Corrosion inhibition study of 6061 aluminium alloy in the presence of ethyl 5-methyl-1-(4-nitrophenyl)-1H-1,2,3-triazole-4-carboxylate (NTE) in hydrochloric acid

The inhibitory effect of an ethyl 5-methyl-1-(4-nitrophenyl)-1H-1,2,3-triazole-4-carboxylate (NTE) was investigated on the corrosion of Al (AA6061) alloy at different temperatures (303-333 K) by Electrochemical impedance spectroscopy (EIS), Potentiodynamic polarization (PDP), and weight loss techniques. It was found that NTE molecules protect the aluminium against corrosion and its ability increases with increasing concentrations, and temperature resulting in better inhibitory performance. At all concentrations and temperature ranges, NTE exhibited mixed inhibitor action and complied with the Langmuir isotherm. At 100 ppm and 333 K, NTE demonstrated the highest inhibition efficiency (94%). The EIS results and the PDP results had a good level of concordance. A suitable mechanism for the corrosion prevention of AA6061 alloy was proposed. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) were used to confirm the adsorption of an inhibitor onto the aluminium alloy surface. The electrochemical results were validated by morphological examination, which demonstrated that NTE prevents uniform corrosion of aluminium alloy in acid chloride solutions. The activation energy and thermodynamic parameters were computed, and the results were discussed.

Diabetes Prediction Using Enhanced SVM and Deep Neural Network Learning Techniques

Diabetes, caused by the rise in level of glucose in blood, has many latest devices to identify from blood samples. Diabetes, when unnoticed, may bring many serious diseases like heart attack, kidney disease. In this way, there is a requirement for solid research and learning model’s enhancement in the field of gestational diabetes identification and analysis. SVM is one of the powerful classification models in machine learning, and similarly, Deep Neural Network is powerful under deep learning models. In this work, we applied Enhanced Support Vector Machine and Deep Learning model Deep Neural Network for diabetes prediction and screening. The proposed method uses Deep Neural Network obtaining its input from the output of Enhanced Support Vector Machine, thus having a combined efficacy. The dataset we considered includes 768 patients’ data with eight major features and a target column with result “Positive” or “Negative”. Experiment is done with Python and the outcome of our demonstration shows that the deep Learning model gives more efficiency for diabetes prediction.

Artificial Flora Algorithm-Based Feature Selection with Gradient Boosted Tree Model for Diabetes Classification

PURPOSE

Classification of medical data is essential to determine diabetic treatment options; therefore, the objective of the study was to develop a model to classify the three diabetes type diagnoses according to multiple patient attributes.

METHODS

Three different datasets are used to develop a novel medical data classification model. The proposed model involved preprocessing, artificial flora algorithm (AFA)-based feature selection, and gradient boosted tree (GBT)-based classification. Then, the processing occurred in two steps, namely, format conversion and data transformation. AFA was applied for selecting features, such as demographics, vital signs, laboratory tests, medications, from the patients' electronic health records. Lastly, the GBT-based classification model was applied for classifying the patients' cases to type I, type II, or gestational diabetes mellitus.

RESULTS

The effectiveness of the proposed AFA-GBT model was validated using three diabetes datasets to classify patient cases into one of the three different types of diabetes. The proposed model showed a maximum average precision of 91.64%, a recall of 97.46%, an accuracy of 99.93%, an F-score of 94.19%, and a kappa of 96.61%.

CONCLUSION

The AFA-GBT model could classify patient diagnoses into the three diabetes types efficiently.

Abstract 11: Novel dual inhibitors of LSD1-HDAC6/8 for treatment of cancer

Introduction: Lysine Specific Demethylase 1 (LSD1) is a flavin adenine dinucleotide (FAD)-dependent amine oxidase that has been reported to be over-expressed in many malignant tumors. Down-regulation of LSD1 has been shown to effectively treat cancers by inducing re-expression of aberrantly silenced genes. Studies have shown that LSD1 may contribute to acute myelogenous leukemia pathogenesis by inhibiting the normal pro-differentiative function of ATRA, paving the way for new combinatorial therapies for AML. Similarly, HDAC isoform selective inhibitors are beginning to be explored as less toxic alternatives to panHDAC inhibitors in select cancers. Further, combined inhibition of LSD1 and HDAC has been shown to be more efficacious in inhibiting multiple cancers. Here, we show that JBI-295, a dual inhibitor targeting both LSD1 and HDAC6/8 shows stronger efficacy without enhancing systemic toxicity, in a subset of AML and JAK-dependent myeloproliferative cancer.

Methods: Computational chemistry approaches were used to design LSD1 specific and LSD1-HDAC dual inhibitors. To assess in vitro LSD1 potency, TR-FRET assay was used. For assessing in vitro HDAC activity fluorescence based HDAC6 activity assay was performed. Western blotting was used to assess biomarkers of LSD1 and HDAC inhibition. Alamar blue cytotoxicity assay was used to assess cell proliferation.

Results: Several compounds from this series show strong in vitro potency against LSD1 with more than excellent selectivity against MAOs. JBI-295 one of the lead dual molecules, showed strong LSD1 potency (IC50 of 0.07 μM) and isoform selective HDAC6/8 activity (IC50 of 0.006 and 0.08 µM on HDAC6 and HDAC8, respectively), with about 100 fold selectivity against other HDAC isoforms. JBI-295 showed strong anti-proliferative activity on leukaemia and multiple myeloma cell lines. In cell based and in vivo target engagement studies there was a concomitant increase in CD11b, CD86 and GFI1b and tubulin acetylation levels. JBI-295 was more efficacious in inhibiting the growth of leukemia HEL92.1.7 xenograft by oral administration when compared to IP administration of ACY-1215, a HDAC6 selective inhibitor. Stronger tumor growth inhibition was also observed in melanoma A375 xenograft model as compared to inhibitors with single activity. In addition, JBI-295 showed single agent activity in a syngeneic murine colon cancer model CT26 and also resulted in stronger tumor growth inhibition when combined with anti-PDL1 antibody.

Conclusion: The data obtained demonstrate that dual LSD1-HDAC6/8 inhibitors could serve as novel, effective therapeutic agents for treatment of select subset of cancer. Advanced efficacy and toxicology studies with JBI-295 are in progress.

Citation Format: Dhanalakshmi Sivanandhan, Sridharan Rajagopal, Sreekala Nair, Chandru Gajendran, Dimpy Ghosh, P Nagaraj, Subramanyam J. Tantry, Purushottam Dewang, Mahanandeesha S. Hallur, Kannan Murugan, Srinatha K. C, Damodara Kuntrapaku, M Dilipkumar, R Sharma, S Meghashree, Durga Prasanna Kumar, Suraj P. Ingle, Mohd Zainuddin, A B. Vinod, Sriram Rajagopal. Novel dual inhibitors of LSD1-HDAC6/8 for treatment of cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 11.

Psycho-behavioural risks of low back pain in railway workers

BACKGROUND

Low back pain (LBP) is the most costly ailment in the work force. Risky work behaviour and psychological stress are established risk factors.

AIMS

To explore the associations between workplace risk factors, psychological stress and LBP among Malaysian railway workers.

METHODS

A cross-sectional study was carried out on railway workers in Malaysia. Socio-demographics, workplace risk factors for LBP, perceived psychological stress and history of LBP over the previous month were obtained by direct interviews using a structured closed-ended questionnaire. Descriptive, bivariate and logistic regression analyses were conducted.

RESULTS

There were 513 study participants (70% response rate). The prevalence of LBP in the previous month was 69%. Multivariate analysis yielded four significant predictors of LBP: employment of ≥ 10 years, lifting and lowering heavy loads, prolonged standing posture and psychological stress.

CONCLUSIONS

The high prevalence of LBP and its significant associations with physical and psychological stress factors in railway workers points to an urgent need for preventive measures, particularly among workers in high-risk occupations.

Spectrophotometric method for the determination of chromium (VI) in water samples

A simple and sensitive spectrophotometric method for the determination of chromium has been developed. The method is based on the diazotization of Dapsone in hydroxylamine hydrochloride medium and coupling with N-(1-Napthyl) Ethylene Diamine Dihydrochloride by electrophilic substitution to produce an intense pink azo-dye, which has absorption maximum at 540 nm. The Beer's law is obeyed from 0.02-1.0 microg mL(-1) and the molar absorptivity is 3.4854 L mol(-1) cm(-1). The Limits of quantification and Limit of detection of the proposed method are 0.0012 microg mL(-1) and 0.0039 microg mL(-1) respectively. The method has been successfully applied for the determination of chromium in water samples and the results were statistically evaluated with that of the reference method.

Sensitive spectrophotometric methods for the assessment of nitrite in water sample

Two spectrophotometric methods have been developed for the determination of nitrite using dapsone (DAP) with alpha-naphthol and 4-amino-5-hydroxynapthalene-2,7-disulphonic acid monosodium salt (AHNDMS) as chromogenic reagents with maximum absorbance wavelength at 540 and 520 nm respectively. For the method that utilizes dapsone with alpha-naphthol (DAP-alpha-naphthol), the beer's law range is obeyed between 0.05-0.8 microg ml(-1) with molar absorptivity of 5.749 x 104 l mol(-1) cm(-1). The second method that uses dapsone with AHNDMS (DAP-AHNDMS), the beer's law is valid over the range 0.2-1.4 mug ml(-1) and molar absorptivity 2.44 x 104 l mol(-1) cm(-1). The common interfering ions in the analytical procedures have been studied. This proposed methods are reliable, reproducible and have been successfully applied to determine nitrite in various water sources of environmental interest.

Determination of closed form solution for acceptance sampling using ANN

Tabled sampling schemes such as MIL-STD-105D offer limited flexibility to quality control engineers in designing sampling plans to meet specific needs. We describe a closed form solution to determine the AQL indexed single sampling plan using an artificial neural network (ANN). To determine the sample size and the acceptance number, feed-forward neural networks with sigmoid neural function are trained by a back propagation algorithm for normal, tightened, and reduced inspections. From these trained ANNs, the relevant weight and bias values are obtained. The closed form solutions to determine the sampling plans are obtained using these values. Numerical examples are provided for using these closed form solutions to determine sampling plans for normal, tightened, and reduced inspections. The proposed method does not involve table look-ups or complex calculations. Sampling plan can be determined by using this method, for any required acceptable quality level and lot size. Suggestions are provided to duplicate this idea for applying to other standard sampling table schemes.