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Comparison of quantitative laser speckle contrast and indocyanine green imaging for intestinal perfusion measurements in robot assisted surgery
Abstract Adequate perfusion is essential to prevent anastomotic leakage in intestinal and rectal surgeries. This study compares Laser Speckle Contrast Imaging (LSCI) and Indocyanine Green Fluorescence Angiography (ICG-FA) for assessing blood flow in intestinal anastomoses during robot-assisted surgeries in pigs. Intestinal perfusion was evaluated in three pigs using LSCI and ICG-FA, before and after clamping the main arterial supply, with measurements taken across ten regions of interest (ROIs). Pearson correlation coefficients were used to compare the two techniques. ROIs were normalized for analysis to facilitate direct comparison of the perfusion patterns. The results showed a strong correlation between the maximum fluorescence intensity from ICG-FA and LSCI values after clamping (r = 0.7293), with comparable perfusion patterns observed post-unclamping. LSCI provides continuous monitoring, while ICG-FA captures contrast-enhanced snapshots, explaining weaker correlations for static values. No significant difference was found in normalized measurements between the two methods. This study supports the use of both LSCI and ICG-FA in clinical practice, highlighting their complementary roles in assessing perfusion during surgeries. Further research is needed to explore their combined utility.
Combined association of triglyceride–glucose index and systemic inflammation index on all-cause and cardiovascular mortality
How to speed up peer review: make applicants mark one another
Crystal structure and catalytic mechanism of drimenol synthase, an unusual bifunctional terpene cyclase–phosphatase
Drimenol synthase from Aquimarina spongiae (AsDMS) is a highly unusual chimera that integrates two distinct, sequential isoprenoid processing activities within a single polypeptide chain. AsDMS catalyzes the class II cyclization of farnesyl diphosphate (FPP) to form drimenyl diphosphate, which then undergoes enzyme-catalyzed hydrolysis to yield drimenol, a bioactive sesquiterpene alcohol with antifungal and anticancer properties. Here, we report the X-ray crystal structures of AsDMS and its complex with a sesquiterpene thiol. The AsDMS structure exhibits a didomain architecture consisting of a terpene cyclase β domain and a haloacid dehalogenase-like phosphatase domain, with two distinct active sites located on opposite sides of the protein. Mechanistic studies show that dephosphorylation of the drimenyl diphosphate intermediate proceeds through stepwise hydrolysis such that two equivalents of inorganic phosphate rather than inorganic pyrophosphate are coproducts of the reaction sequence. When the AsDMS reaction is performed in H 2 18 O, 18 O is not incorporated into drimenol, indicating that the hydroxyl oxygen of drimenol originates from the prenyl oxygen of FPP rather than a water molecule from bulk solution. These results correct a mechanistic proposal previously advanced by another group. Surprisingly, AsDMS exhibits substrate promiscuity, catalyzing the conversion of the slowly reactive substrate mimic farnesyl- S -thiolodiphosphate into cyclic and linear sesquiterpene products. Structural and mechanistic insights gained from AsDMS illustrate the functional diversity of terpene biosynthetic enzymes and provide a foundation for engineering “designer cyclase” assemblies capable of generating a wide variety of terpenoid products.
Identifying artificial intelligence-generated content using the DistilBERT transformer and NLP techniques
ADRB2 is regulated by TRIM22 and facilitates lung adenocarcinoma progression via JAK2/STAT3 signaling pathway
Efficacy and safety of stem cell therapy for acute and subacute ischemic stroke: a systematic review and meta-analysis
Trees vs neural networks for enhancing tau lepton real-time selection in proton-proton collisions
Abstract This paper introduces supervised learning techniques for real-time selection (triggering) of hadronically decaying tau leptons in proton-proton colliders. By implementing traditional machine learning decision trees and advanced deep learning models, such as Multi-Layer Perceptron or residual neural networks, visible improvements in performance compared to standard rule-based tau triggers are observed. We show how such an implementation may lower selection energy thresholds, thus increasing the sensitivity of searches for new phenomena in proton-proton collisions classified by low-energy tau leptons. Moreover, we analyze when it is better to use neural networks vs decision trees for tau triggers with conclusions relevant to other problems in physics.