Carotid endarterectomy and transcarotid artery revascularization can be performed with acceptable morbidity and mortality in patients with chronic kidney disease.

OBJECTIVE: Patients with chronic kidney disease (CKD) are considered a high-risk population, and the optimal approach to the treatment of carotid disease remains unclear. Thus, we compared outcomes following carotid revascularization for patients with CKD by operative approach of carotid endarterectomy (CEA), transfemoral carotid artery stenting (TFCAS), and transcarotid arterial revascularization (TCAR). METHODS: The Vascular Quality Initiative was analyzed for patients undergoing carotid revascularizations (CEA, TFCAS, and TCAR) from 2016 to 2021. Patients with normal renal function (estimated glomular filtration rate >90 mL/min/1.72 m2) were excluded. Asymptomatic and symptomatic carotid stenosis were assessed separately. Preoperative demographics, operative details, and outcomes of 30-day mortality, stroke, myocardial infarction (MI), and composite variable of stroke/death were compared. Multivariable analysis adjusted for differences in groups, including CKD stage. RESULTS: A total of 90,343 patients with CKD underwent revascularization (CEA, n = 66,870; TCAR, n = 13,459; and TFCAS, n = 10,014; asymptomatic, 63%; symptomatic, 37%). Composite 30-day mortality/stroke rates were: asymptomatic: CEA, 1.4%; TCAR, 1.2%; TFCAS, 1.8%; and symptomatic: CEA, 2.7%; TCAR, 2.3%; TFCAS, 3.7%. In adjusted analysis, TCAR had lower 30-day mortality compared with CEA (asymptomatic: adjusted odds ratio [aOR], 0.4; 95% confidence interval [CI], 0.3-0.7; symptomatic: aOR, 0.5; 95% CI, 0.3-0.7), and no difference in stroke, MI, or the composite outcome of stroke/death in both symptom cohorts. TCAR had lower risk of other cardiac complications compared with CEA in asymptomatic patients (aOR, 0.7; 95% CI, 0.6-0.9) and had similar risk in symptomatic patients. Compared with TFCAS, TCAR patients had lower 30-day mortality (asymptomatic: aOR, 0.5; 95% CI, 0.2-0.95; symptomatic: aOR, 0.3; 95% CI, 0.2-0.4), stroke (symptomatic: aOR, 0.7; 95% CI, 0.5-0.97), and stroke/death (asymptomatic: aOR, 0.7; 95% CI, 0.5-0.97; symptomatic: aOR, 0.6; 95% CI, 0.4-0.7), but no differences in MI or other cardiac complications. Patients treated with TFCAS had higher 30-day mortality (aOR, 1.8; 95% CI, 1.2-2.5) and stroke risk (aOR, 1.3; 95% CI, 1.02-1.7) in symptomatic patients compared with CEA. There were no differences in MI or other cardiac complications. CONCLUSIONS: Among patients with CKD, TCAR and CEA showed rates of stroke/death less than 2% for asymptomatic patients and less than 3% for symptomatic patients. Given the increased risk of major morbidity and mortality, TFCAS should not be performed in patients with CKD who are otherwise anatomic candidates for TCAR or CEA.

Direction-dependent conductivity in planar Hall set-ups with tilted Weyl/multi-Weyl semimetals.

We compute the magnetoelectric conductivity tensors in planar Hall set-ups, which are built with tilted Weyl semimetals (WSMs) and multi-Weyl semimetals (mWSMs), considering all possible relative orientations of the electromagnetic fields (EandB) and the direction of the tilt. The non-Drude part of the response arises from a nonzero Berry curvature in the vicinity of the WSM/mWSM node under consideration. Only in the presence of a nonzero tilt do we find linear-in-|B|terms in set-ups where the tilt-axis is not perpendicular to the plane spanned byEandB. The advantage of the emergence of the linear-in-|B|terms is that, unlike the various|B|2-dependent terms that can contribute to experimental observations, they have purely a topological origin, and they dominate the overall response-characteristics in the realistic parameter regimes. The important signatures of these terms are that they (1) change the periodicity of the response fromπto 2π, when we consider their dependence on the angleθbetweenEandB; and (2) lead to an overall change in sign of the conductivity depending onθ, when measured with respect to theB=0case.

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States.

Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the "black-box" nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having "no change" (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

Antibiotic induced adipose tissue browning in C57BL/6 mice: An association with the metabolic profile and the gut microbiota.

AIMS: The use of antibiotics affects health. The gut microbial dysbiosis by antibiotics is thought to be an essential pathway to influence health. It is important to have optimized energy utilization, in which adipose tissues (AT) play crucial roles in maintaining health. Adipocytes regulate the balance between energy expenditure and storage. While it is known that white adipose tissue (WAT) stores energy and brown adipose tissue (BAT) produces energy by thermogenesis, the role of an intermediate AT plays an important role in balancing host internal energy. In the current study, we tried to understand how treating an antibiotic cocktail transforms WAT into BAT or, more precisely, into beige adipose tissue (BeAT). METHODS: Since antibiotic treatment perturbs the host microbiota, we wanted to understand the role of gut microbial dysbiosis in transforming WAT into BeAT in C57BL/6 mice. We further correlated the metabolic profile at the systemic level with this BeAT transformation and gut microbiota profile. KEY FINDINGS: In the present study, we have reported that the antibiotic cocktail treatment increases the Proteobacteria and Actinobacteria while reducing the Bacteroidetes phylum. We observed that prolonged antibiotic treatment could induce the formation of BeAT in the inguinal and perigonadal AT. The correlation analysis showed an association between the gut microbiota phyla, beige adipose tissue markers, and serum metabolites. SIGNIFICANCE: Our study revealed that the gut microbiota has a significant role in regulating the metabolic health of the host via microbiota-adipose axis communication.

Automated catheter segmentation and tip detection in cerebral angiography with topology-aware geometric deep learning.

BACKGROUND: Visual perception of catheters and guidewires on x-ray fluoroscopy is essential for neurointervention. Endovascular robots with teleoperation capabilities are being developed, but they cannot 'see' intravascular devices, which precludes artificial intelligence (AI) augmentation that could improve precision and autonomy. Deep learning has not been explored for neurointervention and prior works in cardiovascular scenarios are inadequate as they only segment device tips, while neurointervention requires segmentation of the entire structure due to coaxial devices. Therefore, this study develops an automatic and accurate image-based catheter segmentation method in cerebral angiography using deep learning. METHODS: Catheters and guidewires were manually annotated on 3831 fluoroscopy frames collected prospectively from 40 patients undergoing cerebral angiography. We proposed a topology-aware geometric deep learning method (TAG-DL) and compared it with the state-of-the-art deep learning segmentation models, UNet, nnUNet and TransUNet. All models were trained on frontal view sequences and tested on both frontal and lateral view sequences from unseen patients. Results were assessed with centerline Dice score and tip-distance error. RESULTS: The TAG-DL and nnUNet models outperformed TransUNet and UNet. The best performing model was nnUNet, achieving a mean centerline-Dice score of 0.98 ±0.01 and a median tip-distance error of 0.43 (IQR 0.88) mm. Incorporating digital subtraction masks, with or without contrast, significantly improved performance on unseen patients, further enabling exceptional performance on lateral view fluoroscopy despite not being trained on this view. CONCLUSIONS: These results are the first step towards AI augmentation for robotic neurointervention that could amplify the reach, productivity, and safety of a limited neurointerventional workforce.

Cobalt-Catalyzed Chemodivergent Synthesis of Cyclic Amines and Lactams from Ketoacids and Anilines Using Hydrosilylation.

Here, commercially available Co2(CO)8 was utilized as an efficient catalyst for chemodivergent synthesis of pyrrolidines and pyrrolidones from levulinic acid and aromatic amines under slightly different hydrosilylation conditions. 1.5 and 3 equiv of phenylsilane selectively yielded pyrrolidone and pyrrolidine, respectively. Various ketoacids and amines were successfully tested. Plausible mechanism involves the condensation of levulinic acid and amine to form an imine, which cyclizes to 3-pyrrolidin-2-one followed by reduction to pyrrolidone. The final reduction of pyrrolidone gave pyrrolidine.

Analogue Hawking Radiation as a Tunneling in a Two-Level PT-Symmetric System.

In light of a general scenario of a two-level non-Hermitian PT-symmetric Hamiltonian, we apply the tetrad-based method to analyze the possibility of analogue Hawking radiation. We carry this out by making use of the conventional null-geodesic approach, wherein the associated Hawking radiation is described as a quantum tunneling process across a classically forbidden barrier on which the event horizon imposes. An interesting aspect of our result is that our estimate for the tunneling probability is independent of the non-Hermitian parameter that defines the guiding Hamiltonian.

Harnessing heterogeneity in space with statistically guided meta-learning.

Spatial data are ubiquitous, massively collected, and widely used to support critical decision-making in many societal domains, including public health (e.g., COVID-19 pandemic control), agricultural crop monitoring, transportation, etc. While recent advances in machine learning and deep learning offer new promising ways to mine such rich datasets (e.g., satellite imagery, COVID statistics), spatial heterogeneity-an intrinsic characteristic embedded in spatial data-poses a major challenge as data distributions or generative processes often vary across space at different scales, with their spatial extents unknown. Recent studies (e.g., SVANN, spatial ensemble) targeting this difficult problem either require a known space-partitioning as the input, or can only support very limited number of partitions or classes (e.g., two) due to the decrease in training data size and the complexity of analysis. To address these limitations, we propose a model-agnostic framework to automatically transform a deep learning model into a spatial-heterogeneity-aware architecture, where the learning of arbitrary space partitionings is guided by a learning-engaged generalization of multivariate scan statistic and parameters are shared based on spatial relationships. Moreover, we propose a spatial moderator to generalize learned space partitionings to new test regions. Finally, we extend the framework by integrating meta-learning-based training strategies into both spatial transformation and moderation to enhance knowledge sharing and adaptation among different processes. Experiment results on real-world datasets show that the framework can effectively capture flexibly shaped heterogeneous footprints and substantially improve prediction performances.

Cobalt catalyzed chemoselective reduction of nitroarenes: hydrosilylation under thermal and photochemical reaction conditions.

Commercially available Co2(CO)8 was used as an effective catalyst for the hydrosilylation of nitroarenes under both thermal and photochemical conditions. A wide variety of nitroarenes with various functionalities were selectively reduced to aromatic amines. Syntheses of drug molecules expand the potential utility of this protocol. Experimental evidence suggested a radical pathway.

Manganese-Catalyzed Chemoselective Hydrosilylation of Nitroarenes: Sustainable Route to Aromatic Amines.

Herein we report efficient catalytic hydrosilylations of nitroarenes to form the corresponding aromatic amines using a well-defined manganese(II)-NNO pincer complex with a low catalyst loading (1 mol %) under solvent-free conditions. This base-metal-catalyzed hydrosilylation is an easy and sustainable alternative to classical hydrogenation. A large variety of nitroarenes bearing various functionalities were selectively transformed into the corresponding aromatic amines in good yields. The potential utility of the present catalytic protocol was demonstrated by the preparation of commercial drug molecules.

Automatic Segmentation in Acute Ischemic Stroke: Prognostic Significance of Topological Stroke Volumes on Stroke Outcome.

BACKGROUND: Stroke infarct volume predicts patient disability and has utility for clinical trial outcomes. Accurate infarct volume measurement requires manual segmentation of stroke boundaries in diffusion-weighted magnetic resonance imaging scans which is time-consuming and subject to variability. Automatic infarct segmentation should be robust to rotation and reflection; however, prior work has not encoded this property into deep learning architecture. Here, we use rotation-reflection equivariance and train a deep learning model to segment stroke volumes in a large cohort of well-characterized patients with acute ischemic stroke in different vascular territories. METHODS: In this retrospective study, patients were selected from a stroke registry at Houston Methodist Hospital. Eight hundred seventy-five patients with acute ischemic stroke in any brain area who had magnetic resonance imaging with diffusion-weighted imaging were included for analysis and split 80/20 for training/testing. Infarct volumes were manually segmented by consensus of 3 independent clinical experts and cross-referenced against radiology reports. A rotation-reflection equivariant model was developed based on U-Net and grouped convolutions. Segmentation performance was evaluated using Dice score, precision, and recall. Ninety-day modified Rankin Scale outcome prediction was also evaluated using clinical variables and segmented stroke volumes in different brain regions. RESULTS: Segmentation model Dice scores are 0.88 (95% CI, 0.87-0.89; training) and 0.85 (0.82-0.88; testing). The modified Rankin Scale outcome prediction AUC using stroke volume in 30 refined brain regions based upon modified Rankin Scale-relevance areas adjusted for clinical variables was 0.80 (0.76-0.83) with an accuracy of 0.75 (0.72-0.78). CONCLUSIONS: We trained a deep learning model with encoded rotation-reflection equivariance to segment acute ischemic stroke lesions in diffusion- weighted imaging using a large data set from the Houston Methodist stroke center. The model achieved competitive performance in 175 well-balanced hold-out testing cases that include strokes from different vascular territories. Furthermore, the location specific stroke volume segmentations from the deep learning model combined with clinical factors demonstrated high AUC and accuracy for 90-day modified Rankin Scale in an outcome prediction model.

Retrospective study of deep learning to reduce noise in non-contrast head CT images.

PURPOSE: Presented herein is a novel CT denoising method uses a skip residual encoder-decoder framework with group convolutions and a novel loss function to improve the subjective and objective image quality for improved disease detection in patients with acute ischemic stroke (AIS). MATERIALS AND METHODS: In this retrospective study, confirmed AIS patients with full-dose NCCT head scans were randomly selected from a stroke registry between 2016 and 2020. 325 patients (67 ± 15 years, 176 men) were included. 18 patients each with 4-7 NCCTs performed within 5-day timeframe (83 total scans) were used for model training; 307 patients each with 1-4 NCCTs performed within 5-day timeframe (380 total scans) were used for hold-out testing. In the training group, a mean CT was created from the patient's co-registered scans for each input CT to train a rotation-reflection equivariant U-Net with skip and residual connections, as well as a group convolutional neural network (SRED-GCNN) using a custom loss function to remove image noise. Denoising performance was compared to the standard Block-matching and 3D filtering (BM3D) method and RED-CNN quantitatively and visually. Signal-to-noise ratio (SNR) and contrast-to-noise (CNR) were measured in manually drawn regions-of-interest in grey matter (GM), white matter (WM) and deep grey matter (DG). Visual comparison and impact on spatial resolution were assessed through phantom images. RESULTS: SRED-GCNN reduced the original CT image noise significantly better than BM3D, with SNR improvements in GM, WM, and DG by 2.47x, 2.83x, and 2.64x respectively and CNR improvements in DG/WM and GM/WM by 2.30x and 2.16x respectively. Compared to the proposed SRED-GCNN, RED-CNN reduces noise effectively though the results are visibly blurred. Scans denoised by the SRED-GCNN are shown to be visually clearer with preserved anatomy. CONCLUSION: The proposed SRED-GCNN model significantly reduces image noise and improves signal-to-noise and contrast-to-noise ratios in 380 unseen head NCCT cases.

Efficient α-Alkylation of Arylacetonitriles with Secondary Alcohols Catalyzed by a Phosphine-Free Air-Stable Iridium(III) Complex.

A well-defined and readily available air-stable dimeric iridium(III) complex catalyzed α-alkylation of arylacetonitriles using secondary alcohols with the liberation of water as the only byproduct is reported. The α-alkylations were efficiently performed at 120 °C under solvent-free conditions with very low (0.1-0.01 mol %) catalyst loading. Various secondary alcohols including cyclic and acyclic alcohols and a wide variety of arylacetonitriles bearing different functional groups were converted into the corresponding α-alkylated products in good yields. Mechanistic study revealed that the reaction proceeds via alcohol activation by metal-ligand cooperation with the formation of reactive iridium-hydride species.

Hydrosilylation of Esters Catalyzed by Bisphosphine Manganese(I) Complex: Selective Transformation of Esters to Alcohols.

Selective and efficient hydrosilylations of esters to alcohols by a well-defined manganese(I) complex with a commercially available bisphosphine ligand are described. These reactions are easy alternatives for stoichiometric hydride reduction or hydrogenation, and employing cheap, abundant, and nonprecious metal is attractive. The hydrosilylations were performed at 100 °C under solvent-free conditions with low catalyst loading. A large variety of aromatic, aliphatic, and cyclic esters bearing different functional groups were selectively converted into the corresponding alcohols in good yields.