Frailty and Other Factors Associated With Early Outcomes in Middle-to Older Age Trauma Patients: A Prospective Cohort Study.

OBJECTIVES: To prospectively investigate associations of frailty and other predictor variables with functional recovery and health outcomes in middle-aged and older patients with trauma. DESIGN: Single-center prospective cohort study. SETTING: Emergency department of Wan Fang Hospital in Taiwan. PARTICIPANTS: Trauma patients aged 45 and older. MEASUREMENTS: Frailty was assessed with the Clinical Frailty Scale (CFS). Injury mechanisms, pre-existing diseases, and fracture locations were recorded at baseline. The primary outcome was functional recovery assessed using the Barthel Index (BI). Secondary outcomes were new care needs, unscheduled return visits, and falls 3 months postinjury. RESULTS: A total of 588 participants were included in the final analysis. For every one-point increase in the CFS, the multivariable-adjusted odds ratio (OR, 95% confidence interval [CI]) of failure to retain the preinjury BI was 1.34 (1.16-1.55); associations were consistent across levels of age and injury severities. Significant joint associations of frailty and age with poor functional recovery were observed. CFS was also associated with new care needs (OR for every one-point increase, 1.36, 95% CI, 1.17-1.58), unscheduled return visits (OR 1.26, 95% CI, 1.04-1.51), and falls (OR 1.23, 95% CI, 1.01-1.51). Other variables associated with failure to retain preinjury BI included road traffic accident and presence of hip fracture. CONCLUSION: Frailty was significantly associated with poor functional and health outcomes regardless of injury severity in middle-aged and older patients with trauma. Injury mechanisms and fracture locations were also significant predictors of functional recovery postinjury.

Prediction of Changes in Functional Outcomes During the First Year After Inpatient Stroke Rehabilitation: A Longitudinal Study.

OBJECTIVE: To identify meaningful changes in patients in 3 functional domains (basic mobility [BM], daily activity [DA], and applied cognition [AC]) after discharge from inpatient stroke rehabilitation and to identify the predictors of 1-year functional improvement. DESIGN: A longitudinal, multicenter, prospective cohort study. SETTING: The acute care wards of 3 hospitals in the Greater Taipei area of Taiwan. PARTICIPANTS: Five hundred patients with stroke in acute care wards (mean age=60±12.2 years, 62% men, N=500). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE(S): The Mandarin version of the Activity Measure for Post-Acute Care (AM-PAC) short forms were assessed at discharge and 3-, 6-, and 12-month follow-up. The minimal detectable change (MDC) was used to categorize changes in the scores as improved and unimproved at the 4 time points. RESULTS: The mean scores of the AM-PAC BM and DA subscales substantially increased over the first 3 months after discharge (86% of participants exhibited improvement) and slightly increased during the subsequent 9 months (5∼26% of participants exhibited improvement). However, the mean score of the AC subscale decreased within the first 3 months and increased over the subsequent 9 months (22-23% of participants exhibited improvement). The BM, AC scores at discharge were the dominant predictors of subsequent functional improvement (P<.05). Patients with a higher functional stage at discharge were more likely to experience significant improvement. CONCLUSION: This study established the capacity of the AM-PAC to predict functional improvement in 3 domains during the early, middle, and late stages of recovery. The findings can assist clinicians in identifying patients at risk of unfavorable long-term functional recovery and providing such patients with tailored interventions during the early stage of rehabilitation.

Development of a Dyad-Focused Intervention for Stroke Survivors and Their Family Caregivers: A Feasibility Study.

IMPORTANCE: Transitioning from the hospital to the community poses significant challenges for stroke survivors and their caregivers. OBJECTIVE: To examine the feasibility and preliminary effects of a dyad-focused strategy training intervention. DESIGN: Single-arm trial with data collection at baseline, postintervention, and 3-mo follow-up. SETTING: Rehabilitation settings in Taiwan. PARTICIPANTS: Sixteen stroke survivor-caregiver dyads. INTERVENTIONS: Dyad-focused strategy training was provided to stroke survivor-caregiver dyads twice a week over 6 wk. The training included shared decision-making, goal setting, performance evaluation, strategy development and implementation, and therapeutic guided discovery. OUTCOMES AND MEASURES: Feasibility indicators were Goal Attainment Scaling, Dyadic Relationship Scale, Participation Measure-3 Domains, 4 Dimensions, Activity Measure for Post-Acute Care, Montreal Cognitive Assessment, Trail Making Test, Stroop Color and Word Test, Preparedness for Caregiving Scale, and Zarit Burden Interview. RESULTS: In total, 15 dyads completed all intervention sessions with full attendance. Both stroke survivors and their caregivers demonstrated high engagement and comprehension and reported moderate to high satisfaction with the intervention. From baseline to postintervention, the effects on goal attainment, frequency and perceived difficulty of community participation, executive function, mobility function, and caregiver preparedness were significant and positive. CONCLUSIONS AND RELEVANCE: Our study supports the feasibility and preliminary efficacy of dyad-focused strategy training for stroke survivor-caregiver dyads transitioning from the hospital to the community in Taiwan. Our preliminary evidence indicates that dyads who receive strategy training exhibit advancement toward their goals and experience considerable enhancements in their individual outcomes. Plain-Language Summary: This study addresses the scarcity of interventions catering to both stroke survivors and their caregivers. By demonstrating the feasibility of our dyad-focused intervention, the research offers preliminary evidence that supports the potential advantages of involving both stroke survivors and their caregivers in the intervention process.

Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI.

BACKGROUND: Timely diagnosis of meniscus injuries is key for preventing knee joint dysfunction and improving patient outcomes because it decreases morbidity and facilitates treatment planning. PURPOSE: To train and evaluate a deep learning model for automated detection of meniscus tears on knee magnetic resonance imaging (MRI). STUDY TYPE: Bicentric retrospective study. SUBJECTS: In total, 584 knee MRI studies, divided among training (n = 234), testing (n = 200), and external validation (n = 150) data sets, were used in this study. The public data set MRNet was used as a second external validation data set to evaluate the performance of the model. SEQUENCE: A 3 T, coronal, and sagittal images from T1-weighted proton density (PD) fast spin-echo (FSE) with fat saturation and T2-weighted FSE with fat saturation sequences. ASSESSMENT: The detection system for meniscus tear was based on the improved YOLOv4 model with Darknet-53 as the backbone. The performance of the model was also compared with that of three radiologists of varying levels of experience. The determination of the presence of a meniscus tear from surgery reports was used as the ground truth for the images. STATISTICAL TESTS: Sensitivity, specificity, prevalence, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curve were used to evaluate the performance of the detection model. Two-way analysis of variance, Wilcoxon signed-rank test, and Tukey's multiple tests were used to evaluate differences in performance between the model and radiologists. RESULTS: The overall accuracies for detecting meniscus tears using our model on the internal testing, internal validation, and external validation data sets were 95.4%, 95.8%, and 78.8%, respectively. One radiologist had significantly lower performance than our model in detecting meniscal tears (accuracy: 0.9025 ± 0.093 vs. 0.9580 ± 0.025). DATA CONCLUSION: The proposed model had high sensitivity, specificity, and accuracy for detecting meniscus tears on knee MRIs. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Using Transfer Learning of Convolutional Neural Network on Neck Radiographs to Identify Acute Epiglottitis.

Acute epiglottitis (AE) is a life-threatening condition and needs to be recognized timely. Diagnosis of AE with a lateral neck radiograph yields poor reliability and sensitivity. Convolutional neural networks (CNN) are powerful tools to assist the analysis of medical images. This study aimed to develop an artificial intelligence model using CNN-based transfer learning to identify AE in lateral neck radiographs. All cases in this study are from two hospitals, a medical center, and a local teaching hospital in Taiwan. In this retrospective study, we collected 251 lateral neck radiographs of patients with AE and 936 individuals without AE. Neck radiographs obtained from patients without and with AE were used as the input for model transfer learning in a pre-trained CNN including Inception V3, Densenet201, Resnet101, VGG19, and Inception V2 to select the optimal model. We used five-fold cross-validation to estimate the performance of the selected model. The confusion matrix of the final model was analyzed. We found that Inception V3 yielded the best results as the optimal model among all pre-train models. Based on the average value of the fivefold cross-validation, the confusion metrics were obtained: accuracy = 0.92, precision = 0.94, recall = 0.90, and area under the curve (AUC) = 0.96. Using the Inception V3-based model can provide an excellent performance to identify AE based on radiographic images. We suggest using the CNN-based model which can offer a non-invasive, accurate, and fast diagnostic method for AE in the future.

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability.

OBJECTIVE: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. BACKGROUND: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. METHOD: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. RESULTS: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. CONCLUSION: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. APPLICATION: The established models can be used in realistic driving scenarios.

Elevated tau and ß-amyloid in the serum of fibromyalgia patients.

OBJECTIVE: Fibromyalgia (FM) is a chronic widespread pain syndrome. Although its mechanism remains relatively unknown, accelerated neurodegeneration in the brain has been reported in patients with FM. Sleep disturbance can increase the risk of neurocognitive disorders, which are associated with tau and beta-amyloid (Aß) protein accumulation. We hypothesize neurodegeneration in patients with FM may be associated with sleep disturbance. METHODS: In this case-control study, we analyzed serum tau and Aß levels and their association with symptom profiles for patients with FM, by recruiting 22 patients with FM and 22 age-matched healthy participants. The visual analog scale, Fibromyalgia Impact Questionnaire, pressure pain threshold test, Pittsburgh Sleep Quality Index (PSQI), Beck Depression Inventory-II, Beck Anxiety Inventory, and serum tau and beta-amyloid-42 (Aß-42) levels were recorded. The Mann-Whitney test was conducted to compare questionnaire and protein level results between the groups. Pearson correlation test was conducted to investigate the correlation of questionnaire scores with tau and Aß-42 levels in patients with FM. The significance level was set at P < .05. RESULTS: Serum tau and Aß-42 levels were significantly higher in patients with FM than in controls. A positive correlation between serum tau levels and PSQI scores was observed in patients with FM (r = 0.476, P = .025). We found that only sleep disturbance in patients with FM was significantly associated with higher serum tau levels among all symptom scores. CONCLUSIONS: We suggest sleep disturbance may play a vital role in the pathomechanism of accelerated neurodegeneration in FM.

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.

Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders.

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.

Novel design for a dynamic ankle foot orthosis with motion feedback used for training in patients with hemiplegic gait: a pilot study.

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 .

Chronic pulmonary exposure to traffic-related fine particulate matter causes brain impairment in adult rats.

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 < 1 µm (PM1), high-efficiency particulate air (HEPA)-filtered air, and clean air on the brain structure, behavioral changes, brainwaves, and bioreactivity of the brain (cortex, cerebellum, and hippocampus), olfactory bulb, and serum after 3 and 6 months of whole-body exposure in 6-month-old Sprague Dawley rats. RESULTS: The rats were exposed to 16.3 ± 8.2 (4.7~ 68.8) µg/m3 of PM1 during the study period. An MRI analysis showed that whole-brain and hippocampal volumes increased with 3 and 6 months of PM1 exposure. A short-term memory deficiency occurred with 3 months of exposure to PM1 as determined by a novel object recognition (NOR) task, but there were no significant changes in motor functions. There were no changes in frequency bands or multiscale entropy of brainwaves. Exposure to 3 months of PM1 increased 8-isoporstance in the cortex, cerebellum, and hippocampus as well as hippocampal inflammation (interleukin (IL)-6), but not in the olfactory bulb. Systemic CCL11 (at 3 and 6 months) and IL-4 (at 6 months) increased after PM1 exposure. Light chain 3 (LC3) expression increased in the hippocampus after 6 months of exposure. Spongiosis and neuronal shrinkage were observed in the cortex, cerebellum, and hippocampus (neuronal shrinkage) after exposure to air pollution. Additionally, microabscesses were observed in the cortex after 6 months of PM1 exposure. CONCLUSIONS: Our study first observed cerebral edema and brain impairment in adult rats after chronic exposure to traffic-related air pollution.

The effects of add-on non-invasive brain stimulation in fibromyalgia: a meta-analysis and meta-regression of randomized controlled trials.

OBJECTIVES: The effects of non-invasive brain stimulation (NBS), including repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (TDCS), in treating FM remain inconclusive. The aim of this study was to investigate present evidence of using NBS as an add-on treatment in treating FM. METHODS: We conducted a database search of the Medline, Embase, PsycINFO and Cochrane Library electronic databases, from inception to July 2015, to analyse randomized controlled trials of NBS in treating FM. A total of 16 studies were included in the current meta-analysis. RESULTS: The pooled mean effect sizes of the 16 included studies revealed significant favourable effects of NBS. The weighted mean effect size in reducing pain, depression, fatigue, sleep disturbance and tender points and improving general health/function were 0.667 (95% CI 0.446, 0.889), 0.322 (95% CI 0.140, 0.504), 0.511 (95% CI 0.247, 0.774), 0.682 (95% CI 0.350, 1.014), 0.867 (95% CI 0.310, 1.425) and 0.473 (95% CI 0.285, 0.661), respectively. rTMS stimulation yielded a greater effect size compared with that of TDCS (effect size 0.698 and 0.568, respectively; P < 0.0001). The primary motor cortex (M1) stimulation yielded a subtle greater effect size in pain reduction compared with that of the dorsolateral prefrontal cortex (effect size 0.709 and 0.693, respectively; P < 0.0001). No linear relationships were found between the effect sizes and treatment regimens and dose. Most of reported adverse effects were minor. CONCLUSIONS: Both rTMS and TDCS may be feasible and safe modalities for treating FM. The general effects of rTMS and TDCS are compatible in FM patients. M1 stimulation may be better in pain reduction and the dorsolateral prefrontal cortex may be better in depression improvement.

Association between ankylosing spondylitis and chronic periodontitis: a population-based study.

OBJECTIVE: Ankylosing spondylitis (AS) is one type of chronic inflammatory rheumatic disease. It has been suggested that rheumatic diseases may have additional underlying factors in common with chronic periodontitis. However, few studies have addressed the possible link between AS and chronic periodontitis. We undertook the present case-control study to investigate the possible association between AS and chronic periodontitis using a population-based data set in Taiwan. METHODS: We conducted this study by using administrative claims data sourced from the Taiwan National Health Insurance program database. Our study included 6,821 AS patients and 34,105 randomly selected controls. Conditional logistic regression analyses were performed to calculate the odds ratios (ORs) and 95% confidence intervals (95% CIs) for prior chronic periodontitis among AS patients and matched controls. RESULTS: There was a significant difference in the prevalence of prior chronic periodontitis between patients and controls (41.5% versus 25.9%; P<0.001). Conditional logistic regression analysis revealed that patients were more likely than controls to have been previously diagnosed as having chronic periodontitis (adjusted OR 1.84 [95% CI 1.74-1.98]). We further found that patients were only 1.70 (95% CI 1.56-1.89) times more likely than controls to have undergone a gingivectomy or periodontal flap operation (all patients had a history of chronic periodontitis) within the 5 years preceding the index date. After excluding those who had undergone either a gingivectomy or periodontal flap surgery, patients were even more likely than controls to have been previously diagnosed as having chronic periodontitis (adjusted OR 2.04 [95% CI 1.93-2.15]). CONCLUSION: This study detected an association between AS and a prior diagnosis of chronic periodontitis.

Association of dysmenorrhea with interstitial cystitis/bladder pain syndrome: a case-control study.

OBJECTIVE: Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic disabling condition of the urological system. Many gynecological conditions are reported to be associated with IC/BPS. This study presents epidemiological evidence of a possible association between dysmenorrhea and IC/BPS, using population-based data. DESIGN: A case-control study. SETTING: Taiwan. SAMPLE: The study sample was retrieved from Taiwan's Longitudinal Health Insurance Database 2000, i.e. 291 women aged 18-45 years with a diagnosis of IC/BPS between January 2000 and December 2010 (cases) and 873 randomly selected controls matched on age and index date of ambulatory care visit. We used logistic regression conditioned on age to calculate the odds ratio of cases having a prior diagnosis of dysmenorrhea relative to controls. RESULTS: Prior dysmenorrhea was found in 87 (29.9%) cases and in 163 (18.7%) of the controls. Conditional logistic regression showed a crude odds ratio of 1.86 (95% confidence interval 1.37-2.52, p < 0.001) for prior dysmenorrhea among cases vs. controls. The adjusted odds ratio was 1.59 (95% confidence interval 1.13-2.23, p = 0.007) after adjusting for medical co-morbidities. CONCLUSION: This population-based study found that there is an association between IC/BPS and prior dysmenorrhea.

Peripheral pulse harmonic analysis and its clinical application: A systematic review.

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.

Implementing strategy training in Taiwan: perspectives of individuals with Acquired brain injury.

PURPOSE: Strategy training is a rehabilitation intervention that aims to enhance problem-solving skills with respect to daily activity-related challenges and has achieved favorable results in Western countries. This study explored the perspectives of individuals with acquired brain injury (ABI) in Taiwan who received strategy training. MATERIALS AND METHODS: Semi-structured interviews with community-dwelling adults with ABI were conducted, and reflective memos made by research team members were recorded. Interviews and memos were analyzed through thematic analysis. RESULTS: This study included 55 participants. The analysis of the participants' interview responses and memos yielded nine themes under three categories: 1) expectations regarding strategy training, 2) perceived benefits of strategy training, and 3) barriers affecting the process and outcomes of strategy training. CONCLUSIONS: All the participants endorsed strategy training through different gains. Most participants' expectations before the intervention were uncertain. Including family members into the strategy training is of key importance for a successfulness of their goals. The participants' experiences about strategy training were affected by various barriers (i.e., health and medical problems, the physical environment, and natural events). Clinicians and researchers should consider these expectations, benefits, and barriers when studying and implementing strategy training in non-Western contexts.IMPLICATIONS FOR REHABILITATIONStrategy training provides clients the opportunity to actively engage in their own goal setting and decision making.Strategy training increases the client's confidence in their ability to participate in the community, communicate, and perform daily living and physical activities.Therapists should consider the health conditions and physical environment of clients when helping them set goals and before facilitating their engagement in the community.Taiwanese family members play a crucial role in supporting acquired brain injury survivors in strategy training.

Associations Between Brain-Gut Axis and Psychological Distress in Fibromyalgia: A Microbiota and Magnetic Resonance Imaging Study.

An altered brain-gut axis is suspected to be one of the pathomechanisms in fibromyalgia (FM). This cross-sectional study investigated the associations among altered microbiota, psychological distress, and brain functional connectivity (FC) in FM. We recruited 25 FM patients and 25 healthy people in the present study. Psychological distress was measured using standardized questionnaires. Microbiota analysis was performed on the participants' stools. Functional magnetic resonance imaging data were acquired, and seed-based resting-state FC (rs-FC) analysis was conducted with the salience network nodes as seeds. Linear regression and mediation analyses evaluated microbiota, symptoms, and rs-FCs associations. We found altered microbiota diversity in FM, of which Phascolarctobacterium and Lachnoclostridium taxa increased the most and Faecalibacterium taxon decreased the most compared to controls. The Phascolarctobacterium abundance significantly predicted Beck depression inventory (BDI-II) scores in FM (ß = 6.83; P = .033). Rs-FCs from salience network nodes were reduced in FM, of which rs-FCs from the right lateral rostral prefrontal cortex (RPFC) to the lateral occipital cortex, superior division right (RPFC-sLOC) could be predicted by BDI-II scores in patients (ß = -.0064; P = .0054). In addition, the BDI-II score was a mediator in the association between Phascolarctobacterium abundance and rs-FCs of RPFC-sLOC (ab = -.06; 95% CI: -.16 to -9.10-3). In conclusion, microbial dysbiosis might be associated with altered neural networks mediated by psychological distress in FM, emphasizing the critical role of the brain-gut axis in FM's non-pain symptoms and supporting further analysis of mechanism-targeted therapies to reduce FM symptoms. PERSPECTIVE: Our study suggests microbial dysbiosis might be associated with psychological distress and the altered salience network, supporting the role of brain-gut axis dysfunction in fibromyalgia pathomechanisms. Further targeting therapies for microbial dysbiosis should be investigated to manage fibromyalgia patients in the future.

Association Between Leg Muscle Thickness and Walking Test with the Haemophilia-Specific Functional Parameters.

OBJECTIVE: To evaluate the muscle thickness and walking test in people with haemophilia A (PWH) and their correlation to joint health and functional impairments. DESIGN: Cross-sectional study. RESULTS: 29 severe/moderate PWH were enrolled. Muscle thickness of quadriceps and medial gastrocnemius were measured using ultrasound. Joint health and functional capacity were assessed using Haemophilia Joint Health Score (HJHS), Haemophilia Early Arthropathy Detection with Ultrasound (HEAD-US), 6-Minute Walking test (6MWT), Haemophilia Quality of Life Questionnaire for Adults (Haem-A-QoL), and Haemophilia Activities List (HAL). Quadriceps muscle thickness significantly correlated with HJHS knee, HEAD-US knee, and HAL. Calf muscle thickness significantly correlated with the HJHS ankle. After adjusted age and BMI, calf muscle thickness was inversely associated with the HJHS ankle. 6MWT was found to significantly correlate with HJHS total, HEAD-US total, Haem-A-QoL, and HAL. CONCLUSION: Muscle thickness and the distance of 6MWT were linked to assessment of joint health, quality of life and activity participation in PWH. Ultrasound measurement of muscle thickness and walking test appear to be useful tools for the assessment of joint health and functional status in PWH.

Multi-Class Deep Learning Model for Detecting Pediatric Distal Forearm Fractures Based on the AO/OTA Classification.

Common pediatric distal forearm fractures necessitate precise detection. To support prompt treatment planning by clinicians, our study aimed to create a multi-class convolutional neural network (CNN) model for pediatric distal forearm fractures, guided by the AO Foundation/Orthopaedic Trauma Association (AO/ATO) classification system for pediatric fractures. The GRAZPEDWRI-DX dataset (2008-2018) of wrist X-ray images was used. We labeled images into four fracture classes (FRM, FUM, FRE, and FUE with F, fracture; R, radius; U, ulna; M, metaphysis; and E, epiphysis) based on the pediatric AO/ATO classification. We performed multi-class classification by training a YOLOv4-based CNN object detection model with 7006 images from 1809 patients (80% for training and 20% for validation). An 88-image test set from 34 patients was used to evaluate the model performance, which was then compared to the diagnosis performances of two readers-an orthopedist and a radiologist. The overall mean average precision levels on the validation set in four classes of the model were 0.97, 0.92, 0.95, and 0.94, respectively. On the test set, the model's performance included sensitivities of 0.86, 0.71, 0.88, and 0.89; specificities of 0.88, 0.94, 0.97, and 0.98; and area under the curve (AUC) values of 0.87, 0.83, 0.93, and 0.94, respectively. The best performance among the three readers belonged to the radiologist, with a mean AUC of 0.922, followed by our model (0.892) and the orthopedist (0.830). Therefore, using the AO/OTA concept, our multi-class fracture detection model excelled in identifying pediatric distal forearm fractures.

Combining wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of two levels of OSA severity (i.e., moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS: We conducted a prospective, simultaneous study using the wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index (AHI), results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between AHI and RDIPSG_TST were identified. In terms of the agreement between the two devices, the average difference between PSG-based AHI and radar-based RDIPSG_TST was 0.59 events/h, while 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy, and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work.