Massage ameliorates lumbar disc herniation-related radicular pain in rats by suppressing TLR4/NLRP3 inflammasome signaling transduction.

BACKGROUND: Lumbar disc herniation (LDH) is a common spinal disease that can cause severe radicular pain. Massage, also known as Tuina in Chinese, has been indicated to exert an analgesic effect in patients with LDH. Nonetheless, the mechanism underlying this effect of massage on LDH remains unclarified. METHODS: Forty Sprague-Dawley rats were randomly divided into four groups. A rat LDH model was established by autologous nucleus pulpous (NP) implantation, followed by treatment with or without massage. A toll-like receptor 4 (TLR4) antagonist TAK-242 was administrated to rats for blocking TLR4. Behavioral tests were conducted to examine rat mechanical and thermal sensitivities. Western blotting was employed for determining TLR4 and NLRP3 inflammasome-associated protein levels in the spinal dorsal horn (SDH). Immunofluorescence staining was implemented for estimating the microglial marker Iba-1 expression in rat SDH tissue. RESULTS: NP implantation induced mechanical allodynia and thermal hyperalgesia in rat ipsilateral hindpaws and activated TLR4/NLRP3 inflammasome signaling transduction in the ipsilateral SDH. Massage therapy or TAK-242 administration relieved NP implantation-triggered pain behaviors in rats. Massage or TAK-242 hindered microglia activation and blocked TLR4/NLRP3 inflammasome activation in ipsilateral SDH of LDH rats. CONCLUSION: Massage ameliorates LDH-related radicular pain in rats by suppressing microglia activation and TLR4/NLRP3 inflammasome signaling transduction.

Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning.

Purpose: To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC). Methods: Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, n = 216; test cohort, n = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images. The t-test and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimensions and select key features. Handcrafted radiomics, deep radiomics, and clinical features were combined to construct models. Models I (handcrafted radiomic features), II (Model I plus deep radiomic features), and III (Model II plus clinical features) were built using three machine learning methods: support vector machine (SVM), adaptive boosting (AdaBoost), and random forest (RF). The best model was compared with the results of two radiologists, and its performance was evaluated in terms of sensitivity, specificity, accuracy, area under the curve (AUC), and receiver operating characteristic (ROC) curve analysis. Results: No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features (p < 0.05). Model III could discriminate between patients with and without LNM better than the diagnostic results of the two radiologists. Conclusion: The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.