Significant impact of forcing uncertainty in a large ensemble of climate model simulations.

Forcing due to solar and volcanic variability, on the natural side, and greenhouse gas and aerosol emissions, on the anthropogenic side, are the main inputs to climate models. Reliable climate model simulations of past and future climate change depend crucially upon them. Here we analyze large ensembles of simulations using a comprehensive Earth System Model to quantify uncertainties in global climate change attributable to differences in prescribed forcings. The different forcings considered here are those used in the two most recent phases of the Coupled Model Intercomparison Project (CMIP), namely CMIP5 and CMIP6. We show significant differences in simulated global surface air temperature due to volcanic aerosol forcing in the second half of the 19th century and in the early 21st century. The latter arise from small-to-moderate eruptions incorporated in CMIP6 simulations but not in CMIP5 simulations. We also find significant differences in global surface air temperature and Arctic sea ice area due to anthropogenic aerosol forcing in the second half of the 20th century and early 21st century. These differences are as large as those obtained in different versions of an Earth System Model employing identical forcings. In simulations from 2015 to 2100, we find significant differences in the rates of projected global warming arising from CMIP5 and CMIP6 concentration pathways that differ slightly but are equivalent in terms of their nominal radiative forcing levels in 2100. Our results highlight the influence of assumptions about natural and anthropogenic aerosol loadings on carbon budgets, the likelihood of meeting Paris targets, and the equivalence of future forcing scenarios.

Virtually delivered guided self-help for binge eating disorder and bulimia nervosa: findings from a service evaluation.

BACKGROUND: Timely intervention is beneficial to the effectiveness of eating disorder (ED) treatment, but limited capacity within ED services means that these disorders are often not treated with sufficient speed. This service evaluation extends previous research into guided self-help (GSH) for adults with bulimic spectrum EDs by assessing the feasibility, acceptability, and preliminary effectiveness of virtually delivered GSH using videoconferencing. METHOD: Patients with bulimia nervosa (BN), binge eating disorder (BED) and other specified feeding and eating disorders (OSFED) waiting for treatment in a large specialist adult ED out-patient service were offered virtually delivered GSH. The programme used an evidence-based cognitive behavioural self-help book. Individuals were supported by non-expert coaches, who delivered the eight-session programme via videoconferencing. RESULTS: One hundred and thirty patients were allocated to a GSH coach between 1 September 2020 and 30 September 2022; 106 (82%) started treatment and 78 (60%) completed treatment. Amongst completers, there were large reductions in ED behaviours and attitudinal symptoms, measured by the ED-15. The largest effect sizes for change between pre- and post-treatment were seen for binge eating episode frequency (d = -0.89) and concerns around eating (d = -1.72). Patients from minoritised ethnic groups were over-represented in the non-completer group. CONCLUSIONS: Virtually delivered GSH is feasible, acceptable and effective in reducing ED symptoms amongst those with bulimic spectrum disorders. Implementing virtually delivered GSH reduced waiting times, offering a potential solution for long waiting times for ED treatment. Further research is needed to compare GSH to other brief therapies and investigate barriers for patients from culturally diverse groups.

Augmented Reality for Enhanced Visualization of MOF Adsorbents.

Augmented reality (AR) is an emerging technique used to improve visualization and comprehension of complex 3D materials. This approach has been applied not only in the field of chemistry but also in real estate, physics, mechanical engineering, and many other areas. Here, we demonstrate the workflow for an app-free AR technique for visualization of metal-organic frameworks (MOFs) and other porous materials to investigate their crystal structures, topology, and gas adsorption sites. We think this workflow will serve as an additional tool for computational and experimental scientists working in the field for both research and educational purposes.

Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard.

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs. TRIAL REGISTRATION. Clinicaltrials.gov NCT02173275.

Fragment Hotspot Mapping to Identify Selectivity-Determining Regions between Related Proteins.

Selectivity is a crucial property in small molecule development. Binding site comparisons within a protein family are a key piece of information when aiming to modulate the selectivity profile of a compound. Binding site differences can be exploited to confer selectivity for a specific target, while shared areas can provide insights into polypharmacology. As the quantity of structural data grows, automated methods are needed to process, summarize, and present these data to users. We present a computational method that provides quantitative and data-driven summaries of the available binding site information from an ensemble of structures of the same protein. The resulting ensemble maps identify the key interactions important for ligand binding in the ensemble. The comparison of ensemble maps of related proteins enables the identification of selectivity-determining regions within a protein family. We applied the method to three examples from the well-researched human bromodomain and kinase families, demonstrating that the method is able to identify selectivity-determining regions that have been used to introduce selectivity in past drug discovery campaigns. We then illustrate how the resulting maps can be used to automate comparisons across a target protein family.

A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.

We release a new, high quality data set of 1162 PDE10A inhibitors with experimentally determined binding affinities together with 77 PDE10A X-ray co-crystal structures from a Roche legacy project. This data set is used to compare the performance of different 2D- and 3D-machine learning (ML) as well as empirical scoring functions for predicting binding affinities with high throughput. We simulate use cases that are relevant in the lead optimization phase of early drug discovery. ML methods perform well at interpolation, but poorly in extrapolation scenarios-which are most relevant to a real-world application. Moreover, we find that investing into the docking workflow for binding pose generation using multi-template docking is rewarded with an improved scoring performance. A combination of 2D-ML and 3D scoring using a modified piecewise linear potential shows best overall performance, combining information on the protein environment with learning from existing SAR data.

Development of Novel Immunoprophylactic Agents against Multidrug-Resistant Gram-Negative Bacterial Infections.

The widespread emergence of antibiotic resistance, including multidrug resistance in Gram-negative (G-) bacterial pathogens, poses a critical challenge to the current antimicrobial armamentarium. Antibody-drug conjugates (ADCs), primarily used in anticancer therapy, offer a promising treatment alternative due to their ability to deliver a therapeutic molecule while simultaneously activating the host immune response. The Cloudbreak platform is being used to develop ADCs to treat infectious diseases, composed of a therapeutic targeting moiety (TM) attached via a noncleavable linker to an effector moiety (EM) to treat infectious diseases. In this proof-of-concept study, 21 novel dimeric peptidic molecules (TMs) were evaluated for activity against a screening panel of G- pathogens. The activities of the TMs were not impacted by existing drug resistance. Potent TMs were conjugated to the Fc fragment of human IgG1 (EM), resulting in 4 novel ADCs. These ADCs were evaluated for immunoprophylactic efficacy in a neutropenic mouse model of deep thigh infection. In colistin-sensitive infections, 3 of the 4 ADCs offered protection similar to that of therapeutically dosed colistin, while CTC-171 offered enhanced protection. The efficacy of these ADCs was unchanged in colistin-resistant infections. Together, these results indicate that the ADCs used here are capable of potent binding to G- pathogens regardless of lipopolysaccharide (LPS) modifications that otherwise lead to antibiotic resistance and support further exploration of ADCs in the treatment of infections caused by drug-resistant G- bacteria.

Use of molecular docking computational tools in drug discovery.

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Validation of a Field-Based Ligand Screener Using a Novel Benchmarking Data Set for Assessing 3D-Based Virtual Screening Methods.

Ligand-based methods play a crucial role in virtual screening when the 3D structure of the target is not available. This study discusses the results of a validation study of the CSD field-based ligand screener using a novel benchmarking data set containing 56 targets. The data set was created starting from the target UniProt IDs in a previously published data set (i.e., the AZ data set), by mining ChEMBL to find known active molecules for these targets and by using DUD-E to generate property-matched decoys of the identified actives. Several experiments were performed to assess the virtual screening performance of the new method. One of its strengths is that it can use an overlay of multiple flexible ligands as a query without the need to run several parallel calculations with one ligand at a time. Here, we discuss how changes to different parameter settings or adoption of different query models can influence the final performance compared to the performance when using the experimentally observed overlay of ligands. We have also generated the enrichment scores based on three external benchmark data sets to enable the comparison with existing methods previously validated using these data sets. Here, we present results for the standard DUD-E data set, the DUD-E+ data set, as well as the DUD_Lib_VS_1.0 data set which was designed for ligand-based virtual screening validation and hence is more suitable for this type of methods.

An Observational Constraint on Aviation-Induced Cirrus From the COVID-19-Induced Flight Disruption.

Global aviation dropped precipitously during the covid-19 pandemic, providing an unprecedented opportunity to study aviation-induced cirrus (AIC). AIC is believed to be responsible for over half of aviation-related radiative forcing, but until now, its radiative impact has only been estimated from simulations. Here, we show that satellite observations of cirrus cloud do not exhibit a detectable global response to the dramatic aviation reductions of spring 2020. These results indicate that previous model-based estimates may overestimate AIC. In addition, we find no significant response of diurnal surface air temperature range to the 2020 aviation changes, reinforcing the findings of previous studies. Though aviation influences the climate through multiple pathways, our analysis suggests that its warming effect from cirrus changes may be smaller than previously estimated.

The Climate Response to Emissions Reductions Due to COVID-19: Initial Results From CovidMIP.

Many nations responded to the corona virus disease-2019 (COVID-19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial-condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near-surface temperature or rainfall during 2020-2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID-19-related emission reductions on near-term climate.

High-resolution µCT of a mouse embryo using a compact laser-driven X-ray betatron source.

In the field of X-ray microcomputed tomography (µCT) there is a growing need to reduce acquisition times at high spatial resolution (approximate micrometers) to facilitate in vivo and high-throughput operations. The state of the art represented by synchrotron light sources is not practical for certain applications, and therefore the development of high-brightness laboratory-scale sources is crucial. We present here imaging of a fixed embryonic mouse sample using a compact laser-plasma-based X-ray light source and compare the results to images obtained using a commercial X-ray µCT scanner. The radiation is generated by the betatron motion of electrons inside a dilute and transient plasma, which circumvents the flux limitations imposed by the solid or liquid anodes used in conventional electron-impact X-ray tubes. This X-ray source is pulsed (duration <30 fs), bright (>1010 photons per pulse), small (diameter <1 µm), and has a critical energy >15 keV. Stable X-ray performance enabled tomographic imaging of equivalent quality to that of the µCT scanner, an important confirmation of the suitability of the laser-driven source for applications. The X-ray flux achievable with this approach scales with the laser repetition rate without compromising the source size, which will allow the recording of high-resolution µCT scans in minutes.

Identification of Noncompetitive Protein-Ligand Interactions for Structural Optimization.

For efficient structure-guided drug design, it is important to have an excellent understanding of the quality of interactions between the target receptor and bound ligands. Identification and characterization of poor intermolecular contacts offers the possibility to focus design efforts directly on ligand regions with suboptimal molecular recognition. To enable a more straightforward identification of these in a structural model, we use a suitably enhanced version of our previously introduced statistical ratio of frequencies (RF) approach. This allows us to highlight protein-ligand interactions and geometries that occur much less often in the Protein Data Bank than would be expected from the exposed surface areas of the interacting atoms. We provide a comprehensive overview of such noncompetitive interactions and geometries for a set of common ligand substituents. Through retrospective case studies on congeneric series and single-point mutations for several pharmaceutical targets, we illustrate how knowledge of noncompetitive interactions could be exploited in the drug design process.

Hotspots API: A Python Package for the Detection of Small Molecule Binding Hotspots and Application to Structure-Based Drug Design.

Methods that survey protein surfaces for binding hotspots can help to evaluate target tractability and guide exploration of potential ligand binding regions. Fragment Hotspot Maps builds upon interaction data mined from the CSD (Cambridge Structural Database) and exploits the idea of identifying hotspots using small chemical fragments, which is now widely used to design new drug leads. Prior to this publication, Fragment Hotspot Maps was only publicly available through a web application. To increase the accessibility of this algorithm we present the Hotspots API (application programming interface), a toolkit that offers programmatic access to the core Fragment Hotspot Maps algorithm, thereby facilitating the interpretation and application of the analysis. To demonstrate the package's utility, we present a workflow which automatically derives protein hydrogen-bond constraints for molecular docking with GOLD. The Hotspots API is available from https://github.com/prcurran/hotspots under the MIT license and is dependent upon the commercial CSD Python API.

Use of the Gore Viabahn VBX balloon-expandable endoprosthesis in the congenital heart disease population.

The Gore Viabahn VBX balloon-expandable endoprosthesis (W. L. Gore & Associates, Flagstaff, AZ) is a flexible covered stent that is FDA-approved for the treatment of iliac artery stenosis, including lesions at the aortic bifurcation. In this case series, we report the first use of the VBX covered stent in congenital heart disease, highlighting several of its unique advantages.

A Rare Case of an Intracardiac Myoepithelial Carcinoma in an Infant.

Myoepithelial carcinomas (MC) represent aggressive tumors that occur in a myriad of ages and anatomic locations. The rarity and histologic similarity with other tumors make them difficult to diagnosis. We report an extremely rare case of a right ventricular outflow tract mass identified to be an intracardiac MC in a 4-month-old male infant. Pathology revealed an EWS-KLF15 translocation. Treatment included gross total resection and intensive chemotherapy. Recurrent cardiac mass with brain metastasis was seen 16 months after primary diagnosis. We describe the rarity of intracardiac MC in pediatric patients and the challenges encountered in the multimodal management of this patient.

Coiled-coil destabilizing residues in the group A Streptococcus M1 protein are required for functional interaction.

The sequences of M proteins, the major surface-associated virulence factors of the widespread bacterial pathogen group A Streptococcus, are antigenically variable but have in common a strong propensity to form coiled coils. Paradoxically, these sequences are also replete with coiled-coil destabilizing residues. These features are evident in the irregular coiled-coil structure and thermal instability of M proteins. We present an explanation for this paradox through studies of the B repeats of the medically important M1 protein. The B repeats are required for interaction of M1 with fibrinogen (Fg) and consequent proinflammatory activation. The B repeats sample multiple conformations, including intrinsically disordered, dissociated, as well as two alternate coiled-coil conformations: a Fg-nonbinding register 1 and a Fg-binding register 2. Stabilization of M1 in the Fg-nonbinding register 1 resulted in attenuation of Fg binding as expected, but counterintuitively, so did stabilization in the Fg-binding register 2. Strikingly, these register-stabilized M1 proteins gained the ability to bind Fg when they were destabilized by a chaotrope. These results indicate that M1 stability is antithetical to Fg interaction and that M1 conformational dynamics, as specified by destabilizing residues, are essential for interaction. A "capture-and-collapse" model of association accounts for these observations, in which M1 captures Fg through a dynamic conformation and then collapses into a register 2-coiled coil as a result of stabilization provided by binding energy. Our results support the general conclusion that destabilizing residues are evolutionarily conserved in M proteins to enable functional interactions necessary for pathogenesis.

Outcomes of anatomical versus functional testing for coronary artery disease.

BACKGROUND: Many patients have symptoms suggestive of coronary artery disease (CAD) and are often evaluated with the use of diagnostic testing, although there are limited data from randomized trials to guide care. METHODS: We randomly assigned 10,003 symptomatic patients to a strategy of initial anatomical testing with the use of coronary computed tomographic angiography (CTA) or to functional testing (exercise electrocardiography, nuclear stress testing, or stress echocardiography). The composite primary end point was death, myocardial infarction, hospitalization for unstable angina, or major procedural complication. Secondary end points included invasive cardiac catheterization that did not show obstructive CAD and radiation exposure. RESULTS: The mean age of the patients was 60.8±8.3 years, 52.7% were women, and 87.7% had chest pain or dyspnea on exertion. The mean pretest likelihood of obstructive CAD was 53.3±21.4%. Over a median follow-up period of 25 months, a primary end-point event occurred in 164 of 4996 patients in the CTA group (3.3%) and in 151 of 5007 (3.0%) in the functional-testing group (adjusted hazard ratio, 1.04; 95% confidence interval, 0.83 to 1.29; P=0.75). CTA was associated with fewer catheterizations showing no obstructive CAD than was functional testing (3.4% vs. 4.3%, P=0.02), although more patients in the CTA group underwent catheterization within 90 days after randomization (12.2% vs. 8.1%). The median cumulative radiation exposure per patient was lower in the CTA group than in the functional-testing group (10.0 mSv vs. 11.3 mSv), but 32.6% of the patients in the functional-testing group had no exposure, so the overall exposure was higher in the CTA group (mean, 12.0 mSv vs. 10.1 mSv; P<0.001). CONCLUSIONS: In symptomatic patients with suspected CAD who required noninvasive testing, a strategy of initial CTA, as compared with functional testing, did not improve clinical outcomes over a median follow-up of 2 years. (Funded by the National Heart, Lung, and Blood Institute; PROMISE ClinicalTrials.gov number, NCT01174550.).

Steatorrhea and Hyperoxaluria in Severely Obese Patients Before and After Roux-en-Y Gastric Bypass.

BACKGROUND AND AIMS: Hyperoxaluria after Roux-en-Y gastric bypass (RYGB) is generally attributed to fat malabsorption. If hyperoxaluria is indeed caused by fat malabsorption, magnitudes of hyperoxaluria and steatorrhea should correlate. Severely obese patients, prior to bypass, ingest excess dietary fat that can produce hyperphagic steatorrhea. The primary objective of the study was to determine whether urine oxalate excretion correlates with elements of fat balance in severely obese patients before and after RYGB. METHODS: Fat balance and urine oxalate excretion were measured simultaneously in 26 severely obese patients before and 1 year after RYGB, while patients consumed their usual diet. At these time points, stool and urine samples were collected. Steatorrhea and hyperoxaluria were defined as fecal fat >7 g/day and urine oxalate >40 mg/day. Differences were evaluated using paired 2-tailed t tests. RESULTS: Prior to RYGB, 12 of 26 patients had mild to moderate steatorrhea. Average urine oxalate excretion was 61 mg/day; there was no correlation between fecal fat and urine oxalate excretion. After RYGB, 24 of 26 patients had steatorrhea and urine oxalate excretion averaged 69 mg/day, with a positive correlation between fecal fat and urine oxalate excretions (r = 0.71, P < .001). For each 10 g/day increase in fecal fat output, fecal water excretion increased only 46 mL/day. CONCLUSIONS: Steatorrhea and hyperoxaluria were common in obese patients before bypass, but hyperoxaluria was not caused by excess unabsorbed fatty acids. Hyperphagia, obesity, or metabolic syndrome could have produced this previously unrecognized hyperoxaluric state by stimulating absorption or endogenous synthesis of oxalate. Hyperoxaluria after RYGB correlated with steatorrhea and was presumably caused by excess fatty acids in the intestinal lumen. Because post-bypass steatorrhea caused little increase in fecal water excretion, most patients with steatorrhea did not consider themselves to have diarrhea. Before and after RYGB, high oxalate intake contributed to the severity of hyperoxaluria.

Development of the Autosomal Dominant Polycystic Kidney Disease Impact Scale: A New Health-Related Quality-of-Life Instrument.

BACKGROUND: The impact of autosomal dominant polycystic kidney disease (ADPKD) on health-related quality of life (HRQoL) is not well understood due to a lack of instruments specific to the condition. STUDY DESIGN: Content for a new self-administered patient-reported outcome (PRO) questionnaire to assess ADPKD-related HRQoL was developed through clinical expert and patient focus group discussions. The new PRO instrument was administered to study patients with ADPKD to evaluate its reliability and validity. SETTING & PARTICIPANTS: 1,674 adult patients with ADPKD participated in this research: 285 patients in focus groups to generate questionnaire content, 15 patients in debriefing interviews to refine the PRO questionnaire, and 1,374 patients to assess the performance and measurement properties of the PRO questionnaire. OUTCOME: A new PRO questionnaire. RESULTS: The ADPKD Impact Scale (ADPKD-IS), consisting of 14 items representing 3 conceptual domains (physical, emotional, and fatigue) plus 4 additional questions, was developed. The instrument's reliability (regarding internal consistency and test-retest consistency) and validity (content and construct) were supported. LIMITATIONS: Need for more responsiveness testing when more data from clinical use become available over time. Complex concepts such as ADPKD-related pain and impact on a patient's HRQoL need further evaluation. CONCLUSIONS: The ADPKD-IS is a new patient-centric tool that reliably and validly provides a standardized method for assessing HRQoL and overall disease burden in patients with ADPKD.