Regioselective hydroformylation of propene catalysed by rhodium-zeolite.

Hydroformylation is an industrial process for the production of aldehydes from alkenes1,2. Regioselective hydroformylation of propene to high-value n-butanal is particularly important, owing to a wide range of bulk applications of n-butanal in the manufacture of various necessities in human daily life3. Supported rhodium (Rh) hydroformylation catalysts, which often excel in catalyst recyclability, ease of separation and adaptability for continuous-flow processes, have been greatly exploited4. Nonetheless, they usually consist of rotationally flexible and sterically unconstrained Rh hydride dicarbonyl centres, only affording limited regioselectivity to n-butanal5-8. Here we show that proper encapsulation of Rh species comprising Rh(I)-gem-dicarbonyl centres within a MEL zeolite framework allows the breaking of the above model. The optimized catalyst exhibits more than 99% regioselectivity to n-butanal and more than 99% selectivity to aldehydes at a product formation turnover frequency (TOF) of 6,500 h-1, surpassing the performance of all heterogeneous and most homogeneous catalysts developed so far. Our comprehensive studies show that the zeolite framework can act as a scaffold to steer the reaction pathway of the intermediates confined in the space between the zeolite framework and Rh centres towards the exclusive formation of n-butanal.

Measurement of 19F(p, γ)20Ne reaction suggests CNO breakout in first stars.

Proposed mechanisms for the production of calcium in the first stars (population III stars)-primordial stars that formed out of the matter of the Big Bang-are at odds with observations1. Advanced nuclear burning and supernovae were thought to be the dominant source of the calcium production seen in all stars2. Here we suggest a qualitatively different path to calcium production through breakout from the 'warm' carbon-nitrogen-oxygen (CNO) cycle through a direct experimental measurement of the 19F(p, γ)20Ne breakout reaction down to a very low energy point of 186 kiloelectronvolts, reporting a key resonance at 225 kiloelectronvolts. In the domain of astrophysical interest2, at around 0.1 gigakelvin, this thermonuclear 19F(p, γ)20Ne rate is up to a factor of 7.4 larger than the previous recommended rate3. Our stellar models show a stronger breakout during stellar hydrogen burning than previously thought1,4,5, and may reveal the nature of calcium production in population III stars imprinted on the oldest known ultra-iron-poor star, SMSS0313-67086. Our experimental result was obtained in the China JinPing Underground Laboratory7, which offers an environment with an extremely low cosmic-ray-induced background8. Our rate showcases the effect that faint population III star supernovae can have on the nucleosynthesis observed in the oldest known stars and first galaxies, which are key mission targets of the James Webb Space Telescope9.

Oxidation-induced superelasticity in metallic glass nanotubes.

Although metallic nanostructures have been attracting tremendous research interest in nanoscience and nanotechnologies, it is known that environmental attacks, such as surface oxidation, can easily initiate cracking on the surface of metals, thus deteriorating their overall functional/structural properties1-3. In sharp contrast, here we report that severely oxidized metallic glass nanotubes can attain an ultrahigh recoverable elastic strain of up to ~14% at room temperature, which outperform bulk metallic glasses, metallic glass nanowires and many other superelastic metals hitherto reported. Through in situ experiments and atomistic simulations, we reveal that the physical mechanisms underpinning the observed superelasticity can be attributed to the formation of a percolating oxide network in metallic glass nanotubes, which not only restricts atomic-scale plastic events during loading but also leads to the recovery of elastic rigidity on unloading. Our discovery implies that oxidation in low-dimensional metallic glasses can result in unique properties for applications in nanodevices.

Evolution of Human Brain Left-Right Asymmetry: Old Genes with New Functions.

The human brain is generally anatomically symmetrical, boasting mirror-like brain regions in the left and right hemispheres. Despite this symmetry, fine-scale structural asymmetries are prevalent and are believed to be responsible for distinct functional divisions within the brain. Prior studies propose that these asymmetric structures are predominantly primate specific or even unique to humans, suggesting that the genes contributing to the structural asymmetry of the human brain might have evolved recently. In our study, we identified approximately 1,500 traits associated with human brain asymmetry by collecting paired brain magnetic resonance imaging features from the UK Biobank. Each trait is measured in a specific region of one hemisphere and mirrored in the corresponding region of the other hemisphere. Conducting genome-wide association studies on these traits, we identified over 1,000 quantitative trait loci. Around these index single nucleotide polymorphisms, we found approximately 200 genes that are enriched in brain-related Gene Ontology terms and are predominantly upregulated in brain tissues. Interestingly, most of these genes are evolutionarily old, originating just prior to the emergence of Bilateria (bilaterally symmetrical animals) and Euteleostomi (bony vertebrates with a brain), at a significantly higher ratio than expected. Further analyses of these genes reveal a brain-specific upregulation in humans relative to other mammalian species. This suggests that the structural asymmetry of the human brain has been shaped by evolutionarily ancient genes that have assumed new functions over time.

Multi-Functional 2D Covalent Organic Frameworks with Diketopyrrolopyrrole as Electron Acceptor.

2D covalent organic framework (COF) materials with extended conjugated structure and periodic columnar π-arrays exhibit promising applications in organic optoelectronics. However, there is a scarcity of reports on optoelectronic COFs, mainly due to the lack of suitable π-skeletons. Here, two multi-functional optoelectronic 2D COFs DPP-TPP-COF and DPP-TBB-COF are constructed with diketopyrrolopyrrole as electron acceptor (A), and 1,3,6,8-tetraphenylpyrene and 1,3,5-triphenylbenzene as electron donor (D) through imine bonds. Both 2D COFs showed good crystallinities and AA stacking with a rhombic framework for DPP-TPP-COF and hexagonal one for DPP-TBB-COF, respectively. The electron D-A and ordered intermolecular packing structures endow the COFs with broad UV-vis absorptions and narrow bandgaps along with suitable HOMO/LUMO energy levels, resulting in multi-functional optoelectronic properties, including photothermal conversion, supercapacitor property, and ambipolar semiconducting behavior. Among them, DPP-TPP-COF exhibits a high photothermal conversion efficiency of 47% under 660 nm laser irradiation, while DPP-TBB-COF exhibits superior specific capacitance of 384 F g-1. Moreover, P-type doping and N-type doping are achieved by iodine and tetrakis(dimethylamino)ethylene on a single host COF, resulting in ambipolar semiconducting behavior. These results provide a paradigm for the application of multi-functional optoelectronic COF materials.

Organic-Inorganic Rare-Earth Double Perovskite Ferroelectric with Large Piezoelectric Response and Ferroelasticity for Flexible Composite Energy Harvesters.

Hybrid organic-inorganic perovskite (HOIP) ferroelectric materials have great potential for developing self-powered electronic transducers owing to their impressive piezoelectric performance, structural tunability and low processing temperatures. Nevertheless, their inherent brittle and low elastic moduli limit their application in electromechanical conversion. Integration of HOIP ferroelectrics and soft polymers is a promising solution. In this work, a hybrid organic-inorganic rare-earth double perovskite ferroelectric, [RM3HQ]2RbPr(NO3)6 (RM3HQ = (R)-N-methyl-3-hydroxylquinuclidinium) is presented, which possesses multiaxial nature, ferroelasticity and satisfactory piezoelectric properties, including piezoelectric charge coefficient (d33) of 102.3 pC N-1 and piezoelectric voltage coefficient (g33) of 680 × 10-3 V m N-1. The piezoelectric generators (PEG) based on composite films of [RM3HQ]2RbPr(NO3)6@polyurethane (PU) can generate an open-circuit voltage (Voc) of 30 V and short-circuit current (Isc) of 18 µA, representing one of the state-of-the-art PEGs to date. This work has promoted the exploration of new HOIP ferroelectrics and their development of applications in electromechanical conversion devices.

Mapping single-cell-resolution cell phylogeny reveals cell population dynamics during organ development.

Mapping the cell phylogeny of a complex multicellular organism relies on somatic mutations accumulated from zygote to adult. Available cell barcoding methods can record about three mutations per barcode, enabling only low-resolution mapping of the cell phylogeny of complex organisms. Here we developed SMALT, a substitution mutation-aided lineage-tracing system that outperforms the available cell barcoding methods in mapping cell phylogeny. We applied SMALT to Drosophila melanogaster and obtained on average more than 20 mutations on a three-kilobase-pair barcoding sequence in early-adult cells. Using the barcoding mutations, we obtained high-quality cell phylogenetic trees, each comprising several thousand internal nodes with 84-93% median bootstrap support. The obtained cell phylogenies enabled a population genetic analysis that estimates the longitudinal dynamics of the number of actively dividing parental cells (Np) in each organ through development. The Np dynamics revealed the trajectory of cell births and provided insight into the balance of symmetric and asymmetric cell division.

SULT2B1-CS-DOCK2 axis regulates effector T-cell exhaustion in HCC microenvironment.

BACKGROUND AND AIMS: HCC is a malignant disease. Compared with tyrosine kinase inhibitors (the classical therapy), immune checkpoint inhibitors are more effective in the treatment of HCC, despite their limited efficacy. Among these restricted factors, exhaustion of tumor-infiltrated lymphocytes, especially CD8 + T cells, is a core event. We aimed to determine the key factors contributing to CD8 + T-cell infiltration in HCC and investigate the underlying mechanisms. APPROACH AND RESULTS: Using machine learning and multiplex immunohistochemistry analysis, we showed that dedicator of cytokinesis protein 2 (DOCK2) was a potential indicator of infiltrated CD8 + T cells in HCC. Using RNA sequencing, flow cytometry analysis, and mouse HCC models, we demonstrated that DOCK2 inactivation accounted for infiltrated CD8 + T-cell exhaustion in tumors. Using quasi-targeted metabolomics, mass spectrum, and mass cytometry by time of flight analysis, we found that cholesterol sulfate synthesized by sulfotransferase 2B1 in tumor cells suppressed DOCK2 enzymatic activity of T cells. Through virtual screening, molecular docking simulation, and experiments validation, we demonstrated that tolazamide reversed DOCK2 inactivation-mediated CD8 + T-cell exhaustion and enhanced anti-programmed death-ligand 1 antibody+apatinib immunotherapeutic effects on HCC. CONCLUSIONS: This study indicates that DOCK2 controls CD8 + T-cell infiltration in HCC, and cholesterol sulfate synthesized by sulfotransferase 2B1 in tumor cells promotes effector T-cell exhaustion. The findings suggest that the usage of conventional drugs affects immunotherapy efficacy in HCC patients.

Microcavity induced by a few-layer GaSe crystal on a silicon photonic crystal waveguide for efficient optical frequency conversion.

We demonstrate the post-induction of high-quality microcavities on a silicon photonic crystal (PC) waveguide by integrating a few-layer GaSe crystal, which promises efficient on-chip optical frequency conversions. The integration of GaSe shifts the dispersion bands of the PC waveguide mode into the bandgap, resulting in localized modes confined by the bare PC waveguides. Thanks to the small contrast of refractive index at the boundaries of the microcavity, it is reliable to obtain quality factors exceeding 104. With the enhanced light-GaSe interaction by the microcavity modes and GaSe's high second-order nonlinearity, remarkable second-harmonic generation (SHG) and sum-frequency generation (SFG) are achieved with continuous-wave (CW) lasers.

Anion-π+ AIEgens for Fluorescence Imaging and Photodynamic Therapy.

Fluorescence imaging-guided photodynamic therapy (PDT) has attracted extensive attention due to its potential of real-time monitoring the lesion locations and visualizing the treatment process with high sensitivity and resolution. Aggregation-induced emission luminogens (AIEgens) show enhanced fluorescence and reactive oxygen species (ROS) generation after cellular uptake, giving them significant advantages in bioimaging and PDT applications. However, most AIEgens are unfavorable for the application in organisms due to their severe hydrophobicity. Anion-π+ type AIEgens carry intrinsic charges that can effectively alleviate their hydrophobicity and improve their binding capability to cells, which is expected to enhance the bioimaging quality and PDT performance. This concept summarizes the applications of anion-π+ type AIEgens in fluorescence imaging, fluorescence imaging-guided photodynamic anticancer and antimicrobial therapy in recent years, hoping to provide some new ideas for the construction of robust photosensitizers. Finally, the current problems and future challenges of anion-π+ AIEgens are discussed.

Effect of strontium on transcription factors identified by transcriptome analyses of bovine ruminal epithelial cells.

BACKGROUND: Strontium (Sr) has similar physicochemical properties as calcium (Ca) and is often used to evaluate the absorption of this mineral. Because the major route of Ca absorption in the bovine occurs in the rumen, it is essential to understand whether Sr impacts the ruminal epithelial cells and to what extent. RESULTS: In the present study, RNA sequencing and assembled transcriptome assembly were used to identify transcription factors (TFs), screening and bioinformatics analysis in bovine ruminal epithelial cells treated with Sr. A total of 1405 TFs were identified and classified into 64 families based on an alignment of conserved domains. A total of 174 differently expressed TFs (DE-TFs) were increased and 52 DE-TFs were decreased; the biological process-epithelial cell differentiation was inhibited according to the GSEA-GO analysis of TFs; The GO analysis of DE-TFs was enriched in the DNA binding. Protein-protein interaction network (PPI) found 12 hubs, including SMAD4, SMAD2, SMAD3, SP1, GATA2, NR3C1, PPARG, FOXO1, MEF2A, NCOA2, LEF1, and ETS1, which verified genes expression levels by real-time PCR. CONCLUSIONS: In this study, SMAD2, PPARG, LEF1, ETS1, GATA2, MEF2A, and NCOA2 are potential candidates that could be targeted by Sr to mediate cell proliferation and differentiation, as well as lipid metabolism. Hence, these results enhance the comprehension of Sr in the regulation of transcription factors and provide new insight into the study of Sr biological function in ruminant animals.

Tumor Promoting Effect of Long Non-Coding RNA RP11-556E13.1 and its Clinical Significance in Hepatocellular Carcinoma.

BACKGROUND: The aim of this study was to reveal the function of the long non-coding RNA (lncRNA) RP11-556E13.1 (RP11) and its clinical significance in hepatocellular carcinoma (HCC). METHODS: LncRNA and mRNA expression profiling was performed using lncRNA and mRNA microarrays in HCC and adjacent tissues. Human tissue samples were analyzed by semiquantitative real-time polymerase chain reaction (sqRT-PCR) to evaluate the expression of RP11. Smart silencer RNA (siRNA) was used to knockdown the expression of RP11 in HCC cells. The function of RP11 was determined by some cell function experiments in HCC cells. Western blotting (WB) was performed to detect proteins that were presumably associated with these function changes. An Affymetrix Human HTA2.0 microarray was used to detect the underlying mechanism of RP11 in HCC. RESULTS: lncRNA RP11 was the most significantly upregulated lncRNA in HCC tissues compared with the adjacent tissues (p < 0.05, fold change = 20.24). The expression of RP11 was significantly higher in HCC tissues compared to adjacent tissues in 112 tissue pairs (p < 0.05). The higher the expression of RP11 in HCC tissues, the bigger the tumor size, the poorer the histological differentiation, and the lower the overall survival rate of the patients (all p < 0.05). After the knockdown of RP11, HCC cells displayed inhibited proliferation, increased apoptosis rate, and G1/S arrest. Moreover, the expression of cleaved PARP1 and cleaved caspase-3 was increased. GO enrichment and KEGG pathway enrichment analysis showed some important pathways that might be related to the knockdown of RP11 in HCC cells. CONCLUSIONS: lncRNA RP11 is an HCC-promoting gene and a potential prognostic predictor of HCC.

Blood metabolomic and postpartum depression: a mendelian randomization study.

BACKGROUND: Postpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR). METHODS: This study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study's clinical expert groups, which included leading experts in their respective medical fields. RESULTS: The MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation. CONCLUSIONS: This study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.

Application of computer vision and deep learning models to automatically classify medically important mosquitoes in North Borneo, Malaysia.

Mosquito-borne diseases have emerged in North Borneo in Malaysia due to rapid changes in the forest landscape, and mosquito surveillance is key to understanding disease transmission. However, surveillance programmes involving sampling and taxonomic identification require well-trained personnel, are time-consuming and labour-intensive. In this study, we aim to use a deep leaning model (DL) to develop an application capable of automatically detecting mosquito vectors collected from urban and suburban areas in North Borneo, Malaysia. Specifically, a DL model called MobileNetV2 was developed using a total of 4880 images of Aedes aegypti, Aedes albopictus and Culex quinquefasciatus mosquitoes, which are widely distributed in Malaysia. More importantly, the model was deployed as an application that can be used in the field. The model was fine-tuned with hyperparameters of learning rate 0.0001, 0.0005, 0.001, 0.01 and the performance of the model was tested for accuracy, precision, recall and F1 score. Inference time was also considered during development to assess the feasibility of the model as an app in the real world. The model showed an accuracy of at least 97%, a precision of 96% and a recall of 97% on the test set. When used as an app in the field to detect mosquitoes with the elements of different background environments, the model was able to achieve an accuracy of 76% with an inference time of 47.33 ms. Our result demonstrates the practicality of computer vision and DL in the real world of vector and pest surveillance programmes. In the future, more image data and robust DL architecture can be explored to improve the prediction result.

Plasma and milk metabolomics profiles in dairy cows with subclinical and clinical ketosis.

Ketosis, a commonly observed energy metabolism disorder in dairy cows during the peripartal period, is distinguished by increased concentrations of ß-hydroxybutyrate (BHB) in blood. This condition has a negative impact on milk production and quality, causing financial losses. An untargeted metabolomics approach was performed on plasma samples from cows between 5 and 7 DIM diagnosed as controls (CON, BHB <1.2 mM, n = 30), subclinically ketotic (SCK, 1.2 < BHB <3.0 mM, n = 30), or clinically ketotic (CK, BHB >3.0 mM, n = 30). Cows were selected from a commercial farm of 214 Holstein cows (average 305-d yield in the previous lactation of 35.42 ± 7.23 kg/d; parity, 2.41 ± 1.12; body condition score, 3.1 ± 0.45). All plasma and milk samples (n = 90) were subjected to Liquid Chromatography-Mass Spectrometry (LC-MS)-based metabolomic analysis. Statistical analyses was performed using the Graph Pad Prism 8.0, MetaboAnalyst 4.0 and R packages (version 4.1.3). Compared with the CON group, both SCK and CK groups had greater milk fat, freezing point, and fat-to-protein ratio and lower milk protein, lactose, solids-nonfat, and milk density. Within 21 d after calving, compared with CON, the SCK group experienced a reduction of 2.65 kg/d in milk yield, while the CK group experienced a decrease of 7.7 kg/d. Untargeted metabolomics analysis facilitated the annotation of a total of 5,259 and 8,423 metabolites in plasma and milk. Differentially affected metabolites were screened in CON vs. SCK, CON vs. CK, and SCK vs. CK (unpaired t-test, False discovery rate <0.05; and absolute value of log(2)-fold change >1.5). A total of 1,544 and 1,888 differentially affected metabolites were detected in plasma and milk. In plasma, glycerophospholipid metabolism, pyrimidine metabolism, tryptophan metabolism, sphingolipid metabolism, amino sugar and nucleotide sugar metabolism, phenylalanine metabolism, steroid hormone biosynthesis were identified as significant pathways. Weighted gene co-expression network analysis (WGCNA) indicated that tryptophan metabolism is a key pathway associated with the occurrence and development of ketosis. Increases in 5-Hydroxytryptophan and decreases in kynurenine and 3-indoleacetic acid in SCK and CK were suggestive of an impact at the gut level. The decrease of most glycerophospholipids indicated that ketosis is associated with disordered lipid metabolism. For milk, pyrimidine metabolism, purine metabolism, pantothenate and CoA biosynthesis, amino sugar and nucleotide sugar metabolism, nicotinate and nicotinamide metabolism, sphingolipid metabolism, fatty acid degradation were identified as significant pathways. The WGCNA indicated that purine and pyrimidine metabolism in plasma was highly correlated with milk yield during the peripartal period. Alterations in purine and pyrimidine metabolism characterized ketosis, with lower levels of these metabolites in both milk and blood underscoring reduced efficiency in nitrogen metabolism. Our results may help to establish a foundation for future research investigating mechanisms responsible for the occurrence and development of ketosis in peripartal cows.

The Effect of Water Co-Feeding on the Catalytic Performance of Zn/HZSM-5 in Ethylene Aromatization Reactions.

During the methanol-to-aromatics (MTA) process, a large amount of water is generated, while the influence and mechanism of water on the activity and selectivity of the light olefin aromatization reaction are still unclear. Therefore, a study was conducted to systematically investigate the effects of water on the reactivity and the product distribution in ethylene aromatization using infrared spectroscopy (IR), intelligent gravitation analyzer (IGA), and X-ray absorption fine structure (XAFS) characterizations. The results demonstrated that the presence of water reduced ethylene conversion and aromatic selectivity while increasing hydrogen selectivity at the same contact time. This indicated that water had an effect on the reaction pathway by promoting the dehydrogenation reaction and suppressing the hydrogen transfer reaction. A detailed analysis using linear combination fitting (LCF) of Zn K-edge X-ray absorption near-edge spectroscopy (XANES) on Zn/HZSM-5 catalysts showed significant variations in the state of existence and the distribution of Zn species on the deactivated catalysts, depending on different reaction atmospheres and water contents. The presence of water strongly hindered the conversion of ZnOH+ species, which served as the active centers for the dehydrogenation reaction, to ZnO on the catalyst. As a result, the dehydrogenation activity remained high in the presence of water. This study using IR and IGA techniques revealed that water on the Zn/HZSM-5 catalyst inhibited the adsorption of ethylene on the zeolite, resulting in a noticeable decrease in ethylene conversion and a decrease in aromatic selectivity. These findings contribute to a deeper understanding of the aromatization reaction process and provide data support for the design of efficient aromatization catalysts.

Microbial mechanisms of C/N/S geochemical cycling during low-water-level sediment remediation in urban rivers.

Low-water-level regulation has been effectively implemented in the restoration of urban river sediments in Guangzhou City, China. Further investigation is needed to understand the microbial mechanisms involved in pollutant degradation in low-water-level environments. This study examined sediment samples from nine rivers, including low-water-level rivers (LW), tidal waterways (TW), and enclosed rivers (ER). Metagenomic high-throughput sequencing and the Diting pipeline were utilized to investigate the microbial mechanisms involved in sediment C/N/S geochemical cycling during low-water-level regulation. The results reveal that the degree of pollution in LW sediment is lower compared to TW and ER sediment. LW sediment exhibits a higher capacity for pollutant degradation and elimination of black, odorous substances due to its stronger microbial methane oxidation, nitrification, denitrification, anammox, and oxidation of sulfide, sulfite, and thiosulfate. Conversely, TW and ER sediment showcase greater microbial methanogenesis, anaerobic fermentation, and sulfide generation abilities, leading to the persistence of black, odorous substances. Factors such as grit and silt content, nitrate, and ammonia concentrations impacted microbial metabolic pathways. Low-water-level regulation improved the micro-environment for functional microbes, facilitating pollutant removal and preventing black odorous substance accumulation. These findings provide insights into the microbial mechanisms underlying low-water-level regulation technology for sediment restoration in urban rivers.

A Fine Analysis of Zn Species Structure and Distribution in Zn/ZSM-5 Catalysts by Linear Combination Fitting Analysis of XANES Spectra.

Investigating the distribution of different Zn species on Zn-containing zeolite catalysts is crucial for identifying the active sites and establishing the relationship between the catalyst's structure and its activity in the process of ethylene aromatization. By utilizing X-ray absorption near edge spectra (XANES) of various reference samples, this study employed linear combination fitting (LCF) analysis on XANES spectra of real samples to accurately measure the changes in the distribution of Zn species in Zn-containing HZSM-5 zeolites under different Zn sources and loadings. The results showed that ZnOH+, ZnO clusters, and ZnO crystalline structures coexist in Zn/HZSM-5 catalysts prepared through physical mixing and incipient wet impregnation methods. A similar trend was observed for catalysts prepared using different methods, with an increase in Zn content resulting in a decrease in the proportion of ZnOH+ and a significant increase in the amount of larger ZnO crystals. Furthermore, ZnO clusters were confined within the zeolite pores. The findings of this study established a direct correlation between the amount of ZnOH+ determined through LCF analysis and both the rate of hydrogen production and the rate of aromatics formation, providing strong evidence for the catalytic role of ZnOH+ as an active center for dehydrogenation, which plays a key role in promoting the formation of aromatics. The method of LCF analysis on XANES spectra allows for the determination of the local structure of Zn species, facilitating a more precise analysis based on the distribution of these species. This method not only provides detailed information about the Zn species but also enhances the accuracy of the overall analysis.

Combination of serum alpha-fetoprotein, PIVKA-â ¡ and glypican-3 in diagnosis of hepatocellular carcinoma: a meta-analysis. / è¡€æ¸ å­¦æ ‡å¿—ç‰©ç”²èƒŽè›‹ç™½ã€PIVKA-â ¡å’Œç£·è„‚é °è‚Œé†‡è›‹ç™½èšç³–3联合诊断肝癌的meta分析.

OBJECTIVES: To assess the value of serum alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-Ⅱ (PIVKA-Ⅱ) and glypican-3 (GPC-3) in the diagnosis of hepatocellular carcinoma (HCC). METHODS: Studies of AFP, PIVKA-Ⅱ, GPC-3 or in combination for the diagnosis of HCC since 2002 were searched in PubMed, Web of Science and Embase databases. The literature was screened according to the inclusion and exclusion criteria, the quality of the included articles was evaluated by QUADAS checklist, and relevant data were extracted by Meta DiSc, Review Manager 5.4 and Stata 15.1. The diagnostic values of AFP, PIVKA-Ⅱ and GPC-3 alone or in combination for HCC were assessed with receiver operating characteristic (ROC) curve. RESULTS: A total of 32 articles were included in the study. Meta-analysis showed that when a single marker was used to diagnose HCC, the area under the ROC curve (AUC) of PIVKA-Ⅱ was the highest (0.88, 95%CI: 0.85-0.91), followed by GPC-3 and AFP. The AUC of combination of serum markers was higher than that of a single marker, and the AUC of PIVKA-Ⅱ combined with GPC-3 was the highest (0.90, 95%CI: 0.87-0.92). When a single marker was used for diagnosis, the sensitivity of PIVKA-Ⅱ and GPC-3 were relatively high (0.75 and 0.76), while the specificity of PIVKA-Ⅱ (0.88) and AFP (0.87) were higher than that of GPC-3 (0.81). The sensitivity of the combination of serum markers was higher than that of a single marker, while the specificity was not significantly improved. When a single marker is used to diagnose HCC, the diagnostic odds ratio (DOR) of PIVKA-Ⅱ was the highest (22, 95%CI: 13-36), followed by GPC-3 and AFP. The DOR of the combination of two markers in the diagnosis of HCC was higher than that of a single marker, and the DOR of AFP combined with GPC-3 was the highest (25, 95%CI: 9-67). The DOR of the combination of the three markers was significantly reduced to 10 (95%CI: 7-45). CONCLUSIONS: When a single marker is used, PIVKA-Ⅱ has a higher diagnostic value for HCC. The combination of two markers can significantly improve the diagnostic sensitivity, and AFP combined with PIVKA-Ⅱ is recommended for the diagnosis of HCC. The combination of all three markers failed to further improve the diagnostic value.

Irisin deficiency exacerbates diet-induced insulin resistance and cardiac dysfunction in type II diabetes in mice.

Irisin is involved in the regulation of a variety of physiological conditions, metabolism, and survival. We and others have demonstrated that irisin contributes critically to modulation of insulin resistance and the improvement of cardiac function. However, whether the deletion of irisin will regulate cardiac function and insulin sensitivity in type II diabetes remains unclear. We utilized the CRISPR/Cas-9 genome-editing system to delete irisin globally in mice and high-fat diet (HFD)-induced type II diabetes model. We found that irisin deficiency did not result in developmental abnormality during the adult stage, which illustrates normal cardiac function and insulin sensitivity assessed by glucose tolerance test in the absence of stress. The ultrastructural analysis of the transmission electronic microscope (TEM) indicated that deletion of irisin did not change the morphology of mitochondria in myocardium. Gene expression profiling showed that several key signaling pathways related to integrin signaling, extracellular matrix, and insulin-like growth factors signaling were coordinately downregulated by deletion of irisin. However, when mice were fed a high-fat diet and chow food for 16 wk, ablation of irisin in mice exposed to HFD resulted in much more severe insulin resistance, metabolic derangements, profound cardiac dysfunction, and hypertrophic response and remodeling as compared with wild-type control mice. Taken together, our results indicate that the loss of irisin exacerbates insulin resistance, metabolic disorders, and cardiac dysfunction in response to HFD and promotes myocardial remodeling and hypertrophic response. This evidence reveals the molecular evidence and the critical role of irisin in modulating insulin resistance and cardiac function in type II diabetes.NEW & NOTEWORTHY By utilizing the CRISPR/Cas-9 genome-editing system and high-fat diet (HFD)-induced type II diabetes model, our results provide direct evidence showing that the loss of irisin exacerbates cardiac dysfunction and insulin resistance while promoting myocardial remodeling and a hypertrophic response in HFD-induced diabetes. This study provides new insight into understanding the molecular evidence and the critical role of irisin in modulating insulin resistance and cardiac function in type II diabetes.