Integrative big transcriptomics data analysis implicates crucial role of MUC13 in pancreatic cancer

Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review

BACKGROUND

The use of Artificial Intelligence (AI) is exponentially rising in the healthcare sector. This change influences various domains of early identification, diagnosis, and treatment of diseases.

PURPOSE

This study examines the integration of AI in healthcare, focusing on its transformative potential in diagnostics and treatment, and the challenges and methodologies. shaping its future development.

METHODS

The review included 68 academic studies retracted from different databases (WOS, Scopus and Pubmed) from January 2020 and April 2024. After careful review and data analysis, AI methodologies, benefits and challenges, were summarized.

RESULTS

The number of studies showed a steady rise from 2020 to 2023. Most of them were the results of a collaborative work with international universities (92.1%). The majority (66.7%) were published in top-tier (Q1) journals and 40% were cited 2-10 times. The results have shown that AI tools such as deep learning methods and machine learning continue to significantly improve accuracy and timely execution of medical processes. Benefits were discussed from both the organizational and the patient perspective in the categories of diagnosis, treatment, consultation and health monitoring of diseases. However, some challenges may exist, despite these benefits, and are related to data integration, errors related to data processing and decision making, and patient safety.

CONCLUSION

The article examines the present status of AI in medical applications and explores its potential future applications. The findings of this review are useful for healthcare professionals to acquire deeper knowledge on the use of medical AI from design to implementation stage. However, a thorough assessment is essential to gather more insights into whether AI benefits outweigh its risks. Additionally, ethical and privacy issues need careful consideration.

Deciphering cellular and molecular mechanism of MUC13 mucin involved in cancer cell plasticity and drug resistance

There has been a surge of interest in recent years in understanding the intricate mechanisms underlying cancer progression and treatment resistance. One molecule that has recently emerged in these mechanisms is MUC13 mucin, a transmembrane glycoprotein. Researchers have begun to unravel the molecular complexity of MUC13 and its impact on cancer biology. Studies have shown that MUC13 overexpression can disrupt normal cellular polarity, leading to the acquisition of malignant traits. Furthermore, MUC13 has been associated with increased cancer plasticity, allowing cells to undergo epithelial-mesenchymal transition (EMT) and metastasize. Notably, MUC13 has also been implicated in the development of chemoresistance, rendering cancer cells less responsive to traditional treatment options. Understanding the precise role of MUC13 in cellular plasticity, and chemoresistance could pave the way for the development of targeted therapies to combat cancer progression and enhance treatment efficacy.

A review of big data technology and its application in cancer care

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.

Stabilization challenges and aggregation in protein-based therapeutics in the pharmaceutical industry

Protein-based therapeutics have revolutionized the pharmaceutical industry and become vital components in the development of future therapeutics. They offer several advantages over traditional small molecule drugs, including high affinity, potency and specificity, while demonstrating low toxicity and minimal adverse effects. However, the development and manufacturing processes of protein-based therapeutics presents challenges related to protein folding, purification, stability and immunogenicity that should be addressed. These proteins, like other biological molecules, are prone to chemical and physical instabilities. The stability of protein-based drugs throughout the entire manufacturing, storage and delivery process is essential. The occurrence of structural instability resulting from misfolding, unfolding, and modifications, as well as aggregation, poses a significant risk to the efficacy of these drugs, overshadowing their promising attributes. Gaining insight into structural alterations caused by aggregation and their impact on immunogenicity is vital for the advancement and refinement of protein therapeutics. Hence, in this review, we have discussed some features of protein aggregation during production, formulation and storage as well as stabilization strategies in protein engineering and computational methods to prevent aggregation.

An integrated computational biology approach defines the crucial role of TRIP13 in pancreatic cancer

Pancreatic cancer (PanCa) is one of the most aggressive forms of cancer and its incidence rate is continuously increasing every year. It is expected that by 2030, PanCa will become the 2nd leading cause of cancer-related deaths in the United States due to the lack of early diagnosis and extremely poor survival. Despite great advancements in biomedical research, there are very limited early diagnostic modalities available for the early detection of PanCa. Thus, understanding of disease biology and identification of newer diagnostic and therapeutic modalities are high priority. Herein, we have utilized high dimensional omics data along with some wet laboratory experiments to decipher the expression level of hormone receptor interactor 13 (TRIP13) in various pathological staging including functional enrichment analysis. The functional enrichment analyses specifically suggest that TRIP13 and its related oncogenic network genes are involved in very important patho-physiological pathways. These analyses are supported by qPCR, immunoblotting and IHC analysis. Based on our study we proposed TRIP13 as a novel molecular target for PanCa diagnosis and therapeutic interventions. Overall, we have demonstrated a crucial role of TRIP13 in pathogenic events and progression of PanCa through applied integrated computational biology approaches.