It's an idea that sounds straight from the latest science fiction blockbuster. But scientists at the University of Miami have warned that the world's oceans are teeming with'giant viruses', also known as giruses. Most viruses are less than 0.5 per cent the width of a human hair – too small to be seen with the naked human eye. In contrast, the researchers say that the giant viruses are five times bigger, rivaling bacteria in terms of size. Concerningly, all 230 giant viruses are previously unknown to science.

This study introduces the Supervised Magnitude-Altitude Scoring (SMAS) methodology, a machine learning-based approach, for analyzing gene expression data obtained from nonhuman primates (NHPs) infected with Ebola virus (EBOV). We utilize a comprehensive dataset of NanoString gene expression profiles from Ebola-infected NHPs, deploying the SMAS system for nuanced host-pathogen interaction analysis. SMAS effectively combines gene selection based on statistical significance and expression changes, employing linear classifiers such as logistic regression to accurately differentiate between RT-qPCR positive and negative NHP samples. A key finding of our research is the identification of IFI6 and IFI27 as critical biomarkers, demonstrating exceptional predictive performance with 100% accuracy and Area Under the Curve (AUC) metrics in classifying various stages of Ebola infection. Alongside IFI6 and IFI27, genes, including MX1, OAS1, and ISG15, were significantly upregulated, highlighting their essential roles in the immune response to EBOV. Our results underscore the efficacy of the SMAS method in revealing complex genetic interactions and response mechanisms during EBOV infection. This research provides valuable insights into EBOV pathogenesis and aids in developing more precise diagnostic tools and therapeutic strategies to address EBOV infection in particular and viral infection in general.

In this study, a Virus vs. Bacteria machine learning model was developed to discern between these infection types using 16 routine blood test results, C-reactive protein levels, biological sex, and age. With a dataset of 44,120 cases from a single medical center, the Virus vs. Bacteria model demonstrated remarkable accuracy of 82.2%, a Brier score of 0.129, and an area under the ROC curve of 0.91, surpassing the performance of traditional CRP decision rule models. The model demonstrates substantially improved accuracy within the CRP range of 10-40 mg/L, an interval in which CRP alone offers limited diagnostic value for distinguishing between bacterial and viral infections. These findings underscore the importance of considering multiple blood parameters for diagnostic decision-making and suggest that the Virus vs. Bacteria model could contribute to the creation of innovative diagnostic tools. Such tools would harness machine learning and relevant biomarkers to support enhanced clinical decision-making in managing infections.

A group of Northeastern researchers is tapping into the power of machine learning to develop new models for identifying patients who may have post-acute sequelae of SARS-CoV-2 infection, or so-called "long COVID." Using electronic health records from the National COVID Cohort Collaborative, a federal database that compiles medical information about COVID-19 patients, researchers were able to develop models that helped identify COVID long haulers across a range of features--from past COVID diagnosis, to the types of medications they've been prescribed, according to new research published in Lancet Digital Health. The data harmonization effort drew from a variety of information sources to construct a picture of what long COVID looks like in the U.S.--and who is most likely to have it. Those sources include demographic data, healthcare visit details, diagnoses and medications for 97,995 adults with COVID-19, the study says. Patients most likely suffering from the post-infection illness, which is estimated to plague between 10-30% of people who contract COVID-19, are often characterized as having new or lingering symptoms that are present 90 days after being diagnosed with the viral infection--a criteria researchers also used to determine their base population in their analysis.

IMAGE: Deep Learning detects virus infected cells and predicts acute, severe infections. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to completely suppress and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly. This can lead to serious acute diseases of the lungs or nervous system. The research group of Urs Greber, Professor at the Department of Molecular Life Sciences at the University of Zurich (UZH), has now shown for the first time that a machine-learning algorithm can recognize the cells infected with herpes or adenoviruses based solely on the fluorescence of the cell nucleus.

When viruses infect a cell, changes in the cell nucleus occur, and these can be observed through fluorescence microscopy. Using fluoresence images made in live cells, researchers at the University of Zurich have trained an artificial neural network to reliably recognize cells that are infected by adenoviruses or herpes viruses. The procedure also identifies severe acute infections at an early stage. In most cases, this does not lead to the production of new virus particles, as the viruses are suppressed by the immune system. However, adenoviruses and herpes viruses can cause persistent infections that the immune system is unable to keep completely in check and that produce viral particles for years. These same viruses can also cause sudden, violent infections where affected cells release large amounts of viruses, such that the infection spreads rapidly.

The image shows a glimpse of glycan diversity, showcasing several classes of glycans from various kingdoms of life. A new model that applies artificial intelligence to carbohydrates improves the understanding of the infection process and could help predict which viruses are likely to spread from animals to humans. This is reported in a recent study led by researchers at the University of Gothenburg. Carbohydrates participate in nearly all biological processes - yet they are still not well understood. Referred to as glycans, these carbohydrates are crucial to making our body work the way it is supposed to.

New York: A team of researchers has used an Artificial Intelligence (AI) algorithm to sift through terabytes of gene expression data to look for shared patterns in patients with past pandemic viral infections, including SARS, MERS and swine flu. The researchers, including Pradipta Ghosh from the University of California San Diego, indicated two telltale signatures. One, a set of 166 genes, reveals how the human immune system responds to viral infections. A second set of 20 signature genes predicts the severity of a patient's disease. For example, the need to hospitalise or use a mechanical ventilator.

Washington: Gene expression patterns associated with pandemic viral infections provide a map to help define patients' immune responses, measure disease severity, predict outcomes and test therapies, for current and future pandemics. Researchers at the University of California San Diego School of Medicine used an artificial intelligence (AI) algorithm to sift through terabytes of gene expression data, which genes are "on" or "off" during infection and to look for shared patterns in patients with past pandemic viral infections, including SARS, MERS and swine flu. Two telltale signatures emerged from the study, published in eBiomedicine called "AI-guided discovery of the invariant host response to viral pandemics". One, a set of 166 genes, reveals how the human immune system responds to viral infections. The second set of 20 signature genes predicts the severity of a patient's disease.

Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID-19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results represent a significant contribution to improvements in COVID-19 diagnosis.