Fabrication and characterization of an alginate-based film incorporated with cinnamaldehyde for fruit preservation.

Sodium alginate (SA) is widely used in the food, biomedical, and chemical industries due to its biocompatibility, biodegradability, and excellent film-forming properties. This article introduces a simple method for preparing uniform alginate-based packaging materials with exceptional properties for fruit preservation. The alginate was uniformly crosslinked by gradually releasing calcium ions triggered by the sustained hydrolysis of gluconolactone (GDL). A cinnamaldehyde (CA) emulsion, stabilized by xanthan without the use of traditional surfactants, was tightly incorporated into the alginate film to enhance its antimicrobial, antioxidant, and UV shielding properties. The alginate-based film effectively blocked ultraviolet rays in the range of 400-200 nm, while allowing for a visible light transmittance of up to 70 %. Additionally, it showed an increased water contact angle and decreased water vapor permeability. The alginate-based film was also employed in the preparation of coated paper through the commonly used coating process in the papermaking industry. The alginate-based material displayed excellent antioxidant properties and antimicrobial activity against Escherichia coli, Staphylococcus aureus and Botrytis cinerea, successfully extending the shelf life of strawberries to 7 days at room temperature. This low-cost and facile method has the potential to drive advancements in the food and biomedical fields by tightly incorporating active oil onto a wide range of biomacromolecule substrates.

High expression of CCNB2 is an independent predictive poor prognostic biomarker and correlates with immune infiltrates in breast carcinoma.

Background: Cyclin B2 (CCNB2) is associated with cell cycle progression, acting as a cell cycle checkpoint in progression of G2/M transition. In many cancer patients, it has been observed that overexpression of CCNB2 enhances tumor invasiveness and leads to adverse prognosis. However, the association of CCNB2 with the tumor microenvironment remains unclear. Therefore, it is necessary to clarify the associations of CCNB2 with the immune status and prognosis of breast carcinoma (BRCA). Methods: Gene expression and clinical data for BRCA were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, followed by association analyses of CCNB2 expression with prognosis, immune cell infiltration, and immune checkpoints. This study further performed drug sensitivity analysis and constructed a prognostic nomogram for CCNB2. Results: 3619 differentially expressed genes were identified in BRCA, including CCNB2 that emerged as a key gene in the network. High CCNB2 expression correlated with poor prognosis. Functional analysis demonstrated enrichment of CCNB2 co-expressed genes with the cell cycle, cancer progression, cell energy, and immune pathways. Microsatellite instability and tumor mutation burden analyses indicated CCNB2 as a candidate immunotherapy target. Tumor-infiltrating myeloid-derived suppressor cells, regulatory T cells, and T helper 2 cells were associated with CCNB2-related tumor progression and metastasis. CCNB2 expression positively correlated with immune checkpoints, indicating that high CCNB2 expression might facilitate tumor immune escape. Tumors with high CCNB2 expression showed sensitivity to phosphoinositide 3-kinase-protein kinase B-mammalian target of rapamycin and cyclin-dependent kinase (CDK) 4/6 inhibitors, and the nomogram had good prognostic predictive ability for patients with BRCA. Conclusions: CCNB2 may play a crucial role in tumorigenesis and serve as an independent prognostic biomarker associated with tumor microenvironment, tumor immune infiltration and immunotherapy in BRCA.

Mesoporous CdO/CdGa2O4 microsphere for rapidly detecting triethylamine at ppb level.

Developing fast, accurate and sensitive triethylamine gas sensors with low detection limits is paramount to ensure the safety of workers and the public. However, sensors based on single metal oxide materials still suffer from drawbacks such as low response sensitivity and long response and recovery times. To address these challenges, in this work, a series of mesoporous CdO/CdGa2O4 microspheres were synthesized. We optimized the sensor's sensing performance to triethylamine by fine-tuning the ratio of CdO to CdGa2O4. Among them, CdO:3CdGa2O4-based sensor demonstrates a rapid response time of 2 s to detect 100 ppm of triethylamine, with a high response value of 211 and exceptional selectivity. Furthermore, it exhibits a low detection limit of 20 ppb for triethylamine, making it suitable for practically testing fish freshness. Crucially, electron transfer between the heterojunctions increases the chemically adsorbed oxygen on the materials' surface, thereby enhancing the sensor's response sensitivity to triethylamine. This discovery provides new insights and methodologies for the design of highly efficient triethylamine gas sensors.

Quantitative determination of flow rate variations in reservoir Eco-scheduling: A case study of Yangqu dam in the upper yellow river.

Accurate quantification of flow dynamics during reservoir ecological scheduling hinders the maintenance of normal reproductive activities in downstream riverine fish. This study proposed a quantitative method for determining the flow rate changes in reservoir ecological scheduling. The approach utilized the daily flow rate and daily flow-rate increment to characterize the flow process. Adopting the perspective of shifting spawning grounds of adhesive egg-laying fish species in response to flow rate variations, we introduced the Spawning Ground Overlap Rate as an indicator and utilized it to determine flow rate changes. Focusing on the downstream area of the Yangqu Hydropower Station in the upper reaches of the Yellow River, we calculated the distribution of spawning grounds and the Spawning Ground Overlap Rate in the region. We set a threshold for the Spawning Ground Overlap Rate to restrict the flow rate changes. The results indicated that during the fish spawning period, the ecological flow range in the downstream area of the Yangqu Dam was 480-1200 m3/s. It was required to maintain a daily flow rate change of less than 49.45 m3/(s·d) and a maximum seven-day flow difference of less than 227.76 m3/s to maintain the optimal level of spawning ground overlap rate. Additionally, it was necessary to keep the daily flow rate change below 123.83 m3/(s·d) and the maximum seven-day flow difference below 368.84 m3/s to maintain the minimum spawning ground overlap rate. The findings provide foundational data for determining flow dynamics during the ecological scheduling of the spawning period for viscous-spawning fish.

Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction.

Uncovering novel drug-drug interactions (DDIs) plays a pivotal role in advancing drug development and improving clinical treatment. The outstanding effectiveness of graph neural networks (GNNs) has garnered significant interest in the field of DDI prediction. Consequently, there has been a notable surge in the development of network-based computational approaches for predicting DDIs. However, current approaches face limitations in capturing the spatial relationships between neighboring nodes and their higher-level features during the aggregation of neighbor representations. To address this issue, this study introduces a novel model, KGCNN, designed to comprehensively tackle DDI prediction tasks by considering spatial relationships between molecules within the biomedical knowledge graph (BKG). KGCNN is built upon a message-passing GNN framework, consisting of propagation and aggregation. In the context of the BKG, KGCNN governs the propagation of information based on semantic relationships, which determine the flow and exchange of information between different molecules. In contrast to traditional linear aggregators, KGCNN introduces a spatial-aware capsule aggregator, which effectively captures the spatial relationships among neighboring molecules and their higher-level features within the graph structure. The ultimate goal is to leverage these learned drug representations to predict potential DDIs. To evaluate the effectiveness of KGCNN, it undergoes testing on two datasets. Extensive experimental results demonstrate its superiority in DDI predictions and quantified performance.

Nanocavity in hollow sandwiched catalysts as substrate regulator for boosting hydrodeoxygenation of biomass-derived carbonyl compounds.

Hydrodeoxygenation of oxygen-rich molecules toward hydrocarbons is attractive yet challenging in the sustainable biomass upgrading. The typical supported metal catalysts often display unstable catalytic performances owing to the migration and aggregation of metal nanoparticles (NPs) into large sizes under harsh conditions. Here, we develop a crystal growth and post-synthetic etching method to construct hollow chromium terephthalate MIL-101 (named as HoMIL-101) with one layer of sandwiched Ru NPs as robust catalysts. Impressively, HoMIL-101@Ru@MIL-101 exhibits the excellent activity and stability for hydrodeoxygenation of biomass-derived levulinic acid to gamma-valerolactone under 50°C and 1-megapascal H2, and its activity is about six times of solid sandwich counterparts, outperforming the state-of-the-art heterogeneous catalysts. Control experiments and theoretical simulation clearly indicate that the enrichment of levulinic acid and H2 by nanocavity as substrate regulator enables self-regulating the backwash of both substrates toward Ru NPs sandwiched in MIL-101 shells for promoting reaction with respect to solid counterparts, thus leading to the substantially enhanced performance.

FAM83B promotes cell proliferation via regulating the expression of CDK4/CDK6/CCND1 complex in laryngeal squamous cell carcinoma.

FAM83B, as one of the FAM83 family members, has been closely involved in cell transformation, and a growing number of scholars have been studied its role in tumours over the years. Whereas the effect and potential mechanism of FAM83B in laryngeal squamous cell carcinoma (LSCC) have not been investigated. In this research, we discovered that the expression quantity of FAM83B was remarkably higher in LSCC tissues (79.65 ± 35.98) than in matched adjacent tissues (59.34 ± 32.59) by tissue microarrays and immunohistochemistry. Furthermore, expression of FAM83B was knocked down in HEP-2 and TU177 cell lines via lentivirus, and in the course of intracorporal and extracorporeal experiments, FAM83B knockdown showed the inhibition of tumour growth, migration, and invasion ability. Moreover, cell cycle assay showed that FAM83B knockdown leads to an apparent accumulation of cells in the G1 phase, indicating that FAM83B knockdown can inhibit cell proliferation. Meanwhile, western blotting (WB) demonstrated that FAM83B knockdown led to a significant reduction in CDK4/CDK6/CCND1 protein expression, which may have decelerated cell cycle progression. Collectively, this study demonstrates that FAM83B serves as an oncogene in LSCC, promoting cell proliferation by controlling the protein expression of CDK4, CDK6, and CCND1, thus inducing a transference of the G1 stage to S stage in cell-cycle of LSCC cells. These results provide an academic foundation for elucidating the mechanism of LSCC occurrence and evolution and for developing treatment strategies for LSCC.

Shexiang Tongxin Dropping Pill Promotes Angiogenesis through VEGF/eNOS Signaling Pathway on Diabetic Coronary Microcirculation Dysfunction.

OBJECTIVE: To study the effect of Shexiang Tongxin Dropping Pill (STDP) on angiogenesis in diabetic cardiomyopathy mice with coronary microcirculation dysfunction (CMD). METHODS: According to a random number table, 6 of 36 SPF male C57BL/6 mice were randomly selected as the control group, and the remaining 30 mice were injected with streptozotocin intraperitoneally to replicate the type 1 diabetes model. Mice successfully copied the diabetes model were randomly divided into the model group, STDP low-dose group [15 mg/(kg·d)], medium-dose group [30 mg/(kg·d)], high-dose group [60 mg/(kg·d)], and nicorandil group [15 mg/(kg·d)], 6 in each group. The drug was given by continuous gavage for 12 weeks. The cardiac function of mice in each group was detected at the end of the experiment, and coronary flow reserve (CFR) was detected by chest Doppler technique. Pathological changes of myocardium were observed by hematoxylin-eosin staining, collagen fiber deposition was detected by masson staining, the number of myocardial capillaries was detected by platelet endothelial cell adhesion molecule-1 staining, and the degree of myocardial hypertrophy was detected by wheat germ agglutinin staining. The expression of the vascular endothlial growth factor (VEGF)/endothelial nitric oxide synthase (eNOS) signaling pathway-related proteins in myocardial tissue was detected by Western blot. RESULTS: Compared with the model group, medium- and high-dose STDP significantly increased the left ventricular ejection fraction and left ventricular fraction shortening (P<0.01), obviously repaired the disordered cardiac muscle structure, reduced myocardial fibrosis, reduced myocardial cell area, increased capillary density, and increased CFR level (all P<0.01). Western blot showed that high-dose STDP could significantly increase the expression of VEGF and promote the phosphorylation of vascular endothelial growth factor receptor 2, phosphoinositide 3-kinase, protein kinase B, and eNOS (P<0.05 or P<0.01). CONCLUSION: STDP has a definite therapeutic effect on diabetic CMD, and its mechanism may be related to promoting angiogenesis through the VEGF/eNOS signaling pathway.

Enhanced quantum secret sharing protocol for anonymous secure communication utilizing W states.

Quantum secret sharing (QSS) represents the fusion of quantum mechanics principles with secret information sharing, allowing a sender to distribute a secret among receivers for collective recovery. This paper introduces the concept of quantum anonymous secret sharing (QASS) to enhance the practicality of such protocols. We propose a QASS protocol leveraging W states, ensuring both recover-security and anonymity of shared secrets. Our protocol undergoes rigorous evaluation verifying their accuracy and fortifying their security against scenarios involving the active adversary. Additionally, acknowledging the imperfections inherent in real-world communication channels, we conduct a comprehensive analysis of protocol security and efficacy in noisy quantum networks. Our investigations reveal that W states exhibit good performance in mitigating noise interference, making them apt for practical applications.

Hydrophobic association-improved multi-functional hydrogels with liquid metal droplets stabilized by xanthan gum and PEDOT:PSS for strain sensors.

The synthesis of liquid metal-infused hydrogels, typically constituted by polyacrylamide networks crosslinked through covalent bonds, often encounters a conundrum: they exhibit restricted extensibility and a diminished capacity for self-repair, owing to the inherently irreversible nature of the covalent linkages. This study introduces a hydrophobically associated hydrogel embedding gallium (Ga)-droplets, realized through the in situ free radical copolymerization of hydrophobic hexadecyl methacrylate (HMA) and hydrophilic acrylamide (AM) in a milieu containing xanthan gum (XG) and PEDOT:PSS, which co-stabilizes the Ga-droplets. The Ga-droplets, synergistically functioning as conductive agents alongside PEDOT:PSS, also expedite the hydrogel's formation. The resultant XG/PEDOT:PSS-Ga-P(AM-HMA) hydrogel is distinguished by its remarkable extensibility (2950 %), exceptional toughness (3.28 MJ/m3), superior adherence to hydrophobic, smooth substrates, and an innate ability for hydrophobic-driven self-healing. As a strain sensing medium, this hydrogel-based sensor exhibits heightened sensitivity (gauge factor = 12.66), low detection threshold (0.1 %), and robust durability (>500 cycles). Furthermore, the inclusion of glycerol endows the XG/PEDOT:PSS-Ga-P(AM-HMA) hydrogel with anti-freezing properties without compromising its mechanical integrity and sensing acumen. This sensor adeptly captures a spectrum of human movements, from the nuanced radial pulse to extensive joint articulations. This research heralds a novel approach for fabricating multifaceted PAM-based hydrogels with toughness and superior sensing capabilities.

Essential role of lattice oxygen in hydrogen sensing reaction.

Understanding the sensing mechanism of metal oxide semiconductors is imperative to the development of high-performance sensors. The traditional sensing mechanism only recognizes the effect of surface chemisorbed oxygen from the air but ignores surface lattice oxygen. Herein, using in-situ characterizations, we provide direct experimental evidence that the surface chemisorbed oxygen participated in the sensing process can come from lattice oxygen of the oxides. Further density functional theory (DFT) calculations prove that the p-band center of O serves as a state of art for regulating the participation of lattice oxygen in gas-sensing reactions. Based on our experimental data and theoretical calculations, we discuss mechanisms that are fundamentally different from the conventional mechanism and show that the easily participation of lattice oxygen is helpful for the high response value of the materials.

Deformation and Failure Mechanisms of Element-Substituted Thermoelectric Type-I and Type-VIII Clathrates.

Clathrates are potential "phonon-glass, electron-crystal" thermoelectric semiconductors, whose structure of polyhedron stacks is very attractive. However, their mechanical properties have not yet met the requirements of industrial applications. Here, we report the ideal strength of element-substituted type-I and type-VIII clathrates and the shear deformation mechanism by using density functional theory. The results show that the framework element is the determinant of the intrinsic mechanical properties of the clathrates and is affected by sequential weakening of Si-Ge-Sn. The highest ideal shear strength is 8.71 GPa for I-Ba8Au6Si40 along the (110)/[001] slip system, which is attributed to the formation of higher-energy Si-Si covalent bonds. Meanwhile, the ideal shear strength of Ba-filled I/VIII clathrates (4.51/2.65 GPa) is higher than that of Sr-filled clathrates (3.64 GPa/1.91 GPa). In addition, the strength and ultimate strain of VIII-Ba8Ga16Sn30 can be significantly increased by the structural coordination accommodating with the stiffness of the Ga-Ge bond to achieve simultaneous bond breaking. Our findings demonstrate that the element substitution strategy is an effective approach for designing highly robust clathrates.

Exploring Carbonization Temperature to Create Closed Pores for Hard Carbon as High-Performance Sodium-Ion Battery Anodes.

Biomass-derived porous carbon materials are meaningful to employ as a hard carbon precursor for anode materials of sodium-ion batteries (SIBs) from a sustainability perspective. Here, a straightforward approach is proposed to develop rich closed pores in pinenut-derived carbon, with the aim of improving Na+ plateau storage by adjusting the pyrolysis temperature. The optimized sample, namely the pinenut-derived carbon at 1300 °C, demonstrates remarkable reversible specific capacity of 278 mAh g-1, along with a high initial Coulomb efficiency of 85% and robust cycling stability (with a capacity retention of 89% after 800 cycles at 0.2 A g-1). In situ and ex situ analyses unveil that the developed closed pores play a significant role in enhancing the plateau capacity, providing compelling evidence for the "adsorption-filling" mechanism. Moreover, the corresponding full-cell achieves a high energy density of 245.7 Wh kg-1 (based on the total weight of both electrode active materials) and exhibits outstanding rate capability (191.4 mAh g-1 at 3 A g-1).

Molecular subtyping and the construction of a predictive model of colorectal cancer based on ion channel genes.

PURPOSE: Colorectal cancer (CRC) is a highly heterogeneous malignancy with an unfavorable prognosis. The purpose of this study was to address the heterogeneity of CRC by categorizing it into ion channel subtypes, and to develop a predictive modeling based on ion channel genes to predict the survival and immunological states of patients with CRC. The model will provide guidance for personalized immunotherapy and drug treatment. METHODS: A consistent clustering method was used to classify 619 CRC samples based on the expression of 279 ion channel genes. Such a method was allowed to investigate the relationship between molecular subtypes, prognosis, and immune infiltration. Furthermore, a predictive modeling was constructed for ion channels to evaluate the ion channel properties of individual tumors using the least absolute shrinkage and selection operator. The expression patterns of the characteristic genes were validated through molecular biology experiments. The effect of potassium channel tetramerization domain containing 9 (KCTD9) on CRC was verified by cellular functional experiments. RESULTS: Four distinct ion channel subtypes were identified in CRC, each characterized by unique prognosis and immune infiltration patterns. Notably, Ion Cluster3 exhibited high levels of immune infiltration and a favorable prognosis, while Ion Cluster4 showed relatively lower levels of immune infiltration and a poorer prognosis. The ion channel score could predict overall survival, with lower scores correlated with longer survival. This score served as an independent prognostic factor and presented an excellent predictive efficacy in the nomogram. In addition, the score was closely related to immune infiltration, immunotherapy response, and chemotherapy sensitivity. Experimental evidence further confirmed that low expression of KCTD9 in tumor tissues was associated with an unfavorable prognosis in patients with CRC. The cellular functional experiments demonstrated that KCTD9 inhibited the proliferation, migration and invasion capabilities of LOVO cells. CONCLUSIONS: Ion channel subtyping and scoring can effectively predict the prognosis and evaluate the immune microenvironment, immunotherapy response, and drug sensitivity in patients with CRC.

Motif-Aware miRNA-Disease Association Prediction via Hierarchical Attention Network.

As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level. To overcome this problem, we propose a novel motif-aware MDA prediction model, namely MotifMDA, by fusing a variety of high- and low-order structural information. In particular, we first design several motifs of interest considering their ability to characterize how miRNAs are associated with diseases through different network structural patterns. Then, MotifMDA adopts a two-layer hierarchical attention to identify novel MDAs. Specifically, the first attention layer learns high-order motif preferences based on their occurrences in the given MDA network, while the second one learns the final embeddings of miRNAs and diseases through coupling high- and low-order preferences. Experimental results on two benchmark datasets have demonstrated the superior performance of MotifMDA over several state-of-the-art prediction models. This strongly indicates that accurate MDA prediction can be achieved by relying solely on MDA network information. Furthermore, our case studies indicate that the incorporation of motif-level structure information allows MotifMDA to discover novel MDAs from different perspectives.

Application of machine learning combined with population pharmacokinetics to improve individual prediction of vancomycin clearance in simulated adult patients.

Background and aim: Vancomycin, a glycopeptide antimicrobial drug. PPK has problems such as difficulty in accurately reflecting inter-individual differences, and the PPK model may not be accurate enough to predict individual pharmacokinetic parameters. Therefore, the aim of this study is to investigate whether the application of machine learning combined with the PPK method can improve the prediction of vancomycin CL in adult Chinese patients. Methods: In the first step, a vancomycin CL prediction model for Chinese adult patients is given by PPK and Hamilton Monte Carlo sampling is used to obtain the reference CL of 1,000 patients; the second step is to obtain the final prediction model by machine learning using an appropriate model for the predictive factor and the reference CL; and the third step is to randomly select, in the simulated data, a total of 250 patients for prediction effect evaluation. Results: XGBoost model is selected as final machine learning model. More than four-fifths of the subjects' predictive values regarding vancomycin CL are improved by machine learning combined with PPK. Machine learning combined with PPK models is more stable in performance than the PPK method alone for predicting models. Conclusion: The first combination of PPK and machine learning for predictive modeling of vancomycin clearance in adult patients. It provides a reference for clinical pharmacists or clinicians to optimize the initial dosage given to ensure the effectiveness and safety of drug therapy for each patient.

Electrocatalytic Hydrogenation Using Palladium Membrane Reactors.

Hydrogenation is a crucial chemical process employed in a myriad of industries, often facilitated by metals such as Pd, Pt, and Ni as catalysts. Traditional thermocatalytic hydrogenation usually necessitates high temperature and elevated pressure, making the process energy intensive. Electrocatalytic hydrogenation offers an alternative but suffers from issues such as competing H2 evolution, electrolyte separation, and limited solvent selection. This Perspective introduces the evolution and advantages of the electrocatalytic Pd membrane reactor (ePMR) as a solution to these challenges. ePMR utilizes a Pd membrane to physically separate the electrochemical chamber from the hydrogenation chamber, permitting the use of water as the hydrogen source and eliminating the need for H2 gas. This setup allows for greater control over reaction conditions, such as solvent and electrolyte selection, while mitigating issues such as low Faradaic efficiency and complex product separation. Several representative hydrogenation reactions (e.g., hydrogenation of C=C, C≡C, C=O, C≡N, and O=O bonds) achieved via ePMR over the past 30 years were concisely discussed to highlight the unique advantages of ePMR. Promising research directions along with the advancement of ePMR for more challenging hydrogenation reactions are also proposed. Finally, we provide a prospect for future development of this distinctive hydrogenation strategy using hydrogen-permeable membrane electrodes.

Effects of Glutamine Synthetase on Neovascularization in Glioma: In Vivo MR Vessel Size Imaging and Histology.

BACKGROUND: Glutamine Synthetase (GS) could induce vascular sprouting through the improvement of endothelial cell migration in inflammatory diseases. MR vessel-size imaging has been proposed as a valuable approach for visualizing the underlying angiogenic processes in the brain. OBJECTIVE: This study aims to investigate the role of GS in the neovascularization of gliomas through the utilization of MR vessel-size imaging and histopathological techniques. METHODS: In this exploratory animal study, we randomly divided the C6 glioma rat models into a control group and an L-methionine sulfoximine (MSO) treatment group. Daily intraperitoneal injections were administered for three consecutive days, starting from day 10 following the implantation of C6 glioma cells in rats. Subsequently, MR vessel size imaging was conducted using a BRUKER 7 T/200 mm MRI scanner, and the MRI results were validated through histopathological examination. RESULTS: A significant decrease in microvessel density was observed in both the tumor periphery and center areas in the MSO treatment group compared to that in the control group. The mean vessel diameter (mVD) and vessel size index (VSI) did not exhibit significant changes compared to the control group. Moreover, the staining intensity of platelet endothelial cell adhesion molecule-1 (CD31) and GS in the tumor periphery was significantly decreased in the MSO treatment group. Additionally, the MSO treatment demonstrated a substantial inhibition of tumor growth. CONCLUSION: GS inhibitors significantly reduced angiogenesis in the periphery area of C6 glioma, exerting an inhibitory effect on tumor progression. Thus, GS inhibitors could be potential therapeutic agents for treating glioma. Additionally, in vivo MR vessel size imaging detects changes in vascularrelated parameters after tumor treatment, making it a promising method for detecting neovascularization in glioma.

Incidence and risk factors for pulmonary hemorrhage after percutaneous CT-guided pulmonary nodule biopsy: an observational study.

To evaluate the current incidence of pulmonary hemorrhage and the potential factors contributing to its increased risk after percutaneous CT-guided pulmonary nodule biopsy and to summarize the technical recommendations for its treatment. In this observational study, patient data were collected from ten medical centers from April 2021 to April 2022. The incidence of pulmonary hemorrhage was as follows: grade 0, 36.1% (214/593); grade 1, 36.8% (218/593); grade 2, 18.9% (112/593); grade 3, 3.5% (21/593); and grade 4, 4.7% (28/593). High-grade hemorrhage (HGH) occurred in 27.2% (161/593) of the patients. The use of preoperative breathing exercises (PBE, p =0.000), semiautomatic cutting needles (SCN, p = 0.004), immediate contrast enhancement (ICE, p =0.021), and the coaxial technique (CoT, p = 0.000) were found to be protective factors for HGH. A greater length of puncture (p =0.021), the presence of hilar nodules (p = 0.001), the presence of intermediate nodules (p = 0.026), a main pulmonary artery diameter (mPAD) larger than 29 mm (p = 0.015), and a small nodule size (p = 0.014) were risk factors for high-grade hemorrhage. The area under the curve (AUC) was 0.783. These findings contribute to a deeper understanding of the risks associated with percutaneous CT-guided pulmonary nodule biopsy and provide valuable insights for developing strategies to minimize pulmonary hemorrhage.

Machine Learning Method to Explore the Correlation between Fly Ash Content and Chloride Resistance.

Chloride ion corrosion has been considered to be one of the main reasons for durability deterioration of reinforced concrete structures in marine or chlorine-containing deicing salt environments. This paper studies the relationship between the amount of fly ash and the durability of concrete, especially the resistance to chloride ion erosion. The heat trend map of total chloride ion factor correlation displayed that the ranking of factor correlations was as follows: sampling depth > cement dosage > fly ash dosage. In order to verify the effect of fly ash dosage on chloride ion resistance, three different machine learning algorithms (RF, GBR, DT) are employed to predict the total chloride content of fly ash proportioned concrete with varying admixture ratios, which are evaluated based on R2, MSE, RMSE, and MAE. The results predicted by the RF model show that the threshold of fly ash admixture in chlorinated salt environments is 30-40%. Replacing part of cement with fly ash in the mixture of concrete below this threshold of fly ash, it could change the phase structure and pore structure, which could improve the permeability of fly ash concrete and reduce the content of free chloride ions in the system. Machine learning modeling using sample data can accurately predict concrete properties, which effectively reduce engineering tests. The development of machine learning models is essential for the decarbonization and intelligence of engineering.