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Abstract Background Helicobacter pylori (H. pylori) is the predominant etiological agent of gastritis and disrupts the integrity of the gastric mucosal barrier through various pathogenic mechanisms. After H. pylori invades the gastric mucosa, it interacts with immune cells in the lamina propria. Macrophages are central players in the inflammatory response, and H. pylori stimulates them to secrete a variety of inflammatory factors, leading to the chronic damage of the gastric mucosa. Therefore, the study aims to explore the mechanism of gastric mucosal injury caused by inflammatory factors secreted by macrophages, which may provide a new mechanism for the development of H. pylori-related gastritis. Methods The expression and secretion of CCL3 from H. pylori infected macrophages were detected by RT-qPCR, Western blot and ELISA. The effect of H. pylori-infected macrophage culture medium and CCL3 on gastric epithelial cells tight junctions were analyzed by Western blot, immunofluorescence and transepithelial electrical resistance. EdU and apoptotic flow cytometry assays were used to detect cell proliferation and apoptosis levels. Dual-luciferase reporter assays and chromatin immunoprecipitation assays were used to study CCL3 transcription factors. Finally, gastric mucosal tissue inflammation and CCL3 expression were analyzed by hematoxylin and eosin staining and immunohistochemistry. Results After H. pylori infection, CCL3 expressed and secreted from macrophages were increased. H. pylori-infected macrophage culture medium and CCL3 disrupted gastric epithelial cells tight junctions, while CCL3 neutralizing antibody and receptor inhibitor of CCL3 improved the disruption of tight junctions between cells. In addition, H. pylori-infected macrophage culture medium and CCL3 recombinant proteins stimulated P38 phosphorylation, and P38 phosphorylation inhibitor improved the disruption of tight junctions between cells. Besides, it was identified that STAT1 was a transcription factor of CCL3 and H. pylori stimulated macrophage to secret CCL3 through the JAK1-STAT1 pathway. Finally, after mice were injected with murine CCL3 recombinant protein, the gastric mucosal injury and inflammation were aggravated, and the phosphorylation level of P38 was increased. Conclusions In summary, our findings demonstrate that H. pylori infection stimulates macrophages to secrete CCL3 via the JAK1-STAT1 pathway. Subsequently, CCL3 damages gastric epithelial tight junctions through the phosphorylation of P38. This may be a novel mechanism of gastric mucosal injury in H. pylori-associated gastritis.

ObjectiveIn recent years, there has been a significant increase in the severity of leaf diseases in maize, with a noticeable trend of mixed occurrence. This poses a serious threat to the yield and quality of maize. However, there is a lack of studies that combine the identification of different types of leaf diseases and their severity classification, which cannot meet the needs of disease prevention and control under the mixed occurrence of different diseases and different severities in actual maize fields.MethodsA method was proposed for identifying the types of typical leaf diseases in maize and classifying their severity using hyperspectral technology. Hyperspectral data of three leaf diseases of maize: northern corn leaf blight (NCLB), southern corn leaf blight (SCLB) and southern corn rust (SCR), were obtained through greenhouse pathogen inoculation and natural inoculation. The spectral data were preprocessed by spectral standardization, SG filtering, sensitive band extraction and vegetation index calculation, to explore the spectral characteristics of the three leaf diseases of maize. Then, the inverse frequency weighting method was utilized to balance the number of samples to reduce the overfitting phenomenon caused by sample imbalance. Relief-F and variable selection using random forests (VSURF) method were employed to optimize the sensitive spectral features, including band features and vegetation index features, to construct models for disease type identification based on the full stages of disease development (including all disease severities) and for individual disease severities using several representative machine learning approaches, demonstrating the effectiveness of the research method. Furthermore, the study individual occurrence severity classification models were also constructed for each single maize leaf disease, including the NCLB, SCLB and SCR severity classification models, respectively, aiming to achieve full-process recognition and disease severity classification for different leaf diseases. Overall accuracy (OA) and Macro F1 were used to evaluate the model accuracy in this study.Results and DiscussionThe research results showed significant spectrum changes of three kinds of maize leaf diseases primarily focusing on the visible (550-680 nm), red edge (740-760 nm), near-infrared (760-1 000 nm) and shortwave infrared (1 300-1 800 nm) bands. Disease-specific spectral features, optimized based on disease spectral response rules, effectively identified disease species and classify their severity. Moreover, vegetation index features were more effective in identifying disease-specific information than sensitive band features. This was primarily due to the noise and information redundancy present in the selected hyperspectral sensitive bands, whereas vegetation index could reduce the influence of background and atmospheric noise to a certain extent by integrating relevant spectral signals through band calculation, so as to achieve higher precision in the model. Among several machine learning algorithms, the support vector machine (SVM) method exhibited better robustness than random forest (RF) and decision tree (DT). In the full stage of disease development, the optimal overall accuracy (OA) of the disease classification model constructed by SVM based on vegetation index reached 77.51%, with a Macro F1 of 0.77, representing a 28.75% increase in OA and 0.30 higher of Macro F1 compared to the model based on sensitive bands. Additionally, the accuracy of the disease classification model with a single severity of the disease increased with the severity of the disease. The accuracy of disease classification during the early stage of disease development (OA=70.31%) closely approached that of the full disease development stage (OA=77.51%). Subsequently, in the moderate disease severity stage, the optimal accuracy of disease classification (OA=80.00%) surpassed the optimal accuracy of disease classification in the full disease development stage. Furthermore, the optimal accuracy of disease classification under severe severity reached 95.06%, with a Macro F1 of 0.94. This heightened accuracy during the severity stage can be attributed to significant changes in pigment content, water content and cell structure of the diseased leaves, intensifying the spectral response of each disease and enhancing the differentiation between different diseases. In disease severity classification model, the optimal accuracy of the three models for maize leaf disease severity all exceeded 70%. Among the three kinds of disease severity classification results, the NCLB severity classification model exhibited the best performance. The NCLB severity classification model, utilizing SVM based on the optimal vegetation index features, achieved an OA of 86.25%, with a Macro F1 of 0.85. In comparison, the accuracy of the SCLB severity classification model (OA=70.35%, Macro F1=0.70) and SCR severity classification model (OA=71.39%, Macro F1=0.69) were lower than that of NCLB.ConclusionsThe aforementioned results demonstrate the potential to effectively identify and classify the types and severity of common leaf diseases in maize using hyperspectral data. This lays the groundwork for research and provides a theoretical basis for large-scale crop disease monitoring, contributing to precision prevention and control as well as promoting green agriculture.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

BackgroudIn recent years, as the number of people with obesity has surged, the number of morbidly obese patients has also grown. The pathophysiological changes in morbid obesity can lead to combined lung diseases, which may result in hypoventilation, hypoxemia, acute upper airway obstruction, acute respiratory distress syndrome, and sleep apnea syndrome, posing serious challenges to anesthesia management. Here, we describe a case of the administration of remimazolam combined with remifentanil in a patient with morbid obesity undergoing gastroscopy. This has rarely been reported in clinical practice, and we present our management experience here with the aim of providing a reference for clinical work.Case presentationWe report the case of a 32-year-old male hypertensive patient with a height of 180 cm, weight of 145 kg, and body mass index of 44.8 kg/m2. The patient’s main complaint was intermittent hunger pain for more than 1 year, and duodenal polyps were found. Considering the patient’s morbid obesity and the combination of sleep apnea syndrome and hypertension, we administered remimazolam along with remifentanil to ensure perioperative safety.ConclusionThe procedure lasted 30 min, and the anesthesia was satisfactory with no complications. Remimazolam combined with remifentanil intravenous anesthesia is safe for short gastroscopy in patients with morbidly obesity. The administration of a small dose of split-titration delivery facilitates the maintenance of stable vital signs.

A novel tungsten carbide (WC)-reinforced nickel (Ni)-based laser cladding layer was prepared through an in-situ synthesis process. The mechanism of the effect of scanning rates and laser power on the microstructures, wear and corrosion resistance of in-situ synthesized WC-reinforced Ni-based cladding layer were analyzed. The results revealed that the optimal in-situ synthesized WC laser cladding process entailed a scan rate of 3 mm/s and a laser power of 3000 W. The generation of WC phase has a significant strengthening effect on the cladding layer, which exhibits outstanding hardness (1261HV0.2). There is no clear linear relationship between scanning rate and abrasion resistance, and the coating abrasion resistance increased with increasing laser power. Furthermore, increasing the laser power improves the corrosion resistance of the cladding layer, while the scanning speed has a minimal effect on corrosion resistance.

Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture. However, the unique agronomic practice (i.e., varied stubble height treatment) in rice ratooning could lead to inconsistent rice phenology, which had a significant impact on yield prediction of ratoon rice. Multi-temporal unmanned aerial vehicle (UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods. Thus, in this study, we explored the performance of combination of agronomic practice information (API) and single-phase, multi-spectral features [vegetation indices (VIs) and texture (Tex) features] in predicting ratoon rice yield, and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice. The results showed that the integrated use of VIs, Tex and API (VIs & Tex + API) improved the accuracy of yield prediction than single-phase UAV imagery-based feature, with the panicle initiation stage being the best period for yield prediction (R2 as 0.732, RMSE as 0.406, RRMSE as 0.101). More importantly, compared with previous multi-temporal UAV-based methods, our proposed multi- temporal method (multi-temporal model VIs & Tex: R2 as 0.795, RMSE as 0.298, RRMSE as 0.072) can increase R2 by 0.020–0.111 and decrease RMSE by 0.020–0.080 in crop yield forecasting. This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture, which is of great significance to take timely means for ensuring ratoon rice production and food security.