: 3.80, 95% CI: 3.42-4.23, respectively). The length of hospitalization (LOH) ≥ 15 or ≥ 31 days (HR: 1.39, 95% CI: 1.30-1.48; and HR: 1.59, 95% CI: 1.43-1.77, respectively) and injury to the head (HR: 1.59, 95% CI: 1.53-1.65) or spine (HR: 1.66, 95% CI: 1.59-1.74) were also found to be major risk factors.Our findings show that the LOH and head and spine injuries are associated with an increased risk of recurrent injurious falls leading to hospitalization. The risk of recurrent injurious falls requiring hospitalization increased significantly among adults older than 40 years. We suggest further research on the effects of injury characteristics associated with the first injurious fall requiring hospitalization and resultant anatomical damages on the risk of recurrent injurious falls requiring hospitalization. High-risk patients should receive tailored rehabilitation addressing their respective injuries within 3 months after hospital discharge.

This study investigated the association between statin use and herpes zoster (HZ) occurrence in a population-based case-control study.Study subjects were retrieved from the Taiwan Longitudinal Health Insurance Database 2000. This study included 47,359 cases with HZ and 142,077 controls. We performed conditional logistic regression analyses to calculate the odds ratio (OR) to present the association between HZ and having previously been prescribed statin.We found that 13.0% of the sampled subjects had used statins, at 15.5% and 12.1% for cases and controls, respectively (p

BACKGROUND:While an association between zolpidem use and fracture and road accident was previously proposed, this study aimed to further explore the frequency and risk of a wide spectrum of injuries in subjects prescribed with zolpidem in Taiwan. METHODS:We identified 77,036 subjects who received Zolpidem treatment between 2005 and 2007. We randomly selected 77,036 comparison subjects who were frequency-matched based-on their demographic profiles. We individually tracked each subject for a 90-day period to identify those who subsequently suffered an injury. Cox proportional hazards regressions were performed to calculate the hazard ratio of injury between the two groups. RESULTS:The incidence rate of injury during the 90-day follow-up period for the total subjects was 18.11 (95% CI = 17.69-18.54) per 100 person-years; this was 24.35 (95% CI = 23.66-25.05) and 11.86 (95% CI = 11.39-12.36) for the study and comparison cohort, respectively. After adjusting for demographic variables, the hazard ratio (HR) of injury during the 90-day follow-up period for study subjects was 1.83 (95% CI = 1.73-1.94) that of comparison subjects. Additionally, compared to comparison subjects, the adjusted HR of injury during the 90-day follow-up period for study subjects who were prescribed Zolpidem for >30 days was as high as 2.17 (95% CI = 2.05-2.32). The adjusted HR of injury to blood vessels for study subjects was particularly high when compared to comparison subjects (HR = 6.34; 95% CI = 1.37-29.38). CONCLUSIONS:We found that patients prescribed with Zolpidem were at a higher risk for a wide range of injuries.

Objectives. Current approaches via physiological features detecting aberrant driving behaviour (ADB), including speeding, abrupt steering, hard braking and aggressive acceleration, are developing. This study proposes using machine learning approaches incorporating heart rate variability (HRV) parameters to predict ADB occurrence. Methods. Naturalistic driving data of 10 highway bus drivers in Taiwan from their daily routes were collected for 4 consecutive days. Their driving behaviours and physiological data during a driving task were determined using a navigation mobile application and heart rate watch. Participants' self-reported data on sleep, driving-related experience, open-source data on weather and the traffic congestion level were obtained. Five machine learning models - logistic regression, random forest, naive Bayes, support vector machine and gated recurrent unit (GRU) - were employed to predict ADBs. Results. Most drivers with ADB had low sleep efficiency (≤ 80%), with significantly higher scores in driver behaviour questionnaire subcategories of lapses and errors and in the Karolinska sleepiness scale than those without ADBs. Moreover, HRV parameters were significantly different between baseline and pre-ADB event measurements. GRU had the highest accuracy (81.16-84.22%). Conclusions. Sleep deficit may be related to the increased fatigue level and ADB occurrence predicted from HRV-based models among bus drivers. [ABSTRACT FROM AUTHOR]