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 investigate the clinicopathological characteristics and molecular variants of chromophobe renal cell carcinoma with small cell components/neuroendocrine-like features (ChRCC-SC/ND-L).Methods:There were 7 cases of ChRCC-SC/ND-L diagnosed by light microscopy and immunohistochemical staining were collected from the Affiliated Hospital of Qingdao University (5 cases) and 971 Hospital of the People′s Liberation Army Navy (2 cases) between January 2010 and December 2023. The clinical data, histological characteristics, and immunohistochemical staining results of the patients were summarized. Among them, 4 cases underwent whole exome sequencing.Results:Among the 7 cases, 5 cases were male and 2 cases were female. The mean age was 53 (43,58)years,with a range of 36 to 76 years. Gross examination showed that the mean maximum tumor diameter was 7.9 (6.0,9.0) cm,with a range of 5.5 to 13.0 cm. The tumors were nodular, well-defined, gray, red or yellow in color with a solid cut surface, except for 1 case with cystic and solid on cut surface. One case showed visible necrosis, and 1 case invaded the renal pelvis and sinus. Microscopically, the tumors had clear boundaries. Typical ChRCC components (5 cases of classical type, 2 cases of eosinophilic type) were found in all cases, accompanied by varying amounts of small cell components (5%-90%). The two components were mixed in 6 cases or directly adjacent to each other in 1 case. The small cell components were arranged in clusters, dense acinar and nest-like structures, beam-like, fence-like, chrysanthemum-shaped clusters, and ribbon-like patterns. Three cases exhibited patchy necrosis. Intravascular tumor thrombus was found in 1 case. Immunohistochemically, EMA was expressed consistently in the small cell and typical ChRCC components (7/7); whilst both CK7 and CD117 were negative in 1 case with typical ChRCC component (6/7). Small cell components in 3 cases were positive for CD56, whereas all 7 cases were negative for CgA, Syn, and INSM1. The Ki-67 proliferation index was less than 1% in both components. Whole exome sequencing revealed that the 4 cases exhibited different genetic aberrations including 1 case with multiple chromosomal deletions, while 2 cases showed amplification of chromosome 12 and deletion of chromosome 11, respectively. The 7 cases were followed up for 25 to 172 months. Except for 1 patient that died with unknown causes 25 months after surgery, the remaining 6 cases were still alive (average 103.8 months, median 101 months).Conclusions:ChRCC-SC/ND-L is a very rare subtype of ChRCC. The small cell component does not represent true neuroendocrine differentiation and might indicate a morphological heterogeneity of the tumor. The presence of typical chromophobe cell carcinoma components is helpful for the diagnosis of ChRCC-SC/ND-L and they do not have consistent molecular characteristics. ChRCC-SC/ND-L has a good prognosis and the small cell components/neuroendocrine-like components might not have a significant impact on the outcome of patients with the tumor.

Objective:To investigate the role and molecular mechanisms of transcription factor EB (TFEB) and its target genes in the treatment of multiple myeloma (MM) with bortezomib.Methods:TFEB target genes were predicted using the GTRD database (http://gtrd.biouml.org/), identifying Ptch1 gene for further study. Expression changes of Ptch1 in RPMI8226 and U266 MM cell lines after bortezomib treatment were assessed by real time fluorogenic quantitative PCR (RT-qPCR) and Western blot. RPMI8226 and U266 cell lines were transfected with siRNA-TFEB, and mRNA and protein levels of key factors (Ptch1, Gli1) in the Ptch1/Hedgehog signaling pathway were measured by RT-qPCR and Western blot. Furthermore, Ptch1 was overexpressed in MM cell lines via lentiviral transduction. Autophagy was evaluated by acridine orange staining, and protein levels of LC3B, Beclin-1, and Lamp-1 were measured by Western blot. Lysosomal quantity changes were assessed by lysosomal fluorescent probes.Results:Bortezomib (6.0×10 -6 mmol/L, 24 h) significantly reduced Ptch1 mRNA and protein levels in both cell lines compared with blank control group (all P<0.05). siRNA-TFEB transfection reversed bortezomib’s inhibition of Hedgehog pathway key factors Ptch1 and Gli. Ptch1 overexpression in bortezomib-treated RPMI8226 and U266 cells significantly reduced the relative expression of autophagy-related proteins LC3B, Beclin-1, and Lamp-1 (all P=0.001). Acridine orange staining showed fewer acidic vesicular organelles within two cell lines (all P=0.001). The relative fluorescence expressions of lysosomal probes reflecting the number of lysosomes were also decreased ( P values of RPMI8226 and U266 cell lines were 0.001 and 0.007, respectively) . Conclusion:The knockdown of TFEB can specifically promote the expression of the Ptch1/Hedgehog signaling pathway, thereby reducing bortezomib-induced autophagy in MM cells and reversing the inhibitory effect of bortezomib on the proliferation of MM cell lines.

OBJECTIVE To study the chemical constituents in the root bark of Schisandra sphenanthera and their cytotoxic activ-ities.METHODS The compounds were isolated and purified by silica,Sephadex LH-20 and semi preparative-HPLC and the chem-ical structures were identified by 1 H-NMR,13 C-NMR and MS data analysis.The cytotoxic activities of the compounds were deter-mined by MTT method.RESULTS Twenty lignans were isolated and deduced as:Matairesinol(1),2-Hydroxy-2-(3′,4′-di-hydroxyphenyl)methyl-3-(3″,4″-dimethoxyphenyl)methyl-gamma-butyrolactone(2),(+)-Nortrachelogenin(3),2-Hydroxy-2-(4′-O-β-D-glucopyranosyl-3′-hydroxyphenyl)methyl-3-(3″,4″-dimethoxyphenyl)methyl-γ-butyrolactone(4),Nortracheloside(5),Burselignan(6),(+)-Cycloolivil(7),5-Methoxy-(+)-isolariciresinol(8),(-)-Isolariciresinol 3α-O-β-D-glucopyranoside(9),(+)-9-O-β-D-Glucopyranosyl lyoniresinol(10),(-)-Secoisolariciresinol(11),Licarin A(12),Cedrusin(13),Mataires-inol 4′-O-β-D-glucopyranoside(14),Pregomisin(15),Meso-dihydroguaiaretic acid(16),7S,8R-Erythro-4,9,9′-trihydroxy-3,3′-dimethoxy-8-O-4′-neolignan-7-O-β-D-glucopyranoside(17),Gomisin M2(18),Gomisin M3(19),Pinoresinol(20).Com-pounds 1-3,12,15,16,18 and 19 showed cytotoxic activity against A549,HCT116 and SW620 cell lines with IC50 values ranging from 1.4 to 22.9 μmol·L-1.CONCLUSION Compounds 1-4,6-12,14,17-19 are isolated from the plant for the first time,com-pounds 1-3,12,15,16,18 and 19 exhibit cytotoxic activities.

Objective To explore the clinical efficacy of Xingqi Tongjiang tablets in treating postprandial distress syndrome(PDS)of liver-stomach disharmony syndrome and its impact on electrogastrogram indicators.Methods A total of 300 patients with PDS were prospectively selected and randomly divided into the control group(treated with mosapride citrate+placebo of Xingqi Tongjiang tablets,n=151)and the observation group(treated with mosapride citrate+Xingqi Tongjiang tablets,n=149)according to the random number table method.The levels of traditional Chinese medicine(TCM)syndrome scores,clinical efficacy,electrogastrography indicators,negative emotion scores,quality of life scores and adverse reactions were compared between the two groups.Results After treatment,the TCM syndrome scores of both groups decreased,and the scores of the observation group were lower than those of the control group(P＜0.05).The total effective rate of the observation group was higher than that of the control group(P＜0.05).After treatment,the percentages of normal gastric slow waves(NGSW)in the overall stomach and gastric antrum before and after meals of both groups increased,and the proportion of slow waves(SR)decreased.Moreover,the increased NGSW and the decreased SR in the observation group were more significant(P＜0.05).The scores of the Hamilton Anxiety Scale(HAMA)and the Hamilton Depression Scale(HAMD)decreased after treatment in both groups,and the scores of the observation group were lower(P＜0.05).The scores of the Nepean Dyspepsia Quality of Life Index(NDLQI)increased after treatment in both groups,and the scores of the observation group were higher(P＜0.05).There were no significant differences in adverse reactions between the two groups.Conclusion Xingqi Tongjiang tablets can effectively improve the electrogastrogram indicators of patients with postprandial distress syndrome of liver-stomach disharmony syndrome,and enhance clinical efficacy.

To investigate the transcriptionally regulatory mechanism of the senp8 promoter in yellow cat-fish(Pelteobagrus fulvidraco);this study used P.fulvidraco as the research subject.Dual-luciferase re-porter assay and electrophoretic mobility shift assay were employed to analyze the functional activity of the promoter;coupled with in vivo experiments.The results indicated that the 2 045 bp senp8 promoter se-quence contained key transcription factor binding sites such as SP1;TATA-Box;CCAAT-Box;SREBP1;PPARα;and PPARγ.The binding sites of SREBP1(-901/-910 bp);PPARα(-1 291/-1 308 bp);and PPARγ(-1 292/-1 306 bp)in the senp8 promoter positively regulate its activity;and oleic acid or palmitic acid promote this binding.Furthermore;high-fat feeding promoted the expression of the senp8 gene and its protein in the liver of P.fulvidraco;oleic acid or palmitic acid treatment significantly en-hanced the activity of the senp8 promoter;and this enhancement could be achieved through the regulatory effects of SREBP1;PPARα;and PPARγ response elements.Additionally;high-fat feeding influenced the mRNA and protein expression levels of genes related to deSUMOylation modification in the liver of P.fulvidraco.This study provides new insights into the relationship between deSUMOylation modification and the regulation of lipid metabolism in the vertebrates.

[Objective] To explore the strategy of blood management in patients with massive transfusion in the frigid plateau region. [Methods] The treatment process of a patient with liver rupture in the frigid plateau region was analyzed, and the blood management strategy of the frigid plateau region was discussed in combination with the difficulties of blood transfusion and literature review. [Results] The preoperative complete blood count (CBC) test results of the patient were as follows: RBC 3.14×10¹²/L, Hb 10⁶ g/L, HCT 30.40%, PLT 115.00×10⁹/L; coagulation function: PT 18.9 s, FiB 1.31 g/L, DD > 6 μg/mL, FDP 25.86 μg/mL; ultrasound examination and imaging manifestations suggested liver contusion and laceration / intraparenchymal hematoma, splenic contusion and laceration, and massive blood accumulation in the abdominal cavity; it was estimated that the patient's blood loss was ≥ 2 000 mL, and massive blood transfusion was required during the operation; red blood cell components were timely transfused during the operation, and the blood component transfusion was guided according to the patient's CBC and coagulation function test results, providing strong support and guarantee for the successful treatment of the patient. The patient recovered well after the operation, and the CBC test results were as follows: RBC 4.32×10¹²/L, Hb 144 g/L, HCT 39.50%, PLT 329.00×10⁹/L; coagulation function: APTT 29.3 s, PT 12.1 s, FiB 2.728 g/L, DD>6 μg/mL, FDP 25.86 μg/mL. The patient was discharged after 20 days, and regular follow-up reexamination showed no abnormal results. [Conclusion] Individualized blood management strategy should comprehensively consider the patient’s clinical symptoms, the degree of hemoglobin decline, dynamic coagulation test results and existing treatment conditions. Efficient and reasonable patient blood management strategies can effectively improve the clinical outcomes of massive transfusion patients in the frigid plateau region.