Isolation and Identification of Lactic Acid Bacteria from a Traditional Fermented Fish Sauce (Mahyaveh) in Fars Province, Iran

Background: Mahyaveh is a fermented fish sauce in southern parts of Iran. Lactic acid bacteria (LAB) are commonly dominant microorganisms in fermented fish products. These bacteria develop organoleptic characteristics of fermented foods and play a significant role in promoting their quality and safety. The present study aimed to identify LAB isolated from Mahyaveh using 16SrDNA gene sequences. Methods: Mahyaveh samples were collected from different regions of Fars province, southern Iran. Then, LAB colonies were isolated using specific media and identified by microscopic observations and biochemical tests. Afterwards, DNA was extracted, PCR was done by general primers of 16S rDNA, and the bacteria were recognized. Results: The 16S rDNA sequence of all isolates was related to Lactobacillus plantarum and Enterococcus faecium type strains. Conclusion: L. plantarum and E. faecium were shown to be prevalent LAB strains that could be used as starters in Mahyaveh fermentation in southern Iran

The human RECQ1 helicase is highly expressed in glioblastoma and plays an important role in tumor cell proliferation

<p>Abstract</p> <p>Background</p> <p>RecQ helicases play an essential role in the maintenance of genome stability. In humans, loss of RecQ helicase function is linked with predisposition to cancer and/or premature ageing. Current data show that the specific depletion of the human RECQ1 helicase leads to mitotic catastrophe in cancer cells and inhibition of tumor growth in mice.</p> <p>Results</p> <p>Here, we show that RECQ1 is highly expressed in various types of solid tumors. However, only in the case of brain gliomas, the high expression of RECQ1 in glioblastoma tissues is paralleled by a lower expression in the control samples due to the poor expression of RECQ1 in non-dividing tissues. This conclusion is validated by immunohistochemical analysis of a tissue microarray containing 63 primary glioblastomas and 19 perilesional tissue samples, as control. We also show that acute depletion of RECQ1 by RNAi results in a significant reduction of cellular proliferation, perturbation of S-phase progression, and spontaneous γ-H2AX foci formation in T98G and U-87 glioblastoma cells. Moreover, RECQ1 depleted T98G and U-87 cells are hypersensitive to HU or temozolomide treatment.</p> <p>Conclusions</p> <p>Collectively, these results indicate that RECQ1 has a unique and important role in the maintenance of genome integrity. Our results also suggest that RECQ1 might represent a new suitable target for anti cancer therapies aimed to arrest cell proliferation in brain gliomas.</p

Phylogeny of urate oxidase producing bacteria: on the basis of gene sequences of 16S rRNA and uricase protein: Phylogeny of urate oxidase producing bacteria

Uricase or Urate oxidase (urate:oxygen oxidoreductase, EC 1.7.3.3), a peroxisomal enzyme which is found in many bacteria, catalyzes the oxidative opening of the purine ring of urate to yield allantoin, carbon dioxide, and hydrogen peroxide. In this study, the phylogeny of urate oxidase (uricase) producing bacteria was studied based on gene sequences of 16S rRNA and uricase protein. Representative and type strains (52 strains total) of most of the known species were analyzed. The acquired sequences (rDNA sequences of the 16S rRNA genes and the amino acid sequences of uricase) were aligned with the Clustal W program using MEGA software version 4.0. Phylogenetic trees were constructed with the neighbor-joining method, and were bootstrapped with 500 replications of each sequence. The large congruence ofphylogenetic relationship between the uricase gene and of 16S rRNA gives considerable support to the phylogeny of urate oxidase producing bacteria which was previously suggested on the basis of 16S rRNA sequences. The observed consistency promotes the idea that each of these genes shared a common evolutionary history in uricase producing bacteria we have analyzed

Medium optimization for extracellular urate oxidase production by a newly isolated Aspergillus Niger

Urate oxidase is a peroxisomal enzyme with four equal subunits that convert uric acid to allantoin, a more soluble metabolite for excretion. The usage of uricase as a drug in medicine is to treat hyperuricemia. Many microorganisms have been used for uricase production such as Streptomyces exfoliates, Pseudomonas aeruginosa, and Aspergillus flavus. In this study, soil samples were collected and then cultured in a screening medium including uric acid as the sole carbon source. Samples with the higher ability of uricase production were selected for enzyme assay. Enzyme activity was measured by spectrophotometry and the sample with the maximal uricase activity was identified as Aspergillus niger and selected for further studies. According to the results of experiments, the optimized temperature for enzyme production by Aspergillus niger was determined to be 35±2°C. The best carbon and nitrogen source was glucose and NH4NO3, and the highest enzyme activity was observed in the presence of Cu2+ ion

Determination and evaluation of effective criteria to location selection the optimal for establishing fluting paper mills from agricultural residues of Mazandaran province

Location selection for factory competitiveness in the market place plays an important role and should be chosen so that will leads achievement of the strategic advantages compared with other competitors. The objective of this study was determination of the effective criteria for decision making to select the most suitable location for establishing a fluting paper mills from agricultural residues. For this purpose, effective criteria were divided into five major groups: Material and Product, Facilities and limitations of regional (infrastructure), Technical and Human, Economical, Rules & Regulations as well as 33 sub-criteria, after preliminary investigation, preparatory observation, and an interview with some of the paper producers and relevant experts. A hierarchy was designed based on five major groups of criteria and then the priority rates of obtained criteria and sub-criteria were determined by Analytical Hierarchy Process (AHP) after compiling expert's opinions via questionnaire. Results have shown that among 33 determined effective criteria in location selection of fluting paper mills from agricultural residues , the sub-criteria of Supply residual amount, Ensure the supply of residual, Cost purchasing of raw material, Cost of transporting raw material have the highest priorities, respectivel

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Background: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. Methods: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. Results: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. Conclusion: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.</p