Experimental Verification Research Articles

Testing of wood shear modulus based on cantilevered square plate torsional vibration method

ABSTRACT The application of the energy method has proposed a dynamic testing method for the shear modulus in two mutually perpendicular directions on the principal surface of wood using a cantilevered square plate torsional vibration method. In the study, this method was applied to measure the shear modulus of larch wood in the LT and TL directions, as well as the longitudinal and transverse shear modulus of Laminated Veneer Lumber (LVL) sheets. The validity of the results was confirmed through experimental verification using the asymmetric four-point bending beam method, the free plate torsional vibration method, and the free square plate torsional mode method. Simulation calculations of the principal shear modulus for six species of trees with width-to-thickness ratios ranging from 10 to 25 were also conducted using this method. The conclusions drawn suggest that a width-to-thickness ratio of 15 for the cantilevered square plate provides sufficient accuracy for testing the principal shear modulus of wood. Furthermore, the difference in shear modulus between two mutually perpendicular directions on the principal surface of wood is dependent on the width-to-thickness ratio of the cantilevered square plate, indicating that this method outperforms other dynamic methods for testing wood shear modulus.

Enhanced through-plane thermal conductivity of graphene membranes by the interlayer microstructure manipulation: Multiscale numerical simulations and experimental verifications

Graphene, with ultrahigh in-plane thermal conductivity (IPTC), has been widely applied in the thermal dissipation of integrated circuits. However, the extremely low through-plane thermal conductivity (TPTC) of graphene limits its further application. In this work, a novel strategy was proposed to enhance the TPTC of graphene membranes (GNMs) by improving the interlayer bonding state through interlayer decoration with metal elements. Molecular dynamics simulations demonstrated that decoration with interlayer elements (Al, Ti, and Ni) enhanced the interfacial thermal conductance. This was attributed to interfacial chemical bonding through strong interfacial interactions (intensive electron localization) formed at the interface between metal and GN sheets as implied by the first principles calculations. Furthermore, the composite GNMs decorated with interlayer metal nanoparticles (Al, Ni, and Co) were prepared through co-reduction using thermal decomposition method. The corresponding TPTC was tested using time-domain thermoreflectance (TDTR) method which was much higher for the composite GNMs (175–400 W/m·K) than for pure GNM (∼140 W/m·K), and this was in consistent with the simulation results. Interlayer decoration, which enhanced the TPTC of GNMs, provides a novel and effective strategy for enhancing the overall thermal management performance of graphene-based materials in both vertical and horizontal directions.

Mechanical and tribological properties of layered epoxy coatings synergistically enhanced by graphene and polytetrafluoroethylene: Molecular dynamics simulations and experimental verification

Epoxy resin coatings have high adhesion properties and excellent chemical resistance, and are widely used in the fields of ships, automobiles and electronic appliances. However, the shortcomings of epoxy resin, such as high brittleness, poor thermal stability and insufficient abrasion resistance, seriously affect the durability of the coating. In this paper, the mechanisms of mechanical and tribological properties of graphene (Gr) and polytetrafluoroethylene (PTFE) synergistically reinforced epoxy resin (EP) double layer coatings (with pure epoxy resin as the bottom layer) on Cu substrates were investigated by molecular dynamics simulations. The modeling includes pure epoxy double layer coating (Pure EP-EP), PTFE reinforced epoxy double layer coating (PTFE/EP-EP), and graphene and PTFE synergistically reinforced epoxy double layer coating (Gr/PTFE/EP-EP). The counter frictional part for these coatings is Cu metal. The simulation results show that by analyzing the interaction energy between the coating and the Cu substrate, the double layer coating with Pure EP as the bottom layer is found to have a better adsorption capacity, which can effectively extend the service life of the coating, compared with those with PTFE/EP or Gr/PTFE/EP as bottom layer. By calculating the shear modulus, Young's modulus and bulk modulus of the Gr/PTFE/EP-EP coating, the synergistic enhancement mechanism of the mechanical properties of the Gr/PTFE/EP-EP coating was revealed. In particular, the PTFE/EP-EP coating exhibited a reduction in mechanical properties and the mechanical property simulation results were verified experimentally. Compared with the Pure EP-EP double layer coating, the PTFE/EP-EP and Gr/PTFE/EP-EP double layer coatings showed a reduction in friction coefficient of 9.52 % and 23.81 %, wear rate of 46.69 % and 76.63 %, and mean square displacements (MSD) of 22.4 % and 81.34 %, respectively. The synergistic effect of Gr and PTFE on the tribological performance of Gr/PTFE/EP-EP coatings was better than that of PTFE/EP-EP coatings with PTFE only. The interaction of Gr and PTFE with EP was analyzed by comparing the changes in radius distribution function (RDF), relative concentration, temperature and velocity in the thickness direction of PTFE/EP-EP coatings and Gr/PTFE/EP-EP coatings, which further revealed the synergistic enhancement mechanism by Gr and PTFE.

Automata-Based Quantum Circuit Design Patterns Identification: A Novel Approach and Experimental Verification

Automata-Based Quantum Circuit Design Patterns Identification: A Novel Approach and Experimental Verification

Design, development, and testing of active valve piezo-hydraulic pump

Valves play a major role in rectifying the fluid flow through piezo-hydraulic pumps, which may be active or passive. Passive valves operate due to changes in the fluid pressure whereas active valves are operated through control signals. This paper presents a novel design of an active valve chamber assembly for a piezo-hydraulic pump. It is a novel design since none of the research publications has addressed the application of a Flexurally amplified piezoelectric actuator (FAPA) for valve actuation in active valve chamber assembly. The piezo-hydraulic pump design is done using solid edge design software. FAPA’s are used for pumping and valve actuation. Structural analysis with experimental verification for the valve chamber component is presented. The valve opening and the pumping displacement are mathematically modeled in MATLAB Simulink software. Also, mathematical simulations for pump flow rates at different actuation frequencies and voltage are discussed. The piezo-hydraulic pump components are fabricated and assembled with the actuators. Experimental investigations on the flow rate performance of the active valve piezo-hydraulic pump are carried out with changing frequency and voltage amplitude of actuators. The interpretation of results is done by comparing the experimental and simulation results. During experiments, the pump delivered a maximum flow rate of 4.5 ml min−1 at 140 V when actuated at 0.25 Hz actuation frequency.

Quantitative analysis of interface heat transport at the Si3N4/SiO2 van-der Waals point contact

Si3N4 and SiO2 have been widely used in lithium batteries and optical devices. The high energy density brought by device integration makes the interfacial heat transport at the nano scale a hot spot. To optimize interfacial heat transfer, the roughness of the device surface is continuously reduced down to the nanometer. However, due to the limitation of spatial resolution, it is difficult to realize the measurement of thermal boundary resistance in the case of nanoscale contact by the existing measurement means. There is a great discrepancy between the measurement and simulation results, making the optimization of interfacial heat transfer lack of experimental verification. In this paper, the measurement of thermal boundary resistance of Si3N4/SiO2 interface at the nano-scale, was firstly realized by the optimized 3ω-SThM system. The effect of air tunnel on the heat transfer between real surfaces was also analyzed. The progress made so far provides an effective experimental tool to measure interfacial heat transport at nanoscale point contact.

Molecular profiling of core immune-escape genes highlights TNFAIP3 as an immune-related prognostic biomarker in neuroblastoma.

Neuroblastoma (NB) is the most prevalent and deadliest pediatric solid tumor. With of over 50% of high-risk neuroblastoma cases relapse, the imperative for novel drug targets and therapeutic strategies is accentuated. In neuroblastoma, the existence of tumor-associated macrophages (TAMs) correlates with an unfavorable patient prognosis. However, the clinical relevance and prognostic implications of regulatory genes linked to TAMs infiltration in neuroblastoma remain unclear, and further study is required. We conducted a comprehensive analysis utilizing transcriptome expression profiles from three primary datasets associated with neuroblastoma (GSE45547, GSE49710, TARGET) to identify hub genes implicated in immune evasion within neuroblastoma. Subsequently, we utilized single-cell RNA sequencing analysis on 17 clinical neuroblastoma samples to investigate the expression and distribution of these hub genes, leading to the identification of TNFAIP3. The above three public databases were merged to allowed for the validation of TNFAIP3's molecular functions through GO and KEGG analysis. Furthermore, we assessed TNFAIP3's correlation with immune infiltration and its potential immunotherapeutic impact by multiple algorithms. Our single-cell transcriptome data revealed the role of TNFAIP3 in macrophage polarization. Finally, preliminary experimental verifications to confirm the biological functions of TNFAIP3-mediated TAMs in NB. A total of 6 genes related to immune evasion were screened and we found that TNFAIP3 exhibited notably higher expression in macrophages than other immune cell types, based on the scRNA-sequencing data. GO and KEGG analysis showed that low expression of TNFAIP3 significantly correlated with the activation of multiple oncogenic pathways as well as immune-related pathways. Then validation affirmed that individuals within the TNFAIP3 high-expression cohort could potentially derive greater advantages from immunotherapeutic interventions, alongside exhibiting heightened immune responsiveness. Deciphering the pseudotime trajectory of macrophages, we revealed the potential of TNFAIP3 in inducing the polarization of macrophages towards the M1 phenotype. Finally, we confirmed that patients in the TNFAIP3 high expression group might benefit more from immunotherapy or chemotherapy as substantiated by RT-qPCR and immunofluorescence examinations. Moreover, the role of TNFAIP3 in macrophage polarization was validated. Preliminary experiment showed that TNFAIP3-mediated TAMs inhibit the proliferation, migration and invasion capabilities of NB cells. Our results suggest that TNFAIP3 was first identified as a promising biomarker for immunotherapy and potential molecular target in NB. Besides, the presence of TNFAIP3 within TAMs may offer a novel therapeutic strategy for NB.

Prognostic value and immune infiltration of the NEK family in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a fatal urological malignancy. Members of the never-in mitosis gene A (NIMA)-related kinase (NEK) family have been found to participate in the progression of several cancers and could be used as target genes to treat corresponding diseases. Nonetheless, the prognostic value and immune infiltration levels of NEK family genes in ccRCC remain unknown. The GSCA, TIMER, and GEPIA databases were utilized to examine the differential expression of NEK family members in ccRCC, and the Kaplan-Meier plotter was utilized to analyze the prognosis. The STRING database was used to construct a protein-protein interaction network. Analysis of function was performed by the Sangerbox tool. In addition, the relationship between NEK family genes and immune cells was explored using the TIMER and TISIDB databases. Finally, we used quantitative real-time PCR (qPCR) and immunohistochemistry (IHC) for experimental verification. Transcriptional levels of NEK2, NEK3, NEK5, NEK6, and NEK11 significantly differed between ccRCC and normal tissues. Moreover, there was a significant correlation between NEK1, NEK2, NEK4, NEK8, NEK9, and NEK10 and their clinicopathological stages in patients with ccRCC. Based on survival analysis, ccRCC patients with high transcriptional levels of NEK2, NEK3, NEK8, and NEK10 and low transcriptional levels of NEK1, NEK4, NEK5, NEK6, NEK7, NEK9, NEK11 had shorter survival times. Additionally, a significant relationship was observed between NEK family members and immune cell infiltration, immune cell markers, and immune subtypes. These results indicate that NEK family members are significantly differentially expressed in ccRCC, and a significant correlation exists between the NEK family and prognosis and immune infiltration. NEK family members may act as therapeutic targets and prognostic indicators in ccRCC.

Core antibiotic resistance genes mediate gut microbiota to intervene in the treatment of major depressive disorder

IntroductionThe relationship between depression and gut microbiota remains unclear, but an important role of gut microbiota has been verified. The relationship between gut microbiota and antibiotic resistance genes (ARGs) may be a potential new explanatory pathway. MethodsWe collected samples from 63 depressed patients and 30 healthy controls for metagenomic sequencing. The two groups' microbiota characteristics, functional characteristics, and ARG differences were analyzed. ResultsWe obtained 30 differential KEGG orthologs (KOs) and their producers in 5 genera and 7 species by HUMAnN3. We found 6 KOs from Weissella_cibaria and Lactobacillus_plantaru are potentially coring functional mechanism of gut microbiota. Different metabolites including sphingolipids, pyrans, prenol lipids, and isoflavonoids also showed significance between MDD and HC. We detected 48 significantly different ARGs: 5 ARGs up-regulated and 43 ARGs down-regulated in MDD compared to HC. Based on Cox model results, Three ARGs significantly affected drug efficacy (ARG29, ARG105, and ARG111). Eggerthella, Weissella, and Lactobacillus were correlated with different core ARGs, which indicated different mechanisms in affecting MDD. LimitationsThe present study needs to be replicated in different ethnic groups. At the same time, a larger Chinese cohort study and detailed experimental verification are also the key to further discussion. ConclusionOur findings suggest that ARGs play a role in the interplay between major depressive disorder and gut microbiota. The role of ARGs should be taken into account when understanding the relationship between depression and gut microbiota.

Combining network pharmacology and experimental verification to study the anti-colon cancer effect and mechanism of sulforaphene.

Sulforaphene is a derivative of glucosinolate and a potential bioactive substance used for treating colon cancer. This study aimed to evaluate the potential inhibitory effect and mechanisms of sulforaphene in human colon cancer Caco-2 cells. Network pharmacology, molecular docking, and experimental verification were performed to elucidate potential sulforaphene mechanisms in the treatment of this condition. Network pharmacology predicted 27 intersection target genes between sulforaphene and colon cancer cell inhibition. Key sulforaphene targets associated with colon cancer cell inhibition were identified as EGFR, MAPK14, MCL1, GSK3B, PARP1, PTPRC, NOS2, CTSS, TLR9, and CTSK. Gene ontology functional enrichment analysis revealed that the above genes were primarily related to the positive regulation of peptidase activity, cytokine production in the inflammatory response, and the cell receptor signaling pathway. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that sulforaphene mainly inhibited the proliferation of cancer cells by affecting apoptosis as well as the signaling pathways of PD-1, Toll-like receptor, T cell receptor, and P13k-Akt. Molecular docking results further confirmed that CTSS, GSK3B, and NOS2 were significantly up-regulated and had good binding affinity with sulforaphene. In vitro experiments also indicated that sulforaphene had a significant inhibitory effect on human colon cancer Caco-2 cells. This paper revealed the pharmacodynamic mechanism of sulforaphene in the treatment of colon cancer for the first time. It provides scientific insight into the development of sulforaphene as a medicinal resource. © 2024 Society of Chemical Industry.

A rotary self-sensing magnetorheological damper based on triboelectric nanogenerator

A rotary self-sensing magnetorheological (MR) damper(RSMRD) based on a triboelectric nanogenerator is proposed in this study, which can provide variable torque and state self-sensing without a power supply. Compared with traditional self-sensing devices, the sensing part of RSMRD has the advantages of small size, no external power supply, and good low-frequency response. The feasibility of the angular velocity self-sensing (AVS) function is verified through theoretical derivation and experimental verification. The experimental results demonstrate a linear relationship between the output voltage generated by the AVS component and the rotational speed of the rotating shaft. Additionally, the torque characteristics of the rotary self-sensing MR damper are tested, revealing a torque generation of approximately 9.26 N ⋅ m at a current of 1.6 A. Furthermore, a fuzzy control algorithm for vehicle braking is proposed, based on the model parameters of RSMRD. The simulink software is used to establish a dynamic model of 1/4 car braking, with an initial braking speed of 15 m s−1. The results indicate that the vehicle comes to a complete stop after 1.64 s, with a braking distance of 10.93 m. Throughout the braking process, the vehicle slip rate remains close to the optimal slip rate of 0.2.

Multi-condition adaptive detail characterization model of magnetorheological dampers and experimental verification

The theoretical model for predicting the damping characteristics of magnetorheological dampers (MRDs) is significant for enhancing the design efficiency of the control algorithm. However, some existing theoretical models face limitations in characterizing MRD damping characteristics simultaneously in terms of nonlinear detail characterization and adaptability to variable working conditions. Therefore, this paper proposed the Composite Double-Boltzmann (CDB) model combining the Double-Boltzmann (DB) function widely used in the field of biology and chemistry for its strong nonlinear characterization capability. Utilizing this model to fit the sinusoidal vibration testing data of the MRD prototype under variable combination working conditions, obtaining quantitative relationships between the undetermined parameters in the CDB model and the excitation current, vibration frequency, and amplitude to enable the model to address both the nonlinear details characterization of MRDs and adaptability to variable working conditions. Subsequently, the validity of the quantitative relationships were verified by comparing the calculated parameter values using the quantitative relationships with the original accurate parameter values. In order to verify the validity of the CDB model, extensive unknown working condition vibration tests were conducted on the MRD prototype under variable excitation currents, vibration frequencies, amplitudes and random excitation working conditions, employing the CDB and Tanh models to predict the damping characteristics, to compare to demonstrate the CDB model’s capability of adapting to variable working conditions while accurately characterizing the nonlinear details of MRD damping characteristics.

A Crystal Plasticity-Based Simulation to Predict Fracture Initiation Toughness of Reactor-Grade Aluminium: Experimental Verification and Study of Effect of Crystal Orientation

A Crystal Plasticity-Based Simulation to Predict Fracture Initiation Toughness of Reactor-Grade Aluminium: Experimental Verification and Study of Effect of Crystal Orientation

Intelligent Monitoring System for Highway Roadbed Based on Combination Neural Network Algorithm

To solve the problem of the frequent occurrence of roadbed faults, we studied the highway roadbed intelligent monitoring system based on a combined neural network algorithm. Based on the embedded system, with a variety of sensors, we completed the construction of the roadbed monitoring system. In the selection of the data processing algorithm model, the combined neural network algorithm based on an artificial immune algorithm and probabilistic neural network (PNN) is selected. The accurate acquisition of data characteristics is realized by data preprocessing, data smoothing and data fitting. Through experimental verification, the accuracy of the research model in identifying roadbed settlements has been improved by about 5% compared to traditional models. Furthermore, the processing time of the model has been shortened by about 19.5%, proving the effectiveness of the model. In terms of fault identification, compared with other classic models, the final recognition accuracy of this model reached 96.7%, far exceeding the comparison model. This provides new ideas for the monitoring and protection of roadbed faults.

A novel splice-altering TNC variant (c.5247A > T, p.Gly1749Gly) in an Chinese family with autosomal dominant non-syndromic hearing loss

BackgroundThis study aims to analyze the pathogenic gene in a Chinese family with non-syndromic hearing loss and identify a novel mutation site in the TNC gene.MethodsA five-generation Chinese family from Anhui Province, presenting with autosomal dominant non-syndromic hearing loss, was recruited for this study. By analyzing the family history, conducting clinical examinations, and performing genetic analysis, we have thoroughly investigated potential pathogenic factors in this family. The peripheral blood samples were obtained from 20 family members, and the pathogenic genes were identified through whole exome sequencing. Subsequently, the mutation of gene locus was confirmed using Sanger sequencing. The conservation of TNC mutation sites was assessed using Clustal Omega software. We utilized functional prediction software including dbscSNV_AdaBoost, dbscSNV_RandomForest, NNSplice, NetGene2, and Mutation Taster to accurately predict the pathogenicity of these mutations. Furthermore, exon deletions were validated through RT-PCR analysis.ResultsThe family exhibited autosomal dominant, progressive, post-lingual, non-syndromic hearing loss. A novel synonymous variant (c.5247A > T, p.Gly1749Gly) in TNC was identified in affected members. This variant is situated at the exon–intron junction boundary towards the end of exon 18. Notably, glycine residue at position 1749 is highly conserved across various species. Bioinformatics analysis indicates that this synonymous mutation leads to the disruption of the 5' end donor splicing site in the 18th intron of the TNC gene. Meanwhile, verification experiments have demonstrated that this synonymous mutation disrupts the splicing process of exon 18, leading to complete exon 18 skipping and direct splicing between exons 17 and 19.ConclusionThis novel splice-altering variant (c.5247A > T, p.Gly1749Gly) in exon 18 of the TNC gene disrupts normal gene splicing and causes hearing loss among HBD families.

Investigation of intra-fractionated range guided adaptive proton therapy (RGAPT): II. Range-shift compensated on-line treatment adaptation and verification

We previously proposed range-guided adaptive proton therapy (RGAPT) that uses mid-range treatment beams as probing beams and intra-fractionated range measurements for online adaptation. In this work, we demonstrated experimental verification and reported the dosimetric accuracy for RGAPT. A STEEV phantom was used for the experiments, and a 3 × 3 × 3 cm3 cube inside the phantom was assigned to be the treatment target. We simulated three online range shift scenarios: reference, overshoot, and undershoot, by placing upstream Lucite sheets, 4, 0, and 8 that corresponded to changes of 0, 6.8, and −6.8 mm, respectively, in water-equivalent path length. The reference treatment plan was to deliver single-field uniform target doses in pencil beam scanning mode and generated on the Eclipse treatment planning system. Different numbers of mid-range layers, including single, three, and five layers, were selected as probing beams to evaluate beam range (BR) measurement accuracy in positron emission tomography (PET). Online plans were modified to adapt to BR shifts and compensate for probing beam doses. In contrast, non-adaptive plans were also delivered and compared to adaptive plans by film measurements. The mid-range probing beams of three (5.55MU) and five layers (8.71MU) yielded accurate range shift measurements in 60 s of PET acquisition with uncertainty of 0.5 mm while the single-layer probing (1.65MU) was not sufficient for measurements. The adaptive plans achieved an average gamma (2%/2 mm) passing rate of 95%. In contrast, the non-adaptive plans only had an average passing rate of 69%. RGAPT planning and delivery are feasible and verified by the experiments. The probing beam delivery, range measurements, and adaptive planning and delivery added a small increase in treatment delivery workflow time but resulted in substantial dose improvement. The three-layer mid-range probing was most suitable considering the balance of high range measurement accuracy and the low number of probing beam layers.

AOP-Anchored Transcriptome Analysis Catalogue Accelerates the Discovery of Environmental Toxicants in Zebrafish.

Current toxicity screening approaches to evaluate the vast number of environmental chemicals that require assessment are hampered due to their significant costs, time requirements, and reliance on live animal testing. The aim of the present study was to develop an adverse outcome pathway (AOP)-anchored transcriptome analysis (AATA) catalogue to expedite the discovery of environmental toxicants. 437 AOPs from the AOPwiki (https://aopwiki.org/) and 2280 transcriptomics data sets from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI ArrayExpress (AE) repositories were comprehensively reviewed and analyzed. By using the differentially expressed molecular key event (mKE) genes as connection nodes, we created a large-scale environmental substance─target gene (mKE)─predicted adverse outcomes (SGAs) network that included 78 substances, 1099 genes, and 354 adverse outcomes (AOs). To validate the reliability of the network, comprehensive literature verification was conducted. We demonstrated that 164 of the 354 AOs identified have been previously characterized in the literature. The results for 136 of these AOs were consistent with the predictions of the AATA catalogue, representing an accuracy rate of 82.9%. Besides, distinct patterns in molecular KEs and AOs among categories of substances, such as biocides and metals, were demonstrated. Some representative substances, including atrazine and copper, pose significant risks to fish at various levels of biological organization. Moreover, experimental verification of the AATA predictions was conducted, including exposures of zebrafish to perfluorooctanesulfonate, cresyl diphenyl phosphate, and lanthanum. Results demonstrated consistency with predictions of the AATA catalogue, with an accuracy rate of 92.3%. Collectively, the present findings support the AATA catalogue as an efficient and promising platform for identifying environmental toxicants to fish and thereby provide novel insights into the understanding of potential risks of environmental contaminants.

Network pharmacology analysis and molecular mechanism of paeoniflorin and its metabolite in prolactinoma cells.

Prolactinoma was the most common functionalpituitary neuroendocrine tumor tissue type, which was caused by excessive proliferation of pituitary prolactin (PRL) cells. Drug therapy of dopamine receptoragonists was generally considered as the prior treatment for prolactinoma patients. However, there were still prolactinoma patients who were resistant to dopamine agonists. Studies have been reported that paeoniflorin can inhibit the secretion of PRL in prolactinoma cells lacking dopamine D2 receptor (D2R) expression, and paeoniflorin can be metabolized into albiflorin by intestinal flora in rats. The effect of albiflorin on prolactinoma has not been reported yet. In this study, network pharmacology was used to analyze the mechanism of paeoniflorin and its metabolite albiflorin as multi-target therapy for prolactinoma, and the experimental verification was carried out. In order to clarify the complex relationship among paeoniflorin, albiflorin and prolactinoma, we constructed a component-target-disease network, and further constructed interaction network, MMP9, EGFR, FGF2, FGFR1 and LGALS3 were screened as the core targets. Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that paeoniflorin and albiflorin may be involved in various pathways in the treatment of prolactinoma, included relaxin signaling pathway and PI3K-Akt signaling pathway. Molecular docking analysis showed that paeoniflorin and albiflorin had good binding activity with MMP9. Western blotting results showed that paeoniflorin and albiflorin could significantly reduce the expression of MMP9, and ELISA results showed that paeoniflorin and albiflorin could significantly reduce the concentration of PRL in GH3 cells, and the reduce degree of albiflorin was stronger than paeoniflorin at 50μM, which indicated that albiflorin might be a potential drug to treat prolactinoma, which can regulate prolactinoma through MMP9 and reduce the concentration of PRL. Our study provided a new therapeutic strategy for prolactinoma.

Exploring the Molecular Mechanism of Schisandrin C for the Treatment of Atherosclerosis via the PI3K/AKT/mTOR Autophagy Pathway.

Atherosclerosis (AS) is a common cardiovascular disease that poses a major threat to health. Schisandra chinensis is a medicinal and edible plant that is commonly used to treat cardiovascular diseases. In this paper, HPLC was used to detect and analyze 5 different components in Schisandra chinensis. Network pharmacological predictions highlight the PI3K/AKT/mTOR pathway as an important pharmacological pathway. The effective ingredient Schisandrin C was screened by the molecular docking technique. ox-LDL-induced HUVECs were used to construct the atherosclerosis model for further experimental verification. The results showed that Schisandrin C interfered with the PI3K/AKT/mTOR autophagy pathway. This study lays a foundation for the further application of Schisandrin C in the prevention and treatment of atherosclerosis in the future.

Abnormal Behavior Recognition Based on 3D Dense Connections.

Abnormal behavior recognition is an important technology used to detect and identify activities or events that deviate from normal behavior patterns. It has wide applications in various fields such as network security, financial fraud detection, and video surveillance. In recent years, Deep Convolution Networks (ConvNets) have been widely applied in abnormal behavior recognition algorithms and have achieved significant results. However, existing abnormal behavior detection algorithms mainly focus on improving the accuracy of the algorithms and have not explored the real-time nature of abnormal behavior recognition. This is crucial to quickly identify abnormal behavior in public places and improve urban public safety. Therefore, this paper proposes an abnormal behavior recognition algorithm based on three-dimensional (3D) dense connections. The proposed algorithm uses a multi-instance learning strategy to classify various types of abnormal behaviors, and employs dense connection modules and soft-threshold attention mechanisms to reduce the model's parameter count and enhance network computational efficiency. Finally, redundant information in the sequence is reduced by attention allocation to mitigate its negative impact on recognition results. Experimental verification shows that our method achieves a recognition accuracy of 95.61% on the UCF-crime dataset. Comparative experiments demonstrate that our model has strong performance in terms of recognition accuracy and speed.