Systematic characterization of protein structural features of alternative splicing isoforms using AlphaFold 2.

Alternative splicing is an important cellular process in eukaryotes, altering pre-mRNA to yield multiple protein isoforms from a single gene. However, our understanding of the impact of alternative splicing events on protein structures is currently constrained by a lack of sufficient protein structural data. To address this limitation, we employed AlphaFold 2, a cutting-edge protein structure prediction tool, to conduct a comprehensive analysis of alternative splicing for approximately 3,000 human genes, providing valuable insights into its impact on the protein structural. Our investigation employed state of the art high-performance computing infrastructure to systematically characterize structural features in alternatively spliced regions and identified changes in protein structure following alternative splicing events. Notably, we found that alternative splicing tends to alter the structure of residues primarily located in coils and beta-sheets. Our research highlighted a significant enrichment of loops and highly exposed residues within human alternatively spliced regions. Specifically, our examination of the Septin-9 protein revealed potential associations between loops and alternative splicing, providing insights into its evolutionary role. Furthermore, our analysis uncovered two missense mutations in the Tau protein that could influence alternative splicing, potentially contributing to the pathogenesis of Alzheimer's disease. In summary, our work, through a thorough statistical analysis of extensive protein structural data, sheds new light on the intricate relationship between alternative splicing, evolution, and human disease.

Ensemble pretrained language models to extract biomedical knowledge from literature.

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking. MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and taxonomy names and annotated 70 more abstracts as additional corpus. We further finetuned the T0pp model, with 11 billion parameters, to boost the performance on relation extraction and leveraged entites' location information (eg, title, background) to enhance novelty prediction performance in relation extraction (RE). RESULTS: Our pioneering NLP system designed for this challenge secured first place in Phase I-NER and second place in Phase II-relation extraction and novelty prediction, outpacing over 200 teams. We tested OpenAI ChatGPT 3.5 and ChatGPT 4 in a Zero-Shot setting using the same test set, revealing that our finetuned model considerably surpasses these broad-spectrum large language models. DISCUSSION AND CONCLUSION: Our outcomes depict a robust NLP system excelling in NER and RE across various biomedical entities, emphasizing that task-specific models remain superior to generic large ones. Such insights are valuable for endeavors like knowledge graph development and hypothesis formulation in biomedical research.

A Review of 10-Year Survivability of Immunotherapy in the Management of Colon Cancer.

Colon cancer is one of the most common cancers in the United States of America. In addition to conventional treatment approaches such as surgery, chemotherapy, and radiation for colorectal cancer, immunotherapy has gained recognition over the past few years. However, its effectiveness in colorectal cancer treatment is controversial. Our study investigates the survival and progression-free rates of immunotherapy for different types of colorectal cancer over the last 10 years. We conducted literature reviews from various clinical trials and research studies to evaluate immunotherapy's role in colorectal cancer treatment. We also investigated how it affects clinical outcomes. We discovered a range of effective immunotherapy approaches targeting various growth factors and signaling pathways. These modalities include monoclonal antibodies aimed at growth factors such as epidermal growth factor (EGF), vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), human epidermal growth factor receptor 2 (HER2), and downstream signaling pathways such as mitogen-activated protein kinase (MAPK), kirsten rat sarcoma viral oncogene (KRAS), B-raf proto-oncogene, serine/threonine kinase (BRAF), and phosphatase and tensin homolog (PTEN). Additionally, we identified immune checkpoint inhibitors, such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitors and programmed cell death ligand 1 (PD-L1) inhibitors, as well as target therapy and adoptive cell therapy as promising immunotherapeutic options. Nevertheless, the application of immunotherapy remains highly limited due to various factors influencing survival and progression-free rates, including tumor microenvironment, microsatellite instability, immune checkpoint expression, and gut microbiome. Additionally, its effectiveness is restricted to a small subgroup of patients, accompanied by side effects and the development of drug resistance mechanisms. To unlock its full potential, further clinical trials and research on molecular pathways in colorectal cancer are imperative. This will ultimately enhance drug discovery success and lead to more effective clinical management approaches.

The Burden of Obesity in the Rural Adult Population of America.

There is an epidemic of obesity in adults in rural America. It is estimated that about 19% of the population resides in rural areas, which encompasses 97% of America's total landmass. Although rural America makes up a fraction of America's total population, it has been estimated that the prevalence of obesity is approximately 6.2 times higher than in urban America. This illustrates an apparent disparity that exists between the rural population and urban populations that needs to be examined. The prevalence of obesity, especially in rural America, is a growing concern in the medical community in recent years. Obesity has been identified as a significant risk factor for cardiovascular disease, cancer, and type 2 diabetes mellitus, which are leading causes of morbidity and mortality in the US. To better understand the disparity in the prevalence of adult obesity between rural and urban America, researchers have identified risk factors that are associated with the high incidence and prevalence of obesity in the rural American adult population. Low income and lack of physical activity have been identified as factors that predispose rural Americans to increased risk of obesity, arguing that low-income Americans may not have access to the resources available to assist them in weight reduction. With rural Americans being at an income disadvantage, it creates a risk for obesity, which further predisposes them to chronic diseases such as hypertension, obstructive sleep apnea (OSA), diabetes, and coronary artery disease. As obesity continues to rise among the American population, the burden on the rural population is incredibly evident. Despite ongoing efforts by the US government and strategies implemented by the Common Community Measures for Obesity Prevention, there is still much to be done to tackle the epidemic. With an existing strategy in place, such as the 12 Common Community Measures for Obesity Prevention (COCOMO) strategies to fight obesity with physical activity, Americans are a step closer to conquering this epidemic. However, until other disparities such as income are addressed, rural Americans may continue to be severely impacted by the rising incidence of obesity and subsequent higher mortality rates from associated diseases.