Long-Term Efficacy of an AI-Based Health Coaching Mobile App in Slowing the Progression of Non-Dialysis Dependent CKD: A Retrospective Cohort Study.

BACKGROUND: Chronic kidney disease (CKD) is a significant public health concern, with an escalating global prevalence ranging from 11% to 13%. Therefore, practical strategies for slowing CKD progression and improving patient outcomes are imperative. There is limited evidence to substantiate the efficacy of mobile app-based nursing systems for decelerating CKD progression. OBJECTIVE: This study aimed to evaluate the long-term efficacy of the KidneyOnline intelligent care system in slowing the progression of non-dialysis-dependent CKD. METHODS: In this retrospective study, the KidneyOnline app was utilized for CKD patients in China who were registered between January 2017 and April 2023. Patients were divided into two groups: an intervention group using the app's nurse-led, patient-oriented management system and a conventional care group that didn't use the app. Patients' uploaded health data were processed via deep learning optical character recognition, and the AI system provided personalized healthcare plans and interventions. Conversely, the conventional care group received suggestions from nephrologists during regular visits without AI assistance. Monitoring extended for an average duration of 2.1 years post-recruitment, with the study's objective being to assess the app's effectiveness in preserving kidney function. The primary outcome was the eGFR slope over the follow-up period, and secondary outcomes included changes in albumin-to-creatinine ratio (ACR) and mean arterial pressure (MAP). RESULTS: A total of 12297 eligible patients who registered on the KidneyOnline app from January 2017 to April 2023 were enrolled for the analysis. Among them, 808 patients were successfully matched using 1:1 propensity score matching, resulting in 404(50%) patients in the KidneyOnline care system group and another 404(50%) patients in the conventional care group. The eGFR slope in the KidneyOnline care group was significantly lower than that in the conventional care group (-1.3, 95% CI: -2.4, -0.1 mL/min/1.73 m2 per year vs. -2.8, 95% CI: -3.8, -1.9 mL/min/1.73 m2 per year，P=.009). Subgroup analysis revealed that the effect of the KidneyOnline care group was more significant in males, patients over 45 years old, and patients with worse baseline kidney function, higher blood pressure, and heavier proteinuria. After 3 and 6 months, the MAP in the KidneyOnline care group decreased to 85.6 (SD 9.2) and 83.6 (SD 10.5) mmHg, respectively, compared to 94.9 (SD 10.6) and 95.2 (SD 11.6) mmHg in the conventional care group (P<.001). The ACR in the KidneyOnline care group showed a more significant reduction after 3 and 6 months (736 mg/g vs. 980 mg/g and 572 mg/g vs. 840 mg/g, P=.074, .027)); however, there was no significant difference in ACR between the two groups at the end of the follow-up period (618 mg/g vs. 639 mg/g, P=.904). CONCLUSIONS: The utilization of KidneyOnline, an AI-based, nurse-led, patient-centered care system, may be beneficial in slowing the progression of non-dialysis-dependent CKD.

Clinical significance of glomerular IgM deposit in IgA nephropathy: a 5-year follow-up study.

The significance of glomerular IgM deposit intensity in IgA Nephropathy (IgAN) remained ambiguous and requires further research. Patients with biopsy-proven IgAN in our hospital from January 2018 to May 2023 were recruited into this retrospective single-center study. Patients who presented with positive IgM deposit were included in IgM + cohort while patients with negative IgM deposit were included in IgM- cohort. Of the IgM+, patients whose IF intensity of IgM deposits exceeded 1+ formed IgM-H cohort while patients whose IF intensity of IgM deposits was equal to 1+ consisted IgM-L cohort. Pairwise comparisons were performed among these cohorts to determine clinical disparities, following the propensity score matching process. Among 982 IgAN patients, 539 patients presented with positive IgM deposit. The Kaplan-Meier analysis showed that the IgM deposit did not contribute adversely to the outcomes (eGFR decreased from the baseline ≥ 50% continuously or reached end-stage renal disease). However, the Cox regression analysis showed that increased intensity of IgM deposit was an independent risk factor (p = 0.03) in IgM+. The IgM-H exhibited more pronounced segmental glomerulosclerosis (p = 0.02) than the IgM-L, which may also be associated more directly with higher urine protein levels (p = 0.02). Moreover, our generalized linear mixed model demonstrated a remarkably higher urine albumin/creatinine ratio (p < 0.01) and serum creatinine (p = 0.04) levels as well as lower serum albumin (p < 0.01) level in IgM-H persistently during the 5-year follow-up. This study concluded that increased intensity of glomerular IgM deposits may contribute adversely to clinicopathologic presentation and outcome in those IgM + patients.

Postoperative Bleeding in Children With Tonsillectomy: A Retrospective Cohort.

BACKGROUND: It is necessary to evaluate the risk factors of secondary bleeding after tonsillectomy in children, to provide reference for clinical children management and care. METHODS: Children who underwent tonsillectomy from January 1, 2018, to December 31, 2022, in our hospital were retrospectively included. The characteristics of children with and without secondary bleeding were analyzed. RESULTS: A total of 1068 children undergoing tonsillectomy were included, and the incidence of secondary bleeding children undergoing tonsillectomy was 4.87%. Age ≥ 9 years old (odds ratio [OR] = 2.609, 95% confidence interval [CI] = 2.112-3.437), type of surgery (OR = 1.764, 95% CI = 1.148-2.291), and degree of tonsil embedding (OR = 2.116, 95% CI = 1.805-2.644) were the risk factors of secondary bleeding after tonsillectomy in children (all P < .05). CONCLUSION: Medical staff should proactively monitor and address the identified risk factors by implementing timely warning systems and preventive strategies to reduce the postoperative bleeding following tonsillectomy.

Fractional exhaled nitric oxide, a potential biomarker for evaluating glucocorticoids treatment and prognosis in allergic bronchopulmonary aspergillosis.

BACKGROUND: Allergic bronchopulmonary aspergillosis (ABPA) is characterized by enhanced TH2 inflammatory response. Fractional exhaled nitric oxide (FeNO) measurement has been used as a valuable tool in predicting the development and management of asthma, another typical TH2 inflammation. However, the clinical significance of FeNO in ABPA remains unclear. OBJECTIVE: To investigate the association between FeNO and the prognosis of patients with ABPA to provide a basis for the use of FeNO in evaluating the efficacy of glucocorticoids in ABPA treatment. METHODS: This study comprised 2 parts; 58 patients were enrolled in the retrospective study. Clinical indexes in patients with different prognoses were compared, and receiver operating characteristic curve analysis was used to determine the threshold value. The prospective observational study involved 61 patients who were regularly followed up at 4 to 6 weeks and 6 months since the initial treatment. Patients were grouped on the basis of baseline FeNO values; correlation analysis was performed in the clinical data. RESULTS: Different prognoses were observed between patients with high and low baseline FeNO values, with a threshold value of 57 parts per billion. The percentage of Aspergillus fumigatus-specific IgE, percentage of positive A fumigatus-specific IgG, and relapse/exacerbation rate differed significantly between the high and low FeNO groups. Patients with higher FeNO needed longer treatment duration and showed shorter interval between glucocorticoid withdrawal and the next relapse/exacerbation. CONCLUSION: Our findings indicate that the level of FeNO is associated with the prognosis of ABPA. It can serve as an independent and valuable biomarker for evaluating the effectiveness of glucocorticoid treatment.

Th2-skewed peripheral T-helper cells drive B-cells in allergic bronchopulmonary aspergillosis.

INTRODUCTION: Patients with allergic bronchopulmonary aspergillosis (ABPA) suffer from repeated exacerbations. The involvement of T-cell subsets remains unclear. METHODS: We enrolled ABPA patients, asthma patients and healthy controls. T-helper type 1 (Th1), 2 (Th2) and 17 (Th17) cells, regulatory T-cells (Treg) and interleukin (IL)-21+CD4+T-cells in total or sorted subsets of peripheral blood mononuclear cells and ABPA bronchoalveolar lavage fluid (BALF) were analysed using flow cytometry. RNA sequencing of subsets of CD4+T-cells was done in exacerbated ABPA patients and healthy controls. Antibodies of T-/B-cell co-cultures in vitro were measured. RESULTS: ABPA patients had increased Th2 cells, similar numbers of Treg cells and decreased circulating Th1 and Th17 cells. IL-5+IL-13+IL-21+CD4+T-cells were rarely detected in healthy controls, but significantly elevated in the blood of ABPA patients, especially the exacerbated ones. We found that IL-5+IL-13+IL-21+CD4+T-cells were mainly peripheral T-helper (Tph) cells (PD-1+CXCR5-), which also presented in the BALF of ABPA patients. The proportions of circulating Tph cells were similar among ABPA patients, asthma patients and healthy controls, while IL-5+IL-13+IL-21+ Tph cells significantly increased in ABPA patients. Transcriptome data showed that Tph cells of ABPA patients were Th2-skewed and exhibited signatures of follicular T-helper cells. When co-cultured in vitro, Tph cells of ABPA patients induced the differentiation of autologous B-cells into plasmablasts and significantly enhanced the production of IgE. CONCLUSION: We identified a distinctly elevated population of circulating Th2-skewed Tph cells that induced the production of IgE in ABPA patients. It may be a biomarker and therapeutic target for ABPA.

Root endophyte-mediated alteration in plant H2O2 homeostasis regulates symbiosis outcome and reshapes the rhizosphere microbiota.

Endophytic symbioses between plants and fungi are a dominant feature of many terrestrial ecosystems, yet little is known about the signaling that defines these symbiotic associations. Hydrogen peroxide (H2O2) is recognized as a key signal mediating the plant adaptive response to both biotic and abiotic stresses. However, the role of H2O2 in plant-fungal symbiosis remains elusive. Using a combination of physiological analysis, plant and fungal deletion mutants, and comparative transcriptomics, we reported that various environmental conditions differentially affect the interaction between Arabidopsis and the root endophyte Phomopsis liquidambaris, and link this process to alterations in H2O2 levels and H2O2 fluxes across root tips. We found that enhanced H2O2 efflux leading to a moderate increase in H2O2 levels at the plant-fungal interface is required for maintaining plant-fungal symbiosis. Disturbance of plant H2O2 homeostasis compromises the symbiotic ability of plant roots. Moreover, the fungus-regulated H2O2 dynamics modulate the rhizosphere microbiome by selectively enriching for the phylum Cyanobacteria, with strong antioxidant defenses. Our results demonstrated that the regulation of H2O2 dynamics at the plant-fungal interface affects the symbiotic outcome in response to external conditions and highlight the importance of the root endophyte in reshaping the rhizosphere microbiota.

Unravelling the Impact of Metal Dopants and Oxygen Vacancies on Syngas Conversion over Oxides: A Machine Learning-Accelerated Study of CO Activation on Cr-Doped ZnO Surfaces.

As a critical component of the OX-ZEO composite catalysts toward syngas conversion, the Cr-doped ZnO ternary system can be considered as a model system for understanding oxide catalysts. However, due to the complexity of its structures, traditional approaches, both experimental and theoretical, encounter significant challenges. Herein, we employ machine learning-accelerated methods, including grand canonical Monte Carlo and genetic algorithm, to explore the ZnO(1010) surface with various Cr and oxygen vacancy (OV) concentrations. Stable surfaces with varied Cr and OV concentrations were then systematically investigated to examine their influence on the CO activation via density functional theory calculations. We observe that Cr tends to preferentially appear on the surface of ZnO(1010) rather than in its interior regions and Cr-doped structures incline to form rectangular islands along the [0001] direction at high Cr and OV concentrations. Additionally, detailed calculations of CO reactivity unveil an inverse relationship between the reaction barrier (Ea) for C-O bond dissociation and the Cr and OV concentrations, and a linear relationship is observed between OV formation energy and Ea for CO activation. Further analyses indicate that the C-O bond dissociation is much more favored when the adjacent OVs are geometrically aligned in the [1210] direction, and Cr is doped around the reactive sites. These findings provide a deeper insight into CO activation over the Cr-doped ZnO surface and offer valuable guidance for the rational design of effective catalysts for syngas conversion.

DEL-1, as an anti-neutrophil transepithelial migration molecule, inhibits airway neutrophilic inflammation in asthma.

BACKGROUND: Neutrophil migration into the airways is a key process in neutrophilic asthma. Developmental endothelial locus-1 (DEL-1), an extracellular matrix protein, is a neutrophil adhesion inhibitor that attenuates neutrophilic inflammation. METHODS: Levels of DEL-1 were measured in exhaled breath condensate (EBC) and serum in asthma patients by ELISA. DEL-1 modulation of neutrophil adhesion and transepithelial migration was examined in a co-culture model in vitro. The effects of DEL-1-adenoviral vector-mediated overexpression on ovalbumin/lipopolysaccharide (OVA/LPS)-induced neutrophilic asthma were studied in mice in vivo. RESULTS: DEL-1 was primarily expressed in human bronchial epithelial cells and was decreased in asthma patients. Serum DEL-1 concentrations were reduced in patients with severe asthma compared with normal subjects (567.1 ± 75.3 vs. 276.8 ± 29.36 pg/mL, p < .001) and were negatively correlated to blood neutrophils (r = -0.2881, p = .0384) and neutrophil-to-lymphocyte ratio (NLR) (r = -0.5469, p < .0001). DEL-1 concentrations in the EBC of severe asthmatic patients (113.2 ± 8.09 pg/mL) were also lower than normal subjects (193.0 ± 7.61 pg/mL, p < .001) and were positively correlated with the asthma control test (ACT) score (r = 0.3678, p = .0035) and negatively related to EBC IL-17 (r = -0.3756, p = .0131), myeloperoxidase (MPO) (r = -0.5967, p = .0055), and neutrophil elastase (NE) (r = -0.5488, p = .0009) expression in asthma patients. Neutrophil adhesion and transepithelial migration in asthma patients were associated with LFA-1 binding to ICAM-1 and inhibited by DEL-1. DEL-1 mRNA and protein expression in human bronchial epithelial cells were regulated by IL-17. Exogenous DEL-1 inhibited IL-17-enhanced neutrophil adhesion and migration. DEL-1 expression was decreased while neutrophil infiltration was increased in the airway of a murine model of neutrophilic asthma. This was prevented by DEL-1 overexpression. CONCLUSIONS: DEL-1 down-regulation leads to increased neutrophil migration across bronchial epithelial cells and is associated with neutrophilic airway inflammation in asthma.

DeePMD-kit v2: A software package for deep potential models.

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H2 Activation Using Machine Learning-Accelerated First-Principles Simulations.

Oxygen vacancies (OVs) play important roles on any oxide catalysts. In this work, using an investigation of the OV effects on ZnO(101̅0) for CO and H2 activation as an example, we demonstrate, via machine learning potentials (MLPs), genetic algorithm (GA)-based global optimization, and density functional theory (DFT) validations, that the ZnO(101̅0) surface with 0.33 ML OVs is the most likely surface configuration under experimental conditions (673 K and 2.5 MPa syngas (H2:CO = 1.5)). It is found that a surface reconstruction from the wurtzite structure to a body-centered-tetragonal one would occur in the presence of OVs. We show that the OVs create a Zn3 cluster site, allowing H2 homolysis and C-O bond cleavage to occur. Furthermore, the activity of intrinsic sites (Zn3c and O3c sites) is almost invariable, while the activity of the generated OV sites is strongly dependent on the concentration of the OVs. It is also found that OV distributions on the surface can considerably affect the reactions; the barrier of C-O bond dissociation is significantly reduced when the OVs are aligned along the [12̅10] direction. These findings may be general in the systems with metal oxides in heterogeneous catalysis and may have significant impacts on the field of catalyst design by regulating the concentration and distribution of the OVs.

The relationship between triglyceride, cholesterol and lipoprotein levels, and immune responses to hepatitis B vaccine.

Cholesterol homeostasis disorder and hypertriglyceridemia, as common metabolic conditions, have rarely been reported to affect the immune responses to the hepatitis B vaccine. Our study found that higher high-density lipoprotein (HDL) level showed a significant relationship with positive anti-HBs results (cOR = 1.479, 95% CI: 1.150, 1.901, p = 0.002; aOR = 1.304, 95% CI: 1.006, 1.691, p = 0.045), especially in individuals aged 18- to 40-year-old, female, smoking more than 100 cigarettes in life, and drinking more than 12 times every year. Lower low-density lipoprotein (LDL) level was associated with a negative anti-HBs result among participants aged 18- to 40-year-old, and participants who were obese. Higher level of HDL and lower level of LDL may be protective factors of better immune effect of hepatitis B vaccine. More research should be conducted to investigate the influence of the cholesterol level on the immune responses to the hepatitis B vaccine, and more in-depth research should be performed to uncover the mechanism.

Rheological and microstructural properties of polysaccharide obtained from the gelatinous Tremella fuciformis fungus.

The gelatinous feature of Tremella fuciformis polysaccharide (TFP) has attracted growing interest in its application as a thickening agent in the food industry. This study aims to reveal the microstructure and rheological properties of TFP. Results showed that TFP randomly distributed in aqueous solutions in an irregular worm-like morphology and formed a more extensive entangled network and stiffer chains at higher concentration solutions. The further rheological study indicated that the TFP solutions exhibited a shear-thinning behavior. Multiple results of dynamic oscillation tests confirmed the viscoelastic properties of TFP. Frequency sweep data display that TFP solutions exhibit solid-like behavior at high frequencies, showing the oscillatory behavior of entangled polymers. The temperature sweep demonstrated that the rheological behavior of TFP is thermally reversible. These results enriched the understanding of the rheology-microstructure relationship of TFP solution and were beneficial to expanding the application of TFP in food processing.

Achieving Theory-Experiment Parity for Activity and Selectivity in Heterogeneous Catalysis Using Microkinetic Modeling.

ConspectusMicrokinetic modeling based on density functional theory (DFT) energies plays an essential role in heterogeneous catalysis because it reveals the fundamental chemistry for catalytic reactions and bridges the microscopic understanding from theoretical calculations and experimental observations. Microkinetic modeling requires building a set of ordinary differential equations (ODEs) based on the calculation results of thermodynamic properties of adsorbates and kinetic parameters for the reaction elementary steps. Solving a microkinetic model can extract information on catalytic chemistry, including critical reaction intermediates, reaction pathways, the surface species distribution, activity, and selectivity, thus providing vital guidelines for altering catalysts.However, the quantitative reliability of traditional microkinetic models is often insufficient to conclusively extrapolate the mechanistic details of complex reaction systems. This can be attributed to several factors, the most important of which is the limitation of obtaining an accurate estimation of the energy inputs via traditional calculation methods. These limitations include the difficulty of using static DFT methods to calculate reaction energies of adsorption/desorption processes, often rate-controlling or selectivity-determining steps, and the inadequate consideration of surface coverage effects. In addition, the robust microkinetic software is rare, which also complicates the resolution of complex catalytic systems.In this Account, we review our recent works toward refining the predictions of microkinetic modeling in heterogeneous catalysis and achieving theory-experiment parity for activity and selectivity. First, we introduce CATKINAS, a microkinetic software developed in our group, and show how it disentangles the problem that traditional microkinetic software has and how it can now be applied to obtain kinetic results for more sophisticated reaction systems. Second, we describe a molecular dynamics method developed recently to obtain the free-energy changes for the adsorption/desorption process to fill in the missing energy inputs. Third, we show that a rigorous consideration of surface coverage effects is pivotal for building more realistic models and obtaining accurate kinetic results. Following a series of studies on acetylene hydrogenation reactions on Pd catalysts, we demonstrate how this new approach can provide an improved quantitative understanding of the mechanism, active site, and intrinsic structural sensitivity. Finally, we conclude with a brief outlook and the remaining challenges in this field.

Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials.

There is an increasing demand for free-energy calculations using ab initio molecular dynamics these days. Metadynamics (MetaD) is frequently utilized to reconstruct the free-energy surface, but it is often computationally intractable for the first-principles calculations. Machine learning potentials (MLPs) have become popular alternatives. However, the training could be a long and arduous process before using them in practical applications. To accelerate MetaD use with MLPs for the free-energy calculation in an easy manner, we propose the adaptive machine learning potential-accelerated metadynamics (AMLP-MetaD). In this method, the MLP in the form of a Gaussian approximation potential (GAP) can adapt itself based on its uncertainty estimation, which decides whether to accept the model prediction or recalculate it with a reference method (usually density functional theory) for further training during the MetaD simulation. We demonstrate that the free-energy landscape similar to the ab initio one can be obtained using AMLP-MetaD with a 10-time speedup. Moreover, the quality of the free-energy results can be deeply improved using Δ-MLP, which is the GAP-corrected density functional tight binding in our case. We exemplify this novel method with two model systems, CO adsorption on the Pt13 cluster and the Pt(111) surface, which are of vital importance in heterogeneous catalysis. The successful application in these two tests highlights that our proposed method can be used in both cluster and periodic systems and for up to two collective variables.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis.

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

The Entire Mitochondrial Genome of Macrophthalmus abbreviatus Reveals Insights into the Phylogeny and Gene Rearrangements of Brachyura.

Brachyuran crabs comprise the most species-rich clades among extant Decapoda and are divided into several major superfamilies. However, the phylogeny of Brachyuran remains controversial, comprehensive analysis of the overall phylogeny is still lacking. Complete mitochondrial genome (mitogenome) can indicate phylogenetic relationships, as well as useful information for gene rearrangement mechanisms and molecular evolution. In this study, we firstly sequenced and annotated the complete mitogenome of Macrophthalmus abbreviatus (Brachyura; Macrophthalmidae). The mitogenome length of M. abbreviatus is 16,322 bp, containing the entire set of 37 genes and a control region typically observed in Brachyuran mitogenomes. The genome composition of M. abbreviatus was highly A+T biased 76.3% showing positive AT-skew (0.033) and negative GC-skew (- 0.351). In M. abbreviatus mitogenome, most tRNA genes were folded into the clover-leaf secondary structure except trnH, trnS1 and trnC, which was similar to the other species in Macrophthalmidae. Phylogenetic analysis showed that all families form a monophyletic, and Varunidae and Macrophthalmidae clustered into a monophyletic clade as sister groups. Comparative analyses of rearrangement among Brachyura revealed that Varunidae (Grapsoidea) and Macrophthalmidae (Ocypodoidea) had the same gene order, which reinforced the result of phylogeny. The combined results of two aspects revealed that the polyphyly of Ocypodoidea and Grapsoidea were well supported. In general, the results obtained in this research will contribute to further studies on molecular based for the classification and gene rearrangements of Macrophthalmidae or even Brachyura.

Applicability of Equilibrium Sampling in Informing Tissue Residues and Dietary Risks of Legacy and Current-Use Organic Chemicals in Aquaculture.

Equilibrium sampling based on silicone polydimethylsiloxane (PDMS) has been used to determine the concentrations of freely dissolved hydrophobic organic compounds (HOCs) and assess the thermodynamic potentials for bioaccumulation of these compounds in the aquatic environment. This allows the use of PDMS-based sampling techniques in assisting conventional sampling and extraction methods for the determination of the concentrations of HOCs in aquaculture products. The present study is an ex situ demonstration of how well PDMS can inform the tissue residues and dietary risks of legacy or current-use organic chemicals in aquaculture species from farm ponds in eastern China. For legacy contaminants such as polybrominated diphenyl ethers (PBDEs, n = 10), good agreement between the predicted concentrations based on PDMS and the measured lipid-normalized concentrations was observed for 60% of the studied biota, including both pelagic and benthic species. For pesticides currently used, such as pyrethroid (PE) (n = 4) and organophosphate pesticides (OPPs, n = 7), the measured tissue residues were consistently higher than those predicted by PDMS, possibly caused by the continuous input from the surroundings. For the organochlorine pesticides (OCPs, n = 5), the only detected chemical was also underestimated. Adjusted by ingestion rates of aquaculture products and toxicology data, the target hazard quotients of these chemicals predicted from PDMS were generally comparable to those derived from measured concentrations in tissue because of the predominance of PBDEs. Overall, PDMS-based equilibrium sampling offered an alternative approach for the prediction of tissue residues and dietary risks of PBDEs. Moreover, it should be applied with caution for PEs, OPPs, and OCPs. Improving the application of PDMS for these chemicals in farm ponds warrants future study. Environ Toxicol Chem 2021;40:79-87. © 2020 SETAC.

Epithelial expression and role of secreted STC1 on asthma airway hyperresponsiveness through calcium channel modulation.

BACKGROUND: Asthma is characterized by airway hyperresponsiveness (AHR), inflammation, and airway remodeling. Airway hyperresponsiveness results from enhanced airway smooth muscle (ASM) contraction potentially under the control of an epithelium-derived relaxing factor (EpDRF). However, relatively rare is known about EpDRF. We aimed to elucidate the role of epithelium-derived stanniocalcin-1 (STC1) on AHR and ASM contraction. METHODS: Stanniocalcin-1 levels in the serum of asthmatic patients and healthy volunteers and in bronchoalveolar lavage fluid (BALF) from ovalbumin (OVA)-challenged mice were measured by ELISA. The effects of exogenous STC1 on AHR and on inflammation were examined in mice. IL-13 modulation of STC1 mRNA and protein levels was studied in human bronchial epithelial cell lines (16HBE). The function of STC1 on Ca2+ influx and ASM contraction was examined ex vivo. RESULTS: Serum STC1 was decreased in asthma (n = 93) compared with healthy volunteers (1071 ± 30.4 vs 1414 ± 75.1 pg/ml, p < 0.0001, n = 23) and correlated with asthma control (p = 0.0270), lung function (FEV1, p = 0.0130), and serum IL-13 levels (p = 0.0009). Treatment of ten asthmatic subjects with inhaled corticosteroids/long-acting beta2-agonists (ICS/LABA) for 1 year enhanced STC1 expression which correlated with improved asthma control (p = 0.022). STC1 was mainly expressed in bronchial epithelium and intranasal administration of recombinant human STC1 (rhSTC1) reduced AHR and inflammation in mice. IL-13 suppressed STC1 release from 16HBE, whereas rhSTC1 blocked store-operated Ca2+ entry (SOCE) by suppressing stromal interaction molecule 1 (STIM1) and further inhibited ASM cell contractility by suppressing Ca2+ -dependent myosin light chain (MLC) phosphorylation. CONCLUSION: Our data indicate that STC1 deficiency in asthmatic airways promotes STIM1 hyperactivity, enhanced ASM contraction, and AHR. STC1 may be a candidate EpDRF.

An approach to calculate the free energy changes of surface reactions using free energy decomposition on ab initio brute-force molecular dynamics trajectories.

To understand the mechanisms and kinetics of catalytic reactions in heterogeneous catalysis, ab initio molecular dynamics is one of the powerful methods used to explore the free energy surface (FES) of surface elementary steps. The most significant aspect of performing such calculations is to choose the specific collective variable (CV) of the reaction. Here, we take CO oxidation on Pt(111) at 300 K as an example to demonstrate the protocol of selecting CVs guided by the free energy decomposition which quantifies individual bond free energy contributions. The basic concept is to conduct the brute-force molecular dynamics initiated from the transition state on the FES, which is refined from the one on the potential energy surface, to generate the reaction path at a finite temperature. The validity of this reaction path is further demonstrated by a 2-D free energy landscape spanned by the path-CV. By choosing CVs including other bond distances, we find that CO oxidation cannot be well understood by umbrella sampling or constrained molecular dynamics (CMD) solely along the OC-O bond distance. The free energy decomposition analysis suggests that not only the OC-O bond but also two O-Pt bonds are responsible for the free energy change. The further CMD simulations along selected CVs based on the insights from our protocol capture different reaction stages and give solid estimations of free energy barriers.

Ezrin, a Membrane Cytoskeleton Cross-Linker Protein, as a Marker of Epithelial Damage in Asthma.

RATIONALE: Bronchial epithelial cell damage occurs in patients with bronchial asthma. Ezrin, a membrane-cytoskeleton protein, maintains cellular morphology and intercellular adhesion and protects the barrier function of epithelial cells. OBJECTIVES: To study the role of ezrin in bronchial epithelial cells injury and correlate its expression with asthma severity. METHODS: Levels of ezrin were measured in exhaled breath condensate (EBC) and serum in patients with asthma and BAL fluid (BALF) from a mouse model of asthma by ELISA. The regulation of IL-13 on ezrin protein levels was studied in primary bronchial epithelial cells. Ezrin knockdown using shRNA was studied in human bronchial epithelial 16HBE cells. MEASUREMENTS AND MAIN RESULTS: Ezrin levels were decreased in asthmatic EBC (92.7 ± 34.99 vs. 150.5 ± 10.22 pg/ml, P < 0.0001) and serum (700.7 ± 55.59 vs. 279.2 ± 25.83 pg/ml, P < 0.0001) compared with normal subjects. Levels were much lower in uncontrolled (P < 0.001) and partly controlled patients (P < 0.01) compared with well-controlled subjects. EBC and serum ezrin levels correlated with lung function in patients with asthma and serum ezrin levels were negatively correlated with serum IL-13 and periostin. IL-13-induced downregulation of ezrin expression in primary bronchial epithelial cells was significantly attenuated by the Janus tyrosine kinase 2 inhibitor, TG101348. Ezrin knockdown changed 16HBE cell morphology, enlarged intercellular spaces, and increased their permeability. Ezrin expression was decreased in the lung tissue and BALF of "asthmatic" mice and negatively correlated with BALF IL-13 level. CONCLUSIONS: Ezrin downregulation is associated with IL-13-induced epithelial damage and might be a potential biomarker of asthma control.