Investigation of Electrical Responses to Acupuncture Stimulation: The Effect of Electrical Grounding and Insulation Conditions

AbstractAcupuncture in Oriental medicine has been widely used as a core therapeutic method due to its minimal side-effects and therapeutic efficacy. However, the electrical response to acupuncture stimulation (ERAS) has not been clearly studied under acupuncture conditions that might affect the efficacy of acupuncture therapy. In this study, the ERAS was objectively investigated by measuring meridian electric potentials (MEPs) when the electrical grounding conditions of the operator and subject were varied, and when the insulation conditions of acupuncture needle were varied. MEPs between Sang-geoheo (ST37) and Ha-geoheo (ST39) of the Stomach Meridian (ST) were measured by stimulating Jok-samni (ST36) with an acupuncture needle. For non-insulated acupuncture stimulation (NIAS), the average MEP peak was 148.6 ± 20.6 when neither the operator nor the subject were electrically grounded, 23.1 ± 8.8 when the subject only was electrically grounded, 348 ± 76.8 when the operator only was electrically grounded, and 19.9 ± 4.7 when both the operator and the subject were electrically grounded. The MEPs presented various magnitudes and patterns depending on the electrical grounding conditions. The MEP pattern was very similar to that of the charge and discharge of a capacitor. For insulated acupuncture stimulation (IAS), the average MEP peak was 20 ± 4 in all electrical grounding conditions, which is not a significant electric response for acupuncture stimulation. In terms of electricity, this study verified that acupuncture therapy might be affected by acupuncture conditions such as (1) the electrical grounding condition of the operator and the subject and (2) the insulation condition of the acupuncture needle

Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification

Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data. Moreover, the respiratory sound samples are collected from a variety of electronic stethoscopes, which could potentially introduce biases into the trained models. When a significant distribution shift occurs within the test dataset or in a practical scenario, it can substantially decrease the performance. To tackle this issue, we introduce cross-domain adaptation techniques, which transfer the knowledge from a source domain to a distinct target domain. In particular, by considering different stethoscope types as individual domains, we propose a novel stethoscope-guided supervised contrastive learning approach. This method can mitigate any domain-related disparities and thus enables the model to distinguish respiratory sounds of the recording variation of the stethoscope. The experimental results on the ICBHI dataset demonstrate that the proposed methods are effective in reducing the domain dependency and achieving the ICBHI Score of 61.71%, which is a significant improvement of 2.16% over the baseline.Comment: accepted to ICASSP 202

Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification

Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medical care based on electronic stethoscopes. To this end, cutting-edge deep learning models have been developed to diagnose lung diseases; however, it is still challenging due to the scarcity of medical data. In this study, we demonstrate that the pretrained model on large-scale visual and audio datasets can be generalized to the respiratory sound classification task. In addition, we introduce a straightforward Patch-Mix augmentation, which randomly mixes patches between different samples, with Audio Spectrogram Transformer (AST). We further propose a novel and effective Patch-Mix Contrastive Learning to distinguish the mixed representations in the latent space. Our method achieves state-of-the-art performance on the ICBHI dataset, outperforming the prior leading score by an improvement of 4.08%.Comment: INTERSPEECH 2023, Code URL: https://github.com/raymin0223/patch-mix_contrastive_learnin

A Deep Learning Approach with Data Augmentation to Predict Novel Spider Neurotoxic Peptides

As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these peptides. However, the major hurdle in developing a deep learning model is the limited data on neurotoxic peptides. Here, we present a peptide data augmentation method that improves the recognition of neurotoxic peptides via a convolutional neural network model. The neurotoxic peptides were augmented with the known neurotoxic peptides from UniProt database, and the models were trained using a training set with or without the generated sequences to verify the augmented data. The model trained with the augmented dataset outperformed the one with the unaugmented dataset, achieving accuracy of 0.9953, precision of 0.9922, recall of 0.9984, and F1 score of 0.9953 in simulation dataset. From the set of all RNA transcripts of Callobius koreanus spider, we discovered neurotoxic peptides via the model, resulting in 275 putative peptides of which 252 novel sequences and only 23 sequences showing homology with the known peptides by Basic Local Alignment Search Tool. Among these 275 peptides, four were selected and shown to have neuromodulatory effects on the human neuroblastoma cell line SH-SY5Y. The augmentation method presented here may be applied to the identification of other functional peptides from biological resources with insufficient data

Effects of dietary supplementation of bacteriophage with or without zinc oxide on the performance and gut development of weanling pigs

The present study investigates the effect of zinc oxide (ZN), bacteriophage (BAC) or their combination on the growth performance and gut development in weaning pigs. A total of 200 weaned pigs were allotted to four treatments including two levels (0 and 0.34%) of ZN and two levels (0 and 0.10%) of BAC cocktail. Supplementation of both BAC and ZN in the diet improved average daily gain and gain to feed ratio in all three phases. The apparent total tract digestibility (ATTD) of dry matter was consistently increased in BAC. A higher digestibility of dry matter was observed in ZN group at phase-I and II. The ATTD of crude protein was increased in BAC group at phase-I and III. ZN increased ATTD of crude protein during phase III. In all phases, the population of total anaerobic bacteria, Bifidobacterium spp., Lactobacillus spp., Clostridium spp. and coliforms were higher in BAC and ZN groups with the exception for coliforms in ZN at the end of experiment. The duodenum (p < 0.05) and jejunum (p < 0.01) villus heights were considerably increased in BAC group but the ileal villus height was not affected by the addition of BAC in the diet. Similar increase (p < 0.05) in the duodenal (p = 0.06) and jejunal (p < 0.01) villus heights were also observed in ZN supplemented groups. The overall faecal score was reduced (p < 0.01) by BAC and tended to decrease (p = 0.07) by ZN. Thus both ZN and BAC are useful for improving the performance and gut health in weaning pigs without any interactive effects