Seaweeds are a group of marine multicellular algae; the presence of antioxidant phytochemical constituents in Seaweed Chaetomorpha sp. extracts has received attention for their role in the prevention of human diseases. This study explores the phytochemical constituents, antioxidant, and anticancer properties of the Cladophoraceae, Chaetomorpha sp. Energy dispersive x-ray spectroscopy (EDX), and Gas chromatography-mass spectrometry (GC/MS) were performed to study the chemical structure and chemical formula. Different concentrations of ethanol and aqueous extracts of Chaetomorpha were used to estimate antioxidant activity by 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and total flavonoid, phenolic, and tannins content assays. Anti-tumor activity against breast cancer cell lines (MCF-7 and MDA-MB-231) was assessed by 3-(4,5-Dimethylthiazol-2-cyl)-2,5-Diphenyltetrazolium Bromide (MTT) assay. The EDX analysis indicated the presence of oxygen, silicon, and calcium as dominant elements. Antioxidant assays indicated that the ethanol extracts of Chaetomorpha consisted of a total of 189.14 ± 0.99 mg QE/g flavonoid content, 21.92 ± 0.43 mg GAE/g phenolic content and 21.81 ± 0.04 mg GAE/g tannins content. The DPPH radical scavenging assay exhibited higher antioxidant activity IC₅₀ (9.41 ± 0.54 mg/mL) in the ethanol extract. Moreover, it showed high anticancer activity by growth inhibition in the MDA-MB-231 breast cancer cell line and low IC₅₀ (225.18 ± 0.61 µg/mL). GC/MS analysis revealed the presence of Dichloracetic acid (DCA) as the active antitumor constituent of Chaetomorpha sp.; other anticancer compounds identified were Oximes and L-α-Terpinol. The results revealed that the type of Chaetomorpha sp. studied here possesses very unique and novel constituents and active potent antitumor chemical constituents and it can act as a promising antioxidant and anticancer agent for future applications in pharmaceutical industries.

Bone mass reduction due to an imbalance in osteogenesis and osteolysis is characterized by low bone mineral density (LBMD) and is clinically classified as osteopenia (ON) or osteoporosis (OP), which is more severe. Multiple biomarkers for diagnosing OP and its progression have been reported; however, most of these lack specificity. This cohort study aimed to investigate sensitive and specific LBMD-associated protein biomarkers in patients diagnosed with ON and OP. A label-free liquid chromatography-mass spectrometry (LC-MS) proteomics approach was used to analyze serum samples. Patients’ proteomics profiles were filtered for potential confounding effects, such as age, sex, chronic diseases, and medication. A distinctive proteomics profile between the control, ON, and OP groups (Q² = 0.7295, R² = 0.9180) was identified, and significant dysregulation in a panel of proteins (n = 20) was common among the three groups. A comparison of these proteins showed that the levels of eight proteins were upregulated in ON, compared to those in the control and the OP groups, while the levels of eleven proteins were downregulated in the ON group compared to those in the control group. Interestingly, only one protein, myosin heavy chain 14 (MYH14), showed a linear increase from the control to the ON group, with the highest abundance in the OP group. A significant separation in the proteomics profile between the ON and OP groups (Q² = 0.8760, R² = 0.991) was also noted. Furthermore, a total of twenty-six proteins were found to be dysregulated between the ON and the OP groups, with fourteen upregulated and twelve downregulated proteins in the OP, compared to that in the ON group. Most of the identified dysregulated proteins were immunoglobulins, complement proteins, cytoskeletal proteins, coagulation factors, and various enzymes. Of these identified proteins, the highest area under the curve (AUC) in the receiver operating characteristic (ROC) analysis was related to three proteins (immunoglobulin Lambda constant 1 (IGLC1), RNA binding protein (MEX3B), and fibulin 1 (FBLN1)). Multiple reaction monitoring (MRM), LC-MS, was used to validate some of the identified proteins. A network pathway analysis of the differentially abundant proteins demonstrated dysregulation of inflammatory signaling pathways in the LBMD patients, including the tumor necrosis factor (TNF), toll-like receptor (TL4), and interferon-γ (IFNG) signaling pathways. These results reveal the existence of potentially sensitive protein biomarkers that could be used in further investigations of bone health and OP progression.

Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is the most common inherited mitochondrial metabolic disease of fatty acid β-oxidation, especially in newborns. MCADD is clinically diagnosed using Newborn Bloodspot Screening (NBS) and genetic testing. Still, these methods have limitations, such as false negatives or positives in NBS and the variants of uncertain significance in genetic testing. Thus, complementary diagnostic approaches for MCADD are needed. Recently, untargeted metabolomics has been proposed as a diagnostic approach for inherited metabolic diseases (IMDs) due to its ability to detect a wide range of metabolic alterations. We performed an untargeted metabolic profiling of dried blood spots (DBS) from MCADD newborns (n = 14) and healthy controls (n = 14) to discover potential metabolic biomarkers/pathways associated with MCADD. Extracted metabolites from DBS samples were analyzed using UPLC-QToF-MS for untargeted metabolomics analyses. Multivariate and univariate analyses were used to analyze the metabolomics data, and pathway and biomarker analyses were also performed on the significantly identified endogenous metabolites. The MCADD newborns had 1034 significantly dysregulated metabolites compared to healthy newborns (moderated t-test, no correction, p-value ≤ 0.05, FC 1.5). A total of 23 endogenous metabolites were up-regulated, while 84 endogenous metabolites were down-regulated. Pathway analyses showed phenylalanine, tyrosine, and tryptophan biosynthesis as the most affected pathways. Potential metabolic biomarkers for MCADD were PGP (a21:0/PG/F1alpha) and glutathione, with an area under the curve (AUC) of 0.949 and 0.898, respectively. PGP (a21:0/PG/F1alpha) was the first oxidized lipid in the top 15 biomarker list affected by MCADD. Additionally, glutathione was chosen to indicate oxidative stress events that could happen during fatty acid oxidation defects. Our findings suggest that MCADD newborns may have oxidative stress events as signs of the disease. However, further validations of these biomarkers are needed in future studies to ensure their accuracy and reliability as complementary markers with established MCADD markers for clinical diagnosis.

Introduction: Gestational Diabetes Mellitus (GDM) is a metabolic disorder marked by Q10 hyperglycemia that can negatively affect both mothers and newborns. The increasing prevalence of GDM and the limitations associated with the standard diagnostic test highlight the urgent need for early screening strategies that promote timely interventions.Methods: This study aims to investigate the metabolic profile associated with GDM through an untargeted metabolomic analysis using mass spectrometry (MS)- based omics. Serum samples were collected from 40 pregnant women at weeks 24–28 of gestation based on the 2-h 75-g oral glucose tolerance test (OGTT); 50% were diagnosed with GDM (n = 20), and the remaining were considered a control group.Results and discussion: The results showed distinct metabolic differences between women with GDM and those without, with 222 significantly dysregulated metabolites, 120 up- and 102 downregulated in GDM compared to the control group. Key metabolic pathways, such as tryptophan, inositol phosphate, phenylalanine, and histidine metabolism, were notably dysregulated in GDM. The study also found that specific metabolites, like N-Acetylproline and Serylmethionine, with area under the curve (AUC) of 0.978 and 0.968, respectively, showed high accuracy in distinguishing between GDM and non-GDM women. This study would enhance our understanding of metabolic alterations in GDM and could contribute to early prediction and management strategies.