RepoMed (Medizinische Hochschule Hannover)
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Geschlechtsspezifische Unterschiede der epigenetischen Regulation bei Alkoholbezogener Störung am Beispiel des GABAB1-Rezeptors
No full textQuantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations.
No full textIntroduction Over the past few decades, numerous researchers have explored the application of machine learning for assessing children's neurological development. Developmental changes in the brain could be utilized to gauge the alignment of its maturation status with the child's chronological age. AI is trained to analyze changes in different modalities and estimate the brain age of subjects. Disparities between the predicted and chronological age can be viewed as a biomarker for a pathological condition. This literature review aims to illuminate research studies that have employed AI to predict children's brain age. Methods The inclusion criteria for this study were predicting brain age via AI in healthy children up to 12 years. The search term was centered around the keywords "pediatric," "artificial intelligence," and "brain age" and was utilized in PubMed and IEEEXplore. The selected literature was then examined for information on data acquisition methods, the age range of the study population, pre-processing, methods and AI techniques utilized, the quality of the respective techniques, model explanation, and clinical applications. Results Fifty one publications from 2012 to 2024 were included in the analysis. The primary modality of data acquisition was MRI, followed by EEG. Structural and functional MRI-based studies commonly used publicly available datasets, while EEG-based studies typically relied on self-recruitment. Many studies utilized pre-processing pipelines provided by toolkit suites, particularly in MRI-based research. The most frequently used model type was kernel-based learning algorithms, followed by convolutional neural networks. Overall, prediction accuracy may improve when multiple acquisition modalities are used, but comparing studies is challenging. In EEG, the prediction error decreases as the number of electrodes increases. Approximately one-third of the studies used explainable artificial intelligence methods to explain the model and chosen parameters. However, there is a significant clinical translation gap as no study has tested their model in a clinical routine setting. Discussion Further research should test on external datasets and include low-quality routine images for MRI. T2-weighted MRI was underrepresented. Furthermore, different kernel types should be compared on the same dataset. Implementing modern model architectures, such as convolutional neural networks, should be the next step in EEG-based research studies
Vitamin D3, Proteine der DNA-Schadensantwort und die Regulation von oxidativem Stress in der Präeklampsie
No full textEinfluss ausgewählter Immunsuppressiva auf die Expression der Oberflächenproteine PD-L1 und PD-L2 in humanen Keratinozyten und Plattenepithelkarzinomzellen
No full textBlockade der Th2-Schlüsselzytokine IL-4 und IL-13 in Modellsystemen für die Behandlung der atopischen Dermatitis
No full textKonditionelle Ablation von P16INK4a in proximalen Tubuluszellen: Auswirkung auf den Ischämie/Reperfusionsschaden
No full textExpressionsanalyse des Fibroblastenwachstumsfaktors 23 im gesunden Mausherz sowie bei Hochdruck-induzierter kardialer Hypertrophie
No full textLaparoscopic pyeloplasty in neonates and infants is safe and efficient.
No full textIntroduction Dismembered laparoscopic pyeloplasty (LP) is a well-accepted treatment modality for ureteropelvic junction obstruction (UPJO) in children. However, its efficacy and safety in infants, particularly neonates, remain uncertain. To address this significant knowledge gap, we aimed to compare outcomes between a cohort of neonates and infants undergoing LP vs. open pyeloplasty (OP) at less than 6 months and 6 weeks of age. Material and methods We conducted a retrospective analysis of data from patients who underwent primary pyeloplasty at our institution between 2000 and 2022. Only patients aged 6 months or less at the time of surgery were included, excluding redo-procedures or conversions. Ethical approval was obtained, and data were assessed for redo-pyeloplasty and postoperative complications, classified according to the Clavien-Madadi classification. A standard postoperative assessment was performed 6 weeks postoperatively. This included an isotope scan and a routine ultrasound up to the year 2020. Results A total of 91 eligible patients were identified, of which 49 underwent LP and 42 underwent OP. Patients receiving LP had a median age of 11.4 (1-25.4) weeks, compared to 13.8 (0.5-25.9) weeks for those receiving OP (p > 0.31). Both groups in our main cohort had an age range of 0-6 months at the time of surgery. Nineteen patients were younger than 6 weeks at the time of surgery. The mean operating time was longer for LP (161 ± 43 min) than that for OP (109 ± 32 min, p < 0.001). However, the mean operating time was not longer in the patient group receiving LP at ≤6 weeks (145 ± 21.6) compared to that in our main cohort receiving LP. There was no significant difference in the length of stay between the groups. Four patients after LP required emergency nephrostomy compared to one patient after OP. The rate of revision pyeloplasty in our main cohort aged 0-6 months at surgery was 8% in the patient group receiving LP and 14% in the patient group receiving OP (not significant). Three revisions after LP were due to persistent UPJO, and one was due to stent migration. Only one patient requiring revision pyeloplasty was less than 6 weeks old. Conclusion To our knowledge, this is one of the largest collectives of laparoscopic pyeloplasty performed in infants, and it is the youngest cohort published to date. Based on our experience, LP in neonates and infants under 6 months appears to be as effective as open surgery
