Sub-band common spatial pattern (SBCSP) for brain-computer interface

Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. Different imagery activities can be classified based on the changes in mu and/or beta rhythms and their spatial distributions. However, the change in these rhythmic patterns varies from one subject to another. This causes an unavoidable time-consuming fine-tuning process in building a BCI for every subject. To address this issue, we propose a new method called sub-band common spatial pattern (SBCSP) to solve the problem. First, we decompose the EEG signals into sub-bands using a filter bank. Subsequently, we apply a discriminative analysis to extract SBCSP features. The SBCSP features are then fed into linear discriminant analyzers (LDA) to obtain scores which reflect the classification capability of each frequency band. Finally, the scores are fused to make decision. We evaluate two fusion methods: recursive band elimination (RBE) and meta-classifier (MC). We assess our approaches on a standard database from BCI Competition III. We also compare our method with two other approaches that address the same issue. The results show that our method outperforms the other two approaches and achieves similar result as compared to the best one in the literature which was obtained by a time-consuming fine-tuning process

A Deep Learning Architecture with Spatio-Temporal Focusing for Detecting Respiratory Anomalies

This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Our system initially performs audio feature extraction using Continuous Wavelet transformation. This transformation converts the respiratory sound input into a two-dimensional spectrogram where both spectral and temporal features are presented. Then, our proposed deep learning architecture inspired by the Inception-residual-based backbone performs the spatial-temporal focusing and multi-head attention mechanism to classify respiratory anomalies. In this work, we evaluate our proposed models on the benchmark SPRSound (The Open-Source SJTU Paediatric Respiratory Sound) database proposed by the IEEE BioCAS 2023 challenge. As regards the Score computed by an average between the average score and harmonic score, our robust system has achieved Top-1 performance with Scores of 0.810, 0.667, 0.744, and 0.608 in Tasks 1-1, 1-2, 2-1, and 2-2, respectively.Comment: arXiv admin note: text overlap with arXiv:2303.0410

An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies

This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Initially, our system begins with audio feature extraction using Gammatone and Continuous Wavelet transformation. This step aims to transform the respiratory sound input into a two-dimensional spectrogram where both spectral and temporal features are presented. Then, our proposed system integrates Inception-residual-based backbone models combined with multi-head attention and multi-objective loss to classify respiratory anomalies. Instead of applying a simple concatenation approach by combining results from various spectrograms, we propose a Linear combination, which has the ability to regulate equally the contribution of each individual spectrogram throughout the training process. To evaluate the performance, we conducted experiments over the benchmark dataset of SPRSound (The Open-Source SJTU Paediatric Respiratory Sound) proposed by the IEEE BioCAS 2022 challenge. As regards the Score computed by an average between the average score and harmonic score, our proposed system gained significant improvements of 9.7%, 15.8%, 17.8%, and 16.1% in Task 1-1, Task 1-2, Task 2-1, and Task 2-2, respectively, compared to the challenge baseline system. Notably, we achieved the Top-1 performance in Task 2-1 and Task 2-2 with the highest Score of 74.5% and 53.9%, respectively

Elevated Levels of Cell-Free Circulating DNA in Patients with Acute Dengue Virus Infection

BACKGROUND: Apoptosis is thought to play a role in the pathogenesis of severe dengue and the release of cell-free DNA into the circulatory system in several medical conditions. Therefore, we investigated circulating DNA as a potential biomarker for severe dengue. METHODS AND FINDINGS: A direct fluorometric degradation assay using PicoGreen was performed to quantify cell-free DNA from patient plasma. Circulating DNA levels were significantly higher in patients with dengue virus infection than with other febrile illnesses and healthy controls. Remarkably, the increase of DNA levels correlated with the severity of dengue. Additionally, multivariate logistic regression analysis showed that circulating DNA levels independently correlated with dengue shock syndrome. CONCLUSIONS: Circulating DNA levels were increased in dengue patients and correlated with dengue severity. Additional studies are required to show the benefits of this biomarker in early dengue diagnosis and for the prognosis of shock complication

Differentiation Effect of Two Alkaloid Fractions from Vietnamese Lycopodiaceae on Mouse Neural Stem Cells

Various Lycopodium alkaloids have been studied for their various biological activities including anti-inflammatory, antioxidant, immunomodulatory, and neuroprotective activities. Moreover, these alkaloid compounds have high potential in the treatment of neuron degenerative disease. This study has been carried out to test the effect of Huperzia serrata (Thunb.) Trevis, and Lycopodium clavatum L alkaloid fractions on the mouse neural stem cells (NSCs). Firstly, the alkaloid fractions were used to verify its toxicity on NSCs. The multiple concentrations of alkaloid fractions from H. serrata (0.044; 0.088; 0.175; 0.35; 0.7; 1.4 mg/ml) and L. clavatum (0.031; 0.063; 0.125; 0.25; 0.50; 1.0; 2.0 mg/ml) have been used for the treatment of NSCs at period of 48h incubation. Results of the study suggested that the IC50 value of H. serrata and L. clavatum was 0.56 mg/ml and 0.50 mg/ml, respectively. Then, the NSCs were differentiated in the presence of 5 and 10 µg/ml of alkaloid fraction from H. serrata; 0.625 and 1.25 µg/ml of alkaloid fraction from L. clavatum for 6 days. Here, we observed the primary NSCs treated with alkaloid fraction extract from H. serrata showed the increased gene expression level of early neuron TUBB3 and neuron-specific cytoskeleton MAP2. On the other hand, the L. clavatum alkaloid fraction increased the expression of neural stem cell marker genes (Nestin and PAX6) and decreased neuron marker genes. In conclusion, these results established that alkaloid fraction from H. serrata promoted differentiation of the mouse NSCs to neuron cells, and L. clavatum extract had a capacity for stemness maintenance

Penicillium digitatum as a Model Fungus for Detecting Antifungal Activity of Botanicals: An Evaluation on Vietnamese Medicinal Plant Extracts.

peer reviewedMedicinal plants play important roles in traditional medicine, and numerous compounds among them have been recognized for their antimicrobial activity. However, little is known about the potential of Vietnamese medicinal plants for antifungal activity. In this study, we examined the antagonistic activity of twelve medicinal plant species collected in Northern Vietnam against Penicillium digitatum, Aspergillus flavus, Aspergillus fumigatus, and Candida albicans. The results showed that the antifungal activities of the crude extracts from Mahonia bealei, Ficus semicordata, and Gnetum montanum were clearly detected with the citrus postharvest pathogen P. digitatum. These extracts could fully inhibit the growth of P. digitatum on the agar medium, and on the infected citrus fruits at concentrations of 300-1000 µg/mL. Meanwhile, the other tested fungi were less sensitive to the antagonistic activity of the plant extracts. In particular, we found that the ethanolic extract of M. bealei displayed a broad-spectrum antifungal activity against all four pathogenic fungi. Analysis of this crude extract by enrichment coupled with high-performance liquid chromatography revealed that berberine and palmatine are major metabolites. Additional inspections indicated berberine as the key compound responsible for the antifungal activity of the M. bealei ethanolic extract. Our study provides a better understanding of the potential of Vietnamese medicinal plant resources for combating fungal pathogens. This work also highlights that the citrus pathogen P. digitatum can be employed as a model fungus for screening the antifungal activity of botanicals

The Association of Cytokines with Severe Dengue in Children

Background: Dengue virus infection is a major public health problem. A hypothesis put forward for severe dengue is the cytokine storm, a sudden increase in cytokines that induces vascular permeability. Previous studies and our recent meta-analysis showed that IL-6, IL-8, IFNγ, TNFα, VEGF-A and VCAM-1 are associated with dengue shock syndrome. Therefore, in this study we aim to validate the association of these cytokines with severe dengue. Methods & Findings: In a hospital based-case control study in Vietnam, children with dengue fever, other febrile illness and healthy controls were recruited. Dengue virus infection was confirmed by several diagnostic tests. Multiplex immunoassay using Luminex technology was used to measure cytokines simultaneously. A positive association with dengue shock syndrome was found for VCAM-1, whereas a negative association was found for IFNγ. Furthermore, multivariate logistic analysis also showed that VCAM-1 and IFNγ were independently correlated with dengue shock syndrome. Conclusion: IFNγ and VCAM-1 were associated with dengue shock syndrome, although their role in the severe dengue pathogenesis remains unclear. Additional studies are required to shed further light on the function of these cytokines in severe dengue

Insulin signaling and its application

The discovery of insulin in 1921 introduced a new branch of research into insulin activity and insulin resistance. Many discoveries in this field have been applied to diagnosing and treating diseases related to insulin resistance. In this mini-review, the authors attempt to synthesize the updated discoveries to unravel the related mechanisms and inform the development of novel applications. Firstly, we depict the insulin signaling pathway to explain the physiology of insulin action starting at the receptor sites of insulin and downstream the signaling of the insulin signaling pathway. Based on this, the next part will analyze the mechanisms of insulin resistance with two major provenances: the defects caused by receptors and the defects due to extra-receptor causes, but in this study, we focus on post-receptor causes. Finally, we discuss the recent applications including the diseases related to insulin resistance (obesity, cardiovascular disease, Alzheimer’s disease, and cancer) and the potential treatment of those based on insulin resistance mechanisms

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary objective of the current paper is to summarize the results and lessons learned based on the twelve sub-systems and their fusion submitted to SRE'18. It is also our intention to present a shared view on the advancements, progresses, and major paradigm shifts that we have witnessed as an SRE participant in the past decade from SRE'08 to SRE'18. In this regard, we have seen, among others, a paradigm shift from supervector representation to deep speaker embedding, and a switch of research challenge from channel compensation to domain adaptation.Comment: 5 page

Pharmacists’ Perspectives on the Use of Telepharmacy in Response to COVID-19 Pandemic in Ho Chi Minh City, Vietnam

Introduction: Telepharmacy, the application of information and communication technologies in healthcare services, has been adopted in many countries to provide patients with pharmaceutical care. However, it has yet to be widely used in Vietnam. This study was conducted to assess the current status of use and the factors associated with the willingness to use telepharmacy of pharmacists in Vietnam. Methods: A descriptive cross-sectional study was conducted from February to July 2021; 414 pharmacists were recruited to fill in an online survey. Results: Overall, 86.7% of participants have used telepharmacy application and 87.2% of them were willing to apply telepharmacy in pharmacy practice. According to our multivariate analysis, the level of readiness was associated with positive attitude (odds ratio [OR] = 4.67; 95% confidence interval [CI]: 2.26-9.66), and a good behavior (OR = 11.34; 95% CI: 3.84-33.45). Discussion: Developing a telepharmacy system with appropriate features is essential to meet the requirements of pharmacy practice amid the spread of the COVID-19 pandemic