The synthesis of four new FeII(N4S(thiolate)) complexes as models of the thiol dioxygenases are described. They are composed of derivatives of the neutral, tridentate ligand triazacyclononane (R3TACN; R = Me, iPr) and 2-aminobenzenethiolate (abtx; X = H, CF3), a non-native substrate for cysteine dioxygenase (CDO). The coordination number of these complexes depends on the identity of the TACN derivative, giving 6-coordinate (6-coord) complexes for FeII(Me3TACN)(abtx)(OTf) (1: X = H; 2: X = CF3), and 5-coordinate (5-coord) complexes for [FeII(iPr3TACN)(abtx)](OTf) (3: X = H; 4: X = CF3). Complexes 1 – 4 were examined by UV-vis, 1H/19F NMR, and Mössbauer spectroscopies, and density functional theory (DFT) calculations were employed to support the data. Mössbauer spectroscopy reveals that the 6-coord 1 – 2 and 5-coord 3 – 4 exhibit distinct spectra, and these data are compared with that for cysteine-bound CDO, helping to clarify the coordination environment of the cys-bound FeII active site. Reaction of 1 or 2 with O2 at −95 °C leads to S-oxygenation of the abt ligand, and in the case of 2, a rare di(sulfinato)-bridged complex, [Fe2III(µ-O)(O2S(NH2)C6H3CF3)2](OTf)2 (5), was obtained. Parallel enzymatic studies on the CDO variant C93G were carried out with the abt substrate, and show that reaction with O2 leads to disulfide formation, as opposed to S-oxygenation. The combined model and enzyme studies show that the thiol dioxygenases can operate via a 6-coord FeII center, in contrast to the accepted mechanism for nonheme iron dioxygenases, and that proper substrate chelation to Fe appears to be critical for S-oxygenation..

The ability of resonant X-ray emission spectroscopy (XES) to recover physical oxidation state information, which may often be ambiguous in conventional X-ray spectroscopy, is demonstrated. By combining Kb XES with resonant excitation in the XAS pre-edge region, resonant Kb XES (or 1s3p RXES) data are obtained, which probe the 3d n+1 final-state configuration. Comparison of the non-resonant and resonant XES for as eries of high-spin ferrous and ferric complexes shows that oxidation state assignments that were previously unclear are now easily made.T he present study spans iron tetrachlorides,iron sulfur clusters,and the MoFeprotein of nitrogenase. While 1s3p RXES studies have previously been reported, to our knowledge,1s3p RXES has not been previously utilized to resolve questions of metal valency in highly covalent systems. As such, the approach presented herein provides chemists with means to more rigorously and quantitatively address challenging electronic-structure questions.

The present study employs a suite of spectroscopic techniques to evaluate the electronic and bonding characteristics of the interstitial carbide in a set of iron-carbonyl-carbide clusters, one of which is substituted with a molybdenum atom. The M6C and M5C clusters are the dianions (Et4N)2[Fe6(μ6-C)(μ2-CO)2(CO)14] (1), [K(benzo-18-crown-6)]2[Fe5(μ5-C)(μ2-CO)1(CO)13] (2), and [K(benzo-18-crown-6)]2[Fe5Mo(μ6-C)(μ2-CO)2(CO)15] (3). Because 1 and 2 have the same overall cluster charge (2−) but different numbers of iron sites (1: 6 sites → 2: 5 sites), the metal atoms of 2 are formally oxidized compared to those in 1. Despite this, Mössbauer studies indicate that the iron sites in 2 possess significantly greater electron density (lower spectroscopic oxidation state) compared with those in 1. Iron K-edge X-ray absorption and valence-to-core X-ray emission spectroscopy measurements, paired with density functional theory spectral calculations, revealed the presence of significant metal-to-metal and carbide 2p-based character in the filled valence and low-lying unfilled electronic manifolds. In all of the above experiments, the presence of the molybdenum atom in 3 (Fe5Mo) results in somewhat unremarkable spectroscopic properties that are essentially a “hybrid” of 1 (Fe6) and 2 (Fe5). The overall electronic portrait that emerges illustrates that the central inorganic carbide ligand is essential for distributing charge and maximizing electronic communication throughout the cluster. It is evident that the carbide coordination environment is quite flexible and adaptive: it can drastically modify the covalency of individual Fe–C bonds based on local structural changes and redox manipulation of the clusters. In light of these findings, our data and calculations suggest a potential role for the central carbon atom in FeMoco, which likely performs a similar function in order to maintain cluster integrity through multiple redox and ligand binding events.

Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.