The objectives of this study were to assess the efficacy and reliability of whole-body turbo short tau inversion recovery (STIR) magnetic resonance imaging (MRI) for detecting skeletal metastasis and to compare the results with those of bone scintigraphy. Twenty-six patients with primary cancer (mean age=56 years, age range: 34-75 years) were assessed for bone metastasis with whole-body MRI and bone scintigraphy. Eight bone regions in each patient were assessed (total of 208 sites) with each of these 2 techniques. A turbo STIR sequence and panoramic table were used during MRI. Whole-body MRI and scintigraphy findings were compared with biopsy or follow-up imaging results. After at least 12 months of follow-up, 9 patients had bone metastases in a total of 31 sites. Whole-body MRI showed 29 metastases (94%) in the total 208 skeletal sites investigated in the 26 patients. Bone scintigraphy revealed metastases in 16 (52%) of the 208 sites. Whole-body turbo STIR MRI is a reliable method for screening patients with suspected skeletal metastases. This technique is also advantageous in that it reveals extraskeletal organ and soft tissue metastases.

The prognostic value of HER2 expression in patients with advanced non-small cell lung cancer remains controversial. The relationship between HER2 expression, and platinum resistance and patient survival, was investigated.

Today, drug discovery and development is one of the fields where Artificial Intelligence (AI) is used extensively. Therefore, this study aims to systematically analyze the scientific literature on the application of AI in drug discovery and development to understand the evolution, trends, and key contributors within this rapidly growing field. By leveraging various bibliometric indicators and visualization techniques, we seek to explore the growth patterns, influential authors and institutions, collaboration networks, and emerging research trends within this domain. Bibliometric and network analysis methods (co-occurrence, co-authorship, and collaboration, etc.) were used to achieve this goal. Bibliometric visualization tools such as Bibliometrix R package software, VOSviewer, and Litmaps were used for comprehensive data analysis. Scientific publications on AI in drug discovery and development were retrieved from the Web of Science Core Collection (WoS CC) database covering 1990–2023. In addition to visualization programs, the InCites database was also used for analysis and visualization. A total of 4059 scientific publications written by 13,932 authors and published in 1071 journals were included in the analysis. The results reveal that the most prolific authors are Ekins (n = 67), Schneider (n = 52), Hou Tj (n = 43), and Cao Ds (n = 34), while the most active institutions are the “Chinese Academy of Science” and “University of California.” The leading scientific journals are “Journal of Chemical Information and Modelling,” “Briefings in Bioinformatics,” and “Journal of Cheminformatics.” The most frequently used author keywords include “protein folding,” “QSAR,” “gene expression data,” “coronavirus,” and “genome rearrangement.” The average number of citations per scientific publication is 28.62, indicating a high impact of research in this field. A significant increase in publications was observed after 2014, with a peak in 2022, followed by a slight decline. International collaboration accounts for 28.06% of the publications, with the USA and China leading in both productivity and influence. The study also identifies key funding organizations, such as the National Natural Science Foundation of China (NSFC) and the United States Department of Health & Human Services, which have significantly supported advancements in this field. In conclusion, this study highlights the transformative role of AI in drug discovery and development, showcasing its potential to accelerate innovation and improve efficiency. The findings provide valuable insights into the current state of research, emerging trends, and future directions, offering a roadmap for researchers, industry professionals, and policymakers to further explore and leverage AI technologies in this domain. Scientific contributionThis study provides a comprehensive bibliometric analysis of 4,059 scientific publications (1990–2023) to map the evolution, trends, and key contributors in AI-driven drug discovery, identifying prolific authors (e.g., Ekins, Schneider), leading institutions (e.g., Chinese Academy of Sciences, University of California), and high-impact journals (Journal of Chemical Information and Modelling). It reveals critical collaboration patterns (28.06% international co-authorships), dominant funding sources (e.g., NSFC, NIH), and emerging research hotspots (e.g., protein folding, QSAR, coronavirus), while highlighting the transformative role of deep learning post-2014. By synthesizing these insights, the study offers a strategic roadmap for researchers and policymakers to optimize AI applications in drug development, addressing both current challenges and future opportunities in the field.

To report on possible adverse interaction between capecitabine and warfarin in a patient with cancer, who developed subconjunctival and nose bleeding during treatment with these drugs and review of the previously reported five cases in the literature. In the second week of capecitabine treatment the patient was hospitalized owing to subconjunctival hemorrhage and nose bleeding. Her international normalized ratio (INR) level was found to have increased, and both drugs were discontinued. Fresh frozen plasma replacement was administered. Warfarin and capecitabine treatment were restarted again but the warfarin dose was decreased. The patients INR was kept between 2.5-3 with the reduced dose of warfarin. Capecitabine is an orally active prodrug of fluorouracil (FU) and is extensively used as an antineoplastic agent. It is converted to 5-FU in the liver and tumor tissues. Warfarin is an antithrombolytic agent and is metabolized by liver cytochorom P450 (CYP) isoenzymes in liver. Preclinical in vitro studies using human liver microsomes report no inhibitory effects between capecitabine and substrates of CYP. However, the concomitant administration of capecitabine and warfarin resulted in gastrointestinal, retroperitoneal bleeding and hemorrhagic blisters in the five cases previously reported. The exact mechanism of this interaction is unknown; however, a significant pharmacokinetic interaction between capecitabine and S-warfarin resulting in exaggerated anticoagulant activity has recently been demonstrated. Here, we describe another case and use of the Naranjo adverse drug reaction (ADR) probability scale, which indicated a probable relationship between subconjunctival bleeding and epistaxis in this patient after concomitant warfarin and capecitabine use. Capecitabine is extensively used in outpatient clinics, and physicians should be aware of ADRs arising from combined used of capecitabine and warfarin. In the light of the current data, INR levels should be closely monitored in patients using this medication regimen.