OBJECTIVE To confirm trigger items for adverse drug reaction （ADR） induced by bevacizumab， to identify and analyze the occurrence of related ADR， and to establish a predictive model for severe adverse reaction （SAR） caused by this drug. METHODS Based on the global trigger tool （GTT） theory， and referencing the GTT White Paper， drug package inserts and relevant literature， trigger items for bevacizumab-related ADR were confirmed using a single-round Delphi method. Utilizing these established items， electronic medical records of relevant patients at Guilin People’s Hospital from January 2020 to September 2024 were actively screened via the China Hospital Pharmacovigilance System. Pharmacists then identified and tallied the occurrence of bevacizumab-induced ADR. Data from patients with any positive trigger item served as the study subjects （divided into training and test sets at a ratio of 7∶3）， candidate feature variables were selected from 39 related variables using the Boruta algorithm， and the multivariable Logistic regression analysis was performed with the occurrence of SAR as the dependent variable. Based on these candidate features， Logistic Regression， Extreme Gradient Boosting， Light Gradient Boosting Machine， Random Forest， and Categorical Boosting models were constructed. Model performance was evaluated using metrics including the area under the curve （AUC） of receiver operating characteristic curve and recall rate. The Shapley Additive exPlanations （SHAP） method was applied to analyze and interpret the contribution of each variable. A nomogram was constructed based on the optimal model. RESULTS A total of 38 trigger items for active monitoring of bevacizumab-related ADR were determined， comprising 17 laboratory indicators， 13 clinical manifestations， and 8 intervention measures. In total， 483 patients with positive trigger items were included， and 318 patients with bevacizumab-induced ADR were identified， including 83 SARs. The positive predictive values for the trigger items and cases were 43.57% （708/1 625） and 63.84% （318/483）， respectively. Bevacizumab-induced ADR involved 7 systems/organs， with the hematological system being the most frequently involved （64.15%）. The Boruta algorithm selected 7 vari ables： serum potassium， hematocrit， albumin-to-globulin ratio， prealbumin， hypertension history， age and red blood cell count. Multivariable Logistic regression showed that elevated serum potassium levels were associated with a decreased risk of bevacizumab-induced SAR （OR＝0.234， P ＝0.002）， while a history of hypertension （OR＝2.642， P ＝0.006） and increased age （OR＝1.040， P ＝0.025） were associated with an increased risk. The Logistic Regression model demonstrated superior performance with higher AUC， F1 score and recall rate （0.761， 0.447， 0.607）， compared to other models. SHAP evaluation results indicated that variables such as serum potassium， hematocrit， and age ranked highest in importance. CONCLUSIONS Totally 38 trigger entries have been successfully identified for active screening of bevacizumab-related ADR. Elevated serum potassium levels are a protective factor against bevacizumab-induced SAR， whereas the hypertension history and increased age are risk factors. The Logistic Regression model is the optimal predictive model.

ObjectiveTo investigate the expression level of HBV cccDNA in patients in the convalescence stage of hepatitis B virus-related acute-on-chronic liver failure （HBV-ACLF） and its correlation with HBV markers and liver histopathological changes. MethodsA total of 30 patients in the convalescence stage of HBV-ACL who were hospitalized in The Ninth Hospital of Nanchang from January 2015 to October 2023 were enrolled as liver failure group， and 9 patients with chronic hepatitis B （CHB）， matched for sex and age， were enrolled as control group. The content of HBV cccDNA in liver tissue was measured， and its correlation with clinical data and laboratory markers was analyzed. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups， and a one-way analysis of variance or the Kruskal-Wallis H test was used for comparison between multiple groups； the Fisher’s exact test was used for comparison of categorical data between groups. A Spearman correlation analysis was performed. ResultsThe liver failure group had a significantly lower content of HBV cccDNA in liver tissue than the control group （-0.92±0.70 log₁₀ copies/cell vs -0.13±0.91 log₁₀ copies/cell， t=2.761， P=0.009）. In the liver failure group， there was no significant difference in the content of HBV cccDNA in liver tissue between the HBeAg-positive patients and the HBeAg-negative patients （P>0.05）； there was no significant difference in the content of HBV cccDNA in liver tissue between the patients with different grades （G0-G2， G3， and G4） of liver inflammatory activity （P>0.05）； there was no significant difference in the content of HBV cccDNA in liver tissue between the patients with different stages （S0-S2， S3， and S4） of liver fibrosis （P>0.05）； there was no significant difference in the content of HBV cccDNA in liver tissue between the patients with negative HBV DNA and those with positive HBV DNA （P>0.05）. For the liver failure group， the content of HBV cccDNA in liver tissue was positively correlated with the content of HBV DNA in liver tissue （r=0.426， P=0.043） and was not significantly correlated with the content of HBV DNA in serum （P>0.05）. ConclusionThere is a significant reduction in the content of HBV cccDNA in liver tissue in the convalescence stage of HBV-ACLF. HBV cccDNA exists continuously and stably in liver tissue and can better reflect the persistent infection and replication of HBV than HBV DNA in serum and liver tissue.

ObjectiveTo explore the pharmacological mechanism involved in the treatment of allergic cough （AC） by Xiaoer Huatan Zhike granules （XEHT） based on proteomics and network pharmacology. MethodsAfter sensitization by intraperitoneal injection of 1 mL suspension containing 2 mg ovalbumin （OVA） and 100 mg aluminum hydroxide， a guinea pig model of allergic cough was constructed by nebulization with 1% OVA. The modeled guinea pigs were randomized into the model， low-， medium- and high-dose （1， 5， 20 g·kg^-1， respectively） XEHT， and sodium montelukast （1 mg·kg^-1） groups （n=6）， and another 6 guinea pigs were selected as the blank group. The guinea pigs in drug administration groups were administrated with the corresponding drugs by gavage， and those in the blank and model groups received the same volume of normal saline by gavage， 1 time·d^-1. After 10 consecutive days of drug administration， the guinea pigs were stimulated by 1% OVA nebulization， and the coughs were observed. The pathological changes in the lung tissue were observed by hematoxylin-eosin staining. The enzyme-linked immunosorbent assay was performed to measure the levels of C-reactive protein （CRP）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， superoxide dismutase （SOD）， and malondialdehyde （MDA） in the bronchoalveolar lavage fluid （BALF） and immunoglobulin G （IgG） and immunoglobulin A （IgA） in the serum. Immunohistochemistry （IHC） was employed to observe the expression of IL-6 and TNF-α in the lung tissue. Transmission electron microscopy was employed observe the alveolar type Ⅱ epithelial cell ultrastructure. Real-time PCR was employed to determine the mRNA levels of IL-6， interleukin-1β （IL-1β）， and TNF-α in the lung tissue. Label-free proteomics was used to detect the differential proteins among groups. Network pharmacology was used to predict the targets of XEHT in treating AC. The Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis was performed to search for the same pathways from the results of proteomics and network pharmacology. ResultsCompared with the blank group， the model group showed increased coughs （P<0.01）， elevated levels of CRP， TNF-α， IL-6， and MDA and lowered level of SOD in the BALF （P<0.05， P<0.01）， elevated levels of IgA and IgG in the serum （P<0.05， P<0.01）， congestion of the lung tissue and infiltration of inflammatory cells， increased expression of IL-6 and TNF-α （P<0.01）， large areas of low electron density edema in type Ⅱ epithelial cells， obvious swelling and vacuolization of the organelles， karyopyknosis or sparse and dissolved chromatin， and up-regulated mRNA levels of IL-6， IL-1β， and TNF-α （P<0.01）. Compared with the model group， the drug administration groups showed reduced coughs （P<0.01）， lowered levels of CRP， TNF-α， IL-6， and MDA and elevated level of SOD in the BALF （P<0.05， P<0.01）， alleviated lung tissue congestion， inflammatory cell infiltration， and type Ⅱ epithelial cell injury， and decreased expression of IL-6 and TNF-α （P<0.01）. In addition， the medium-dose XEHT group and the montelukast sodium group showcased lowered serum levels of IgA and IgG （P<0.05， P<0.01）. The medium- and high-dose XEHT groups and the montelukast sodium showed down-regulated mRNA levels of IL-6， IL-1β， and TNF-α and the low-dose XEHT group showed down-regulated mRNA levels of IL-6 and TNF-α （P<0.05， P<0.01）. Phospholipase D， mammalian target of rapamycin （mTOR）， and epidermal growth factor receptor family of receptor tyrosine kinase （ErbB） signaling pathways were the common pathways predicted by both proteomics and network pharmacology. ConclusionProteomics combined with network pharmacology reveal that XEHT can ameliorate AC by regulating the phospholipase D， mTOR， and ErbB signaling pathways.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

OBJECTIVE: To investigate the role of MK2 inhibitor MMI-0100 on inflammatory response after allogeneic hematopoietic stem cell transplantation (allo-HSCT) and related mechanisms. METHODS: An allo-HSCT mouse model was established. Recipient rats were randomly divided into BMT+NaCl group and BMT+MMI-0100 group, and were injected with NaCl and MMI-0100 every day after transplantation, respectively. Samples of the two groups were collected on d 7 and 14, femur paraffin sections were stained with HE, and pathological changes in the bone marrow cavity were observed under the light microscope. The gene and protein expression levels of pro-inflammatory cytokines IL-1β and IL-18 were detected by qPCR and Western blot. Macrophage typing was detected by flow cytometry. The expression levels of NLRP3 and Caspase-1 were detected by Western blot. RESULTS: Inflammatory cell infiltration in the bone marrow cavity was significantly reduced in the BMT+MMI-0100 group. Western blot results showed that the protein expression levels of IL-1β and IL-18 in the BMT+MMI-0100 group were decreased compared to the BMT+NaCl group on day 7 and day 14 (all P <0.01). The qPCR results showed that compared to the BMT+NaCl group, the IL-18 gene expression levels in the BMT+MMI-0100 group were significantly reduced on day 7 and day 14 (both P <0.01). In the BMT+MMI-0100 group, the expression level of IL-1β gene decreased on day 7 (P <0.05), but increased and was higher than that in the BMT+NaCl group on day 14 (P <0.05). Flow cytometry results showed that the expression of M1 macrophages and M1/M2 ratio decreased in the BMT+MMI-0100 group compared to BMT+NaCl group (all P <0.05). Western blot results showed that the protein expression levels of NLRP3 and Caspase-1 in the BMT+MMI-0100 group were lower than those in the BMT+NaCl group (all P <0.05). CONCLUSION MMI-0100 can ameliorate bone marrow inflammatory injury after allo-HSCT and may act by reducing NLRP3 expression to promote M2 polarization.
Animals ; Interleukin-1beta/metabolism* ; Rats ; Interleukin-18/metabolism* ; Hematopoietic Stem Cell Transplantation/adverse effects* ; Mice ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammation ; Bone Marrow/pathology* ; Protein Serine-Threonine Kinases/metabolism* ; Intracellular Signaling Peptides and Proteins/antagonists & inhibitors* ; Caspase 1/metabolism* ; Macrophages ; Transplantation, Homologous

Neutrophil extracellular traps (NETs), intricate reticular structures released by activated neutrophils, play a pivotal regulatory role in the pathogenesis of malignant tumors. Lung cancer is one of the most prevalent malignancies globally, with persistently high incidence and mortality rates. Recent studies have revealed that NETs dynamically modulate the tumor microenvironment through unique pathological mechanisms, exhibiting complex immunoregulatory characteristics during the progression of lung cancer, and this discovery has increasingly become a focal point in tumor immunology research. This paper provides a comprehensive review of the latest advancements in NETs research related to lung cancer, offering an in-depth analysis of their impact on lung cancer progression, their potential diagnostic value, and the current state of research on targeting NETs for lung cancer prevention and treatment. The aim is to propose novel strategies to enhance therapeutic outcomes and improve the prognosis for lung cancer patients.
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Extracellular Traps/immunology* ; Humans ; Lung Neoplasms/metabolism* ; Neutrophils/metabolism* ; Animals ; Tumor Microenvironment

Disorders of consciousness (DOC) are pathological conditions characterized by severely suppressed brain function and the persistent interruption or loss of consciousness. Accurate diagnosis and evaluation of DOC are prerequisites for precise treatment. Traditional assessment methods are primarily based on behavioral scales, which are inherently subjective and rely on observable behaviors. Moreover, traditional methods have a high misdiagnosis rate, particularly in distinguishing minimally conscious state (MCS) from vegetative state/unresponsive wakefulness syndrome (VS/UWS). This diagnostic uncertainty has driven the exploration of objective, reliable, and efficient assessment tools. Among these tools, electroencephalography (EEG) has garnered significant attention for its non-invasive nature, portability, and ability to capture real-time neurodynamics. This paper systematically reviews the application of EEG biomarkers in DOC assessment. These biomarkers are categorized into 3 main types: resting-state EEG features, task-related EEG features, and features derived from transcranial magnetic stimulation-EEG (TMS-EEG). Resting-state EEG biomarkers include features based on spectrum, microstates, nonlinear dynamics, and brain network metrics. These biomarkers provide baseline representations of brain activity in DOC patients. Studies have shown their ability to distinguish different levels of consciousness and predict clinical outcomes. However, because they are not task-specific, they are challenging to directly associate with specific brain functions or cognitive processes. Strengthening the correlation between resting-state EEG features and consciousness-related networks could offer more direct evidence for the pathophysiological mechanisms of DOC. Task-related EEG features include event-related potentials, event-related spectral modulations, and phase-related features. These features reveal the brain’s responses to external stimuli and provide dynamic information about residual cognitive functions, reflecting neurophysiological changes associated with specific cognitive, sensory, or behavioral tasks. Although these biomarkers demonstrate substantial value, their effectiveness rely on patient cooperation and task design. Developing experimental paradigms that are more effective at eliciting specific EEG features or creating composite paradigms capable of simultaneously inducing multiple features may more effectively capture the brain activity characteristics of DOC patients, thereby supporting clinical applications. TMS-EEG is a technique for probing the neurodynamics within thalamocortical networks without involving sensory, motor, or cognitive functions. Parameters such as the perturbational complexity index (PCI) have been proposed as reliable indicators of consciousness, providing objective quantification of cortical dynamics. However, despite its high sensitivity and objectivity compared to traditional EEG methods, TMS-EEG is constrained by physiological artifacts, operational complexity, and variability in stimulation parameters and targets across individuals. Future research should aim to standardize experimental protocols, optimize stimulation parameters, and develop automated analysis techniques to improve the feasibility of TMS-EEG in clinical applications. Our analysis suggests that no single EEG biomarker currently achieves an ideal balance between accuracy, robustness, and generalizability. Progress is constrained by inconsistencies in analysis methods, parameter settings, and experimental conditions. Additionally, the heterogeneity of DOC etiologies and dynamic changes in brain function add to the complexity of assessment. Future research should focus on the standardization of EEG biomarker research, integrating features from resting-state, task-related, and TMS-EEG paradigms to construct multimodal diagnostic models that enhance evaluation efficiency and accuracy. Multimodal data integration (e.g., combining EEG with functional near-infrared spectroscopy) and advancements in source localization algorithms can further improve the spatial precision of biomarkers. Leveraging machine learning and artificial intelligence technologies to develop intelligent diagnostic tools will accelerate the clinical adoption of EEG biomarkers in DOC diagnosis and prognosis, allowing for more precise evaluations of consciousness states and personalized treatment strategies.

[摘 要] 目的：构建程序性死亡受体1（PD-1）高表达的细菌质膜纳米囊泡（BMV）BMV-PD-1，评估其对小鼠肺癌移植瘤组织的靶向性。方法：通过质粒转化将PD-1与膜孔蛋白细胞溶素A（ClyA）融合质粒ClyA-PD-1-EGFP转入大肠杆菌BL21-Codonplus，使用激光共聚焦显微镜、SDS-PAGE和WB法检测融合蛋白ClyA-PD-1-EGFP的表达。提取质膜并采用挤出法，利用挤出器制备BMV-PD-1。采用透射电子显微镜（TEM）、纳米粒子跟踪分析（NTA）技术分别对BMV-PD-1的形态、粒径和膜电位进行检测，用WB鉴定PD-1蛋白的携带情况。采用激光共聚焦成像检测Lewis肺癌LLC细胞对BMV-PD-1的摄取。建立肺癌LLC细胞C57BL/6J小鼠皮下移植瘤模型，采用小动物活体成像系统评估BMV-PD-1的肿瘤靶向性。结果：激光共聚焦显微成像结果显示，质粒ClyA-PD-1-EGFP被转入BL21-Codonplus并成功表达蛋白。SDS-PAGE结果表明，ClyA-PD-1-EGFP在BL21-Codonplus中过表达。WB分析表明，PD-1在细菌中表达且在BMV-PD-1上呈高表达（P < 0.001）状态。NTA和TEM分析表明，BMV-PD-1是一种粒径为（145 ± 14） nm、表面呈负电性的球状囊泡。激光共聚焦成像显示，PD-1高表达能显著提升肺癌细胞对BMV-PD-1的摄取（P < 0.01），小动物活体成像也进一步证实PD-1高表达能有效提升BMV-PD-1的肿瘤靶向性（P < 0.01）。结论：本研究成功构建了PD-1高表达的细菌纳米囊泡BMV-PD-1，发现PD-1高表达可显著提高BMV-PD-1的肺癌LLC细胞移植瘤组织的靶向性，为进一步开发以BMV-PD-1为载体的肿瘤靶向药物递送系统奠定基础。

A 20-year-old male patient presented to the Department of Dermatology of Peking Union Medical College Hospital with complaints of an 8-year history of facial scarring, swelling of the lower limbs, and a 4-year history of scalp thickening. Physical examination showed thickening furrowing wrinkling of the skin on the face and behind the ears, ciliary body hirsutism, blepharoptosis, and cutis verticis gyrate. Both lower limbs were swollen, especially the knees and ankles. The skin of the palms and soles of the feet was keratinized and thickened. Laboratory examination using bone and joint X-ray showed periostosis of the proximal middle phalanges and metacarpals of both hands, distal ulna and radius, tibia and fibula, distal femurs, and metatarsals.Genetic testing revealed two variants in SLCO2A1, c.1658T > A and c.96+4A > C.Through multidisciplinary consultation, we confirmed the diagnosis of pachydermoperiostosis.

ObjectiveTo analyze the clinical treatment plan and traditional Chinese medicine （TCM） medication rule of children with primary nephrotic syndrome （PNS） in the First Affiliated Hospital of Henan University of Chinese Medicine. MethodsThe gender and age of children firstly diagnosed with nephrotic syndrome in the pediatric nephrology department of the First Affiliated Hospital of Henan University of Chinese Medicine from November 2019 to December 2022 were collected， and the use of immunosuppressive agents and related frequencies were counted. According to the inclusion and exclusion criteria， an independent TCM prescription database for children with nephrotic syndrome was established. Excel was used to analyze the relevant information of the literature. The frequency counting， association rule analysis， and cluster analysis were carried out on TCM in the prescription， and the high-frequent drugs were analyzed. Results（1） General information： A total of 711 children were included， consisting of 522 males （73.42%） and 189 females （26.58%）. The ratio of male to female was about 2.76∶1. The disease mainly occurred in infants and preschool age， and the average age of onset was （4.74 ± 3.48） years old. （2） Clinical treatment plan and use of immunosuppressive agents： Of the 711 children with PNS， 237 were treated with hormone alone （32.33%）， and 474 （66.67%） received immunosuppressive agents combined with hormones. In the initial treatment， hormone combined with Tacrolimus （TAC） was the preferred treatment （32.91%）. For children with refractory PNS who exhibited poor clinical efficacy， Rituximab （RTX） was mostly used for treatment， with a ratio of up to 23.63%. （3） TCM syndrome and medication rule： In PNS syndrome differentiation， Qi and Yin deficiency was identified as the main syndrome. This involved a total of 477 cases， accounting for 67.09%. Yang deficiency of spleen and kidney was observed in 118 cases， accounting for 16.60%. A total of 711 children were included， of which 706 children were treated with TCM. This involved a total of 706 prescriptions， 226 TCM， and 9 793 frequencies. There were 30 herbs used more than 95 times. The top five TCM were Radix et Rhizoma Glycyrrhizae （81.16%）， Radix Astragali （71.81%）， Poria （68.84%）， Rhizoma Atractylodis Macrocephalae （63.60%）， and Fructus Corni （57.37%）. The drug association rules and network diagram showed that the combination of ''Radix Astragali-Rhizoma Atractylodis Macrocephalae-Poria'' was the closest， and five types of combinations were obtained by cluster analysis. ConclusionIn the diagnosis and treatment of PNS in children， TAC combined with hormones shows good clinical efficacy and high safety. For children with refractory PNS， RTX combined with hormones can be used. TCM medication for PNS should follow the basic principles of strengthening the body and vital Qi and make good use of drugs such as Radix Astragali， Poria， Rhizoma Atractylodis Macrocephalae， and cornus to regulate the Yin and Yang balance and achieve better clinical efficacy.