Background: Acute kidney injury (AKI) in critically ill patients is associated with a significant increase in mortality as well as long-term renal dysfunction and chronic kidney disease (CKD). Serum creatinine (SCr), the most widely used biomarker to evaluate kidney function, does not always accurately predict the glomerular filtration rate (GFR), since it is affected by some non-GFR determinants such as muscle mass and recent meat ingestion. Researchers and clinicians have gained interest in cystatin C (CysC), another biomarker of kidney function. The study objective was to compare GFR estimation using SCr and CysC in detecting CKD over a 1-year follow-up after an AKI stage-3 event in the ICU, as well as to analyze the association between eGFR (using SCr and CysC) and mortality after the AKI event. Method: This prospective observational study used the medical records of ICU patients diagnosed with AKI stage 3. SCr and CysC were measured twice during the ICU stay and four times following diagnosis of AKI. The eGFR was calculated using the EKFC equation for SCr and FAS equation for CysC in order to check the prevalence of CKD (defined as eGFR < 60 mL/min/1.73 m2). Results: The study enrolled 101 patients, 36.6% of whom were female, with a median age of 74 years (30–92), and a median length of stay of 14.5 days in intensive care. A significant difference was observed in the estimation of GFR when comparing formulas based on SCrand CysC, resulting in large differences in the prediction of CKD. Three months after the AKI event, eGFRCysC < 25 mL/min/1.73 m2 was a predictive factor of mortality later on; however, this was not the case for eGFRSCr. Conclusion: The incidence of CKD was highly discrepant with eGFRCysC versus eGFRSCr during the follow-up period. CysC detects more CKD events compared to SCr in the follow-up phase and eGFRCysC is a predictor for mortality in follow-up but not eGFRSCr. Determining the proper marker to estimate GFR in the post-ICU period in AKI stage-3 populations needs further study to improve risk stratification.

Shotcrete used for rock tunnel linings calls for skilled technicians, which is the key aspect to control the rebound. 3D concrete printing of tunnel linings has the potential to reduce manual labor for construction workers and to eliminate rebound, especially at overhead positions. In this study, the sag resistance and bond properties of printable concrete for overhead applications were explored. Mixtures with the addition of redispersible polymer powders and cellulose ethers were formulated. Roughened concrete slabs were used to replace the tunnel wall rock. A tack test with a loading control mode and a stress growth test were performed. To verify the results of the tack test and the stress growth test, a 3D concrete printing test, involving upside-down printing against the lower face of a supported concrete slab, was performed afterwards. Also, a pull-off test was performed to measure the bond strength of the printed layers in the hardened stage. The results showed that sag resistance of printable concrete is related to two aspects including the adhesion at the interface and the shear resistance of the fresh material itself. The adhesion and shear resistance properties determined two different failure modes (i.e. adhesion failure and cohesion failure). The results also demonstrated that the tack test results were more consistent with the upside-down printing test results, compared to the stress growth test.

Compressive membrane action can considerably improve the load bearing capacity of concrete slabs and beams in case of excessive loaded due to an accidental event. Currently, only limited research has been focusing on compressive membrane action in prestressed concrete elements, or on concrete elements with large cavities, such as precast concrete hollow core slabs. Therefore, a novel real-scale test setup has been developed in order to assess this effect in precast hollow core slabs, and how it can enhance the load-carrying capacity in accidental events. In parallel with these tests, a numerical finite element model has been developed in order to perform a more detailed structural analysis of this phenomenon, and to study the influence of various input parameters. The details of this test setup are briefly explained, and some relevant experimental test results are provided. Considering the experimental findings and validated numerical model, this contribution aims to quantify the influence of compressive membrane action on the structural reliability of precast concrete hollow core slabs. To this end, probabilistic models for the most important material and geometric variables are gathered, and the structural reliability is assessed using Latin Hypercube sampling. Overall, the results indicate that considering the formation of compressive membrane action strongly influences the variability of the ultimate load-carrying capacity of precast concrete hollow core slabs.

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Background Acute Kidney Injury (AKI) is frequently seen in hospitalized and critically ill patients. Studies have shown that AKI is a risk factor for the development of acute kidney disease (AKD), chronic kidney disease (CKD), and mortality. Methods A systematic review is performed on validated risk prediction models for developing poor renal outcomes after AKI scenarios. Medline, EMBASE, Cochrane, and Web of Science were searched for articles that developed or validated a prediction model. Moreover, studies that report prediction models for recovery after AKI also have been included. This review was registered with PROSPERO (CRD42022303197). Result We screened 25,812 potentially relevant abstracts. Among the 149 remaining articles in the first selection, eight met the inclusion criteria. All of the included models developed more than one prediction model with different variables. The models included between 3 and 28 independent variables and c-statistics ranged from 0.55 to 1. Conclusion Few validated risk prediction models targeting the development of renal insufficiency after experiencing AKI have been developed, most of which are based on simple statistical or machine learning models. While some of these models have been externally validated, none of these models are available in a way that can be used or evaluated in a clinical setting.