Multicenter Evaluation of Interpretable AI for Coronary Artery Disease Diagnosis from PET Biomarkers.

Background: Positron emission tomography (PET)/CT for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluated separately. We developed an artificial intelligence (AI) model integrating key clinical PET MPI parameters to improve the diagnosis of obstructive coronary artery disease (CAD).

Methods: From 17,348 patients undergoing cardiac PET/CT across four sites, we retrospectively enrolled 1,664 subjects who had invasive coronary angiography within 180 days and no prior CAD.

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study.

Background: Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for all-cause mortality prediction.

Methods: This study included 27039 consecutive patients [57% male] with MPI scans from nine sites.

AI-based identification of patients who benefit from revascularization: a multicenter study.

Background And Aims: Revascularization in stable coronary artery disease often relies on ischemia severity, but we introduce an AI-driven approach that uses clinical and imaging data to estimate individualized treatment effects and guide personalized decisions.

Methods: Using a large, international registry from 13 centers, we developed an AI model to estimate individual treatment effects by simulating outcomes under alternative therapeutic strategies. The model was trained on an internal cohort constructed using 1:1 propensity score matching to emulate randomized controlled trials (RCTs), creating balanced patient pairs in which only the treatment strategy-early revascularization (defined as any procedure within 90 days of MPI) versus medical therapy-differed.

AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study.

Background: CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification.

Methods: We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues.

Polyphenol-modified 3D Nanoassemblies: A novel antibacterial immunoglobulins loading platform for rapid detection of Salmonella typhimurium.

Within the realm of lateral flow assay (LFIA), the conjugation efficiency between signal tracers and antibody constitutes a pivotal determinant for the sensitivity of the detection system. In this study, three-dimensional (3D) complex flower-like MoS self-assembled from 2D MoS, and natural plant polyphenols "Tannic acid" were introduced for surface modification. This composite material exhibits distinct colorimetric signals, excellent monoclonal antibody coupling efficiency, and commendable photothermal properties.

Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment.

Aims: Identification of proximal coronary artery calcium (CAC) may improve prediction of major adverse cardiac events (MACE) beyond the CAC score, particularly in patients with low CAC burden. We investigated whether the proximal CAC can be detected on gated cardiac CT and whether it provides prognostic significance with artificial intelligence (AI).

Methods And Results: A total of 2016 asymptomatic adults with baseline CAC CT scans from a single site were followed up for MACE for 14 years.

Bacterial communication intelligently regulates their interactions in anammox consortia under decreasing temperatures.

Bacterial communication could affect their interactions, but whether this regulation has "intelligence" is still unknown. Here, we operated an anammox reactor under temperature gradient from 35 °C to 15 °C. As results, expression abundance of bacterial communication genes increased by 12 % significantly after temperature declined.

Communication leads to bacterial heterogeneous adaptation to changing conditions in partial nitrification reactor.

Recently, it is reported that bacterial communication coordinates the whole consortia to jointly resist the adverse environments. Here, we found the bacterial communication inevitably distinguished bacterial adaptation among different species in partial nitrification reactor under decreasing temperatures. We operated a partial nitrification reactor under temperature gradient from 30 °C to 5 °C and found the promotion of bacterial communication on adaptation of ammonia-oxidizing bacteria (AOB) was greater than that of nitrite-oxidizing bacteria (NOB).

Blue-light irradiation induced partial nitrification.

The role of ray radiation from the sunlight acting on organisms has long-term been investigated. However, how the light with different wavelengths affects nitrification and the involved nitrifiers are still elusive. Here, we found more than 60 % of differentially expressed genes (DEGs) in nitrifiers were observed under irradiation of blue light with wavelengths of 440-480 nm, which were 13.

Communication mediated interaction between bacteria and microalgae advances photogranulation.

Recently, photogranules composed of bacteria and microalgae for carbon-negative nitrogen removal receive extensive attention worldwide, yet which type of bacteria is helpful for rapid formation of photogranules and whether they depend on signaling communication remain elusive. Varied signaling communication was analyzed using metagenomic method among bacteria and microalgae in via of two types of experimentally verified signaling molecule from bacteria to microalgae, which include indole-3-acetic acid (IAA) and N-acyl homoserine lactones (AHLs) during the operation of photo-bioreactors. Signaling communication is helpful for the adaptability of bacteria to survive with algae.

Chameleon-like Anammox Bacteria for Surface Color Change after Suffering Starvation.

Bacteria are often exposed to long-term starvation during transportation and storage, during which a series of enzymes and metabolic pathways are activated to ensure survival. However, why the surface color of the bacteria changes during starvation is still not well-known. In this study, we found black anammox consortia suffering from long-term starvation contained 0.

Fast Broad Multiview Multi-Instance Multilabel Learning (FBM3L) With Viewwise Intercorrelation.

Multiview multi-instance multilabel learning (M3L) is a popular research topic during the past few years in modeling complex real-world objects such as medical images and subtitled video. However, existing M3L methods suffer from relatively low accuracy and training efficiency for large datasets due to several issues: 1) the viewwise intercorrelation (i.e.

Consensus Sparsity: Multi-Context Sparse Image Representation via L-Induced Matrix Variate.

The sparsity is an attractive property that has been widely and intensively utilized in various image processing fields (e.g., robust image representation, image compression, image analysis, etc.

Bacterial Communication Coordinated Behaviors of Whole Communities to Cope with Environmental Changes.

Bacterial communication plays an important role in coordinating microbial behaviors in a community. However, how bacterial communication organizes the entire community for anaerobes to cope with varied anaerobic-aerobic conditions remains unclear. We constructed a local bacterial communication gene (BCG) database comprising 19 BCG subtypes and 20279 protein sequences.

Composite carrier enhanced bacterial adhesion and nitrogen removal in partial nitrification/anammox process.

The rapid start-up and stable operation of one-stage (Partial nitrification/anammox) PN/A process for low-ammonium wastewater are difficult to be achieved, and many carriers are designed to solve this problem. Here, a composite carrier was developed, in which sepiolite and non-woven fabrics were assembled in polypropylene spherical shells. At the start-up phase, P reactor using the composite carriers reached a higher nitrogen removal rate of 134.

Potential bacterial isolation by dosing metabolites in cross-feedings.

Metabolic cross-feeding, in which species use metabolites of other members to promote their own growth, is vital for bacterial growth and survival. Thus, whether the unculturable bacteria can be isolated or purified from consortia by adding these essential metabolites remains elusive. In this study, mass spectrometry imaging vividly pictured symbionts supplied folate and gluconate to anammox bacteria to support their growth.

Missing-view completion for fatty liver disease detection.

Fatty liver disease is a common disease that causes extra fat storage in an individual's liver. Patients with fatty liver disease may progress to cirrhosis and liver failure, further leading to liver cancer. The prevalence of fatty liver disease ranges from 10% to 30% in many countries.

Fuzzy Graph Subspace Convolutional Network.

Graph convolutional networks (GCNs) are a popular approach to learn the feature embedding of graph-structured data, which has shown to be highly effective as well as efficient in performing node classification in an inductive way. However, with massive nongraph-organized data existing in application scenarios nowadays, it is critical to exploit the relationships behind the given groups of data, which makes better use of GCN and broadens the application field. In this article, we propose the f uzzy g raph s ubspace c onvolutional n etwork (FGSCN) to provide a brand-new paradigm for feature embedding and node classification with graph convolution (GC) when given an arbitrary collection of data.

Response of amino acid metabolism to decreased temperatures in anammox consortia: Strong, efficient and flexible.

Although amino acid (AA) metabolism is basis of bacterial activities, unique characteristics of its response to decreased temperatures are not fully understood. Achieving nitrogen removal rate of 130-150 mg N/ (L∙d), metabolic differences of anammox consortia between 35 °C and four decreased temperatures (15-30 °C) were revealed respectively. 0-11.

Cross-feeding among microalgae facilitates nitrogen recovery at low C/N.

Although co-culture of microalgae has been found as a feasible strategy to improve biomass production, their interspecies relationships are not fully understood. Here, two algae taxa, Chlorella sp. and Phormidium sp.

Two-phase non-invasive multi-disease detection via sublingual region.

Non-invasive multi-disease detection is an active technology that detects human diseases automatically. By observing images of the human body, computers can make inferences on disease detection based on artificial intelligence and computer vision techniques. The sublingual vein, lying on the lower part of the human tongue, is a critical identifier in non-invasive multi-disease detection, reflecting health status.

Multi-feature representation for burn depth classification via burn images.

Burns are a common and severe problem in public health. Early and timely classification of burn depth is effective for patients to receive targeted treatment, which can save their lives. However, identifying burn depth from burn images requires physicians to have a lot of medical experience.