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The methanolic extract of the marine sponge Hemimycale sp. yielded two new compounds; 1-(2'-methyl heptadecyl) phenol (1) and a new pyrazole derivative; 4-(hydroxymethyl)-1H-pyrazol-3-ol (2), together with previously isolated (2′R)-2′-hydroxy-N-((2S,3S,4R)-1,3,4-trihydroxy-16-methylpentadecan-2-yl)docosanamide (3), cholesterol (4), 5, 8-epi-dioxycholest-6-en-3-ol (5) and 3-acetylsesterstatin 3 (6), which were firstly reported from family Hymedesmiidae. Their structure elucidation was based on extensive nuclear magnetic resonance spectroscopy and high resolution-electrospray ionization-mass spectrometry. The isolated compounds were evaluated for their anti-leishmanial and cytotoxic activities. Compound 5 showed remarkable anti-leishmanial activity with IC50 value of 15.8 ± 0.92 µg/mL comparable with the standard miltefosine (IC50 = 3.2 ± 0.07 µg/mL), while compound 3 exhibited noteworthy cytotoxicity against A594 cell line with IC50 value of 29.6 ± 1.68 µg/mL compared to etoposide (IC50 = 10.9 ± 1.30 µg/mL). [ABSTRACT FROM AUTHOR]

Background: Coronary artery calcium score (CACS) and carotid intima-media thickness (CIMT) are important markers for early detection of coronary atherosclerosis. The study aimed to investigate the relationship between the CIMT and the CACS as markers for atherosclerotic coronary artery disease (CAD). Methods: This single-center prospective study enrolled 41 cases with suspected CAD. The cases underwent carotid artery US and CA noncontrast CT (NCCT). A radiologist with eight years of experience in vascular US imaging performed the carotid artery US, while two radiologists with ten years of experience in cardiac imaging analyzed the coronary artery CT images and assigned a CAC-DRS category for each case. We used the Pearson correlation test to calculate the correlation between CIMT and CACS. The receiver operating characteristics (ROC) curve was used to estimate the best cutoff value of CIMT in predicting CA heavy calcification. Results: The study included 41 cases of suspected CAD, of which 12-showed heavy, and 29 showed non-heavy CA calcification. The mean CACS was 163, and the highest CIMT was 0.785 mm among the included patients. There was a significant positive correlation between the CACS and the highest CIMT (r 0.606 and P <0.001). A CIMT > 0.8 mm was the best cutoff for predicting heavy calcification, with a sensitivity of 91.7%, a specificity of 65.5%, and an accuracy of 73.2% based on ROC curve analysis. Conclusions: CIMT and CACS can be considered non-invasive predictive measures for CA atherosclerosis and subsequent CAD. Moreover, these markers might help cardiologists in clinical management. [ABSTRACT FROM AUTHOR]

Background: Diabetic kidney disease (DKD) is a time progressive problem, give rise in uncontrolled Diabetics increasing risks for chronic kidney disease (CKD) and /or end-stage renal disease (ESRD). The vulnerability to renal dysfunction manifested with sudden glomerular hypofiltration associated with micro-to macroalbuminuria passing to renal failure. So that, screening of specific enzymes shifts, or urinary albumin may predict onset diabetic nephropathy. Objective:The assessment of urinary alkaline phosphatase (ALP), alanine aminopeptidase (AAP), acid phosphatase (ACP) and microalbuminuria (MAU) for type II diabetic patients. Patients and Methods: In this study,120 type II diabetic patients were compared to 90 healthy volunteers of matched age and sex in Al-Leith General Hospital, Al-Leith Kidney Unit (AKU), Al-Leith, Makkah area, KSA in which random urine samples were collected for testing of MAU, ALP, AAP, ACP and Cr. Results: Mean values of measured biomarkers in patient group for MAU, ALP, AAP, ACP and Cr were 51.92 mg/I, 41.55 U/L, 20.17 U/L, 570.10 U/L and 2.92 mg/dl VS in control group were 12.59 mg/I, 8.84 U/L, 6.94 U/L, 385.87U/L and 1.07 mg/dl respectively. Additionally, there were statistically positive correlation between AAP with MAU and ALP; ACP with MAU, ALP and AAP; Cr level with MAU, ALP, AAP and ACP; on the other hand, there were positive significant correlation between duration of diabetes with all studied markers. Conclusion: Using of MAU in addition to other urinary enzymes could be beneficial non-invasive indicators for renal deterioration in type II diabetics.

Depression is a common mental disturbance that can be categorized as mild, moderate or severe. Mesemberine alkaloids, the main recognized phytoconstituents of some plants belonging to family Mesembryanthemaceae, are well-known as serotonin reuptake inhibitors. Therefore, the objective of this study is to evaluate the antidepressant activity of the alkaloidal fraction of Mesembryanthemum cordifolium L.f. (Aptenia cordifolia) roots, family Mesembryanthemaceae using forced swimming test, assisted by metabolomic analysis and in silico ligand-based and structure-based screening. Results showed that the alkaloidal fraction displayed an antidepressant activity superior to imipramine hydrochloride, a standard antidepressant agent. Nine alkaloids were annotated from the metabolomic analysis. Interestingly, among the dereplicated constituents, mesembrane (5) displayed strong binding affinity to SERT protein, which is slightly higher than the antidepressant drug venlafaxine. In conclusion, the alkaloidal fraction of the M. cordifolium (A. cordifolia) root exhibits an antidepressant activity which can be attributed in part to mesembrane (5). [ABSTRACT FROM AUTHOR]

Developing information technology led to raise the intricacy of information systems intensive, hence techniques with effectiveness and efficiency are required. These techniques are used to support users in using the information for rapid and correct decision-making. Conventional text mining and managing systems mainly use the presence or absence of keywords to discover and analyze useful information from textual documents. However, simple word counting and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper has been primarily concerned with constructing text representation model and exploiting that in mining and managing operations such as gathering, searching, filtering, retrieving, extracting, clustering, classifying, and summarizing. This representation model is based on semantic notions to represent text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. This model reflects the existing relations among concepts and facilitates accurate relatedness measurements that result in better mining performance. The experimental evaluations were carried out on real datasets from various domains, showing the importance of the proposed model. [ABSTRACT FROM AUTHOR]

Tremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analyze useful information from textual documents. However, simple word counts and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper proposes an efficient methodology for constructing hierarchy/graph-based texts organization and representation scheme based on semantic annotation and Q-learning. This methodology is based on semantic notions to represent the text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. The representation scheme reflects the existing relationships among concepts and facilitates accurate relatedness measurements that result in a better mining performance. An extensive experimental evaluation is conducted on real datasets from various domains, indicating the importance of the proposed approach. [ABSTRACT FROM AUTHOR]

Tremendous growth in the number of textual documents has produced daily requirements for effective development to explore, analyze, and discover knowledge from these textual documents. Conventional text mining and managing systems mainly use the presence or absence of key words to discover and analyze useful information from textual documents. However, simple word counts and frequency distributions of term appearances do not capture the meaning behind the words, which results in limiting the ability to mine the texts. This paper proposes an efficient methodology for constructing hierarchy/graph-based texts organization and representation scheme based on semantic annotation and Q-learning. This methodology is based on semantic notions to represent the text in documents, to infer unknown dependencies and relationships among concepts in a text, to measure the relatedness between text documents, and to apply mining processes using the representation and the relatedness measure. The representation scheme reflects the existing relationships among concepts and facilitates accurate relatedness measurements that result in a better mining performance. An extensive experimental evaluation is conducted on real datasets from various domains, indicating the importance of the proposed approach.

The natural history of warts shows considerable variations between individuals, ranging from spontaneous regression to prolonged persistence. The level of cytokines participate in the immune response to human papilloma virus. This work investigates local expression of T-helper1 (Th1)/ T-helper2 (Th2)/ cytokines in a trial to disclose the different immunological mechanisms affecting the natural history of warts. A total of thirty patients suffering from different types (common, plane, plantar and anogenital) of viral warts were included. An excision biopsy was used to assess tumor necrosis factor-alpha (TNF-alpha) and interleukin-4 (IL-4) expression. Local expression of TNF-alpha (Th1) and IL-4 (Th2) in wart tissue was determined using in-situ hybridization technique. The cytokines probes used in this assay detect messenger RNA (mRNA) in wart tissue. Local expression of TNF-alpha mRNA was higher in viral wart patients compared to healthy control (P < 0.01), while local expression of IL-4 was not significantly higher in patients compared to control (P > 0.05). Statistical analysis was done to determine whether variations in cytokine mRNA expression depend on wart location or clinical types. In conclusion, immunoreactivity to warts is likely to be associated with a predominant Th-1 or mixed Th-1/ Th-2 cytokine mRNA expression profile. However, it does not appear to be a simple causal relationship between expression of a Th-1 pattern and clearance.