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Fulltext Condorcet and borda count fusion method for ligand-based virtual screening

Ahmed A, Saeed F, Salim N, Abdo A

J Cheminform, 2014;6:19.
PMID: 24883114 DOI: 10.1186/1758-2946-6-19

BACKGROUND: It is known that any individual similarity measure will not always give the best recall of active molecule structure for all types of activity classes. Recently, the effectiveness of ligand-based virtual screening approaches can be enhanced by using data fusion. Data fusion can be implemented using two different approaches: group fusion and similarity fusion. Similarity fusion involves searching using multiple similarity measures. The similarity scores, or ranking, for each similarity measure are combined to obtain the final ranking of the compounds in the database.
RESULTS: The Condorcet fusion method was examined. This approach combines the outputs of similarity searches from eleven association and distance similarity coefficients, and then the winner measure for each class of molecules, based on Condorcet fusion, was chosen to be the best method of searching. The recall of retrieved active molecules at top 5% and significant test are used to evaluate our proposed method. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints.
CONCLUSIONS: Simulated virtual screening experiments with the standard two data sets show that the use of Condorcet fusion provides a very simple way of improving the ligand-based virtual screening, especially when the active molecules being sought have a lowest degree of structural heterogeneity. However, the effectiveness of the Condorcet fusion was increased slightly when structural sets of high diversity activities were being sought.
Combining multiple clusterings of chemical structures using cluster-based similarity partitioning algorithm

Saeed F, Salim N, Abdo A

Int J Comput Biol Drug Des, 2014 01 09;7(1):31-44.
PMID: 24429501 DOI: 10.1504/IJCBDD.2014.058584

Many types of clustering techniques for chemical structures have been used in the literature, but it is known that any single method will not always give the best results for all types of applications. Recent work on consensus clustering methods is motivated because of the successes of combining multiple classifiers in many areas and the ability of consensus clustering to improve the robustness, novelty, consistency and stability of individual clusterings. In this paper, the Cluster-based Similarity Partitioning Algorithm (CSPA) was examined for improving the quality of chemical structures clustering. The effectiveness of clustering was evaluated based on the ability to separate active from inactive molecules in each cluster and the results were compared with the Ward's clustering method. The chemical dataset MDL Drug Data Report (MDDR) database was used for experiments. The results, obtained by combining multiple clusterings, showed that the consensus clustering method can improve the robustness, novelty and stability of chemical structures clustering.
Consensus methods for combining multiple clusterings of chemical structures

Saeed F, Salim N, Abdo A

J Chem Inf Model, 2013 May 24;53(5):1026-34.
PMID: 23581471 DOI: 10.1021/ci300442u

The goal of consensus clustering methods is to find a consensus partition that optimally summarizes an ensemble and improves the quality of clustering compared with single clustering algorithms. In this paper, an enhanced voting-based consensus method was introduced and compared with other consensus clustering methods, including co-association-based, graph-based, and voting-based consensus methods. The MDDR and MUV data sets were used for the experiments and were represented by three 2D fingerprints: ALOGP, ECFP_4, and ECFC_4. The results were evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster using four criteria: F-measure, Quality Partition Index (QPI), Rand Index (RI), and Fowlkes-Mallows Index (FMI). The experiments suggest that the consensus methods can deliver significant improvements for the effectiveness of chemical structures clustering.
Information Theory and Voting Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures

Saeed F, Salim N, Abdo A

Mol Inform, 2013 Jul;32(7):591-8.
PMID: 27481767 DOI: 10.1002/minf.201300004

Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster, and the results were compared with Ward's method. The chemical dataset MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) dataset were used. Experiments suggest that the adaptive cumulative voting-based consensus method can improve the effectiveness of combining multiple clusterings of chemical structures.
Fulltext Voting-based consensus clustering for combining multiple clusterings of chemical structures

Saeed F, Salim N, Abdo A

J Cheminform, 2012 Dec 17;4(1):37.
PMID: 23244782 DOI: 10.1186/1758-2946-4-37

BACKGROUND: Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster.
RESULTS: The cumulative voting-based aggregation algorithm (CVAA), cluster-based similarity partitioning algorithm (CSPA) and hyper-graph partitioning algorithm (HGPA) were examined. The F-measure and Quality Partition Index method (QPI) were used to evaluate the clusterings and the results were compared to the Ward's clustering method. The MDL Drug Data Report (MDDR) dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward's method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward's method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria.
CONCLUSIONS: The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA) was the method of choice among consensus clustering methods.
Knowledge, attitudes and practice regarding copper intrauterine contraceptive devices among doctors in Malaysia

Chew KT, Salim N, Abu MA, Abdul Karim AK

BMJ Sex Reprod Health, 2018 Jun 01.
PMID: 29972367 DOI: 10.1136/bmjsrh-2017-101869

BACKGROUND AND OBJECTIVE: Intrauterine contraceptive devices (IUDs) are an important method to reduce unmet need for family planning and for prevention of unintended pregnancy. However, IUD use in Malaysia is still low. Doctors play a major role in influencing IUD uptake among women. This study was designed to evaluate doctors' knowledge, attitudes and perceptions towards IUDs and factors associated with their current practice.
METHODS: A questionnaire was mailed to public and private contraceptive providers who practise in Kuala Lumpur, Malaysia.
RESULTS: A total of 400 doctors were invited and 240 (60%) of them responded to the survey. Of the respondents, 161 (65.9%) were from the public or government sector and 89 (34.1%) were from the private sector. The knowledge score of doctors was classed as 'average', and correlated well with their previous training level, working position, number of patients seen in a week and number of contraceptive methods available in their facilities. The age, gender, working duration, availability of IUDs in the premises and number of IUD insertions in a month were not statistically associated with the providers' knowledge. The use of IUDs was low, especially among private doctors, and was significantly related to their knowledge of the method. Knowledge scores, perception and practice were significantly lower in the private sector.
Excipient selection and aerodynamic characterization of nebulized lipid-based nanoemulsion loaded with docetaxel for lung cancer treatment

Asmawi AA, Salim N, Ngan CL, Ahmad H, Abdulmalek E, Masarudin MJ, et al.

Drug Deliv Transl Res, 2019 04;9(2):543-554.
PMID: 29691812 DOI: 10.1007/s13346-018-0526-4

Docetaxel has demonstrated extraordinary anticancer effects on lung cancer. However, lack of optimal bioavailability due to poor solubility and high toxicity at its therapeutic dose has hampered the clinical use of this anticancer drug. Development of nanoemulsion formulation along with biocompatible excipients aimed for pulmonary delivery is a potential strategy to deliver this poorly aqueous soluble drug with improved bioavailability and biocompatibility. In this work, screening and selection of pharmaceutically acceptable excipients at their minimal optimal concentration have been conducted. The selected nanoemulsion formulations were prepared using high-energy emulsification technique and subjected to physicochemical and aerodynamic characterizations. The formulated nanoemulsion had mean particle size and ζ-potential in the range of 90 to 110 nm and - 30 to - 40 mV respectively, indicating high colloidal stability. The pH, osmolality, and viscosity of the systems met the ideal requirement for pulmonary application. The DNE4 formulation exhibited slow drug release and excellent stability even under the influence of extreme environmental conditions. This was further confirmed by transmission electron microscopy as uniform spherical droplets in nanometer range were observed after storage at 45 ± 1 °C for 3 months indicating high thermal stability. The nebulized DNE4 exhibited desirable aerosolization properties for pulmonary delivery application and found to be more selective on human lung carcinoma cell (A549) than normal cell (MRC-5). Hence, these characteristics make the formulation a great candidate for the potential use as a carrier system for docetaxel in targeting lung cancer via pulmonary delivery.
In vitro evaluation of the inhalable quercetin loaded nanoemulsion for pulmonary delivery

Arbain NH, Salim N, Masoumi HRF, Wong TW, Basri M, Abdul Rahman MB

Drug Deliv Transl Res, 2019 04;9(2):497-507.
PMID: 29541999 DOI: 10.1007/s13346-018-0509-5

Bioavailability of quercetin, a flavonoid potentially known to combat cancer, is challenging due to hydrophobic nature. Oil-in-water (O/W) nanoemulsion system could be used as nanocarrier for quercertin to be delivered to lung via pulmonary delivery. The novelty of this nanoformulation was introduced by using palm oil ester/ricinoleic acid as oil phase which formed spherical shape nanoemulsion as measured by transmission electron microscopy and Zetasizer analyses. High energy emulsification method and D-optimal mixture design were used to optimize the composition towards the volume median diameter. The droplet size, polydispersity index, and zeta potential of the optimized formulation were 131.4 nm, 0.257, and 51.1 mV, respectively. The formulation exhibited high drug entrapment efficiency and good stability against phase separation and storage at temperature 4 °C for 3 months. It was discovered that the system had an acceptable median mass aerodynamic diameter (3.09 ± 0.05 μm) and geometric standard deviation (1.77 ± 0.03) with high fine particle fraction (90.52 ± 0.10%), percent dispersed (83.12 ± 1.29%), and percent inhaled (81.26 ± 1.28%) for deposition in deep lung. The in vitro release study demonstrated that the sustained release pattern of quercetin from naneomulsion formulation up to 48 h of about 26.75% release and it was in adherence to Korsmeyer's Peppas mechanism. The cytotoxicity study demonstrated that the optimized nanoemulsion can potentially induce cyctotoxicity towards A549 lung cancer cells without affecting the normal cells. These results of the study suggest that nanoemulsion is a potential carrier system for pulmonary delivery of molecules with low water solubility like quercetin.
Fulltext Size-Controlled Preparation of Docetaxel- and Curcumin-Loaded Nanoemulsions for Potential Pulmonary Delivery

Asmawi AA, Salim N, Abdulmalek E, Abdul Rahman MB

Pharmaceutics, 2023 Feb 15;15(2).
PMID: 36839974 DOI: 10.3390/pharmaceutics15020652

Lung cancer is one of the deadliest pulmonary diseases in the world. Although docetaxel (DTX) has exhibited superior efficacy in lung cancer treatment, it has demonstrated numerous adverse effects and poor bioavailability. The natural product extract, curcumin (CCM), has reportedly reduced toxicity and synergistically improved DTX bioavailability. Nonetheless, the hydrophobic nature of DTX and CCM limits their clinical use. Nanoemulsion pulmonary delivery of DTX and CCM has demonstrated potential as a drug carrier to alleviate these drawbacks. The controlled preparation of inhalable DTX- and CCM-loaded nanoemulsions within the 100 to 200 nm range was explored in this study. A response surface methodology (RSM) based on a central composite design (CCD) was utilized to fabricate the desired size of the nanoemulsion under optimized conditions. Different process parameters were employed to control the size of the nanoemulsions procured through a high-energy emulsification technique. The size of the resultant nanoemulsions decreased with increasing energy input. The actual response according to the targeted sizes for DTX- and CCM-loaded nanoemulsion models exhibited excellent agreement with the predicted value at below 5% residual standard error under optimized conditions. The nanoemulsion of 100 nm particle size demonstrated better membrane permeability than their larger counterparts. Moreover, the formulations documented favorable physicochemical and aerodynamic pulmonary delivery properties and reduced toxicity in human lung fibroblast (MRC-5) cells. Hence, this tunable size of nanoemulsions could be a suitable alternative drug delivery for pulmonary diseases with increased local lung concentration.
Fulltext Modeling the Effect of Composition on Formation of Aerosolized Nanoemulsion System Encapsulating Docetaxel and Curcumin Using D-Optimal Mixture Experimental Design

Asmawi AA, Salim N, Abdulmalek E, Abdul Rahman MB

Int J Mol Sci, 2020 Jun 19;21(12).
PMID: 32575390 DOI: 10.3390/ijms21124357

The synergistic anticancer effect of docetaxel (DTX) and curcumin (CCM) has emerged as an attractive therapeutic candidate for lung cancer treatment. However, the lack of optimal bioavailability because of high toxicity, low stability, and poor solubility has limited their clinical success. Given this, an aerosolized nanoemulsion system for pulmonary delivery is recommended to mitigate these drawbacks. In this study, DTX- and CCM-loaded nanoemulsions were optimized using the D-optimal mixture experimental design (MED). The effect of nanoemulsion compositions towards two response variables, namely, particle size and aerosol size, was studied. The optimized formulations for both DTX- and CCM-loaded nanoemulsions were determined, and their physicochemical and aerodynamic properties were evaluated as well. The MED models achieved the optimum formulation for DTX- and CCM-loaded nanoemulsions containing a 6.0 wt% mixture of palm kernel oil ester (PKOE) and safflower seed oils (1:1), 2.5 wt% of lecithin, 2.0 wt% mixture of Tween 85 and Span 85 (9:1), and 2.5 wt% of glycerol in the aqueous phase. The actual values of the optimized formulations were in line with the predicted values obtained from the MED, and they exhibited desirable attributes of physicochemical and aerodynamic properties for inhalation therapy. Thus, the optimized formulations have potential use as a drug delivery system for a pulmonary application.
Fulltext Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach

Awan MJ, Rahim MSM, Salim N, Mohammed MA, Garcia-Zapirain B, Abdulkareem KH

Diagnostics (Basel), 2021 Jan 11;11(1).
PMID: 33440798 DOI: 10.3390/diagnostics11010105

The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.
Fulltext Optimization of Processing Parameters of Nanoemulsion Containing Aripiprazole Using Response Surface Methodology

Samiun WS, Ashari SE, Salim N, Ahmad S

Int J Nanomedicine, 2020;15:1585-1594.
PMID: 32210553 DOI: 10.2147/IJN.S198914

BACKGROUND: Aripiprazole, which is a quinolinone derivative, has been widely used to treat schizophrenia, major depressive disorder, and bipolar disorder.
PURPOSE: A Central Composite Rotatable Design (CCRD) of Response Surface Methodology (RSM) was used purposely to optimize process parameters conditions for formulating nanoemulsion containing aripiprazole using high emulsification methods.
METHODS: This design is used to investigate the influences of four independent variables (overhead stirring time (A), shear rate (B), shear time (C), and the cycle of high-pressure homogenizer (D)) on the response variable namely, a droplet size (Y) of nanoemulsion containing aripiprazole.
RESULTS: The optimum conditions suggested by the predicted model were: 120 min of overhead stirring time, 15 min of high shear homogenizer time, 4400 rpm of high shear homogenizer rate and 11 cycles of high-pressure homogenizer, giving a desirable droplet size of nanoemulsion containing aripiprazole of 64.52 nm for experimental value and 62.59 nm for predicted value. The analysis of variance (ANOVA) showed the quadratic polynomial fitted the experimental values with F-value (9.53), a low p-value (0.0003) and a non-significant lack of-fit. It proved that the models were adequate to predict the relevance response. The optimized formulation with a viscosity value of 3.72 mPa.s and pH value of 7.4 showed good osmolality value (297 mOsm/kg) and remained stable for three months in three different temperatures (4°C, 25°C, and 45°C).
CONCLUSION: This proven that response surface methodology is an efficient tool to produce desirable droplet size of nanoemulsion containing aripiprazole for parenteral delivery application.
Implementing relevance feedback in ligand-based virtual screening using Bayesian inference network

Abdo A, Salim N, Ahmed A

J Biomol Screen, 2011 Oct;16(9):1081-8.
PMID: 21862688 DOI: 10.1177/1087057111416658

Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
Fulltext Adapting Document Similarity Measures for Ligand-Based Virtual Screening

Himmat M, Salim N, Al-Dabbagh MM, Saeed F, Ahmed A

Molecules, 2016 Apr 13;21(4):476.
PMID: 27089312 DOI: 10.3390/molecules21040476

Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cut-offs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods.
Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals

Elhaj FA, Salim N, Harris AR, Swee TT, Ahmed T

Comput Methods Programs Biomed, 2016 Apr;127:52-63.
PMID: 27000289 DOI: 10.1016/j.cmpb.2015.12.024

Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support vector machine and radial basis function method.
Fulltext Intelligent bar chart plagiarism detection in documents

Al-Dabbagh MM, Salim N, Rehman A, Alkawaz MH, Saba T, Al-Rodhaan M, et al.

ScientificWorldJournal, 2014;2014:612787.
PMID: 25309952 DOI: 10.1155/2014/612787

This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.
High-intensity interval training induced PGC-1∝ and AdipoR1 gene expressions and improved insulin sensitivity in obese individuals

Asilah Za'don NH, Amirul Farhana MK, Farhanim I, Sharifah Izwan TO, Appukutty M, Salim N, et al.

Med J Malaysia, 2019 12;74(6):461-467.
PMID: 31929469

INTRODUCTION: High-intensity interval training (HIIT) has been found to improve cardiometabolic health outcome as compared to moderate-intensity continuous exercise. However, there is still limited data on the benefits of HIIT on the expression of regulatory proteins that are linked to skeletal muscle metabolism and insulin sensitivity in obese adults. This study investigated the effects of HIIT intervention on expressions of peroxisome proliferatoractivated receptor-γ coactivator 1-∝ (PGC-1∝) and adiponectin receptor-1 (AdipoR1), insulin sensitivity (HOMAIR index), and body composition in overweight/obese individuals.
METHODS: Fifty overweight/obese individuals aged 22-29 years were assigned to either no-exercise control (n=25) or HIIT (n=25) group. The HIIT group underwent a 12-week intervention, three days/week, with intensity of 65-80% of age-based maximum heart rate. Anthropometric measurements, homeostatic model of insulin resistance (HOMA-IR) and gene expression analysis were conducted at baseline and post intervention.
RESULTS: Significant time-by-group interactions (p<0.001) were found for body weight, BMI, waist circumference and body fat percentage. The HIIT group had lower body weight (2.3%, p<0.001), BMI (2.7%, p<0.001), waist circumference (2.4%, p<0.001) and body fat percentage (4.3%, p<0.001) post intervention. Compared to baseline, expressions of PGC-1∝ and AdipoR1 were increased by approximately three-fold (p=0.019) and two-fold (p=0.003) respectively, along with improved insulin sensitivity (33%, p=0.019) in the HIIT group.
CONCLUSION: Findings suggest that HIIT possibly improved insulin sensitivity through modulation of PGC-1∝ and AdipoR1. This study also showed that improved metabolic responses can occur despite modest reduction in body weight in overweight/obese individuals undergoing HIIT intervention.
Fulltext Response Factorial Design Analysis on Papain-Generated Hydrolysates from Actinopyga lecanora for Determination of Antioxidant and Antityrosinase Activities

Bahari AN, Saari N, Salim N, Ashari SE

Molecules, 2020 Jun 08;25(11).
PMID: 32521731 DOI: 10.3390/molecules25112663

Actinopyga lecanora (A. lecanora) is classified among the edible species of sea cucumber, known to be rich in protein. Its hydrolysates were reported to contain relatively high antioxidant activity. Antioxidants are one of the essential properties in cosmeceutical products especially to alleviate skin aging. In the present study, pH, reaction temperature, reaction time and enzyme/substrate ratio (E/S) have been identified as the parameters in the papain enzymatic hydrolysis of A. lecanora. The degree of hydrolysis (DH) with antioxidant activities of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric-reducing antioxidant power (FRAP) assays were used as the responses in the optimization. Analysis of variance (ANOVA), normal plot of residuals and 3D contour plots were evaluated to study the effects and interactions between parameters. The best conditions selected from the optimization were at pH 5.00, 70 °C of reaction temperature, 9 h of hydrolysis time and 1.00% enzyme/substrate (E/S) ratio, with the hydrolysates having 51.90% of DH, 42.70% of DPPH activity and 109.90 Fe2+μg/mL of FRAP activity. The A. lecanora hydrolysates (ALH) showed a high amount of hydrophobic amino acids (286.40 mg/g sample) that might be responsible for antioxidant and antityrosinase activities. Scanning electron microscopy (SEM) image of ALH shows smooth structures with pores. Antityrosinase activity of ALH exhibited inhibition of 31.50% for L-tyrosine substrate and 25.40% for L-DOPA substrate. This condition suggests that the optimized ALH acquired has the potential to be used as a bioactive ingredient for cosmeceutical applications.
Fulltext Optimization of nanoemulsion containing gemcitabine and evaluation of its cytotoxicity towards human fetal lung fibroblast (MRC5) and human lung carcinoma (A549) cells

Wahgiman NA, Salim N, Abdul Rahman MB, Ashari SE

Int J Nanomedicine, 2019;14:7323-7338.
PMID: 31686809 DOI: 10.2147/IJN.S212635

Background: Gemcitabine (GEM) is a chemotherapeutic agent, which is known to battle cancer but challenging due to its hydrophilic nature. Nanoemulsion is water-in-oil (W/O) nanoemulsion shows potential as a carrier system in delivering gemcitabine to the cancer cell.
Methods: The behaviour of GEM in MCT/surfactants/NaCl systems was studied in the ternary system at different ratios of Tween 80 and Span 80. The system with surfactant ratio 3:7 of Tween 80 and Span 80 was chosen for further study on the preparation of nanoemulsion formulation due to the highest isotropic region. Based on the selected ternary phase diagram, a composition of F1 was chosen and used for optimization by using the D-optimal mixture design. The interaction variables between medium chain triglyceride (MCT), surfactant mixture Tween 80: Span 80 (ratio 3:7), 0.9 % sodium chloride solution and gemcitabine were evaluated towards particle size as a response.
Results: The results showed that NaCl solution and GEM gave more effects on particle size, polydispersity index and zeta potential of 141.57±0.05 nm, 0.168 and -37.10 mV, respectively. The optimized nanoemulsion showed good stability (no phase separation) against centrifugation test and storage at three different temperatures. The in vitro release of gemcitabine at different pH buffer solution was evaluated. The results showed the release of GEM in buffer pH 6.5 (45.19%) was higher than GEM in buffer pH 7.4 (13.62%). The cytotoxicity study showed that the optimized nanoemulsion containing GEM induced cytotoxicity towards A549 cell and at the same time reduced cytotoxicity towards MRC5 when compared to the control (GEM solution).
MGACA-Net: a novel deep learning based multi-scale guided attention and context aggregation for localization of knee anterior cruciate ligament tears region in MRI images

Awan MJ, Mohd Rahim MS, Salim N, Nobanee H, Asif AA, Attiq MO

PeerJ Comput Sci, 2023;9:e1483.
PMID: 37547408 DOI: 10.7717/peerj-cs.1483

Anterior cruciate ligament (ACL) tears are a common knee injury that can have serious consequences and require medical intervention. Magnetic resonance imaging (MRI) is the preferred method for ACL tear diagnosis. However, manual segmentation of the ACL in MRI images is prone to human error and can be time-consuming. This study presents a new approach that uses deep learning technique for localizing the ACL tear region in MRI images. The proposed multi-scale guided attention-based context aggregation (MGACA) method applies attention mechanisms at different scales within the DeepLabv3+ architecture to aggregate context information and achieve enhanced localization results. The model was trained and evaluated on a dataset of 917 knee MRI images, resulting in 15265 slices, obtaining state-of-the-art results with accuracy scores of 98.63%, intersection over union (IOU) scores of 95.39%, Dice coefficient scores (DCS) of 97.64%, recall scores of 97.5%, precision scores of 98.21%, and F1 Scores of 97.86% on validation set data. Moreover, our method performed well in terms of loss values, with binary cross entropy combined with Dice loss (BCE_Dice_loss) and Dice_loss values of 0.0564 and 0.0236, respectively, on the validation set. The findings suggest that MGACA provides an accurate and efficient solution for automating the localization of ACL in knee MRI images, surpassing other state-of-the-art models in terms of accuracy and loss values. However, in order to improve robustness of the approach and assess its performance on larger data sets, further research is needed.

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