Search

Lung cancer is one of the most common malignant tumors, and patients are often diagnosed at an advanced stage, posing a substantial risk to human health, so it is crucial to establish a model to forecast the prognosis of patients with lung cancer. Recent research has indicated that proteasome 20S subunit 6 (PSMB6) may be closely associated with anti-apoptotic pathways, and proliferation transduction signals in tumor cells of different tumors. However, the precise role of PSMB6 in the immunoregulatory processes within lung adenocarcinoma (LUAD) is yet to be elucidated. By analyzing the TCGA database, we discovered a positive correlation between the expression of PSMB6 and tumor growth trends, and lung adenocarcinoma patients with elevated PSMB6 expression levels had a worse prognosis. Our findings suggest a close correlation between PSMB6 expression levels, immune cell infiltration and immune checkpoint gene expression, which suggests that PSMB6 may become a new independent prognostic indicator. In addition, we developed a prognostic model of PSMB6-regulated immune infiltration-associated genes by analyzing the link between PSMB6 and the immune microenvironment. This model can not only predict the prognosis of lung adenocarcinoma but also forecasts the patient’s reaction to immunotherapy. The validity of this research outcome has been confirmed by the GSE31210 and IMvigor210 cohorts. Analysis of the Kaplan-Meier Plotter database indicates that individuals with elevated levels of PSMB6 expression exhibit a poorer prognosis. Additionally, in vitro experiments demonstrated that knockdown of PSMB6 inhibits the proliferation, migration, and invasion of lung adenocarcinoma cells while promoting their apoptosis. Overall, our findings suggest that PSMB6 could remarkably influence the management and treatment of lung adenocarcinoma, opening new avenues for targeted immunotherapeutic strategies.

BackgroundPolycystic ovary syndrome (PCOS), a prevalent endocrine disorder in women of reproductive age, is mainly ameliorated through drugs or lifestyle changes, with limited treatment options. To date, numerous researchers have found that fertility nutrient supplements may benefit female reproductive health, but their direct impact on polycystic ovary syndrome risk remains unclear.MethodsOur research employs Mendelian Randomization to assess how fertility nutrients affect PCOS risk. Initially, we reviewed 49 nutrients and focused on 10: omega-3 fatty acids, calcium, dehydroepiandrosterone, vitamin D, betaine, D-Inositol, berberine, curcumin, epigallocatechin gallate, and metformin. Using methodologies of Inverse Variance Weighting and Mendelian Randomization-Egger regression, we examined their potential causal relationships with PCOS risk.ResultsOur findings indicate omega-3 fatty acids reduced PCOS risk (OR=0.73, 95% CI: 0.57-0.94, P=0.016), whereas betaine increased it (OR=2.60, 95% CI: 1.09-6.17, P=0.031). No definitive causal relations were observed for calcium, dehydroepiandrosterone, vitamin D, D-Inositol, and metformin (P>0.05). Drug target Mendelian Randomization analysis suggested that increased expression of the berberine target gene BIRC5 in various tissues may raise PCOS risk (OR: 3.00-4.88; P: 0.014-0.018), while elevated expressions of curcumin target gene CBR1 in Stomach and epigallocatechin gallate target gene AHR in Adrenal Gland were associated with reduced PCOS risk (OR=0.48, P=0.048; OR=0.02, P=0.018, respectively).ConclusionsOur research reveals that specific fertility nutrients supplementation, such as omega-3 fatty acids, berberine, and curcumin, may reduce the risk of PCOS by improving metabolic and reproductive abnormalities associated with it.

Heavy metal pollution of the soil affects the environment and human health. Masson pine is a good candidate for phytoremediation of heavy metal in mining areas. Microorganisms in the rhizosphere can help with the accumulation of heavy metal in host plants. However, studies on its rhizosphere bacterial communities under heavy metal pollution are still limited. Therefore, in this study, the chemical and bacterial characteristics of Masson pine rhizosphere under four different levels of heavy metal pollution were investigated using 16 S rRNA gene sequencing, soil chemistry and analysis of plant enzyme activities. The results showed that soil heavy metal content, plant oxidative stress and microbial diversity damage were lower the farther they were from the mine dump. The co-occurrence network relationship of slightly polluted soils (C1 and C2) was more complicated than that of highly polluted soils (C3 and C4). Relative abundance analysis indicated Sphingomonas and Pseudolabrys were more abundant in slightly polluted soils (C1 and C2), while Gaiella and Haliangium were more abundant in highly polluted soils (C3 and C4). LEfSe analysis indicated Burkholderiaceae, Xanthobacteraceae, Gemmatimonadaceae, Gaiellaceae were significantly enriched in C1 to C4 site, respectively. Mantel analysis showed that available cadmium (Cd) contents of soil was the most important factor influencing the bacterial community assembly. Correlation analysis showed that eight bacterial genus were significantly positively associated with soil available Cd content. To the best of our knowledge, this is the first study to investigate the rhizospheric bacterial community of Masson pine trees under different degrees of heavy metal contamination, which lays the foundation for beneficial bacteria-based phytoremediation using Masson pines in the future.

Lung cancer has one of the highest mortality rates worldwide, with non-small-cell lung cancer (NSCLC) constituting approximately 85% of all cases. Demethylzeylasteral (DEM), extracted from Tripterygium wilfordii Hook F, exhibits notable anti-tumor properties. In this study, we revealed that DEM could effectively induce NSCLC cell apoptosis. Specifically, DEM can dose-dependently suppress the viability and migration of human NSCLC cells. RNA-seq analysis revealed that DEM regulates the P53-signaling pathway, which was further validated by assessing crucial proteins involved in this pathway. Biacore analysis indicated that DEM has high affinity with the P53 protein. The CDX model demonstrated DEM's anti-tumor actions. This work provided evidence that DEM-P53 interaction stabilizes P53 protein and triggers downstream anti-tumor activities. These findings indicate that DEM treatment holds promise as a potential therapeutic approach for NSCLC, which warrants further clinical assessment in patients with NSCLC.

Abstract An edge-cloud computing collaborative dust concentration detection architecture is proposed for real-time operation of intelligent algorithms to reduce the warning delay. And, an end-to-end three-channel convolutional neural network (E2E-SCNN) method is proposed in the paper to facilitate intelligent monitoring and management of dust concentration in tobacco production workshops. This model, which includes three sub-networks-a local feature branch, a global feature branch, and a spatial feature branch, learns the detail texture, overall layout, and spatial distribution information of the input image respectively. A fusion of the three complementary features is performed at the end of the network for the final dust concentration regression prediction. The design, when compared with the single network structure that directly regresses the entire image, is shown to more fully represent the overall information of the image and enhance monitoring performance. A richly annotated image dataset of tobacco production workshops is constructed to verify the effectiveness of the proposed method. The prediction error of E2E-SCNN is compared with existing image estimation algorithms, dual-channel networks, and other methods on this dataset using indicators such as Mean Absolute Error (MAE) and $${R}^{2}$$ R 2 . It is shown by the results that excellent performance is achieved by the E2E-SCNN algorithm, significantly surpassing other comparison methods. The paper demonstrates that the accuracy and robustness of dust concentration prediction can be greatly improved by using a three-channel convolutional neural network spatial information monitoring framework. This achievement provides an effective means for dust supervision and governance during the tobacco production process and offers a technical route that can be referred to for image analysis tasks in other similar fields.

Underdetermined blind source separation (UBSS) is a critical technique in the field of intelligent mechanical operation and maintenance that allows for the disentanglement of source signals from their mixtures without the need for prior knowledge or extensive sensor information. The accuracy of source signal recovery depends on the estimation of the mixing matrix. To promote sparsity in source signals, we employed methods such as sparse representation and sparse component analysis. Traditional approaches, such as the Short-Time Fourier Transform (STFT), often suffer from limited time-frequency resolution, motivating the adoption of the Synchronous Extraction Transformation (SET) algorithm, which closely approximates the ideal time-frequency transform. SET significantly enhances the sparsity of the signals, thus facilitating the separation of the mixed signals. In the context of sparse component analysis, we introduce an improved density peaks clustering (DPC) method that successfully resolves source number estimation issues and robustly eliminates outliers. This improvement leads to a more accurate mixing matrix estimation. To determine the cluster centers, we employed K-means clustering, further refining our source separation process. In summary, our study presents an innovative approach that combines the Synchronous Extraction Transformation (SET) with ‘an improved density peaks clustering (DPC)’ method and K-means for mixing matrix estimation. Source signal recovery was achieved using the shortest-path algorithm. Extensive simulations and experiments validate the effectiveness of the method, outperforming the traditional techniques. When applied to rolling bearing fault diagnosis, the proposed approach effectively separates complex signals and accurately identifies the fault characteristic frequencies.

We attained wurtzite ScxAl1−xN (0.16 ≤ x ≤ 0.37) thin films by varying the Sc and Al fluxes at a fixed active nitrogen flux during plasma-assisted molecular beam epitaxy. Atomic fluxes of Sc and Al sources via measured Sc percentage in as-grown ScxAl1−xN thin films were derived as the feedback for precise determination of the ScxAl1−xN growth diagram. We identified an optimal III/N atomic flux ratio of 0.78 for smooth Sc0.18Al0.82N thin films. Further increasing the III/N ratio led to phase separation under N-rich conditions, validated by the observation of high-Sc-content hillocks with energy-dispersive X-ray spectroscopy mapping. At the fixed III/N ratio of 0.78, we found that phase separation with high-Al-content hillocks occurs for x > 0.37, which is substantially lower than the thermodynamically dictated threshold Sc content of ~0.55 in wurtzite ScxAl1−xN. We postulate that these wurtzite-phase purity degradation scenarios are correlated with adatom diffusion and the competitive incorporation process of Sc and Al. Therefore, the ScxAl1−xN growth window is severely restricted by the adatom kinetics. We obtained single crystalline Sc0.37Al0.63N thin films with X-ray diffraction (002)/(102) ω rocking curve full-width at half-maximums of 2156 arcsec and 209 arcsec and surface roughness of 1.70 nm. Piezoelectric force microscopy probing of the Sc0.37Al0.63N epilayer validates unambiguous polarization flipping by 180°.

Due to their robust migration capabilities, slow degradation, and propensity for adsorbing environmental pollutants, micro(nano)plastics (MNPs) are pervasive across diverse ecosystems. They infiltrate various organisms within different food chains through multiple pathways including inhalation and dermal contact, and pose a significant environmental challenge in the 21st century. Research indicates that MNPs pose health threats to a broad range of organisms, including humans. Currently, extensive detection data and studies using experimental animals and in vitro cell culture indicate that MNPs can trigger various forms of programmed cell death (PCD) and can induce various diseases. This review provides a comprehensive and systematic analysis of different MNP-induced PCD processes, including pyroptosis, ferroptosis, autophagy, necroptosis, and apoptosis, based on recent research findings and focuses on elucidating the links between PCD and diseases. Additionally, targeted therapeutic interventions for these diseases are described. This review provides original insights into the opportunities and challenges posed by current research findings. This review evaluates ways to mitigate various diseases resulting from cell death patterns. Moreover, this paper enhances the understanding of the biohazards associated with MNPs by providing a systematic reference for subsequent toxicological research and health risk mitigation efforts.

N6-methyladenosine (m6A) is the most abundant chemical modification in eukaryotic cells. It is a post-transcriptional modification of mRNA, a dynamic reversible process catalyzed by methyltransferase, demethylase, and binding proteins. Ferroptosis, a unique iron-dependent cell death, is regulated by various cell metabolic events, including many disease-related signaling pathways. And different ferroptosis inducers or inhibitors have been identified that can induce or inhibit the onset of ferroptosis through various targets and mechanisms. They have potential clinical value in the treatment of diverse diseases. Until now, it has been shown that in several cancer diseases m6A can be involved in the regulation of ferroptosis, which can impact subsequent treatment. This paper focuses on the concept, function, and biological role of m6A methylation modification and the interaction between m6A and ferroptosis, to provide new therapeutic strategies for treating malignant diseases and protecting the organism by targeting m6A to regulate ferroptosis.

Lung cancer is the leading cause of cancer-related deaths worldwide, and ∼85% of lung cancers are classified as nonsmall cell lung cancer (NSCLC). These malignancies can proliferate indefinitely, in part due to dysregulation of the cell cycle and the resulting abnormal cell growth. The specific activation of cyclin-dependent kinases 4 and 6 (CDK4/6) is closely linked to tumour proliferation. Approximately 80% of human tumours exhibit abnormalities in the cyclin D-CDK4/6-INK4-RB pathway. Specifically, CDK4/6 inhibitors either as monotherapy or combination therapy have been investigated in pre-clinical and clinical studies for the treatment of NSCLC, and promising results have been achieved. This review article focuses on research regarding the use of CDK4/6 inhibitors in NSCLC, including the characteristics and mechanisms of action of approved drugs and progress of pre-clinical and clinical research.

Flash droughts have attracted worldwide attention because of their rapid outbreak and extensive influence. However, studies regarding the characteristics and effects of flash droughts in grassland ecosystems are insufficient. In this study, the frequency and intensity characteristics of flash droughts in the Xilinguole Grassland in China were studied. The response characteristics of the productivity of different types of grassland to flash droughts and the relationship between these characteristics and the drought tolerance of grassland were revealed. The results show that (1) flash droughts had the greatest impact on grassland net primary productivity (NPP) and rain use efficiency (RUE) in summer and spring, respectively, with a level of intensity above that of moderate drought. Strong evapotranspiration flash droughts (SEFD) require more attention from decision-makers than heat wave flash droughts (HWFD). A higher frequency and intensity of flash droughts had a greater impact on vegetation. (2) Flash droughts caused moderate negative anomalies in the NPP and RUE indices in more than 90 % of the grasslands. The longest lag time of the NPP response to flash droughts was 2 months, and NPP anomalies were affected by flash droughts for nearly 2 months. RUE was more sensitive to flash droughts than NPP. RUE responded to flash droughts within 10 days, with a decrease of more than 80 % in magnitude, which was 30 % higher than that of the NPP, and the duration of the anomaly was half that of the NPP. (3) Grasslands with a high sensitivity to flash droughts had shorter response durations, fewer abnormalities, better recovery abilities and better drought tolerance. The drought tolerance of grasslands did not increase in association with large NPP and RUE values. Desert grasslands were the most drought tolerant, while meadow grasslands were the least drought tolerant, with the highest risk of flash droughts. This study provides theoretical support for improving the ability of an ecosystem to cope with flash drought risk and scientific grassland management.

Abstract To achieve real‐time and effective prediction of industrial site risks, this paper proposes an industrial risk prediction framework for multimodal data based on edge computing. First, the authors gather and annotate industrial risk multimodal data that consist of text descriptions, images, and videos. Then, the authors transfer the data to the edge server, and apply deep learning models such as Bidirectional Encoder Representations from Transformers (BERT), ResNet etc., to extract features and learn representations for text, image, and video data respectively. The authors input the fused feature data into an enhanced long short term memory (LSTM) model and train it on the dataset. Finally, the authors perform the risk prediction based on the collected multimodal data. The experimental results demonstrate that the method proposed in this paper exhibits superior performance, achieving a 1.4% enhancement in predictive accuracy.

The nonhomogeneity of conglomerate in terms of organization and the complexity of fracture extension make the design and effective implementation of fracturing in conglomerate reservoirs challenging. Considering the limitations of physical experiments and two-dimensional (2D) numerical modeling, this paper adopts the continuum-discontinue element method (CDEM) to carry out numerical simulation of three-dimensional (3D) conglomerate fracturing considering pore-fracture seepage. By introducing multiple parameters to quantify the correlation between fracture geometry, fracture complexity, and damage mode, the evolution mechanism of fracture morphology under the influence of multiple factors is systematically investigated. The results show that the numerical simulation experiments can control the variables well, but the random distribution of gravel leads to the unpredictability of fracture extension, and the concluding patterns obtained still show large fluctuations. The high permeability of the gravel promotes the development of gravel-penetrating fractures but has less effect on the overall morphology of the fractures. High-strength gravel promotes the development of branching fractures in the initiation phase, which acts as a barrier to expanding fractures, and the most complex fracture development occurs when the gravel strength is approximately 4–5 times that of the matrix. In the weakly cemented state, fracture development around the gravel contributes to the shear failure of the conglomerate, but the strength of the cemented interface has no obvious control on the overall fracture morphology. The correlation between gravel content and conglomerate damage mode is significant, with the highest degree of fracture complexity occurring when the gravel content is approximately 30%. Stress differential is the most significant controlling factor affecting fracture morphology, followed by minimum principal stress. When the stress difference reaches 8 MPa, the fracture morphology begins to stabilize, and too high a stress difference will cause the phenomenon that the fracture stops expanding, affecting the fracturing effect. A high level of minimum stress promotes tensile failure in conglomerate, and the scale and complexity of fracture decrease. High injection displacement promotes branch fracture development and reduces the effect of in situ stress on fracture extension, and too high a displacement leads to inhibition of main fracture development.

Micro- and nano-plastics (MNPs) are ubiquitously distributed in the environment, infiltrate organisms through multiple pathways, and accumulate, thus posing potential threats to human health. MNP exposure elicits changes in microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), thereby precipitating immune, neurological, and other toxic effects. The investigation of MNP exposure and its effect on miRNA expression has garnered increasing attention. Following MNP exposure, circRNAs serve as miRNA sponges by modulating gene expression, while lncRNAs function as competing endogenous RNAs (ceRNAs) by fine-tuning target gene expression and consequently impacting protein translation and physiological processes in cells. Dysregulated miRNA expression mediates mitochondrial dysfunction, inflammation, and oxidative stress, thereby increasing the risk of neurodegenerative diseases, cardiovascular diseases, and cancer. This tract, blood, urine, feces, placenta, and review delves into the biotoxicity arising from dysregulated miRNA expression due to MNP exposure and addresses the challenges encountered in this field. This study provides novel insights into the connections between MNPs and disease risk.

Abstract Background Non-small cell lung cancer (NSCLC) is the most common lung cancer with high mortality across the world, but it is challenging to develop an effective therapy for NSCLC. Celastrol is a natural bioactive compound, which has been found to possess potential antitumor activity. However, the underlying molecular mechanisms of celastrol activity in NSCLC remain elusive. Methods Cellular function assays were performed to study the suppressive role of celastrol in human NSCLC cells (H460, PC-9, and H520) and human bronchial epithelial cells BEAS-2B. Cell apoptosis levels were analyzed by flow cytometry, Hoechst 33342, caspase-3 activity analysis, and western blot analysis. Intracellular reactive oxygen species (ROS) were analyzed by flow cytometry and fluorescence microscope. Expression levels of endoplasmic reticulum (ER) stress-related proteins and phosphorylated signal transducer and activator of transcription 3 (P-STAT3) were identified via western blot analysis. A heterograft model in nude mice was employed to evaluate the effect of celastrol in vivo. Results Celastrol suppressed the growth, proliferation, and metastasis of NSCLC cells. Celastrol significantly increased the level of intracellular ROS; thus, triggering the activation of the ER stress pathway and inhibition of the P-STAT3 pathway, and eventually leading to cell apoptosis, and the effects were reversed by the pre-treatment with N-Acetyl-l-cysteine (NAC). Celastrol also suppressed tumor growth in vivo. Conclusion The outcomes revealed that celastrol plays a potent suppressive role in NSCLC in vitro and in vivo. Celastrol induces apoptosis via causing mitochondrial ROS accumulation to suppress the STAT3 pathway. Celastrol may have potential application prospects in the therapy of NSCLC.

As important organelles of energetic and metabolism, changes in the dynamic state of mitochondria affect the homeostasis of cellular metabolism. Mitochondrial dynamics include mitochondrial fusion and mitochondrial fission. The former is coordinated by mitofusin-1 (Mfn1), mitofusin-2 (Mfn2), and optic atrophy 1 (Opa1), and the latter is mediated by dynamin related protein 1 (Drp1), mitochondrial fission 1 (Fis1) and mitochondrial fission factor (MFF). Mitochondrial fusion and fission are generally in dynamic balance and this balance is important to preserve the proper mitochondrial morphology, function and distribution. Diabetic conditions lead to disturbances in mitochondrial dynamics, which in return causes a series of abnormalities in metabolism, including decreased bioenergy production, excessive production of reactive oxygen species (ROS), defective mitophagy and apoptosis, which are ultimately closely linked to multiple chronic complications of diabetes. Multiple researches have shown that the incidence of diabetic complications is connected with increased mitochondrial fission, for example, there is an excessive mitochondrial fission and impaired mitochondrial fusion in diabetic cardiomyocytes, and that the development of cardiac dysfunction induced by diabetes can be attenuated by inhibiting mitochondrial fission. Therefore, targeting the restoration of mitochondrial dynamics would be a promising therapeutic target within type II diabetes (T2D) and its complications. The molecular approaches to mitochondrial dynamics, their impairment in the context of T2D and its complications, and pharmacological approaches targeting mitochondrial dynamics are discussed in this review and promise benefits for the therapy of T2D and its comorbidities.

Plasmids are commonly used tools in microbiology and molecular biology and have important and wide-ranging applications in the study of gene function, protein expression, and compound synthesis. The complex relationship between necessary antibiotic addition, compatibility between multiple plasmids, and the growth burden of host bacteria has plagued the wider use of compatibility plasmids. In this study, we constructed the terminal polymerization pathway of PSA by exogenously expressing the neuA, neuD, and neuS genes after the knockdown of Eschesrichia coli BL21 (DE3). Duet series vectors were utilized to regulate the expression level of neuA, neuD, and neuS genes to study the gene expression level, plasmid copy number growth burden, pressure of antibiotic addition, stability of compatible plasmids, and the level of expression stability of exogenous genes, as well as the effect on the biological reaction process. The results showed that the three genes, neuA, neuD, and neuS, were enhanced in the recombinant strain E. coli NA-05, with low copy, medium copy, and high copy, respectively. The effect of PSA synthesis under standard antibiotic pressure was remarkable. The results of this thesis suggest the use of a Duet series of compatible expression vectors to achieve the stable existence and co-expression of multiple genes in recombinant bacteria, which is a good reason for further research.

IntroductionThe peat carbon pool stores 30% of the total global soil carbon accounting for 3–4% of the global land surface. The stability of the peatland carbon pool is a key factor affecting global carbon cycling that is seriously disturbed by climate change and regional human activities. However, the impact of these factors on carbon pool stability remains poorly understood.MethodsBased on the physicochemical properties and carbon compounds of 973 peat samples from Jilin Province (China), which are widely distributed in different altitude regions of the Changbai Mountains, we investigated the stability of the carbon pool in different dominant plants and degradation types of peatlands and assessed the effects of regional environmental factors on the peatland carbon pool.Results and DiscussionOur results showed that the carbohydrate content of peat soils in different peatland types ranged from 33.2 ± 6.9% to 40.5 ± 4.8%, and the aromatic content ranged from 19.8 ± 1.2% to 22.7 ± 2.3%. Bulk density is the most important physicochemical factor, and annual average temperature is the most important environmental factor that influences carbon stability. The effects of selected environmental factors on the peatland carbon pool covered by different plants were different, and the carbon stability in shrub peatlands is more sensitive to climate characteristics than in peatlands dominated by the other two plant types. Peatland degradation decreases the carbon stability in herb and herb/shrub peatlands and increases the carbon stability in shrub peatlands, leading the peatland carbon pool to be more easily influenced by regional human activities than natural peatlands.

Piperine (1-piperoylpiperidine) is the major pungent component of black pepper (Piper nigrum) and exhibits a spectrum of pharmacological activities. The molecular bases for many of piperine’s biological effects are incompletely defined. We noted that the chemical structure of piperine generally conforms to a pharmacophore model for small bioactive molecules that activate mitofusin (MFN)-mediated mitochondrial fusion. Piperine, but not its isomer chavicine, stimulated mitochondrial fusion in MFN-deficient cells with EC50 of ~8 nM. We synthesized piperine analogs having structural features predicted to optimize mitofusin activation and defined structure-activity relationships (SAR) in live-cell mitochondrial elongation assays. When optimal spacing was maintained between amide and aromatic groups the derivatives were potent mitofusin activators. Compared to the prototype phenylhexanamide mitofusin activator, 2, novel molecules containing the piperidine structure of piperine exhibited markedly enhanced passive membrane permeability with no loss of fusogenic potency. Lead compounds 5 and 8 enhanced mitochondrial motility in cultured murine Charcot-Marie-Tooth disease type 2A (CMT2A) neurons, but only 8 improved mitochondrial transport in sciatic nerve axons of CMT2A mice. Piperine analogs represent a new chemical class of mitofusin activators with potential pharmaceutical advantages.

The effectiveness of feature extraction is a critical aspect of fault diagnosis for petrochemical machinery and equipment. Traditional entropy analysis methods are prone to disruption by noise, parameter sensitivity, and sudden entropy variations. This study establishes a high-dimensional mapping entropy (HDME) method characterized by robust noise resistance, addressing the issues of parameter sensitivity and inadequate noise suppression inherent in traditional feature extraction methodologies. A mapping theory of high-dimensional space based on kernel function pattern recognition is proposed, which reassembles the sample vector after phase space reconstruction of time series. The multi-scale high-dimensional mapping entropy (MHDME) and refined composite multi-scale high-dimensional mapping entropy (RCMHDME) algorithms are further studied based on the idea of refined composite multi-scale. Application to simulated signals shows that the suggested methods reduce parameter sensitivity and enhance entropy smoothness. The development of a methodology to identify faults through MHDME is proposed. This approach integrates signal preprocessing and intelligent preference techniques to achieve pattern recognition of reciprocating compressor bearings in various wear conditions. Moreover, the identification findings demonstrate that the suggested approach can effectively extract the characteristics of the signal and accurately distinguish the effects caused by different faults.

Cancer is a serious disease with an abnormal proliferation of organ tissues; it is characterized by malignant infiltration and growth that affects human life. Traditional cancer therapies such as resection, radiotherapy and chemotherapy have a low cure rate and often cause irreversible damage to the body. In recent years, since the traditional treatment of cancer is still very far from perfect, researchers have begun to focus on non-invasive near-infrared (NIR)-responsive natural macromolecular hydrogel assembly drugs (NIR-NMHADs). Due to their unique biocompatibility and extremely high drug encapsulation, coupling with the spatiotemporal controllability of NIR, synergistic photothermal therapy (PTT), photothermal therapy (PDT), chemotherapy (CT) and immunotherapy (IT) has created excellent effects and good prospects for cancer treatment. In addition, some emerging bioengineering technologies can also improve the effectiveness of drug delivery systems. This review will discuss the properties of NIR light, the NIR-functional hydrogels commonly used in current research, the cancer therapy corresponding to the materials encapsulated in them and the bioengineering technology that can assist drug delivery systems. The review provides a constructive reference for the optimization of NIR-NMHAD experimental ideas and its application to human body.

Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields.

Abstract Oxidative stress (OS) is one of the primary factors leading to male infertility. Oral administration of antioxidants has thus far been found to significantly improve the quality of human sperm. Therefore, antioxidant treatment has become the consensus among international experts on male infertility. In this study, peroxisomal biogenesis factor 3 (Pex3)-knockout (KO, −/−) mice were used as a model to compare the efficacy of three types of traditional Chinese medicine (TCM) granules (Epimedium [YYH], Cuscuta [TSZ], and Rhodiola [HJT]) for male reproductive function rescue. YYH was revealed to be the best and exerted a rescue effect on Pex3−/− mice with spermatogenesis defects. In addition, YYH prominently reduced ROS levels in the testes, inhibited DNA oxidative damage in spermatogenic cells, promoted the proliferation of spermatogenic cells, and inhibited apoptosis in Pex3−/− male mice. Furthermore, the mechanism by which YYH ameliorated dyszoospermia was confirmed via the establishment of cyclin-dependent kinase inhibitor 2 A (P16Ink4a)-KO mice. Specifically, Pex3−/− mice produced elevated amounts of ROS, which damaged germ cell DNA and further activated the signaling pathway of the cell senescence regulatory protein P16-CDK6, resulting in cell cycle arrest and eventually contributing to spermatogenesis dysfunction. YYH supplementation partially corrected the associated phenotype in gene KO mice by affecting P16 expression levels, thus improving the reproductive outcome to a certain extent.

Ovarian age assessment is an important indicator to evaluate the ovarian reserve function and reproductive potential of women. At present, the application of ovarian age prediction model in China needs further improvement and optimization to make it more suitable for the actual situation of women in China. In this study, we collected subjects and their data in three ways: firstly, we collected clinical data from a number of women go to local hospital, including healthy women and women with DOR or PCOS; secondly, we obtained data by recruited healthy women through CRO companies for a fee; thirdly, we collected data from a number of healthy women using WeChat applet. Using the data collected by CRO company and WeChat applet, we applied the generalized linear model to optimize the ovarian age prediction model. The optimized formula is: OvAge = exp (3.5254-0.0001*PRL-0.0231*AMH), where P = 0.8195 for PRL and P = 0.0003 for AMH. Applying the formula to the hospital population data set for testing, it showed that the predicted ovarian age in the healthy women was comparable to their actual age, with a root mean squared error (RMSE) = 5.6324. The prediction accuracy was high. These data suggest that our modification of the ovarian age prediction model is feasible and that the formula is currently a more appropriate model for ovarian age assessment in healthy Chinese women. This study explored a new way to collect clinical data, namely, an online ovarian age calculator developed based on a WeChat applet, which can collect data from a large number of subjects in a short period of time and is more economical, efficient, and convenient. In addition, this study introduced real data to optimize the model, which could provide insights for model localization and improvement.

Lung adenocarcinoma (LUAD) is associated with poor prognosis. Identifying novel cancer targets and helpful therapeutic strategies remains a serious clinical challenge. This study detected differentially expressed genes in The Cancer Genome Atlas (TCGA) LUAD data collection. We also identified a predictive DNA biomarker, G protein-coupled receptor 37 (GPR37), which was verified as a prognostic biomarker with a critical role in tumor progression. In human LUAD specimens and microarray analyses, we determined that GPR37 was significantly upregulated and associated with a poor prognosis. GPR37 downregulation markedly inhibited the proliferation and migration of LUAD both in vitro and in vivo. Mechanistically, GPR37 could bind to CDK6, thereby facilitating tumor progression in LUAD by inducing cell cycle arrest at the G1 phase. GPR37 also facilitates tumorigenesis in xenograft tumors in vivo. High-throughput screening for GPR37-targeted drugs was performed using the Natural Products Library, which revealed the potential of Hypocrellin B to inhibit GPR37 and cell growth in LUAD. We demonstrated that Hypocrellin B suppressed LUAD cell proliferation and migration both in vitro and in vivo via GPR37 inhibition. Collectively, our findings reveal the role of GPR37 in LUAD progression and migration and the potential of GPR37 as a target for the treatment of LUAD. Thus, the specific inhibition of GPR37 by the natural product Hypocrellin B may possess the potential for the treatment of LUAD.

The accuracy and stability of the envelope estimation function are enduring issues throughout the research process of LMD. This paper presents double interpolation and mutation interval reconstruction local mean decomposition (DIMIRLMD) to improve the stability of the demodulation process and the accuracy of PF components. DIMIRLMD first proposes a mutation interval reconstruction envelope algorithm using extreme symmetry points to suppress the demodulation mutation phenomenon, which disturbs the stability of the demodulation process, and then selects the optimal PF component from a double interpolation PF component library based on the index of orthogonality (IO) for a better hierarchical property. DIMIRLMD was employed to analyze the simulation signal and vibration signal of a reciprocating compressor in an oversized bearing clearance state, and the results illustrate its performances are more excellent than those of three other LMD methods. Furthermore, the envelope frequency spectrum obtained from the proposed LMD presents a clear double rotation fault frequency and lower noise disturbance.

Fish not only constitute an important trophic level in aquatic ecosystems but also serve as an important source of protein for human beings. The health of fish is related to the sustained and healthy development of their entire aquatic ecosystem. Due to the widespread use, mass production, high disposal frequency, and degradation resistance of plastics, these pollutants are released into aquatic environments on a large scale. They have become one of the fastest growing pollutants and have a substantial toxic effect on fish. Microplastics have intrinsic toxicity and can absorb heavy metals discharged into water. The adsorption of heavy metals onto microplastics in aquatic environments is affected by many factors and serves as a convenient way for heavy metals to migrate from the environment to organisms. Fish are exposed to both microplastics and heavy metals. In this paper, the toxic effects of heavy metal adsorption by microplastics on fish are reviewed, and the focus is on the toxic effects at the individual (survival, feeding activity and swimming, energy reserves and respiration, intestinal microorganisms, development and growth, and reproduction), cellular (cytotoxicity, oxidative damage, inflammatory response, neurotoxicity, and metabolism) and molecular (gene expression) levels. This facilitates an assessment of the pollutants’ impact on ecotoxicity and contributes to the regulation of these pollutants in the environment.

The underdetermined blind source separation (UBSS) has been considered to be a novel signal processing technique, which can separate the fault source signals from their mixtures. The mixing matrix estimation is a major step in the UBSS, this paper focuses on boosting the accuracy level of the estimated mixing matrix in the underdetermined case. Since the traditional clustering algorithms may not capture the signal characteristics well and secure a satisfactory estimation of the mixing matrix, an effective two-stage clustering algorithm is proposed to estimate the mixing matrix through a combination of hierarchical clustering and K-means. More specifically, first, the sum of frequency points energy in the time-frequency (TF) domain is calculated to estimate the number of source signals before clustering, and the initial clustering centers are obtained with a hierarchical clustering algorithm. Second, after eliminating outliers deviating from the initial clustering centers with the cosine distance, the new clustering centers are obtained by recalculating the mean value of each sub-cluster. Finally, the new clustering centers are set as the initial clustering centers of the K-means algorithm to estimate the mixing matrix. Extensive simulations and experiments show that the proposed method can effectively separate the source signals and ensure an estimate of the mixing matrix that is substantially more accurate than the K-means algorithm alone.

The bearing vibration signal of reciprocating compressor has complex, non-stationary, nonlinear, and feature coupling characteristics. A method for sub-health recognition of sliding bearings based on curve adaptive grasshopper optimization algorithm optimize the parameters of variational mode decomposition (CAGOA-VMD) and generalized refine composite multiscale dispersion entropy (GRCMDE) is used. First, the CAGOA was used to search the best influence parameter combination of the VMD algorithm, and determine the bandwidth parameters and the number of decompositions that need to be set by the VMD algorithm, decompose the bearing fault signal to obtain a series of IMF. Then, the kurtosis and correlation coefficient criteria are used to select a group of components that contain the most information, and the fault signal is reconstructed on this component, and then the reconstructed signal is analyzed by GRCMDE to form a fault eigenvector. Finally, KPCA is used for dimensionality reduction to select input features and input into KELM for classification and recognition. The experimental results show that this method can effectively extract the bearing fault features of reciprocating compressors, and the eigenvectors have good separability, and realize the sub-health recognition of bearing fault features of reciprocating compressors.

In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving the serious modal aliasing problem of local mean decomposition (LMD) and the dependence of permutation entropy on the length of the original time series. First, by adding a sine wave with a uniform phase as a masking signal, adaptively selecting the amplitude of the added sine wave, the optimal decomposition result is screened by the orthogonality and the signal is reconstructed based on the kurtosis value to remove the signal noise. Secondly, in the RTSMWPE method, the fault feature extraction is realized by considering the signal amplitude information and replacing the traditional coarse-grained multi-scale method with a time-shifted multi-scale method. Finally, the proposed method is applied to the analysis of the experimental data of the reciprocating compressor valve; the analysis results demonstrate the effectiveness of the proposed method.

Non-small cell lung carcinoma (NSCLC) is a major neoplastic disease with a high mortality worldwide; however, effective treatment of this disease remains a challenge. Flubendazole, a traditional anthelmintic drug, possesses potent antitumor properties; however, the detailed molecular mechanism of flubendazole activity in NSCLC needs to be further explored. In the present study, flubendazole was found to exhibit valid antitumor activity in vitro as well as in vivo. Flubendazole blocked phosphorylation of STAT3 in a dose- and time-dependent manner and regulated the transcription of STAT3 target genes encoding apoptotic proteins. Further, flubendazole inhibited STAT3 activation by inhibiting its phosphorylation and nuclear localization induced by interleukin-6 (IL-6). Notably, the autophagic flux of NSCLC cell lines was increased after flubendazole treatment. Furthermore, flubendazole downregulated the expression of BCL2, P62, and phosphorylated-mTOR, but it upregulated LC3-I/II and Beclin-1 expression, which are the main genes associated with autophagy. Collectively, these data contribute to elucidating the efficacy of flubendazole as an anticancer drug, demonstrating its potential as a therapeutic agent via its suppression of STAT3 activity and the activation of autophagy in NSCLC.

Abstract Background Signal transducer and activator of transcription 3 (STAT3) is an oncogene, which upregulates in approximately 70% of human cancers. Autophagy is an evolutionarily conserved process which maintains cellular homeostasis and eliminates damaged cellular components. Moreover, the STAT3 signaling pathway, which may be triggered by cancer cells, has been implicated in the autophagic process. Methods In this study, we found that the anthelmintic flubendazole exerts potent antitumor activity in three human colorectal cancer (CRC) cell lines and in the nude mouse model. The inhibition of cell proliferation in vitro by flubendazole was evaluated using a clonogenic assay and the MTT assay. Western blot analysis, flow cytometry analysis, siRNA growth experiment and cytoplasmic and nuclear protein extraction were used to investigate the mechanisms of inhibiting STAT3 signaling and activation of autophagy induced by flubendazole. Additionally, the expression of STAT3 and mTOR was analyzed in paired colorectal cancer and normal tissues collected from clinical patients. Results Flubendazole blocked the IL6-induced nuclear translocation of STAT3, which led to inhibition of the transcription of STAT3 target genes, such as MCL1, VEGF and BIRC5. In addition, flubendazole also reduced the expression of P-mTOR, P62, BCL2, and upregulated Beclin1 and LC3-I/II, which are major autophagy-related genes. These processes induced potent cell apoptosis in CRC cells. In addition, flubendazole displayed a synergistic effect with the chemotherapeutic agent 5-fluorouracil in the treatment of CRC. Conclusions Taken together, these results indicate that flubendazole exerts antitumor activities by blocking STAT3 signaling and inevitably affects the autophagy pathway. Flubendazole maybe a novel anticancer drug and offers a distinctive therapeutic strategy in neoadjuvant chemotherapy of CRC.

BackgroundColorectal cancer (CRC) is the second deadliest malignant disease in the world and the leukemia inhibitory factor receptor/signal transducers and activators of transcriptions (LIFR/STATs) signaling axis plays an important role in the molecular biology of CRC.MethodsCell function tests were performed to observe the inhibitory effect of cynaropicrin on human CRC cells (RKO, HCT116, and DLD-1). Expression levels of LIFR, P-STAT3, P-STAT4, and apoptotic proteins were detected by Western blotting. Immunoprecipitation confirmed the presence of LIFR/STAT3/STAT4 complex. Cell immunofluorescence assay was used to observe the subcellular localization of STAT3 and STAT4. In vivo efficacy of cynaropicrin was evaluated by a xenotransplantation model in nude mice.ResultsCynaropicrin significantly reduced the survival ability of human CRC cells and promoted apoptosis in a dose-dependent manner. Western blotting results suggested that the antitumor effects of cynaropicrin might be mediated by inhibition of the LIFR/STATs axis. Cynaropicrin reduced the formation of STAT3/STAT4 heterodimers and blocked their entry into the nucleus. Cynaropicrin also suppressed tumor growth in the xenograft model.ConclusionThe results showed that cynaropicrin exerted a strong inhibitory effect on CRC in vitro and in vivo. Our study concluded that cynaropicrin has potential application prospects in the field of anti-CRC therapy.

Sphingosine-1-phosphate receptor 1 (S1PR1) plays a crucial role in infectious diseases. Targeting S1PR1 provides protection against pathogens, such as influenza viruses. This study is aimed at investigating S1PR1 in response to bacterial infection by assessing S1PR1 expression in S. aureus-infected mice. A rodent local muscle bacterial infection model was developed by injecting S. aureus to the lower hind limb of Balb/c mice. The changes of S1PR1 expression in response to bacterial infection and blocking treatment were assessed using ex vivo biodistribution and in vivo positron emission tomography (PET) after intravenous injection of an S1PR1-specific radiotracer [18F]TZ4877. The specificity of [18F]TZ4877 was assessed using S1PR1-specific antagonist, NIBR-0213, and S1PR1-specific DsiRNA pretreated the animals. Immunohistochemical studies were performed to confirm the increase of S1PR1 expression in response to infection. Ex vivo biodistribution data showed that the uptake of [18F]TZ4877 was increased 30.6%, 54.3%, 74.3%, and 115.3% in the liver, kidney, pancreas, and thymus of the infected mice, respectively, compared to that in normal control mice, indicating that S1PR1 is involved in the early immune response to bacterial infection. NIBR-0213 or S1PR1-specific DsiRNA pretreatment reduced the tissue uptake of [18F]TZ4877, suggesting that uptake of [18F]TZ4877 is specific. Our PET/CT study data also confirmed that infected mice have increased [18F]TZ4877 uptake in several organs comparing to that in normal control mice. Particularly, compared to control mice, a 39% increase of [18F]TZ4877 uptake was observed in the infected muscle of S. aureus mice, indicating that S1PR1 expression was directly involved in the inflammatory response to infection. Overall, our study suggested that S1PR1 plays an important role in the early immune response to bacterial infection. The uptake of [18F]TZ4877 is tightly correlated with the S1R1 expression in response to S. aureus infection. PET with S1PR1-specific radiotracer [18F]TZ4877 could provide a noninvasive tool for detecting the early S1PR1 immune response to infectious diseases.

Aims and Hypothesis: This study aims to explore the specific molecular mechanism of folliculin (FLCN)-induced proliferation, migration, and invasion in clear cell renal cell carcinoma (ccRCC) and to investigate the relationship of FLCN and HIF2α. Folliculin was identified as a tumor suppressor gene. Its deletions and mutations are associated with a potential risk of renal cancer. At present, the specific molecular mechanism of FLCN-induced proliferation, invasion, and migration in ccRCC remains elusive.Methods: Cell proliferation was measured by flow cytometry analysis, while cell migration and invasion were measured by wound healing assay and Matrigel invasion assay. The expression of FLCN, HIF2α, MMP9, and p-AKT was examined by Western blotting. The cells were transfected with plasmids or siRNA to upregulate or downregulate the expression of FLCN. Immunofluorescence microscopy was carried out to display the HIF2α location. We also determined the correlation of FLCN and HIF2α in human renal cancer samples.Results: FLCN was combined with HIF2α in renal tubular epithelial and cancer cells, and it effectively alleviates the deterioration of renal cancer cells by degrading HIF2α. The silencing of FLCN showed a promotion of HIF2α protein expression via PI3K/mTORC2 pathway, which in turn led to an increase in downstream target genes Cyclin D1 and MMP9. Moreover, interfering with siFLCN advanced the time of HIF2α entry into the nucleus.Conclusions: Our study illustrated that FLCN could be a new therapeutic target in ccRCC. FLCN combined with HIF2α and identified a novel PI3K/mTORC2/HIF2α signaling in ccRCC cells.

In practical engineering applications, the vibration signals collected by sensors often contain outliers, resulting in the separation accuracy of source signals from the observed signals being seriously affected. The mixing matrix estimation is crucial to the underdetermined blind source separation (UBSS), determining the accuracy level of the source signals recovery. Therefore, a two-stage clustering method is proposed by combining hierarchical clustering and K-means to improve the reliability of the estimated mixing matrix in this paper. The proposed method is used to solve the two major problems in the K-means algorithm: the random selection of initial cluster centers and the sensitivity of the algorithm to outliers. Firstly, the observed signals are clustered by hierarchical clustering to get the cluster centers. Secondly, the cosine distance is used to eliminate the outliers deviating from cluster centers. Then, the initial cluster centers are obtained by calculating the mean value of each remaining cluster. Finally, the mixing matrix is estimated with the improved K-means, and the sources are recovered using the least square method. Simulation and the reciprocating compressor fault experiments demonstrate the effectiveness of the proposed method.

In this paper, four binary hard sphere crystals were numerically constructed by discrete element method (DEM) through different packing modes under three-dimensional (3D) mechanical vibration. For each crystal, a modified Voronoi tessellation method (called radical tessellation) was utilized to quantitatively investigate the topological and metrical properties of radical polyhedra (RPs). The topological properties such as the number of faces, edges, vertices per RP and the number of edges per RP face as well as the metrical properties such as perimeter, surface area, volume, and relative pore size per RP were systematically characterized and compared. Meanwhile, the mechanism of the binary hard sphere crystallization was also investigated. The results show that the packing sequence and pattern of the large spheres can determine the structure of the binary hard sphere crystal. The RP structures and their metrical and topological properties of the four binary hard sphere crystals (even the packing density of the two crystals is the same) are quite different. Each property can clearly reflect the specific characteristics of the corresponding binary hard sphere crystalline structure. The obtained quantitative results would be useful for the deep understanding of the structure and resultant properties of binary hard sphere crystals.

Bottom pressure of confined granular assemblies saturates at a certain value even this packing bed is being continuously charged. Corresponding formulation has been established to describe this interesting phenomenon. In this work, the influences of particle size and friction on the bottom stresses of granular matter were numerically investigated by discrete element method (DEM). It is found that the Janssen model can well predict the stress profile only when the size ratio of the container versus the particle is larger than 16. Moreover, a hydrostatic linear relation between apparent mass and filling mass can be obtained when the friction coefficient becomes insignificant (μ ≤ 0.01). To further interpret the Janssen effects, the granular assemblies are characterized and evaluated from the overall interactions with sidewalls, angular distribution function, void size distribution, coordination number, contact networks, contact orientation and distributions of contact forces within the packing structure. It is believed that these results will be helpful to comprehend the granular behaviors and may offer instructive reference to industrial processes in related fields.

The original article [1] contains an accidental omission in the Acknowledgements.