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Leveraging LSTM, tactile sensors, and haptic feedback to augment prosthetic control via grasp type prediction and grasp type feedback
Novel environmental and sustainable approach for concurrent assay of antineoplastics in VMP regimen with a comprehensive Pharmacokinetic study
Abstract Multiple myeloma (MM) is a blood cancer that, unfortunately, has a high morbidity and mortality rate. The VMP regimen, which includes bortezomib (BOR), melphalan (MEL), and prednisolone (PRD), is a safe and effective salvage regimen for refractory or relapsed MM. Up to now, there is no established analytical method to determine the VMP regimen, nor has any study investigated the kinetic interactions among its components, thereby highlighting the need for further clinical investigation. In light of this, an environmentally friendly, fast, sensitive, and precise LC-MS/MS method was established to determine bortezomib, melphalan, prednisolone, and sildenafil (an internal standard) simultaneously as part of the in vivo pharmacokinetics research carried out on rats. The established LC-MS/MS method was applied using a mobile phase composed of a mixture of methanol: 0.1% aqueous solution of formic acid and a ZORBAX Eclipse Plus C18 column (4.6 mm × 150 mm, 5 μm) as a stationary phase. The cited drugs were ionized through positive ionization and detected using multi-reaction monitoring (MRM) mode with the following precursor→product transitions: m/z 367.3→226.3 for BOR, m/z 305.0→168.2 for MEL, m/z 545.0→147.5 for PRD, and m/z 475.3→283.4 for SIL. Following FDA guidelines, the developed method was validated and showed acceptable ranges. Subsequently, it was employed in an in vivo investigation using rats, where the quantitative assessment of each drug was performed following both single and combined treatment. This allowed for the investigation of potential drug-drug interactions and the calculation of all pharmacokinetic parameters to monitor the therapeutic effects of those medications. To ensure the safety and environmental friendliness of the developed method, four assessment tools were applied: the assessment of green profile (AGP), blue applicability grade index (BAGI), analytical greenness metric for sample preparation (AGREEprep), and green analytical procedure index (GAPI).
Study on the pore structure evolution and microscopic seepage characteristics of coal under high pressure air blasting
PTML models of self assembled ligand free nanoparticle catalysts for cross coupling reactions
Abstract Cross-coupling reactions have transformed the synthesis of complex and valuable compounds used in pharmaceuticals, materials science, and chemical synthesis. Transition metal nanoparticle (NP) catalysts represent a promising strategy within this field, but their behavior and efficiency continue under investigation. The use of computational models enables rapid design, optimization, and understanding of the behavior of these molecules, thereby reducing the costs and time. In this study, the perturbation theory and machine learning (PTML) approach was used to construct a predictive model for estimating yield after multiple reuses (up to 10) of self-assembled Au- or glass-supported transition metal NP catalysts under ligand-free conditions and diverse cross-coupling reactions. The studied reactions include Suzuki–Miyaura, Kumada, Negishi, Buchwald-Hartwig, C(sp2)- and C(sp3)-H functionalization, and double carbonylation. A comprehensive dataset was built, and multiple linear regression (MLR) and artificial neural network (ANN) models were built and compared. The best MLR model achieved MAE = 7.4% and RMSE = 12.2% on the test set, demonstrating robust performance for yield prediction. Among the ANN models, MLP (9:9-20-9-1:1) and RBF (9:9-70-1:1) regression models showed similar results, with test MAE of 5.9% and 5.8% respectively, and both showed test RMSE of 9.8%. MLP (9:9-20-18-1:1) classification model showed high precision (97.0%) and recall (93.8%), effectively distinguishing high- and low-yielding reactions. These results highlight the potential of PTML-based models to guide catalyst and reaction condition selection, optimize catalytic systems, and minimize synthesis costs and environmental impact.
Structure guided discovery of small molecule ligands targeting the oncomiR-1 NPSL2 hairpin
Coadministered Cagrilintide and Semaglutide in Adults with Overweight or Obesity
Automated insect detection and biomass monitoring via AI and electrical field sensor technology
Abstract Insects, vital for ecosystem stability, are declining globally necessitating improved monitoring methods. Trap-based approaches are labor-intensive, invasive, and limited in scope. This study therefore presents a novel, automated, non-invasive insect monitoring system that detects atmospheric electrical field modulations caused by flying insects. In-field sensors monitor insect activity and biomass without physical trapping, using differential electric field measurements and convolutional neural networks for detection and wing-beat frequency analysis. Furthermore, a biomass algorithm that estimates taxon-specific weights is introduced. To validate this method, paired sensor and Townes Malaise trap deployments were conducted at two sites in a Danish nature reserve. Results showed moderate to strong correlations between sensors and traps, particularly at one site (Spearman’s $$\rho =0.725$$ for counts; 0.644 for biomass), supporting the method’s viability. A discrepancy in biomass estimates between methods, greater than that of counts, suggests the need for further refinement of the sensor’s biomass estimation. For inter-method consistency, sensor-sensor correlations ( $$\rho =0.758$$ for counts; 0.867 for biomass) exceeded Malaise-Malaise correlations ( $$\rho =0.597$$ for counts; 0.641 for biomass), though not significantly so ( $$P=0.304$$ for counts; $$P=0.057$$ for biomass). Overall, the study concludes that while further work is needed, this innovative approach shows promise for future insect monitoring and ecological research.
Dismantling Public Health Infrastructure, Endangering American Lives
Muscone suppresses inflammation and senescence of nucleus pulposus via p53 signalling during intervertebral disc degeneration
Ocular Gnathostomiasis
Research on quality and safety risk identification of import and export toys based on the WOA-BP model
Cagrilintide–Semaglutide in Adults with Overweight or Obesity and Type 2 Diabetes
Enhancing track and field training feedback through 6G enabled transparent optical sensor networks
Reviewers for the <i>Journal</i> , January–June 2025
Enhanced activity localization and microscale dosimetry in alpha-emitter radiopharmaceutical therapy using integrated autoradiography and histological imaging
Abstract Alpha-emitter radiopharmaceutical therapy delivers highly localized radiation, offering potent therapeutic effects. However, microscale heterogeneity remains poorly characterized in vivo and may affect efficacy. This underscores the critical need for sub-organ dosimetry to better understand αRPT radiobiology and guide treatment optimization. While autoradiography enables high-resolution activity mapping, conventional approaches lack anatomical context for accurate dose mapping. To address this, we propose a comprehensive workflow integrating quantitative autoradiography with histological imaging. Tissues from αRPT-treated mice bearing HER2 + breast tumors were snap-frozen, sectioned, and imaged using autoradiography. The same sections were histologically stained and used for precise autoradiography-histology integration. These anatomical contexts were then used to accurately stack multiple sections in a 3D volume, and were used for subsequent microscale dosimetry. Both tumor and kidney tissues were analyzed. Snap-freezing in isopentane preserved tissue morphology optimally. Our method enabled precise activity localization, revealing significant accumulation in the kidney cortex region close to glomeruli. Anatomical context improved 3D reconstructions needed for accurate dose estimations in tumor tissue. This methodology enhances αRPT dosimetry by precise spatial mapping of autoradiography unto the underlying tissue morphology. These advancements provide crucial insights into αRPT spatial radiobiology at the near-cellular level and will aid in optimizing radiopharmaceutical design and treatment planning.