Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1177/0091270006288213 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Toll-like receptor 1/2 activation reduces immunoglobulin free light chain production by multiple myeloma cells in the context of bone marrow stromal cells and fibronectin.
PLoS One
January 2025
Division of Pharmacology, Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Toll-like receptor (TLRs) activation in multiple myeloma (MM) cells induces heterogeneous functional responses including cell growth and proliferation, survival or apoptosis. These effects have been suggested to be partly due to increase in secretion of cytokines such as IL-6 or IFNα among others from MM cells following TLR activation. However, whether triggering of these receptors also modulates production of immunoglobulin free light chains (FLCs), which largely contribute to MM pathology, has not been investigated in MM cells before.
View Article and Find Full Text PDFObjective: Aim: To investigate the effect of succinic acid on the humoral component of the immune system in rats.
Patients And Methods: Materials and Methods: The study was conducted on two groups of mature non-linear white rats (males) of similar weight (200-270 g, aged 6-8 months), with 5 animals in each group. The control group was fed a standard diet with free access to water throughout the experiment.
Triethylamine-mediated protonation-deprotonation unlocks dual-drug self assembly to suppress breast cancer progression and metastasis.
Proc Natl Acad Sci U S A
February 2025
Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, People's Republic of China.
Carrier-free nanomedicines exhibited significant potential in elevating drug efficacy and safety for tumor management, yet their self assembly typically relied on chemical modifications of drugs or the incorporation of surfactants, thereby compromising the drug's inherent pharmacological activity. To address this challenge, we proposed a triethylamine (TEA)-mediated protonation-deprotonation strategy that enabled the adjustable-proportion self assembly of dual drugs without chemical modification, achieving nearly 100% drug loading capacity. Molecular dynamic simulations, supported by experiment evidence, elucidated the underlying self-assembly mechanism.
View Article and Find Full Text PDFClinical derivation and data simulated validation of rule-out and rule-in algorithms for the Siemens Atellica IM high-sensitivity cardiac troponin I assay.
Eur Heart J Acute Cardiovasc Care
January 2025
Department of Heart Disease, Haukeland University Hospital, Bergen, Norway.
Background: This prospective, two-centre study derived and validated predictive algorithms for the Siemens Atellica IM high-sensitivity cardiac troponin I (hs-cTnI) assay in the emergency department (ED).
Methods: Algorithms for predicting 30-day myocardial infarction type 1 and 2 (MI) and death or non-ST-elevation myocardial infarction (NSTEMI, type 1 and 2) at index admission were developed from a derivation cohort of 1896 patients and validated using a synthetic dataset with nearly 1 million patient cases. Performance was compared to the European Society of Cardiology algorithms for hs-cTnT (Roche Diagnostics) and hs-cTnI (Abbott Diagnostics).
Leveraging Network Target Theory for Efficient Prediction of Drug-Disease Interactions: A Transfer Learning Approach.
Adv Sci (Weinh)
January 2025
Department of Molecular Pharmacology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin, 300060, China.
Efficient virtual screening methods can expedite drug discovery and facilitate the development of innovative therapeutics. This study presents a novel transfer learning model based on network target theory, integrating deep learning techniques with diverse biological molecular networks to predict drug-disease interactions. By incorporating network techniques that leverage vast existing knowledge, the approach enables the extraction of more precise and informative drug features, resulting in the identification of 88,161 drug-disease interactions involving 7,940 drugs and 2,986 diseases.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!