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Characterizing root response phenotypes by neural network analysis.

Roots play an immediate role as the interface for water acquisition. To improve sustainability in low-water environments, breeders of major crops must therefore pay closer attention to advantageous root phenotypes; however, the complexity of root architecture in response to stress can be difficult to quantify. Here, the Sholl method, an established technique from neurobiology used for the characte...

Hemoglobin-based oxygen carriers promote systemic hyperfibrinolysis that is both dependent and independent of plasmin.

BACKGROUND: Hyperfibrinolysis plays an integral role in the genesis of trauma-induced coagulopathy. Recent data demonstrate that red blood cell lysis promotes fibrinolysis; however, the mechanism is unclear. Hemoglobin-based oxygen carriers (HBOCs) have been developed for resuscitation and have been associated with coagulopathy. We hypothesize that replacement of whole blood (WB) using an HBOC res...

Utilizing Telemedicine in the Trauma Intensive Care Unit: Does It Impact Teamwork?

BACKGROUND: The aim of this study was to examine the impact of a telemedical robot on trauma intensive care unit (TICU) clinician teamwork (i.e., team...

Bridging a translational gap: using machine learning to improve the prediction of PTSD.

BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level ri...

Partial splenectomy in the era of minimally invasive surgery: the current laparoscopic and robotic experiences.

BACKGROUND: Partial splenectomy (PS) is a spleen-preserving technique that is applied as a result of trauma, focal lesions or hematological conditions...

Surgical robots for SPL and NOTES: a review.

INTRODUCTION: Single port laparoscopy (SPL) and natural orifice transluminal endoscopic surgery (NOTES) are next-generation minimally invasive surgery...

Robotic natural orifice transluminal endoscopic surgery (R-NOTES): literature review and prototype system.

In minimally invasive surgery methods such as laparoscopic surgery, surgical instruments are introduced through small incisions to minimize patient tr...

Artificial neural networks in neurosurgery.

Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on ...

Machine learning-based augmented reality for improved surgical scene understanding.

In orthopedic and trauma surgery, AR technology can support surgeons in the challenging task of understanding the spatial relationships between the an...

Estimation of optimal pivot point for remote center of motion alignment in surgery.

PURPOSE: The determination of an optimal pivot point ([Formula: see text]) is important for instrument manipulation in minimally invasive surgery. Suc...

SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models

Delta-Rule recurrent models maintain a fixed-size state, enabling $O(1)$ inference memory but potentially becoming unstable under extreme-context extr...

Limitations of Synthetic Data Generation in Specialized Data-Scarce Domains

Advances in diffusion-based generative models have motivated the use of synthetic image generation to alleviate data scarcity in vision tasks. While t...

A Cloud-Edge System for Multimodal Clinical Screening in Resource-Constrained Rural Settings

Medical AI has demonstrated specialist-level diagnostic accuracy, yet these capabilities remain largely inaccessible in resource-constrained rural set...

First-24-hour machine learning for 30-day mortality prediction in ICU trauma patients: development in MIMIC-III and cross-database evaluation in MIMIC-IV

ICU trauma patients are clinically heterogeneous, and early mortality risk stratification may support monitoring and resource allocation. We developed...

Infrared Imaging Empowered by Artificial Intelligence for Pediatric Skeletal Triage: A Narrative Review and Future Perspectives

Background. Pediatric musculoskeletal trauma represents up to 18% of pediatric ED visits, yet diagnosis still depends on ionizing radiography. Cumulat...

Learned ultrasound segmentation and deformable CT fusion for augmented reality endovascular surgery

Minimally invasive endovascular procedures offer reduced surgical trauma, shorter recovery times, and improved outcomes, but rely on 2D fluoroscopic X...

Is simple better? Comparing Computational Cost and Carbon Impact of Machine Learning Models for Traumatic Brain Injury Prediction; A Case Study for Sustainable Digital Health Implementation

Background Machine learning (ML) models for traumatic brain injury (TBI) prediction increasingly demand extensive data, computational resources, and e...

Comparative Performance of Clinical Scoring Systems for Early Mortality Prediction in Blunt Traumatic Brain Injury

Background: Early risk stratification in traumatic brain injury (TBI) is essential for timely triage, resource allocation, and clinical decision-makin...

Integrating Genetic, Environmental, Cognitive, and Temperament Data for ADHD Prediction in Explainable Deep Learning Models

Objective: Attention-deficit/hyperactivity disorder (ADHD) is clinically and etiologically heterogeneous, and diagnostic decisions may benefit from in...

Golden Hour Divide: Trauma Care Accessibility and Resource Vulnerability in Sri Lanka

Timely intensive care dictates survival, yet emergency infrastructure remains unevenly distributed across Sri Lanka. While pre-hospital services have ...