Keloid-derived, plasma/fibrin-based skin equivalents generate de novo dermal and epidermal pathology of keloid fibrosis in a mouse model.

Keloids are wounding-induced tumor-like human scars. Unclear etiology and lack of animal models to reveal disease mechanisms and invent therapies deepen the grievous health and psychosocial state of vulnerable individuals. Epitomizing the injury-repair environment which triggers and fosters keloid formation and essential dermal/epidermal interactions in disease development, the novel animal model was established by implanting porous polyethylene ring-supported plasma/fibrin-based epidermal-dermal skin constructs on the dorsum of athymic NU/J mice. The implants were stable to 18 weeks, contained abundant human cells, and remodeled to yield scar architecture characteristic of keloid fibrosis compared with normal implants and clinical specimens: (1) macroscopic convex or nodular scar morphology; (2) morphogenesis and accumulation of large collagen bundles from collagen-null initial constructs; (3) epidermal hyperplasia, aberrant epidermal-dermal patency, and features of EMT; (4) increased vasculature, macrophage influx, and aggregation; and (5) temporal-spatial increased collagen-inducing PAI-1 and its interactive partner uPAR expression. Development of such pathology in the NU/J host suggests that T-cell participation is less important at this stage than at keloid initiation. These accessible implants also healed secondary excisional wounds, enabling clinically relevant contemporaneous wounding and treatment strategies, and evaluation. The model provides a robust platform for studying keloid formation and testing knowledge-based therapies.

Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model.

We propose score dynamics (SD), a general framework for learning accelerated evolution operators with large timesteps from molecular dynamics (MD) simulations. SD is centered around scores or derivatives of the transition log-probability with respect to the dynamical degrees of freedom. The latter play the same role as force fields in MD but are used in denoising diffusion probability models to generate discrete transitions of the dynamical variables in an SD time step, which can be orders of magnitude larger than a typical MD time step. In this work, we construct graph neural network-based SD models of realistic molecular systems that are evolved with 10 ps timesteps. We demonstrate the efficacy of SD with case studies of the alanine dipeptide and short alkanes in aqueous solution. Both equilibrium predictions derived from the stationary distributions of the conditional probability and kinetic predictions for the transition rates and transition paths are in good agreement with MD. Our current SD implementation is about 2 orders of magnitude faster than the MD counterpart for the systems studied in this work. Open challenges and possible future remedies to improve SD are also discussed.

Quantifying disorder one atom at a time using an interpretable graph neural network paradigm.

Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena.

The ADAS-cog in Alzheimer's disease clinical trials: psychometric evaluation of the sum and its parts.

BACKGROUND: The Alzheimer's Disease Assessment Scale Cognitive Behavior Section (ADAS-cog), a measure of cognitive performance, has been used widely in Alzheimer's disease trials. Its key role in clinical trials should be supported by evidence that it is both clinically meaningful and scientifically sound. Its conceptual and neuropsychological underpinnings are well-considered, but its performance as an instrument of measurement has received less attention. Objective To examine the traditional psychometric properties of the ADAS-cog in a large sample of people with Alzheimer's disease. METHODS: Data from three clinical trials of donepezil (Aricept) in mild-to-moderate Alzheimer's disease (n=1421; MMSE 10-26) were analysed at both the scale and component level. Five psychometric properties were examined using traditional psychometric methods. These methods of examination underpin upcoming Food and Drug Administration recommendations for patient rating scale evaluation. RESULTS: At the scale-level, criteria tested for data completeness, scaling assumptions (eg, component total correlations: 0.39-0.67), targeting (no floor or ceiling effects), reliability (eg, Cronbach's α: = 0.84; test-retest intraclass correlations: 0.93) and validity (correlation with MMSE: -0.63) were satisfied. At the component level, 7 of 11 ADAS-cog components had substantial ceiling effects (range 40-64%). CONCLUSIONS: Performance was satisfactory at the scale level, but most ADAS-cog components were too easy for many patients in this sample and did not reflect the expected depth and range of cognitive performance. The clinical implication of this finding is that the ADAS-cog's estimate of cognitive ability, and its potential ability to detect differences in cognitive performance under treatment, could be improved. However, because of the limitations of traditional psychometric methods, further evaluations would be desirable using additional rating scale analysis techniques to pinpoint specific improvements.

High performance modeling of heterogeneous SOFC electrode microstructures using the MOOSE framework: ERMINE (Electrochemical Reactions in MIcrostructural NEtworks).

Electrochemical energy devices, such as batteries and fuel cells, contain active electrode components that have highly porous, multiphase microstructures for improved performance. Predictive electrochemical models of solid oxide fuel cell (SOFC) electrode performance based on measured microstructures have been limited to small length scales, a small number of simulations, and/or relatively homogeneous microstructures. To overcome the difficulty in modeling electrochemical activity of inhomogeneous microstructures at considerable length scales, we have developed a high-throughput simulation application that operates on high-performance computing platforms. The open-source application, named Electrochemical Reactions in MIcrostructural NEtworks (ERMINE), is implemented within the MOOSE computational framework, and solves species transport coupled to both three-phase boundary and two-phase boundary electrochemical reactions. As the core component, this application is further incorporated into a high-throughput computational workflow. The main advantages of the workflow include:•Straightforward image-based volumetric meshing that conforms to complex, multi-phased microstructural features•Computation of local electrochemical fields in morphology-resolved microstructures at considerable length scales•Implementation on high performance computing platforms, leading to fast, high-throughput computations.

Efficient and Highly Selective Solvent-Free Oxidation of Primary Alcohols to Aldehydes Using Bucky Nanodiamond.

Selective oxidation of alcohols to aldehydes is widely applicable to the synthesis of various green chemicals. The poor chemoselectivity for complicated primary aldehydes over state-of-the-art metal-free or metal-based catalysts represents a major obstacle for industrial application. Bucky nanodiamond is a potential green catalyst that exhibits excellent chemoselectivity and cycling stability for the selective oxidation of primary alcohols in diverse structures (22 examples, including aromatic, substituted aromatic, unsaturated, heterocyclic, and linear chain alcohols) to their corresponding aldehydes. The results are comparable to reported transition-metal catalysts including conventional Pt/C and Ru/C catalysts for certain substrates under solvent-free conditions. The possible activation process of the oxidant and substrates by the surface oxygen groups and defect species are revealed with model catalysts, ex situ electrochemical measurements, and ex situ attenuated total reflectance. The zigzag edges of sp2 carbon planes are shown to play a key role in these reactions.

Processing and Properties of Bioactive Surface-Porous PEKK.

Bioactive surface-porous polyetherketoneketone materials (PEKK-BSP) were prepared by first leaching hydroxyapatite (HA) microsphere templates from compression molded PEKK/HA composites (i.e., PEKK-P) followed by sulfonation with 80% sulfuric acid for 3 h (i.e., PEKK-SP) and then soaking in a simulated body fluid (SBF) for 5 days. The combination of both physical and chemical processes created a surface-porous PEKK material (PEKK-SP) that had both structurally interconnected and open macropores (200-600 µm) because of templating of HA microspheres, and micropores (<10 µm) due to effects of the sulfonation. The in vitro bioactivity of PEKK-SP was demonstrated by the development of a dense layer of bone-like apatite inside and on the surfaces after SBF treatment. The sulfonation process followed SBF treatment also significantly reduced the water contact angle of PEKK material. Mechanical tests showed that both PEKK-BSP and PEKK-P were a little better than PEKK-SP in terms of compressive strength and modulus. In vitro cell culture using rabbits' mesenchymal stem cells (MSCs) demonstrated that PEKK-BSP promoted better cell growth and proliferation than other groups of PEKK materials. Moreover, compared to PEKK material alone, PEKK-BSP elevated gene expressions of osteocalcin (OCN), Type I collagen (Col-I), alkaline phosphatase (ALP), and runt-related transcription factor 2 (Runx2). This bioactive PEKK-BSP material has potential application for spinal interbody fusion devices to enhance their in vivo osseointegration.

High-temperature poly(phthalazinone ether ketone) thin films for dielectric energy storage.

The synthesis and characterization of poly(phthalazinone ether ketone) (PPEK) for high-temperature electric energy storage applications is described. It was found that PPEK displayed excellent stability of the dielectric properties over a broad frequency and temperature range. Little change in the breakdown field and discharge time has been observed in PPEK with the increase of temperature up to 190 degrees C. A linear correlation between the AC conductance and the angular frequency implied that the hopping as a dominant conduction process contributed to the dielectric loss. Superior energy densities, remarkable breakdown strengths, and fast discharge speeds have been demonstrated in PPEK at various temperatures.

Newly weaned nonobese diabetic mice show heightened early diabetes sensitivity to multiple low doses of streptozotocin than nondiabetes-prone CD-1 mice: initial beta-cell damage a key trigger for type 1 diabetes?

OBJECTIVES: We determined if newly weaned female nonobese diabetic (NOD) mice show greater diabetes sensitivity to dose-adjusted regimens of multiple low doses of streptozotocin (Stz) than nondiabetes-prone CD-1 mice. METHODS: Female NOD mice received 5 daily doses of Stz from day 21 (0, 5, 10, 15, 20, 30, and 40 mg/kg body weight) and CD-1 mice 20, 30, and 40 mg. RESULTS: : Streptozotocin, at the 15-, 20-, 30-, and 40-mg dose, induced rapid diabetes in NOD mice. By day 100, 90% to 95% of NOD mice became diabetic after the 40- and 30-mg dose and 33% to 40% with the 15- and 20-mg dose. In comparison, only about 50% and 33% of CD-1 mice developed diabetes with the 40- and 30-mg dose, respectively, and 5.5% with the 20-mg dose. In NOD mice, the 20-mg dose also partially suppressed spontaneous diabetes. All diabetic mice displayed insulitis, variable immunostaining for insulin, and redistribution of glucagon and somatostatin cells. Glucose transporter-2 was markedly attenuated in selective beta cells. CONCLUSIONS: Newly weaned female NOD mice show heightened early sensitivity to low doses of Stz than CD-1 mice. At diabetes, several beta cells remain and show variable immunostaining for insulin and an attenuated expression for glucose transporter-2. Specific low doses of Stz may also suppress spontaneous diabetes.