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ObjectivesTo evaluate the diagnostic performance of attenuation imaging (ATI) in the detection of hepatic steatosis compared with a histopathology gold standard.MethodsWe prospectively enrolled 108 consecutive patients (35 males; median age, 54.0 years) who underwent percutaneous liver biopsy for evaluation of diffuse liver disease between January 2018 and November 2018 in a tertiary academic center. Grayscale ultrasound examination with ATI was performed just before biopsy, and an attenuation coefficient (AC) was obtained from each patient. The degree of hepatic steatosis, fibrosis stage, and necroinflammatory activity were assessed on histopathologic examination. The significant factor associated with the AC was found by a linear regression analysis, and the diagnostic performance of the AC for the classification into each hepatic steatosis stage was evaluated by receiver operating characteristic (ROC) analysis.ResultsThe distribution of hepatic steatosis grade on histopathology was 53/11/22/16/6 for none/mild (< 10%)/mild (≥ 10%)/moderate/severe steatosis, respectively. The area under the ROC curve, sensitivity, specificity, and optimal cutoff AC value for detection of hepatic steatosis ranged from 0.843–0.926, 74.5–100.0%, 77.4–82.8%, and 0.635–0.745, respectively. Multivariate analysis revealed that the degree of steatosis was the only significant determinant factor for the AC.ConclusionsThe AC from ATI provided good diagnostic performance in detecting the varying degrees of hepatic steatosis. The degree of steatosis was the only significant factor affecting the AC, whereas fibrosis and inflammation were not.Key Points• Attenuation imaging (ATI) is based on two-dimensional grayscale ultrasound images that can incorporate into routine ultrasound examinations with less than 2 min of acquisition time.• ATI provided good diagnostic performance in detecting the varying degrees of hepatic steatosis with an area under the ROC curves ranging from 0.843 to 0.926, and there was no technical failure in this study indicating high applicability of this technique.• The degree of hepatic steatosis was the only significant factor affecting the result of ATI examination.

There has been a tremendous amount of research in the past decade to optimize the mechanical properties and degradation behavior of the biodegradable Mg alloy for orthopedic implant. Despite the feasibility of degrading implant, the lack of fundamental understanding about biocompatibility and underlying bone formation mechanism is currently limiting the use in clinical applications. Herein, we report the result of long-term clinical study and systematic investigation of bone formation mechanism of the biodegradable Mg-5wt%Ca-1wt%Zn alloy implant through simultaneous observation of changes in element composition and crystallinity within degrading interface at hierarchical levels. Controlled degradation of Mg-5wt%Ca-1wt%Zn alloy results in the formation of biomimicking calcification matrix at the degrading interface to initiate the bone formation process. This process facilitates early bone healing and allows the complete replacement of biodegradable Mg implant by the new bone within 1 y of implantation, as demonstrated in 53 cases of successful long-term clinical study.

A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF), is proposed. A popular method for removing impulsive noise is a median filter whereas the weighted median filter and center weighted median filter were also investigated. ADWMF is based on weighted median filter. In ADWMF, instead of fixed weights, weightages of the filter are dynamically assigned with the results of noise detection. A simple and efficient noise detection method is also used to detect noise candidates and dynamically assign zero or small weights to the noise candidates in the window. This paper proposes an adaptive method which increases the window size according to the amounts of impulsive noise. Simulation results show that the AMWMF works better for both images with low and high density of impulsive noise than existing methods work.

ObjectivesTo validate new diagnostic criteria for hepatocellular carcinoma (HCC) on gadoxetic acid-enhanced MR imaging (Gd-EOB-MRI) using hypointensity on the hepatobiliary phase (HBP) as an alternative to washout in combination with ancillary features.MethodsThis retrospective study included 288 patients at high risk for HCC with 387 nodules (HCCs, n=292; non-HCCs, n=95) showing arterial phase hyper-enhancement (APHE) ≥1 cm on Gd-EOB-MRI. Imaging diagnoses of HCCs were made using different criteria: APHE plus hypointensity on the portal venous phase (PVP) (criterion 1), APHE plus hypointensity on the PVP and/or transitional phase (TP) (criterion 2), APHE plus hypointensity on the PVP and/or TP and/or HBP (criterion 3), and criterion 3 plus non-LR-1/2/M according to the Liver Imaging Reporting and Data System (LI-RADS) v2017 considering ancillary features (criterion 4). Sensitivities and specificities of those criteria were compared using McNemar’s test.ResultsAmong diagnostic criteria for HCCs, criteria 3 and 4 showed significantly higher sensitivities (93.8% and 92.5%, respectively) than criteria 1 and 2 (70.9% and 86.6%, respectively) (p values <0.001). The specificity of criterion 4 (87.4%) was shown to be significantly higher than that of criterion 3 (48.4%, p<0.001), albeit comparable to criterion 2 (86.3%, p>0.999) and significantly lower than criterion 1 (97.9%, p=0.002).ConclusionsIn the non-invasive diagnosis of HCCs on Gd-EOB-MRI, HBP hypointensity may be used as an alternative to washout enabling a highly sensitive diagnosis with little loss in specificity if it is used after excluding nodules considered to be benignities or non-HCC malignancies based on characteristic imaging features.Key Points• Gd-EOB-MRI enhancement and ancillary features can be used to diagnose HCC.• Exclusion of LR-1/2/M improves specificity when HBP hypointensity is used.

Colorectal cancer (CRC) is the most common type cancers in the world. CRC occurs sporadically in the majority of cases, indicating the predominant cause of the disease are environmental factors. Diet-induced changes in gut-microbiome are recently supposed to contribute on epidemics of CRC. This study was aimed to investigate the association of metagenomics and metabolomics in gut extracellular vesicles (EVs) of CRC and healthy subjects. A total of 40 healthy volunteers and 32 patients with CRC were enrolled in this study. Metagenomic profiling by sequencing 16 S rDNA was performed for assessing microbial codiversity. We explored the small molecule metabolites using gas chromatography-time-of-flight mass spectrometry. In total, stool EVs were prepared from 40 healthy volunteers and 32 patients with CRC. Metagenomic profiling demonstrated that bacterial phyla, particularly of Firmicutes and Proteobacteria , were significantly altered in patients with colorectal cancer. Through metabolomics profiling, we determined seven amino acids, four carboxylic acids, and four fatty acids; including short-chain to long chain fatty acids that altered in the disease group. Binary logistic regression was further tested to evaluate the diagnostic performance. In summary, the present findings suggest that gut flora dysbiosis may result in alternation of amino acid metabolism, which may be correlated with the pathogenesis of CRC.

ObjectivesTo investigate the performance of the Liver Imaging Reporting and Data System (LI-RADS) v2017 for combined hepatocellular cholangiocarcinoma (cHCC-CCA) in the differential diagnosis from hepatocellular carcinoma (HCC) and prediction of prognosis on gadoxetic acid-enhanced MRI (Gd-EOB-MRI).MethodsPatients at high risk of HCC with pathologically confirmed cHCC-CCAs (n = 70) and a matched control of HCCs (n = 70) who had undergone Gd-EOB-MRI were included. LI-RADS category was assigned for each lesion by two radiologists. Imaging features and surgical outcomes were compared between cHCC-CCAs of LR-M and LR-5/4 using the χ2 test or Fisher’s exact test. Recurrence-free survival (RFS) was estimated using Kaplan–Meier survival curves and compared using the log-rank test.ResultscHCC-CCAs and HCCs were categorised as LR-M, LR-5/4 and LR-TIV in 61.4% (43/70), 37.1% (26/70) and 1.4% (1/70) and 10.0% (7/70), 88.6% (62/70) and 1.4% (1/70), respectively. cHCC-CCAs of LR-5/4, in comparison to LR-M, showed significantly higher frequencies of major HCC features: arterial hyperenhancement (96.2% (25/26) vs. 58.1% (25/43), p = 0.001), washout appearance (80.8% (21/26) vs. 48.8% (21/43), p = 0.011) and enhancing capsule (34.6% (9/26) vs. 11.6% (5/43), p = 0.031). After curative surgery, patients with cHCC-CCAs of LR-M showed a higher early recurrence rate (≤ 6 months) than did those with LR-5/4 (27.8% (10/36) vs. 4.8% (1/21), p = 0.041), whereas no significant difference was observed in RFS (log-rank p = 0.084).ConclusionsBy using LI-RADS on Gd-EOB-MRI, a substantial proportion of cHCC-CCAs can be categorised as non-LR-M. In addition, cHCC-CCAs mimicking HCCs on imaging (LR-5/4) may indicate better surgical outcomes with regard to early recurrence than those of LR-M.Key Points• cHCC-CCAs can be categorised as either LR-M or non-LR-M on Gd-EOB-MRI.• cHCC-CCAs of LR-5/4 frequently demonstrate major HCC imaging features.• LI-RADS categorisation may provide prognostic information after surgery in cHCC-CCAs.

The accurate and timely prediction of Napa cabbage fresh weight is essential for optimizing harvest timing, crop management, and supply chain logistics, which ultimately contributes to food security and price stabilization. Traditional manual sampling methods are labor-intensive and lack precision. This study introduces an artificial intelligence (AI)-powered model that utilizes unmanned aerial systems (UAS)-based multi-sensor data to predict Napa cabbage fresh weight. The model was developed using high-resolution RGB, multispectral (MSP), and thermal infrared (TIR) imagery collected throughout the 2020 growing season. The imagery was used to extract various vegetation indices, crop features (vegetation fraction, crop height model), and a water stress indicator (CWSI). The deep neural network (DNN) model consistently outperformed support vector machine (SVM) and random forest (RF) models, achieving the highest accuracy (R2 = 0.82, RMSE = 0.47 kg) during the mid-to-late rosette growth stage (35–42 days after planting, DAP). The model’s accuracy improved with cabbage maturity, emphasizing the importance of the heading stage for fresh weight estimation. The model slightly underestimated the weight of Napa cabbages exceeding 5 kg, potentially due to limited samples and saturation effects of vegetation indices. The overall error rate was less than 5%, demonstrating the feasibility of this approach. Spatial analysis further revealed that the model accurately captured variability in Napa cabbage growth across different soil types and irrigation conditions, particularly reflecting the positive impact of drip irrigation. This study highlights the potential of UAS-based multi-sensor data and AI for accurate and non-invasive prediction of Napa cabbage fresh weight, providing a valuable tool for optimizing harvest timing and crop management. Future research should focus on refining the model for specific weight ranges and diverse environmental conditions, and extending its application to other crops.

Alcoholic liver disease (ALD) refers to the damages to the liver and its functions due to alcohol overconsumption. It consists of fatty liver/steatosis, alcoholic hepatitis, steatohepatitis, chronic hepatitis with liver fibrosis or cirrhosis, and hepatocellular carcinoma. However, the mechanisms behind the pathogenesis of alcoholic liver disease are extremely complicated due to the involvement of immune cells, adipose tissues, and genetic diversity. Clinically, the diagnosis of ALD is not yet well developed. Therefore, the number of patients in advanced stages has increased due to the failure of proper early detection and treatment. At present, abstinence and nutritional therapy remain the conventional therapeutic interventions for ALD. Moreover, the therapies which target the TNF receptor superfamily, hormones, antioxidant signals, and MicroRNAs are used as treatments for ALD. In particular, mesenchymal stem cells (MSCs) are gaining attention as a potential therapeutic target of ALD. Therefore, in this review, we have summarized the current understandings of the pathogenesis and diagnosis of ALD. Moreover, we also discuss the various existing treatment strategies while focusing on promising therapeutic approaches for ALD.

To compare the clinical and radiological outcomes between patients with long posterior cervical fusion (PCF) in which fusion stopped at C7 versus patients in which fusion crossed the cervicothoracic junction (CTJ). The patients were divided into 2 groups on the basis of the lower-most instrumented vertebra (LIV); C7 group patients (n = 25) and upper thoracic (UT) group (n = 21). We analyzed the visual analogue scale of arm/neck pain, Japanese Orthopedic Association (JOA) score, and neck disability index (NDI). And we also measured the following parameters: (1) pseudomotion of fused segments; (2) C2-C7 sagittal vertical axis; (3) T1 slope; and (4) C2-C7 lordosis. Arm and neck pain were similar in both groups pre- and postoperatively. Interestingly, mean postoperative NDI score in the UT group was significant worse when compared with the C7 group (9.7±4.6 vs. 14.2±3.7, p = 0.006). Although UT patients had longer fusion levels, the fusion rates were not significantly different between the C7 and UT groups (96.0% vs. 90.5%; p = 0.577). The radiographic parameters did not show any significant differences between the groups at final follow-up. Our study demonstrates that multi-level PCF stopping at C7 does not negatively affect C7-T1 segment failure, fusion rate, neck pain, neurologic outcomes, and global sagittal alignment of the cervical spine. Hence, it is unnecessary to extend the long PCF levels caudally across the healthy CTJ for fear of development of adjacent segmental disease (ASD) at the C7-T1 segment.

Objectives Post-hepatectomy liver failure (PHLF) can occur as a major complication after hepatic resection (HR) in patients with hepatocellular carcinoma (HCC) and negatively affects the prognosis. We aimed to retrospectively assess whether the spleen volume (SV) measured from preoperative CT images would be associated with the development of PHLF and overall survival (OS) after HR in patients with HCC. Methods We enrolled 317 consecutive patients with very early/early stage HCC who underwent a preoperative CT and HR between January 2010 and December 2016. The SV was obtained from preoperative CT images using semi-automated volumetric software and was divided by body surface area to yield SV BSA . Receiver operating characteristic (ROC) curves and logistic regression analyses were performed to identify factors affecting the development of PHLF. The Cox proportional hazard model was used to identify prognostic factors for OS. Results PHLF was observed in 72 patients (22.7% [72/317]). SV BSA was associated with the development of PHLF (odds ratio, 2.321; 95% CI, 1.347–4.001; p = 0.002) with the area under the ROC curve of 0.663 using the cutoff value of 107.5 cm 3 ( p < 0.001). SV BSA was also an influencing factor for OS (hazard ratio, 3.935; 95% CI 1.520–10.184; p = 0.005), with the optimal cutoff of 146 cm 3 . The 5-year OS rate was higher in 245 patients with a SV BSA ≤ 146 cm 3 than in 72 patients with a SV BSA > 146 cm 3 (95.0% vs. 78.7%, p < 0.001). Conclusions In patients with HCC, a larger SV BSA was associated with a higher rate of PHLF and worse OS after HR. The SV BSA may be useful in selecting good surgical candidates. Key Points • A significantly higher spleen volume divided by body surface area was observed in patients who experienced post-hepatectomy liver failure than in patients who did not (148 cm 3 vs. 112 cm 3 , p < 0.001). • The area under the receiver operating characteristic curve of spleen volume divided by body surface area to predict the development of post-hepatectomy liver failure was 0.663 (p < 0.001). • Spleen volume divided by body surface area was a significant influencing factor for overall survival (hazard ratio, 3.935; 95% CI, 1.520–10.184; p < 0.001), with the optimal cutoff of 146 cm 3 .