Imbalance of Mg Homeostasis as a Potential Biomarker in Colon Cancer

Soft X-ray Fluorescence and Near-Edge Absorption Microscopy for Investigating Metabolic Features in Biological Systems: A Review

Scanning transmission X-ray microscopy (STXM) provides the imaging of biological specimens allowing the parallel collection of localized spectroscopic information by X-ray fluorescence (XRF) and/or X-ray Absorption Near Edge Spectroscopy (XANES). The complex metabolic mechanisms which can take place in biological systems can be explored by these techniques by tracing even small quantities of the chemical elements involved in the metabolic pathways. Here, we present a review of the most recent publications in the synchrotrons' scenario where soft X-ray spectro-microscopy has been employed in life science as well as in environmental research.

A dual-adjuvant neoantigen nanovaccine loaded with imiquimod and magnesium enhances anti-tumor immune responses of melanoma

A dual-adjuvant neoantigen nanoparticle loaded with imiquimod and magnesium (R837-Pep@HM NP) can enhance the anti-tumor immune response. The dendritic cells activated and presented neoantigens, which in turn activated T cells and killed tumor cells.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia

BACKGROUND

The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters.

METHODS

Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster's key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed.

RESULTS

In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality.

CONCLUSION

Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.

Why Has Metabolomics So Far Not Managed to Efficiently Contribute to the Improvement of Assisted Reproduction Outcomes? The Answer through a Review of the Best Available Current Evidence

Metabolomics emerged to give clinicians the necessary information on the competence, in terms of physiology and function, of gametes, embryos, and the endometrium towards a targeted infertility treatment, namely, assisted reproduction techniques (ART). Our minireview aims to investigate the current status of the use of metabolomics in assisted reproduction, the potential flaws in its use, and to propose specific solutions towards the improvement of ART outcomes through the use of the intervention. We used published reports assessing the role of metabolomic investigation of the endometrium, oocytes, and embryos in improving clinical outcomes in women undergoing ART. We initially found that there is no evidence to support that fertility outcomes can be improved through metabolomics profiling. In contrast, it may be helpful for understanding and appraising the nutritional environment of oocytes and embryos. The causes include the different infertility populations, the difference between animals and humans, technical limitations, and the great heterogeneity in the variables employed. Suggested steps include the standardization of variables of the method itself, the universal creation of a panel where all biomarkers are stored concerning specific infertile populations with different phenotypes or etiologies, specific bioinformatics contribution, significant computing power for data processing, and importantly, properly conducted trials.