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Background: Fungal infections are associated with high morbidity and mortality in the intensive care unit (ICU), but their diagnosis is difficult. In this study, machine learning was applied to design and define the predictive model of ICU-acquired fungi (ICU-AF) in the early stage of fungal infections using Random Forest., Objectives: This study aimed to provide evidence for the early warning and management of fungal infections., Methods: We analyzed the data of patients with culture-positive fungi during their admission to seven ICUs of the First Affiliated Hospital of Chongqing Medical University from January 1, 2015, to December 31, 2019. Patients whose first culture was positive for fungi longer than 48 h after ICU admission were included in the ICU-AF cohort. A predictive model of ICU-AF was obtained using the Least Absolute Shrinkage and Selection Operator and machine learning, and the relationship between the features within the model and the disease severity and mortality of patients was analyzed. Finally, the relationships between the ICU-AF model, antifungal therapy and empirical antifungal therapy were analyzed., Results: A total of 1,434 cases were included finally. We used lasso dimensionality reduction for all features and selected six features with importance ≥0.05 in the optimal model, namely, times of arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation. The area under the curve of the model for predicting ICU-AF was 0.981 in the test set, with a sensitivity of 0.960 and specificity of 0.990. The times of arterial catheter ( p  = 0.011, OR = 1.057, 95% CI = 1.053-1.104) and invasive mechanical ventilation ( p  = 0.007, OR = 1.056, 95%CI = 1.015-1.098) were independent risk factors for antifungal therapy in ICU-AF. The times of arterial catheter ( p  = 0.004, OR = 1.098, 95%CI = 0.855-0.970) were an independent risk factor for empirical antifungal therapy., Conclusion: The most important risk factors for ICU-AF are the six time-related features of clinical parameters (arterial catheter, enteral nutrition, corticosteroids, broadspectrum antibiotics, urinary catheter, and invasive mechanical ventilation), which provide early warning for the occurrence of fungal infection. Furthermore, this model can help ICU physicians to assess whether empiric antifungal therapy should be administered to ICU patients who are susceptible to fungal infections., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhao, Lai, Tang, Luo, He, Huang, Wang, Zhang, Lin, Qin and Xu.)

Alcoholic fermentation by an oenological strain of Torulaspora delbrueckii in association with an oenological strain of Saccharomyces cerevisiae was studied in mixed and sequential cultures. Experiments were performed in a synthetic grape must medium in a membrane bioreactor, a special tool designed to study indirect interactions between microorganisms. Results showed that the S. cerevisiae strain had a negative impact on the T. delbrueckii strain, leading to a viability decrease as soon as S. cerevisiae was inoculated. Even for high inoculation of T. delbrueckii (more than 20× S. cerevisiae) in mixed cultures, T. delbrueckii growth was inhibited. Substrate competition and cell-to-cell contact mechanism could be eliminated as explanations of the observed interaction, which was probably an inhibition by a metabolite produced by S. cerevisiae. S. cerevisiae should be inoculated 48 h after T. delbrueckii in order to ensure the growth of T. delbrueckii and consequently a decrease of volatile acidity and a higher isoamyl acetate production. In this case, in a medium with a high concentration of assimilable nitrogen (324 mg L(-1)), S. cerevisiae growth was not affected by T. delbrueckii. But in a sequential fermentation in a medium containing 176 mg L(-1) initial assimilable nitrogen, S. cerevisiae was not able to develop because of nitrogen exhaustion by T. delbrueckii growth during the first 48 h, leading to sluggish fermentation.

Torulaspora delbrueckii metabolism was assessed in a synthetic culture medium similar to grape must under various conditions: no aeration and three different oxygen feeds, in order to determine the effect of oxygen on metabolism. Carbon and nitrogen mass balances were calculated to quantify metabolic fluxes. The effect of oxygen was to decrease the flux of carbon going into the fermentation pathway in favor of growth. In the absence of aeration, higher amounts of glycerol were produced, probably to maintain the redox balance. The oxygen requirement of this strain was high, since even for the highest air supply oxygen became limiting after 24 h. Nevertheless, this strain developed well in the absence of oxygen and consumed 220 g/L of sugars (glucose/fructose) in 166 h at 20 °C, giving a good ethanol yield (0.50 g/g).

We describe a method for investigating the reaction mechanism of fuel cell systems by designing a spectroelectrochemical cell with functions of temperature and flow control to mimic the reaction condition of fuel cell systems and utilizing Au core Pt shell (Au@Pt) nanoparticles to enhance the Raman signal of the surface species on the surface of electrocatalysts. The cell consists of three parts: a thin-layer spectroelectrochemical reaction chamber with an optical window for Raman measurement, the heating chamber right beneath the reaction chamber, and a long spiral flow channel to preheat the solution to the desired temperature and effectively exchange the solution. The temperature of the solution can be easily controlled from room temperature to 80 degrees C, and the flow rate can be as high as 945 microl s(-1). The temperature and flow control is demonstrated by monitoring the changes in the cyclic voltammograms and the Raman signals. By synthesizing Au@Pt nanoparticles and assembling them on a Pt substrate, we can significantly enhance the Raman signal of surface species on the Pt shell surface, which allows us to detect strong signal of CO as the dissociative product of formic acid as well as the intermediate species of the oxidation process. The further development and perspectives of using SERS to study the electrocatalytic systems are discussed.