BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

ObjectiveMigraine is a leading neurological disorder and the fourth most common cause of years lived with disability worldwide, affecting nearly 116 million individuals. Although pharmacological treatments are available, their efficacy is often limited by side effects and variable response rates. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) offers a safe, non-invasive alternative for migraine management. However, the neurophysiological mechanisms, particularly how rTMS modulates local cortical excitability and distributed pain-related circuits, remain poorly understood. Elucidating these mechanisms is essential for optimizing treatment protocols and improving clinical outcomes. MethodsThis study employed concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) to investigate neuroplastic and neurocircuitry mechanisms of DLPFC-rTMS in migraine. Study 1 compared 30 migraineurs and 28 healthy controls to identify abnormalities in TMS-evoked potentials (TEPs) and significant current density (SCD) within sensory-discriminative regions including the primary somatosensory cortex (S1) and posterior insula (pINS), cognitive-affective regions including the anterior insula (aINS) and midcingulate cortex (MCC), and a descending modulatory region, the periaqueductal gray (PAG). Study 2 used a single-blind, crossover, sham-controlled design in 34 healthy participants. Each participant received both active (10 Hz, 80% RMT, 1 500 pulses) and sham DLPFC-rTMS in counterbalanced order. TMS-EEG and cold pain tolerance were assessed before and after each session. ResultsIn Study 1, migraineurs showed a significantly less negative N120 amplitude compared to healthy controls (P=0.027, Cohen’s d=0.60), indicating local intracortical disinhibition. No group differences were observed for N40, P60, or P180 components. At the source level, migraineurs exhibited significantly higher SCD in the S1, pINS, aINS, and MCC (allQ<0.05), but not in the ventroposterior thalamus (vpTHAL), mediodorsal thalamus (mdTHAL), or PAG. In Study 2, active rTMS significantly reduced SCD from pre- to post-stimulation in the S1, aINS, and MCC (all Q<0.05). Sham stimulation also reduced SCD in the S1 (Q<0.05) but not in the aINS or MCC. Although no significant group-level analgesic effect was observed between active and sham conditions (P=0.107), correlation analyses revealed that greater SCD reductions in the S1 and MCC were significantly associated with higher post-rTMS pain tolerance (R=-0.487 and -0.495, both Q<0.01) and larger improvements in pain tolerance(R=-0.487 and -0.451, both Q<0.05). No such correlations were found following sham stimulation, suggesting that the behavioural relevance of neural changes is specific to active rTMS. ConclusionThis study provides novel evidence that migraineurs exhibit both local neuroplastic abnormalities (reduced N120 amplitude) and hyperactivity in key pain-processing regions (S1, pINS, aINS, MCC). A single session of DLPFC-rTMS reduced hyperactivity in the aINS, MCC, and S1. Notably, greater reductions in the S1 and MCC were associated with improved pain tolerance. These findings identify distinct cortical circuitries, particularly within the cognitive-affective pain network, that may serve as potential biomarkers for optimizing rTMS treatment in migraine and other chronic pain conditions. Future studies should validate these results in patient populations experiencing spontaneous migraine attacks and explore multi-session or accelerated rTMS protocols.

ObjectiveTo systematically characterize the in vitro chemical constituents of Entada phaseoloides and its absorbed prototype compounds and metabolites in a neuropathic pain （NP） model， thereby elucidating the potential pharmacological material basis underlying its analgesic activity. MethodsUltra-high-performance liquid chromatography coupled with quadrupole-Orbitrap high-resolution mass spectrometry （UHPLC-Q-Orbitrap HRMS） was used to analyze the constituents of the 80% methanol extract of E. phaseoloides. Thirty-six mice were divided into a blank group， low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， an aspirin group （400 mg·kg^-1）， and a morphine group （10 mg·kg^-1）， with six mice in each group. The tail-flick threshold was measured. Twenty-four mice were randomly divided into a blank group and low-， medium-， and high-dose E. phaseoloides extract groups （25， 50， and 100 mg·kg^-1， respectively）， with six mice in each group， and motor coordination was evaluated. A spinal nerve ligation （SNL） model was established in mice. Twenty-four mice were randomly divided into a sham-operated （Sham） group， an SNL group， an SNL + E. phaseoloides extract group （750 mg·kg^-1）， and an SNL + pregabalin group （20 mg·kg^-1）， with 6 mice in each group. Mechanical allodynia and thermal hyperalgesia were evaluated. Behavioral assessments were combined to determine the optimal onset time of the analgesic effect， and plasma samples were collected at this time point. Absorbed components and metabolites were systematically identified based on accurate mass， MS/MS fragmentation patterns， retention time， and comparisons with literature data and reference standards. Meanwhile， network pharmacology， target similarity analysis， and molecular docking validation were integrated to evaluate the potential effects of absorbed components on disease intervention. ResultsCompared with the blank group， the high-dose E. phaseoloides extract group and morphine group showed significantly increased pain response thresholds （P<0.01）， without affecting motor coordination in normal mice. Compared with the Sham group， the SNL group exhibited significantly decreased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. Compared with the SNL group， the SNL + E. phaseoloides extract group and SNL + pregabalin group showed significantly increased thresholds for mechanical allodynia and thermal hyperalgesia （P<0.01）. The analgesic effect of E. phaseoloides was most significant at 2 h after administration. A total of 70 constituents were identified in vitro， and 22 prototype compounds and 29 metabolites were detected in plasma. The targets of the absorbed components were mainly enriched in biological processes associated with pain signal perception and transmission and showed a high degree of overlap with disease genes related to neuropathic pain. In addition， multiple components exhibited strong binding energies with core pain-related targets. Based on integrated analysis of in vitro content， plasma exposure levels， and target associations， ketanthamide A-β-D-pyranoside and ketengzide were identified as the most representative pharmacologically active substances responsible for the analgesic effect. ConclusionThis study systematically elucidates the material basis of E. phaseoloides intervention in neuropathic pain from three aspects， including chemical constituent characteristics， in vivo behavioral characteristics， and potential targets， providing important theoretical foundations and experimental support for further studies on its pharmacological mechanism and analgesic drug development.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

The recombinant human thyroid-stimulating hormone (rhTSH) can be used for differentiated thyroid cancer (DTC) preparation before ¹³¹I therapy, and follow-up. Since its approval by the American Food and Drug Administration (FDA) in 1998, rhTSH has been clinically used in European countries, the United States, and other countries for nearly 30 years. In April 2024, China’s first independently developed rhTSH agent was approved. Based on clinical experience and evidence from domestic and international studies, an expert consensus on the application of rhTSH has been developed following thorough consultations among experts. The consensus offers evidence-based recommendations to guide the appropriate use of rhTSH in clinical practice.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.