Comparison of nasal microbiota between preterm and full-term infants in early life.

Background: The respiratory microbiota influences infant immune system maturation. Little is known about how perinatal, physiological, and environmental exposures impact the nasal microbiota in preterm infants after discharge, or nasal microbiota differences between preterm and healthy full-term infants.

Methods: Nasal swabs (from 136 preterm and 299 full-term infants at mean postmenstrual age of 45 weeks from the prospective Basel-Bern Infant Lung Development cohort) were analyzed by 16S-rRNA gene amplification and sequencing (Illumina MiSeq).

Differences in autophagy marker levels at birth in preterm vs. term infants.

Background: Preterm infants are susceptible to oxidative stress and prone to respiratory diseases. Autophagy is an important defense mechanism against oxidative-stress-induced cell damage and involved in lung development and respiratory morbidity. We hypothesized that autophagy marker levels differ between preterm and term infants.

Low-dimensional neural ODEs and their application in pharmacokinetics.

Machine Learning (ML) is a fast-evolving field, integrated in many of today's scientific disciplines. With the recent development of neural ordinary differential equations (NODEs), ML provides a new tool to model dynamical systems in the field of pharmacology and pharmacometrics, such as pharmacokinetics (PK) or pharmacodynamics. The novel and conceptionally different approach of NODEs compared to classical PK modeling creates challenges but also provides opportunities for its application.

Evaluation of machine learning methods for covariate data imputation in pharmacometrics.

Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, such as population pharmacokinetic and pharmacodynamic analyses. To this end, various statistical methods have been widely adopted.

Machine learning-based algorithm as an innovative approach for the differentiation between diabetes insipidus and primary polydipsia in clinical practice.

Objective: Differentiation between central diabetes insipidus (cDI) and primary polydipsia (PP) remains challenging in clinical practice. Although the hypertonic saline infusion test led to high diagnostic accuracy, it is a laborious test requiring close monitoring of plasma sodium levels. As such, we leverage machine learning (ML) to facilitate differential diagnosis of cDI.

Augmenting camera images with gamma detector data : A novel approach to support sentinel lymph node biopsy.

Background: Squamous cell carcinoma in the head and neck region is one of the most widespread cancers with high morbidity. Classic treatment comprises the complete removal of the lymphatics together with the cancerous tissue. Recent studies have shown that such interventions are only required in 30% of the patients.

Sentinel lymph node fingerprinting.

Background: When locating the sentinel lymph node (SLN), surgeons use state-of-the-art imaging devices, such as a 1D gamma probe or less widely spread a 2D gamma camera. These devices project the 3D subspace onto a 1D respectively 2D space, hence loosing accuracy and the depth of the SLN which is very important, especially in the head and neck area with many critical structures in close vicinity. Recent methods which use a multi-pinhole collimator and a single gamma detector image try to gain a depth estimation of the SLN.