Towards energetically viable asymmetric deprotonations : selectivity at more elevated temperatures with C2-symmetric magnesium bisamides

A novel chiral magnesium bisamide has enabled the development of effective asymmetric deprotonation protocols at substantially more elevated temperatures. This new, structurally simple, C2-symmetric magnesium complex displays excellent levels of asymmetric efficiency and energy reduction in the synthesis of enantioenriched enol silane

Clinical Acceptability of Automatically Generated Lymph Node Levels and Structures of Deglutition and Mastication for Head and Neck Radiation Therapy

BACKGROUND AND PURPOSE: Auto-contouring of complex anatomy in computed tomography (CT) scans is a highly anticipated solution to many problems in radiotherapy. In this study, artificial intelligence (AI)-based auto-contouring models were clinically validated for lymph node levels and structures of swallowing and chewing in the head and neck. MATERIALS AND METHODS: CT scans of 145 head and neck radiotherapy patients were retrospectively curated. One cohort (n = 47) was used to analyze seven lymph node levels and the other (n = 98) used to analyze 17 swallowing and chewing structures. Separate nnUnet models were trained and validated using the separate cohorts. For the lymph node levels, preference and clinical acceptability of AI vs human contours were scored. For the swallowing and chewing structures, clinical acceptability was scored. Quantitative analyses of the test sets were performed for AI vs human contours for all structures using overlap and distance metrics. RESULTS: Median Dice Similarity Coefficient ranged from 0.77 to 0.89 for lymph node levels and 0.86 to 0.96 for chewing and swallowing structures. The AI contours were superior to or equally preferred to the manual contours at rates ranging from 75% to 91%; there was not a significant difference in clinical acceptability for nodal levels I-V for manual versus AI contours. Across all AI-generated lymph node level contours, 92% were rated as usable with stylistic to no edits. Of the 340 contours in the chewing and swallowing cohort, 4% required minor edits. CONCLUSIONS: An accurate approach was developed to auto-contour lymph node levels and chewing and swallowing structures on CT images for patients with intact nodal anatomy. Only a small portion of test set auto-contours required minor edits

Deep Learning-Based Dose Prediction To Improve the Plan Quality of Volumetric Modulated Arc Therapy for Gynecologic Cancers

Background: In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. Purpose: To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. Methods: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. Results: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D1% and D98% were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. Conclusions: Deep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization

Association of the phosphodiesterase 4D (PDE4D) gene and cardioembolic stroke in an Australian cohort

Background: Large-scale epidemiological studies support an important role for susceptibility genes in the pathogenesis of ischemic stroke, with phosphodiesterase 4D identified as the first gene predisposing to ischemic stroke. Several single nucleotide polymorphisms within the phosphodiesterase 4D gene have been implicated in the pathogenesis of stroke. Aim: Undertake a multivariate analysis of six single nucleotide polymorphisms within the phosphodiesterase 4D gene in a previously defined Australian stroke cohort, to determine whether these single nucleotide polymorphisms have an association with ischemic stroke. Methods: This case–control study was performed using an existing genetic database of 180 ischemic stroke patients and 301 community controls, evaluated previously for cerebrovascular risk factors (hypertension, hypercholesterolemia, diabetes, paroxysmal atrial fibrillation, smoking and history of stroke in a first-degree relative). Based on previously reported associations with large vessel disease, ischemic stroke, cardioembolic stroke or a mixture of these, six single nucleotide polymorphisms in the phosphodiesterase 4D gene were selected for study, these being single nucleotide polymorphisms 13, 19, rs152312, 45, 83 and 87, based on previously utilized DeCODE nomenclature. Single nucleotide polymorphisms were genotyped using a sequence-specific polymerase chain reaction method and gel electrophoresis. Logistic regression was undertaken to determine the relevance of each polymorphism to stroke. Further analysis was undertaken to determine the risk of stroke following stratification for stroke sub-type and etiology. Results: Significant odds ratios were found to be associated with cardioembolic strokes in two single nucleotide polymorphisms: rs152312 and SNP 45 (P<0·05). Conclusions: Our findings demonstrated an association between cardioembolic stroke and phosphodiesterase 4D single nucleotide polymorphisms rs152312 and 45. No significant association was found for the other four single nucleotide polymorphisms investigated within the phosphodiesterase 4D gene. We propose that the results from this Australian population support the concept that a large prospective international study is required to investigate the role of phosphodiesterase 4D in the cardiogenic cause of ischemic stroke.Austin G. Milton, Verna M. Aykanat, M. Anne Hamilton-Bruce, Mark Nezic, Jim Jannes, Simon A. Kobla

Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

PURPOSE: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model\u27s performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists. RESULTS: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D CONCLUSIONS: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality

Direct Functionalization of Nitrogen Heterocycles via Rh-Catalyzed C−H Bond Activation

Nitrogen heterocycles are present in many compounds of enormous practical importance, ranging from pharmaceutical agents and biological probes to electroactive materials. Direct functionalization of nitrogen heterocycles through C−H bond activation constitutes a powerful means of regioselectively introducing a variety of substituents with diverse functional groups onto the heterocycle scaffold. Working together, our two groups have developed a family of Rh-catalyzed heterocycle alkylation and arylation reactions that are notable for their high level of functional-group compatibility. This Account describes our work in this area, emphasizing the relevant mechanistic insights that enabled synthetic advances and distinguished the resulting transformations from other methods. We initially discovered an intramolecular Rh-catalyzed C-2 alkylation of azoles by alkenyl groups. That reaction provided access to a number of di-, tri-, and tetracyclic azole derivatives. We then developed conditions that exploited microwave heating to expedite these reactions. While investigating the mechanism of this transformation, we discovered that a novel substrate-derived Rh−N-heterocyclic carbene (NHC) complex was involved as an intermediate. We then synthesized analogous Rh−NHC complexes directly by treating precursors to the intermediate [RhCl(PCy3)2] with N-methylbenzimidazole, 3-methyl-3,4-dihydroquinazoline, and 1-methyl-1,4-benzodiazepine-2-one. Extensive kinetic analysis and DFT calculations supported a mechanism for carbene formation in which the catalytically active RhCl(PCy3)2 fragment coordinates to the heterocycle before intramolecular activation of the C−H bond occurs. The resulting Rh−H intermediate ultimately tautomerizes to the observed carbene complex. With this mechanistic information and the discovery that acid cocatalysts accelerate the alkylation, we developed conditions that efficiently and intermolecularly alkylate a variety of heterocycles, including azoles, azolines, dihydroquinazolines, pyridines, and quinolines, with a wide range of functionalized olefins. We demonstrated the utility of this methodology in the synthesis of natural products, drug candidates, and other biologically active molecules. In addition, we developed conditions to directly arylate these heterocycles with aryl halides. Our initial conditions that used PCy3 as a ligand were successful only for aryl iodides. However, efforts designed to avoid catalyst decomposition led to the development of ligands based on 9-phosphabicyclo[4.2.1]nonane (phoban) that also facilitated the coupling of aryl bromides. We then replicated the unique coordination environment, stability, and catalytic activity of this complex using the much simpler tetrahydrophosphepine ligands and developed conditions that coupled aryl bromides bearing diverse functional groups without the use of a glovebox or purified reagents. With further mechanistic inquiry, we anticipate that researchers will better understand the details of the aforementioned Rh-catalyzed C−H bond functionalization reactions, resulting in the design of more efficient and robust catalysts, expanded substrate scope, and new transformations

Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy.

Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy

Antiplatelet therapy with aspirin, clopidogrel, and dipyridamole versus clopidogrel alone or aspirin and dipyridamole in patients with acute cerebral ischaemia (TARDIS): a randomised, open-label, phase 3 superiority trial

Background: Intensive antiplatelet therapy with three agents might be more effective than guideline treatment for preventing recurrent events in patients with acute cerebral ischaemia. We aimed to compare the safety and efficacy of intensive antiplatelet therapy (combined aspirin, clopidogrel, and dipyridamole) with that of guideline-based antiplatelet therapy. Methods: We did an international, prospective, randomised, open-label, blinded-endpoint trial in adult participants with ischaemic stroke or transient ischaemic attack (TIA) within 48 h of onset. Participants were assigned in a 1:1 ratio using computer randomisation to receive loading doses and then 30 days of intensive antiplatelet therapy (combined aspirin 75 mg, clopidogrel 75 mg, and dipyridamole 200 mg twice daily) or guideline-based therapy (comprising either clopidogrel alone or combined aspirin and dipyridamole). Randomisation was stratified by country and index event, and minimised with prognostic baseline factors, medication use, time to randomisation, stroke-related factors, and thrombolysis. The ordinal primary outcome was the combined incidence and severity of any recurrent stroke (ischaemic or haemorrhagic; assessed using the modified Rankin Scale) or TIA within 90 days, as assessed by central telephone follow-up with masking to treatment assignment, and analysed by intention to treat. This trial is registered with the ISRCTN registry, number ISRCTN47823388. Findings: 3096 participants (1556 in the intensive antiplatelet therapy group, 1540 in the guideline antiplatelet therapy group) were recruited from 106 hospitals in four countries between April 7, 2009, and March 18, 2016. The trial was stopped early on the recommendation of the data monitoring committee. The incidence and severity of recurrent stroke or TIA did not differ between intensive and guideline therapy (93 [6%] participants vs 105 [7%]; adjusted common odds ratio [cOR] 0·90, 95% CI 0·67–1·20, p=0·47). By contrast, intensive antiplatelet therapy was associated with more, and more severe, bleeding (adjusted cOR 2·54, 95% CI 2·05–3·16, p<0·0001). Interpretation: Among patients with recent cerebral ischaemia, intensive antiplatelet therapy did not reduce the incidence and severity of recurrent stroke or TIA, but did significantly increase the risk of major bleeding. Triple antiplatelet therapy should not be used in routine clinical practice

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease