Cost-Effective and Wireless Portable Device for Rapid and Sensitive Quantification of Micro/Nanoplastics.

The accumulation of micro/nanoplastics (MNPs) in ecosystems poses tremendous environmental risks for terrestrial and aquatic organisms. Designing rapid, field-deployable, and sensitive devices for assessing the potential risks of MNPs pollution is critical. However, current techniques for MNPs detection have limited effectiveness. Here, we design a wireless portable device that allows rapid, sensitive, and on-site detection of MNPs, followed by remote data processing via machine learning algorithms for quantitative fluorescence imaging. We utilized a supramolecular labeling strategy, employing luminescent metal-phenolic networks composed of zirconium ions, tannic acid, and rhodamine B, to efficiently label various sizes of MNPs (e.g., 50 nm-10 µm). Results showed that our device can quantify MNPs as low as 330 microplastics and 3.08 × 106 nanoplastics in less than 20 min. We demonstrated the applicability of the device to real-world samples through determination of MNPs released from plastic cups after hot water and flow induction and nanoplastics in tap water. Moreover, the device is user-friendly and operative by untrained personnel to conduct data processing on the APP remotely. The analytical platform integrating quantitative imaging, customized data processing, decision tree model, and low-cost analysis ($0.015 per assay) has great potential for high-throughput screening of MNPs in agrifood and environmental systems.

US Markers and Necroinflammation, Steatosis, and Fibrosis in Metabolic Dysfunction-associated Steatotic Liver Disease: The iLEAD Study.

Background Attenuation coefficient (AC) and shear-wave speed (SWS) are established US markers for assessing patients with metabolic dysfunction-associated steatotic liver disease (MASLD), while shear-wave dispersion slope (DS) is not. Purpose To assess the relationship between the multiparametric US imaging markers DS, AC, and SWS and liver histopathologic necroinflammation in patients with MASLD. Materials and Methods This international multicenter prospective study enrolled consecutive patients with biopsy-proven MASLD between June 2019 and March 2023. Before biopsy, all participants underwent multiparametric US, and measurements of DS, AC, and SWS were obtained. Multivariable linear regression analyses were performed to assess the association of clinical variables and imaging markers with pathologic findings. The diagnostic performance of imaging markers for determining inflammation grade, steatosis grade, and fibrosis stage was assessed using the area under the receiver operating characteristic curve (AUC). Results A total of 124 participants (mean age, 53 years ± 15 [SD]; 62 males) were evaluated. In multivariable regression, lobular inflammation was associated with DS (regression coefficient, 0.06; P = .02), alanine aminotransferase level (regression coefficient, 0.002; P = .002), and Hispanic or Latino ethnicity (regression coefficient, -0.68; P = .047), while steatosis was associated with AC (regression coefficient, 3.66; P < .001) and fibrosis was associated with SWS (regression coefficient, 2.02; P < .001) and body mass index (regression coefficient, 0.05; P = .02). DS achieved an AUC of 0.72 (95% CI: 0.63, 0.82) for identifying participants with inflammation grade A2 or higher (moderate to severe inflammation). AC showed excellent performance for identifying participants with grade S1 (mild) or higher steatosis (AUC, 0.92 [95% CI: 0.87, 0.97]), while SWS showed excellent performance for identifying participants with fibrosis stage F2 or higher (clinically significant fibrosis) (AUC, 0.91 [95% CI: 0.86, 0.96]). Of the three US markers, SWS showed the highest AUC (0.81 [95% CI: 0.74, 0.89]) for the diagnosis of metabolic dysfunction-associated steatohepatitis. Conclusion Of the three US imaging markers (DS, AC, and SWS), DS was most associated with lobular inflammation grade at histologic examination and demonstrated fair diagnostic performance in distinguishing moderate to severe lobular inflammation. ClinicalTrials.gov Identifier: NCT04012242 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Yin in this issue.

Ecotoxicological Assessment of Microplastics and Cellulose Particles in the Galápagos Islands and Galápagos Penguin Food Web.

Microplastic pollution threatens some of the world's most iconic locations for marine biodiversity, including the remote Galápagos Islands, Ecuador. Using the Galápagos penguin (Spheniscus mendiculus) as a sentinel species, the present study assessed microplastics and suspected anthropogenic cellulose concentrations in surface seawater and zooplankton near Santa Cruz and Galápagos penguin colonies (Floreana, Isabela, Santiago), as well as in penguin potential prey (anchovies, mullets, milkfish) and penguin scat. On average, 0.40 ± 0.32 microplastics L-1 were found in surface seawater (<10 µm; n = 63 L), while 0.003, 0.27, and 5.12 microplastics individual-1 were found in zooplankton (n = 3372), anchovies (n = 11), and mullets (n = 6), respectively. The highest concentration (27 microplastics individual-1) was observed in a single milkfish. Calculations based on microplastics per gram of prey, in a potential diet composition scenario, suggest that the Galápagos penguin may consume 2881 to 9602 microplastics daily from prey. Despite this, no microplastics or cellulose were identified in 3.40 g of guano collected from two penguins. Our study confirms microplastic exposure in the pelagic food web and endangered penguin species within the UNESCO World Heritage site Galápagos Islands, which can be used to inform regional and international policies to mitigate plastic pollution and conserve biodiversity in the global ocean. Environ Toxicol Chem 2024;43:1442-1457. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Electrophoretic Deposition Interferometric Scattering Mass Photometry.

Interferometric scattering microscopy (iSCAT) has rapidly developed as a quantitative tool for the label-free detection of single macromolecules and nanoparticles. In practice, this measurement records the interferometric scattering signal of individual nanoparticles in solution as they land and stick on a coverslip, exhibiting an intensity that varies linearly with particle volume and an adsorption rate that reflects the solution-phase transport kinetics of the system. Together, such measurements provide a multidimensional gauge of the particle size and concentration in solution over time. However, the landing kinetics of particles in solution also manifest a measurement frequency limitation imposed by the slow long-range mobility of particle diffusion to the measurement interface. Here we introduce an effective means to overcome the inherent diffusion-controlled sampling limitation of spontaneous mass photometry. We term this methodology electrophoretic deposition interferometric scattering microscopy (EPD-iSCAT). This approach uses a coverslip supporting a conductive thin film of indium tin oxide (ITO). Charging this ITO film to a potential of around +1 V electrophoretically draws charged nanoparticles from solution and binds them in the focal plane of the microscope. Regulating this potential offers a direct means of controlling particle deposition. Thus, we find for a 0.1 nM solution of 50 nm polystyrene nanoparticles that the application of +1 V to an EPD-iSCAT coverslip assembly drives an electrophoretic deposition rate constant of 1.7 s-1 µm-2 nM-1. Removal of the potential causes deposition to cease. This user control of EPD-iSCAT affords a means to apply single-molecule mass photometry to monitor long-term changes in solution, owing to slow kinetic processes. In contrast with conventional coverslips chemically derivatized with charged thin films, EPD-iSCAT maintains a deposition rate that varies linearly with the bulk concentration.

Professionalization of Clinical Ethics Consultants: A Need for Liability Protection?

Clinical Ethics Consultation (CEC) has grown significantly in the last decade, and efforts are being made to professionalize the practice. The American Society for Bioethics and Humanities (ASBH) has been instrumental in this process, having published the Code of Ethics and Professional Responsibilities for Healthcare Ethics Consultants and founded and endorsed the creation of the Healthcare Ethics Consultant Certified (HCEC) Certification Commission. The ASBH also published "core competencies" for healthcare ethics consultants and has delineated a clear identity and role of such consultants distinct from that other healthcare professionals. In addition, more enter the field armed with advanced degrees (MA and PhD) or certification in clinical ethics consultation. While some have questioned the trend toward professionalization, the momentum is clearly in its favor. This paper explores three questions: Does the professionalization of healthcare ethics consultation expose those engaged in the field to the types of liability claims faced by professionals in other fields? What specific liabilities could affect a healthcare ethics consultant? And finally, what should healthcare ethics consultants do to protect themselves against liability claims? We conclude that while the risk of liability remains low, those engaged in the field should accept that risk just as part of their status as professionals and, like those in allied professions, seek appropriate protection in the form of liability insurance.

Deep learning unmasks the ECG signature of Brugada syndrome.

One in 10 cases of sudden cardiac death strikes without warning as the result of an inherited arrhythmic cardiomyopathy, such as Brugada Syndrome (BrS). Normal physiological variations often obscure visible signs of this and related life-threatening channelopathies in conventional electrocardiograms (ECGs). Sodium channel blockers can reveal previously hidden diagnostic ECG features, however, their use carries the risk of life-threatening proarrhythmic side effects. The absence of a nonintrusive test places a grossly underestimated fraction of the population at risk of SCD. Here, we present a machine-learning algorithm that extracts, aligns, and classifies ECG waveforms for the presence of BrS. This protocol, which succeeds without the use of a sodium channel blocker (88.4% accuracy, 0.934 AUC in validation), can aid clinicians in identifying the presence of this potentially life-threatening heart disease.

International Expert Consensus on US Lexicon for Thyroid Nodules.

Multiple US-based systems for risk stratification of thyroid nodules are in use worldwide. Unfortunately, the malignancy probability assigned to a nodule varies, and terms and definitions are not consistent, leading to confusion and making it challenging to compare study results and craft revisions. Consistent application of these systems is further hampered by interobserver variability in identifying the sonographic features on which they are founded. In 2018, an international multidisciplinary group of 19 physicians with expertise in thyroid sonography (termed the International Thyroid Nodule Ultrasound Working Group) was convened with the goal of developing an international system, tentatively called the International Thyroid Imaging Reporting and Data System, or I-TIRADS, in two phases: (phase I) creation of a lexicon and atlas of US descriptors of thyroid nodules and (phase II) development of a system that estimates the malignancy risk of a thyroid nodule. This article presents the methods and results of phase I. The purpose herein is to show what has been accomplished thus far, as well as generate interest in and support for this effort in the global thyroid community.

Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic-Mineral Mixtures.

Important decisions in local agricultural policy and practice often hinge on the soil's chemical composition. Raman spectroscopy offers a rapid noninvasive means to quantify the constituents of complex organic systems. But the application of Raman spectroscopy to soils presents a multifaceted challenge due to organic/mineral compositional complexity and spectral interference arising from overwhelming fluorescence. The present work compares methodologies with the capacity to help overcome common obstacles that arise in the analysis of soils. We created conditions representative of these challenges by combining varying proportions of six amino acids commonly found in soils with fluorescent bentonite clay and coarse mineral components. Referring to an extensive data set of Raman spectra, we compare the performance of the convolutional neural network (CNN) and partial least-squares regression (PLSR) multivariate models for amino acid composition. Strategies employing volume-averaged spectral sampling and data preprocessing algorithms improve the predictive power of these models. Our average test R2 for PLSR models exceeds 0.89 and approaches 0.98, depending on the complexity of the matrix, whereas CNN yields an R2 range from 0.91 to 0.97, demonstrating that classic PLSR and CNN perform comparably, except in cases where the signal-to-noise ratio of the organic component is very low, whereupon CNN models outperform. Artificially isolating two of the most prevalent obstacles in evaluating the Raman spectra of soils, we have characterized the effect of each obstacle on the performance of machine learning models in the absence of other complexities. These results highlight important considerations and modeling strategies necessary to improve the Raman analysis of organic compounds in complex mixtures in the presence of mineral spectral components and significant fluorescence.

Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation.

Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet 600,000 FNAs are still performed annually, with a PPV of 5-7% (up to 30%). Artificial intelligence (AI) systems have the capacity to improve diagnostic accuracy and workflow efficiency when integrated into clinical decision pathways. Previous studies have evaluated AI systems against physicians, whereas we aim to compare the benefits of incorporating AI into their final diagnostic decision. This work analyzed the potential for artificial intelligence (AI)-based decision support systems to improve physician accuracy, variability, and efficiency. The decision support system (DSS) assessed was Koios DS, which provides automated sonographic nodule descriptor predictions and a direct cancer risk assessment aligned to ACR TI-RADS. The study was conducted retrospectively between (08/2020) and (10/2020). The set of cases used included 650 patients (21% male, 79% female) of age 53 ± 15. Fifteen physicians assessed each of the cases in the set, both unassisted and aided by the DSS. The order of the reading condition was randomized, and reading blocks were separated by a period of 4 weeks. The system's impact on reader accuracy was measured by comparing the area under the ROC curve (AUC), sensitivity, and specificity of readers with and without the DSS with FNA as ground truth. The impact on reader variability was evaluated using Pearson's correlation coefficient. The impact on efficiency was determined by comparing the average time per read. There was a statistically significant increase in average AUC of 0.083 [0.066, 0.099] and an increase in sensitivity and specificity of 8.4% [5.4%, 11.3%] and 14% [12.5%, 15.5%], respectively, when aided by Koios DS. The average time per case decreased by 23.6% (p = 0.00017), and the observed Pearson's correlation coefficient increased from r = 0.622 to r = 0.876 when aided by Koios DS. These results indicate that providing physicians with automated clinical decision support significantly improved diagnostic accuracy, as measured by AUC, sensitivity, and specificity, and reduced inter-reader variability and interpretation times.

Bictegravir/emtricitabine/tenofovir alafenamide plus doravirine in highly treatment-experienced men with multidrug-resistant HIV.

OBJECTIVE: To evaluate the safety and efficacy of switching highly treatment-experienced people with HIV (HTE PWH) from rilpivirine/emtricitabine/tenofovir alafenamide (RPV/FTC/TAF) plus dolutegravir (DTG) to bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) plus doravirine (DOR). A pharmacokinetic (PK) analysis was conducted to assess the potential interaction between BIC and DOR. DESIGN AND METHODS: This open-label switch trial enrolled HTE PWH from a primary care private practice in the United States. Eligible participants were male, aged ≥45 years, with documented viral resistance to protease inhibitors, nucleoside reverse transcriptase inhibitors, and/or nonnucleoside reverse transcriptase inhibitors but no resistance to RPV or DOR, and no K65R or T69 insertion mutations. Virologic suppression (≤50 copies/ml) while on RPV/FTC/TAF plus DTG for ≥6 months was required prior to enrollment. The primary endpoint of the study was virologic suppression (<50 and <200 copies/ml) at 48 weeks. Secondary endpoints included safety, tolerability, changes in body mass index (BMI), and identification of PK parameters of BIC and DOR. RESULTS: Twenty males [median age: 65 years (range, 46-74), median time since HIV diagnosis: 37 years (range, 12-42)] completed the study. BIC/FTC/TAF plus DOR was well tolerated with no serious or treatment-related adverse events reported and no appreciable changes in BMI from baseline to Week 48. At Week 48, 100% of participants had <50 viral copies/ml. PK parameters for BIC and DOR ( n  = 10) were consistent with published data. CONCLUSIONS: Switching from RPV/FTC/TAF plus DTG to BIC/FTC/TAF plus DOR was well tolerated and efficacious in HTE men aged ≥45 years with HIV.

Catalyst Aggregation Matters for Immobilized Molecular CO2RR Electrocatalysts.

Here, we detail how the catalytic behavior of immobilized molecular electrocatalysts for the CO2 reduction reaction (CO2RR) can be impacted by catalyst aggregation. Operando Raman spectroscopy was used to study the CO2RR mediated by a layer of cobalt phthalocyanine (CoPc) immobilized on the cathode of an electrochemical flow reactor. We demonstrate that during electrolysis, the oxidation state of CoPc in the catalyst layer is dependent upon the degree of catalyst aggregation. Our data indicate that immobilized molecular catalysts must be dispersed on conductive supports to mitigate the formation of aggregates and produce meaningful performance data. We leveraged insights from this mechanistic study to engineer an improved CO-forming immobilized molecular catalyst─cobalt octaethoxyphthalocyanine (EtO8-CoPc)─that exhibited high selectivity (FECO ≥ 95%), high partial current density (JCO ≥ 300 mA/cm2), and high durability (ΔFECO < 0.1%/h at 150 mA/cm2) in a flow cell. This work demonstrates how to accurately identify CO2RR active species of molecular catalysts using operando Raman spectroscopy and how to use this information to implement improved molecular electrocatalysts into flow cells. This work also shows that the active site of CoPc during CO2RR catalysis in a flow cell is the metal center.

Right ventricular epicardial arrhythmogenic substrate in long-QT syndrome patients at risk of sudden death.

AIMS: The long-QT syndrome (LQTS) represents a leading cause of sudden cardiac death (SCD). The aim of this study was to assess the presence of an underlying electroanatomical arrhythmogenic substrate in high-risk LQTS patients. METHODS AND RESULTS: The present study enrolled 11 consecutive LQTS patients who had experienced frequent implantable cardioverter-defibrillator (ICD discharges triggered by ventricular fibrillation (VF). We acquired electroanatomical biventricular maps of both endo and epicardial regions for all patients and analyzed electrograms sampled from several myocardial regions. Abnormal electrical activities were targeted and eliminated by the means of radiofrequency catheter ablation. VF episodes caused a median of four ICD discharges in eleven patients (6 male, 54.5%; mean age 44.0 ± 7.8 years, range 22-53) prior to our mapping and ablation procedures. The average QTc interval was 500.0 ± 30.2 ms. Endo-epicardial biventricular maps displayed abnormally fragmented, low-voltage (0.9 ± 0.2 mV) and prolonged electrograms (89.9 ± 24.1 ms) exclusively localized in the right ventricular epicardium. We found electrical abnormalities extending over a mean epicardial area of 15.7 ± 3.1 cm2. Catheter ablation of the abnormal epicardial area completely suppressed malignant arrhythmias over a mean 12 months of follow-up (median VF episodes before vs. after ablation, 4 vs. 0; P = 0.003). After the procedure, the QTc interval measured in a 12-lead ECG analysis shortened to a mean of 461.8 ± 23.6 ms (P = 0.004). CONCLUSION: This study reveals that, among high-risk LQTS patients, regions localized in the epicardium of the right ventricle harbour structural electrophysiological abnormalities. Elimination of these abnormal electrical activities successfully prevented malignant ventricular arrhythmia recurrences.

An open-label, proof-of-concept study assessing the effects of bromelain-based enzymatic debridement on biofilm and microbial loads in patients with venous leg ulcers and diabetic foot ulcers.

BACKGROUND: Most chronic wounds contain biofilm, and debridement remains the centerpiece of treatment. Enzymatic debridement is an effective tool in removing nonviable tissue, however, there is little evidence supporting its effect on planktonic and biofilm bacteria. OBJECTIVE: This study evaluated the effects of a novel BBD agent on removal of nonviable tissue, biofilm, and microbial loads in patients with chronic ulcers. MATERIALS AND METHODS: Twelve patients with DFU or VLU were treated with up to 8 once-daily applications of BBD and then followed for an additional 2 weeks. Punch biopsy specimens were collected and analyzed for biofilm, and fluorescence imaging was used to measure bacterial load. RESULTS: Ten patients completed treatment, and 7 achieved complete debridement within a median of 2 applications (range, 2-8). By the end of the 2-week follow-up period, the mean ± SD reduction in wound area was 35% ± 38. In all 6 patients who were positive for biofilm at baseline, the biofilm was reduced to single individual or no detected microorganisms by the end of treatment. Red fluorescence for Staphylococcus aureus decreased from a mean of 1.09 cm² ± 0.58 before treatment to 0.39 cm² ± 0.25 after treatment. BBD was safe and well tolerated. CONCLUSION: Preliminary data suggest that BBD is safe and that it can be used to effectively debride DFU and VLU, reduce biofilm and planktonic bacterial load, and promote reduction in wound size.

Detected prenatal perfluorooctanoic acid (PFOA) exposure is associated with decreased fetal head biometric parameters in participants experiencing higher perceived stress during pregnancy in the MADRES cohort.

Background: Perfluoroalkyl substances (PFAS) are ubiquitous synthetic chemicals with long half-lives and are known to cross the placenta during pregnancy. We examined the influence of maternal PFAS levels on in utero fetal growth trajectories and assessed whether maternal stress modified these associations. Methods: Blood serum concentrations of five PFAS (PFOS, PFHxS, PFNA, PFOA, PFDA) were measured in 335 prenatal specimens (mean gestational age (GA): 21±9 weeks) in the MADRES cohort. Fetal growth outcomes (head circumference (HC), abdominal circumference (AC), biparietal diameter (BPD), femur length (FL), and estimated fetal weight (EFW)) were abstracted from ultrasound medical records and measured at the 3rd trimester study visit (N = 833 scans, GA range 10-42 weeks, mean 2.4 scans/participant). Adjusted linear mixed models with a GA quadratic growth curve were used for each PFAS exposure and growth outcome. PFOS and PFHxS were modeled continuously (100% sample detection), while PFOA, PFNA, and PFDA were modeled categorically (57-70% sample detection). Scores on the Perceived Stress Scale (PSS) measured in pregnancy were dichotomized at the median (<13 vs. ≥ 13) in stratified models. Results: Participants were on average 29±6 years old and predominately Hispanic (76%). Median serum concentrations of PFOS, PFHxS, PFNA, PFOA and PFDA were 1.34, 1.10, 0.07, 0.12, and 0.04 ng/mL, respectively. Participants with detected PFOA concentrations had fetuses with -2.5 mm (95% CI -4.2, -0.8) smaller HC and-0.7 mm (95% CI -1.3, -0.2) smaller BPD on average for a fixed GA than those without detected PFOA concentrations. In models stratified by PSS level, the effects of PFOA on fetal growth parameters were stronger and only significant in participants with higher stress levels (HC: ß= -3.5, 95% CI -5.8, -1.4; BPD: ß = -0.8, 95% CI -1.6, -1.1). Conclusions: Prenatal PFOA exposure adversely impacted fetal head biometric parameters in participants experiencing higher stress during pregnancy.

Identifying pre-conception and pre-natal periods in which ambient air pollution exposure affects fetal growth in the predominately Hispanic MADRES cohort.

BACKGROUND: It is well documented that persons of color experience disproportionate exposure to environmental contaminants, including air pollution, and have poorer pregnancy outcomes. This study assessed the critical windows of exposure to ambient air pollution on in utero fetal growth among structurally marginalized populations in urban Los Angeles. METHODS: Participants (N = 281) from the larger ongoing MADRES pregnancy cohort study were included in this analysis. Fetal growth outcomes were measured on average at 32 [Formula: see text] 2 weeks of gestation by a certified sonographer and included estimated fetal weight, abdominal circumference, head circumference, biparietal diameter and femur length. Daily ambient air pollutant concentrations were estimated for four pollutants (particulate matter less than 2.5 µm (PM2.5) and less than 10 µm (PM10) in aerodynamic diameter, nitrogen dioxide (NO2), and 8-h maximum ozone (O3)) at participant residences using inverse-distance squared spatial interpolation from ambient monitoring data. Weekly gestational averages were calculated from 12 weeks prior to conception to 32 weeks of gestation (44 total weeks), and their associations with growth outcomes were modeled using adjusted distributed lag models (DLMs). RESULTS: Participants were on average 29 years [Formula: see text] 6 old and predominately Hispanic (82%). We identified a significant sensitive window of PM2.5 exposure (per IQR increase of 6 [Formula: see text]3) between gestational weeks 4-16 for lower estimated fetal weight [Formula: see text] averaged4-16 = -8.7 g; 95% CI -16.7, -0.8). Exposure to PM2.5 during gestational weeks 1-23 was also significantly associated with smaller fetal abdominal circumference ([Formula: see text] averaged1-23 = -0.6 mm; 95% CI -1.1, -0.2). Additionally, prenatal exposure to PM10 (per IQR increase of 13 [Formula: see text]3) between weeks 6-15 of pregnancy was significantly associated with smaller fetal abdominal circumference ([Formula: see text] averaged6-15 = -0.4 mm; 95% CI -0.8, -0.1). DISCUSSION: These results suggest that exposure to particulate matter in early to mid-pregnancy, but not preconception or late pregnancy, may have critical implications on fetal growth.