Browse Articles

Elevated IAP in critically ill patients associated with increased AKI incidence: a cohort study from the MIMIC-IV database

Protective effects of Rosa roxburghii Tratt. extract against UVB-induced inflammaging through inhibiting the IL-17 pathway

Establishment of a novel experimental animal model for the treatment of tibial segmental bone defects in juvenile sheep

Higher dominant muscle strength is mediated by motor unit discharge rates and proportion of common synaptic inputs

Exploring a patient-specific in vitro pipeline for stratification and drug response prediction of microglia-based therapeutics

Risk factors and an optimized prediction model for urosepsis in diabetic patients with upper urinary tract stones

Abstract To identify independent risk factors for urosepsis in diabetic patients with upper urinary tract stones (UUTS) and develop a prediction model to facilitate early detection and diagnosis, we retrospectively reviewed medical records of patients admitted between January 2020 and June 2023. Patients were divided based on the quick Sequential Organ Failure Assessment (qSOFA) score. The least absolute shrinkage and selection operator (LASSO) regression analysis was used for variable selection to form a preliminary model. The model was optimized and validated using the receiver operating characteristic (ROC) curve, the Hosmer-Lemeshow test and calibration curve, and decision curve analysis (DCA). A nomogram was constructed for visualization. A total of 434 patients were enrolled, with 66 cases and 368 controls. Six optimal predictors were identified: underweight, sarcopenia, poor performance status, midstream urine culture, urinary leukocyte count, and albumin-globulin ratio (AGR). The midstream urine culture was excluded due to its inability to provide rapid results. The final model demonstrated good prediction accuracy and clinical utility, with no significant difference in performance compared to the initial model. The study developed a prediction model for urosepsis risk in diabetic patients with UUTS, presenting a convenient tool for timely diagnosis, particularly in non-operated patients.

Enhanced organic matter removal and fouling mitigation in seawater desalination using electrocoagulation pretreatment using ZnO coated Fe electrodes

Index-Based selection of Chickpea (Cicer arietinum L.) genotypes for enhanced drought tolerance

Vision Mamba and xLSTM-UNet for medical image segmentation

Discovery of the widespread site-specific single-stranded nuclease family Ssn

Author Correction: Identifying representative sequences of protein families using submodular optimization

Accelerating discovery of bioactive ligands with pharmacophore-informed generative models

Abstract Deep generative models have advanced drug discovery but often generate compounds with limited structural novelty, providing constrained inspiration for medicinal chemists. To address this, we develop TransPharmer, a generative model that integrates ligand-based interpretable pharmacophore fingerprints with a generative pre-training transformer (GPT)-based framework for de novo molecule generation. TransPharmer excels in unconditioned distribution learning, de novo generation, and scaffold elaboration under pharmacophoric constraints. Its unique exploration mode could enhance scaffold hopping, producing structurally distinct but pharmaceutically related compounds. Its efficacy is validated through two case studies involving the dopamine receptor D2 (DRD2) and polo-like kinase 1 (PLK1). Notably, three out of four synthesized PLK1-targeting compounds show submicromolar activities, with the most potent, IIP0943, exhibiting a potency of 5.1 nM. Featuring a new 4-(benzo[b]thiophen-7-yloxy)pyrimidine scaffold, IIP0943 also has high PLK1 selectivity and submicromolar inhibitory activity in HCT116 cell proliferation. TransPharmer offers a promising tool for discovering structurally novel and bioactive ligands.

Study on migration instability characteristics and mine pressure control of roof overburden rock in thin coal seams mining

Gradient all-nanostructured aerogel fibers for enhanced thermal insulation and mechanical properties

Evaluating the environmental effects of bitcoin mining on energy and water use in the context of energy transition

In vivo prime editing rescues photoreceptor degeneration in nonsense mutant retinitis pigmentosa

Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images

Topologically reconfigurable room-temperature polariton condensates from bound states in the continuum in organic metasurfaces

Application of multi-sensor fusion localization algorithm based on recurrent neural networks

Mitochondria are positioned at dendritic branch induction sites, a process requiring rhotekin2 and syndapin I

Abstract Proper neuronal development, function and survival critically rely on mitochondrial functions. Yet, how developing neurons ensure spatiotemporal distribution of mitochondria during expansion of their dendritic arbor remained unclear. We demonstrate the existence of effective mitochondrial positioning and tethering mechanisms during dendritic arborization. We identify rhotekin2 as outer mitochondrial membrane-associated protein that tethers mitochondria to dendritic branch induction sites. Rhotekin2-deficient neurons failed to correctly position mitochondria at these sites and also lacked the reduction in mitochondrial dynamics observed at wild-type nascent dendritic branch sites. Rhotekin2 hereby serves as important anchor for the plasma membrane-binding and membrane curvature-inducing F-BAR protein syndapin I (PACSIN1). Consistently, syndapin I loss-of-function phenocopied the rhotekin2 loss-of-function phenotype in mitochondrial positioning at dendritic branch induction sites. The finding that rhotekin2 deficiency impaired dendritic branch induction and that a syndapin binding-deficient rhotekin2 mutant failed to rescue this phenotype highlighted the physiological importance of rhotekin2 functions for neuronal network formation.