Poor Overlap Research Articles

Deciphering a shared transcriptomic regulation and the relative contribution of each regulator type through endometrial gene expression signatures

BackgorundWhile various endometrial biomarkers have been characterized at the transcriptomic and functional level, there is generally a poor overlap among studies, making it unclear to what extent their upstream regulators (e.g., ovarian hormones, transcription factors (TFs) and microRNAs (miRNAs)) realistically contribute to menstrual cycle progression and function. Unmasking the intricacies of the molecular interactions in the endometrium from a novel systemic point of view will help gain a more accurate perspective of endometrial regulation and a better explanation the molecular etiology of endometrial-factor infertility.MethodsAn in-silico analysis was carried out to identify which regulators consistently target the gene biomarkers proposed in studies related to endometrial progression and implantation failure (19 gene lists/signatures were included). The roles of these regulators, and of genes related to progesterone and estrogens, were then analysed in transcriptomic datasets compiled from samples collected throughout the menstrual cycle (n = 129), and the expression of selected TFs were prospectively validated in an independent cohort of healthy participants (n = 19).ResultsA total of 3,608 distinct genes from the 19 gene lists were associated with endometrial progression and implantation failure. The lists’ regulation was significantly favoured by TFs (89% (17/19) of gene lists) and progesterone (47% (8 /19) of gene lists), rather than miRNAs (5% (1/19) of gene lists) or estrogen (0% (0/19) of gene lists), respectively (FDR < 0.05). Exceptionally, two gene lists that were previously associated with implantation failure and unexplained infertility were less hormone-dependent, but primarily regulated by estrogen. Although endometrial progression genes were mainly targeted by hormones rather than non-hormonal contributors (odds ratio = 91.94, FDR < 0.05), we identified 311 TFs and 595 miRNAs not previously associated with ovarian hormones. We highlight CTCF, GATA6, hsa-miR-15a-5p, hsa-miR-218-5p, hsa-miR-107, hsa-miR-103a-3p, and hsa-miR-128-3p, as overlapping novel master regulators of endometrial function. The gene expression changes of selected regulators throughout the menstrual cycle (FDR < 0.05), dually validated in-silico and through endometrial biopsies, corroborated their potential regulatory roles in the endometrium.ConclusionsThis study revealed novel hormonal and non-hormonal regulators and their relative contributions to endometrial progression and pathology, providing new leads for the potential causes of endometrial-factor infertility.

P15.02.A EVALUATION OF [68GA]GA-PSMA-617 PET AS A DIAGNOSTIC AGENT IN RECURRENT GLIOBLASTOMA PATIENTS: RESULTS OF THE GENESIS GBM 001 PHASE I/II STUDY

Abstract BACKGROUND PSMA is a cell-surface protein that is highly expressed in the endothelium of the tumour neovasculature of several solid tumours, including glioblastoma (GBM), making it an excellent target for antibody-drug conjugates or peptide receptor radionuclide therapy. Several [68Ga]Ga-PSMA-based radioligands have shown utility in the diagnosis and treatment response assessment of primary GBM patients in a number of case studies. The small-molecule ligand [68Ga]Ga-PSMA-617 has the advantage of being suitable for radiolabelling with different radioisotopes, including 68Ga, 177Lu, 111In and 90Y, which raised the possibility of using it in a theranostic application in glioblastoma. The aim of this phase I/II prospective study was to evaluate the potential of [68Ga]Ga-PSMA-617 as a PET imaging biomarker and as a candidate ligand for targeted radionuclide therapy in patients with recurrent GBM. MATERIAL AND METHODS Ten patients with recurrent glioblastoma were recruited for the study between October 2018 and September 2021. Patients underwent [68Ga]Ga-PSMA-617 and [18F]F-FET PET brain scans done on two separate days. Two patients had full-body [68Ga]Ga-PSMA-617 scans. Tumour selectivity of [68Ga]Ga-PSMA-617 was assessed by measuring tumour volume on [68Ga]Ga-PSMA-617 PET scans and comparing it with tumour volume measured on [18F]F-FET PET scans, which are more widely used clinically. Tumour specificity of [68Ga]Ga-PSMA-617 was assessed by comparing its standard uptake value (SUV) and tumour-to-brain-ratio (TBR) with those of [18F]F-FET TBR, and by measuring [68Ga]Ga-PSMA-617 tumour-to-liver ratio (TLR). RESULTS PSMA-defined tumour volume was on average 1.87±1.10 times larger than the FET-defined tumour volume (p=0.084), and the two volumes had a DICE similarity coefficient of 0.578±0.175. Mean and maximum TBR was significantly (p=0.002) higher (~10x) for PSMA than for FET. Mean SUV in the tumour was significantly higher for FET than for PSMA (p=0.002), while maximum SUV was slightly higher for PSMA than for FET (p=0.08). For the two patients with full-body [68Ga]Ga-PSMA PET scan available, PSMA mean TLR was 0.91 and 0.68, and maximum TLR was 1.20 and 1.07, respectively. CONCLUSION [68Ga] Ga-PSMA PET delineates a larger tumour volumes compared to [18F]F-FET PET, suggesting there are areas of the tumour with emerging tumour neovasculature that are not yet metabolically active. The poor spatial overlap between PSMA and FET tumour volume suggests the complementary diagnostic role of the two tracers. The higher TBR of PSMA compared to FET demonstrates the higher tumour specificity of the former, and thus its potential ability as a diagnostic agent for recurrent tumour volume delineation. Relatively low values of PSMA SUV and TLR suggest that PSMA specific uptake is not sufficient to use this ligand for targeted radionuclide therapy in glioblastoma when conjugated to [177Lu].

Propensity score weighting methods for causal subgroup analysis with time-to-event outcomes.

Evaluating causal effects of an intervention in pre-specified subgroups is a standard goal in comparative effectiveness research. Despite recent advancements in causal subgroup analysis, research on time-to-event outcomes has been lacking. This article investigates the propensity score weighting method for causal subgroup survival analysis. We introduce two causal estimands, the subgroup marginal hazard ratio and subgroup restricted average causal effect, and provide corresponding propensity score weighting estimators. We analytically established that the bias of subgroup-restricted average causal effect is determined by subgroup covariate balance. Using extensive simulations, we compare the performance of various combinations of propensity score models (logistic regression, random forests, least absolute shrinkage and selection operator, and generalized boosted models) and weighting schemes (inverse probability weighting, and overlap weighting) for estimating the causal estimands. We find that the logistic model with subgroup-covariate interactions selected by least absolute shrinkage and selection operator consistently outperforms other propensity score models. Also, overlap weighting generally outperforms inverse probability weighting in terms of balance, bias and variance, and the advantage is particularly pronounced in small subgroups and/or in the presence of poor overlap. We applied the methods to the observational Comparing Options for Management: PAtient-centered REsults for Uterine Fibroids study to evaluate the causal effects of myomectomy versus hysterectomy on the time to disease recurrence in a number of pre-specified subgroups of patients with uterine fibroids.

Accurate and Efficient Multilevel Free Energy Simulations with Neural Network-Assisted Enhanced Sampling.

Free energy differences (ΔF) are essential to quantitative characterization and understanding of chemical and biological processes. Their direct estimation with an accurate quantum mechanical potential is of great interest and yet impractical due to high computational cost and incompatibility with typical alchemical free energy protocols. One promising solution is the multilevel free energy simulation in which the estimate of ΔF at an inexpensive low level of theory is combined with the correction toward a higher level of theory. The poor configurational overlap generally expected between the two levels of theory, however, presents a major challenge. We overcome this challenge by using a deep neural network model and enhanced sampling simulations. An adversarial autoencoder is used to identify a low-dimensional (latent) space that compactly represents the degrees of freedom that encode the distinct distributions at the two levels of theory. Enhanced sampling in this latent space is then used to drive the sampling of configurations that predominantly contribute to the free energy correction. Results for both gas phase and condensed phase systems demonstrate that this data-driven approach offers high accuracy and efficiency with great potential for scalability to complex systems.

Isoniazid resistance-conferring mutations are associated with highly variable phenotypic resistance

BackgroundHigh-dose isoniazid is recommended in the 9–12 months short-course regimen for multidrug-resistant tuberculosis with inhA mutation. However, there is insufficient evidence to support the assumption of genotypic-phenotypic concordance. This study aimed to identify the genetic mutations associated with high-level phenotypic isoniazid resistance. MethodsClinical isolates from patients with drug-resistant tuberculosis were profiled by whole-genome sequencing and subjected to minimum inhibitory concentration (MIC) testing using MGIT based-method. MICs were performed in concentration ranges based on the mutation present: isolates with no isoniazid resistance-conferring mutations and H37Rv, 0.016–0.256 µg/ml; inhA, 0.256–4.0 µg/ml, katG 1.0–16.0 µg/ml; and inhA + katG, 4.0–64.0 µg/ml. Isolates demonstrating resistance at the upper limit of the concentration range were tested up to the maximum of 64.0 µg/ml. Bootstrap of the mean MICs was performed to increase the robustness of the estimates and an overlap index was used to compare the distributions of the MICs for each mutation profile. ResultsA total of 52 clinical isolates were included in this analysis. Bootstrap MIC means for inhA, katG and inhA + katG were 33.64 (95% CI, 9.47, 56.90), 6.79 (4.45, 9.70) and 52.34 (42.750, 61.66) µg/ml, respectively. There was high overlap between inhA and inhA + katG mutations (eta = 0.45) but not with inhA and katG (eta = 0.19). Furthermore, katG showed poor overlap with inhA + katG mutations (eta = 0.09). Unexpectedly, 4/8 (50.0%) of all InhA mutants demonstrated high-level resistance, while 20/24 (83.3%) of katG mutants demonstrated moderate-level resistance. ConclusionsInhA mutations demonstrated unexpectedly high MICs and showed high overlap with inhA + katG. Contrary to the common belief that katG mutants are associated with high-level resistance, this mutation primarily showed moderate-level resistance.

Using Balancing Weights to Target the Treatment Effect on the Treated when Overlap is Poor.

Inverse probability weights are commonly used in epidemiology to estimate causal effects in observational studies. Researchers often focus on either the average treatment effect or the average treatment effect on the treated with inverse probability weighting estimators. However, poor overlap in the baseline covariates between the treated and control groups can produce extreme weights that can result in biased treatment effect estimates. One alternative to inverse probability weights are overlap weights, which target the population with the most overlap on observed covariates. Although estimates based on overlap weights produce less bias in such contexts, the causal estimand can be difficult to interpret. An alternative to model-based inverse probability weights are balancing weights, which directly target imbalances during the estimation process, rather than model fit. Here, we explore whether balancing weights allow analysts to target the average treatment effect on the treated in cases where inverse probability weights lead to biased estimates due to poor overlap. We conduct three simulation studies and an empirical application. We find that balancing weights often allow the analyst to still target the average treatment effect on the treated even when overlap is poor. We show that although overlap weights remain a key tool, more familiar estimands can sometimes be targeted by using balancing weights.

Quantitative Analysis of Isoform Switching in Cancer.

Over the past 8 years, multiple studies examined the phenomenon of isoform switching in human cancers and discovered that isoform switching is widespread, with hundreds to thousands of such events per cancer type. Although all of these studies used slightly different definitions of isoform switching, which in part led to a rather poor overlap of their results, they all leveraged transcript usage, a proportion of the transcript's expression in the total expression level of the parent gene, to detect isoform switching. However, how changes in transcript usage correlate with changes in transcript expression is not sufficiently explored. In this article, we adopt the most common definition of isoform switching and use a state-of-the-art tool for the analysis of differential transcript usage, SatuRn, to detect isoform switching events in 12 cancer types. We analyze the detected events in terms of changes in transcript usage and the relationship between transcript usage and transcript expression on a global scale. The results of our analysis suggest that the relationship between changes in transcript usage and changes in transcript expression is far from straightforward, and that such quantitative information can be effectively used for prioritizing isoform switching events for downstream analyses.

Characterizing the targets of transcription regulators by aggregating ChIP-seq and perturbation expression data sets.

Mapping the gene targets of chromatin-associated transcription regulators (TRs) is a major goal of genomics research. ChIP-seq of TRs and experiments that perturb a TR and measure the differential abundance of gene transcripts are a primary means by which direct relationships are tested on a genomic scale. It has been reported that there is a poor overlap in the evidence across gene regulation strategies, emphasizing the need for integrating results from multiple experiments. Although research consortia interested in gene regulation have produced a valuable trove of high-quality data, there is an even greater volume of TR-specific data throughout the literature. In this study, we show a workflow for the identification, uniform processing, and aggregation of ChIP-seq and TR perturbation experiments for the ultimate purpose of ranking human and mouse TR-target interactions. Focusing on an initial set of eight regulators (ASCL1, HES1, MECP2, MEF2C, NEUROD1, PAX6, RUNX1, and TCF4), we identified 497 experiments suitable for analysis. We used this corpus to examine data concordance, to identify systematic patterns of the two data types, and to identify putative orthologous interactions between human and mouse. We build upon commonly used strategies to forward a procedure for aggregating and combining these two genomic methodologies, assessing these rankings against independent literature-curated evidence. Beyond a framework extensible to other TRs, our work also provides empirically ranked TR-target listings, as well as transparent experiment-level gene summaries for community use.

Outcomes for an arboreal folivore after rehabilitation and implications for management

Wildlife rehabilitation is a critical part of animal welfare that contributes to species conservation. Despite the resources that go into rehabilitation, how animals fare after release from care is unknown. This is particularly true for cryptic arboreal species where specialist diets in care and low detectability in the wild present challenges for both care and post-release monitoring. We evaluated post-release outcomes for koalas and assessed if koalas were fed appropriately while in care. We monitored 36 koalas that had experienced one of three categories of medical intervention (none, minor, major) during rehabilitation. We examined the drivers of (i) koala survival and (ii) movements post-release, and (iii) evaluated variation between the species of browse fed in care versus browse selected by koalas in-situ. Overall, the post release survival rate of koalas was 58.5%, with only koalas that received medical intervention experiencing mortality. A critical threshold for mortality occurred at two weeks post-release and mortality was related to the measurable indicators of low body condition and poor climbing ability at time of release. In the month following their release, animals translocated furthest from their capture point moved the furthest. There was poor overlap between the tree species that koalas were fed in care and those they utilized post-release. We provide recommendations to address critical gaps in rehabilitation practices, as well as priorities for monitoring animals post-release to improve outcomes for arboreal folivores.

Solvent Dependence on Cooperative Vibrational Strong Coupling and Cavity Catalysis.

Strong light-matter coupling offers a unique way to control chemical reactions at the molecular level. Here, we compare the solvent effect on an ester solvolysis process under cooperative vibrational strong coupling (VSC). Three reactants, para-nitrophenylacetate, 3-methyl-para-nitrophenylbenzoate, and bis-(2, 4-dinitrophenyl) oxalate are chosen to study the effect of VSC on the solvolysis reaction rates. Two solvents, ethyl acetate and cyclopentanone, are also considered to compare the cavity catalysis by coupling the C=O stretching band of the reactant and the solvent molecules to a Fabry-Perot cavity mode. Interestingly, both solvents enhance the chemical reaction rate of para-nitrophenylacetate and 3-methyl-para-nitrophenylbenzoate under cooperative VSC conditions. However, the resonance effect is observed at different temperatures for different solvents, which is further confirmed by thermodynamic studies. Bis-(2, 4-dinitrophenyl) oxalate doesn't respond to VSC in either of the solvent systems due to poor overlap of reactant and solvent C=O vibrational bands. Cavity detuning and other control experiments suggest that cooperative VSC of the solvent plays a crucial role in modifying the activation free-energy of the reaction. These findings, along with other observations, cement the concept of polaritonic chemistry.

A cautionary note on the use of G-computation in population adjustment.

In a recent issue of the Journal; Remiro-Azócar et al. introduce a new method to adjust for population difference between two trials; when the individual patient data (IPD) are only accessible for one study. The proposed method generates the covariate data for the trial without IPD; then using a G-computation approach to transport information about the treatment effect from the other study with IPD to this trial. The authors advocate the use of G-computation over matching-adjusted indirect comparison because (i) the former allows for "useful extrapolation" when there is poor case-mix overlap between populations; and (ii) nonparametric; data-adaptive methods can be used to reduce the risk of (outcome) model misspecification. In this commentary; we provide a different perspective from these arguments. Despite certain disagreements; we believe that the proposed data generation approaches can open new and interesting research directions for population adjustment methodology in the future.

In vivo singlet state filtered nuclear magnetic resonance: towards monitoring toxic responses inside living organisms.

In line with recent paradigm shifts in toxicity testing, in vivo nuclear magnetic resonance (NMR) is a powerful tool for studying the biological impacts and perturbations caused by toxicants in living organisms. However, despite the excellent molecular insights that can be obtained through this technique, in vivo NMR applications are hampered by considerable experimental challenges such as poor line shape and spectral overlap. Here, we demonstrate the application of singlet-filtered NMR to target specific metabolites and facilitate the study of metabolite fluxes in living Daphnia magna, an aquatic keystone species and model organism. Informed by mathematical simulations and experiments on ex vivo organisms, singlet state NMR is used to monitor the flux of metabolites such as d-glucose and serine in living D. magna, during the environmentally relevant processes of anoxic stress and reduced food availability. Overall, singlet state NMR is shown to have significant future potential for studying metabolic processes in vivo.

The prevalence of sarcopenia and cachexia in older patients with localized colorectal cancer

IntroductionThe incidence of colorectal cancer (CRC) increases with age. In combination with an ageing population, the number of older patients undergoing surgical treatment for CRC is therefore expected to increase. Sarcopenia and cachexia are potentially modifiable risk factors of a negative surgical outcome. Sarcopenia can be categorized into primary (age-related) and secondary where diseases, such as malignancy, are influential factors. We aimed to investigate the prevalence of preoperative sarcopenia and cachexia in older (≥65 years) vulnerable patients with localized CRC. Materials and MethodsPatients included in the randomized study “Geriatric assessment and intervention in older vulnerable patients undergoing resection for colorectal cancer,” were screened for sarcopenia and cachexia prior to surgery. All patients in the present cohort were considered vulnerable with Geriatric 8 ≤ 14 points. Sarcopenia was defined according to European Guidelines (EWGSOP2), based on low muscle strength—low handgrip-strength and/or slow 5xChair-Stand-Test—and low appendicular lean mass assessed by dual-energy X-ray absorptiometry. Cachexia was defined as self-reported unintended weight loss >5% within three months or 2–5% with body mass index <20 kg/m2. ResultsSixty-four patients (mean age 79.6 years ±6.4 years, 36 women) were assessed. Of these, 28% (n = 18, 11 women) had low muscle strength and 13% (n = 8, 4 women) fulfilled the criteria for sarcopenia, however, 33% (n = 21, 13 women) had low muscle mass. There was no correlation between low muscle strength and low muscle mass (r = 0.16, P = 0.22). The prevalence of cachexia was 36% (n = 23, 16 women). Low muscle mass was associated with cachexia (φ = 0.38, P = 0.005), but there was no association between sarcopenia and cachexia (φ = 0.01, P = 1.0). DiscussionDespite the included patients who fulfilled the criteria for vulnerability according to G8, relatively few (28%) had low muscle strength. Moreover, there was poor overlap between the prevalence of sarcopenia according to the EWGSOP2 guidelines (13%) and prevalence of low muscle mass (33%) in older patients with CRC. Of note also, there was no association between sarcopenia and cachexia, but an association between cachexia and low muscle mass, which highlights the importance of assessing muscle mass in patients with cancer.Trial registration: The GEPOC trial has been prospectively registered at http://clinicaltrials.gov (NCT03719573).

MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization

Out of the thousands of metabolites in a given specimen, most metabolomics experiments measure only hundreds, with poor overlap across experimental platforms. Here, we describe Metabolite Imputation via Rank-Transformation and Harmonization (MIRTH), a method to impute unmeasured metabolite abundances by jointly modeling metabolite covariation across datasets which have heterogeneous coverage of metabolite features. MIRTH successfully recovers masked metabolite abundances both within single datasets and across multiple, independently-profiled datasets. MIRTH demonstrates that latent information about otherwise unmeasured metabolites is embedded within existing metabolomics data, and can be used to generate novel hypotheses and simplify existing metabolomic workflows.

Methodological considerations for the use of mutual information: Examining the role of context in collocation research

There is a vast literature on collocation, and mutual information scores are frequently used to justify the listing, targeting or indexing of collocations within studies. However, despite methodologically focused papers that discuss the limitations of mutual information scores and the importance of uncertainty estimates in corpus linguistic data, very few applied papers consider the implications of these observations for their analyses. This paper provides a methodologically focused, empirical case study of mutual information scores to examine the extent to which such concerns have implications for study design and the interpretation of results. The paper reports mutual information scores for 100 node words in general American English, and then compares the lists of strong collocates identified with those identified in four corpora representing narrower domains of American English. In so doing, it highlights the range of collocations observed and the relatively poor overlap between conventionally defined strong collocates in general English and those that are identified in narrow domains. The results are discussed in relation to their implications for research involving listing collocates, pedagogical targeting of collocates, and the use of collocation as a correlate of broader constructs such as proficiency. Suggestions are made for calculations that could be used in a test-retest approach to address the issues identified.

Comparison of lasing characteristics of GaN microdisks with different structures

The lasing characteristics of optically-pumped GaN microdisks of different configurations, including microdisks with undercuts, microdisks with cladding layers and thin-film microdisks are investigated in this paper. The microdisks, fabricated from a range of epitaxial structures containing blue-light emitting InGaN/GaN multi-quantum wells grown on Si, sapphire or GaN substrates, undergo different processes to form 8 µm diameter whispering-gallery mode microdisks with different degrees of optical confinement. The microdisks have lasing thresholds ranging from 2.1–8.3 mJ cm−2 and quality factors of 1400–4200. The lasing characteristics are correlated to the material qualities, optical confinement as well as the overlap of the mode with the multi-quantum wells in the microdisk structures. The undercut microdisks benefit from high optical confinement factors but poor overlap factor, while the thin film structures have high overlap factors but low confinement due to absorption by the metallic bonding layers. The findings provide useful insight on ways to optimize GaN microdisk for improving lasing performances.

Detection of features predictive of microRNA targets by integration of network data.

Gene activity is controlled by multiple molecular mechanisms, for instance through transcription factors or by microRNAs (miRNAs), among others. Established bioinformatics tools for the prediction of miRNA target genes face the challenge of ensuring accuracy, due to high false positive rates. Further, these tools present poor overlap. However, we demonstrated that it is possible to filter good predictions of miRNA targets from the bulk of all predictions by using information from the gene regulatory network. Here, we take advantage of this strategy that selects a large subset of predicted microRNA binding sites as more likely to possess less false-positives because of their over-representation in RE1 silencing transcription factor (REST)-regulated genes from the background of TargetScanHuman 7.2 predictions to identify useful features for the prediction of microRNA targets. These enriched miRNA families would have silencing activity for neural transcripts overlapping the repressive activity on neural genes of REST. We analyze properties of associated microRNA binding sites and contrast the outcome to the background. We found that the selected subset presents significant differences respect to the background: (i) lower GC-content in the vicinity of the predicted miRNA binding site, (ii) more target genes with multiple identical microRNA binding sites and (iii) a higher density of predicted microRNA binding sites close to the 3’ terminal end of the 3’-UTR. These results suggest that network selection of miRNA-mRNA pairs could provide useful features to improve microRNA target prediction.

Parametric G-computation for compatible indirect treatment comparisons with limited individual patient data.

Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are increasingly used to compare marginal treatment effects when there are cross-trial differences in effect modifiers and limited patient-level data. MAIC is based on propensity score weighting, which is sensitive to poor covariate overlap and cannot extrapolate beyond the observed covariate space. Current outcome regression-based alternatives can extrapolate but target a conditional treatment effect that is incompatible in the indirect comparison. When adjusting for covariates, one must integrate or average the conditional estimate over the relevant population to recover a compatible marginal treatment effect. We propose a marginalization method based on parametric G-computation that can be easily applied where the outcome regression is a generalized linear model or a Cox model. The approach views the covariate adjustment regression as a nuisance model and separates its estimation from the evaluation of the marginal treatment effect of interest. The method can accommodate a Bayesian statistical framework, which naturally integrates the analysis into a probabilistic framework. A simulation study provides proof-of-principle and benchmarks the method's performance against MAIC and the conventional outcome regression. Parametric G-computation achieves more precise and more accurate estimates than MAIC, particularly when covariate overlap is poor, and yields unbiased marginal treatment effect estimates under no failures of assumptions. Furthermore, the marginalized regression-adjusted estimates provide greater precision and accuracy than the conditional estimates produced by the conventional outcome regression, which are systematically biased because the measure of effect is non-collapsible.

Polycyclic Aromatic Hydrocarbons (PAHs) in Interstellar Ices: A Computational Study into How the Ice Matrix Influences the Ionic State of PAH Photoproducts.

It has been experimentally observed that water–ice-embedded polycyclic aromatic hydrocarbons (PAHs) form radical cations when exposed to vacuum UV irradiation, whereas ammonia-embedded PAHs lead to the formation of radical anions. In this study, we explain this phenomenon by investigating the fundamental electronic differences between water and ammonia, the implications of these differences on the PAH–water and PAH–ammonia interaction, and the possible ionization pathways in these complexes using density functional theory (DFT) computations. In the framework of the Kohn–Sham molecular orbital (MO) theory, we show that the ionic state of the PAH photoproducts results from the degree of occupied–occupied MO mixing between the PAHs and the matrix molecules. When interacting with the PAH, the lone pair-type highest occupied molecular orbital (HOMO) of water has poor orbital overlap and is too low in energy to mix with the filled π-orbitals of the PAH. As the lone-pair HOMO of ammonia is significantly higher in energy and has better overlap with filled π-orbitals of the PAH, the subsequent Pauli repulsion leads to mixed MOs with both PAH and ammonia character. By time-dependent DFT calculations, we demonstrate that the formation of mixed PAH–ammonia MOs opens alternative charge-transfer excitation pathways as now electronic density from ammonia can be transferred to unoccupied PAH levels, yielding anionic PAHs. As this pathway is much less available for water-embedded PAHs, charge transfer mainly occurs from localized PAH MOs to mixed PAH–water virtual levels, leading to cationic PAHs.

Reproducibility of repeated breathhold and impact of breathhold failure in whole breast and regional nodal irradiation in prone crawl position

In whole breast and regional nodal irradiation (WB + RNI), breathhold increases organ at risk (OAR) sparing. WB + RNI is usually performed in supine position, because positioning materials obstruct beam paths in prone position. Recent advancements allow prone WB + RNI (pWB + RNI) with increased sparing of OARs compared to supine WB + RNI. We evaluate positional and dosimetrical impact of repeated breathhold (RBH) and failure to breathhold (FTBH) in pWB + RNI. Twenty left-sided breast cancer patients were scanned twice in breathhold (baseline and RBH) and once free breathing (i.e. FTBH). Positional impact was evaluated using overlap index (OI) and Dice similarity coefficient (DSC). Dosimetrical impact was assessed by beam transposition from the baseline plan. Mean OI and DSC ranges were 0.01–0.98 and 0.01–0.92 for FTBH, and 0.73–1 and 0.69–1 for RBH. Dosimetric impact of RBH was negligible. FTBH significantly decreased minimal dose to CTV WBI, level II and the internal mammary nodes, with adequate mean doses. FTBH significantly increased heart, LAD, left lung and esophagus dose. OI and DSC for RBH and FTBH show reproducible large ROI positions. Small ROIs show poor overlap. FTBH maintained adequate target coverage but increased heart, LAD, ipsilateral lung and esophagus dose. RBH is a robust technique in pWB + RNI. (Clinicaltrials.gov: NCT05179161, registered 05/01/2022).