Given the increasing use of bevacizumab in combinatorial drug therapy for a multitude of different cancer types, there is a need for therapeutic drug monitoring to analyze the possible correlation between drug trough concentration, and therapeutic effect and adverse reactions. An ultra-performance liquid chromatography tandem-mass spectrometry method was then developed and validated to determine bevacizumab levels in human plasma samples. Chromatographic separation was achieved on a Shimadzu InertSustainBio C18 HP column, whereas subsequent mass spectrometric analysis was performed using a Shimadzu 8050CL triple quadrupole mass spectrometer equipped with an electro-spray ionization source in the positive ion mode. In total, three multiple reaction monitoring transitions of each of the surrogate peptides were chosen with 'FTFSLDTSK' applied as the quantification peptide whereas 'VLIYFTSSLHSGVPSR' and 'STAYLQMNSLR' were designated as the verification peptides using the Skyline software. This analytical method was then fully validated, with specificity, linearity, lower limit of quantitation, accuracy, precision, stability, matrix effect and recovery calculated. The linearity of this method was developed to be within the concentration range 5-400 µg/ml for bevacizumab in human plasma. Subsequently, eight patients with non-small cell lung cancer (NSCLC) were recruited and injected with bevacizumab over three periods of treatment to analyze their steady-state trough concentration and differences. To conclude, the results of the present study suggest that bevacizumab can be monitored in a therapeutic setting in patients with NSCLC.
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http://dx.doi.org/10.3892/ol.2024.14356 | DOI Listing |
J Magn Reson Imaging
January 2025
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.
Purpose: This work tests the viability of semi-supervision for brain metastases segmentation.
Adv Skin Wound Care
January 2025
Ling Jia Goh, MClin Res, MHA, Adv Dip (CCNC), BHS (Nursing), Dip (Nursing with Merit), is Nurse Manager (Research), Department of Nursing, National Healthcare Group Polyclinics, Singapore. Xiaoli Zhu, MN, RN, is Wound Care Senior Nurse Clinician, National Healthcare Group Polyclinics, and PhD candidate, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
Background: Patient adherence to performing self-wound care (SWC) has a direct influence on the success of telewound care, a healthcare delivery mode that emerged in 2016 in National Healthcare Group Polyclinics in Singapore to relieve the healthcare burden. This mode of delivery was useful during the pandemic, when nonurgent face-to-face visits were switched to the use of telecommunications for consultation. Telewound care requires that patients be willing to perform wound care on their own; however, whether patients are willing to do so remains unknown.
View Article and Find Full Text PDFMol Divers
January 2025
Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases Ministry of Education, Jiangxi Province Key Laboratory of Biomaterials and Biofabrication for Tissue Engineering, Gannan Medical University, Ganzhou, 341000, Jiangxi, China.
Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise.
View Article and Find Full Text PDFOtolaryngol Head Neck Surg
January 2025
Department of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle, Washington, USA.
Objective: To validate the use of neural radiance fields (NeRF), a state-of-the-art computer vision technique, for rapid, high-fidelity 3-dimensional (3D) reconstruction in endoscopic sinus surgery (ESS).
Study Design: An experimental cadaveric pilot study.
Setting: Academic medical center.
The emergence of artificial intelligence (AI) in drug discovery represents a transformative development in addressing neglected diseases, particularly in the context of the developing world. Neglected diseases, often overlooked by traditional pharmaceutical research due to limited commercial profitability, pose significant public health challenges in low- and middle-income countries. AI-powered drug discovery offers a promising solution by accelerating the identification of potential drug candidates, optimising the drug development process, and reducing the time and cost associated with bringing new treatments to market.
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