Development and construction of the Multidimensional Self-Efficacy Scale for Epilepsy (MSESE) and its psychometric properties.

PURPOSE: It has become evident that patients with epilepsy require strong self-efficacy support in various domains, including work, social interaction, and academic performance, to ensure their complete social functioning. Nevertheless, previous studies have predominantly assessed the self-efficacy of individuals with epilepsy from a singular perspective of disease management. This study aimed to develop the Multidimensional Self-Efficacy Scale for Epilepsy (MSESE) to assess multiple dimensions and establish its psychometric properties. METHODS: We compiled a total of 25 questions for the initial version of the questionnaire based on a review of the literature and insights from experts, patients, and family members. The study included 180 adult patients with epilepsy who met the research criteria, with 126 of them serving as pre-test samples. All participants completed the MSESE, Brief Symptom Rating Scale-50 (BSRS-50), Rosenberg Self-Esteem Scale-Chinese version (RSES-C), and General Self-Efficacy Scale (GSES). RESULTS: The final scale consisted of 12 items across four dimensions, with item factor loadings ranging from .51 to .90. Most of the fit indices indicated a good fit. Construct validity was established through significant correlations with the BSRS-50, RSES-C, and GSES (r = -0.51 to 0.69, p < 0.01). Internal consistency coefficients for the MSESE were strong at .90, with individual dimensions ranging from 0.71 to 0.89. The MSESE also demonstrated a satisfactory test-retest reliability of 0.72. CONCLUSIONS: The MSESE is a convenient, multidimensional, and easy-to-use scale with good psychometric properties, making it suitable for both clinical assessments and research purposes.

Standardized patients' experiences of portraying characters in difficult communication scenarios: Narrative inquiry.

BACKGROUND: There are limited studies that explored the preparation and challenges faced by standardized patients (SPs) in portraying characters in difficult communication scenarios, and the strategies used to overcome these challenges. The purpose of this study was to understand the experience of SPs in interpreting difficult communication situations and the learning needs of performing similar scenarios. And it allows the researchers to explore the meaning, beliefs, values, and aspiration associated with their role as SPs. The findings could shade light on the significance of their experiences and provide valuable insights for the development of future SP training programs. METHODS: The design of this study is framed by a narrative inquiry, using semi-structured guidelines to conduct in-depth interviews with 11 SPs who have participated in the performances of difficult communication situations. Research data were analyzed by Polkinghorne narrative analysis, and Riessman's four criteria were used to establish rigor. RESULTS: Analysis revealed the following five themes: scenarios to real life connections, process of preparing for a performance, methods to detach from character, obtaining unexpected rewards, and needs for performance training. There are two to three subthemes that are subsumed under each theme. CONCLUSIONS: To strengthen training in difficult communication for healthcare professionals, the use of SPs to interpret challenging difficult communication scenarios will continue to increase. Educators need to ensure that SPs are fully prepared physically and emotionally before, during and after their performance. Offering of continuing education and training in feedback techniques are crucial to extend the tenure of SPs, reduce their frustration, prevent attrition, and ultimately, reduce training costs. In the future, SP training should also include detachment and feedback techniques to alleviate SPs' stress.

A simple, organized web-based system improved the transfer efficiency and patient outcomes for endovascular thrombectomy in regional stroke network.

BACKGROUND: Endovascular thrombectomy (EVT) is a time-sensitive treatment for acute ischemic stroke with large vessel occlusion. To optimize transfer efficiency, a web-based platform was introduced in the Tainan Stroke Network (TSN). We assessed its application and effectiveness in regional stroke care. METHOD: This new web-based platform containing a questionnaire-style interface was introduced on October 1, 2021. To assess the transfer efficiency and patient outcomes, acute stroke patients transferred from PSCs to CSC for EVT from April 01, 2020, to December 30, 2022, were enrolled. The patients were classified into the traditional transferal pathway (TTP) group and the new transferal pathway (NTP) group depending on mode of transfer. Patient characteristics, time segments after stroke onset and outcome were compared between groups. RESULT: A total of 104 patients were enrolled, with 77 in the TTP group and 27 in the NTP group. Compared to the TTP group, the NTP group had a significantly shorter onset-to-CSC door time (TTP vs. NTP: 267 vs. 198 min; p = 0.041) and a higher EVT rate (TTP vs. NTP: 18.2% vs. 48.1%, p = 0.002). Among EVT patients, those in the NTP group had a significantly shorter CSC door-to-puncture time (TTP vs. NTP: 131.5 vs. 110 min; p = 0.029). The NTP group had a higher rate of good functional outcomes at 3 months (TTP vs. NTP: 21% vs. 61.5%; p = 0.034). CONCLUSION: This new web-based EVT transfer system provides notable improvements in clinical outcomes, transfer efficiency, and EVT execution for potential EVT candidates without markedly changing the regional stroke care paradigm.

Novel Software for High-level Virological Testing: Self-Designed Immersive Virtual Reality Training Approach.

BACKGROUND: To ensure the timely diagnosis of emerging infectious diseases, high-tech molecular biotechnology is often used to detect pathogens and has gradually become the gold standard for virological testing. However, beginners and students are often unable to practice their skills due to the higher costs associated with high-level virological testing, the increasing complexity of the equipment, and the limited number of specimens from patients. Therefore, a new training program is necessary to increase training and reduce the risk of test failure. OBJECTIVE: The aim of the study is to (1) develop and implement a virtual reality (VR) software for simulated and interactive high-level virological testing that can be applied in clinical practice and skills building or training settings and (2) evaluate the VR simulation's effectiveness on reaction, learning, and behavior of the students (trainees). METHODS: Viral nucleic acid tests on a BD MAX instrument were selected for our VR project because it is a high-tech automatic detection system. There was cooperation between teachers of medical technology and biomedical engineering. Medical technology teachers were responsible for designing the lesson plan, and the biomedical engineering personnel developed the VR software. We designed a novel VR teaching software to simulate cognitive learning via various procedure scenarios and interactive models. The VR software contains 2D VR "cognitive test and learning" lessons and 3D VR "practical skills training" lessons. We evaluated students' learning effectiveness pre- and posttraining and then recorded their behavior patterns when answering questions, performing repeated exercises, and engaging in clinical practice. RESULTS: The results showed that the use of the VR software met participants' needs and enhanced their interest in learning. The average posttraining scores of participants exposed to 2D and 3D VR training were significantly higher than participants who were exposed solely to traditional demonstration teaching (P<.001). Behavioral assessments of students pre- and posttraining showed that students exposed to VR-based training to acquire relevant knowledge of advanced virological testing exhibited significantly improved knowledge of specific items posttraining (P<.01). A higher participant score led to fewer attempts when responding to each item in a matching task. Thus, VR can enhance students' understanding of difficult topics. CONCLUSIONS: The VR program designed for this study can reduce the costs associated with virological testing training, thus, increasing their accessibility for students and beginners. It can also reduce the risk of viral infections particularly during disease outbreaks (eg, the COVID-19 pandemic) and also enhance students' learning motivation to strengthen their practical skills.

Exploration of students' reaction in medical error events and the impact of personalized training on the speaking-up behavior in medical error events.

BACKGROUND: The ability of medical students to speak up before a medical error occurs is a timely and necessary interaction to prevent potential patient harm. As it may be crucial to improve patient safety, we explored how medical students react to a medical error and provided them appropriate training regarding speaking up about medical issues. METHODS: A quasi-experimental study was conducted in Taiwan involving 153 medical students who participated in a speaking-up simulation course. They were divided into two groups. The first group participated in a non-life-threatening scenario before the intervention, followed by a personalized debriefing session, then a life-threatening scenario after the intervention. The second group participated in a life-threatening scenario before the intervention, followed by a personalized debriefing session, then a non-life-threatening scenario after the intervention. Students also completed patient safety attitude survey. RESULTS: During the preintervention scenario, the overall medical students' speaking-up rate to medical error was 45.1%. The speaking-up rate of medical students in life-threatening scenario was significantly higher than the rate in non-life-threatening scenario before the intervention (64.6% vs 24.3%, p < 0.001). After personalized debriefing, the speaking-up rate to medical errors was significantly improved both in life-threatening scenarios (95.9%, p < 0.001) and in non-life-threatening scenarios (100%, p < 0.001). Male medical students had significantly higher speaking-up rates than female students in life-threatening scenario (76.2% vs 51.4%, p = 0.02). On post-intervention surveys, students provided several reasons for their likelihood of speaking up or remaining silent during a medical error event. CONCLUSIONS: Medical students' rate of speaking-up to medical error was higher in a simulated life-threatening scenario than in a simulated non-life-threatening scenario. Faculty-led personalized debriefing can facilitate medical students' adoption of communication strategies to speak up more in medical error events. Educators should also consider gender differences when they design effective assertive communication courses.[Box: see text].

Out of chaos: Phylogenomics of Asian Sonerileae.

Sonerileae is a diverse Melastomataceae lineage comprising ca. 1000 species in 44 genera, with >70% of genera and species distributed in Asia. Asian Sonerileae are taxonomically intractable with obscure generic circumscriptions. The backbone phylogeny of this group remains poorly resolved, possibly due to complexity caused by rapid species radiation in early and middle Miocene, which hampers further systematic study. Here, we used genome resequencing data to reconstruct the phylogeny of Asian Sonerileae. Three parallel datasets, viz. single-copy ortholog (SCO), genomic SNPs, and whole plastome, were assembled from genome resequencing data of 205 species for this purpose. Based on these genome-scale data, we provided the first well resolved phylogeny of Asian Sonerileae, with 34 major clades identified and 74% of the interclade relationships consistently resolved by both SCO and genomic data. Meanwhile, widespread phylogenetic discordance was detected among SCO gene trees as well as species trees reconstructed using different tree estimation methods (concatenation/site-based coalescent method/summary method) or different datasets (SCO/genomic/plastome). We explored sources of discordance using multiple approaches and found that the observed discordance in Asian Sonerileae was mainly caused by a combination of biased distribution of missing data, random noise from uninformative genes, incomplete lineage sorting, and hybridization/introgression. Exploration of these sources can enable us to generate hypotheses for future testing, which is the first step towards understanding the evolution of Asian Sonerileae. We also detected high levels of homoplasy for some characters traditionally used in taxonomy, which explains current chaotic generic delimitations. The backbone phylogeny of Asian Sonerileae revealed in this study offers a solid basis for future taxonomic revision at the generic level.

Does ultrasound education improve anatomy learning? Effects of the Parallel Ultrasound Hands-on (PUSH) undergraduate medicine course.

BACKGROUND: As ultrasound has become increasingly prominent in medicine, portable ultrasound is perceived as the visual stethoscope of the twenty-first century. Many studies have shown that exposing preclinical students to ultrasound training can increase their motivation and ultrasound competency. However, few studies have discussed the effect of ultrasound training on anatomy learning. METHOD: The Parallel Ultrasound Hands-on (PUSH) course was designed to investigate whether or not ultrasonography training affects anatomy knowledge acquisition. The PUSH course included anatomical structures located in the chest and abdomen (target anatomy) and was conducted in parallel to the compulsory gross anatomy course. Learners (n = 140) voluntarily participated in this elective course (learners in the course before the midterm examination (Group 1, n = 69), or after the midterm examination (Group 2, n = 71)). Anatomy examination scores (written and laboratory tests) were utilized to compare the effects of the PUSH course. RESULT: Group 1 obtained significantly higher written test scores on the midterm examination (mean difference [MD] = 1.5(7.6%), P = 0.014, Cohen's d = 0.43). There was no significant difference in the final examination scores between the two groups (Written Test: MD = 0.3(1.6%), P = 0.472). In Laboratory test, both mid-term (MD:0.7(2.8%), P = 0.308) and final examination (MD:0.3(1.5%), P = 0.592) showed no significant difference between two groups. Students provided positive feedback in overall learning self-efficacy after the PUSH course (Mean = 3.68, SD = ±0.56 on a 5-point Likert scale). Learning self-efficacy in the cognitive domain was significantly higher than that in the affective domain (MD = 0.58; P < 0.001) and psychomotor domain (MD = 0.12; P = 0.011). CONCLUSION: The PUSH course featured a hands-on learning design that empowered medical students to improve their anatomy learning.

Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks.

Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart's surface using the potentials recorded at the body's surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs' ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.

Evaluation of an AI-Based TB AFB Smear Screening System for Laboratory Diagnosis on Routine Practice.

The most robust and economical method for laboratory diagnosis of tuberculosis (TB) is to identify mycobacteria acid-fast bacilli (AFB) under acid-fast staining, despite its disadvantages of low sensitivity and labor intensity. In recent years, artificial intelligence (AI) has been used in TB-smear microscopy to assist medical technologists with routine AFB smear microscopy. In this study, we evaluated the performance of a TB automated system consisting of a microscopic scanner and recognition program powered by artificial intelligence and machine learning. This AI-based system can detect AFB and classify the level from 0 to 4+. A total of 5930 smears were evaluated on the performance of this automatic system in identifying AFB in daily lab practice. At the first stage, 120 images were analyzed per smear, and the accuracy, sensitivity, and specificity were 91.3%, 60.0%, and 95.7%, respectively. In the second stage, 200 images were analyzed per smear, and the accuracy, sensitivity, and specificity were increased to 93.7%, 77.4%, and 96.6%. After removing disqualifying smears caused by poor staining quality and smear preparation, the accuracy, sensitivity, and specificity were improved to 95.2%, 85.7%, and 96.9%, respectively. Furthermore, the automated system recovered 85 positive smears initially identified as negative by manual screening. Our results suggested that the automated TB system could achieve higher sensitivity and laboratory efficiency than manual microscopy under the quality control of smear preparation. Automated TB smear screening systems can serve as a screening tool at the first screen before manual microcopy.

Bone-Targeting Nanoparticles of a Dendritic (Aspartic acid)3-Functionalized PEG-PLGA Biopolymer Encapsulating Simvastatin for the Treatment of Osteoporosis in Rat Models.

Simvastatin (SIM) is a lipid-lowering drug that also promotes bone formation, but its high liver specificity may cause muscle damage, and the low solubility of lipophilic drugs limits the systemic administration of SIM, especially in osteoporosis (OP) studies. In this study, we utilized the bone-targeting moiety of dendritic oligopeptides consisting of three aspartic acid moieties (dAsp3) and amphiphilic polymers (poly(ethylene glycol)-block-poly(lactic-co-glycolic acid); PEG-PLGA) to create dAsp3-PEG-PLGA (APP) nanoparticles (NPs), which can carry SIM to treat OP. An in vivo imaging system showed that gold nanocluster (GNC)-PLGA/APP NPs had a significantly higher accumulation rate in representative bone tissues. In vivo experiments comparing low-dose SIM treatment (0.25 mg/kg per time, 2 times per week) showed that bone-targeting SIM/APP NPs could increase the bone formation effect compared with non-bone-targeting SIM/PP NPs in a local bone loss of hindlimb suspension (disuse) model, but did not demonstrate good bone formation in a postmenopausal (ovariectomized) model of systemic bone loss. The APP NPs could effectively target high mineral levels in bone tissue and were expected to reduce side effects in other organs affected by SIM. However, in vivo OP model testing showed that the same lower dose could not be used to treat different types of OP.

Effects of vibrotactile-enhanced music-based intervention on sensorimotor control capacity in the hand of an aging brain: a pilot feasibility randomized crossover trial.

BACKGROUND: Music-based interventions (MBI), using music as a therapeutic medium, has been utilized as a promising strategy for motor relearning and shaping. However, currently, MBI with active performance training is restricted to being extensively applied for patients with various levels of defects in fine motor skills and cognitive functions. Therefore, the integration of vibrotactile stimulation with MBI has been adopted as a motor training strategy intended to enhance motor learning through use of vibration stimuli. The current study was designed to investigate differences in the sensorimotor performance of older adults' hands under baseline, a single session of active MBI, and vibrotactile-enriched MBI conditions. METHODS: Thirty healthy older adults were recruited and randomized to receive either the single session of 30-min of vibrotactile-enriched MBI or 30-min of active MBI at the beginning of the experiment. After a one-week washout period, they switched their treatment programs and then were assessed to study the training effects of both approaches through measuring precision pinch performance, hand function, and sensory status. RESULTS: The results of the Pinch-Holding-Up Activity test revealed a statistically significant difference in the FRpeak parameter (F = 14.37, p < 0.001, η2p = 0.507) under the vibrotactile-enriched MBI condition compared to the baseline and active MBI conditions. In addition, significant beneficial effects were found on the results of the barognosis (F = 19.126, p < 0.001, η2p = 0. 577) and roughness differentiation subtests (F = 15.036, p < 0.001, η2p = 0.518) in the Manual Tactile Test for the participants in the vibrotactile-enriched MBI group. In addition, the participants under both the active MBI and vibrotactile-enriched MBI conditions exhibited better performance in the three subtests of the Purdue Pegboard Test as compared to under the baseline condition (p < 0.016). CONCLUSIONS: The findings indicated that vibrotactile-enriched MBI potentially improves the precision pinch performance of hands in healthy older adults. In addition, the add-on effect of vibrotactile stimulation to the MBI condition provides beneficial effects on the sensory functions of the upper extremities. TRIAL REGISTRATION: NCT04802564 . Date of registration: 15/03/2021. The first posted date: 17/03/2021.

Why do pre-clinical medical students learn ultrasound? Exploring learning motivation through ERG theory.

BACKGROUND: In recent years, point-of-care ultrasound (POCUS) has become an essential field of medical education. Bedside ultrasound has become a necessary skill for clinical physicians. Previous studies have already discussed the importance of advancements in ultrasound education. However, learning motivations for ultrasound education have seldom been analyzed in the literature. For medical students, learning ultrasound could have a relevance for their future career. The Existence, Relatedness and Growth (ERG) theory extended Maslow's hierarchy of needs through these three concepts. This theory has been widely used in the workplace to analyze employee job performance but has not yet been applied in medical education. In this study ERG theory was applied to analyze pre-clinical medical students' learning motivation toward ultrasound education. METHOD: This mixed method study used online questionnaires consisting of open-ended questions as a data collection tool, and based on these results, both qualitative and quantitative analysis were conducted. Participants answered a series of neutral and open-ended questions regarding their motivations to learn ultrasonography. After data collection, a three-step analysis was conducted based on the grounded theory approach. Finally, the results of the thematic coding were used to complete additional quantitative analysis. RESULTS: The study involved 140 pre-clinical medical students, and their responses fell into 13 specific categories. The analysis demonstrated that students' motivations toward ultrasound education were unbalanced across the three ERG domains (F = 41.257, p < .001). Pairwise comparisons showed that students mentioned existence motivation (MD = 39.3%; p < .001) and growth motivation (MD = 40.7%; p < .001) more frequently than relatedness motivation. However, there was no significant difference between existence motivation and growth motivation (MD = - 1.4%; p = .830). CONCLUSION: The results revealed that students placed a high value on existence and growth needs rather than relatedness based on the survey. In addition, the findings suggest that ERG theory can be a useful tool to conduct medical education motivation analysis.

Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time-Frequency Features for the Detection and Severity Classification of Parkinson's Disease.

Conventional approaches to diagnosing Parkinson's disease (PD) and rating its severity level are based on medical specialists' clinical assessment of symptoms, which are subjective and can be inaccurate. These techniques are not very reliable, particularly in the early stages of the disease. A novel detection and severity classification algorithm using deep learning approaches was developed in this research to classify the PD severity level based on vertical ground reaction force (vGRF) signals. Different variations in force patterns generated by the irregularity in vGRF signals due to the gait abnormalities of PD patients can indicate their severity. The main purpose of this research is to aid physicians in detecting early stages of PD, planning efficient treatment, and monitoring disease progression. The detection algorithm comprises preprocessing, feature transformation, and classification processes. In preprocessing, the vGRF signal is divided into 10, 15, and 30 s successive time windows. In the feature transformation process, the time domain vGRF signal in windows with varying time lengths is modified into a time-frequency spectrogram using a continuous wavelet transform (CWT). Then, principal component analysis (PCA) is used for feature enhancement. Finally, different types of convolutional neural networks (CNNs) are employed as deep learning classifiers for classification. The algorithm performance was evaluated using k-fold cross-validation (kfoldCV). The best average accuracy of the proposed detection algorithm in classifying the PD severity stage classification was 96.52% using ResNet-50 with vGRF data from the PhysioNet database. The proposed detection algorithm can effectively differentiate gait patterns based on time-frequency spectrograms of vGRF signals associated with different PD severity levels.

Using Virtual Reality-Based Rehabilitation in Sarcopenic Older Adults in Rural Health Care Facilities-A Quasi-Experimental Study.

Because of a shortage of health care providers, providing rehabilitation in health care facilities is difficult. Virtual reality-based rehabilitation is effective in older populations. There are only a few studies among patients with sarcopenia. This is a quasi-experimental, single-group, pretest-posttest design evaluating the clinical effectiveness of virtual reality-based progressive resistance training among residents aged over 60 years with sarcopenia in rural care facilities. The authors used Oculus Rift with headsets to provide the virtual reality-based progressive resistance training. The authors administered the program twice per week, 30 min per session, for 12 weeks. The primary outcomes were dominant handgrip strength, walking speed, and appendicular skeletal muscle mass index. Data from 30 participants were analyzed. Significant improvements in handgrip strength and walking speed were observed. Although an increasing trend in appendicular skeletal muscle mass index was observed, it did not reach statistical significance. The authors concluded that the virtual reality-based progressive resistance training is partially effective in older sarcopenic adults in health care facilities.

Evaluation of Vertical Ground Reaction Forces Pattern Visualization in Neurodegenerative Diseases Identification Using Deep Learning and Recurrence Plot Image Feature Extraction.

To diagnose neurodegenerative diseases (NDDs), physicians have been clinically evaluating symptoms. However, these symptoms are not very dependable-particularly in the early stages of the diseases. This study has therefore proposed a novel classification algorithm that uses a deep learning approach to classify NDDs based on the recurrence plot of gait vertical ground reaction force (vGRF) data. The irregular gait patterns of NDDs exhibited by vGRF data can indicate different variations of force patterns compared with healthy controls (HC). The classification algorithm in this study comprises three processes: a preprocessing, feature transformation and classification. In the preprocessing process, the 5-min vGRF data divided into 10-s successive time windows. In the feature transformation process, the time-domain vGRF data are modified into an image using a recurrence plot. The total recurrence plots are 1312 plots for HC (16 subjects), 1066 plots for ALS (13 patients), 1230 plots for PD (15 patients) and 1640 plots for HD (20 subjects). The principal component analysis (PCA) is used in this stage for feature enhancement. Lastly, the convolutional neural network (CNN), as a deep learning classifier, is employed in the classification process and evaluated using the leave-one-out cross-validation (LOOCV). Gait data from HC subjects and patients with amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD) obtained from the PhysioNet Gait Dynamics in Neurodegenerative disease were used to validate the proposed algorithm. The experimental results included two-class and multiclass classifications. In the two-class classification, the results included classification of the NDD and the HC groups and classification among the NDDs. The classification accuracy for (HC vs. ALS), (HC vs. HD), (HC vs. PD), (ALS vs. PD), (ALS vs. HD), (PD vs. HD) and (NDDs vs. HC) were 100%, 98.41%, 100%, 95.95%, 100%, 97.25% and 98.91%, respectively. In the multiclass classification, a four-class gait classification among HC, ALS, PD and HD was conducted and the classification accuracy of HC, ALS, PD and HD were 98.99%, 98.32%, 97.41% and 96.74%, respectively. The proposed method can achieve high accuracy compare to the existing results, but with shorter length of input signal (Input of existing literature using the same database is 5-min gait signal, but the proposed method only needs 10-s gait signal).

Development of a Neurodegenerative Disease Gait Classification Algorithm Using Multiscale Sample Entropy and Machine Learning Classifiers.

The prevalence of neurodegenerative diseases (NDD) has grown rapidly in recent years and NDD screening receives much attention. NDD could cause gait abnormalities so that to screen NDD using gait signal is feasible. The research aim of this study is to develop an NDD classification algorithm via gait force (GF) using multiscale sample entropy (MSE) and machine learning models. The Physionet NDD gait database is utilized to validate the proposed algorithm. In the preprocessing stage of the proposed algorithm, new signals were generated by taking one and two times of differential on GF and are divided into various time windows (10/20/30/60-sec). In feature extraction, the GF signal is used to calculate statistical and MSE values. Owing to the imbalanced nature of the Physionet NDD gait database, the synthetic minority oversampling technique (SMOTE) was used to rebalance data of each class. Support vector machine (SVM) and k-nearest neighbors (KNN) were used as the classifiers. The best classification accuracies for the healthy controls (HC) vs. Parkinson's disease (PD), HC vs. Huntington's disease (HD), HC vs. amyotrophic lateral sclerosis (ALS), PD vs. HD, PD vs. ALS, HD vs. ALS, HC vs. PD vs. HD vs. ALS, were 99.90%, 99.80%, 100%, 99.75%, 99.90%, 99.55%, and 99.68% under 10-sec time window with KNN. This study successfully developed an NDD gait classification based on MSE and machine learning classifiers.

Three-dimensional spherical gelatin bubble-based scaffold improves the myotube formation of H9c2 myoblasts.

Microenvironmental factors including physical and chemical cues can regulate stem cells as well as terminally differentiated cells to modulate their biological function and differentiation. However, one of the physical cues, the substrate's dimensionality, has not been studied extensively. In this study, the flow-focusing method with a microfluidic device was used to generate gelatin bubbles to fabricate highly ordered three-dimensional (3D) scaffolds. Rat H9c2 myoblasts were seeded into the 3D gelatin bubble-based scaffolds and compared to those grown on 2D gelatin-coating substrates to demonstrate the influences of spatial cues on cell behaviors. Relative to cells on the 2D substrates, the H9c2 myoblasts were featured by a good survival and normal mitochondrial activity but slower cell proliferation within the 3D scaffolds. The cortical actin filaments of H9c2 cells were localized close to the cell membrane when cultured on the 2D substrates, while the F-actins distributed uniformly and occupied most of the cell cytoplasm within the 3D scaffolds. H9c2 myoblasts fused as multinuclear myotubes within the 3D scaffolds without any induction but cells cultured on the 2D substrates had a relatively lower fusion index even differentiation medium was provided. Although there was no difference in actin α 1 and myosin heavy chain 1, H9c2 cells had a higher myogenin messenger RNA level in the 3D scaffolds than those of on the 2D substrates. This study reveals that the dimensionality influences differentiation and fusion of myoblasts.

Heart Rate Variability is Associated with Memory in Females.

Research into the association between heart rate variability (HRV) and cognitive function is scarce, particularly with regard to gender differences. HRV in 182 healthy volunteers was assessed by the root mean square of the successive difference (RMSSD) and spectrum analysis, while the Wechsler Memory Scale-Revised (WMS-R) was used to determine memory function. Robust and significant associations were found to exist between HRV (RMSSD and high-frequency HRV) and domains of the WMS-R in females. Caution should therefore be taken to control for gender when conducting studies on the relationships between HRV and cognitive variables.

Combination of a Bioceramic Scaffold and Simvastatin Nanoparticles as a Synthetic Alternative to Autologous Bone Grafting.

The fragile nature of porous bioceramic substitutes cannot match the toughness of bone, which limits the use of these materials in clinical load-bearing applications. Statins can enhance bone healing, but it could show rhabdomyolysis/inflammatory response after overdosing. In this study, the drug-containing bone grafts were developed from poly(lactic acid-co-glycolic acid)-polyethylene glycol (PLGA-PEG) nanoparticles encapsulating simvastatin (SIM) (SIM-PP NPs) loaded within an appropriately mechanical bioceramic scaffold (BC). The combination bone graft provides dual functions of osteoconduction and osteoinduction. The mechanical properties of the bioceramic are enhanced mainly based on the admixture of a combustible reverse-negative thermoresponsive hydrogel (poly(N-isopropylacrylamide base). We showed that SIM-PP NPs can increase the activity of alkaline phosphatase and osteogenic differentiation of bone marrow stem cells. To verify the bone-healing efficacy of this drug-containing bone grafts, a nonunion radial endochondral ossification bone defect rabbit model (N = 3/group) and a nonunion calvarial intramembranous defect Sprague Dawley (SD) rat model (N = 5/group) were used. The results indicated that SIM-PP NPs combined with BC can improve the healing of nonunion bone defects of the radial bone and calvarial bone. Therefore, the BC containing SIM-PP NPs may be appropriate for clinical use as a synthetic alternative to autologous bone grafting that can overcome the problem of determining the clinical dosage of simvastatin drugs to promote bone healing.

Chemical Constituents of the Rhizomes of Bletilla formosana and Their Potential Anti-inflammatory Activity.

Nine new phenanthrenes (1-9) and a new benzyl glycoside (10) together with 45 known compounds were isolated from the rhizomes of Bletilla formosana. The structures of 1-10 were elucidated primarily on the basis of their 1D and 2D NMR spectroscopic data. Most of the isolated compounds were evaluated for their anti-inflammatory activities. The results showed that IC50 values for the inhibition of superoxide anion generation and elastase release ranged from 0.2 to 6.5 µM and 0.3 to 5.7 µM, respectively. Structure-activity relationships of the isolated compounds were also investigated. The inhibitory potencies were determined as phenanthrenes > bibenzyls > biphenanthrenes.