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Diabetic wounds represent a common chronic condition, posing significant challenges in the treatment process due to bacterial infections, increased generation of reactive oxygen species (ROS) and exacerbated inflammation. Fenofibrate (FEN) is a clinical medication used for lipid regulation. In this study, it was utilized for the first time as an effective component of wound dressings for treating diabetic ulcers, exploring its novel applications further. Therefore, we prepared a polyvinyl alcohol/chitosan/FEN (PCF) hydrogel using a freeze–thaw method and conducted physicochemical characterization of the PCF hydrogel to further elucidate its biological functions. In vitrostudies demonstrated that the PCF hydrogel exhibits excellent biocompatibility along with significant antimicrobial, pro-angiogenic, ROS-scavenging, and anti-inflammatory properties. Subsequent animal experiments indicated that the PCF hydrogel has the ability to promote blood vessel formation and collagen deposition. Additionally, the PCF hydrogel showed a significant inhibitory effect on the inflammatory response, as evidenced by the reductions in the levels of tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6). These compelling findings accentuate the promising application of the PCF hydrogel in the treatment of diabetic wounds.

Diffusion Monte Carlo (DMC) is one of the most accurate techniques available for calculating the electronic properties of molecules and materials, yet it often remains a challenge to economically compute forces using this technique. As a result, ab initio molecular dynamics simulations and geometry optimizations that employ Diffusion Monte Carlo forces are often out of reach. One potential approach for accelerating the computation of “DMC forces” is to machine learn these forces from DMC energy calculations. In this work, we employ Behler–Parrinello Neural Networks to learn DMC forces from DMC energy calculations for geometry optimization and molecular dynamics simulations of small molecules. We illustrate the unique challenges that stem from learning forces without explicit force data and from noisy energy data by making rigorous comparisons of potential energy surface, dynamics, and optimization predictions among ab initio density functional theory (DFT) simulations and machine-learning models trained on DFT energies with forces, DFT energies without forces, and DMC energies without forces. We show for three small molecules─C2, H2O, and CH3Cl─that machine-learned DMC dynamics can reproduce average bond lengths and angles within a few percent of known experimental results at one hundredth of the typical cost. Our work describes a much-needed means of performing dynamics simulations on high-accuracy, DMC PESs and for generating DMC-quality molecular geometries given current algorithmic constraints.

This paper describes measurements of charge transport by tunneling through molecular junctions comprising a self-assembled monolayer (SAM) supported by a template-stripped metal bottom electrode (MTS), which has been immersed in an organic liquid and contacted by a conical Ga2O3/EGaIn top electrode. These junctions formed in organic liquids are robust; they show stabilities and yields similar to those formed in air. We formed junctions under seven external environments: (I) air, (II) perfluorocarbons, (III) linear hydrocarbons, (IV) cyclic hydrocarbons, (V) aromatic compounds, (VI) large, irregularly shaped hydrocarbons, and (VII) dimethyl siloxanes. Several different lengths of SAMs of n-alkanethiolates, S(CH2)n−1CH3with n= 4–18, and two different kinds of bottom electrodes (AgTSor AuTS) are employed to assess the mechanism underlying the observed changes in tunneling currents. Measurements of current density through junctions immersed in perfluorocarbons (II) are comparable to junctions measured in air. Junctions immersed in other organic liquids show reductions in the values of current density, compared to the values in air, ranging from 1 (III) to 5 orders of magnitude (IV). We interpret the most plausible mechanism for these reductions in current densities to be an increase in the length of the tunneling pathway, reflecting the formation of thin (0.5–1.5 nm) liquid films at the interface between the SAM and the Ga2O3/EGaIn electrode. Remarkably, the thickness of the liquid film─estimated by the simplified Simmons model, measurements of electrical breakdown of the junction, and simulations of molecular dynamics─is consistent with the existing observations of structured liquid layers that form between two flat interfaces from measurements obtained by the surface force apparatus. These results suggest the use of the EGaIn junction and measurements of charge transport by tunneling as a new form of surface analysis, with the applications in the study of near-surface, weak, molecular interactions and the behavior of liquid films adjacent to non-polar interfaces.

A majority of the electricity currently generated is regrettably lost as heat. Engineering high-efficiency thermoelectric materials which can convert waste heat back into electricity is therefore vital for reducing our energy fingerprint. ZT, a dimensionless figure of merit, acts as a beacon of promising thermoelectric materials. However, engineering materials with large ZT values is practically challenging, since maximizing ZT requires optimizing many interdependent material properties. Motivated by recent studies on bulk indium selenide that suggest it may have favorable thermoelectric properties, here we present the thermoelectric properties of monolayer indium selenide in the presence of uniaxial strain using first-principles calculations conjoined with semiclassical Boltzmann transport theory. Our calculations indicate that conduction band convergence occurs at a compressive strain of −6% along the zigzag direction and results in an enhancement of ZT for p-type indium selenide at room temperature. Further enhancements occur at −7% as the valence bands similarly converge, reaching a maximum ZT value of 0.46, which is one of the largest monolayer InSe figures of merit recorded to date at room temperature. The importance of strain is directly reflected by the enhanced transport coefficients observed at strains nearing those which give rise to the band degeneracies we observe. Our studies demonstrate that strain-induced transitions can play a key role in the engineering of promising thermoelectric materials.

Background: The roles of gut microbiota in the pathogenesis of SLE have been receiving much attention during recent years. However, it remains unknown how fecal microbiota transplantation (FMT) and microbial metabolites affect immune responses and lupus progression. Methods: We transferred fecal microbiota from MRL/lpr (Lpr) mice and MRL/Mpj (Mpj) mice or PBS to pristane-induced lupus mice and observed disease development. We also screened gut microbiota and metabolite spectrums of pristane-induced lupus mice with FMT via 16S rRNA sequencing, metagenomic sequencing, and metabolomics, followed by correlation analysis. Results: FMT from MRL/lpr mice promoted the pathogenesis of pristane-induced lupus and affected immune cell profiles in the intestine, particularly the plasma cells. The structure and composition of microbial communities in the gut of the FMT-Lpr mice were different from those of the FMT-Mpj mice and FMT-PBS mice. The abundances of specific microbes such as prevotella taxa were predominantly elevated in the gut microbiome of the FMT-Lpr mice, which were positively associated with functional pathways such as cyanoamino acid metabolism. Differential metabolites such as valine and L-isoleucine were identified with varied abundances among the three groups. The abundance alterations of the prevotella taxa may affect the phenotypic changes such as proteinuria levels in the pristane-induced lupus mice. Conclusion: These findings further confirm that gut microbiota play an important role in the pathogenesis of lupus. Thus, altering the gut microbiome may provide a novel way to treat lupus.

We constructed an expression vector for a fusion protein [ANG II type 1a receptor-green fluorescent protein (AT1aR-GFP)] consisting of enhanced GFP attached to the COOH terminus of the rat AT1aR. Chinese hamster ovary (CHO) cells transfected with AT1aR-GFP demonstrated specific, high-affinity 125I-labeled ANG II binding (IC5021 nM). ANG II exposure stimulated sodium-proton exchange and cytoplasmic calcium release to a similar extent in cells transfected with AT1aR or AT1aR-GFP; these responses were desensitized by prior exposure to ANG II and were sensitive to the AT1R blocker losartan. ANG II-driven internalization of AT1aR-GFP in transfected CHO cells was demonstrated both by radioligand binding and by laser scanning confocal microscopy. Colocalization of GFP fluorescence with that of the nuclear stain TOTO-3 in confocal images was increased more than twofold after 1 h of ANG II exposure. We conclude that AT1aR-GFP exhibits similar pharmacological behavior to that of the native AT1aR. Our observations also support previous evidence for the presence of AT1aR in the nucleus and suggest that the density of AT1aR in the nucleus may be regulated by exposure to its ligand.

The activation of the gene for interstitial collagenase in myometrial smooth muscle cells is absolutely dependent upon the presence of serotonin. Our previous studies investigating the mechanisms of this induction demonstrated that the mRNAs of both interleukin-1 (IL-1) isoforms, IL-1α and IL-1β, are induced by serotonin and that the induction of IL-1 is required for the subsequent induction of collagenase. These data provided compelling evidence that serotonin-induced IL-1 acts via an autocrine loop in activating the collagenase gene. The experiments described here were designed to examine the potential role of each IL-1 isoform in collagenase production by using neutralizing antisera specific to each isoform of the cytokine. The antisera were examined for their ability to inhibit the serotonin-dependent production of the mRNA for collagenase and of the cytokines themselves. Neutralizing antiserum against IL-1α, but not against IL-1β, inhibited the induction of the mRNA for collagenase and of the mRNAs for both IL-1α and IL-1β. Western analysis indicated that detectable levels of IL-1α protein, but not that of IL-1β, are produced at the time of serotonin-dependent collagenase induction. In contrast, significant levels of IL-1β protein are detected only when bacterial lipopolysaccharide is added to the cells. Taken together, the results of our study indicate that IL-1α, but not IL-1β, plays an obligatory role in multiple serotonin-mediated gene regulations in the myometrial smooth muscle cell. In addition, the data suggest that IL-1β production has the potential for modifying myometrial function in pathological settings, particularly that of uterine infection.