Effect of corneal cross-linking on biomechanical changes following transepithelial photorefractive keratectomy and femtosecond laser-assisted LASIK.

Purpose: To evaluate the change in corneal biomechanics in patients with postoperative ectasia risk when combining two common laser vision correction procedures (tPRK and FS-LASIK) with cross-linking (in tPRK Xtra and FS-LASIK Xtra). Methods: The study included 143 eyes of 143 myopic, astigmatic patients that were divided into non-cross-linked refractive surgery groups (non-Xtra groups, tPRK and FS-LASIK) and cross-linked groups (Xtra groups, tPRK Xtra and FS-LASIK Xtra) according to an ectasia risk scoring system. The eyes were subjected to measurements including the stress-strain index (SSI), the stiffness parameter at first applanation (SP-A1), the integrated inverse radius (IIR), the deformation amplitude at apex (DA), and the ratio of deformation amplitude between apex and 2 mm from apex (DARatio2mm). The measurements were taken preoperatively and at 1, 3, and 6 months postoperatively (pos1m, pos3m, and pos6m). Posterior demarcation line depth from the endothelium (PDLD) and from the ablation surface (DLA) were recorded at pos1m. Results: SP-A1 significantly decreased, while IIR, deformation amplitude, and DARatio2mm increased significantly postoperatively in all four groups (p < 0.01)-all denoting stiffness decreases. In the FS-LASIK group, the changes in IIR, DA, and DARatio2mm were 32.7 ± 15.1%, 12.9 ± 7.1%, and 27.2 ± 12.0% respectively, which were significantly higher (p < 0.05) compared to 20.1 ± 12.8%, 6.4 ± 8.2%, and 19.7 ± 10.4% in the FS-LASIK Xtra group. In the tPRK group, the change in IIR was 27.3 ± 15.5%, significantly larger than 16.9 ± 13.4% in the tPRK Xtra group. The changes of SSI were minimal in the tPRK (-1.5 ± 21.7%, p = 1.000), tPRK Xtra (8.4 ± 17.9%, p = 0.053), and FS-LASIK Xtra (5.6 ± 12.7%, p = 0.634) groups, but was significant in the FS-LASIK group (-12.1 ± 7.9%, p < 0.01). After correcting for baseline biomechanical metrics, preoperative bIOP and the change in central corneal thickness (△CCT) from pre to pos6m, the changes in the IIR in both FS-LASIK and tPRK groups, as well as DA, DARatio2mm and SSI in the FS-LASIK group remained statistically greater than their corresponding Xtra groups (all p < 0.05). Most importantly, after correcting for these covariates, the changes in DARatio2mm in the FS-LASIK Xtra became statistically smaller than in the tPRK Xtra (p = 0.017). Conclusion: The statistical analysis results indicate that tPRK Xtra and FS-LASIK Xtra effectively reduced the biomechanical losses caused by refractive surgery (tPRK and FS-LASIK). The decrease in corneal overall stiffness was greater in FS-LASIK than in tPRK, and the biomechanical enhancement of CXL was also higher following LASIK than after tPRK.

A metastasis-associated Pannexin1 mutant (Panx1 1-89 ) forms a minimalist ATP release channel.

A truncated form of the ATP release channel pannexin 1 (Panx1), Panx1 1-89 , is enriched in metastatic breast cancer cells and has been proposed to mediate metastatic cell survival by increasing ATP release through mechanosensitive Panx1 channels. However, whether Panx1 1-89 on its own (without the presence of wtPanx1) mediates ATP release has not been tested. Here, we show that Panx1 1-89 by itself can form a constitutively active membrane channel, capable of releasing ATP even in the absence of wild type Panx1. Our biophysical characterization reveals that most basic structure-function features of the channel pore are conserved in the truncated Panx1 1-89 peptide. Thus, augmenting extracellular potassium ion concentrations enhances Panx1 1-89 -mediated conductance. Moreover, despite the severe truncation, Panx1 1-89 retains the sensitivity to most of wtPanx1 channel inhibitors and can thus be targeted. Therefore, Panx1 blockers have the potential to be of therapeutic value to combat metastatic cell survival. Our study not only elucidates a mechanism for ATP release from cancer cells, but it also supports that the Panx1 1-89 mutant should facilitate structure-function analysis of Panx1 channels.

A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators with Functional Group Information and Hypergraph Structure.

Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-specific. Recently, deep learning models based on graph neural networks have made remarkable progress in molecular representation learning. However, many graph-based approaches ignore molecular hierarchical structure modeling guided by domain knowledge. In chemistry, the functional groups of a molecule determine its interaction with specific targets. Therefore, we propose a hierarchical graph neural network framework (called HiGPPIM) for predicting PPIMs by integrating atom-level and functional group-level features of molecules. HiGPPIM constructs atom-level and functional group-level graphs based on chemical knowledge and learns graph representations using graph attention networks. Furthermore, a hypergraph attention network is designed in HiGPPIM to aggregate and transform two-level graph information. We evaluate the performance of HiGPPIM on eight PPI families and two prediction tasks, namely PPIM identification and potency prediction. Experimental results demonstrate that HiGPPIM achieves state-of-the-art performance on both tasks and that using functional group information to guide PPIM prediction is effective. The source code and datasets are freely available at https://github.com/1zzt/HiGPPIM.

Tumor treating fields in glioblastoma: long-term treatment and high compliance as favorable prognostic factors.

Introduction: Tumor treating fields (TTFields) have earned substantial attention in recent years as a novel therapeutic approach with the potential to improve the prognosis of glioblastoma (GBM) patients. However, the impact of TTFields remains a subject of ongoing debate. This study aimed to offer real-world evidence on TTFields therapy for GBM, and to investigate the clinical determinants affecting its efficacy. Methods: We have reported a retrospective analysis of 81 newly diagnosed Chinese GBM patients who received TTFields/Stupp treatment in the Second Affiliated Hospital of Zhejiang University. Overall survival (OS) and progression-free survival (PFS) were analyzed using Kaplan-Meier method. Cox regression models with time-dependent covariates were utilized to address non-proportional hazards and to assess the influence of clinical variables on PFS and OS. Results: The median PFS and OS following TTFields/STUPP treatment was 12.6 months (95% CI 11.0-14.1) and 21.3 months (95% CI 10.0-32.6) respectively. Long-term TTFields treatment (>2 months) exhibits significant improvements in PFS and OS compared to the short-term treatment group (≤2 months). Time-dependent covariate COX analysis revealed that longer TTFields treatment was correlated with enhanced PFS and OS for up to 12 and 13 months, respectively. Higher compliance to TTFields (≥ 0.8) significantly reduced the death risk (HR=0.297, 95%CI 0.108-0.819). Complete surgical resection and MGMT promoter methylation were associated with significantly lower risk of progression (HR=0.337, 95% CI 0.176-0.643; HR=0.156, 95% CI 0.065-0.378) and death (HR=0.276, 95% CI 0.105-0.727; HR=0.249, 95% CI 0.087-0.710). Conclusion: The TTFields/Stupp treatment may prolong median OS and PFS in GBM patients, with long-term TTFields treatment, higher TTFields compliance, complete surgical resection, and MGMT promoter methylation significantly improving prognosis.

Centrosymmetry-Breaking Morphotropic Phase Boundary: A Pathway to Highly Sensitive and Strong Pressure-Responsive Nonlinear Optical Switches.

The quest for high-performance piezoelectric materials has been synonymous with the pursuit of the morphotropic phase boundary (MPB), yet the full potential of MPBs remains largely untapped outside of the realm of ferroelectrics. In this study, we reveal a new class of MPB by creating continuous molecular-based solid solutions between centro- and noncentrosymmetric compounds, exemplified by (tert-butylammonium)1-x(tert-amylammonium)xFeCl4 (0 ≤ x ≤ 1), where the MPB is formed due to disorder of molecular cations. Near the MPB, we discovered an exceptionally sensitive nonlinear optical material in the centrosymmetric phase, capable of activation at pressures as low as 0.12-0.27 GPa, and producing tunable second-harmonic generation (SHG) signals from zero to 18.8 times that of KH2PO4 (KDP). Meanwhile, synchrotron diffraction experiments have unveiled a third competing phase (P212121) appearing at low pressure, forming a triple-phase point near the MPB, thereby providing insight into the mechanism underpinning the nonlinear optical (NLO) switch behavior. These findings highlight the opportunity to harness exceptional physical properties in symmetry-breaking solid solution systems by strategically designing novel MPBs.

PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction.

MOTIVATION: Drug combination therapies have shown promise in clinical cancer treatments. However, it is hard to experimentally identify all drug combinations for synergistic interaction even with high-throughput screening due to the vast space of potential combinations. Although a number of computational methods for drug synergy prediction have proven successful in narrowing down this space, fusing drug pairs and cell line features effectively still lacks study, hindering current algorithms from understanding the complex interaction between drugs and cell lines. RESULTS: In this paper, we proposed a Permutable feature fusion network for Drug-Drug Synergy prediction, named PermuteDDS. PermuteDDS takes multiple representations of drugs and cell lines as input and employs a permutable fusion mechanism to combine drug and cell line features. In experiments, PermuteDDS exhibits state-of-the-art performance on two benchmark data sets. Additionally, the results on independent test set grouped by different tissues reveal that PermuteDDS has good generalization performance. We believed that PermuteDDS is an effective and valuable tool for identifying synergistic drug combinations. It is publicly available at https://github.com/littlewei-lazy/PermuteDDS . SCIENTIFIC CONTRIBUTION: First, this paper proposes a permutable feature fusion network for predicting drug synergy termed PermuteDDS, which extract diverse information from multiple drug representations and cell line representations. Second, the permutable fusion mechanism combine the drug and cell line features by integrating information of different channels, enabling the utilization of complex relationships between drugs and cell lines. Third, comparative and ablation experiments provide evidence of the efficacy of PermuteDDS in predicting drug-drug synergy.

The Molecular Architecture of the Nuclear Basket.

The nuclear pore complex (NPC) is the sole mediator of nucleocytoplasmic transport. Despite great advances in understanding its conserved core architecture, the peripheral regions can exhibit considerable variation within and between species. One such structure is the cage-like nuclear basket. Despite its crucial roles in mRNA surveillance and chromatin organization, an architectural understanding has remained elusive. Using in-cell cryo-electron tomography and subtomogram analysis, we explored the NPC's structural variations and the nuclear basket across fungi (yeast; S. cerevisiae), mammals (mouse; M. musculus), and protozoa (T. gondii). Using integrative structural modeling, we computed a model of the basket in yeast and mammals that revealed how a hub of Nups in the nuclear ring binds to basket-forming Mlp/Tpr proteins: the coiled-coil domains of Mlp/Tpr form the struts of the basket, while their unstructured termini constitute the basket distal densities, which potentially serve as a docking site for mRNA preprocessing before nucleocytoplasmic transport.

Spatiotemporal characteristics analysis of multi-factorial air pollution in the Jing-Jin-Ji region based on improved sequential ICI method and novel grey spatiotemporal incidence models.

Air pollution shares the attributes of multi-factorial influence and spatiotemporal complexity, leading to air pollution control assistance models easily falling into a state of failure. To address this issue, we design a framework containing improved data fusion method, novel grey incidence models and air pollution spatiotemporal analysis to analyze the complex characteristics of air pollution under the fusion of multiple factors. Firstly, we improve the existing data fusion method for multi-factor fusion. Subsequently, we construct two grey spatiotemporal incidence models to examine the spatiotemporal characteristics of multi-factorial air pollution in network relationships and changing trends. Furthermore, we propose two new properties that can manifest the performance of grey incidence analysis, and we provide detailed proof of the properties of the new models. Finally, in the Jing-Jin-Ji region, the novel models are used to study the network relationships and trend changes of air pollution. The findings are as follows: (1) Two highly polluted belts in the region require attention. (2) Although the air pollution network under multi-factorial fusion obeys the first law of geography, the network density and node density exhibit significant variations. (3) From 2013 to 2021, all pollutants except O3 show improvement. (4) Recommendations for responses are presented based on the above-mentioned results. (5) The parameter analyses, model comparisons, Monte Carlo experiments and model feature summaries illustrate that the proposed models are practical, interpretable and considerably outperform various prevailing competitors with remarkable universality.

QTL mapping for plant height and ear height using bi-parental immortalized heterozygous populations in maize.

Introduction: Plant height (PH) and ear height (EH) are key plant architectural traits in maize, which will affect the photosynthetic efficiency, high plant density tolerance, suitability for mechanical harvesting. Methods: QTL mapping were conducted for PH and EH using a recombinant inbred line (RIL) population and two corresponding immortalized backcross (IB) populations obtained from crosses between the RIL population and the two parental lines. Results: A total of 17 and 15 QTL were detected in the RIL and IB populations, respectively. Two QTL, qPH1-1 (qEH1-1) and qPH1-2 (qEH1-4) in the RIL, were simultaneously identified for PH and EH. Combing reported genome-wide association and cloned PH-related genes, co-expression network analyses were constructed, then five candidate genes with high confidence in major QTL were identified including Zm00001d011117 and Zm00001d011108, whose homologs have been confirmed to play a role in determining PH in maize and soybean. Discussion: QTL mapping used a immortalized backcross population is a new strategy. These identified genes in this study can provide new insights for improving the plant architecture in maize.

Ozone degradation of tetracycline hydrochloride enhanced by magnetic nanofluid composed of Fe3O4 nanoparticles.

Magnetic Fe3O4 nanoparticles were added into the aqueous phase to form nanofluid systems, in which ozone was used for the oxidation of tetracycline hydrochloride (TC) in the solution. The nanomaterials were characterized using SEM, XRD, EDS, and FT-IR. The effects of nanoparticles size, addition ratio, and number of cycles on the process of ozone oxidation of TC were investigated. The results indicated that the addition ratio of nanoparticles have a certain impact on the performance of ozone oxidation. When the addition ratio increased from 0.02% to 0.4%, the removal rate of TC in the solution was improved significantly. Besides, the particle size of nanoparticles showed a greater impact on ozone oxidation. At the nanoscale, Fe3O4 nanoparticles exhibited significant strengthening properties, which is attributed to the construction of nanofluid systems. The removal rate of TC in solution decreased obviously with the increase of nanoparticles size. The Fe3O4 nanoparticles with particle size of 20 nm showed the most significant effect on TC degradation. The recycling experiment showed that magnetic Fe3O4 nanoparticles had stable regeneration performance. For three times of recycling treatment, with a Fe3O4 addition ratio of 0.4%, the removal rate of TC reached 98.7%, 97.21%, and 96%, respectively. Based on the characterization results, the strengthening mechanism was analyzed. The experimental results indicated that construction of nanofluids systems could improve the utilization rate of ozone, and Fe3O4 nanoparticles were reusable and easily recyclable.

Adaptive Force Field Parameter Optimization for Expanding Reaction Simulations within Wide-Ranged Temperature.

Large-scale and long-term simulation of chemical reactions are key research topics in computational chemistry. However, there are still difficulties in simulating high-temperature reactions, such as polymer thermal decomposition. Herein, we introduce an adaptive potential parameter optimization framework designed to automatically fine-tune parameters, and the application of it to optimize ReaxFF parameters enhances the accuracy of chemical reaction simulations conducted at experimental temperatures. To achieve this, we leverage the power of Random Forests and interpretable machine learning techniques that enable the identification and selection of parameters that exert a substantial influence on the target attribute. By training deep neural network (NN) models, we established optimized parameter associations with reference properties. We train deep neural network (NN) models to establish the relationship between the optimized parameters and reference properties. We employ a Genetic Algorithm (GA) to utilize the surrogate NN model and the quantum mechanical targets to speed up the search for the optimal parameters. Our simulation results of resin pyrolysis show that the adaptive optimized ReaxFF can predict the peak temperature more accurately and obtain reasonable product composition under conditions that more closely resemble experimental scenarios. This work facilitates advances in force field parameter optimization for more accurate and universal reaction simulations.

Mendelian Randomisation Analysis of Causal Association between Lifestyle, Health Factors, and Keratoconus.

Keratoconus (KC), a leading cause of vision impairment, has an unclear aetiology. This study used Mendelian randomization (MR) to explore the causal links between various factors (smoking, asthma, Down syndrome, inflammatory bowel disease, atopic dermatitis, and serum 25-hydroxyvitamin D levels) and KC. A two-sample MR design, grounded in genome-wide association study (GWAS) summary statistics, was adopted using data from FinnGen, UK Biobank, and other GWAS-related articles. The inverse-variance weighted (IVW) method was employed, complemented by the Wald ratio method for factors with only one single-nucleotide polymorphism (SNP). Sensitivity and stability were assessed through Cochrane's Q test, the MR-Egger intercept test, MR-PRESSO outlier test, and the leave-one-out analysis. The IVW results for the ORA (Ocular Response Analyzer) biomechanical parameters indicated significant associations between tobacco smoking (CH: p < 0.001; CRF: p = 0.009) and inflammatory bowel disease (CH: p = 0.032; CRF: p = 0.001) and corneal biomechanics. The Wald ratio method showed tobacco smoking was associated with a lower risk of KC (p = 0.024). Conversely, asthma (p = 0.009), atopic dermatitis (p = 0.012), inflammatory bowel disease (p = 0.017), and serum 25-hydroxyvitamin D levels (p = 0.039) were associated with a higher risk of KC by IVW, and the same applied to Down syndrome (p = 0.004) using the Wald ratio. These results underscore the role of corneal biomechanics as potential mediators in KC risk, warranting further investigation using Corvis ST and Brillouin microscopy. The findings emphasise the importance of timely screening for specific populations in KC prevention and management.

Defect Engineering of Hexagonal MAB Phase Ti2 InB2 as Anode of Lithium-Ion Battery with Excellent Cycling Stability.

Hexagonal MAB phases （h-MAB） have attracted attention due to their potential to exfoliate into MBenes, similar to MXenes, which are predicted to be promising for Li-ion battery applications. However, the high cost of synthesizing MBenes poses challenges for their use in batteries. This study presents a novel approach where a simple ball-milling treatment is employed to enhance the purity of the h-MAB phase Ti2 InB2 and introduce significant indium defects, resulting in improved conductivity and the creation of abundant active sites. The synthesized Ti2 InB2 with indium defects (VIn -Ti2 InB2 ) exhibits excellent electrochemical properties, particularly exceptional long-cycle stability at current densities of 5 A g-1 (5000 cycles, average capacity decay of 0.0018%) and 10 A g-1 (15 000 cycles, average capacity decay of 0.093%). The charge storage mechanism of VIn -Ti2 InB2 , involving a dual redox reaction, is proposed, where defects promote the In-Li alloy reaction and a redox reaction with Li in the TiB layer. Finally, a Li-ion full cell demonstrates cycling stability at 0.5 A g-1 after 350 cycles. This work presents the first accessible and scalable application of VIn -Ti2 InB2 as a Li-ion anode, unlocking a wealth of possibilities for sustainable electrochemical applications of h-MAB phases.

Sensing of mitochondrial DNA by ZBP1 promotes RIPK3-mediated necroptosis and ferroptosis in response to diquat poisoning.

Diquat (DQ) poisoning is a severe medical condition associated with life-threatening implications and multiorgan dysfunction. Despite its clinical significance, the precise underlying mechanism remains inadequately understood. This study elucidates that DQ induces instability in the mitochondrial genome of endothelial cells, resulting in the accumulation of Z-form DNA. This process activates Z-DNA binding protein 1 (ZBP1), which then interacts with receptor-interacting protein kinase 3 (RIPK3), ultimately leading to RIPK3-dependent necroptotic and ferroptotic signaling cascades. Specific deletion of either Zbp1 or Ripk3 in endothelial cells simultaneously inhibits both necroptosis and ferroptosis. This dual inhibition significantly reduces organ damage and lowers mortality rate. Notably, our investigation reveals that RIPK3 has a dual role. It not only phosphorylates MLKL to induce necroptosis but also phosphorylates FSP1 to inhibit its enzymatic activity, promoting ferroptosis. The study further shows that deletion of mixed lineage kinase domain-like (Mlkl) and the augmentation of ferroptosis suppressor protein 1 (FSP1)-dependent non-canonical vitamin K cycling can provide partial protection against DQ-induced organ damage. Combining Mlkl deletion with vitamin K treatment demonstrates a heightened efficacy in ameliorating multiorgan damage and lethality induced by DQ. Taken together, this study identifies ZBP1 as a crucial sensor for DQ-induced mitochondrial Z-form DNA, initiating RIPK3-dependent necroptosis and ferroptosis. These findings suggest that targeting the ZBP1/RIPK3-dependent necroptotic and ferroptotic pathways could be a promising approach for drug interventions aimed at mitigating the adverse consequences of DQ poisoning.

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data.

BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. METHODS: This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. RESULTS: In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. CONCLUSION: This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

Direct Z-scheme system of UiO-66 cubes wrapped with Zn0.5Cd0.5S nanoparticles for photocatalytic hydrogen generation synchronized with organic pollutant degradation.

Optimized fabrication of Z-scheme photocatalyst based on MOF materials offers sustainable energy generation and environmental improvement due to their attractive properties. The Z-scheme heterojunctions consisting of UiO-66 cubes covered with Zn0.5Cd0.5S nanoparticles were fabricated by a facile solvothermal method. Thanks to the Z-scheme carrier transport under simulated sunlight irradiation, UiO-66@Zn0.5Cd0.5S exhibited enhanced photocatalytic performance of H2 generation synchronized with organic pollutant degradation in fluoroquinolone antibiotic wastewater. Synergistically, the highest comprehensive performance was obtained in ciprofloxacin solution. The H2 yield reached 224 µmol∙ g-1∙ h-1 and simultaneously the removal efficiency was up to 83.6 %. The degradation pathways revealed that the process of piperazine ring cleavage and decarboxylation also generates H protons, further promoting the production of H2. Therefore, the effective spatial separation and transfer of the photoinduced carriers are attributed to the good band structure, large specific surface area, and cooperative reduction and oxidation reactions of UiO-66@Zn0.5Cd0.5S, resulting in significant photocatalytic activity. The toxicity assessment of antibiotics and intermediate products during the photocatalytic reaction also verifies the reduction of environmental risk. This study highlights a promising way to expand the application of the MOFs-based photocatalyst in clean energy conversion coupling with water remediation.

Retraction: Strontium-doped gelatin scaffolds promote M2 macrophage switch and angiogenesis through modulating the polarization of neutrophils.

Retraction of 'Strontium-doped gelatin scaffolds promote M2 macrophage switch and angiogenesis through modulating the polarization of neutrophils' by Tao Li et al., Biomater. Sci., 2021, 9, 2931-2946, https://doi.org/10.1039/D0BM02126A.

Review on physical performance, modification mechanisms, carbon emissions and economic costs of recycled aggregates modified with physical enhancement technologies.

With the continuous advancement of urban renewal, the application of recycled aggregates (RA) is a win-win measure to solve the treatment of construction waste and provide the required building materials. However, the existence of a large amount of old adhesive mortar (OAM) makes it difficult for RA to equivalently replace natural aggregates (NA) due to their higher water absorption and crushing index, as well as a lower apparent density. From the published literature on enhancing RA, the most mature and easiest method for construction is physical enhancement technology. Therefore, through a review of recent related researches, this article summarizes and compares the modification effects of mechanical grinding technology, traditional heating and grinding technology, and microwave heating technology on the physical properties of RA, including water absorption, apparent density, and crushing value. The related modification mechanisms were discussed. Additionally, the impacts of different physical enhancement technologies on the environment and economy effects are assessed from the perspectives of carbon emissions and cost required during processing. Based on multi-criteria analysis, microwave heating technology is more efficient and cleaner, which is the most recommended in the future.

The acute toxic effect of Chinese medicine Fuzi is exacerbated in kidney yang deficiency mice due to metabolic difference.

ETHNOPHARMACOLOGICAL RELEVANCE: The proper application of toxic medicines is one of the characteristics of traditional Chinese medicines, and the use of traditional Chinese medicines follows the principle of dialectical treatment. It is necessary to combine different "syndrome" or "disease" states with the toxicity of traditional Chinese medicines to form a reliable toxicity evaluation system. Fuzi, the lateral root of Aconitum carmichaelii Debx, is recognized as a panacea for kidney yang deficiency syndrome, however, its toxic effects significantly limit its clinical application. AIM OF THE STUDY: Herein, our research aimed to explore the toxic effects of Fuzi on syndrome models, and tried to reveal the underlying mechanisms. MATERIALS AND METHODS: Firstly, the mouse model of kidney yang deficiency syndrome was established through intramuscular injection of 25 mg/kg hydrocortisone per day for 10 consecutive days. Then, the acute toxicity of Fuzi in normal mice and kidney yang deficiency model mice was explored. Finally, the plasma metabolite concentrations and liver CYP3A4 enzyme activity were analyzed to reveal the possible mechanisms of the different pharmacological and toxicological effects of Fuzi in individuals with different physical constitutions. RESULTS: It was found that the treatment with Fuzi (138 g/kg) had serious toxic effects on kidney yang deficiency mice, leading to the death of 80% of the mice, whereas it showed no lethal toxicity in normal mice. This indicates that Fuzi induced greater toxicity in kidney yang deficiency mice than in normal ones. The liver CYP3A4 enzyme activity in kidney yang deficiency mice was decreased by 20% compared to the controls, resulting in slower metabolism of the toxic diester diterpenoid alkaloids in Fuzi. CONCLUSION: In conclusion, our study showed that changes of the metabolic enzyme activity in individuals with different syndromes led to different toxic effects of Chinese medicines, emphasizing the crucial importance of considering individual physical syndromes in the clinical application of traditional Chinese medicine, and the significance of conducting safety evaluations and dose predictions on animal models with specific syndromes for traditional Chinese medicines.

Transcriptomic regulatory analysis of skeletal muscle development in landrace pigs.

The development of pig skeletal muscle is a complex dynamic regulation process, which mainly includes the formation of primary and secondary muscle fibers, the remodeling of muscle fibers, and the maturation of skeletal muscle; However, the regulatory mechanism of the entire developmental process remains unclear. This study analyzed the whole-transcriptome data of skeletal muscles at 27 developmental nodes (E33-D180) in Landrace pigs, and their key regulatory factors in the development process were identified using the bioinformatics method. Firstly, we constructed a transcriptome expression map of skeletal muscle development from embryo to adulthood in Landrace pig. Subsequently, due to drastic change in gene expression, the perinatal periods including E105, D0 and D9, were focused, and the genes related to the process of muscle fiber remodeling and volume expansion were revealed. Then, though conjoint analysis with miRNA and lncRNA transcripts, a ceRNA network were identified, which consist of 11 key regulatory genes (such as CHAC1, RTN4IP1 and SESN1), 7 miRNAs and 43 lncRNAs, and they potentially play an important role in the process of muscle fiber differentiation, muscle fiber remodeling and volume expansion, intramuscular fat deposition, and other skeletal muscle developmental events. In summary, we reveal candidate genes and underlying molecular regulatory networks associated with perinatal skeletal muscle fiber type remodeling and expansion. These data provide new insights into the molecular regulation of mammalian skeletal muscle development and diversity.