rAAV TGF-ß and FGF-2 Overexpression via pNaSS-Grafted PCL Films Stimulates the Reparative Activities of Human ACL Fibroblasts.

Lesions in the human anterior cruciate ligament (ACL) are frequent, unsolved clinical issues due to the limited self-healing ability of the ACL and lack of treatments supporting full, durable ACL repair. Gene therapy guided through the use of biomaterials may steadily activate the processes of repair in sites of ACL injury. The goal of the present study was to test the hypothesis that functionalized poly(sodium styrene sulfonate)-grafted poly(ε-caprolactone) (pNaSS-grafted PCL) films can effectively deliver recombinant adeno-associated virus (rAAV) vectors as a means of overexpressing two reparative factors (transforming growth factor beta-TGF-ß and basic fibroblast growth factor-FGF-2) in primary human ACL fibroblasts. Effective, durable rAAV reporter red fluorescent protein and candidate TGF-ß and FGF-2 gene overexpression was achieved in the cells for at least 21 days, especially when pNaSS-grafted PCL films were used versus control conditions, such as ungrafted films and systems lacking vectors or films (between 1.8- and 5.2-fold differences), showing interactive regulation of growth factor production. The expression of TGF-ß and FGF-2 from rAAV via PCL films safely enhanced extracellular matrix depositions of type-I/-III collagen, proteoglycans/decorin, and tenascin-C (between 1.4- and 4.5-fold differences) in the cells over time with increased levels of expression of the specific transcription factors Mohawk and scleraxis (between 1.7- and 3.7-fold differences) and without the activation of the inflammatory mediators IL-1ß and TNF-α, most particularly with pNaSS-grafted PCL films relative to the controls. This work shows the value of combining rAAV gene therapy with functionalized PCL films to enhance ACL repair.

PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children.

Computer-aided diagnosis systems in adult chest radiography (CXR) have recently achieved great success thanks to the availability of large-scale, annotated datasets and the advent of high-performance supervised learning algorithms. However, the development of diagnostic models for detecting and diagnosing pediatric diseases in CXR scans is undertaken due to the lack of high-quality physician-annotated datasets. To overcome this challenge, we introduce and release PediCXR, a new pediatric CXR dataset of 9,125 studies retrospectively collected from a major pediatric hospital in Vietnam between 2020 and 2021. Each scan was manually annotated by a pediatric radiologist with more than ten years of experience. The dataset was labeled for the presence of 36 critical findings and 15 diseases. In particular, each abnormal finding was identified via a rectangle bounding box on the image. To the best of our knowledge, this is the first and largest pediatric CXR dataset containing lesion-level annotations and image-level labels for the detection of multiple findings and diseases. For algorithm development, the dataset was divided into a training set of 7,728 and a test set of 1,397. To encourage new advances in pediatric CXR interpretation using data-driven approaches, we provide a detailed description of the PediCXR data sample and make the dataset publicly available on https://physionet.org/content/vindr-pcxr/1.0.0/ .

WGS-Based Analysis of Carbapenem-Resistant Acinetobacter baumannii in Vietnam and Molecular Characterization of Antimicrobial Determinants and MLST in Southeast Asia.

Carbapenem-resistant Acinetobacter baumannii (A. baumannii, CRAb) is an emerging global threat for healthcare systems, particularly in Southeast Asia. Next-generation sequencing (NGS) technology was employed to map genes associated with antimicrobial resistance (AMR) and to identify multilocus sequence types (MLST). Eleven strains isolated from humans in Vietnam were sequenced, and their AMR genes and MLST were compared to published genomes of strains originating from Southeast Asia, i.e., Thailand (n = 49), Myanmar (n = 38), Malaysia (n = 11), Singapore (n = 4) and Taiwan (n = 1). Ten out of eleven Vietnamese strains were CRAb and were susceptible only to colistin. All strains harbored ant(3")-IIa, armA, aph(6)-Id and aph(3") genes conferring resistance to aminoglycosides, and blaOXA-51 variants and blaADC-25 conferring resistance to ß-lactams. More than half of the strains harbored genes that confer resistance to tetracyclines, sulfonamides and macrolides. The strains showed high diversity, where six were assigned to sequence type (ST)/2, and two were allocated to two new STs (ST/1411-1412). MLST analyses of 108 strains from Southeast Asia identified 19 sequence types (ST), and ST/2 was the most prevalent found in 62 strains. A broad range of AMR genes was identified mediating resistance to ß-lactams, including cephalosporins and carbapenems (e.g., blaOXA-51-like, blaOXA-23, blaADC-25, blaADC-73, blaTEM-1, blaNDM-1), aminoglycosides (e.g., ant(3")-IIa, aph(3")-Ib, aph(6)-Id, armA and aph(3')-Ia), phenicoles (e.g., catB8), tetracyclines (e.g., tet.B and tet.39), sulfonamides (e.g., sul.1 and sul.2), macrolides and lincosamide (e.g., mph.E, msr.E and abaF). MLST and core genome MLST (cgMLST) showed an extreme diversity among the strains. Several strains isolated from different countries clustered together by cgMLST; however, different clusters shared the same ST. Developing an action plan on AMR, increasing awareness and prohibiting the selling of antibiotics without prescription must be mandatory for this region. Such efforts are critical for enforcing targeted policies on the rational use of carbapenem compounds and controlling AMR dissemination and emergence in general.

Phenotypic and WGS-derived antimicrobial resistance profiles of clinical and non-clinical Acinetobacter baumannii isolates from Germany and Vietnam.

OBJECTIVES: This study aimed to combine in vitro phenotyping analysis and whole-genome-sequencing (WGS) to characterise the phenotype and genetic determinants associated with intrinsic resistance in 100 clinical and non-clinical Acinetobacter baumannii strains originating from Germany and Vietnam. Moreover, it aimed to assess whether powdered milk as a food source functions as a potential reservoir of antibiotic resistance and possesses similar antimicrobial resistance (AMR) genes as in clinical strains isolated from Germany. METHODS: Antimicrobial susceptibility testing was performed using the broth microdilution method and the minimum inhibitory concentration (MIC) was determined for 18 antibiotics. The WGS data from all isolates were mapped to intrinsic genes known to be associated with phenotypic AMR. RESULTS: The highest resistance frequency was observed for chloramphenicol (100%), followed by fosfomycin (96%) and cefotaxime (95%). The lowest resistant rates were observed for colistin (3%), trimethoprim/sulfamethoxazole (17%), tigecycline (19%), and amikacin (19%). Thirty-five percent of tested strains displayed resistance to at least one of the carbapenems. Resistance to fluoroquinolones, aminoglycosides, tigecycline, penicillins, trimethoprim/sulfamethoxazole, and fourth-generation cephalosporins was determined only in human strains. About one-quarter of isolates (24%) was multidrug-resistant (MDR) and all were of human origin. Among them, 16 isolates were extensively drug resistant (XDR) and 10 from those 16 isolates showed resistance to all tested antibiotics except colistin. In silico detection of intrinsic AMR genes revealed the presence of 36 ß-lactamases and 24 non-ß-lactamase resistance genes. Two colistin-resistant and 10 ertapenem-resistant strains were isolated from powdered milk produced in Germany. Thirty-eight AMR genes associated with resistance to antibiotics were found in isolates recovered from milk powder. Several resistance mechanisms towards many classes of antibiotics existed in A. baumannii including ß-lactamases, multidrug efflux pumps and aminoglycoside-modifying enzymes. CONCLUSION: The use of WGS for routine public health surveillance is a reliable method for the rapid detection of emerging AMR in A. baumannii isolates. Milk powder poses a risk to contain MDR Acinetobacter strains or resistance genes in Germany.

Evaluating an owner-to-worker training intervention in California nail salons using personal air monitoring.

BACKGROUND: Chemicals in nail products have been linked to numerous health concerns. METHODS: We recruited Vietnamese-American nail salon owners and workers in California and randomized salons into an intervention or control group. Owners in the intervention group received training and then provided education to workers in their salons on best practices to reduce workplace chemical exposures. Methyl methacrylate (MMA), toluene, and total volatile organic compounds (TVOCs) were measured using personal air monitors worn by workers during the work-shift. RESULTS: We enrolled 77 salons (37 intervention and 40 control) and 200 workers. There was no significant intervention effect between the two groups. However, MMA and TVOCs were higher for workers who used gel polish and acrylic nails as well as in busy salons. CONCLUSIONS: Although the intervention did not show reductions in chemical levels, identifying worker tasks and salon characteristics that predict chemical levels can inform future interventions to reduce exposures.

Prediction of Freezing of Gait in Patients with Parkinson's Disease Using EEG Signals.

Freezing of gait (FOG) is an episodic gait disturbance affecting initiation and continuation of locomotion in many Parkinson's disease (PD) patients, causing falls and a poor quality of life. FOG can be experienced on turning and start hesitation, passing through doorways or crowded areas dual tasking, and in stressful situations. Electroencephalography (EEG) offers an innovative technique that may be able to effectively foresee an impending FOG. From data of 16 PD patients, using directed transfer function (DTF) and independent component analysis (ICA) as data pre-processing, and an optimal Bayesian neural network as a predictor of a transition of 5 seconds before the impending FOG occurs in 11 in-group PD patients, we achieved sensitivity and specificity of 85.86% and 80.25% respectively in the test set (5 out-group PD patients). This study therefore contributes to the development of a non-invasive device to prevent freezing of gait in PD.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

Classification of EEG based-mental fatigue using principal component analysis and Bayesian neural network.

This paper presents an electroencephalography (EEG) based-classification of between pre- and post-mental load tasks for mental fatigue detection from 65 healthy participants. During the data collection, eye closed and eye open tasks were collected before and after conducting the mental load tasks. For the computational intelligence, the system uses the combination of principal component analysis (PCA) as the dimension reduction method of the original 26 channels of EEG data, power spectral density (PSD) as feature extractor and Bayesian neural network (BNN) as classifier. After applying the PCA, the dimension of the data is reduced from 26 EEG channels in 6 principal components (PCs) with above 90% of information retained. Based on this reduced dimension of 6 PCs of data, during eyes open, the classification pre-task (alert) vs. post-task (fatigue) using Bayesian neural network resulted in sensitivity of 76.8 %, specificity of 75.1% and accuracy of 76% Also based on data from the 6 PCs, during eye closed, the classification between pre- and post-task resulted in a sensitivity of 76.1%, specificity of 74.5% and accuracy of 75.3%. Further, the classification results of using only 6 PCs data are comparable to the result using the original 26 EEG channels. This finding will help in reducing the computational complexity of data analysis based on 26 channels of EEG for mental fatigue detection.

An EEG study of turning freeze in Parkinson's disease patients: The alteration of brain dynamic on the motor and visual cortex.

Freezing of gait is a very debilitating symptom affecting many patients with Parkinson's disease, leading to a reduced mobility and increased risk for falls. Turning is known to be the most provocative trigger for freezing of gait. However, the underlying brain dynamic changes associated with a turning freeze remain unknown. This study therefore used ambulatory EEG to investigate the brain dynamic changes associated with freezing of gait during turning. In addition, this study aimed to determine the most suitable EEG sensor location to detect freezing of gait during turning using our classification system. Data from four Parkinson's disease patients with freezing of gait was analysed using power spectral density and brain effective connectivity, comparing periods of successful turning with freezing of gait during turning. Results showed that freezing of gait during turning is associated with significant alterations in the high beta and theta power spectral densities across the occipital and parietal areas. Furthermore, brain effective connectivity showed that freezing during turning was associated with increased connectivity towards the visual area, which also had the highest accuracy to detect freezing episodes in the O1 regions by using power spectral density in our classification analyses. This is the first study to show cortical dynamic changes associated with freezing of gait during turning, providing valuable information to enhance the performance of future freezing of gait detection systems.

Comparing features extractors in EEG-based cognitive fatigue detection of demanding computer tasks.

An electroencephalography (EEG)-based classification system could be used as a tool for detecting cognitive fatigue from demanding computer tasks. The most widely used feature extractor in EEG-based fatigue classification is power spectral density (PSD). This paper investigates PSD and three alternative feature extraction methods, in order to find the best feature extractor for the classification of cognitive fatigue during cognitively demanding tasks. These compared methods are power spectral entropy (PSE), wavelet, and autoregressive (AR). Bayesian neural network was selected as the classifier in this study. The results showed that the use of PSD and PSE methods provide an average accuracy of 60% for each computer task. This finding is slightly improved using the wavelet method which has an average accuracy of 61%. The AR method is the best feature extractor compared with the PSD, PSE and wavelet in this study with accuracy of 75.95% in AX-continuous performance test (AX-CPT), 75.23% in psychomotor vigilance test (PVT) and 76.02% in Stroop task (p-value <; 0.05).

Analysis and Prediction of the Freezing of Gait Using EEG Brain Dynamics.

Freezing of Gait (FOG) is a common symptom in the advanced stages of Parkinson's disease (PD), which significantly affects patients' quality of life. Treatment options offer limited benefit and there are currently no mechanisms able to effectively detect FOG before it occurs, allowing time for a sufferer to avert a freezing episode. Electroencephalography (EEG) offers a novel technique that may be able to address this problem. In this paper, we investigated the univariate and multivariate EEG features determined by both Fourier and wavelet analysis in the confirmation and prediction of FOG. The EEG power measures and network properties from 16 patients with PD and FOG were extracted and analyzed. It was found that both power spectral density and wavelet energy could potentially act as biomarkers during FOG. Information in the frequency domain of the EEG was found to provide better discrimination of EEG signals during transition to freezing than information coded in the time domain. The performance of the FOG prediction systems improved when the information from both domains was used. This combination resulted in a sensitivity of 86.0%, specificity of 74.4%, and accuracy of 80.2% when predicting episodes of freezing, outperforming current accelerometry-based tools for the prediction of FOG.

Prediction of freezing of gait using analysis of brain effective connectivity.

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), in which patients experience sudden difficulties in starting or continuing locomotion. It is described by patients as the sensation that their feet are suddenly glued to the ground. This, disturbs their balance, and hence often leads to falls. In this study, directed transfer function (DTF) and partial directed coherence (PDC) were used to calculate the effective connectivity of neural networks, as the input features for systems that can detect FOG based on a Multilayer Perceptron Neural Network, as well as means for assessing the causal relationships in neurophysiological neural networks during FOG episodes. The sensitivity, specificity and accuracy obtained in subject dependent analysis were 82%, 77%, and 78%, respectively. This is a significant improvement compared to previously used methods for detecting FOG, bringing this detection system one step closer to a final version that can be used by the patients to improve their symptoms.

Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients.

Parkinson's Disease (PD) patients with Freezing of Gait (FOG) often experience sudden and unpredictable failure in their ability to start or continue walking, making it potentially a dangerous symptom. Emerging knowledge about brain connectivity is leading to new insights into the pathophysiology of FOG and has suggested that electroencephalogram (EEG) may offer a novel technique for understanding and predicting FOG. In this study we have integrated spatial, spectral, and temporal features of the EEG signals utilizing wavelet coefficients as our input for the Multilayer Perceptron Neural Network and k-Nearest Neighbor classifier. This approach allowed us to predict transition from walking to freezing with 87 % sensitivity and 73 % accuracy. This preliminary data affirms the functional breakdown between areas in the brain during FOG and suggests that EEG offers potential as a therapeutic strategy in advanced PD.

The detection of Freezing of Gait in Parkinson's disease patients using EEG signals based on Wavelet decomposition.

Freezing of Gait (FOG) is one of the most disabling gait disturbances of Parkinson's disease (PD). The experience has often been described as "feeling like their feet have been glued to the floor while trying to walk" and as such it is a common cause of falling in PD patients. In this paper, EEG subbands Wavelet Energy and Total Wavelet Entropy were extracted using the multiresolution decomposition of EEG signal based on the Discrete Wavelet Transform and were used to analyze the dynamics in the EEG during freezing. The Back Propagation Neural Network classifier has the ability to identify the onset of freezing of PD patients during walking using these features with average values of accuracy, sensitivity and specificity are around 75 %. This results have proved the feasibility of utilized EEG in future treatment of FOG.

Performance of dry electrode with bristle in recording EEG rhythms across brain state changes.

In this paper we evaluate the physiological performance of a silver-silver chloride dry electrode with bristle (B-Electrode) in recording EEG data. For this purpose, we compare the performance of the bristle electrode in recording EEG data with the standard wet gold-plated cup electrode (G-Electrode) using two different brain state change tasks including resting condition with eyes-closed and performing mathematical task with eyes-open. Using a 2 channel recording device, eyes-closed command data were collected from each of 6 participants for a period of 20 sec and the same procedure was applied for the mathematical calculation task. These data were used for statistical and classification analyse. Although, B-electrode has shown a slightly higher performance compared with G-electrode in both tasks, but analyse did not reveal any significant differences between both electrodes in all six subjects tested.

Fast tracking of a given heart rate profile in treadmill exercise.

This paper investigates the application of a multi-loop PID controller in an automated treadmill exercise machine. The approach is to design a computer-controlled treadmill control system for the regulation of heart rate (HR) during treadmill exercise. A single-input and multiple-output (SIMO) controller was implemented to fast track a given heart rate profile in treadmill exercise. Two separate single-input and single-output (SISO) PID control systems are initially implemented to modify either the treadmill speed or its angle of inclination in order to achieve a desired HR. The purpose of this paper is to apply a SIMO control system by implementing a control algorithm which includes the two PID controllers working simultaneously to track the desired HR profile. The performance of the SIMO and SISO control systems are compared through the closed loop responses recorded during experimentation. This would also help future development of safe treadmill exercise system.

The C-terminal region of human NFATc2 binds cJun to synergistically activate interleukin-2 transcription.

At eukaryotic promoters, multi-faceted protein-protein and protein-DNA interactions can result in synergistic transcriptional activation. NFAT and AP-1 proteins induce interleukin-2 (IL-2) transcription in stimulated T cells, but the contributions of individual members of these activator families to synergistically activating IL-2 transcription is not known. To investigate the combinatorial regulation of IL-2 transcription we tested the ability of different combinations of NFATc2, NFATc1, cJun, and cFos to synergistically activate transcription from the IL-2 promoter. We found that NFATc2 and cJun are exclusive in their ability to synergistically activate human IL-2 transcription. Protein-protein interaction assays revealed that in the absence of DNA, NFATc2, but not NFATc1, bound directly to cJun/cJun dimers, but not to cFos/cJun heterodimers. A region of NFATc2 C-terminal of the DNA binding domain was necessary and sufficient for interaction with cJun in the absence of DNA, and this same region of NFATc2 was required for the synergistic activation of IL-2 transcription in T cells. Moreover, expression of this C-terminal region of NFATc2 specifically repressed the synergistic activation of IL-2 transcription. These studies show that a previously unidentified interaction between human NFATc2 and cJun is necessary for synergistic activation of IL-2 transcription in T cells.

Solid-state NMR structural studies of the fibril form of a mutant mouse prion peptide PrP89-143(P101L).

The peptide fragment 89-143 of the prion protein (carrying a P101L mutation) is biologically active in transgenic mice when in a fibrillar form. Injection of these fibrils into transgenic mice (expressing full length PrP with the P101L mutation) induces a neurodegenerative prion disease (Kaneko et al., J. Mol. Biol. 295 (2000) 997). Here we present solid-state NMR studies of PrP(89-143)(P101L) fibrils, probing the conformation of residues in the hydrophobic segment 112-124 with chemical shifts. The conformations of glycine residues were analyzed using doubly (13)C=O labeled peptides by two-dimensional (2D) double-quantum correlation, and double-quantum filtered dephasing distance measurements. MQ-NMR experiments were carried out to probe the relative alignment of the individual peptides fibrils. These NMR studies indicate that the 112-124 segment adopts an extended beta-sheet conformation, though not in a parallel, in register alignment. There is evidence for conformational variability at Gly 113. DQ correlation experiments provide useful information in regions with conformational heterogeneity.