Three-Dimensional Cell Culture Models to Study Respiratory Virus Infections Including COVID-19

Mannose-Binding Lectins as Potent Antivirals against SARS-CoV-2

The SARS-CoV-2 entry into host cells is mainly mediated by the interactions between the viral spike protein (S) and the ACE-2 cell receptor, which are highly glycosylated. Therefore, carbohydrate binding agents may represent potential candidates to abrogate virus infection. Here, we evaluated the in vitro anti-SARS-CoV-2 activity of two mannose-binding lectins isolated from the Brazilian plants Canavalia brasiliensis and Dioclea violacea (ConBR and DVL). These lectins inhibited SARS-CoV-2 Wuhan-Hu-1 strain and variants Gamma and Omicron infections, with selectivity indexes (SI) of 7, 1.7, and 6.5, respectively for ConBR; and 25, 16.8, and 22.3, for DVL. ConBR and DVL inhibited over 95% of the early stages of the viral infection, with strong virucidal effect, and also protected cells from infection and presented post-entry inhibition. The presence of mannose resulted in the complete lack of anti-SARS-CoV-2 activity by ConBR and DVL, recovering virus titers. ATR-FTIR, molecular docking, and dynamic simulation between SARS-CoV-2 S and either lectins indicated molecular interactions with predicted binding energies of -85.4 and -72.0 Kcal/Mol, respectively. Our findings show that ConBR and DVL lectins possess strong activities against SARS-CoV-2, potentially by interacting with glycans and blocking virus entry into cells, representing potential candidates for the development of novel antiviral drugs.

In vitro high-content tissue models to address precision medicine challenges

The field of precision medicine allows for tailor-made treatments specific to a patient and thereby improve the efficiency and accuracy of disease prevention, diagnosis, and treatment and at the same time would reduce the cost, redundant treatment, and side effects of current treatments. Here, the combination of organ-on-a-chip and bioprinting into engineering high-content in vitro tissue models is envisioned to address some precision medicine challenges. This strategy could be employed to tackle the current coronavirus disease 2019 (COVID-19), which has made a significant impact and paradigm shift in our society. Nevertheless, despite that vaccines against COVID-19 have been successfully developed and vaccination programs are already being deployed worldwide, it will likely require some time before it is available to everyone. Furthermore, there are still some uncertainties and lack of a full understanding of the virus as demonstrated in the high number new mutations arising worldwide and reinfections of already vaccinated individuals. To this end, efficient diagnostic tools and treatments are still urgently needed. In this context, the convergence of bioprinting and organ-on-a-chip technologies, either used alone or in combination, could possibly function as a prominent tool in addressing the current pandemic. This could enable facile advances of important tools, diagnostics, and better physiologically representative in vitro models specific to individuals allowing for faster and more accurate screening of therapeutics evaluating their efficacy and toxicity. This review will cover such technological advances and highlight what is needed for the field to mature for tackling the various needs for current and future pandemics as well as their relevancy towards precision medicine.

Biology-Informed Recurrent Neural Network for Pandemic Prediction Using Multimodal Data

In the biomedical field, the time interval from infection to medical diagnosis is a random variable that obeys the log-normal distribution in general. Inspired by this biological law, we propose a novel back-projection infected-susceptible-infected-based long short-term memory (BPISI-LSTM) neural network for pandemic prediction. The multimodal data, including disease-related data and migration information, are used to model the impact of social contact on disease transmission. The proposed model not only predicts the number of confirmed cases, but also estimates the number of infected cases. We evaluate the proposed model on the COVID-19 datasets from India, Austria, and Indonesia. In terms of predicting the number of confirmed cases, our model outperforms the latest epidemiological modeling methods, such as vSIR, and intelligent algorithms, such as LSTM, for both short-term and long-term predictions, which shows the superiority of bio-inspired intelligent algorithms. In general, the use of mobility information improves the prediction accuracy of the model. Moreover, the number of infected cases in these three countries is also estimated, which is an unobservable but crucial indicator for the control of the pandemic.

Better In Vitro Tools for Exploring Chlamydia trachomatis Pathogenesis

Currently, Chlamydia trachomatis still possesses a significant impact on public health, with more than 130 million new cases each year, alongside a high prevalence of asymptomatic infections (approximately 80% in women and 50% in men). C. trachomatis infection involves a wide range of different cell types, from cervical epithelial cells, testicular Sertoli cells to Synovial cells, leading to a broad spectrum of pathologies of varying severity both in women and in men. Several two-dimensional in vitro cellular models have been employed for investigating C. trachomatis host–cell interaction, although they present several limitations, such as the inability to mimic the complex and dynamically changing structure of in vivo human host-tissues. Here, we present a brief overview of the most cutting-edge three-dimensional cell-culture models that mimic the pathophysiology of in vivo human tissues and organs for better translating experimental findings into a clinical setting. Future perspectives in the field of C. trachomatis research are also provided.