Spectrophotometric Analysis of Urinary Iodine in 18-22 Year-Old Women in Central Minnesota

Iodine is an overlooked but incredibly important micronutrient, especially with regard to early fetus development. The United States does not mandate salt iodization, so widely consumed processed foods are not likely to contain iodized salt, which may potentially put the population at risk for developing iodine deficiencies. Therefore, the purpose of this study is to correspond iodine intake to iodine status and establish effective methods for determining a relationship between the two. Subjects (n=23) completed a food survey and supplied 50mL urine samples to compare an estimated average daily iodine intake based on foods over the course of a week and urinary iodine content determined via spectrophotometric analysis of the Sandell-Kolthoff reaction. Most subjects fell into the optimal intake and urinary iodine categories, but the survey inconsistently correlated intake and urine categories, most likely due to the use of a single spot urine sample. Future studies should investigate more representative intake surveillance, potentially through the use of 24-hour recalls or food diaries, and more representative urine sample collection methods, as with a 24-hour collection or a control for hydration

Investigation of the Effects of Water Stress on \u3ci\u3eVigna radiata\u3c/i\u3e and \u3ci\u3eBrassica rapus\u3c/i\u3e

Water is crucial to photosynthesis because it provides electrons for the light-dependent reactions. Additionally, plants decrease transpiration rate during drought in an effort to minimize water loss, resulting in changes in CO2 uptake and photosynthetic rate (Vico, 2008; Özenc, 2008; Galmés et al., 2007). Water use efficiency (WUE), the ability of a plant to maintain photosynthesis despite water loss, is an essential component of determining plant performance in drought conditions. Previous studies have shown an association between water stress and increased WUE (Zhang et al., 2010). Therefore, we hypothesize that the photosynthetic rates in both the Vigna radiata (mung beans) and Brassica rapus (rutabagas) will decrease after withholding water until exhibition of water stress symptoms, and the WUE of water-stressed plants will be higher than watered counterparts

Recent advances in mass spectrometry-based computational metabolomics

The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled “Computational Metabolomics: From Spectra to Knowledge”

Precision information extraction for rare disease epidemiology at scale

Abstract Background The United Nations recently made a call to address the challenges of an estimated 300 million persons worldwide living with a rare disease through the collection, analysis, and dissemination of disaggregated data. Epidemiologic Information (EI) regarding prevalence and incidence data of rare diseases is sparse and current paradigms of identifying, extracting, and curating EI rely upon time-intensive, error-prone manual processes. With these limitations, a clear understanding of the variation in epidemiology and outcomes for rare disease patients is hampered. This challenges the public health of rare diseases patients through a lack of information necessary to prioritize research, policy decisions, therapeutic development, and health system allocations. Methods In this study, we developed a newly curated epidemiology corpus for Named Entity Recognition (NER), a deep learning framework, and a novel rare disease epidemiologic information pipeline named EpiPipeline4RD consisting of a web interface and Restful API. For the corpus creation, we programmatically gathered a representative sample of rare disease epidemiologic abstracts, utilized weakly-supervised machine learning techniques to label the dataset, and manually validated the labeled dataset. For the deep learning framework development, we fine-tuned our dataset and adapted the BioBERT model for NER. We measured the performance of our BioBERT model for epidemiology entity recognition quantitatively with precision, recall, and F1 and qualitatively through a comparison with Orphanet. We demonstrated the ability for our pipeline to gather, identify, and extract epidemiology information from rare disease abstracts through three case studies. Results We developed a deep learning model to extract EI with overall F1 scores of 0.817 and 0.878, evaluated at the entity-level and token-level respectively, and which achieved comparable qualitative results to Orphanet’s collection paradigm. Additionally, case studies of the rare diseases Classic homocystinuria, GRACILE syndrome, Phenylketonuria demonstrated the adequate recall of abstracts with epidemiology information, high precision of epidemiology information extraction through our deep learning model, and the increased efficiency of EpiPipeline4RD compared to a manual curation paradigm. Conclusions EpiPipeline4RD demonstrated high performance of EI extraction from rare disease literature to augment manual curation processes. This automated information curation paradigm will not only effectively empower development of the NIH Genetic and Rare Diseases Information Center (GARD), but also support the public health of the rare disease community

The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseasesResearch in context

Summary: Background: As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles. Methods: We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence. Findings: We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10−08, ORmortality = 4.7, p-value = 1.6 × 10−04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10−4, βrecurrence = 1.03; p-value = 5.1 × 10−3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two–and potentially more–IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis. Interpretations: These metabolites may be identified prior to infection to enable protective measures for these individuals. Funding: The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health