Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.

BACKGROUND: While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. OBJECTIVE: In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool. METHODS: We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. RESULTS: UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. CONCLUSIONS: The outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2022.12.14.22283419.

Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.

[This corrects the article DOI: 10.2196/45767.].

The FDA-Approved Drug Nelfinavir Inhibits Lytic Cell-Free but Not Cell-Associated Nonlytic Transmission of Human Adenovirus.

Adenoviruses (AdVs) are prevalent and give rise to chronic and recurrent disease. Human AdV (HAdV) species B and C, such as HAdV-C2, -C5, and -B14, cause respiratory disease and constitute a health threat for immunocompromised individuals. HAdV-Cs are well known for lysing cells owing to the E3 CR1-ß-encoded adenovirus death protein (ADP). We previously reported a high-throughput image-based screening framework and identified an inhibitor of HAdV-C2 multiround infection, nelfinavir mesylate. Nelfinavir is the active ingredient of Viracept, an FDA-approved inhibitor of human immunodeficiency virus (HIV) aspartyl protease that is used to treat AIDS. It is not effective against single-round HAdV infections. Here, we show that nelfinavir inhibits lytic cell-free transmission of HAdV, indicated by the suppression of comet-shaped infection foci in cell culture. Comet-shaped foci occur upon convection-based transmission of cell-free viral particles from an infected cell to neighboring uninfected cells. HAdV lacking ADP was insensitive to nelfinavir but gave rise to comet-shaped foci, indicating that ADP enhances but is not required for cell lysis. This was supported by the notion that HAdV-B14 and -B14p1 lacking ADP were highly sensitive to nelfinavir, although HAdV-A31, -B3, -B7, -B11, -B16, -B21, -D8, -D30, and -D37 were less sensitive. Conspicuously, nelfinavir uncovered slow-growing round HAdV-C2 foci, independent of neutralizing antibodies in the medium, indicative of nonlytic cell-to-cell transmission. Our study demonstrates the repurposing potential of nelfinavir with postexposure efficacy against different HAdVs and describes an alternative nonlytic cell-to-cell transmission mode of HAdV.

The nuclear export factor CRM1 controls juxta-nuclear microtubule-dependent virus transport.

Transport of large cargo through the cytoplasm requires motor proteins and polarized filaments. Viruses that replicate in the nucleus of post-mitotic cells use microtubules and the dynein-dynactin motor to traffic to the nuclear membrane and deliver their genome through nuclear pore complexes (NPCs) into the nucleus. How virus particles (virions) or cellular cargo are transferred from microtubules to the NPC is unknown. Here, we analyzed trafficking of incoming cytoplasmic adenoviruses by single-particle tracking and super-resolution microscopy. We provide evidence for a regulatory role of CRM1 (chromosome-region-maintenance-1; also known as XPO1, exportin-1) in juxta-nuclear microtubule-dependent adenovirus transport. Leptomycin B (LMB) abolishes nuclear targeting of adenovirus. It binds to CRM1, precludes CRM1-cargo binding and blocks signal-dependent nuclear export. LMB-inhibited CRM1 did not compete with adenovirus for binding to the nucleoporin Nup214 at the NPC. Instead, CRM1 inhibition selectively enhanced virion association with microtubules, and boosted virion motions on microtubules less than ∼2 µm from the nuclear membrane. The data show that the nucleus provides positional information for incoming virions to detach from microtubules, engage a slower microtubule-independent motility to the NPC and enhance infection.

Inhibition of Poxvirus Gene Expression and Genome Replication by Bisbenzimide Derivatives.

Virus infection of humans and livestock can be devastating for individuals and populations, sometimes resulting in large economic and societal impact. Prevention of virus disease by vaccination or antiviral agents is difficult to achieve. A notable exception was the eradication of human smallpox by vaccination over 30 years ago. Today, humans and animals remain susceptible to poxvirus infections, including zoonotic poxvirus transmission. Here we identified a small molecule, bisbenzimide (bisbenzimidazole), and its derivatives as potent agents against prototypic poxvirus infection in cell culture. We show that bisbenzimide derivatives, which preferentially bind the minor groove of double-stranded DNA, inhibit vaccinia virus infection by blocking viral DNA replication and abrogating postreplicative intermediate and late gene transcription. The bisbenzimide derivatives are potent against vaccinia virus and other poxviruses but ineffective against a range of other DNA and RNA viruses. The bisbenzimide derivatives are the first inhibitors of their class, which appear to directly target the viral genome without affecting cell viability.IMPORTANCE Smallpox was one of the most devastating diseases in human history until it was eradicated by a worldwide vaccination campaign. Due to discontinuation of routine vaccination more than 30 years ago, the majority of today's human population remains susceptible to infection with poxviruses. Here we present a family of bisbenzimide (bisbenzimidazole) derivatives, known as Hoechst nuclear stains, with high potency against poxvirus infection. Results from a variety of assays used to dissect the poxvirus life cycle demonstrate that bisbenzimides inhibit viral gene expression and genome replication. These findings can lead to the development of novel antiviral drugs that target viral genomes and block viral replication.

Cell Cycle-Dependent Expression of Adeno-Associated Virus 2 (AAV2) Rep in Coinfections with Herpes Simplex Virus 1 (HSV-1) Gives Rise to a Mosaic of Cells Replicating either AAV2 or HSV-1.

Adeno-associated virus 2 (AAV2) depends on the simultaneous presence of a helper virus such as herpes simplex virus 1 (HSV-1) for productive replication. At the same time, AAV2 efficiently blocks the replication of HSV-1, which would eventually limit its own replication by diminishing the helper virus reservoir. This discrepancy begs the question of how AAV2 and HSV-1 can coexist in a cell population. Here we show that in coinfected cultures, AAV2 DNA replication takes place almost exclusively in S/G2-phase cells, while HSV-1 DNA replication is restricted to G1 phase. Live microscopy revealed that not only wild-type AAV2 (wtAAV2) replication but also reporter gene expression from both single-stranded and double-stranded (self-complementary) recombinant AAV2 vectors preferentially occurs in S/G2-phase cells, suggesting that the preference for S/G2 phase is independent of the nature of the viral genome. Interestingly, however, a substantial proportion of S/G2-phase cells transduced by the double-stranded but not the single-stranded recombinant AAV2 vectors progressed through mitosis in the absence of the helper virus. We conclude that cell cycle-dependent AAV2 rep expression facilitates cell cycle-dependent AAV2 DNA replication and inhibits HSV-1 DNA replication. This may limit competition for cellular and viral helper factors and, hence, creates a biological niche for either virus to replicate.IMPORTANCE Adeno-associated virus 2 (AAV2) differs from most other viruses, as it requires not only a host cell for replication but also a helper virus such as an adenovirus or a herpesvirus. This situation inevitably leads to competition for cellular resources. AAV2 has been shown to efficiently inhibit the replication of helper viruses. Here we present a new facet of the interaction between AAV2 and one of its helper viruses, herpes simplex virus 1 (HSV-1). We observed that AAV2 rep gene expression is cell cycle dependent and gives rise to distinct time-controlled windows for HSV-1 replication. High Rep protein levels in S/G2 phase support AAV2 replication and inhibit HSV-1 replication. Conversely, low Rep protein levels in G1 phase permit HSV-1 replication but are insufficient for AAV2 replication. This allows both viruses to productively replicate in distinct sets of dividing cells.

Chemical induction of unfolded protein response enhances cancer cell killing through lytic virus infection.

UNLABELLED: Cancer cells are susceptible to oncolytic viruses, albeit variably. Human adenoviruses (HAdVs) are widely used oncolytic agents that have been engineered to produce progeny within the tumor and elicit bystander effects. We searched for host factors enhancing bystander effects and conducted a targeted RNA interference screen against guanine nucleotide exchange factors (GEFs) of small GTPases. We show that the unfolded protein response (UPR), which is readily inducible in aggressive tumor cells, enhances melanoma or epithelial cancer cell killing upon HAdV infection. UPR was triggered by knockdown of Golgi-specific brefeldin A-resistant guanine nucleotide exchange factor 1 (GBF-1) or the GBF-1 inhibitor golgicide A (GCA) and stimulated HAdV infection. GBF-1 is a GEF for ADP ribosylation factors (Arfs) regulating endoplasmic reticulum (ER)-to-Golgi apparatus and intra-Golgi apparatus membrane transport. Cells treated with GCA enhanced HAdV-induced cytopathic effects in epithelial and melanoma cancer cells but not normal cells, if the drug was applied several hours prior to HAdV inoculation. This was shown by real-time label-free impedance measurements using the xCELLigence system. GCA-treated cells contained fewer incoming HAdVs than control cells, but GCA treatment boosted HAdV titers and spreading in cancer cells. GCA enhanced viral gene expression or transgene expression from the cytomegalovirus promoter of B- or C-species HAdVs but did not enhance viral early region 1A (E1A) expression in uninfected cell lines or cells transfected with plasmid reporter DNA. The UPR-enhanced cell killing required the nuclease activity of the UPR sensor inositol-requiring enzyme 1 (IRE-1) and X box binding protein 1 (XBP-1), which alleviate ER stress. The collective results show that chemical UPR induction and viruses boost tumor cell killing by enhancing oncolytic viral efficacy. IMPORTANCE: Cancer is difficult to combat. A wide range of oncolytic viruses show promise for killing cancer cells, yet the efficacy of oncolytic killing is low. We searched for host factors enhancing adenovirus cancer cell killing and found that the knockdown of Golgi-specific brefeldin A-resistant guanine nucleotide exchange factor 1 (GBF-1) or chemical inhibition of GBF-1 enhanced adenovirus infection by triggering the IRE-1/XBP-1 branch of the unfolded protein response (UPR). IRE-1/XBP-1 promote cell survival and enhanced the levels of the adenoviral immediate early gene product E1A, virus spreading, and killing of cancer cells. Aggressive tumor cells depend on a readily inducible UPR and, hence, present prime targets for a combined strategy involving adenoviruses and small chemicals inducing UPR.

Simultaneous analysis of large-scale RNAi screens for pathogen entry.

BACKGROUND: Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries. RESULTS: We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied. CONCLUSIONS: Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.

Toward the novel AI tasks in infection biology.

Machine learning and artificial intelligence (AI) are becoming more common in infection biology laboratories around the world. Yet, as they gain traction in research, novel frontiers arise. Novel artificial intelligence algorithms are capable of addressing advanced tasks like image generation and question answering. However, similar algorithms can prove useful in addressing advanced questions in infection biology like prediction of host-pathogen interactions or inferring virus protein conformations. Addressing such tasks requires large annotated data sets, which are often scarce in biomedical research. In this review, I bring together several successful examples where such tasks were addressed. I underline the importance of formulating novel AI tasks in infection biology accompanied by freely available benchmark data sets to address these tasks. Furthermore, I discuss the current state of the field and potential future trends. I argue that one such trend involves AI tools becoming more versatile.

A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy.

High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day machine learning and deep learning algorithms, this is widely impeded by the lack of suitable datasets. To address this, here we present a purpose-created open dataset of high-content microscopy sample preparation artefact. It consists of high-content microscopy of laboratory dust titrated on fixed cell culture specimens imaged with fluorescence filters covering the complete spectral range. To ensure this dataset is suitable for supervised machine learning tasks like image classification or segmentation we propose rule-based annotation strategies on categorical and pixel levels. We demonstrate the applicability of our dataset for deep learning by training a convolutional-neural-network-based classifier.

Bisbenzimide compounds inhibit the replication of prototype and pandemic potential poxviruses.

We previously identified the bisbenzimide Hoechst 33342 (H42) as a potent multi-stage inhibitor of the prototypic poxvirus, the vaccinia virus (VACV), and several parapoxviruses. A recent report showed that novel bisbenzimide compounds similar in structure to H42 could prevent human cytomegalovirus replication. Here, we assessed whether these compounds could also serve as poxvirus inhibitors. Using virological assays, we show that these bisbenzimide compounds inhibit VACV spread, plaque formation, and the production of infectious progeny VACV with relatively low cell toxicity. Further analysis of the VACV lifecycle indicated that the effective bisbenzimide compounds had little impact on VACV early gene expression but inhibited VACV late gene expression and truncated the formation of VACV replication sites. Additionally, we found that bisbenzimide compounds, including H42, can inhibit both monkeypox and a VACV mutant resistant to the widely used anti-poxvirus drug TPOXX (Tecovirimat). Therefore, the tested bisbenzimide compounds were inhibitors of both prototypic and pandemic potential poxviruses and could be developed for use in situations where anti-poxvirus drug resistance may occur. Additionally, these data suggest that bisbenzimide compounds may serve as broad-activity antiviral compounds, targeting diverse DNA viruses such as poxviruses and betaherpesviruses.IMPORTANCEThe 2022 mpox (monkeypox) outbreak served as a stark reminder that due to the cessation of smallpox vaccination over 40 years ago, most of the human population remains susceptible to poxvirus infection. With only two antivirals approved for the treatment of smallpox infection in humans, the need for additional anti-poxvirus compounds is evident. Having shown that the bisbenzimide H33342 is a potent inhibitor of poxvirus gene expression and DNA replication, here we extend these findings to include a set of novel bisbenzimide compounds that show anti-viral activity against mpox and a drug-resistant prototype poxvirus mutant. These results suggest that further development of bisbenzimides for the treatment of pandemic potential poxviruses is warranted.

A clinical microscopy dataset to develop a deep learning diagnostic test for urinary tract infection.

Urinary tract infection (UTI) is a common disorder. Its diagnosis can be made by microscopic examination of voided urine for markers of infection. This manual technique is technically difficult, time-consuming and prone to inter-observer errors. The application of computer vision to this domain has been slow due to the lack of a clinical image dataset from UTI patients. We present an open dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant cell types. It is an enriched dataset acquired from the unstained and untreated urine of patients with symptomatic UTI using a simple imaging system. We demonstrate that this dataset can be used to train a Patch U-Net, a novel deep learning architecture with a random patch generator to recognise urinary cells. Our hope is, with this dataset, UTI diagnosis will be made possible in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques.

Vaccinia virus subverts xenophagy through phosphorylation and nuclear targeting of p62.

Autophagy is an essential degradation program required for cell homeostasis. Among its functions is the engulfment and destruction of cytosolic pathogens, termed xenophagy. Not surprisingly, many pathogens use various strategies to circumvent or co-opt autophagic degradation. For poxviruses, it is known that infection activates autophagy, which however is not required for successful replication. Even though these complex viruses replicate exclusively in the cytoplasm, autophagy-mediated control of poxvirus infection has not been extensively explored. Using the prototypic poxvirus, vaccinia virus (VACV), we show that overexpression of the xenophagy receptors p62, NDP52, and Tax1Bp1 restricts poxvirus infection. While NDP52 and Tax1Bp1 were degraded, p62 initially targeted cytoplasmic virions before being shunted to the nucleus. Nuclear translocation of p62 was dependent upon p62 NLS2 and correlated with VACV kinase mediated phosphorylation of p62 T269/S272. This suggests that VACV targets p62 during the early stages of infection to avoid destruction and further implies that poxviruses exhibit multi-layered control of autophagy to facilitate cytoplasmic replication.

Cell-free transmission of human adenovirus by passive mass transfer in cell culture simulated in a computer model.

Viruses spread between cells, tissues, and organisms by cell-free and cell-cell transmissions. Both mechanisms enhance disease development, but it is difficult to distinguish between them. Here, we analyzed the transmission mode of human adenovirus (HAdV) in monolayers of epithelial cells by wet laboratory experimentation and a computer simulation. Using live-cell fluorescence microscopy and replication-competent HAdV2 expressing green fluorescent protein, we found that the spread of infection invariably occurred after cell lysis. It was affected by convection and blocked by neutralizing antibodies but was independent of second-round infections. If cells were overlaid with agarose, convection was blocked and round plaques developed around lytic infected cells. Infected cells that did not lyse did not give rise to plaques, highlighting the importance of cell-free transmission. Key parameters for cell-free virus transmission were the time from infection to lysis, the dose of free viruses determining infection probability, and the diffusion of single HAdV particles in aqueous medium. With these parameters, we developed an in silico model using multiscale hybrid dynamics, cellular automata, and particle strength exchange. This so-called white box model is based on experimentally determined parameters and reproduces viral infection spreading as a function of the local concentration of free viruses. These analyses imply that the extent of lytic infections can be determined by either direct plaque assays or can be predicted by calculations of virus diffusion constants and modeling.

A weak-labelling and deep learning approach for in-focus object segmentation in 3D widefield microscopy.

Three-dimensional information is crucial to our understanding of biological phenomena. The vast majority of biological microscopy specimens are inherently three-dimensional. However, conventional light microscopy is largely geared towards 2D images, while 3D microscopy and image reconstruction remain feasible only with specialised equipment and techniques. Inspired by the working principles of one such technique-confocal microscopy, we propose a novel approach to 3D widefield microscopy reconstruction through semantic segmentation of in-focus and out-of-focus pixels. For this, we explore a number of rule-based algorithms commonly used for software-based autofocusing and apply them to a dataset of widefield focal stacks. We propose a computation scheme allowing the calculation of lateral focus score maps of the slices of each stack using these algorithms. Furthermore, we identify algorithms preferable for obtaining such maps. Finally, to ensure the practicality of our approach, we propose a surrogate model based on a deep neural network, capable of segmenting in-focus pixels from the out-of-focus background in a fast and reliable fashion. The deep-neural-network-based approach allows a major speedup for data processing making it usable for online data processing.

Roadmap on Deep Learning for Microscopy.

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

Sequencing meets machine learning to fight emerging pathogens: A preview.

In searching for SARS-CoV variants-of-concern, pathogen sequencing is generating an impressive amount of data. However, beyond epidemiological use, these data contain cues fundamental to our understanding of pathogen evolution in the human population. Yet, to harness them, further development of computational methodology, such as machine learning, may be required. This preview discusses updates in machine learning to understand emerging pathogens.

A tandem segmentation-classification approach for the localization of morphological predictors of C. elegans lifespan and motility.

C. elegans is an established model organism for studying genetic and drug effects on aging, many of which are conserved in humans. It is also an important model for basic research, and C. elegans pathologies is a new emerging field. Here we develop a proof-of-principal convolutional neural network-based platform to segment C. elegans and extract features that might be useful for lifespan prediction. We use a dataset of 734 worms tracked throughout their lifespan and classify worms into long-lived and short-lived. We designed WormNet - a convolutional neural network (CNN) to predict the worm lifespan class based on young adult images (day 1 - day 3 old adults) and showed that WormNet, as well as, InceptionV3 CNN can successfully classify lifespan. Based on U-Net architecture we develop HydraNet CNNs which allow segmenting worms accurately into anterior, mid-body and posterior parts. We combine HydraNet segmentation, WormNet prediction and the class activation map approach to determine the segments most important for lifespan classification. Such a tandem segmentation-classification approach shows the posterior part of the worm might be more important for classifying long-lived worms. Our approach can be useful for the acceleration of anti-aging drug discovery and for studying C. elegans pathologies.

Open-Source Biomedical Image Analysis Models: A Meta-Analysis and Continuous Survey.

Open-source research software has proven indispensable in modern biomedical image analysis. A multitude of open-source platforms drive image analysis pipelines and help disseminate novel analytical approaches and algorithms. Recent advances in machine learning allow for unprecedented improvement in these approaches. However, these novel algorithms come with new requirements in order to remain open source. To understand how these requirements are met, we have collected 50 biomedical image analysis models and performed a meta-analysis of their respective papers, source code, dataset, and trained model parameters. We concluded that while there are many positive trends in openness, only a fraction of all publications makes all necessary elements available to the research community.

High-content, arrayed compound screens with rhinovirus, influenza A virus and herpes simplex virus infections.

Viruses are genetically and structurally diverse, and outnumber cells by orders of magnitude. They can cause acute and chronic infections, suppress, or exacerbate immunity, or dysregulate survival and growth of cells. To identify chemical agents with pro- or antiviral effects we conducted arrayed high-content image-based multi-cycle infection screens of 1,280 mainly FDA-approved compounds with three human viruses, rhinovirus (RV), influenza A virus (IAV), and herpes simplex virus (HSV) differing in genome organization, composition, presence of an envelope, and tropism. Based on Z'-factors assessing screening quality and Z-scores ranking individual compounds, we identified potent inhibitors and enhancers of infection: the RNA mutagen 5-Azacytidine against RV-A16; the broad-spectrum antimycotic drug Clotrimazole inhibiting IAV-WSN; the chemotherapeutic agent Raltitrexed blocking HSV-1; and Clobetasol enhancing HSV-1. Remarkably, the topical antiseptic compound Aminacrine, which is clinically used against bacterial and fungal agents, inhibited all three viruses. Our data underscore the versatility and potency of image-based, full cycle virus propagation assays in cell-based screenings for antiviral agents.