Search

The pharmacokinetics of doxorubicin (DOX) and doxorubicinol (DOXol) was studied in six patients with various advanced neoplastic diseases who received 28-72 mg/m2 DOX (nine courses). Plasma and parotid saliva were collected over a 48-h period, and DOX and DOXol were quantified by high-performance liquid chromatography with fluorescence detection. As reported previously, a wide range of plasma levels were found among our patients. It appears that in addition to being quickly cleared from the plasma, both DOX and DOXol are excreted in detectable amounts in parotid saliva, a route of elimination that has been given little attention, if any. Excretion in the saliva exposes the mucosa of the upper gastrointestinal tract to drug and may play a role in causing stomatitis in patients receiving DOX by the i.v. route. Since huge interindividual and pronounced intraindividual differences were found in S/P ratios that mostly were not systematically related to the plasma drug concentration, the concentration in parotid saliva was not useful in predicting the level of free DOX and DOXol in plasma. For the parent drug and its metabolite, the S/P ratios increased significantly with time during the 48-h period after dosing. [ABSTRACT FROM AUTHOR]

Doxorubicin was given by short i.v. infusion (dose range 25-72 mg/m2) to 18 patients who underwent three to seven successive courses of chemotherapy (total, 57 courses). Plasma levels of doxorubicin and its major metabolite doxorubicinol were determined by high-performance liquid chromatography over a 48-h period after the infusion. Pharmacokinetic parameters for the parent drug and its metabolite were calculated for each course of treatment. The results show considerable inter- and intraindividual variations for most parameters. The coefficients of variation (CV) ranged from 37% to 93% (inter-individual) and from 6% to 59% (intra-individual). Nevertheless, we observed a good stability over successive courses for terminal half-life in six patients (CV, 6%-25%) and for clearance and AUC in four subjects (CV, 10%-22%). The ratio of the AUCs for doxorubicinol: doxorubicin averaged 0.514. The pharmacokinetic pattern of doxorubicinol was biphasic in plasma of the majority of patients. We propose a model for curve-fitting of these metabolite plasma concentrations that is based on two successive releases of the compound in the plasma compartment, separated by a lag time. [ABSTRACT FROM AUTHOR]

Background: Artificial Intelligence (AI) in dental diagnostics is evolving, offering innovative approaches for conducting cephalometric analysis with less manual input and overcoming the limitations of traditional imaging methods. To enhance the diagnostic processes in dentistry, an open-source software that utilises AI to improve the extraction of cephalometric values from limited field of view (FOV) images was created., Material and Methods: Reduced FOV images lack several vital cephalometric landmarks, prompting the creation of predictive models to estimate missing values. The GridSearchCV algorithm and other algorithms were used to construct predictive models using software and to select the best models. The software was validated by comparing the predicted values with the actual measurements and calculating the mean squared error using Excel. Further validation involved a randomly selected cohort of 25 untreated orthodontic cases., Results: Evaluation of the software showed that it was effective in accurately predicting key cephalometric measurements, suggesting that it could be a reliable tool for clinical use. However, some variations were noted in its predictive accuracy across different measurements, indicating areas that could benefit from further development. The software could align closely with the actual cephalometric measurements through detailed statistical analysis., Conclusions: The integration of AI into cephalometric analysis with the software could represent progress, potentially leading to more efficient dental diagnostics and a reduction in the need for additional X-rays. This study aimed to advance the integration and refinement of AI in healthcare, focusing on minimising bias and understanding its impact on clinical decisions. In future studies, the application of AI in dental practice should be expanded to address these challenges., Clinical Significance Statement: This software integrates AI into clinical practice to enhance the diagnostic and therapeutic phases for patient benefit. It enables precise and comprehensive cephalometric analyses using data previously considered insufficient, thereby reducing the need for X-rays and improving patient care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

An atomic-level understanding of radiation-induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. Ensembles of quantum simulations of radiation damage in a polyethylene analog are performed using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out-gassing products are computed. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out-gassing products, though these correlations decrease with increasing atom recoil energy. The results present relatively simple chemical descriptors as possible indications of network rearrangements in the middle range of excitation energies. Ultimately, the work provides a computational framework for determining the coupling between nonequilibrium chemistry in polymers and potential changes to macro-scale properties that can aid in the interpretation of future radiation damage experiments on plastic materials., (© 2024 The Author(s). Macromolecular Rapid Communications published by Wiley‐VCH GmbH.)

Nanoengineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within three-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood. To address such knowledge gaps, in this study, we report a robust and transferable machine learning-based potential (MLP) that is capable of describing the structure, stability, and dynamics of zeolite-confined gold nanoclusters. Specifically, we show that the resulting MLP maintains ab initio accuracy across a range of temperatures (300-1000 K) and can be used to investigate time scales (>10 ns), length scales (ca. 10,000 atoms), and phenomena (e.g., ensemble-averaged stability and diffusivity) that are typically inaccessible using density functional theory (DFT). Taken together, this study represents an important step in enabling the rational theory-guided design of metal@zeolite catalysts.

Three polyanionic tellurides, A Ba 6 Cu 31 Te 22 ( A = K, Rb, Cs), were synthesized in salt flux. The isostructural tellurides crystallize in a new structure type, in the cubic Pa 3 space group with a Wyckoff sequence of d 10 c 2 b 1 and large unit cell volumes of over 5500 Å 3 . The structures feature a framework of [CuTe 4 ] tetrahedra and [CuTe 3 ] trigonal pyramids with disorder in the Cu sites. The polyanionic frameworks have large square antiprism and cuboctahedral voids where Ba and alkali metal cations are situated, forming [BaTe 8 ] and [ A Te 12 ], respectively. The overall compositions are close to being charge balanced. The large [ A Te 12 ] cuboctahedra allowed for significant anisotropic displacement of the A cations, as observed from both single crystal X-ray diffraction and heat capacity studies. Alkali cations rattling together with Cu atom displacement and disorder leads to the dispersion of phonons, thus softening the lattice and subsequently reducing the thermal conductivity. Evaluations of the electronic band structure revealed the occurrence of a narrow bandgap together with the presence of a flat band near the valence band maximum, giving rise to the high thermopower. The Cs and Rb analogues show a slope change in the temperature dependence of electrical resistivity around room temperature, which is typical for semimetals or degenerate semiconductors. For the as-synthesized and unoptimized materials, high values of the thermoelectric figure-of-merit of ∼0.2 were observed at 623 K.

Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order-disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of the hydrogen atoms. Despite numerous previous studies, a comprehensive understanding of the nuclear quantum effects (NQEs) on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness efficient machine-learning potentials and many-body perturbation theory to assess how NQEs impact the electronic bands of water and hexagonal ice. By comparing path-integral and classical simulations, we find that NQEs lead to a larger renormalization of the fundamental gap of ice, compared to that of water, ultimately yielding similar bandgaps in the two systems, consistent with experimental estimates. Our calculations suggest that the increased quantum mechanical delocalization of protons in ice, relative to water, is a key factor leading to the enhancement of NQEs on the electronic structure of ice.

In the interstellar medium, diols and other prebiotic molecules adsorb onto icy mantles surrounding dust grains. Water in the ice may affect the reactivity and photoionization of these diols. Ethylene glycol (EG), 1,2-propylene glycol, and 1,3-propylene glycol clusters with water clusters were used as a proxy to study these interactions. The diol-water clusters were generated in a continuous supersonic molecular beam, photoionized by synchrotron-based vacuum ultraviolet light from the Advanced Light Source, and subsequently detected by reflectron time-of-flight mass spectrometry. The appearance energies for the detected clusters were determined from the mass spectra, collected at increasing photon energy. Clusters of both diol fragments and unfragmented diols with water were detected. The lowest energy geometry optimized conformers for the observed EG-water clusters and EG fragment-water clusters have been visualized using density functional theory (DFT), providing insight into hydrogen bonding networks and how these affect fragmentation and appearance energy. As the number of water molecules clustered around EG fragments (m/z 31 and 32) increased, the appearance energy for the cluster decreased, indicating a stabilization by water. This trend was supported by DFT calculations. Fragment clusters from 1,2-propylene glycol exhibited a similar trend, but with a smaller energy decrease, and no trend was observed from 1,3-propylene glycol. We discuss and suggest that the reactivity and photoionization of diols in the presence of water depend on the size of the diol, the location of the hydroxyl group, and the number of waters clustered around the diol., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

The zinc-antimony phase space has been heavily investigated due to the structural complexity and abundance of high-performing thermoelectric materials. Consequentially, the desire to use zinc and antimony as framework elements to encage rattling cations and achieve phonon-glass-electron-crystal-type properties has remained an enticing goal with only two alkali metal clathrates to date, Cs 8 Zn 18 Sb 28 and K 58 Zn 122 Sb 207 . Guided by Zintl electron-counting predictions, we explored the Ba-Zn- Pn ( Pn = As, Sb) phase space proximal to the expected composition of the type-I clathrate. In situ powder X-ray diffraction studies revealed two "hidden" compounds which can only be synthesized in a narrow temperature range. The ex situ synthesis and crystal growth unveiled that instead of type-I clathrates, compositionally close but structurally different new clathrate-like compounds formed, Ba 2 Zn 5 As 6 and Ba 2 Zn 5 Sb 6 . These materials crystallize in a unique structure, in the orthorhombic space group Pmna with the Wyckoff sequence i 2 h 6 gfe . Single-phase synthesis enabled the exploration of their transport properties. Rattling of the Ba cations in oversized cages manifested low thermal conductivity, which, coupled with the high Seebeck coefficients observed, are prerequisites for a promising thermoelectric material. Potential for further optimization of the thermoelectric performance by aliovalent doping was computationally analyzed.

We characterise the structural properties of the quasi-liquid layer (QLL) at two low-index ice surfaces in the presence of sodium chloride (Na + /Cl - ) ions by molecular dynamics simulations. We find that the presence of a high surface density of Na + /Cl - pairs changes the surface melting behaviour from step-wise to gradual melting. The ions lead to an overall increase of the thickness and the disorder of the QLL, and to a low-temperature roughening transition of the air-ice interface. The local molecular structure of the QLL is similar to that of liquid water, and the differences between the basal and primary prismatic surface are attenuated by the presence of Na + /Cl - pairs. These changes modify the crystal growth rates of different facets and the solvation environment at the surface of sea-water ice with a potential impact on light scattering and environmental chemical reactions.

Predicting UV-visible absorption spectra is essential to understand photochemical processes and design energy materials. Quantum chemical methods can deliver accurate calculations of UV-visible absorption spectra, but they are computationally expensive, especially for large systems or when one computes line shapes from thermal averages. Here, we present an approach to predict UV-visible absorption spectra of solvated aromatic molecules by quantum chemistry (QC) and machine learning (ML). We show that a ML model, trained on the high-level QC calculation of the excitation energy of a set of aromatic molecules, can accurately predict the line shape of the lowest-energy UV-visible absorption band of several related molecules with less than 0.1 eV deviation with respect to reference experimental spectra. Applying linear decomposition analysis on the excitation energies, we unveil that our ML models probe vertical excitations of these aromatic molecules primarily by learning the atomic environment of their phenyl rings, which align with the physical origin of the π →π* electronic transition. Our study provides an effective workflow that combines ML with quantum chemical methods to accelerate the calculations of UV-visible absorption spectra for various molecular systems.

Layering two-dimensional van der Waals materials provides a high degree of control over atomic placement, which could enable tailoring of vibrational spectra and heat flow at the sub-nanometer scale. Here, using spatially resolved ultrafast thermoreflectance and spectroscopy, we uncover the design rules governing cross-plane heat transport in superlattices assembled from monolayers of graphene (G) and MoS 2 (M). Using a combinatorial experimental approach, we probe nine different stacking sequences, G, GG, MG, GGG, GMG, GGMG, GMGG, GMMG, and GMGMG, and identify the effects of vibrational mismatch, interlayer adhesion, and junction asymmetry on thermal transport. Pure G sequences display evidence of quasi-ballistic transport, whereas adding even a single M layer strongly disrupts heat conduction. The experimental data are described well by molecular dynamics simulations, which include thermal expansion, accounting for the effect of finite temperature on the interlayer spacing. The simulations show that an increase of ∼2.4% in the layer separation of GMGMG, relative to its value at 300 K, can lead to a doubling of the thermal resistance. Using these design rules, we experimentally demonstrate a five-layer GMGMG superlattice "thermal metamaterial" with an ultralow effective cross-plane thermal conductivity comparable to that of air.

The first arsenic-based clathrate exhibiting superstructural ordering due to optimization of Au-As, As-As, and Ba-Au bonding is reported. Ba 8 Au 16 As 30 crystallizes in a unique P 2 1 / c monoclinic clathrate structure. The synthesis, crystal and electronic structure, and transport properties are discussed.

Solid solutions of Yb 2- x A x CdSb 2 (A = Ca, Sr, Eu; x ≤ 1) are of interest for their promising thermoelectric (TE) properties. Of these solid solutions, Yb 2- x Ca x CdSb 2 has end members with different crystal structures. Yb 2 CdSb 2 crystallizes in the polar space group Cmc 2 1 , whereas Ca 2 CdSb 2 crystallizes in the centrosymmetric space group Pnma . Other solid solutions, Yb 2- x A x CdSb 2 (A = Sr, Eu), crystallize in the polar space group for x ≤ 1, and compositions with x ≥ 1 have not been reported. Both structure types are composed of corner-sharing CdSb 4 tetrahedra condensed into sheets that differ by the stacking of the layers. Single crystals of the solid solution Yb 2- x Ca x CdSb 2 ( x = 0-1) were studied to elucidate the structural transition between the Yb 2 CdSb 2 and Ca 2 CdSb 2 structure types. For x ≤ 1, the structures remain in the polar space group Cmc 2 1 . As the Ca content is increased, a positional disorder arises in the intralayer cation sites (Yb2/Ca2) and the Cd site, resulting in inversion of the CdSb 4 tetrahedral chain. This phenomenon could be indicative of an intergrowth of the opposing space group. The TE properties of polycrystalline samples of Yb 2- x Ca x CdSb 2 ( x ≤ 1) were measured from 300 to 525 K. The lattice thermal conductivity is extremely low (0.3-0.4 W/m·K) and the Seebeck coefficients are high (100-180 μV/K) across the temperature range. First-principles calculations show a minimum in the thermal conductivity for the x = 0.3 composition, in good agreement with experimental data. The low thermal conductivity stems from the acoustic branches being confined to low frequencies and a large number of phonon scattering channels provided by the localized optical branches. The TE quality factor of the Yb 1.7 A 0.3 CdSb 2 (A = Ca, Sr, Eu) series has been calculated and predicts that the A = Ca and Sr solid solutions may not improve with carrier concentration optimization but that the Eu series is worthy of additional modifications. Overall, the x = 0.3 compositions provide the highest zT because they provide the best electronic properties with the lowest thermal conductivity.

Water is one of the most important, yet least understood, liquids in nature. Many anomalous properties of liquid water originate from its well-connected hydrogen bond network 1 , including unusually efficient vibrational energy redistribution and relaxation 2 . An accurate description of the ultrafast vibrational motion of water molecules is essential for understanding the nature of hydrogen bonds and many solution-phase chemical reactions. Most existing knowledge of vibrational relaxation in water is built upon ultrafast spectroscopy experiments 2-7 . However, these experiments cannot directly resolve the motion of the atomic positions and require difficult translation of spectral dynamics into hydrogen bond dynamics. Here, we measure the ultrafast structural response to the excitation of the OH stretching vibration in liquid water with femtosecond temporal and atomic spatial resolution using liquid ultrafast electron scattering. We observed a transient hydrogen bond contraction of roughly 0.04 Å on a timescale of 80 femtoseconds, followed by a thermalization on a timescale of approximately 1 picosecond. Molecular dynamics simulations reveal the need to treat the distribution of the shared proton in the hydrogen bond quantum mechanically to capture the structural dynamics on femtosecond timescales. Our experiment and simulations unveil the intermolecular character of the water vibration preceding the relaxation of the OH stretch., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Anomalous heat transport in one-dimensional nanostructures, such as nanotubes and nanowires, is a widely debated problem in condensed matter and statistical physics, with contradicting pieces of evidence from experiments and simulations. Using a comprehensive modeling approach, comprised of lattice dynamics and molecular dynamics simulations, we proved that the infinite length limit of the thermal conductivity of a (10,0) single-wall carbon nanotube is finite but this limit is reached only for macroscopic lengths due to a thermal phonon mean free path of several millimeters. Our calculations showed that the extremely high thermal conductivity of this system at room temperature is dictated by quantum effects. Modal analysis showed that the divergent nature of thermal conductivity, observed in one-dimensional model systems, is suppressed in carbon nanotubes by anharmonic scattering channels provided by the flexural and optical modes with polarization in the plane orthogonal to the transport direction.

Some organic pollutants in snowpacks undergo faster photodegradation than in solution. One possible explanation for such effect is that their UV-visible absorption spectra are shifted toward lower energy when the molecules are adsorbed at the air-ice interface. However, such bathochromic shift is difficult to measure experimentally. Here, we employ a multiscale/multimodel approach that combines classical and first-principles molecular dynamics, quantum chemical methods, and statistical learning to compute the light absorption spectra of two phenolic molecules in different solvation environments at the relevant thermodynamic conditions. Our calculations provide an accurate estimate of the bathochromic shift of the lowest-energy UV-visible absorption band when these molecules are adsorbed at the air-ice interface, and they shed light into its molecular origin.

Intercalation offers a promising way to alter the physical properties of two-dimensional (2D) layered materials. Here, we investigate the electronic and vibrational properties of 2D layered MoSe 2 intercalated with atomic manganese at ambient and high pressure up to 7 GPa by Raman scattering and electronic structure calculations. The behavior of optical phonons is studied experimentally with a diamond anvil cell and computationally through density functional theory calculations. Experiment and theory show excellent agreement in optical phonon behavior. The previously Raman inactive A 2u mode is activated and enhanced with intercalation and pressure, and a new Raman mode appears upon decompression, indicating a possible onset of a localized structural transition, involving the bonding or trapping of the intercalant in 2D layered materials. Density functional theory calculations reveal a shift of the Fermi level into the conduction band and spin polarization in Mn x MoSe 2 that increases at low Mn concentrations and low pressure. Our results suggest that intercalation and pressurization of van der Waals materials may allow one to obtain dilute magnetic semiconductors with controllable properties, providing a viable route for the development of new materials for spintronic applications.

Snowpacks contain a wide variety of inorganic and organic compounds, including some that absorb sunlight and undergo direct photoreactions. How the rates of these reactions in, and on, ice compare to rates in water is unclear: some studies report similar rates, while others find faster rates in/on ice. Further complicating our understanding, there is conflicting evidence whether chemicals react more quickly at the air-ice interface compared to in liquid-like regions (LLRs) within the ice. To address these questions, we measured the photodegradation rate of guaiacol (2-methoxyphenol) in various sample types, including in solution, in ice, and at the air-ice interface of nature-identical snow. Compared to aqueous solution, we find modest rate constant enhancements (increases of 3- to 6-fold) in ice LLRs, and much larger enhancements (of 17- to 77-fold) at the air-ice interface of nature-identical snow. Our computational modeling suggests the absorption spectrum for guaiacol red-shifts and increases on ice surfaces, leading to more light absorption, but these changes explain only a small portion (roughly 2 to 9%) of the observed rate constant enhancements in/on ice. This indicates that increases in the quantum yield are primarily responsible for the increased photoreactivity of guaiacol on ice; relative to solution, our results suggest that the quantum yield is larger by a factor of roughly 3-6 in liquid-like regions and 12-40 at the air-ice interface.

We investigate the effect of pressure, temperature and acidity on the composition of water-rich carbon-bearing fluids under thermodynamic conditions that correspond to the Earth's deep crust and upper mantle. Our first-principles molecular dynamics simulations provide mechanistic insight into the hydration shell of carbon dioxide, bicarbonate and carbonate ions, and into the pathways of the acid/base reactions that convert these carbon species into one another in aqueous solutions. At temperatures of 1000 K and higher, our simulations can sample the chemical equilibrium of these acid/base reactions, thus allowing us to estimate the chemical composition of diluted carbon dioxide and (bi)carbonate ions as a function of acidity and thermodynamic conditions. We find that, especially at the highest temperature, the acidity of the solution is essential to determine the stability domain of CO 2 vs. HCO 3 - vs. CO 3 2- .