Hormone Rhythms and Breast Cancer Chronoepidemiology: Salivary Progesterone Concentrations in Pre- and Post-Menarchal Girls and in Normal Premenopausal Women

Orcadian and circatrigintan time series of salivary progesterone levels in premenarchal and adolescent girls and healthy mature premenopausal women have been investigated as a possible determinant for breast cancer risk. Circadian variations in progesterone appear to be more random than systematic and estimates of total daily progesterone output are better represented by samples pooled from several 2-hr specimens. Different patterns of circatrigintan progesterone secretion in girls are recognised and relate to those experienced in infertile and fertile women, though their relation to chronological or menarchal age is as yet uncertain. These data suggest that the measurement of salivary progesterone at premenarche, adolescence and maturity is a feasible, though statistically difficult, study for prospective identification of individuals at risk for breast cancer.     

A high‐throughput method to quantify feeding rates in aquatic organisms: A case study with Daphnia

1. Food ingestion is one of the most basic features of all organisms. However, obtaining precise—and high‐throughput—estimates of feeding rates remains challenging, particularly for small, aquatic herbivores such as zooplankton, snails, and tadpoles. These animals typically consume low volumes of food that are time‐consuming to accurately measure.
2. We extend a standard high‐throughput fluorometry technique, which uses a microplate reader and 96‐well plates, as a practical tool for studies in ecology, evolution, and disease biology. We outline technical and methodological details to optimize quantification of individual feeding rates, improve accuracy, and minimize sampling error.
3. This high‐throughput assay offers several advantages over previous methods, including i) substantially reduced time allotments per sample to facilitate larger, more efficient experiments; ii) technical replicates; and iii) conversion of in vivo measurements to units (mL^‐1 hr^‐1 ind^‐1) which enables broad‐scale comparisons across an array of taxa and studies.
4. To evaluate the accuracy and feasibility of our approach, we use the zooplankton, Daphnia dentifera, as a case study. Our results indicate that this procedure accurately quantifies feeding rates and highlights differences among seven genotypes.
5. The method detailed here has broad applicability to a diverse array of aquatic taxa, their resources, environmental contaminants (e.g., plastics), and infectious agents. We discuss simple extensions to quantify epidemiologically relevant traits, such as pathogen exposure and transmission rates, for infectious agents with oral or trophic transmission.

     

Validating Landsat-based landscape metrics with fine-grained land cover data

ContextModerate-grained data may not always represent landscape structure in adequate detail which could cause misleading results. Certain metrics have been shown to be predictable with changes in scale; however, no studies have verified such predictions using independent fine-grained data.ObjectivesOur objective was to use independently derived land cover datasets to assess relationships between metrics based on fine- and moderate-grained data for a range of analysis extents. We focus on metrics that previous literature has shown to have predictable relationships across scales.MethodsThe study area was located in eastern Connecticut. We compared a 1 m land cover dataset to a 30 m resampled dataset, derived from the 1 m data, as well as two Landsat-based datasets. We examined 11 metrics which included cover areas and patch metrics. Metrics were analyzed using analysis extents ranging from 100 to 1400 m in radius.ResultsThe resampled data had very strong linear relationships to the 1 m data, from which it was derived, for all metrics regardless of the analysis extent size. Landsat-based data had strong correlations for most cover area metrics but had little or no correlation for patch metrics. Increasing analysis areas improved correlations.ConclusionsRelationships between coarse- and fine-grained data tend to be much weaker when comparing independent land cover datasets. Thus, trends across scales that are found by resampling land cover are likely to be unsuitable for predicting the effects of finer-scale elements in the landscape. Nevertheless, coarser data shows promise in predicting fine-grained for cover area metrics provided the analysis area used is sufficiently large.     

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Impact of High Deductible Health Plans on Diabetes Care Quality and Outcomes: Systematic Review

ObjectiveTo provide a review of the impact of high deductible health plans (HDHPs) on the utilizations of services required for optimal management of diabetes and subsequent health outcomes.MethodsSystematic literature review of studies published between January 1, 2000, and May 7, 2021, was conducted that examined the impact of HDHP on diabetes monitoring (eg, recommended laboratory and surveillance testing), routine care (eg, ambulatory appointments), medication management (eg, medication initiation, adherence), and acute health care utilization (eg, emergency department visits, hospitalizations, incident complications).ResultsOf the 303 reviewed articles, 8 were relevant. These studies demonstrated that HDHPs lower spending at the expense of reduced high-value diabetes monitoring, routine care, and medication adherence, potentially contributing to the observed increases in acute health care utilization. Additionally, patient out-of-pocket costs for recommended screenings doubled, and total health care expenditures increased by 49.4% for HDHP enrollees compared with enrollees in traditional health plans. Reductions in disease monitoring and routine care and increases in acute health care utilization were greatest in lower-income patients. None of the studies examined the impact of HDHPs on access to diabetes self-management education, technology use, or glycemic control.ConclusionAlthough HDHPs reduce some health care utilization and costs, they appear to do so at the expense of limiting high-value care and medication adherence. Policymakers, providers, and payers should be more cognizant of the potential for negative consequences of HDHPs on patients’ health.     

An Extreme Value Analysis of Pollutant Concentrations in Surface Soils Due to Atmospheric Deposition

Risk assessments often rely on deterministic models using long-term averages or “steady-state” values of input variables. Such models do not provide the information needed to estimate acute exposures. This study uses extreme value theory to examine the frequency and magnitude of daily pollutant concentrations in surface soils predicted at six U.S. locations. Concentrations are predicted using a deposition-leaching model and 50 years of historical precipitation data. A stochastic model also is used to generate 1000 years of precipitation data as modeling inputs for each location. The annual maximum concentrations at each site are fitted to a Gumbel type I distribution to estimate occurrence probability. For soluble pollutants, the predicted concentration varied substantially with precipitation, and the maximum daily concentrations exceeded annual averages by 4 to 8 times. Observed and synthetic precipitation data produced similar results at most study locations, though the synthetic data provided a slightly better fit to the Gumbel type I distribution. The precipitation model allows the generation of representative precipitation data that extend limited historical records. The extreme value analysis facilitates the evaluation of maximum pollutant concentrations, return periods, and other statistics that are important in evaluating acute exposures.     

Mitochondrial Oxidative Phosphorylation Complex Regulates NLRP3 Inflammasome Activation and Predicts Patient Survival in Nasopharyngeal Carcinoma

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Highlights

• •Flow cytometry analysis is used to isolate ASC speck⁽⁺⁾ NPC cells.
• •Proteome analysis of ASC speck⁽⁺⁾ NPC cells reveals enriched mitochondrial OxPhos proteins.
• •OxPhos proteins mediate NLRP3 inflammasome activation through mtROS.
• •OxPhos proteins, NDUFB8 and ATP5B are correlated with NPC local recurrence.

     

The transient and constitutive inflammatory signaling in tumorigenesis

Comment on: Rokavec M, et al. Mol Cell 2012; 45:777-89.     

Associations of demographic and perinatal factors with childhood neuroblastoma in Texas, 1995–2011

BackgroundNeuroblastoma, the most common extracranial solid tumor in children, contributes disproportionately to childhood cancer mortality and few risk factors have been identified. Our objective was to evaluate associations between parental and infant characteristics and neuroblastoma incidence.MethodsChildren born in Texas between January 1995 and December 2011 were eligible for the present study. Cases (N = 637) were diagnosed with neuroblastoma in Texas during the same period; controls (N = 6370) matched on year of birth were randomly selected from birth certificates that did not link to a record in the Texas Cancer Registry. We obtained data on birth and parental demographic/reproductive characteristics from birth certificates, and estimated odds ratios (OR) and 95% confidence intervals (CIs) for neuroblastoma using logistic regression.ResultsGestational age 34–36 weeks at birth was associated with neuroblastoma (OR 1.45, CI 1.09–1.90), whereas female sex was inversely associated (OR 0.68, CI 0.58–0.81). Relative to children of non-Hispanic White women, children of Hispanic (OR 0.53, CI 0.43–0.64) or non-Hispanic Black (OR 0.52, CI 0.38–0.71) women were at reduced odds of neuroblastoma. When maternal and paternal race/ethnicity were evaluated jointly, similar patterns were observed (two non-Hispanic Black parents: OR 0.55, 95%CI 0.36–0.79; two Hispanic parents: OR 0.53, 95%CI 0.41–0.67). Older maternal age was also positively associated with neuroblastoma (OR 1.41, CI 1.04–1.90 for 35–39 years; OR 1.62, CI 0.87–2.81 for ≥40 years, relative to 25–29 years).ConclusionsFindings provide further evidence of racial/ethnic disparities in neuroblastoma incidence, determinants of which are unknown. In contrast to most published studies, we observed an association between maternal age and neuroblastoma. Further studies with more robust control for confounding are warranted.