Investigating the Prognostic Accuracy of Different Biomarkers for Detection of Developmental Language Disorder in Children With Neonatal Encephalopathy

The goal of this ambispective cohort study is to reveal the early indicators of delayed language development in children born with hypoxic-ischemic encephalopathy (HIE).
We will examine the prognostic accuracy of different biomarkers, with a special focus on the ADC values of the corpus callosum.
The main questions it aims to answer are:
1. To what extent does hypoxic-ischemic encephalopathy (HIE) in infancy affect intellectual development (IQ), receptive and expressive language abilities at different levels of the language system, and memory capacities related to language development?
2. What is the relationship between early biomarkers of brain injury-such as blood gas levels, lactate, aEEG, and MRI findings (Weeke scoring system, ADC values of the corpus callosum)-and long-term cognitive developmental outcomes?
3. What is the incidence of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) in this high-risk population of infants with HIE?
4. Is there an association between the Weeke Total Score and long-term language developmental outcomes?
5. Can restricted diffusion (ADC values) of the splenium of the corpus callosum serve as an early neuroradiological marker of developmental language disorder (DLD)?
Our participants are children born between 2017 and 2023 with moderate to severe HIE, treated with therapeutic hypothermia at Semmelwies University Children's Hospital. During their first days of life, several neonatal measurements were taken (blood gas markers, aEEG etc.), and at day 4-5, they had an MRI scan of their brain. The MRI scans will be reanalyzed, using the Weeke MRI scoring system. These children underwent a neurodevelopmental follow-up at the age of 2 years and currently, they will have another follow-up at the age of 4-7 years.

Start Date01 Apr 2025

Sponsor / Collaborator

Semmelweis University

[+3]

NCT07156825

/ RecruitingNot ApplicableIIT

Methodological Guide for the Adaptation of Pediatric Speech Audiometry Tests Into Other Languages

The objective of this study was to offer a comprehensive framework for the adaptation of speech audiometric tests into other languages. To date, this is the first universal protocol of its kind that systematically considers linguistic, phonological, and audiological aspects.
The present paper provides a protocol and an example for adaptation and standardization of the Mainzer Audiometric Test for Children (MATCH) to another language.

Start Date05 Aug 2024

Sponsor / Collaborator

Semmelweis University

[+1]

NCT06290310

/ Not yet recruitingNot ApplicableIIT

Assessment of Patient-ventilator Asynchrony by Electric Impedance Tomography and Artificial Intelligence

Patient-ventilator asynchrony (PVA) has deleterious effects on the lungs. PVA can lead to acute lung injury and worsening hypoxemia through biotrauma. Little is known about how PVA affects lung aeration estimated by electric impedance tomography (EIT). Artificial intelligence can promote the detection of PVA and with its help, EIT measurements can be correlated to asynchrony.

Start Date12 Apr 2024

Sponsor / Collaborator

University of Szeged

[+2]

100 Clinical Results associated with Budapest University of Technology & Economics

0 Patents (Medical) associated with Budapest University of Technology & Economics

6,651

Literatures (Medical) associated with Budapest University of Technology & Economics

01 Apr 2026·TALANTA

Detection of anthraquinone natural dyes in silk artifacts based on KI-modified silver nanoparticles SERS technology

Review

Author: Wang, Ruqian ; Yao, Mingyi ; Liu, Jian ; Zhang, Chengming ; Tóth, Tünde ; Moór, Anikó

Silk artifacts are commonly dyed with natural dyes, the composition of which reflects their historical context. In this study, silver nanoparticles (AgNPs) were modified with KI solution at room temperature to produce KI-functionalized Surface-enhanced Raman spectroscopy (SERS) substrates. The optimized substrate, prepared at a 3:1 ratio AgNPs-to-KI ratio solution, exhibited superior localized surface plasmon resonance (LSPR) properties, which significantly increased Raman signals of dye molecules while effectively reducing background interference from the substrate. The relative standard deviations (RSD) of alizarin as a molecular probe was 3.47 %, with an enhancement factor (EF) of approximately 6.16 × 10⁶. SERS reference fingerprint spectra were established for five representative anthraquinone natural dyes: dyer's madder (Rubia tinctorum), Indian madder (Rubia cordifolia), Japanese madder (Rubia argyi), lac insect (Kerria lacca) and cochineal (Dactylopius coccus). The method was then applied to analyze dyes in silk artifacts from the collection of the Hungarian Museum of Applied Arts, where alizarin, purpurin, and laccaic acid were detected in a single silk fiber. The proposed KI-AgNPs SERS technique provides a minimally invasive, rapid, reliable and highly specific tool for identifying trace natural dyes in silk artifacts.

01 Apr 2026·Journal of the Mechanical Behavior of Biomedical Materials

Modeling the mechanical anisotropy in the trabecular bone with the measurement and consideration of the structural anisotropy

Article

Author: Lakatos, Éva I ; Németh, Róbert K

The most accurate understanding of the material properties of the trabecular bone tissue of the jawbone is essential for certain oral surgery procedures and for the design of bone replacement materials and implants. The material properties obtained from micro-structural analyses can be used to study the behavior of dental implants and other prostheses implanted in the jawbone. The structural anisotropy of trabecular bone samples from the jawbone was measured using the method of inserted ellipsoids. Using the developed method, it has been shown that bone samples from the close environment of the living tooth-root show anisotropy that can be effectively measured using micro-CT. In this article, we present a method that uses the eigenvalues of the fabric tensor describing structural anisotropy to generate a micro-structural frame model of the trabecular bone. A homogenization method is applied to describe macro-mechanical behavior of the orthotropic bone tissue, which uses the normal-, bending- and torsional stiffness of the beams in the elementary cell in an elastic spring model. With the homogenization of the frame model, the orthotropic material properties of the trabecular bone could be estimated. The method developed is demonstrated using the micro-CT of a bone sample with 0.2636 relative density. The eigenvalues of the fabric tensor of the sample were measured to be 0.5386, 0.3330 and 0.1306, which, after the homogenization of the elementary cell with an identical fabric tensor, resulted in a mechanically orthotropic macro-structure. The apparent moduli obtained were calculated to be 0.6920 GPa, 1.3668 GPa and 0.0503 GPa.

01 Apr 2026·INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

Incorporating patient history into the insulin sensitivity prediction in intensive care by feedforward neural network models

Article

Author: Szabó, Bálint ; Chase, J Geoffrey ; Benyó, Balázs

BACKGROUND AND OBJECTIVE:

Insulin sensitivity prediction is crucial for model-based treatment in Intensive Care Unit patients, particularly those with hyperglycemia. However, predicting insulin sensitivity is challenging due to inter- and intra-patient variability.

METHODS:

Different neural network models are proposed and compared for predicting insulin sensitivity, including recurrent and feedforward versions of the Classification Deep Neural Network and Mixture Density Network models. These models were trained using 1879 patient records containing 123,988 insulin sensitivity values from three intensive care patient cohorts in three different countries.

RESULTS:

Results show that using patient history in prediction models can improve the accuracy of insulin sensitivity predictions. The Mixture Density Network model provided more accurate predictions, measured by a problem-specific metric that expresses clinical requirements. We demonstrated that even using up to 12 h of historical data can improve prediction accuracy.

CONCLUSION:

This study highlights the potential of recurrent neural network models in predicting insulin sensitivity in Intensive Care Unit patients. Our findings suggest that using recurrent neural networks and incorporating patient history can lead to more accurate predictions. These results are generalizable due to the large and diverse dataset employed, which included patients from three different cohorts in three care settings.

100 Deals associated with Budapest University of Technology & Economics

100 Translational Medicine associated with Budapest University of Technology & Economics

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