Efficacy of Alum-Adjuvanted Peptide and Carbohydrate Conjugate Vaccine Candidates against Group A Streptococcus Pharyngeal Infection in a Non-Human Primate Model.

Vaccine development against group A Streptococcus (GAS) has gained traction in the last decade, fuelled by recognition of the significant worldwide burden of the disease. Several vaccine candidates are currently being evaluated in preclinical and early clinical studies. Here, we investigate two conjugate vaccine candidates that have shown promise in mouse models of infection. Two antigens, the J8 peptide from the conserved C-terminal end of the M protein, and the group A carbohydrate lacking N-acetylglucosamine side chain (ΔGAC) were each conjugated to arginine deiminase (ADI), an anchorless surface protein from GAS. Both conjugate vaccine candidates combined with alum adjuvant were tested in a non-human primate (NHP) model of pharyngeal infection. High antibody titres were detected against J8 and ADI antigens, while high background antibody titres in NHP sera hindered accurate quantification of ΔGAC-specific antibodies. The severity of pharyngitis and tonsillitis signs, as well as the level of GAS colonisation, showed no significant differences in NHPs immunised with either conjugate vaccine candidate compared to NHPs in the negative control group.

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.

Impact of Atherosclerosis Imaging-Quantitative Computed Tomography on Diagnostic Certainty, Downstream Testing, Coronary Revascularization and Medical Therapy: The CERTAIN Study.

AIMS: The incremental impact of Atherosclerosis Imaging-Quantitative Computed Tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of the present study was to compare the clinical utility of routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. METHODS AND RESULTS: In this multicenter crossover study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnosis and plans for downstream non-invasive testing, coronary intervention and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years, 433 (57.7%) were male. Compared to conventional site CCTA evaluation, AI-QCT analysis improved physician's confidence 2-5-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; p < 0.001), including for such measures as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; p < 0.001) and plaque burden (197; 26.3%; p < 0.001). After AI-QCT including ischemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (p < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (p < 0.001) and 23.0% (p < 0.001) of patients, respectively. CONCLUSIONS: Use of AI-QCT improves diagnostic certainty, and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy.

Percutaneous Stenting of a Stenotic Berlin Heart Outflow Cannula Graft in a 2 Year Old Child.

Left ventricular assist device (LVAD) outflow obstruction is a rare complication of long-term LVAD support. We present the first case of successful percutaneous stent implantation in a pediatric patient with LVAD outflow obstruction.

Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations.

Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conformers by AtNN prediction. AtNN ranking was compared with bioactivity-unaware baselines such as ascending Sage force field energy ranking, and a slower bioactivity-based baseline ranking by ascending Torsion Fingerprint Deviation to the Maximum Common Substructure to the most similar molecule in the training set (TFD2SimRefMCS). On test sets from random ligand splits of PDBbind, ranking conformers using ComENet, the AtNN encoding the most 3D information, leads to early enrichment of bioactive-like conformations with a median BEDROC of 0.29 ± 0.02, outperforming the best bioactivity-unaware Sage energy ranking baseline (median BEDROC of 0.18 ± 0.02), and performing on a par with the bioactivity-based TFD2SimRefMCS baseline (median BEDROC of 0.31 ± 0.02). The improved performance of the AtNN and TFD2SimRefMCS baseline is mostly observed on test set ligands that bind proteins similar to proteins observed in the training set. On a more challenging subset of flexible molecules, the bioactivity-unaware baselines showed median BEDROCs up to 0.02, while AtNNs and TFD2SimRefMCS showed median BEDROCs between 0.09 and 0.13. When performing rigid ligand re-docking of PDBbind ligands with GOLD using the 1% top-ranked conformers, ComENet ranked conformers showed a higher successful docking rate than bioactivity-unaware baselines, with a rate of 0.48 ± 0.02 compared to CSD probability baseline with a rate of 0.39 ± 0.02. Similarly, on a pharmacophore searching experiment, selecting the 20% top-ranked conformers ranked by ComENet showed higher hit rate compared to baselines. Hence, the approach presented here uses AtNNs successfully to focus conformer ensembles towards bioactive-like conformations, representing an opportunity to reduce computational expense in virtual screening applications on known targets that require input conformations.

Open science discovery of potent noncovalent SARS-CoV-2 main protease inhibitors.

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property-free knowledge base for future anticoronavirus drug discovery.

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.

Relation of Gender to Atherosclerotic Plaque Characteristics by Differing Angiographic Stenosis Severity.

It is unknown whether gender influences the atherosclerotic plaque characteristics (APCs) of lesions of varying angiographic stenosis severity. This study evaluated the imaging data of 303 symptomatic patients from the derivation arm of the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial, all of whom underwent coronary computed tomographic angiography and clinically indicated nonemergent invasive coronary angiography upon study enrollment. Index tests were interpreted by 2 blinded core laboratories, one of which performed quantitative coronary computed tomographic angiography using an artificial intelligence application to characterize and quantify APCs, including percent atheroma volume (PAV), low-density noncalcified plaque (LD-NCP), noncalcified plaque (NCP), calcified plaque (CP), lesion length, positive arterial remodeling, and high-risk plaque (a combination of LD-NCP and positive remodeling ≥1.10); the other classified lesions as obstructive (≥50% diameter stenosis) or nonobstructive (<50% diameter stenosis) based on quantitative invasive coronary angiography. The relation between APCs and angiographic stenosis was further examined by gender. The mean age of the study cohort was 64.4 ± 10.2 years (29.0% female). In patients with obstructive disease, men had more LD-NCP PAV (0.5 ± 0.4 vs 0.3 ± 0.8, p = 0.03) and women had more CP PAV (11.7 ± 1.6 vs 8.0 ± 0.8, p = 0.04). Obstructive lesions had more NCP PAV compared with their nonobstructive lesions in both genders, however, obstructive lesions in women also demonstrated greater LD-NCP PAV (0.4 ± 0.5 vs 1.0 ± 1.8, p = 0.03), and CP PAV (17.4 ± 16.5 vs 25.9 ± 18.7, p = 0.03) than nonobstructive lesions. Comparing the composition of obstructive lesions by gender, women had more CP PAV (26.3 ± 3.4 vs 15.8 ± 1.5, p = 0.005) whereas men had more NCP PAV (33.0 ± 1.6 vs 26.7 ± 2.5, p = 0.04). Men had more LD-NCP PAV in nonobstructive lesions compared with women (1.2 ± 0.2 vs 0.6 ± 0.2, p = 0.02). In conclusion, there are gender-specific differences in plaque composition based on stenosis severity.

DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining.

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

Deep generative models for 3D molecular structure.

Deep generative models have gained recent popularity for chemical design. Many of these models have historically operated in 2D space; however, more recently explicit 3D molecular generative models have become of interest, which are the topic of this article. Dozens of published models have been developed in the last few years to generate molecules directly in 3D, outputting both the atom types and coordinates, either in one-shot or adding atoms or fragments step-by-step. These 3D generative models can also be guided by structural information such as a binding pocket representation to successfully generate molecules with docking score ranges similar to known actives, but still showing lower computational efficiency and generation throughput than 1D/2D generative models and sometimes producing unrealistic conformations. We advocate for a unified benchmark of metrics to evaluate generation and propose perspectives to be addressed in next implementations.

Development of a Patient-Reported Outcomes Tool to Assess Pain and Discomfort in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Pain has been identified as a core outcome for individuals with autosomal dominant polycystic kidney disease (ADPKD), but no disease-specific pain assessment has been developed using current development methodology for patient-reported outcomes (PRO) instruments. We developed and validated an ADPKD-specific pain questionnaire: the ADPKD Pain and Discomfort Scale (ADPKD-PDS). METHODS: Conceptual underpinnings were drawn from literature review, concept elicitation, expert consultation, and measurement performance. In the qualitative analysis phase, concepts were elicited from focus groups of adults with ADPKD, and the resulting draft instrument was refined using cognitive debriefing interviews with individuals with ADPKD. For quantitative analysis, adults with ADPKD completed the draft instrument and other PRO tools in an online survey, and a follow-up survey was conducted 3-4 weeks later. Survey responses were analyzed for item-level descriptive statistics, latent model fit statistics, item discrimination, item- and domain-level psychometric statistics, test-retest reliability, responsiveness to change, and convergent validity. RESULTS: In the qualitative phase, 46 focus groups were conducted in 18 countries with 293 participants. Focus groups described three conceptually distinct types of ADPKD-related pain and discomfort (dull kidney pain, sharp kidney pain, and fullness/discomfort). In the quantitative phase, 298 adults with ADPKD completed the online survey, and 108 participants completed the follow-up survey. After iterative refinement of the instrument, latent variable measurement models showed very good fit (comparative fit and nonnormed fit indices both 0.99), as did item- and domain-level psychometric characteristics. The final ADPKD-PDS contains 20 items assessing pain severity and interference with activities over a 7-day recall period. CONCLUSIONS: The ADPKD-PDS is the first validated tool for systematically assessing pain and discomfort in ADPKD.

Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial.

AIMS: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). METHODS: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. RESULTS: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. CONCLUSIONS: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.

Caregiver Burden of Autosomal Dominant Polycystic Kidney Disease: A Qualitative Study.

Rationale & Objective: There is limited published research on how autosomal dominant polycystic kidney disease (ADPKD) impacts caregivers. This study explored how caregivers of individuals with ADPKD perceive the burdens placed on them by the disease. Study Design: Qualitative study consisting of focus groups and interviews. Discussions were conducted by trained interviewers using semi-structured interview guides. Setting & Participants: The research was conducted in 14 countries in North America, South America, Asia, Australia, and Europe. Eligible participants were greater than or equal to 18 years old and caring for a child or adult diagnosed with ADPKD. Analytical Approach: The concepts reported were coded using qualitative research software. Data saturation was reached when subsequent discussions introduced no new key concepts. Results: Focus groups and interviews were held with 139 participants (mean age, 44.9 years; 66.9% female), including 25 participants who had a diagnosis of ADPKD themselves. Caregivers reported significant impact on their emotional (74.1%) and social life (38.1%), lost work productivity (26.6%), and reduced sleep (25.2%). Caregivers also reported worry about their financial situation (23.7%). In general, similar frequencies of impact were reported among caregivers with ADPKD versus caregivers without ADPKD, with the exception of sleep (8.0% vs 28.9%, respectively), leisure activities (28.0% vs 40.4% respectively), and work/employment (12.0% vs 29.8%, respectively). Limitations: The study was observational and designed to elicit concepts, and only descriptive analyses were conducted. Conclusions: These findings highlight the unique burden on caregivers in ADPKD, which results in substantial emotional, social, and professional/financial impact.

Diabetes, Atherosclerosis, and Stenosis by AI.

OBJECTIVE: This study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Whether APCs differ based on lesion severity and diabetes status is unknown. RESEARCH DESIGN AND METHODS: We retrospectively evaluated 303 subjects from the Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia (CREDENCE) trial referred for invasive coronary angiography with coronary computed tomographic angiography (CCTA) and classified lesions as obstructive (≥50% stenosed) or nonobstructive using blinded core laboratory analysis of quantitative coronary angiography. CCTA quantified APCs, including plaque volume (PV), calcified plaque (CP), noncalcified plaque (NCP), low-density NCP (LD-NCP), lesion length, positive remodeling (PR), high-risk plaque (HRP), and percentage of atheroma volume (PAV; PV normalized for vessel volume). The relationship between APCs, stenosis severity, and diabetes status was assessed. RESULTS: Among the 303 patients, 95 (31.4%) had diabetes. There were 117 lesions in the cohort with diabetes, 58.1% of which were obstructive. Patients with diabetes had greater plaque burden (P = 0.004). Patients with diabetes and nonobstructive disease had greater PV (P = 0.02), PAV (P = 0.02), NCP (P = 0.03), PAV NCP (P = 0.02), diseased vessels (P = 0.03), and maximum stenosis (P = 0.02) than patients without diabetes with nonobstructive disease. APCs were similar between patients with diabetes with nonobstructive disease and patients without diabetes with obstructive disease. Diabetes status did not affect HRP or PR. Patients with diabetes had similar APCs in obstructive and nonobstructive lesions. CONCLUSIONS: Patients with diabetes and nonobstructive stenosis had an association to similar APCs as patients without diabetes who had obstructive stenosis. Among patients with nonobstructive disease, patients with diabetes had more total PV and NCP.

AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy.

BACKGROUND: Clinical reads of coronary computed tomography angiography (CTA), especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. Artificial intelligence (AI)-based solutions applied to coronary CTA may overcome these limitations. OBJECTIVES: This study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography (AI-QCT) angiography analyses to core lab-interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).

Identifying a Hidden Conglomerate Chiral Pool in the CSD.

Conglomerate crystallization is the spontaneous generation of individually enantioenriched crystals from a nonenantioenriched material. This behavior is responsible for spontaneous resolution and the discovery of molecular chirality by Pasteur. The phenomenon of conglomerate crystallization of chiral organic molecules has been left largely undocumented, with no actively curated list available in the literature. While other crystallographic behaviors can be interrogated by automated searching, conglomerate crystallizations are not identified within the Cambridge Structural Database (CSD) and are therefore not accessible by conventional automated searching. By conducting a manual search of the CSD and literature, a list of over 1800 chiral species capable of conglomerate crystallization was curated by inspection of the racemic synthetic routes described in each publication. The majority of chiral conglomerate crystals are produced and published by synthetic chemists who seldom note and rarely exploit the implications this phenomenon can have on the enantiopurity of their crystalline materials. With their structures revealed, we propose that this list of compounds represents a new chiral pool which is not tied to biological sources of chirality.

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.

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.

Polycystic Kidney Disease-Related Disease Burden in Adolescents With Autosomal Dominant Polycystic Kidney Disease: An International Qualitative Study.

Rationale & Objective: Little is known about symptoms and disease impacts in adolescents with autosomal dominant polycystic kidney disease (ADPKD). The objective of the study was to explore these issues from the adolescent patient's perspective. Study Design: Observational, qualitative study. Setting & Participants: Eligible participants were 12-17 years old and had a diagnosis of ADPKD. Semi-structured interviews were conducted in 18 cities in 13 countries to elicit participant experiences of ADPKD-related symptoms and physical, social, and emotional impacts. Analytical Approach: Interviews were recorded, transcribed, and coded. Symptom and impact frequencies from the interviews were calculated, and representative quotes concerning elicited concepts were collated. Results: Thirty-three participants (mean age, 14.6 years; 42.4% female) completed interviews. Frequently reported symptoms included urinary urgency (n = 10; 30.3%) and back pain (n = 9; 27.3%). Consistent with previous findings in adults, participants experienced 3 primary types of pain: dull kidney pain, severe or sharp kidney pain, and a feeling of fullness and/or discomfort. Reported disease impacts included avoiding sports and physical activity (n = 10; 30.3%), missing school (n = 6; 18.2%) and social activities (n = 6; 18.2%), and feeling worried (n = 6; 18.2%), sad (n = 4; 12.1%), or frustrated (n = 3; 9.1%) about the disease and their future. Approximately one-fifth of participants (n = 7; 21.2%) reported that they were bothered or impacted by dietary limitations (primarily the need for reduced sodium intake and increased water intake). Limitations: The study had a small sample size. The researchers were unable to conduct focus groups with participants because of parental preferences. Conclusions: The findings from this exploratory study indicate that a substantial proportion of adolescents with ADPKD experience physical, social, and emotional impacts from their disease.