Effects of <it>Panax</it> ginseng-containing herbal plasters on compressed intervertebral discs in an <it>in vivo</it> rat tail model

<p>Abstract</p> <p>Background</p> <p><it>Tienchi</it> (<it>Panax notoginseng</it>) has been used in conservative treatments for back pain as a major ingredient of many herbal medicines. This study aims to investigate the effects of a herbal medicine containing <it>tienchi</it> on compressed intervertebral discs in rats.</p> <p>Methods</p> <p>Using an <it>in vivo</it> rat tail model, intervertebral disc compression was simulated in the caudal 8–9 discs of 25 rats by continuous static compression (11 N) for 2 weeks. An herbal medicine plaster (in which the major ingredient was <it>tienchi</it>) was externally applied to the compressed disc (n=9) for three weeks, and held in place by an adhesive bandage, in animals in the Chinese Medicine (CM) group. The effect of the bandage was evaluated in a separate placebo group (n=9), while no intervention with unrestricted motion was provided to rats in an additional control group (n=7). Disc structural properties were quantified by <it>in vivo</it> disc height measurement and <it>in vitro</it> morphological analysis.</p> <p>Results</p> <p>Disc height decreased after the application of compression (<it>P</it> < 0.001). The disc height decreased continuously in the control (<it>P</it> = 0.006) and placebo (<it>P</it> = 0.003) groups, but was maintained in the CM group (<it>P</it> = 0.494). No obvious differences in disc morphology were observed among the three groups (<it>P</it> = 0.896).</p> <p>Conclusion</p> <p>The <it>tienchi</it>-containing herbal plaster had no significant effect on the morphology of compressed discs, but maintained disc height in rats.</p

Radiomics from Various Tumour Volume Sizes for Prognosis Prediction of Head and Neck Squamous Cell Carcinoma: A Voted Ensemble Machine Learning Approach

Background: Traditionally, cancer prognosis was determined by tumours size, lymph node spread and presence of metastasis (TNM staging). Radiomics of tumour volume has recently been used for prognosis prediction. In the present study, we evaluated the effect of various sizes of tumour volume. A voted ensemble approach with a combination of multiple machine learning algorithms is proposed for prognosis prediction for head and neck squamous cell carcinoma (HNSCC). Methods: A total of 215 HNSCC CT image sets with radiotherapy structure sets were acquired from The Cancer Imaging Archive (TCIA). Six tumour volumes, including gross tumour volume (GTV), diminished GTV, extended GTV, planning target volume (PTV), diminished PTV and extended PTV were delineated. The extracted radiomics features were analysed by decision tree, random forest, extreme boost, support vector machine and generalized linear algorithms. A voted ensemble machine learning (VEML) model that optimizes the above algorithms was used. The receiver operating characteristic area under the curve (ROC-AUC) were used to compare the performance of machine learning methods, including accuracy, sensitivity and specificity. Results: The VEML model demonstrated good prognosis prediction ability for all sizes of tumour volumes with reference to GTV and PTV with high accuracy of up to 88.3%, sensitivity of up to 79.9% and specificity of up to 96.6%. There was no significant difference between the various target volumes for the prognostic prediction of HNSCC patients (chi-square test, p > 0.05). Conclusions: Our study demonstrates that the proposed VEML model can accurately predict the prognosis of HNSCC patients using radiomics features from various tumour volumes