Molecular Assessment of Proadipogenic Effects for Common-Use Contraceptives and Their Mixtures.

Hormonal contraceptives are widely prescribed due to their effectiveness and convenience and have become an integral part of family planning strategies worldwide. In the United States, approximately 65% of reproductive-aged women are estimated to be using contraceptive options, with approximately 33% using one or a combination of hormonal contraceptives. While these methods have undeniably contributed to improved reproductive health, recent studies have raised concerns regarding their potential effect on metabolic health. Despite widespread anecdotal reports, epidemiological research has been mixed as to whether hormonal contraceptives contribute to metabolic health effects. As such, the goals of this study were to assess the adipogenic activity of common hormonal contraceptive chemicals and their mixtures. Five different models of adipogenesis were used to provide a rigorous assessment of metabolism-disrupting effects. Interestingly, every individual contraceptive (both estrogens and progestins) and each mixture promoted significant adipogenesis (eg, triglyceride accumulation and/or preadipocyte proliferation). These effects appeared to be mediated in part through estrogen receptor signaling, particularly for the contraceptive mixtures, as cotreatment with fulvestrant acted to inhibit contraceptive-mediated proadipogenic effects on triglyceride accumulation. In conclusion, this research provides valuable insights into the complex interactions between hormonal contraceptives and adipocyte development. The results suggest that both progestins and estrogens within these contraceptives can influence adipogenesis, and the specific effects may vary based on the receptor disruption profiles. Further research is warranted to establish translation of these findings to in vivo models and to further assess causal mechanisms underlying these effects.

In Search of the Truth: Choice of Ground Truth for Predictive Modeling of Trauma Team Activation in Pediatric Trauma.

BACKGROUND: Assigning trauma team activation (TTA) levels for trauma patients is a classification task that machine learning models can help optimize. However, performance is dependent on the "ground-truth" labels used for training. Our purpose was to investigate 2 ground truths, the Cribari matrix and the Need for Trauma Intervention (NFTI), for labeling training data. STUDY DESIGN: Data were retrospectively collected from the institutional trauma registry and electronic medical record, including all pediatric patients (age <18 years) who triggered a TTA (January 2014 to December 2021). Three ground truths were used to label training data: (1) Cribari (Injury Severity Score >15 = full activation), (2) NFTI (positive for any of 6 criteria = full activation), and (3) the union of Cribari+NFTI (either positive = full activation). RESULTS: Of 1,366 patients triaged by trained staff, 143 (10.47%) were considered undertriaged using Cribari, 210 (15.37%) using NFTI, and 273 (19.99%) using Cribari+NFTI. NFTI and Cribari+NFTI were more sensitive to undertriage in patients with penetrating mechanisms of injury (p = 0.006), specifically stab wounds (p = 0.014), compared with Cribari, but Cribari indicated overtriage in more patients who required prehospital airway management (p < 0.001), CPR (p = 0.017), and who had mean lower Glasgow Coma Scale scores on presentation (p < 0.001). The mortality rate was higher in the Cribari overtriage group (7.14%, n = 9) compared with NFTI and Cribari+NFTI (0.00%, n = 0, p = 0.005). CONCLUSIONS: To prioritize patient safety, Cribari+NFTI appears best for training a machine learning algorithm to predict the TTA level.

Machine Learning Improves the Accuracy of Trauma Team Activation Level Assignments in Pediatric Patients.

BACKGROUND: The assignment of trauma team activation levels can be conceptualized as a classification task. Machine learning models can be used to optimize classification predictions. Our purpose was to demonstrate proof-of-concept for a machine learning tool for predicting trauma team activation levels in pediatric patients with traumatic injuries. METHODS: Following IRB approval, we retrospectively collected data from the institutional trauma registry and electronic medical record at our Pediatric Trauma Center for all patients (age <18 y) who triggered a trauma team activation (1/2014-12/2021), including: demographics, mechanisms of injury, comorbidities, pre-hospital interventions, numeric variables, and the six "Need for Trauma Intervention (NFTI)" criteria. Three machine learning models (Logistic Regression, Random Forest, Support Vector Machine) were tested 1000 times in separate trials using the union of the Cribari and NFTI metrics as ground-truth (Injury Severity Score >15 or positive for any of 6 NFTI criteria = full activation). Model performance was quantified and compared to emergency department (ED) staff. RESULTS: ED staff had 75% accuracy, an area under the curve (AUC) of 0.73 ± 0.04, and an F1 score of 0.49. The best performing of all machine learning models, the support vector machine, had 80% accuracy, AUC 0.81 ± 4.1e-5, F1 Score 0.80, with less variance compared to other models and ED staff. CONCLUSIONS: All machine learning models outperformed ED staff in all performance metrics. These results suggest that data-driven methods can optimize trauma team activations in the ED, with potential improvements in both patient safety and hospital resource utilization. TYPE OF STUDY: Economic/Decision Analysis or Modeling Studies. LEVEL OF EVIDENCE: II.

Adipogenic and endocrine disrupting mixture effects of organic and inorganic pollutant mixtures.

Chronic health conditions are rapidly increasing in prevalence and cost to society worldwide: in the US, >42 % of adults aged 20 and older are currently classified as obese. Exposure to endocrine disrupting chemicals (EDCs) has been implicated as a causal factor; some EDCs, termed "obesogens", can increase weight and lipid accumulation and/or perturb metabolic homeostasis. This project aimed to assess the potential combination effects of diverse inorganic and organic contaminant mixtures, which more closely reflect environmentally realistic exposures, on nuclear receptor activation/inhibition and adipocyte differentiation. Herein, we focused on two polychlorinated biphenyls (PCB-77 and 153), two perfluoroalkyl substances (PFOA and PFOS), two brominated flame retardants (PBB-153 and BDE-47), and three inorganic contaminants (lead, arsenic, and cadmium). We examined adipogenesis using human mesenchymal stem cells and receptor bioactivities using luciferase reporter gene assays in human cell lines. We observed significantly greater effects for several receptor bioactivities by various contaminant mixtures relative to individual components. All nine contaminants promoted triglyceride accumulation and/or pre-adipocyte proliferation in human mesenchymal stem cells. Comparing simple component mixtures to individual components at 10 % and 50 % effect levels revealed putative synergistic effects for each of the mixtures for at least one of the concentrations relative to the individual component chemicals, some of which also exhibited significantly greater effects than the component contaminants. Our results support further testing of more realistic and complex contaminant mixtures that better reflect environmental exposures, in order to more conclusively define mixture responses both in vitro and in vivo.

Cetyl Alcohol Polyethoxylates Disrupt Metabolic Health in Developmentally Exposed Zebrafish.

Alcohol polyethoxylates (AEOs), such as cetyl alcohol ethoxylates (CetAEOs), are high-production-volume surfactants used in laundry detergents, hard-surface cleaners, pesticide formulations, textile production, oils, paints, and other products. AEOs have been suggested as lower toxicity replacements for alkylphenol polyethoxylates (APEOs), such as the nonylphenol and octylphenol polyethoxylates. We previously demonstrated that nonylphenol polyethoxylates induced triglyceride accumulation in several in vitro adipogenesis models and promoted adiposity and increased body weights in developmentally exposed zebrafish. We also demonstrated that diverse APEOs and AEOs were able to increase triglyceride accumulation and/or pre-adipocyte proliferation in a murine pre-adipocyte model. As such, the goals of this study were to assess the potential of CetAEOs to promote adiposity and alter growth and/or development (toxicity, length, weight, behavior, energy expenditure) of developmentally exposed zebrafish (Danio rerio). We also sought to expand our understanding of ethoxylate chain-length dependent effects through interrogation of varying chain-length CetAEOs. We demonstrated consistent adipogenic effects in two separate human bone-marrow-derived mesenchymal stem cell models as well as murine pre-adipocytes. Immediately following chemical exposures in zebrafish, we reported disrupted neurodevelopment and aberrant behavior in light/dark activity testing, with medium chain-length CetAEO-exposed fish exhibiting hyperactivity across both light and dark phases. By day 30, we demonstrated that cetyl alcohol and CetAEOs disrupted adipose deposition in developmentally exposed zebrafish, despite no apparent impacts on standard length or gross body weight. This research suggests metabolic health concerns for these common environmental contaminants, suggesting further need to assess molecular mechanisms and better characterize environmental concentrations for human health risk assessments.

Correction: Kassotis et al. Nonylphenol Polyethoxylates Enhance Adipose Deposition in Developmentally Exposed Zebrafish. Toxics 2022, 10, 99.

In the original publication [...].

Prehospital factors predict outcomes in pediatric trauma: A principal component analysis.

BACKGROUND: Trauma team activation leveling decisions are complex and based on many variables. Accurate triage decisions improve patient safety and resource utilization. Our purpose was to establish proof-of-concept for using principal component analysis (PCA) to identify multivariate predictors of injury severity and to assess their ability to predict outcomes in pediatric trauma patients. We hypothesized that we could identify significant principal components (PCs) among variables used for decisions regarding trauma team activation and that PC scores would be predictive of outcomes in pediatric trauma. METHODS: We conducted a retrospective review of the trauma registry (January 2014 to December 2020) at our pediatric trauma center, including all pediatric patients (age <18 years) who triggered a trauma team activation. Data included patient demographics, prehospital report, Injury Severity Score, and outcomes. Four significant principal components were identified using PCA. Differences in outcome variables between the highest and lowest quartile for PC score were examined. RESULTS: There were 1,090 pediatric patients included. The four significant PCs accounted for greater than 96% of the overall data variance. The first PC was a composite of prehospital Glasgow Coma Scale and Revised Trauma Score and was predictive of outcomes, including injury severity, length of stay, and mortality. The second PC was characterized primarily by prehospital systolic blood pressure and high PC scores were associated with increased length of stay. The third and fourth PCs were characterized by patient age and by prehospital Revised Trauma Score and systolic blood pressure, respectively. CONCLUSION: We demonstrate that, using information available at the time of trauma team activation, PCA can be used to identify key predictors of patient outcome. While the ultimate goal is to create a machine learning-based predictive tool to support and improve clinical decision making, this study serves as a crucial step toward developing a deep understanding of the features of the model and their behavior with actual clinical data. LEVEL OF EVIDENCE: Diagnostic Test or Criteria; Level III.

Predictors and biomarkers of food allergy and sensitization in early childhood.

OBJECTIVE: To review existing literature on the early risk factors for and biomarkers of food allergy (FA) and food sensitization (FS) and highlight opportunities for future research that will further the understanding of FA pathogenesis in infancy and toddlerhood. DATA SOURCES: PubMed search of English-language articles related to FA and atopic disease. STUDY SELECTIONS: Human studies with outcomes related to FA, FS, and other atopic disease in childhood were selected and reviewed. Studies published after 2015 were prioritized. RESULTS: The prevalence of FA has greatly increased in recent decades and is now a global public health concern. A complex network of early life risk factors has been associated with development of FA and FS in childhood. Food allergy has a genetic component, but recent evidence suggests that interactions between risk alleles and other environmental exposures are important for disease pathogenesis, potentially through epigenetic mechanisms. Lifestyle factors, such as delivery mode, antibiotic use, and pet exposure also influence FA risk, which may be through their effect on the early life gut microbiome. How these early life risk factors, along with route and timing of antigen exposure, collectively target the developing immune system remains an ongoing and important area of study. CONCLUSION: The current body of evidence emphasizes the first 1000 days of life as a critical period for FA development. More observational studies and adequately powered clinical trials spanning early pregnancy through childhood are needed to identify novel biomarkers and risk factors that can predict susceptibility toward or protection against FA.

Nonylphenol Polyethoxylates Enhance Adipose Deposition in Developmentally Exposed Zebrafish.

Alkylphenol polyethoxylates (APEOs), such as nonylphenol ethoxylates (NPEOs), are high-production-volume surfactants used in laundry detergents, hard-surface cleaners, pesticide formulations, textile production, oils, paints, and other products. NPEOs comprise -80% of the total production of APEOs and are widely reported across diverse environmental matrices. Despite a growing push for replacement products, APEOs continue to be released into the environment through wastewater at significant levels. Research into related nonionic surfactants from varying sources has reported metabolic health impacts, and we have previously demonstrated that diverse APEOs and alcohol polyethoxylates promote adipogenesis in the murine 3T3-L1 pre-adipocyte model. These effects appeared to be independent of the base alkylphenol and related to the ethoxylate chain length, though limited research has evaluated NPEO exposures in animal models. The goals of this study were to assess the potential of NPEOs to promote adiposity (Nile red fluorescence quantification) and alter growth and/or development (toxicity, length, weight, and energy expenditure) of developmentally exposed zebrafish (Danio rerio). We also sought to expand our understanding of the ability to promote adiposity through evaluation in human mesenchymal stem cells. Herein, we demonstrated consistent adipogenic effects in two separate human bone-marrow-derived mesenchymal stem cell models, and that nonylphenol and its ethoxylates promoted weight gain and increased adipose deposition in developmentally exposed zebrafish. Notably, across both cell and zebrafish models we report increasing adipogenic/obesogenic activity with increasing ethoxylate chain lengths up to maximums around NPEO-6 and then decreasing activity with the longest ethoxylate chain lengths. This research suggests metabolic health concerns for these common obesogens, suggesting further need to assess molecular mechanisms and better characterize environmental concentrations for human health risk assessments.