Griseofulvin stabilizes microtubule dynamics, activates p53 and inhibits the proliferation of MCF-7 cells synergistically with vinblastine

<p>Abstract</p> <p>Background</p> <p>Griseofulvin, an antifungal drug, has recently been shown to inhibit proliferation of various types of cancer cells and to inhibit tumor growth in athymic mice. Due to its low toxicity, griseofulvin has drawn considerable attention for its potential use in cancer chemotherapy. This work aims to understand how griseofulvin suppresses microtubule dynamics in living cells and sought to elucidate the antimitotic and antiproliferative action of the drug.</p> <p>Methods</p> <p>The effects of griseofulvin on the dynamics of individual microtubules in live MCF-7 cells were measured by confocal microscopy. Immunofluorescence microscopy, western blotting and flow cytometry were used to analyze the effects of griseofulvin on spindle microtubule organization, cell cycle progression and apoptosis. Further, interactions of purified tubulin with griseofulvin were studied <it>in vitro </it>by spectrophotometry and spectrofluorimetry. Docking analysis was performed using autodock4 and LigandFit module of Discovery Studio 2.1.</p> <p>Results</p> <p>Griseofulvin strongly suppressed the dynamic instability of individual microtubules in live MCF-7 cells by reducing the rate and extent of the growing and shortening phases. At or near half-maximal proliferation inhibitory concentration, griseofulvin dampened the dynamicity of microtubules in MCF-7 cells without significantly disrupting the microtubule network. Griseofulvin-induced mitotic arrest was associated with several mitotic abnormalities like misaligned chromosomes, multipolar spindles, misegregated chromosomes resulting in cells containing fragmented nuclei. These fragmented nuclei were found to contain increased concentration of p53. Using both computational and experimental approaches, we provided evidence suggesting that griseofulvin binds to tubulin in two different sites; one site overlaps with the paclitaxel binding site while the second site is located at the αβ intra-dimer interface. In combination studies, griseofulvin and vinblastine were found to exert synergistic effects against MCF-7 cell proliferation.</p> <p>Conclusions</p> <p>The study provided evidence suggesting that griseofulvin shares its binding site in tubulin with paclitaxel and kinetically suppresses microtubule dynamics in a similar manner. The results revealed the antimitotic mechanism of action of griseofulvin and provided evidence suggesting that griseofulvin alone and/or in combination with vinblastine may have promising role in breast cancer chemotherapy.</p

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Introduction Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality. Methods Prospective cohort study in 109 institutions in 41 countries. Inclusion criteria: children &lt;18 years who were newly diagnosed with or undergoing active treatment for acute lymphoblastic leukaemia, non-Hodgkin's lymphoma, Hodgkin lymphoma, retinoblastoma, Wilms tumour, glioma, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma, medulloblastoma and neuroblastoma. Of 2327 cases, 2118 patients were included in the study. The primary outcome measure was all-cause mortality at 30 days, 90 days and 12 months. Results All-cause mortality was 3.4% (n=71/2084) at 30-day follow-up, 5.7% (n=113/1969) at 90-day follow-up and 13.0% (n=206/1581) at 12-month follow-up. The median time from diagnosis to multidisciplinary team (MDT) plan was longest in low-income countries (7 days, IQR 3-11). Multivariable analysis revealed several factors associated with 12-month mortality, including low-income (OR 6.99 (95% CI 2.49 to 19.68); p&lt;0.001), lower middle income (OR 3.32 (95% CI 1.96 to 5.61); p&lt;0.001) and upper middle income (OR 3.49 (95% CI 2.02 to 6.03); p&lt;0.001) country status and chemotherapy (OR 0.55 (95% CI 0.36 to 0.86); p=0.008) and immunotherapy (OR 0.27 (95% CI 0.08 to 0.91); p=0.035) within 30 days from MDT plan. Multivariable analysis revealed laboratory-confirmed SARS-CoV-2 infection (OR 5.33 (95% CI 1.19 to 23.84); p=0.029) was associated with 30-day mortality. Conclusions Children with cancer are more likely to die within 30 days if infected with SARS-CoV-2. However, timely treatment reduced odds of death. This report provides crucial information to balance the benefits of providing anticancer therapy against the risks of SARS-CoV-2 infection in children with cancer

Clinical Text Mining for Context Sequences Identification

Part 2: MAKE-TextInternational audienceThis paper presents an approach based on sequence mining for identification of context models of diseases described by different medical specialists in clinical text. Clinical narratives contain rich medical terminology, specific abbreviations, and various numerical values. Usually raw clinical texts contain too many typos. Due to the telegraphic style of the text and incomplete sentences, the general part of speech taggers and syntax parsers are not efficient in text processing of non-English clinical text. The proposed approach is language independent. Thus, the method is suitable for processing clinical texts in low resource languages. The experiments are done on pseudonimized outpatient records in Bulgarian language produced by four different specialists for the same cohort of patients suffering from similar disorders. The results show that from the clinical documents can be identified the specialty of the physician. Even the close vocabulary is used in the patient status description there are slight differences in the language used by different physicians. The depth and the details of the description allow to determine different aspects and to identify the focus in the text. The proposed data driven approach will help for automatic clinical text classification depending on the specialty of the physician who wrote the document. The experimental results show high precision and recall in classification task for all classes of specialist represented in the dataset. The comparison of the proposed method with bag of words method show some improvement of the results in document classification task