MagMet: A fully automated web server for targeted nuclear magnetic resonance metabolomics of plasma and serum

Nuclear magnetic resonance (NMR) spectral analysis of biofluids can be a time-consuming process, requiring the expertise of a trained operator. With NMR becoming increasingly popular in the field of metabolomics, there is a growing need to change this paradigm and to automate the process. Here we introduce MagMet, an online web server, that automates the processing and quantification of 1D 1 H NMR spectra from biofluids-specifically, human serum/plasma metabolites, including those associated with inborn errors of metabolism (IEM). MagMet uses a highly efficient data processing procedure that performs automatic Fourier Transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking/peak alignment. MagMet then uses the peak positions, linewidth information, and J-couplings from its own specially prepared standard metabolite reference spectral NMR library of 85 serum/plasma compounds to identify and quantify compounds from experimentally acquired NMR spectra of serum/plasma. MagMet employs linewidth adjustment for more consistent quantification of metabolites from higher field instruments and incorporates a highly efficient data processing procedure for more rapid and accurate detection and quantification of metabolites. This optimized algorithm allows the MagMet webserver to quickly detect and quantify 58 serum/plasma metabolites in 2.6 min per spectrum (when processing a dataset of 50-100 spectra). MagMet's performance was also assessed using spectra collected from defined mixtures (simulating other biofluids), with >100 previously measured plasma spectra, and from spiked serum/plasma samples simulating known IEMs. In all cases, MagMet performed with precision and accuracy matching the performance of human spectral profiling experts. MagMet is available at http://magmet.ca.

Predictors of ICU admission in patients with peripartum cardiomyopathy

Background

Peripartum cardiomyopathy (PPCM) is a dilated cardiomyopathy that affects women in the peripartum period. The incidence of PPCM in the United States is 1,100 women per year. We studied the admission of women diagnosed with PPCM to the intensive care unit (ICU) with the goal of identifying factors that increase risk for adverse outcomes.

Methods

This was a retrospective study of 2,286 patients with a diagnosis of PPCM admitted to a private hospital system across the United States between the years 2017 and 2021. Logistical regression analysis was utilized to identify specific patient characteristics and risk factors that were associated with admission to the ICU.

Results

The average age of patients was 32.13 years. The patient cohort was 39.76% black, 39.15% white, and 13.12% Hispanic. The average length of stay was 3.35 days. Preeclampsia was present in 342 (14.96%) patients, cerebral infarction in 31 (1.36%) patients, eclampsia in 48 (2.10%) patients, myocardial infarction (MI) in 52 (2.27%) patients, and HELLP (hemolysis, elevated liver enzymes, low platelets) syndrome in 24 (1.05%) patients. There was a total of 504 (22.05%) patients admitted to the ICU. Preeclampsia (adjusted odds ratio [adj. OR] 1.588, 95% confidence interval [CI] 1.215–2.074, p<0.001), eclampsia (adj. OR 3.131, 95% CI 1.718–5.708, p<0.001), cerebral infarction (adj. OR 4.381, 95% CI 2.028–9.463, p<0.001), age (adj. OR 0.967, 95% CI 0.953–0.982, p<0.001), MI (adj. OR 2.961, 95% CI 1.608–5.455, p<0.001), and HELLP syndrome (adj. OR 4.655, 95% CI 1.926–11.256, p<0.001) were all associated with increased odds of requiring ICU level of care. However, hypertension, d-dimer level, troponin level, active tobacco smoking, alcohol use, and diabetes mellitus were not associated with increased likelihood of admission to the ICU.

Conclusion

In our study of 2,286 women with PPCM, we found statistically significant predictors of ICU admission included age, HELLP syndrome, eclampsia, preeclampsia, MI, and cerebral infarction. This data may be useful for the early identification and triaging of patients with PPCM that require higher level of care. These results can be utilized in developing a risk score to assist in this goal.

Funding Acknowledgement

Type of funding sources: Private hospital(s). Main funding source(s): HCA-Riverside Community Hospital

PlantSimLab - a modeling and simulation web tool for plant biologists

BACKGROUND

At the molecular level, nonlinear networks of heterogeneous molecules control many biological processes, so that systems biology provides a valuable approach in this field, building on the integration of experimental biology with mathematical modeling. One of the biggest challenges to making this integration a reality is that many life scientists do not possess the mathematical expertise needed to build and manipulate mathematical models well enough to use them as tools for hypothesis generation. Available modeling software packages often assume some modeling expertise. There is a need for software tools that are easy to use and intuitive for experimentalists.

RESULTS

This paper introduces PlantSimLab, a web-based application developed to allow plant biologists to construct dynamic mathematical models of molecular networks, interrogate them in a manner similar to what is done in the laboratory, and use them as a tool for biological hypothesis generation. It is designed to be used by experimentalists, without direct assistance from mathematical modelers.

CONCLUSIONS

Mathematical modeling techniques are a useful tool for analyzing complex biological systems, and there is a need for accessible, efficient analysis tools within the biological community. PlantSimLab enables users to build, validate, and use intuitive qualitative dynamic computer models, with a graphical user interface that does not require mathematical modeling expertise. It makes analysis of complex models accessible to a larger community, as it is platform-independent and does not require extensive mathematical expertise.

The prevalence and expression of inherited connexin 26 mutations associated with nonsyndromic hearing loss in the Israeli population

Connexin 26 (GJB2) mutations lead to hearing loss in a significant proportion of all populations studied so far, despite the fact that at least 50 other genes are also associated with hearing loss. The entire coding region of connexin 26 was sequenced in 75 hearing impaired children and adults in Israel in order to determine the percentage of hearing loss attributed to connexin 26 and the types of mutations in this population. Age of onset in the screened population was both prelingual and postlingual, with hearing loss ranging from moderate to profound. Almost 39% of all persons tested harbored GJB2 mutations, the majority of which were 35delG and 167delT mutations. A novel mutation, involving both a deletion and insertion, 51del12insA, was identified in a family originating from Uzbekistan. Several parameters were examined to establish whether genotype-phenotype correlations exist, including age of onset, severity of hearing loss and audiological characteristics, including pure-tone audiometry, tympanometry, auditory brainstem response (ABR), and transient evoked otoacoustic emissions (TEOAE). All GJB2 mutations were associated with prelingual hearing loss, though severity ranged from moderate to profound, with variability even among hearing impaired siblings. We have not found a significant difference in hearing levels between individuals with 35delG and 167delT mutations. Our results suggest that, in Israel, clinicians should first screen for the common 167delT and 35delG mutations by simple and inexpensive restriction enzyme analysis, although if these are not found, sequencing should be done to rule out additional mutations due to the ethnic diversity in this region.