Unraveling the role of tumor sidedness in prognosis of stage II colon cancer.

Background: Stage II colon cancer has varying risks for metastasis, and treatment strategies depend on molecular and clinicopathological features. While tumor-sidedness is a well-accepted prognostic factor for stage III/IV colon cancer, its role in stage II is controversial. Understanding its effect in stage II is crucial for improving treatment strategies.

Diagnostic Value of Inflammation-Related Indicators in Distinguishing Early Colon Cancer and Adenomatous Polyps.

Background: There are few clinical symptoms in early colorectal cancer, so it is necessary to find a simple and economical tumor detection index for auxiliary diagnosis. This study aims to explore the diagnostic value of preoperative inflammation-related indicators, such as neutrophil, lymphocyte, platelet count, platelet to lymphocyte ratio (PLA), neutrophil to lymphocyte ratio (NLR), and systemic immune-inflammation index (SII), for early colorectal cancer, and determine whether inflammation-related indicators can provide more accurate diagnostic judgment for patients.

Methods: This study was a retrospective study.

Sarcobesity, but not visceral fat, is an independent risk factor for complications after radical resection of colorectal cancer.

Background: The influence of body composition on the outcome of colorectal cancer surgery is controversial. The aim of this study was to evaluate the effects of visceral obesity and sarcobesity on the incidence of total and surgical complications after radical resection of colorectal cancer.

Methods: We collected a total of 426 patients who underwent elective radical resection of colorectal cancer at Beijing Friendship Hospital, Capital Medical University from January 2017 to May 2018.

Hierarchical Weight Averaging for Deep Neural Networks.

Despite simplicity, stochastic gradient descent (SGD)-like algorithms are successful in training deep neural networks (DNNs). Among various attempts to improve SGD, weight averaging (WA), which averages the weights of multiple models, has recently received much attention in the literature. Broadly, WA falls into two categories: 1) online WA, which averages the weights of multiple models trained in parallel, is designed for reducing the gradient communication overhead of parallel mini-batch SGD and 2) offline WA, which averages the weights of one model at different checkpoints, is typically used to improve the generalization ability of DNNs.

Impact of preoperative biliary drainage on postoperative complications and prognosis after pancreaticoduodenectomy: A single-center retrospective cohort study.

Background And Objectives: Obstructive jaundice is common in patients with pancreaticobiliary malignancies. Preoperative biliary drainage (PBD) can alleviate cholestasis; however, no consensus has been reached on the impact of PBD on the incidence of surgery-related complications and patient survival. This study aimed to evaluate the effect among patients treated with PBD.

Efficient Spiking Neural Networks With Radix Encoding.

Spiking neural networks (SNNs) have advantages in latency and energy efficiency over traditional artificial neural networks (ANNs) due to their event-driven computation mechanism and the replacement of energy-consuming weight multiplication with addition. However, to achieve high accuracy, it usually requires long spike trains to ensure accuracy, usually more than 1000 time steps. This offsets the computation efficiency brought by SNNs because a longer spike train means a larger number of operations and larger latency.

Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network.

The design of nanophotonic structures based on deep learning is emerging rapidly in the research community. Design methods using Deep Neural Networks (DNN) are outperforming conventional physics-based simulations performed iteratively by human experts. Here, a self-adaptive and regularized DNN based on Convolutional Neural Networks (CNNs) for the smart and fast characterization of nanophotonic structures in high-dimensional design parameter space is presented.