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Biochemical evaluation and medicinal ability of Jatropha mollissima in hepatic disorders

Iqbal MO, Naeem M, Mumtaz A, Ahmed MM, Ahmad A, Riaz R, et al.

Am J Transl Res, 2022;14(10):7178-7188.
PMID: 36398251

OBJECTIVE: Jatropha mollissima is one of the most valuable medicinal plants used for the treatment of hepatic disorders. It is evident that 500 mg/kg of sodium valproate causes the hepatotoxicity, ototoxicity, gastrotoxicity, bone marrow suppression, and inflammation. This study was designed to explore the medicinal uses of Jatropha mollissima in hepatic disorders.
METHODS: Hepatotoxicity was induced in Wister albino rats by injecting sodium valproate at the rate of 500 mg/kg once daily for fourteen days. Six male rats, each weighing 220-270 g, were placed into four separate groups for the study. The first group was treated with normal saline. Treatment of the second group was carried out by SVP for four days consecutively together with saline for three weeks. Group three and four were treated with sodium valproate and Jm hydroalcoholic extract applied in the concentrations of the 200 mg/kg and 400 mg/kg for the period of the three weeks. Phytochemical screening and HPLC analysis were conducted to identify the phytochemical nature and polyphenols in extract, respectively. DPPH, SOD, and NO tests were performed to measure the antioxidant activity.
RESULTS: With the initial dose of treatments to rats, anatomic, physiological, or histopathologic abnormalities were detected. After three weeks, extract of Jatropha mollissima was used to treat the valproic acid-induced hepatotoxicity (P < 0.05).
CONCLUSION: It was concluded that sodium valproate (SVP) and Jm extract were administered together. The hepatoprotective effects were extraordinarily high, with high concentrations of 400 mg/kg.
Fulltext Development and Characterization of Calcium-Alginate Beads of Apigenin: In Vitro Antitumor, Antibacterial, and Antioxidant Activities

Aldawsari MF, Ahmed MM, Fatima F, Anwer MK, Katakam P, Khan A

Mar Drugs, 2021 Aug 20;19(8).
PMID: 34436306 DOI: 10.3390/md19080467

The objective of this work was to develop sustained-release Ca-alginate beads of apigenin using sodium alginate, a natural polysaccharide. Six batches were prepared by applying the ionotropic gelation technique, wherein calcium chloride was used as a crosslinking agent. The beads were evaluated for particle size, drug loading, percentage yield, and in vitro drug release. Particle size was found to decrease, and drug entrapment efficiency was enhanced with an increase in the polymer concentration. The dissolution study showed sustained drug release from the apigenin-loaded alginate beads with an increase in the polymer proportion. Based on the dissolution profiles, BD6 formulation was optimized and characterized for FTIR, DSC, XRD, and SEM, results of which indicated successful development of apigenin-loaded Ca alginate beads. MTT assay demonstrated a potential anticancer effect against the breast cancer MCF-7 cell lines. The antimicrobial activity exhibited effective inhibition in the bacterial and fungal growth rate. The DPPH measurement revealed that the formulation had substantial antioxidant activity, with EC50 value slightly lowered compared to pure apigenin. A stability study demonstrated that the BD6 was stable with similar (f2) drug release profiles in harsh condition. In conclusion, alginate-based beads could be used for sustaining the drug release of poorly water-soluble apigenin while also improving in vitro antitumor, antimicrobial, and antioxidant activity.
Fulltext Antioxidant and Anti-inflammatory Applications of Aerva persica Aqueous-Root Extract-Mediated Synthesis of ZnO Nanoparticles

Fatima K, Asif M, Farooq U, Gilani SJ, Bin Jumah MN, Ahmed MM

ACS Omega, 2024 Apr 09;9(14):15882-15892.
PMID: 38617686 DOI: 10.1021/acsomega.3c08143

In the present study, ZnO nanoparticles were synthesized by using aqueous extracts of Aerva persica roots. Characterization of as-prepared ZnO nanoparticles was carried out using different techniques, including powder X-ray diffraction (XRD), UV-vis diffuse reflectance spectroscopy (DRS), Fourier transform infrared (FTIR) spectroscopy, field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM) and BET surface area analysis. Morphological analysis confirmed the small, aggregated flake-shaped morphology of as-synthesized ZnO nanostructures. The as-prepared ZnO nanoparticles were analyzed for their potential application as anti-inflammatory (using in vivo inhibition of carrageenan induced paw edema) and antioxidant (using in vitro radical scavenging activity) agents. The ZnO nanoparticles were found to have a potent antioxidant and anti-inflammatory activity comparable to that of standard ascorbic acid (antioxidant) and indomethacin (anti-inflammatory drug). Therefore, due to their ecofriendly synthesis, nontoxicity, and biocompatible nature, zinc oxide nanoparticles synthesized successfully from roots extract of the plant Aerva persica with potent efficiencies can be utilized for different biomedical applications.
Fulltext Fake news stance detection using selective features and FakeNET

Aljrees T, Cheng X, Ahmed MM, Umer M, Majeed R, Alnowaiser K, et al.

PLoS One, 2023;18(7):e0287298.
PMID: 37523404 DOI: 10.1371/journal.pone.0287298

The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising 'agree', 'disagree', 'discuss', and 'unrelated' classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article's perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.