Synthesis and anticancer activity of Pt(0)-olefin complexes bearing 1,3,5-triaza-7-phosphaadamantane and N-heterocyclic carbene ligands

A series of Pt(0)-η2-olefin complexes bearing 1,3,5-triaza-7-phosphaadamantane (PTA) or N-heterocyclic carbenes are prepared following different synthetic strategies depending on the nature of coordinated alkene and spectator ligands. These new platinum(0) derivatives have been tested in vitro as anticancer agents toward three different tumor (human ovarian cancer A2780 and A2780cis and K562 myelogenous leukemia) and one non-tumor (Hacat keratinocytes) cell lines, proving to be in several cases highly and selectively cytotoxic against ovarian cancer cells. Furthermore, this antiproliferative effect is associated with the activation of an apoptosis process. In particular, complexes equipped with PTA as spectator ligand give comparable IC50 values on A2780 (cisplatin sensitive) and A2780cis (cisplatin resistant) cell lines, indirectly proving that these new Pt(0) substrates act with a mechanism of action conceivably different from cisplatin. This hypothesis is also confirmed by the fact that our compounds, in contrast to cisplatin, are not able to promote erythroid-differentiation activity on the K562 myelogenous leukemia cell line

FONTI 4.0: Evaluating speech-to-text automatic transcription of digitized historical oral sources

Conducting “manual” transcriptions and analyses is unsustainable for most historical oral archives because they require a remarkable amount of funds and time. The FONTI 4.0 project aims at exploring the suitability of automatic transcription and information extraction technologies for making historical oral sources available. In this work, we conducted an experiment to test the performance of two commercial speech-to-text services (Google Cloud Speech-to-text and Amazon Transcribe) on digitized oral sources. We created an eight-hour corpus made of manually transcribed and annotated historical speech recordings in TEI format. The results clearly show how audio quality and disturbing elements (e.g., overlaps, foreign words, etc.) impact on the automatic transcription, showing what needs to be improved for implementing an unsupervised transcription chain