Insight from untargeted metabolomics: Revealing the potential marker compounds changes in refrigerated pork based on random forests machine learning algorithm

Unraveling the spoilage characteristics of refrigerated pork using high-throughput sequencing coupled with UHPLC-MS/MS-based non-targeted metabolomics

The spoilage of refrigerated pork involves nutrient depletion and the production of spoilage metabolites by spoilage bacteria, yet the microbe-metabolite interactions during this process remain unclear. This study employed 16S rRNA high-throughput sequencing and non-targeted metabolomics based on ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) to reveal the core microbiota and metabolite profiles of pork during refrigeration. A total of 45 potential biomarkers were screened through random forest model analysis. Metabolic pathway analysis indicated that eleven pathways, including biogenic amine metabolism, pentose metabolism, purine metabolism, pyrimidine metabolism, phospholipid metabolism, and fatty acid degradation, were potential mechanisms of pork spoilage. Correlation analysis revealed nine metabolites-histamine, tyramine, tryptamine, D-gluconic acid, UDP-d-glucose, xanthine, glutamine, phosphatidylcholine, and hexadecanoic acid-as spoilage biomarkers, with Pseudomonas, Serratia, and Photobacterium playing significant roles. This study provides new insights into the changes in microbial and metabolic characteristics during the spoilage of refrigerated pork.

Characterization and exploration of dynamic variation of volatile compounds in vine tea during processing by GC-IMS and HS-SPME/GC-MS combined with machine learning algorithm

It is imperative to unravel the dynamic variation of volatile components of vine tea during processing to provide guidance for tea quality evaluation. In this study, the dynamic changes of volatile compounds of vine tea during processing were characterized by GC-IMS and HS-SPME/GC-MS. As a result, 103 volatile compounds were characterized by the two technologies with three overlapped ones. The random forest approach was employed to develop the models and explore key volatile compounds. 23 key compounds were explored, among which 13 were derived from GC-IMS and ten were from HS-SPME/GC-MS. Moreover, the area under the receiver operating characteristics curve with 100 cross validations by the pair-wised models were all 1 for the established models. Furthermore, the primary aroma formation mechanism for the key volatile compounds were mainly involved in fatty acid and amino acid metabolism. Besides, this study provides a theoretical support for directed processing and quality control of vine tea.

Metabolomics integrated with mass spectrometry imaging reveals novel action of tetramethylpyrazine in migraine

Migraine as a common neurological disorder still lacks effective therapies. Tetramethylpyrazine (TMP) is the main bioactive component from Ligusticum chuanxiong hort., a traditional edible-medicinal herb. This study aimed to investigate the action of TMP on migraine by metabolomics with mass spectrometry imaging (MSI) analysis and molecular exploring, including random forest model analysis, KEGG enrichment analysis and metabolite-metabolite interaction network analysis. The results indicated that 26 key representative metabolic biomarkers were identified, especially γ-glu-cys, which were highly related to glutathione (GSH) metabolism. MSI found the abundance of eleven endogenous metabolites were modulated by TMP, particularly glucose, the most important energy metabolism molecule, and GSH were increased that maintains intracellular redox balance, which was consistent with activation of Nrf2 signals by TMP. These findings provide insights into the effectiveness of metabolomics integrated with MSI in explaining the metabolic mechanisms of TMP, and afford valuable information for healthy development of TMP in migraine.

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays

Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring.

A comparative metabolomics analysis of phytochemcials and antioxidant activity between broccoli floret and by-products (leaves and stalks)

Leaves and stalks, which account for about 45% and 25% of broccoli biomass, respectively, are usually discarded during broccoli production, leading to the waste of green resources. In this study, the phytochemical composition and antioxidant capacity of broccoli florets and their by-products (leaves and stalks) were comprehensively analyzed. The metabolomics identified several unique metabolites (e.g., scopoletin, Harpagoside, and sinalbin) in the leaves and stalks compared to florets. Notably, the leaves were found to be a rich source of flavonoids and coumarins, with superior antioxidant capacity. The random forest model and correlation analysis indicated that flavonoids, coumarin, and indole compounds were the important factors contributing to the antioxidant activity. Moreover, the stalks contained higher levels of carbohydrates and exhibited better antioxidant enzyme activity. Together, these results provided valuable data to support the comprehensive utilization of broccoli waste, the development of new products, and the expansion of the broccoli industry chain.

Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma

Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.

On-site quantitation of xanthine in fish and serum using a smartphone-based spectrophotometer integrated with a dual-readout nanosensing assay

Rapid and quantitative biochemical analysis at points-of-need is imperative for food safety inspection. This work reports on: 1) a stand-alone smartphone-based "two-in-one" spectrophotometer (the SAFS) installed with a self-developed application (the SAFS-App) which can precisely collect both absorption spectra and fluorescence spectra in a reproducible manner within 5 s; and 2) a straightforward protocol for xanthine detection using fluorescent carbon nanodots and silver nanoparticles. The assay performed with the SAFS demonstrates high specificity towards xanthine, and a linear range of 1-60 μM with LODs of 0.38 and 0.58 μM for colorimetric and fluorometric readouts, respectively. The reliability and robustness of the SAFS are validated by on-site quantitation of xanthine in fish and serum samples, with comparable accuracy to HPLC method. More importantly, the SAFS presents itself as an appealing device which is accessible to everyone through the Internet of Things and can be tailored for diverse point-of-care testing applications.