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Introduction: Recent studies have revealed that inflammation is a key factor in the causation of opioid analgesic tolerance. Opioids can induce a massive release of inflammatory cytokines and disruption of intestinal barrier function by activating Toll-like receptors 2/4 (TLR2/4), eventually resulting to sustained bacterial transmission and persistent systemic inflammation. However, most of the relevant analyses available were conducted at the level of animal experiments. It is necessary to explore the potential association between opioid tolerance and inflammatory cytokines and gut microbiota in patients with cancer pain. Methods: We retrospectively analyzed cytokines, lymphocyte subsets and blood cells in 186 cancer patients to examine the effect of oral opioids on inflammatory cytokines in patients with moderate to severe cancer pain. The control group constituted tumor patients without cancer pain, while patients with moderate to severe cancer pain taking oral opioids made up the observation group. Fecal samples collected from 25 cancer patients were also analyzed for the composition and diversity of gut microbiota using 16S rRNA sequencing to explore the association between oral opioids and dynamic changes in gut microbiota. Results: Patients with moderate to severe cancer pain taking oxycodone had significantly higher levels of IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ than those in the control group (p < 0.001). The difference in the relative abundance of Lactobacillus (p = 0.025), Anaerostipes (p = 0.034), Megamonas (p = 0.0080), Monoglobus (p = 0.0080), and the Rikenellaceae_RC9_gut_group (p = 0.022) between the opioid and control group was significant. Conclusion: Oral oxycodone can cause abnormal changes in cytokine levels and gut microbiota of patients with moderate to severe cancer pain, prompting chronic systemic inflammation. Analgesic tolerance induced by long-term oxycodone use could be closely related to the consistent upregulation of IL-6 and TNF-α levels. [ABSTRACT FROM AUTHOR]

Introduction: Studies have shown that genetic variation and environmental factors are associated with individual differences in therapeutic efficacy and side effects of opioids. However, the focus of these studies has been on a single factor of single-nucleotide polymorphisms (SNPs) or haplotypes, for which results have rarely been validated. For complex traits, such as cancer pain and opioid response, interactions between multiple genetic variation and environmental factors need to be considered to explain the opioid individual differences. Methods: We conducted an exploratory two-stage cross-sectional study with 1027 Chinese patients who were taking strong opioid medications for their cancer pain, and genotyped 110 SNPs to explore the association of SNPs, haplotypes, gene–gene and gene–environment interactions with opioid dose, pain relief, and opioid-induced constipation. Results: Due to the failure to meet Benjamini–Hochberg criteria in the discovery stage or to be validated in replication stage, no association was found between SNPs, haplotypes, paired SNP–SNP interactions or multi-dimensional gene–gene interactions and opioid response. However, for gene–environment interactions, optimal models have been constructed in all phenotypes of opioid response. Conclusions: This study reveals for the first time that construction of multidimensional gene–environment interactions enables better interpretations of the effect of genetic variation and environmental factors on the opioid response in patients with cancer pain. Trial registration: Chictr.org.cn, identifier, ChiCTR2000033576. [ABSTRACT FROM AUTHOR]

In recent years, orexin (ORX) and melanin-concentrating hormone (MCH) have been demonstrated to exert neuroprotective roles in cerebral ischemia. Hence, this study investigated the regulatory function of ORX and MCH in neurological function following ischemic stroke and explored the molecular mechanism underlying these functions. A rat model of ischemic stroke was developed by middle cerebral artery occlusion (MCAO), and Longa scoring was employed to evaluate the degree of neurological function deficit. The expression patterns of ORX and MCH were examined by real-time polymerase chain reaction in the brain tissues of rats with ischemic stroke induced by middle cerebral artery occlusion (MCAO). Moreover, electroencephalography (EEG) analysis and high-performance liquid chromatography (HPLC) were respectively performed to detect rapid-eye movement (REM) sleep, the glutamate (Glu) uptake, and the expression of γ-aminobutyric acid B receptor (GABAB). Immunoblotting was performed to test the levels of autophagic markers LC3, BECLIN-1, and p62. Immunohistochemistry (IHC) staining and TUNEL assays were respectively used to assess the autophagy and neuronal apoptosis. Results demonstrated that ORX and MCH were lowly expressed in brain of rats with ischemic stroke. ORX or MCH overexpression decreased neuronal apoptosis and autophagy, and improved the sleep architecture of post-stroke rats, while rescuing Glu uptake and GABA expression. ORX or MCH upregulation exerted protective effects on neurological function. Taken together, ORX and/or MCH protect against ischemic stroke in a rat model, highlighting their value as targets for the clinical treatment of ischemic stroke. [ABSTRACT FROM AUTHOR]

Agriculture has undergone a remarkable transformation, transitioning from traditional methods that were used for centuries to technology-driven practices. The advent of image processing and computational intelligence has revolutionized crop production and plant health monitoring. From drones capturing detailed crop growth data to sensors meticulously measuring soil moisture levels, the possibilities are boundless. This review delves into the cutting-edge research advancements in the application of image processing and computational intelligence techniques for botanical fields, with a particular focus on plant health monitoring. First, it provides a comprehensive overview of the diverse imaging sensors employed in agriculture, including visible, near-infrared, thermal, and hyperspectral imaging. Subsequently, it carefully analyzes the advantages and limitations of each sensor type, along with illustrative examples of their utilization in plant health monitoring. The review further explores the application of machine learning and deep learning for automated plant disease identification, highlighting the critical need for standardized datasets, benchmarking protocols, and domain-specific knowledge for effective implementation. In conclusion, the review emphasizes the future challenges and trends in this rapidly evolving field. It serves as a valuable resource, providing insights into the latest trends in computer vision-based plant disease monitoring and identifying gaps that demand further attention from the scientific community. [ABSTRACT FROM AUTHOR]

The rapid urbanization process is escalating the urban waste problem, and ineffective management has worsened the issue, leading to severe consequences to the population health and economy. Although many countries have started to charge money for large household items, it is time-consuming and challenging for collectors to distinguish various types of bulky waste manually. As a result, this study introduces a mobile-based automatic bulky waste classification system. The original contributions include (1) a fine-tuned VGG-19 model is proposed to classify 95 types of bulky wastes; (2) three hybrid models are introduced to efficiently handle the imbalanced data problem, including class-weight VGG-19 (CW_VGG19), eXtreme Gradient Boosting VGG-19 (XGB_VGG19), and Light Gradient Boosting Machine VGG-19 (LGB_VGG19); (3) a large dataset that includes 95 classes, and each class contains over 500 images; and (4) the development of a mobile application that used the proposed model. Experiments show that the model obtained an accuracy of 86.19%, which outperforms existing models in classifying bulky waste. Moreover, the proposed hybrid models showed their robustness against imbalanced data under various scenarios. [ABSTRACT FROM AUTHOR]

For carrying out the high-quality cropping cross-sections, it is critical to obtain the variable frequency-loading curve rapidly and accurately. This paper proposes a processing method on the loading force acted on the bar based on the separate treatment at the crack initiation stage and the subsection treatment at the crack propagation stage. At the crack initiation stage, the formula of stress concentration factor of V-shaped notch is given by the polygonal section assumption method when the bottom corner radius of V-shaped notch is 0.2-0.5 mm, and the critical initial load acted on the experimental bar is about 1405 N. According to the principle and characteristic of the cropping method, the critical stability-loading curve between the rotation frequency of main shaft and the crack propagation time is given and the transition curve is obtained by the data fitting method; the mutation force is zero when the bottom corner radius is 0.1915 mm. The cropping experimental results show that the critical variable frequency-loading curve, by which the cross-section roughness, the area and the eccentricity of final rupture region are all very small, is an ideal control mode compared with other frequency-loading curves mentioned in this paper. [ABSTRACT FROM AUTHOR]

Wear phenomenon has caused severe damage or failure of fracturing tools in oil and gas industry. In this paper, influence of heat treatment on the mechanical properties and wear resistance of fracturing tool made of lamellar graphite grey cast iron were investigated. The surface composition and microstructure were characterized by X-ray diffraction (XRD) and metallographic microscope. Sliding wear tests were performed to study the tribological behavior. Tests results showed that wear rates of treated specimens decreased by 33 %. Besides, worn morphology and wear debris were analyzed using Scanning electron microscope (SEM) and Energy dispersive X-ray spectra (EDS). Wear failure mechanisms of specimens were identified. Furthermore, on-site experiment results indicated that wear loss of treated samples decreased by 37.5 %. The wear life of hydraulic fracturing tools can be improved obviously by the heat treatment. [ABSTRACT FROM AUTHOR]

Microglia, the major immune cells of the central nervous system (CNS), can be driven to adopt M1 and M2 phenotypes. Recently, the distinct functions of M1 and M2 microglia have been intensively studied. M1-activation microglia are pro-inflammatory and may contribute to the development of several CSN disorders, while M2-activation microglia are anti-inflammatory and may promote tissue reconstruction. TRAM1 is a protein involved in translocation of nascent polypeptides and functions as a sorting adaptor of TLR4. Here, we found that TRAM1 plays an important role in microglia M1 polarization. Our results showed that the expression of TRAM1 is highly induced in LPS/interferon (IFN)-γ-stimulated BV2 cells and primary microglia cells. Flag-TRAM1 transfection, but not Flag-GFP used as a control, significantly enhanced M1 polarization by strongly increasing expression of M1 makers, such as IL-6, IL-1β, iNOS, and so on. Silence of TRAM1 effectively inhibited LPS/IFN-γ-induced expression of M1-related genes in BV2 cells. In addition, TRAM1 was found to cooperate with TLR4 to induce an M1 genetic program in Flag-TRAM1-transfected and LPS/IFN-γ-induced BV2 cells. TRAM1 is essential for LPS/IFN-γ induced expressions of adapter molecule (IRAK1, phosphorylation of TBK1, and IRF3) of TLR4. TRAM1 is also essential for phosphorylation of IκB and P65 and for P65-NF-kB translocation to nucleus. Overall, our findings showed that TRAM1 could promote microglia M1 polarization. [ABSTRACT FROM AUTHOR]