A Randomised Control Trial Comparing Opioid and Paracetamol for Pain Control after Carpal Tunnel Release.

Background: Opioids are frequently used for pain control after carpal tunnel release (CTR). However, few studies have examined whether the use of opioids is necessary for pain control after CTR. The aim of this study is to compare the effectiveness of codeine versus paracetamol for pain control after CTR. Methods: This is a prospective comparison of consecutive patients' subjective perception of pain after outpatient CTR. Patients were randomised to receive either codeine or paracetamol for pain control after CTR. Visual analogue scale for pain of both groups was compared on the day of surgery and the first three post-operative days (POD) using non-inferiority test. Results: In the codeine group, the mean pain score was 5.2, 4.0, 2.6 and 1.6 on the day of surgery and the first, second and third days after surgery, respectively. In the paracetamol group, the mean VAS score in the same period was 4.3, 3.5, 2.8 and 2.3. There was no significant difference in the mean visual analogue pain scores between the two groups from the day of surgery to the third POD. Conclusions: Paracetamol was as effective as codeine for relieving the pain after outpatient CTR. Our results suggest that the clinicians might avoid unnecessary prescription of the opioid after outpatient CTR. Level of Evidence: Level III (Therapeutic).

Stack LSTM-Based User Identification Using Smart Shoes with Accelerometer Data.

In this study, we propose a long short-term memory (LSTM)-based user identification method using accelerometer data from smart shoes. In general, for the user identification with human walking data, we require a pre-processing stage in order to divide human walking data into individual steps. Next, user identification can be made with divided step data. In these approaches, when there exist partial data that cannot complete a single step, it is difficult to apply those data to the classification. Considering these facts, in this study, we present a stack LSTM-based user identification method for smart-shoes data. Rather than using a complicated analysis method, we designed an LSTM network for user identification with accelerometer data of smart shoes. In order to learn partial data, the LSTM network was trained using walking data with random sizes and random locations. Then, the identification can be made without any additional analysis such as step division. In the experiments, user walking data with 10 m were used. The experimental results show that the average recognition rate was about 93.41%, 97.19%, and 98.26% by using walking data of 2.6, 3.9, and 5.2 s, respectively. With the experimental results, we show that the proposed method can classify users effectively.

Adaptive Accumulation of Plantar Pressure for Ambulatory Activity Recognition and Pedestrian Identification.

In this paper, we propose a novel method for ambulatory activity recognition and pedestrian identification based on temporally adaptive weighting accumulation-based features extracted from categorical plantar pressure. The method relies on three pressure-related features, which are calculated by accumulating the pressure of the standing foot in each step over three different temporal weighting forms. In addition, we consider a feature reflecting the pressure variation. These four features characterize the standing posture in a step by differently weighting step pressure data over time. We use these features to analyze the standing foot during walking and then recognize ambulatory activities and identify pedestrians based on multilayer multiclass support vector machine classifiers. Experimental results show that the proposed method achieves 97% accuracy for the two tasks when analyzing eight consecutive steps. For faster processing, the method reaches 89.9% and 91.3% accuracy for ambulatory activity recognition and pedestrian identification considering two consecutive steps, respectively, whereas the accuracy drops to 83.3% and 82.3% when considering one step for the respective tasks. Comparative results demonstrated the high performance of the proposed method regarding accuracy and temporal sensitivity.

Comparison of Open Anterior Syndesmotic Repair Augmented With Suture-Tape and Trans-syndesmotic Screw Fixation: A Biomechanical Study.

The treatment of syndesmotic injuries with ankle fractures is controversial. The purpose of this study was to compare the biomechanical properties of open anterior syndesmotic repair with those of screw fixation. Ten matched pairs of human cadaver specimens were subjected to open syndesmotic repair or screw fixation. Each specimen underwent initial intact physiologic loading, consisting of 10 cycles of external torsional loading with a peak torque of 7.5 Nm at 0.05 Hz. Injuries of the anterior inferior tibiofibular ligament, tibiofibular interosseous membrane, and deltoid ligament were applied to each specimen. Postfixation cyclic loading consisted of 50 cycles of combined axial and external rotation loading with peak torques of 750 N and 7.5 Nm at a rate of 0.05 Hz. After postfixation loading, each specimen underwent failure loading by external rotation at 0.25 degrees/second. Failure torque and failure angle were measured. The paired t test and Wilcoxon signed-rank test were used to analyze the data. Mean failure torques were 95.63 Nm in the open anterior syndesmotic repair augmented with suture-tape group and 108.61 Nm in the screw group. Mean failure angles were 34.93 degrees in the open anterior syndesmotic repair augmented with suture-tape group and 43.55 degrees in the screw group. These data were not statistically significantly different between the groups (p= .7682 and .4133, respectively). Open anterior syndesmotic repair augmented with suture tape for ankle syndesmotic injury provides similar torsional strength to that of screw fixation. Therefore, this technique can be considered as an alternative treatment option for syndesmosis injury.

Proximal vs Distal Approach of Ultrasound-guided Suprascapular Nerve Block for Patients With Adhesive Capsulitis of the Shoulder: Prospective Randomized Controlled Trial.

OBJECTIVES: To evaluate the early clinical outcomes of ultrasound (US)-guided suprascapular nerve block (SSNB) using a proximal approach compared with a distal approach for outpatient treatment of adhesive capsulitis. DESIGN: Randomized controlled trial. SETTING: Outpatient clinic PARTICIPANTS: Participants (N=47) with symptomatic adhesive capsulitis. INTERVENTIONS: Participants were randomly assigned to either US-guided SSNB using a proximal approach (n=23, proximal group) or a distal approach (n=24, distal group). MAIN OUTCOME MEASURES: The primary outcome measure was the visual analog scale (VAS) for pain at week 12. Secondary outcomes included the American Shoulder Elbow Surgeon's (ASES) score, University California Los Angeles score, Short Form-36 mental and physical component summaries, and range of motion. All patients completed clinical follow-up at 2, 6, and 12 weeks after treatment. On US images, depth and insertion angle of needle during injection were measured. RESULTS: The VAS significantly improved in both groups at week 12. After treatment, no significant differences were found in early clinical outcomes (weeks 2, 6, and 12) between groups (all P>.05), except that ASES at 2 weeks showed a significantly higher score in the distal group than in the proximal group (87.1±4.8 and 83.0±6.3, respectively; P=.014). The mean depth and insertion angle of needle was significantly lower (depth: 13.4±3.9 and 30.6±4.3 mm, respectively; P<.001; insertion angle: 19.6°±6.4° and 38.7°±5.8°; P<.001) in the proximal group than in the distal group. CONCLUSIONS: This study demonstrated that proximal approach of US-guided SSNB provided favorable short-term outcomes of pain and functional improvement and that outcomes were comparable to those of the distal approach in adhesive capsulitis. The suprascapular nerve was located more superficially and easily identified in the proximal approach, suggesting that this method might improve the accuracy of injection.

Data reconstruction using iteratively reweighted L1-principal component analysis for an electronic nose system.

We propose a method to reconstruct damaged data based on statistical learning during data acquisition. In the process of measuring the data using a sensor, the damage of the data caused by the defect of the sensor or the environmental factor greatly degrades the performance of data classification. Instead of the traditional PCA based on L2-norm, the PCA features were extracted based on L1-norm and updated by iteratively reweighted fitting using the generalized objective function to obtain robust features for the outlier data. The damaged data samples were reconstructed using weighted linear combination using these features and the projection vectors of L1-norm based PCA. The experimental results on various types of volatile organic compounds (VOCs) data show that the proposed method can be used to reconstruct the damaged data to the original form of the undamaged data and to prevent degradation of classification performance due to data corruption through data reconstruction.

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band.

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2⁻4.2% depending on the type of wrist activities.

Robust Thin Film Surface with a Selective Antibacterial Property Enabled via a Cross-Linked Ionic Polymer Coating for Infection-Resistant Medical Applications.

Fabrication of new antibacterial surfaces has become a primary strategy for preventing device-associated infections (DAIs). Although considerable progress has recently been made in reducing DAIs, current antibacterial coating methods are technically complex and do not allow selective bacterial killing. Here, we propose novel anti-infective surfaces made of a cross-linked ionic polymer film that achieve selective bacteria killing while simultaneously favoring the survival of mammalian cells. A one-step polymerization process known as initiated chemical vapor deposition was used to generate a cross-linked ionic polymer film from 4-vinylbenzyl chloride and 2-(dimethylamino) ethyl methacrylate monomers in the vapor phase. In particular, the deposition process produced a polymer network with quaternary ammonium cross-linking sites, which provided the surface with an ionic moiety with an excellent antibacterial contact-killing property. This method confers substrate compatibility, which enables various materials to be coated with ionic polymer films for use in medical implants. Moreover, the ionic polymer-deposited surfaces supported the healthy growth of mammalian cells while selectively inhibiting bacterial growth in coculture models without any detectable cytotoxicity. Thus, the cross-linked ionic polymer-based antibacterial surface developed in this study can serve as an ideal platform for biomedical applications that require a highly sterile environment.

Daily wrist activity classification using a smart band.

OBJECTIVE: In this letter, we propose a novel method for classifying daily wrist activities by using a smart band. APPROACH: Triaxial acceleration data are collected by built-in sensors of the smart band during experiments regarding five activities, i.e. texting, calling, placing a hand in a pocket, carrying a suitcase, and swinging a hand. We analyze patterns in the sensor signals during these activities based on three types of features, i.e. norm, norm-variance, and frequency-domain features. After extracting the significant features, a multi-class support vector machine algorithm is applied to classify these activities. MAIN RESULTS: We obtained recognition error rates of approximately 2.7% by applying the proposed method to the experimental dataset.

Step-Detection and Adaptive Step-Length Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone.

We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone. First, we apply a fast Fourier transform (FFT)-based smoother on the acceleration data collected by the smartphone to remove the interference signals. Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length. To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels. The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy.

Stride Counting in Human Walking and Walking Distance Estimation Using Insole Sensors.

This paper proposes a novel method of estimating walking distance based on a precise counting of walking strides using insole sensors. We use an inertial triaxial accelerometer and eight pressure sensors installed in the insole of a shoe to record walkers' movement data. The data is then transmitted to a smartphone to filter out noise and determine stance and swing phases. Based on phase information, we count the number of strides traveled and estimate the movement distance. To evaluate the accuracy of the proposed method, we created two walking databases on seven healthy participants and tested the proposed method. The first database, which is called the short distance database, consists of collected data from all seven healthy subjects walking on a 16 m distance. The second one, named the long distance database, is constructed from walking data of three healthy subjects who have participated in the short database for an 89 m distance. The experimental results show that the proposed method performs walking distance estimation accurately with the mean error rates of 4.8% and 3.1% for the short and long distance databases, respectively. Moreover, the maximum difference of the swing phase determination with respect to time is 0.08 s and 0.06 s for starting and stopping points of swing phases, respectively. Therefore, the stride counting method provides a highly precise result when subjects walk.

Development of high-affinity single chain Fv against foot-and-mouth disease virus.

Foot-and-mouth disease (FMD) is caused by the FMD virus (FMDV) and results in severe economic losses in livestock farming. For rapid FMD diagnostic and therapeutic purposes, an effective antibody against FMDV is needed. Here, we developed a high-affinity antibody against FMDV by FACS-based high throughput screening of a random library. With the FITC-conjugated VP1 epitope of FMDV and high-speed FACS sorting, we screened the synthetic antibody (scFv) library in which antibody variants are displayed in the periplasm of Escherichia coli. After three rounds of sorting, we isolated one antibody fragment (#138-scFv) against the VP1 epitope of FMDV. Next, to improve its affinity, a mutation library of #138-scFV was constructed by error-prone PCR and screened by FACS. After three rounds of sorting, we isolated one antibody (AM-32 scFv), which has a higher binding affinity (KD=42.7nM) than that of the original #138-scFv. We also confirmed that it specifically binds to whole inactivated FMDV.

Rapid isolation of antibody from a synthetic human antibody library by repeated fluorescence-activated cell sorting (FACS).

Antibodies and their derivatives are the most important agents in therapeutics and diagnostics. Even after the significant progress in the technology for antibody screening from huge libraries, it takes a long time to isolate an antibody, which prevents a prompt action against the spread of a disease. Here, we report a new strategy for isolating desired antibodies from a combinatorial library in one day by repeated fluorescence-activated cell sorting (FACS). First, we constructed a library of synthetic human antibody in which single-chain variable fragment (scFv) was expressed in the periplasm of Escherichia coli. After labeling the cells with fluorescent antigen probes, the highly fluorescent cells were sorted by using a high-speed cell sorter, and these cells were reused without regeneration in the next round of sorting. After repeating this sorting, the positive clones were completely enriched in several hours. Thus, we screened the library against three viral antigens, including the H1N1 influenza virus, Hepatitis B virus, and Foot-and-mouth disease virus. Finally, the potential antibody candidates, which show K(D) values between 10 and 100 nM against the target antigens, could be successfully isolated even though the library was relatively small (∼ 10(6)). These results show that repeated FACS screening without regeneration of the sorted cells can be a powerful method when a rapid response to a spreading disease is required.

High-level production of Fc-fused kringle domain in Pichia pastoris.

Recently, as a new non-immunoglobulin-based protein scaffold, a human kringle domain was successfully engineered toward biologically functional agonists and antagonists. In this study, the fed-batch cultivation conditions were optimized for enhanced production of an Fc-fused kringle domain (KD548-Fc) in Pichia pastoris. Fed-batch cultivations were performed in 5-l laboratory-scale bioreactors, and in order to find the optimal conditions for high-level production of KD548-Fc, several parameters including the initial carbon source (glycerol) concentration, temperature, and pH were investigated. When cells were cultivated at pH 4.0 and 25 °C with 9.5 % glycerol in the initial medium, the highest production yield (635 mg/l) was achieved with high productivity (7.2 mg/l/h). Furthermore, functional KD548-Fc was successfully purified from the culture broth using a simple purification procedure with high purity and recovery yield.

A discriminant distance based composite vector selection method for odor classification.

We present a composite vector selection method for an effective electronic nose system that performs well even in noisy environments. Each composite vector generated from a electronic nose data sample is evaluated by computing the discriminant distance. By quantitatively measuring the amount of discriminative information in each composite vector, composite vectors containing informative variables can be distinguished and the final composite features for odor classification are extracted using the selected composite vectors. Using the only informative composite vectors can be also helpful to extract better composite features instead of using all the generated composite vectors. Experimental results with different volatile organic compound data show that the proposed system has good classification performance even in a noisy environment compared to other methods.

A human kringle domain-based fluorescence-linked immunosorbent assay system.

As a non-immunoglobulin protein scaffold, human kringle domain (KD) has attractive properties such as high specificity, stability, and production in bacterial hosts. Here, we developed a rapid and sensitive fluorescence-linked immunosorbent assay (FLISA) system using a fluorescent kringle domain (fluoKD), a fusion protein of a green fluorescent protein (GFP), and a kringle domain variant (KD548). Two kinds of fluoKDs in which KD was fused to the N terminus of GFP (N-fluoKD) or the C terminus of GFP (C-fluoKD) were constructed and characterized. In Escherichia coli host, both fluoKDs were produced in high yield and solubility and were successfully purified by a simple procedure. The purified fluoKDs exhibited strong fluorescent activities and high affinities to the target antigen. Furthermore, it was successfully demonstrated that the FLISA with purified fluoKDs allowed for more rapid detection of target antigens with higher sensitivity compared with conventional enzyme-linked immunosorbent assay (ELISA), indicating that a simple, rapid, and sensitive immunoassay system could be developed by using KD instead of antibody or antibody fragments.

Pseudo optimization of e-nose data using region selection with feature feedback based on regularized linear discriminant analysis.

In this paper, we present a pseudo optimization method for electronic nose (e-nose) data using region selection with feature feedback based on regularized linear discriminant analysis (R-LDA) to enhance the performance and cost functions of an e-nose system. To implement cost- and performance-effective e-nose systems, the number of channels, sampling time and sensing time of the e-nose must be considered. We propose a method to select both important channels and an important time-horizon by analyzing e-nose sensor data. By extending previous feature feedback results, we obtain a two-dimensional discriminant information map consisting of channels and time units by reverse mapping the feature space to the data space based on R-LDA. The discriminant information map enables optimal channels and time units to be heuristically selected to improve the performance and cost functions. The efficacy of the proposed method is demonstrated experimentally for different volatile organic compounds. In particular, our method is both cost and performance effective for the real implementation of e-nose systems.

Classification of odorants in the vapor phase using composite features for a portable e-nose system.

We present an effective portable e-nose system that performs well even in noisy environments. Considering the characteristics of the e-nose data, we use an image covariance matrix-based method for extracting discriminant features for vapor classification. To construct composite vectors, primitive variables of the data measured by a sensor array are rearranged. Then, composite features are extracted by utilizing the information about the statistical dependency among multiple primitive variables, and a classifier for vapor classification is designed with these composite features. Experimental results with different volatile organic compounds data show that the proposed system has better classification performance than other methods in a noisy environment.

Data refinement and channel selection for a portable e-nose system by the use of feature feedback.

We propose a data refinement and channel selection method for vapor classification in a portable e-nose system. For the robust e-nose system in a real environment, we propose to reduce the noise in the data measured by sensor arrays and distinguish the important part in the data by the use of feature feedback. Experimental results on different volatile organic compounds data show that the proposed data refinement method gives good clustering for different classes and improves the classification performance. Also, we design a new sensor array that consists only of the useful channels. For this purpose, each channel is evaluated by measuring its discriminative power based on the feature mask used in the data refinement. Through the experimental results, we show that the new sensor array improves both the classification rates and the efficiency in computation and data storage.