The role of macrophage ion channels in the progression of atherosclerosis

Atherosclerosis is a complex inflammatory disease that affects the arteries and can lead to severe complications such as heart attack and stroke. Macrophages, a type of immune cell, play a crucial role in atherosclerosis initiation and progression. Emerging studies revealed that ion channels regulate macrophage activation, polarization, phagocytosis, and cytokine secretion. Moreover, macrophage ion channel dysfunction is implicated in macrophage-derived foam cell formation and atherogenesis. In this context, exploring the regulatory role of ion channels in macrophage function and their impacts on the progression of atherosclerosis emerges as a promising avenue for research. Studies in the field will provide insights into novel therapeutic targets for the treatment of atherosclerosis.

Inflammatory risk stratification individualizes anti-inflammatory pharmacotherapy for acute type A aortic dissection

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016-2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%-41.6%; RR 1.196 [0.698-2.049] for the predicted risk of <23.5%; RR 0.922 [95% CI 0.816-1.042] for the predicted risk of >41.6%). By using artificial intelligence to define an individual's benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.

Insight of a lipid metabolism prognostic model to identify immune landscape and potential target for retroperitoneal liposarcoma

Introduction

The exploration of lipid metabolism dysregulation may provide novel perspectives for retroperitoneal liposarcoma (RPLS). In our study, we aimed to investigate potential targets and facilitate further understanding of immune landscape in RPLS, through lipid metabolism-associated genes (LMAGs) based prognostic model.

Methods

Gene expression profiles and corresponding clinical information of 234 cases were enrolled from two public databases and the largest retroperitoneal tumor research center of East China, including cohort-TCGA (n=58), cohort-GSE30929 (n=92), cohort-FD (n=50), cohort-scRNA-seq (n=4) and cohort-validation (n=30). Consensus clustering analysis was performed to identify lipid metabolism-associated molecular subtypes (LMSs). A prognostic risk model containing 13 LMAGs was established using LASSO algorithm and multivariate Cox analysis in cohort-TCGA. ESTIMATE, CIBERSORT, XCELL and MCP analyses were performed to visualize the immune landscape. WGCNA was used to identify three hub genes among the 13 model LMAGs, and preliminarily validated in both cohort-GSE30929 and cohort-FD. Moreover, TIMER was used to visualize the correlation between antigen-presenting cells and potential targets. Finally, single-cell RNA-sequencing (scRNA-seq) analysis of four RPLS and multiplexed immunohistochemistry (mIHC) were performed in cohort-validation to validate the discoveries of bioinformatics analysis.

Results

LMS1 and LMS2 were characterized as immune-infiltrated and -excluded tumors, with significant differences in molecular features and clinical prognosis, respectively. Elongation of very long chain fatty acids protein 2 (ELOVL2), the enzyme that catalyzed the elongation of long chain fatty acids, involved in the maintenance of lipid metabolism and cellular homeostasis in normal cells, was identified and negatively correlated with antigen-presenting cells and identified as a potential target in RPLS. Furthermore, ELOVL2 was enriched in LMS2 with significantly lower immunoscore and unfavorable prognosis. Finally, a high-resolution dissection through scRNA-seq was performed in four RPLS, revealing the entire tumor ecosystem and validated previous findings.

Discussion

The LMS subgroups and risk model based on LMAGs proposed in our study were both promising prognostic classifications for RPLS. ELOVL2 is a potential target linking lipid metabolism to immune regulations against RPLS, specifically for patients with LMS2 tumors.

LS-MolGen: Ligand-and-Structure Dual-Driven Deep Reinforcement Learning for Target-Specific Molecular Generation Improves Binding Affinity and Novelty

Deep learning-based molecular generative models have garnered considerable interest in the field of de novo drug design. However, most extant models focus on either ligand-based or structure-based strategies, thereby failing to effectively harness the combined knowledge derived from both ligands and the structure of the binding target. In this article, we introduce LS-MolGen, a novel ligand and structure-integrated molecular generative model. This model synergistically combines representation learning, transfer learning, and reinforcement learning. The targeted knowledge assimilation from transfer learning, coupled with an advanced exploration strategy in reinforcement learning, empowers LS-MolGen to efficiently generate novel and high-affinity molecules efficiently. The comparable performance of our model is affirmed through multiple evaluations, including EGFR, DRD3, CDK2, AA2AR, ADRB2, and a dedicated case study of inhibitor design for SARS-CoV-2 Mpro. The results indicate that LS-MolGen performs better than other ligand-based or structure-based generative models in de novo designing promising compounds with novel scaffolds and high binding affinity. This proof-of-concept study signifies the potential of our ligand- and structure-based generative model, LS-MolGen, as a promising new tool for target-specific molecular generation and drug design.

Statistical Mechanical Model of the Giant Electrocaloric Effect in Ferroelectric Polymers

The development of highly efficient cooling technologies has been identified as a key strategy to address the mitigation of global warming. Especially, electrocaloric materials have emerged as promising candidates for cooling applications, owing to their potential to provide high cooling capacity with low energy consumption. To advance the development of electrocaloric materials with a significant electrocaloric effect (ECE), a thorough understanding of the underlying mechanisms is required. Previous studies have estimated the maximum ECE temperature change by calculating the entropy change between two assumed states of a dipole model, assuming polarization saturation with a sufficiently large electric field. However, it is more relevant to assess the ECE under continuously changing electric fields as this is more reflective of real-world conditions. To this end, we establish a continuous transition between the complete disorder state and the polarization saturation state using the partition function to derive the entropy change. Our results demonstrate excellent agreement with experimental data, and our analysis of energy items within the partition function attributes the increase in the ECE entropy change with decreasing crystal size to interfacial effects. This statistical mechanical model reveals the in-depth ferroelectric polymers producing the ECE and offers significant potential for predicting the ECE in ferroelectric polymers and thus guides the design of high-performance ECE materials.

Effects of Alzheimer's disease and related dementias on dental care usage and economic burden in older adults: a cross-sectional study

AIM

Distinct subtypes of Alzheimer's disease (AD) and related dementias (RD) might have different effects on dental care usage and economic burden. To determine the effects of AD and RD on specific types of dental care usage (preventive and treatment visits) and dental care costs from different payers (total and out-of-pocket costs).

METHODS

A cross-sectional study was conducted using the Medicare Current Beneficiary Survey in 2016. This study identified 4268 community dwelling older adults with and without Alzheimer's disease and related dementias (ADRD) from a nationally representative sample of Medicare beneficiaries. Dental care usage and costs are based on self-reported data. Preventive dental events included preventive and diagnosis events. Treatment dental events included restorative, oral surgery and other events.

RESULTS

This study identified 4268 (weighted N=30 423 885) older adults, including 94.48% without ADRD, 1.90% with AD and 3.63% with RD. Compared with older adults without ADRD, those with AD had similar dental care usage, but those with RD were 38% less likely to have treatment visit (OR: 0.62; 95% CI: 0.41 to 0.94) and had a 40% reduced number of total treatment visits (incidence rate ratio: 0.60; 95% CI: 0.37 to 0.98). RD was not associated with dental care costs, but AD was associated with higher total costs (β: 1.08; 95% CI: 0.14 to 2.01) and higher out-of-pocket costs (β: 1.25; 95% CI: 0.17 to 2.32).

CONCLUSIONS

Patients with ADRD were more likely to have adverse dental care outcomes. Specifically, RD was associated with lower treatment dental care usage and AD was associated with higher total and out-of-pocket dental care costs. Effective patient-centred strategies should be used to improve dental care outcomes in patients with distinct subtypes of ADRD.

The relationship between false-lumen area ratio and renal replacement therapy after acute aortic dissection repair on bilateral artery cannulation: a cross-sectional study

Background

Malperfusion poses a serious challenge to organ function in the repair of acute aortic dissection (AAD). The purpose of this study was to explore the segment change of false-lumen area ratio (FLAR; the ratio of the maximal false-lumen area to the total lumen area) in the descending aorta and its relationship with renal replacement therapy (RRT) after total aortic arch (TAA) surgery.

Methods

A total of 228 patients with AAD who received TAA using perfusion mode right axillary and femur artery cannulation between March 2013 and March 2022 were included in a cross-sectional study. The descending aorta was divided into 3 segments: the descending thoracic aorta (S1), the abdominal aorta above the ostium of the renal artery (S2), and the abdominal aorta between the ostium of the renal artery and the iliac bifurcation (S3). The primary outcomes were postoperative segmental FLAR changes in the descending aorta, which were observed using computed tomography angiography before the patients were discharged from the hospital. The secondary outcomes were RRT and 30-day mortality.

Results

The total potencies in the false lumen were 71.1%, 95.2%, and 88.2% in S1, S2, and S3. The postoperative/preoperative ratio of the FLAR was higher in S2 than that in S1 and S3 (S1: 67%±14%; S2: 80%±8%; S3: 57%±12%; all P values <0.001). For the patients undergoing RRT, there was a higher postoperative/preoperative ratio of the FLAR for the S2 segment (85%±7% vs. 79%±8%; P<0.001) and higher mortality (28.9% vs. 7.7%; P<0.001) after AAD repair compared with patients in the no-RRT group.

Conclusions

This study demonstrated there was less attenuation of the FLAR on the abdominal aorta above the ostium of the renal artery in the whole descending aorta after AAD repair with intraoperative right axillary and femur artery perfusion mode. The patients needing RRT were associated with less postoperative/preoperative change of the FLAR and worse clinical outcomes.

Observation of structural switch in nascent SAM-VI riboswitch during transcription at single-nucleotide and single-molecule resolution

Growing RNAs fold differently as they are transcribed, which modulates their finally adopted structures. Riboswitches regulate gene expression by structural change, which are sensitive to co-transcriptionally structural biology. Here we develop a strategy to track the structural change of RNAs during transcription at single-nucleotide and single-molecule resolution and use it to monitor individual transcripts of the SAM-VI riboswitch (riboSAM) as transcription proceeds, observing co-existence of five states in riboSAM. We report a bifurcated helix in one newly identified state from NMR and single-molecule FRET (smFRET) results, and its presence directs the translation inhibition in our cellular translation experiments. A model is proposed to illustrate the distinct switch patterns and gene-regulatory outcome of riboSAM when SAM is present or absent. Our strategy enables the precise mapping of RNAs' conformational landscape during transcription, and may combine with detection methods other than smFRET for structural studies of RNAs in general.

AcrNET: Predicting anti-CRISPR with Deep Learning

MOTIVATION

As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e., CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance.

RESULTS

On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly.

AVAILABILITY AND IMPLEMENTATION

Web server: https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/. Training code and pre-trained model are available at https://github.com/banma12956/AcrNET.

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Supplementary Information.

Norepinephrine combined with phenylephrine versus norepinephrine in patients with septic shock: a retrospective cohort study

BACKGROUND

Phenylephrine (PE) and norepinephrine (NE) may be used to maintain adequate blood pressure and tissue perfusion in patients with septic shock, but the effect of NE combined with PE (NE-PE) on mortality remains unclear. We hypothesized that NE-PE would not inferior to NE alone for all-cause hospital mortality in patients with septic shock.

METHODS

This single-center, retrospective cohort study included adult patients with septic shock. According to the infusion type, patients were divided into the NE-PE or NE group. Multivariate logistic regression, propensity score matching and doubly robust estimation were used to analyze the differences between groups. The primary outcome was the all-cause hospital mortality rate after NE-PE or NE infusion.

RESULTS

Among 1, 747 included patients, 1, 055 received NE and 692 received NE-PE. For the primary outcome, the hospital mortality rate was higher in patients who received NE-PE than in those who received NE (49.7% vs. 34.5%, p < 0.001), and NE-PE was independently associated with higher hospital mortality (odds ratio = 1.76, 95% confidence interval = 1.36-2.28, p < 0.001). Regarding secondary outcomes, patients in the NE-PE group had longer lengths of stay in ICU and hospitals. Patients in the NE-PE group also received mechanical ventilation for longer durations.

CONCLUSIONS

NE combined with PE was inferior to NE alone in patients with septic shock, and it was associated with a higher hospital mortality rate.

Thymidine Phosphorylase Deficiency or Inhibition Preserves Cardiac Function in Mice With Acute Myocardial Infarction

Background Ischemic cardiovascular disease is the leading cause of death worldwide. Current pharmacologic therapy has multiple limitations, and patients remain symptomatic despite maximal medical therapies. Deficiency or inhibition of thymidine phosphorylase (TYMP) in mice reduces thrombosis, suggesting that TYMP could be a novel therapeutic target for patients with acute myocardial infarction (AMI). Methods and Results A mouse AMI model was established by ligation of the left anterior descending coronary artery in C57BL/6J wild-type and TYMP-deficient (Tymp-/-) mice. Cardiac function was monitored by echocardiography or Langendorff assay. TYMP-deficient hearts had lower baseline contractility. However, cardiac function, systolic left ventricle anterior wall thickness, and diastolic wall strain were significantly greater 4 weeks after AMI compared with wild-type hearts. TYMP deficiency reduced microthrombus formation after AMI. TYMP deficiency did not affect angiogenesis in either normal or infarcted myocardium but increased arteriogenesis post-AMI. TYMP deficiency enhanced the mobilization of bone marrow stem cells and promoted mesenchymal stem cell (MSC) proliferation, migration, and resistance to inflammation and hypoxia. TYMP deficiency increased the number of larger MSCs and decreased matrix metalloproteinase-2 expression, resulting in a high homing capability. TYMP deficiency induced constitutive AKT phosphorylation in MSCs but reduced expression of genes associated with retinoid-interferon-induced mortality-19, a molecule that enhances cell death. Inhibition of TYMP with its selective inhibitor, tipiracil, phenocopied TYMP deficiency, improved post-AMI cardiac function and systolic left ventricle anterior wall thickness, attenuated diastolic stiffness, and reduced infarct size. Conclusions This study demonstrated that TYMP plays an adverse role after AMI. Targeting TYMP may be a novel therapy for patients with AMI.

Sensitive Room-Temperature Graphene Photothermoelectric Terahertz Detector Based on Asymmetric Antenna Coupling Structure

A highly sensitive room-temperature graphene photothermoelectric terahertz detector, with an efficient optical coupling structure of asymmetric logarithmic antenna, was fabricated by planar micro-nano processing technology and two-dimensional material transfer techniques. The designed logarithmic antenna acts as an optical coupling structure to effectively localize the incident terahertz waves at the source end, thus forming a temperature gradient in the device channel and inducing the thermoelectric terahertz response. At zero bias, the device has a high photoresponsivity of 1.54 A/W, a noise equivalent power of 19.8 pW/Hz^1/2, and a response time of 900 ns at 105 GHz. Through qualitative analysis of the response mechanism of graphene PTE devices, we find that the electrode-induced doping of graphene channel near the metal-graphene contacts play a key role in the terahertz PTE response. This work provides an effective way to realize high sensitivity terahertz detectors at room temperature.

Creatinase: Using Increased Entropy to Improve the Activity and Thermostability

Improving protein thermostability in mutagenesis-based enzyme engineering was often achieved by enhancing interresidue interactions via mutation to increase the enthalpy penalty of unfolding. However, this approach may trade off the functional activity due to the loss of structural flexibility of the biomolecule. Here, by performing X-ray crystallography, enzymatic kinetic experiments, neutron scattering, and thermodynamical measurements, we compared the structures, catalytic behaviors, dynamics, and thermostability between a wild-type creatinase and its four-point mutant. We found that the mutant is an entropy-driven thermostable protein with higher structural flexibility, i.e., higher conformational entropy, in the folded state compared to the wild type. The increased conformational entropy of the mutant in the folded state can reduce the entropy gain during unfolding and thus renders it greater thermostability. Moreover, the increased structural flexibility, particularly around the catalytic site, can broaden the mutant's working temperature range and considerably improve its activity at ambient conditions, which is crucial for its application in diagnosing kidney diseases. Complementary all-atom molecular dynamics simulations indicated that the four mutations replaced several of the strong interresidue interactions (electrostatic interactions and hydrogen bonds) with weak hydrophobic interactions. These substitutions not only release the structural flexibility to promote the thermostability and enzymatic activity of the protein but they also preserve the protein structure from collapsing. Our findings may pave a route for the entropy-driven strategy to design proteins with high thermostability and activity.

Orthogonal Optimization, Characterization, and In Vitro Anticancer Activity Evaluation of a Hydrogen Peroxide-Responsive and Oxygen-Reserving Nanoemulsion for Hypoxic Tumor Photodynamic Therapy

Tumor hypoxia can seriously impede the effectiveness of photodynamic therapy (PDT). To address this issue, two approaches, termed in situ oxygen generation and oxygen delivery, were developed. The in situ oxygen generation method uses catalysts such as catalase to decompose excess H₂O₂ produced by tumors. It offers specificity for tumors, but its effectiveness is limited by the low H₂O₂ concentration often present in tumors. The oxygen delivery strategy relies on the high oxygen solubility of perfluorocarbon, etc., to transport oxygen. It is effective, but lacks tumor specificity. In an effort to integrate the merits of the two approaches, we designed a multifunctional nanoemulsion system named CCIPN and prepared it using a sonication-phase inversion composition-sonication method with orthogonal optimization. CCIPN included catalase, the methyl ester of 2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oic acid (CDDO-Me), photosensitizer IR780, and perfluoropolyether. Perfluoropolyether may reserve the oxygen generated by catalase within the same nanoformulation for PDT. CCIPN contained spherical droplets below 100 nm and showed reasonable cytocompatibility. It presented a stronger ability to generate cytotoxic reactive oxygen species and consequently destroy tumor cells upon light irradiation, in comparison with its counterpart without catalase or perfluoropolyether. This study contributes to the design and preparation of oxygen-supplementing PDT nanomaterials.

Association of Critical Value With 28-Day Mortality After Cardiac Surgery

OBJECTIVE

The emergence of critical values gives a warning to the medical safety of hospitalized patients, especially Cardiosurgery Intensive Care Unit (CSICU) patients. The aim of this study was to investigate the association between early postoperative critical values and the prognosis of patients after cardiac surgery.

METHODS

Clinical data of the patients were obtained from the Cardiac Critical Care Clinical Database of the Cardiovascular Intensive Care Unit of Nanjing First Hospital. A total of 1,598 consecutive patients undergoing cardiac surgery were enrolled in this retrospective cohort study, during the period from July 2019 to December 2020. According to whether critical value occurred within 7 days after cardiac surgery, patients were divided into two groups: the critical value group and control group. COX regression and survival analysis were performed to analyze the clinical data of the two groups. The area under the receiver operating characteristic curve (ROC) was used to assess the critical value's predictive value and determine the optimal cutoff value.

RESULTS

With patients in the critical value group, the 28-day mortality after cardiac surgery was 21.98%, significantly higher than that of the control group (P < 0.05). Logistic regression analysis revealed the APACHE II score (Adjusted HR-1.11, 95% CI-1.043-1.185) and critical value group (Adjusted HR-13.57, 95% CI-6.714-27.435 ) were independent predictors of 28-day mortality after cardiac surgery. The ROC curve showed that the critical value case model (AUC = 0.748 ± 0.052, P < 0.05) could effectively predict the 28-day mortality, and the optimum cutoff was 1 case (sensitivity 52.63%, specificity 95.70%).

CONCLUSIONS

One or more reported cases of critical values in the early postoperative period could be an independent risk factor for 28-day mortality in patients undergoing cardiac surgery. The predictive model based on critical value might be effective in clinical therapy and risk stratification.

Synthesis and Biological Evaluation of Phthalideisoquinoline Derivatives

A photo and Cu-mediated radical-radical approach enabling the one-step synthesis of the phthalideisoquinoline skeleton has been reported. Under mild reaction conditions, a series of N-aryl phthalideisoquinolines containing various substituents were synthesized in moderate to good yields. Bioactivity data demonstrated that a new compound 4x can efficiently inhibit the growth of multiple tumor cell lines with enhancements of more than 10-fold by significantly increasing G₂/M arrest compared with noscapine.

SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction

Drug-target binding affinity prediction is a fundamental task for drug discovery and has been studied for decades. Most methods follow the canonical paradigm that processes the inputs of the protein (target) and the ligand (drug) separately and then combines them together. In this study we demonstrate, surprisingly, that a model is able to achieve even superior performance without access to any protein-sequence-related information. Instead, a protein is characterized completely by the ligands that it interacts. Specifically, we treat different proteins separately, which are jointly trained in a multi-head manner, so as to learn a robust and universal representation of ligands that is generalizable across proteins. Empirical evidences show that the novel paradigm outperforms its competitive sequence-based counterpart, with the Mean Squared Error (MSE) of 0.4261 versus 0.7612 and the R-Square of 0.7984 versus 0.6570 compared with DeepAffinity. We also investigate the transfer learning scenario where unseen proteins are encountered after the initial training, and the cross-dataset evaluation for prospective studies. The results reveals the robustness of the proposed model in generalizing to unseen proteins as well as in predicting future data. Source codes and data are available at https://github.com/huzqatpku/SAM-DTA.

Buoyancy-Induced Convection Driven by Frontal Polymerization

In this Letter, we study the interaction between a self-sustaining exothermic reaction front propagating in a direction perpendicular to that of gravity and the buoyancy-driven convective flow during frontal polymerization (FP) of a low-viscosity monomer resin. As the polymerization front transforms the liquid monomer into the solid polymer, the large thermal gradients associated with the propagating front sustain a natural convection of the fluid ahead of the front. The fluid convection in turn affects the reaction-diffusion (RD) dynamics and the shape of the front. Detailed multiphysics numerical analyses and particle image velocimetry experiments reveal this coupling between natural convection and frontal polymerization. The frontal Rayleigh (Ra) number affects the magnitude of the velocity field and the inclination of the front. A higher Ra number drives instability during FP, leading to the observation of thermal-chemical patterns with tunable wavelengths and magnitudes.

Association between glucocorticoid use and all-cause mortality in critically ill patients with heart failure: A cohort study based on the MIMIC-III database

Background: Heart failure (HF) is the terminal stage of various heart diseases. Conventional treatments have poor efficacy, and diuretic resistance can present. Previous studies have found that the use of glucocorticoids can enhance the diuretic effect of patients with heart failure and reduce heart failure symptoms. However, the relationship between glucocorticoid use and mortality in patients with heart failure in intensive care units is unclear. Objectives: The aim of this study was to determine the association between glucocorticoid use and all-cause mortality in critically ill patients with heart failure. Methods: The information on patients with heart failure in this study was extracted from the MIMIC-III (Medical Information Mart for Intensive Care-III) database. Patients in the glucocorticoid and non-glucocorticoid groups were matched using propensity scores. The Kaplan-Meier method was used to explore the difference in survival probability between the two groups. A Cox proportional-hazards regression model was used to analyze the hazard ratios (HRs) for the two patient groups. Subgroup analyses were performed with prespecified stratification variables to demonstrate the robustness of the results. Results: The study included 9,482 patients: 2,099 in the glucocorticoid group and 7,383 in the non-glucocorticoid group. There were 2,055 patients in each group after propensity-score matching. The results indicated that the non-glucocorticoid group was not significantly associated with reduced mortality in patients with heart failure during the 14-day follow-up period [HRs = .901, 95% confidence interval (CI) = .767-1.059]. During the follow-up periods of 15-30 and 15-90 days, the mortality risk was significantly lower in the non-glucocorticoid group than in the glucocorticoid group (HRs = .497 and 95% CI = .370-.668, and HRs = .400 and 95% CI = .310-.517, respectively). Subgroup analyses indicated no interaction among each stratification variable and glucocorticoid use. Conclusion: Glucocorticoid use was associated with an increased mortality risk in critically ill patients with heart failure.

The Evolution of Anionic Nanoclusters at the Electrode Interface in Water-in-salt Electrolytes

Water-in-salt electrolytes (WiSEs) have attracted extensive attention as promising alternatives to organic electrolytes. The limited electrochemical stability windows (ESWs) of aqueous electrolytes are significantly widened by WiSEs. However, the actual...