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.

ObjectiveTreadmill exercise intervention was evaluated for its effects on addictive behavior, learning and memory, and hippocampal neuroinflammation in methamphetamine (MA)-addicted mice, and transcriptomic screening was performed to identify candidate molecules involved in exercise-mediated attenuation of MA-related neuronal injury. MethodsTwo-month-old male C57BL/6 mice were randomly assigned to four groups: control (C), exercise control (CE), methamphetamine (M), and methamphetamine plus exercise (ME). Mice in the M and ME groups received intraperitoneal injections of MA (1 mg/kg) once daily for 7 consecutive days to establish the addiction model, whereas mice in the C and CE groups received equal volumes of saline. Subsequently, mice in the CE and ME groups underwent treadmill exercise at 12 m/min for 60 min/d for 2 weeks. Conditioned place preference (CPP), the Y-maze, and the Morris water maze (MWM) were used to assess addictive behavior, spatial recognition memory, and spatial learning and memory, respectively. Hippocampal neuronal ultrastructure was examined by transmission electron microscopy (TEM), and microglia-related responses were evaluated by immunofluorescence staining for ionized calcium-binding adapter molecule 1 (Iba-1) in the hippocampal CA1 region. Previously generated and publicly available RNA-seq data from brain tissue of the C, M, and ME groups were reanalyzed, with three biological samples per group. Differentially expressed genes (DEGs) were re-screened using the criteria of P<0.05 and fold change>1.5 or <0.67, followed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. NLR family CARD domain containing 5 (Nlrc5) was further validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and Western blot was used to determine the protein expression of NLRC5, nuclear factor kappa B (NF‑κB), interleukin-1β (IL-1β), postsynaptic density-95 (PSD-95), and synaptophysin. ResultsTreadmill exercise significantly attenuated MA-induced conditioned place preference and improved learning and memory performance in both the Y-maze and MWM tests. Exercise increased exploration time and distance in the novel arm of the Y-maze, shortened escape latency, and increased the number of platform crossings in the MWM. Average swimming speed during the probe trial did not differ significantly among groups, suggesting that the MWM differences were not attributable to obvious differences in swimming performance. TEM revealed that MA exposure induced mitochondrial swelling, disrupted cristae, reduced organelle abundance, and nuclear envelope abnormalities in hippocampal neurons, whereas these ultrastructural changes were alleviated after exercise. Immunofluorescence staining showed that MA increased Iba-1 fluorescence intensity in the hippocampal CA1 region, while treadmill exercise significantly reduced this change. Transcriptomic analysis identified 160 DEGs associated with MA exposure and 58 DEGs associated with exercise intervention, with 9 overlapping DEGs. KEGG enrichment analysis showed that MA-related DEGs were enriched in the NOD-like receptor signaling pathway. Among the overlapping DEGs, Nlrc5, which was downregulated after MA exposure and restored following exercise intervention, was selected as a candidate gene for further validation. RT-qPCR confirmed that treadmill exercise reversed the MA-induced decrease in Nlrc5 mRNA expression. Western blot analysis further showed that treadmill exercise increased NLRC5, PSD-95, and synaptophysin protein expression while reducing NF-κB and IL-1β expression. ConclusionTwo weeks of treadmill exercise attenuated MA-induced conditioned place preference and ameliorated learning and memory impairment, alleviated hippocampal neuroinflammation and neuronal ultrastructural damage, and restored the expression of synapse-related proteins. Transcriptomic screening combined with molecular validation demonstrated that exercise intervention was accompanied by restoration of NLRC5 expression, reduced NF‑κB and IL-1β expression, and increased PSD-95 and synaptophysin expression. These findings suggest that NLRC5 may represent a candidate molecule involved in exercise-mediated amelioration of MA-related neuronal injury. However, the present results mainly demonstrate coordinated changes in NLRC5 expression, inflammatory indicators, and cognitive outcomes rather than a direct causal relationship, and the specific role of NLRC5 requires further functional validation.

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.

Based on ultra-high performance liquid chromatography-quadrupole-Exactive Orbitrap mass spectrometry(UPLC-Q-Exactive Orbitrap-MS) technology and network pharmacology, this study explored the pharmacodynamic substances and potential mechanisms of Huazhuo Sanjie Chubi Decoction in the treatment of gouty arthritis(GA). UPLC-Q-Exactive Orbitrap-MS technology was used to identify the components in Huazhuo Sanjie Chubi Decoction, and the qualitative analysis of its active ingredients was carried out, with a total of 184 active ingredients identified. A total of 897 active ingredient targets were screened through the PharmMapper database, and 491 GA-related disease targets were obtained from the OMIM, GeneCards, CTD databases. After Venn analysis, 60 intersecting targets were obtained. The component target-GA target network was constructed through the Cytoscape platform, and the STRING database was used to construct a protein-protein interaction network, with 16 core targets screened. The core targets were subjected to Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analyses, and the component-target-pathway network was constructed. It was found that the main active ingredients of the formula for the treatment of GA were phenols, flavonoids, alkaloids, and terpenoids, and the key targets were SRC, MMP3, MMP9, REN, ALB, IGF1R, PPARG, MAPK1, HPRT1, and CASP1. Through GO analysis, it was found that the treatment of GA mainly involved biological processes such as lipid response, bacterial response, and biostimulus response. KEGG analysis showed that the pathways related to the treatment of GA included lipids and atherosclerosis, neutrophil extracellular traps(NETs), IL-17, and so on. In summary, phenols, flavonoids, alkaloids, and terpenoids may be the core pharmacodynamic substances of Huazhuo Sanjie Chubi Decoction in the treatment of GA, and the pharmacodynamic mechanism may be related to SRC, MMP3, MMP9, and other targets, as well as lipids and atherosclerosis, NETs, IL-17, and other pathways.
Drugs, Chinese Herbal/therapeutic use* ; Network Pharmacology ; Arthritis, Gouty/metabolism* ; Chromatography, High Pressure Liquid/methods* ; Humans ; Mass Spectrometry/methods* ; Protein Interaction Maps/drug effects*

In order to investigate whether the effect of Yuxuebi Tablets on the peripheral and central inflammation resolution of mice with inflammatory pain is related to their regulation of G protein-coupled receptor 37(GPR37), an inflammatory pain model was established by injecting complete Freund's adjuvant(CFA) into the paws of mice, with a sham-operated group receiving a similar volume of normal saline. The mice were assigned randomly to the sham-operated group, model group, ibuprofen group(91 mg·kg~(-1)), and low-, medium-, and high-dose groups of Yuxuebi Tablets(60, 120, and 240 mg·kg~(-1)). The drug was administered orally from days 1 to 19 after modeling. Von Frey method and the hot plate test were used to detect mechanical pain thresholds and heat hyperalgesia. The levels of interleukin-10(IL-10) and transforming growth factor-beta(TGF-β) in the spinal cord were quantified using enzyme-linked immunosorbent assay(ELISA), and the mRNA and protein expression of GPR37 in the spinal cord was measured by real-time quantitative reverse transcription PCR(qRT-PCR) and Western blot. Additionally, immunofluorescence was used to detect the expression of macrosialin antigen(CD68), mannose receptor(MRC1 or CD206), and GPR37 in dorsal root ganglia, as well as the expression of calcium-binding adapter molecule 1(IBA1), CD206, and GPR37 in the dorsal horn of the spinal cord. The results showed that compared with those of the sham-operated group, the mechanical pain thresholds and hot withdrawal latency of the model group significantly declined, and the expression of CD68 in the dorsal root ganglia and the expression of IBA1 in the dorsal horn of the spinal cord significantly increased. The expression of CD206 and GPR37 significantly decreased in the dorsal root ganglion and dorsal horn of the spinal cord, and IL-10 and TGF-β levels in the spinal cord were significantly decreased. Compared with those of the model group, the mechanical pain thresholds and hot withdrawal latency of the high-dose group of Yuxuebi Tablets significantly increased, and the expression of CD68 in the dorsal root ganglion and IBA1 in the dorsal horn of the spinal cord significantly decreased. The expression of CD206 and GPR37 in the dorsal root ganglion and dorsal horn of the spinal cord significantly increased, as well as IL-10 and TGF-β levels in the spinal cord. These findings indicated that Yuxuebi Tablets may reduce macrophage(microglial) infiltration and foster M2 macrophage polarization by enhancing GPR37 expression in the dorsal root ganglia and dorsal horn of the spinal cord of CFA-induced mice, so as to improve IL-10 and TGF-β levels, promote resolution of both peripheral and central inflammation, and play analgesic effects.
Inflammation/genetics* ; Pain/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Mice ; Freund's Adjuvant/pharmacology* ; Ibuprofen ; Pain Threshold/drug effects* ; Hyperalgesia/genetics* ; Ganglia, Spinal ; Interleukin-10/genetics* ; Transforming Growth Factor beta/genetics* ; Reverse Transcriptase Polymerase Chain Reaction ; Tablets ; Receptors, G-Protein-Coupled

OBJECTIVE: To explore whether hydrocortisone can improve the prognosis of patients with severe community-acquired pneumonia (sCAP) by Meta-analysis. METHODS: Randomized controlled trial (RCT) on hydrocortisone in the treatment of sCAP were extracted from the database including PubMed, Cochrane library, Web of Science, and Embase, and the search time was up to April 29, 2023. The patients in the standard treatment group received standard treatment such as antibiotics and supportive care, while those in the hydrocortisone group received hydrocortisone treatment on the basis of standard treatment. Meta-analysis was used to compare the mortality, duration of mechanical ventilation, mechanical ventilation rate and incidence of adverse reactions (hyperglycemia, gastrointestinal bleeding, secondary infection) between the two groups. The risk of literature bias was assessed. The studies that might have publication bias were corrected by the subtraction and complementation method. At the same time, trial sequential analysis (TSA) was conducted. RESULTS: A total of 5 RCTs involving 1 031 patients were finally enrolled, including 494 patients in the standard treatment group and 537 patients in the hydrocortisone group. Among the 5 studies, the research site of 2 studies was in the mixed ward. Considering the inclusion characteristics of the study population, there was doubt whether its research object was sCAP patients, which might have a certain impact on the results and introduce potential bias. Meta-analysis showed that the mortality in the hydrocortisone group was significantly lower than that in the standard treatment group [6.0% vs. 14.0%; odds ratio (OR) = 0.38, 95% confidence interval (95%CI) was 0.25-0.59, P < 0.01; I² = 9%]. The studies that were asymmetric were corrected by the reduction and supplementation method. Even after filling the missing studies, hydrocortisone could still reduce the death risk of the patient (OR = 0.49, 95%CI was 0.32-0.73, P < 0.01; I² = 31%). TSA showed that the average mortality of the standard treatment group was about 14.0%, and that of the hydrocortisone group was about 6.0%, with a relative risk reduction (RRR) = 57%. The calculated sample size was 699 cases, and the actual sample size was 1 031 cases. The actual sample size exceeded the required sample size, and the Z-curve crossed the O'Brien-Fleming boundary and the curve corresponding to P = 0.05, it meant that hydrocortisone could effectively reduce the mortality of sCAP. Compared with the standard treatment group, no statistical difference in the duration of mechanical ventilation was found in the hydrocortisone group [mean difference (MD) = -3.26, 95%CI was -6.72-0.21, P = 0.07; I² = 0%], but the 8-day mechanical ventilation rate was significantly lowered (19.5% vs. 55.4%; OR = 0.24, 95%CI was 0.12-0.45, P < 0.01; I² = 0%), and also no significantly difference was found in the incidence of hyperglycemia (54.3% vs. 44.6%, OR = 1.26, 95%CI was 0.56-2.84, P = 0.58; I² = 61%), gastrointestinal bleeding (2.5% vs. 3.6%; OR = 0.70, 95%CI was 0.34-1.46, P = 0.34; I² = 0%) and secondary infection (9.2% vs. 11.5%; OR = 0.46, 95%CI was 0.06-3.35, P = 0.45; I² = 53%). CONCLUSION Hydrocortisone can reduce the mortality rate of sCAP patients, decrease their need for mechanical ventilation, and does not increase the risk of hyperglycemia, gastrointestinal bleeding, or secondary infections.
Humans ; Hydrocortisone/therapeutic use* ; Community-Acquired Infections/drug therapy* ; Pneumonia/drug therapy* ; Randomized Controlled Trials as Topic ; Respiration, Artificial ; Community-Acquired Pneumonia

OBJECTIVE: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

Listeria brainstem encephalitis with myelitis is extremely rare in clinical practice.Since the clinical manifestations are non-specific,MRI is helpful for diagnosis.Positive cerebrospinal fluid culture is considered the gold standard for diagnosis.This article reports a case of an immunocompetent individual with listeria brainstem encephalitis with myelitis,aiming to enhance the awareness of this condition.
Humans ; Brain Stem/pathology* ; Diagnostic Errors ; Encephalitis/complications* ; Encephalomyelitis, Acute Disseminated/diagnosis* ; Listeriosis/complications* ; Myelitis/complications*

Objective To evaluate the diagnostic value of transrectal contrast-enhanced ultrasound (CEUS) for rectal cancer with intestinal stenosis caused by tumors. Methods Forty-nine patients with rectal cancer underwent transrectal CEUS and magnetic resonance imaging (MRI) before surgery.Intraoperative tumor localization and postoperative pathological results were taken as the gold standard for diagnosis.The differences in T stage,localization,and tumor length of rectal cancer were compared between the two methods. Results The total accuracy rates of transrectal CEUS and MRI in diagnosing T stage were 75.5% (36/49) and 67.3% (33/49),which had no significant difference (χ²=0.8,P=0.371).The total accuracy rates of transrectal CEUS and MRI in judging tumor localization were 79.5% (39/49) and 77.5% (38/49),which had no significant difference (χ²=0.061,P=0.806).The measurement results of tumor length in pathological examination had no significant difference from the transrectal CEUS results (t=1.42,P=0.162) but a significant difference from the MRI results (t=3.38,P=0.001).Furthermore,transrectal CEUS detected 8 (16.3%) cases of colonic polyps among the 49 patients,while MRI did not detect colon lesions. Conclusions Transrectal CEUS has good consistency with MRI in T staging and localization judgement of rectal cancer with intestinal stenosis,and this method can more accurately evaluate the tumor length and simultaneously evaluate whether there is a lesion in the entire colon at the proximal end of stenosis.It can be used as a supplementary examination before rectal cancer treatment in clinical practice.
Humans ; Rectal Neoplasms/complications* ; Male ; Middle Aged ; Female ; Aged ; Contrast Media ; Ultrasonography ; Adult ; Magnetic Resonance Imaging ; Constriction, Pathologic/diagnostic imaging* ; Aged, 80 and over ; Intestinal Obstruction/etiology*