Dicentric chromosome analysis (DCA) has limitations in its use for the evaluation of the radiation dose upon the development of medical radiation equipment due to its time/labour-consuming procedure and the requirement of highly trained experts. Therefore, we aimed to construct a dose-response curve using a semi-automatic cytokinesis-block micronucleus (CBMN) analysis method that can be easily analysed and utilised by anyone. CHO-K1 cells were exposed to gamma rays at various doses (0-4 Gy). For the CBMN assay, the bi-nucleated cells were selected and captured, and micronuclei (MN) scoring was automatically performed using the Metafer4 system. The MN scores were manually confirmed and corrected by analysts. Using the frequency distributions of MN according to the radiation dose, the dose-response calibration curve was generated using Dose Estimate v5.2 software. The equation of dose-response calibration curve is Y = 0.0299 (±0.0057) + 0.1502 (±0.0151) × D + 0.0111 (±0.0048) × D2. The goodness-of-fit parameters were also calculated (chi-squared [χ 2] = 39.45, degrees of freedom = 5, p = 0.0000). The semi-automated CBMN assay consist of two steps: the automated MN capture/scoring step and the manual confirmation/correction step. Using an established dose-response calibration curve and the procedure of the semi-automated CBMN assay, the dose-estimation of gamma-irradiated (0.5 or 2 Gy) CHO-K1 cells were performed by two analysts individually, and it was inter-compared to verify the accuracy, the results showed that the estimated doses were a good fit the applied doses of radiation. The CBMN assay using CHO-K1 cells can be easily used as a biodosimetry tool for dose assessment of medical radiation equipment due to the advantage of being simple, easy, and quick to measure the dose.
Artificial intelligence (AI) offers a broad range of enhancements in medicine. Machine learning and deep learning techniques have shown significant potential in improving diagnosis and treatment outcomes, from assisting clinicians in diagnosing medical images to ascertaining effective drugs for a specific disease. Despite the prospective benefits, adopting AI in clinical settings requires careful consideration, particularly concerning data generalisation and model explainability. This commentary aims to discuss two potential use cases for AI in the field of medicine and the overarching challenges involved in their implementation.
Breast cancer (BC) recurrence is a major concern for both patients and healthcare providers. Accurately predicting the risk of BC recurrence can help guide treatment decisions and improve patient outcomes for a disease-free survival. There are several approaches and models that have been developed to predict BC recurrence risk. These include derived clinical assays such as genetic profiling (Oncotye Dx, MammaPrint, CanAssist and others), and algorithm derived open access tools such as Magee equations (ME), CTS5 Calculator and Predict Breast cancer. All the clinical assays are well accepted, but affordability and feasibility remain the challenge due to a noteworthy price tag of USD 3000. As per The American Society of Clinical Oncology (ASCO) updates, open access tools are possible substitutes but the availability of limited information on their applicability is a concern. These tools take into consideration the histopathologic parameters and immunohistochemistry (IHC) biomarkers data for estrogen receptor/progesterone (ER/PR), human epidermal growth factor receptor 2 (HER2), and Ki67. The current study focuses on the application of these tools in a subset of 55 Indian BC patients considering the influence of the androgen receptor (AR) IHC expression profile. AR is a potent target and a close interacting neighbor protein to ER and available literature also suggests their crosstalk expression in BC clinical models. Our comparative recurrence scores (RSs) predictive data showed a statistically significant AR expression correlation with average ME scores. No significance was noted across different prediction tools. The findings are suggestive that ME predictive scores are more relevant and informative compared to other online tools and with an additional AR IHC expression analysis the recurrence prediction might prove to be beneficial and feasible to many deprived BC patients.
Physiological processes that govern the normal functioning of mammalian cells are regulated by a myriad of signalling pathways. Mammalian mitogen-activated protein (MAP) kinases constitute one of the major signalling arms and have been broadly classified into four groups that include extracellular signal-regulated protein kinase (ERK), c-Jun N-terminal kinase (JNK), p38, and ERK5. Each signalling cascade is governed by a wide array of external and cellular stimuli, which play a critical part in mammalian cells in the regulation of various key responses, such as mitogenic growth, differentiation, stress responses, as well as inflammation. This evolutionarily conserved MAP kinase signalling arm is also important for metabolic maintenance, which is tightly coordinated via complicated mechanisms that include the intricate interaction of scaffold proteins, recognition through cognate motifs, action of phosphatases, distinct subcellular localisation, and even post-translational modifications. Aberration in the signalling pathway itself or their regulation has been implicated in the disruption of metabolic homeostasis, which provides a pathophysiological foundation in the development of metabolic syndrome. Metabolic syndrome is an umbrella term that usually includes a group of closely associated metabolic diseases such as hyperglycaemia, hyperlipidaemia, and hypertension. These risk factors exacerbate the development of obesity, diabetes, atherosclerosis, cardiovascular diseases, and hepatic diseases, which have accounted for an increase in the worldwide morbidity and mortality rate. This review aims to summarise recent findings that have implicated MAP kinase signalling in the development of metabolic diseases, highlighting the potential therapeutic targets of this pathway to be investigated further for the attenuation of these diseases.
Breast cancer is the most common cause of malignancy and the second most common cause of death due to cancer in women. This heterogeneous disease is currently broadly classified as estrogen receptor (ER), progesterone receptor (PR) positive luminal tumors, human epidermal growth factor receptor 2 (HER2) amplified tumors and triple-negative breast cancers (TNBC). Phytochemicals are proven to be promising anti-cancer chemotherapeutics agents with minimal cytotoxic effects on normal cells. Plumbagin (5-hydroxy-2-methyl-1, 4-naphthoquinone) is a phytochemical derived from the roots of Plumbago zeylanica and it is known to possess anti-cancer properties similar to other compounds of naphthoquinones. In about 90% of cancer cells, the telomerase enzyme activity is revived to add telomeric repeats to evade apoptosis. In this study, a combinatorial approach of combining the anti-cancer compound plumbagin to induce genotoxicity and a potent telomerase inhibitor, MST-312 (synthetic derivative of tea catechins), was used to determine the combinational treatment-induced lethality in breast cancer cells such as MDA-MB-231 (TNBC) and MCF-7 (lumina) cells. MDA-MB-231 cells were responsive to combination treatment in both short-term (48 h) and long-term treatment (14 days) in a synergistic manner, whereas in MCF-7, the combination treatment was more effective in the long-term regimen. Furthermore, the cytotoxic effects of the plumbagin and MST-312 combination treatment were not recoverable after the short-term treatment. In conclusion, a combination treatment of MST-312 and plumbagin is proven to be more effective than a single plumbagin compound treatment in inducing DNA damage and telomere dysfunction leading to greater genome instability, cell cycle arrest and eventually cell death in cancer cells.
