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Patients with gastroesophageal reflux disease (GERD) display enhanced laryngeal reflex reactivity to stimuli that may be due to sensitization of the laryngeal C-fibers by acid and pepsin. Menthol, a ligand of transient receptor potential melastatin-8 (TRPM8), relieves throat irritation. However, the possibility that GERD induces laryngeal C-fiber hypersensitivity to cigarette smoke (CS) and that menthol suppresses this event has not been investigated. We delivered CS into functionally isolated larynxes of 160 anesthetized rats. Laryngeal pH 5-pepsin treatment, but not pH 5-denatured pepsin, augmented the apneic response to CS, which was blocked by denervation or perineural capsaicin treatment (a procedure that blocks the conduction of C fibers) of the superior laryngeal nerves. This augmented apnea was partially attenuated by capsazepine [an transient receptor potential vanilloid 1 (TRPV1) antagonist], SB-366791 (a TRPV1 antagonist), and HC030031 [a transient receptor potential ankyrin 1 (TRPA1) antagonist] and was completely prevented by a combination of TRPV1 and TRPA1 antagonists. Local application of menthol significantly suppressed the augmented apnea and this effect was reversed by pretreatment with AMTB (a TRPM8 antagonist). Our electrophysiological studies consistently revealed that laryngeal pH 5-pepsin treatment increased the sensitivity of laryngeal C-fibers to CS. Likewise, menthol suppressed this laryngeal C-fiber hypersensitivity and its effect could be reversed by pretreatment with AMTB. Our results suggest that laryngeal pH 5-pepsin treatment increases sensitivity to CS of both TRPV1 and TRPA1, which are presumably located at the terminals of laryngeal C-fibers. This sensory sensitization leads to enhanced laryngeal reflex reactivity and augmentation of the laryngeal C-fiber responses to CS, which can be suppressed by menthol acting via TRPM8.

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PurposeTo investigate the risk factors for recurrence in patients with early-stage hepatocellular carcinoma (HCC) after minimally invasive treatment with curative intent, then to construct a prediction model based on Lasso-Cox regression and visualize the model built.MethodsClinical data were collected from 547 patients that received minimally invasive treatment in our hospital from January 1, 2012, to December 31, 2016. Lasso regression was used to screen risk factors for recurrence. Then we established Cox proportional hazard regression model and random survival forest model including several parameters screened by Lasso regression. An optimal model was selected by comparing the values of C-index, then the model was visualized and the nomogram was finally plotted.ResultsThe variables screened by Lasso regression including age, gender, cirrhosis, tumor number, tumor size, platelet-albumin-bilirubin index (PALBI), and viral load were incorporated in the Cox model and random survival forest model (P<0.05). The C-index of these two models in the training sets was 0.729 and 0.708, and was 0.726 and 0.700 in the validation sets, respectively. So we finally chose Lasso-Cox regression model, and the calibration curve in the validation set performed well, indicating that the model built has a better predictive ability. And then a nomogram was plotted based on the model chosen to visualize the results.ConclusionsThe present study established a nomogram for predicting recurrence in patients with early-stage HCC based on the Lasso-Cox regression model. This nomogram was of some guiding significance for screening populations at high risk of recurrence after treatment, by which doctors can formulate individualized follow-up strategies or treatment protocols according to the predicted risk of relapse for patients to improve the long-term prognosis.

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