Integrated modeling of labile and glycated hemoglobin with glucose for enhanced diabetes detection and short-term monitoring.

Metabolic biomarkers, particularly glycated hemoglobin and fasting plasma glucose, are pivotal in the diagnosis and control of diabetes mellitus. Despite their importance, they exhibit limitations in assessing short-term glucose variations. In this study, we propose labile hemoglobin as an additional biomarker, providing insightful perspectives into these fluctuations. By utilizing datasets from 40,652 retrospective general participants and conducting glucose tolerance tests on 60 prospective pediatric subjects, we explored the relationship between plasma glucose and labile hemoglobin. A mathematical model was developed to encapsulate short-term glucose kinetics in the pediatric group. Applying dimensionality reduction techniques, we successfully identified participant subclusters, facilitating the differentiation between diabetic and non-diabetic individuals. Intriguingly, by integrating labile hemoglobin measurements with plasma glucose values, we were able to predict the likelihood of diabetes in pediatric subjects, underscoring the potential of labile hemoglobin as a significant glycemic biomarker for diabetes research.

Variable selection for nonlinear dimensionality reduction of biological datasets through bootstrapping of correlation networks.

Identifying the most relevant variables or features in massive datasets for dimensionality reduction can lead to improved and more informative display, faster computation times, and more explainable models of complex systems. Despite significant advances and available algorithms, this task generally remains challenging, especially in unsupervised settings. In this work, we propose a method that constructs correlation networks using all intervening variables and then selects the most informative ones based on network bootstrapping. The method can be applied in both supervised and unsupervised scenarios. We demonstrate its functionality by applying Uniform Manifold Approximation and Projection for dimensionality reduction to several high-dimensional biological datasets, derived from 4D live imaging recordings of hundreds of morpho-kinetic variables, describing the dynamics of thousands of individual leukocytes at sites of prominent inflammation. We compare our method with other standard ones in the field, such as Principal Component Analysis and Elastic Net, showing that it outperforms them. The proposed method can be employed in a wide range of applications, encompassing data analysis and machine learning.

Behavioural immune landscapes of inflammation.

Transcriptional and proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues1,2. These approaches, however, do not describe dynamic scenarios in which cells continuously change their biochemical properties and downstream 'behavioural' outputs3-5. Here we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamics of individual leukocytes at sites of active inflammation. By analysing more than 100,000 reconstructions of cell shapes and tracks over time, we obtained behavioural descriptors of individual cells and used these high-dimensional datasets to build behavioural landscapes. These landscapes recognized leukocyte identities in the inflamed skin and trachea, and uncovered a continuum of neutrophil states inside blood vessels, including a large, sessile state that was embraced by the underlying endothelium and associated with pathogenic inflammation. Behavioural screening in 24 mouse mutants identified the kinase Fgr as a driver of this pathogenic state, and interference with Fgr protected mice from inflammatory injury. Thus, behavioural landscapes report distinct properties of dynamic environments at high cellular resolution.

SURFWET: A biokinetic model for surface flow constructed wetlands.

Constructed wetlands are an alternative biotechnology for wastewater treatment that have several advantages over conventional systems. In this work, a biokinetic model for surface flow constructed wetlands is presented (SURFWET). SURFWET belongs to a class of models that are not only interesting from a theoretical viewpoint, as they allow to improve the understanding of the underlying processes; but also from a practical viewpoint, because they can be useful for optimal designs of constructed wetlands, complementing current empirical methods. The proposed model is centered on the intervening physical and biochemical processes involved in pollutant removal in wastewater (organic matter, nitrogen, phosphorus, suspended solids), capturing the interplay of the main agents on contaminant removal (bacteria, macrophytes and phytoplankton). Furthermore, the hydraulic model considers water volume as a variable depending on the outlet hydraulic capacity, and dissolved oxygen has also been introduced as a key driver of reaction kinetics of wetlands. Beyond putting forward a theoretical framework, SURFWET has been applied to simulate a specific case to demonstrate its robustness, in a 12-year-interval simulation. The results show the typical seasonality of this biotechnology, highlighting the importance of dissolved oxygen, which is a key limiting factor on a large number of biochemical processes.

Effects of flooding regime and meteorological variability on the removal efficiency of treatment wetlands under a Mediterranean climate.

The modeling of free-water surface constructed wetlands (FWS-CWs) provides an improved understanding of their processes and constitutes a useful tool for the design and management of these systems. In this work, a dynamic simulation model for FWS-CWs was developed and used to simulate the operation of a FWS-CW proposed for improving the treatment of sewage effluents entering the Tablas de Daimiel National Park in central Spain. The process-based model simulates carbon, nitrogen and phosphorus dynamics, including key hydrological processes for wetlands under a fluctuating Mediterranean semiarid climate. The model allows for the simulation of the operation of FWS-CWs with variable flooding regimes, relating the surface water level to the flooded area and the water outflow. Simulations of the proposed FWS-CW under different water management schemes and scenarios were run, and the consequences of those management strategies on the treatment efficiency were analyzed. Under the Mediterranean climate and geology of the study area, namely, high water losses through evapotranspiration and infiltration, the decrease in nutrient concentrations was higher when the flooded area was reduced in summer than when a constant flooded area was maintained. Moreover, the meteorological variability introduced in different scenarios produced different results in terms of water outflow, but differences in terms of nutrient concentrations were not significant. The ability of the model to simulate different hydrological scenarios and their consequences on water quality makes it a useful decision-support tool.