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.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

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.

AIM To study the chemical constituents from the water fraction of rhizoma of Smilax trinervula Miq.and their biological activities.METHODS Polyamide,silica gel,Sephadex LH-20,ODS and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activities were determined by MTT mothod,and the inhibitory activities on α-glucosidase were determined by PNPG method.RESULTS Eleven compounds were isolated and identified as tyrosine(1),uridine(2),2-(2',3',4'-trihydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(3),2-(1',2',3',4'-tetrahydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(4),2-(1',2',3',4'-tetrahydroxybutyl)-5-(2",3",4"-trihydroxybutyl)-pyrazine(5),uracil(6),2-(1',2',3',4'-tetrahydroxybutyl)-5-(1",2",3",4"-tetrahydroxybutyl)-pyrazine(7),dioscin(8),shikimic acid(9),pyrazine(10),3,4-dihydroxyphenyethyl alcohol 8-O-β-D-glycopyranoside(11).The IC50 values of compounds 8 to human breast cancer cell MCF-7 was(2.36±0.26)μg/mL,and the IC50 values of compounds 3-5 and 7 to α-glucosidase were(1.54±0.15)-(10.53±0.38)μg/mL.CONCLUSION Compounds 1-7,10 are isolated from Smilax genus for the first time,and compound 9,11 are first isolated from this plant.Compound 8 has anti-tumor activity,and compounds 3-5,7 have α-glucosidase inhibitory activities.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

To investigate the effects of Biyan Jiedu Capsules on the proliferation and apoptosis of nasopharyngeal carcinoma cells and their molecular mechanism, nasopharyngeal carcinoma cells CNE1 and CNE2 were used. They were divided into control group(30% blank serum medium), low-(10% drug-containing serum + 20% blank serum medium), medium-(20% drug-containing serum + 10% blank serum medium), and high-(30% drug-containing serum medium) concentration group of Biyan Jiedu Capsules according to in vitro experiment. After 24 h of intervention, the effects of Biyan Jiedu Capsules on the proliferation of CNE1 and CNE2 were detected by CCK-8 assay, clonal formation experiment, and EdU staining. The effect of Biyan Jiedu Capsules on apoptosis of CNE1 and CNE2 was detected by flow cytometry. Western blot was used to detect the effect of Biyan Jiedu Capsules on the expression of X-linked apoptosis inhibitor protein(XIAP), survivin, proliferating cell nuclear antigen(PCNA), and PI3K/Akt pathway-related proteins in CNE1 and CNE2. The results showed that compared with the control group, the survival rate of CNE1 and CNE2 in the medium and high concentration groups of Biyan Jiedu Capsules could be decreased in a concentration-dependent way(P<0.05, P<0.01). At the same time, EdU staining and clonal formation experiments showed that the proliferation of CNE1 and CNE2 was significantly inhibited in the medium and high concentration groups of Biyan Jiedu Capsules(P<0.05, P<0.01). Flow cytometry showed that the apoptosis rate of CNE1 and CNE2 was significantly increased in all concentration groups of Biyan Jiedu Capsules(P<0.01), and the apoptosis rate was concentration-dependent. Western blot showed that the expressions of XIAP, survivin, PCNA, p-PI3K, and p-Akt in all concentration groups of Biyan Jiedu Capsules were significantly down-regulated(P<0.05, P<0.01). In conclusion, Biyan Jiedu Capsules can inhibit the proliferation and induce apoptosis of nasopharyngeal carcinoma cells possibly by down-regulating the PI3K/Akt signaling pathway.
Humans ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Nasopharyngeal Carcinoma ; Nasopharyngeal Neoplasms/physiopathology* ; Proto-Oncogene Proteins c-akt/genetics* ; Cell Line, Tumor ; Drugs, Chinese Herbal/pharmacology* ; Phosphatidylinositol 3-Kinases/genetics* ; Signal Transduction/drug effects* ; Capsules ; Carcinoma/drug therapy*

Jiuwei Xifeng Granules have become a Chinese patent medicine in the market. Because the formula contains Os Draconis, a top-level protected fossil of ancient organisms, the formula was to be improved by replacing Os Draconis with Ostreae Concha. To evaluate whether the improved formula has the same effectiveness and safety as the original formula, a randomized, double-blind, parallel-controlled, equivalence clinical trial was conducted. This study enrolled 288 tic disorder(TD) of children and assigned them into two groups in 1∶1. The treatment group and control group took the modified formula and original formula, respectively. The treatment lasted for 6 weeks, and follow-up visits were conducted at weeks 2, 4, and 6. The primary efficacy endpoint was the difference in Yale global tic severity scale(YGTSS)-total tic severity(TTS) score from baseline after 6 weeks of treatment. The results showed that after 6 weeks of treatment, the declines in YGTSS-TSS score showed no statistically significant difference between the two groups. The difference in YGTSS-TSS score(treatment group-control group) and the 95%CI of the full analysis set(FAS) were-0.17[-1.42, 1.08] and those of per-protocol set(PPS) were 0.29[-0.97, 1.56], which were within the equivalence boundary [-3, 3]. The equivalence test was therefore concluded. The two groups showed no significant differences in the secondary efficacy endpoints of effective rate for TD, total score and factor scores of YGTSS, clinical global impressions-severity(CGI-S) score, traditional Chinese medicine(TCM) response rate, or symptom disappearance rate, and thus a complete evidence chain with the primary outcome was formed. A total of 6 adverse reactions were reported, including 4(2.82%) cases in the treatment group and 2(1.41%) cases in the control group, which showed no statistically significant difference between the two groups. No serious suspected unexpected adverse reactions were reported, and no laboratory test results indicated serious clinically significant abnormalities. The results support the replacement of Os Draconis by Ostreae Concha in the original formula, and the efficacy and safety of the modified formula are consistent with those of the original formula.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Male ; Double-Blind Method ; Drugs, Chinese Herbal/therapeutic use* ; Tic Disorders/drug therapy* ; Treatment Outcome

Based on serum metabolomics and gut microbiota technology, this study explores the effects and mechanisms of the water extract of Ziziphi Spinosae Semen(SZRW) and the petroleum ether extract of Ziziphi Spinosae Semen(SZRO) in improving depressive-like behaviors induced by sleep deprivation. A modified multi-platform water environment method was employed to establish a rat model of sleep deprivation. Depressive-like behaviors in rats were assessed through the sucrose preference test and forced swim test. The expression of barrier proteins, such as Occludin, in the colon was determined by immunofluorescence. UPLC-Q-Orbitrap MS was utilized to analyze the serum metabolic profiles of sleep-deprived rats, screen for differential metabolites, and analyze metabolic pathways. The diversity of the gut microbiota was detected using 16S rRNA gene sequencing. Spearman correlation coefficient analysis was conducted to assess the correlation between differential metabolites and gut microbiota. The results indicated that SZRO significantly increased the sucrose preference index and decreased the immobility time in the forced swim test in rats. A total of 34 differential metabolites were identified through serum metabolomics. SZRW and SZRO shared five metabolic pathways, including phenylalanine metabolism. SZRW uniquely featured taurine and hypotaurine metabolism, while SZRO uniquely featured linoleic acid metabolism and tyrosine metabolism. Correlation analysis revealed that SZRW could upregulate the abundance of Bilophila, promoting the production of indole-3-propionic acid and subsequently upregulating the expression levels of intestinal tight junction proteins such as ZO-1, Occludin, and Claudin-1. SZRO could indirectly influence metabolic pathways such as arginine metabolism and linoleic acid metabolism by upregulating the abundance of gut microbiota such as Coprococcus and Eubacterium species. Both SZRW and SZRO can regulate endogenous metabolism, including amino acids, energy, and lipids, alter the gut microbiota microecology, and improve depressive-like behaviors. SZRO demonstrated superior effects in regulating metabolic pathways and gut microbiota structure compared to SZRW. The findings of this study provide a scientific basis for elucidating the pharmacodynamic material basis of Ziziphi Spinosae Semen.
Animals ; Rats ; Gastrointestinal Microbiome/drug effects* ; Male ; Metabolomics ; Drugs, Chinese Herbal/administration & dosage* ; Depression/blood* ; Rats, Sprague-Dawley ; Sleep Deprivation/complications* ; Ziziphus/chemistry* ; Antidepressive Agents/administration & dosage* ; Behavior, Animal/drug effects* ; Humans

OBJECTIVE: To compare the efficacy of percutaneous pedicle screw combined with unilateral nail placement combined with bone cement strengthening and bilateral nail placement in the treatment of osteoporotic thoracic and lumbar fractures. METHODS: A retrospective case-control study was used to analyze the clinical data of 78 patients with osteoporotic thoracic and lumbar fractures admitted from October 2017 to May 2019. According to the surgical method, it was divided into percutaneous pedicle screw combined with unilateral nail placement combined with unilateral bone cement strengthening group(bone cement group) and percutaneous pedicle screw combined with bilateral nail placement(screw group). In the bone cement group, 40 patients included 16 males and 24 females, with a mean age of (62.1±8.1) years old. In the screw group, 38 patients included 18 males and 20 females with a mean age of (65.1±9.3) years old. The operation time, intraoperative blood loss, length of hospital stay and postoperative complications were compared between two groups. The kyphosis Cobb angle, anterior edge height ratio, central height ratio and pain visual analogue score(VAS) were compared. RESULTS: All patients were followed up for 25 to 36 months. The operation time (70.1±17.3) min of the cement group was shorter than that of the screw group (78.6±18.2) min(P<0.05). There were no significant differences in intraoperative blood loss and length of hospital stay(P>0.05). The VAS in the cement group 1 year 1.5±0.5 and the latest follow-up 0.5±0.3 after operation were lower than 1 year 1.8±0.3 and the latest follow-up 0.8±0.4 in the screw group(P<0.05). The kyphosis Cobb angle, anterior edge height ratio, central height ratio in bone cement group, 1 year (6.2±1.2)°, (86.6±3.5)%, (91.1±2.5)%, the last follow-up (6.4±0.7)°, (85.5±3.3)%, (90.5±6.3)% were better than that of the screw group 1 year (6.8±1.4)°, (83.1±2.4)%, (89.9±3.4)% and the latest follow-up (7.1±1.1)°, (82.6±4.1)%, (87.6±5.9)%(P<0.05). There were 3 cases of bone cement leakage in the cement group, all of which had no clinical symptoms;and 2 cases of pedicle screws were extracted in the screw group, and the screws were removed at the last follow-up. CONCLUSION Percutaneous pedicle screw combined with unilateral nail placement combined with bone cement strengthening and bilateral nail placement in the treatment of osteoporotic thoracic and lumbar compression fractures in the elderly can achieve satisfactory efficacy and effectively relieve the pain of patients, but the former internal fixation system is more stable, and the long-term follow-up can effectively maintain the height of the anterior middle column and the correction of kyphosis deformity, and the incidence of chronic low back pain is lower.
Humans ; Male ; Female ; Aged ; Bone Cements ; Middle Aged ; Thoracic Vertebrae/surgery* ; Lumbar Vertebrae/surgery* ; Retrospective Studies ; Spinal Fractures/surgery* ; Osteoporotic Fractures/surgery* ; Case-Control Studies ; Bone Nails ; Pedicle Screws