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 To explore the efficacy of goal management training(GMT)on core symptoms and mental status in college students with attention deficit hyperactivity disorder(ADHD)inattentive type.Methods Delphi method was used to construct a GMT program for college students with inattentive ADHD.Then,totally 68 college students with inattentive ADHD were recruited through advertisements published by hospitals and universities(Second Affiliated Hospital of Army Medical University,and 3 universities in Chongqing from March to June 2024.The subjects were randomly divided into an intervention group(n=34)and a control group(n=34).The intervention group received GMT for 2 h,once a week,for 7 weeks,and the control group did not receive training for the time being.The 2 groups were evaluated within 1 week before and in 7 weeks after intervention by using Adult ADHD Self-Report Scale(ASRS),Dysregulation of Emotions Rating Scale(DERS),Generalized Anxiety Disorde-7(GAD-7),Patient Health Questionnaire-9(PHQ-9),Self-Compassion Scale(SCS),and Satisfaction with Life Scale(SWLS).Results ① The expert authority coefficient(Cr)of 2 rounds of expert consultation was 0.83,with a questionnaire recovery rate of 100%and 95%,respectively,the Kendall's coordination coefficient was 0.081(P<0.01)and 0.226(P<0.01),and the coefficient of variation was<0.3,indicating the results of the expert consultation were reliable.The constructed GMT program includes 1 first-level indicator,7 second-level indicators,and 20 third-level indicators.② After 7 weeks of GMT intervention,the interaction between the 2 groups and time showed that the experimental group obtained significant improvements than the control group in terms of inattention symptoms(Wald Chi-square=28.35,P<0.001),dysregulation of emotions(Wald Chi-square=23.81,P<0.001),anxiety(Wald Chi-square=22.79,P<0.001),depression(Wald Chi-square=20.52,P<0.001),self-compassion(Wald Chi-square=9.36,P<0.01),and life satisfaction(Wald Chi-square=3.97,P<0.05).Conclusion GMT intervention can significantly improve the core symptoms of college students with inattentive ADHD,reduce anxiety and depression levels,enhance their emotion regulation and self-compassion abilities,and improve their life satisfaction.

Objective To develop and validate a nomogram incorporating postoperative blood pressure and other multifactorial predictors for the risk of delayed cerebral ischemia(DCI)following surgical clipping in patients with aneurysmal subarachnoid hemorrhage(aSAH).Methods This retrospective cohort study consecutively enrolled 272 aSAH patients who underwent aneurysm clipping at the Department of Neurosurgery,Second Affiliated Hospital of Chongqing Medical University from January 2018 to January 2022.Demographic,clinical,and imaging data were collected.Predictors were screened using LASSO regression,and a multifactorial logistic regression-based nomogram was constructed.The dataset was randomly divided into training(n=190)and validation(n=82)sets at a 7∶3 ratio.Model discrimination,calibration,and clinical utility were evaluated using the area under the receiver operating characteristic curve(AUC),calibration curves,and decision curve analysis(DCA),respectively.Results Among 272 enrolled patients,98(36%)developed DCI.Multivariate analysis identified age(OR=0.97;95%CI:0.94～0.99;P=0.036),World Federation of Neurological Surgeons(WFNS)grade(OR=1.36;95%CI:1.02～1.82;P=0.038),Fisher grade(OR=1.44;95%CI:1.01～2.05;P=0.048),and mean arterial pressure on postoperative day 2(OR=1.06;95%CI:1.03～1.09;P<0.001)as independent risk factors for DCI.The model achieved C-indices of 0.739(95%CI:0.668～0.810)in the training set and 0.760(95%CI:0.647～0.872)in the validation set.Calibration curves demonstrated high agreement between predicted and observed probabilities(mean absolute error=0.027),while DCA confirmed significant net clinical benefit at threshold probabilities of 15%～65%.Conclusion We successfully developed a predictive model incorporating age,WFNS grade,Fisher grade,and postoperative day 2 mean arterial pressure,with elevated postoperative blood pressure identified as an early critical indicator for DCI development.

Molecularly imprinted polymers(MIPs)of colchicine(COL)were prepared by precipitation polymerization with COL as template,methacrylic acid(MAA)as monomer,ethylene glycol dimethacrylate(EGDMA)as crosslinking agent,azobisisobutyronitrile(AIBN)as initiator and chloroform as porogen.The basic structure of the prepared MIPs was characterized by scanning electron microscopy(SEM),transmission electron microscopy(TEM)and Fourier transform infrared(FT-IR)spectrometry,and the adsorption properties of MIPs were evaluated by kinetic adsorption,isothermal adsorption and selective adsorption experiments.The results indicated that the prepared MIPs had uniform particles size,with a maximum adsorption capacity of 62.61 mg/g for COL and an imprinting factor of 3.74,and was suitable for sample pretreatment in determination of COL blood concentration,enabling the enrichment and separation of trace or even ultra-trace target components,thereby improving analytical capabilities.

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.

This study investigated the functions and regulatory mechanism of Portulacae Herba and its chemical components on the Toll-like receptor 4(TLR4)/myeloid differentiation primary response 88(MyD88)/nuclear factor kappa B(NF-κB) inflammatory signaling pathway in the colon tissue of mice with dextran sodium sulfate(DSS)-induced ulcerative colitis(UC). A total of 35 mice were randomly divided into groups, including a blank group, a model group, a mesalazine group(0. 5 g·kg~(-1)), and low, medium,and high dose alkaloids from Portulacae Herba groups(9, 18, 36 mg·kg~(-1)), and a combination treatment group, with 5 mice in each group. The blank group was given purified water, while the other groups were continuously given a 3% DSS solution for 7 days to induce the UC model. From day 8 onwards, the treatment group received oral gavage according to the prescribed doses for 14 days. The overall condition, body weight, stool characteristics, and presence of blood in the stool were recorded daily. After the experiment, the disease activity index(DAI) was assessed for each group, and colon length was measured. Histopathological changes in colon tissue were examined using hematoxylin-eosin(HE) staining. The levels of pro-inflammatory cytokines, tumor necrosis factor-α(TNF-α),and interleukin-1β( IL-1β) in serum were measured by enzyme-linked immunosorbent assay( ELISA). The protein and m RNA expression of TLR4, MyD88, and NF-κB in colon tissue were measured using Western blot and quantitative real-time PCR(qPCR).Compared to the blank group, the model group showed a significant decrease in body weight, a notable increase in DAI scores, a significant shortening of colon length, and evident histopathological damage. The levels of inflammatory cytokines TNF-α and IL-1β in the serum were significantly elevated, and the protein and m RNA expression of TLR4, MyD88, and NF-κB in colon tissue were significantly up-regulated. In contrast, the alkaloids from Portulacae Herba treatment groups significantly improved symptoms and reduced body weight loss in mice, decreased DAI scores, alleviated colon shortening, lowered serum levels of TNF-α and IL-1β,significantly down-regulated the expression levels of TLR4, MyD88, and NF-κB proteins and genes in colon tissue, as well as reduced histopathological damage. Therefore, the study suggests that alkaloids from Portulacae Herba can alleviate intestinal inflammation damage in DSS-induced UC mice, with its mechanism involving the TLR4/MyD88/NF-κB signaling pathway.
Animals ; Colitis, Ulcerative/immunology* ; Toll-Like Receptor 4/immunology* ; Myeloid Differentiation Factor 88/metabolism* ; Mice ; NF-kappa B/metabolism* ; Signal Transduction/drug effects* ; Male ; Alkaloids/administration & dosage* ; Drugs, Chinese Herbal/administration & dosage* ; Humans ; Female ; Colon/metabolism* ; Disease Models, Animal

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.

The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

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.