The Rhizome Mixture of Anemarrhena asphodeloides

We investigated the effect of DWac on the gut microbiota composition in mice with 2,3,6-trinitrobenzenesulfonic acid- (TNBS-) induced colitis. Treatment with DWac restored TNBS-disturbed gut microbiota composition and attenuated TNBS-induced colitis. Moreover, we examined the effect of DWac in mice with mesalazine-resistant colitis (MRC). Intrarectal injection of TNBS in MRC mice caused severe colitis, as well as colon shortening, edema, and increased myeloperoxidase activity. Treatment with mesalazine (30 mg/kg) did not attenuate TNBS-induced colitis in MRC mice, whereas treatment with DWac (30 mg/kg) significantly attenuated TNBS-induced colitis. Moreover, treatment with the mixture of mesalazine (15 mg/kg) and DWac (15 mg/kg) additively attenuated colitis in MRC mice. Treatment with DWac and its mixture with mesalazine inhibited TNBS-induced activation of NF-κB and expression of M1 macrophage markers but increased TNBS-suppressed expression of M2 macrophage markers. Furthermore, these inhibited TNBS-induced T-bet, RORγt, TNF-α, and IL-17 expression but increased TNBS-suppressed Foxp3 and IL-10 expression. However, Th2 cell differentiation and GATA3 and IL-5 expression were not affected. These findings suggest that DWac can ameliorate MRC by increasing the polarization of M2 macrophage and correcting the disturbance of gut microbiota and Th1/Th17/Treg, as well as additively attenuating MRC along with mesalazine

Experiences of Sanhujori Facility Use among the First Time Mothers by the Focus Group Interview

PURPOSE: The purpose of this study was to examine the experiences of Sanhujori facility use among the first time mothers in Korea. METHODS: A qualitative study was conducted, using focus group interview. Data were collected from the 24 first time mothers of 4 focus groups, who had given birth within 6 month and had used one of the Sanhujori facilities located in C province, Korea. After obtaining written informed consent from all participants, each session of the focus group was audio-taped and transcribed into verbatim. Data were analyzed using content analysis in order to identify significant themes. RESULTS: Four major themes that emerged from the data were as follows. 1) Promoting postpartum physical recovery through a enough time with only focusing on herself, 2) Promoting postpartum psychological recovery through emotional and informational support with peer mothers, 3) Experiencing breast feeding difficulties and disappointing with unsatisfied help from health professionals, and 4) Lack of the professional education programs regarding parenting. CONCLUSION: Based on these results, it will be suggested that the various support programs by not only the peer mothers co-resided in Sanhujori facilities but also the health care professionals in the Sanhujori facilities should be developed for helping a "becoming a mother" of the first time mother in the Sanhujori facilities. In addition, qualified education and counseling program, especially for the successful breast feeding, should be provided by the health care professionals for improving mothering ability of the first time mother in the Sanhujori facilities

Liquid biopsy in lung cancer: Clinical applications of circulating biomarkers (CTCs and ctDNA)

Lung cancer is by far the leading cause of cancer death worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Recent advances in the understanding of the biology of tumors and in highly sensitive detection technologies for molecular analysis offer targeted therapies, such as epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors. However, our understanding of an individual patient's lung cancer is often limited by tumor accessibility because of the high risk and invasive nature of current tissue biopsy procedures. Liquid biopsy, the analysis of circulating biomarkers from peripheral blood, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), offers a new source of cancer-derived materials that may reflect the status of the disease better and thereby contribute to more personalized treatment. In this review, we examined the clinical significance and uniqueness of CTCs and ctDNA from NSCLC patients, isolation and detection methods developed to analyze each type of circulating biomarker, and examples of clinical studies of potential applications for early diagnosis, prognosis, treatment monitoring, and prediction of resistance to therapy. We also discuss challenges that remain to be addressed before such tools are implemented for routine use in clinical settings

Convolution channel separation and frequency sub-bands aggregation for music genre classification

In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative. A music genre classification (MGC) system should be able to analyze these features. In this research, we propose a novel framework that can extract and aggregate both short- and long-term features hierarchically. Our framework is based on ECAPA-TDNN, where all the layers that extract short-term features are affected by the layers that extract long-term features because of the back-propagation training. To prevent the distortion of short-term features, we devised the convolution channel separation technique that separates short-term features from long-term feature extraction paths. To extract more diverse features from our framework, we incorporated the frequency sub-bands aggregation method, which divides the input spectrogram along frequency bandwidths and processes each segment. We evaluated our framework using the Melon Playlist dataset which is a large-scale dataset containing 600 times more data than GTZAN which is a widely used dataset in MGC studies. As the result, our framework achieved 70.4% accuracy, which was improved by 16.9% compared to a conventional framework

Integrated Parameter-Efficient Tuning for General-Purpose Audio Models

The advent of hyper-scale and general-purpose pre-trained models is shifting the paradigm of building task-specific models for target tasks. In the field of audio research, task-agnostic pre-trained models with high transferability and adaptability have achieved state-of-the-art performances through fine-tuning for downstream tasks. Nevertheless, re-training all the parameters of these massive models entails an enormous amount of time and cost, along with a huge carbon footprint. To overcome these limitations, the present study explores and applies efficient transfer learning methods in the audio domain. We also propose an integrated parameter-efficient tuning (IPET) framework by aggregating the embedding prompt (a prompt-based learning approach), and the adapter (an effective transfer learning method). We demonstrate the efficacy of the proposed framework using two backbone pre-trained audio models with different characteristics: the audio spectrogram transformer and wav2vec 2.0. The proposed IPET framework exhibits remarkable performance compared to fine-tuning method with fewer trainable parameters in four downstream tasks: sound event classification, music genre classification, keyword spotting, and speaker verification. Furthermore, the authors identify and analyze the shortcomings of the IPET framework, providing lessons and research directions for parameter efficient tuning in the audio domain.Comment: 5 pages, 3 figures, submit to ICASSP202

One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification

The application of speech self-supervised learning (SSL) models has achieved remarkable performance in speaker verification (SV). However, there is a computational cost hurdle in employing them, which makes development and deployment difficult. Several studies have simply compressed SSL models through knowledge distillation (KD) without considering the target task. Consequently, these methods could not extract SV-tailored features. This paper suggests One-Step Knowledge Distillation and Fine-Tuning (OS-KDFT), which incorporates KD and fine-tuning (FT). We optimize a student model for SV during KD training to avert the distillation of inappropriate information for the SV. OS-KDFT could downsize Wav2Vec 2.0 based ECAPA-TDNN size by approximately 76.2%, and reduce the SSL model's inference time by 79% while presenting an EER of 0.98%. The proposed OS-KDFT is validated across VoxCeleb1 and VoxCeleb2 datasets and W2V2 and HuBERT SSL models. Experiments are available on our GitHub

Elevated intracellular cAMP exacerbates vulnerability to oxidative stress in optic nerve head astrocytes.

Glaucoma is characterized by a progressive loss of retinal ganglion cells and their axons, but the underlying biological basis for the accompanying neurodegeneration is not known. Accumulating evidence indicates that structural and functional abnormalities of astrocytes within the optic nerve head (ONH) have a role. However, whether the activation of cyclic adenosine 3',5'-monophosphate (cAMP) signaling pathway is associated with astrocyte dysfunction in the ONH remains unknown. We report here that the cAMP/protein kinase A (PKA) pathway is critical to ONH astrocyte dysfunction, leading to caspase-3 activation and cell death via the AKT/Bim/Bax signaling pathway. Furthermore, elevated intracellular cAMP exacerbates vulnerability to oxidative stress in ONH astrocytes, and this may contribute to axonal damage in glaucomatous neurodegeneration. Inhibition of intracellular cAMP/PKA signaling activation protects ONH astrocytes by increasing AKT phosphorylation against oxidative stress. These results strongly indicate that activation of cAMP/PKA pathway has an important role in astrocyte dysfunction, and suggest that modulating cAMP/PKA pathway has therapeutic potential for glaucomatous ONH degeneration