Comparison of thyroid surgery techniques: a retrospective cohort study and meta-analysis of traditional electric knife vs. straight bipolar electrocoagulation forceps.

Background: In recent years, advancements in surgical techniques for thyroidectomy have led to varying outcomes and efficiencies. Understanding these differences is crucial to optimize patient care and surgical success. This study compared intra- and postoperative parameters of thyroid surgery for thyroidectomy or thyroid cancer. One approach involved the traditional electric knife, employing traditional clamp-ligation skills and an electric knife. The other approach utilized straight bipolar electrocoagulation forceps for micro-hemostasis and micro-cutting. Methods: Data were analyzed retrospectively for 228 patients who underwent thyroidectomy at the Third Affiliated Hospital of Kunming Medical University from January 2014 to November 2018. Surgery was performed either as traditional open surgery (n=150) or as a meticulous anatomical procedure involving bipolar electrocoagulation (n=78). In addition, data from published studies comparing the two surgical procedures were meta-analyzed. Results: The bipolar electrocoagulation procedure was associated with significantly shorter total operation time, lower intraoperative blood loss and lower rate of hypocalcemia. The two procedures were associated with similar rates of hoarseness. Meta-analysis of eight studies involving 2,080 patients showed that bipolar electrocoagulation was associated with significantly shorter total operation time than the traditional approach (mean difference =-21.29 min, 95% CI: -26.32 to -16.27) and with less intraoperative bleeding (mean difference =-12.87 min, 95% CI: -23.81 to -1.93). Conclusions: Straight bipolar electrocoagulation forceps can be used to perform fine dissection during thyroid surgery. Performing "micro-hemostasis" and "micro-cutting" manipulations with these straight bipolar forceps can smoothly dissect nerves and parathyroid glands and may reduce intraoperative bleeding, operation time and rates of postoperative complications, might accelerate recovery after surgery.

Photothermolysis Mediated by Gold Nanorods Conjugated with Epidermal Growth Factor Receptor (EGFR) Monoclonal Antibody Induces Apoptosis via the Mitochondrial Apoptosis Pathway in Laryngeal Squamous Cell Cancer.

Gold nanorods (AuNRs) have unique optical properties and biological affinity and can be used to treat tumors when conjugated with other protein molecules. Our previous studies have shown that EGFR monoclonal antibody (EGFRmAb)-modified AuNRs exert strong antitumor activity in vitro by inducing apoptosis. In this study, we tested the effects of EGFRmAb-modified AuNRs on laryngeal squamous cell cancer (LSCC) in vitro and in vivo. The in vitro results showed that EGFRmAb-modified AuNRs inhibited NP-69, BEAS-2B and Hep-2 cell growth and induced mitochondria-dependent apoptosis. The mitochondrial membrane potential was reduced, leading to the release of cytochrome C (Cyt C) and consequent activation of the intrinsic mitochondrial apoptosis pathway. Moreover, we observed that the occurrence of mitochondrial apoptosis is related to the destruction of the lysosome-mitochondria axis. To verify the effects in vivo, we also established a laryngeal tumor model in nude mice by subcutaneous transplantation. In model mice treated with EGFRmAb-modified AuNRs and irradiated with an NIR laser, tumor cell apoptosis and tumor growth were inhibited. These results suggest that EGFRmAb-modified AuNRs induced apoptosis through the intrinsic mitochondrial apoptotic pathway and are a potential candidate for cancer therapy.

Optimization of endothelial growth factor receptor monoclonal antibody-gold nanorods photothermal therapy for laryngeal squamous cell carcinoma.

It reported that heat generated by near-infrared laser irradiation of gold nanorods (AuNRs) effectively inhibited tumor cells, and the conjugate of epidermal growth factor receptor monoclonal antibody (EGFRmAb) and gold nanorods could selectively binded to the surface of cancer cell membrane expressing EGFR. However, there are few research reports on EGFRmAb-AuNRs in laryngeal squamous cell carcinoma. Therefore, our study aimed to investigate the photothermal effect of EGFRmAb modified AuNRs in inducing tumor cell death in an animal model of laryngeal squamous cell carcinoma. We showed that the conjugates of EGFRmAb and AuNRs selectively entered laryngeal squamous cell carcinoma cells. We analyzed the parameters of laser irradiation by controlling the near-infrared to optimize the condition and procedure of targeted treatment in nude mice treated with EGFRmAb and AuNRs. In addition, we examined the safety of the combined therapy. Test results showed that EGFRmAb-AuNRs inhibited the growth of Hep-2 and CNE-2 cells but not normal epithelial cells, and the semi-inhibitor concentration of EGFRmAb in Hep-2 and CNE-2 cells was 4 pmol/ml and 2 pmol/ml, respectively. AuNRs injected into the tumor and irradiated by near-infrared laser effectively inhibited tumor growth in nude mice without toxic effect in mice. We further confirmed that the apoptosis and necrosis rates of tumor cells in mice were highest under 3 W/cm2 near-infrared laser irradiation and AuNRs minimum concentration of 280 µg/kg. In conclusion, we developed a new method of targeting EGFRmAb combined with AuNRs to achieve photothermal effect in the treatment of laryngeal squamous cell carcinoma.

Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence.

BACKGROUND: The sensitivity of endoscopy in diagnosing chronic atrophic gastritis is only 42%, and multipoint biopsy, despite being more accurate, is not always available. AIMS: This study aimed to construct a convolutional neural network to improve the diagnostic rate of chronic atrophic gastritis. METHODS: We collected 5470 images of the gastric antrums of 1699 patients and labeled them with their pathological findings. Of these, 3042 images depicted atrophic gastritis and 2428 did not. We designed and trained a convolutional neural network-chronic atrophic gastritis model to diagnose atrophic gastritis accurately, verified by five-fold cross-validation. Moreover, the diagnoses of the deep learning model were compared with those of three experts. RESULTS: The diagnostic accuracy, sensitivity, and specificity of the convolutional neural network-chronic atrophic gastritis model in diagnosing atrophic gastritis were 0.942, 0.945, and 0.940, respectively, which were higher than those of the experts. The detection rates of mild, moderate, and severe atrophic gastritis were 93%, 95%, and 99%, respectively. CONCLUSION: Chronic atrophic gastritis could be diagnosed by gastroscopic images using the convolutional neural network-chronic atrophic gastritis model. This may greatly reduce the burden on endoscopy physicians, simplify diagnostic routines, and reduce costs for doctors and patients.