Prediction of 30-day mortality for ICU patients with Sepsis-3.

BACKGROUND: There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mortality prediction for Sepsis-3 patients to improve hospital resource allocation. METHODS: In this study, we developed a Machine Learning (ML) framework to predict the 30-day mortality rate of ICU patients with Sepsis-3 using the MIMIC-III database. Advanced big data extraction tools like Snowflake were used to identify eligible patients. Decision tree models and Entropy Analyses helped refine feature selection, resulting in 30 relevant features curated with clinical experts. We employed the Light Gradient Boosting Machine (LightGBM) model for its efficiency and predictive power. RESULTS: The study comprised a cohort of 9118 Sepsis-3 patients. Our preprocessing techniques significantly improved both the AUC and accuracy metrics. The LightGBM model achieved an impressive AUC of 0.983 (95% CI: [0.980-0.990]), an accuracy of 0.966, and an F1-score of 0.910. Notably, LightGBM showed a substantial 6% improvement over our best baseline model and a 14% enhancement over the best existing literature. These advancements are attributed to (I) the inclusion of the novel and pivotal feature Hospital Length of Stay (HOSP_LOS), absent in previous studies, and (II) LightGBM's gradient boosting architecture, enabling robust predictions with high-dimensional data while maintaining computational efficiency, as demonstrated by its learning curve. CONCLUSIONS: Our preprocessing methodology reduced the number of relevant features and identified a crucial feature overlooked in previous studies. The proposed model demonstrated high predictive power and generalization capability, highlighting the potential of ML in ICU settings. This model can streamline ICU resource allocation and provide tailored interventions for Sepsis-3 patients.

Structure-function analysis of an ancient TsaD-TsaC-SUA5-TcdA modular enzyme reveals a prototype of tRNA t6A and ct6A synthetases.

N 6-threonylcarbamoyladenosine (t6A) is a post-transcriptional modification found uniquely at position 37 of tRNAs that decipher ANN-codons in the three domains of life. tRNA t6A plays a pivotal role in promoting translational fidelity and maintaining protein homeostasis. The biosynthesis of tRNA t6A requires members from two evolutionarily conserved protein families TsaC/Sua5 and TsaD/Kae1/Qri7, and a varying number of auxiliary proteins. Furthermore, tRNA t6A is modified into a cyclic hydantoin form of t6A (ct6A) by TcdA in bacteria. In this work, we have identified a TsaD-TsaC-SUA5-TcdA modular protein (TsaN) from Pandoraviruses and determined a 3.2 Å resolution cryo-EM structure of P. salinus TsaN. The four domains of TsaN share strong structural similarities with TsaD/Kae1/Qri7 proteins, TsaC/Sua5 proteins, and Escherichia coli TcdA. TsaN catalyzes the formation of threonylcarbamoyladenylate (TC-AMP) using L-threonine, HCO3- and ATP, but does not participate further in tRNA t6A biosynthesis. We report for the first time that TsaN catalyzes a tRNA-independent threonylcarbamoyl modification of adenosine phosphates, leading to t6ADP and t6ATP. Moreover, TsaN is also active in catalyzing tRNA-independent conversion of t6A nucleoside to ct6A. Our results imply that TsaN from Pandoraviruses might be a prototype of the tRNA t6A- and ct6A-modifying enzymes in some cellular organisms.

[Effects of shade-humid environment on the growth characteristics of different maize (Zea mays) hybrids].

The growth traits of 18 maize hybrids were studied in natural and artificial simulation shade-humid environments. Significant differences were observed between the natural and shade-humid environments, and the air relative humidity in the shade-humid environment increased 15.0%-16.4%, the soil moisture increased 27.0%-78.4%, the illumination intensity decreased 72.9%-77.9%, and the quantum decreased 72.8%-79.6%. Shade did not affect the ambient temperature. The 7th leaf width, effective functional leaves, plant total leaves, tassel branch number, stem diameter, plant height, ear height, ear length, ear diameter, rows per ear, kernels per row, 100-grain mass and grain yield per plant under the shade-humid environment showed negative variations (reduction in phenotypic values), with the grain yield per plant and plant height being reduced by 72.3% and 7.1% respectively, and the declining changes of the remaining traits ranging from 14.8%-53.8%. However, the 7th leaf length, 7th leaf length-width ratio, anthesis to silking (ASI) duration, southern leaf blight (SLB) index and sheath blight index showed positive variations (increase in phenotypic values), with increases by 39.8%, 80.5%, 114.3%, 73.0% and 54.8%, respectively. The comprehensive shade-humid-tolerant coefficient calculated from the seven traits of ASI, tassel branches, plant total leaves, plant height, individual grain yield, southern leaf blight and sheath blight index could be easily and reliably used to evaluate the shade-humid-tolerant ability of the maize hybrids. According to this coefficient, the 18 hybrids could be classified into three categories, strongly-resistant, moderately-resistant and weakly-resistant to the shade-humid environment.