Variable importance for projection (VIP) scores for analyzing the contribution of risk factors in severe adverse events to Xiyanping injection

Metabolic profiling reveals distinctive ripening dynamics in ethylene-treated Musa balbisiana cv. 'Pisang Klutuk Wulung' compared to commercial Cavendish banana

As an important crop, bananas still encounter fruit quality and shelf-life problems that are affected by the ripening process. Improving postharvest technologies may effectively address these challenges, such as by studying the ripening mechanism of banana cultivars with a slow ripening process. A banana cultivar that exhibits this characteristic is Musa balbisiana cv. 'Pisang Klutuk Wulung' (BB Group) or Pisang Klutuk Wulung (PKW), which has a ripening duration of 14-28 days. However, the metabolomics study on the ripening mechanism of this banana is still limited. This study aimed to analyze metabolite changes in ethylene-treated Pisang Klutuk Wulung during ripening in comparison to commercial bananas (Cavendish). Both bananas were subjected to exogenous ethylene treatment and analyzed using gas chromatography-mass spectrometry to perform metabolite profiling throughout the ripening process. The principal component analysis showed sample separation based on the ripening stages and banana species in pulp and peel. Orthogonal projection to latent structure analysis suggested that metabolite changes accompanied the ripening stages. Potential metabolite markers that distinguished the ripening of PKW and Cavendish were found, such as quinic acid, inositol, and 2-aminoethanol. This study shows differences in metabolite profiles between these bananas, especially the metabolites involved in sugar metabolism, cell wall metabolism, stress response, and biosynthesis of aromatic compounds. This study provides novel insights into the metabolic changes occurring during PKW ripening, contributing to the improvement of banana postharvest strategies.

Differential Chemical Components Analysis of Periplocae Cortex, Lycii Cortex, and Acanthopanacis Cortex Based on Mass Spectrometry Data and Chemometrics

Background:Periplocae Cortex (PC), Acanthopanacis Cortex (AC), and Lycii Cortex (LC), as traditional Chinese medicines, are all dried root bark, presented in a roll, light and brittle, easy to break, have a fragrant scent, etc. Due to their similar appearances, it is tough to distinguish them, and they are often confused and adulterated in markets and clinical applications. To realize the identification and quality control of three herbs, in this paper, Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry Expression (UHPLC-QTOF-MSE) combined with chemometric analysis was used to explore the different chemical compositions. Methods: LC, AC, and PC were analyzed by UHPLC-QTOF-MSE, and the quantized MS data combined with Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to explore the different chemical compositions with Variable Importance Projection (VIP) > 1.0. Further, the different chemical compositions were identified according to the chemical standard substances, related literature, and databases. Results: AC, PC, and LC can be obviously distinguished in PCA and PLS-DA analysis with the VIP of 2661 ions > 1.0. We preliminarily identified 17 differential chemical constituents in AC, PC, and LC with significant differences (p < 0.01) and VIP > 1.0; for example, Lycium B and Periploside H2 are LC and PC's proprietary ingredients, respectively, and 2-Hydroxy-4-methoxybenzaldehyde, Periplocoside C, and 3,5-Di-O-caffeoylquinic acid are the shared components of the three herbs. Conclusions: UHPLC-QTOF-MSE combined with chemometric analysis is conducive to exploring the differential chemical compositions of three herbs. Moreover, the proprietary ingredients, Lycium B (LC) and Periploside H2 (PC), are beneficial in strengthening the quality control of AC, PC, and LC. In addition, limits on the content of shared components can be set to enhance the quality control of LC, PC, and AC.

Metabolic profiling and combined therapeutic strategies unveil the cytotoxic potential of selenium-chrysin (SeChry) in NSCLC cells

Lung cancer ranks as the predominant cause of cancer-related mortalities on a global scale. Despite progress in therapeutic interventions, encompassing surgical procedures, radiation, chemotherapy, targeted therapies and immunotherapy, the overall prognosis remains unfavorable. Imbalances in redox equilibrium and disrupted redox signaling, common traits in tumors, play crucial roles in malignant progression and treatment resistance. Cancer cells, often characterized by persistent high levels of reactive oxygen species (ROS) resulting from genetic, metabolic, and microenvironmental alterations, counterbalance this by enhancing their antioxidant capacity. Cysteine availability emerges as a critical factor in chemoresistance, shaping the survival dynamics of non-small cell lung cancer (NSCLC) cells. Selenium-chrysin (SeChry) was disclosed as a modulator of cysteine intracellular availability. This study comprehensively characterizes the metabolism of SeChry and investigates its cytotoxic effects in NSCLC. SeChry treatment induces notable metabolic shifts, particularly in selenocompound metabolism, impacting crucial pathways such as glycolysis, gluconeogenesis, the tricarboxylic acid (TCA) cycle, and amino acid metabolism. Additionally, SeChry affects the levels of key metabolites such as acetate, lactate, glucose, and amino acids, contributing to disruptions in redox homeostasis and cellular biosynthesis. The combination of SeChry with other treatments, such as glycolysis inhibition and chemotherapy, results in greater efficacy. Furthermore, by exploiting NSCLC's capacity to consume lactate, the use of lactic acid-conjugated dendrimer nanoparticles for SeChry delivery is investigated, showing specificity to cancer cells expressing monocarboxylate transporters.

A Comprehensive Analysis of Fel Ursi and Its Common Adulterants Based on UHPLC-QTOF-MSE and Chemometrics

BACKGROUND

As one of the four most valuable animal medicines, Fel Ursi, named Xiong Dan (XD) in China, has the effect of clearing heat, calming the liver, and brightening the eyes. However, due to the special source of XD and its high price, other animals' bile is often sold as XD or mixed with XD on the market, seriously affecting its clinical efficacy and consumers' rights and interests. In order to realize identification and adulteration analysis of XD, UHPLC-QTOF-MSE and multivariate statistical analysis were used to explore the differences in XD and six other animals' bile.

METHODS

XD, pig gall (Zhu Dan, ZD), cow gall (Niu Dan, ND), rabbit gallbladder (Tu Dan, TD), duck gall (Yan Dan, YD), sheep gall (Yang Dan, YND), and chicken gall (Ji Dan, JD) were analyzed by UHPLC-QTOF-MSE, and the MS data, combined with multivariate analysis methods, were used to distinguish between them. Meanwhile, the potential chemical composition markers that contribute to their differences were further explored.

RESULTS

The results showed that XD and six other animals' bile can be distinguished from each other obviously, with 27 ions with VIP > 1.0. We preliminarily identified 10 different bile acid-like components in XD and the other animals' bile with significant differences (p < 0.01) and VIP > 1.0, such as tauroursodeoxycholic acid, Glycohyodeoxycholic acid, and Glycodeoxycholic acid.

CONCLUSIONS

The developed method was efficient and rapid in accurately distinguishing between XD and six other animals' bile. Based on the obtained chemical composition markers, it is beneficial to strengthen quality control for bile medicines.

Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning

OBJECTIVE

To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more accurately than the Charlson Comorbidity Index (CCI) for use in health services research.

METHODS

We utilized SEER-Medicare data to identify patients with stage IIIC and IV ovarian cancer, diagnosed in 2010-2015. We employed partial least squares regression, a supervised machine learning algorithm, to develop the MCI by extracting latent factors that optimally captured the variation in health insurance claims made in the year preceding cancer diagnosis, and 1-year mortality. We assessed the discrimination and calibration of the MCI for 1-year mortality and compared its performance to the commonly-used CCI. Finally, we evaluated the MCI's ability to reduce confounding in the association of neoadjuvant chemotherapy (NACT) and all-cause mortality.

RESULTS

We included 4723 patients in the development cohort and 933 in the validation cohort. The MCI demonstrated good discrimination for 1-year mortality (c-index: 0.75, 95% CI: 0.72-0.79), while the CCI had poor discrimination (c-index: 0.59, 95% CI: 0.56-0.63). Calibration plots showed better agreement between predicted and observed 1-year mortality risk for the MCI compared with CCI. When comparing all-cause mortality between NACT with primary cytoreductive surgery, NACT was associated with a higher hazard of death (HR: 1.13, 95% CI: 1.04-1.23) after controlling for tumor characteristics, demographic factors, and the CCI. However, when controlling for the MCI instead of the CCI, there was no longer a significant difference (HR: 1.05, 95% CI: 0.96-1.14).

CONCLUSIONS

The MCI outperformed the conventional CCI in predicting 1-year mortality, and reducing confounding due to differences in baseline health status in comparative effectiveness analysis of NACT versus primary surgery.