Dose-Volume Predictors of Radiation Pneumonitis After Thoracic Hypofractionated Radiation Therapy.

PURPOSE: Hypofractionated radiation therapy (HFRT) is a common treatment for thoracic tumors, typically delivered as 60 Gy in 15 fractions. We aimed to identify dosimetric risk factors associated with radiation pneumonitis in patients receiving HFRT at 4 Gy per fraction, focusing on lung V20, mean lung dose (MLD), and lung V5 as potential predictors of grade ≥2 pneumonitis. METHODS AND MATERIALS: All patients were treated with thoracic HFRT to 60 Gy in 15 fractions or 72 Gy in 18 fractions at a single health care system from 2013 to 2020. Tumors near critical structures (trachea, proximal tracheobronchial tree, esophagus, spinal cord, or heart) were considered central (within 2 cm), and those closer were classified as ultracentral (within 1 cm). The primary endpoint was grade ≥2 pneumonitis. Logistic regression analyses, adjusting for target size and dosimetric variables, were used to establish a dose threshold associated with <20% risk of grade ≥2 pneumonitis. RESULTS: During a median 24.3-month follow-up, 18 patients (16.8%) developed grade ≥2 radiation pneumonitis, with no significant difference between the 2 dose regimens (17.3% vs 16.3%, P = .88). Four patients (3.7%) experienced grade ≥3 pneumonitis, including 2 grade 5 cases. Patients with grade ≥2 pneumonitis had significantly higher lung V20 (mean 23.4% vs 14.5%, P < .001), MLD (mean 13.0 Gy vs 9.5 Gy, P < .001), and lung V5 (mean 49.6% vs 40.6%, P = .01). Dose thresholds for a 20% risk of grade ≥2 pneumonitis were lung V20 <17.7%, MLD <10.6 Gy, and V5 <41.3%. Multivariable analysis revealed a significant association between lung V20 and grade ≥2 pneumonitis (adjusted odds ratio, 1.48, P = .03). CONCLUSIONS: To minimize the risk of grade ≥2 radiation pneumonitis when delivering 4 Gy per fraction at either 60 Gy or 72 Gy, it is advisable to maintain lung V20<17.7%. MLD <10.6 Gy and V5<41.3% can also be considered as lower-priority constraints. However, additional validation is necessary before incorporating these constraints into clinical practice or trial planning guidelines.

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks1-6, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions; however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluate their utility as personal DNA interpreters. We used paired whole genome sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learned sequence motif grammar and suggest new model training strategies to improve performance.

Impact on splicing in Saccharomyces cerevisiae of random 50-base sequences inserted into an intron.

Intron splicing is a key regulatory step in gene expression in eukaryotes. Three sequence elements required for splicing-5' and 3' splice sites and a branchpoint-are especially well-characterized in Saccharomyces cerevisiae, but our understanding of additional intron features that impact splicing in this organism is incomplete, due largely to its small number of introns. To overcome this limitation, we constructed a library in S. cerevisiae of random 50-nt (N50) elements individually inserted into the intron of a reporter gene and quantified canonical splicing and the use of cryptic splice sites by sequencing analysis. More than 70% of approximately 140,000 N50 elements reduced splicing by at least 20%. N50 features, including higher GC content, presence of GU repeats, and stronger predicted secondary structure of its pre-mRNA, correlated with reduced splicing efficiency. A likely basis for the reduced splicing of such a large proportion of variants is the formation of RNA structures that pair N50 bases-such as the GU repeats-with other bases specifically within the reporter pre-mRNA analyzed. However, multiple models were unable to explain more than a small fraction of the variance in splicing efficiency across the library, suggesting that complex nonlinear interactions in RNA structures are not accurately captured by RNA structure prediction methods. Our results imply that the specific context of a pre-mRNA may determine the bases allowable in an intron to prevent secondary structures that reduce splicing. This large data set can serve as a resource for further exploration of splicing mechanisms.

A multimodality approach to a rare case of ruptured sinus of Valsalva aneurysm with tricuspid valve involvement: a case report.

Background: Ruptured sinus of Valsalva aneurysm is a rare disease entity that is potentially life-threatening if left untreated. While imaging is the mainstay of diagnosis, resultant tricuspid valve involvement may mask typical findings providing a diagnostic challenge. Disruption of the tricuspid valve during ruptured sinus of Valsalva aneurysm with consequent tricuspid regurgitation is rare and infrequently described in the literature. Description of the utility and limitations of multimodality imaging in this scenario is equally scarce. Case summary: We review the case of a young patient presenting with acute ruptured sinus of Valsalva aneurysm and involvement of the tricuspid valve on a background of severe aortic regurgitation requiring multimodality imaging for diagnostic and pre-surgical assessment. Discussion: In young patients presenting with acute decompensation and pre-existing bicuspid aortic valve regurgitation, an increased clinical suspicion of a sinus of Valsalva aneurysm rupture is imperative. Doppler and 3D transoesophageal echocardiographic assessment should be pursued to characterize abnormal flows and clarify aetiology in the context of tricuspid involvement and resultant tricuspid regurgitation. A large-volume left-right shunt in proximity to the tricuspid annulus may result in disproportionately severe tricuspid regurgitation in the absence of annular disruption due to forced systolic opening of the leaflets by shunt flow and 'windsock' prolapse. Multimodality imaging can be essential in these cases to adequately assess the extent of the ruptured sinus of Valsalva aneurysm and overcome limitations of single modality imaging.

The interweaved signatures of common-gamma-chain cytokines across immunologic lineages.

"γc" cytokines are a family whose receptors share a "common-gamma-chain" signaling moiety, and play central roles in differentiation, homeostasis, and communications of all immunocyte lineages. As a resource to better understand their range and specificity of action, we profiled by RNAseq the immediate-early responses to the main γc cytokines across all immunocyte lineages. The results reveal an unprecedented landscape: broader, with extensive overlap between cytokines (one cytokine doing in one cell what another does elsewhere) and essentially no effects unique to any one cytokine. Responses include a major downregulation component and a broad Myc-controlled resetting of biosynthetic and metabolic pathways. Various mechanisms appear involved: fast transcriptional activation, chromatin remodeling, and mRNA destabilization. Other surprises were uncovered: IL2 effects in mast cells, shifts between follicular and marginal zone B cells, paradoxical and cell-specific cross-talk between interferon and γc signatures, or an NKT-like program induced by IL21 in CD8+ T cells.

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Deep learning methods have recently become the state-of-the-art in a variety of regulatory genomic tasks1-6 including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions, however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluates their utility as personal DNA interpreters. We used paired Whole Genome Sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learnt sequence motif grammar, and suggest new model training strategies to improve performance.

Cholesterol treatment in patients with acute coronary syndromes: does stating a target improve management?

AIM: Previous research in New Zealand has demonstrated high rates of statin prescription in patients with acute coronary syndromes (ACS), but how widely a treat to target approach is adopted is unclear. METHODS: We retrospectively examined cholesterol management in 100 consecutive patients admitted with confirmed ACS. The primary end point was reaching low-density lipoprotein-cholesterol (LDL-C) target of <1.8 mmol/L within six months. Following this a change in practice was implemented, documenting patients' current LDL-C and the LDL-C target of <1.8mmol/L in the discharge summary. A prompt to arrange a follow-up lipid test was also added to the discharge process. A second cohort of 100 patients with confirmed ACS was prospectively examined and the same endpoints reassessed. RESULTS: Lipid testing increased post intervention, both in-hospital (70% vs 98%, P<0.001) and during outpatient follow-up (60% vs 82%, P=0.01). In the intervention group, the primary outcome was achieved in more frequently (47% vs. 64% P=0.02) and follow-up LDL-C was lower (2.01.1 mmol/L vs 1.730.77 mmol/L, P=0.002). Non-statin cholesterol medication was rarely used. CONCLUSION: At baseline a treat to target approach was infrequent. Stating a target in discharge documentation was associated with significant improvements in lipid testing and patients achieving LDL-C targets.

Hypofractionated vs. standard radiotherapy for locally advanced limited-stage small cell lung cancer.

Background: Hypofractionated radiotherapy in locally advanced limited-stage small cell lung cancer is preferred in many Western countries but not used regularly in the United States. We examined practice patterns and overall survival with definitive hypofractionated radiotherapy and chemotherapy vs. standard radiotherapy in this setting. Methods: We included patients in the National Cancer Database with unresected primary stage II-III small cell lung cancer in 2008-2016 who underwent chemotherapy within six months of either hypofractionated radiotherapy (40-45 Gy/15 fractions) or standard radiotherapy (45 Gy/30 fractions or 60-70 Gy/30-35 fractions) in this retrospective cohort study. Patient characteristics were assessed with univariable and multivariable logistic regression. Kaplan-Meier estimator, log-rank test, and multivariable Cox regression were used to evaluate overall survival. Propensity score matching (PSM) was performed as a sensitivity analysis. Early concurrent chemotherapy consisted of radiotherapy and chemotherapy initiated within 30 days of each other. Results: Seven thousand and one hundred forty-three patients were included: 97.9% received standard radiotherapy and 2.1% hypofractionated radiotherapy. Multivariable analysis on the whole cohort yielded comparable overall survival (HR for hypofractionated radiotherapy 1.09, CI: 0.90-1.32, P=0.37). On PSM (N=292), median overall survival was similar between standard radiotherapy [22.9 months (95% CI: 18.2-30.4 months)] vs. hypofractionated radiotherapy [21.2 months (CI: 16.3-24.7 months); P=0.13]. Overall survival was shorter with hypofractionated radiotherapy in the early concurrent chemotherapy subset (15.8 vs. 22.1 months, P=0.007) and longer with hypofractionated radiotherapy in the non-early concurrent chemotherapy subset (29.5 vs. 18.5 months, P=0.027). Conclusions: Overall survival with hypofractionated radiotherapy appears similar to standard radiotherapy in locally advanced limited-stage small cell lung cancer. Chemotherapy timing may modify the effect of fractionation on overall survival, though larger numbers must confirm. Hypofractionated radiotherapy may be considered in those unable to receive early concurrent chemotherapy.

Noninvasive Myocardial Work Index: Characterizing the Normal and Ischemic Response to Exercise.

BACKGROUND: Myocardial work index (MWI) and work efficiency are new parameters for assessing left ventricular function. We aim to characterize the normal response to exercise in a mixed population and determine whether MWI can identify patients with inducible ischemia. METHODS: Patients were retrospectively enrolled from an existing database of exercise stress echocardiography. Inclusion criteria were a clinical indication of possible ischemia and technical suitability to calculate MWI. Exclusion criteria were abnormal baseline left ventricular function or inadequate image quality. Echocardiograms positive for ischemia were defined by independent visual assessment and compared with angiographic findings where available. Myocardial work index was determined using a proprietary algorithm and efficiency calculated as constructive work divided by the sum of constructive and wasted work. RESULTS: A total of 177 patients met inclusion criteria; 117 were excluded, leaving 40 normal and 20 positive tests. During normal exercise, global MWI increased 54% (from 2,296 to 3,523 mm Hg%) and efficiency remained at 96%. However, in patients with inducible ischemia, MWI decreased in affected segments, global MWI did not increase (2,069-2,070 mm Hg%), and global efficiency fell from 93% to 87%. The receiver operating characteristic curve for MWI had an area under the curve of 0.94. CONCLUSIONS: During normal exercise, MWI increases and efficiency remains unchanged. However, during exercise-induced ischemia, MWI paradoxically decreases in affected segments, while globally MWI fails to increase and efficiency decreases. We have demonstrated that MWI can be applied to stress echocardiography to identify ischemia, but its utility remains uncertain. Further research that makes comparisons with an objective measure of functional ischemia is needed.

Similarity regression predicts evolution of transcription factor sequence specificity.

Transcription factor (TF) binding specificities (motifs) are essential for the analysis of gene regulation. Accurate prediction of TF motifs is critical, because it is infeasible to assay all TFs in all sequenced eukaryotic genomes. There is ongoing controversy regarding the degree of motif diversification among related species that is, in part, because of uncertainty in motif prediction methods. Here we describe similarity regression, a significantly improved method for predicting motifs, which we use to update and expand the Cis-BP database. Similarity regression inherently quantifies TF motif evolution, and shows that previous claims of near-complete conservation of motifs between human and Drosophila are inflated, with nearly half of the motifs in each species absent from the other, largely due to extensive divergence in C2H2 zinc finger proteins. We conclude that diversification in DNA-binding motifs is pervasive, and present a new tool and updated resource to study TF diversity and gene regulation across eukaryotes.

Improving the Prescribing Gap For Guideline Recommended Medications Post Myocardial Infarction.

BACKGROUND: We assessed the effect of a pre-discharge medication checklist on discharge prescription rates of guideline recommended medications following myocardial infarction. In addition, we assessed what proportion of the residual prescribing gap following implementation of the checklist was due to the presence of contraindications. METHODS: We examined baseline prescription rates of guideline recommended medications in 100 patients discharged from our institution following acute myocardial infarction. We then introduced a pre-discharge checklist and reassessed discharge medications and reasons for non-prescription of guideline recommended medications in 447 patients with acute myocardial infarction. RESULTS: We demonstrated a significant gap in the prescription of guideline recommended secondary prevention medications at the time of discharge in our pre-intervention cohort. Introduction of a pre-discharge checklist resulted in a significant improvement in the prescription rates of all guideline recommended secondary prevention medications, with aspirin increasing from 90% to 97% (p=0.004), Adenosine diphosphate (ADP) receptor antagonist from 84% to 96% (p=0.0001), B-blocker from 79% to 87% (p=0.03), statin from 88% to 96% (p=0.002) and angiotensin converting enzyme (ACE) inhibitor from 58% to 70% (p=0.03). The residual gap in prescribing was largely explained by the presence of contraindications or absence of an indication in the case of ACE-inhibitors. Once these were taken into account there was a residual gap of 0-4% which represents genuine non-adherence to the guidelines. CONCLUSIONS: Introduction of a pre-discharge checklist led to significant improvement in prescription rates of all five guideline recommended secondary prevention medications. The residual gap in medication prescription following introduction of the checklist was largely due to the presence of contraindications rather than non-adherence.

Motif models for RNA-binding proteins.

Identifying the binding preferences of RNA-binding proteins (RBPs) is important in understanding their contribution to post-transcriptional regulation. Here, we review the current state-of-the art of RNA motif identification tools for RBPs. New in vivo and in vitro data sets provide sufficient statistical power to enable detection of relatively long and complex sequence and sequence-structure binding preferences, and recent computational methods are geared towards quantitative identification of these patterns. We classify methods by their motif model's representational power and describe the underlying considerations for RNA-protein interactions. All classical motif identification algorithms apply physically motivated architectures, consisting of a motif and an occupancy model, we call these explicit motif models. Recent methods, such as convolutional neural networks and support vector machines, abandon the classical architecture and implicitly model RNA binding without defining a motif model. Although they achieve high accuracy on held-out data they may be unsuitable to solve the ultimate goal of the field, using motifs trained on in vitro data to predict in vivo binding sites. For this task methods need to separate intrinsic binding preferences from cellular effects from protein and RNA concentrations, cooperativity, and competition. To tackle this problem, we advocate for the use of a `three-layer' architecture, consisting of motif model, occupancy model, and extrinsic factor model, which enables separation and adjustment to cellular conditions.

One-pot Microwave-assisted Conversion of Anomeric Nitrate-esters to Trichloroacetimidates.

The goal of the following procedure is to provide a demonstration of the one-pot conversion of a 2-azido-1-nitrate-ester to a trichloroacetimidate glycosyl donor. Following azido-nitration of a glycal, the product 2-azido-1-nitrate ester can be hydrolyzed under microwave-assisted irradiation. This transformation is usually achieved using strongly nucleophilic reagents and extended reaction times. Microwave irradiation induces hydrolysis, in the absence of reagents, with short reaction times. Following denitration, the intermediate anomeric alcohol is converted, in the same pot, to the corresponding 2-azido-1-trichloroacetimidate.

Comparison of Multiplate and VerifyNow platelet function tests in predicting clinical outcome in patients with acute coronary syndromes.

INTRODUCTION: This study examined the ability of two widely used "point of care" platelet function assays, VerifyNow and Multiplate, to predict adverse outcomes in patients with acute coronary syndromes (ACS). METHODS: We examined platelet reactivity using VerifyNow and Multiplate P2Y12 assays in patients with ACS and the relationship between platelet reactivity and both MACE (defined as a composite of death, myocardial infarction, stroke, stent thrombosis and unplanned revascularisation) and TIMI major bleeding at 1year. RESULTS: In 619 ACS patients, 65 patients (10.5%) had experienced MACE at 1year and 6 patients (1%) had TIMI major bleeding events. The two measures of platelet reactivity were only moderately correlated (Rho=0.43, p=0.0001). Both measures demonstrated a statistically significant relationship with MACE, with area under the curve for VerifyNow of 0.632 (0.001) and for Multiplate of 0.577 (p=0.04), and neither measure showed a significant relationship with bleeding. Logistic regression analysis found that only VerifyNow was a statistical predictor of MACE (p=0.01). MACE occurred in 16% of those classified as having HPR using VerifyNow compared to 7% in those without HPR (odds ratio of 2.6 (95% CI 1.5-4.4, p=0.001). In those classified as having HPR by the Multiplate assay, MACE occurred in 13% compared to 9% of those without HPR (Odds ratio 1.5 95% CI 0.9-2.5, p=0.11). CONCLUSION: The two points of care platelet function tests examined in this study were only moderately correlated. The VerifyNow assay demonstrated a stronger relationship to MACE than the Multiplate assay.

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.

Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scoring step is one of the bottlenecks in the performance of many state-of-the-art protocols. The performance of scoring functions depends on the quality of the generated structures and its coupling to the sampling algorithm. A tool kit, GRADSCOPT (GRid Accelerated Directly SCoring OPTimizing), was designed to allow rapid development and optimization of different knowledge-based scoring potentials for specific objectives in protein-protein docking. Different atomistic and coarse-grained potentials can be created by a grid-accelerated directly scoring dependent Monte-Carlo annealing or by a linear regression optimization. We demonstrate that the scoring functions generated by our approach are similar to or even outperform state-of-the-art scoring functions for predicting near-native solutions. Of additional importance, we find that potentials specifically trained to identify the native bound complex perform rather poorly on identifying acceptable or medium quality (near-native) solutions. In contrast, atomistic long-range contact potentials can increase the average fraction of near-native poses by up to a factor 2.5 in the best scored 1% decoys (compared to existing scoring), emphasizing the need of specific docking potentials for different steps in the docking protocol.

SAXS Data Alone can Generate High-Quality Models of Protein-Protein Complexes.

Modeling driven by small-angle X-ray scattering (SAXS) combines low-resolution data with computational modeling to predict the structure of biomolecular assemblies. A new protocol, ATTRACT-SAXS, has been developed and tested on a large protein-protein docking benchmark with simulated SAXS data. For 88% of cases, high-quality solutions were generated using SAXS data alone without a physiochemical force field (interface-RMSD ≦ 2 Å or ligand-RMSD ≦ 5 Å; and more than 30% native contacts). ATTRACT-SAXS gave significant improvements compared with previous approaches that filter by SAXS a posteriori. When combining SAXS and interface properties for scoring, the protocol placed high-quality models in 70% of cases among the top-ranked 100 clusters. ATTRACT-SAXS also gave good results when tested on experimental data if the native complex structure was compatible with the SAXS profile. Our results show that, in principle, SAXS on its own can contain enough information to generate high-quality models of protein-protein complexes.