Digital twins in medicine

Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.

High-throughput tracking of single yeast cells in a microfluidic imaging matrix

Time-lapse live cell imaging is a powerful tool for studying signaling network dynamics and complexity and is uniquely suited to single cell studies of response dynamics, noise, and heritable differences. Although conventional imaging formats have the temporal and spatial resolution needed for such studies, they do not provide the simultaneous advantages of cell tracking, experimental throughput, and precise chemical control. This is particularly problematic for system-level studies using non-adherent model organisms such as yeast, where the motion of cells complicates tracking and where large-scale analysis under a variety of genetic and chemical perturbations is desired. We present here a high-throughput microfluidic imaging system capable of tracking single cells over multiple generations in 128 simultaneous experiments with programmable and precise chemical control. High-resolution imaging and robust cell tracking are achieved through immobilization of yeast cells using a combination of mechanical clamping and polymerization in an agarose gel. The channel and valve architecture of our device allows for the formation of a matrix of 128 integrated agarose gel pads, each allowing for an independent imaging experiment with fully programmable medium exchange via diffusion. We demonstrate our system in the combinatorial and quantitative analysis of the yeast pheromone signaling response across 8 genotypes and 16 conditions, and show that lineage-dependent effects contribute to observed variability at stimulation conditions near the critical threshold for cellular decision making.

Determination of soil nitrate and water content using attenuated total reflectance spectroscopy

Direct determination of nitrate and soil moisture can significantly improve N-application management and thus reduce N-derived environmental pollution related to agriculture. Several studies have shown that Fourier transform infrared attenuated total reflectance (FT-IR/ATR) spectroscopy could be used to estimate the nitrate content of standardized soil pastes. Paste standardization appeared to be the main obstacle to in situ application of this approach, and the present study shows how FT-IR/ATR can be used to estimate both water content and nitrate concentration of field soil samples. Water content and nitrate concentration are determined sequentially using two subsamples of the initial soil sample. An a priori determined amount of highly concentrated nitrate solution is added to the first subsample and the ATR spectrum of this paste is used to estimate the sample water content. It is then possible to calculate the amount of water that should be added to the second subsample so that the resulting paste is very close to the ideal standard paste. Nitrate concentration, mg [N]/kg [dry soil], is estimated using the FT-IR/ATR spectrum of this second paste. Results are presented for a laboratory experiment with four agricultural soils, as well as for a field trial with a calcareous soil. For water content, the determination errors range from 0.01 to 0.02 g [water]/g [dry soil]. For nitrate concentration, the errors for three of the soils range from 5.9 to 8.4 mg [N]/kg [dry soil], while for the fourth, calcareous clay soil, the determination error is 13.6 mg [N]/kg [dry soil]. The determination errors obtained for the field trial are similar to the ones obtained for a similar soil under laboratory conditions, which shows the potential usefulness of the approach for improving N-application management and reducing environmental pollution.

Determination of Protein Concentration in Raw Milk by Mid-Infrared Fourier Transform Infrared/Attenuated Total Reflectance Spectroscopy

This study investigates the potential use of attenuated total reflectance spectroscopy in the mid-infrared range for determining protein concentration in raw cow milk. The determination of protein concentration is based on the characteristic absorbance of milk proteins, which includes 2 absorbance bands in the 1500 to 1700 cm(-1) range, known as the amide I and amide II bands, and absorbance in the 1060 to 1100 cm(-1) range, which is associated with phosphate groups covalently bound to casein proteins. To minimize the influence of the strong water band (centered around 1640 cm(-1)) that overlaps with the amide I and amide II bands, an optimized automatic procedure for accurate water subtraction was applied. Following water subtraction, the spectra were analyzed by 3 methods, namely simple band integration, partial least squares (PLS) and neural networks. For the neural network models, the spectra were first decomposed by principal component analysis (PCA), and the neural network inputs were the spectra principal components scores. In addition, the concentrations of 2 constituents expected to interact with the protein (i.e., fat and lactose) were also used as inputs. These approaches were tested with 235 spectra of standardized raw milk samples, corresponding to 26 protein concentrations in the 2.47 to 3.90% (weight per volume) range. The simple integration method led to very poor results, whereas PLS resulted in prediction errors of about 0.22% protein. The neural network approach led to prediction errors of 0.20% protein when based on PCA scores only, and 0.08% protein when lactose and fat concentrations were also included in the model. These results indicate the potential usefulness of Fourier transform infrared/attenuated total reflectance spectroscopy for rapid, possibly online, determination of protein concentration in raw milk.

Complexity of the consistency problem for certain Post classes

The complexity of the consistency problem for several important classes of Boolean functions is analyzed. The classes of functions under investigation are those which are closed under function composition or superposition. Several of these so-called Post classes are considered within the context of machine learning with an application to breast cancer diagnosis. The considered Post classes furnish a user-selectable measure of reliability. It is shown that for realistic situations which may arise in practice, the consistency problem for these classes of functions is polynomial-time solvable.