Continuous isomerisation of 2,5-dimethylfuran to 2,4-dimethylfuran over Ga-silicate.

2,4-dimethylfuran has a rare disubstitution pattern in the five-membered heterocyclic furan ring that is highly interesting chemically but challenging to access synthetically. We present a heterogeneously catalysed route to synthesise 2,4-dimethylfuran from commonly available 2,5-dimethylfuran using a zeolite packed-bed flow reactor. As supported by DFT calculations, the reaction occurs inside the zeolite channels, where the acid sites catalyse proton transfer followed by migration of a methyl group. The zeotype Ga-silicate (MFI type) appears superior to an aluminium-containing ZSM-5 by demonstrating higher selectivities and slower catalyst deactivation. This work provides new opportunities for the continuous valorisation of bio-feedstock molecules in the perspective of the emerging biorefinery era.

Evaluating equitable care in the ICU: Creating a causal inference framework to assess the impact of life-sustaining interventions across racial and ethnic groups.

Background: Variability in the provision of intensive care unit (ICU)-interventions may lead to disparities between socially defined racial-ethnic groups. Research Question: We used causal inference to examine the use of invasive mechanical ventilation (IMV), renal replacement therapy (RRT), and vasopressor agents (VP) to identify disparities in outcomes across race-ethnicity in patients with sepsis. Study Design and Methods: Single-center, academic referral hospital in Boston, Massachusetts, USA. Retrospective analysis of treatment effect with a targeted trial design categorized by treatment assignment within the first 24 hours in the MIMIC-IV dataset (2008- 2019) using targeted maximum likelihood estimation. Of 76,943 ICU stays in MIMIC-IV, 32,971 adult stays fulfilling sepsis-3 criteria were included. The primary outcome was in-hospital mortality. Secondary outcomes were hospital-free days, and occurrence of nosocomial infection stratified by predicted mortality probability ranges and self-reported race-ethnicity. Average treatment effects by treatment type and race-ethnicity, Racial-ethnic group (REG) or White group (WG), were estimated. Results: Of 19,419 admissions that met inclusion criteria, median age was 68 years, 57.4% were women, 82% were White, and mortality was 18.2%. There was no difference in mortality benefit associated with the administration of IMV, RRT, or VP between the REG and the WG. There was also no difference in hospital-free days or nosocomial infections. These findings are unchanged with different eligibility periods. Interpretation: There were no differences in the treatment outcomes from three life-sustaining interventions in the ICU according to race-ethnicity. While there was no discernable harm from the treatments across mortality risk, there was also no measurable benefit. These findings highlight the need for research to understand better the risk-benefit of life-sustaining interventions in the ICU.

Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.

BACKGROUND: Atrial Fibrillation (AF) is the most common arrhythmia in the intensive care unit (ICU) and is associated with increased morbidity and mortality. Identification of patients at risk for AF is not routinely performed as AF prediction models are almost solely developed for the general population or for particular ICU populations. However, early AF risk identification could help to take targeted preemptive actions and possibly reduce morbidity and mortality. Predictive models need to be validated across hospitals with different standards of care and convey their predictions in a clinically useful manner. Therefore, we designed AF risk models for ICU patients using uncertainty quantification to provide a risk score and evaluated them on multiple ICU datasets. METHODS: Three CatBoost models, utilizing feature windows comprising data 1.5-13.5, 6-18, or 12-24 hours before AF occurrence, were built using 2-repeat-10-fold cross-validation on AmsterdamUMCdb, the first freely available European ICU database. Furthermore, AF Patients were matched with no-AF patients for training. Transferability was validated using a direct and a recalibration evaluation on two independent external datasets, MIMIC-IV and GUH. The calibration of the predicted probability, used as an AF risk score, was measured using the Expected Calibration Error (ECE) and the presented Expected Signed Calibration Error (ESCE). Additionally, all models were evaluated across time during the ICU stay. RESULTS: The model performance reached Areas Under the Curve (AUCs) of 0.81 at internal validation. Direct external validation showed partial generalizability with AUCs reaching 0.77. However, recalibration resulted in performances matching or exceeding that of the internal validation. All models furthermore showed calibration capabilities demonstrating adequate risk prediction competence. CONCLUSION: Ultimately, recalibrating models reduces the challenge of generalization to unseen datasets. Moreover, utilizing the patient-matching methodology together with the assessment of uncertainty calibration can serve as a step toward the development of clinical AF prediction models.

Leveraging electronic health records for data science: common pitfalls and how to avoid them.

Analysis of electronic health records (EHRs) is an increasingly common approach for studying real-world patient data. Use of routinely collected data offers several advantages compared with other study designs, including reduced administrative costs, the ability to update analysis as practice patterns evolve, and larger sample sizes. Methodologically, EHR analysis is subject to distinct challenges because data are not collected for research purposes. In this Viewpoint, we elaborate on the importance of in-depth knowledge of clinical workflows and describe six potential pitfalls to be avoided when working with EHR data, drawing on examples from the literature and our experience. We propose solutions for prevention or mitigation of factors associated with each of these six pitfalls-sample selection bias, imprecise variable definitions, limitations to deployment, variable measurement frequency, subjective treatment allocation, and model overfitting. Ultimately, we hope that this Viewpoint will guide researchers to further improve the methodological robustness of EHR analysis.

Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists.

OBJECTIVE: As data science and artificial intelligence continue to rapidly gain traction, the publication of freely available ICU datasets has become invaluable to propel data-driven clinical research. In this guide for clinicians and researchers, we aim to: 1) systematically search and identify all publicly available adult clinical ICU datasets, 2) compare their characteristics, data quality, and richness and critically appraise their strengths and weaknesses, and 3) provide researchers with suggestions, which datasets are appropriate for answering their clinical question. DATA SOURCES: A systematic search was performed in Pubmed, ArXiv, MedRxiv, and BioRxiv. STUDY SELECTION: We selected all studies that reported on publicly available adult patient-level intensive care datasets. DATA EXTRACTION: A total of four publicly available, adult, critical care, patient-level databases were included (Amsterdam University Medical Center data base [AmsterdamUMCdb], eICU Collaborative Research Database eICU CRD], High time-resolution intensive care unit dataset [HiRID], and Medical Information Mart for Intensive Care-IV). Databases were compared using a priori defined categories, including demographics, patient characteristics, and data richness. The study protocol and search strategy were prospectively registered. DATA SYNTHESIS: Four ICU databases fulfilled all criteria for inclusion and were queried using SQL (PostgreSQL version 12; PostgreSQL Global Development Group) and analyzed using R (R Foundation for Statistical Computing, Vienna, Austria). The number of unique patient admissions varied between 23,106 (AmsterdamUMCdb) and 200,859 (eICU-CRD). Frequency of laboratory values and vital signs was highest in HiRID, for example, 5.2 (±3.4) lactate values per day and 29.7 (±10.2) systolic blood pressure values per hour. Treatment intensity varied with vasopressor and ventilatory support in 69.0% and 83.0% of patients in AmsterdamUMCdb versus 12.0% and 21.0% in eICU-CRD, respectively. ICU mortality ranged from 5.5% in eICU-CRD to 9.9% in AmsterdamUMCdb. CONCLUSIONS: We identified four publicly available adult clinical ICU datasets. Sample size, severity of illness, treatment intensity, and frequency of reported parameters differ markedly between the databases. This should guide clinicians and researchers which databases to best answer their clinical questions.

Multiplexed quantitative neuropeptidomics via DiLeu isobaric tagging.

Neuropeptides are key signaling molecules in many pathways and can serve as potential biomarkers or therapeutics. Mass spectrometry has emerged as a powerful tool for studying neuropeptides with high sensitivity and accuracy. Isobaric tagging can further enhance this method by improving throughput and reducing sampling needs. In this chapter, we discuss the benefits and limitations of using isobaric tags to analyze neuropeptides. Methods for optimizing the data acquisition are also presented to enable a greater number of neuropeptides to be identified and quantified when using isobaric tags, specifically N,N-dimethyl leucine (DiLeu).

Understanding critically ill sepsis patients with normal serum lactate levels: results from U.S. and European ICU cohorts.

While serum lactate level is a predictor of poor clinical outcomes among critically ill patients with sepsis, many have normal serum lactate. A better understanding of this discordance may help differentiate sepsis phenotypes and offer clues to sepsis pathophysiology. Three intensive care unit datasets were utilized. Adult sepsis patients in the highest quartile of illness severity scores were identified. Logistic regression, random forests, and partial least square models were built for each data set. Features differentiating patients with normal/high serum lactate on day 1 were reported. To exclude that differences between the groups were due to potential confounding by pre-resuscitation hyperlactatemia, the analyses were repeated for day 2. Of 4861 patients included, 47% had normal lactate levels. Patients with normal serum lactate levels had lower 28-day mortality rates than those with high lactate levels (17% versus 40%) despite comparable physiologic phenotypes. While performance varied between datasets, logistic regression consistently performed best (area under the receiver operator curve 87-99%). The variables most strongly associated with normal serum lactate were serum bicarbonate, chloride, and pulmonary disease, while serum sodium, AST and liver disease were associated with high serum lactate. Future studies should confirm these findings and establish the underlying pathophysiological mechanisms, thus disentangling association and causation.

On-Line Composition Analysis of Complex Hydrocarbon Streams by Time-Resolved Fourier Transform Infrared Spectroscopy and Ion-Molecule Reaction Mass Spectrometry.

On-line composition analysis of complex hydrocarbon mixtures is highly desirable to determine the composition of process streams and to study chemical reactions in heterogeneous catalysis. Here, we show how the combination of time-resolved Fourier transform infrared spectroscopy and ion-molecule-reaction mass spectrometry (IMR-MS) can be used for compositional analysis of processed plant biomass streams. The method is based on the biomass-derived model compound 2,5-dimethylfuran and its potential catalytic conversion to valuable green aromatics, for example, benzene, toluene, and xylenes (BTX) over zeolite ß. Numerous conversion products can be determined and quantified simultaneously in a temporal resolution of 4 min-1 without separation of individual compounds. The realization of this method enables us to study activity, selectivity, and changes in composition under transient reaction conditions. For example, increasing isomerization of 2,5-dimethylfuran to 2,4-dimethylfuran, 2-methyl-2-cyclopenten-1-one, and 2-methyl-2-cyclopenten-1-one is observed as the catalyst is exposed to the reactant, while BTX and olefin formation is decreasing.

Developing mass spectrometry for the quantitative analysis of neuropeptides.

INTRODUCTION: Neuropeptides are signaling molecules originating in the neuroendocrine system that can act as neurotransmitters and hormones in many biochemical processes. Their exact function is difficult to characterize, however, due to dependence on concentration, post-translational modifications, and the presence of other comodulating neuropeptides. Mass spectrometry enables sensitive, accurate, and global peptidomic analyses that can profile neuropeptide expression changes to understand their roles in many biological problems, such as neurodegenerative disorders and metabolic function. AREAS COVERED: We provide a brief overview of the fundamentals of neuropeptidomic research, limitations of existing methods, and recent progress in the field. This review is focused on developments in mass spectrometry and encompasses labeling strategies, post-translational modification analysis, mass spectrometry imaging, and integrated multi-omic workflows, with discussion emphasizing quantitative advancements. EXPERT OPINION: Neuropeptidomics is critical for future clinical research with impacts in biomarker discovery, receptor identification, and drug design. While advancements are being made to improve sensitivity and accuracy, there is still room for improvement. Better quantitative strategies are required for clinical analyses, and these methods also need to be amenable to mass spectrometry imaging, post-translational modification analysis, and multi-omics to facilitate understanding and future treatment of many diseases.

Fringe Projection Profilometry in Production Metrology: A Multi-Scale Comparison in Sheet-Bulk Metal Forming.

Fringe projection profilometry in combination with other optical measuring technologies has established itself over the last decades as an essential complement to conventional, tactile measuring devices. The non-contact, holistic reconstruction of complex geometries within fractions of a second in conjunction with the lightweight and transportable sensor design open up many fields of application in production metrology. Furthermore, triangulation-based measuring principles feature good scalability, which has led to 3D scanners for various scale ranges. Innovative and modern production processes, such as sheet-bulk metal forming, thus, utilize fringe projection profilometry in many respects to monitor the process, quantify possible wear and improve production technology. Therefore, it is essential to identify the appropriate 3D scanner for each application and to properly evaluate the acquired data. Through precise knowledge of the measurement volume and the relative uncertainty with respect to the specimen and scanner position, adapted measurement strategies and integrated production concepts can be realized. Although there are extensive industrial standards and guidelines for the quantification of sensor performance, evaluation and tolerancing is mainly global and can, therefore, neither provide assistance in the correct, application-specific positioning and alignment of the sensor nor reflect the local characteristics within the measuring volume. Therefore, this article compares fringe projection systems across various scale ranges by positioning and scanning a calibrated sphere in a high resolution grid.

Mass Spectrometric Profiling of Neuropeptides in Response to Copper Toxicity via Isobaric Tagging.

Copper is a necessary nutrient but quickly becomes toxic at elevated levels. To properly handle environmental copper influxes and maintain metal homeostasis, organisms utilize various methods to chelate, excrete, and metabolize heavy metals. These mechanisms are believed to involve complex signaling pathways mediated by neuropeptides. This study incorporates custom N,N-dimethyl leucine isobaric tags to characterize the neuropeptidomic changes after different time points (1, 2, and 4 h) of copper exposure in a model organism, blue crab, Callinectes sapidus. Using a modified simplex optimization strategy, the number of identifiable and quantifiable neuropeptides was increased 3-fold to facilitate a deeper understanding of the signaling pathways involved in responding to heavy metal exposure. The time course exposure showed many interesting findings, including upregulation of inhibitory allatostatin peptides in the pericardial organs. Additionally, there was evidence of transport of a pigment dispersing hormone from the sinus glands to the brain. Overall, this study improves the multiplexing capabilities of neuropeptidomic studies to understand the temporal changes associated with copper toxicity.

Acute delayed onset hemodynamic and respiratory compromise due to thoracic translocation of hemourinoma after percutaneous nephrolithotomy.

Percutaneous nephrolithotomy (PCNL) despite its minimally invasive approach has an high complication rate, with the most common complications being extravasation of urine and perioperative bleeding requiring transfusion. While most of these complications are minor, many serious and life-threatening complications do occur. One such complication is the development of hemothorax or hydrothorax which usually develops in the early postoperative period with blood or urine passing from the surgical site through a newly established pleural-peritoneal fistula. Here we describe an unusual presentation and clinical management of delayed onset acute urohemothorax and hemodynamic collapse several days following PCNL.

Temporal Study of the Perturbation of Crustacean Neuropeptides Due to Severe Hypoxia Using 4-Plex Reductive Dimethylation.

Hypoxia (i.e., low oxygen (O2) levels) is a common environmental challenge for several aquatic species, including fish and invertebrates. To survive or escape these conditions, these animals have developed novel biological mechanisms, some regulated by neuropeptides. By utilizing mass spectrometry (MS), this study aims to provide a global perspective of neuropeptides in the blue crab, Callinectes sapidus, and their changes over time (0, 1, 4, and 8 h) due to acute, severe hypoxia (∼10% O2 water saturation) stress using a 4-plex reductive dimethylation strategy to increase throughput. Using both electrospray ionization and matrix-assisted laser desorption/ionization (MALDI) MS, this study provides complementary coverage, allowing 88 neuropeptides to be identified. Interesting trends include (1) an overall decrease in neuropeptide expression due to hypoxia exposure, (2) a return to basal levels after 4 or 8 h of exposure following an initial response, (3) changes only after 4+ h exposure, and (4) an oscillating pattern. Overall, this study boosts the power of multiplexed quantitation to understand the large-scale changes due to severe hypoxia stress over time.

Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.

Patients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86-0.89) classify an individual patient's baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.

Improved Survival of Cancer Patients Admitted to the Intensive Care Unit between 2002 and 2011 at a U.S. Teaching Hospital.

PURPOSE: Cancer patients are at increased risk of treatment- or disease-related admission to the intensive care unit. Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that the number of cancer patients admitted to the intensive care unit (ICU) and survival have increased. MATERIALS AND METHODS: MIMIC-III was used to study trends and outcomes of cancer patients admitted to the ICU between 2002 and 2011. Multiple logistic regression analysis was performed to adjust for confounders of mortality. RESULTS: Among 41,468 patients analyzed, 1,083 were hemato-oncologic, 4,330 were oncologic and 66 patients had both a hematological and solid malignancy. Admission numbers more than doubled and the proportion of cancer patients in the ICU increased steadily from 2002 to 2011. In both the univariate and multivariate analyses, solid cancers and hematologic cancers were strongly associated with 28-day mortality. This association was even stronger for 1-year mortality, with odds ratios of 4.02 (95% confidence interval [CI], 3.69 to 4.38) and 2.25 (95% CI, 1.93 to 2.62), respectively. Over the 10-year study period, both 28-day and 1-year mortality decreased, with hematologic patients showing the strongest annual adjusted decrease in the odds of death. There was considerable heterogeneity among solid cancer types. CONCLUSION: Between 2002 and 2011, the number of cancer patients admitted to the ICU more than doubled, while clinical severity scores remained overall unchanged, suggesting improved treatment. Although cancer patients had higher mortality rates, both 28-day and 1-year mortality of hematologic patients decreased faster than that of non-cancer patients, while mortality rates of cancer patients strongly depended on cancer type.

Recent Advances and New Perspectives in Capillary Electrophoresis-Mass Spectrometry for Single Cell "Omics".

Accurate clinical therapeutics rely on understanding the metabolic responses of individual cells. However, the high level of heterogeneity between cells means that simply sampling from large populations of cells is not necessarily a reliable approximation of an individual cell's response. As a result, there have been numerous developments in the field of single-cell analysis to address this lack of knowledge. Many of these developments have focused on the coupling of capillary electrophoresis (CE), a separation technique with low sample consumption and high resolving power, and mass spectrometry (MS), a sensitive detection method for interrogating all ions in a sample in a single analysis. In recent years, there have been many notable advancements at each step of the single-cell CE-MS analysis workflow, including sampling, manipulation, separation, and MS analysis. In each of these areas, the combined improvements in analytical instrumentation and achievements of numerous researchers have served to drive the field forward to new frontiers. Consequently, notable biological discoveries have been made possible by the implementation of these methods. Although there is still room in the field for numerous further advances, researchers have effectively minimized various limitations in detection of analytes, and it is expected that there will be many more developments in the near future.

Neuroendocrine Key Regulator Gene Expression in Merkel Cell Carcinoma.

Merkel cell carcinoma (MCC) is a highly aggressive non-melanoma skin cancer of the elderly which is associated with the Merkel cell polyomavirus (MCPyV). MCC reveals a trilinear differentiation characterized by neuroendocrine, epithelial and pre/pro B-cell lymphocytic gene expression disguising the cellular origin of MCC. Here we investigated the expression of the neuroendocrine key regulators RE1 silencing transcription factor (REST), neurogenic differentiation 1 (NeuroD1) and the Achaete-scute homolog 1 (ASCL1) in MCC. All MCCs were devoid of REST and were positive for NeuroD1 expression. Only one MCC tissue revealed focal ASCL1 expression. This was confirmed in MCPyV-positive MCC cell lines. Of interest, MCPyV-negative cell lines did express REST. The introduction of REST expression in REST-negative, MCPyV-positive MCC cells downregulated the neuroendocrine gene expression. The lack of the neuroendocrine master regulator ASCL1 in almost all tested MCCs points to an important role of the absence of the negative regulator REST towards the MCC neuroendocrine phenotype. This is underlined by the expression of the REST-regulated microRNAs miR-9/9* in REST-negative MCC cell lines. These data might provide the basis for the understanding of neuroendocrine gene expression profile which is expected to help to elucidate the cellular origin of MCC.

Feature selection and prediction of treatment failure in tuberculosis.

BACKGROUND: Tuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control. OBJECTIVE: To identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure. METHODS: On a multi-country dataset managed by the National Institute of Allergy and Infectious Diseases we applied various machine learning techniques to identify factors statistically associated with treatment failure and to predict treatment failure based on baseline demographic and clinical characteristics alone. RESULTS: The complete-case analysis database consisted of 587 patients (68% males) with a median (p25-p75) age of 40 (30-51) years. Treatment failure occurred in approximately one fourth of the patients. The features most associated with treatment failure were patterns of drug sensitivity, imaging findings, findings in the microscopy Ziehl-Nielsen stain, education status, and employment status. The most predictive model was forward stepwise selection (AUC: 0.74), although most models performed at or above AUC 0.7. A sensitivity analysis using the 643 original patients filling the missing values with multiple imputation showed similar predictive features and generally increased predictive performance. CONCLUSION: Machine learning can help to identify patients at higher risk of treatment failure. Closer monitoring of these patients may decrease treatment failure rates and prevent emergence of antibiotic resistance. The use of inexpensive basic demographic and clinical features makes this approach attractive in low and middle-income countries.

Exploring the Nonconserved Sequence Space of Synthetic Expression Modules in Bacillus subtilis.

Increasing protein expression levels is a key step in the commercial production of enzymes. Predicting promoter activity and translation initiation efficiency based solely on consensus sequences have so far met with mixed results. Here, we addressed this challenge using a "brute-force" approach by designing and synthesizing a large combinatorial library comprising ∼12 000 unique synthetic expression modules (SEMs) for Bacillus subtilis. Using GFP fluorescence as a reporter of gene expression, we obtained a dynamic expression range that spanned 5 orders of magnitude, as well as a maximal 13-fold increase in expression compared with that of the already strong veg expression module. Analyses of the synthetic modules indicated that sequences at the 5'-end of the mRNA were the most important contributing factor to the differences in expression levels, presumably by preventing formation of strong secondary mRNA structures that affect translation initiation. When the gfp coding region was replaced by the coding region of the xynA gene, encoding the industrially relevant B. subtilis xylanase enzyme, only a 3-fold improvement in xylanase production was observed. Moreover, the correlation between GFP and xylanase expression levels was weak. This suggests that the differences in expression levels between the gfp and xynA constructs were due to differences in 5'-end mRNA folding and consequential differences in the rates of translation initiation. Our data show that the use of large libraries of SEMs, in combination with high-throughput technologies, is a powerful approach to improve the production of a specific protein, but that the outcome cannot necessarily be extrapolated to other proteins.