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Background: The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. Purpose: To assess the performance of the deep learning–based artificial intelligence (AI) software in identifying and measuring lesions on contrast‐enhanced magnetic resonance imaging (MRI) images in patients with FLLs. Study Type: Retrospective. Subjects: 395 patients with 1149 FLLs. Field Strength/Sequence: The 1.5 T and 3 T scanners, including T1‐, T2‐, diffusion‐weighted imaging, in/out‐phase imaging, and dynamic contrast‐enhanced imaging. Assessment: The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut‐off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10–20, 20–40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. Statistical Tests: McNemar test, Bland–Altman analyses, Friedman test, Pearson's chi‐squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P‐value <0.05 was considered statistically significant. Results: The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). Data Conclusion: AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. Level of Evidence: 4. Technical Efficacy: Stage 2. [ABSTRACT FROM AUTHOR]

Purpose: This study compares the diagnostic efficacy of non-contrast abbreviated MRI protocols with Gadoxetic acid-enhanced abbreviated MRI for detecting colorectal liver metastasis (CRLM), focusing on lesion characterization and surveillance. Methods: Ninety-four patients, including 55 with pathologically verified CRLM, were enrolled, totaling 422 lesions (287 metastatic, 135 benign). Two independent readers assessed three MRI protocols per patient: Protocol 1 included non-contrast sequences (T2-weighted turbo spin-echo, T1-weighted Dixon, diffusion-weighted imaging (DWI), and ADC mapping). Protocol 2 included gadoxetic acid enhancement with hepatobiliary phase imaging, T2 TSE, DWI, and ADC maps. Protocol 3 utilized the standard Gadoxetic Acid-enhanced MRI sequence, which included pre-contrast T1-weighted imaging, T1-weighted Dixon sequences, post-contrast T1-weighted imaging (including arterial, portal venous, transitional and hepatobiliary phases), and additional T2-weighted and DWI sequences. Diagnoses were scored on a 5-point scale (benign = 1; malignant = 5), with scores ≥ 3 indicating CRLM. ROC curves analyzed diagnostic accuracy, comparing area under the curve (AUC) values across protocols. Results: No significant difference in AUCs was observed between Protocol 1 (0.899–0.909) and Protocol 2 (0.906–0.931) versus Protocol 3 (0.935–0.939) (p = 0.091–0.195). For lesions ≤ 10 mm, Protocol 1 was slightly inferior to Protocol 3 (p = 0.002–0.032), while Protocol 2 remained comparably effective (p = 0.096–0.179). These findings held when using a threshold of ≥ 4 to define CRLM. Conclusion: The non-enhanced abbreviated MRI protocol is as effective as the gadoxetic acid-enhanced protocol in identifying CRLM. The proposed Ab-MRI approach may be a viable alternative for CRLM surveillance. Critical relevance statement: The non-enhanced abbreviated MRI (Ab-MRI) protocol is as effective as the gadoxetic acid-enhanced protocol in identifying colorectal liver metastasis (CRLM). The proposed Ab-MRI approach may be a viable alternative for CRLM surveillance. Key Points: Two abbreviated protocols are proposed for colorectal liver metastasis (CRLM) surveillance. The non-enhanced protocol showed equivalent efficacy and was more cost-effective. The non-enhanced protocol may be a viable alternative for CRLM surveillance. [ABSTRACT FROM AUTHOR]

The swift incorporation of artificial intelligence (AI) into higher education has significantly propelled the digital transformation of education. This advancement is crucial for educators aiming to augment teaching quality through AI technologies, such as ChatGPT. However, the acceptance of ChatGPT among college students remains underexplored. This paper aims to clarify the determinants influencing college students' acceptance of ChatGPT and to facilitate its widespread adoption in higher education. To achieve this, we integrate the Technology Readiness Index (TRI), Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB) to develop a novel research framework. Employing a mixed-method approach that includes PLS-SEM and fsQCA, we analyze data from 298 Chinese college students. Our findings indicate that discomfort and insecurity adversely affect Perceived Ease of Use (PEU) and Perceived Usefulness (PU) in the context of ChatGPT adoption. Additionally, both PEU and PU positively impact attitudes, which, in conjunction with Subjective Norms (SN) and Perceived Behavioral Control (PBC), bolster the intention to accept ChatGPT. Insights from fsQCA reveal that the acceptance of ChatGPT among students is not driven by a singular factor but by an amalgamation of these elements, underscoring the complex nature of technology adoption. The paper concludes with practical recommendations for educators and designers to refine curriculum design and teaching methodologies, boost student engagement and learning efficacy, and promote the broader adoption of educational technology. Plain language summary: Factors influencing the acceptance of ChatGPT in high education This study contributes to the existing literature by amalgamating the Technology Readiness Index (TRI), Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB) into a novel research model to identify significant factors that influence Chinese college students' acceptance of ChatGPT via a mixed approach that combines PLS-SEM and fsQCA within higher educational settings. [ABSTRACT FROM AUTHOR]

Purpose: To study the MRI features (based on LI-RADS) and clinical characteristics of P53-mutated hepatocellular carcinoma (HCC) patients. Patients and Methods: This study enrolled 344 patients with histopathologically confirmed HCC (P53-mutated group [n = 196], non-P53-mutated group [n = 148]). We retrospectively evaluated the preoperative MRI features, clinical and pathologic features of the lesions and assigned each lesion according to the LI-RADS. MRI findings, clinical features, and pathologic findings were compared using the Student's t test, χ 2 test, and multivariable regression analysis. Results: Most HCC patients were categorized as LR-5. On multivariate analysis, the Edmondson–Steiner grade (odds ratio, 2.280; 95% CI: 1.268, 4.101; p = 0.006) and rim enhancement (odds ratio, 2.517; 95% CI: 1.095, 5.784; p = 0.030) were found to be independent variables associated with P53-mutated HCC. In the group of HCC lesions with the largest tumor diameter (LTD) greater than or equal to 10mm and less than or equal to 20mm, enhancing capsule was an independent predictor of P53-mutated HCC (odds ratio, 6.200; 95% CI: 1.116, 34.449; p = 0.037). Among the HCC lesions (20 mm ˂ LTD ≤ 50 mm), corona enhancement (odds ratio, 2.102; 95% CI: 1.022, 4.322; p = 0.043) and nodule-in-nodule architecture (odds ratio, 2.157; 95% CI: 1.033, 4.504; p = 0.041) were found to be independent risk factors for P53 mutation. Among the HCC lesions (50 mm ˂ LTD ≤ 100 mm), diameter (odds ratio, 1.035; 95% CI: 1.001, 1.069; p = 0.044) and AFP ≥ 400 (ng/mL) (odds ratio, 3.336; 95% CI: 1.052, 10.577; p = 0.041) were found to be independent variables associated with P53-mutated HCC. Conclusion: Poor differentiation and rim enhancement are potential predictive biomarkers for P53-mutated HCC, while HCCs of different diameters have different risk factors for predicting P53 mutations. [ABSTRACT FROM AUTHOR]

We present an interface-type memristor utilizing native silicon oxide nanostructures achieved through controlled plasma oxidation engineering at room temperature. This process effectively reduces the concentration of oxygen vacancies on the surface of the native silicon oxide, facilitating oxygen-vacancy-mediated interfacial resistive switching with excellent COMS compatibility and low switching variability (1.03% for C2C and 3.96% for D2D variability). Moreover, this dynamic memristor exhibits favorable nonlinearity and short-term memory (STM) effects, which are crucial properties for the development of memristor-based reservoir computing (RC) systems. Compared with the conventional neural network, the RC system requires fewer trainable weights to achieve an accuracy of 96.7% for the Iris classification task. By the addition of virtual nodes to the reservoir with masks, the accuracy can be further increased to 100%. A simple digit recognition task is also employed, and noisy images that are not included in the original training set can be recognized correctly. These results highlight the great potential of our device for constructing reservoir computing hardware. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) has a poor prognosis, often characterized by microvascular invasion (MVI). Radiomics and habitat imaging offer potential for preoperative MVI assessment., Purpose: To identify MVI in HCC by habitat imaging, tumor radiomic analysis, and peritumor habitat-derived radiomic analysis., Study Type: Retrospective., Subjects: Three hundred eighteen patients (53 ± 11.42 years old; male = 276) with pathologically confirmed HCC (training:testing = 224:94)., Field Strength/sequence: 1.5 T, T2WI (spin echo), and precontrast and dynamic T1WI using three-dimensional gradient echo sequence., Assessment: Clinical model, habitat model, single sequence radiomic models, the peritumor habitat-derived radiomic model, and the combined models were constructed for evaluating MVI. Follow-up clinical data were obtained by a review of medical records or telephone interviews., Statistical Tests: Univariable and multivariable logistic regression, receiver operating characteristic (ROC) curve, calibration, decision curve, Delong test, K-M curves, log rank test. A P-value less than 0.05 (two sides) was considered to indicate statistical significance., Results: Habitat imaging revealed a positive correlation between the number of subregions and MVI probability. The Radiomic-Pre model demonstrated AUCs of 0.815 (95% CI: 0.752-0.878) and 0.708 (95% CI: 0.599-0.817) for detecting MVI in the training and testing cohorts, respectively. Similarly, the AUCs for MVI detection using Radiomic-HBP were 0.790 (95% CI: 0.724-0.855) for the training cohort and 0.712 (95% CI: 0.604-0.820) for the test cohort. Combination models exhibited improved performance, with the Radiomics + Habitat + Dilation + Habitat 2 + Clinical Model (Model 7) achieving the higher AUC than Model 1-4 and 6 (0.825 vs. 0.688, 0.726, 0.785, 0.757, 0.804, P = 0.013, 0.048, 0.035, 0.041, 0.039, respectively) in the testing cohort. High-risk patients (cutoff value >0.11) identified by this model showed shorter recurrence-free survival., Data Conclusion: The combined model including tumor size, habitat imaging, radiomic analysis exhibited the best performance in predicting MVI, while also assessing prognostic risk., Evidence Level: 3 TECHNICAL EFFICACY: Stage 2., (© 2024 International Society for Magnetic Resonance in Medicine.)

Background: Growing evidence emphasizes the importance of xanthophyll carotenoids and omega-3 fatty acids in eye health. However, the beneficial effects of such supplementation have not been thoroughly discussed among adults with high screen exposure. Current trial evidence on lutein bioavailability is contradictory, and the interactions of dietary intervention with host-related factors remain elusive. This study aims to investigate the comparative effectiveness of supplementation with macular xanthophylls and omega-3 fatty acids on macular pigment optical density (MPOD) and visual function, access the bioavailability of free lutein and lutein ester, and explore the complex interplay between genetic variations, intestinal microbiota, and the dietary intervention in Chinese adults with long-term exposure to digital devices., Methods: The Lutein, Zeaxanthin, and Omega-3 (LZO) clinical trial is a 24-week multicenter, randomized, double-blind, placebo-controlled trial of 600 participants recruited from research centers, universities, and communities. Individuals are eligible to participate if they are aged over 18 years and use digital devices for over 8 h daily in the last 2 years, and will be randomized to six arms. A total of three visits will be scheduled at baseline, 12 and 24 weeks. The primary outcome is the change in MPOD over the 24-week intervention. The secondary outcomes are changes in visual function (visual acuity, best-corrected visual acuity, contrast and glare sensitivity, critical flicker fusion, reaction time, visuognosis persistence, symptoms and signs of dry eye, retinal thickness, and optical quality), and changes in serum lutein and zeaxanthin concentrations, and erythrocyte membrane omega-3 fatty acids. Genetic variations will be determined using genome-wide genotyping at baseline. 16S rRNA gene sequencing will be utilized to assess microbiome compositional changes before and after intervention., Discussion: The trial is anticipated to establish early interventions to prevent photochemical ocular damage and delay the onset of vision impairment in young adults with long-term repeated exposure to screen-based electronic devices, and provide valuable insights for the development of precision nutrition strategies for maintaining eye health., Clinical Trial Registration: www.clinicaltrials.in.th, Identifier, TCTR20220904002., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Ma, Li, Pei, Wang, Li, Yang, Liu, Tang, Hao, Jiang, Fu, Xiao, Wang, Cui, Su, Bai, Tang, Wang, Shan, Jiang, Deng, Kong, Hui and Ma.)