Shanxi Zhendong Pharmaceutical (China)

Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability to adapt to heterogeneous data across patients. In contrast, Dynamic Ensemble Selection (DES) improves clinical decision-making and therapeutic interventions by dynamically adapting to variations in data characteristics. Here, we developed and validated a risk prediction model for DVT using electronic medical record data from fracture patients upon admission. By employing the DES method to optimize the prediction process, the model generates patient-specific probabilities of DVT occurrence, enabling personalized clinical risk assessment. Validation results showed that the DES model achieved strong performance in predicting DVT, with an accuracy of 0.875 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.906. Notably, it demonstrated a high recall of 0.918 for DVT. Furthermore, in the prospective test set, DES exhibited excellent generalization capability, maintaining robust performance with an accuracy of 0.813 and an AUC of 0.876. We further developed an interactive clinical tool based on the DES algorithm to facilitate model interpretation and implementation. By integrating this user-friendly solution into clinical workflows, DES not only improves early DVT detection but also optimizes the allocation of healthcare resources.

Nonoxidative ethanol metabolites have been reported as ethanol biomarkers in clinical and forensic cases. However, their forensic toxicokinetics are still unclear. The study aimed to simultaneously research the postmortem distribution and dynamic distribution of ethanol and its nonoxidative metabolites in 62 poisoned rabbits. Firstly, 32 rabbits were randomly divided into three groups and sacrificed after three doses ethanol, and their organs were collected to study the postmortem distribution of ethanol and its nonoxidative metabolites. The results showed that the distribution trends of ethanol and its nonoxidative metabolites were basically same between different groups, and ethanol could be better detected by collecting body fluids, while the nonoxidative metabolites could be better detected by collecting the heart, liver, kidney and testis. Secondly, 30 rabbits after 1/2LD50 ethanol intragastric administration were sacrificed at 10 time points, and their organs were collected to study the dynamic distribution of ethanol and its nonoxidative metabolites. The results showed the concentrations of ethanol and its nonoxidative metabolites increased with time and then decreased. All analytes could be detected in all samples within 8 h, with the exception of ethyl oleate, which kept high level in muscle and vitreous humor, and could be detected within 4 h.

Ketamine has recently become an anesthetic drug used in human and veterinary clinical medicine for illicit abuse worldwide, but the detection of illicit abuse and inference of time intervals following ketamine abuse are challenging issues in forensic toxicological investigations. Here, we developed methods to estimate time intervals since ketamine use is based on significant metabolite changes in rat serum over time after a single intraperitoneal injection of ketamine, and global metabolomics was quantified by ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF–MS). Thirty-five rats were treated with saline (control) or ketamine at 3 doses (30, 60, and 90 mg/kg), and the serum was collected at 21 time points (0 h to 29 d). Time-dependent rather than dose-dependent features were observed. Thirty-nine potential biomarkers were identified, including ketamine and its metabolites, lipids, serotonin and other molecules, which were used for building a random forest model to estimate time intervals up to 29 days after ketamine treatment. The accuracy of the model was 85.37% in the cross-validation set and 58.33% in the validation set. This study provides further understanding of the time-dependent changes in metabolites induced by ketamine abuse.

BackgroundAstragalus mongolicus Bunge is used in traditional Chinese medicine and is thus cultivated in bulk. The cultivation of A. mongolicus requires a large amount of nitrogen fertilizer, increasing the planting cost of medicinal materials and polluting the environment. Isolation and screening of plant growth-promoting rhizobacteria (PGPR) and exploring the nitrogen fixation potential of A. mongolicus rhizosphere microorganisms would effectively reduce the production cost of A. mongolicus.ResultsThis study used A. mongolicus roots and rhizosphere soil samples from Longxi County of Gansu Province, Jingle County, and Hunyuan County of Shanxi Province, China, to isolate and identify nitrogen-fixing bacteria. Through nitrogen fixation efficiency test, single strain inoculation test, and plant growth-promoting characteristics, three strains, Bacillus sp. J1, Arthrobacter sp. J2, and Bacillus sp. G4 were selected from 86 strains of potential nitrogen-fixing bacteria, which were the most effective in promoting the A. mongolicus growth and increasing the nitrogen, phosphorus, and potassium content in plants. The antagonistic test showed that these bacteria could grow smoothly under the co-culture conditions. The J1, J2, and G4 strains were used in a mixed inoculum and found to enhance the biomass of A. mongolicus plants and the accumulation of the main medicinal components in the field experiment. Mixed bacterial agent inoculation also increased bacterial diversity and changed the structure of the bacterial community in rhizosphere soil. Meanwhile, the relative abundance of Proteobacteria increased significantly after inoculation, suggesting that Proteobacteria play an important role in plant growth promotion.ConclusionsThese findings indicate that specific and efficient PGPRs have a significant promoting effect on the growth of A. mongolicus, while also having a positive impact on the structure of the host rhizosphere bacteria community. This study provides a basis for developing a nitrogen-fixing bacterial fertilizer and improving the ecological planting efficiency of A. mongolicus.