Abstract.Signaling of the Toll-like receptor (TLR) is closely associated with tumor development and progression processes including cell proliferation, angiogenesis, metastasis, and immunosuppression. In this study, we examined the expression of TLR5 in gastric cancer cells and its function in cell proliferation. RT-PcR revealed that the TLR5 gene was expressed in all gastric cancer cell lines examined, SNU638, SNU601, SNU216, and AGS. The TLR5 agonist, flagellin, induced IL-8 production and NF-κB activation in the gastric cancer cell lines. In addition, flagellin enhanced the proliferation of all gastric cancer cells examined, whereas LPS did not affect that of SNU638 cells. Blockade of TLR5 using an antibody, restored the proliferation of SNU638 cells enhanced by flagellin, indicating that TLR5 is essential for cell proliferation by flagellin. Flagellin also led to phosphorylation of ERK in SNU638 cells. The ERK inhibitor, Pd98059, restored the proliferation ability of SNU638 cells enhanced by flagellin, suggesting that ERK may play an important role in the proliferation of gastric cancer cells. These findings suggest that TLR5 may play an important role in tumor progression of gastric cancer via the regulation of cell proliferation.

Objective: The study evaluated the clinical intraoperative effects of intrathecal administration of fentanyl on shoulder tip pain in patients undergoing laparoscopic total extraperitoneal inguinal hernia repair (TEP) under spinal anaesthesia. Methods: Patients undergoing TEP were allocated in a double-blinded, prospective, randomized manner to two groups. Spinal anaesthesia was induced by intrathecal administration of 2.8 ml of 0.5% hyperbaric bupivacaine (14 mg) in the control group and with 2.6 ml of 0.5% hyperbaric bupivacaine (13 mg) and 10 mg fentanyl (0.2 ml) in the experimental group.Results: The quality of muscle relaxation, adequacy of operative space and incidence of pneumoperitoneum were similar in the two groups (n ¼ 36 per group). Compared with the control group, the experimental group had significantly fewer cases of hypotension (12 [

We investigated the thermographic findings of carpal tunnel syndrome (CTS). We enrolled 304 hands with electrodiagnostically identified CTS and 88 control hands. CTS hands were assigned to duration groups (D1, < 3 months; D2, 3‒6 months; D3, 6‒12 months; D4, ≥ 12 months) and severity groups (S1, very mild; S2, mild; S3, moderate; S4, severe). The temperature difference between the median and ulnar nerve territories (ΔM-U territories) decreased as CTS duration and severity increased. Significant differences in ΔM-U territories between the D1 and D3, D1 and D4, D2 and D4, and S1 and S4 groups (P = 0.003, 0.001, 0.001, and < 0.001, respectively) were observed. Thermal anisometry increased as CTS duration and severity increased. Significant differences in thermal anisometry between the D1 and D4 as well as the D2 and D4 groups (P = 0.005 and 0.04, respectively) were noted. Thermal anisometry was higher in the S4 group than in the S1, S2, and S3 groups (P = 0.009, < 0.001, and 0.003, respectively). As CTS progresses, skin temperature tends to decrease and thermal variation tends to increase in the median nerve-innervated area. Thermographic findings reflect the physiological changes of the entrapped median nerve.

Identifying the severity of carpal tunnel syndrome (CTS) is essential to providing appropriate therapeutic interventions. We developed and validated machine-learning (ML) models for classifying CTS severity. Here, 1037 CTS hands with 11 variables each were retrospectively analyzed. CTS was confirmed using electrodiagnosis, and its severity was classified into three grades: mild, moderate, and severe. The dataset was randomly split into a training (70%) and test (30%) set. A total of 507 mild, 276 moderate, and 254 severe CTS hands were included. Extreme gradient boosting (XGB) showed the highest external validation accuracy in the multi-class classification at 76.6% (95% confidence interval [CI] 71.2–81.5). XGB also had an optimal model training accuracy of 76.1%. Random forest (RF) and k-nearest neighbors had the second-highest external validation accuracy of 75.6% (95% CI 70.0–80.5). For the RF and XGB models, the numeric rating scale of pain was the most important variable, and body mass index was the second most important. The one-versus-rest classification yielded improved external validation accuracies for each severity grade compared with the multi-class classification (mild, 83.6%; moderate, 78.8%; severe, 90.9%). The CTS severity classification based on the ML model was validated and is readily applicable to aiding clinical evaluations.