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Ten new ruthenium compounds based on the N,N,N,N-chelate Me2bpbMe2 (bpb = 1,2-bis(pyridine-2-carboximido)benzene) have prepared and characterized by 1H NMR and IR spectroscopy. The monocarbonyl compound (Me2bpbMe2)Ru(CO)(H2O) compound was generated from the reaction of the free base Me2bpbMe2H2 with Ru3(CO)12 in refluxing DMF. Isoamyl nitrite reacts with this compound to yield the trans-addition nitrosyl alkoxide (Me2bpbMe2)Ru(NO)(O-i-C5H11). Nitrosothiols similarly add in a formal trans-addition manner to yield (Me2bpbMe2)Ru(NO)(SR/Ar) (SR/Ar = S-i-C5H11, SPh, SC6F4H, SC(Me)2CHNHC(O)Me) derivatives. The (Me2bpbMe2)Ru(NO)(O-i-C5H11) compound undergoes alkoxide exchange reactions with PhOH and HOC6F4H to generate (Me2bpbMe2)Ru(NO)(OPh) and (Me2bpbMe2)Ru(NO)(OC6F4H), respectively. The neutral alkoxide/aryloxide nitrosyl compounds exhibit higher NO bands (1809–1842 cm-1) relative to their thiolate analogues (1755–1823 cm-1). The X-ray crystal structures of (Me2bpbMe2)Ru(NO)(OPh), (Me2bpbMe2)Ru(NO)(OC6F4H), and (Me2bpbMe2)Ru(NO)(SPh), have been determined, and reveal linear axial (O)N–Ru–O/S linkages consistent with trans positioning of the NO and aryloxide and -thiolate groups, and near-linear Ru–N–O moieties (164–174°) consistent with these complexes being formulated as {RuNO}6 species. The electrooxidation behavior of (Me2bpbMe2)Ru(NO)(OC6F4H), (Me2bpbMe2)Ru(NO)(SC6F4H), and (Me2bpbMe2)Ru(NO)(SPh) were examined by cyclic voltammetry and IR spectroelectrochemistry in CH2Cl2. (Me2bpbMe2)Ru(NO)(OC6F4H) and (Me2bpbMe2)Ru(NO)(SC6F4H) display reversible first oxidations, whereas (Me2bpbMe2)Ru(NO)(SPh) displays an irreversible first oxidation with likely loss of the thiolate ligand. Chemical reactivity of (Me2bpbMe2)Ru(NO)(SPh) with H+ and Me+ results in the generation of the free thiol PhSH and thioether PhSMe, respectively. 

Abstract The synthesis, characterization, and redox behavior of aryloxide complexes containing an increasing number of internal hydrogen bonds (OEP)Ru(NO)(OAr_xH) (OEP=octaethylporphyrinato dianion; x=0, 1, 2) are reported. These nitrosyl aryloxide compounds were characterized by X‐ray crystallography, IR and¹H NMR spectroscopy. The IR spectra displayed υ_NOfrequencies in the 1823–1843 cm⁻¹range with compounds possessing more internal hydrogen bonds demonstrating higher υ_NOfrequencies due to diminished π‐backdonation to the Ru−NO fragment. Comparison of the distinct υ_NHand δ_N−Hsignals in the IR and¹H NMR spectra of the free and complexed OAr_1H/OAr_2Hligands support the notion of additional electron density being removed via intramolecular hydrogen bonding. Results of DFT calculations on the (porphine)Ru(NO)(OAr_xH) models (porphine=unsubstituted porphyrin) reveal that the HOMOs of these complexes have significant axial ligand contributions, whereas the HOMOs of the five‐coordinate [(porphine)RuNO)]⁺cation resides mostly on the equatorial porphyrin macrocycle. The electrochemical results of these (OEP)Ru(NO)(OAr_xH) complexes in CH₂Cl₂reveal first oxidations that occur at increasingly positive potentials when more internal hydrogen bonds are present. Based on the DFT and preliminary IR spectroelectrochemical results, we propose that the electrooxidations result in eventual dissociation of the axial aryloxide ligands. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.