Minimally Invasive Versus Open Distal Gastrectomy for Locally Advanced Gastric Cancer: Trial Sequential Analysis of Randomized Trials.

Background: Minimally invasive distal gastrectomy (MIDG) has been shown to be associated with improved short-term outcomes compared to open distal gastrectomy (ODG) in patients with locally advanced gastric cancer (LAGC). The impact of MIDG on long-term patient survival remains debated. Aim was to compare the MIDG vs.

Anomaly Detection for Power Quality Analysis Using Smart Metering Systems.

The problem of Power Quality analysis is becoming crucial to ensuring the proper functioning of complex systems and big plants. In this regard, it is essential to rapidly detect anomalies in voltage and current signals to ensure a prompt response and maximize the system's availability with the minimum maintenance cost. In this paper, anomaly detection algorithms based on machine learning, such as One Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Angle-Based Outlier Detection (ABOD), are used as a first tool for rapid and effective clustering of the measured voltage and current signals directly on-line on the sensing unit.

Antioxidant Marine Hydrolysates Isolated from Tuna Mixed Byproducts: An Example of Fishery Side Streams Upcycling.

The aim of this research is to propose simple and scalable processes to obtain bioactive peptides extensively hydrolyzed starting from a tuna mixed biomass. The upcycling of this powdered biomass is challenging since it comes from the unsorted industrial side streams of the tuna canning process (cooked residues from fillet trimming) after a patented mild dehydration useful for preventing its degradation until its exploitation. Two different protocols were proposed, with and without the inclusion of an exogenous enzyme (Enzymatic-Assisted Extraction, EAE), with no relevant differences in yields (24% vs.

Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features.

Purpose: We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients.

Materials And Methods: The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient.