Two New Monascus Metabolites with Strong Blue Fluorescence Isolated from Red Yeast Rice

Red yeast rice obtained as cultures of Monascus AS3.4444 on rice was extracted and analyzed by high-performance liquid chromatography (HPLC). Two new Monascus metabolites with similar fluorescence spectra (λex = 396 nm, λem = 460 nm) and UV absorption spectra (λmax = 386 nm) were detected. They were isolated by rechromatography on a silica gel column and semipreparative HPLC, and two strong blue fluorescent compounds were obtained. Their structures were elucidated by electrospray ionization mass spectrometry (ESI−MS), electrospray ionization tandem mass spectrometry (ESI−MS/MS), intensive ESI−MS, and nuclear magnetic resonance spectroscopy (1H NMR, 13C NMR, COSY, and HMBC) studies. High-resolution mass spectrometry indicated the molecular formulas C21H24O5 and C23H28O5. The two new compounds, named monasfluore A and monasfluore B, respectively, contain a alkyl side chain, γ-lactone, and propenyl group, whereas the more lipophilic compound, monasfluore B, is a higher homologue of monasfluore A, with the more lipophilic octanoyl instead of the hexanoyl side chain

Metabolic Effects of the <i>pksCT</i> Gene on Monascus aurantiacus Li As3.4384 Using Gas Chromatography–Time-of-Flight Mass Spectrometry-Based Metabolomics

Monascus spp. have been used for the production of natural pigments and bioactive compounds in China for several centuries. Monascus can also produce the mycotoxin citrinin, restricting its use. Disruption of the <i>pksCT</i> gene in Monascus aurantiacus Li AS3.4384 reduces citrinin production capacity of this strain (Monascus PHDS26) by over 98%. However, it is unclear how other metabolites of M. aurantiacus Li AS3.4384 (the wild-type strain) are affected by the <i>pksCT</i> gene. Here, we used metabolomic analyses to compare red yeast rice (RYR) metabolite profiles of the wild-type strain and Monascus PHDS26 at different stages of solid-state fermentation. A total of 18 metabolites forming components within the glycolysis, acetyl-CoA, amino acid, and tricarboxylic acid (TCA) cycle metabolic processes were found to be altered between the wild-type strain and Monascus PHDS26 at different stages of solid-state fermentation. Thus, these findings provide important insights into the metabolic pathways affected by the <i>pksCT</i> gene in M. aurantiacus

Mechanism of Synergistic Effect on Electron Transfer over Co–Ce/MCM-48 during Ozonation of Pharmaceuticals in Water

The same amount of metal was deposited on the surface of three-dimensional mesoporous MCM-48 by a facile impregnation–calcination method for catalytic ozonation of pharmaceutical and personal-care products in the liquid phase. At 120 min reaction time, Co/MCM-48 and Ce/MCM-48 showed 46.6 and 63.8% mineralization for clofibric acid (CA) degradation, respectively. Less than 33% mineralization was achieved with Co/MCM-48 and Ce/MCM-48 during sulfamethazine (SMZ) ozonation. In the presence of monometallic oxides modified MCM-48 catalysts, total organic carbon (TOC) removal of diclofenac sodium (DCF) was around 80%. The composite Co–Ce/MCM-48 catalyst exhibited significantly higher activity in terms of TOC removal of CA (83.6%), SMZ (51.7%) and DCF (86.8%). Co–Ce/MCM-48 inhibited efficiently the accumulation of small molecular carboxyl acids during ozonation. A detailed research was conducted to detect the nature of material structure and mechanism of catalytic ozonation by using a series of characterizations. The main reaction pathway of CA was determined by the analysis of liquid chromatography-mass spectrometry, in line with the results of frontier electron density calculations that reactive oxygen species (ROSs) were easy to attack negative regions of pharmaceuticals. The Si–O–Si, Co···HO–Si–O–Si–OH···Ce, and O3···Co–HO–Si–O–Si–OH···Ce–OH···O3 basic units in catalysts were constructed to detect the orbit-energy-level difference. The results revealed that a synergistic effect existed at the interface between cobalt and cerium oxides over MCM-48, which facilitated the ROSs sequence in solution with ozone. Therefore, the multivalence redox coupling of Ce4+/Ce3+ and Co3+/Co2+ along with electron transfer played an important role in catalytic ozonation process

Novel biomarker panel for the diagnosis and prognosis assessment of sepsis based on machine learning - Supplementary Tables

Background: The authors investigated a panel of novel biomarkers for diagnosis and prognosis assessment of sepsis usingmachine learning (ML) methods. Methods: Hematological parameters, liver function indices and inflammatory marker levels of 332 subjects were retrospectively analyzed. Results: The authors constructed sepsis diagnosis models and identified the random forest (RF) model to be the most optimal. Compared with PCT (procalcitonin) and CRP (C-reactive protein), the RF model identified sepsis patients at an earlier stage. The sepsis group had a mortality rate of 36.3%, and the RF model had greater predictive ability for the 30-day mortality risk of sepsis patients. Conclusion: The RF model facilitated the identification of sepsis patients and showed greater accuracy in predicting the 30-day mortality risk of sepsis patients.</p

The relationship between miR-26a expression and clinicopathological parameters in breast cancer.

<p>The relationship between miR-26a expression and clinicopathological parameters in breast cancer.</p

MCL-1 is the target of miR-26a.

<p>A. Putative miR-26a binding sites in the 3′UTR region of MCL-1 and interspecies conservation of seed matching sequences (gray box). B. Expression of MCL-1 in the 2 immortalized normal mammary epithelium cell lines and 4 breast cancer cell lines. C. Western blot assay for MCL-1 expression after MDA-MB-231 and MCF-7 cells were transfected with miR-26a for 48 hours. *P<0.05 compared with control.</p

Novel biomarker panel for the diagnosis and prognosis assessment of sepsis based on machine learning - Supplementary Figure 1

Background: The authors investigated a panel of novel biomarkers for diagnosis and prognosis assessment of sepsis usingmachine learning (ML) methods. Methods: Hematological parameters, liver function indices and inflammatory marker levels of 332 subjects were retrospectively analyzed. Results: The authors constructed sepsis diagnosis models and identified the random forest (RF) model to be the most optimal. Compared with PCT (procalcitonin) and CRP (C-reactive protein), the RF model identified sepsis patients at an earlier stage. The sepsis group had a mortality rate of 36.3%, and the RF model had greater predictive ability for the 30-day mortality risk of sepsis patients. Conclusion: The RF model facilitated the identification of sepsis patients and showed greater accuracy in predicting the 30-day mortality risk of sepsis patients.</p

Knockdown of MCL-1 suppresses cell proliferation, clonogenicity and induces cell apoptosis.

<p>A. Does effect and time effect of transfection of MCL-1-siRNA on the proliferation of MDA-MB-231 and MCF-7 cells. B. The functional role of MCL-1 in breast cancer cell growth was analyzed by colony formation of MDA-MB-231 and MCF-7 cells. The evaluation of colony numbers was shown in the panel. C. Influence of MCL-1 on apoptosis in breast cancer cells was monitored by flow cytometry. The percentage of Annexin V-FITC positive cells to the total cells was shown in the bar graphs. *P<0.05 compared with control.</p

Overexpression of miR-26a lead to reduced cell viability and decreased clonogenicity.

<p>A. Dose effect. Cells were transfected with miR-26a at the indicated concentrations for 48 hours. B. Time effect. Cells were transfected with 50 µM of miR-26a for indicated periods. C. Morphologic changes of MDA-MB-231 and MCF-7 cells in response to miR-26a inhibition. D. Influence of miR-26a on colony formation of MDA-MB-231 and MCF-7 cells. Representative dishes are presented (left). The number of colony was counted for each well of six-well plates and the evaluation of colony numbers was shown in the y-axis of the right panel. *P<0.05 compared with control.</p

The expression of miR-26a was reduced in breast cancer cell lines and clinical specimens.

<p>A. Expression of miR-26a in the 2 immortalized normal mammary epithelium cell lines and 4 breast cancer cell lines. B. Average expression level of miR-26a in human breast cancer tissues and normal breast tissues. MiRNA abundance was normalized to 5s rRNA. *P<0.05 compared with control.</p