Artificial Intelligence in Minimally Invasive Adrenalectomy: Using Deep Learning to Identify the Left Adrenal Vein.

BACKGROUND: Minimally invasive adrenalectomy is the main surgical treatment option for the resection of adrenal masses. Recognition and ligation of adrenal veins are critical parts of adrenal surgery. The utilization of artificial intelligence and deep learning algorithms to identify anatomic structures during laparoscopic and robot-assisted surgery can be used to provide real-time guidance. METHODS: In this experimental feasibility study, intraoperative videos of patients who underwent minimally invasive transabdominal left adrenalectomy procedures between 2011 and 2022 in a tertiary endocrine referral center were retrospectively analyzed and used to develop an artificial intelligence model. Semantic segmentation of the left adrenal vein with deep learning was performed. To train a model, 50 random images per patient were captured during the identification and dissection of the left adrenal vein. A randomly selected 70% of data was used to train models while 15% for testing and 15% for validation with 3 efficient stage-wise feature pyramid networks (ESFPNet). Dice similarity coefficient (DSC) and intersection over union scores were used to evaluate segmentation accuracy. RESULTS: A total of 40 videos were analyzed. Annotation of the left adrenal vein was performed in 2000 images. The segmentation network training on 1400 images was used to identify the left adrenal vein in 300 test images. The mean DSC and sensitivity for the highest scoring efficient stage-wise feature pyramid network B-2 network were 0.77 (±0.16 SD) and 0.82 (±0.15 SD), respectively, while the maximum DSC was 0.93, suggesting a successful prediction of anatomy. CONCLUSIONS: Deep learning algorithms can predict the left adrenal vein anatomy with high performance and can potentially be utilized to identify critical anatomy during adrenal surgery and provide real-time guidance in the near future.

Overweight and obesity in preschool children in Turkey: A multilevel analysis.

Childhood obesity/overweight is a worldwide concern and its prevalence is increasing in many countries. The first aim of this study is to analyse the trends in overweight and obesity among children under the age of five in Turkey based on the new World Health Organization (WHO) standards, using data from the 'five-round of the Turkey Demographic and Health Surveys' (TDHSs). The second aim is to examine whether or not the maternal/household and individual-level factors are associated with overweight/obesity using TDHS 2003, 2008, and 2013 datasets. A total sample of 14,231 children under the age of five were extracted from the TDHS in 1993, 1998, 2003, 2008, and 2013 to determine the prevalence of the trend. Pooled data from 8,812 children were included in the analysis to examine factors associated with overweight/obesity. Taking into account the clustered data structure, multilevel logistic regression models were utilised. In 1993, 1998, 2003, 2008, and 2013 the prevalence of overweight children was 5.3%, 4.9%, 10.0%, 11% and 11.6%, respectively. The factors that were independently associated with overweight/obesity were as follows: living in single-parent households (adjusted odds ratio (aOR) = 2.27, 95%CI = 1.21-4.26), compared to living in dual-parent households; having an obese mother (aOR = 4.25, 95%CI = 1.73-10.44), overweight mother (aOR = 3.15, 95%CI = 1.29-7.69), and a normal-weight mother (aOR = 2.70, 95%CI = 1.11-6.59) compared to having an underweight mother; being aged between 13-24 months (aOR = 1.72, 95%CI = 1.30 to 2.27), compared to being aged 0-12 months; male gender (aOR = 1.30, 95%CI = 1.11 to 1.53); being stunted (aOR = 2.18, 95%CI = 1.74 to 2.73); high birth weight (aOR = 1.55, 95%CI = 1.08 to 2.23) compared to low birth weight. In addition, overweight was higher in children of mothers who had completed primary school (aOR = 1.21, 95%CI = 1.01 to 1.59) than children of mothers who had not completed primary school. These findings reveal that, over the years, there has been a substantial increase in obesity/overweight among children which demonstrates the importance of evaluating the overweight indicators at the maternal/household level.

Structures of MERS-CoV macro domain in aqueous solution with dynamics: Impacts of parallel tempering simulation techniques and CHARMM36m and AMBER99SB force field parameters.

A novel virus, severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), causing coronavirus disease 2019 (COVID-19) worldwide appeared in 2019. Detailed scientific knowledge of the members of the Coronaviridae family, including the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is currently lacking. Structural studies of the MERS-CoV proteins in the current literature are extremely limited. We present here detailed characterization of the structural properties of MERS-CoV macro domain in aqueous solution. Additionally, we studied the impacts of chosen force field parameters and parallel tempering simulation techniques on the predicted structural properties of MERS-CoV macro domain in aqueous solution. For this purpose, we conducted extensive Hamiltonian-replica exchange molecular dynamics simulations and Temperature-replica exchange molecular dynamics simulations using the CHARMM36m and AMBER99SB parameters for the macro domain. This study shows that the predicted secondary structure properties including their propensities depend on the chosen simulation technique and force field parameter. We perform structural clustering based on the radius of gyration and end-to-end distance of MERS-CoV macro domain in aqueous solution. We also report and analyze the residue-level intrinsic disorder features, flexibility and secondary structure. Furthermore, we study the propensities of this macro domain for protein-protein interactions and for the RNA and DNA binding. Overall, results are in agreement with available nuclear magnetic resonance spectroscopy findings and present more detailed insights into the structural properties of MERS CoV macro domain in aqueous solution. All in all, we present the structural properties of the aqueous MERS-CoV macro domain using different parallel tempering simulation techniques, force field parameters and bioinformatics tools.