AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B.

Background & Aims: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by incorporating imaging biomarkers derived from abdominal computed tomography (CT) images along with clinical variables.

Methods: An AI prediction model employing a gradient-boosting machine algorithm was developed utilizing imaging biomarkers extracted by DeepFore, a deep learning-based CT auto-segmentation software.

Development and functions of the area opaca of the chick embryo.

Before radial symmetry-breaking of the blastoderm, the chick embryo is distinctly divided into a central area pellucida and a surrounding region, the area opaca. In this review, we focus on the area opaca and its functions. First, we survey current knowledge about how the area opaca is formed during the intrauterine period and how it sets up its initial tissue structure.

Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients.

Delirium can result in undesirable outcomes including increased length of stays and mortality in patients admitted to the intensive care unit (ICU). Dexmedetomidine has emerged for delirium prevention in these patients; however, optimal dosing is challenging. A reinforcement learning-based Artificial Intelligence model for Delirium prevention (AID) is proposed to optimize dexmedetomidine dosing.

Comparison of NLP machine learning models with human physicians for ASA Physical Status classification.

The American Society of Anesthesiologist's Physical Status (ASA-PS) classification system assesses comorbidities before sedation and analgesia, but inconsistencies among raters have hindered its objective use. This study aimed to develop natural language processing (NLP) models to classify ASA-PS using pre-anesthesia evaluation summaries, comparing their performance to human physicians. Data from 717,389 surgical cases in a tertiary hospital (October 2004-May 2023) was split into training, tuning, and test datasets.

Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study.

Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the early post-transplant period. This multicenter retrospective cohort study aimed to develop and validate a machine-learning algorithm to predict tacrolimus concentration. Data from 443 patients undergoing liver transplantation between 2017 and 2020 at an academic hospital in South Korea were collected to train machine-learning models.

Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

Purpose: Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors.

Antagonistic Efficacy of Symbiotic Bacterium sp. SCG against spp.

The inhabitation and parasitism of root-knot nematodes (RKNs) can be difficult to control, as its symptoms can be easily confused with other plant diseases; hence, identifying and controlling the occurrence of RKNs in plants remains an ongoing challenge. Moreover, there are only a few biological agents for controlling these harmful nematodes. In this study, sp.

Risk factor analysis and clinical experience of treating capsular contracture after prepectoral implant-based breast reconstruction.

Background: Capsular contracture is one of the most common and severe complications after implant-based breast reconstruction. Recently, prepectoral implant-based breast reconstruction using acellular dermal matrix (ADM) has become an alternative to subpectoral implant-based reconstruction. However, risk factors for capsular contracture associated with recent prepectoral reconstruction trends are not well refined yet.

Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data.

Objective: Failure to receive prompt blood transfusion leads to severe complications if massive bleeding occurs during surgery. For the timely preparation of blood products, predicting the possibility of massive transfusion (MT) is essential to decrease morbidity and mortality. This study aimed to develop a model for predicting MT 10 min in advance using non-invasive bio-signal waveforms that change in real-time.

INSPIRE, a publicly available research dataset for perioperative medicine.

We present the INSPIRE dataset, a publicly available research dataset in perioperative medicine, which includes approximately 130,000 surgical operations at an academic institution in South Korea over a ten-year period between 2011 and 2020. This comprehensive dataset includes patient characteristics such as age, sex, American Society of Anesthesiologists physical status classification, diagnosis, surgical procedure code, department, and type of anaesthesia. The dataset also includes vital signs in the operating theatre, general wards, and intensive care units (ICUs), laboratory results from six months before admission to six months after discharge, and medication during hospitalisation.

Association Between High Preoperative White Blood Cell-to-Hemoglobin Ratio and Postoperative Symptomatic Cerebral Vasospasm in Patients With Aneurysmal Subarachnoid Hemorrhage.

Background: Cerebral vasospasm after aneurysmal subarachnoid hemorrhage (ASAH) is a serious complication and has a strong relationship with systemic inflammatory responses. Given previously reported relationships between leukocytosis and anemia with ASAH-related cerebral vasospasm, this study examined the association between the preoperative white blood cell-to-hemoglobin ratio (WHR) and postoperative symptomatic cerebral vasospasm (SCV) in patients with ASAH.

Methods: Demographic, preoperative (comorbidities, ASAH characteristics, laboratory findings), intraoperative (operation and anesthesia), and postoperative (SCV, other neurological complications, clinical course) data were retrospectively analyzed in patients with ASAH who underwent surgical or endovascular treatment of the culprit aneurysm.

Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation.

Background: A real-time model for predicting short-term mortality in critically ill patients is needed to identify patients at imminent risk. However, the performance of the model needs to be validated in various clinical settings and ethnicities before its clinical application. In this study, we aim to develop an ensemble machine learning model using routinely measured clinical variables at a single academic institution in South Korea.

Effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin measurements in liver transplantation recipients.

This study evaluated the effect of hyperbilirubinemia on the accuracy of continuous non-invasive hemoglobin (SpHb) measurements in liver transplantation recipients. Overall, 1465 SpHb and laboratory hemoglobin (Hb) measurement pairs (n = 296 patients) were analyzed. Patients were grouped into normal (< 1.

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

Background: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery.

Methods: Easily extractable electronic health record (EHR) variables that do not require subjective assessment by clinicians were used.

Regulation of long-range BMP gradients and embryonic polarity by propagation of local calcium-firing activity.

Many amniote vertebrate species including humans can form identical twins from a single embryo, but this only occurs rarely. It has been suggested that the primitive-streak-forming embryonic region emits signals that inhibit streak formation elsewhere but the signals involved, how they are transmitted and how they act has not been elucidated. Here we show that short tracks of calcium firing activity propagate through extraembryonic tissue via gap junctions and prevent ectopic primitive streak formation in chick embryos.

High Intraoperative Serum Lactate Level is Associated with Acute Kidney Injury after Brain Tumor Resection.

Background: Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Identification of risk factors for postoperative AKI is clinically important. Serum lactate can increase in situations of inadequate oxygen delivery and is widely used to assess a patient's clinical course.

Real-time machine learning model to predict in-hospital cardiac arrest using heart rate variability in ICU.

Predicting in-hospital cardiac arrest in patients admitted to an intensive care unit (ICU) allows prompt interventions to improve patient outcomes. We developed and validated a machine learning-based real-time model for in-hospital cardiac arrest predictions using electrocardiogram (ECG)-based heart rate variability (HRV) measures. The HRV measures, including time/frequency domains and nonlinear measures, were calculated from 5 min epochs of ECG signals from ICU patients.

Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and Transformer architecture.

Objectives: Automatic detection of atrial fibrillation and flutter (AF/AFL) is a significant concern in preventing stroke and mitigating hemodynamic instability. Herein, we developed a Transformer-based deep learning model for AF/AFL segmentation in single-lead electrocardiograms (ECGs) by self-supervised learning with masked signal modeling (MSM).

Materials And Methods: We retrieved data from 11 open-source databases on PhysioNet; 7 of these databases included labeled ECGs, while the other 4 were without labels.

Comparison of invasive and non-invasive measurements of cardiac index and systemic vascular resistance in living-donor liver transplantation: a prospective, observational study.

Background: Based on the controversy surrounding pulmonary artery catheterization (PAC) in surgical patients, we investigated the interchangeability of cardiac index (CI) and systemic vascular resistance (SVR) measurements between ClearSight™ and PAC during living-donor liver transplantation (LDLT).

Methods: This prospective study included consecutively selected LDLT patients. ClearSight™-based CI and SVR measurements were compared with those from PAC at seven LDLT-stage time points.

Predicting optimal endotracheal tube size and depth in pediatric patients using demographic data and machine learning techniques.

Background: Use of endotracheal tubes (ETTs) with appropriate size and depth can help minimize intubation-related complications in pediatric patients. Existing age-based formulae for selecting the optimal ETT size present several inaccuracies. We developed a machine learning model that predicts the optimal size and depth of ETTs in pediatric patients using demographic data, enabling clinical applications.