Terretonin, ophiobolin, and drimane terpenes with absolute configurations from an algicolous Aspergillus ustus

One new meroterpene, 1,2-dihydroterretonin F (2), five new sesterterpenes, (6a)-21-deoxyophiobolin G(3), (6a)-16,17-dihydro-21-deoxyophiobolin G (4), phiobolin U (5), ophiobolin V (6), and ophiobolin W (7),two new sesquiterpenes, (6-strobilactone-B) esters of (E,E)-6,7-epoxy-2,4-octadienoic acids (13 and 14),and twelve known terpenes (1, 8–12, and 15–20) were isolated from Aspergillus ustus, a fungus from the fresh tissue of marine green alga Codium fragile. Their structures and absolute configurations were identified by NMR and mass spectroscopic methods as well as quantum chemical calculations. Some of the isolates exhibited antibacterial activity and brine shrimp toxicity.One new meroterpene, 1,2-dihydroterretonin F (2), five new sesterterpenes, (6 alpha)-21-deoxyophiobolin G (3), (6 alpha)-16,17-dihydro-21-deoxyophiobolin G (4), ophiobolin U (5), ophiobolin V (6), and ophiobolin W(7), two new sesquiterpenes, (6-strobilactone-B) esters of (E,E)-6,7-epoxy-2,4-octadienoic acids (13 and 14), and twelve known terpenes (1, 8-12, and 15-20) were isolated from Aspergillus ustus, a fungus from the fresh tissue of marine green alga Codium fragile. Their structures and absolute configurations were identified by NMR and mass spectroscopic methods as well as quantum chemical calculations. Some of the isolates exhibited antibacterial activity and brine shrimp toxicity

Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis.

BACKGROUND: Staging diagnosis of liver fibrosis is a prerequisite for timely diagnosis and therapy in patients with chronic hepatitis B. In recent years, ultrasound elastography has become an important method for clinical noninvasive assessment of liver fibrosis stage, but its diagnostic value for early liver fibrosis still needs to be further improved. In this study, the texture analysis was carried out on the basis of two dimensional shear wave elastography (2D-SWE), and the feasibility of 2D-SWE plus texture analysis in the diagnosis of early liver fibrosis was discussed. AIM: To assess the diagnostic value of 2D-SWE combined with textural analysis in liver fibrosis staging. METHODS: This study recruited 46 patients with chronic hepatitis B. Patients underwent 2D-SWE and texture analysis; Young\u27s modulus values and textural patterns were obtained, respectively. Textural pattern was analyzed with regard to contrast, correlation, angular second moment (ASM), and homogeneity. Pathological results of biopsy specimens were the gold standard; comparison and assessment of the diagnosis efficiency were conducted for 2D-SWE, texture analysis and their combination. RESULTS: 2D-SWE displayed diagnosis efficiency in early fibrosis, significant fibrosis, severe fibrosis, and early cirrhosis (AUC \u3e 0.7, P \u3c 0.05) with respective AUC values of 0.823 (0.678-0.921), 0.808 (0.662-0.911), 0.920 (0.798-0.980), and 0.855 (0.716-0.943). Contrast and homogeneity displayed independent diagnosis efficiency in liver fibrosis stage (AUC \u3e 0.7, P \u3c 0.05), whereas correlation and ASM showed limited values. AUC of contrast and homogeneity were respectively 0.906 (0.779-0.973), 0.835 (0.693-0.930), 0.807 (0.660-0.910) and 0.925 (0.805-0.983), 0.789 (0.639-0.897), 0.736 (0.582-0.858), 0.705 (0.549-0.883) and 0.798 (0.650-0.904) in four liver fibrosis stages, which exhibited equivalence to 2D-SWE in diagnostic efficiency (P \u3e 0.05). Combined diagnosis (PRE) displayed diagnostic efficiency (AUC \u3e 0.7, P \u3c 0.01) for all fibrosis stages with respective AUC of 0.952 (0.841-0.994), 0.896 (0.766-0.967), 0.978 (0.881-0.999), 0.947 (0.835-0.992). The combined diagnosis showed higher diagnosis efficiency over 2D-SWE in early liver fibrosis (P \u3c 0.05), whereas no significant differences were observed in other comparisons (P \u3e 0.05). CONCLUSION: Texture analysis was capable of diagnosing liver fibrosis stage, combined diagnosis had obvious advantages in early liver fibrosis, liver fibrosis stage might be related to the hepatic tissue hardness distribution

Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide

BackgroundAcute kidney injury (AKI) is a serious clinical complication associated with adverse short-term and long-term outcomes. In recent years, with the rapid popularization of electronic health records and artificial intelligence machine learning technology, the detection rate and treatment of AKI have been greatly improved. At present, there are many studies in this field, and a large number of articles have been published, but we do not know much about the quality of research production in this field, as well as the focus and trend of current research.MethodsBased on the Web of Science Core Collection, studies reporting machine learning-based AKI research that were published from 2013 to 2022 were retrieved and collected after manual review. VOSviewer and other software were used for bibliometric visualization analysis, including publication trends, geographical distribution characteristics, journal distribution characteristics, author contributions, citations, funding source characteristics, and keyword clustering.ResultsA total of 336 documents were analyzed. Since 2018, publications and citations have increased dramatically, with the United States (143) and China (101) as the main contributors. Regarding authors, Bihorac, A and Ozrazgat-Baslanti, T from the University of Florida have published 10 articles. Regarding institutions, the University of California (18) had the most publications. Approximately 1/3 of the publications were published in Q1 and Q2 journals, of which Scientific Reports (19) was the most prolific journal. Tomašev et al.'s study that was published in 2019 has been widely cited by researchers. The results of cluster analysis of co-occurrence keywords suggest that the construction of AKI prediction model related to critical patients and sepsis patients is the research frontier, and XGBoost algorithm is also popular.ConclusionThis study first provides an updated perspective on machine learning-based AKI research, which may be beneficial for subsequent researchers to choose suitable journals and collaborators and may provide a more convenient and in-depth understanding of the research basis, hotspots and frontiers

Galaxy Optical Variability of Virgo Cluster: New Tracer for Environmental Influences on Galaxies

We investigate the relationship between the optical variability of galaxies and their distances from the centre of the Virgo Cluster using Palomar Transient Factory data. We define the ratio between the standard deviation of the galaxy brightness and the mean value of the standard deviation as a measure of a galaxy's optical variability. A sample of 814 Virgo galaxies with 230263 observations shows a monotonically decreasing trend of optical variability with increasing clustercentric distance. The variability level inside the cluster is 3.2$\sigma$ higher than the level outside. We fit the variability with a linear function and find that the data reject a distance-independent model. We examine 217 background galaxies for comparison and find no significant trend in galaxy variability. We assess the relation with Monte Carlo simulation by rebuilding the brightness of each galaxy. The simulation shows a monotonically decreasing relation for member galaxy variability and a distance-independent relation for background galaxies. Our result is consistent with the theory that the cold gas flowing inwards the cluster centre fuels AGN activity. This work is a new implementation of the method using optical variability to investigate the relation between galaxies evolution and their environment.Comment: 5 pages, 4 figures, in Press (accepted by MNRAS Letters