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Abstract High expression of cellular self‐activated immunosuppressive molecules and extensive infiltration of suppressive immune cells in the tumor microenvironment are the main factors contributing to glioma's resistance to immunotherapy. Nonetheless, technology to modify the expression of glioma cellular self‐molecules through gene editing requires further development. This project advances cell therapy strategies to reverse the immunosuppressive microenvironment of glioma (TIME). Bone marrow‐derived mesenchymal stem cells (MSCs) are engineered to express bioactive proteins and demonstrate tumor‐homing characteristics upon activation by TGF‐β. These MSCs are designed to secrete the anti‐tumor immune cytokine IL‐12 and the nCD47‐SLAMF7 fusion protein, which regulates T‐cell activity and macrophage phagocytosis. The engineered MSCs are then injected in situ into the glioma site, circumventing the blood‐brain barrier to deliver high local concentrations of bioactive proteins. This approach aims to enhance the M1 polarization of infiltrating macrophages, stimulate macrophage‐mediated tumor cell phagocytosis, activate antigen‐presenting cells, and promote effector CD8+ T cell infiltration, effectively controlling glioma. Additionally, the engineered MSCs may serve as a universal treatment for other tumors that express TGF‐β at high levels. This study proposes a novel treatment strategy for the clinical management of glioma patients.

Prolonged use of opioids can lead to increased sensitivity to painful stimuli, a condition referred to as opioid-induced hyperalgesia (OIH). However, the mechanisms underlying this contradictory situation remain unclear. This study elucidates the pivotal role of the paratenial thalamic nucleus (PT)-anterior cingulate cortex (ACC) neuronal circuit in the development of OIH in male rats. Immunofluorescence and electrophysiology experiments demonstrated aberrant activation of PT glutamatergic neurons (PTGlu) in rats with OIH. Optogenetic or chemogenetic activation of the PTGlu-ACC circuit aggravates mechanical and thermal hyperalgesia. Conversely, the inhibition of neuronal circuits showed analgesic effects. Additionally, PTGlu neurons project to both ACC pyramidal neurons and interneurons. Moreover, OIH affects the function of the ACC microcircuit, leading to decreased feedforward inhibition and an inhibitory/excitatory (I/E) imbalance in ACC pyramidal neurons. In conclusion, our findings highlighted the role of the PTGlu-ACC neuronal circuit in the development of opioid-induced hyperalgesia, suggesting that this circuit is a promising therapeutic target for addressing the side effects of opioids.

Many correlation analysis methods can capture a wide range of functional types of variables. However, the influence of uncertainty and distribution status in data is not considered, which leads to the neglect of the regularity information between variables, so that the correlation of variables that contain functional relationship but subject to specific distributions cannot be well identified. Therefore, a novel correlation analysis framework for detecting associations between variables with randomness (RVCR-CA) is proposed. The new method calculates the normalized RMSE to evaluate the degree of functional relationship between variables, calculates entropy difference to measure the degree of uncertainty in variables and constructs the copula function to evaluate the degree of dependence on random variables with distributions. Then, the weighted sum method is performed to the above three indicators to obtain the final correlation coefficient R. In the study, which considers the degree of functional relationship between variables, the uncertainty in variables and the degree of dependence on the variables containing distributions, cannot only measure the correlation of functional relationship variables with specific distributions, but also can better evaluate the correlation of variables without clear functional relationships. In experiments on the data with functional relationship between variables that contain specific distributions, UCI data and synthetic data, the results show that the proposed method has more comprehensive evaluation ability and better evaluation effect than the traditional method of correlation analysis.

A novel regression model, monotonic inner relation-based non-linear partial least squares (MIR-PLS), is proposed to address complex issues like limited observations, multicollinearity, and nonlinearity in Chinese Medicine (CM) dose-effect relationship experimental data. MIR-PLS uses a piecewise mapping function based on monotonic cubic splines to model the non-linear inner relations between input and output score vectors. Additionally, a new weight updating strategy (WUS) is developed by leveraging the properties of monotonic functions. The proposed MIR-PLS method was compared with five well-known PLS variants: standard PLS, quadratic PLS (QPLS), error-based QPLS (EB-QPLS), neural network PLS (NNPLS), and spline PLS (SPL-PLS), using CM dose-effect relationship datasets and near-infrared (NIR) spectroscopy datasets. Experimental results demonstrate that MIR-PLS exhibits general applicability, achieving excellent predictive performances in the presence or absence of significant non-linear relationships. Furthermore, the model is not limited to CM dose-effect relationship research and can be applied to other regression tasks.

Metabolomics data has high-dimensional features and a small sample size, which is typical of high-dimensional small sample (HDSS) data. Too high a dimensionality leads to the curse of dimensionality, and too small a sample size tends to trigger overfitting, which poses a challenge to deeper mining in metabolomics. Feature selection is a valuable technique for effectively handling the challenges HDSS data poses. For the feature selection problem of HDSS data in metabolomics, a hybrid Max-Relevance and Min-Redundancy (mRMR) and multi-objective particle swarm feature selection method (MCMOPSO) is proposed. Experimental results using metabolomics data and various University of California, Irvine (UCI) public datasets demonstrate the effectiveness of MCMOPSO in selecting feature subsets with a limited number of high-quality features. MCMOPSO achieves this by efficiently eliminating irrelevant and redundant features, showcasing its efficacy. Therefore, MCMOPSO is a powerful approach for selecting features from high-dimensional metabolomics data with limited sample sizes.

Particle swarm optimization (PSO) has been successfully applied to various complex optimization problems due to its simplicity and efficiency. However, the update strategy of the standard PSO algorithm is to learn from the global best particle, making it difficult to maintain diversity in the population and prone to premature convergence due to being trapped in local optima. Chaos search mechanism is an optimization technique based on chaotic dynamics, which utilizes the randomness and nonlinearity of a chaotic system for global search and can escape from local optima. To overcome the limitations of PSO, an improved particle swarm optimization combined with double-chaos search (DCS-PSO) is proposed in this paper. In DCS-PSO, we first introduce double-chaos search mechanism to narrow the search space, which enables PSO to focus on the neighborhood of the optimal solution and reduces the probability that the swarm gets trapped into a local optimum. Second, to enhance the population diversity, the logistic map is employed to perform a global search in the narrowed search space and the best solution found by both the logistic and population search guides the population to converge. Experimental results show that DCS-PSO can effectively narrow the search space and has better convergence accuracy and speed in most cases.

Neighborhood rough set is considered an essential approach for dealing with incomplete data and inexact knowledge representation, and it has been widely applied in feature selection. The Gini index is an indicator used to evaluate the impurity of a dataset and is also commonly employed to measure the importance of features in feature selection. This article proposes a novel feature selection methodology based on these two concepts. In this methodology, we present the neighborhood Gini index and the neighborhood class Gini index and then extensively discuss their properties and relationships with attributes. Subsequently, two forward greedy feature selection algorithms are developed using these two metrics as a foundation. Finally, to comprehensively evaluate the performance of the algorithm proposed in this article, comparative experiments were conducted on 16 UCI datasets from various domains, including industry, food, medicine, and pharmacology, against four classical neighborhood rough set-based feature selection algorithms. The experimental results indicate that the proposed algorithm improves the average classification accuracy on the 16 datasets by over 6%, with improvements exceeding 10% in five. Furthermore, statistical tests reveal no significant differences between the proposed algorithm and the four classical neighborhood rough set-based feature selection algorithms. However, the proposed algorithm demonstrates high stability, eliminating most redundant or irrelevant features effectively while enhancing classification accuracy. In summary, the algorithm proposed in this article outperforms classical neighborhood rough set-based feature selection algorithms.

ObjectivesThis study aimed to make a systematic analysis of cuproptosis-related genes (CRGs) in immunological characterization and predictive drugs in Alzheimer’s disease (AD) through bioinformatics and biological experiments.MethodsThe molecular clusters related to CRGs and associated immune cell infiltrations in AD were investigated. The diagnostic models were constructed for AD and different AD subtypes. Moreover, drug prediction and molecular docking were also performed. Subsequently, a molecular dynamics (MD) simulation was conducted to further verify the findings. Finally, RT-qPCR validation was performed.ResultsThe characterization of 12 AD-related CRGs was evaluated in AD, and a diagnostic model for AD showed a satisfying discrimination power based on five CRGs by LASSO regression analysis. The dysregulated CRGs and activated immune responses partially differed between patients with AD and healthy subjects. Furthermore, two molecular subtypes (clusters A and B) with different immune infiltration characteristics in AD were identified. Similarly, a diagnostic model for different AD subtypes was built with nine CRGs, which achieved a good performance. Molecular docking revealed the optimum conformation of CHEMBL261454 and its target gene CSNK1D, which was further validated by MD simulation. The RT-qPCR results were consistent with those of the comprehensive analysis.ConclusionThis study systematically elucidated the complex relationship between cuproptosis and AD, providing novel molecular targets for treatment and diagnosis biomarkers of AD.

Glass fiber-reinforced polymer (GFRP) composites were prepared with ortho-phthalic unsaturated polyester resin as the matrix. Mass productions were carried out on a production line using infrared laser radiation and tunnel-kiln heating. Infrared laser radiation heating can realize internal and external collaborative curing by utilizing the deep penetration of the infrared laser, whereas, the curing of tunnel kilns mainly depends on heat conduction and heat convection. The tensile strength of the bar cured by laser radiation was approximately 25% higher than that of the bar cured by the tunnel kiln. The temperature field of the GFRP bars under the two curing modes was simulated using the finite element method, and The fiber at the fracture of reinforcement was analyzed using scanning electron microscopy and FTIR spectrum. The results showed that there was more fiber adhesion resin in the GFRP bars heated by infrared laser radiation, whereas the adhesion resin on the fibers was less scattered in the tunnel–kiln-heated GFRP bars.

Hepatic ischemia-reperfusion injury (HIRI)–induced acute lung injury (ALI) is characterized by high incidence and poor prognosis. The regulatory role of microRNA-146a (miR-146a) in HIRI has been reported, but if miR-146a could affect the progression of HIRI-induced ALI has not been reported. The mice HIRI model was established by ligating left hepatic portal vein and hepatic artery for 60 minutes and then treating with reperfusion for 4 hours. Hypoxia-reoxygenation (HR) was performed to establish cell model. The binding site between miR-146a and Peroxidase 1 (PRDX1) was predicted and validated. The levels of inflammation factors and redox markers were detected with commercial kits. Significant lower expression of miR-146a and higher expression of PRDX1 in HIRI animal model were observed. miR-146a inhibited the liver injury after HIRI induction through targeting PRDX1. miR-146a inhibited the lung injury caused by HIRI via regulating PRDX1. The inhibition of cell apoptosis and inflammation factors by miR-146a were reversed by pcDNA-PRDX1. This research demonstrated that miR-146a improved ALI caused by HIRI by inhibiting apoptosis, inflammation, oxidative condition through targeting PRDX1. This study might provide a novel thought for the prevention and treatment of ALI caused by HIRI by regulating miR-146a/PRDX1 axis.

The gelation of scallop Patinopecten yessoensis male gonad hydrolysates (SMGHs) and κ-carrageenan (KC) subjected to pH (2–8, 3–9) and NaCl/KCl stimuli-response was investigated. SMGHs/KC gels subjected to a NaCl response exhibited an increasing storage modulus G′from 2028.6 to 3418.4 Pa as the pH decreased from pH 8 to 2, with corresponding T23 fluctuating from 966.40 to 365.64 ms. For the KCl-treated group, SMGHs/KC gels showed an even greater G′ from 4646.7 to 10996.5 Pa, with T23 fluctuating from 622.2 to 276.98 ms as the pH decreased from 9 to 3. The improved gel strength could be ascribed to the blueshift and redshift of hydroxyl groups and amide I peaks, enhanced enthalpy and peak temperature, and gathered characteristic diffraction peaks from SMGHs, KC, NaCl, and KCl. The CLSM and cryo-SEM images further reflected that SMGHs/KC gels showed more flocculation formation and denser and more homogeneous networks with smaller pore sizes in more acidic domains, especially when subjected to the KCl response. This research gives a theoretical and methodological understanding of the construction of salt- and pH-responsive SMGHs/KC hydrogels as novel functional soft biomaterials applied in food and biological fields.

Sea cucumbers Apostichopus japonicus will vomit their intestines during certain stimulations, and the collagen of the body wall will then be degraded. To define the effect of the sea cucumber intestine extracts on the body wall, the intestinal extracts and crude collagen fibers (CCF) of sea cucumber A. japonicus were prepared. According to the gelatin zymography, the type of endogenous enzymes in intestinal extracts were mainly serine endopeptidases with optimal activities at pH 9.0 and 40 °C. According to the rheology results, the viscosity of 3% CCF decreased from 32.7 Pa·s to 5.3 Pa·s by adding intestine extracts. The serine protease inhibitor phenylmethanesulfonyl fluoride inhibited the activity of intestinal extracts and increased the viscosity of collagen fibers to 25.7 Pa·s. The results proved that serine protease in the intestinal extracts participated in the process of body wall softening in sea cucumbers.

Volatile profiles of peas under 9 kinds of different treatments including native, washing, blanching, precooling, freezing, steaming, boiling, frying, and freeze-drying were characterized by GC-IMS and GC-MS. The differences of volatile compounds in different peas were observed from the characteristic fingerprints by GC-IMS. The Venn diagram found that the common flavor substances codetected by GC-IMS and GC-MS were n-hexanal, nonanal, 1-octene-3-ol, benzaldehyde, 6-methyl-5-hepten-2-one, trans-2-octenal, and 2-ethyl-3,5-dimethylpyrazine, which were speculated to be the key flavor substances of peas. The cluster analysis of the heat map conducted towards the differences of volatile components in peas under different treatments; the results indicated that peas could be mainly divided into four groups, which was consistent with the above conclusion of GC-IMS. Eight sensory descriptors were used to evaluate the aroma notes: sweet flowers, fat fragrance, waxy aldehydes, mushroom hay, roasted potato with nuts, vegetable-like bean, spicy dry tar, and bitter almond from the sensory analysis, and the sensory analysis also showed good agreement with the results of GC-IMS and GC-MS. The results indicated that the volatile compounds of peas under different treatments could be visualized and identified quickly via GC-IMS, and the samples could be clearly classified based on the difference of volatile compounds. Practical Application. In the study, fingerprints coupled with cluster analysis were a visualized method for the identification of volatile compounds. Meanwhile, a new method, the Venn diagram with OAV, was used to identify the key-aroma of products. Finally, a rapid method is established to classify products by GC-IMS. In future practical applications, GC-IMS can be used to classify products from different origins and different manufacturers. Similarly, it can identify fake and inferior products and whether the products have deteriorated. In addition, this research will provide a new strategy to find the relationship between flavor compounds and various processed technology towards different cereals.

With low porosity and low permeability, shale reservoirs cannot be mined economically without large-scale hydraulic fracturing operation. However, abundant fracturing fluid will enter the reservoirs during the process of fracture. Nevertheless, there have not been specific research findings on the imbibition law of Fuling shale gas reservoir in China. In this study, an imbibition experiment was carried out on the shale core of Jiaoshiba block of Fuling shale gas reservoir to learn spontaneous imbibition characteristic of Fuling shale gas reservoir. Based on the experimental results, the imbibition process of Fuling shale gas reservoir fracturing fluid is divided into two stages. During the first stage, i.e., the former 30 hours, imbibition velocity is high, with the cumulative imbibition occupying more than 70% of the total imbibition; during the second stage, i.e., the latter 30 hours, the imbibition velocity substantially drops towards balance. There is a typical power function relationship between the average imbibition velocity and imbibition time, and this function relationship runs throughout the whole imbibition process. Nonetheless, the imbibition process of shale core cannot be described directly by the Handy equation. The imbibition velocity is closely related to clay mineral content and pore structure characteristics of shale core. The higher the clay mineral content, the higher the imbibition velocity. According to the relationship between the average imbibition velocity and imbibition time, we derived the estimation equation of fracture area formed by fractured shale gas well to estimate the fracture scale formed by shale gas well fracturing.

Objective The aim of this study was to investigate the reversibility and safety of KISS1 metastasis suppressor (KISS1) gene vaccine in immunocastration. Methods Six eight-week old ram lambs were randomly divided into vaccinated and control groups. The vaccine (1 mg/ram lamb) was injected at weeks 0, 3, and 6 of the study. Blood samples were collected from the jugular vein before primary immunization and at weeks 2, 4, 6, 10, 14, 22, and 30 after primary immunization. All ram lambs were slaughtered at 38 weeks of age, and samples were collected. Results The specific anti-KISS1 antibody titers in vaccinated animals were significantly higher and the serum testosterone level was significantly lower than those in the control groups from week 4 to 14 after primary immunization (p

Partial least squares method has many advantages in multivariate linear regression modeling, but its internal cross-checking method will lead to a sharp reduction of the principal component, thereby reducing the accuracy of the regression equation, and the selection of principal components about the traditional Chinese medicine data is particularly sensitive. This paper proposes a kind of partial least squares method based on deep belief nets. This method mainly uses the deep learning model to extract the upper-level features of the original data, putting the extracted features into the partial least squares model for multiple linear regression and evading the problem that selects the number of principal components, continuously adjusting the model parameters until satisfied well-pleased accuracy condition. Using Dachengqitang experimental data and data sets in the UCI Machine Learning Repository, the experimental results show that the partial least squares analysis method based on deep belief nets has good adaptability to TCM data.

Objective: This study aimed to evaluate the incidence and related factors of acute asymptomatic brain infarcts (aABI) following percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) via the right radial approach. Patients and methods: Two hundred ten consecutive patients with AMI were enrolled. Multiple factors were compared between patients with (n = 37) and without (n = 138) aABI in 175 patients who underwent PCI. Results: Of 175 patients with AMI, 37 (21.14%) developed aABI as determined by diffusion-weighted magnetic resonance imaging (MRI). Incidence of hypertension, proportion of primary PCI (p-PCI), Killip > 1 on admission, duration of procedure, and the frequency of device insertion into the coronary artery significantly differed between the aABI and non-aABI groups. These significant factors were reevaluated using logistic regression. Proportion of p-PCI, duration of the procedure, and the frequency of device insertion into the coronary artery were indicated as independent factors related to the incidence of aABI and others did not. Conclusion: Cranial MRI imaging following PCI revealed that 21.14% of the patients with AMI had aABI. The independent factors related to aABI following PCI were p-PCI, duration of the procedure, and the frequency of device insertion into the coronary artery.

We aim to investigate the effects of adipose-derived stem cells (ASCs) transplantation on irradiation-induced skeletal muscle fibrosis. Sixty-four rabbits were randomly divided into ASCs group and PBS group followed by irradiation at unilateral hip with a single dose of 80 Gy. Nonirradiated side with normal skeletal muscle served as normal control. Skeletal muscle tissues were collected from eight rabbits in each group at 1 w, 4 w, 8 w, and 26 w after irradiation. Migration of ASCs was observed in the peripheral tissues along the needle passage in the injured muscle. The proportion of the area of collagen fibers to the total area in sections of ASCs group was lower than those of PBS groups at 4 w, 8 w, and 26 w after irradiation. Significant decrease was noted in the integrated optimal density of the transforming growth factor β1 (TGF-β1) in the ASCs group compared with those of PBS group at 4 w, 8 w, and 26 w after irradiation. Moreover, the expression of TGF-β1 was lower in the ASCs group compared to those of the PBS group at each time point determined by Western blot analysis. ASCs transplantation could alleviate irradiation fibrosis by suppressing the level of TGF-β1 in the irradiated skeletal muscle.

Objective: To explore the effect of Gegen Qinlian Decoction (GQD) combined with dietary management in the treatment of patients with Type-2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) (T2DM MetS). Methods: This is a retrospective analysis of 102 cases of T2DM in the Brain Hospital of Hunan Province, China from April 2020 to February 2023. Of them, 49 patients received conventional drug treatment (control group), and 53 patients received GQD combined with dietary management on the basis of conventional drugs (observation group). Treatment efficacy was calculated, and blood glucose levels before and after the treatment, blood lipid-related indicators, tumor necrosis factor-α (TNF-α) and adiponectin (ADP) levels, and incidence of adverse reactions were compared between the two groups. Results: The total efficacy of the observation group (92.45%) was significantly higher than that of the control group (75.51%) (P<0.05). After the treatment, blood glucose and lipid indicators in both groups were significantly improved compared to pretreatment levels, and were significantly better in the observation group than in the control group (P<0.05). After the treatment, TNF-α levels in both groups decreased compared to before the treatment, and were significantly lower in the observation group compared to the control group. Levels of ADP after the treatment increased, and were significantly higher in the observation group compared to the control group (P<0.05). Conclusions: When taken as an adjunct to the conventional drug regimen, GQD combined with dietary management can effectively regulate blood glucose and lipid metabolism in patients with T2DM and MetS (T2DM MetS), improve TNF-α and ADP levels, and enhance disease treatment effectiveness. [ABSTRACT FROM AUTHOR]

Mild traumatic brain injury (mTBI)-induced post-traumatic headache (PTH) is a pressing public health concern and leading cause of disability worldwide. Although PTH is often accompanied by neurological disorders, the exact underlying mechanism remains largely unknown. Identifying potential biomarkers may prompt the diagnosis and development of effective treatments for mTBI-induced PTH. In this study, a mouse model of mTBI-induced PTH was established to investigate its effects on cerebral structure and function during short-term recovery. Results indicated that mice with mTBI-induced PTH exhibited balance deficits during the early post-injury stage. Metabolic kinetics revealed that variations in neurotransmitters were most prominent in the cerebellum, temporal lobe/cortex, and hippocampal regions during the early stages of PTH. Additionally, variations in brain functional activities and connectivity were further detected in the early stage of PTH, particularly in the cerebellum and temporal cortex, suggesting that these regions play central roles in the mechanism underlying PTH. Moreover, our results suggested that GABA and glutamate may serve as potential diagnostic or prognostic biomarkers for PTH. Future studies should explore the specific neural circuits involved in the regulation of PTH by the cerebellum and temporal cortex, with these two regions potentially utilized as targets for non-invasive stimulation in future clinical treatment [ABSTRACT FROM AUTHOR]