The effect of oral massage intervention on oral hygiene at home care.

INTRODUCTION: Oral hygiene for individuals with disabilities living at home heavily relies on caregiver assistance. This study investigates differences in mouth opening, dental plaque index and halitosis grade among home-dwelling individuals with and without oral massage intervention. It is important to consider that extended bedridden periods, the presence of nasogastric tubes and difficulties in mouth opening can affect caregivers' oral cleaning effectiveness. METHOD: This single-blind controlled experiment spanned 12 weeks. Primary caregivers underwent a 15- to 20-min demonstration. The experimental group received guidance on the Bass brushing method, instructions for using oral cleaning tools and training in oral massage. The control group received guidance on the Bass brushing method and instructions for oral cleaning tools. Measurement tools included basic participant information, oral examination records, mouth opening assessments, dental plaque index evaluations and halitosis ratings. RESULTS: The study included 38 subjects, all over 80 years old with disabilities. Dental plaque index significantly improved after massage (p = 0.001). Compared to their pre-massage conditions, the experimental group showed significant improvements in maximum mouth opening (11.65-20.71 mm, p = 0.001), dental plaque index (98.04%-59.74%, p < 0.0001) and halitosis rating (4.12-3.76, p = 0.034). CONCLUSIONS: Oral massage effectively improved mouth opening, reduced the dental plaque index and lowered halitosis ratings in the experimental group, underscoring its positive impact on oral hygiene. The study's insights can significantly benefit oral care for individuals with disabilities living at home.

An Acute Kidney Injury Prediction Model for 24-hour Ultramarathon Runners.

Acute kidney injury (AKI) is frequently seen in ultrarunners, and in this study, an AKI prediction model for 24-hour ultrarunners was built based on the runner's prerace blood, urine, and body composition data. Twenty-two ultrarunners participated in the study. The risk of acquiring AKI was evaluated by a support vector machine (SVM) model, which is a statistical model commonly used for classification tasks. The inputs of the SVM model were the data collected 1 hour before the race, and the output of the SVM model was the decision of acquiring AKI. Our best AKI prediction model achieved accuracy of 96% in training and 90% in cross-validation tests. In addition, the sensitivity and specificity of the model were 90% and 100%, respectively. In accordance with the AKI prediction model components, ultra-runners are suggested to have high muscle mass and undergo regular ultra-endurance sports training to reduce the risk of acquiring AKI after participating in a 24-hour ultramarathon.

Effective licorice gargle juice for aphthous ulcer pain relief: A randomized double-blind placebo-controlled trial.

The aim of this study was to determine the effects of licorice gargle juice on aphthous ulcers, which is a common and painful disease that causes loss of normal mucous tissues and results in inflammatory ulcers in the oral mucosa. A randomized double-blind placebo-controlled trial involving primary care patients suffering from aphthous ulcer was performed. The intervention group received licorice gargle juice for 1 and 2 days. Of the 54 patients that participated in the study, 30 were included in the intervention group and 24 in the placebo group. A 10-point visual analog scale (VAS) was used to assess the patients' self-assessed pain levels before and after treatment. Statistical analyses were performed by using the nonparametric Mann-Whitney test. The licorice gargle juice group had a significantly reduced pain level rate compared with the placebo group at day 1 (mean VAS, 2.47 [95% CI, 1.95-2.98] vs. 4.75 [3.96-5.54]; P<0.001) and day 2 (mean VAS, 1.07 [95% CI, 0.81-1.32] vs. 4.08 [3.23-4.94]; P<0.001).The current study indicates that licorice gargle juice rapidly reduce pain and healing time and thus can improve the quality of life of a patient with aphthous ulcer.

Detection of Chronic Blast-Related Mild Traumatic Brain Injury with Diffusion Tensor Imaging and Support Vector Machines.

Blast-related mild traumatic brain injury (bmTBI) often leads to long-term sequalae, but diagnostic approaches are lacking due to insufficient knowledge about the predominant pathophysiology. This study aimed to build a diagnostic model for future verification by applying machine-learning based support vector machine (SVM) modeling to diffusion tensor imaging (DTI) datasets to elucidate white-matter features that distinguish bmTBI from healthy controls (HC). Twenty subacute/chronic bmTBI and 19 HC combat-deployed personnel underwent DTI. Clinically relevant features for modeling were selected using tract-based analyses that identified group differences throughout white-matter tracts in five DTI metrics to elucidate the pathogenesis of injury. These features were then analyzed using SVM modeling with cross validation. Tract-based analyses revealed abnormally decreased radial diffusivity (RD), increased fractional anisotropy (FA) and axial/radial diffusivity ratio (AD/RD) in the bmTBI group, mostly in anterior tracts (29 features). SVM models showed that FA of the anterior/superior corona radiata and AD/RD of the corpus callosum and anterior limbs of the internal capsule (5 features) best distinguished bmTBI from HCs with 89% accuracy. This is the first application of SVM to identify prominent features of bmTBI solely based on DTI metrics in well-defined tracts, which if successfully validated could promote targeted treatment interventions.

Swelling-Resistant, Crosslinked Polyvinyl Alcohol Membranes with High ZIF-8 Nanofiller Loadings as Effective Solid Electrolytes for Alkaline Fuel Cells.

The present work investigates the direct mixing of aqueous zeolitic imidazolate framework-8 (ZIF-8) suspension into a polyvinyl alcohol (PVA) and crosslinked with glutaraldehyde (GA) to form swelling-resistant, mechanically robust and conductivity retentive composite membranes. This drying-free nanofiller incorporation method enhances the homogeneous ZIF-8 distributions in the PVA/ZIF-8/GA composites to overcome the nanofiller aggregation problem in the mixed matrix membranes. Various ZIF-8 concentrations (25.4, 40.5 and 45.4 wt.%) are used to study the suitability of the resulting GA-crosslinked composites for direct alkaline methanol fuel cell (DAMFC). Surface morphological analysis confirmed homogeneous ZIF-8 particle distribution in the GA-crosslinked composites with a defect- and crack-free structure. The increased ionic conductivity (21% higher than the ZIF-free base material) and suppressed alcohol permeability (94% lower from the base material) of PVA/40.5%ZIF-8/GA resulted in the highest selectivity among the prepared composites. In addition, the GA-crosslinked composites' selectivity increased to 1.5−2 times that of those without crosslink. Moreover, the ZIF-8 nanofillers improved the mechanical strength and alkaline stability of the composites. This was due to the negligible volume swelling ratio (<1.4%) of high (>40%) ZIF-8-loaded composites. After 168 h of alkaline treatment, the PVA/40.5%ZIF-8/GA composite had almost negligible ionic conductivity loss (0.19%) compared with the initial material. The maximum power density (Pmax) of PVA/40.5%ZIF-8/GA composite was 190.5 mW cm−2 at 60 °C, an increase of 181% from the PVA/GA membrane. Moreover, the Pmax of PVA/40.5%ZIF-8/GA was 10% higher than that without GA crosslinking. These swelling-resistant and stable solid electrolytes are promising in alkaline fuel cell applications.

Motion Artifact Resilient SCG-based Biometric Authentication Using Machine Learning.

On account of privacy preserving issue and health-care monitoring, physiological signal biometric authentication system has gained popularity in recent years. Seismocardiogram (SCG) is now easily accessible owing to the advance of wearable sensor technology. However, SCG biometric has not been widely explored due to the challenging motion artifact removal. In this paper, we design placing the sensors at different body parts under different activities to determine the best sensor location. In addition, we develop SCG noise removal algorithm and utilize machine learning approach to perform biometric authentication tasks. We validate the proposed methods on 20 healthy young adults. The dataset contains acceleration data of sitting, standing, walking, and sitting post-exercise activities with the sensor placed at the wrists, neck, heart and sternum. We demonstrate that vertical and dorsal-ventral SCG near the heart and the sternum produce reliable SCG biometric evidenced by achieving the state-of-the-art performance. Moreover, we present the efficacy of the devised noise removal procedure in the authentication during walking motion.Clinical relevance- A seismocardiography-based biometric authentication system can help serve privacy preserving and reveal cardiovascular functioning information in clinics.

Star-ECG: Visualization of Electrocardiograms for Arrhythmia and Heart Rate Variability.

Conventional electrocardiograms (ECG) are displayed in one dimension. Reading one-dimensional ECG waveform becomes challenging when one wants to visualize the heart rate variability with naked eye. Some ECG visualization techniques have been proposed. However, they rely on domain knowledge to comprehend the heart rate variability. To improve the readability for patients and non-experts, we introduce Star-ECG, a novel ECG visualization approach. Such approach projects ECG waveforms onto a two-dimensional plane in a circular form. We demonstrate that Star-ECG offers not only easily deciphered visualization of cardiac abnormalities and heart rate variability, but also the application of state-of-the-art arrhythmia classification with integrated deep neural networks. We also report positive user feedback from both experts and non-experts that Star-ECG can provide readable and helpful information to monitor cardiac activities.

Motion Artifact Resilient Cuff-Less Blood Pressure Monitoring Using a Fusion of Multi-Dimensional Seismocardiograms.

Blood pressure (BP) monitoring is critical to raise awareness of hypertension and hypotension, yet the commonly used techniques require the person staying still along with a cuff around the arm. Some cuff-less approaches have been researched, but all hinder the person from moving around. To address the challenge, we propose using a fusion of accelerometers to achieve motion artifact resilient blood pressure monitoring. Such technique is accomplished with the motion artifact removal process and feature extraction from multi-dimensional seismocardiograms. The efficacy of our BP monitoring models is validated in 19 young healthy adults. Both the diastolic and systolic BP monitoring models fulfill the AAMI standard and British Hypertension Society protocol. For sitting still BP monitoring, the mean and standard deviation of diastolic and systolic difference errors (DE) are 0.09 ± 4.10 and -0.25 ± 5.45 mmHg; moreover, the mean absolute difference errors (MADE) are 3.62 and 4.73 mmHg. In walking motions, the DE are 1.15 ±4.47 mmHg for diastolic BP and -0.38 ± 6.67 for systolic BP; furthermore, the MADE are 3.36 and 5.07 mmHg, respectively. The motion artifact resilient cuff-less BP monitoring reveals the potential of portable BP monitoring in healthcare environments.

Heart Rate and Respiratory Rate Monitoring Using Seismocardiography.

Vital signs monitoring is critical for healthcare. Currently, at-home vital signs monitoring is obstructed by the complicated device, unaffordable cost, and inconvenience. In this study, we develop a simultaneous heart rate and respiratory rate monitoring technique that requires only one tri-axial accelerometer placing on the sternum. We devise a signal processing technique to generate seismocardiography and respiratory vibration from the raw acceleration data; furthermore, we formulate the algorithms to compute the heart rate and respiratory rate from the processed signals. We tested the methodology on 20 young healthy adults during pre-exercise and post-exercise sitting. The accuracy of 98.3% and 97.3% are achieved in heart rate monitoring during pre-exercise and post-exercise sitting. For respiratory rate, an accuracy of 96.8% is accomplished. Given the accuracy, affordable cost and convenience, the acceleration-based technique shows great promise for at-home vital signs monitoring.Clinical relevance- Portable heart rate and respiratory rate monitoring is substantial in elevating the quality of healthcare environment.

Transcriptome-level assessment of the impact of deformed wing virus on honey bee larvae.

Deformed wing virus (DWV) prevalence is high in honey bee (Apis mellifera) populations. The virus infects honey bees through vertical and horizontal transmission, leading to behavioural changes, wing deformity, and early mortality. To better understand the impacts of viral infection in the larval stage of honey bees, artificially reared honey bee larvae were infected with DWV (1.55 × 1010 copies/per larva). No significant mortality occurred in infected honey bee larvae, while the survival rates decreased significantly at the pupal stage. Examination of DWV replication revealed that viral replication began at 2 days post inoculation (d.p.i.), increased dramatically to 4 d.p.i., and then continuously increased in the pupal stage. To better understand the impact of DWV on the larval stage, DWV-infected and control groups were subjected to transcriptomic analysis at 4 d.p.i. Two hundred fifty-five differentially expressed genes (DEGs) (fold change ≥ 2 or ≤ -2) were identified. Of these DEGs, 168 genes were downregulated, and 87 genes were upregulated. Gene Ontology (GO) analysis showed that 141 DEGs (55.3%) were categorized into molecular functions, cellular components and biological processes. One hundred eleven genes (38 upregulated and 73 downregulated) were annotated by KO (KEGG Orthology) pathway mapping and involved metabolic pathways, biosynthesis of secondary metabolites and glycine, serine and threonine metabolism pathways. Validation of DEGs was performed, and the related gene expression levels showed a similar tendency to the DEG predictions at 4 d.p.i.; cell wall integrity and stress response component 1 (wsc1), cuticular protein and myo-inositol 2-dehydrogenase (iolG) were significantly upregulated, and small conductance calcium-activated potassium channel protein (SK) was significantly downregulated at 4 d.p.i. Related gene expression levels at different d.p.i. revealed that these DEGs were significantly regulated from the larval stage to the pupal stage, indicating the potential impacts of gene expression levels from the larval to the pupal stages. Taken together, DWV infection in the honey bee larval stage potentially influences the gene expression levels from larvae to pupae and reduces the survival rate of the pupal stage. This information emphasizes the consequences of DWV prevalence in honey bee larvae for apiculture.

Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing.

Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In this study, we present a waveform-based signal processing (WBSP) method to produce state-of-the-art performance in arrhythmia classification. When performing WBSP, we first filtered ECG signals, searched local minima, and removed baseline wandering. Subsequently, we fit the processed ECG signals with Gaussians and extracted the parameters. Afterwards, we exploited the products of WBSP to accomplish arrhythmia classification with our proposed machine learning-based and deep learning-based classifiers. We utilized MIT-BIH Arrhythmia Database to validate WBSP. Our best classifier achieved 98.8% accuracy. Moreover, it reached 96.3% sensitivity in class V and 98.6% sensitivity in class Q, which both share one of the best among the related works. In addition, our machine learning-based classifier accomplished identifying four waveform components essential for automated arrhythmia classification: the similarity of QRS complex to a Gaussian curve, the sharpness of the QRS complex, the duration of and the area enclosed by P-wave.Clinical relevance- Early diagnosis and automated classification of arrhythmia is clinically essential.

R-peak Detection Using a Hybrid of Gaussian and Threshold Sensitivity.

We present an enhanced R-peak detection technique that incorporates both waveform shape recognition and threshold sensitivity enhancement. Waveform shape recognition was achieved with signal processing and Gaussian curve parameterization; threshold sensitivity was accomplished with the famous Pan-Tompkins algorithm. We tested all 48 records in MIT-BIH Arrhythmia Database to validate the proposed method. Our method achieved 97.41% sensitivity against a tolerance window of 10% averaged R-R interval, which improves the current state-of-the-art Pan-Tompkins algorithm by 1%. More importantly, we demonstrate that our approach outperforms the Pan-Tompkins' algorithm in 81% of the records in MIT-BIH Arrhythmia Database.Clinical relevance: High sensitivity R-peak detection is substantial in various cardiovascular disease diagnosis.

Cuff-Less Blood Pressure Monitoring with a 3-Axis Accelerometer.

OBJECTIVE: The aim was to propose a cuff-less, cost-efficient, and ultra-convenient blood pressure monitoring technique with a 3-axis accelerometer. METHODS: The efficacy of the proposed approach was examined in 8 young healthy volunteers undergoing different activities with a 3-axis accelerometer leveled on their upper chest. The 3-dimensional accelerations were exploited to select features for the calculation of systolic pressure (SP) and diastolic pressure (DP); the whole process involved signal processing, feature extraction, linear multivariate regression, and leave-one-out cross validations (LOOCV). RESULTS: DP and SP could be approximated with the linear combination of the extracted features: the L2 norm of lateral acceleration for both DP and SP, state variation (defined in the proposed algorithm) of vertical acceleration for SP, and I-J interval (defined in ballistocardiogram) of vertical acceleration for DP. The correlation coefficient (r) of the estimated and the measured DP was 0.97, and for SP, r = 0.96. In LOOCV, our best validated results in difference errors were -0.02±3.82 mmHg for DP and -0.59 ± 7.46 mmHg for SP. CONCLUSION: Compared to AAMI criteria, the proposed acceleration-based technique fulfilled the requirement. The accelerometer-based technique showed the potential to monitor blood pressure cuff-lessly, cost-efficiently, ultra-conveniently, and to be embedded in a long-term wearable device for clinical usage.

Highly Zeolite-Loaded Polyvinyl Alcohol Composite Membranes for Alkaline Fuel-Cell Electrolytes.

Having a secure and stable energy supply is a top priority for the global community. Fuel-cell technology is recognized as a promising electrical energy generation system for the twenty-first century. Polyvinyl alcohol/zeolitic imidazolate framework-8 (PVA/ZIF-8) composite membranes were successfully prepared in this work from direct ZIF-8 suspension solution (0⁻45.4 wt %) and PVA mixing to prevent filler aggregation for direct methanol alkaline fuel cells (DMAFCs). The ZIF-8 fillers were chosen for the appropriate cavity size as a screening aid to allow water and suppress methanol transport. Increased ionic conductivities and suppressed methanol permeabilities were achieved for the PVA/40.5% ZIF-8 composites, compared to other samples. A high power density of 173.2 mW cm-2 was achieved using a KOH-doped PVA/40.5% ZIF-8 membrane in a DMAFC at 60 °C with 1⁻2 mg cm-2 catalyst loads. As the filler content was raised beyond 45.4 wt %, adverse effects resulted and the DMAFC performance (144.9 mW cm-2) was not improved further. Therefore, the optimal ZIF-8 content was approximately 40.5 wt % in the polymeric matrix. The specific power output was higher (58 mW mg-1) than most membranes reported in the literature (3⁻18 mW mg-1).