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Fulltext Chemical Compounds of Malacca Leaf (Phyllanthus emblica) after Triple Extraction with N-Hexane, Ethyl Acetate, and Ethanol

Asmilia N, Fahrimal Y, Abrar M, Rinidar R

ScientificWorldJournal, 2020;2020:2739056.
PMID: 32395086 DOI: 10.1155/2020/2739056

Malacca (Phyllanthus emblica) is one of the plants that is often by the community in the Aceh Besar district of Indonesia as a traditional medicine for the treatment of various diseases such as antimicrobial, antibacterial, antifungals, antivirals, antimutagenic, antimalaria, and antiallergic. This research was conducted to analyze the content of chemical compounds in the ethanol extract of the Malacca leaf (EEDM) using a gas chromatography-mass spectrophotometer (GC-MS). Malacca leaves were extracted by the maceration method using n-hexane, ethyl acetate, and ethanol. The GC-MS analysis showed EEDM contained 22 chemical compounds. The highest chemical content of EEDM is octadecanoic acid reaching 22.93%, 9,12-octadecanoic acid 14.99%, octadecanoic acid 7.59%, 9-hexadecenoic acid 6.17%, octadecanoic acid 5.95%, octadecanal 5.59%, 9,12-octadecanoic acid 5.06%, 3-eicosyne 4.75%, 1-hexadecenoic acid 4.08%, 11-tetradecen-1-ol 2.92%, 2-furanmethanol 2.83%, delta-guaiene 2.43%, cyclohexane 2.13%, hexadecanoic acid 1.99%, sativen 1.87%, octadecanoic acid 1.52%, 1H-cyclopropaanaphthalene 1.40%, tetradecanoic acid 1.40%, 3,7,11-tridecatrienenitrile 1.20%, caryophellene 1.11%, 2H-pyran 1.07%, and trans-caryophellene 1.03%. This study clearly shows the presence of fatty acids which play a major role in the efficacy of these traditional medicines particularly as antioxidant and antimalarial.
Fulltext Malacca leaf ethanolic extract (Phyllanthus emblica) as a hepatoprotector of the liver of mice (Mus musculus) infected with Plasmodium berghei

Asmilia N, Aliza D, Fahrimal Y, Abrar M, Ashary S

Vet World, 2020 Jul;13(7):1457-1461.
PMID: 32848324 DOI: 10.14202/vetworld.2020.1457-1461

Background and Aim: Although existing research confirms the antiparasitic effect of the Malacca plant against Plasmodium, its effect on the liver, one of the target organs of Plasmodium has not been investigated. Therefore, this study was conducted to explore the potential of the ethanolic extract of Malacca (Phyllanthus emblica) leaves in preventing liver damage in mice (Mus musculus) caused by Plasmodium berghei infection.
Materials and Methods: This study was conducted using the livers of 18 mice fixed in 10% neutral-buffered formalin. A completely randomized design with a unidirectional pattern comprising six treatments was used in this study, with each treatment consisting of three replications. Treatment 0 was the negative control group infected with P. berghei, treatment 1 was the positive control group infected with P. berghei followed by chloroquine administration at a dose of 5 mg/kg BW, and treatments 2, 3, 4, and 5 were groups infected with P. berghei and administered Malacca leaf ethanolic extracts at doses of 100, 300, 600, and 1200 mg/kg BW, respectively. The extracts were administered orally using a gastric tube for 4 consecutive days. Mice were sacrificed on the 7th day and livers were collected for histopathological examination.
Results: Histopathological examination of the livers of mice infected with P. berghei demonstrated the presence of hemosiderin, hydropic degeneration, fat degeneration, necrosis, and megalocytosis. However, all these histopathological changes were reduced in the livers of P. berghei-infected mice treated with various doses of Malacca leaf ethanolic extract. The differences between the treatments were found be statistically significant (p<0.05).
Conclusion: Ethanolic extract of Malacca leaves has the potential to protect against liver damage in mice infected with P. berghei. The dose of 600 mg/kg BW was found to be the most effective compared with the doses of 100, 300, and 1200 mg/kg BW.
Fulltext Advanced neural network-based model for predicting court decisions on child custody

Abrar M, Salam A, Ullah F, Nadeem M, AlSalman H, Mukred M, et al.

PeerJ Comput Sci, 2024;10:e2293.
PMID: 39650418 DOI: 10.7717/peerj-cs.2293

Predicting court rulings has gained attention over the past years. The court rulings are among the most important documents in all legal systems, profoundly impacting the lives of the children in case of divorce or separation. It is evident from literature that Natural language processing (NLP) and machine learning (ML) are widely used in the prediction of court rulings. In general, the court decisions comprise several pages and require a lot of space. In addition, extracting valuable information and predicting legal decisions task is difficult. Moreover, the legal system's complexity and massive litigation make this problem more serious. Thus to solve this issue, we propose a new neural network-based model for predicting court decisions on child custody. Our proposed model efficiently performs an efficient search from a massive court decisions database and accurately identifies specific ones that especially deal with copyright claims. More specially, our proposed model performs a careful analysis of court decisions, especially on child custody, and pinpoints the plaintiff's custody request, the court's ruling, and the pivotal arguments. The working mechanism of our proposed model is performed in two phases. In the first phase, the isolation of pertinent sentences within the court ruling encapsulates the essence of the proceedings performed. In the second phase, these documents were annotated independently by using two legal professionals. In this phase, NLP and transformer-based models were employed and thus processed 3,000 annotated court rulings. We have used a massive dataset for the training and refining of our proposed model. The novelty of the proposed model is the integration of bidirectional encoder representations from transformers (BERT) and bidirectional long short-term memory (Bi_LSTM). The traditional methods are primarily based on support vector machines (SVM), and logistic regression. We have performed a comparison with the state-of-the-art model. The efficient results indicate that our proposed model efficiently navigates the complex terrain of legal language and court decision structures. The efficiency of the proposed model is measured in terms of the F1 score. The achieved results show that scores range from 0.66 to 0.93 and Kappa indices from 0.57 to 0.80 across the board. The performance is achieved at times surpassing the inter-annotator agreement, underscoring the model's adeptness at extracting and understanding nuanced legal concepts. The efficient results proved the potential of the proposed neural network model, particularly those based on transformers, to effectively discern and categorize key elements within legal texts, even amidst the intricacies of judicial language and the layered complexity of appellate rulings.
Fulltext DeepSplice: a deep learning approach for accurate prediction of alternative splicing events in the human genome

Abrar M, Hussain D, Khan IA, Ullah F, Haq MA, Aleisa MA, et al.

Front Genet, 2024;15:1349546.
PMID: 38974384 DOI: 10.3389/fgene.2024.1349546

Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease.
Fulltext Impact of Cobalt Doping on the Structural, Optical, and Dielectric Properties of MgAl2O4 Spinel Material

Ullah K, Khan SA, Zaman A, Sarker MR, Ali A, Tirth V, et al.

ACS Omega, 2023 Aug 22;8(33):29959-29965.
PMID: 37636967 DOI: 10.1021/acsomega.3c00541

Nanomaterials (NMs) with structural, optical, and dielectric properties are called functional or smart materials and have favorable applications in various fields of material science and nanotechnology. Pure and Co-doped MgAl2O4 were synthesized by using the sol-gel combustion method. A systematic investigation was carried out to understand the effects of the Co concentration on the crystalline phase, morphology, and optical and dielectric properties of Co-doped MgAl2O4. X-ray diffraction confirmed the cubic spinel structure with the Fd3̅m space group, and there was no impurity phase, while the surface morphology of the samples was investigated by scanning electron microscopy. The dielectric properties of the synthesized material are investigated using an LCR meter with respect to the variation in frequency (1-2 GHz), and their elemental composition has been examined through the energy-dispersive X-ray technique. The existence of the metal-oxygen Mg-Al-O bond has been confirmed by Fourier transform infrared spectroscopy. The value of the dielectric constant decreases with the increasing frequency and Co concentration. The optical behaviors of the Co2+-doped MgAl2O4 reveal that the optical properties were enhanced by increasing the cobalt concentration, which ultimately led to a narrower band gap, which make them exquisite and suitable for energy storage applications, especially for super capacitors. This work aims to focus on the effect of cobalt ions in different concentrations on structural, optical, and dielectric properties.