Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

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.

To investigate the association between sleep-related phenotypes and the risk of malignancy in pulmonary nodules, and to provide complementary evidence from a general population cohort and genetic analyses.

BACKGROUND:Wogonin is a flavonoid extracted from the root of Scutellaria baicalensis.Previous studies have shown that baicalein has protective effects against cerebral ischemia-reperfusion injury,and can also reduce blood sugar and complications in diabetic mice,but its role and mechanism in diabetic cerebral infarction remain unclear. OBJECTIVE:To explore the effect of wogonin on nerve injury in rats with diabetic cerebral infarction and its mechanism. METHODS:Sprague-Dawley rats were randomly divided into six groups:control group,model group,low-dose wogonin group,medium-dose wogonin group,high-dose wogonin group,and high-dose wogonin+Ras homolog gene family member A(RhoA)activator group.Except for the control group,the other rats were established with diabetes and cerebral ischemia models using intraperitoneal injection of streptozotocin and middle cerebral artery occlusion.Low,medium-and high-dose wogonin groups were intragastrically given 10,20,40 mg/kg wogonin,respectively;high-dose wogonin+RhoA activator group was intragastrically given 40 mg/kg wogonin and intraperitoneally injected 10 mg/kg lysophosphatidic acid;control group and model group were given the same amount of normal saline once a day for 7 consecutive days.Rats in each group were evaluated for neurological deficits and their blood glucose levels were measured after the last dose.TTC staining was applied to detect the volume of cerebral infarction.Hematoxylin-eosin staining was applied to observe pathological changes in brain tissue.ELISA kit was applied to detect tumor necrosis factor-α,interleukin-6,malondialdehyde,and superoxide dismutase levels in brain tissue.Western blot was applied to detect the protein expression of RhoA and Rho-associated protein kinase(ROCK)2 in brain tissue. RESULTS AND CONCLUSION:Compared with the control group,the neuronal structure of rats in the model group was severely damaged,with cell necrosis and degeneration,the neurological deficit score,blood glucose level,and infarct volume were significantly elevated(P<0.05),the levels of tumor necrosis factor-α,interleukin-6,and malondialdehyde,and the protein expression of RhoA and ROCK2 in brain tissue were significantly increased(P<0.05),and the superoxide dismutase level was decreased(P<0.05).Compared with the model group,the low-,medium-,and high-dose wogonin groups showed improved neuronal damage,reduced cell degeneration and necrosis,a significant reduction in neurological deficit score,blood glucose level,infarct volume,and the levels of tumor necrosis factor-α,interleukin-6,and malondialdehyde,and the protein expression of RhoA and ROCK2 in brain tissue,and an increase in the superoxide dismutase level(P<0.05).Compared with the high-dose wogonin group,the high-dose wogonin+RhoA activator group significantly weakened the improvement in the above indexes of rats with diabetic cerebral infarction(P<0.05).To conclude,wogonin can improve the blood glucose level in rats with diabetic cerebral infarction,reduce cerebral infarction and nerve injury,and its mechanism may be related to the inhibition of RhoA/ROCK signaling pathway.

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.

Objective To investigate the role of Insulin like growth factor 2 mRNA binding protein 1(IGF2BP1)and long non-coding RNA LINC00160(LINC00160)in gastric cancer,and its potential mechanism of regulating the proliferation and invasion of gastric cancer cells.Methods Quantitative real time polymerase chain reaction(qRT-PCR)was used to detect the expression level of LINC00160 in gastric cancer tissues and cells.Bioinformatics prediction,RNA-binding protein immunoprecipitation(RIP)and methylated RNA immunoprecipitation(MeRIP)were used to verify the binding effect of LINC00160 and IGF2BP1.The correlation between the expression of LINC00160 and IGF2BP1 in gastric cancer tissues was analyzed by Pearson assay.CCK-8 assay and Transwell assay were used to detect cell proliferation and invasion.The changes of aerobic glycolysis index[glucose intake,lactate production,and Adenosine-triphosphate(ATP),extracellular acidification rate(ECAR)and oxygen consumption rate(OCR)]were detected and analyzed.Results Compared with normal tissues,the expression of LINC00160 in gastric cancer tissues(5.13±0.62 vs 1.02±0.03)was significantly up-regulated,and the difference was statistically significant(t=-36.266,P<0.001).The expression level of LINC00160 in gastric cancer cells was higher than that of human normal gastric epithelial cell line GES-1,and the difference was statistically significant(F=24.595,P<0.001).Compared with the control group,silenting LINC00160 significantly inhibited the proliferation(0.42±0.03 vs 1.03±0.04)and invasion(22.13%±1.97%vs 42.15%±2.67%)of AGS cells,decreased glucose uptake(2.11±0.26mmol/L vs 4.22±0.37mmol/L)and lactate production(6.84±1.25mmol/L vs 11.68±1.55mmol/L),decreased ECAR,and increased ATP(3.34±0.29mmol/L vs 1.87±0.24mmol/L)levels and OCR,and the differences were statistically significant(t=4.188～24.423,all P<0.01).The expression of IGF2BP1 protein in gastric cancer tissues(4.07±0.36)was significantly higher than that in adjacent tissues(1.01±0.03),and the difference was statistically significant(t=-46.396,P<0.01),and was positively correlated with the expression of LINC00160(r2=0.774 5,P<0.01).Mechanistic studies revealed that IGF2BP1 upregulated LINC00160 expression by binding m6A modified LINC00160 to promote its stability.Silencing IGF2BP1 significantly inhibited the expression of LINC00160 and the proliferation,invasion and aerobic glycolysis of gastric cancer cells,and the differences were statistically significant(t=4.386～11.989,all P<0.01).Overexpression of LINC00160 reversed the effect of IGF2BP1 silencing on AGS cells.Conclusion LINC00160 is significantly up-regulated in gastric cancer,and IGF2BP1 may stably regulate the expression of LINC00160 through m6A modification,promote the aerobic glycolysis of tumor cells,and participate in the occurrence and development of gastric cancer.

Background::An evidence gap still exists regarding the efficacy and safety of tegoprazan in patients with erosive esophagitis (EE) in China. This study aimed to verify the efficacy and safety of tegoprazan vs. esomeprazole in patients with EE in China. Methods::This study was a multicenter, randomized, double-blind, parallel, active-controlled, non-inferiority phase III trial of patients with EE randomized 1:1 to tegoprazan 50 mg/day vs. esomeprazole 40 mg/day. This study was conducted in 32 sites between October 24, 2018 and October 18, 2019. The primary endpoint was the cumulative endoscopic healing rate at week 8. The secondary endpoint included endoscopic healing rate at week 4, changes in the reflux disease questionnaire (RDQ) and gastroesophageal reflux disease health-related quality of life (GERD-HRQL) scores, and symptom improvement. Results::A total of 261 patients were randomized: 132 to the tegoprazan group and 129 to the esomeprazole group. The cumulative endoscopic healing rate at 8 weeks in the tegoprazan group was non-inferior to that of the esomeprazole group (91.1% vs. 92.8%, difference: -1.7%, 95% confidence interval [CI]: -8.5%, 5.0%, P = 0.008). There were no statistically significant differences in the changes in RDQ (total, severity, and frequency) and GERD-HRQL scores between the two groups (all P >0.05). The percentages of days without symptoms, including daytime and nighttime symptoms based on patients' diaries, were similar between the two groups (all P >0.05). In the tegoprazan and esomeprazole groups, 71.5% (93/130) and 61.7% (79/128) of the participants reported adverse events (AEs), 2.3% and 0 experienced serious AEs, while 70.0% and 60.2% had treatment-emergent AEs, respectively. Conclusion::Tegoprazan 50 mg/day demonstrated non-inferior efficacy in healing EE, symptom improvement, and quality of life, and it has similar tolerability compared with esomeprazole 40 mg/day.

Time rhythm and the immune system play crucial roles in the pathogenesis of allergic rhinitis.Traditional Chinese medicine(TCM)believes that the circulation of qi and blood in lung meridian follows the principle of"yang during the day,yin at night",which has a defensive function to protect the body from external pathogens.Qi stagnation in lung meridian and impaired qi and blood circulation can lead to the invasion of external pathogens,exacerbating allergic reactions,especially during the active period of the lung meridian,when the immune system is most sensitive to allergens.Based on this,this paper proposes the concept of"bidirectional regulation among meridian,time rhythm,and immune system",and in combination with TCM theory of midnight-noon and ebb-flow doctrine,analyzes the fluctuations of allergic rhinitis symptoms and the temporal changes of meridian qi and blood,and reveals the rhythmic relationship between meridian activity and immune response.This paper combines existing clinical and experimental studies to support this hypothesis,integrating the time dimension into traditional TCM syndrome differentiation and treatment,offering a more individualized treatment approach.This concept not only provides novel scientific evidence for TCM treatment of allergic rhinitis,but also offers theoretical support for optimizing treatment timing and intervention strategies.

Nanoparticle delivery systems have good application prospects in the field of precision therapy, but the preparation process of nanomaterial has problems such as short in vivo circulation time, easy recognition, and clearance by the immune system in the body. In recent years, biomimetic nanoparticle delivery systems mediated by natural cell membranes have become a research hotspot to address these issues. The natural membrane biomimetic nanoparticle delivery system cleverly integrates the advantages of natural biofilm "autologous" and "artificial" functional carriers by using endogenous cell membranes to modify the surface of nanocarriers, endowing them with characteristics such as tumor targeting, low immunogenicity, and long blood circulation. Currently, biomimetic nanoparticle delivery systems have been used in the treatment of malignant tumors, cardiovascular diseases, bacterial infections, and other diseases. This paper analyzes the development status and current research hotspots of natural cell membrane camouflaged biomimetic nanoparticle delivery systems mainly reviews the latest research progress of red blood cell membrane camouflaged biomimetic nanoparticle delivery systems in the field of disease treatment in recent years. It focuses on exploring the advantages, future development prospects, and limitations of biomimetic nanoparticle delivery systems based on red blood cell membrane camouflage in improving drug delivery, to provide a reference for the in-depth research and development of this system.