Synthesis, structure, and opto-electronic properties of organic-based nanoscale heterojunctions

Enormous research effort has been put into optimizing organic-based opto-electronic systems for efficient generation of free charge carriers. This optimization is mainly due to typically high dissociation energy (0.1-1 eV) and short diffusion length (10 nm) of excitons in organic materials. Inherently, interplay of microscopic structural, chemical, and opto-electronic properties plays crucial role. We show that employing and combining advanced scanning probe techniques can provide us significant insight into the correlation of these properties. By adjusting parameters of contact- and tapping-mode atomic force microscopy (AFM), we perform morphologic and mechanical characterizations (nanoshaving) of organic layers, measure their electrical conductivity by current-sensing AFM, and deduce work functions and surface photovoltage (SPV) effects by Kelvin force microscopy using high spatial resolution. These data are further correlated with local material composition detected using micro-Raman spectroscopy and with other electronic transport data. We demonstrate benefits of this multi-dimensional characterizations on (i) bulk heterojunction of fully organic composite films, indicating differences in blend quality and component segregation leading to local shunts of photovoltaic cell, and (ii) thin-film heterojunction of polypyrrole (PPy) electropolymerized on hydrogen-terminated diamond, indicating covalent bonding and transfer of charge carriers from PPy to diamond

Enhanced Growth and Osteogenic Differentiation of Human Osteoblast-Like Cells on Boron-Doped Nanocrystalline Diamond Thin Films

Intrinsic nanocrystalline diamond (NCD) films have been proven to be promising substrates for the adhesion, growth and osteogenic differentiation of bone-derived cells. To understand the role of various degrees of doping (semiconducting to metallic-like), the NCD films were deposited on silicon substrates by a microwave plasma-enhanced CVD process and their boron doping was achieved by adding trimethylboron to the CH4:H2 gas mixture, the B∶C ratio was 133, 1000 and 6700 ppm. The room temperature electrical resistivity of the films decreased from >10 MΩ (undoped films) to 55 kΩ, 0.6 kΩ, and 0.3 kΩ (doped films with 133, 1000 and 6700 ppm of B, respectively). The increase in the number of human osteoblast-like MG 63 cells in 7-day-old cultures on NCD films was most apparent on the NCD films doped with 133 and 1000 ppm of B (153,000±14,000 and 152,000±10,000 cells/cm2, respectively, compared to 113,000±10,000 cells/cm2 on undoped NCD films). As measured by ELISA per mg of total protein, the cells on NCD with 133 and 1000 ppm of B also contained the highest concentrations of collagen I and alkaline phosphatase, respectively. On the NCD films with 6700 ppm of B, the cells contained the highest concentration of focal adhesion protein vinculin, and the highest amount of collagen I was adsorbed. The concentration of osteocalcin also increased with increasing level of B doping. The cell viability on all tested NCD films was almost 100%. Measurements of the concentration of ICAM-1, i.e. an immunoglobuline adhesion molecule binding inflammatory cells, suggested that the cells on the NCD films did not undergo significant immune activation. Thus, the potential of NCD films for bone tissue regeneration can be further enhanced and tailored by B doping and that B doping up to metallic-like levels is not detrimental for cells

In Vivo Biotransformation of 3,3′,4,4′-Tetrachlorobiphenyl by Whole Plants−Poplars and Switchgrass

Polychlorinated biphenyls (PCBs) are widely distributed persistent organic pollutants. In vitro research has shown that plant cell cultures might transform lower chlorinated congeners to hydroxylated PCBs, but there are few studies on in vivo metabolism of PCBs by intact whole plants. In this research, poplar plants (Populus deltoides × nigra, DN34) and switchgrass (Panicum vigratum, Alamo) were hydroponically exposed to 3,3′,4,4′-tetrachlorobiphenyl (CB77). Metabolism in plants occurred rapidly, and metabolites were detected after only a 24 h exposure. Rearrangement of chlorine atoms and dechlorination of CB77 by plants was unexpectedly observed. In addition, poplars were able to hydroxylate CB77 and the metabolite 6-hydroxy-3,3′,4,4′-tetrachlorobiphenyl (6-OH-CB77) was identified and quantified. Hybrid poplar was able to hydroxylate CB77, but switchgrass was not, suggesting that enzymatic transformations are plant specific. Sulfur-containing metabolites (from the action of sulfotransferases) were investigated in this study, but they were not detected in either poplar or switchgrass

Entropy and Complexity Analyses in Alzheimer’s Disease: An MEG Study

Alzheimer’s disease (AD) is one of the most frequent disorders among elderly population and it is considered the main cause of dementia in western countries. This irreversible brain disorder is characterized by neural loss and the appearance of neurofibrillary tangles and senile plaques. The aim of the present study was the analysis of the magnetoencephalogram (MEG) background activity from AD patients and elderly control subjects. MEG recordings from 36 AD patients and 26 controls were analyzed by means of six entropy and complexity measures: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi’s fractal dimension (HFD), Maragos and Sun’s fractal dimension (MSFD), and Lempel-Ziv complexity (LZC). SSE is an irregularity estimator in terms of the flatness of the spectrum, whereas ApEn and SampEn are embbeding entropies that quantify the signal regularity. The complexity measures HFD and MSFD were applied to MEG signals to estimate their fractal dimension. Finally, LZC measures the number of different substrings and the rate of their recurrence along the original time series. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in several brain regions using all these methods, with the exception of MSFD (p-value < 0.05, Welch’s t-test with Bonferroni’s correction). Using receiver operating characteristic curves with a leave-one-out cross-validation procedure, the highest accuracy was achieved with SSE: 77.42%. We conclude that entropy and complexity analyses from MEG background activity could be useful to help in AD diagnosis

Vitamin C Enhances Vitamin E Status and Reduces Oxidative Stress Indicators in Sea Bass Larvae Fed High DHA Microdiets

Docosahexaenoic acid (DHA) is an essential fatty acid necessary for many biochemical, cellular and physiological functions in fish. However, high dietary levels of DHA increase free radical injury in sea bass (Dicentrarchus labrax) larvae muscle, even when vitamin E (&alpha;-tocopherol, &alpha;-TOH) is increased. Therefore, the inclusion of other nutrients with complementary antioxidant functions, such as vitamin C (ascorbic acid, vitC), could further contribute to prevent these lesions. The objective of the present study was to determine the effect of vitC inclusion (3,600 mg/kg) in high DHA (5 % DW) and &alpha;-TOH (3,000 mg/kg) microdiets (diets 5/3,000 and 5/3,000 + vitC) in comparison to a control diet (1 % DHA DW and 1,500 mg/kg of &alpha;-TOH; diet 1/1,500) on sea bass larvae growth, survival, whole body biochemical composition and thiobarbituric acid reactive substances (TBARS) content, muscle morphology, skeletal deformities and antioxidant enzymes, insulin-like growth factors (IGFs) and myosin expression (MyHC). Larvae fed diet 1/1,500 showed the best performance in terms of total length, incidence of muscular lesions and ossification degree. IGFs gene expression was elevated in 5/3,000 diet larvae, suggesting an increased muscle mitogenesis that was confirmed by the increase in the mRNA copies of MyHC. vitC effectively controlled oxidative damages in muscle, increased &alpha;-TOH larval contents and reduced TBARS content and the occurrence of skull deformities. The results of the present study showed the antioxidant synergism between vitamins E and C when high contents of DHA are included in sea bass larvae diets

Track A Basic Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138319/1/jia218438.pd

Current clinical practice and outcome of neoadjuvant chemotherapy for early breast cancer: analysis of individual data from 94,638 patients treated in 55 breast cancer centers

Neoadjuvant chemotherapy (NACT) is frequently used in patients with early breast cancer. Randomized controlled trials have demonstrated similar survival after NACT or adjuvant chemotherapy (ACT). However, certain subtypes may benefit more when NACT contains regimes leading to high rates of pathologic complete response (pCR) rates. In this study we analyzed data using the OncoBox research from 94,638 patients treated in 55 breast cancer centers to describe the current clinical practice of and outcomes after NACT under routine conditions. These data were compared to patients treated with ACT. 40% of all patients received chemotherapy. The use of NACT increased over time from 5% in 2007 up to 17.3% in 2016. The proportion of patients receiving NACT varied by subtype. It was low in patients with HR-positive/HER2-negative breast cancer (5.8%). However, 31.8% of patients with triple-negative, 31.9% with HR-negative/HER2-positive, and 26.5% with HR-positive/HER2-positive breast cancer received NACT. The rates of pCR were higher in patients with HR-positive/HER2-positive, HR negative/HER2-positive and triple-negative tumors (36, 53 and 38%) compared to HR-positive/HER2-negative tumors (12%). PCR was achieved more often in HER2-positive and triple-negative tumors over time. This is the largest study on use and effects of NACT in German breast cancer centers. It demonstrates the increased use of NACT based on recommendations in current clinical guidelines. An improvement of pCR was shown in particular in HER2-positive and triple-negative breast cancer, which is consistent with data from randomized controlled trails

Bayesian approaches to Gaussian mixture modeling

A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an “optimal” number of components in the model and so partition data sets. The performance of the Bayesian method is compared to other methods of optimal model selection and found to give good results. The methods are tested on synthetic and real data sets