Clinical and Cellular Evidence of Glycyrrhiza glabra and Platycodon grandiflorus for Vocal Fold Nodules Complementary Treatment.

Vocal fold nodules (VFNs) are the most frequent cause of hoarseness. The management comprised medical, surgical and physical therapy but the effectiveness is not always satisfactory. In this study, we try to figure out an alternative treatment from our clinical experience summary. We retrospectively reviewed VFNs patients who received traditional Chinese medicine (TCM) treatments from July 2018 to August 2020 and traced their Chinese Voice Handicap Index-10 (VHI-C10) and multidimensional voice program (MDVP) analysis results. For further evaluation, we conducted an inflammatory response of porcine vocal fold epithelial (PVFE) cells with 50 ng/mL TNF-alpha. The inflamed PVFE cells were separately cultured in the aqueous extract of Glycyrrhiza glabra (G. glabra) and Platycodon grandifloras (P. grandifloras). In these VFNs patients (n = 22), the average VHI-C10 score decreased from 17.6 to 6.6 (p < 0.001). MDVP analysis revealed improvements in jitter, shimmer, noise-harmonic ratio, and GRBAS scoring system. Of the TCM prescription patterns, G. glabra and P. grandiflorus were used most frequently. In the MTT assay of PVFE cells, no adverse effects of our extracts were observed at doses of 1-200 µg/mL. Western blot analysis revealed downregulation of p65 and mitogen activated protein kinase pathway proteins. The results from both the clinical and in vitro aspects of this study revealed that the herbs G. glabra and P. grandiflorus may offer beneficial outcomes as alternative treatments for VFNs after precise diagnosis.

Flow versus permeability weighting in estimating the forward volumetric transfer constant (Ktrans) obtained by DCE-MRI with contrast agents of differing molecular sizes.

PURPOSE: To quantify the differential plasma flow- (Fp-) and permeability surface area product per unit mass of tissue- (PS-) weighting in forward volumetric transfer constant (Ktrans) estimates by using a low molecular (Gd-DTPA) versus high molecular (Gadomer) weight contrast agent in dynamic contrast enhanced (DCE) MRI. MATERIALS AND METHODS: DCE MRI was performed using a 7T animal scanner in 14 C57BL/6J mice syngeneic for TRAMP tumors, by administering Gd-DTPA (0.9kD) in eight mice and Gadomer (35kD) in the remainder. The acquisition time was 10min with a sampling rate of one image every 2s. Pharmacokinetic modeling was performed to obtain Ktrans by using Extended Tofts model (ETM). In addition, the adiabatic approximation to the tissue homogeneity (AATH) model was employed to obtain the relative contributions of Fp and PS. RESULTS: The Ktrans values derived from DCE-MRI with Gd-DTPA showed significant correlations with both PS (r2=0.64, p=0.009) and Fp (r2=0.57, p=0.016), whereas those with Gadomer were found only significantly correlated with PS (r2=0.96, p=0.0003) but not with Fp (r2=0.34, p=0.111). A voxel-based analysis showed that Ktrans approximated PS (<30% difference) in 78.3% of perfused tumor volume for Gadomer, but only 37.3% for Gd-DTPA. CONCLUSIONS: The differential contributions of Fp and PS in estimating Ktrans values vary with the molecular weight of the contrast agent used. The macromolecular contrast agent resulted in Ktrans values that were much less dependent on flow. These findings support the use of macromolecular contrast agents for estimating tumor vessel permeability with DCE-MRI.

Assessment of vessel permeability by combining dynamic contrast-enhanced and arterial spin labeling MRI.

The forward volumetric transfer constant (K(trans)), a physiological parameter extracted from dynamic contrast-enhanced (DCE) MRI, is weighted by vessel permeability and tissue blood flow. The permeability × surface area product per unit mass of tissue (PS) in brain tumors was estimated in this study by combining the blood flow obtained through pseudo-continuous arterial spin labeling (PCASL) and K(trans) obtained through DCE MRI. An analytical analysis and a numerical simulation were conducted to understand how errors in the flow and K(trans) estimates would propagate to the resulting PS. Fourteen pediatric patients with brain tumors were scanned on a clinical 3-T MRI scanner. PCASL perfusion imaging was performed using a three-dimensional (3D) fast-spin-echo readout module to determine blood flow. DCE imaging was performed using a 3D spoiled gradient-echo sequence, and the K(trans) map was obtained with the extended Tofts model. The numerical analysis demonstrated that the uncertainty of PS was predominantly dependent on that of K(trans) and was relatively insensitive to the flow. The average PS values of the whole tumors ranged from 0.006 to 0.217 min(-1), with a mean of 0.050 min(-1) among the patients. The mean K(trans) value was 18% lower than the PS value, with a maximum discrepancy of 25%. When the parametric maps were compared on a voxel-by-voxel basis, the discrepancies between PS and K(trans) appeared to be heterogeneous within the tumors. The PS values could be more than two-fold higher than the K(trans) values for voxels with high K(trans) levels. This study proposes a method that is easy to implement in clinical practice and has the potential to improve the quantification of the microvascular properties of brain tumors.

The changes of monocarboxylate transporter-2 in spinal cord horn in a rat model of chronic inflammatory pain / 中国应用生理学杂志

<p><b>OBJECTIVE</b>To investigate the changes in the levels of monocarboxylate transporter-2 in spinal cord horn in a rat model of chronic inflammatory pain.</p><p><b>METHODS</b>Male SD rats weighting 180 - 220 g were randomly divided into two groups(n = 48): normal saline group (NS group), complete Freund's adjuvant group (CFA group). Rats were given injections of CFA 100 µl in left hind paw in group CFA, and an equal volume of saline was given injection in group NS. Mechanical withdraw threshold(MWT) and thermal withdraw latency(TWL) were measured at before injection(T0 and 3 h, 1 d, 3 d, 7 d, 14 d, and 21 d after injection(T1-7). Four rats were chosen from each group at T0-7 and sacrificed, and L4-5 segments of the spinal cord horn were removed for measurement of the expression of monocarboxylate transporter-2 by Western blot analysis.</p><p><b>RESULTS</b>In CFA group, mechanical hyperalgesia and allodynia appeared on the 3 h after CFA injection, then until the day 14. The expression of monocarboxylate transporter-2 in the spinal dorsal horn of rats in CFA group was significantly higher than that in normal control group at T1-6(P <0.05). The protein level of monocarboxylate transporter-2 was apparently correlated with MWT and TWL(P <0.01 and P <0.05) in CFA group.</p><p><b>CONCLUSION</b>The level of monocarboxylate transporter-2 in spinal dorsal horn is significantly increased in a rat model of chronic inflammatory pain and the change may involve in the formation and maintenance of central sensitization in spinal cord of chronic inflammatory uain.</p>

Reversible blood-brain barrier disruption by repeated transcranial focused ultrasound allows enhanced extravasation.

The permeability of blood-brain barrier (BBB) for albumin can be enhanced by focused ultrasound (FUS) in a targeted region when this is applied in the presence of ultrasound contrast agent (UCA). In this study, we demonstrate that, using this noninvasive treatment, Evans Blue (EB) extravasation can be enhanced by repeated sonication. Sonications were applied at an ultrasound frequency of 1 MHz with a 5% duty cycle, and a repetition frequency of 1 Hz. The brains of male Sprague-Dawley rats were subjected to FUS exposure at the same targeted site. At the same acoustic power, the extravasation caused by leakage of EB into the brain was found to be dependent on the applied sonication time. In vivo, both single and repeated sonications increased the extravasation of the albumin binding EB, especially for the repeated sonication group. In the retreatment experiment, there was a nearly twofold increase in EB extravasation in groups with a second sonication compared with the single sonication group. BBB disruption can be prolonged by repeated FUS sonication and the duration is dependent on the time point of the resonication after the first sonication. Compared to a single sonication, the MR imaging analysis and histological examination of the affected brains indicated that the pattern of contrast enhancement was changed and that vacuolation occurred after repeated sonication. This noninvasive technology offers the possibility of controlling the extent of drug delivery by means of repeated treatment and adjusting the duration and interval between sonications.

Association between contrast-enhanced MR images and blood-brain barrier disruption following transcranial focused ultrasound.

PURPOSE: To investigate the correlation between the contrast-enhanced magnetic resonance imaging (MRI) signal and the duration of blood-brain barrier (BBB) disruption induced by focused ultrasound (FUS). MATERIALS AND METHODS: FUS was applied to 45 rat brains in the presence of microbubbles, and these rats were scanned on a 3T MRI system at several timepoints. The rat brains were then studied using contrast-enhanced spin echo T1-weighted images. At the same time, BBB disruption was evaluated based on Evans blue (EB) extravasation. The relationship between the normalized signal intensity change of the MRI and EB extravasation was analyzed by least-squares linear regression and the calculation of correlation coefficients. RESULTS: When MRI enhancement was quantitatively evaluated by EB extravasation, a strong correlation between the normalized signal intensity change of the MRI and EB extravasation was identified during BBB disruption after sonication. However, the correlation coefficient decreased as BBB closure occurred after sonication ended. CONCLUSION: The contrast-enhanced MRI signal can potentially be used to evaluate the amount of chemotherapeutic agents entering the targeted tissue, but the accuracy of the assessment will be affected by the time interval since sonication.

A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images.

MOTIVATION: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise. RESULTS: We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification. Calibration is essential for a practical quantification method because we need to measure the confidence of the quantification. We applied our method to quantify the degree of partial fragmentation of mitochondria in collections of fluorescent cell images. We show that with transformation, the quantification can be more accurate and sensitive than that without transformation. We also show that our method outperforms competing methods, including neighbourhood component analysis and the multi-variate drug profiling method by Loo et al. We illustrate its utility with a study of Annonaceous acetogenins, a family of compounds with drug potential. Our result reveals that squamocin induces more fragmented mitochondria than muricin A. AVAILABILITY: Mitochondrial cell images, their corresponding feature sets (SSLF and WSLF) and the source code of our proposed method are available at http://aiia.iis.sinica.edu.tw/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[Substances transport in an underground river of typical karst watershed during storm events].

Hydrologic process, turbidity, suspended particles matters (SPM), major cations and TOC concentrations during two storm events in late April 2008 were monitored at Jiangjia Spring which is the outlet of Qingmu Guan underground river system. Scanning electron microscopy (SEM) and energy disperse spectroscopy (EDS) analyses of SPM were also performed in order to investigate the transport characteristics of substances, such as SPM, turbidity and major cations in the underground river of typical karst watershed. The results show that at a single and well-developed karst conduit of Jiangjia Spring, discharge, turbidity, and concentrations of SPM, major cations and TOC respond promptly to the rainfall. The carbonate-derived cations including Ca2+, Mg2+ and Sr2+ are subject to dilution effect during the rising limb of discharge. The elevation in turbidity and SPM concentration is a result of the gradual increase of allochthonous substances (soil) flux input from the surface. Al3+, Fe, Mn, Ba2+ and TOC are concomitant substances of SPM. And their concentrations are ascending with turbid rise. The flux of SPM in diameter > 0.45 microm in the underground river is about 9.7 tons during the events. The bad water quality suggests us that the spring water is unfit to drink without purification during the period of rising and recession time of discharge at Jiangjia Spring. Thus, soil erosion and nutrient losing not only strongly destroy the fragile karst ecological environment, but also lead to non-point source pollution, and seriously threaten the drinking water safety of locals.

Overview of BioCreative II gene mention recognition.

Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a statistical analysis of the results. We also demonstrate that, by combining the results from all submissions, an F score of 0.9066 is feasible, and furthermore that the best result makes use of the lowest scoring submissions.

Integrating high dimensional bi-directional parsing models for gene mention tagging.

MOTIVATION: Tagging gene and gene product mentions in scientific text is an important initial step of literature mining. In this article, we describe in detail our gene mention tagger participated in BioCreative 2 challenge and analyze what contributes to its good performance. Our tagger is based on the conditional random fields model (CRF), the most prevailing method for the gene mention tagging task in BioCreative 2. Our tagger is interesting because it accomplished the highest F-scores among CRF-based methods and second over all. Moreover, we obtained our results by mostly applying open source packages, making it easy to duplicate our results. RESULTS: We first describe in detail how we developed our CRF-based tagger. We designed a very high dimensional feature set that includes most of information that may be relevant. We trained bi-directional CRF models with the same set of features, one applies forward parsing and the other backward, and integrated two models based on the output scores and dictionary filtering. One of the most prominent factors that contributes to the good performance of our tagger is the integration of an additional backward parsing model. However, from the definition of CRF, it appears that a CRF model is symmetric and bi-directional parsing models will produce the same results. We show that due to different feature settings, a CRF model can be asymmetric and the feature setting for our tagger in BioCreative 2 not only produces different results but also gives backward parsing models slight but constant advantage over forward parsing model. To fully explore the potential of integrating bi-directional parsing models, we applied different asymmetric feature settings to generate many bi-directional parsing models and integrate them based on the output scores. Experimental results show that this integrated model can achieve even higher F-score solely based on the training corpus for gene mention tagging. AVAILABILITY: Data sets, programs and an on-line service of our gene mention tagger can be accessed at http://aiia.iis.sinica.edu.tw/biocreative2.htm.

Boosting multiclass learning with repeating codes and weak detectors for protein subcellular localization.

MOTIVATION: Determining locations of protein expression is essential to understand protein function. Advances in green fluorescence protein (GFP) fusion proteins and automated fluorescence microscopy allow for rapid acquisition of large collections of protein localization images. Recognition of these cell images requires an automated image analysis system. Approaches taken by previous work concentrated on designing a set of optimal features and then applying standard machine-learning algorithms. In fact, trends of recent advances in machine learning and computer vision can be applied to improve the performance. One trend is the advances in multiclass learning with error-correcting output codes (ECOC). Another trend is the use of a large number of weak detectors with boosting for detecting objects in images of real-world scenes. RESULTS: We take advantage of these advances to propose a new learning algorithm, AdaBoost.ERC, coupled with weak and strong detectors, to improve the performance of automatic recognition of protein subcellular locations in cell images. We prepared two image data sets of CHO and Vero cells and downloaded a HeLa cell image data set in the public domain to evaluate our new method. We show that AdaBoost.ERC outperforms other AdaBoost extensions. We demonstrate the benefit of weak detectors by showing significant performance improvements over classifiers using only strong detectors. We also empirically test our method's capability of generalizing to heterogeneous image collections. Compared with previous work, our method performs reasonably well for the HeLa cell images. AVAILABILITY: CHO and Vero cell images, their corresponding feature sets (SSLF and WSLF), our new learning algorithm, AdaBoost.ERC, and Supplementary Material are available at http://aiia.iis.sinica.edu.tw/