Plastic additives in the ocean: Use of a comprehensive dataset for meta-analysis and method development.

In excess of 13,000 chemicals are added to plastics ('additives') to improve performance, durability, and production of plastic products. They are categorized into numerous chemical classes including flame retardants, light stabilizers, antioxidants, and plasticizers. While research on plastic additives in the marine environment has increased over the past decade, there is a lack of methodological standardization. To direct future measurement of plastic additives, we compiled a first-of-its-kind dataset of literature assessing plastic additives in marine environments, delineated by sample type (plastic debris, seawater, sediment, biota). Using this dataset, we performed a meta-analysis to summarize the state of the science. Currently, our dataset includes 217 publications published between 1978 and May 2023. The majority of publications analyzed plastic additives in biota collected from Europe and Asia. Analyses concentrated on plasticizers, brominated flame retardants, and bisphenols. Common sample preparation techniques included Solvent - Agitation extraction for plastic, sediment, and biota samples, and Solid Phase Extraction for seawater samples with dichloromethane and solvent mixtures including dichloromethane as the organic extraction solvent. Finally, most analyses were performed utilizing gas chromatography/mass spectrometry. There are a variety of data gaps illuminated by this meta-analysis, most notably the small number of compounds that have been targeted for detection compared to the large number of additives used in plastic production. The provided dataset facilitates future investigation of trends in plastic additive concentration data in the marine environment (allowing for comparison to toxicity thresholds) and acts as a starting point for optimizing and harmonizing plastic additive analytical methods.

Previous successes and untapped potential of pyrolysis-GC/MS for the analysis of plastic pollution.

There is growing concern from scientists, policy makers, and the public about the contamination of natural and indoor environments with plastics, particularly micro/nanoplastics. Typically, characterizing microplastics in environmental samples requires extensive sample processing to isolate particles, followed by spectroscopic methodologies to identify particle polymer composition. Spectroscopic techniques are limited in their ability to provide polymer mass or advanced chemical composition (e.g., chemical additive content), which are important for toxicological assessments. To achieve mass fraction quantification and chemical characterization of plastics in environmental samples, many researchers have turned to thermoanalytical spectrometric approaches, particularly pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Sample preparation for Py-GC/MS may be approached similarly to techniques needed for spectroscopic approaches (e.g., isolate particles on a filter), employ pressurized solvent extraction, or use ultrafiltration techniques to concentrate nanoplastics. Great strides have been made in using calibration curves to quantify plastics in complex matrices. However, the approaches to the pyrolysis thermal program, as well as calibrant and sample preparation, are inconsistent, requiring refinement and harmonization. This review provides a critical synthesis of previous Py-GC/MS work and highlights opportunities for novel and improved Py-GC/MS analysis of plastics in the future.

Microplastics exacerbate virus-mediated mortality in fish.

Microplastics are a persistent and increasing environmental hazard. They have been reported to interact with a variety of biotic and abiotic environmental stressors, but the ramifications of such interactions are largely unknown. We investigated virus-induced mortalities in a commercially important salmonid following exposure to microplastics, plastic microfibers, and natural (non-plastic) microparticles. Microplastics or microparticles alone were not lethal. Mortality increased significantly when fish were co-exposed to virus and microplastics, particularly microfibers, compared to virus alone. This presents the unique finding that microplastics (not natural microparticulate matter) may have a significant impact on population health when presented with another stressor. Further, we found that mortality correlated with host viral load, mild gill inflammation, immune responses, and transmission potential. We hypothesize that microplastics can compromise host tissues, allowing pathogens to bypass defenses. Further research regarding this mechanism and the interplay between microplastics and infectious disease are paramount, considering microplastics increasing environmental burden.

Polystyrene microplastics reduce abundance of developing B cells in rainbow trout (Oncorhynchus mykiss) primary cultures.

Environmental microplastic pollution (including polystyrene, PS) may have detrimental effects on the health of aquatic organisms. Accumulation of PS microplastics has been reported to affect innate immune cells and inflammatory responses in fish. To date, knowledge on effects of microplastics on the antibody response is still very limited. Here, we investigated effects of small (0.8-20 µm) PS microplastics on the abundance of B lineage cells in primary cultures of developing immune cells from the anterior kidney of rainbow trout. Both purchased PS microbeads and PS microparticles generated from consumer products were used as microplastic sources. We first show that rainbow trout phagocytic B cells efficiently took up small (0.83-3.1 µm) PS microbeads within hours of exposure. In addition, our data revealed that PS microplastic exposure most significantly decreased the abundance of a population of non-phagocytic developing B cells, using both flow cytometry and RT-qPCR. PS microplastics-induced loss of developing B cells further correlated with reduced gene expression of RAG1 and the membrane form of immunoglobulin heavy chains mu and tau. Based on the induced loss of developing B cells observed in our in vitro studies, we speculate that in vivo, chronic PS microplastic-exposure may lead to suboptimal IgM/IgT levels in response to pathogens in teleost species. Considering the highly conserved nature of vertebrate B lymphopoiesis it is likely that PS microplastics will similarly reduce antibody responses in higher vertebrate species, including humans. Further, RAG1 provides an effective biomarker to determine effects of PS microplastics on B cell development in teleost species.

Microplastics affect sedimentary microbial communities and nitrogen cycling.

Microplastics are ubiquitous in estuarine, coastal, and deep sea sediments. The impacts of microplastics on sedimentary microbial ecosystems and biogeochemical carbon and nitrogen cycles, however, have not been well reported. To evaluate if microplastics influence the composition and function of sedimentary microbial communities, we conducted a microcosm experiment using salt marsh sediment amended with polyethylene (PE), polyvinyl chloride (PVC), polyurethane foam (PUF) or polylactic acid (PLA) microplastics. We report that the presence of microplastics alters sediment microbial community composition and nitrogen cycling processes. Compared to control sediments without microplastic, PUF- and PLA-amended sediments promote nitrification and denitrification, while PVC amendment inhibits both processes. These results indicate that nitrogen cycling processes in sediments can be significantly affected by different microplastics, which may serve as organic carbon substrates for microbial communities. Considering this evidence and increasing microplastic pollution, the impact of plastics on global ecosystems and biogeochemical cycling merits critical investigation.

Evaluation of a Serum Lung Cancer Biomarker Panel.

BACKGROUND: A panel of 3 serum proteins and 1 autoantibody has been developed to assist with the detection of lung cancer. We aimed to validate the accuracy of the biomarker panel in an independent test set and explore the impact of adding a fourth serum protein to the panel, as well as the impact of combining molecular and clinical variables. METHODS: The training set of serum samples was purchased from commercially available biorepositories. The testing set was from a biorepository at the Cleveland Clinic. All lung cancer and control subjects were >50 years old and had smoked a minimum of 20 pack-years. A panel of biomarkers including CEA (carcinoembryonic antigen), CYFRA21-1 (cytokeratin-19 fragment 21-1), CA125 (carbohydrate antigen 125), HGF (hepatocyte growth factor), and NY-ESO-1 (New York esophageal cancer-1 antibody) was measured using immunoassay techniques. The multiple of the median method, multivariate logistic regression, and random forest modeling was used to analyze the results. RESULTS: The training set consisted of 604 patient samples (268 with lung cancer and 336 controls) and the testing set of 400 patient samples (155 with lung cancer and 245 controls). With a threshold established from the training set, the sensitivity and specificity of both the 4- and 5-biomarker panels on the testing set was 49% and 96%, respectively. Models built on the testing set using only clinical variables had an area under the receiver operating characteristic curve of 0.68, using the biomarker panel 0.81 and by combining clinical and biomarker variables 0.86. CONCLUSIONS: This study validates the accuracy of a panel of proteins and an autoantibody in a population relevant to lung cancer detection and suggests a benefit to combining clinical features with the biomarker results.

Impact of a Lung Cancer Screening Counseling and Shared Decision-Making Visit.

BACKGROUND: Lung cancer screening is a complex balance of benefits and harms. A counseling and shared decision-making visit has been mandated to assist patients with the decision about participation in screening. To our knowledge, the impact of this visit on patient understanding and decisions has not been studied. METHODS: We developed a centralized counseling and shared decision-making visit for our lung cancer screening program. The visit included confirmation of eligibility for screening, education supported by a narrated slide show, individualized risk assessment with a decision aid, time for answering questions, and data collection. We surveyed consecutive patients prior to the visit, immediately after the visit, and 1 month after the visit to determine the impact of the visit on their knowledge. RESULTS: Twenty-three of 423 patients (5.4%) who had a visit did not proceed to the screening CT scan. One hundred twenty-five consecutive patients completed the initial survey, 122 completed the postvisit survey, and 113 completed the 1-month follow-up survey. Prior to the visit, the patients had a poor level of understanding about the age and smoking eligibility criteria (8.8% and 13.6% correct, respectively) and the benefits and harms of screening (55.2% and 38.4% correct, respectively). There was a significant improvement in knowledge noted after the visit for all questions (P = .03 to P < .0001). Knowledge waned by the 1-month follow-up but remained higher than it was before the visit. CONCLUSIONS: A centralized counseling and shared decision-making visit impacts the patient's knowledge about the eligibility criteria, benefits, and harms of lung cancer screening with LDCT, helping patients make value-based decisions.

Metabolite Profiles of the Serum of Patients with Non-Small Cell Carcinoma.

INTRODUCTION: Alterations of serum metabolites may allow us to identify individuals with lung cancer and advance our understanding of the nature and treatment of their cancer. We aimed to identify serum metabolites that differentiate patients with lung cancer from at-risk controls. METHODS: Serum samples from patients with biopsy-confirmed untreated stage I through stage III non-small cell lung cancer and at-risk controls were divided into fractions for analysis by ultrahigh-performance liquid chromatography-tandem mass spectrometry and gas chromatography-mass spectrometry. Compounds were identified by comparison with library entries of purified standards. Differences in concentrations of single metabolites and metabolite ratios were identified. Prediction models were developed. RESULTS: Serum samples from 284 subjects was analyzed. The subjects' mean age was 67 and 48% were female. Ninety-four patients had lung cancer (50 had adenocarcinoma and 44 had squamous cell carcinoma), 44% had stage I disease, 17% had stage II disease, and 39% had stage III disease. The patients with cancer were slightly older than the controls (68.7 versus 66.2 years, p = 0.013). A total of 534 metabolites were identified in eight metabolite superpathways and 73 subpathways. The concentrations of 149 metabolites differed significantly (q values <0.05) between the cancer and control groups (70 were lower in the cancer group and 79 were higher), and 9723 metabolite ratios differed significantly (q values <0.001) between the cancer and control groups. The accuracies of the models (cancer and cancer subtypes versus control) trained on 70% of the subjects and tested on 30% (expressed as C-statistics) ranged from 0.748 to 0.858. CONCLUSIONS: Differences in the serum metabolite profile exist between patients with stage I through stage III non-small cell lung cancer and matched controls.

Accuracy of volatile urine biomarkers for the detection and characterization of lung cancer.

BACKGROUND: The mixture of volatile organic compounds in the headspace gas of urine may be able to distinguish lung cancer patients from relevant control populations. METHODS: Subjects with biopsy confirmed untreated lung cancer, and others at risk for developing lung cancer, provided a urine sample. A colorimetric sensor array was exposed to the headspace gas of neat and pre-treated urine samples. Random forest models were trained from the sensor output of 70% of the study subjects and were tested against the remaining 30%. Models were developed to separate cancer and cancer subgroups from control, and to characterize the cancer. An additional model was developed on the largest clinical subgroup. RESULTS: 90 subjects with lung cancer and 55 control subjects participated. The accuracies, reported as C-statistics, for models of cancer or cancer subgroups vs. control ranged from 0.795 - 0.917. A model of lung cancer vs. control built using only subjects from the largest available clinical subgroup (30 subjects) had a C-statistic of 0.970. Models developed and tested to characterize cancer histology, and to compare early to late stage cancer, had C-statistics of 0.849 and 0.922 respectively. CONCLUSIONS: The colorimetric sensor array signature of volatile organic compounds in the urine headspace may be capable of distinguishing lung cancer patients from clinically relevant controls. The incorporation of clinical phenotypes into the development of this biomarker may optimize its accuracy.

Progress in the development of volatile exhaled breath signatures of lung cancer.

RATIONALE: Volatile organic compounds present in the exhaled breath have shown promise as biomarkers of lung cancer. Advances in colorimetric sensor array technology, breath collection methods, and clinical phenotyping may lead to the development of a more accurate breath biomarker. OBJECTIVES: Perform a discovery-level assessment of the accuracy of a colorimetric sensor array-based volatile breath biomarker. METHODS: Subjects with biopsy-confirmed untreated lung cancer, and others at risk for developing lung cancer, performed tidal breathing into a breath collection instrument designed to expose a colorimetric sensor array to the alveolar portion of the breath. Random forest models were built from the sensor output of 70% of the study subjects and were tested against the remaining 30%. Models were developed to separate cancer and subgroups from control, and to characterize the cancer. Additional models were developed after matching the clinical phenotypes of cancer and control subjects. MEASUREMENTS AND MAIN RESULTS: Ninety-seven subjects with lung cancer and 182 control subjects participated. The accuracies, reported as C-statistics, for models of cancer and subgroups versus control ranged from 0.794 to 0.861. The accuracy was improved by developing models for cancer and control groups selected through propensity matching for clinical variables. A model built using only subjects from the largest available clinical subgroup (49 subjects) had a C-statistic of 0.982. Models developed and tested to characterize cancer histology, and to compare early- with late-stage cancer, had C-statistics of 0.881-0.960. CONCLUSIONS: The colorimetric sensor array signature of exhaled breath volatile organic compounds was capable of distinguishing patients with lung cancer from clinically relevant control subjects in a discovery level trial. The incorporation of clinical phenotypes into the further development of this biomarker may optimize its accuracy.