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Applications of photodynamic therapy in keratitis

Anwar A, Khan NA, Alharbi AM, Alhazmi A, Siddiqui R

Int Ophthalmol, 2024 Mar 15;44(1):140.
PMID: 38491335 DOI: 10.1007/s10792-024-03062-4

Keratitis is corneal inflammatory disease which may be caused by several reason such as an injury, allergy, as well as a microbial infection. Besides these, overexposure to ultraviolet light and unhygienic practice of contact lenses are also associated with keratitis. Based on the cause of keratitis, different lines of treatments are recommended. Photodynamic therapy is a promising approach that utilizes light activated compounds to instigate either killing or healing mechanism to treat various diseases including both communicable and non-communicable diseases. This review focuses on clinically-important patent applications and the recent literature for the use of photodynamic therapy against keratitis.
A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directions

Alhazmi A, Mahmud R, Idris N, Mohamed Abo ME, Eke C

PeerJ Comput Sci, 2024;10:e1966.
PMID: 38660217 DOI: 10.7717/peerj-cs.1966

The automatic speech identification in Arabic tweets has generated substantial attention among academics in the fields of text mining and natural language processing (NLP). The quantity of studies done on this subject has experienced significant growth. This study aims to provide an overview of this field by conducting a systematic review of literature that focuses on automatic hate speech identification, particularly in the Arabic language. The goal is to examine the research trends in Arabic hate speech identification and offer guidance to researchers by highlighting the most significant studies published between 2018 and 2023. This systematic study addresses five specific research questions concerning the types of the Arabic language used, hate speech categories, classification techniques, feature engineering techniques, performance metrics, validation methods, existing challenges faced by researchers, and potential future research directions. Through a comprehensive search across nine academic databases, 24 studies that met the predefined inclusion criteria and quality assessment were identified. The review findings revealed the existence of many Arabic linguistic varieties used in hate speech on Twitter, with modern standard Arabic (MSA) being the most prominent. In identification techniques, machine learning categories are the most used technique for Arabic hate speech identification. The result also shows different feature engineering techniques used and indicates that N-gram and CBOW are the most used techniques. F1-score, precision, recall, and accuracy were also identified as the most used performance metric. The review also shows that the most used validation method is the train/test split method. Therefore, the findings of this study can serve as valuable guidance for researchers in enhancing the efficacy of their models in future investigations. Besides, algorithm development, policy rule regulation, community management, and legal and ethical consideration are other real-world applications that can be reaped from this research.
Fulltext Code-mixing unveiled: Enhancing the hate speech detection in Arabic dialect tweets using machine learning models

Alhazmi A, Mahmud R, Idris N, Mohamed Abo ME, Eke CI

PLoS One, 2024;19(7):e0305657.
PMID: 39018339 DOI: 10.1371/journal.pone.0305657

Technological developments over the past few decades have changed the way people communicate, with platforms like social media and blogs becoming vital channels for international conversation. Even though hate speech is vigorously suppressed on social media, it is still a concern that needs to be constantly recognized and observed. The Arabic language poses particular difficulties in the detection of hate speech, despite the considerable efforts made in this area for English-language social media content. Arabic calls for particular consideration when it comes to hate speech detection because of its many dialects and linguistic nuances. Another degree of complication is added by the widespread practice of "code-mixing," in which users merge various languages smoothly. Recognizing this research vacuum, the study aims to close it by examining how well machine learning models containing variation features can detect hate speech, especially when it comes to Arabic tweets featuring code-mixing. Therefore, the objective of this study is to assess and compare the effectiveness of different features and machine learning models for hate speech detection on Arabic hate speech and code-mixing hate speech datasets. To achieve the objectives, the methodology used includes data collection, data pre-processing, feature extraction, the construction of classification models, and the evaluation of the constructed classification models. The findings from the analysis revealed that the TF-IDF feature, when employed with the SGD model, attained the highest accuracy, reaching 98.21%. Subsequently, these results were contrasted with outcomes from three existing studies, and the proposed method outperformed them, underscoring the significance of the proposed method. Consequently, our study carries practical implications and serves as a foundational exploration in the realm of automated hate speech detection in text.
Metal Oxide Nanoparticles Exhibit Anti-Acanthamoeba castellanii Properties by Inducing Necrotic Cell Death

Ahmed U, Gew LT, Siddiqui R, Khan NA, Alharbi AM, Alhazmi A, et al.

Acta Parasitol, 2024 Sep;69(3):1717-1723.
PMID: 39153011 DOI: 10.1007/s11686-024-00891-2

PURPOSE: The treatment of amoebic infections is often problematic, largely due to delayed diagnosis, amoebae transformation into resistant cyst form, and lack of availability of effective chemotherapeutic agents. Herein, we determined anti-Acanthamoeba castellanii properties of three metal oxide nanoparticles (TiO2, ZrO2, and Al2O3).
METHODS: Amoebicidal assays were performed to determine whether metal oxide nanoparticles inhibit amoebae viability. Encystation assays were performed to test whether metal oxide nanoparticles inhibit cyst formation. By measuring lactate dehydrogenase release, cytotoxicity assays were performed to determine human cell damage. Hoechst 33342/PI staining was performed to determine programmed cell death (apoptosis) and necrosis in A. castellanii.
RESULTS: TiO2-NPs significantly inhibited amoebae viability as observed through amoebicidal assays, as well as inhibited their phenotypic transformation as evident using encystation assays, and showed limited human cell damage as observed by measuring lactate dehydrogenase assays. Furthermore, TiO2-NPs altered parasite membranes and resulted in necrotic cell death as determined using double staining cell death assays with Hoechst33342/Propidium iodide (PI) observed through chromatin condensation. These findings suggest that TiO2-NPs offers a potential viable avenue in the rationale development of therapeutic interventions against Acanthamoeba infections.
Fulltext In silico proteolysis and analysis of bioactive peptides from sequences of fatty acid desaturase 3 (FAD3) of flaxseed protein

Langyan S, Khan FN, Yadava P, Alhazmi A, Mahmoud SF, Saleh DI, et al.

Saudi J Biol Sci, 2021 Oct;28(10):5480-5489.
PMID: 34588858 DOI: 10.1016/j.sjbs.2021.08.027

Flaxseed (Linum usitatissimum), commonly known as linseed is an oilseed crop, emerging as an important and functional ingredient of food and has been paid more attention due to its nutritional value as well as beneficial effects. It is mainly rich in is α-linolenic acid (ALA, omega-3 fatty acid), fibres and lignans that have potential health benefits in reducing cardiovascular diseases, diabetes, osteoporosis, atherosclerosis, cancer, arthritis, neurological and autoimmune disorders. Due to its richness in omega-3 fatty acid, a group of enzymes known as fatty acid desaturases (FADs) mainly introduce double bonds into fatty acids' (FAs) hydrocarbon chains that produce unsaturated fatty acids. Fatty acid desaturase 3 (FAD3), the commonest microsomal enzyme of omega-3 fatty acid, synthesizes linolenic acid (C18:3) from linoleic acid located in endoplasmic reticulum (ER) facing towards the cytosol. The emerging field of bioinformatics and large number of databases of bioactive peptides, helps in providing time-saving and efficient method for identification of potential bioactivities of any protein. In this study, 10 unique sequences of FAD3 from flaxseed protein have been used for in silico proteolysis and releasing of various bioactive peptides using three plant proteases, namely ficin, papain and stem bromelain, that are evaluated with the help of BIOPEP database. Overall, 20 biological activities were identified from these proteins. The results showed that FAD3 protein is a potential source of peptides with angiotensin-I-converting enzyme (ACE) inhibitory and dipeptidyl peptidase-IV (DPP-IV) activities, and also various parameters such as ∑A, ∑B, AE, W, BE, V and DHt were also calculated. Furthermore, PeptideRanker have been used for screening of novel promising bioactive peptides. Various bioinformatics tools also used to study protein's physicochemical properties, peptide's score, toxicity, allergenicity aggregation, water solubility, and drug likeliness. The present work suggests that flaxseed protein can be a good source of bioactive peptides for the synthesis of good quality and quantity of oil, and in silico method helps in investigating and production of functional peptides.
Fulltext Designing a highly immunogenic multi epitope based subunit vaccine against Bacillus cereus

Asif Rasheed M, Awais M, Aldhahrani A, Althobaiti F, Alhazmi A, Sattar S, et al.

Saudi J Biol Sci, 2021 Sep;28(9):4859-4866.
PMID: 34466059 DOI: 10.1016/j.sjbs.2021.06.082

Objective: Serious non-gastrointestinal-tract infections and food poisoning are caused by Bacillus cereus. Vaccination against B. cereus is very important. The aim of this study was to identify and analyze B and T cell epitopes for chromate transporter protein of the bacteria.
Methods: Multiple sequence alignment with the Clustal Omega method was used to identify conserved regions and Geneious Prime was used to produce a consensus sequence. T and B cell epitopes were predicted by various computational tools from the NetCTL and Immune Epitope Database (IEDB), respectively.
Results: Altogether, 6 HTL cells and 11 CTL epitopes were predicted. This vaccine's molecular docking is done with Patch Dock and LigPlot to verify interactions. The immune server (C-IMMSIM) was used to develop In silico immune response in order to assess the multi-epitope vaccine's immunogenic profile.
Conclusion: We designed universal vaccine against B. cereus responsible for food poisoning. The disease may be avoided with the aid of the proposed epitope-based vaccine.
Fulltext Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna

Haque MA, Saha D, Al-Bawri SS, Paul LC, Rahman MA, Alshanketi F, et al.

Heliyon, 2023 Sep;9(9):e19548.
PMID: 37809766 DOI: 10.1016/j.heliyon.2023.e19548

In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535λ0×0.714λ0, it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.
Fulltext Melanin pigment of Streptomyces puniceus RHPR9 exhibits antibacterial, antioxidant and anticancer activities

Polapally R, Mansani M, Rajkumar K, Burgula S, Hameeda B, Alhazmi A, et al.

PLoS One, 2022;17(4):e0266676.
PMID: 35468144 DOI: 10.1371/journal.pone.0266676

The present study reveals the production of dark, extracellular melanin pigment (386 mg/L) on peptone yeast extract iron agar medium by Streptomyces puniceus RHPR9 using the gravimetric method. UV-Visible, Fourier Transform Infrared (FTIR), and Nuclear Magnetic Resonance (1H) (NMR) spectroscopy confirmed the presence of melanin. Extracted melanin showed antibacterial activity against human pathogens such as Bacillus cereus, Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli except for Klebsiella pneumoniae. A potent free radical scavenging activity was observed at 100 μg/mL of melanin by the DPPH method with a concentration of 89.01±0.05% compared with ascorbic acid 96.16±0.01%. Antitumor activity of melanin was evaluated by MTT assay against HEK 293, HeLa, and SK-MEL-28 cell lines with IC50 values of 64.11±0.00, 14.43±0.02, and 13.31±0.01 μg/mL respectively. Melanin showed maximum anti-inflammatory activity with human red blood cells (hRBC) (78.63 ± 0.01%) and minimum hemolysis of 21.37±0.2%. The wound healing potential of the pigment was confirmed on HeLa cells, cell migration was calculated, and it was observed that cell migration efficiency decreased with an increase in the concentration of melanin. To our knowledge, this is the first evidence of melanin produced from S. puniceus RHPR9 that exhibited profound scavenging, anti-inflammatory and cytotoxic activities.
Fulltext Pomegranate peels waste hydrolyzate optimization by Response Surface Methodology for Bioethanol production

Chaudhary A, Hussain Z, Aihetasham A, El-Sharnouby M, Abdul Rehman R, Azmat Ullah Khan M, et al.

Saudi J Biol Sci, 2021 Sep;28(9):4867-4875.
PMID: 34466060 DOI: 10.1016/j.sjbs.2021.06.081

Unwanted agricultural waste is largely comprised of lignocellulosic substrate which could be transformed into sugars. The production of bioethanol from garbage manifested an agreeable proposal towards waste management as well as energy causation. The goal of this work is to optimize parameters for generation of bioethanol through fermentation by different yeast strains while Saccharomyces cerevisiae used as standard strain. The low cost fermentable sugars from pomegranate peels waste (PPW) were obtained by hydrolysis with HNO3 (1 to 5%). The optimum levels of hydrolysis time and temperature were elucidated via RSM (CCD) ranging from 30 to 60 min and 50 to 100 °C respectively. The result shows that optimum values (g/L) for reducing sugars was 61.45 ± 0.01 while for total carbohydrates was 236 ± 0.01. These values were found when PPW was hydrolyzed with 3% HNO3, at 75 °C for one hour. The hydrolyzates obtained from the dilute HNO3 pretreated PPW yielded a maximum of 0.43 ± 0.04, 0.41 ± 0.03 g ethanol per g of reducing sugars by both Metchnikowia sp. Y31 and M. cibodasensis Y34 at day 7 of ethanologenic experiment. The current study exhibited that by fermentation of dilute HNO3 hydrolyzates of PPW could develop copious amount of ethanol by optimized conditions.