ObjectiveTomatoes are one of the highest-yielding and most widely cultivated economic crops globally, playing a crucial role in agricultural production and providing significant economic benefits to farmers and related industries. However, early blight in tomatoes is known for its rapid infection, widespread transmission, and severe destructiveness, which significantly impacts both the yield and quality of tomatoes, leading to substantial economic losses for farmers. Therefore, accurately identifying early symptoms of tomato early blight is essential for the scientific prevention and control of this disease. Additionally, visualizing affected areas can provide precise guidance for farmers, effectively reducing economic losses. This study combines hyperspectral imaging technology with machine learning algorithms to develop a model for the early identification of symptoms of tomato early blight, facilitating early detection of the disease and visual localization of affected areas. MethodsTo address noise interference present in hyperspectral images, robust principal component analysis (RPCA) is employed for effective denoising, enhancing the accuracy of subsequent analyses. To avoid insufficient information representation caused by the subjective selection of regions of interest, the Otsu’s thresholding method is utilized to extract tomato leaves effectively from the background, with the average spectrum of the entire leaf taken as the primary object of study. Furthermore, a comprehensive spectral preprocessing workflow is established by integrating multivariate scatter correction (MSC) and standardization methods, ensuring the reliability and effectiveness of the data. Based on the processed spectral data, a discriminant model utilizing a linear kernel function support vector machine (SVM) is constructed, focusing on characteristic wavelengths to improve the model's discriminative capability. ResultsCompared to full-spectrum modeling, this approach results in an 8.33% increase in accuracy on the test set. After optimizing the parameters of the SVM model, when C=1.64, the accuracies of the training set and test set reach 91.67% and 94.44%, respectively, demonstrating a 1.19% increase in training set accuracy compared to the unoptimized model, while maintaining the same accuracy on the test set, effectively alleviating issues of underfitting. ConclusionThis study successfully establishes an early discriminant model for tomato early blight using hyperspectral imaging and achieves visualization of early symptoms. Experimental results indicate that the SVM discriminant model based on characteristic wavelengths and a linear kernel function can effectively identify early symptoms of tomato early blight. Visualization of these symptoms in terms of disease probability allows for a more intuitive detection of early diseases and timely implementation of corresponding control measures. This visual analysis not only enhances the efficiency of disease identification but also provides farmers with more straightforward and practical information, aiding them in formulating more reasonable prevention strategies. These research findings provide valuable references for the early identification and visualization of plant diseases, holding significant practical implications for monitoring, identifying, and scientifically preventing crop diseases. Future research could further explore how to apply this model to disease detection in other crops and how to integrate IoT technology to create intelligent disease monitoring systems, enhancing the scientific and efficient management of crops.

[Objective] To evaluate the efficacy and practicality of autologous platelet-rich plasma (aPRP) in patients undergoing total aortic arch replacement for aortic dissection. [Methods] A retrospective analysis was performed on 483 patients diagnosed with type A aortic dissection who underwent total aortic arch replacement between January 2016 and November 2023. Patients were categorized into two groups based on whether they received aPRP. Baseline characteristics, intraoperative blood product usage and postoperative outcomes were compared between the two groups. [Results] The aPRP group exhibited reduced usage of allogeneic platelets (1.55±1.04 vs 1.60±1.27)U, allogeneic plasma (480.89±432.49 vs 746.50±508.81)mL, allogeneic RBC (red blood cell)(5.95±1.91 vs 6.17±3.52)U, bivalirudin (2.66±1.51 vs 3.31±1.59)U and coagulation factor Ⅶ (0.67±1.03 vs 1.22±1.43)mg compared to the non-aPRP group (P<0.05). The incidence of postoperative hypoxemia was lower in the aPRP group (43.98% vs 48.41%), and the duration of mechanical ventilation was significantly shorter[median 50.91 (interquartile range 18.71, 113.71) vs 83.40 (37.73, 151.98) hours]. There were no significant differences between the two groups in terms of postoperative mortality, continuous bedside hemofiltration, cerebral infarction, cerebral hemorrhage, paraplegia or re-exploration for hemostasis(P>0.05). [Conclusion] The application of aPRP in total aortic arch replacement effectively diminishes intraoperative blood product usage and the incidence of lung injury-related complications. However, it does not demonstrate significant benefits in terms of mortality, cerebral infarction and other complications.

The Chinese Pharmacopoeia is the legal technical standard which should be followed during the research, production, use, and administration of drugs. At present, the new edition of the Chinese Pharmacopoeia is planned to be promulgated and implemented. This article summarizes and analyzes the main characteristics and the content of updates and amendments of the Chinese Pharmacopoeia 2025 Edition(Volume Ⅰ), to provide a reference for the correct understanding and accurate implementation the new edition of the pharmacopoeia.

ObjectiveTo characterize the changes of metabolites of Blumea balsamifera in the process of sweating by non-targeted metabolomics， and to investigate the influence of sweating processing on the constituents of B. balsamifera. MethodsUltra performance liquid chromatography-quadrupole/electrostatic field orbitrap high resolution mass spectrometry（UPLC-Q-Exactive Orbitrap-MS） metabolomics was used to identify the metabolites in no sweating group（F1）， sweating 2 d group（F2） and sweating 4 d group（F3）， the differences of metabolites between the groups were compared by principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）， and differential metabolites were screened according to the variable importance in the projection（VIP） value>1 and P<0.05， and the pathway enrichment of the differential metabolites was analyzed by Kyoto Encyclopedia of Genes and Genomes（KEGG）. ResultsThe results of PCA and OPLS-DA showed a clear distinction between the three groups of samples， indicating significant differences in the compositions of the three groups of samples. A total of 433 differential metabolites were screened between the F1 and F2， with 154 up-regulated and 279 down-regulated， the significant up-regulated metabolites were tangeritin， 5-O-demethylnobiletin and so on， while the metabolites with significant down-regulation included alternariol， fortunellin， etc. A total of 379 differential metabolites were screened between the F2 and F3， with 150 up-regulated and 229 down-regulated， the significant up-regulated metabolites were isoimperatorin， helianyl octanoate and so on， and the significant down-regulated metabolites were hovenoside I， goyasaponin Ⅲ， etc. KEGG pathway enrichment analysis showed that tyrosine metabolism， isoquinoline alkaloid biosynthesis， phenylalanine， tyrosine and tryptophan biosynthesis， tryptophan metabolism， valine， leucine and isoleucine biosynthesis， pantothenate and coenzyme A biosynthesis may be the key pathways affecting metabolite differences of B. balsamifera after sweating treatment. ConclusionSweating can reduce the content of endophytic mycotoxins in B. balsamifera and has a great impact on the synthesis and metabolic pathways of total flavonoids and auxin. This study can provide a reference for the process research on the sweating conditions of B. balsamifera.

The abnormal accumulation of neutrophil extracellular traps （NETs） can promote the initiation and progression of pancreatic cancer， which is considered a potential therapeutic target for this disease. The Miraculous Pivot·Inquiry About Statement （《灵枢·口问》） have recorded the concept of "defensive qi stagnation". Based on the recognition that the function of defensive qi is similar to the immune function of neutrophils， and combining traditional Chinese medicine theory with clinical practice， it is proposed that the abnormal accumulation of NETs may be a pathological product of "defensive qi stagnation"， with the spleen being the critical site of pathology. Further exploring the application strategy of cleaning spleen and restoring defensive qi method in pancreatic cancer treatment， it is proposed to employ three approaches such as dredging method to eliminate spleen stagnation and inhibit pancreatic cancer proliferation， cleaning method to remove spleen dampness and suppress the inflammatory micro-environment， and tonifying method to strengthen Weiqi and to improve the immune microenvironment， which aims to provide new insights for the clinical treatment of pancreatic cancer with traditional Chinese medicine.

This article combined the pathogenic characteristics of "latent wind" with the theory of collateral diseases to clarify the pathological features of tumor blood vessels， including their active proliferation， high permeabi-lity， and promotion of metastasis. The theory framework of "latent wind in collaterals" as the tumor mechanism was proposed， which suggests that at the site of tumor lesions， the collaterals inherit the nature of latent wind to grow excessively， adopt an open and discharge nature to leak essence， and tumor toxins， characterized by their rapid movement and frequent changes， spread and metastasize， driving the progression of malignant tumors. Focusing on the fundamental pathogenesis of "latent wind in collaterals"， specific clinical treatment principles and methods centered on treating wind are proposed， including regulating qi and dispelling wind， clearing heat and extinguishing wind， unblocking collaterals and expelling wind， and reinforcing healthy qi to calm wind， so as to provide references for enhancing the precision of traditional Chinese medicine in treating malignant tumors.

BACKGROUND: Neoadjuvant chemoradiotherapy followed by radical surgery has been a common practice for patients with locally advanced rectal cancer, but the response rate varies among patients. This study aimed to develop a ResNet-Vision Transformer based magnetic resonance imaging (MRI)-endoscopy fusion model to precisely predict treatment response and provide personalized treatment. METHODS: In this multicenter study, 366 eligible patients who had undergone neoadjuvant chemoradiotherapy followed by radical surgery at eight Chinese tertiary hospitals between January 2017 and June 2024 were recruited, with 2928 pretreatment colonic endoscopic images and 366 pelvic MRI images. An MRI-endoscopy fusion model was constructed based on the ResNet backbone and Transformer network using pretreatment MRI and endoscopic images. Treatment response was defined as good response or non-good response based on the tumor regression grade. The Delong test and the Hanley-McNeil test were utilized to compare prediction performance among different models and different subgroups, respectively. The predictive performance of the MRI-endoscopy fusion model was comprehensively validated in the test sets and was further compared to that of the single-modal MRI model and single-modal endoscopy model. RESULTS: The MRI-endoscopy fusion model demonstrated favorable prediction performance. In the internal validation set, the area under the curve (AUC) and accuracy were 0.852 (95% confidence interval [CI]: 0.744-0.940) and 0.737 (95% CI: 0.712-0.844), respectively. Moreover, the AUC and accuracy reached 0.769 (95% CI: 0.678-0.861) and 0.729 (95% CI: 0.628-0.821), respectively, in the external test set. In addition, the MRI-endoscopy fusion model outperformed the single-modal MRI model (AUC: 0.692 [95% CI: 0.609-0.783], accuracy: 0.659 [95% CI: 0.565-0.775]) and the single-modal endoscopy model (AUC: 0.720 [95% CI: 0.617-0.823], accuracy: 0.713 [95% CI: 0.612-0.809]) in the external test set. CONCLUSION The MRI-endoscopy fusion model based on ResNet-Vision Transformer achieved favorable performance in predicting treatment response to neoadjuvant chemoradiotherapy and holds tremendous potential for enabling personalized treatment regimens for locally advanced rectal cancer patients.
Humans ; Rectal Neoplasms/diagnostic imaging* ; Magnetic Resonance Imaging/methods* ; Male ; Female ; Middle Aged ; Neoadjuvant Therapy/methods* ; Aged ; Adult ; Chemoradiotherapy/methods* ; Endoscopy/methods* ; Treatment Outcome

BACKGROUND: Pre-transplant exposure to immune checkpoint inhibitors (ICIs) significantly increases the risk of allograft rejection after liver transplantation (LT); however, whether ICI-related rejection leads to increased graft loss remains controversial. Therefore, this study aimed to investigate the association between ICI-related allograft rejection and perioperative graft loss. METHODS: This was a retrospective analysis of adult liver transplant recipients with early biopsy-proven T-cell-mediated rejection (TCMR) at Liver Transplantation Center of Sun Yat-sen Memorial Hospital from June 2019 to September 2024. The pathological features, clinical characteristics, and perioperative graft survival were analyzed. RESULTS: Twenty-eight patients who underwent early TCMR between June 2019 and September 2024 were included. Based on pre-LT ICI exposure, recipients were categorized into ICI-related TCMR (irTCMR, n = 12) and conventional TCMR (cTCMR, n = 16) groups. Recipients with irTCMR had a higher median Banff rejection activity index (RAI) (6 vs . 5, P = 0.012) and more aggressive tissue damage and inflammation. Recipients with irTCMR showed higher proportion of treatment resistance, achieving a complete resolution rate of only 8/12 compared to 16/16 for cTCMR. Graft loss occurred in 5/12 of irTCMR recipients within 90 days after LT, with no graft loss in cTCMRs recipients. Cox analysis demonstrated that irTCMR with an ICI washout period of <30 days was an independent risk factor for perioperative graft loss (hazard ratio [HR], 6.540; 95% confidence interval [CI], 1.067-40.067, P = 0.042). CONCLUSION IrTCMR is associated with severe pathological features, increased resistance to treatment, and higher graft loss in adult liver transplant recipients.
Humans ; Liver Transplantation/adverse effects* ; Male ; Female ; Middle Aged ; Retrospective Studies ; Graft Rejection/immunology* ; Immune Checkpoint Inhibitors/therapeutic use* ; Adult ; T-Lymphocytes/drug effects* ; Graft Survival/immunology* ; Aged

In a healthy human, the airway mucus forms a thin, protective liquid layer covering the surface of the respiratory tract. It comprises a complex blend of mucin, multiple antibacterial proteins, metabolic substances, water, and electrolytes. This mucus plays a pivotal role in the lungs' innate immune system by maintaining airway hydration and capturing airborne particles and pathogens. However, heightened mucus secretion in the airway can compromise ciliary clearance, obstruct the respiratory tract, and increase the risk of pathogen colonization and recurrent infections. Consequently, a thorough exploration of the mechanisms driving excessive airway mucus secretion is crucial for establishing a theoretical foundation for the eventual development of targeted drugs designed to reduce mucus production. Across a range of lung diseases, excessive airway mucus secretion manifests with unique characteristics and regulatory mechanisms, all intricately linked to mucin. This article provides a comprehensive overview of the characteristics and regulatory mechanisms associated with excessive airway mucus secretion in several prevalent lung diseases.
Humans ; Mucus/metabolism* ; Mucins/physiology* ; Lung Diseases/metabolism* ; Respiratory Mucosa/metabolism* ; Pulmonary Disease, Chronic Obstructive/physiopathology* ; Asthma/physiopathology* ; Cystic Fibrosis/physiopathology* ; Mucociliary Clearance/physiology*