Interpretable machine learning-based predictive modeling of patient outcomes following cardiac surgery.

Background: The clinical applicability of machine learning predictions of patient outcomes following cardiac surgery remains unclear. We applied machine learning to predict patient outcomes associated with high morbidity and mortality after cardiac surgery and identified the importance of variables to the derived model's performance.

Methods: We applied machine learning to the Society of Thoracic Surgeons Adult Cardiac Surgery Database to predict postoperative hemorrhage requiring reoperation, venous thromboembolism (VTE), and stroke.

Resurrecting ancestral antibiotics: unveiling the origins of modern lipid II targeting glycopeptides.

Antibiotics are central to modern medicine, and yet they are mainly the products of intra and inter-kingdom evolutionary warfare. To understand how nature evolves antibiotics around a common mechanism of action, we investigated the origins of an extremely valuable class of compounds, lipid II targeting glycopeptide antibiotics (GPAs, exemplified by teicoplanin and vancomycin), which are used as last resort for the treatment of antibiotic resistant bacterial infections. Using a molecule-centred approach and computational techniques, we first predicted the nonribosomal peptide synthetase assembly line of paleomycin, the ancestral parent of lipid II targeting GPAs.

Multimodal learning in clinical proteomics: enhancing antimicrobial resistance prediction models with chemical information.

Motivation: Large-scale clinical proteomics datasets of infectious pathogens, combined with antimicrobial resistance outcomes, have recently opened the door for machine learning models which aim to improve clinical treatment by predicting resistance early. However, existing prediction frameworks typically train a separate model for each antimicrobial and species in order to predict a pathogen's resistance outcome, resulting in missed opportunities for chemical knowledge transfer and generalizability.

Results: We demonstrate the effectiveness of multimodal learning over proteomic and chemical features by exploring two clinically relevant tasks for our proposed deep learning models: drug recommendation and generalized resistance prediction.

Precursor deconvolution error estimation: The missing puzzle piece in false discovery rate in top-down proteomics.

Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics (BUP) that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification.

Small proteins modulate ion-channel-like ACD6 to regulate immunity in Arabidopsis thaliana.

The multi-pass transmembrane protein ACCELERATED CELL DEATH 6 (ACD6) is an immune regulator in Arabidopsis thaliana with an unclear biochemical mode of action. We have identified two loci, MODULATOR OF HYPERACTIVE ACD6 1 (MHA1) and its paralog MHA1-LIKE (MHA1L), that code for ∼7 kDa proteins, which differentially interact with specific ACD6 variants. MHA1L enhances the accumulation of an ACD6 complex, thereby increasing the activity of the ACD6 standard allele for regulating plant growth and defenses.

Air channels create a directional light signal to regulate hypocotyl phototropism.

In plants, light direction is perceived by the phototropin photoreceptors, which trigger directional growth responses known as phototropism. The formation of a phototropin activation gradient across a photosensitive organ initiates this response. However, the optical tissue properties that functionally contribute to phototropism remain unclear.

Spatial metatranscriptomics resolves host-bacteria-fungi interactomes.

The interactions of microorganisms among themselves and with their multicellular host take place at the microscale, forming complex networks and spatial patterns. Existing technology does not allow the simultaneous investigation of spatial interactions between a host and the multitude of its colonizing microorganisms, which limits our understanding of host-microorganism interactions within a plant or animal tissue. Here we present spatial metatranscriptomics (SmT), a sequencing-based approach that leverages 16S/18S/ITS/poly-d(T) multimodal arrays for simultaneous host transcriptome- and microbiome-wide characterization of tissues at 55-µm resolution.

KIPEs3: Automatic annotation of biosynthesis pathways.

Flavonoids and carotenoids are pigments involved in stress mitigation and numerous other processes. Both pigment classes can contribute to flower and fruit coloration. Flavonoid aglycones and carotenoids are produced by a pathway that is largely conserved across land plants.

Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer Development.

Introduction: Since the emergence of SARS-CoV-2 viruses, multiple mutant strains have been identified. Infection with SARS-CoV-2 virus leads to alterations in host cell phosphorylation signal, which systematically modulates the immune response.

Methods: Identification and analysis of SARS-CoV-2 virus infection phosphorylation sites enable insight into the mechanisms of viral infection and effects on host cells, providing important fundamental data for the study and development of potent drugs for the treatment of immune inflammatory diseases.

Dynamics and durability of HIV-1 neutralization are determined by viral replication.

Human immunodeficiency virus type 1 (HIV-1)-neutralizing antibodies (nAbs) that prevent infection are the main goal of HIV vaccine discovery. But as no nAb-eliciting vaccines are yet available, only data from HIV-1 neutralizers-persons with HIV-1 who naturally develop broad and potent nAbs-can inform about the dynamics and durability of nAb responses in humans, knowledge which is crucial for the design of future HIV-1 vaccine regimens. To address this, we assessed HIV-1-neutralizing immunoglobulin G (IgG) from 2,354 persons with HIV-1 on or off antiretroviral therapy (ART).

A recent update on development, synthesis methods, properties and application of natural products derived carbon dots.

Natural resources are practically infinitely abundant in nature, which stimulates scientists to create new materials with inventive uses and minimal environmental impact. Due to the various benefits of natural carbon dots (NCDs) from them has received a lot of attention recently. Natural products-derived carbon dots have recently emerged as a highly promising class of nanomaterials, showcasing exceptional properties and eco-friendly nature, which make them appealing for diverse applications in various fields such as biomedical, environmental sensing and monitoring, energy storage and conversion, optoelectronics and photonics, agriculture, quantum computing, nanomedicine and cancer therapy.

The plant immune receptor SNC1 monitors helper NLRs targeted by a bacterial effector.

Plants deploy intracellular receptors to counteract pathogen effectors that suppress cell-surface-receptor-mediated immunity. To what extent pathogens manipulate intracellular receptor-mediated immunity, and how plants tackle such manipulation, remains unknown. Arabidopsis thaliana encodes three similar ADR1 class helper nucleotide-binding domain leucine-rich repeat receptors (ADR1, ADR1-L1, and ADR1-L2), which are crucial in plant immunity initiated by intracellular receptors.

Genome-scale metabolic models consistently predict characteristics of .

Genome-scale metabolic models (GEMs) are organism-specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. However, the validity of predictions for bacterial proliferation in settings is hardly investigated. The present work combines and approaches to create and curate strain-specific genome-scale metabolic models of .

A comprehension on structure guided alignment dependent 3D-QSAR modelling, and molecular dynamics simulation on 2,4-thiazolidinediones as aldose reductase inhibitors for the management of diabetic complications.

Aldose reductase is an oxo-reductase enzyme belonging to the aldo-keto reductase class. Compounds having thiazolidine-2,4-dione scaffold are reported as potential aldose reductase inhibitors for diabetic complications. The present work uses structure-guided alignment-dependent Gaussian field- and atom-based 3D-QSAR on a dataset of 84 molecules.

Bacterial genome-wide association study substantiates papGII of Escherichia coli as a major risk factor for urosepsis.

Background: Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, often caused by uropathogenic Escherichia coli. Multiple bacterial virulence factors or patient characteristics have been linked separately to progressive, more invasive infections. In this study, we aim to identify pathogen- and patient-specific factors that drive the progression to urosepsis by jointly analysing bacterial and host characteristics.

The International Virus Bioinformatics Meeting 2023.

The 2023 International Virus Bioinformatics Meeting was held in Valencia, Spain, from 24-26 May 2023, attracting approximately 180 participants worldwide. The primary objective of the conference was to establish a dynamic scientific environment conducive to discussion, collaboration, and the generation of novel research ideas. As the first in-person event following the SARS-CoV-2 pandemic, the meeting facilitated highly interactive exchanges among attendees.

plASgraph2: using graph neural networks to detect plasmid contigs from an assembly graph.

Identification of plasmids from sequencing data is an important and challenging problem related to antimicrobial resistance spread and other One-Health issues. We provide a new architecture for identifying plasmid contigs in fragmented genome assemblies built from short-read data. We employ graph neural networks (GNNs) and the assembly graph to propagate the information from nearby nodes, which leads to more accurate classification, especially for short contigs that are difficult to classify based on sequence features or database searches alone.

Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients.

A growing number of druggable targets and national initiatives for precision oncology necessitate broad genomic profiling for many cancer patients. Whole exome sequencing (WES) offers unbiased analysis of the entire coding sequence, segmentation-based detection of copy number alterations (CNAs), and accurate determination of complex biomarkers including tumor mutational burden (TMB), homologous recombination repair deficiency (HRD), and microsatellite instability (MSI). To assess the inter-institution variability of clinical WES, we performed a comparative pilot study between German Centers of Personalized Medicine (ZPMs) from five participating institutions.

An integrated systems biology approach reveals differences in formate metabolism in the genus .

Methanogenesis allows methanogenic archaea to generate cellular energy for their growth while producing methane. Thermophilic hydrogenotrophic species of the genus have been recognized as robust biocatalysts for a circular carbon economy and are already applied in power-to-gas technology with biomethanation, which is a platform to store renewable energy and utilize captured carbon dioxide. Here, we generated curated genome-scale metabolic reconstructions for three strains and investigated differences in the growth performance of these same strains in chemostat bioreactor experiments with hydrogen and carbon dioxide or formate as substrates.

EuCARE-hospitalised study protocol: a cohort study of patients hospitalised with COVID-19 in the EuCARE project.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), can lead to hospitalisation, particularly in elderly, immunocompromised, and non-vaccinated or partially vaccinated individuals. Although vaccination provides protection, the duration of this protection wanes over time. Additional doses can restore immunity, but the influence of viral variants, specific sequences, and vaccine-induced immune responses on disease severity remains unclear.

EuCARE-POSTCOVID Study: a multicentre cohort study on long-term post-COVID-19 manifestations.

Background: Post-COVID-19 condition refers to persistent or new onset symptoms occurring three months after acute COVID-19, which are unrelated to alternative diagnoses. Symptoms include fatigue, breathlessness, palpitations, pain, concentration difficulties ("brain fog"), sleep disorders, and anxiety/depression. The prevalence of post-COVID-19 condition ranges widely across studies, affecting 10-20% of patients and reaching 50-60% in certain cohorts, while the associated risk factors remain poorly understood.

Leveraging Unlabeled Electroencephalographic Data to Predict Neurological Recovery for Comatose Patients Following Cardiac Arrest.

In response to the 2023 George B. Moody PhysioNet Challenge, we propose an automated, unsupervised pre-training approach to boost the performance of models that predict neurologic outcomes after cardiac arrest. Our team, (BrownBAI), developed a model architecture consisting of three parts: a pre-processor to convert raw electroencephalograms (EEGs) into two-dimensional spectrograms, a three-layer convolutional neural network (CNN) encoder for unsupervised pre-training, and a time series transformer (TST) model.

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites.

Investigation of the anti-cancer potential of epoxyazadiradione in neuroblastoma: experimental assays and molecular analysis.

Neuroblastoma, the most common childhood solid tumor, originates from primitive sympathetic nervous system cells. Epoxyazadiradione (EAD) is a limonoid derived from , belonging to the family Meliaceae. In this study, we isolated the EAD from seed and studied the anti-cancer potential against neuroblastoma.