Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes.

In silico (quantitative) structure-activity relationship modeling is an approach that provides a fast and cost-effective alternative to assess the genotoxic potential of chemicals. However, one of the limiting factors for model development is the availability of consolidated experimental datasets. In the present study, we collected experimental data on micronuclei in vitro and in vivo, utilizing databases and conducting a PubMed search, aided by text mining using the BioBERT large language model. Chemotype enrichment analysis on the updated datasets was performed to identify enriched substructures. Additionally, chemotypes common for both endpoints were found. Five machine learning models in combination with molecular descriptors, twelve fingerprints and two data balancing techniques were applied to construct individual models. The best-performing individual models were selected for the ensemble construction. The curated final dataset consists of 981 chemicals for micronuclei in vitro and 1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. The ensemble model exhibited high accuracy and sensitivity when applied to an external test set of in vitro data. A good balanced predictive performance was also achieved for the micronucleus in vivo endpoint.

Investigating residual leukemic cells in acute lymphoblastic leukemia: a practical approach using a streamlined interphase fluorescence in situ hybridization method on cerebrospinal fluid.

INTRODUCTION: A precise diagnosis of central nervous system involvement in acute lymphoblastic leukemia (ALL) requires comprehensive knowledge of morphological analysis, with a focus on the quantity and quality of cells being examined. Some research has utilized techniques such as immunocytochemistry, flow cytometry, polymerase chain reaction (PCR), and interphase fluorescence in situ hybridization (iFISH) on cerebrospinal fluid (CSF) cytospin samples to detect any remaining leukemic cells in the CSF. To obtain reliable results using immunocytochemistry and flow cytometry, it is essential to use freshly collected specimens within a limited timeframe. At the same time, PCR requires a sufficient number of cells for DNA extraction. On the other hand, the iFISH procedure on CSF cytospin samples can be challenging and requires practice. Therefore, there is a need for a fast, easy method that will be affordable and marketable in laboratories where the above methods are not available, or the sample is insufficient to use those methods. METHODS: The samples were prepared by centrifugation of 1 mL aliquots of CSF collected into EDTA tubes. The CSF sample was centrifuged at 3000 rpm for 3 min, the supernatant was removed, and the pellet was placed in KCl hypotonic solution for 5 min at 37 °C. Other steps (fixation, hybridization, wash steps, and analysis) were the same as in the standard protocol for blood samples. The BCR-ABL1 rearrangements were performed and evaluated in 200 interphase cells. RESULTS: 90% of Ph(+) cells were found in CSF. CONCLUSION: We propose a significantly streamlined iFISH method for detecting blast/residual leukemic cells in acute lymphoblastic leukemia using CSF as a complementary test option.

Improving VAE based molecular representations for compound property prediction.

Collecting labeled data for many important tasks in chemoinformatics is time consuming and requires expensive experiments. In recent years, machine learning has been used to learn rich representations of molecules using large scale unlabeled molecular datasets and transfer the knowledge to solve the more challenging tasks with limited datasets. Variational autoencoders are one of the tools that have been proposed to perform the transfer for both chemical property prediction and molecular generation tasks. In this work we propose a simple method to improve chemical property prediction performance of machine learning models by incorporating additional information on correlated molecular descriptors in the representations learned by variational autoencoders. We verify the method on three property prediction tasks. We explore the impact of the number of incorporated descriptors, correlation between the descriptors and the target properties, sizes of the datasets etc. Finally, we show the relation between the performance of property prediction models and the distance between property prediction dataset and the larger unlabeled dataset in the representation space.

Antimicrobial and antioxidant potentials of non-cytotoxic extracts of corticolous lichens sampled in Armenia.

Due to wide range of secondary metabolites, lichens were used from antiquity as sources of colorants, perfumes and medicaments. This research focuses on exploring the antioxidant, antimicrobial and cytotoxic activities of methanol, ethanol, acetone extracts and aqueous infusions of corticolous lichens sampled from Armenia. Methanol, ethanol and acetone extracts from all tested lichens were active against Gram-positive bacterial strains. The most effective solvent to retrieve antimicrobial compounds was methanol. Aqueous infusions of tested lichens didn't show any significant antibacterial and antifungal activity. The highest antimicrobial activity was observed for methanol extract of Ramalina sinensis. The minimum inhibitory concentration of methanol extract of Ramalina sinensis were 0.9-1.8 mg mL- 1. Pseudevernia furfuracea demonstrated antifungal activity (Ø 12 mm). Methanol extract of Parmelia sulcata demonstrated largest 1,1-diphenyl-2-picryl-hydrazil (DPPH) radical scavenging activity (71 %). The cytotoxicity was measured on human HeLa (cervical carcinoma) cell lines using microculture tetrazolium test assay. The IC50 values estimated for methanol extracts of Peltigera praetextata, Evernia prunastri, Ramalina sinensis and Ramalina farinacea species in HeLa cell line were within 1.8-2.8 mg mL- 1 and considered as non-cytotoxic. Obtained results suggest that studied lichens can be prospective in biotechnologies as alternative sources of antimicrobial and antioxidant substances.

Ochratoxin A induces global DNA hypomethylation and oxidative stress in neuronal cells in vitro.

Recently, it was reported that ochratoxin A (OTA) mycotoxin, produced by a number of Aspergillus and Penicillium fungal species, may cause neuropsychological impairment or mental and emotional disorders but the mechanism of neurotoxicity remains unknown. Adverse effects of OTA in human (SHSY5Y) and mouse (HT22) neuronal cell lines were studied in vitro. OTA was found to be non-cytotoxic in both cell lines at concentrations 2.5-30 µmol/l, which are above the levels reported for human and animal plasma. OTA led to slightly elevated chromosomal instability in HT22 cells at concentrations of 15-30 µmol/l after 48 h, while in SHSY5Y cells, no evidence for genotoxic effects was observed at concentrations of 2.5-30 µmol/l. OTA treatment at 10 µmol/l resulted in elevated levels of unmethylated cytosines in CpG dinucleotides (up to 1.4-fold), elevated levels of intracellular reactive oxygen species (up to 1.6-fold), and in elevated levels of oxidized DNA purines (up to 2.2-fold) in both cell lines. Detected global DNA hypomethylation and oxidative stress were found to be reversible in 96 h and 24-72 h, respectively. In general, the observed pattern of OTA-induced effects in both cell lines was similar, but HT22 cells exhibited higher sensitivity, as well as better repair capacity in response to OTA toxicity. In conclusion, the results suggest that oxidative stress and epigenetic changes are directly involved in OTA-induced neurotoxicity, while cytotoxicity and genotoxicity cannot be considered as primary cause of toxicity in neuronal cells in vitro.

Laser-Driven Ultrashort Pulsed Electron Beam Radiation at Doses of 0.5 and 1.0 Gy Induces Apoptosis in Human Fibroblasts.

Rapidly evolving laser technologies have led to the development of laser-generated particle accelerators as an alternative to conventional facilities. However, the radiobiological characteristics need to be determined to enhance their applications in biology and medicine. In this study, the radiobiological effects of ultrashort pulsed electron beam (UPEB) and X-ray radiation in human lung fibroblasts (MRC-5 cell line) exposed to doses of 0.1, 0.5, and 1 Gy are compared. The changes of γH2AX foci number as a marker of DNA double-strand breaks (DSBs) were analyzed. In addition, the micronuclei induction and cell death via apoptosis were studied. We found that the biological action of UPEB-radiation compared to X-rays was characterized by significantly slower γH2AX foci elimination (with a dose of 1 Gy) and strong apoptosis induction (with doses of 0.5 and 1.0 Gy), accompanied by a slight increase in micronuclei formation (dose of 1 Gy). Our data suggest that UPEB radiation produces more complex DNA damage than X-ray radiation, leading to cell death rather than cytogenetic disturbance.