ObjectiveTo investigate the contamination levels of zearalenone (ZEN) in commercially available edible vegetable oils in Ningbo City and to assess its health risks to local residents. MethodsA total of 330 samples of commercially available edible vegetable oil samples (50 each of peanut oil, corn oil, and olive oil; 40 each of rapeseed oil and blended oil; 30 each of soybean oil, rice oil, and sunflower seed oil; and 10 of camellia oil) were collected in 2024. The samples were tested for ZEN using the first method specified in GB 5009.209‒2016 National Food Safety Standard―Determination of Zearalenone in Food, namely the liquid chromatography method, and the contamination status was analyzed. Additionally, combined with dietary consumption data of residents, the Monte Carlo simulation method was employed to evaluate the health risks of ZEN in edible vegetable oils. ResultsZEN was detected in 267 out of 330 samples, with a detection rate of 80.91%, and the median (P₅₀) and the 25th, 75th percentiles (P₂₅, P₇₅) of ZEN concentrations were 2.02 (0.37, 17.90) μg·kg^-1, with a maximum value of 342.00 μg·kg^-1. The ZEN detection rates in corn oil, peanut oil, and blended oil were all 100.00%. The daily average exposure (P₅₀) and daily high exposure (P₉₅) to ZEN via edible vegetable oils among Ningbo residents were 0.001 μg·kg^-1 (normalized to body weight, same below) and 0.060 μg·kg^-1, respectively. However, 1.22% of Ningbo residents had a daily ZEN exposure exceeding the tolerable daily intake (TDI) of 0.25 μg·kg^-1. The hazard quotients (HQ) for the daily average exposure (P₅₀) and daily high exposure (P₉₅) levels were 0.004 and 0.020, respectively, both substantially below 1. Nevertheless, the probability of health risk for Ningbo residents due to ZEN exposure from vegetable oil consumption remained at 1.02%. ConclusionEdible vegetable oils in Ningbo City were contaminated with ZEN, but the probability of ZEN exposure exceeding the TDI through edible vegetable oils was relatively low, and the associated health risk probability were also minimal, indicating an overall insignificant health risk.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

This paper summarizes Professor HAN Mingxiang's clinical experience in the use of Zeqi （Euphorbia HelioscopiaL.）. It is believed that Zeqi （Euphorbia HelioscopiaL.） has the effects of promoting qi， relieving water retention and swelling， resolving phlegm， stopping cough， dissipating masses， activating blood， removing stasis， and detoxifying. In clinical practice， Zeqi （Euphorbia HelioscopiaL.） is flexibly applied in the treatment of skin diseases， respiratory diseases， tumors， etc. For instance， in treating psoriasis with the pathogenesis of damp-heat toxin， a compound prescription of Zeqi Decoction （泽漆汤） is formulated. For bronchial asthma with kidney deficiency and water retention， Zeqi Decoction is commonly combined with Wuling Powder （五苓散） in adjusted doses. For lung nodules with a combination of deficiency， phlegm， stasis， and toxin， a Lung Nodule Prescription is proposed. For advanced lung cancer with both qi and yin deficiency and toxin accumulation， Qiyu Sanlong Decoction （芪玉三龙汤） is suggested， and for cancer-related ascites with qi deficiency and water retention， Wuling Powder combined with Zeqi （Euphorbia HelioscopiaL.）is chosen.

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 observe the therapeutic efficacy of Jisheng Shenqi Pills combined with endocrine therapy in alleviating lower urinary tract obstruction symptoms of patients with advanced prostatecancer(PCa).Methods A total of 68 patients with advanced PCa complicated by lower uri-nary tract obstruction symptoms were randomly divided into observation group and control group,with 34 cases in each group.The control group received endocrine therapy alone,while the observation group received Jisheng Shenqi Pills on the basis of the control group.Both groups underwent a course of 3-month treatment.The total therapeutic effectiveness rate,traditional Chinese medicine(TCM)syndrome scores,maximum urinary flow rate(Qmax),residual urinary volume(RUV),International Prostate Symptom Score(IPSS),prostate-specific antigen(PSA)levels,and Quality of Life(QOL)Scale scores were compared between the two groups.Results The total therapeutic effectiveness rate in the observation group was 94.12％,which was significantly higher than 82.35％in the control group(P＜0.05).After treatment,TCM syndrome scores decreased in both groups,with the obser-vation group had significantly lower scores than the control group(P＜0.05).Both groups exhibited an increase in Qmax and a decrease in RUV after treatment,with the observation group had significant-ly higher Qmax and lower RUV compared to the control group(P＜0.05).PSA level decreased signif-icantly in both groups after treatment(P＜0.05),but no significant difference was observed between the two groups(P＞0.05).IPSS score was decreased while QOL score was increased in both groups after treatment,with the observation group had significantly lower IPSS score and higher QOL score compared to the control group(P＜0.05).Conclusion Jisheng Shenqi Pills combined with endo-crine therapy effectively alleviates urinary obstruction symptoms,improves TCM syndromes,and en-hances quality of life in patients with advanced PCa.

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

Objective:To explore the value of the vesical imaging-reporting and data system (VI-RADS) score based on multiparametric MRI (mpMRI) combined with quantitative tumor MRI parameters in assessing the muscle invasion of bladder cancer.Methods:The study was a case-control study. The data of 87 bladder cancer patients confirmed by pathology who underwent mpMRI of the bladder were retrospectively collected from the First Medical Center of Chinese PLA General Hospital between January 2019 and April 2023 The pathological findings were used as the gold standard to categorize them into the muscle invasive bladder cancer (MIBC) group (29 cases) and non-muscle invasive bladder cancer (NMIBC) group (58 cases). Quantitative parameters were measured based on preoperative mpMRI images, including the length of tumor bladder wall contact, the perpendicular distance between the bladder tumor and the tangent of the bladder wall, the maximal diameter of the bladder tumor, and the volume of the bladder tumor. Bladder cancer was classified according to the VI-RADS scoring criteria. The Mann-Whitney U test was used for intergroup comparisons. Multivariate logistic regression analysis was performed to obtain the independent risk factors related to muscle invasion of bladder cancer and to establish the model. The receiver operating characteristic curves were analyzed for MRI quantitative parameters and logistic regression models, and area under the curve (AUC) comparisons were performed using the DeLong test. Results:The differences in tumor bladder wall contact length, perpendicular distance from the tumor to the tangent line of the bladder wall, maximum diameter, bladder tumor volume, and the VI-RADS scores were statistically significant between the MIBC group and the NMIBC group ( P<0.05). Multifactorial logistic regression analysis showed that tumor bladder wall contact length ( OR=21.07, 95% CI 3.56-124.89, P=0.001) and VI-RADS score ( OR=11.90, 95% CI 3.53-40.12, P<0.001) were the independent risk factors for evaluating the muscle invasion of bladder cancer. The difference between the VI-RADS score and the tumor bladder wall contact length for assessing muscular infiltration of bladder cancer had AUCs of 0.802 (95% CI 0.704-0.899) and 0.759 (95% CI 0.652-0.865). The combined model of VI-RADS score combined with tumor bladder wall contact length had an AUC of 0.891 (95% CI 0.812-0.970), which was higher than the diagnostic efficacy of applying tumor bladder wall contact length or VI-RADS score alone ( Z=3.05, 2.37, P=0.002, 0.018). Conclusion:Tumor contact length with the bladder wall is an independent risk factor for assessing muscle invasion of bladder cancer and the combination of VI-RADS score may enhances diagnostic accuracy.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Objective To preliminarily design a wide-energy-spectrum CR-39 solid-state nuclear track individual neutron dosimeter with different energy sections. Methods The thickness of the converter was optimized using the Monte Carlo SRIM program to broaden the energy range of the dosimeter. The self-made wide-energy-spectrum CR-39 individual neutron dosimeter was calibrated using ²⁴¹Am-Be, ²⁵²Cf, and thermal neutron sources to evaluate its dosimetric performance, including linearity, energy response, and neutron energy resolution. Results The linear correlation coefficient of the measurement system exceeded 0.98. The relative deviations of the energy response were 35.0% for blank section and 42.0% for polyethylene section, falling within the range of −50% to + 100% and meeting the monitoring requirements. The detection sensitivity for thermal neutron dose was 67 137.2 tr·cm⁻²·mSv⁻¹, and the detection sensitivity for thermal neutron fluence was 0.98 × 10⁻³ tr·n⁻¹, demonstrating good thermal neutron detection capability. Conclusion The self-made wide-energy-spectrum CR-39 individual neutron dosimeter fundamentally meets the requirements for individual neutron dose monitoring and is suitable for individual neutron dose monitoring in the energy range of thermal neutrons (up to approximately 15 MeV).