Potential of oligonucleotide- and protein/peptide-based therapeutics in the management of toxicant/stressor-induced diseases.

Exposure to toxicants/stressors has been linked to the development of many human diseases. They could affect various cellular components, such as DNA, proteins, lipids, and non-coding RNAs (ncRNA), thereby triggering various cellular pathways, particularly oxidative stress, inflammatory responses, and apoptosis, which can contribute to pathophysiological states. Accordingly, modulation of these pathways has been the focus of numerous investigations for managing related diseases. The involvement of various ncRNAs, such as small interfering RNA (siRNA), microRNAs (miRNA), and long non-coding RNAs (lncRNA), as well as various proteins and peptides in mediating these pathways, provides many target sites for pharmaceutical intervention. In this regard, various oligonucleotide- and protein/peptide-based therapies have been developed to treat toxicity-induced diseases, which have shown promising results in vitro and in vivo. This comprehensive review provides information about various aspects of toxicity-related diseases including their causing factors, main underlying mechanisms and intermediates, and their roles in pathophysiological states. Particularly, it highlights the principles and mechanisms of oligonucleotide- and protein/peptide-based therapies in the treatment of toxicity-related diseases. Furthermore, various issues of oligonucleotides and proteins/peptides for clinical usage and potential solutions are discussed.

Natural language processing for identification of refractory status epilepticus in children.

OBJECTIVE: Pediatric status epilepticus is one of the most frequent pediatric emergencies, with high mortality and morbidity. Utilizing electronic health records (EHRs) permits analysis of care approaches and disease outcomes at a lower cost than prospective research. However, reviewing EHR manually is time intensive. We aimed to compare refractory status epilepticus (rSE) cases identified by human EHR review with a natural language processing (NLP)-assisted rSE screen followed by a manual review. METHODS: We used the NLP screening tool Document Review Tool (DrT) to generate regular expressions, trained a bag-of-words NLP classifier on EHRs from 2017 to 2019, and then tested our algorithm on data from February to December 2012. We compared results from manual review to NLP-assisted search followed by manual review. RESULTS: Our algorithm identified 1528 notes in the test set. After removing notes pertaining to the same event by DrT, the user reviewed a total number of 400 notes to find patients with rSE. Within these 400 notes, we identified 31 rSE cases, including 12 new cases not found in manual review, and 19 of the 20 previously identified cases. The NLP-assisted model found 31 of 32 cases, with a sensitivity of 96.88% (95% CI = 82%-99.84%), whereas manual review identified 20 of 32 cases, with a sensitivity of 62.5% (95% CI = 43.75%-78.34%). SIGNIFICANCE: DrT provided a highly sensitive model compared to human review and an increase in patient identification through EHRs. The use of DrT is a suitable application of NLP for identifying patients with a history of recent rSE, which ultimately contributes to the implementation of monitoring techniques and treatments in near real time.