Clustering broiler farmers based on their behavioural differences towards biosecurity to prevent highly pathogenic avian influenza.

Highly pathogenic avian influenza (HPAI) is an important zoonotic disease. The study aims to identify farmer behaviour types to inform the design of behaviour change programmes for mitigating the transmission of HPAI. Therefore, the study utilised multivariate statistical analysis for gaining a better understanding of the relationships among farmers' 30 biosecurity behaviours, the risk of HPAI infection, and distinct features of commercial broiler farmers, which is different from using simple and few binary biosecurity measures. Convenience sampling was used to collect data from 303 Taiwan's farmers among which 40 farmers (13.2%) self-reported having had a HPAI outbreak in the study year while 16 farmers (5.3%) self-reported having had a HPAI outbreak in the past two years. Using categorical principal components analysis and a two-stage cluster analysis, four farmer clusters were identified with distinct features: 1)'Reserved' (4.6%) tended to choose 'No idea' for answering specific questions about HPAI; 2)'Secure' (76.3%) had a higher biosecurity status than the other farms; 3) 'Jeopardised' (16.8%) had a lower biosecurity status than the other farms; 4) 'No-response' (2.3%) tended to skip specific questions about HPAI. The biosecurity status of the 'Reserved' and 'No-response' clusters was undetermined, placing these farms at risk of HPAI infection. Compared to the 'Secure' cluster, the 'Jeopardised' cluster exhibited higher odds of self-reported HPAI in the study year (OR: 2.61, 95% CI: 1.22-5.58) and in the past two years (OR: 4.28, 95% CI: 1.39-13.19). Additionally, the 'Jeopardised' cluster showed increased odds of HPAI recurrence (OR: 4.01, 95% CI: 1.41-11.43). Our study demonstrates that inadequate biosecurity practices can elevate the occurrence or recurrence of HPAI outbreaks. The findings underscore the importance of distinguishing between these clusters to accurately assess the risk of HPAI infection across farms. Furthermore, understanding farmers' behaviours can inform the development of strategies aimed at behaviour change among farmers.

Evaluating artificial intelligence-enhanced digital urine cytology for bladder cancer diagnosis.

BACKGROUND: This study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence-based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time. METHODS: One cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole-slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS-guided and binary diagnosis, inter- and intraobserver agreement, and screening time, were compared across all methods and reviewers. RESULTS: AIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%-30.6% to 63.9%), positive predictive value (PPV; from 21.6%-24.3% to 31.1%), and negative predictive value (NPV; from 91.3%-91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high-grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%-27.3% to 33.3%), PPV (from 31.3%-47.4% to 61.1%), and NPV (from 91.6%-92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%-82.2% to 90.0%) and NPV (from 91.7%-93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57-0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75-0.88). AIxURO significantly reduced screening time by 52.3%-83.2% from microscopy and 43.6%-86.7% from WSI review across all reviewers. Screening-positive (AUC+) cases required more time than negative cases across all methods and reviewers. CONCLUSIONS: AIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.

Impact of background music listening on anxiety in cancer patients undergoing initial radiation therapy: a randomized clinical trial.

BACKGROUND: Patients undergoing radiation therapy (RT) often experience anxiety, which may jeopardize the treatment success. The efficacy of music interventions in reducing anxiety remains contentious. This randomized trial aimed to evaluate the impact of music listening on anxiety symptoms in patients undergoing initial RT. METHODS: First-time RT patients were randomly allocated to experimental and control groups. The Brief Symptom Rating Scale (BSRS-5), Distress Thermometer (DT), and Beck Anxiety Inventory (BAI-C) were administered pre- and post-RT. Changes in physiological anxiety symptoms were monitored over 10 consecutive days starting from the first day of RT. The experimental group received music during RT; the control group did not. The generalized linear mixed model was used to estimate the pre-post difference in the BSRS-5, DT, and BAI-C scores between the music intervention and control group. RESULTS: This study included 50 patients each in the experimental and control groups. BSRS-5 and DT scores were significantly reduced in the experimental group post-RT (p = 0.0114 and p = 0.0023, respectively). When music listening was discontinued, these scores rebounded. While the posttest BAI-C score was significantly lower in the experimental group (p < 0.0001), the pre-post difference between the two groups was not significant (p = 0.0619). On cessation of music listening, the BAI-C score also rebounded. CONCLUSIONS: For cancer patients undergoing initial RT, music listening intervention significantly reduced anxiety symptoms measured using the BSRS-5, DT, and BAI-C scores after two weeks. Our results demonstrate the effectiveness of music listening intervention in reducing anxiety symptoms, thereby potentially improving the quality of life of cancer patients undergoing RT.

Neuroprotection effects of kynurenic acid-loaded micelles for the Parkinson's disease models.

Anti-glutamatergic agents may have neuroprotective effects against excitotoxicity that is known to be involved in the pathogenesis of Parkinson's disease (PD). One of these agents is kynurenic acid (KYNA), a tryptophan metabolite, which is an endogenous N-methyl-D-aspartic acid (NMDA) receptor antagonist. However, its pharmacological properties of poor water solubility and limited blood-brain barrier (BBB) permeability rules out its systemic administration in disorders affecting the central nervous system. Our aim in the present study was to investigate the neuroprotective effects of KYNA-loaded micelles (KYNA-MICs) against PD in vitro and in vivo. Lipid-based micelles (MICs) in conjunction with KYNA drug delivery have the potential to enhance the penetration of therapeutic drugs into a diseased brain without BBB obstacles. KYNA-MICs were characterized by particle size (105.8 ± 12.1 nm), loading efficiency (78.3 ± 4.23%), and in vitro drug release (approximately 30% at 24 h). The in vitro experiments showed that KYNA-MICs effectively reduced 2-fold protein aggregation. The in vivo studies revealed that KYNA was successfully delivered by 5-fold increase in neurotoxin-induced PD brains. The results showed significant enhancement of KYNA delivery into brain. We also found that the KYNA-MICs exhibited several therapeutic effects. The KYNA-MICs reduced protein aggregation of an in vitro PD model, ameliorated motor functions, and prevented loss of the striatal neurons in a PD animal model. The beneficial effects of KYNA-MICs are probably explained by the anti-excitotoxic activity of the treatment's complex. As the KYNA-MICs did not induce any appreciable side-effects at the protective dose applied to a chronic PD mouse model, our results demonstrate that KYNA provides neuroprotection and attenuates PD pathology.

Evaluating Urine Cytology Slide Digitization Efficiency: A Comparative Study Using an Artificial Intelligence-Based Heuristic Scanning Simulation and Multiple Z-Plane Scanning.

INTRODUCTION: Digitizing cytology slides presents challenges because of their three-dimensional features and uneven cell distribution. While multi-Z-plane scan is a prevalent solution, its adoption in clinical digital cytopathology is hindered by prolonged scanning times, increased image file sizes, and the requirement for cytopathologists to review multiple Z-plane images. METHODS: This study presents heuristic scan as a novel solution, using an artificial intelligence (AI)-based approach specifically designed for cytology slide scanning as an alternative to the multi-Z-plane scan. Both the 21 Z-plane scan and the heuristic scan simulation methods were used on 52 urine cytology slides from three distinct cytopreparations (Cytospin, ThinPrep, and BD CytoRich™ [SurePath]), generating whole-slide images (WSIs) via the Leica Aperio AT2 digital scanner. The AI algorithm inferred the WSI from 21 Z-planes to quantitate the total number of suspicious for high-grade urothelial carcinoma or more severe cells (SHGUC+) cells. The heuristic scan simulation calculated the total number of SHGUC+ cells from the 21 Z-plane scan data. Performance metrics including SHGUC+ cell coverage rates (calculated by dividing the number of SHGUC+ cells identified in multiple Z-planes or heuristic scan simulation by the total SHGUC+ cells in the 21 Z-planes for each WSI), scanning time, and file size were analyzed to compare the performance of each scanning method. The heuristic scan's metrics were linearly estimated from the 21 Z-plane scan data. Additionally, AI-aided interpretations of WSIs with scant SHGUC+ cells followed The Paris System guidelines and were compared with original diagnoses. RESULTS: The heuristic scan achieved median SHGUC+ cell coverage rates similar to 5 Z-plane scans across three cytopreparations (0.78-0.91 vs. 0.75-0.88, p = 0.451-0.578). Notably, it substantially reduced both scanning time (137.2-635.0 s vs. 332.6-1,278.8 s, p < 0.05) and image file size (0.51-2.10 GB vs. 1.16-3.10 GB, p < 0.05). Importantly, the heuristic scan yielded higher rates of accurate AI-aided interpretations compared to the single Z-plane scan (62.5% vs. 37.5%). CONCLUSION: We demonstrated that the heuristic scan offers a cost-effective alternative to the conventional multi-Z-plane scan in digital cytopathology. It achieves comparable SHGUC+ cell capture rates while reducing both scanning time and image file size, promising to aid digital urine cytology interpretations with a higher accuracy rate compared to the conventional single (optimal) plane scan. Further studies are needed to assess the integration of this new technology into compatible digital scanners for practical cytology slide scanning.

Comparative evaluation of slide scanners, scan settings, and cytopreparations for digital urine cytology.

Background: Acquiring well-focused digital images of cytology slides with scanners can be challenging due to the 3-dimensional nature of the slides. This study evaluates performances of whole-slide images (WSIs) obtained from 2 different cytopreparations by 2 distinct scanners with 3 focus modes. Methods: Fourteen urine specimens were collected from patients with urothelial carcinoma. Each specimen was equally divided into 2 portions, prepared with Cytospin and ThinPrep methods and scanned for WSIs using Leica (Aperio AT2) and Hamamatsu (NanoZoomer S360) scanners, respectively. The scan settings included 3 focus modes (default, semi-auto, and manual) for single-layer scanning, along with a manual focus mode for 21 Z-layers scanning. Performance metrics were evaluated including scanning success rate, artificial intelligence (AI) algorithm-inferred atypical cell numbers and coverage rate (atypical cell numbers in single or multiple Z-layers divided by the total atypical cell numbers in 21 Z-layers), scanning time, and image file size. Results: The default mode had scanning success rates of 85.7% or 92.9%, depending on the scanner used. The semi-auto mode increased success to 92.9% or 100%, and manual even further to 100%. However, these changes did not affect the standardized median atypical cell numbers and coverage rates. The selection of scanners, cytopreparations, and Z-stacking influenced standardized median atypical cell numbers and coverage rates, scanning times, and image file sizes. Discussion: Both scanners showed satisfactory scanning. We recommend using semi-auto or manual focus modes to achieve a scanning success rate of up to 100%. Additionally, a minimum of 9-layer Z-stacking at 1 µm intervals is required to cover 80% of atypical cells. These advanced focus methods do not impact the number of atypical cells or their coverage rate. While Z-stacking enhances the AI algorithm's inferred quantity and coverage rates of atypical cells, it simultaneously results in longer scanning times and larger image file sizes.

A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database.

The opioid epidemic has become a serious national crisis in the United States. An indepth systematic analysis of opioid-related adverse events (AEs) can clarify the risks presented by opioid exposure, as well as the individual risk profiles of specific opioid drugs and the potential relationships among the opioids. In this study, 92 opioids were identified from the list of all Food and Drug Administration (FDA)-approved drugs, annotated by RxNorm and were classified into 13 opioid groups: buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, tapentadol, and tramadol. A total of 14,970,399 AE reports were retrieved and downloaded from the FDA Adverse Events Reporting System (FAERS) from 2004, Quarter 1 to 2020, Quarter 3. After data processing, Empirical Bayes Geometric Mean (EBGM) was then applied which identified 3317 pairs of potential risk signals within the 13 opioid groups. Based on these potential safety signals, a comparative analysis was pursued to provide a global overview of opioid-related AEs for all 13 groups of FDA-approved prescription opioids. The top 10 most reported AEs for each opioid class were then presented. Both network analysis and hierarchical clustering analysis were conducted to further explore the relationship between opioids. Results from the network analysis revealed a close association among fentanyl, oxycodone, hydrocodone, and hydromorphone, which shared more than 22 AEs. In addition, much less commonly reported AEs were shared among dihydrocodeine, meperidine, oxymorphone, and tapentadol. On the contrary, the hierarchical clustering analysis further categorized the 13 opioid classes into two groups by comparing the full profiles of presence/absence of AEs. The results of network analysis and hierarchical clustering analysis were not only consistent and cross-validated each other but also provided a better and deeper understanding of the associations and relationships between the 13 opioid groups with respect to their adverse effect profiles.

B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness.

Patients who experience upper-limb paralysis after stroke require continual rehabilitation. Rehabilitation must be evaluated for appropriate treatment adjustment; such evaluation can be performed using inertial measurement units (IMUs) instead of standard scales or subjective evaluations. However, IMUs produce large quantities of discretized data, and using these data directly is challenging. In this study, B-splines were used to estimate IMU trajectory data for objective evaluations of hand function and stability by using machine learning classifiers and mathematical indices. IMU trajectory data from a 2018 study on upper-limb rehabilitation were used to validate the proposed method. Features extracted from B -spline trajectories could be used to classify individuals in the 2018 study with high accuracy, and the proposed indices revealed differences between these groups. Compared with conventional rehabilitation evaluation methods, the proposed method is more objective and effective.

mRNA markers associated with malignant pleural effusion.

Malignant pleural effusions (MPE) commonly result from malignant tumors and represent advanced-stage cancers. Thus, in clinical practice, early recognition of MPE is valuable. However, the current diagnosis of MPE is based on pleural fluid cytology or histologic analysis of pleural biopsies with a low diagnostic rate. This research aimed to assess the diagnostic ability of eight previously identified Non-Small Cell Lung Cancer (NSCLC)-associated genes for MPE. In the study, eighty-two individuals with pleural effusion were recruited. There were thirty-three patients with MPE and forty-nine patients with benign transudate. mRNA was isolated from the pleural effusion and amplified by Quantitative real-time PCR. The logistic models were further applied to evaluate the diagnostic performance of those genes. Four significant MPE-associated genes were discovered in our study, including Dual-specificity phosphatase 6 (DUSP6), MDM2 proto-oncogene (MDM2), Ring finger protein 4 (RNF4), and WEE1 G2 Checkpoint Kinase (WEE1). Pleural effusion with higher expression levels of MDM2 and WEE1 and lower expression levels of RNF4 and DUSP6 had a higher possibility of being MPE. The four-gene model had an excellent performance distinguishing MPE and benign pleural effusion, especially for pathologically negative effusions. Therefore, the gene combination is a suitable candidate for MPE screening in patients with pleural effusion. We also identified three survival-associated genes, WEE1, Neurofibromin 1 (NF1), and DNA polymerase delta interacting protein 2 (POLDIP2), which could predict the overall survival of patients with MPE.

The attitude-behaviour gap in biosecurity: Applying social theories to understand the relationships between commercial chicken farmers' attitudes and behaviours.

Introduction: Traditionally, it is believed that people's behaviours align with their attitudes; however, during COVID-19 pandemic, an attitude-behaviour gap in relation to preventive measures has been observed in recent studies. As such, the mixed-methods research was used to examine the relationships between farmers' biosecurity attitudes and behaviours in Taiwan's chicken industry based on the cognitive consistency theory. Methods: Content analysis of face-to-face interviews with 15 commercial chicken farmers identified their biosecurity responses to infectious disease threats. Results: The results indicated the mismatch of farmers' attitudes and behaviours towards specific biosecurity measures, in that they act differently than they think. The findings of the qualitative research allowed the research team to conduct the subsequent quantitative, confirmatory assessment to investigate the mismatch of farmers' attitudes and behaviours in 303 commercial broiler farmers. Survey data were analyzed to discover the relationships between farmers' attitudes and behaviours in relation to 29 biosecurity measures. The results show a mixed picture. The percentage of the farmers who had the attitude-behaviour gap towards 29 biosecurity measures ranged from 13.9 to 58.7%. Additionally, at the 5% significant level, there is an association between farmers' attitudes and behaviours for 12 biosecurity measures. In contrast, a significant association does not exist for the other 17 biosecurity measures. Specifically, out of the 17 biosecurity measures, the disconnection of farmers' attitudes and behaviours was observed in three specific biosecurity measures such as using a carcass storage area. Discussion: Based on a fairly large sample of farmers in Taiwan, this study confirms the existence of an attitude-behaviour gap in context and applies social theories to provide an in-depth understanding of how infectious diseases are managed in the animal health context. As the results demonstrate the necessity of tailoring biosecurity strategies to address the gap, it is time to reconsider the current approach by understanding farmers' real attitudes and behaviours in relation to biosecurity for the success of animal disease prevention and control at the farm level.

Gene-Function-Based Clusters Explore Intricate Networks of Gene Expression of Circulating Tumor Cells in Patients with Colorectal Cancer.

Colorectal cancer (CRC) is a complex disease characterized by dynamically deregulated gene expression and crosstalk between signaling pathways. In this study, a new approach based on gene-function-based clusters was introduced to explore the CRC-associated networks of gene expression. Each cluster contained genes involved in coordinated regulatory activity, such as RAS signaling, the cell cycle process, transcription, or translation. A retrospective case-control study was conducted with the inclusion of 119 patients with histologically confirmed colorectal cancer and 308 controls. The quantitative expression data of 15 genes were obtained from the peripheral blood samples of all participants to investigate cluster-gene and gene-gene interactions. DUSP6, MDM2, and EIF2S3 were consistently selected as CRC-associated factors with high significance in all logistic models. CPEB4 became an insignificant factor only when combined with the clusters for cell cycle processes and for transcription. The CPEB4/DUSP6 complex was a prerequisite for the significance of MMD, whereas EXT2, RNF4, ZNF264, WEE1, and MCM4 were affected by more than two clusters. Intricate networks among MMD, RAS signaling factors (DUSP6, GRB2, and NF1), and translation factors (EIF2S3, CPEB4, and EXT2) were also revealed. Our results suggest that limited G1/S transition, uncontrolled DNA replication, and the cap-independent initiation of translation may be dominant and concurrent scenarios in circulating tumor cells derived from colorectal cancer. This gene-function-based cluster approach is simple and useful for revealing intricate CRC-associated gene expression networks. These findings may provide clues to the metastatic mechanisms of circulating tumor cells in patients with colorectal cancer.

Misspecification of a binary dependent variable in the logistic model controlling for the repeated longitudinal measures.

Many medical applications are interested to know the disease status. The disease status can be related to multiple serial measurements. Nevertheless, owing to various reasons, the binary outcome can be measured incorrectly. The estimators derived from the misspecified outcome can be biased. This paper derives the complete data likelihood function to incorporate both the multiple serial measurements and the misspecified outcome. Owing to the latent variables, EM algorithm is used to derive the maximum-likelihood estimators. Monte Carlo simulations are conducted to compare the impact of misspecification on the estimates. A retrospective data for the recurrence of atrial fibrillation is used to illustrate the usage of the proposed model.

Causes of Hospitalization among End-Stage Kidney Disease Cohort before and after Hemodialysis.

Patients with end-stage kidney disease (ESKD) have a greater risk of comorbidities, including diabetes and anemia, and have higher hospital admission rates than patients with other diseases. The cause of hospital admissions is associated with ESKD prognosis. This retrospective cohort study involved patients with ESKD who received hemodialysis and investigated whether the cause of hospital admission changed before versus after they started hemodialysis. This study recruited 592 patients with ESKD who received hemodialysis at any period between January 2005 and November 2017 and had been assigned the International Classification of Diseases Ninth Revision Clinical Modification (ICD-9-CM) code for ESKD. The patients' demographic data and hospitalization status one year before and two years after they received hemodialysis were analyzed. A McNemar test was conducted to analyze the diagnostic changes from before to after hemodialysis in patients with ESKD. The study's sample of patients with ESKD comprised more women (51.86%) than men and had an average age of 67.15 years. The numbers of patients admitted to the hospital for the following conditions all decreased significantly after they received hemodialysis: type 2 (non-insulin-dependent and adult-onset) diabetes; native atherosclerosis; urinary tract infection; gastric ulcer without mention of hemorrhage, perforation, or obstruction; pneumonia; reflux esophagitis; duodenal ulcer without mention of hemorrhage, perforation, or obstruction; and bacteremia. Most patients exhibited one or more of the following comorbidities: diabetes (n = 407, 68.75%), hypertension (n = 491, 82.94%), congestive heart failure (n = 161, 27.20%), ischemic heart disease (n = 125, 21.11%), cerebrovascular accident (n = 93, 15.71%), and gout (n = 96, 16.22%). An analysis of variance (ANOVA) indicated that changes in the ICD-9-CM codes for native atherosclerosis, urinary tract infection, pneumonia, and hyperkalemia were associated with age. Patients who developed pneumonia before or after they received hemodialysis tended to be older (range: 69-70 years old). This study investigated the causes of hospital admission among patients with ESKD one year before and two years after they received hemodialysis. This study's results revealed hypertension to be the most common comorbidity. Regarding the cause of admission, pneumonia was more prevalent in older than in younger patients. Moreover, changes in the ICD-9-CM codes of native atherosclerosis, urinary tract infection, pneumonia, and hyperkalemia were significantly correlated with age. Therefore, when administering comprehensive nursing care and treatment for ESKD, clinicians should not only focus on comorbidities but also consider factors (e.g., age) that can affect patient prognosis.

Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models.

Women often wear high-heeled shoes for professional or esthetic reasons. However, high-heeled shoes can cause discomfort and injury and can change the body's center of gravity when maintaining balance. This study developed an assessment system for predicting the maximal safe range for heel height by recording the plantar pressure of participants' feet by using force-sensing resistor (FSR) sensors and conducting analyses using regression models. Specifically, 100 young healthy women stood on an adjustable platform while physicians estimated the maximal safe height of high-heeled shoes. The collected FSR data combined with and without personal features were analyzed using regression models. The experimental results showed that the regression model based on the pressure data for the right foot had better predictive power than that based on data for the left foot, regardless of the module. The model with two heights had higher predictive power than that with a single height. Furthermore, adding personal features under the condition of two heights afforded the best predictive effect. These results can help wearers choose maximal safe high-heeled shoes to reduce injuries to the bones and lower limbs.

Sample size determination for comparing accuracies between two diagnostic tests under a paired design.

With the progressive technology, many medical researches are aimed to develop diagnostic tests that can detect diseases faster and accurately. The assessment of the accuracy of the diagnostic test for classifying two groups is through the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). When a paired design is considered, the sample size determination requires the information about two AUC estimates and the corresponding variance and covariance of two AUC estimators. This paper derives the nonparametric estimators of the variance and covariance of two AUC estimators. The result is used to derive the sample size formula when the paired sample is planned. Since most of the results do not have a closed form, numerical results are provided under various scenarios.

Use of Functional Data to Model the Trajectory of an Inertial Measurement Unit and Classify Levels of Motor Impairment for Stroke Patients.

Motor impairment evaluations are key rehabilitation-related assessments for patients with stroke. Currently, such evaluations are subjective; they are based on physicians' judgements regarding the actions performed by patients. This leads to inconsistent clinical results. Many inertial sensing elements for motion detection have been designed. However, to more easily and rapidly evaluate motor impairment, we require a system that can collect data effectively to predict the degree of motor impairment. Lin et al. used data gloves equipped with an inertial measurement unit (IMU) to collect movement trajectories for motor impairment evaluations in patients with stroke. The present study used functional data analysis to model data trajectories to reduce the influence of noise from IMU data and proposed using coefficients of function as features for classifying motor impairment. To verify the appropriateness of feature construction, five classification methods were used to evaluate the extracted features in terms of the overall and sensor-specific ability to classify levels of motor impairment. The results indicated that the features derived from cubic smoothing splines could effectively reflect key data characteristics, and a support vector machine yielded relatively high overall and sensor-specific accuracy for distinguishing between levels of motion impairment in patients with stroke. Future data glove systems can contain cubic smoothing splines to extract hand function features and then classify motion impairment for appropriate rehabilitation programs to be prescribed.

Plasma neurofilament light chain level predicts outcomes in stroke patients receiving endovascular thrombectomy.

BACKGROUND: Timely endovascular thrombectomy (EVT) significantly improves outcomes in patients with acute ischemic stroke (AIS) with large vessel occlusion type. However, whether certain central nervous system-specific plasma biomarkers correlate with the outcomes is unknown. We evaluated the temporal changes and prognostic roles of the levels of these biomarkers in patients with AIS undergoing EVT. METHODS: We enrolled 60 patients who received EVT for AIS and 14 controls. The levels of plasma biomarkers, namely neurofilament light chain (NfL), glial fibrillary astrocytic protein (GFAP), tau, and ubiquitin C-terminal hydrolase L1 (UCHL1), were measured with an ultrasensitive single molecule array before, immediately after, and 24 h after EVT (T1, T2, and T3, respectively). The outcomes of interest were death or disability at 90 days (defined as a modified Rankin Scale score of 3-6) and types of hemorrhagic transformation (hemorrhagic infarction or parenchymal hemorrhage). RESULTS: Of the 180 blood samples from the 60 patients who received EVT, the plasma NfL, GFAP, and UCHL1 levels at T1 were significantly higher than those of the controls, and the levels of all four biomarkers were significantly higher at T3. Patients with parenchymal hemorrhage had a significantly higher rate of increase in GFAP (Pinteraction = 0.005) and UCHL1 (Pinteraction = 0.007) levels compared with those without parenchymal hemorrhage. In a multivariable analysis with adjustment for age, sex, National Institute of Health Stroke Scale score, history of atrial fibrillation, and recanalization status, higher NfL levels at T1 (odds ratio [OR] 2.05; 95% confidence interval [CI], 1.03-4.08), T2 (OR, 2.08; 95% CI, 1.05-4.01), and T3 (OR, 3.94; 95% CI, 1.44-10.79) were independent predictors of death or disability at 90 days. CONCLUSION: Among patients with AIS who received EVT, those with hemorrhagic transformation exhibited significant increase in plasma GFAP and UCHL1 levels over time. Higher plasma NfL were predictive of unfavorable functional outcomes.

Adding mirabegron after intravesical onabotulinumtoxinA injection improves therapeutic effects in patients with refractory overactive bladder.

OBJECTIVES: To investigate whether adding an anticholinergic or beta-3 agonist can improve the therapeutic effect of intravesical onabotuliumtoxinA injection in patients with refractory overactive bladder (OAB). METHODS: Ninety OAB patients who received an intravesical 100-U onabotulinumtoxinA injection 1 month previously were consecutively invited into a prospective, randomized, open-label study. They were randomly adding on solifenacin 5 mg daily (QD) (30 patients), mirabegron 50 mg QD (31 patients), or no medication (29 patients, control). All enrolled patients completed a 3-day voiding diary, Overactive Bladder Symptom Score (OABSS) and Urgency Severity Scale (USS) questionnaires, Global Response Assessment (GRA) scale, and uroflowmetry at baseline (1 month after intravesical onabotulinumtoxinA injection) and 3-, 6-, 9-, and 12-month follow-up. The primary end point was the effective therapeutic outcome defined as no OAB wet during the 12-month period. The secondary end point included changes of GRA, OABSS, and the parameters of the voiding diary at 3 months. RESULTS: The baseline data were comparable among the three groups. The percentage of OAB wet in the mirabegron-added-on group was significantly less than that in the solifenacin-added-on and onabotulinumtoxinA-only groups at four different time points (P = .02). At 3 months, the changes of GRA, OABSS, USS, urge urinary incontinence, frequency, nocturia episodes, and functional bladder capacity in the mirabegron-added-on group were significantly greater than those in the other groups. No serious adverse events were reported. CONCLUSIONS: Adding mirabegron could increase the therapeutic effects, mainly on OAB symptoms and GRA scale, after intravesical onabotulinumtoxinA injection in refractory OAB patients.

Exploring the Outcomes and Satisfaction of Automated Physiological Monitoring Systems Among Nurses.

Vital signs are central to the assessment of physiologic functions of patients and must be included in the electronic health record. The purpose of this retrospective and cross-sectional design study was to evaluate use of-and satisfaction with-automated physiological monitoring systems. Usage data from a hospital database were analyzed 3, 6, and 12 months after implementation of the automated system (June 2018 to May 2019). In addition, questionnaires were completed by 168 nurses, and 20 nurses were interviewed between August/September 2020 and October/November 2020, respectively. Results revealed that usage frequency of automated physiological monitoring devices increased steadily with user familiarity. Although respondents indicated general satisfaction with the devices, system downtime, sufficiency of the battery charge, and data transmission speed were identified as needing correction to smooth workflow and boost work efficiency. Although most interviewees considered devices easy to use, some mentioned transmission speed of the gateway, scanner sensitivity, and accuracy of the ear thermometer as needing improvement. For nurses to use automated physiological monitoring devices fully, a user-friendly design in functions and features is vital, and in-service training and a streamlined workflow are recommended to facilitate technology adoption.

Urinary heavy metals, DNA methylation, and subclinical atherosclerosis.

PURPOSE: Lead (Pb) or cadmium (Cd) exposure has been linked to atherosclerosis. Co-exposure of these two heavy metals often occurs in humans. Recent evidence has indicated a crucial role of DNA methylation in atherosclerosis, while Pb or Cd exposure has also been shown to alter DNA methylation. However, it is still unknown whether DNA methylation plays a role in the pathological mechanism of these two heavy metals in atherosclerosis. APPROACH AND RESULTS: We enrolled 738 participants (12-30 years) to investigate the association among concentrations of urine Pb or Cd, the 5mdC/dG value (a global DNA methylation marker) and the carotid intima-media thickness (CIMT). When each heavy metal was modeled separately, the results showed urine Pb and Cd concentrations were positively associated with the 5mdC/dG value and CIMT, respectively. When the two heavy metals were analyzed in the same model, urinary Pb concentrations were positively associated with the 5mdC/dG value and CIMT, while urinary Cd concentrations were only positively associated with the CIMT. When Pb and Cd are simultaneously considered in the same logistic regression model, the odds ratios (OR) of thicker CIMT (greater than 75th percentile) with one unit increase in ln-Pb level was 1.67 (95% C.I. = 1.17-2.46, P = 0.005) when levels of 5mdC/dG were above 50th percentile, which is higher than 5mdC/dG bellow the 50th percentile (OR = 1.50 (95% C.I. = 0.96-2.35), P = 0.076). In structural equation model (SEM), Pb or Cd levels are directly associated with CIMT. Moreover, Pb or Cd had an indirect association with CIMT through the 5mdC/dG. When we considered Pb and Cd together, Pb levels had a direct association with CIMT and an indirect association with CIMT through the 5mdC/dG value, while Cd only had a direct association with CIMT. CONCLUSIONS: Our findings imply that Pb and Cd exposure might be associated with subclinical atherosclerosis, and global DNA methylation might mediate Pb-associated subclinical atherosclerosis in this young population. Future effort is necessary to elucidate the causal relationship.