Text summarization for pharmaceutical sciences using hierarchical clustering with a weighted evaluation methodology.

In the pharmaceutical industry, there is an abundance of regulatory documents used to understand the current regulatory landscape and proactively make project decisions. Due to the size of these documents, it is helpful for project teams to have informative summaries. We propose a novel solution, MedicoVerse, to summarize such documents using advanced machine learning techniques. MedicoVerse uses a multi-stage approach, combining word embeddings using the SapBERT model on regulatory documents. These embeddings are put through a critical hierarchical agglomerative clustering step, and the clusters are organized through a custom data structure. Each cluster is summarized using the bart-large-cnn-samsum model, and each summary is merged to create a comprehensive summary of the original document. We compare MedicoVerse results with established models T5, Google Pegasus, Facebook BART, and large language models such as Mixtral 8 × 7b instruct, GPT 3.5, and Llama-2-70b by introducing a scoring system that considers four factors: ROUGE score, BERTScore, business entities and the Flesch Reading Ease. Our results show that MedicoVerse outperforms the compared models, thus producing informative summaries of large regulatory documents.

COVID-19 serum can be cross-reactive and neutralizing against the dengue virus, as observed by the dengue virus neutralization test.

OBJECTIVES: Observing the serological cross-reactivity between SARS-CoV-2 and dengue virus (DV), we aimed to elucidate its effect on dengue serodiagnosis and infectivity in a highly dengue-endemic city in India. METHODS: A total of 52 COVID-19 (reverse transcription-polymerase chain reaction [RT-PCR] positive) serum samples were tested in rapid lateral flow immunoassays and DV immunoglobulin G (IgG) enzyme-linked immunosorbent assay (ELISA) to detect DV or SARS-CoV-2 IgG/immunoglobulin M. The COVID-19 antibody (Ab) positive samples were subjected to a virus neutralization test (Huh7 cells) using DV type 1 (DV1) clinical isolate. RESULTS: Most (93%) of the SARS-CoV-2 Ab-positive serum samples cross-reacted with DV in rapid or ELISA tests. All were DV RNA and nonstructural protein 1 (NS1) antigen-negative. COVID-19 serum samples that were DV cross-reactive neutralized DV1. Of these, 57% had no evidence of DV pre-exposure (DV NS1 Ab-negative). The computational study also supported potential interactions between SARS-CoV-2 Ab and DV1. CONCLUSION: DV serodiagnosis will be inconclusive in areas co-endemic for both viruses. The COVID-19 pandemic appears to impart a protective response against DV in DV-endemic populations.