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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.

Objectives. Labyrinthitis significantly reduces quality of life due to prolonged vestibular symptoms in patients experiencing sudden sensorineural hearing loss with vertigo (SSNHLV). This study employed a novel coherence analysis in the video head impulse test (vHIT) to explore vertigo outcomes in SSNHLV patients. Methods. A retrospective review was conducted on 48 SSNHLV patients who completed high-dose steroid treatment between December 2016 and April 2023. Additionally, 38 healthy volunteers were prospectively enrolled from November 2022 to April 2023 at our academic tertiary referral center. The magnitude-squared wavelet coherence between eye and head velocities during the vHIT was measured to assess correlations across frequency bands. Recovery from vertigo, determined by a visual analog scale (VAS) score of 0 at both 2 weeks and 2 months, was analyzed using multivariable Cox regression. Results. The mean VAS for patients with SSNHLV was 5.73±2.45. Higher coherent frequencies in the horizontal semicircular canal (SCC), posterior SCC, and the mean and minimal coherent frequencies of all three SCCs combined were significantly associated with early complete remission of vertigo 2 weeks posttreatment. In the multivariate analysis, the minimal coherent frequency among the three SCCs emerged as an independent factor (hazard ratio, 2.040; 95% CI, 1.776–2.304). Two months posttreatment, in addition to the previously significant parameters, abnormalities in the vestibulo-ocular reflex (VOR) in the posterior SCC, gains in the horizontal and posterior SCCs, total and overt saccades in the horizontal SCC, coherent frequency in the anterior SCC, and mean VOR gain of all three SCCs combined were also statistically significantly related to total relief from vertigo. Conclusion. The highest minimal coherent frequency among the three SCCs significantly contributed to earlier vertigo relief in patients with SSNHLV. Coherence analysis in vHIT may offer greater sensitivity than time series analysis for predicting the prognosis of vertigo.

In recent years, the development of biodegradable hydrogels as an alternative over the traditional wound dressing has become increasingly significant. These specific hydrogels are able to offer suitable microenvironments to further aid the process of tissue or organ regeneration. However, application of biodegradable hydrogels in clinical medicine remains uncommon due to most biodegradable hydrogels struggle with achieving satisfactory adhesiveness property, high mechanical support and cell compatibility simultaneously. In order to overcome these constraints and enhance the applicability of biodegradable hydrogels, methods have been employed in this study.By reacting gellan gum with methacrylic anhydride and incorporating a biodegradable protein, keratin, we endowed the hydrogels with high pliability via photo-polymerization chain extension, thereby obtaining a biodegradable hydrogel with exceptional properties. Through a series of in vitro tests, GGMA/keratin hydrogels exhibited great cell compatibility via providing an appropriate environment for cell proliferation. Furthermore, this hydrogel not only exhibits extraordinary adhesive ability on visceral tissues but also extends to scenarios involving skin or organ damage, offering valuable assistance in wound healing. Our design provides a suitable platform for cell proliferation and tissue regeneration, which shows prospects for future medical research and clinical applications.

Abstract Objective This study aimed to clarify the relationship between oral frailty and oral dysbiosis among hospitalized patients aged ≥ 50 years. Methods A prospective observational study was conducted. Number of teeth, masticatory ability, articulatory oral motor skill, tongue pressure, swallowing pressure, and choking were used to assess oral frailty. Saliva samples were collected from the oral cavity for bacterial culture. Results A total 103 in patients enrolled and 53.4% suffered from oral frailty. Oral frailty was found to have a 3.07‐fold correlation with the presence of Enterobacterales in the oral cavity (p = 0.037), especially in poor articulatory oral motor skill, which showed at greater risk of Enterobacterales isolated from the oral cavity by 5.58‐fold (p = 0.01). Conclusion Half of hospitalized patients was found to have oral frailty that was related to more Enterobacterales in the oral cavity. This evidence suggests that the enhancement of articulatory oral motor skills may serve as a potential strategy for mitigating the presence of Enterobacterales within the oral cavity.

Abstract Reactive oxygen species (ROS) have been recognized as prevalent contributors to the development of inner retinal injuries including optic neuropathies such as glaucoma, non‐arteritic anterior ischemic optic neuropathy, traumatic optic neuropathy, and Leber hereditary optic neuropathy, among others. This underscores the pivotal significance of oxidative stress in the damage inflicted upon retinal tissue. To combat ROS‐related challenges, this study focuses on creating an injectable and tissue‐adhesive hydrogel with tailored antioxidant properties for retinal applications. GelCA, a gelatin‐modified hydrogel with photo‐crosslinkable and injectable properties, is developed. To enhance its antioxidant capabilities, curcumin‐loaded polydopamine nanoparticles (Cur@PDA NPs) are incorporated into the GelCA matrix, resulting in a multifunctional nanocomposite hydrogel referred to as Cur@PDA@GelCA. This hydrogel exhibits excellent biocompatibility in both in vitro and in vivo assessments, along with enhanced tissue adhesion facilitated by NPs in an in vivo model. Importantly, Cur@PDA@GelCA demonstrates the potential to mitigate oxidative stress when administered via intravitreal injection in retinal injury models such as the optic nerve crush model. These findings underscore its promise in advancing retinal tissue engineering and providing an innovative strategy for acute neuroprotection in the context of inner retinal injuries.

This study aimed to determine the impact on hearing prognosis of the coherent frequency with high magnitude-squared wavelet coherence (MSWC) in video head impulse test (vHIT) among patients with sudden sensorineural hearing loss with vertigo (SSNHLV) undergoing high-dose steroid treatment. This study was a retrospective cohort study. SSNHLV patients treated at our referral center from December 2016 to December 2020 were examined. The cohort comprised 64 patients with SSNHLV undergoing high-dose steroid treatment. MSWC was measured by calculating the wavelet coherence analysis (WCA) at various frequencies from a vHIT. The hearing prognosis were analyzed using a multivariable Cox regression model and convolution neural network (CNN) of WCA. There were 64 patients with a male-to-female ratio of 1:1.67. The greater highest coherent frequency of the posterior semicircular canal (SCC) was associated with the complete recovery (CR) of hearing. After adjustment for other factors, the result remained robust (hazard ratio [HR] 2.11, 95% confidence interval [CI] 1.86-2.35). In the feature extraction with Resnet-50 and proceeding SVM in the horizontal image cropping style, the classification accuracy [STD] for (CR vs. partial + no recovery [PR + NR]), (over-sampling of CR vs. PR + NR), (extensive data extraction of CR vs. PR + NR), and (interpolation of time series of CR vs. PR + NR) were 83.6% [7.4], 92.1% [6.8], 88.9% [7.5], and 91.6% [6.4], respectively. The high coherent frequency of the posterior SCC was a significantly independent factor that was associated with good hearing prognosis in the patients who have SSNHLV. WCA may be provided with comprehensive ability in vestibulo-ocular reflex (VOR) evaluation. CNN could be utilized to classify WCA, predict treatment outcomes, and facilitate vHIT interpretation. Feature extraction in CNN with proceeding SVM and horizontal cropping style of wavelet coherence plot performed better accuracy and offered more stable model for hearing outcomes in patients with SSNHLV than pure CNN classification. Clinical and Translational Impact Statement—High coherent frequency in vHIT results in good hearing outcomes in SSNHLV and facilitates AI classification.

BackgroundTo 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. ObjectiveThe 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). MethodsViral 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. ResultsThe 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

Monascin (MS) and ankaflavin (AK) produced by Monascus purpureus fermented red mold dioscorea (RMD) elicit an anti-diabetes effect in streptozocin-induced type 1 diabetes. However, their effects on preventing metabolic disorder with type 2 diabetes induced by long-term high fat and high fructose diet (HFFD) remain unclear. In this study, RMD, MS, and AK were orally administered to HFFD-fed rats for 10 weeks. The result showed that orally administered MS, AK, and RMD were able to prevent the develop-ment. MS had a more potent effect on lowering blood glucose, fructosamine, and insulin resistance, and increasing hepatic glucose transporters-2, adipocyte glucose transporters-4, and hypoxia-inducible factor 1-α. AK had a greater effect on lowering blood lipid, adipose pad, and adipose inflammatory factors (interlukin-1β, tumor necrosis factor-α, and interlukin-6) levels. Therefore, the prevention of type 2 diabetes and metabolic disorders of RMD could be mainly attributed to the synergistic effect of MS and AK.

Monascus purpureus-produced monascin (MS) and ankaflavin (AK) have the potential to improve memory deficiency in Alzheimer's disease rat models; however, the mechanisms are still unclear. We aimed to investigate the effects and mechanisms of MS and AK on preventing Aβ40-induced oxidative stress and inflammatory response through in vitro and in vivo studies. Aβ40-treated PC-12 cells and intracerebroventricularly Aβ40-infused rats were used as the in vitro and in vivo models, respectively. MS and AK enhanced cell viability in Aβ40-treated PC-12 cells. Furthermore, the expression levels of oxidative and inflammatory factors, including COX-2, IL-1β, IL-6, iNOS, NF-κB, and TNF-α, were repressed by MS and AK treatment in the in vitro and in vivo models. The anti-oxidative and anti-inflammatory effects were the probable reasons for repressing Aβ40-induced p-tau protein expression, Aβ fibrils formation, neurotoxicity, and memory deficiency.

Abstract 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

Objective: A neurodegenerative disease (NDD) detection algorithm using a convolutional neural network (CNN) and wavelet coherence spectrogram of gait synchronization was developed to classify NDD based on gait force signals. The main purpose of this research was to help physicians with screening for NDD for early diagnosis, efficient treatment planning, and monitoring of disease progression. Methods: The NDD detection algorithm was evaluated using the existing online database from Physionet by Hausdorff et al., called gait in neurodegenerative disease database, comprised of windowing, feature transformation, and classification processes. Force pattern variations among healthy control (HC) and patients with ALS, HD, and PD were distinctly observed from feature-extracted wavelet coherence spectrogram images. Results: HC was balanced because their left and right feet supported each other when walking. In patients with ALS, the left-right foot correlation was weaker than that in HC. In patients with HD, walking velocity varied, which indicated that only one foot (right or left) was dominant and sustained the entire body’s balance during movement. The left and right feet of patients with PD were correlated and coordinated in terms of supporting lower-body movements. The right foot was always on the ground to support the entire body when walking. Conclusion: The proposed NDD detection algorithm effectively differentiates gait patterns on the basis of a time-frequency spectrogram of gait force signals between HC and NDD patients with an overall sensitivity of 94.34%, specificity of 96.98%, accuracy of 96.37%, and AUC value of 0.97 using 5-fold cross-validation.

ANKASCIN 568-R is an extract derived from red mold rice (RMR) fermented using Monascus purpureus NTU 568. RMR fermented using M. purpureus NTU 568 prevents cardiovascular diseases and decreases blood lipid levels. This study evaluates the safety of ANKASCIN 568-R, since it has not determined yet. After daily oral ANKASCIN 568-R for 13 consecutive weeks, we evaluated the toxicity tolerance of Sprague-Dawley rats and performed dose formulation analysis on monascin and ankaflavin. The dose formulation analysis showed that ANKASCIN 568-R concentrations were lower than the target concentration and out of range ( ± 15%) at week 8 and on the last dosing day for both monascin (all dose groups) and ankaflavin at the 100 mg/kg dose. The lowest reported concentrations for the low, middle, and high dose formulations were 34.7, 115.2, and 398.1 mg/mL, respectively. We also evaluated the genotoxicity of ANKASCIN 568-R and showed no genotoxicity potential at all ANKASCIN 568-R doses investigated. The no observed adverse effect level of ANKASCIN 568-R was determined to be 796.2 mg/kg/day. This study revealed the first toxicity evaluation data of ANKASICN 568-R, and the data demonstrated ANKASICN 568-R was safe and can be used in daily life.

Abstract 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

The COVID-19 pandemic has become a major cause of rapid globalization and digitization of educational institutions, including medical education. The adaptation to digital technologies is the purpose of best education and training practices in the development of the academic medical curriculum. Virtual reality (VR) is embraced by the 3D environment and network resources which allow the expansion of VR from the entertainment industry to the education industry. This brief communication explains our understanding and the challenges in adopting VR technologies for medical training at an academic medical center. Advancement in VR technology assists medical institutes to strategize for the further development of medical training and education. There is a timely need for persistence to make the VR content accessible widely and open source. There is an urgent need for collaboration of medical institutes and technology industries on the development of education-related VR content and simulations.

(1) Background: Parkinson’s disease (PD) is the second most common neurodegenerative disease. Early diagnosis and reliable clinical assessments are essential for appropriate therapy and improving patients’ quality of life. Keystroke biometrics, which capture unique typing behavior, have shown potential for early PD diagnosis. This study aimed to evaluate keystroke biometric parameters from two datasets to identify indicators that can effectively distinguish de novo PD patients from healthy controls. (2) Methods: Data from natural typing tasks in Physionet were analyzed to estimate keystroke biometric parameters. The parameters investigated included alternating-finger tapping (afTap) and standard deviations of interkey latencies (ILSD) and release latencies (RLSD). Sensitivity rates were calculated to assess the discriminatory ability of these parameters. (3) Results: Significant differences were observed in three parameters, namely afTap, ILSD, and RLSD, between de novo PD patients and healthy controls. The sensitivity rates were high, with values of 83%, 88%, and 96% for afTap, ILSD, and RLSD, respectively. Correlation analysis revealed a significantly negative correlation between typing speed and number of words typed with the standard motor assessment for PD, UPDRS-III, in patients with early PD. (4) Conclusions: Simple algorithms utilizing keystroke biometric parameters can serve as effective screening tests in distinguishing de novo PD patients from healthy controls. Moreover, typing speed and number of words typed were identified as reliable tools for assessing clinical statuses in PD patients. These findings underscore the potential of keystroke biometrics for early PD diagnosis and clinical severity assessment.

Abstract 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

ABSTRAK Cardiac arrest merupakan permasalahan kesehatan yang signifikan. CPR dapat mengurangi resiko, namun tidak semua orang dapat melakukan CPR dengan benar. Berdasarkan permasalahan tersebut maka dibuat simulator pelatihan CPR yang dapat mengamati kedalaman dan frekuensi penekanan saat melakukan CPR. Simulator pelatihan CPR dibuat dengan sistem akuisisi data berupa estimasi kedalaman dan panduan kompresi. Sensor yang digunakan adalah accelerometer MMA 7361 dan force sensor FSR 406. Kedalaman dari akselerasi dapat diperoleh dengan mengintegralkan data accelerometer sebanyak dua kali. Integral dilakukan persiklus kompresi untuk mengurangi akumulasi error. Sistem panduan kompresi terdiri atas metronom sebagai acuan kecepatan kompresi, dan LED indikator frekuensi kompresi. Simulator pelatihan CPR ini memberikan estimasi kedalaman yang baik pada spons dan bantal menggunakan sampling rate sebesar 1 kHz dan integral per siklus kompresi. Kata kunci: Pelatihan CPR, Akuisisi Kedalaman, FSR 406, MMA7361 Â ABSTRACT Cardiac arrest is a significant health problem. CPR can reduce risk, but not everyone can perform CPR correctly. Based on these problems, a CPR training simulator was created that can observe the depth and frequency of compressions when performing CPR. The CPR training simulator is built with a data acquisition system in the form of depth estimation and compression guidance. The sensors used are the MMA 7361 accelerometer and the FSR 406 force sensor. The depth of acceleration can be obtained by integrating the accelerometer data 2 times. The integral is performed per cycle of compression to reduce the accumulation of errors. The compression guidance system consists of a metronome as a reference for compression speed, and a compression frequency indicator LED. This CPRÂ training simulator provides good depth estimation in sponges and pillows using a sampling rate of 1 kHz and integrals per compression cycle. Keywords: CPR Training, Depth Acquisition, FSR 406, MMA7361

Objective: This study presents a low-memory-usage ectopic beat classification convolutional neural network (CNN) (LMUEBCNet) and a correlation-based oversampling (Corr-OS) method for ectopic beat data augmentation. Methods: A LMUEBCNet classifier consists of four VGG-based convolution layers and two fully connected layers with the continuous wavelet transform (CWT) spectrogram of a QRS complex (0.712 s) segment as the input of the LMUEBCNet. A Corr-OS method augmented a synthetic beat using the top K correlation heartbeat of all mixed subjects for balancing the training set. This study validates data via a 10-fold cross-validation in the following three scenarios: training/testing with native data (CV1), training/testing with augmented data (CV2), and training with augmented data but testing with native data (CV3). Experiments: The PhysioNet MIT-BIH arrhythmia ECG database was used for verifying the proposed algorithm. This database consists of a total of 109,443 heartbeats categorized into five classes according to AAMI EC57: non-ectopic beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), a fusion of ventricular and normal beats (F), and unknown beats (Q), with 90,586/2781/7236/803/8039 heartbeats, respectively. Three pre-trained CNNs: AlexNet/ResNet18/VGG19 were utilized in this study to compare the ectopic beat classification performance of the LMUEBCNet. The effectiveness of using Corr-OS data augmentation was determined by comparing (1) with/without using the Corr-OS method and (2) the Next-OS data augmentation method. Next-OS augmented the synthetic beat using the next heartbeat of one subject. Results: The proposed LMUEBCNet can achieve a 99.4% classification accuracy under the CV2 and CV3 cross-validation scenarios. The accuracy of the proposed LMUEBCNet is 0.4–0.5% less than the performance obtained from AlexNet/ResNet18/VGG19 under the same data augmentation and cross-validation scenario, but the parameter usage is only 10% or less than that of the AlexNet/ResNet18/VGG19 method. The proposed Corr-OS method can improve ectopic beat classification accuracy by 0.3%. Conclusion: This study developed a LMUEBCNet that can achieve a high ectopic beat classification accuracy with efficient parameter usage and utilized the Corr-OS method for balancing datasets to improve the classification performance.

Mirror therapy (MT) has been proposed as an essential component of upper limb neurorehabilitation, using mirror illusion of the unaffected hand movement and superimposing it on the affected hand to improve neuroplasticity for improving motor recovery in stroke patients. In this paper, we developed a virtual reality mirror therapy (VRMT) system, and examined the performance of the proposed VRMT system. The VRMT system consists of a motion-tracking device, a VR goggle, and a VRMT software. Young, healthy participants, and stroke patients with mild-to-severe hemiparesis were recruited in this study, to validate the effectiveness of the proposed VRMT system. Experimental results based on the pinch-holding-up activity (PHUA) test revealed a significant effect on the precision pinch performance of young-healthy participants receiving VRMT, whereas traditional MT could not improve the precision pinch performance in young-healthy participants (Wilcoxon signed-rank test, Z = -2.693, p = 0.007). For patients having suffered a stroke and being treated with VRMT, significant beneficial effects examined by mixed effect model were found on the total score of FMA (p = 0.033) and hand part of FMA (p = 0.008). The findings in this study indicate that the VRMT system has a potentially positive effects on the sensorimotor performance of hands in healthy participants. In addition, add-on effect of VR to MT provides beneficial effects on motor function of the upper extremity in chronic stroke patients.

Abstract Background With currently 1980 described species, the mega-diverse Begonia is now perhaps the 5th largest flowering plant genus, expanding rapidly from ca. 900 species in 1997 to its current size in merely two decades. In continuation of our studies of Asian Begonia, we report six additional new species from Guangxi, the region/province harboring the second richest Begonia flora of China. Results Based on morphological and molecular data, the new species B. aurora belongs to Begonia sect. Platycentrum, while the other five new species (viz. B. larvata, B. longiornithophylla, B. lui, B. scabrifolia, and B. zhuoyuniae) are members of Sect. Coelocentrum. Somatic chromosome numbers of B. longiornithophylla and B. zhuoyuniae at metaphase were counted as 2n = 30, consistent with previously reports for Sect. Coelocentrum. Conclusions With the addition of the six new species, the total number of Begonia species in Guangxi increases from 86 to 92. Detailed description, line drawings, and color plates are provided to aid in identification.

Background: A new wearable pulse audiogram (PAG) of radial artery was developed with the main purpose to quickly screen atrial fibrillation (AF) and monitor pulse amplitude variation. Methods: Subjects with sinus rhythm (SR), AF, ectopic arrhythmia (EA), and a pacemaker rhythm (PM) were recruited to measure the PAG of radial artery. In total, 91 subjects were recruited: SR (n =45), AF (n = 21), EA (n =11), and PM (n =14). For signal processing, the inter-pulse interval (IPI) and pulse height (PH) were extracted. Then, an automatic classification algorithm combining fuzzy c-means (FCM) or sample entropy (CEn) with an adaptive-network-based fuzzy inference system was constructed. The PAG data were divided into different segment lengths (10 to 30 beats) to investigate the robustness of the algorithm in short intervals. Furthermore, linear regression was performed to evaluate the relation between the normalized IPI and PH in the AF group. Results: The identification rate of AF increased with the number of beats and decreased with the number of classified types of arrhythmia. Results of combining CEn and FCM, or of FCM alone were better than those of CEn alone. When the combined method was used for the two types of arrhythmia and the number of beats was greater than 10, the rate of successful identification was greater than 90%, validating the technique. Furthermore, for the AF group, PH increased with IPI, while the amplitude of electrocardiogram (ECG) did not. Conclusions: Results indicated that our PAG can effectively identify AF, even in a time window as short as 10 beats. In addition, PAG can monitor the trend of pulse amplitude, possibility that cannot be offered by an ECG.

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.

Bredia (Melastomataceae) is an Asian genus that extends from central and southern mainland China to Taiwan and the Ryukyu islands. Molecular phylogenetic analyses reveal that the type of Bredia is nested in a clade of 20 species, while Tashiroea, a genus previously synonymized in Bredia, falls in another distantly related clade of 10 species. Our morphological survey shows that the two clades can be distinguished by several diagnostic features including leaf indumentum, texture, leaf surface sculpture under SEM, presence/absence of yellowish uniseriate trichomes, and capsule morphology. Based on molecular and morphological evidence, Bredia is recircumscribed and Tashiroea is resurrected. Description and a list of species are provided for the two genera with the description of a new species, T. villosa.

Abstract Background Melastoma has undergone rapid species radiation during the last one million years, and circumscription of some species in the genus has remained controversial. Melastoma kudoi, an erect species narrowly endemic to central Taiwan was previously treated as a synonym of M. intermedium, a semicreeping hybrid between the erect species M. candidum and the creeping M. dodecandrum, making its identity questionable. We addressed this question based on molecular and morphological data. Results Phylogenetic analyses based on nrITS sequence data revealed that M. kudoi is most closely related to M. dodecandrum. Further analyses of six nuclear genes (cam, chi, gapC, gbss, tpi and vr) and two chloroplast markers (trnL–trnF and psbA) showed that M. kudoi is well diverged from its close relatives. Morphologically, it is also easily distinguished from related species by its erect habit, center-positioned stigma, and spreading, basally enlarged hairs on the hypanthium. Conclusions Both molecular phylogenetic and morphological data suggest that M. kudoi is well separated from M. intermedium, M. dodecandrum, and O. scaberrima, and should be treated as a distinct species. Taxonomic treatment and detailed description of M. kudoi are provided.

Background: Although polysomnography (PSG) is a gold standard tool for diagnosing sleep apnea (SA), it can reduce the patient’s sleep quality by the placement of several disturbing sensors and can only be interpreted by a highly trained sleep technician or scientist. In recent years, electrocardiogram (ECG)-derived respiration (EDR) and heart rate variability (HRV) have been used to automatically diagnose SA and reduce the drawbacks of PSG. Up to now, most of the proposed approaches focus on machine-learning (ML) algorithms and feature engineering, which require prior expert knowledge and experience. The present study proposes an SA detection algorithm to differentiate a normal and apnea event using a deep-learning (DL) framework based on 1D and 2D deep CNN with empirical mode decomposition (EMD) of a preprocessed ECG signal. The EMD is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. In addition, the simple and compact architecture of 1D deep CNN, which only performs 1D convolutions, and pretrained 2D deep CNNs, are suitable for real-time and low-cost hardware implementation. Method: This study was validated using 7 h to nearly 10 h overnight ECG recordings from 33 subjects with an average apnea-hypopnea index (AHI) of 30.23/h originated from PhysioNet Apnea-ECG database (PAED). In preprocessing, the raw ECG signal was normalized and filtered using the FIR band pass filter. The preprocessed ECG signal was then decomposed using the empirical mode decomposition (EMD) technique to generate several features. Several important generated features were selected using neighborhood component analysis (NCA). Finally, deep learning algorithm based on 1D and 2D deep CNN were used to perform the classification of normal and apnea event. The synthetic minority oversampling technique (SMOTE) was also applied to evaluate the influence of the imbalanced data problem. Results: The segment-level classification performance had 93.8% accuracy with 94.9% sensitivity and 92.7% specificity based on 5-fold cross-validation (5fold-CV), meanwhile, the subject-level classification performance had 83.5% accuracy with 75.9% sensitivity and 88.7% specificity based on leave-one-subject-out cross-validation (LOSO-CV). Conclusion: A novel and robust SA detection algorithm based on the ECG decomposed signal using EMD and deep CNN was successfully developed in this study.

Study objectiveThe purpose of this feasibility study is to develop and validate a new assessment tool and scoring system for multitasking competency for physicians in-training in a timed simulated setting. The multitasking competency includes ability to appropriately prioritize and implement tasks for different patients who present simultaneously.MethodsWe designed three single task stations with different levels of difficulty and priority. These skill stations were then combined to create a multitasking simulation scenario. Skill checklists and the global rating scale were utilized to assess the participants' performance. A multitasking score, multitasking index, and priority score were developed to measure the multitasking ability of participants.ResultsThirty-three first-year postgraduate physicians were recruited for this prospective study. The total performance scores were significantly higher for the single-tasking stations than for the multitasking scenario. In terms of the time needed to complete the tasks, the participants spent more time on the multitasking scenario than on the single-tasking scenario. There were significant correlations between the global rating scale and the multitasking score (rho = 0.693, p < 0.001) and between the global rating scale and the multitasking index (rho = 0.515, p < 0.001). The multitasking score, multitasking index, and priority score did not have any significant correlations with the total single-tasking score.ConclusionWe demonstrated that the use of a simulated multitasking scenario could be an effective method of assessing multitasking ability and allow assessors to offer better quality feedback.

PurposeThe use of Virtual Reality (VR) in health professions education has increased dramatically in recent years, yet there is limited evidence of its impact on educational outcomes. The purpose of the study was to assess the impact of VR anatomy instruction on the ultrasound competency of novice learners participating in a ultrasonography workshop.MethodWe designed a VR-enhanced ultrasonography training program and utilized a plane transection tool to interact with a three-dimensional (3D) VR model of the human body which facilitated the 3D conceptualization of the spatial relationship of anatomical structures, leading to faster and better development of ultrasonographic competency. This was a randomized control study which enrolled third-year medical students (n = 101) without previous exposure to formal or informal ultrasonography training. The participants were randomly divided into an intervention and control group. We assessed participants' competency through ultrasound performance stations on live subjects, we also measured anatomical and ultrasound image identification ability using multiple choice tests.ResultParticipants in the intervention group (median = 16; interquartile 13 to 19) had significantly higher scores in ultrasonography task performance tests than the control group (median = 10; interquartile 7 to 14; Mann-Whitney U = 595; P < 0.01). In sub-group analysis, the intervention group performed significantly better in the six out of ten ultrasound tasks. Participants in the intervention group also had greater improvement in ultrasonographic image identification MCQ tests than the control group (Mann-Whitney U = 914; P < 0.05).ConclusionThis study suggests that VR-enhanced anatomical training could be of significant benefit in ultrasonography training by promoting a better understanding of the spatial relationships of anatomical structures and the development of early psychomotor skills transferable to the handling of ultrasonographic probes.

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.

A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression. The detection algorithm comprises a preprocessing process, a feature transformation process, and a classification process. In the preprocessing process, the five-minute vertical ground reaction force signal was divided into 10, 30, and 60 s successive time windows. In the feature transformation process, the time–domain vGRF signal was modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, feature enhancement with principal component analysis (PCA) was utilized. Finally, a convolutional neural network, as a deep learning classifier, was employed in the classification process of the proposed detection algorithm and evaluated using leave-one-out cross-validation (LOOCV) and k-fold cross-validation (k-fold CV, k = 5). The proposed detection algorithm can effectively differentiate gait patterns based on a time–frequency spectrogram of a vGRF signal between HC subjects and patients with neurodegenerative diseases.

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.

Abstract Background The flora of Panay Island is under-collected compared with the other islands of the Philippines. In a joint expedition to the island, botanists from Taiwan and the Philippines found three unknown Begonia species and compared them with potentially allied species. Results The three species are clearly assignable to Begonia sect. Baryandra which is largely endemic to the Philippines. Studies of literature, herbarium specimens, and living plants support the recognition of the three new species: Begonia culasiensis, B. merrilliana, and B. sykakiengii. Somatic chromosomes at metaphase were determined to be 2n = 30 for B. culasiensis and 2n = 28 for both B. merrilliana and B. sykakiengii, congruent with those of most species in sect. Baryandra. Molecular phylogenetic evidence is consistent with B. culasiensis being a relict from the late Miocene and B. merrilliana and B. sykakiengii being younger species of Pleistocene origin. Conclusion The continuing discovery of endemic Philippine species means the remaining fragments of both primary and secondary native vegetation in the archipelago are of increasing value in terms of natural capital. A secure future for the species could be realized through ex situ conservation collections and raising awareness with community groups.

Sonerileae/Dissochaeteae (Melastomataceae) comprises ca. 50 genera, two thirds of which occur in Southeast Asia. Phylogenetic relationships within this clade remain largely unclear, which hampers our understanding of its origin, evolution, and biogeography. Here, we explored the use of chloroplast genomes in phylogenetic reconstruction of Sonerileae/Dissochaeteae, by sampling 138 species and 23 genera in this clade. A total of 151 complete plastid genomes were assembled for this study. Plastid genomic data provided better support for the backbone of the Sonerileae/Dissochaeteae phylogeny, and also for relationships among most closely related species, but failed to resolve the short internodes likely resulted from rapid radiation. Trees inferred from plastid genome and nrITS sequences were largely congruent regarding the major lineages of Sonerileae/Dissochaeteae. The present analyses recovered 15 major lineages well recognized in both nrITS and plastid phylogeny. Molecular dating and biogeographical analyses indicated a South American origin for Sonerileae/Dissochaeteae during late Eocene (stem age: 34.78 Mya). Two dispersal events from South America to the Old World were detected in late Eocene (33.96 Mya) and Mid Oligocene (28.33 Mya) respectively. The core Asian clade began to diversify around early Miocene in Indo-Burma and dispersed subsequently to Malesia and Sino-Japanese regions, possibly promoted by global temperature changes and East Asian monsoon activity. Our analyses supported previous hypothesis that Medinilla reached Madagascar by transoceanic dispersal in Miocene. In addition, generic limits of some genera concerned were discussed.

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.

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).

The Philippine island of Palawan is highly biodiverse. During fieldwork there in 2011 & 2014 we found five unknown species in the large genus Begonia. The species are similar in their rhizomatous stems, four-tepaled flowers, inferior two- or three-locular ovaries with bilamellate placentas, and are assignable to Begonia sect. Baryandra. Our observations support the recognition of these as five new species endemic to Palawan: B. elnidoensis, B. gironellae, B. quinquealata, B. tabonensis and B. tenuibracteata which are described here. The five new species were added to phylogenies based Bayesian analysis of nrDNA (ITS) and chloroplast DNA (ndhA, ndhF-rpl32, rpl32-trnL, trnC-trnD), along with 45 other allied ingroup species. A majority of the species show incongruent positions in the two phylogenies, with evidence of prevalent chloroplast capture. Models show chloroplast capture is more likely in plant populations with high levels of inbreeding following a reduction in selfing rate after hybridisation; we suggest that this is a possible explanation for the massive amount of chloroplast exchange seen in our phylogeny, as Begonia species often exist as small isolated populations and may be prone to inbreeding depression. Our data also indicate a level of nuclear genetic exchange between species. The high prevalence of hybrid events in Begonia is potentially an important factor in driving genomic change and species evolution in this mega-diverse genus.

Although the central autonomic network (CAN) has been well researched in animal models, the CAN in humans is still unclear, especially for cardiovascular control. This study aimed to investigate which areas of the cerebral cortices are associated with the peripheral cardiac autonomic control involved in the CAN in uremic patients with autonomic dysfunction and normal controls. The central and peripheral autonomic network in 19 uremic patients with significant autonomic dysfunction and 24 age- and sex-matched controls [mean age ± standard deviation (SD), 55.16 ± 10.45 years and 55.42 ± 5.42 years, respectively] were evaluated by simultaneous spectral analysis of electroencephalography (EEG) and electrocardiography recording (ECG), along with serial autonomic tests [autonomic questionnaire and orthostatic blood pressure (BP) change]. Only frequency-domain heart rate variability (f-HRV) during the deep-breathing stage could differentiate the two groups. Although there is no significant difference in f-HRV during the quiet-breathing stage, different patterns of central oscillation and their correlation with peripheral cardiac autonomic indices could be found for the two groups. Although the power of specific EEG bands under electrode T3 and T6 correlated significantly with the power of peripheral HRV indices in the control group, those under electrodes P3 and Pz had significant correlations in the uremic group suggesting a role of functional connectivity between them. In addition, sympathetic activity is correlated with slow wave EEG (theta/delta) power whereas parasympathetic activity is correlated with fast wave EEG (beta) power. In conclusion, there is functional connectivity between the parietal cortex and the peripheral cardiac autonomic system (PAN) in uremics and the pattern of central autonomic connectivity differs between uremic patients with autonomic dysfunction and normal controls.

Neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Parkinson’s diseases (PD), and Huntington’s disease (HD) are not rare neurological diseases. They affect different neurological systems and present various characteristic gait abnormalities. We retrieved gait signals of the right and left feet from a public domain on the Physionet. There were 13 patients with ALS, 15 patients with PD, 20 patients with HD and 16 healthy controls (HC). We used multiscale approximate entropy (MAE) to analyze ground reaction force on both feet. Our study shows that MAE increases with scales in all tested subjects. The group HD has the highest MAE and group ALS has the lowest MAE. We can differentiate ALS from HC by MAE, while scale factors >10 in the left foot. There are few significant differences of MAE between the HC and HD. We found a good correlation of MAE between both feet in group ALS. In conclusion, our results indicate that MAE analysis of gait signals can be used for diagnosis and long-term assessment for ALS and probably HD. Similarity of MAE between both feet can also be a diagnostic marker for ALS.

This paper proposes a new optical design that will cooperate with 3D image technology, infrared spectrum technology, future medical diagnostics, the cloud, and big data analysis. We first conducted image recognition experiments to compare the pros and cons of 2D and 3D frameworks in order to make sure that the optical and mechanical framework of a glasses-type 3D ophthalmoscope would be a better choice. The experimental results showed that a 3D image recognition rate (90%) was higher than a 2D image recognition rate (84%), and hence the 3D mechanism design was selected. The glasses-type 3D ophthalmoscope design is primarily based on the specification of indirect ophthalmoscope requirements and two working spectrums: a near infrared and a visible spectrum. The design is a 2.5x magnification fixed focal telecentric relay system with a right-angle prism, which uses a large aperture to increase the amount of incident light (F/# = 2.0). As the infrared spectrums that have better transmittance towards human eye tissue are 965 nm and 985 nm, so that we took account of the visible spectrum and the near-infrared spectrum simultaneously to increase the basis of the physician’s diagnosis. In this research, we conclude that a wearable ophthalmoscope can be designed optically and mechanically with 3D technology, an infrared and a visible working spectrum and further, possibly in cooperation with the cloud and big data analysis.

We demonstrate the performance of a novel cyan light-emitting diode (LED) on a patterned sapphire (PS) substrate as a light source for plastic optical fiber (POF) communications with the central wavelength at 500 nm. To further enhance the external quantum efficiency (EQE) and output power of this miniaturized high-speed LED, a LED with a PS substrate is adopted. Furthermore, by greatly reducing the number of active $\hbox{In}_{\rm x}\hbox{Ga}_{1 - {\rm x}}\hbox{N/GaN}$ multiple quantum wells (MQWs) to four and minimizing the device active area, we can achieve a record-high electrical-to-optical (E–O) bandwidth (as high as 400 MHz) among all the reported high-speed visible LEDs under a very small dc bias current (40 mA). The fiber coupling efficiency has been improved in 4 dB using lens with a 500-$\mu \hbox{m}$ diameter mounted on the LED chip. Thus, the maximum fiber-coupled power was $-$2.67 dBm at the bias current of 40 mA. The 1.07-Gb/s data transmissions over a 50-m SI-POF fiber have been successfully demonstrated using this device at the bias current of 40 mA.

Methylene blue (MB) is a widely used dye and photodynamic therapy (PDT) agent that can produce reactive oxygen species (ROS) after light exposure, triggering apoptosis. However, it is hard for the dye to penetrate through the cell membrane, leading to poor cellular uptake; thus, drug carriers, which could enhance the cellular uptake, are a suitable solution. In addition, the defective vessels resulting from fast vessel outgrowth leads to an enhanced permeability and retention (EPR) effect, which gives nanoscale drug carriers a promising potential. In this study, we applied poly(12-(methacryloyloxy)dodecyl phosphorylcholine), a zwitterionic polymer-lipid, to self-assemble into liposomes and encapsulate MB (MB-liposome). Its properties of high stability and fast intracellular uptake were confirmed, and the higher in vitro ROS generation ability of MB-liposomes than that of free MB was also verified. For in vivo tests, we examined the toxicity in mice via tail vein injection. With the features found, MB-liposome has the potential of being an effective PDT nano agent for cancer therapy.

The intermetallic compounds formed during the diffusion soldering of a Bi2Te2.55Se0.45 thermoelectric material with a Cu electrode are investigated. For this bonding process, Bi2Te2.55Se0.45 was pre-coated with a 1 μm Sn thin film on the thermoelectric element and pre-heated at 250 °C for 3 min before being electroplated with a Ni barrier layer and a Ag reaction layer. The pre-treated thermoelectric element was bonded with a Ag-coated Cu electrode using a 4 μm Sn interlayer at temperatures between 250 and 325 °C. The results indicated that a multi-layer of Bi–Te–Se/Sn–Te–Se–Bi/Ni3Sn4 phases formed at the Bi2Te2.55Se0.45/Ni interface, ensuring sound cohesion between the Bi2Te2.55Se0.45 thermoelectric material and Ni barrier. The molten Sn interlayer reacted rapidly with both Ag reaction layers to form an Ag3Sn intermetallic layer until it was completely exhausted and the Ag/Sn/Ag sandwich transformed into a Ag/Ag3Sn/Ag joint. Satisfactory shear strengths ranging from 19.3 and 21.8 MPa were achieved in Bi2Te2.55Se0.45/Cu joints bonded at 250 to 300 °C for 5 to 30 min, dropping to values of about 11 MPa for 60 min, bonding at 275 and 300 °C. In addition, poor strengths of about 7 MPa resulted from bonding at a higher temperature of 325 °C for 5 to 60 min.

Thismia (Thismiaceae), whose distributions are currently known from Asia, Australia, and South America, is newly recorded in the Solomon Islands. Through extensive field explorations, three new species of Thismia were discovered in the rainforest of the Solomon Islands and are herein delineated as T. occasa, T. chicoreoides, and T. solomonensis, all belonging to sect. Sarcosiphon. Comprehensive species descriptions, line drawings, and color plates are furnished for each species, accompanied by a distribution map delineating their respective ranges. [ABSTRACT FROM AUTHOR]

Sonerila quangnamensis, a new species from Truong Son Mountain Range, Vietnam, is described and illustrated here. Like S. plagiocardia, S. quangnamensis has obliquely ovate leaves, which are unequal in size within each pair. However, S. quangnamensis is obviously distinct in several characters: the cross-section of its stems and petioles is terete (vs. 4-winged), the adaxial surface of its leaves is covered with hispidulous-pilose (vs. puberulous), the hypanthium is unribbed (vs. 6-ribbed), and its anthers are uniformly yellow (vs. purple to purplish-pink, yellow-tinged towards the base). [ABSTRACT FROM AUTHOR]