Analysis of Legacy and Novel Neutral Per- and Polyfluoroalkyl Substances in Soils from an Industrial Manufacturing Facility.

Per- and polyfluoroalkyl substances (PFASs) have been detected in an array of environmental media due to their ubiquitous use in industrial and consumer products as well as potential release from fluorochemical manufacturing facilities. During their manufacture, many fluorotelomer (FT) facilities rely on neutral intermediates in polymer production including the FT-alcohols (FTOHs). These PFAS are known to transform to the terminal acids (perfluoro carboxylic acids; PFCAs) at rates that vary with environmental conditions. In the current study on soils from a FT facility, we employed gas chromatography coupled with conventional- and high-resolution mass spectrometry (GC-MS and GC-HRMS) to investigate the profile of these precursor compounds, the intermediary secondary alcohols (sFTOHs), FT-acrylates (FTAcr), and FT-acetates (FTAce) in soils around the former FT-production facility. Of these precursors, the general trend in detection intensity was [FTOHs] > [sFTOHs] > [FTAcrs], while for the FTOHs, homologue intensities generally were [12:2 FTOH] > [14:2 FTOH] > [16:2 FTOH] > [10:2 FTOH] > [18:2 FTOH] > [20:2 FTOH] > [8:2 FTOH] ∼ [6:2 FTOH]. The corresponding terminal acids were also detected in all soil samples and positively correlated with the precursor concentrations. GC-HRMS confirmed the presence of industrial manufacturing byproducts such as FT-ethers and FT-esters and aided in the tentative identification of previously unreported dimers and other compounds. The application of GC-HRMS to the measurement and identification of precursor PFAS is in its infancy, but the methodologies described here will help refine its use in tentatively identifying these compounds in the environment.

AbMelt: learning antibody thermostability from molecular dynamics.

Antibody thermostability is challenging to predict from sequence and/or structure. This difficulty is likely due to the absence of direct entropic information. Herein, we present AbMelt where we model the inherent flexibility of homologous antibody structures using molecular dynamics (MD) simulations at three temperatures and learn the relevant descriptors to predict the temperatures of aggregation (Tagg), melt onset (Tm,on), and melt (Tm). We observed that the radius of gyration deviation of the complementarity determining regions (CDRs) at 400K is the highest Pearson correlated descriptor with aggregation temperature (rp = -0.68 ± 0.23) and the deviation of internal molecular contacts at 350K is the highest correlated descriptor with both Tm,on (rp = -0.74 ± 0.04) as well as Tm (rp = -0.69 ± 0.03). Moreover, after descriptor selection and machine learning (ML) regression, we predict on a held-out test set containing both internal and public data and achieve robust performance for all endpoints compared to baseline models (Tagg R2 = 0.57 ± 0.11, Tm,on R2 = 0.56 ± 0.01, and Tm R2 = 0.60 ± 0.06). Additionally, the robustness of the AbMelt MD methodology is demonstrated by only training on <5% of the data and outperforming more traditional ML models trained on the entire dataset of more than 500 internal antibodies. Users can predict thermostability measurements for antibody variable fragments (Fvs) by collecting descriptors and using AbMelt, which we are making available at https://doi.org/10.5281/zenodo.10815667.

Multi-Organ System Metabolic Stress and Sex-Divergent Vascular Associations.

Introduction: Women experience excess cardiovascular risk compared to men in the setting of similar metabolic disease burden. This consistent finding could be related to sex differences in the vascular response to various forms of metabolic stress. In this study we examine the association of both systemic and organ-specific metabolic stress with vascular health in women and men. Methods: We conducted an observational study of 4,299 adult participants (52% women, aged 59±13 years) of the National Health and Nutrition Examination Survey (NHANES) 2017-2018 cohort and 110,225 adult outpatients (55% women, aged 64±16 years) of the Cedars-Sinai Medical Center (CSMC) 2019 cohort. We used natural splines to examine the association of systemic and organ-specific measures of metabolic stress including body mass index (BMI), hemoglobin A1c (HbA1c), hepatic FIB-4 score, and CKD-EPI estimated glomerular filtration rate (eGFR) on systolic blood pressure (SBP). Piecewise linear models were generated using normal value thresholds (BMI <25 kg/m 2 , HbA1c <5.7%, FIB-4 <1.3, and eGFR ≥90 ml/min), which approximated observed spline breakpoints. The primary outcome was increase in SBP (relative to a sex-specific physiologic baseline SBP) in association with increase in level of each metabolic measure. Results: Women compared to men demonstrated larger magnitudes and an earlier onset of increase in SBP per increment increase across all metabolic stress measures. The slope of SBP increase per increment of each metabolic measure was greater for women than men particularly for metabolic measures within the normal range, with slope differences of 1.71 mmHg per kg/m2 of BMI, 9.61 mmHg per %HbA1c, 6.45 mmHg per FIB-4 unit, and 0.37 mmHg per ml/min decrement of eGFR in the NHANES cohort (P difference <0.05 for all). Overall results were consistent in the CSMC cohort. Conclusions: Women exhibited greater vascular sensitivity in the setting of multiple types of metabolic stress, particularly in periods representing the transition from metabolic health to disease. These findings underscore the importance of involving early metabolic health interventions as part of efforts to mitigate vascular risks in both women and men.

Quantifying the Time to Administer Outpatient Parenteral Antimicrobial Therapy: A Missed Opportunity to Compensate for the Value of Infectious Diseases.

Outpatient parenteral antimicrobial therapy (OPAT) relies on substantial uncompensated provider time. In this study of a large academic OPAT program, the median amount of unbilled OPAT management time was 27 minutes per week, per OPAT course. These data should inform benchmarks in pursuing novel payment approaches for OPAT.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.

PURPOSE: To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. METHODS: A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. RESULTS: In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. CONCLUSION: The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

Using Deep learning to Predict Cardiovascular Magnetic Resonance Findings from Echocardiography Videos.

Background: Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. While deep learning has been shown to uncover findings not recognized by clinicians, it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning. We hypothesized that deep learning applied to echocardiography could predict CMR-based measurements. Methods: In a retrospective single-center study, adult patients with CMRs and echocardiography studies within 30 days were included. A video-based convolutional neural network was trained on echocardiography videos to predict CMR-derived labels including wall motion abnormality (WMA) presence, LGE presence, and abnormal T1, T2 or ECV across echocardiography views. The model performance was evaluated in a held-out test dataset not used for training. Results: The study population included 1,453 adult patients (mean age 56±18 years, 42% female) with 2,556 paired echocardiography studies occurring on average 2 days after CMR (interquartile range 2 days prior to 6 days after). The model had high predictive capability for presence of WMA (AUC 0.873 [95%CI 0.816-0.922]), however, the model was unable to reliably detect the presence of LGE (AUC 0.699 [0.613-0.780]), native T1 (AUC 0.614 [0.500-0.715]), T2 0.553 [0.420-0.692], or ECV 0.564 [0.455-0.691]). Conclusions: Deep learning applied to echocardiography accurately identified CMR-based WMA, but was unable to predict tissue characteristics, suggesting that signal for these tissue characteristics may not be present within ultrasound videos, and that the use of CMR for tissue characterization remains essential within cardiology. Clinical Perspective: Tissue characterization of the heart muscle is useful for clinical diagnosis and prognosis by identifying myocardial fibrosis, inflammation, and infiltration, and can be measured using cardiac MRI. While echocardiography is highly accessible and provides excellent functional information, its ability to provide tissue characterization information is limited at this time. Our study using a deep learning approach to predict cardiac MRI-based tissue characteristics from echocardiography showed limited ability to do so, suggesting that alternative approaches, including non-deep learning methods should be considered in future research.

Challenges in IBD Research 2024: Pragmatic Clinical Research.

Pragmatic clinical research is 1 of the 5 focus areas of the Challenges in IBD Research 2024, a multidisciplinary effort by scientists, clinicians, patients, and funders to identify priorities for patient-centric research. This summary provides a comprehensive overview of current gaps in inflammatory bowel disease (IBD) clinical research and actionable approaches to address them. This review is focused on identifying research that is needed to achieve the best outcomes for patients in clinical practice. Research gaps include understanding the needs of understudied patient groups and addressing barriers to care so all patients receive optimal care, validating and using biomarkers to enable early diagnosis and result in better outcomes for adults and children with IBD, and determining the optimal sequencing of treatments (medical, surgical, adjunct) in children and adults. Inclusive pragmatic research is needed to address these gaps and lead to improvements in patient care and outcomes for all populations of patients with IBD.

Pragmatic clinical research focuses on improving evidence for how to best treat patients to improve quality of life and disease outcomes in real-world practice. This includes evaluating and improving healthcare delivery and decreasing barriers for all patients.

Challenges in IBD Research 2024: Novel Technologies.

Novel technology is one of the five focus areas of the Challenges in Inflammatory Bowel Disease (IBD) Research 2024 document. Building off the Challenges in IBD Research 2019 document, the Foundation aims to provide a comprehensive overview of current gaps in IBD research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of a multidisciplinary collaboration from scientists, clinicians, patients, and funders and represents a valuable resource for patient-centric research prioritization. Specifically, the Novel Technologies section focuses on addressing key research gaps to enable interception and improve remission rates in IBD. This includes testing predictions of disease onset and progression, developing novel technologies tailored to specific phenotypes, and facilitating collaborative translation of science into diagnostics, devices, and therapeutics. Proposed priority actions outlined in the document include real-time measurement of biological changes preceding disease onset, more effective quantification of fibrosis, exploration of technologies for local treatment of fistulas, and the development of drug delivery platforms for precise, location-restricted therapies. Additionally, there is a strong emphasis on fostering collaboration between various stakeholders to accelerate progress in IBD research and treatment. Addressing these research gaps necessitates the exploration and implementation of bio-engineered novel technologies spanning a spectrum from materials to systems. By harnessing innovative ideas and technologies, there's a collective effort to enhance patient care and outcomes for individuals affected by IBD.

Technology drives medical progress, solving clinical challenges and enhancing patient care in inflammatory bowel disease (IBD). Collaborative efforts focus on addressing research gaps to improve interception, restoration, and remission rates, utilizing innovative technologies for better patient outcomes.

Overview to Challenges in IBD 2024-2029.

The mission of the Crohn's & Colitis Foundation is to cure Crohn's disease and ulcerative colitis and to improve the quality of lives of patients living with these diseases-in other words, to care and cure. To achieve these missions, there is a need to identify and prioritize research gaps and approaches to address these gaps, which is the aim of Challenges in IBD 2024. The Foundation convened close to 80 experts in inflammatory bowel disease (IBD), including researchers, clinicians, patients and caregivers, funders, industry representatives, and Foundation scientific staff and organized them into 5 workgroups, one for each of the 5 Challenges topics: Preclinical Human IBD Mechanisms, Environmental Triggers, Precision Medicine, Novel Technologies, and Pragmatic Clinical Research. The findings of these groups outline a research agenda that intends to change the research paradigm in IBD by introducing 2 concepts in the course of IBD that warrant specific focus: interception (during the preclinical phase) and restoration of normal physiology after remission is achieved. We hope these reviews will stimulate innovations in our understanding and management of IBD.

HDR or SDR? A Subjective and Objective Study of Scaled and Compressed Videos.

We conducted a large-scale study of human perceptual quality judgments of High Dynamic Range (HDR) and Standard Dynamic Range (SDR) videos subjected to scaling and compression levels and viewed on three different display devices. While conventional expectations are that HDR quality is better than SDR quality, we have found subject preference of HDR versus SDR depends heavily on the display device, as well as on resolution scaling and bitrate. To study this question, we collected more than 23,000 quality ratings from 67 volunteers who watched 356 videos on OLED, QLED, and LCD televisions, and among many other findings, observed that HDR videos were often rated as lower quality than SDR videos at lower bitrates, particularly when viewed on LCD and QLED displays. Since it is of interest to be able to measure the quality of videos under these scenarios, e.g. to inform decisions regarding scaling, compression, and SDR vs HDR, we tested several well-known full-reference and no-reference video quality models on the new database. Towards advancing progress on this problem, we also developed a novel no-reference model called HDRPatchMAX, that uses a contrast-based analysis of classical and bit-depth features to predict quality more accurately than existing metrics.

MolPROP: Molecular Property prediction with multimodal language and graph fusion.

Pretrained deep learning models self-supervised on large datasets of language, image, and graph representations are often fine-tuned on downstream tasks and have demonstrated remarkable adaptability in a variety of applications including chatbots, autonomous driving, and protein folding. Additional research aims to improve performance on downstream tasks by fusing high dimensional data representations across multiple modalities. In this work, we explore a novel fusion of a pretrained language model, ChemBERTa-2, with graph neural networks for the task of molecular property prediction. We benchmark the MolPROP suite of models on seven scaffold split MoleculeNet datasets and compare with state-of-the-art architectures. We find that (1) multimodal property prediction for small molecules can match or significantly outperform modern architectures on hydration free energy (FreeSolv), experimental water solubility (ESOL), lipophilicity (Lipo), and clinical toxicity tasks (ClinTox), (2) the MolPROP multimodal fusion is predominantly beneficial on regression tasks, (3) the ChemBERTa-2 masked language model pretraining task (MLM) outperformed multitask regression pretraining task (MTR) when fused with graph neural networks for multimodal property prediction, and (4) despite improvements from multimodal fusion on regression tasks MolPROP significantly underperforms on some classification tasks. MolPROP has been made available at https://github.com/merck/MolPROP . SCIENTIFIC CONTRIBUTION: This work explores a novel multimodal fusion of learned language and graph representations of small molecules for the supervised task of molecular property prediction. The MolPROP suite of models demonstrates that language and graph fusion can significantly outperform modern architectures on several regression prediction tasks and also provides the opportunity to explore alternative fusion strategies on classification tasks for multimodal molecular property prediction.

Challenges in IBD Research 2024: Preclinical Human IBD Mechanisms.

Preclinical human inflammatory bowel disease (IBD) mechanisms is one of 5 focus areas of the Challenges in IBD Research 2024 document, which also includes environmental triggers, novel technologies, precision medicine, and pragmatic clinical research. Herein, we provide a comprehensive overview of current gaps in inflammatory bowel diseases research that relate to preclinical research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in IBD interception, remission, and restoration. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders and represents a valuable resource for patient-centric research prioritization. This preclinical human IBD mechanisms section identifies major research gaps whose investigation will elucidate pathways and mechanisms that can be targeted to address unmet medical needs in IBD. Research gaps were identified in the following areas: genetics, risk alleles, and epigenetics; the microbiome; cell states and interactions; barrier function; IBD complications (specifically fibrosis and stricturing); and extraintestinal manifestations. To address these gaps, we share specific opportunities for investigation for basic and translational scientists and identify priority actions.

To address the unmet medical needs of patients with inflammatory bowel diseases (IBD) and move toward cures, preclinical human-relevant research must center on mechanistic questions pertinent to patients with IBD in the 3 areas of disease interception, remission, and restoration.

Skin cancers are the most frequent cancers in fair-skinned populations, but we can prevent them.

Cancers of the skin are the most commonly occurring cancers in humans. In fair-skinned populations, up to 95% of keratinocyte skin cancers and 70-95% of cutaneous melanomas are caused by ultraviolet radiation and are thus theoretically preventable. Currently, however, there is no comprehensive global advice on practical steps to be taken to reduce the toll of skin cancer. To address this gap, an expert working group comprising clinicians and researchers from Africa, America, Asia, Australia, and Europe, together with learned societies (European Association of Dermato-Oncology, Euromelanoma, Euroskin, European Union of Medical Specialists, and the Melanoma World Society) reviewed the extant evidence and issued the following evidence-based recommendations for photoprotection as a strategy to prevent skin cancer. Fair skinned people, especially children, should minimise their exposure to ultraviolet radiation, and are advised to use protective measures when the UV index is forecast to reach 3 or higher. Protective measures include a combination of seeking shade, physical protection (e.g. clothing, hat, sunglasses), and applying broad-spectrum, SPF 30 + sunscreens to uncovered skin. Intentional exposure to solar ultraviolet radiation for the purpose of sunbathing and tanning is considered an unhealthy behaviour and should be avoided. Similarly, use of solaria and other artificial sources of ultraviolet radiation to encourage tanning should be strongly discouraged, through regulation if necessary. Primary prevention of skin cancer has a positive return on investment. We encourage policymakers to communicate these messages to the general public and promote their wider implementation.

Homicide or Happiness: Did Folate Fortification and Public Health Campaigns Influence Homicide Rates and the Great American Crime Decline?

The last several years have witnessed a remarkable growth in research directed at nutrition and behavior, with increased interest in the field of nutritional criminology. It is becoming clear that dietary patterns and specific nutrients play an important role in cognition and behavior, including those related to aggression, violence, and antisocial activity. Included in this expanding knowledge base is the recognition that folate, through multiple pathways, including enzymatic reactions and gut microbiome ecology, plays a critical role in central nervous system functioning. These mechanistic advances allow for a retrospective analysis of a topic that remains unexplained-the sudden and unpredicted drop in homicide and other violent crime rates in the United States and other nations in the 1990s. Here, we revisit this marked reduction in homicide rates through the lens of the coincident public health campaign (and subsequent mandatory fortification) to increase folic acid intake. Based on objectively measured blood folate levels through the National Health and Nutrition Examination Surveys, there is little doubt that tissue folate witnessed a dramatic rise at the national level from 1988 through 2000. Drawing from accumulated and emerging research on the neurobehavioral aspects of folate, it is our contention that this relatively sudden and massive increase in tissue folate levels may have contributed to reductions in violent crime in the United States.

Sex-Specific Disparities in Clinical Outcomes After Transcatheter Aortic Valve Replacement Among Different Racial Populations.

Background: Sex-related disparities in clinical outcomes following transcatheter aortic valve replacement (TAVR) and the impact of sex on clinical outcomes after TAVR among different racial groups are undetermined. Objectives: This study assessed whether sex-specific differences in baseline clinical and anatomical characteristics affect clinical outcomes after TAVR and investigated the impact of sex on clinical outcomes among different racial groups. Methods: The TP-TAVR (Trans-Pacific TAVR) registry is a multinational cohort study of patients with severe aortic stenosis who underwent TAVR at 2 major centers in the United States and 1 major center in South Korea. The primary outcome was a composite of death from any cause, stroke, or rehospitalization after 1 year. Results: The incidence of the primary composite outcome was not significantly different between sexes (27.9% in men vs 28% in women; adjusted HR: 0.97; 95% CI: 0.79-1.20). This pattern was consistent in Asian (23.5% vs 23.3%; adjusted HR: 0.99; 95% CI: 0.69-1.41) and non-Asian (30.8% vs 31.6%; adjusted HR: 0.95; 95% CI: 0.72-1.24) cohorts, without a significant interaction between sex and racial group (P for interaction = 0.74). The adjusted risk for all-cause mortality was similar between sexes, regardless of racial group. However, the adjusted risk of stroke was significantly lower in male patients than in female patients, which was more prominent in the non-Asian cohort. Conclusions: Despite significantly different baseline and procedural characteristics, there were no sex-specific differences in the adjusted 1-year rates of primary composite outcomes and all-cause mortality, regardless of different racial groups. (Transpacific TAVR registry [TP-TAVR]; NCT03826264).

Addition of low sodium does not increase sensitivity to glucose in wild-type mice, or lead to partial glucose taste detection in T1R3 knock-out mice.

The sodium glucose cotransporter 1 (SGLT1) has been proposed as a non-T1R glucosensor contributing to glucose taste. Studies have shown that the addition of NaCl at very weak concentrations to a glucose stimulus can enhance signaling in the gustatory nerves of mice and significantly lower glucose detection thresholds in humans. Here, we trained mice with (wild-type; WT) and without (knockout; KO) a functioning T1R3 subunit on a two-response operant detection task to differentially respond to the presence or absence of a taste stimulus immediately after sampling. After extensive training (∼40 sessions), KO mice were unable to reliably discriminate 2 M glucose+0.01 M NaCl from 0.01 M NaCl alone, but all WT mice could. We then tested WT mice on a descending array of glucose concentrations (2.0-0.03 M) with the addition of 0.01 M NaCl vs. 0.01 M NaCl alone. The concentration series was then repeated with glucose alone vs. water. We found no psychophysical evidence of a non-T1R taste transduction pathway involved in the detection of glucose. The addition of NaCl to glucose did not lower taste detection thresholds in WT mice, nor did it render the stimulus detectable to KO mice, even at 2 M. The proposed pathway must contribute to functions other than sensory-discriminative detection, at least when tested under these conditions. Detection thresholds were also derived for fructose and found to be 1/3 log10 lower than for glucose, but highly correlated (r = 0.88) between the two sugars, suggesting that sensitivity to these stimuli in this task was based on a similar neural process.