Visible-Light-Driven Decarboxylative Borylation: Rapid Access to α- and ß-Amino-boronamides.

In this study, we described a two-step process involving an efficient visible-light-induced decarboxylative borylation of α- and ß-amino redox-active esters with bis(catecholato)diboron, followed by transamination with 1,8-diaminonapthalene (DANH2). A series of boronamides were obtained in moderate to excellent yields in this one-pot procedure. The photochemical process proved to be very efficient even when conducted under flow conditions with shorter reaction durations and scalable synthesis of DAN boronates.

Implications of range of motion requirements for the laxity of ligaments in a lumbar finite element model.

Finite element models of the lumbar spine often adopt ligament properties from tensile tests without accounting for possible differences between testing and in situ initial ligament length. Such differences could result in laxities or preloads at the beginning of a simulation that would affect the ligament forces, tangent stiffness, and the posture at which they fail. In vivo and in vitro human experimental data reported laxities or preloads. However, laxities or preloads, which could also result from postural differences, are often neglected in simulation studies. This study proposes a numerical methodology to identify ranges of ligament laxities or preloads compatible with the selected tensile ligament properties, the model, and the range of motion (RoM) the model aims to simulate. The approach assumes that ligaments should remain in a safe elongation range for the complete RoM, and that each ligament should play a significant mechanical role in at least one load case. The methodology was applied to the functional spinal unit (FSU) models using the RoM from healthy subjects and ligament properties from the literature. Without laxity, some ligaments reached their elongation at failure within the RoM. Laxity ranges varied considerably (from -9.2 mm preload to 10.7 mm laxity) and flexion was the most critical load case to determine them. Their effect on the mobility response was also assessed. The effect on the mobility of a FSU was also assessed. While the proposed method cannot determine an exact laxity value, it is simple and it can be applied to any model to identify a plausible range of ligament initial length.

Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans.

Purpose: To present a method that automatically detects, subtypes, and locates acute or subacute intracranial hemorrhage (ICH) on noncontrast CT (NCCT) head scans; generates detection confidence scores to identify high-confidence data subsets with higher accuracy; and improves radiology worklist prioritization. Such scores may enable clinicians to better use artificial intelligence (AI) tools. Materials and Methods: This retrospective study included 46 057 studies from seven "internal" centers for development (training, architecture selection, hyperparameter tuning, and operating-point calibration; n = 25 946) and evaluation (n = 2947) and three "external" centers for calibration (n = 400) and evaluation (n = 16 764). Internal centers contributed developmental data, whereas external centers did not. Deep neural networks predicted the presence of ICH and subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and/or epidural hemorrhage) and segmentations per case. Two ICH confidence scores are discussed: a calibrated classifier entropy score and a Dempster-Shafer score. Evaluation was completed by using receiver operating characteristic curve analysis and report turnaround time (RTAT) modeling on the evaluation set and on confidence score-defined subsets using bootstrapping. Results: The areas under the receiver operating characteristic curve for ICH were 0.97 (0.97, 0.98) and 0.95 (0.94, 0.95) on internal and external center data, respectively. On 80% of the data stratified by calibrated classifier and Dempster-Shafer scores, the system improved the Youden indexes, increasing them from 0.84 to 0.93 (calibrated classifier) and from 0.84 to 0.92 (Dempster-Shafer) for internal centers and increasing them from 0.78 to 0.88 (calibrated classifier) and from 0.78 to 0.89 (Dempster-Shafer) for external centers (P < .001). Models estimated shorter RTAT for AI-prioritized worklists with confidence measures than for AI-prioritized worklists without confidence measures, shortening RTAT by 27% (calibrated classifier) and 27% (Dempster-Shafer) for internal centers and shortening RTAT by 25% (calibrated classifier) and 27% (Dempster-Shafer) for external centers (P < .001). Conclusion: AI that provided statistical confidence measures for ICH detection on NCCT scans reliably detected and subtyped hemorrhages, identified high-confidence predictions, and improved worklist prioritization in simulation.Keywords: CT, Head/Neck, Hemorrhage, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

Asymmetric Transfer Hydrogenation of gem-Difluorocyclopropenyl Esters: Access to Enantioenriched gem-Difluorocyclopropanes.

Catalytic enantioselective access to disubstituted functionalized gem-difluorocyclopropanes, which are emerging fluorinated motifs of interest in medicinal chemistry, was achieved through asymmetric transfer hydrogenation of gem-difluorocyclopropenyl esters, catalyzed by a Noyori-Ikariya (p-cymene)-ruthenium(II) complex, with (N-tosyl-1,2-diphenylethylenediamine) as the chiral ligand and isopropanol as the hydrogen donor. The resulting cis-gem-difluorocyclopropyl esters were obtained with moderate to high enantioselectivity (ee=66-99 %), and post-functionalization reactions enable access to valuable building blocks incorporating a cis- or trans-gem-difluorocyclopropyl motif.