BRAFV600E mutation does not predict lymph node metastases and recurrence in Chinese papillary thyroid microcarcinoma patients.

OBJECT: Previous studies suggest BRAFV600E mutation is a marker for poor prognosis in papillary thyroid cancer, however, its ability to further risk stratify papillary thyroid microcarcinoma (PTMC) remains controversial. We aimed to explore the association between BRAFV600E mutation and the clinicopathological features and recurrence in Chinese PTMC patients. METHODS: We retrospectively reviewed 2094 PTMC patients who underwent surgery and had a valid BRAFV600E mutation test result. Among them, 1292 patients had complete follow-up data. The mutation incidence was determined. Moreover, the clinicopathological characteristics, disease-free survival (DFS), and response to therapy distribution were compared between the mutation and non-mutation groups. RESULTS: BRAFV600E mutation was observed in 90.6 % of all patients and 89.2 % of patients with complete follow-up data. No significant difference was observed in lymph node metastases (LNM) number categories between the mutation and non-mutation groups among all patients (P = 0.329) and 1292 patients (P = 0.408). Neither the 3-year DFS (97.9 % vs. 98.0 %, P = 0.832) nor the response to therapy distribution (P > 0.05) indicated a significant difference between the mutation and non-mutation groups. The 3-year DFS differs among patients having different LNM number categories (99.8 % vs. 98.5 % vs. 77.3 %, P < 0.001). Multivariate analysis revealed that high-volume (over 5) LNM (Total thyroidectomy (TT): OR = 4.000, 95 % CI 2.390-6.694, P < 0.001; Unilateral thyroidectomy (UT): OR = 4.183, 95 % CI 1.565-11.190, P = 0.004), rather than BRAFV600E mutation (P > 0.05), was an independent risk factor of response to therapy. CONCLUSIONS: Our results suggested that BRAFV600E mutation could not accurately predict LNM or the recurrence of Chinese PTMC patients. Moreover, high-volume LNM is significantly associated with PTMC prognosis.

Modulating ferroptosis sensitivity: environmental and cellular targets within the tumor microenvironment.

Ferroptosis, a novel form of cell death triggered by iron-dependent phospholipid peroxidation, presents significant therapeutic potential across diverse cancer types. Central to cellular metabolism, the metabolic pathways associated with ferroptosis are discernible in both cancerous and immune cells. This review begins by delving into the intricate reciprocal regulation of ferroptosis between cancer and immune cells. It subsequently details how factors within the tumor microenvironment (TME) such as nutrient scarcity, hypoxia, and cellular density modulate ferroptosis sensitivity. We conclude by offering a comprehensive examination of distinct immunophenotypes and environmental and metabolic targets geared towards enhancing ferroptosis responsiveness within the TME. In sum, tailoring precise ferroptosis interventions and combination strategies to suit the unique TME of specific cancers may herald improved patient outcomes.

Automatic bleeding detection in laparoscopic surgery based on a faster region-based convolutional neural network.

Background: Laparoscopic surgery has been in great demand over the past decades; it has also brought several obstacles, such as increasing difficulty in maintaining hemostasis, changes in surgical approach, and reduced field of vision. Locating the bleeding point can help surgeons to control bleeding quickly, however, to date, there have been no tools designed for automatic bleeding tracking in laparoscopic operations. Herein, we have proposed a spatiotemporal hybrid model based on a faster region-based convolutional neural network (RCNN) for bleeding point detection in laparoscopic surgery videos. Methods: Laparoscopic videos performed at our hospital were retrieved and images containing bleeding events were extracted. Spatiotemporal features were extracted by using red-green-blue (RGB) frames and optical flow maps and a spatiotemporal hybrid model was developed based on the faster RCNN. The proposed model contributed to (I) providing real-time bleeding point detection which directly assist surgeons, (II) showing the blood's optical flow which improved bleeding point detection, and (III) detecting both arterial and venous bleeding. Results: In this study, 12 different bleeding videos were included for deep learning model training. Compared with models containing a single RGB or a single optical flow map, our model combining RGB and optical flow achieved great detection results (precision rate of 0.8373, recall rate of 0.8034, and average precision of 0.6818). Conclusions: Our approach performs well in bleeding point location and recognition, indicating its potential value in helping to maintain and re-establish hemostasis during operations.