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Backgrounds: Group B Streptococcus (GBS) is an important opportunistic bacterial pathogen that could cause serious infections, especially in neonates, adults, and the elderly. In GBS isolates, a macrolide resistance phenotype that confers constitutive resistance to macrolide-lincosamide-streptogramin B antibiotics (cMLSB phenotype) has become a global concern. On the other hand, little is known about the genetic relatedness and diversity of GBS isolates isolated from various patients in Iran. Hence, this study aimed to determine the genetic relatedness and molecular typing of cMLSB-GBS isolates using enterobacterial repetitive intergenic consensus-PCR (ERIC- PCR) technique.
Materials & Methods: A total of 100 GBS isolates were collected from patients with urinary tract infections (UTI).  Among them, 52 erythromycin-resistant GBS isolates were selected, and double-disc diffusion (D-zone) technique was applied to determine the MLSB phenotype among the isolates based on CLSI criteria. Then the genetic relatedness of MLSB-GBS isolates was assessed using ERIC-PCR fingerprinting method.
Findings: Among 52 erythromycin-resistant GBS isolates, 38 isolates were identified with cMLSB phenotype, nine isolates with M phenotype, and five isolates with iMLSB phenotype. The analysis of ERIC-PCR patterns revealed eight different ERIC types that were divided into seven clusters (A-G) and one single type. Also, four isolates were non-typeable. ERIC type A/ serotype Ib was the most prevalent clone among the isolates.
Conclusion: The current study findings showed a high level of diversity and multiclonal spread of the cMLSB phenotype in Isfahan. ERIC type A/ serotype Ib is the predominant clone circulating among erythromycin-resistant GBS strains.

 

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One of the important purposes of aero-engine high speed compressor design is to decrease its weight. In order to achieve this purpose, it is required to increase the capability of pressure producing in each individual stage of the compressor. The most common way is to use of high pressure aspect ratio blades. These long and thin blades are exposed to serious vibrations in the high speed flow because of the aeroelastic instability. Mechanical designers link adjacent blades by using Mid-span shroud (damper) to decrease the blade destructive vibrations. This dampers cause flow blockage and turbomachine performance loss. In the previous studies, less attention has been done on the effect of damper on blade shocks, trailing edge vortices and near stall condition. In this paper, aerodynamic performance of a compressor with and without mid-span damper has been investigated and compared. On the other hand, the damper effect on the formation and behavior of shock induced separation has been investigated in each two cases. As a result, presence of damper causes an isentropic efficiency reduction. This damper causes 33% pressure loss on the blades in the region of the extent of 2.7% of the blade span around damper. Turbulence due to the presence of damper leads to the distortion of the vortices pattern on training edge.

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Damage detection is a necessary part for structural health monitoring (SHM), being beneficial to SHM and determining the severity of damage. Application of statistical pattern recognition methods for SHM has gained considerable attention to detect changes in a structure. One of the advantages of these methods is that only data from undamaged state is needed in training phase (unsupervised learning) as opposed to supervised learning where data from both undamaged and damaged conditions is required to train the model. There are different approaches used by researchers and the success of a certain one may depend on the type of structure or structural changes. Most of studies focused on the application of statistical pattern recognition methodologies for SHM utilize the time series analyses for extracting damage-sensitive features. These features are statistical properties of time series models that directly depend on damage. Extracting damage-sensitive features is a fundamental step in damage detection process because pattern recognition algorithms can identify the state of structure unless these features are just dependent on damage.  The change in an environmental and operational condition during the data acquisition process is one of the problems that causes damage features to be depended on factors besides existence of damage. This can lead to incorrect structural state identification. On the other hand, after extracting damage-sensitive features, the application of a statistical novelty detection methodology for decision making on structural state is a significant topic in SHM.
This paper proposes a new application of random decrement (RD) technique in order to choose appropriate and accurate damage features which are independent from environmental and operational conditions of structure. The RD technique transforms time series data of the structural response to free decay vibration form that only consist of dynamic properties information by averaging them at specific time. Moreover, a novel statistical method named as Bhattacharyya measure is applied as a robust method for damage diagnosis. The Bhattacharyya measure determines the discrepancy between damage features from different structural states through partitioning data and utilizing numerical information of each partition. Herein, before extracting damage features, time series data are averaged through RD technique. Then the Autoregressive-Autoregressive with exogenous output model (AR-ARX) is used to fit a mathematical model to the averaged time series data and the residuals are considered as damage features. The Bhattacharyya measure is utilized for damage identification and localization. The data obtained from an experimental study on a three-story frame structure model are exploited to validate the accuracy and reliability of the proposed algorithm. Random excitation is applied by varying amplitude level of the input force, simulating various environmental and operational conditions. Damage is induced at two different locations. The proposed algorithm is conducted on data from various environmental and operational conditions at two different locations. A comparative study is also carried out to demonstrate the superiority of the proposed algorithm over some exiting techniques. Results show that the application of random decrement technique reduces the influence of operational and environmental condition due to averaging and normalizing data and correctly determines the state of structure. In addition, using Bhattacharyya measure improves the structural health monitoring results in damage identification and localization.
 

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Introduction: Colorectal cancer (CRC) is one of the leading cancers, following skin, breast, and stomach cancers. This study aimed to investigate the relationship between mutations in axin1 and axin2 in association with CRC.
Methods: Our study contains 147 fresh frozen samples from CRC patients, 25 normal samples, and 3 cell lines, including HT29, SW480, and CACO-2. The chosen SNPs from databases are placed in exon 5 of axin1, in exon 2 of axin1, and in exon 7 of axin2. By PCR-RFLP method, mutated samples were identified and sequenced.
Results: The results showed that mutations in the single-nucleotide polymorphism (SNP) in axin2 were observed in 1 out of 147 patient samples (0.68%). In the three sequences examined in axin2 (exon 7), mutations in SNP with rs79024445 at A2052C were observed. Statistical analysis of clinical and pathological data of patients showed a significant relationship between the tumor size factor and grade of cancer (P=0.016) as well as the degree of tumor diffusion to the lymph nodes factor with a grade of cancer (P=0.001).
Conclusion: The multi-factorial nature of cancer, the high genetic diversity of the Iranian population, and the limited statistical population could affect these outcomes. The observed mutations in each sample can also indicate the importance of personalized medicine in studying diseases.