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Lithium-Mediated Ammonia Electrosynthesis over Orderly Arranged Dipoles Regulated Solid-Electrolyte Interphase
Automatic segmentation of liver structures in multi-phase MRI using variants of nnU-Net and Swin UNETR
Abstract Accurate segmentation of the liver parenchyma, portal veins, hepatic veins, and lesions from MRI is important for hepatic disease monitoring and treatment. Multi-phase contrast enhanced imaging is superior in distinguishing hepatic structures compared to single-phase approaches, but automated approaches for detailed segmentation of hepatic structures are lacking. This study evaluates deep learning architectures for segmenting liver structures from multi-phase Gd-EOB-DTPA-enhanced T1-weighted VIBE MRI scans. We utilized 458 T1-weighted VIBE scans of pathological livers, with 78 manually labeled for liver parenchyma, hepatic and portal veins, aorta, lesions, and ascites. An additional dataset of 47 labeled subjects was used for cross-scanner evaluation. Three models were evaluated using nested cross-validation: the conventional nnU-Net, the ResEnc nnU-Net, and the Swin UNETR. The late arterial phase was identified as the optimal fixed phase for co-registration. Both nnU-Net variants outperformed Swin UNETR across most tasks. The conventional nnU-Net achieved the highest segmentation performance for liver parenchyma (DSC: 0.97; 95% CI 0.97, 0.98), portal vein (DSC: 0.83; 95% CI 0.80, 0.87), and hepatic vein (DSC: 0.78; 95% CI 0.77, 0.80). Lesion and ascites segmentation proved challenging for all models, with the conventional nnU-Net performing best. This study demonstrates the effectiveness of deep learning, particularly nnU-Net variants, for detailed liver structure segmentation from multi-phase MRI. The developed models and preprocessing pipeline offer potential for improved liver disease assessment and surgical planning in clinical practice.
Antioxidant activity and in vitro fluorescence imaging application of N-, O- functionalized carbon dots
Drp1 Proteins Released from Hydrolysis-Driven Scaffold Disassembly Trigger Nucleotide-Dependent Membrane Remodeling to Promote Scission
Novel protocatechuic acid encapsulated bovine serum albumin functionalized folic acid nanoparticles for targeted therapy in urethane-induced lung cancer model
Abstract Lung cancer mortality rates are rising globally, increasing the need for innovative, natural compounds with fewer side effects. Protocatechuic acid (PCA) is a natural phenolic compound with cytotoxic effects against human lung cancer cells, but its poor water solubility limits its use. We hypothesized that encapsulating PCA within bovine serum albumin (BSA) nanoparticles would enhance its solubility compared to previous PCA-loaded-nanocarriers. So, we encapsulated PCA within BSA-nanoparticles conjugated with folic acid (FA) and evaluated its targeting efficacy in lung cancer cell line and lung cancer mouse model. The resulting nanocomposite (PCA-BSA@FA-NPs) had a 229 nm size. Our in vitro study indicated that PCA-BSA@FA-NPs demonstrated a 92.03% reduction in toxicity compared to doxorubicin, and a 59.67% reduction compared to PCA when tested against normal WI38 cells. In the lung cancer mouse model, the NF-κB expression decreased by 179% in the PCA-BSA@FA-NPs-treated group compared to the PCA-treated group. Both groups showed significant reductions in MAPK and FAK, with greater declines of 172% for MAPK and 316% for FAK in the PCA-BSA@FA-NPs-treated group. In conclusion, PCA-BSA@FA-NPs may serve as a promising therapy for targeting lung cancer with reduced toxicity.
Dynamin-like Proteins Combine Mechano-constriction and Membrane Remodeling to Enable Two-Step Mitochondrial Fission via a “Snap-through” Instability
Spatial heterogeneity and temporal trends of thyroid cancer incidence in Iran from 2014 to 2017
Beyond Blue: Systematic Modulation of Electronic Structure and Redox Properties of Type 1 Copper in Azurin
Cross-sectional associations of physical activity intensities and domains with recovery need and burnout risk among Flemish secondary school teachers
Metformin’s impact on tumor regression grade in diabetic patients with rectal cancer undergoing neoadjuvant chemoradiotherapy
‘Stealth flippers’ helped this extinct mega-predator stalk its prey
Synthesis, Structure, and Electronic Properties of [Cr<sub>6</sub>@Sn<sub>8</sub>Sb<sub>8</sub>(en)<sub>2</sub>]<sup>3–</sup>: A Cr<sub>6</sub> Octahedron Encapsulated in a Zintl-Ion Ligand
Development of a human analogue ADHD diagnostic system for family dogs
Abstract Dogs exhibit natural variability in inattention, hyperactivity and impulsivity traits, sometimes with extreme manifestations resembling to Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms in humans. Using human-standard diagnostic methods, we developed a comprehensive approach for supporting diagnosing ADHD in dogs (N = 1872) based on a validated questionnaire that consists of two parts: a symptom section that provides factor scores for three areas (inattention, hyperactivity and impulsivity), and a functionality section that evaluates the extent to which these symptoms cause functional impairments. Based on the functionality section, we classified dogs as functionally impaired if they showed impairment in at least 4 out of the 7 questions of at least one area (n = 116; 6.2% of the sample). Then, we projected the impaired–non-impaired categories on the ADHD total scores of the symptom section to establish potential cutoff values. The ROC curve analysis resulted in an excellent AUC value (0.861). A cutoff score of 26 was established based on well-defined sensitivity and specificity trade-offs. Finally, we applied the combined thresholds of the two parts of the questionnaire to identify dogs at-risk vs. non-at-risk for ADHD (n = 79; 4.22% of the sample). We offer the first replicable method to screen dogs with suspected ADHD. A final diagnosis could be set after dog-owner pairs participate in relevant behavioural tests and an expert consultation. Allowing for comparisons between ADHD vs. typical groups of family dogs, our method not only facilitates the improvement of the wellbeing of at-risk dogs, but also makes it more feasible to use dogs as a natural model species for studying ADHD.