Isocyanide Substitution in Acridine Orange Shifts DNA Damage-Mediated Phototoxicity to Permeabilization of the Lysosomal Membrane in Cancer Cells.

In cancer therapy, immunogenic cell death eliminates tumor cells more efficiently than conventional apoptosis. During photodynamic therapy (PDT), some photosensitizer (PS) targeting lysosomes divert apoptosis to the immunologically more relevant necrosis-like cell death. Acridine orange (AO) is a PS targeting lysosome. We synthesized a new compound, 3-N,N-dimethylamino-6-isocyanoacridine (DM), a modified AO, aiming to target lysosomes better. To compare DM and AO, we studied optical properties, toxicity, cell internalization, and phototoxicity. In addition, light-mediated effects were monitored by the recently developed QUINESIn method on nuclei, and membrane stability, morphology, and function of lysosomes utilizing fluorescent probes by imaging cytometry in single cells. DM proved to be a better lysosomal marker at 405 nm excitation and lysed lysosomes more efficiently. AO injured DNA and histones more extensively than DM. Remarkably, DM's optical properties helped visualize shockwaves of nuclear DNA released from cells during the PDT. The asymmetric polar modification of the AO leads to a new compound, DM, which has increased efficacy in targeting and disrupting lysosomes. Suitable AO modification may boost adaptive immune response making PDT more efficient.

Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves.

OBJECTIVES: The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. METHODS: We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data. RESULTS: We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low. CONCLUSION: Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.