Development and validation of a novel 7α-hydroxy-4-cholesten-3-one (C4) liquid chromatography tandem mass spectrometry method and its utility to assess pre-analytical stability.

OBJECTIVES: 7α-Hydroxy-4-cholesten-3-one (C4) is the common intermediary of both primary bile acids. C4 is recommended by the British Society of Gastroenterology for the investigation of bile acid diarrhoea (BAD) in patients with chronic diarrhoea. This project aimed to develop and validate an assay to quantitate C4 in serum and assess the stability of C4 in unseparated blood. METHODS: Accuracy was underpinned by calibrating to quantitative nuclear magnetic resonance analysis. C4 was analysed in a 96-well plate format with a deuterated C4 internal standard and liquid-liquid extraction. Validation followed the 2018 Food and Drug Administration guidelines. To assess C4 stability, healthy volunteers (n=12) donated 8 fasted samples each. Samples were incubated at 20 °C for up to 72 h and retrieved, centrifuged, aliquoted and frozen for storage at different time points prior to C4 analysis. RESULTS: The C4 method demonstrated excellent analytical performance and passed all validation criteria. The method was found to be accurate, precise, free from matrix effects and interference. After 72 h of delayed sample separation, C4 concentration gradually declined by up to 14 % from baseline. However, the change was not significant for up to 12 h. CONCLUSIONS: We present a robust method of analysing serum C4, offering a convenient alternative to 75SeHCAT for BAD investigation. C4 was found to decline in unseparated blood over time; however, after 12 h the mean change was <5 % from baseline. Our results suggest C4 is suitable for collection from both primary and secondary care prior to gastroenterology referral.

Airborne metal nanoparticles released by azides detonation: determination and potential public exposure.

Metal azides are highly energetic materials that release a large amount of gas upon detonation. They also release metal particles, generating an aerosol. The most common azide is sodium azide (NaN3), which is used nowadays in car airbags. If the decomposition is not complete, harmful azide particles might be inhaled. Heavy metal azides find application as a primary explosive (primer) in ammunition. Public health officials have raised concerns about heavy metal particles released during training in shooting ranges. We identify a lack of knowledge on airborne metal particles properties released from azide detonation and on the analytical methods applied to characterize them. As a case study, we detonated milligram amounts of silver azide, copper azide, and a mixture of them in a glove box. We then analyse the airborne particles with an ensemble analytical setup, able to measure real-time their particle size distribution and chemical composition. We detected spherical metal nanoparticles in the range of 2-500 nm. These findings and the developed analytical tools may allow identifying airborne nanoparticles the passenger compartments of vehicles after airbag activation as well as in indoor shooting ranges, contributing to the evaluation of public health risks.

Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay.

Benchmark dose (BMD) modeling estimates the dose of a chemical that causes a perturbation from baseline. Transcriptional BMDs have been shown to be relatively consistent with apical end point BMDs, opening the door to using molecular BMDs to derive human health-based guidance values for chemical exposure. Metabolomics measures the responses of small-molecule endogenous metabolites to chemical exposure, complementing transcriptomics by characterizing downstream molecular phenotypes that are more closely associated with apical end points. The aim of this study was to apply BMD modeling to in vivo metabolomics data, to compare metabolic BMDs to both transcriptional and apical end point BMDs. This builds upon our previous application of transcriptomics and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP), applying metabolomics to the same archived tissues. Specifically, liver from rats exposed to five doses of TPhP was investigated using liquid chromatography-mass spectrometry and 1H nuclear magnetic resonance spectroscopy-based metabolomics. Following the application of BMDExpress2 software, 2903 endogenous metabolic features yielded viable dose-response models, confirming a perturbation to the liver metabolome. Metabolic BMD estimates were similarly sensitive to transcriptional BMDs, and more sensitive than both clinical chemistry and apical end point BMDs. Pathway analysis of the multiomics data sets revealed a major effect of TPhP exposure on cholesterol (and downstream) pathways, consistent with clinical chemistry measurements. Additionally, the transcriptomics data indicated that TPhP activated xenobiotic metabolism pathways, which was confirmed by using the underexploited capability of metabolomics to detect xenobiotic-related compounds. Eleven biotransformation products of TPhP were discovered, and their levels were highly correlated with multiple xenobiotic metabolism genes. This work provides a case study showing how metabolomics and transcriptomics can estimate mechanistically anchored points-of-departure. Furthermore, the study demonstrates how metabolomics can also discover biotransformation products, which could be of value within a regulatory setting, for example, as an enhancement of OECD Test Guideline 417 (toxicokinetics).

The glutamate/aspartate transporter EAAT1 is crucial for T-cell acute lymphoblastic leukemia proliferation and survival.

T-cell acute lymphoblastic leukemia (T-ALL) is a cancer of the immune system. Approximately 20% of paediatric and 50% of adult T-ALL patients have refractory disease or relapse and die from the disease. To improve patient outcome new therapeutics are needed. With the aim to identify new therapeutic targets, we combined the analysis of T-ALL gene expression and metabolism to identify the metabolic adaptations that T-ALL cells exhibit. We found that glutamine uptake is essential for T-ALL proliferation. Isotope tracing experiments showed that glutamine fuels aspartate synthesis through the TCA cycle and that glutamine and glutamine-derived aspartate together supply three nitrogen atoms in purines and all but one atom in pyrimidine rings. We show that the glutamate-aspartate transporter EAAT1 (SLC1A3), which is normally expressed in the central nervous system, is crucial for glutamine conversion to aspartate and nucleotides and that T-ALL cell proliferation depends on EAAT1 function. Through this work, we identify EAAT1 as a novel therapeutic target for T-ALL treatment.

LDHB contributes to the regulation of lactate levels and basal insulin secretion in human pancreatic ß cells.

Using 13C6 glucose labeling coupled to gas chromatography-mass spectrometry and 2D 1H-13C heteronuclear single quantum coherence NMR spectroscopy, we have obtained a comparative high-resolution map of glucose fate underpinning ß cell function. In both mouse and human islets, the contribution of glucose to the tricarboxylic acid (TCA) cycle is similar. Pyruvate fueling of the TCA cycle is primarily mediated by the activity of pyruvate dehydrogenase, with lower flux through pyruvate carboxylase. While the conversion of pyruvate to lactate by lactate dehydrogenase (LDH) can be detected in islets of both species, lactate accumulation is 6-fold higher in human islets. Human islets express LDH, with low-moderate LDHA expression and ß cell-specific LDHB expression. LDHB inhibition amplifies LDHA-dependent lactate generation in mouse and human ß cells and increases basal insulin release. Lastly, cis-instrument Mendelian randomization shows that low LDHB expression levels correlate with elevated fasting insulin in humans. Thus, LDHB limits lactate generation in ß cells to maintain appropriate insulin release.

Predicting Successful Weaning from Mechanical Ventilation by Reduction in Positive End-expiratory Pressure Level Using Machine Learning.

Weaning patients from mechanical ventilation (MV) is a critical and resource intensive process in the Intensive Care Unit (ICU) that impacts patient outcomes and healthcare expenses. Weaning methods vary widely among providers. Prolonged MV is associated with adverse events and higher healthcare expenses. Predicting weaning readiness is a non-trivial process in which the positive end-expiratory pressure (PEEP), a crucial component of MV, has potential to be indicative but has not yet been used as the target. We aimed to predict successful weaning from mechanical ventilation by targeting changes in the PEEP-level using a supervised machine learning model. This retrospective study included 12,153 mechanically ventilated patients from Medical Information Mart for Intensive Care (MIMIC-IV) and eICU collaborative research database (eICU-CRD). Two machine learning models (Extreme Gradient Boosting and Logistic Regression) were developed using a continuous PEEP reduction as target. The data is splitted into 80% as training set and 20% as test set. The model's predictive performance was reported using 95% confidence interval (CI), based on evaluation metrics such as area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), F1-Score, Recall, positive predictive value (PPV), and negative predictive value (NPV). The model's descriptive performance was reported as the variable ranking using SHAP (SHapley Additive exPlanations) algorithm. The best model achieved an AUROC of 0.84 (95% CI 0.83-0.85) and an AUPRC of 0.69 (95% CI 0.67-0.70) in predicting successful weaning based on the PEEP reduction. The model demonstrated a Recall of 0.85 (95% CI 0.84-0.86), F1-score of 0.86 (95% CI 0.85-0.87), PPV of 0.87 (95% CI 0.86-0.88), and NPV of 0.64 (95% CI 0.63-0.66). Most of the variables that SHAP algorithm ranked to be important correspond with clinical intuition, such as duration of MV, oxygen saturation (SaO2), PEEP, and Glasgow Coma Score (GCS) components. This study demonstrates the potential application of machine learning in predicting successful weaning from MV based on continuous PEEP reduction. The model's high PPV and moderate NPV suggest that it could be a useful tool to assist clinicians in making decisions regarding ventilator management.

Predicting Hypoxia Using Machine Learning: Systematic Review.

Background: Hypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate time-critical interventions to counter patient health deterioration. Objective: This systematic review aims to summarize and compare previous efforts to predict hypoxic events in the hospital setting using machine learning with respect to their methodology, predictive performance, and assessed population. Methods: A systematic literature search was performed using Web of Science, Ovid with Embase and MEDLINE, and Google Scholar. Studies that investigated hypoxia or hypoxemia of hospitalized patients using machine learning models were considered. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool. Results: After screening, a total of 12 papers were eligible for analysis, from which 32 models were extracted. The included studies showed a variety of population, methodology, and outcome definition. Comparability was further limited due to unclear or high risk of bias for most studies (10/12, 83%). The overall predictive performance ranged from moderate to high. Based on classification metrics, deep learning models performed similar to or outperformed conventional machine learning models within the same studies. Models using only prior peripheral oxygen saturation as a clinical variable showed better performance than models based on multiple variables, with most of these studies (2/3, 67%) using a long short-term memory algorithm. Conclusions: Machine learning models provide the potential to accurately predict the occurrence of hypoxic events based on retrospective data. The heterogeneity of the studies and limited generalizability of their results highlight the need for further validation studies to assess their predictive performance.

Safety outcomes of bariatric surgery in patients with advanced organ disease: the ONWARD study: a prospective cohort study.

INTRODUCTION: Increasing numbers of patients with advanced organ disease are being considered for bariatric and metabolic surgery (BMS). There is no prospective study on the safety of BMS in these patients. This study aimed to capture outcomes for patients with advanced cardiac, renal, or liver disease undergoing BMS. MATERIALS AND METHODS: This was a multinational, prospective cohort study on the safety of elective BMS in adults (≥18 years) with advanced disease of the heart, liver, or kidney. RESULTS: Data on 177 patients with advanced diseases of heart, liver, or kidney were submitted by 75 centres in 33 countries. Mean age and BMI was 48.56±11.23 years and 45.55±7.35 kg/m 2 , respectively. Laparoscopic sleeve gastrectomy was performed in 124 patients (70%). The 30-day morbidity and mortality were 15.9% ( n =28) and 1.1% ( n =2), respectively. Thirty-day morbidity was 16.4%, 11.7%, 20.5%, and 50.0% in patients with advanced heart ( n =11/61), liver ( n =8/68), kidney ( n =9/44), and multi-organ disease ( n =2/4), respectively. Cardiac patients with left ventricular ejection fraction less than or equal to 35% and New York Heart Association classification 3 or 4, liver patients with model for end-stage liver disease score greater than or equal to 12, and patients with advanced renal disease not on dialysis were at increased risk of complications. Comparison with a propensity score-matched cohort found advanced disease of the heart, liver, or kidney to be significantly associated with higher 30-day morbidity. CONCLUSION: Patients with advanced organ disease are at increased risk of 30-day morbidity following BMS. This prospective study quantifies that risk and identifies patients at the highest risk.

Mechanical Power Density Predicts Prolonged Ventilation Following Double Lung Transplantation.

Prolonged mechanical ventilation (PMV) after lung transplantation poses several risks, including higher tracheostomy rates and increased in-hospital mortality. Mechanical power (MP) of artificial ventilation unifies the ventilatory variables that determine gas exchange and may be related to allograft function following transplant, affecting ventilator weaning. We retrospectively analyzed consecutive double lung transplant recipients at a national transplant center, ventilated through endotracheal tubes upon ICU admission, excluding those receiving extracorporeal support. MP and derived indexes assessed up to 36 h after transplant were correlated with invasive ventilation duration using Spearman's coefficient, and we conducted receiver operating characteristic (ROC) curve analysis to evaluate the accuracy in predicting PMV (>72 h), expressed as area under the ROC curve (AUROC). PMV occurred in 82 (35%) out of 237 cases. MP was significantly correlated with invasive ventilation duration (Spearman's ρ = 0.252 [95% CI 0.129-0.369], p < 0.01), with power density (MP normalized to lung-thorax compliance) demonstrating the strongest correlation (ρ = 0.452 [0.345-0.548], p < 0.01) and enhancing PMV prediction (AUROC 0.78 [95% CI 0.72-0.83], p < 0.01) compared to MP (AUROC 0.66 [0.60-0.72], p < 0.01). Mechanical power density may help identify patients at risk for PMV after double lung transplantation.

Mitochondrial Metabolism in the Spotlight: Maintaining Balanced RNAP III Activity Ensures Cellular Homeostasis.

RNA polymerase III (RNAP III) holoenzyme activity and the processing of its products have been linked to several metabolic dysfunctions in lower and higher eukaryotes. Alterations in the activity of RNAP III-driven synthesis of non-coding RNA cause extensive changes in glucose metabolism. Increased RNAP III activity in the S. cerevisiae maf1Δ strain is lethal when grown on a non-fermentable carbon source. This lethal phenotype is suppressed by reducing tRNA synthesis. Neither the cause of the lack of growth nor the underlying molecular mechanism have been deciphered, and this area has been awaiting scientific explanation for a decade. Our previous proteomics data suggested mitochondrial dysfunction in the strain. Using model mutant strains maf1Δ (with increased tRNA abundance) and rpc128-1007 (with reduced tRNA abundance), we collected data showing major changes in the TCA cycle metabolism of the mutants that explain the phenotypic observations. Based on 13C flux data and analysis of TCA enzyme activities, the present study identifies the flux constraints in the mitochondrial metabolic network. The lack of growth is associated with a decrease in TCA cycle activity and downregulation of the flux towards glutamate, aspartate and phosphoenolpyruvate (PEP), the metabolic intermediate feeding the gluconeogenic pathway. rpc128-1007, the strain that is unable to increase tRNA synthesis due to a mutation in the C128 subunit, has increased TCA cycle activity under non-fermentable conditions. To summarize, cells with non-optimal activity of RNAP III undergo substantial adaptation to a new metabolic state, which makes them vulnerable under specific growth conditions. Our results strongly suggest that balanced, non-coding RNA synthesis that is coupled to glucose signaling is a fundamental requirement to sustain a cell's intracellular homeostasis and flexibility under changing growth conditions. The presented results provide insight into the possible role of RNAP III in the mitochondrial metabolism of other cell types.

Towards interoperability in infection control: a standard data model for microbiology.

The COVID-19 pandemic has made it clear: sharing and exchanging data among research institutions is crucial in order to efficiently respond to global health threats. This can be facilitated by defining health data models based on interoperability standards. In Germany, a national effort is in progress to create common data models using international healthcare IT standards. In this context, collaborative work on a data set module for microbiology is of particular importance as the WHO has declared antimicrobial resistance one of the top global public health threats that humanity is facing. In this article, we describe how we developed a common model for microbiology data in an interdisciplinary collaborative effort and how we make use of the standard HL7 FHIR and terminologies such as SNOMED CT or LOINC to ensure syntactic and semantic interoperability. The use of international healthcare standards qualifies our data model to be adopted beyond the environment where it was first developed and used at an international level.

Integrated NMR and MS Analysis of the Plasma Metabolome Reveals Major Changes in One-Carbon, Lipid, and Amino Acid Metabolism in Severe and Fatal Cases of COVID-19.

Brazil has the second-highest COVID-19 death rate worldwide, and Rio de Janeiro is among the states with the highest rate in the country. Although vaccine coverage has been achieved, it is anticipated that COVID-19 will transition into an endemic disease. It is concerning that the molecular mechanisms underlying clinical evolution from mild to severe disease, as well as the mechanisms leading to long COVID-19, are not yet fully understood. NMR and MS-based metabolomics were used to identify metabolites associated with COVID-19 pathophysiology and disease outcome. Severe COVID-19 cases (n = 35) were enrolled in two reference centers in Rio de Janeiro within 72 h of ICU admission, alongside 12 non-infected control subjects. COVID-19 patients were grouped into survivors (n = 18) and non-survivors (n = 17). Choline-related metabolites, serine, glycine, and betaine, were reduced in severe COVID-19, indicating dysregulation in methyl donors. Non-survivors had higher levels of creatine/creatinine, 4-hydroxyproline, gluconic acid, and N-acetylserine, indicating liver and kidney dysfunction. Several changes were greater in women; thus, patients' sex should be considered in pandemic surveillance to achieve better disease stratification and improve outcomes. These metabolic alterations may be useful to monitor organ (dys) function and to understand the pathophysiology of acute and possibly post-acute COVID-19 syndromes.

Data suggested hospitalization as critical indicator of the severity of the COVID-19 pandemic, even at its early stages.

COVID-19 has been spreading widely since January 2020, prompting the implementation of non-pharmaceutical interventions and vaccinations to prevent overwhelming the healthcare system. Our study models four waves of the epidemic in Munich over two years using a deterministic, biology-based mathematical model of SEIR type that incorporates both non-pharmaceutical interventions and vaccinations. We analyzed incidence and hospitalization data from Munich hospitals and used a two-step approach to fit the model parameters: first, we modeled incidence without hospitalization, and then we extended the model to include hospitalization compartments using the previous estimates as a starting point. For the first two waves, changes in key parameters, such as contact reduction and increasing vaccinations, were enough to represent the data. For wave three, the introduction of vaccination compartments was essential. In wave four, reducing contacts and increasing vaccinations were critical parameters for controlling infections. The importance of hospitalization data was highlighted, as it should have been included as a crucial parameter from the outset, along with incidence, to avoid miscommunication with the public. The emergence of milder variants like Omicron and a significant proportion of vaccinated people has made this fact even more evident.

Intermittent Surface Oxygenation Results in Similar Mitochondrial Protection and Maintenance of Aerobic Metabolism as Compared to Continuous Oxygenation during Hypothermic Machine Kidney Machine Perfusion.

Short bubble and subsequent surface oxygenation is an innovative oxygenation technique and alternative for membrane oxygenation during hypothermic machine perfusion (HMP). The metabolic effect of the interruption of surface oxygenation for 4 h (mimicking organ transport) during HMP was compared to continuous surface and membrane oxygenation in a pig kidney ex situ preservation model. After 30 min of warm ischemia by vascular clamping, a kidney of a ±40 kg pig was procured and subsequently preserved according to one of the following groups: (1) 22-h HMP + intermittent surface oxygenation (n = 12); (2) 22-h HMP + continuous membrane oxygenation (n = 6); and (3) 22-h HMP + continuous surface oxygenation (n = 7). Brief perfusate O2 uploading before kidney perfusion was either obtained by direct bubble (groups 1, 3) or by membrane (group 2) oxygenation. Bubble oxygenation during minimum 15 min was as efficient as membrane oxygenation in achieving supraphysiological perfusate pO2 levels before kidney perfusion. Metabolic tissue analysis (i.e., lactate, succinate, ATP, NADH, and FMN) during and at the end of the preservation period demonstrated similar mitochondrial protection between all study groups. Short bubble and subsequent intermittent surface oxygenation of the perfusate of an HMP-kidney might be an effective and cheap preservation strategy to protect mitochondria, eliminating the need/costs of a membrane oxygenator and oxygen source during transport.

Global trends in BMS research using publication as a surrogate marker: A 30 year review.

Obesity is a global pandemic with increasing prevalence and long-term negative health outcomes. Bariatric metabolic surgery (BMS) is the most effective treatment option for achieving long-term weight loss. A systematic search was performed from 1990 to 2020 of BMS procedures using standardised groups. Data were collected on operation type reported, country and continent of publication. North America and Europe were the leading contributors to global publications in BMS, producing 41.3 % (n = 4931) and 37.1 % (n = 4436) of publications respectively, with increasing publications from Asia. Roux-en-Y Gastric Bypass (RYGB) and Sleeve Gastrectomy (SG) were the most studied procedure types with number of publications continuing to increase over time. A plateau and downward trend was seen for Laparoscopic Adjustable Gastric Band (LAGB) publication from 2015 to 2019. An increase in emerging/experimental techniques over the past decade is observed.

Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.

Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases. Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach. The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses.

Aerosol-into-liquid capture and detection of atmospheric soluble metals across the gas-liquid interface using Janus-membrane electrodes.

The soluble fraction of atmospheric transition metals is particularly associated with health effects such as reactive oxygen species compared to total metals. However, direct measurements of the soluble fraction are restricted to sampling and detection units in sequence burdened with a compromise between time resolution and system bulkiness. Here, we propose the concept of aerosol-into-liquid capture and detection, which allowed one-step particle capture and detection via the Janus-membrane electrode at the gas-liquid interface, enabling active enrichment and enhanced mass transport of metal ions. The integrated aerodynamic/electrochemical system was capable of capturing airborne particles with a cutoff size down to 50 nm and detecting Pb(II) with a limit of detection of 95.7 ng. The proposed concept can pave the way for cost-effective and miniaturized systems, for the capture and detection of airborne soluble metals in air quality monitoring, especially for abrupt air pollution events with high airborne metal concentrations (e.g., wildfires and fireworks).

A long-term study on structural changes in calcium aluminate silicate hydrates.

Production of blended cements in which Portland cement is combined with supplementary cementitious materials (SCM) is an effective strategy for reducing the CO2 emissions during cement manufacturing and achieving sustainable concrete production. However, the high Al2O3 and SiO2 contents of SCM change the chemical composition of the main hydration product, calcium aluminate silicate hydrate (C-A-S-H). Herein, spectroscopic and structural data for C-A-S-H gels are reported in a large range of equilibration times from 3 months up to 2 years and Al/Si molar ratios from 0.001 to 0.2. The 27Al MAS NMR spectroscopy and thermogravimetric analysis indicate that in addition to the C-A-S-H phase, secondary phases such as strätlingite, katoite, Al(OH)3 and calcium aluminate hydrate are present at Al/Si ≥ 0.03 limiting the uptake of Al in C-A-S-H. More secondary phases are present at higher Al concentrations; their content decreases with equilibration time while more Al is taken up in the C-A-S-H phase. At low Al contents, Al concentrations decrease strongly with time indicating a slow equilibration, in contrast to high Al contents where a clear change in Al concentrations over time was not observed indicating that the equilibrium has been reached faster. The 27Al NMR studies show that tetrahedrally coordinated Al is incorporated in C-A-S-H and its amount increases with the amount of Al present in the solution. Supplementary Information: The online version contains supplementary material available at 10.1617/s11527-022-02080-x.