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Exploring the role of intrinsic and extrinsic factors on the associations between sarcopenia and falls in older adults
Globally recognized island is losing its trademark glaciers
Revealing the complex dynamics of monkeypox epidemics in heterogeneous networks by the evolutionary game theory
Pathology-oriented multiplexing enables integrative disease mapping
Abstract The expression and location of proteins in tissues represent key determinants of health and disease. Although recent advances in multiplexed imaging have expanded the number of spatially accessible proteins1–3, the integration of biological layers (that is, cell structure, subcellular domains and signalling activity) remains challenging. This is due to limitations in the compositions of antibody panels and image resolution, which together restrict the scope of image analysis. Here we present pathology-oriented multiplexing (PathoPlex), a scalable, quality-controlled and interpretable framework. It combines highly multiplexed imaging at subcellular resolution with a software package to extract and interpret protein co-expression patterns (clusters) across biological layers. PathoPlex was optimized to map more than 140 commercial antibodies at 80 nm per pixel across 95 iterative imaging cycles and provides pragmatic solutions to enable the simultaneous processing of at least 40 archival biopsy specimens. In a proof-of-concept experiment, we identified epithelial JUN activity as a key switch in immune-mediated kidney disease, thereby demonstrating that clusters can capture relevant pathological features. PathoPlex was then used to analyse human diabetic kidney disease. The framework linked patient-level clusters to organ disfunction and identified disease traits with therapeutic potential (that is, calcium-mediated tubular stress). Finally, PathoPlex was used to reveal renal stress-related clusters in individuals with type 2 diabetes without histological kidney disease. Moreover, tissue-based readouts were generated to assess responses to inhibitors of the glucose cotransporter SGLT2. In summary, PathoPlex paves the way towards democratizing multiplexed imaging and establishing integrative image analysis tools in complex tissues to support the development of next-generation pathology atlases.
Small intestinal bacterial overgrowth as one of the diagnostic markers of hypertension
George E. Smith obituary: co-inventor of the charge coupled device, which ushered in an era of digital images
Rhamnolipid from Pseudomonas sp. as a green surfactant for enhanced phytoremediation
Abstract Microbial biosurfactants are valued for their surface activity and emulsifying properties; among them, rhamnolipids—primarily produced by Pseudomonas species—are the most prominent. Pseudomonas sp., a plant growth-promoting rhizobacterium, is also known to enhance heavy metal (HM) uptake in Helianthus annuus L. In this study, we produced biosurfactants from Pseudomonas aeruginosa strain ZF2MGHSO (Rha1) and Pseudomonas sp. strain AHE16 (Rha2). Gas chromatography−mass spectrometry (GC–MS) analysis confirmed that the purified biosurfactant was composed of rhamnolipids. We evaluated the effects of Rha1 and Rha2 on Cd and Zn uptake and HaZIP1 gene expression in sunflower plants grown in contaminated soil. Both rhamnolipids significantly increased Zn and Cd accumulation in roots and shoots, with the highest root Zn (724 ± 3 mg g⁻1 DW) and Cd (173 ± 2 mg g⁻1 DW) levels recorded in Rha1-treated plants. In shoots, Zn concentrations reached 460 ± 4 mg g⁻1 DW with Rha1 and 426 ± 3 mg g⁻1 DW with Rha2, compared to 405 ± 3 mg g⁻1 DW in control. The relative expression of HaZIP1 was significantly upregulated in both roots and shoots under rhamnolipid treatments. In Rha1-treated plants, expression levels increased ~ 6.9-fold in roots and ~ 4.8-fold in shoots compared to control. Rha2 treatment led to ~ 6.0-fold and ~ 4.1-fold increases in roots and shoots, respectively. Our findings suggest that HaZIP1 plays a pivotal role in the uptake and accumulation of zinc and cadmium in sunflower plants grown in contaminated soil. Overall, our study highlights the potential of biosurfactant-enhanced phytoremediation using sunflower plants as an efficient, environmentally sustainable strategy for remediating heavy metal-contaminated soils.
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.