METHODS: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr.
RESULTS: The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection (VIP) value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R2 > 0.990) and high precision (both RMSEC and RMSEE < 5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w).
CONCLUSION: A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.
AIM OF THE STUDY: In the present study, we investigated the effects of TCS against insulin resistance in muscle cells through integrating in vitro experiment and identifying its active biomarker using metabolomics and in molecular docking validation.
MATERIALS AND METHODS: We used centrifugal partition chromatography (CPC) to isolate 33 fractions from methanolic extract of TCS, and then used UHPLC-Orbitrap-HRMS to identify the detectable metabolites in each fraction. We assessed the insulin sensitization activity of each fraction using enzyme-linked immunosorbent assay (ELISA), and then used confocal immunocytochemistry microscopy to measure the translocation of glucose transporter 4 (GLUT4) to the cell membrane. The identified active metabolites were further simulated for its molecular docking interaction using Autodock Tools.
RESULTS: The polar fractions of TCS significantly increased insulin sensitivity, as measured by the inhibition of phosphorylated insulin receptor substrate-1 (pIRS1) at serine-312 residue (ser312) also the increasing number of translocated GLUT4 and glycogen content. We identified 58 metabolites of TCS, including glycosides, flavonoids, alkaloids, coumarins, and nucleotides groups. The metabolomics and molecular docking simulations showed the presence of minor metabolites consisting of tinoscorside D, higenamine, and tinoscorside A as the active compounds.
CONCLUSIONS: Our findings suggest that TCS is a promising new treatment for insulin resistance and the identification of the active metabolites in TCS could lead to the development of new drugs therapies for diabetes that target these pathways.
METHODS: Multinational, multicenter, prospective cohort study at 786 ICUs of 312 hospitals in 147 cities in 37 Latin American, Asian, African, Middle Eastern, and European countries.
RESULTS: Between 07/01/1998 and 02/12/2022, 300,827 patients, followed during 2,167,397 patient-days, acquired 21,371 HAIs. Following mortality risk factors were identified in multiple logistic regression: Central line-associated bloodstream infection (aOR:1.84; P
METHODS: We implemented a multidimensional approach, incorporating an 11-element bundle, education, surveillance of CLABSI rates and clinical outcomes, monitoring compliance with bundle components, feedback of CLABSI rates and clinical outcomes, and performance feedback in 316 ICUs across 30 low- and middle-income countries. Our dependent variables were CLABSI per 1,000-CL-days and in-ICU all-cause mortality rates. These variables were measured at baseline and during the intervention, specifically during the second month, third month, 4 to 16 months, and 17 to 29 months. Comparisons were conducted using a two-sample t test. To explore the exposure-outcome relationship, we used a generalized linear mixed model with a Poisson distribution to model the number of CLABSIs.
RESULTS: During 1,837,750 patient-days, 283,087 patients, used 1,218,882 CL-days. CLABSI per 1,000 CL-days rates decreased from 15.34 at the baseline period to 7.97 in the 2nd month (relative risk (RR) = 0.52; 95% confidence interval [CI] = 0.48-0.56; P
DESIGN: Prospective cohort study.
SETTING: This study was conducted across 743 ICUs of 282 hospitals in 144 cities in 42 Asian, African, European, Latin American, and Middle Eastern countries.
PARTICIPANTS: The study included patients admitted to ICUs across 24 years.
RESULTS: In total, 289,643 patients were followed during 1,951,405 patient days and acquired 8,236 VAPs. We analyzed 10 independent variables. Multiple logistic regression identified the following independent VAP RFs: male sex (adjusted odds ratio [aOR], 1.22; 95% confidence interval [CI], 1.16-1.28; P < .0001); longer length of stay (LOS), which increased the risk 7% per day (aOR, 1.07; 95% CI, 1.07-1.08; P < .0001); mechanical ventilation (MV) utilization ratio (aOR, 1.27; 95% CI, 1.23-1.31; P < .0001); continuous positive airway pressure (CPAP), which was associated with the highest risk (aOR, 13.38; 95% CI, 11.57-15.48; P < .0001); tracheostomy connected to a MV, which was associated with the next-highest risk (aOR, 8.31; 95% CI, 7.21-9.58; P < .0001); endotracheal tube connected to a MV (aOR, 6.76; 95% CI, 6.34-7.21; P < .0001); surgical hospitalization (aOR, 1.23; 95% CI, 1.17-1.29; P < .0001); admission to a public hospital (aOR, 1.59; 95% CI, 1.35-1.86; P < .0001); middle-income country (aOR, 1.22; 95% CI, 15-1.29; P < .0001); admission to an adult-oncology ICU, which was associated with the highest risk (aOR, 4.05; 95% CI, 3.22-5.09; P < .0001), admission to a neurologic ICU, which was associated with the next-highest risk (aOR, 2.48; 95% CI, 1.78-3.45; P < .0001); and admission to a respiratory ICU (aOR, 2.35; 95% CI, 1.79-3.07; P < .0001). Admission to a coronary ICU showed the lowest risk (aOR, 0.63; 95% CI, 0.51-0.77; P < .0001).
CONCLUSIONS: Some identified VAP RFs are unlikely to change: sex, hospitalization type, ICU type, facility ownership, and country income level. Based on our results, we recommend focusing on strategies to reduce LOS, to reduce the MV utilization ratio, to limit CPAP use and implementing a set of evidence-based VAP prevention recommendations.
METHODS: Prospective intensive care unit patient data collected via International Nosocomial Infection Control Consortium Surveillance Online System. Centers for Disease Control and Prevention/National Health Care Safety Network definitions applied for device-associated health care-associated infections (DA-HAI).
RESULTS: We gathered data from 204,770 patients, 1,480,620 patient days, 936,976 central line (CL)-days, 637,850 mechanical ventilators (MV)-days, and 1,005,589 urinary catheter (UC)-days. Our results showed 4,270 CL-associated bloodstream infections, 7,635 ventilator-associated pneumonia, and 3,005 UC-associated urinary tract infections. The combined rates of DA-HAIs were 7.28%, and 10.07 DA-HAIs per 1,000 patient days. CL-associated bloodstream infections occurred at 4.55 per 1,000 CL-days, ventilator-associated pneumonias at 11.96 per 1,000 MV-days, and UC-associated urinary tract infections at 2.91 per 1,000 UC days. In terms of resistance, Pseudomonas aeruginosa showed 50.73% resistance to imipenem, 44.99% to ceftazidime, 37.95% to ciprofloxacin, and 34.05% to amikacin. Meanwhile, Klebsiella spp had resistance rates of 48.29% to imipenem, 72.03% to ceftazidime, 61.78% to ciprofloxacin, and 40.32% to amikacin. Coagulase-negative Staphylococci and Staphylococcus aureus displayed oxacillin resistance in 81.33% and 53.83% of cases, respectively.
CONCLUSIONS: The high rates of DA-HAI and bacterial resistance emphasize the ongoing need for continued efforts to control them.