A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has affected billions of people worldwide, and the lessons learned need to be concluded to get better prepared for the next pandemic. Early identification of high-risk patients is important for appropriate treatment and distribution of medical resources. A generalizable and easy-to-use COVID-19 severity stratification model is vital and may provide references for clinicians. Methods: Three COVID-19 cohorts (one discovery cohort and two validation cohorts) were included. Longitudinal peripheral blood mononuclear cells were collected from the discovery cohort (n = 39, mild = 15, critical = 24). The immune characteristics of COVID-19 and critical COVID-19 were analyzed by comparison with those of healthy volunteers (n = 16) and patients with mild COVID-19 using mass cytometry by time of flight (CyTOF). Subsequently, machine learning models were developed based on immune signatures and the most valuable laboratory parameters that performed well in distinguishing mild from critical cases. Finally, single-cell RNA sequencing data from a published study (n = 43) and electronic health records from a prospective cohort study (n = 840) were used to verify the role of crucial clinical laboratory and immune signature parameters in the stratification of COVID-19 severity. Results: Patients with COVID-19 were determined with disturbed glucose and tryptophan metabolism in two major innate immune clusters. Critical patients were further characterized by significant depletion of classical dendritic cells (cDCs), regulatory T cells (Tregs), and CD4+ central memory T cells (Tcm), along with increased systemic interleukin-6 (IL-6), interleukin-12 (IL-12), and lactate dehydrogenase (LDH). The machine learning models based on the level of cDCs and LDH showed great potential for predicting critical cases. The model performances in severity stratification were validated in two cohorts (AUC = 0.77 and 0.88, respectively) infected with different strains in different periods. The reference limits of cDCs and LDH as biomarkers for predicting critical COVID-19 were 1.2% and 270.5 U/L, respectively. Conclusion: Overall, we developed and validated a generalizable and easy-to-use COVID-19 severity stratification model using machine learning algorithms. The level of cDCs and LDH will assist clinicians in making quick decisions during future pandemics.

Abdominal physical examinations in early stages benefit critically ill patients without primary gastrointestinal diseases: a retrospective cohort study.

Background: Gastrointestinal (GI) function is critical for patients in intensive care units (ICUs). Whether and how much critically ill patients without GI primary diseases benefit from abdominal physical examinations remains unknown. No evidence from big data supports its possible additive value in outcome prediction. Methods: We performed a big data analysis to confirm the value of abdominal physical examinations in ICU patients without GI primary diseases. Patients were selected from the Medical Information Mart for Intensive Care (MIMIC)-IV database and classified into two groups depending on whether they received abdominal palpation and auscultation. The primary outcome was the 28-day mortality. Statistical approaches included Cox regression, propensity score matching, and inverse probability of treatment weighting. Then, the abdominal physical examination group was randomly divided into the training and testing cohorts in an 8:2 ratio. And patients with GI primary diseases were selected as the validation group. Several machine learning algorithms, including Random Forest, Gradient Boosting Decision Tree, Adaboost, Extra Trees, Bagging, and Multi-Layer Perceptron, were used to develop in-hospital mortality predictive models. Results: Abdominal physical examinations were performed in 868 (2.63%) of 33,007 patients without primary GI diseases. A significant benefit in terms of 28-day mortality was observed among the abdominal physical examination group (HR 0.75, 95% CI 0.56-0.99; p = 0.043), and a higher examination frequency was associated with improved outcomes (HR 0.62, 95%CI 0.40-0.98; p = 0.042). Machine learning studies further revealed that abdominal physical examinations were valuable in predicting in-hospital mortality. Considering both model performance and storage space, the Multi-Layer Perceptron model performed the best in predicting mortality (AUC = 0.9548 in the testing set and AUC = 0.9833 in the validation set). Conclusion: Conducting abdominal physical examinations improves outcomes in critically ill patients without GI primary diseases. The results can be used to predict in-hospital mortality using machine learning algorithms.

Divergent impacts of VPD and SWC on ecosystem carbon-water coupling under different dryness conditions.

Ecosystem water use efficiency (WUE) is an indicator of carbon-water interactions and is defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, it is currently unclear how WUE responds to atmospheric and soil drought events in terrestrial ecosystems with different dryness conditions. Additionally, the contributions of GPP and ET to the WUE response remain poorly understood. Based on measurements from 26 flux tower sites distributed worldwide, the binning method and random forest model were employed to separate the sensitivities of daily ecosystem WUE, GPP, and ET to vapor pressure deficit (VPD) and soil water content (SWC) under different dryness conditions (dryness index = potential evapotranspiration/precipitation, DI). Results showed that the sensitivity of WUE to VPD was negative at humid sites (DI < 1), while the sensitivity of WUE to SWC was positive at arid sites (DI > 2). Furthermore, the contribution of GPP to VPD-induced WUE variability was 63 % at humid sites, and the contribution of ET to SWC-induced WUE variability was 68 % when SWC was less than the 60th percentile at arid sites. Consequently, one increasing VPD-induced decrease in GPP was generally linked to a decrease in WUE at humid sites, and one drying soil moisture-caused decrease in ET was linked to a WUE increase under low SWC conditions at arid sites. Finally, VPD had a stronger effect on WUE than SWC when VPD was less than the 90th percentile or SWC was greater than the 50th percentile. Our findings underscore the importance of considering ecosystem dryness when investigating the impacts of VPD and SWC on ecosystem carbon-water coupling.

[A new target of precision medicine in sepsis: gut microbiome modified tryptophan metabolism].

Sepsis is a life-threatening organ dysfunction caused by dysregulated host responses to infection. Despite significant advances in anti-infective, immunomodulatory, and organ function support technologies, the precise and targeted management of sepsis remains a challenge due to its high heterogeneity. Studies have identified disturbed tryptophan (TRP) metabolism as a common mechanism in multiple diseases, which is involved in both immune regulation and the development of multi-organ damages. The rise of research on intestinal microflora has further highlighted the critical role of microflora-regulated TRP metabolism in pathogen-host interactions and the "cross-talk" among multi-organs, making it a potential key target for precision medicine in sepsis. This article reviews TRP metabolism, the regulation of TRP metabolism by the intestinal microflora, and the characteristics of TRP metabolism in sepsis, providing clues for further clinical targeting of TRP metabolism for precision medicine in sepsis.

Physiological and environmental control on ecosystem water use efficiency in response to drought across the northern hemisphere.

Drought, a natural hydrometeorological phenomenon, has been more frequent and more widespread due to climate change. Water availability strongly regulates the coupling (or trade-off) between carbon uptake via photosynthesis and water loss through transpiration, known as water-use efficiency (WUE). Understanding the effects of drought on WUE across different vegetation types and along the wet to dry gradient is paramount to achieving better understanding of ecosystem functioning in response to climate change. We explored the physiological and environmental control on ecosystem WUE in response to drought using observations for 44 eddy covariance flux sites in the Northern Hemisphere. We quantified the response of WUE to drought and the relative contributions of gross primary production (GPP) and evapotranspiration (ET) to the variations of WUE. We also examined the control of physiological and environmental factors on monthly WUE under different moisture conditions. Cropland had a peak WUE value under moderate drought conditions, while grassland, deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF) had peak WUE under slight drought conditions. WUE was mainly driven by GPP for cropland, grassland, DBF, and ENF but was mainly driven by ET for EBF. Vapor pressure deficit (VPD) and canopy conductance (Gc) were the most important factors regulating WUE. Moreover, WUE had negative responses to air temperature, precipitation, and VPD but had a positive response to Gc and ecosystem respiration. Our findings highlight the different effects of biotic and abiotic factors on WUE among different vegetation types and the important roles of VPD and Gc in controlling ecosystem WUE in response to drought.

Molecularly imprinted quartz crystal microbalance sensor based on poly(o-aminothiophenol) membrane and Au nanoparticles for ractopamine determination.

A molecularly imprinted quartz crystal microbalance (QCM) sensor for ractopamine (RAC) detection was developed by electrodepositing a poly-o-aminothiophenol membrane on an Au electrode surface modified by self-assembled Au nanoparticles (AuNPs). The modified electrodes were characterized by cyclic voltammetry, electrochemical impedance spectroscopy and scanning electron microscopy. This molecularly imprinted QCM sensor showed good frequency response in RAC binding measurements and the introduction of AuNPs demonstrated performance improvements. Frequency shifts were found to be proportional to concentration of RAC in the range of 2.5×10(-6) to 1.5×10(-4) mol L(-1) with a detection limit of 1.17×10(-6) mol L(-1) (S/N=3). The sensor showed a good selective affinity for RAC (selectivity coefficient >3) compared with similar molecules and good reproducibility and long-term stability. This research has combined the advantages of high specific surface area of AuNPs, high selectivity from molecularly imprinted electrodeposited membrane and high sensitivity from quartz crystal microgravimetry. In addition, the modified electrode sensor was successfully applied to determine RAC residues in spiked swine feed samples with satisfactory recoveries ranging from 87.7 to 95.2%.

Electrochemical sensor based on molecularly imprinted polymer film via sol-gel technology and multi-walled carbon nanotubes-chitosan functional layer for sensitive determination of quinoxaline-2-carboxylic acid.

Quinoxaline-2-carboxylic acid (QCA) is difficult to measure since only trace levels are present in commercial meat products. In this study, a rapid, sensitive and selective molecularly imprinted electrochemical sensor for QCA determination was successfully constructed by combination of a novel modified glassy carbon electrode (GCE) and differential pulse voltammetry (DPV). The GCE was fabricated via stepwise modification of multi-walled carbon nanotubes (MWNTs)-chitosan (CS) functional composite and a sol-gel molecularly imprinted polymer (MIP) film on the surface. MWNTs-CS composite was used to enhance the electron transfer rate and expand electrode surface area, and consequently amplify QCA reduction electrochemical response. The imprinted mechanism and experimental parameters affecting the performance of MIP film were discussed in detail. The resulting MIP/sol-gel/MWNTs-CS/GCE was characterized using various electrochemical methods involving cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and DPV. The sensor using MIP/sol-gel/MWNTs-CS/GCE as working electrode showed a linear current response to the target QCA concentration in the wide range from 2.0×10(-6) to 1.0×10(-3)molL(-1) with a low detection limit of 4.4×10(-7)molL(-1) (S/N=3). The established sensor with excellent reproductivity and stability was applied to evaluate commercial pork products. At five concentration levels, the recoveries and standard deviations were calculated as 93.5-98.6% and 1.7-3.3%, respectively, suggesting the proposed sensor is promising for the accurate quantification of QCA at trace levels in meat samples.