Pitavastatin-loaded bilosomes for oral treatment of hepatocellular carcinoma: a repurposing approach

Albeit its established efficacy as an anti-hyperlipidemic agent, pitavastatin (PIT) has been shown to have other various therapeutic effects. One of these effects is the anti-cancer activity against hepatocellular carcinoma (HCC). This effect has been evaluated in this study for the first time via its oral delivery loaded in bilosomes both in vitro in hepatocellular carcinoma (HCC) cell line; HepG2 and in vivo in an Ehrlich ascites carcinoma (EAC) model. Moreover, the impact of surface modification of bilosomes with lactoferrin (LF) as an active targeting ligand for HCC was investigated. Bilosomes were prepared by thin-film hydration and different molar phospholipid to bile salt ratios were used to optimize the bilosomal formulation. The molar phospholipid to bile salt ratio was adjusted to 4:1 at pH 7.4. LF-coated bilosomes possessed a particle size, PDI, entrapment efficiency, and zeta potential of 112.28 nm ± 6.35, 0.229 ± 0.06, 90.56% ± 3.22, and −7.86 mV ± 1.13, respectively. LF-coated bilosomes also increased permeation of PIT when tested on Caco-2 cells by 3.1-folds (compared to uncoated ones or free PIT solution). It also improved the cytotoxicity of HepG2 spheroids 44-folds more than PIT-free solution. RT-PCR analysis showed that LF-coated PIT-loaded bilosomes caused an improvement (2-fold increase) in the apoptotic potential of PIT mediated by caspase-3. In conclusion, the optimized LF-coated PIT-loaded bilosomes were cytotoxic to HCC with improved hepatocytes permeation and cellular uptake. Thus, the proposed formula could be a promising treatment for HCC.

Extracellular vesicles miRNA-21: a potential therapeutic tool in premature ovarian dysfunction

Chemotherapy induces an irreversible premature ovarian dysfunction (POD). Amniotic fluid mesenchymal stem cells (AFMSCs) can rescue fertility; however, the notion that stem cells can rejuvenate follicles is highly controversial due to the predetermined ovarian reserve. This study aims to isolate AFMSC-derived extracellular vesicles (EVs) and investigate their abundancy for the anti-apoptotic miRNA-21 as a means of ovarian restoration. Female rats were divided into healthy controls and POD-induced groups. The POD induced groups were subdivided into three groups according to the therapies they received: placebo-treated POD, AFMSC and EVs groups. Rats were assessed for serum anti-Müllerian hormone (AMH) levels, ovarian caspase 3 and PTEN protein levels in the ovarian lysate. Total follicular counts (TFCs) were estimated from stained ovarian sections. Functional recovery was investigated through daily vaginal smears and mating trials. In vitro chemical transfection of the AFMSCs with selective miRNA-21 mimics/inhibitors followed by isolation of EVs for therapy was conducted in two additional groups. At the interval points studied, treatment with AFMSCs and EVs equally restored TFC, AMH levels, regular estrous cycles and fruitful conception, while it both diminished caspase 3 and PTEN levels. EVs carrying miRNA-21 mimics recapitulated the short-term effects. Placebo-treated POD or EVs carrying miRNA-21 inhibitors showed augmented ovarian follicular damage demonstrated the low AMH levels, TFC and high levels of PTEN and caspase 3. miRNA-21 allowed regeneration by modulating PTEN and caspase 3 apoptotic pathways. Our findings exemplify that EVs could serve as an innovative cell-free therapeutic tool functioning through their miRNA content and that miRNA-21 has a chief regenerative role through modulating PTEN and caspase 3 apoptotic pathways.

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Early prediction of liver carcinogenicity due to occupational exposure to pesticides

Several studies linked between pesticides exposure and development of liver cancer, through several mechanisms inform of genotoxicity, cytotoxicity, tumor promotion, immunotoxicity and hormonal actions. This study aimed to estimate novel biomarkers for early prediction of liver malignancy due to occupational exposure to pesticides in two groups of workers with different socioeconomic standard (highly educated urban researchers and low educated rural pesticides sprayers). This study included 50 urban researchers and 50 rural pesticides sprayers occupationally exposed to pesticides. They were compared with 50 non-exposed urban researchers and 50 non-exposed rural subjects. Several tumor biomarkers were estimated; P53 protein, Alfa fetoprotein (AFP), and Alpha-L-fucosidase (AFU). Additionally, telomerase enzyme activity, Relative telomere length (RTL), and DNA damage using comet assay were measured. Furthermore, the glutathione-S-Transferase (GST) gene polymorphisms were identified for both exposed groups. Statistical analysis revealed elevated level of tumor biomarkers among exposed subjects relative to control groups in spite of being within the normal range. Increase in the DNA damage was detected, with shortening of telomere length and decrease in telomerase enzyme activity in pesticides-exposed subjects compared to their controls. Most of these changes were related to the levels of butyrylcholinesterase. Subjects with GSTT1 genotype were suggested to be more susceptible to hepatic carcinogenicity. Telomere relative length and comets assay together with GST genes polymorphisms could be used as early predictors for liver cancer susceptibility among pesticides exposed workers.