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Neonatal screening programs for cystic fibrosis (CF) usually consist of an immunoreactive trypsinogen assay. The objective of this study was to validate the AutoDELFIA® Neonatal immunoreactive trypsinogen kit. We carried out a comparative study between two equipments. The following results were yielded: random error = 26.6%systematic error = 4.95% and analytical error = 31.5%. Several factors contributed to the assay variations in dried blood spot, namely chromatographic, hematocrit and total blood volume effects. These factors should be taken into account in the assessment of validation results. The studied kit can be deployed in neonatal screening routine.

Abstract The theorems of density functional theory (DFT) establish bijective maps between the local external potential of a many-body system and its electron density, wavefunction and, therefore, one-particle reduced density matrix. Building on this foundation, we show that machine learning models based on the one-electron reduced density matrix can be used to generate surrogate electronic structure methods. We generate surrogates of local and hybrid DFT, Hartree-Fock and full configuration interaction theories for systems ranging from small molecules such as water to more complex compounds like benzene and propanol. The surrogate models use the one-electron reduced density matrix as the central quantity to be learned. From the predicted density matrices, we show that either standard quantum chemistry or a second machine-learning model can be used to compute molecular observables, energies, and atomic forces. The surrogate models can generate essentially anything that a standard electronic structure method can, ranging from band gaps and Kohn-Sham orbitals to energy-conserving ab-initio molecular dynamics simulations and infrared spectra, which account for anharmonicity and thermal effects, without the need to employ computationally expensive algorithms such as self-consistent field theory. The algorithms are packaged in an efficient and easy to use Python code, QMLearn, accessible on popular platforms.

We present a methodology for the linear-response time-dependent density functional theory (LR-TDDFT) calculation of the dynamic density response function of warm dense matter in the adiabatic approximation that can be used with any available exchange-correlation (XC) functional across Jacob's ladder and across temperature regimes. The uniqueness of the presented approach is that it can go beyond the adiabatic local density approximation and adiabatic generalized gradient approximation while preserving the self-consistency between the Kohn-Sham (KS) response function and adiabatic XC kernel for extended systems. The key ingredient to the presented method is the combination of the adiabatic XC kernel from the direct perturbation approach with the macroscopic dynamic KS response from the standard LR-TDDFT method using KS orbitals. We demonstrate the application of the method for the example of warm dense hydrogen, for which we perform a detailed analysis of the KS density response function, the random phase approximation result, the total density response function, and of the adiabatic XC kernel. The analysis is performed using local density approximation, generalized gradient approximation, and meta-generalized gradient approximation level approximations for the XC effects. The presented method is directly applicable to disordered systems such as liquid metals, warm dense matter, and dense plasmas.

We introduce a practical hybrid approach that combines orbital-free density functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulations. Equilibrated ionic configurations are generated using orbital-free DFT for subsequent Kohn-Sham DFT molecular dynamics. This leads to a massive reduction of the simulation time without any sacrifice in accuracy. We assess this finding across systems of different sizes and temperature, up to the warm dense matter regime. To that end, we use the cosine distance between the time series of radial distribution functions representing the ionic configurations. Likewise, we show that the equilibrated ionic configurations from this hybrid approach significantly enhance the accuracy of machine-learning models that replace Kohn-Sham DFT. Our hybrid scheme enables systematic first-principles simulations of warm dense matter that are otherwise hampered by the large numbers of atoms and the prevalent high temperatures. Moreover, our finding provides an additional motivation for developing kinetic and noninteracting free-energy functionals for orbital-free DFT.