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Background Acute kidney injury (AKI) is one of the most frequent complications of critical illness. We aimed to explore the predictors of renal function recovery and the short-term reversibility after AKI by comparing logistic regression with four machine learning models. Methods We reviewed patients who were diagnosed with AKI in the MIMIC-IV database between 2008 and 2019. Recovery from AKI within 72 h of the initiating event was typically recognized as the short-term reversal of AKI. Conventional logistic regression and four different machine algorithms (XGBoost algorithm model, Bayesian networks [BNs], random forest [RF] model, and support vector machine [SVM] model) were used to develop and validate prediction models. The performance measures were compared through the area under the receiver operating characteristic curve (AU-ROC), calibration curves, and 10-fold cross-validation. Results A total of 12,321 critically ill adult AKI patients were included in our analysis cohort. The renal function recovery rate after AKI was 67.9%. The maximum and minimum serum creatinine (SCr) within 24 h of AKI diagnosis, the minimum SCr within 24 and 12 h, and antibiotics usage duration were independently associated with renal function recovery after AKI. Among the 8364 recovered patients, the maximum SCr within 24 h of AKI diagnosis, the minimum Glasgow Coma Scale (GCS) score, the maximum blood urea nitrogen (BUN) within 24 h, vasopressin and vancomycin usage, and the maximum lactate within 24 h were the top six predictors for short-term reversibility of AKI. The RF model presented the best performance for predicting both renal functional recovery (AU-ROC [0.8295 ± 0.01]) and early recovery (AU-ROC [0.7683 ± 0.03]) compared with the conventional logistic regression model. Conclusions The maximum SCr within 24 h of AKI diagnosis was a common independent predictor of renal function recovery and the short-term reversibility of AKI. The RF machine learning algorithms showed a superior ability to predict the prognosis of AKI patients in the ICU compared with the traditional regression models. These models may prove to be clinically helpful and can assist clinicians in providing timely interventions, potentially leading to improved prognoses.

Additively manufactured grade 300 maraging steel (AM300) exhibits high strength but low elongation, because the precipitation strengthening effected by aging treatment has not been fully took effect. This work aims to optimize the aging treatment process of AM300. It was aged at 420°C, 500°C and 550°C for 4 h, respectively. It was found that the aged AM300 exhibited the best combination of strength and ductility (ultimate tensile strength (UTS) was 1740 MPa, total elongation (TE) was 10.2%, hardness was 520.4 HV) at 420°C. As compared to the non-aged AM300 (UTS = 1060 MPa, TE = 4.2%, hardness = 349.2 HV), the UTS, TE and hardness were increased by 64.1%, 142% and 49%, respectively.

Verticillium wilt, caused by the soil-borne fungus Verticillium dahliae, is one of the most devastating diseases in cotton (Gossypium spp.). Lignin in the cell wall forms a physical barrier to inhibit pathogen invasion, and defense-induced lignification reinforces secondary cell wall to prevent pathogens from further spreading. Cinnamyl alcohol dehydrogenases (CADs) catalyze the production of three main monolignols, p-coumaryl- (H), coniferyl- (G), and sinapyl-alcohols (S), which are the fundamental blocks of lignin. Here, we identified CAD genes in G. hirsutum, analyzed their expression profiles in cotton leaf, stem, and root from different developmental stages, and selected GhCAD35, GhCAD45, and GhCAD43, which were consistently induced by V. dahliae inoculation in G. hirsutum cultivars resistant or susceptible to V. dahliae. On the basis of confirmation of the in vitro enzymatic activity of the three proteins in generation of the three monolignols, we used virus-induced gene silencing (VIGS) to investigate the effects of silencing of GhCAD35, GhCAD45, or GhCAD43 on resistance to V. dahliae as well as on deposition and the composition of lignin. Silencing each of the three CADs impaired the defense-induced lignification and salicylic acid biosynthesis in stem, and compromised resistance to V. dahliae. Moreover, our study showed that silencing the three GhCADs severely affected the biosynthesis of S-lignin, leading to a decrease of the syringyl/guaiacyl (S/G) ratio. Heterogeneous overexpression of GhCAD35, GhCAD45, or GhCAD43 in Arabidopsis enhanced disease resistance. Taken together, our study demonstrates a role of the three GhCADs in defense-induced lignin biosynthesis and resistance to V. dahliae in G. hirsutum.