Age associated susceptibility to SARS-CoV-2 infection in the K18-hACE2 transgenic mouse model.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still an ongoing global health crisis. Clinical data indicate that the case fatality rate (CFR) is age dependent, with a higher CFR percentage in the elderly population. We compared the pathogenesis of SARS-CoV-2 in young and aged K18-hACE2 transgenic mice. We evaluated morbidity, mortality, viral titers, immune responses, and histopathology in SARS-CoV-2-infected young and old K18-hACE2 transgenic mice. Within the limitation of having a low number of mice per group, our results indicate that SARS-CoV-2 infection resulted in slightly higher morbidity, mortality, and viral replication in the lungs of old mice, which was associated with an impaired IgM response and altered cytokine and chemokine profiles. Results of this study increase our understanding of SARS-CoV-2 infectivity and immuno-pathogenesis in the elderly population.

Aerosolized sulfated hyaluronan derivatives prolong the survival of K18 ACE2 mice infected with a lethal dose of SARS-CoV-2.

Despite several vaccines that are currently approved for human use to control the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent medical need for therapeutic and prophylactic options. SARS-CoV-2 binding and entry in human cells involves interactions of its spike (S) protein with several host cell surface factors, including heparan sulfate proteoglycans (HSPGs), transmembrane protease serine 2 (TMPRSS2), and angiotensin-converting enzyme 2 (ACE2). In this paper we investigated the potential of sulphated Hyaluronic Acid (sHA), a HSPG mimicking polymer, to inhibit the binding of SARS-CoV-2 S protein to human ACE2 receptor. After the assessment of different sulfation degree of sHA backbone, a series of sHA functionalized with different hydrophobic side chains were synthesized and screened. The compound showing the highest binding affinity to the viral S protein was further characterized by surface plasmon resonance (SPR) towards ACE2 and viral S protein binding domain. Selected compounds were formulated as solutions for nebulization and, after being characterized in terms of aerosolization performance and droplet size distribution, their efficacy was assessed in vivo using the K18 human (h)ACE2 transgenic mouse model of SARS-CoV-2 infection.

Aid or Antagonize: Nuclear Long Noncoding RNAs Regulate Host Responses and Outcomes of Viral Infections.

Long noncoding RNAs (lncRNAs) are transcripts measuring >200 bp in length and devoid of protein-coding potential. LncRNAs exceed the number of protein-coding mRNAs and regulate cellular, developmental, and immune pathways through diverse molecular mechanisms. In recent years, lncRNAs have emerged as epigenetic regulators with prominent roles in health and disease. Many lncRNAs, either host or virus-encoded, have been implicated in critical cellular defense processes, such as cytokine and antiviral gene expression, the regulation of cell signaling pathways, and the activation of transcription factors. In addition, cellular and viral lncRNAs regulate virus gene expression. Viral infections and associated immune responses alter the expression of host lncRNAs regulating immune responses, host metabolism, and viral replication. The influence of lncRNAs on the pathogenesis and outcomes of viral infections is being widely explored because virus-induced lncRNAs can serve as diagnostic and therapeutic targets. Future studies should focus on thoroughly characterizing lncRNA expressions in virus-infected primary cells, investigating their role in disease prognosis, and developing biologically relevant animal or organoid models to determine their suitability for specific therapeutic targeting. Many cellular and viral lncRNAs localize in the nucleus and epigenetically modulate viral transcription, latency, and host responses to infection. In this review, we provide an overview of the role of nuclear lncRNAs in the pathogenesis and outcomes of viral infections, such as the Influenza A virus, Sendai Virus, Respiratory Syncytial Virus, Hepatitis C virus, Human Immunodeficiency Virus, and Herpes Simplex Virus. We also address significant advances and barriers in characterizing lncRNA function and explore the potential of lncRNAs as therapeutic targets.

Validation of a Deep Learning Model for Detecting Chest Pathologies from Digital Chest Radiographs.

Purpose: Manual interpretation of chest radiographs is a challenging task and is prone to errors. An automated system capable of categorizing chest radiographs based on the pathologies identified could aid in the timely and efficient diagnosis of chest pathologies. Method: For this retrospective study, 4476 chest radiographs were collected between January and April 2021 from two tertiary care hospitals. Three expert radiologists established the ground truth, and all radiographs were analyzed using a deep-learning AI model to detect suspicious ROIs in the lungs, pleura, and cardiac regions. Three test readers (different from the radiologists who established the ground truth) independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month. Results: The model demonstrated an aggregate AUROC of 91.2% and a sensitivity of 88.4% in detecting suspicious ROIs in the lungs, pleura, and cardiac regions. These results outperform unaided human readers, who achieved an aggregate AUROC of 84.2% and sensitivity of 74.5% for the same task. When using AI, the aided readers obtained an aggregate AUROC of 87.9% and a sensitivity of 85.1%. The average time taken by the test readers to read a chest radiograph decreased by 21% (p < 0.01) when using AI. Conclusion: The model outperformed all three human readers and demonstrated high AUROC and sensitivity across two independent datasets. When compared to unaided interpretations, AI-aided interpretations were associated with significant improvements in reader performance and chest radiograph interpretation time.

Levels of Autonomous Radiology.

Radiology, being one of the younger disciplines of medicine with a history of just over a century, has witnessed tremendous technological advancements and has revolutionized the way we practice medicine today. In the last few decades, medical imaging modalities have generated seismic amounts of medical data. The development and adoption of artificial intelligence applications using this data will lead to the next phase of evolution in radiology. It will include automating laborious manual tasks such as annotations, report generation, etc, along with the initial radiological assessment of patients and imaging features to aid radiologists in their diagnostic and treatment planning workflow. We propose a level-wise classification for the progression of automation in radiology, explaining artificial intelligence assistance at each level with the corresponding challenges and solutions. We hope that such discussions can help us address challenges in a structured way and take the necessary steps to ensure the smooth adoption of new technologies in radiology.

A deep learning approach for automated diagnosis of pulmonary embolism on computed tomographic pulmonary angiography.

BACKGROUND: Computed tomographic pulmonary angiography (CTPA) is the diagnostic standard for confirming pulmonary embolism (PE). Since PE is a life-threatening condition, early diagnosis and treatment are critical to avoid PE-associated morbidity and mortality. However, PE remains subject to misdiagnosis. METHODS: We retrospectively identified 251 CTPAs performed at a tertiary care hospital between January 2018 to January 2021. The scans were classified as positive (n = 55) and negative (n = 196) for PE based on the annotations made by board-certified radiologists. A fully anonymized CT slice served as input for the detection of PE by the 2D segmentation model comprising U-Net architecture with Xception encoder. The diagnostic performance of the model was calculated at both the scan and the slice levels. RESULTS: The model correctly identified 44 out of 55 scans as positive for PE and 146 out of 196 scans as negative for PE with a sensitivity of 0.80 [95% CI 0.68, 0.89], a specificity of 0.74 [95% CI 0.68, 0.80], and an accuracy of 0.76 [95% CI 0.70, 0.81]. On slice level, 4817 out of 5183 slices were marked as positive for the presence of emboli with a specificity of 0.89 [95% CI 0.88, 0.89], a sensitivity of 0.93 [95% CI 0.92, 0.94], and an accuracy of 0.89 [95% CI 0.887, 0.890]. The model also achieved an AUROC of 0.85 [0.78, 0.90] and 0.94 [0.936, 0.941] at scan level and slice level, respectively for the detection of PE. CONCLUSION: The development of an AI model and its use for the identification of pulmonary embolism will support healthcare workers by reducing the rate of missed findings and minimizing the time required to screen the scans.

Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs.

Background: Cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR (>0.55) is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. Purpose: We propose a deep learning (DL)-based model for automatic CTR calculation to assist radiologists with rapid diagnosis of cardiomegaly and thus optimise the radiology flow. Material and Methods: The study population included 1012 posteroanterior CXRs from a single institution. The Attention U-Net DL architecture was used for the automatic calculation of CTR. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence assistance. Results: U-Net model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], specificity >99%, precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. Furthermore, the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Conclusion: Our segmentation-based AI model demonstrated high specificity (>99%) and sensitivity (80%) for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with provision of AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows by reducing radiologists' burden and alerting to an abnormal enlarged heart early on.

Evaluating the Association Between Comorbidities and COVID-19 Severity Scoring on Chest CT Examinations Between the Two Waves of COVID-19: An Imaging Study Using Artificial Intelligence.

Background Coronavirus disease 2019 (COVID-19) has accounted for over 352 million cases and five million deaths globally. Although it affects populations across all nations, developing or transitional, of all genders and ages, the extent of the specific involvement is not very well known. This study aimed to analyze and determine how different were the first and second waves of the COVID-19 pandemic by assessing computed tomography severity scores (CT-SS). Methodology This was a retrospective, cross-sectional, observational study performed at a tertiary care Institution. We included 301 patients who underwent CT of the chest between June and October 2020 and 1,001 patients who underwent CT of the chest between February and April 2021. All included patients were symptomatic and were confirmed to be COVID-19 positive. We compared the CT-SS between the two datasets. In addition, we analyzed the distribution of CT-SS concerning age, comorbidities, and gender, as well as their differences between the two waves of COVID-19. Analysis was performed using the SPSS version 22 (IBM Corp., Armonk, NY, USA). The artificial intelligence platform U-net architecture with Xception encoder was used in the analysis. Results The study data revealed that while the mean CT-SS did not differ statistically between the two waves of COVID-19, the age group most affected in the second wave was almost a decade younger. While overall the disease had a predilection toward affecting males, our findings showed that females were more afflicted in the second wave of COVID-19 compared to the first wave. In particular, the disease had an increased severity in cases with comorbidities such as hypertension, diabetes mellitus, bronchial asthma, and tuberculosis. Conclusions This assessment demonstrated no significant difference in radiological severity score between the two waves of COVID-19. The secondary objective revealed that the two waves showed demographical differences. Hence, we iterate that no demographical subset of the population should be considered low risk as the disease manifestation was heterogeneous.

Localization of infection in neonatal rhesus macaques after oral viral challenge.

Vertical transmission of human immunodeficiency virus (HIV) can occur in utero, during delivery, and through breastfeeding. We utilized Positron Emission Tomography (PET) imaging coupled with fluorescent microscopy of 64Cu-labeled photoactivatable-GFP-HIV (PA-GFP-BaL) to determine how HIV virions distribute and localize in neonatal rhesus macaques two and four hours after oral viral challenge. Our results show that by four hours after oral viral exposure, HIV virions localize to and penetrate the rectal mucosa. We also used a dual viral challenge with a non-replicative viral vector and a replication competent SHIV-1157ipd3N4 to examine viral transduction and dissemination at 96 hours. Our data show that while SHIV-1157ipd3N4 infection can be found in the oral cavity and upper gastrointestinal (GI) tract, the small and large intestine contained the largest number of infected cells. Moreover, we found that T cells were the biggest population of infected immune cells. Thus, thanks to these novel technologies, we are able to visualize and delineate of viral distribution and infection throughout the entire neonatal GI tract during acute viral infection.

6-Thioguanine blocks SARS-CoV-2 replication by inhibition of PLpro.

The emergence of SARS-CoV-2 has led to a global health crisis that, in addition to vaccines and immunomodulatory therapies, calls for the identification of antiviral therapeutics. The papain-like protease (PLpro) activity of nsp3 is an attractive drug target as it is essential for viral polyprotein cleavage and for deconjugation of ISG15, an antiviral ubiquitin-like protein. We show here that 6-Thioguanine (6-TG), an orally available and widely available generic drug, inhibits SARS-CoV-2 replication in Vero-E6 cells with an EC50 of approximately 2 µM. 6-TG also inhibited PLpro-catalyzed polyprotein cleavage and de-ISGylation in cells and inhibited proteolytic activity of the purified PLpro domain in vitro. We therefore propose that 6-TG is a direct-acting antiviral that could potentially be repurposed and incorporated into the set of treatment and prevention options for COVID-19.

Efficient Inhibition of HIV Using CRISPR/Cas13d Nuclease System.

Recently discovered Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas13 proteins are programmable RNA-guided ribonucleases that target single-stranded RNA (ssRNA). CRISPR/Cas13-mediated RNA targeting has emerged as a powerful tool for detecting and eliminating RNA viruses. Here, we demonstrate the effectiveness of CRISPR/Cas13d to inhibit HIV-1 replication. We designed guide RNAs (gRNAs) targeting highly conserved regions of HIV-1. RfxCas13d (CasRx) in combination with HIV-specific gRNAs efficiently inhibited HIV-1 replication in cell line models. Furthermore, simultaneous targeting of four distinct, non-overlapping sites in the HIV-1 transcript resulted in robust inhibition of HIV-1 replication. We also show the effective HIV-1 inhibition in primary CD4+ T-cells and suppression of HIV-1 reactivated from latently infected cells using the CRISPR/Cas13d system. Our study demonstrates the utility of the CRISPR/Cas13d nuclease system to target acute and latent HIV infection and provides an alternative treatment modality against HIV.

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice.

The use of machine learning to develop intelligent software tools for the interpretation of radiology images has gained widespread attention in recent years. The development, deployment, and eventual adoption of these models in clinical practice, however, remains fraught with challenges. In this paper, we propose a list of key considerations that machine learning researchers must recognize and address to make their models accurate, robust, and usable in practice. We discuss insufficient training data, decentralized data sets, high cost of annotations, ambiguous ground truth, imbalance in class representation, asymmetric misclassification costs, relevant performance metrics, generalization of models to unseen data sets, model decay, adversarial attacks, explainability, fairness and bias, and clinical validation. We describe each consideration and identify the techniques used to address it. Although these techniques have been discussed in prior research, by freshly examining them in the context of medical imaging and compiling them in the form of a laundry list, we hope to make them more accessible to researchers, software developers, radiologists, and other stakeholders.

Real-world analysis of artificial intelligence in musculoskeletal trauma.

Musculoskeletal trauma accounts for a large percentage of emergency room visits and is amongst the top causes of unscheduled patient visits to the emergency room. Musculoskeletal trauma results in expenditure of billions of dollars and protracted losses of quality-adjusted life years. New and innovative methods are needed to minimise the impact by ensuring quick and accurate assessment. However, each of the currently utilised radiological procedures, such as radiography, ultrasonography, computed tomography, and magnetic resonance imaging, has resulted in implosion of medical imaging data. Deep learning, a recent advancement in artificial intelligence, has demonstrated the potential to analyse medical images with sensitivity and specificity at par with experts. In this review article, we intend to summarise and showcase the various developments which have occurred in the dynamic field of artificial intelligence and machine learning and how their applicability to different aspects of imaging in trauma can be explored to improvise our existing reporting systems and improvise on patient outcomes.

Mapping functional humoral correlates of protection against malaria challenge following RTS,S/AS01 vaccination.

Vaccine development has the potential to be accelerated by coupling tools such as systems immunology analyses and controlled human infection models to define the protective efficacy of prospective immunogens without expensive and slow phase 2b/3 vaccine studies. Among human challenge models, controlled human malaria infection trials have long been used to evaluate candidate vaccines, and RTS,S/AS01 is the most advanced malaria vaccine candidate, reproducibly demonstrating 40 to 80% protection in human challenge studies in malaria-naïve individuals. Although antibodies are critical for protection after RTS,S/AS01 vaccination, antibody concentrations are inconsistently associated with protection across studies, and the precise mechanism(s) by which vaccine-induced antibodies provide protection remains enigmatic. Using a comprehensive systems serological profiling platform, the humoral correlates of protection against malaria were identified and validated across multiple challenge studies. Rather than antibody concentration, qualitative functional humoral features robustly predicted protection from infection across vaccine regimens. Despite the functional diversity of vaccine-induced immune responses across additional RTS,S/AS01 vaccine studies, the same antibody features, antibody-mediated phagocytosis and engagement of Fc gamma receptor 3A (FCGR3A), were able to predict protection across two additional human challenge studies. Functional validation using monoclonal antibodies confirmed the protective role of Fc-mediated antibody functions in restricting parasite infection both in vitro and in vivo, suggesting that these correlates may mechanistically contribute to parasite restriction and can be used to guide the rational design of an improved vaccine against malaria.

Author Correction: CCR5AS lncRNA variation differentially regulates CCR5, influencing HIV disease outcome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

CCR5AS lncRNA variation differentially regulates CCR5, influencing HIV disease outcome.

Multiple genome-wide studies have identified associations between outcome of human immunodeficiency virus (HIV) infection and polymorphisms in and around the gene encoding the HIV co-receptor CCR5, but the functional basis for the strongest of these associations, rs1015164A/G, is unknown. We found that rs1015164 marks variation in an activating transcription factor 1 binding site that controls expression of the antisense long noncoding RNA (lncRNA) CCR5AS. Knockdown or enhancement of CCR5AS expression resulted in a corresponding change in CCR5 expression on CD4+ T cells. CCR5AS interfered with interactions between the RNA-binding protein Raly and the CCR5 3' untranslated region, protecting CCR5 messenger RNA from Raly-mediated degradation. Reduction in CCR5 expression through inhibition of CCR5AS diminished infection of CD4+ T cells with CCR5-tropic HIV in vitro. These data represent a rare determination of the functional importance of a genome-wide disease association where expression of a lncRNA affects HIV infection and disease progression.

Lymphocytes upregulate CD36 in adipose tissue and liver.

CD36 is a multifunctional scavenger receptor and lipid transporter implicated in metabolic and inflammatory pathologies, as well as cancer progression. CD36 is known to be expressed by adipocytes and monocytes/macrophages, but its expression by T cells is not clearly established. We found that CD4 and CD8 T cells in adipose tissue and liver of humans, monkeys, and mice upregulated CD36 expression (ranging from ~5-40% CD36+), whereas little to no CD36 was expressed by T cells in blood, spleen, and lymph nodes. CD36 was expressed predominantly by resting CD38-, HLA.DR-, and PD-1- adipose tissue T cells in monkeys, and increased during high-fat feeding in mice. Adipose tissue and liver promote a distinct phenotype in resident T cells characterized by CD36 upregulation.

DNA Vaccine-Induced Long-Lasting Cytotoxic T Cells Targeting Conserved Elements of Human Immunodeficiency Virus Gag Are Boosted Upon DNA or Recombinant Modified Vaccinia Ankara Vaccination.

DNA-based vaccines able to induce efficient cytotoxic T-cell responses targeting conserved elements (CE) of human immunodeficiency virus type 1 (HIV-1) Gag have been developed. These CE were selected by stringent conservation, the ability to induce T-cell responses with broad human leukocyte antigen coverage, and the association between recognition of CE epitopes and viral control in HIV-infected individuals. Based on homology to HIV, a simian immunodeficiency virus p27gag CE DNA vaccine has also been developed. This study reports on the durability of the CE-specific T-cell responses induced by HIV and simian immunodeficiency virus CE DNA-based prime/boost vaccine regimens in rhesus macaques, and shows that the initially primed CE-specific T-cell responses were efficiently boosted by a single CE DNA vaccination after the long rest period (up to 2 years). In another cohort of animals, the study shows that a single inoculation with non-replicating recombinant Modified Vaccinia Ankara (rMVA62B) also potently boosted CE-specific responses after around 1.5 years of rest. Both CE DNA and rMVA62B booster vaccinations increased the magnitude and cytotoxicity of the CE-specific responses while maintaining the breadth of CE recognition. Env produced by rMVA62B did not negatively interfere with the recall of the Gag CE responses. rMVA62B could be beneficial to further boosting the immune response to Gag in humans. Vaccine regimens that employ CE DNA as a priming immunogen hold promise for application in HIV prevention and therapy.

Anti-HIV IgM protects against mucosal SHIV transmission.

OBJECTIVE: Worldwide, most new HIV infections occur through mucosal exposure. Immunoglobulin M (IgM) is the first antibody class generated in response to infectious agents; IgM is present in the systemic circulation and in mucosal fluids as secretory IgM. We sought to investigate for the first time the role of IgM in preventing AIDS virus acquisition in vivo. DESIGN: Recombinant polymeric monoclonal IgM was generated from the neutralizing monoclonal IgG1 antibody 33C6-IgG1, tested in vitro, and given by passive intrarectal immunization to rhesus macaques 30 min before intrarectal challenge with simian-human immunodeficiency virus (SHIV) that carries an HIV-1 envelope gene. RESULTS: In vitro, 33C6-IgM captured virions more efficiently and neutralized the challenge SHIV with a 50% inhibitory molar concentration (IC50) that was 1 log lower than that for 33C6-IgG1. The IgM form also exhibited significantly higher affinity and avidity compared with 33C6-IgG1. After intrarectal administration, 33C6-IgM prevented viremia in four out of six rhesus macaques after high-dose intrarectal SHIV challenge. Five out of six rhesus macaques given 33C6-IgG1 were protected at a five times higher molar concentration compared with the IgM form; all untreated controls became highly viremic. Rhesus macaques passively immunized with 33C6-IgM with breakthrough infection had notably early development of autologous neutralizing antibody responses. CONCLUSION: Our primate model data provide the first proof-of-concept that mucosal IgM can prevent mucosal HIV transmission and have implications for HIV prevention and vaccine development.

Mucosal IgA Responses: Damaged in Established HIV Infection-Yet, Effective Weapon against HIV Transmission.

HIV infection not only destroys CD4+ T cells but also inflicts serious damage to the B-cell compartment, such as lymphadenopathy, destruction of normal B-cell follicle architecture, polyclonal hypergammaglobulinemia, increased apoptosis of B cells, and irreversible loss of memory B-cell responses with advanced HIV disease. Subepithelial B cells and plasma cells are also affected, which results in loss of mucosal IgG and IgA antibodies. This leaves the mucosal barrier vulnerable to bacterial translocation. The ensuing immune activation in mucosal tissues adds fuel to the fire of local HIV replication. We postulate that compromised mucosal antibody defenses also facilitate superinfection of HIV-positive individuals with new HIV strains. This in turn sets the stage for the generation of circulating recombinant forms of HIV. What can the mucosal B-cell compartment contribute to protect a healthy, uninfected host against mucosal HIV transmission? Here, we discuss proof-of-principle studies we have performed using passive mucosal immunization, i.e., topical administration of preformed anti-HIV monoclonal antibodies (mAbs) as IgG1, dimeric IgA1 (dIgA1), and dIgA2 isotypes, alone or in combination. Our data indicate that mucosally applied anti-HIV envelope mAbs can provide potent protection against mucosal transmission of simian-human immunodeficiency virus. Our review also discusses the induction of mucosal antibody defenses by active vaccination and potential strategies to interrupt the vicious cycle of bacterial translocation, immune activation, and stimulation of HIV replication in individuals with damaged mucosal barriers.