Experimental and theoretical correlations between vanadium K-edge X-ray absorption and Kβ emission spectra

A series of vanadium compounds was studied by K-edge X-ray absorption (XAS) and K[Formula: see text] X-ray emission spectroscopies (XES). Qualitative trends within the datasets, as well as comparisons between the XAS and XES data, illustrate the information content of both methods. The complementary nature of the chemical insight highlights the success of this dual-technique approach in characterizing both the structural and electronic properties of vanadium sites. In particular, and in contrast to XAS or extended X-ray absorption fine structure (EXAFS), we demonstrate that valence-to-core XES is capable of differentiating between ligating atoms with the same identity but different bonding character. Finally, density functional theory (DFT) and time-dependent DFT calculations enable a more detailed, quantitative interpretation of the data. We also establish correction factors for the computational protocols through calibration to experiment. These hard X-ray methods can probe vanadium ions in any oxidation or spin state, and can readily be applied to sample environments ranging from solid-phase catalysts to biological samples in frozen solution. Thus, the combined XAS and XES approach, coupled with DFT calculations, provides a robust tool for the study of vanadium atoms in bioinorganic chemistry

Comparative electronic structures of nitrogenase FeMoco and FeVco

An investigation of the active site cofactors of the molybdenum and vanadium nitrogenases (FeMoco and FeVco) was performed using high-resolution X-ray spectroscopy. Synthetic heterometallic iron–sulfur cluster models and density functional theory calculations complement the study of the MoFe and VFe holoproteins using both non-resonant and resonant X-ray emission spectroscopy. Spectroscopic data show the presence of direct iron–heterometal bonds, which are found to be weaker in FeVco. Furthermore, the interstitial carbide is found to perturb the electronic structures of the cofactors through highly covalent Fe–C bonding. The implications of these conclusions are discussed in light of the differential reactivity of the molybdenum and vanadium nitrogenases towards various substrates. Possible functional roles for both the heterometal and the interstitial carbide are detailed.This work was supported by the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP/2007–2013) ERC Grant Agreement number 615414 (S. D.) and the ERC N-ABLE project (O. E.). Funding was also provided by the Deutsche Forschungsgemeinschaft grants EI-520/7 and RTG1976 (O. E.), the NIH (R01-GM45881 to J. A. K.), and by the Max-Planck–Gesellschaft (S. D., R. B., J. K. K., and F. A. L.). J. A. R. was funded by a graduate study scholarship from the German Academic Exchange Service (DAAD). R. B. acknowledges support from the Icelandic Research Fund, Grant No. 141218051 and the University of Iceland Research Fund. Matthias Gschell and Florian Schneider are thanked for preparing the extracted FeMoco, and Tabea Hamann is thanked for providing samples of the molybdenum cubane. Stefan Hugenbruch, Benjamin Van Kuiken, Rebeca Gómez Castillo, and Anselm Hahn are thanked for assistance with data collection. The ESRF and CHESS are also acknowledged for providing beamtime, and Sara Lafuerza and Pieter Glatzel at beamline ID-26 (ESRF) and Kenneth D. Finkelstein at beamline C-1 (CHESS) are gratefully acknowledged for technical assistance with measurements. CHESS is supported by the National Science Foundation and the National Institutes of Health/National Institute of General Medical Sciences under NSF award DMR-133220. Open Access funding provided by the Max Planck Society.Peer Reviewe

Degradation, Bioactivity, and Osteogenic Potential of Composites Made of PLGA and Two Different Sol–Gel Bioactive Glasses

We have developed poly(l-lactide-co-glycolide) (PLGA) based composites using sol–gel derived bioactive glasses (S-BG), previously described by our group, as composite components. Two different composite types were manufactured that contained either S2—high content silica S-BG, or A2—high content lime S-BG. The composites were evaluated in the form of sheets and 3D scaffolds. Sheets containing 12, 21, and 33 vol.% of each bioactive glass were characterized for mechanical properties, wettability, hydrolytic degradation, and surface bioactivity. Sheets containing A2 S-BG rapidly formed a hydroxyapatite surface layer after incubation in simulated body fluid. The incorporation of either S-BG increased the tensile strength and Young’s modulus of the composites and tailored their degradation rates compared to starting compounds. Sheets and 3D scaffolds were evaluated for their ability to support growth of human bone marrow cells (BMC) and MG-63 cells, respectively. Cells were grown in non-differentiating, osteogenic or osteoclast-inducing conditions. Osteogenesis was induced with either recombinant human BMP-2 or dexamethasone, and osteoclast formation with M-CSF. BMC viability was lower at higher S-BG content, though specific ALP/cell was significantly higher on PLGA/A2-33 composites. Composites containing S2 S-BG enhanced calcification of extracellular matrix by BMC, whereas incorporation of A2 S-BG in the composites promoted osteoclast formation from BMC. MG-63 osteoblast-like cells seeded in porous scaffolds containing S2 maintained viability and secreted collagen and calcium throughout the scaffolds. Overall, the presented data show functional versatility of the composites studied and indicate their potential to design a wide variety of implant materials differing in physico-chemical properties and biological applications. We propose these sol–gel derived bioactive glass–PLGA composites may prove excellent potential orthopedic and dental biomaterials supporting bone formation and remodeling

Bromodeoxyuridine Labeling Index as an Indicator of Early Tumor Response to Preoperative Radiotherapy in Patients with Rectal Cancer

PURPOSE: Assessment of tumor proliferation rate using Bromodeoxyuridine labeling index (BrdUrdLI) as a possible predictor of rectal cancer response to preoperative radiotherapy (RT). METHODS AND MATERIAL: Ninety-two patients were qualified either to short RT (5 Gy/fraction/5 days) and surgery about 1 week after RT (schedule I), or to short RT and 4–5 weeks interval before surgery (schedule II). Tumor samples were taken twice from each patient: before RT and at the time of surgery. The samples were incubated with BrdUrd for 1 h at 37°C, and the BrdUrdLI was calculated as a percentage of BrdUrd-labeled cells. RESULTS: Thirty-eight patients were treated according to schedule I and 54 patients according to schedule II. Mean BrdUrdLI before RT was 8.5% and its value did not differ between the patients in the two compared groups. After RT tumors showed statistically significant growth inhibition (reduction of BrdUrdLI). As the pretreatment BrdUrd LI was not predictive for early clinical and pathologic tumor response, prognostic role of the ratio of BrdUrdLI after to BrdUrdLI before RT was considered. The ratios were calculated separately for fast (BrdUrd LI > 8.5%) and slowly (BrdUrd LI ≤ 8.5%) proliferating tumors and correlated with overall treatment time (OTT, i.e., time from the first day of RT to surgery). One month after RT, accelerated proliferation was observed only in slowly proliferating tumors. CONCLUSIONS: Pretreatment BrdUrdLI was not predictive for early clinical and pathologic tumor response. The ratio after/before RT BrdUrdLI was correlated to inhibition of proliferation in responsive tumors

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)