Cross-disciplinary mathematical modelling to benefit healthcare - could clinical pharmacology play an enabling role?

Clinical pharmacology is often the nexus in any cross-disciplinary team that is seeking solutions for human healthcare issues. The use and application of real-world data and artificial intelligence to better understand our ecosystem has influenced our view at the world and how we do things. This has resulted in remarkable advancements in the healthcare space and development of personalized medicines with great attributes from the application of models and simulations, contributing to a more efficient healthcare development process. A cross-disciplinary symposium was held in December 2023, whereby experts from different scientific disciplines engaged in a high-level discussion on the opportunities and challenges of mathematical models in different fields, possible future developments and decision making. A strong interlink amongst the disciplines represented was apparent, with clinical pharmacology identified as the one which integrates various scientific disciplines. Deliberate and strategic cross-disciplinary dialogues are required to break out of the silos and implement integration for efficiency and cost-effectiveness of novel interventions.

Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset.

Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red-green-blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen's kappa, κ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the F1w score by 0.14, the accuracy by 0.11, κ by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year's data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at https://github.com/cvims/FHB_classification.

The Transferability of Spectral Grain Yield Prediction in Wheat Breeding across Years and Trial Locations.

Grain yield (GY) prediction based on non-destructive UAV-based spectral sensing could make screening of large field trials more efficient and objective. However, the transfer of models remains challenging, and is affected by location, year-dependent weather conditions and measurement dates. Therefore, this study evaluates GY modelling across years and locations, considering the effect of measurement dates within years. Based on a previous study, we used a normalized difference red edge (NDRE1) index with PLS (partial least squares) regression, trained and tested with the data of individual dates and date combinations, respectively. While strong differences in model performance were observed between test datasets, i.e., different trials, as well as between measurement dates, the effect of the train datasets was comparably small. Generally, within-trials models achieved better predictions (max. R2 = 0.27-0.81), but R2-values for the best across-trials models were lower only by 0.03-0.13. Within train and test datasets, measurement dates had a strong influence on model performance. While measurements during flowering and early milk ripeness were confirmed for within- and across-trials models, later dates were less useful for across-trials models. For most test sets, multi-date models revealed to improve predictions compared to individual-date models.

Iron deficiency in sports - definition, influence on performance and therapy.

Iron deficiency is frequent among athletes. All types of iron deficiency may affect physical performance and should be treated. The main mechanisms by which sport leads to iron deficiency are increased iron demand, elevated iron loss and blockage of iron absorption due to hepcidin bursts. As a baseline set of blood tests, haemoglobin, haematocrit, mean cellular volume, mean cellular haemoglobin and serum ferritin levels help monitor iron deficiency. In healthy male and female athletes >15 years, ferritin values <15 mcg are equivalent to empty, values from 15 to 30 mcg/l to low iron stores. Therefore a cut-off of 30 mcg/l is appropriate. For children aged from 6-12 years and younger adolescents from 12-15 years, cut-offs of 15 and 20 mcg/l, respectively, are recommended. As an exception in adult elite sports, a ferritin value of 50 mcg/l should be attained in athletes prior to altitude training, as iron demands in these situations are increased. Treatment of iron deficiency consists of nutritional counselling, oral iron supplementation or, in specific cases, by intravenous injection. Athletes with repeatedly low ferritin values benefit from intermittent oral substitution. It is important to follow up the athletes on an individual basis, repeating the baseline blood tests listed above twice a year. A long-term daily oral iron intake or i.v. supplementation in the presence of normal or even high ferritin values does not make sense and may be harmful.