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Abstract Background Premature ovarian failure (POF) is a clinical condition characterized by the cessation of ovarian function, leading to infertility. The underlying molecular mechanisms remain unclear, and no predictable biomarkers have been identified. This study aimed to investigate the protein and metabolite contents of serum extracellular vesicles to investigate underlying molecular mechanisms and explore potential biomarkers. Methods This study was conducted on a cohort consisting of 14 POF patients and 16 healthy controls. The extracellular vesicles extracted from the serum of each group were subjected to label-free proteomic and unbiased metabolomic analysis. Differentially expressed proteins and metabolites were annotated. Pathway network clustering was conducted with further correlation analysis. The biomarkers were confirmed by ROC analysis and random forest machine learning. Results The proteomic and metabolomic profiles of POF patients and healthy controls were compared. Two subgroups of POF patients, Pre-POF and Pro-POF, were identified based on the proteomic profile, while all patients displayed a distinguishable metabolomic profile. Proteomic analysis suggested that inflammation serves as an early factor contributing to the infertility of POF patients. For the metabolomic analysis, despite the dysfunction of metabolism, oxidative stress and hormone imbalance were other key factors appearing in POF patients. Signaling pathway clustering of proteomic and metabolomic profiles revealed the progression of dysfunctional energy metabolism during the development of POF. Moreover, correlation analysis identified that differentially expressed proteins and metabolites were highly associated, with six of them being selected as potential biomarkers. ROC curve analysis, together with random forest machine learning, suggested that AFM combined with 2-oxoarginine was the best diagnostic biomarker for POF. Conclusions Omics analysis revealed that inflammation, oxidative stress, and hormone imbalance are factors that damage ovarian tissue, but the progressive dysfunction of energy metabolism might be the critical pathogenic pathway contributing to the development of POF. AFM combined with 2-oxoarginine serves as a precise biomarker for clinical POF diagnosis.

Flavors and fragrances are essential for product quality, yet they are highly susceptible to contamination due to high moisture content and rich nutrients. This study investigates microbial growth, pH changes, volatile compound dynamics, and microbial community changes during the storage of flavors and fragrances. Results indicate that total viable counts (TVC) remained stable for the first three days but increased rapidly afterward, exceeding the acceptable limit of 5 log CFU/mL by day 7. The pH levels initially rose slightly, followed by a steady decline, which indicates spoilage progression. Gas chromatography–mass spectrometry (GC–MS) analysis revealed significant degradation of key aromatic compounds, such as 5-hydroxymethylfurfural (5-HMF), vanillin, and its derivative ethyl vanillin. Whole genome shotgun (WGS) sequencing demonstrated a marked increase in microbial community richness and diversity as storage progressed, with a notable shift in composition. Early storage stages were dominated by fungal species from the Ascomycota phylum, while later stages saw a rise in spoilage-associated bacteria, particularly from the Firmicutes and Proteobacteria phyla. Throughout the storage process, Zygosaccharomyces and its dominant species, Zygosaccharomyces bailii, remained prevalent, though their average relative abundance decreased from 81.26 to 32.29%. In addition, the bacterial species Oceanobacillus sojae and Niallia nealsonii showed significant increases in relative abundance, suggesting that bacteria were one of the key contributors to the spoilage of flavors and fragrances. Functional analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database indicated a shift in metabolic pathways within the microbial community, with heightened metabolic activity correlating with spoilage. These findings provide valuable insights for improving storage methods and quality control of flavors and fragrances.

Lunar surface temperature (LST) is a quantity of special interest for interpreting the thermal character of the regolith. It is affected by the solar irradiance, earthshine, and heat flow for the flat areas on the Moon. We present an improved transient temperature model to calculate temperatures of lunar flat surfaces. The model consists of one-dimensional thermal diffusion equation and two boundary conditions. The improved lunar surface boundary condition allows one to precisely determine the effective solar irradiance (ESI) and earthshine. The simulated surface temperatures suggest consistency with the measured temperatures from the thermocouples of Apollo 15 and 17 heat flow experiments. From the LST simulated with the improved model, it is found the annual-seasonal variations present obvious latitude characters, with the highest surface temperature occurring in late October, November, and December separately at high (>∼72°) latitudes, middle latitudes, and low (

The rock strongly affects the surface and subsurface temperature due to its different thermophysical properties compared to the lunar regolith. The brightness temperature (TB) data observed by Chang’E-1 (CE-1) and Chang’E-2 (CE-2) microwave radiometers (MRM) give us a chance to retrieve the lunar subsurface rock abundance (RA). In this paper, a thermal conductivity model with an undetermined parameter β of the mixture has been employed to estimate the physical temperature profile of the mixed layer (rock and regolith). Parameter β and the physical temperature profile of the mixed layer are constrained by the Diviner Channel 7 observations. Then, the subsurface RA on the 16 large (Diameter > 20 km) Copernican-age craters of the Moon is extracted from the average nighttime TB of the CE-2 37 GHz channel based on our previous rocky TB model. Two conclusions can be derived from the results: (1) the subsurface RA values are usually greater than the surface RA values retrieved from Diviner observations of the studied craters; (2) the spatial distribution of subsurface RA extracted from CE-2 MRM data is not necessarily consistent with the surface RA detected by Diviner data. For example, there are similar RA spatial distributions on both the surface and subsurface in Giordano Bruno, Necho, and Aristarchus craters. However, the distribution of subsurface RA is obviously different from that of surface RA for Copernicus, Ohm, Sharonov, and Tycho craters.