C. Paul Morris

Traditional infectivity-based virion quantification methods, such as 50% tissue culture infectious dose (TCID ₅₀ ) and plaque assays, are typically performed in 6-, 12-, 24-, or 48-well cell culture plates and require manual work and analysis based on legacy protocols. Adaptation of these methods to high-throughput formats (96-, 384- or 1,536-well plates) is challenging due to both assay automation constraints and the lack of well surface area available for reliable analysis. Here, we present a scalable alternative to traditional methods that uses whole-well image thresholding to quantify infectious virions by measuring virus-induced cytopathic effect (CPE) via cell lysis and detachment. The CPE area assay is best positioned as a practical high-throughput preliminary screening tool, effectively quantifying samples within the assay range and flagging samples above or below the concentration thresholds. To improve analysis efficiency and reduce user bias in CPE area selection, we evaluated an nnU-Net model for automated image segmentation against images segmented by manually defined brightness thresholds. The model achieved strong correlation with manual thresholding ( R ² = 1.00), showing minimal differences in the identified CPE area and thus demonstrating that nnU-Net accurately reproduces manual analysis. This approach provides two complementary pipelines: manual thresholding, which tolerates adjustments of hyperparameters, and fully automated segmentation via nnU-Net, which streamlines analysis and enhances throughput. This flexible CPE area assay enables accurate and automated quantification in high-throughput screening formats, thereby greatly accelerating a routine laboratory task while decreasing subjectivity and bias.

Background: Seasonal influenza causes significant global morbidity, mortality, and economic burden. Ongoing viral evolution can lead to vaccine mismatch and the emergence of antiviral resistance, highlighting the importance of genomic surveillance. The 2024–2025 influenza season was characterized by high incidence and increased hospitalizations. Methods: We analyzed influenza A virus (IAV) genomes and clinical characteristics from the 2024–2025 season. Whole-genome sequencing was performed on 648 influenza A–positive clinical specimens collected between October 2024 and April 2025. Results: Hemagglutinin (HA) sequences were recovered from 74.23% (481/648) of samples and used for subtyping and phylogenetic analysis. A(H1N1)pdm09 and A(H3N2) viruses cocirculated, representing 55.5% and 44.5% of cases, respectively. Among A(H1N1)pdm09 viruses, the HA1 substitution T120A, located near the Sa antigenic site, increased more than 2-fold compared with the prior season. Circulating A(H3N2) viruses belonged to multiple HA subclades and exhibited distinct amino acid substitutions at key antigenic sites. Neutralization assays using sera from individuals vaccinated with the 2024–2025 seasonal influenza vaccine demonstrated reduced neutralization of 3 dominant A(H1N1)pdm09 isolates and 2 A(H3N2) isolates compared with vaccine strains, consistent with antigenic drift. In addition, the neuraminidase substitution S247N, previously associated with reduced oseltamivir susceptibility, was detected in 13.9% of A(H1N1)pdm09 samples. Conclusions: These findings demonstrate ongoing antigenic drift and the presence of antiviral resistance–associated mutations during the 2024–2025 influenza season, underscoring the need for continued genomic surveillance to guide vaccine and antiviral strategies.

The liver is a key target of pathogenic Marburg and Ebola viruses (MARV and EBOV, respectively), with injury common in severe filovirid disease, yet high-resolution histopathologic quantification is lacking. We addressed this by deploying deep learning (DL) models for pixel-level quantitative analysis of digitized liver pathology slides in lethal rhesus monkey (RM) models of MARV and 2 EBOV variants, Makona and Kikwit. Our DL model segmented arteries, veins, bile ducts, and hepatic necrosis, achieving interobserver variability in necrosis segmentation comparable to that of 3 pathologists. DL-quantified liver necrosis correlated with exposure virus (f = 6.61, P = .006) and was highest in MARV-exposed RMs (11.0%). While filovirid-exposed RMs showed elevations in aspartate aminotransferase, alanine aminotransferase (ALT), gamma-glutamyl transferase, and alkaline phosphatase, only ALT levels correlated with necrosis severity. Necrosis localization differed—portal tract proximate after MARV exposure and central vein proximate after EBOV exposure. This proof-of-concept work enables future large-scale retrospective “meta-pathologic” analyses.

Traditional infectivity-based virion quantification methods, such as 50% tissue culture infectious dose (TCID ₅₀ ) and plaque assays, are typically performed in 6-, 12-, 24-, or 48-well cell culture plates and require manual work and analysis based on legacy protocols. Adaptation of these methods to high-throughput formats (96-, 384- or 1,536-well plates) is challenging due to both assay automation constraints and the lack of well surface area available for reliable analysis. Here, we present a scalable alternative to traditional methods that uses whole-well image thresholding to quantify infectious virions by measuring virus-induced cytopathic effect (CPE) via cell lysis and detachment. The CPE area assay is best positioned as a practical high-throughput preliminary screening tool, effectively quantifying samples within the assay range and flagging samples above or below the concentration thresholds. To improve analysis efficiency and reduce user bias in CPE area selection, we evaluated an nnU-Net model for automated image segmentation against images segmented by manually defined brightness thresholds. The model achieved perfect correlation with manual thresholding ( R ² =1.00), showing minimal differences in the identified CPE area and thus validating nnU-Net as a reliable alternative to manual analysis. This approach provides two complementary pipelines: manual thresholding, which tolerates adjustments of hyperparameters, and fully automated segmentation via nnU-Net, which streamlines analysis and enhances throughput. This flexible CPE area assay enables accurate and automated quantification in high-throughput screening formats, thereby greatly accelerating a routine laboratory task while decreasing subjectivity and bias. Author summary: Measuring the amount of virus in a biosample is a critical part of viral research and testing of new treatments. Traditional methods are slow, labor-intensive, and not easily scaled up to handle large numbers of samples. In this study, we developed a new approach that makes the process faster, more efficient, scalable, and less reliant on manual steps. We used microscopy imaging to capture how cells respond to virus infections and analyzed these images automatically to identify and measure areas where the virus had damaged cells. The analysis pipeline allows researchers to fine-tune settings or run the process using machine learning. This method is flexible and adaptable to a variety of viruses and experiments. By increasing the number of samples that can be tested at once while reducing the time and effort needed for analysis, our approach has the potential to accelerate research in virology, drug development, and public health.

Background/Objective: Computed tomography (CT) can be used to non-invasively assess the health of the liver; however, radiologist evaluation and simple thresholding alone are insufficient for diagnosis of hepatic steatosis, necessitating biopsies. This study explored CT radiomics and machine learning to enable non-invasive, objective, and quantitative prediction of steatosis severity across the macaque liver. Methods: In this retrospective study, CT images of 42 crab-eating macaques (age [yr] = 6.1 ± 1.7; sex [male/female] = 26/16) with varying degrees of hepatic steatosis were analyzed, and the results were compared to histology-based steatosis scores of livers from the same animals. After extracting radiomic features, a thorough array of statistical analyses, feature selection techniques, and machine learning models were applied to identify a distinct radiomic signature of histologically defined hepatic steatosis. Results: We identified 12 radiomic features that correlated with steatosis scores, and hierarchical clustering based on radiomic attributes alone revealed clusters roughly aligning with steatosis severity groups. The k-nearest neighbors model architecture best predicted histopathologic steatosis scores in both classification and regression tasks (area under the receiver operating characteristic curve [AUC ROC] = 0.89 ± 0.09; root-mean-square error [RMSE] = 0.60 ± 0.10). Feature analyses identified seven key radiomic features (six first-order features and one gray-level co-occurrence matrix feature) that were most important when predicting steatosis. Conclusions: We identified a CT radiomic signature of steatosis and demonstrated that histology-based steatosis scores can be predicted non-invasively and objectively using machine learning and CT radiomics as a potential alternative to invasive core biopsies. Given the strong similarities in liver structure, liver function, and hepatic steatosis pathophysiology between macaques and humans, these findings have the potential to translate to humans.

BACKGROUND: Increased SARS-CoV-2 reinfection rates have been reported recently, with some locations basing reinfection on a second positive PCR test at least 90 days after initial infection. In this study, we used Johns Hopkins SARS-CoV-2 genomic surveillance data to evaluate the frequency of sequencing-validated, confirmed, and inferred reinfections between March 2020 and July 2022. METHODS: Patients who had 2 or more positive SARS-CoV-2 tests in our system, with samples sequenced as a part of our surveillance efforts, were identified as the cohort for our study. SARS-CoV-2 genomes of patients’ initial and later samples were compared. RESULTS: A total of 755 patients (920 samples) had a positive test at least 90 days after the initial test, with a median time between tests of 377 days. Sequencing was attempted on 231 samples and was successful in 127. Rates of successful sequencing spiked during the Omicron surge; there was a higher median number of days from initial infection in these cases compared with those with failed sequences. A total of 122 (98%) patients showed evidence of reinfection; 45 of these patients had sequence-validated reinfection and 77 had inferred reinfections (later sequencing showed a clade that was not circulating when the patient was initially infected). Of the 45 patients with sequence-validated reinfections, 43 (96%) had reinfections that were caused by the Omicron variant, 41 (91%) were symptomatic, 32 (71%) were vaccinated prior to the second infection, 6 (13%) were immunosuppressed, and only 2 (4%) were hospitalized. CONCLUSION: Sequence-validated reinfections increased with the Omicron surge but were generally associated with mild infections. FUNDING: Funding was provided by the Johns Hopkins Center of Excellence in Influenza Research and Surveillance (HHSN272201400007C), CDC (75D30121C11061), Johns Hopkins University President’s Fund Research Response, Johns Hopkins Department of Pathology, and the Maryland Department of Health.

Among 9048 people infected with SARS-CoV-2 between January and May 2021 in Maryland, in regression-adjusted analysis, SARS-CoV-2 viruses carrying the spike protein mutation E484K were disproportionately prevalent among persons infected after full vaccination against COVID-19 compared with infected persons who were not fully vaccinated (aOR, 1.96; 95% CI: 1.36–2.83).

The early COVID-19 pandemic was characterized by rapid global spread. In Maryland and Washington, DC, United States, more than 2500 cases were reported within 3 weeks of the first COVID-19 detection in March 2020. We aimed to use genomic sequencing to understand the initial spread of SARS-CoV-2 — the virus that causes COVID-19 — in the region. We analyzed 620 samples collected from the Johns Hopkins Health System during March 11–31, 2020, comprising 28.6% of the total cases in Maryland and Washington, DC. From these samples, we generated 114 complete viral genomes. Analysis of these genomes alongside a subsampling of over 1000 previously published sequences showed that the diversity in this region rivaled global SARS-CoV-2 genetic diversity at that time and that the sequences belong to all of the major globally circulating lineages, suggesting multiple introductions into the region. We also analyzed these regional SARS-CoV-2 genomes alongside detailed clinical metadata and found that clinically severe cases had viral genomes belonging to all major viral lineages. We conclude that efforts to control local spread of the virus were likely confounded by the number of introductions into the region early in the epidemic and the interconnectedness of the region as a whole.

Background: The early COVID-19 pandemic has been characterized by rapid global spread. In the United States National Capital Region, over 2,000 cases were reported within three weeks of its first detection in March 2020. We aimed to use genomic sequencing to understand the initial spread of SARS-CoV-2, the virus that causes COVID-19, in the region. By correlating genetic information to disease phenotype, we also aimed to gain insight into any correlation between viral genotype and case severity or transmissibility. Methods: We performed whole genome sequencing of clinical SARS-CoV-2 samples collected in March 2020 by the Johns Hopkins Health System. We analyzed these regional SARS-CoV-2 genomes alongside detailed clinical metadata and the global phylogeny to understand early establishment of the virus within the region. Results: We analyzed 620 samples from the Johns Hopkins Health System collected between March 11–31, 2020, comprising 37.3% of the total cases in Maryland during this period. We selected 143 of these samples for sequencing, generating 114 complete viral genomes. These genomes belong to all five major Nextstrain-defined clades, suggesting multiple introductions into the region and underscoring the diversity of the regional epidemic. We also found that clinically severe cases had genomes belonging to all of these clades. Conclusions: We established a pipeline for SARS-CoV-2 sequencing within the Johns Hopkins Health system, which enabled us to capture the significant viral diversity present in the region as early as March 2020. Efforts to control local spread of the virus were likely confounded by the number of introductions into the region early in the epidemic and interconnectedness of the region as a whole.

Repeat molecular testing for SARS-CoV-2 may result in scenarios including multiple positive results, positive test results after negative tests, and repeated false negative results in symptomatic individuals. Consecutively collected specimens from a retrospective cohort of COVID-19 patients at the Johns Hopkins Hospital were assessed for RNA and infectious virus shedding. Whole genome sequencing confirmed the virus genotype in patients with prolonged viral RNA shedding and droplet digital PCR (ddPCR) was used to assess the rate of false negative standard of care PCR results. Recovery of infectious virus was associated with Ct values of 18.8 ± 3.4. Prolonged viral RNA shedding was associated with recovery of infectious virus in specimens collected up to 20 days after the first positive result in patients who were symptomatic at the time of specimen collection. The use of Ct values and clinical symptoms provides a more accurate assessment of the potential for infectious virus shedding.