Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders.

BACKGROUND: Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory, cardiac, and gastrointestinal disorders in children and newborns, mostly due to the complexity of therapeutic management in pediatrics and neonatology, owing to the lack of suitable dosage forms for these patients, often rendering them "therapeutic orphans". The development and application of pediatric drug formulations encounter numerous challenges, including physiological heterogeneity within age groups, limited profitability for the pharmaceutical industry, and ethical and clinical constraints. Many drugs are used unlicensed or off-label, posing a high risk of toxicity and reduced efficacy. Despite these circumstances, some regulatory changes are being performed, thus thrusting research innovation in this field. DATA SOURCES: Up-to-date peer-reviewed journal articles, books, government and institutional reports, data repositories and databases were used as main data sources. RESULTS: Among the main strategies proposed to address the current pediatric care situation, nanotechnology is specially promising for pediatric respiratory diseases since they offer a non-invasive, versatile, tunable, site-specific drug release. Tissue engineering is in the spotlight as strategy to address pediatric cardiac diseases, together with theragnostic systems. The integration of nanotechnology and theragnostic stands poised to refine and propel nanomedicine approaches, ushering in an era of innovative and personalized drug delivery for pediatric patients. Finally, the intersection of drug repurposing and artificial intelligence tools in pediatric healthcare holds great potential. This promises not only to enhance efficiency in drug development in general, but also in the pediatric field, hopefully boosting clinical trials for this population. CONCLUSIONS: Despite the long road ahead, the deepening of nanotechnology, the evolution of tissue engineering, and the combination of traditional techniques with artificial intelligence are the most recently reported strategies in the specific field of pediatric therapeutics.

New proposal of viral genome representation applied in the classification of SARS-CoV-2 with deep learning.

BACKGROUND: In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. RESULTS: In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. CONCLUSIONS: The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs.

A Novel Deep Neural Network Technique for Drug-Target Interaction.

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug-target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models.