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Extracellular proteins play a significant role in shaping microbial communities which, in turn, can impact ecosystem function, human health, and biotechnological processes. Yet, for many ubiquitous microbes, there is limited knowledge regarding the identity and function of secreted proteins. Here, we introduce EXCRETE (enhanced exoproteome characterization by mass spectrometry), a workflow that enables comprehensive description of microbial exoproteomes from minimal starting material. Using cyanobacteria as a case study, we benchmark EXCRETE and show a significant increase over current methods in the identification of extracellular proteins. Subsequently, we show that EXCRETE can be miniaturized and adapted to a 96-well high-throughput format. Application of EXCRETE to cyanobacteria from different habitats (Synechocystis sp. PCC 6803, Synechococcus sp. PCC 11901, and Nostoc punctiforme PCC 73102), and in different cultivation conditions, identified up to 85% of all potentially secreted proteins. Finally, functional analysis reveals that cell envelope maintenance and nutrient acquisition are central functions of the predicted cyanobacterial secretome. Collectively, these findings challenge the general belief that cyanobacteria lack secretory proteins and suggest that multiple functions of the secretome are conserved across freshwater, marine, and terrestrial species.
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http://dx.doi.org/10.1038/s42003-024-06910-2 | DOI Listing |
Curr Opin Infect Dis
August 2025
Transplant and Immunocompromised Host Infectious Diseases, Department of Medicine, Infectious Diseases Division, Massachusetts General Hospital.
Purpose Of Review: Plasma metagenomic next-generation sequencing (mNGS) enables detection of microbial cell-free deoxyribonucleic acid (mcfDNA) in blood without the need for culture or organism-specific primers. Here, we review clinical performance, methodological variability, and real-world application of plasma mNGS for infectious disease diagnosis in immunocompromised hosts (ICHs).
Recent Findings: Plasma mNGS has rapidly gained attention as a novel diagnostic tool for infections in ICHs, offering broad-range pathogen detection from a noninvasive blood sample.
Med Biol Eng Comput
September 2025
Department of Computer Science, Università degli Studi di Bari Aldo Moro, Bari, Italy.
Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging.
View Article and Find Full Text PDFmSystems
September 2025
Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
Genome-scale metabolic models (GEMs) are widely used in systems biology to investigate metabolism and predict perturbation responses. Automatic GEM reconstruction tools generate GEMs with different properties and predictive capacities for the same organism. Since different models can excel at different tasks, combining them can increase metabolic network certainty and enhance model performance.
View Article and Find Full Text PDFFront Digit Health
August 2025
FEN - Graduate School in Engineering, State University of Rio de Janeiro (UERJ), Rio de Janeiro, Brazil.
Background: This paper presents the application of simulation to assess the functionality of a proposed Digital Twin (DT) architecture for immunisation services in primary healthcare centres. The solution is based on Industry 4.0 concepts and technologies, such as IoT, machine learning, and cloud computing, and adheres to the ISO 23247 standard.
View Article and Find Full Text PDFDigit Health
September 2025
Information Technology Department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
Objective: To evaluate the impact of integrating digital shadow technology with Lean Six Sigma methodology on intra-laboratory turnaround time (TAT) in a high-volume clinical laboratory, and to demonstrate how digital shadow architectures can enhance process visibility and drive sustainable operational improvements.
Methods: A retrospective, two-phase study was conducted in a tertiary cancer hospital from January to December 2024. Digital shadow technology was implemented by leveraging real-time, time-stamped data from the laboratory information system (LIS) to map specimen workflow milestones.