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.

Background: Aspiration pneumonia (AP), which can be caused by gastric content inhalation into the lower airways, causes acute lung injury (ALI) through complex mechanisms, including inflammation, oxidative stress, and apoptosis. Here, we evaluated the efficacy of exosomes derived from human placental mesenchymal stem cells (hpMSCs) in mitigating ALI in a murine model of AP. We also investigated the role of hsa-let-7i-5p, the most abundant miRNA in hpMSC-derived exosomes, in this respect. Methods: Adult male C57BL/6 mouse AP models were administered hpMSC-derived exosomes (APExo group) or phosphate-buffered saline (AP group) intra-tracheally. After 48 h, the mice were euthanized and evaluated. The effects of hsa-let-7i-5p were assessed by specific inhibition or overexpression. Results: Compared with the APExo group, the AP group exhibited significantly greater ALI, as evidenced by histological damage, increased lung injury scores, impaired lung function, increased leukocyte infiltration, and elevated tissue edema (all P < 0.05). The untreated AP group also showed more inflammation, characterized by nuclear factor-κB upregulation, macrophage M1 polarization, and cytokine level elevation (tumor necrosis factor-α, interleukin-1β, and interleukin-6), as well as increased oxidation and activation of the apoptosis pathway (all P < 0.05). Notably, the therapeutic effects of hpMSC-derived exosomes were compromised by specific inhibition of hsa-let-7i-5p. Furthermore, engineered exosomes derived from genetically modified RAW264.7 overexpressing hsa-let-7i-5p demonstrated therapeutic effects against AP similar to those obtained with hpMSC-derived exosomes. Conclusions In a murine AP model, intra-tracheal administration of hpMSC-derived exosomes has ALI-mitigating effects, involving inflammation, oxidation, and apoptosis modulation, with hsa-let-7i-5p playing a pivotal mediating role.

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.

Purpose: The study aimed to comprehensively analyze testosterone and precursor concentrations in the testicular interstitial fluid (TIF) of men with azoospermia, exploring their significance in the testicular microenvironment and their correlation with testicular sperm retrieval outcomes. Materials and Methods: We analyzed 37 TIF samples, including 5 from men with obstructive azoospermia (OA) and 32 from men with non-obstructive azoospermia (NOA). Liquid chromatography with tandem mass spectrometry quantified testosterone and precursor levels. Comparative assessments of the outcomes of testicular sperm retrieval were performed between the OA and NOA groups as well as among men with NOA. Results: Men with NOA who had not undergone hormone treatment exhibited significantly higher intratesticular concentrations of testosterone (median 1,528.1 vs. 207.5 ng/mL), androstenedione (median 10.6 vs. 1.9 ng/mL), and 17-OH progesterone (median 13.0 vs. 1.8 ng/mL) than men diagnosed with OA. Notably, in the subgroup of patients with NOA subjected to medical treatment, men with successful sperm retrieval had significantly reduced levels of androstenedione (median androstenedione 5.7 vs. 18.5 ng/mL, p=0.004). Upon a more detailed analysis of these men who underwent hormone manipulation treatment, the testosterone/androstenedione ratio (indicative of HSD17B3 enzyme activity) was markedly increased in men with successful sperm retrieval (median: 365.8 vs. 165.0, p=0.008) compared with individuals with NOA who had unsuccessful sperm recovery. Furthermore, within the subset of men with NOA who did not undergo medical treatment before microdissection testicular sperm extraction but achieved successful sperm retrieval, the ratio of 17-OH progesterone/progesterone (indicative of CYP17A1 activity) was substantially higher. Conclusions The study suggests distinct testosterone biosynthesis pathways in men with compromised spermatogenesis and those with normal spermatogenesis. Among NOA men with successful retrieval after hormone optimization therapy, there was decreased androstenedione and increased HSD17B3 enzyme activity. These findings have diagnostic and therapeutic implications for the future.

Purpose: The study aimed to comprehensively analyze testosterone and precursor concentrations in the testicular interstitial fluid (TIF) of men with azoospermia, exploring their significance in the testicular microenvironment and their correlation with testicular sperm retrieval outcomes. Materials and Methods: We analyzed 37 TIF samples, including 5 from men with obstructive azoospermia (OA) and 32 from men with non-obstructive azoospermia (NOA). Liquid chromatography with tandem mass spectrometry quantified testosterone and precursor levels. Comparative assessments of the outcomes of testicular sperm retrieval were performed between the OA and NOA groups as well as among men with NOA. Results: Men with NOA who had not undergone hormone treatment exhibited significantly higher intratesticular concentrations of testosterone (median 1,528.1 vs. 207.5 ng/mL), androstenedione (median 10.6 vs. 1.9 ng/mL), and 17-OH progesterone (median 13.0 vs. 1.8 ng/mL) than men diagnosed with OA. Notably, in the subgroup of patients with NOA subjected to medical treatment, men with successful sperm retrieval had significantly reduced levels of androstenedione (median androstenedione 5.7 vs. 18.5 ng/mL, p=0.004). Upon a more detailed analysis of these men who underwent hormone manipulation treatment, the testosterone/androstenedione ratio (indicative of HSD17B3 enzyme activity) was markedly increased in men with successful sperm retrieval (median: 365.8 vs. 165.0, p=0.008) compared with individuals with NOA who had unsuccessful sperm recovery. Furthermore, within the subset of men with NOA who did not undergo medical treatment before microdissection testicular sperm extraction but achieved successful sperm retrieval, the ratio of 17-OH progesterone/progesterone (indicative of CYP17A1 activity) was substantially higher. Conclusions The study suggests distinct testosterone biosynthesis pathways in men with compromised spermatogenesis and those with normal spermatogenesis. Among NOA men with successful retrieval after hormone optimization therapy, there was decreased androstenedione and increased HSD17B3 enzyme activity. These findings have diagnostic and therapeutic implications for the future.

OBJECTIVE: To investigate the mechanism of electroacupuncture (EA) in treating diabetic gastroparesis (DGP) by inhibiting the activation of Nod-like receptor family pyrin domain-containing protein 3 (NLRP3) inflammasome and pyroptosis mediated via NLRP3/cysteinyl aspartate specific proteinase-1 (caspase-1)/gasdermin D (GSDMD) signaling pathway. METHODS: Forty Sprague-Dawley rats were randomly divided into 4 groups including the control, DGP model, EA, and MCC950 groups. The DGP model was established by a one-time high-dose intraperitoneal injection of 2% streptozotocin and a high-glucose and high-fat diet for 8 weeks. EA intervention was conducted at Zusanli (ST 36), Liangmen (ST 21) and Sanyinjiao (SP 6) with sparse-dense wave for 15 min, and was administered for 3 courses of 5 days. After intervention, the blood glucose, urine glucose, gastric emptying, and intestinal propulsive rate were observed. Besides, HE staining was used to observe histopathological changes in gastric antrum tissues, and TUNEL staining was utilized to detect DNA damage. Protein expression levels of NLRP3, apoptosis-associated speck-like protein containing CARD (ASC), pro-caspase-1, caspase-1 and GSDMD were measured by Western blot. Immunofluorescence staining was employed to assess the activity of GSDMD-N. Lactate dehydrogenase (LDH) levels were detected by using a biochemical kit. RESULTS: DGP rats showed persistent hyperglycemia and a significant decrease in gastrointestinal motility (P<0.05 or P<0.01), accompanied by pathological damage in their gastric antrum tissues. Cellular DNA was obviously damaged, and the expressions of NLRP3, ASC, pro-caspase-1, caspase-1 and GSDMD proteins were significantly elevated, along with enhanced fluorescence signals of GSDMD-N and increased LDH release (P<0.01). EA mitigated hyperglycemia, improved gastrointestinal motility in DGP rats and alleviated their pathological injury (P<0.05). Furthermore, EA reduced cellular DNA damage, lowered the protein levels of NLRP3, ASC, pro-caspase-1, caspase-1 and GSDMD, suppressed GSDMD-N activity, and decreased LDH release (P<0.05 or P<0.01), demonstrating effects comparable to MCC950. CONCLUSION EA promotes gastrointestinal motility and repairs the pathological damage in DGP rats, and its mechanism may be related to the inhibition of NLRP3 inflammasome and pyroptosis mediated by NLRP3/caspase-1/GSDMD pathway.
Animals ; Electroacupuncture ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Pyroptosis ; Rats, Sprague-Dawley ; Caspase 1/metabolism* ; Gastroparesis/physiopathology* ; Signal Transduction ; Male ; Diabetes Mellitus, Experimental/physiopathology* ; Phosphate-Binding Proteins/metabolism* ; Gastrointestinal Motility ; Rats ; Intracellular Signaling Peptides and Proteins/metabolism* ; Diabetes Complications/physiopathology* ; Gasdermins

OBJECTIVE: To investigate the Wnt signaling pathway and miRNAs mechanism of extracts of Plastrum Testudinis (PT) in the treatment of osteoporosis (OP). METHODS: Thirty female Sprague Dawley rats were randomly divided into 5 groups by random number table method, including sham group, ovariectomized group (OVX), ovariectomized groups treated with high-, medium-, and low-dose PT (160, 80, 40 mg/kg per day, respectively), with 6 rats in each group. Except for the sham group, the other rats underwent bilateral ovariectomy to simulate OP and received PT by oral gavage for 10 consecutive weeks. After treatment, bone mineral density was measured by dual-energy X-ray absorptiometry; bone microstructure was analyzed by micro-computed tomography and hematoxylin and eosin staining; and the expressions of osteogenic differentiation-related factors were detected by immunochemistry, Western blot, and quantitative polymerase chain reaction. In addition, Dickkopf-1 (Dkk-1) was used to inhibit the Wnt signaling pathway in bone marrow mesenchymal stem cells (BMSCs) and miRNA overexpression was used to evaluate the effect of miR-214 on the osteogenic differentiation of BMSCs. Subsequently, PT extract was used to rescue the effects of Dkk-1 and miR-214, and its impacts on the osteogenic differentiation-related factors of BMSCs were evaluated. RESULTS: PT-M and PT-L significantly reduced the weight gain in OVX rats (P<0.05). PT also regulated the bone mass and bone microarchitecture of the femur in OVX rats, and increased the expressions of bone formation-related factors including alkaline phosphatase, bone morphogenetic protein type 2, collagen type I alpha 1, and runt-related transcription factor 2 when compared with the OVX group (P<0.05 or P<0.01). Meanwhile, different doses of PT significantly rescued the inhibition of Wnt signaling pathway-related factors in OVX rats, and increased the mRNA or protein expressions of Wnt3a, β-catenin, glycogen synthase kinase-3β, and low-density lipoprotein receptor-related protein 5 (P<0.05 or P<0.01). PT stimulated the osteogenic differentiation of BMSCs inhibited by Dkk-1 and activated the Wnt signaling pathway. In addition, the expression of miR-214 was decreased in OVX rats (P<0.01), and it was negatively correlated with the osteogenic differentiation of BMSCs (P<0.01). MiR-214 mimic inhibited Wnt signaling pathway in BMSCs (P<0.05 or P<0.01). Conversely, PT effectively counteracted the effect of miR-214 mimic, thereby activating the Wnt signaling pathway and stimulating osteogenic differentiation in BMSCs (P<0.05 or P<0.01). CONCLUSION PT stimulates bone formation in OVX rats through β-catenin-mediated Wnt signaling pathway, which may be related to inhibiting miR-214 in BMSCs.
Animals ; MicroRNAs/genetics* ; Female ; Rats, Sprague-Dawley ; Wnt Signaling Pathway/genetics* ; Osteogenesis/genetics* ; Mesenchymal Stem Cells/cytology* ; Cell Differentiation/drug effects* ; Bone Density/drug effects* ; Ovariectomy ; Osteoporosis/drug therapy* ; beta Catenin/metabolism* ; Rats ; Intercellular Signaling Peptides and Proteins/metabolism* ; Drugs, Chinese Herbal/pharmacology*

Cervical cancer is one of the most common gynecological malignant tumors in China. Given their lack of obviously early symptoms, more than half of patients with cervical cancer are diagnosed in the middle and late stages of this malignancy, resulting in poor prognosis. Finding new therapeutic targets is the current research direction. Metabolomics, as a new omics technology, is expected to provide new targets for tumor precision diagnosis and treatment through the analysis of the changes and potential mechanisms of metabolites in tumor occurrence and development by chromatography, mass spectrometry, and other technologies. Herein, we review the research methods of metabolomics; metabolic characteristics of cervical cancer; and progress of the research on metabolomics in cervical cancer diagnosis, curative effect prediction, and prognosis evaluation to provide new ideas for the precise diagnosis and treatment of cervical cancer.

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.