An update on the sample preparation and analytical methods for synthetic food colorants in food products.

Colorants, especially synthetic colorants, play a crucial role in enhancing the aesthetic qualities of food owing to their cost-effectiveness and stability against environmental factors. Ensuring the safe and regulated use of colorants is essential for maintaining consumer trust in food safety. Various preparation and analytical technologies, which are continuously undergoing improvement, are currently used to quantify of synthetic colorants in food products. This paper reviews recent developments in analytical techniques for synthetic food colorants, detection and compares the operational principles, advantages, and disadvantages of each technology. Additionally, it also explores advancements in these technologies, discussing several invaluable tools of analysis, such as high-performance liquid chromatography, liquid chromatography-tandem mass spectrometry, electrochemical sensors, digital image analysis, near-infrared spectroscopy, and surface-enhanced Raman spectroscopy. This comprehensive overview aims to provide valuable insights into current progress and research in the field of food colorant analysis.

Fucosylation and galactosylation in N-glycans of bovine intestinal alkaline phosphatase and their role in its enzymatic activity.

Bovine intestinal alkaline phosphatase (biALP), a membrane-bound plasma metalloenzyme, maintains intestinal homeostasis, regulates duodenal surface pH, and protects against infections caused by pathogenic bacteria. The N-glycans of biALP regulate its enzymatic activity, protein folding, and thermostability, but their structures are not fully reported. In this study, the structures and quantities of the N-glycans of biALP were analyzed by liquid chromatography-electrospray ionization-high energy collision dissociation-tandem mass spectrometry. In total, 48 N-glycans were identified and quantified, comprising high-mannose [6 N-glycans, 33.1 % (sum of relative quantities of each N-glycan)], hybrid (6, 11.9 %), and complex (36, 55.0 %) structures [bi- (13, 26.1 %), tri- (16, 21.5 %), and tetra-antennary (7, 7.4 %)]. These included bisecting N-acetylglucosamine (33, 56.6 %), mono-to tri-fucosylation (32, 53.3 %), mono-to tri-α-galactosylation (16, 20.7 %), and mono-to tetra-ß-galactosylation (36, 58.5 %). No sialylation was identified. N-glycans with non-bisecting GlcNAc (9, 10.3 %), non-fucosylation (10, 13.6 %), non-α-galactosylation (26, 46.2 %), and non-ß-galactosylation (6, 8.4 %) were also identified. The activity (100 %) of biALP was reduced to 37.3 ± 0.2 % (by de-fucosylation), 32.7 ± 2.9 % (by de-α-galactosylation), and 0.2 ± 0.2 % (by de-ß-galactosylation), comparable to inhibition by 10-4 to 101 mM EDTA, a biALP inhibitor. These results indicate that fucosylated and galactosylated N-glycans, especially ß-galactosylation, affected the activity of biALP. This study is the first to identify 48 diverse N-glycan structures and quantities of bovine as well as human intestinal ALP and to demonstrate the importance of the role of fucosylation and galactosylation for maintaining the activity of biALP.

Tonicity-induced cargo loading into extracellular vesicles.

The current challenge in using extracellular vesicles (EVs) as drug delivery vehicles is to precisely control their membrane permeability, specifically in the ability to switch between permeable and impermeable states without compromising their integrity and functionality. Here, we introduce a rapid, efficient, and gentle loading method for EVs based on tonicity control (TC) using a lab-on-a-disc platform. In this technique, a hypotonic solution was used for temporarily permeabilizing a membrane ("on" state), allowing the influx of molecules into EVs. The subsequent isotonic washing led to an impermeable membrane ("off" state). This loading cycle enables the loading of different cargos into EVs, such as doxorubicin hydrochloride (Dox), ssDNA, and miRNA. The TC approach was shown to be more effective than traditional methods such as sonication or extrusion, with loading yields that were 4.3-fold and 7.2-fold greater, respectively. Finally, the intracellular assessments of miRNA-497-loaded EVs and doxorubicin-loaded EVs confirmed the superior performance of TC-prepared formulations and demonstrated the impact of encapsulation heterogeneity on the therapeutic outcome, signifying potential opportunities for developing novel exosome-based therapeutic systems for clinical applications.

Alix-normalized exosomal programmed death-ligand 1 analysis in urine enables precision monitoring of urothelial cancer.

Anti-programmed death-ligand 1 (PD-L1) Ab-based therapies have demonstrated potential for treating metastatic urothelial cancer with high PD-L1 expression. Urinary exosomes are promising biomarkers for liquid biopsy, but urine's high variability requires normalization for accurate analysis. This study proposes using the PD-L1/Alix ratio to normalize exosomal PD-L1 signal intensity with Alix, an internal exosomal protein less susceptible to heterogeneity concerns than surface protein markers. Extracellular vesicles were isolated using ExoDisc and characterized using various methods, including ExoView to analyze tetraspanins, PD-L1, and Alix on individual exosomes. On-disc ELISA was used to evaluate PD-L1 and Alix-normalized PD-L1 in 15 urothelial cancer patients during the initial treatment cycle with Tecentriq. Results showed that Alix signal range was relatively uniform, whereas tetraspanin marker intensity varied for individual exosome particles. On-disc ELISA was more reliable for detecting exosomal PD-L1 expression than standard plate ELISA-based measurement. Using exosomal Alix expression for normalization is a more reliable approach than conventional methods for monitoring patient status. Overall, the study provides a practical and reliable method for detecting exosomal PD-L1 in urine samples from patients with urothelial cancer.

Association between mechanical power and intensive care unit mortality in Korean patients under pressure-controlled ventilation.

BACKGROUND: Mechanical power (MP) has been reported to be associated with clinical outcomes. Because the original MP equation is derived from paralyzed patients under volume-controlled ventilation, its application in practice could be limited in patients receiving pressure-controlled ventilation (PCV). Recently, a simplified equation for patients under PCV was developed. We investigated the association between MP and intensive care unit (ICU) mortality. METHODS: We conducted a retrospective analysis of Korean data from the Fourth International Study of Mechanical Ventilation. We extracted data of patients under PCV on day 1 and calculated MP using the following simplified equation: MPPCV = 0.098 ∙ respiratory rate ∙ tidal volume ∙ (ΔPinsp + positive end-expiratory pressure), where ΔPinsp is the change in airway pressure during inspiration. Patients were divided into survivors and non-survivors and then compared. Multivariable logistic regression was performed to determine association between MPPCV and ICU mortality. The interaction of MPPCV and use of neuromuscular blocking agent (NMBA) was also analyzed. RESULTS: A total of 125 patients was eligible for final analysis, of whom 38 died in the ICU. MPPCV was higher in non-survivors (17.6 vs. 26.3 J/min, P<0.001). In logistic regression analysis, only MPPCV was significantly associated with ICU mortality (odds ratio, 1.090; 95% confidence interval, 1.029-1.155; P=0.003). There was no significant effect of the interaction between MPPCV and use of NMBA on ICU mortality (P=0.579). CONCLUSIONS: MPPCV is associated with ICU mortality in patients mechanically ventilated with PCV mode, regardless of NMBA use.

A Deep Learning-Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness.

BACKGROUND: Vancomycin pharmacokinetics are highly variable in patients with critical illnesses, and clinicians commonly use population pharmacokinetic (PPK) models based on a Bayesian approach to dose. However, these models are population-dependent, may only sometimes meet the needs of individual patients, and are only used by experienced clinicians as a reference for making treatment decisions. To assist real-world clinicians, we developed a deep learning-based decision-making system that predicts vancomycin therapeutic drug monitoring (TDM) levels in patients in intensive care unit. OBJECTIVE: This study aimed to establish joint multilayer perceptron (JointMLP), a new deep-learning model for predicting vancomycin TDM levels, and compare its performance with the PPK models, extreme gradient boosting (XGBoost), and TabNet. METHODS: We used a 977-case data set split into training and testing groups in a 9:1 ratio. We performed external validation of the model using 1429 cases from Kangwon National University Hospital and 2394 cases from the Medical Information Mart for Intensive Care-IV (MIMIC-IV). In addition, we performed 10-fold cross-validation on the internal training data set and calculated the 95% CIs using the metric. Finally, we evaluated the generalization ability of the JointMLP model using the MIMIC-IV data set. RESULTS: Our JointMLP model outperformed other models in predicting vancomycin TDM levels in internal and external data sets. Compared to PPK, the JointMLP model improved predictive power by up to 31% (mean absolute error [MAE] 6.68 vs 5.11) on the internal data set and 81% (MAE 11.87 vs 6.56) on the external data set. In addition, the JointMLP model significantly outperforms XGBoost and TabNet, with a 13% (MAE 5.75 vs 5.11) and 14% (MAE 5.85 vs 5.11) improvement in predictive accuracy on the inner data set, respectively. On both the internal and external data sets, our JointMLP model performed well compared to XGBoost and TabNet, achieving prediction accuracy improvements of 34% and 14%, respectively. Additionally, our JointMLP model showed higher robustness to outlier data than the other models, as evidenced by its higher root mean squared error performance across all data sets. The mean errors and variances of the JointMLP model were close to zero and smaller than those of the PPK model in internal and external data sets. CONCLUSIONS: Our JointMLP approach can help optimize treatment outcomes in patients with critical illnesses in an intensive care unit setting, reducing side effects associated with suboptimal vancomycin administration. These include increased risk of bacterial resistance, extended hospital stays, and increased health care costs. In addition, the superior performance of our model compared to existing models highlights its potential to help real-world clinicians.