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The application of high throughput surface characterization (HTSC) to the analysis of polymeric biomaterial libraries is an important advancement for the discovery and development of new biomedical materials and is the focus of this review. The potential for HTSC to identify structure/activity relationships for large libraries of materials can be utilized to accelerate materials discovery as well as providing insight into the underlying biological-material interactions. Furthermore, the correlations identified between surface chemical structure and cellular behavior could not have been predicted by a rational design approach based simply on review of bulk structure, which demonstrates the importance of HTSC in the assessment of cell-material and cell-biomolecular interactions that are dependent on surface properties.
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http://dx.doi.org/10.3109/1061186X.2010.521941 | DOI Listing |
J Org Chem
September 2025
State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, P. R. China.
The Buchwald-Hartwig (B-H) reaction graph, a novel graph for deep learning models, is designed to simulate the interactions among multiple chemical components in the B-H reaction by representing each reactant as an individual node within a custom-designed reaction graph, thereby capturing both single-molecule and intermolecular relationship features. Trained on a high-throughput B-H reaction data set, B-H Reaction Graph Neural Network (BH-RGNN) achieves near-state-of-the-art performance with an score of 0.971 while maintaining low computational costs.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFJCO Precis Oncol
September 2025
Division of Hematology and Oncology, University of California Los Angeles, Los Angeles, CA.
Purpose: mutations are classically seen in non-small cell lung cancers (NSCLCs), and EGFR-directed inhibitors have changed the therapeutic landscape in patients with -mutated NSCLC. The real-world prevalence of -mutated ovarian cancers has not been previously described. We aim to determine the prevalence of pathogenic or likely pathogenic mutations in ovarian cancer and describe a case of -mutated metastatic ovarian cancer with a durable response to osimertinib, an EGFR-directed targeted therapy.
View Article and Find Full Text PDFJCO Precis Oncol
September 2025
Cell Biology and Biotherapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy.
Purpose: Tumor comprehensive genomic profiling (CGP) may detect potential germline pathogenic/likely pathogenic (P/LP) alterations as secondary findings. We analyzed the frequency of potentially germline variants and large rearrangements (LRs) in the RATIONAL study, an Italian multicenter, observational clinical trial that collects next-generation sequencing-based tumor profiling data, and evaluated how these findings were managed by the enrolling centers.
Patients And Methods: Patients prospectively enrolled in the pathway-B of the RATIONAL study and undergoing CGP with the FoundationOne CDx assays were included in the analysis.
Environ Technol
September 2025
School of Environmental Science and Engineering, Southwest Jiaotong University, Chengdu, People's Republic of China.
To explore strategies for further reducing aeration energy consumption in the simultaneous nitrification and denitrification (SND) process, an SND reactor was constructed to treat low carbon-to-nitrogen (C/N) ratio domestic wastewater under ultra-low dissolved oxygen (DO) conditions (DO < 0.05 mg·L⁻). The effects of hydraulic retention time (HRT) and C/N ratio on nitrogen removal performance were systematically evaluated, and batch experiments were conducted to determine nitrification and denitrification rates.
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