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Aims Acyl‐CoA synthetase long chain family member 4 (ACSL4) is closely related to tumor genesis and development in certain tissues. However, the function of ACSL4 in early brain injury (EBI) caused by subarachnoid hemorrhage (SAH) is unclear. In this study, we investigated the expression patterns and role of ACSL4 in SAH and post‐SAH EBI using a rat model of SAH. Methods The rat model of SAH was induced by autologous blood injection into the prechiasmatic cistern of rats. We also used two specific inhibitors of ferroptosis (Ferrostatin‐1 and Liproxstatin‐1) to investigate the role of ferroptosis in EBI. Results We found that ACSL4 levels in brain tissue increased significantly in post‐SAH EBI. Inhibiting the expression of ACSL4 using small interfering RNAs alleviated inflammation, blood‐brain barrier (BBB) impairment, oxidative stress, brain edema, and behavioral and cognitive deficits, and increased the number of surviving neurons, after SAH. Similar effects were obtained by suppressing ferroptosis. Conclusions ACSL4 exacerbated SAH‐induced EBI by mediating ferroptosis. These findings may provide a theoretical basis for potential therapy aimed at alleviating post‐SAH EBI. ACSL4 levels in brain tissue of rats increase significantly in EBI and brain damage after SAH could be reduced by down‐regulation of ACSL4. ACSL4 could trigger ferroptosis and aggravate brain damage via catalyzing lipid metabolism. It may provide a theoretical basis for potential therapy to alleviate EBI after SAH

S100 calcium-binding protein A8 (S100A8) is also known as macrophage-related protein 8, which is involved in various pathological processes in the central nervous system post-traumatic brain injury (TBI), and plays a critical role in inducing inflammatory cytokines. Accumulating evidences have indicated that toll-like receptor 4 (TLR4) is considered to be involved in inflammatory responses post TBI. The present study was designed to analyze the hypothesis that S100A8 is the key molecule that induces inflammation TLR4 in TBI. The weight-drop TBI model was used and randomly implemented on mice that were categorized into six groups: Sham, NS, S100A8, S100A8+TAK-242, TBI, and TBI+TAK-242 groups. In the S100A8+TAK-242 and TBI+TAK-242 groups, at half an hour prior to the intracerebroventricular administration of S100A8 or TBI, mice were intraperitoneally treated with TAK-242 that acts as a selective antagonist and inhibitor of TLR4. Furthermore, the protein recombinant of S100A8 was injected into the lateral ventricle of the brain of mice in the S100A8 and S100A8+TAK-242 groups. Sterile normal saline was injected into the lateral ventricle in the NS group. To evaluate the association between S100A8 and TLR4, Western blot, immunofluorescence, enzyme-linked immunosorbent assay (ELISA), and Nissl staining were employed. Simultaneously, the neurological score and brain water content were assessed. In the analysis, BV-2 microglial cells were stimulated with lipopolysaccharide LPS or S100A8 recombinant protein, with or without TAK-242. The expression of the related proteins was subsequently detected by Western blot or enzyme-linked immunosorbent assay. The levels of S100A8 protein and pro-inflammatory cytokines were significantly elevated after TBI. There was a reduction in the neurological scores of non-TBI animals with remarkable severe brain edema after the intracerebroventricular administration of S100A8. Furthermore, the TLR4, p-p65, and myeloid differentiation factor 88 (MyD88) levels were elevated after the administration of S100A8 or TBI, which could be restored by TAK-242. Meanwhile, in the analysis, due to the stimulation of S100A8 or LPS, there was an upregulation of p-p65 and MyD88, which could also be suppressed by TAK-242. The present study demonstrated that the TLR4-MyD88 pathway was activated by S100A8, which is essential for the development of inflammation in the brain after TBI.

Neonatal hypoxic ischemia is one of the leading causes of permanent morbidity and mortality in newborns, which is caused by difficulty in supplying blood and oxygen to brain tissue and is often associated with epilepsy, cerebral palsy, death, short-term or long-term neurological and cognitive impairment. In recent years, the clinical therapeutic effects of noble gases have been gradually discovered and recognized. Numerous studies have shown that noble gases have unique neuroprotective effects to restore damaged nerve and relieve symptoms in patients. Although research on the neuroprotective mechanisms of xenon and argon has yielded a lot of results, studies on helium have stalled. Helium is a colorless, odorless, monoatomic inert gas. The helium has no hemodynamic or neurocognitive side effects and can be used as an ideal pre-adaptor for future clinical applications. In recent years, studies have shown that heliox (a mixture of helium and oxygen) pretreatment can protect the heart, brain, liver and intestine from damage in several animal models, where a variety of signaling pathways have been proved to be involved. There are numerous studies on it even though the mechanism of helium for protecting newborns has not been fully elucidated. It is urgent to find an effective treatment due to the high death rate and disability rate of neonatal hypoxic ischemia. It is believed that helium will be approved safely and effectively for clinical use in the near future.

The characterization of interwell section architecture is critical for revealing reservoir lateral heterogeneity and connectivity. This process integrates well and seismic data with geological knowledge yet faces inherent multiple solutions. Current characterization methods remain hampered by high levels of manual intervention, insufficient automation, and difficulties in evaluating the uncertainty of interwell section architecture. To address these challenges, this study presents an intelligent method for the automated characterization of reservoir architecture along section directions based on a Bayesian expert system. The approach quantifies domain knowledge via prior normal distributions. By utilizing well and seismic data, Bayesian probabilistic reasoning infers the guiding influence of each individual piece of domain knowledge on predicting the interwell distribution of architectural elements. A weighted ensemble decision framework then integrates these inferences to determine the interwell distributions of architectural elements and associated uncertainties. Case studies demonstrate that the method effectively evaluates uncertainty, generates geologically consistent section characterizations, achieves 81% consistency in blind well sand body predictions, and excels in delineating the lateral boundaries and contact relationships of architectural elements.

Sedimentary facies modeling is a critical approach for understanding geological phenomena, yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization. In this study, we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning, which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data. Specifically, we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives. Then, during simulation, to enhance the capability of the network model for finely characterizing complex heterogeneous models, cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features. Additionally, through systematic feature map visualization analysis, we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction, intuitively demonstrating the functional mechanisms of each module. Finally, systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method. The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators. Quantitative comparisons reveal remarkable performance of the method, achieving low Wasserstein distance (0.09), Kernel Inception Distance (0.0017) and Kernel Maximum Mean Discrepancy (0.21). These findings further confirm the high realism of the generated realizations regarding pattern features. This study offers a reliable and practical method for geological reservoir modeling, thereby advancing quantitative, precise geological research with broad application prospects.

High-precision sand prediction is fundamental to improving the efficiency of oil and gas exploration and development. To address the limitations of traditional fixed-weight fusion strategies, particularly under conditions of significant lateral variation in sand body distribution, this study proposes a dynamic weighting–deep neural network (DW-DNN) for adaptive frequency-decomposed attribute fusion. The approach integrates physical constraints with deep learning and introduces two innovations: (i) a priori weight matrices derived from the amplitude–frequency and tuning thickness relationship (amplitude variation with frequency, AVF) are embedded into the attention mechanism to adaptively allocate multiband seismic attributes, emphasizing high-frequency features for thin sands and low-frequency features for thick sands; and (ii) a deep neural network with a composite loss function combining mean squared error (MSE) and AVF-based constraints is designed to jointly optimize weight allocation and prediction accuracy. The method was applied to the Xi 233 area of the Qingcheng Oilfield in the Ordos Basin and compared with conventional approaches. DW-DNN achieved high accuracy and generalizability, with an R2 of 0.92 in the 30% blind-well test, 24.3% higher than conventional methods. In addition, 91% of well-point errors were within 0–3 m, while prediction accuracies for thin (≤3 m) and thick (>3 m) sands reached 88% and 91%, respectively. The model also maintained stable performance under low well-control conditions (training–test ratio 5:5). Predicted sand distributions exhibited improved continuity and geologically plausible geometries, clearly delineating channels, lobes, and estuary bars. The results demonstrate that DW-DNN enhances frequency-decomposed attribute fusion through adaptive weight allocation, providing a robust tool for predicting sand body distributions in complex reservoirs.

We investigated the possibility of producing chicken alpha interferon （ChlFN-α） in transgenic plants. The cDNA encoding ChlFN-α was introduced into lettuce （Lactuca sativa L.） plants by using an agro-infiltration transient expression system. The ChlFN-a gene was correctly transcribed and translated in the lettuce plants according to RT-PCR and ELISA assays. Recombinant protein exhibited antiviral activity in vitro by inhibition of vesicular stomatitis virus （VSV） replication on chicken embryonic fibroblast （CEF）. The results demonstrate that biologically active avian cytokine with potential pharmaceutical applications could be expressed in transgenic lettuce plants and that it is possible to generate interferon protein in forage plants for preventing infectious diseases of poultry.

Background： The properties of subchondral bone influence the integrity of articular cartilage in the pathogenesis of osteoarthritis （OA）. However, the characteristics ofsubchondral bone alterations remain unresolved. The present study aimed to observe the dynamic alterations in the microarchitecture, mineralization, and mechanical properties of subchondral bone during the progression of OA. Methods： A medial meniscal tear （MMT） operation was pertbrmed in 128 adult Sprague Dawley rats to induce OA. At 2, 4, 8, and 12 weeks following the MMT operation, cartilage degeneration was evaluated using toluidine blue O staining, whereas changes in the microarchitecture indices and tissue mineral density （TMD）, mineral-to-collagen ratio, and intrinsic mechanical properties of subchondral bone plates （BPs） and trabecular bones （Tbs） were measured using micro-computed tomography scanning, conibcal Raman microspectroscopy and nanoindentation testing, respectively. Results： Cartilage degeneration occurred and worsened progressively from 2 to 12 weeks after OA induction. Microarchitecture analysis revealed that the subchondral bone shifted from bone resorption early （reduced trabecular BV/TV, trabecular number, connectivity density and trabecular thickness [Tb.Th], and increased trabecular spacing （Tb.Sp） at 2 and 4 weeks） to bone accretion late （increased BV/TV, Tb.Th and thickness of subchondral bone plate, and reduced Tb.Sp at 8 and 12 weeks）. The TMD of both the BP and Tb displayed no significant changes at 2 and 4 weeks but decreased at 8 and 12 weeks. The mineral-to-collagen ratio showed a significant decrease from 4 weeks for the Tb and from 8 weeks for the BP after OA induction. Both the elastic modulus and hardness of the Tb showed a significant decrease from 4 weeks after OA induction. The BP showed a significant decrease in its elastic modulus from 8 weeks and its hardness from 4 weeks. Conclusion： The microarchitecture, mineralization and mechanical properties of subchondral bone changed in a time-dependent manner as OA progressed.

Background： To determine responses of neonatal intestine to maternal increased consumption of DL-methionine（DLM） or DL-2-hydroxy-4-methylthiobutanoic acid（HMTBA）, eighteen primiparous sows（Landrace × Yorkshire） were allocated based on body weight and backfat thickness to the control, DLM and HMTBA groups（n = 6）, with the nutritional treatments introduced from postpartum d0 to d14.Results： The DLM-fed sows showed negative energy balance manifested by lost bodyweight, lower plasma glucose, subdued tricarboxylic acid cycle, and increased plasma lipid metabolites levels. Both villus height and ratio of villus height to crypt depth averaged across the small intestine of piglets were higher in the DLM and HMTBA groups than in the control group. Piglet jejunal oxidized glutathione concentration and ratio of oxidized to reduced glutathione were lower in the HMTBA group than in the DLM and control groups. However, piglet jejunal aminopeptidase A, carnitine transporter 2 and IGF-II precursor m RNA abundances were higher in the DLM group than in the HMTBA and control groups.Conclusion： Increasing maternal consumption of methionine as DLM and HMTBA promoted neonatal intestinal growth by increasing morphological development or up-regulating expression of genes responsible for nutrient metabolism. And increasing maternal consumption of HMTBA promoted neonatal intestinal antioxidant capacity without compromising maternal energy homeostasis during early lactation.

Spinal cord injury (SCI) results in a large amount of tissue cell debris in the lesion site, which interacts with various cytokines, including inflammatory factors, and the intrinsic glial environment of the central nervous system (CNS) to form an inhibitory microenvironment that impedes nerve regeneration. The efficient clearance of tissue debris is crucial for the resolution of the inhibitory microenvironment after SCI. Macrophages are the main cells responsible for tissue debris removal after SCI. However, the high lipid content in tissue debris and the dysregulation of lipid metabolism within macrophages lead to their transformation into foamy macrophages during the phagocytic process. This phenotypic shift is associated with a further pro-inflammatory polarization that may aggravate neurological deterioration and hamper nerve repair. In this review, we summarize the phenotype and metabolism of macrophages under inflammatory conditions, as well as the mechanisms and consequences of foam cell formation after SCI. Moreover, we discuss two strategies for foam cell modulation and several potential therapeutic targets that may enhance the treatment of SCI.