Combining mammaglobin and carcinoembryonic mRNA markers for early detection of micrometastases from breast cancers - a molecular study of 59 patients

Introduction: As many as 30% of node-negative breast cancer patients relapse within five years, suggesting that current histological detection methods are inadequate for identifying metastatic disease. Detecting small number of cancer cells in the breast tissue or lymph node by reverse transcription-polymerase chain reaction (RT-PCR) assays using a combination of tissue and cancer specific markers might be very useful in the early detection or monitoring of the treatment. Mammaglobin is a member of the uteroglobin gene family and appears to be expressed only in breast tissue. Carcinoembryonic antigen has been the preferred molecular marker for detection of micro metastases in lymph nodes in almost all carcinomas. Materials and Methods: Samples were collected from randomly chosen breast cancer patients undergoing modified mastectomy or breast conserving surgery between September 2003 and July 2004. RT-PCR was applied to study the expression of MMG and CEA markers. Breast cancer micrometastases in axillary lymph nodes were also assessed. Results: The MMG marker was positive in 9/10 normal breast tissues, 3/ 3 breast fibroadenomas and 37/39 of breast carcinoma tissues, giving an overall sensitivity of 94%. The sensitivity was 80% for metastatic lymph node samples. On the other hand CEA showed 95% sensitivity for malignant breast tumors and 100% sensitivity for metastatic lymph nodes. Conclusions: RT-PCR using a combination of MMG and CEA markers is a powerful tool to complement current routine histopathology techniques for detection of breast cancer metastasis in axillary nodes

Determining the Reaction Rate of Electrochemical Process for Purification of Polluted Water

Abstract Aims: Turbidity in higher than standard levels, indicates failure in the water treatment plant. An electrochemical disinfection process takes place through electricity transition between two or more electrodes. This research aimed to determine the reaction rate of electrochemical process for purification of polluted water. Materials & Methods: This is a bench scale, experimental study performed in a batch system on synthetic wastewater. 1700ml of prepared synthetic wastewater was put in an electrolytic cell and constant 600mA current was flowed into the cell content through merged aluminum electrodes for 1 hour. Samples were taken from the batch in the beginning and every 10 minutes and were analyzed for, turbidity, Coliform bacteria (probably, confirmed and E. coli) and Heterotrophic Plat Count. Fisher exact test was used to analyze data. Findings: All the parameters of turbidity, HPC, total coliform, confirmed coliform and E. coli were decreased during the time. The electrochemical process reduced the average of turbidity below 3NTU after 50 minutes (91.05 removal). The HPC number reduced from 130n/ml to 2.4n/ml (98.15 removal) after 50 minutes. No coliforms were seen after 40 minutes of the electrochemical process. Conclusion: 40 minutes of electrochemical process in 600mA by aluminum electrodes is the optimum condition for removing the turbidity, Coliform bacteria (total, confirmed and E. coli) and HPC from polluted water

Recurrent neural network and reinforcement learning model for COVID-19 prediction.

Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction. In this work, two learning algorithms, namely deep learning and reinforcement learning, were developed to forecast COVID-19. This article constructs a model using Recurrent Neural Networks (RNN), particularly the Modified Long Short-Term Memory (MLSTM) model, to forecast the count of newly affected individuals, losses, and cures in the following few days. This study also suggests deep learning reinforcement to optimize COVID-19's predictive outcome based on symptoms. Real-world data was utilized to analyze the success of the suggested system. The findings show that the established approach promises prognosticating outcomes concerning the current COVID-19 pandemic and outperformed the Long Short-Term Memory (LSTM) model and the Machine Learning model, Logistic Regresion (LR) in terms of error rate

Estimating CO2-Brine diffusivity using hybrid models of ANFIS and evolutionary algorithms

One of the important parameters illustrating the mass transfer process is the diffusion coefficient of carbon dioxide which has a great impact on carbon dioxide storage in marine ecosystems, saline aquifers, and depleted reservoirs. Due to the complex interpretation approaches and special laboratory equipment for measurement of carbon dioxide-brine system diffusivity, the computational and mathematical methods are preferred. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is coupled with five different evolutionary algorithms for predicting the diffusivity coefficient of carbon dioxide. The R2 values forthe testing phase are 0.9978, 0.9932, 0.9854, 0.9738 and 0.9514 for ANFIS optimized by particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), backpropagation (BP), and differential evolution (DE), respectively. The hybrid machine learning model of ANFIS-PSO outperforms the other models

Introducing genetic markers to identify and distinguish five species of Cyprinidae in the Caspian Sea using PCR-RFLP

Cyprinids are the main and most significant bony fishes found in the Caspian Sea. In this study, 15 specimens of Rutilus fish kutum (Kamensky, 1901), 10 specimens of Barbus capito (Gueldenstaedtii, 1772), 10 specimens of Bream Abramis brama (Linnaeus, 1785), 10 specimens of Redlip, Aspius aspius, and 15 specimens of Kora volba, Rutilus rutilus caspius, were collected from commercial catch stations. DNA from all specimens was extracted using the phenol-chloroform method in 500[1.1 tubes and amplified using PCR method with a pair of primers with cytochrom b gene sequence of Kora volba. For RFLP analysis, PCR products of cytochorome gene b (1117 bp) from each species were digested with five restriction enzymes under suitable conditions of incubation. DNA bands were visualized by gel electrophoresis (polyacrylamide) and staining with silver nitrate. Five enzyme Rsa I, Hinf I. Hha I , Hine II, and Mbo I showed polymorphism. Genotypes obtained from digestion of enzymes Rsa I, Hinf I, Hha I showed haplotypes AAAAA for Barbus capito, BBBB for R. rutilus caspius, CCCC for Bream Abramis brama, DDDD for Aspius aspius and EEEE for R. frisii kutum. Each of these haplotypes serves as a genetic marker for the species and is of significant importance in distinguishing them

Robust adaptive synchronization of a class of uncertain chaotic systems with unknown time-delay

The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems

MyoRing implantation in keratoconic patients: 3 years follow-up data

Purpose: To evaluate long-term follow-up data on implantation of a full-ring intra-corneal implant (MyoRing) for management of keratoconus. Methods: A total of 40 keratoconic eyes of 37 consecutive patients who had undergone MyoRing implantation using the Pocket Maker microkeratome (Dioptex, GmbH, Linz, Austria) and completed 3 years of follow-up appointments were included in this retrospective study. Uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), refraction and keratometry (K) readings were measured and evaluated preoperatively, and 3 years, postoperatively. Results: No intraoperative complications were observed in this case series. Three years postoperatively, there was a significant improvement in UDVA, CDVA, K readings, spherical equivalent (SE), and manifest sphere and cylinder (P < 0.05 for all comparisons). UDVA was significantly improved from 1.14 ± 0.27 to 0.30 ± 0.21 LogMAR (P = 0.001), CDVA was also improved from 0.52 ± 0.23 to 0.18 ± 0.12 LogMAR (P = 0.001), SE was decreased by 4.35 diopters (D) and average keratometric values were reduced by 2.34 D (P = 0.001). Overall, 81 of subjects were moderately to highly satisfied 3 years after surgery and 64.90 agreed to have the fellow eye implanted with MyoRing. Conclusion: MyoRing implantation using the Pocket Maker microkeratome was found to be a minimally invasive procedure for improving visual acuity and refraction in the majority of the patients with keratoconus. © 2016 Journal of Ophthalmic and Vision Research

LASEK for the correction of hyperopia with mitomycin C using SCHWIND AMARIS excimer laser: one-year follow-up

AIM: To evaluate the efficacy, safety and predictability of laser-assisted sub-epithelial keratectomy (LASEK) for the correction of hyperopia using the SCHWIND AMARIS platform. METHODS: This retrospective single-surgeon study includes 66 eyes of 33 patients with hyperopia who underwent LASEK with mitomycin C (MMC). The median age of patients was 35.42±1.12y (ranging 18 to 56y). In each patient LASEK was performed using SCHWIND AMARIS excimer laser. Postoperatively clinical outcomes were evaluated in terms of predictability, safety, efficacy, subjective and objective refractions, uncorrected visual acuity (UCVA), best spectacle-corrected visual acuity (BSCVA) and adverse events. RESULTS: The mean baseline refraction was 3.2±1.6 diopters (D) (ranging 0 to 7 D). The mean pre-operative and postoperative spherical equivalent (SE) were 2.34±1.76 (ranging -1.25 to 7 D) and 0.30±0.84 (ranging -0.2 to 0.8 D) respectively (P=0.001). The mean hyperopia was 0.63±0.84 D (ranging -1.75 to 2.76 D) 6 to 12mo postoperatively. Likewise, the mean astigmatism was 0.68±0.43 D (range 0 to 2 D) with 51 (77.3) and 15 (22.7) eyes within ±1 and ±0.50 D respectively. The safety index and efficacy index were 1.08 and 1.6 respectively. CONCLUSION: LASEK using SCHWIND AMARIS with MMC yields good visual and refractive results for hyperopia. Moreover, there were no serious complications. Copyright 2015 by the IJO Press

Modeling of carbon dioxide solubility in ionic liquids based on group method of data handling

Due to industrial development, the volume of carbon dioxide (CO2) is rapidly increasing.. Several techniques have been used to eliminate CO2 from the output gas mixtures. One of these methods is CO2 capturing by ionic liquids (ILs). Computational models for estimating the CO2 solubility in ILS is of utmost importance. In this research, a white box model in the form of a mathematical correlation using the largest data bank in literature is presented by the group method of data handling (GMDH). This research investigates the application of GMDH intelligent method as a powerful computational approach for predicting CO2 solubility in different ionic liquids with temperature lower and upper than 324 K. In this regard, 4726 data points including the solubility of CO2 in 60 ILs were used for model development Moreover, seven different ionic liquids were selected to perform the external test. To evaluate the validity and efficiency of the suggested model, regression analysis was implemented on the actual and estimated target values. As a result, a proper fit between the experimental and predicted data was obtained and presented by various figures and statistical parameters. It is also worth noting that the predicted negative values in the proposed models are considered zero. Also, the results of the established correlation were compared to other proposed models exist in the literature of ionic liquids. The terminal form of the models suggested by GMDH approach and obtained based on temperature are two simple mathematical correlations by exerting input parameters of temperature (T), pressure (P), critical temperature (Tc ), critical pressure (Pc ) and, acentric factor (ω) which does not suffer from the black box property of other neural network algorithms. The model suggested in this work, would be a promising one which can act as an efficient predictor for CO2 solubility estimation in ILs and is capable of being used in different industries

Optimal type-3 fuzzy system for solving singular multi-pantograph equations

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients