SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistency.

The advance in single-cell RNA-sequencing (scRNA-seq) sheds light on cell-specific transcriptomic studies of cell developments, complex diseases and cancers. Nevertheless, scRNA-seq techniques suffer from 'dropout' events, and imputation tools are proposed to address the sparsity. Here, rather than imputation, we propose a tool, SMURF, to extract the low-dimensional embeddings from cells and genes utilizing matrix factorization with a mixture of Poisson-Gamma divergent as objective while preserving self-consistency. SMURF exhibits feasible cell subpopulation discovery efficacy with obtained cell embeddings on replicated in silico and eight web lab scRNA datasets with ground truth cell types. Furthermore, SMURF can reduce the cell embedding to a 1D-oval space to recover the time course of cell cycle. SMURF can also serve as an imputation tool; the in silico data assessment shows that SMURF parades the most robust gene expression recovery power with low root mean square error and high Pearson correlation. Moreover, SMURF recovers the gene distribution for the WM989 Drop-seq data. SMURF is available at https://github.com/deepomicslab/SMURF.

Role of blood lipids in mediating the effect of dietary factors on gastroesophageal reflux disease: a two-step mendelian randomization study.

BACKGROUND: Growing studies have indicated an association between dietary factors and gastroesophageal reflux disease (GERD). However, whether these associations refer to a causal relationship and the potential mechanism by which dietary factors affect GERD is still unclear. METHODS: A two-step mendelian randomization analysis was performed to obtain causal estimates of dietary factors, blood lipids on GERD. Independent genetic variants associated with 13 kinds of dietary factors and 5 kinds of blood lipids at the genome-wide significance level were selected as instrumental variables. The summary statistics for GERD were obtained from European Bioinformatics Institute, including 129,080 cases and 473,524 controls. Inverse variance weighted was utilized as the main statistical method. MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were performed to evaluate possible heterogeneity and pleiotropy. And the potential reverse causality was assessed using Steiger filtering. RESULTS: The results of the inverse variance weighted method indicated that genetically predicted total pork intake (OR = 2.60, 95% CI: 1.21-5.58, p = 0.0143), total bread intake (OR = 0.68, 95% CI: 0.46-0.99, p = 0.0497), total cereal intake (OR = 0.42, 95% CI: 0.31-0.56, p = 2.98E-06), and total cheese intake (OR = 0.41, 95% CI: 0.27-0.61, p = 1.06E-05) were associated with the risk of GERD. Multivariable Mendelian randomization analysis also revealed a negative association between total cereal intake, total cheese intake and the risk of GERD, but the effect of total pork intake and total bread intake on GERD disappeared after adjustment of smoking, alcohol consumption, use of calcium channel blockers, BMI, physical activity levels, and biological sex (age adjusted). Furthermore, the concentration of low-density lipoprotein cholesterol (LDL-C) is negatively correlated with total cheese intake, which mediates the impact of total cheese intake on GERD. The proportion mediated by LDL-C is 2.27% (95%CI: 1.57%, 4.09%). CONCLUSIONS: This study provides evidence that an increase in total cereal intake and total cheese intake will decrease the risk of GERD. Additionally, LDL-C mediates the causal effect of total cheese intake on GERD. These results provide new insights into the role of dietary factors and blood lipids in GERD, which is beneficial for disease prevention.

Causal relationships between coffee intake, apolipoprotein B and gastric, colorectal, and esophageal cancers: univariable and multivariable Mendelian randomization.

PURPOSE: Coffee intake and apolipoprotein B levels have been linked to gastric, colorectal, and esophageal cancers in numerous recent studies. However, whether these associations are all causal remains unestablished. This study aimed to assess the potential causal associations of apolipoprotein B and coffee intake with the risk of gastric, colorectal, and esophageal cancers using Mendelian randomization analysis. METHODS: In this study, we utilized a two-sample Mendelian randomization analysis to access the causal effects of coffee intake and apolipoprotein B on gastric, colorectal, and esophageal cancers. The summary statistics of coffee intake (n = 428,860) and apolipoprotein B (n = 439,214) were obtained from the UK Biobank. In addition, the summary statistics of gastric cancer, colorectal cancer, and esophageal cancer were obtained from the FinnGen biobank (n = 218,792). Inverse variance weighted, MR-Egger, weighted median, and weighted mode were applied to examine the causal relationship between coffee intake, apolipoprotein B and gastric, colorectal, and esophageal cancers. MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were performed to evaluate possible heterogeneity and pleiotropy. Steiger filtering and bidirectional mendelian randomization analysis were performed to evaluate the possible reverse causality. RESULTS: The result of the inverse variance weighted method indicated that apolipoprotein B levels were significantly associated with a higher risk of gastric cancer (OR = 1.392, 95% CI 1.027-1.889, P = 0.0333) and colorectal cancer (OR = 1.188, 95% CI 1.001-1.411, P = 0.0491). Furthermore, multivariable Mendelian randomization analysis also revealed a positive association between apolipoprotein B levels and colorectal cancer risk, but the effect of apolipoprotein B on gastric cancer risk disappeared after adjustment of coffee intake, body mass index or lipid-related traits. However, we did not discover any conclusive evidence linking coffee intake to gastric, colorectal, or esophageal cancers. CONCLUSIONS: This study suggested a causal association between genetically increased apolipoprotein B levels and higher risk of colorectal cancer. No causal relationship was observed between coffee intake and gastric, colorectal, or esophageal cancers.

Association between monocyte-to-high-density lipoprotein-cholesterol ratio and gallstones in U.S. adults: findings from the National Health and Nutrition Examination Survey 2017-2020.

BACKGROUND: Studies have indicated that monocyte-to-high-density lipoprotein cholesterol ratio (MHR) can be a reliable indicator of various diseases. However, the association between MHR and gallstone prevalence remains unclear. Therefore, this study aimed to explore any potential association between MHR and gallstone prevalence. METHODS: This study used data from the National Health and Nutrition Examination Survey (NHANES) 2017-March 2020. MHR was calculated as the monocyte count ratio to high-density lipoprotein cholesterol levels. Multiple logistic regression models, Cochran-Armitage trend test, and subgroup analyses were used to examine the association between MHR and gallstones. RESULTS: This study included 5907 participants, of whom 636 (10.77%) were gallstone formers. The study participants had a mean age of 50.78 ± 17.33 years. After accounting for multiple covariables, the multiple logistic regression model showed a positive linear association between MHR and gallstone odds. The subgroup analyses and interaction testing results revealed that the association between MHR and gallstones was statistically different across strata, including sex, smoking, asthma, and hypertension. CONCLUSIONS: Gallstone prevalence positively associated with elevated MHR, indicating that MHR can be employed as a clinical indicator to assess gallstone prevalence.

Confinement of 1D Chain and 2D Layered CuI Modules in K-INA-R Frameworks via Coordination Assembly: Structure Regulation and Semiconductivity Tuning.

Herein, we present a new series of CuI-based hybrid materials with tunable structures and semiconducting properties. The CuI inorganic modules can be tailored into a one-dimensional (1D) chain and two-dimensional (2D) layer and confined/stabilized in coordination frameworks of potassium isonicotinic acid (HINA) and its derivatives (HINA-R, R = OH, NO2, and COOH). The resulting CuI-based hybrid materials exhibit interesting semiconducting behaviors associated with the dimensionality of the inorganic module; for instance, the structures containing the 2D-CuI module demonstrate significantly enhanced photoconductivity with a maximum increase of five orders of magnitude compared to that of the structures containing the 1D-CuI module. They also represent the first CuI-bearing hybrid chemiresistive gas sensors for NO2 with boosted sensing performance and sensitivity at multiple orders of magnitude over that of the pristine CuI. Particularly, the sensing ability of CuI-K-INA containing both 1D- and 2D-CuI modules is comparable to those of the best NO2 chemiresistors reported thus far.

Catalytic Transformation of PET and CO2 into High-Value Chemicals.

Polyethylene terephthalate (PET) and CO2 , two chemical wastes that urgently need to be transformed in the environment, are converted simultaneously in a one-pot catalytic process through the synergistic coupling of three reactions: CO2 hydrogenation, PET methanolysis and dimethyl terephthalate (DMT) hydrogenation. More interestingly, the chemical equilibria of both reactions were shifted forward due to a revealed dual-promotion effect, leading to significantly enhanced PET depolymerization. The overall methanol yield from CO2 hydrogenation exceeded the original thermodynamic equilibrium limit since the methanol was in situ consumed in the PET methanolysis. The degradation of PET by a stoichiometric ratio of methanol was significantly enhanced because the primary product, DMT was hydrogenated to dimethyl cyclohexanedicarboxylate (DMCD) or p-xylene (PX). This synergistic catalytic process provides an effective way to simultaneously recycle two wastes, polyesters and CO2 , for producing high-value chemicals.

Csp3-Csp3 Bond-Forming Reductive Elimination from Well-Defined Copper(III) Complexes.

Carbon-carbon bond-forming reductive elimination from elusive organocopper(III) complexes has been considered the key step in many copper-catalyzed and organocuprate reactions. However, organocopper(III) complexes with well-defined structures that can undergo reductive elimination are extremely rare, especially for the formation of Csp3-Csp3 bonds. We report herein a general method for the synthesis of a series of [alkyl-CuIII-(CF3)3]- complexes, the structures of which have been unequivocally characterized by NMR spectroscopy, mass spectrometry, and X-ray crystal diffraction. At elevated temperature, these complexes undergo reductive elimination following first-order kinetics, forming alkyl-CF3 products with good yields (up to 91%). Both kinetic studies and DFT calculations indicate that the reductive elimination to form Csp3-CF3 bonds proceeds through a concerted transition state, with a Δ H⧧ = 20 kcal/mol barrier.

Development of a reactive force field for the Fe-C interaction to investigate the carburization of iron.

The approach of molecular dynamics with Reactive Force Field (ReaxFF) is a promising way to investigate the carburization of iron which is pivotal in the preparation of desired iron-based materials and catalysts. However, it is a challenge to develop a reliable ReaxFF to describe the Fe-C interaction, especially when it involves bond rearrangement. In this work, we develop an exclusive set of Reactive Force Field (ReaxFF) parameters, denoted RPOIC-2017, to describe the diffusion behavior of carbon atoms in the α-Fe system. It inherited some partial parameters in 2012 (ReaxFF-2012) which are suitable for hydrogen adsorption and dissociation. This set of parameters is trained against data from first-principles calculations, including the equations of state of α-Fe, the crystal constant of Fe3C and Fe4C, a variety of periodic surface structures with varying carbon coverages, as well as the barriers of carbon diffusion in the α-Fe bulk and on diverse surfaces. The success in predicting the carbon diffusion coefficient and the diffusion barrier using the developed RPOIC-2017 potential demonstrates that the performance is superior to that of the traditional MEAM potential. The new ReaxFF for the Fe-C interaction developed in this work is not only essential for the design of novel iron based materials, but could also help understand atomic arrangements and the interfacial structure of iron carbides.

Chelating N-Heterocyclic Carbene Ligands Enable Tuning of Electrocatalytic CO2 Reduction to Formate and Carbon Monoxide: Surface Organometallic Chemistry.

Reported here is the chelate effect as a design principle for tuning heterogeneous catalysts for electrochemical CO2 reduction. Palladium functionalized with a chelating tris-N-heterocyclic carbene (NHC) ligand (Pd-timtmbMe ) exhibits a 32-fold increase in activity for electrochemical reduction of CO2 to C1 products with high Faradaic efficiency (FEC1 =86 %) compared to the parent unfunctionalized Pd foil (FE=23 %), and with sustained activity relative to a monodentate NHC-ligated Pd electrode (Pd-mimtmbMe ). The results highlight the contributions of the chelate effect for tailoring and maintaining reactivity at molecular-materials interfaces enabled by surface organometallic chemistry.

Tuning Gold Nanoparticles with Chelating Ligands for Highly Efficient Electrocatalytic CO2 Reduction.

Capped chelating organic molecules are presented as a design principle for tuning heterogeneous nanoparticles for electrochemical catalysis. Gold nanoparticles (AuNPs) functionalized with a chelating tetradentate porphyrin ligand show a 110-fold enhancement compared to the oleylamine-coated AuNP in current density for electrochemical reduction of CO2 to CO in water at an overpotential of 340 mV with Faradaic efficiencies (FEs) of 93 %. These catalysts also show excellent stability without deactivation (<5 % productivity loss) within 72 hours of electrolysis. DFT calculation results further confirm the chelation effect in stabilizing molecule/NP interface and tailoring catalytic activity. This general approach is thus anticipated to be complementary to current NP catalyst design approaches.

Corrigendum: Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

[This corrects the article DOI: 10.3389/fimmu.2024.1368749.].

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

CBLC promotes the development of colorectal cancer by promoting ABI1 degradation to activate the ERK signaling pathway.

CBLC (CBL proto-oncogene C) is an E3 ubiquitin protein ligase that plays a key role in cancers. However, the function and mechanism of CBLC in colorectal cancer (CRC) has not been fully elucidated. The aim of this study was to investigate the function of CBLC in CRC and its underlying molecular mechanism. High CBLC levels were certified in tumor tissues of CRC patients, and its expression was positively associated with TNM stage. Next, we explored the role of CBLC in CRC using gain or loss of function. For biological function analysis, CCK-8 cell proliferation, colony formation, flow cytometry, scratch, and transwell assays collectively suggested that CBLC overexpression promoted cell proliferation, cell cycle progression, migration and invasion. As observed, CBLC knockdown exhibited exactly opposite effects, resulting in impaired tumorigenicity in vitro. Xenograft studies displayed that CBLC overexpression accelerated tumor growth and promoted tumor metastasis to the lung, while the inhibitory effects of CBLC knockdown on tumorigenicity and metastasis ability of CRC cells was also confirmed. Furthermore, the molecular mechanism of CBLC in CRC was explored. CBLC induced the activation of ERK signaling pathway, further leading to its pro-tumor role. Notably, CBLC decreased ABI1 (Abelson interactor protein-1, a candidate tumor suppressor) protein levels through its ubiquitin ligase activity, while ABI1 upregulation abolished the effects of CBLC on the tumorigenesis of CRC. Taken together, these results demonstrate that CBLC acts as a tumor promoter in CRC through triggering the ubiquitination and degradation of ABI1 and activating the ERK signaling pathway. CBLC may be a potential novel target for CRC.

RuCo/ZrO2 Tandem Catalysts with Photothermal Confinement Effect for Enhanced CO2 Methanation.

Photothermal CO2 methanation reaction represents a promising strategy for addressing CO2-related environmental issues. The presence of efficient tandem catalytic sites with a localized high-temperature is an effective pathway to enhance the performance of CO2 methanation. Here the bimetallic RuCo nanoparticles anchored on ZrO2 fiber cotton (RuCo/ZrO2) as a photothermal catalyst for CO2 methanation are prepared. A significant photothermal CO2 methanation performance with optimal CH4 selectivity (99%) and rate (169.93 mmol gcat -1 h-1) is achieved. The photothermal energy of the RuCo bimetallic nanoparticles, confined by the infrared insulation and low thermal conductivity of the ZrO2 fiber cotton (ZrO2 FC), provides a localized high-temperature. In situ spectroscopic experiments on RuCo/ZrO2, Ru/ZrO2, and Co/ZrO2 indicate that the construction of tandem catalytic sites, where the Co site favors CO2 conversion to CO while incorporating Ru enhances CO* adsorption for subsequent hydrogenation, results in a higher selectivity toward CH4. This work opens a new insight into designing tandem catalysts with a photothermal confinement effect in CO2 methanation reaction.

Ambient-pressure alkoxycarbonylation for sustainable synthesis of ester.

Alkoxycarbonylation reactions are common in the chemical industry, yet process sustainability is limited by the inefficient utilization of CO. In this study, we address this issue and demonstrate that significant improvements can be achieved by adopting a heterogeneously catalyzed process, using a Ru/NbOx catalyst. The Ru/NbOx catalyst enables the direct synthesis of methyl propionate, a key industrial commodity, with over 98% selectivity from CO, ethylene and methanol, without any ligands or acid/base promoters. Under ambient CO pressure, a high CO utilization efficiency (336 mmolestermolCO-1h-1) is achieved. Mechanistic investigations reveal that CO undergoes a methoxycarbonyl (COOCH3) intermediate pathway, attacking the terminal carbon atom of alkene and yielding linear esters. The origins of prevailing linear regioselectivity in esters are revealed. The infrared spectroscopic feature of the key COOCH3 species is observed at 1750 cm-1 (C=O vibration) both experimentally and computationally. The broad substrate applicability of Ru/NbOx catalyst for ester production is demonstrated. This process offers a sustainable and efficient approach with high CO utilization and atom economy for the synthesis of esters.

Pulsed laser induced plasma and thermal effects on molybdenum carbide for dry reforming of methane.

Dry reforming of methane (DRM) is a highly endothermic process, with its development hindered by the harsh thermocatalytic conditions required. We propose an innovative DRM approach utilizing a 16 W pulsed laser in combination with a cost-effective Mo2C catalyst, enabling DRM under milder conditions. The pulsed laser serves a dual function by inducing localized high temperatures and generating *CH plasma on the Mo2C surface. This activates CH4 and CO2, significantly accelerating the DRM reaction. Notably, the laser directly generates *CH plasma from CH4 through thermionic emission and cascade ionization, bypassing the traditional step-by-step dehydrogenation process and eliminating the rate-limiting step of methane cracking. This method maintains a carbon-oxygen balanced environment, thus preventing the deactivation of the Mo2C catalyst due to CO2 oxidation. The laser-catalytic DRM achieves high yields of H2 (14300.8 mmol h-1 g-1) and CO (14949.9 mmol h-1 g-1) with satisfactory energy efficiency (0.98 mmol kJ-1), providing a promising alternative for high-energy-consuming catalytic systems.

Stabilized ε-Fe2C catalyst with Mn tuning to suppress C1 byproduct selectivity for high-temperature olefin synthesis.

Accurately controlling the product selectivity in syngas conversion, especially increasing the olefin selectivity while minimizing C1 byproducts, remains a significant challenge. Epsilon Fe2C is deemed a promising candidate catalyst due to its inherently low CO2 selectivity, but its use is hindered by its poor high-temperature stability. Herein, we report the successful synthesis of highly stable ε-Fe2C through a N-induced strategy utilizing pyrolysis of Prussian blue analogs (PBAs). This catalyst, with precisely controlled Mn promoter, not only achieved an olefin selectivity of up to 70.2% but also minimized the selectivity of C1 byproducts to 19.0%, including 11.9% CO2 and 7.1% CH4. The superior performance of our ε-Fe2C-xMn catalysts, particularly in minimizing CO2 formation, is largely attributed to the interface of dispersed MnO cluster and ε-Fe2C, which crucially limits CO to CO2 conversion. Here, we enhance the carbon efficiency and economic viability of the olefin production process while maintaining high catalytic activity.

Atomically Dispersed Pd-N1C3 Sites on a Nitrogen-Doped Carbon Nanosphere for Semi-hydrogenation of Acetylene.

Rationally designing efficient catalysts for semi-hydrogenation of acetylene is significant but challenging. Herein, Pd isolated single-atom sites (ISAS) on a covalent-organic-framework (COF)-derived nanosphere (Pd-ISAS/CN) are synthesized by a COF-absorption-pyrolysis strategy. This synthetic strategy is also applicable for Pt and Ru ISAS catalysts, demonstrating that it is a general method to synthesize noble-metal ISAS on COF-derived carbon materials. Pd-ISAS/CN exhibits outstanding reactivity and high selectivity for semi-hydrogenation of acetylene, with 92% conversion of acetylene, 80% selectivity toward ethylene at 100 °C, and corresponding activity is as high as 712 molacetylene molmetal-1 h-1. Extended X-ray absorption fine structure (EXAFS) measurement and density functional theory (DFT) calculation reveal the Pd-N1C3 sites from Pd-ISAS/CN efficiently boost the reactivity for semi-hydrogenation of acetylene. This work will bring inspiration to rationally design noble-metal-based ISAS catalysts derived from COF materials and boost catalytic performance by optimizing the coordination environment of catalytic sites.

Development of a Library of Disulfide Bond-Containing Cationic Lipids for mRNA Delivery.

Lipid nanoparticles (LNPs) are the commonly used delivery tools for messenger RNA (mRNA) therapy and play an indispensable role in the success of COVID-19 mRNA vaccines. Ionizable cationic lipids are the most important component in LNPs. Herein, we developed a series of new ionizable lipids featuring bioreducible disulfide bonds, and constructed a library of lipids derived from dimercaprol. LNPs prepared from these ionizable lipids could be stored at 4 °C for a long term and are non-toxic toward HepG2 and 293T cells. In vivo experiments demonstrated that the best C4S18A formulations, which embody linoleoyl tails, show strong firefly luciferase (Fluc) mRNA expression in the liver and spleen via intravenous (IV) injection, or at the local injection site via intramuscular injection (IM). The newly designed ionizable lipids can be potentially safe and high-efficiency nanomaterials for mRNA therapy.

Deconvolution of metal apportionment in bulk metal-organic frameworks.

We report a general route to decipher the apportionment of metal ions in bulk metal-organic frameworks (MOFs) by solid-state nuclear magnetic resonance spectroscopy. We demonstrate this route in Mg1-xNix-MOF-74, where we uncover all eight possible atomic-scale Mg/Ni arrangements through identification and quantification of the distinct chemical environments of 13C-labeled carboxylates as a function of the Ni content. Here, we use magnetic susceptibility, bond pathway, and density functional theory calculations to identify local metal bonding configurations. The results refute the notion of random apportionment from solution synthesis; rather, we reveal that only two of eight Mg/Ni arrangements are preferred in the Ni-incorporated MOFs. These preferred structural arrangements manifest themselves in macroscopic adsorption phenomena as illustrated by CO/CO2 breakthrough curves. We envision that this nondestructive methodology can be further applied to analyze bulk assembly of other mixed-metal MOFs, greatly extending the knowledge on structure-property relationships of MOFs and their derived materials.