Displaying publications 1 - 20 of 62 in total

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  1. Al-Dabbagh MM, Salim N, Himmat M, Ahmed A, Saeed F
    Molecules, 2015;20(10):18107-27.
    PMID: 26445039 DOI: 10.3390/molecules201018107
    One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. 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. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure.
  2. Hentabli H, Saeed F, Abdo A, Salim N
    ScientificWorldJournal, 2014;2014:286974.
    PMID: 25140330 DOI: 10.1155/2014/286974
    Molecular similarity is a pervasive concept in drug design. The basic idea underlying molecular similarity is the similar property principle, which states that structurally similar molecules will exhibit similar physicochemical and biological properties. In this paper, a new graph-based molecular descriptor (GBMD) is introduced. The GBMD is a new method of obtaining a rough description of 2D molecular structure in textual form based on the canonical representations of the molecule outline shape and it allows rigorous structure specification using small and natural grammars. Simulated virtual screening experiments with the MDDR database show clearly the superiority of the graph-based descriptor compared to many standard descriptors (ALOGP, MACCS, EPFP4, CDKFP, PCFP, and SMILE) using the Tanimoto coefficient (TAN) and the basic local alignment search tool (BLAST) when searches were carried.
  3. 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.
  4. Kapitonova MY, Salim N, Othman S, Muhd Kamauzaman TM, Ali AM, Nawawi HM, et al.
    Malays J Pathol, 2013 Dec;35(2):153-63.
    PMID: 24362479 MyJurnal
    Experiments involving short-term space flight have shown an adverse effect on the physiology, morphology and functions of cells investigated. The causes for this effect on cells are: microgravity, temperature fluctuations, mechanical stress, hypergravity, nutrient restriction and others. However, the extent to which these adverse effects can be repaired by short-term space flown cells when recultured in conditions of normal gravity remains unclear. Therefore this study aimed to investigate the effect of short-term spaceflight on cytoskeleton distribution and recovery of cell functions of normal human osteoblast cells. The ultrastructure was evaluated using ESEM. Fluorescent staining was done using Hoechst, Mito Tracker CMXRos and Tubulin Tracker Green for cytoskeleton. Gene expression of cell functions was quantified using qPCR. As a result, recovered cells did not show any apoptotic markers when compared with control. Tubulin volume density (p<0.001) was decreased significantly when compared to control, while mitochondria volume density was insignificantly elevated. Gene expression for IL-6 (p<0.05) and sVCAM-1 (p<0.001) was significantly decreased while alkaline phosphatase (p<0.001), osteocalcin and sICAM (p<0.05) were significantly increased in the recovered cells compared to the control ones. The changes in gene and protein expression of collagen 1A, osteonectin, osteoprotegerin and beta-actin, caused by short-term spaceflight, were statistically not significant. These data indicate that short term space flight causes morphological changes in osteoblast cells which are consistent with hypertrophy, reduced cell differentiation and increased release of monocyte attracting proteins. The long-term effect of these changes on bone density and remodeling requires more detailed studies.
  5. 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.
  6. Da'u A, Salim N
    PeerJ Comput Sci, 2019;5:e191.
    PMID: 33816844 DOI: 10.7717/peerj-cs.191
    Aspect extraction is a subtask of sentiment analysis that deals with identifying opinion targets in an opinionated text. Existing approaches to aspect extraction typically rely on using handcrafted features, linear and integrated network architectures. Although these methods can achieve good performances, they are time-consuming and often very complicated. In real-life systems, a simple model with competitive results is generally more effective and preferable over complicated models. In this paper, we present a multichannel convolutional neural network for aspect extraction. The model consists of a deep convolutional neural network with two input channels: a word embedding channel which aims to encode semantic information of the words and a part of speech (POS) tag embedding channel to facilitate the sequential tagging process. To get the vector representation of words, we initialized the word embedding channel and the POS channel using pretrained word2vec and one-hot-vector of POS tags, respectively. Both the word embedding and the POS embedding vectors were fed into the convolutional layer and concatenated to a one-dimensional vector, which is finally pooled and processed using a Softmax function for sequence labeling. We finally conducted a series of experiments using four different datasets. The results indicated better performance compared to the baseline models.
  7. Bostan H, Salim N, Hussein ZA, Klappa P, Shamsir MS
    Adv Bioinformatics, 2012;2012:849830.
    PMID: 23091487 DOI: 10.1155/2012/849830
    Computational approaches to the disulphide bonding state and its connectivity pattern prediction are based on various descriptors. One descriptor is the amino acid sequence motifs flanking the cysteine residue motifs. Despite the existence of disulphide bonding information in many databases and applications, there is no complete reference and motif query available at the moment. Cysteine motif database (CMD) is the first online resource that stores all cysteine residues, their flanking motifs with their secondary structure, and propensity values assignment derived from the laboratory data. We extracted more than 3 million cysteine motifs from PDB and UniProt data, annotated with secondary structure assignment, propensity value assignment, and frequency of occurrence and coefficiency of their bonding status. Removal of redundancies generated 15875 unique flanking motifs that are always bonded and 41577 unique patterns that are always nonbonded. Queries are based on the protein ID, FASTA sequence, sequence motif, and secondary structure individually or in batch format using the provided APIs that allow remote users to query our database via third party software and/or high throughput screening/querying. The CMD offers extensive information about the bonded, free cysteine residues, and their motifs that allows in-depth characterization of the sequence motif composition.
  8. Eltyeb S, Salim N
    J Cheminform, 2014;6:17.
    PMID: 24834132 DOI: 10.1186/1758-2946-6-17
    The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.
  9. Jalila A, Dorny P, Sani R, Salim NB, Vercruysse J
    Vet Parasitol, 1998 Jan 31;74(2-4):165-72.
    PMID: 9561704
    Coccidial infections were studied in goats in the state of Selangor (peninsular Malaysia) during a 12-month period. The study included 10 smallholder farms on which kids were monitored for faecal oocyst counts from birth until 1-year old. Eimeria oocysts were found in 725 (89%) of 815 faecal samples examined. Nine species of Eimeria were identified. The most prevalent were E. arloingi, found in 71% of the samples, E. ninakohlyakimovae (67%), E. christenseni (63%) and E. alijevi (61%). The other species found were, E. hirci, E. jolchijevi, E. caprovina, E. caprina and E. pallida, present in 34, 22, 12, 9 and 4% of the samples, respectively. Oocyst counts were significantly higher in animals of less than 4-months old (P < 0.05). High oocyst counts were mainly caused by non-pathogenic species. Poor hygienic conditions were found to be associated with a higher intensity of coccidial infections. Mortality rates in kids could not be related to the intensity of coccidial infections.
  10. 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.
  11. 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.

  12. 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.
  13. Hentabli H, Bengherbia B, Saeed F, Salim N, Nafea I, Toubal A, et al.
    Int J Mol Sci, 2022 Oct 30;23(21).
    PMID: 36362018 DOI: 10.3390/ijms232113230
    Determining and modeling the possible behaviour and actions of molecules requires investigating the basic structural features and physicochemical properties that determine their behaviour during chemical, physical, biological, and environmental processes. Computational approaches such as machine learning methods are alternatives to predicting the physiochemical properties of molecules based on their structures. However, the limited accuracy and high error rates of such predictions restrict their use. In this paper, a novel technique based on a deep learning convolutional neural network (CNN) for the prediction of chemical compounds' bioactivity is proposed and developed. The molecules are represented in the new matrix format Mol2mat, a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. To evaluate the performance of the proposed methods, a series of experiments were conducted using two standard datasets, namely the MDL Drug Data Report (MDDR) and Sutherland, datasets comprising 10 homogeneous and 14 heterogeneous activity classes. After analysing the eight fingerprints, all the probable combinations were investigated using the five best descriptors. The results showed that a combination of three fingerprints, ECFP4, EPFP4, and ECFC4, along with a CNN activity prediction process, achieved the highest performance of 98% AUC when compared to the state-of-the-art ML algorithms NaiveB, LSVM, and RBFN.
  14. Syed Azhar SNA, Ashari SE, Salim N
    Int J Nanomedicine, 2018;13:6465-6479.
    PMID: 30410332 DOI: 10.2147/IJN.S171532
    Introduction: Kojic monooleate (KMO) is an ester derived from a fungal metabolite of kojic acid with monounsaturated fatty acid, oleic acid, which contains tyrosinase inhibitor to treat skin disorders such as hyperpigmentation. In this study, KMO was formulated in an oil-in-water nanoemulsion as a carrier for better penetration into the skin.

    Methods: The nanoemulsion was prepared by using high and low energy emulsification technique. D-optimal mixture experimental design was generated as a tool for optimizing the composition of nanoemulsions suitable for topical delivery systems. Effects of formulation variables including KMO (2.0%-10.0% w/w), mixture of castor oil (CO):lemon essential oil (LO; 9:1) (1.0%-5.0% w/w), Tween 80 (1.0%-4.0% w/w), xanthan gum (0.5%-1.5% w/w), and deionized water (78.8%-94.8% w/w), on droplet size as a response were determined.

    Results: Analysis of variance showed that the fitness of the quadratic polynomial fits the experimental data with F-value (2,479.87), a low P-value (P<0.0001), and a nonsignificant lack of fit. The optimized formulation of KMO-enriched nanoemulsion with desirable criteria was KMO (10.0% w/w), Tween 80 (3.19% w/w), CO:LO (3.74% w/w), xanthan gum (0.70% w/w), and deionized water (81.68% w/w). This optimum formulation showed good agreement between the actual droplet size (110.01 nm) and the predicted droplet size (111.73 nm) with a residual standard error <2.0%. The optimized formulation with pH values (6.28) showed high conductivity (1,492.00 µScm-1) and remained stable under accelerated stability study during storage at 4°C, 25°C, and 45°C for 90 days, centrifugal force as well as freeze-thaw cycles. Rheology measurement justified that the optimized formulation was more elastic (shear thinning and pseudo-plastic properties) rather than demonstrating viscous characteristics. In vitro cytotoxicity of the optimized KMO formulation and KMO oil showed that IC50 (50% inhibition of cell viability) value was >100 µg/mL.

    Conclusion: The survival rate of 3T3 cell on KMO formulation (54.76%) was found to be higher compared to KMO oil (53.37%) without any toxicity sign. This proved that the KMO formulation was less toxic and can be applied for cosmeceutical applications.

  15. 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.
  16. Salma H, Melha YM, Sonia L, Hamza H, Salim N
    J Pharm Sci, 2021 06;110(6):2531-2543.
    PMID: 33548245 DOI: 10.1016/j.xphs.2021.01.032
    The purpose of this study was to simultaneously predict the drug release and skin permeation of Piroxicam (PX) topical films based on Chitosan (CTS), Xanthan gum (XG) and its Carboxymethyl derivatives (CMXs) as matrix systems. These films were prepared by the solvent casting method, using Tween 80 (T80) as a permeation enhancer. All of the prepared films were assessed for their physicochemical parameters, their in vitro drug release and ex vivo skin permeation studies. Moreover, deep learning models and machine learning models were applied to predict the drug release and permeation rates. The results indicated that all of the films exhibited good consistency and physicochemical properties. Furthermore, it was noticed that when T80 was used in the optimal formulation (F8) based on CTS-CMX3, a satisfactory drug release pattern was found where 99.97% of PX was released and an amount of 1.18 mg/cm2 was permeated after 48 h. Moreover, Generative Adversarial Network (GAN) efficiently enhanced the performance of deep learning models and DNN was chosen as the best predictive approach with MSE values equal to 0.00098 and 0.00182 for the drug release and permeation kinetics, respectively. DNN precisely predicted PX dissolution profiles with f2 values equal to 99.99 for all the formulations.
  17. Musa SH, Basri M, Fard Masoumi HR, Shamsudin N, Salim N
    Int J Nanomedicine, 2017;12:2427-2441.
    PMID: 28405165 DOI: 10.2147/IJN.S125302
    Psoriasis is a chronic autoimmune disease that cannot be cured. It can however be controlled by various forms of treatment, including topical, systemic agents, and phototherapy. Topical treatment is the first-line treatment and favored by most physicians, as this form of therapy has more patient compliance. Introducing a nanoemulsion for transporting cyclosporine as an anti-inflammatory drug to an itchy site of skin disease would enhance the effectiveness of topical treatment for psoriasis. The addition of nutmeg and virgin coconut-oil mixture, with their unique properties, could improve cyclosporine loading and solubility. A high-shear homogenizer was used in formulating a cyclosporine-loaded nanoemulsion. A D-optimal mixture experimental design was used in the optimization of nanoemulsion compositions, in order to understand the relationships behind the effect of independent variables (oil, surfactant, xanthan gum, and water content) on physicochemical response (particle size and polydispersity index) and rheological response (viscosity and k-value). Investigation of these variables suggests two optimized formulations with specific oil (15% and 20%), surfactant (15%), xanthan gum (0.75%), and water content (67.55% and 62.55%), which possessed intended responses and good stability against separation over 3 months' storage at different temperatures. Optimized nanoemulsions of pH 4.5 were further studied with all types of stability analysis: physical stability, coalescence-rate analysis, Ostwald ripening, and freeze-thaw cycles. In vitro release proved the efficacy of nanosize emulsions in carrying cyclosporine across rat skin and a synthetic membrane that best fit the Korsmeyer-Peppas kinetic model. In vivo skin analysis towards healthy volunteers showed a significant improvement in the stratum corneum in skin hydration.
  18. 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.
  19. Ab Aziz NA, Salim N, Zarei M, Saari N, Yusoff FM
    Prep Biochem Biotechnol, 2021;51(1):44-53.
    PMID: 32701046 DOI: 10.1080/10826068.2020.1789991
    The study was conducted to determine anti-tyrosinase and antioxidant activities of the extracted collagen hydrolysate (CH) derived from Malaysian jellyfish, Rhopilema hispidum. Collagen was extracted using 1:1 (w:v) 0.1 M NaOH solution at temperature 25 °C for 48 hr followed by treatment of 1:2 (w:v) distilled water for another 24 hr and freeze-dried. The extracted collagen was hydrolyzed using papain at optimum temperature, pH and enzyme/substrate ratio [E/S] of 60 °C, 7.0 and 1:50, respectively. CH was found to exhibit tyrosinase inhibitory activity, DPPH radical scavenging and metal ion-chelating assays up to 64, 28, and 83%, respectively, after 8 hr of hydrolysis process. The molecular weight of CH was found <10 kDa consisting of mainly Gly (19.219%), Glu (10.428%), and Arg (8.848%). The UV-visible spectrum analysis showed a major and minor peak at 218 and 276 nm, accordingly. The FTIR spectroscopy confirmed the amide groups in CH. The SEM images demonstrated spongy and porous structure of CH. In the cytotoxicity study, CH has no cytotoxicity against mouse embryonic 3T3 fibroblast cell line with IC50 value >500 µg/ml. Results revealed that the CH generated from this study has a potential to be developed as active ingredient in cosmeceutical application.
  20. Kapitonova M, Gupalo S, Alyautdin R, Ibrahim IAA, Salim N, Ahmad A, et al.
    Avicenna J Phytomed, 2022;12(1):30-41.
    PMID: 35145893 DOI: 10.22038/AJP.2021.18113
    OBJECTIVE: Modern treatment of peptic ulcers includes antibacterial and gastroprotective medications. However, current anti-ulcer drugs possess severe side effects. Therefore, all attempts to find new effective medications free from side effects are justified. Though Berberis vulgaris is a medicinal plant commonly used for the treatment of numerous disorders, gastroprotective effect of its leaf extract was not investigated before.

    MATERIALS AND METHODS: Gastric ulcer was modelled in Sprague-Dawley rats after treatment with B. vulgaris leaf extract containing 0.07% of alkaloids, 0.48% of flavonoids and 8.05% of tanning substances, 10 or 50 mg of dry extract/kg, changes in the stomach mucosa were assessed semi-quantitatively, and the gastric wall was evaluated for prostaglandin E2 level using ELISA and assessed histologically by calculation of the lesion index.

    RESULTS: B. vulgaris leaf extract at the dose of 50 mg/kg reduced the macroscopic ulcer score and the microscopic lesion index, increased prostaglandin E2 concentration in the gastric wall significantly higher than atropine and B. vulgaris leaf extract 10 mg/kg.

    CONCLUSION: The gastroprotective effect of the high dose of B. vulgaris leaf extract may be due to stimulation of prostaglandin E2 secretion in the stomach, and anti-oxidative and anti-inflammatory properties of polyphenolic complex of flavonoids and tannins present in the leaves of this plant.

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