Intelligent Bladder Volume Monitoring for Wearable Ultrasound Devices: Enhancing Accuracy through Deep Learning-based Coarse-to-Fine Shape Estimation.

Accurate and continuous bladder volume monitoring is crucial for managing urinary dysfunctions. Wearable ultrasound devices offer a solution by enabling non-invasive and real-time monitoring. Previous studies have limitations in power consumption and computation cost or quantitative volume estimation capability. To alleviate this, we present a novel pipeline that effectively integrates conventional feature extraction and deep learning to achieve continuous quantitative bladder volume monitoring efficiently. Particularly, in the proposed pipeline, bladder shape is coarsely estimated by a simple bladder wall detection algorithm in wearable devices, and the bladder wall coordinates are wirelessly transferred to an external server. Subsequently, a roughly estimated bladder shape from the wall coordinates is refined in an external server with a diffusion-based model. With this approach, power consumption and computation costs on wearable devices remained low, while fully harnessing the potential of deep learning for accurate shape estimation. To evaluate the proposed pipeline, we collected a dataset of bladder ultrasound images and RF signals from 250 patients. By simulating data acquisition from wearable devices using the dataset, we replicated real-world scenarios and validated the proposed method within these scenarios. Experimental results exhibit superior improvements, including +9.32% of IoU value in 2D segmentation and -22.06 of RMSE in bladder volume regression compared to state-of-the-art performance from alternative methods, emphasizing the potential of this approach in continuous bladder volume monitoring in clinical settings. Therefore, this study effectively bridges the gap between accurate bladder volume estimation and the practical deployment of wearable ultrasound devices, promising improved patient care and quality of life.

Deep Learning-Based Framework for Fast and Accurate Acoustic Hologram Generation.

Acoustic holography has been gaining attention for various applications, such as noncontact particle manipulation, noninvasive neuromodulation, and medical imaging. However, only a few studies on how to generate acoustic holograms have been conducted, and even conventional acoustic hologram algorithms show limited performance in the fast and accurate generation of acoustic holograms, thus hindering the development of novel applications. We here propose a deep learning-based framework to achieve fast and accurate acoustic hologram generation. The framework has an autoencoder-like architecture; thus, the unsupervised training is realized without any ground truth. For the framework, we demonstrate a newly developed hologram generator network, the holographic ultrasound generation network (HU-Net), which is suitable for unsupervised learning of hologram generation, and a novel loss function that is devised for energy-efficient holograms. Furthermore, for considering various hologram devices (i.e., ultrasound transducers), we propose a physical constraint (PC) layer. Simulation and experimental studies were carried out for two different hologram devices, such as a 3-D printed lens, attached to a single element transducer, and a 2-D ultrasound array. The proposed framework was compared with the iterative angular spectrum approach (IASA) and the state-of-the-art (SOTA) iterative optimization method, Diff-PAT. In the simulation study, our framework showed a few hundred times faster generation speed, along with comparable or even better reconstruction quality, than those of IASA and Diff-PAT. In the experimental study, the framework was validated with 3-D printed lenses fabricated based on different methods, and the physical effect of the lenses on the reconstruction quality was discussed. The outcomes of the proposed framework in various cases (i.e., hologram generator networks, loss functions, and hologram devices) suggest that our framework may become a very useful alternative tool for other existing acoustic hologram applications, and it can expand novel medical applications.

Efficacy of microneedle radiofrequency therapy in the treatment of senile purpura: A prospective study.

BACKGROUND: Radiofrequency (RF) devices have been used for skin rejuvenation and treating skin laxity. It may also be effective for senile purpura (SP) based on its action of promoting neocollagenesis with minimal epidermal damage. This study aimed to evaluate the efficacy and safety of microneedle RF for treating SP of the forearms in elderly. MATERIAL AND METHODS: In this prospective study, 23 patients who underwent a single session of microneedle RF device (GENIUS, Lutronic Co., Korea) therapy for SP were enrolled. Histopathological features were assessed 1 week before and 8 weeks after therapy. The total amount of collagen and elastic fibers were measured using the computer vision method, and epidermal thickness and the number of blood vessels were analyzed using ImageJ. The clinical improvements were evaluated by blinded evaluators and the patients using investigator global assessment (IGA) and patient global assessment (PGA), respectively. Data regarding the number of purpuric lesions and the size of the largest lesion were collected via a telephone survey. RESULTS: The total amount of collagen and elastic fibers, and mean epidermal thickness tended to improve after RF treatments, although they did not reach statistical significance. The locally estimated scatterplot smoothing curve showed decreasing tendency in both size and number of purpuras as weeks progressed. PGA showed very satisfied in 65% of patients and IGA showed 39% near-total improvement and 43% marked improvement. There were no serious adverse events. CONCLUSIONS: Microneedle RF therapy induces remodeling of dermal circumstances with minimal epidermal impairment. It may be a promising therapeutic option for SP.

Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image.

The performance of computer-aided diagnosis (CAD) systems that are based on ultrasound imaging has been enhanced owing to the advancement in deep learning. However, because of the inherent speckle noise in ultrasound images, the ambiguous boundaries of lesions deteriorate and are difficult to distinguish, resulting in the performance degradation of CAD. Although several methods have been proposed to reduce speckle noise over decades, this task remains a challenge that must be improved to enhance the performance of CAD. In this article, we propose a deep content-aware image prior (DCAIP) with a content-aware attention module (CAAM) for superior despeckling of ultrasound images without clean images. For the image prior, we developed a CAAM to deal with the content information in an input image. In this module, super-pixel pooling (SPP) is used to give attention to salient regions in an ultrasound image. Therefore, it can provide more content information regarding the input image when compared to other attention modules. The DCAIP consists of deep learning networks based on this attention module. The DCAIP is validated by applying it as a preprocessing step for breast tumor segmentation in ultrasound images, which is one of the tasks in CAD. Our method improved the segmentation performance by 15.89% in terms of the area under the precision-recall (PR) curve (AUPRC). The results demonstrate that our method enhances the quality of ultrasound images by effectively reducing speckle noise while preserving important information in the image, promising for the design of superior CAD systems.

Research on Material Design of Medical Products for Elderly Families Based on Artificial Intelligence.

Due to the rapid growth of the elderly around the world, the artificial intelligence control framework can collect information and apply it and perform other tasks. Artificial intelligence plays an important role in focusing on the elderly. For example, it can improve the relationship between the elderly and family members or nursing teams. In addition, AI chat robot can communicate with the elderly without obstacles and can remind the elderly when to take medicine, regular physical examination, etc. A significant number of the AI applications on cell phones accessible today could screen wellbeing information, like every day exercises, diet, and surprisingly the senior's way of life, in a less nosy way. In such cases, it could help in expecting and, subsequently, forestalling any conceivable hypertension or unpredictable heart rate. Essentially, mechanical 'pets' are likewise assisting with fighting off feelings of loneliness, while additionally assisting with upgrading patient consideration simultaneously. One model is Tombot, a little dog like model, which was made to diminish misery and tension among dementia patients. Its head developments, looks, and swaying tail feel basically the same as the real thing, causing occupants to feel as though they have their very own pet to really focus on. One of the issues that growing societies are presently facing is the care of elderly individuals. The dearth of skilled workers in the senior healthcare setting has been exacerbated by the worldwide shift of aging populations. There might be an enhanced need for old nursing since the global older demographic is expected to nearly triple in the coming three decades. There are advancements in computer technologies for supporting the aged plus associated caregivers, checking their wellbeing, and offering company to them. Given the global elderly demographic development possibilities, it is no coincidence that the aided care market is drawing fast advancement, rendering health management for nurses a breeze. While the world's governments manage the aging population next years, these ideas will become extremely vital. They will almost certainly encounter economic and political demands to modify state medical care management, retirement benefits, and social security in order to meet the needs of an aging population. Considering that the demand for physicians is growing, a necessity has developed to deliver individualized care for the aged as well as to respond appropriately in emergencies. As a result, in the technological society, healthcare is exploring artificial intelligence to deliver personalized treatment to individuals in need. The challenges of the aged are determined in this study, and answers are supplied via a tailored computer (robot). With the crucial details given via Internet of Things gadgets, emergency events may be foreseen relatively promptly, and appropriate actions can be proposed by AI technology. Individuals' vital health information is collected using the Internet of Things based on smart technology. The information is evaluated, and decisions are made by AI, while the developed machine performs the appropriate task. This research, therefore, looks at the material design of medical products for elderly people based on artificial intelligence. It goes further and explains some of the challenges encountered in the process and possible remedies.

Effects of transcranial ultrasound stimulation pulsed at 40 Hz on Aß plaques and brain rhythms in 5×FAD mice.

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia, and is characterized by amyloid-ß (Aß) plaques and tauopathy. Reducing Aß has been considered a major AD treatment strategy in pharmacological and non-pharmacological approaches. Impairment of gamma oscillations, which play an important role in perception and cognitive function, has been shown in mouse AD models and human patients. Recently, the therapeutic effect of gamma entrainment in AD mouse models has been reported. Given that ultrasound is an emerging neuromodulation modality, we investigated the effect of ultrasound stimulation pulsed at gamma frequency (40 Hz) in an AD mouse model. METHODS: We implanted electroencephalogram (EEG) electrodes and a piezo-ceramic disc ultrasound transducer on the skull surface of 6-month-old 5×FAD and wild-type control mice (n = 12 and 6, respectively). Six 5×FAD mice were treated with two-hour ultrasound stimulation at 40 Hz daily for two weeks, and the other six mice received sham treatment. Soluble and insoluble Aß levels in the brain were measured by enzyme-linked immunosorbent assay. Spontaneous EEG gamma power was computed by wavelet analysis, and the brain connectivity was examined with phase-locking value and cross-frequency phase-amplitude coupling. RESULTS: We found that the total Aß42 levels, especially insoluble Aß42, in the treatment group decreased in pre- and infra-limbic cortex (PIL) compared to that of the sham treatment group. A reduction in the number of Aß plaques was also observed in the hippocampus. There was no increase in microbleeding in the transcranial ultrasound stimulation (tUS) group. In addition, the length and number of microglial processes decreased in PIL and hippocampus. Encelphalographic spontaneous gamma power was increased, and cross-frequency coupling was normalized, implying functional improvement after tUS stimulation. CONCLUSION: These results suggest that the transcranial ultrasound-based gamma-band entrainment technique can be an effective therapy for AD by reducing the Aß load and improving brain connectivity.

Study on the Viability of Canine Nose Pattern as a Unique Biometric Marker.

The uniqueness of the canine nose pattern was studied. A total of 180 nose images of 60 dogs of diverse age, gender, and breed were collected. The canine nose patterns in these images were examined visually and by a biometric algorithm. It was found that the canine nose pattern remains invariant regardless of when the image is taken; and that the canine nose pattern is indeed unique to each dog. The same study was also performed on an enlarged dataset of 278 nose images of 70 dogs of 19 breeds. The study of the enlarged dataset also leads to the same conclusion. The result of this paper confirms and enhances the claims of earlier works by others that the canine nose pattern is indeed unique to each animal and serves as a unique biometric marker.

Seeded Ising Model and Distributed Biometric Template Storage and Matching.

It is known that a variant of Ising model, called Seeded Ising Model, can be used to recover the information content of a biometric template from a fraction of information therein. The method consists in reconstructing the whole template, which is called the intruder template in this paper, using only a small portion of the given template, a partial template. This reconstruction method may pose a security threat to the integrity of a biometric identity management system. In this paper, based on the Seeded Ising Model, we present a systematic analysis of the possible security breach and its probability of accepting the intruder templates as genuine. Detailed statistical experiments on the intruder match rate are also conducted under various scenarios. In particular, we study (1) how best a template is divided into several small pieces called partial templates, each of which is to be stored in a separate silo; (2) how to do the matching by comparing partial templates in the locked-up silos, and letting only the results of these intra-silo comparisons be sent to the central tallying server for final scoring without requiring the whole templates in one location at any time.

Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.

Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although this type of examination is faster than other diagnostic methods, it can still be time-consuming to evaluate a complete sample; additionally, it possesses the disadvantage of inconsistent reliability as the accuracy of the reading may differ depending on the performer's skill. This study aims at detecting hyphae more quickly, conveniently, and consistently through deep learning using images obtained from microscopy used in real-world practice. An object detection convolutional neural network, YOLO v4, was trained on microscopy images with magnifications of 100×, 40×, and (100+40)×. The study was conducted at the Department of Dermatology at Veterans Health Service Medical Center, Seoul, Korea between January 1, 2019 and December 31, 2019, using 3,707 images (1,255 images for training, 1,645 images for testing). The average precision was used to evaluate the accuracy of object detection. Precision recall curve analysis was performed for the hyphal location determination, and receiver operating characteristic curve analysis was performed on the image classification. The F1 score, sensitivity, and specificity values were used as measures of the overall performance. The sensitivity and specificity were, respectively, 95.2% and 100% in the 100× data model, and 99% and 86.6% in the 40× data model; the sensitivity and specificity in the combined (100+40)× data model were 93.2% and 89%, respectively. The performance of our model had high sensitivity and specificity, indicating that hyphae can be detected with reliable accuracy. Thus, our deep learning-based autodetection model can detect hyphae in microscopic images obtained from real-world practice. We aim to develop an automatic hyphae detection system that can be utilized in real-world practice through continuous research.

Pulse-Modulated Plasma Etching of Copper Thin Films via CH3COOH/Ar.

Pulse-modulated plasma etching of copper masked using SIO2 films was conducted via a CH3COOH/Ar. The etch characteristics were examined under pulse-modulated plasma. As the duty ratio of pulse decreased and the frequency of pulse increased, the etch selectivity and etch profile were improved. X-ray photoelectron spectroscopy and indicated that more copper oxides (Cu2O and CuO) and Cu(CH3COO)2 were formed using pulse-modulated plasma than those formed using continuous-wave (CW) plasma. As the concentration of CH3COOH gas in pulse-modulated plasma increased, the formation of these copper compounds increased, which improved the etch profiles. Optical emission spectroscopy confirmed that the active ingredients of the plasma increased with decreasing pulse duty ratio and increasing frequency. Therefore, the optimized pulsed plasma etching of copper via a CH3COOH/Ar gas provides better etch profile than that by CW plasma etching.

Ultrasonic blood flowmeter with a novel Xero algorithm for a mechanical circulatory support system.

Mechanical circulatory support systems (MCSSs) are crucial devices for transplants in patients with heart failure. The blood flowing through the MCSS can be recirculated or even stagnated in the event of critical blood flow issues. To avoid emergencies due to abnormal changes in the flow, continuous changes of the flowrate should be measured with high accuracy and robustness. For better flowrate measurements, a more advanced ultrasonic blood flowmeter (UFM), which is a noninvasive measurement tool, is needed. In this paper, we propose a novel UFM sensor module using a novel algorithm (Xero) that can exploit the advantages of both conventional cross-correlation (Xcorr) and zero-crossing (Zero) algorithms, using only the zero-crossing-based algorithm. To ensure the capability of our own developed and optimized ultrasonic sensor module for MCSSs, the accuracy, robustness, and continuous monitoring performance of the proposed algorithm were compared to those of conventional algorithms after application to the developed sensor module. The results show that Xero is superior to other algorithms for flowrate measurements under different environments and offers an error rate of at least 0.92%, higher robustness for changing fluid temperatures than conventional algorithms, and sensitive responses to sudden changes in flowrates. Thus, the proposed UFM system with Xero has a great potential for flowrate measurements in MCSSs.

Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear.

A rotator cuff tear (RCT) is an injury in adults that causes difficulty in moving, weakness, and pain. Only limited diagnostic tools such as magnetic resonance imaging (MRI) and ultrasound Imaging (UI) systems can be utilized for an RCT diagnosis. Although UI offers comparable performance at a lower cost to other diagnostic instruments such as MRI, speckle noise can occur the degradation of the image resolution. Conventional vision-based algorithms exhibit inferior performance for the segmentation of diseased regions in UI. In order to achieve a better segmentation for diseased regions in UI, deep-learning-based diagnostic algorithms have been developed. However, it has not yet reached an acceptable level of performance for application in orthopedic surgeries. In this study, we developed a novel end-to-end fully convolutional neural network, denoted as Segmentation Model Adopting a pRe-trained Classification Architecture (SMART-CA), with a novel integrated on positive loss function (IPLF) to accurately diagnose the locations of RCT during an orthopedic examination using UI. Using the pre-trained network, SMART-CA can extract remarkably distinct features that cannot be extracted with a normal encoder. Therefore, it can improve the accuracy of segmentation. In addition, unlike other conventional loss functions, which are not suited for the optimization of deep learning models with an imbalanced dataset such as the RCT dataset, IPLF can efficiently optimize the SMART-CA. Experimental results have shown that SMART-CA offers an improved precision, recall, and dice coefficient of 0.604% (+38.4%), 0.942% (+14.0%) and 0.736% (+38.6%) respectively. The RCT segmentation from a normal ultrasound image offers the improved precision, recall, and dice coefficient of 0.337% (+22.5%), 0.860% (+15.8%) and 0.484% (+28.5%), respectively, in the RCT segmentation from an ultrasound image with severe speckle noise. The experimental results demonstrated the IPLF outperforms other conventional loss functions, and the proposed SMART-CA optimized with the IPLF showed better performance than other state-of-the-art networks for the RCT segmentation with high robustness to speckle noise.

Comparison of maximal isometric forearm supination torque in two elbow positions between subjects with and without limited forearm supination range of motion.

Study Design: Cross-sectional comparison. Introduction: Forearm supination range of motion is often limited by short or stiff pronators. Many studies have examined maximal isometric forearm supination torque (MIFST). However, there is little research comparing MIFST in two elbow positions between subjects with and without limited forearm supination range of motion (LSR). Purpose of the Study: The purpose of this study was to compare MIFST in two elbow positions (90º flexed elbow, extended elbow) between subjects with and without LSR. Methods: Thirteen right-handed subjects (seven men, six women) with LSR and 13 age- and gender-matched healthy controls participated in this study. The forearm supination range of motion and MIFST were measured using a Smart KEMA system. Two-way repeated-measures analysis of variance was used to compare MIFST in two elbow positions between subjects with and without LSR. Results: No significant difference between groups (p > .05) was found. However, there was an interaction effect between elbow position and group on MIFST (p < .05). Independent t-tests were used to confirm the simple effects. In the LSR group, the value of MIFST was significantly lower in the elbow-extended position than in the group without LSR (p < .05). Conclusions: These findings indicate that elbow position should be considered when assessing the strength or increasing the strength of the supinator muscle, especially in patients with LSR.

Research on Processing Technology Product Design and the Application of Nano-Wood-Plastic Composite Materials.

This study focused on the design of wood-plastic composite (WPC) products. In this study, recycled high-density polyethylene plastic was used as the matrix, wood powder was used as the filler, different types of nanofillers and self-synthesized nanofiller treatment agents were added, and the twin-screw extrusion granulation method was used to prepare nano-WPC materials. The effects of different types of nanofillers on the mechanical properties of nano-WPC materials were investigated, and the cross-sectional structures of the materials were analyzed by scanning electron microscopy. The results showed that nanofiller treatment agents improved the interface compatibility of the materials. When the treatment agent content reached 2.5% and the nano-montmorillonite content reached 10%, the mechanical properties of the material reach their maximum values.

Effect of weight-bearing wrist movement with carpal-stabilizing taping on pain and range of motion in subjects with dorsal wrist pain: A randomized controlled trial.

STUDY DESIGN: Randomized control trial. INTRODUCTION: During weight-bearing wrist movement, potential stabilizing forces caused by carpal stabilizing taping (CST) may restrict movement of the carpal bones, allowing greater wrist joint extension. PURPOSE OF THE STUDY: The purpose of study was to investigate the effect of CST during weight-bearing wrist movement on pain intensity and range of motion (ROM) of wrist extension in subjects with dorsal wrist pain. METHODS: Thirty participants with dorsal wrist pain when weight bearing through the hand were randomly allocated into 2 groups: (1) a CST group using rigid tape and (2) placebo taping (PT) group using elastic tape. Subjects performed weight-bearing wrist movements with CST or PT in 6 sessions for 1 week. Active and passive ROM (AROM and PROM), and the visual analog scale (VAS) were assessed at baseline and after the intervention. RESULTS: The AROM and PROM of wrist extension increased significantly in both groups compared with preintervention values (P < .01). Comparing the PT and CST groups, the differences between preintervention and postintervention AROM (mean difference [MD] = +8.6°) and PROM (MD = +6.8°) were significantly greater in the CST group than in the PT group (P < .01). The CST group also showed greater improvement in VAS compared with the PT group (MD = -18 mm) (P < .01). CONCLUSION: We recommend CST during weight-bearing wrist movement as an effective intervention for both increasing wrist extension ROM and decreasing pain in patients with dorsal wrist pain during weight bearing through the hand.

Association between shockable rhythm conversion and outcomes in patients with out-of-hospital cardiac arrest and initial non-shockable rhythm, according to the cause of cardiac arrest.

OBJECTIVE: Conversion to shockable rhythm from an initial non-shockable rhythm is associated with good neurologic prognoses in patients with out-of-hospital cardiac arrest (OHCA). We aimed to investigate whether conversion to shockable rhythm has an association with good neurologic outcomes, according to the etiology of cardiac arrest. METHODS: We conducted a nationwide, population-based, cohort study using the OHCA data from the Korea Centers for Disease Control and Prevention database in 2012-2016. We included patients with OHCA and an initial non-shockable rhythm. The primary outcome was good neurologic outcome at discharge, etiologies of arrest were categorized to medical, non-medical cause. We analyzed the effect of conversion to a shockable rhythm on outcome according to causes of cardiac arrest using multiple regression analysis. RESULTS: Of 114,628 patients with an initial non-shockable rhythm, 25,042 (21.8%) experienced conversion to a shockable rhythm; 83,437 (72.8%) had medical causes and 31,191(27.2%) had non-medical causes. In all patients with OHCA and initial non-shockable rhythm, adjusted odds ratio (OR) of conversion for good neurologic outcome was 2.051 (95% confidence interval [CI] 1.181-2.297). The medical cause group showed an adjusted OR 1.789 (95% CI 1.586-2.019) of conversion for good neurologic outcome. In non-medical cause group, the adjusted OR of conversion was 0.644 (95% CI 0.372-1.114). CONCLUSION: Conversion to shockable rhythm had an association with good neurologic outcome in patients with OHCA with initial non-shockable rhythms, especially due to cardiac cause. However, rhythm conversion was not associated with better outcome in patients with non-medical causes.

Prognostic Role of TMED3 in Clear Cell Renal Cell Carcinoma: A Retrospective Multi-Cohort Analysis.

Transmembrane p24 trafficking protein 3 (TMED3) is a metastatic suppressor in colon cancer and hepatocellular carcinoma. However, its function in the progression of clear cell renal cell carcinoma (ccRCC) is unknown. Here, we report that TMED3 could be a new prognostic marker for ccRCC. Patient data were extracted from cohorts in the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Differential expression of TMED3 was observed between the low stage (Stage I and II) and high stage (Stage III and IV) patients in the TCGA and ICGC cohorts and between the low grade (Grade I and II) and high grade (Grade III and IV) patients in the TCGA cohort. Further, we evaluated TMED3 expression as a prognostic gene using Kaplan-Meier survival analysis, multivariate analysis, the time-dependent area under the curve (AUC) of Uno's C-index, and the AUC of the receiver operating characteristics at 5 years. The Kaplan-Meier analysis revealed that TMED3 overexpression was associated with poor prognosis for ccRCC patients. Analysis of the C-indices and area under the receiver operating characteristic curve further supported this. Multivariate analysis confirmed the prognostic significance of TMED3 expression levels (P = 0.005 and 0.006 for TCGA and ICGC, respectively). Taken together, these findings demonstrate that TMED3 is a potential prognostic factor for ccRCC.

Comparison of Shoulder Protraction Strength and Electromyography Activity of Serratus Anterior and Pectoralis Major in Subjects With or Without a Winged Scapula.

CONTEXT: A winged scapula (WS) is associated with faulty posture caused by weakness of the serratus anterior (SA), which mainly acts as a scapular stabilizer muscle. It is important to accurately assess and train the SA muscle with a focus on scapula stabilizers during musculoskeletal rehabilitation of individuals with a WS. OBJECTIVE: The authors examined muscle activity in the SA and pectoralis major (PM), upper trapezius (UT), and anterior deltoid (AD) as well as shoulder protraction strength during isometric shoulder protraction in individuals with and without a WS. DESIGN: Cross-sectional study. SETTING: A clinical biomechanics laboratory. PARTICIPANTS: In total, 27 males with no shoulder, neck, or upper-extremity pain participated. MAIN OUTCOME MEASURES: Isometric shoulder protraction strength was collected and surface electromyography used to measure the activity of the SA, PM, UT, and AD muscles and selective SA activity ratio to other shoulder muscles. RESULTS: Electromyography activity of the SA muscle and shoulder protraction strength were significantly lower in individuals with a WS compared with the non-WS group (P < .05). In contrast, PM muscle activity and the PM-to-SA, UT-to-SA, and AD-to-SA ratios were significantly greater in individuals with a WS than in individuals without winging (P < .05). CONCLUSIONS: Isometric shoulder protraction for measuring SA strength in individuals with a WS should focus on isolated muscle activity of the SA, and SA strengthening exercises are important for individuals with a WS.

Effect of applying consistent pressure to the stationary and the moving arm on measurement reliability of glenohumeral internal rotation range of motion.

OBJECTIVES: The aim of this study was to determine the inter-rater reliability of glenohumeral internal rotation (GIR) range of motion (ROM) and pressure measurements among four measurement methods: (1) Manual stabilization of the humeral head and coracoid process (MSHC) without applying consistent pressure (ACP) to both the stationary and the moving arms (BSaMA); (2) MSHC with ACP to the moving arm; (3) MSHC with ACP to the stationary arm; and (4) MSHC with ACP to BSaMA. DESIGN: Test-retest analyses. PARTICIPANTS: 39 subjects with GIR deficit. MAIN OUTCOME MEASURES: GIR ROM and pressure were measured on the stationary or moving arm by four examiners using the Clinometer application and a pressure biofeedback unit. RESULTS: GIR ROM measurement with MSHC and ACP to the moving arm resulted in the lowest intra-class correlation coefficient (ICC = .43); the ICC for MSHC without ACP to BSaMA was .54; that for MSHC with ACP to the stationary arm was .77; and that for MSHC with ACP to BSaMA was highest, at .81. CONCLUSION: MSHC with ACP to both arms leads to reliable GIR ROM measurement in a clinical setting.

The Brain Donation Program in South Korea.

PURPOSE: Obtaining brain tissue is critical to definite diagnosis and to furthering understanding of neurodegenerative diseases. The present authors have maintained the National Neuropathology Reference and Diagnostic Laboratories for Dementia in South Korea since 2016. We have built a nationwide brain bank network and are collecting brain tissues from patients with neurodegenerative diseases. We are aiming to facilitate analyses of clinic-pathological and image-pathological correlations of neurodegenerative disease and to broaden understanding thereof. MATERIALS AND METHODS: We recruited participants through two routes: from memory clinics and the community. As a baseline evaluation, clinical interviews, a neurological examination, laboratory tests, neuropsychological tests, and MRI were undertaken. Some patients also underwent amyloid PET. RESULTS: We recruited 105 participants, 70 from clinics and 35 from the community. Among them, 11 died and were autopsied. The clinical diagnoses of the autopsied patients included four with Alzheimer's disease (AD), two with subcortical vascular dementia, two with non-fluent variant primary progressive aphasia, one with leukoencephalopathy, one with frontotemporal dementia (FTD), and one with Creutzfeldt-Jakob disease (CJD). Five patients underwent amyloid PET: two with AD, one with mixed dementia, one with FTD, and one with CJD. CONCLUSION: The clinical and neuropathological information to be obtained from this cohort in the future will provide a deeper understanding of the neuropathological mechanisms of cognitive impairment in Asia, especially Korea.