AIMC Topic: Infant, Newborn

Clear Filters Showing 31 to 40 of 815 articles

Machine learning-based evaluation of risk factors for carbapenem-resistant dissemination in neonatal units.

mSystems
Healthcare-associated infections (HAIs), particularly in neonatal intensive care units (NICUs), pose significant challenges due to neonates' vulnerability and the rapid infection spread. However, risk factors facilitating pathogen persistence and dis...

Young infants with bronchiolitis at low risk of respiratory deterioration in an urban, academic emergency department: prospective cohort study protocol.

BMJ open
INTRODUCTION: Bronchiolitis, a viral lower respiratory tract infection, is the leading cause of hospitalisation for infants, with healthcare utilisation highest among young infants (aged ≤90 days). Clinical models to predict respiratory deterioration...

Predictive model integrating deep learning and clinical features based on ultrasound imaging data for surgical intervention in intussusception in children younger than 8 months.

BMJ open
OBJECTIVES: The objective of this study was to identify risk factors for enema reduction failure and to establish a combined model that integrates deep learning (DL) features and clinical features for predicting surgical intervention in intussuscepti...

Determination of miRNA in tear extracellular vesicles significantly associated with treatment-requiring retinopathy of prematurity: a pilot study.

Scientific reports
Retinopathy of prematurity (ROP) develops in some premature infants and may be characterized by permanent severe retinal damage necessitating early detection and prompt treatment. The purpose of this study was to investigate whether specific miRNAs i...

Deep learning approach for screening neonatal cerebral lesions on ultrasound in China.

Nature communications
Timely and accurate diagnosis of severe neonatal cerebral lesions is critical for preventing long-term neurological damage and addressing life-threatening conditions. Cranial ultrasound is the primary screening tool, but the process is time-consuming...

The prediction models for the optimal timing of surgical intervention for necrotizing enterocolitis: nomogram vs. five machine learning models.

Pediatric surgery international
BACKGROUND: Necrotizing enterocolitis (NEC) is one of the most common diseases that pose serious threats to the life of newborns. In clinical practice, NEC is typically treated by surgical intervention, but it is still difficult to identify the timin...

Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations.

Scientific reports
Sepsis is a condition resulting from the uncontrolled immune response to infection, leading to widespread inflammatory damage and potentially fatal organ dysfunction. Currently, there is a lack of specific prevention and treatment strategies for seps...

Patient Blood Management in Pediatric Patients: Current Strategies and Future Perspectives.

Turkish journal of haematology : official journal of Turkish Society of Haematology
Patient blood management (PBM) is an evidence-based, multidisciplinary approach aimed at optimizing the care of patients who might require transfusion. While PBM has been widely adopted in adult practice, its application in pediatric settings remains...

Toward the Development of a Novel Newborn Screening Modality: In-Depth Nontargeted Proteome Analysis of Dried Blood Spots with a Robotic Pipeline Using Low-Cost Iron Powders.

Analytical chemistry
We developed a simple protein extraction method for dried blood spots (DBS) that potentially meets the throughput required for newborn screening (NBS) and optimizes nontargeted proteomic analysis in combination with liquid chromatography coupled mass...