Practising in a post-truth world: Pandemic ethics can inform patient autonomy and clinical communication.

The COVID-19 pandemic posed an unprecedented challenge to modern bioethical frameworks in the clinical setting. Now, as the pandemic stabilises and we learn to 'live with COVID', the medical community has a duty to evaluate its response to the challenge, and reassess our ethical reasoning, considering how we practise in the future. This article considers a number of clinical and bioethical challenges encountered by the author team and colleagues during the most severe waves of the pandemic. We argue that the changed clinical context may require reframing our ethical thought in such a manner as to adequately accommodate all parties in the clinical interaction. We argue that clinicians have become relatively disempowered by the 'infodemic', and do not necessarily have adequate skills or training to assess the scientific literature being published at an unprecedented rate. Conversely, we acknowledge that patients and families are more empowered by the infodemic, and bring this empowerment to bear on the clinical consultation. Sometimes these interactions can be unpleasant and threatening, and involve inviting clinicians to practise against best evidence or even illegally. Generally, these requests are framed within 'patient autonomy' (which some patients or families perceive to be unlimited), and several factors may prevent clinicians from adequately navigating these requests. In this article, we conclude that embracing a framework of shared decision-making (SDM), which openly acknowledges clinical expertise and in which patient and family autonomy is carefully balanced against other bioethics principles, could serve us well going forward. One such principle is the recognition of clinician expertise as holding weight in the clinical encounter, when framed in terms of non-maleficence and beneficence. Such a framework incorporates much of our learning and experience from advising and treating patients during the pandemic.

Biologically informed deep learning to query gene programs in single-cell atlases.

The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.

Natural Language Processing Techniques for Behavior Analysis in Social Networks of Hispanic American University Communities

The Covid-19 confinement has forced certain human groups to look for alternatives to socialize. University communities did not stay out of this context. The presence of student communities called 'confessions' whose purpose is to anonymously express their problems, desires and interests stands out. The main objective of this research is to determine the topics that highlight the cultural aspects and interests of these communities. Confessions pages from 5 Spanish-speaking countries were analyzed. Experiments were carried out on Facebookand Instagram posts using word embeddings and KMeans to cluster the semantic vector space. Anew evaluation approach based on the state-of-the-art was proposed that allow us to select and analyze topic models through specific keywords. As a result, topics of general interest were identified for each community such as 'Academic life', 'Relationships', 'Politics' and 'Personal problems'. The results vary by country. The collected dataset is publicly available1 for any academic purposes. © 2022 IEEE.

Inverse set estimation and inversion of simultaneous confidence intervals (preprint)

Motivated by the questions of risk assessment in climatology (temperature change in North America) and medicine (impact of statin usage and COVID-19 on hospitalized patients), we address the problem of estimating the set in the domain of a function whose image equals a predefined subset. Existing methods that construct confidence sets require strict assumptions. We generalize the estimation of such sets to dense and non-dense domains with protection against "data peeking" by proving that confidence sets of multiple levels can be simultaneously constructed with the desired confidence non-asymptotically through inverting simultaneous confidence bands. A non-parametric bootstrap algorithm and code are provided.

Mature neutrophils and a NF-κB-to-IFN transition determine the unifying disease recovery dynamics in COVID-19.

Disease recovery dynamics are often difficult to assess, as patients display heterogeneous recovery courses. To model recovery dynamics, exemplified by severe COVID-19, we apply a computational scheme on longitudinally sampled blood transcriptomes, generating recovery states, which we then link to cellular and molecular mechanisms, presenting a framework for studying the kinetics of recovery compared with non-recovery over time and long-term effects of the disease. Specifically, a decrease in mature neutrophils is the strongest cellular effect during recovery, with direct implications on disease outcome. Furthermore, we present strong indications for global regulatory changes in gene programs, decoupled from cell compositional changes, including an early rise in T cell activation and differentiation, resulting in immune rebalancing between interferon and NF-κB activity and restoration of cell homeostasis. Overall, we present a clinically relevant computational framework for modeling disease recovery, paving the way for future studies of the recovery dynamics in other diseases and tissues.

COVIUAM: A mobile app to get information about COVID-19 cases

The COVID-19 pandemic took the world by surprise, its rapid spread and its death rate caused governments to make drastic decisions such as closing borders, establishing curfews, closing businesses, etc. in order to break the chains of infections. In many countries mobile apps were developed to have information on possible contagions and prevent their spread. This paper describes COVIUAM, a mobile app that collects information on suspected or confirmed cases of COVID-19 in members of the Metropolitan Autonomous University. Through the data collected by COVIUAM app, patterns can be identified in the information, for example in symptomatology data. The article highlights the design and architecture of COVIUAM app and presents two evaluations, one quantitative and one qualitative of the information collected and the use of the application. © 2021 IEEE.

Automatic Detection of Levels of Intimate Partner Violence Against Women with Natural Language Processing Using Machine Learning and Deep Learning Techniques

Violence against women continues to claim thousands of lives worldwide each year. The COVID-19 pandemic only aggravated the problem by confining many victims together with their aggressors. When a woman experiences this situation, she usually falls into denial, justifies the aggressive behavior of her partner, or even blames herself for provoking it. The sooner she realizes that she is experiencing intimate partner violence, she can act and prevent her advance in the violence cycle (from psychological violence to physical violence, which could lead to femicide). The work proposes a classifier artificial intelligence model to detect levels of psychological violence against women in written virtual expressions (messages ‘from him to her’ in a couple) to ‘alert her’ about the risk that she runs in that relationship. 5250 records in Spanish were extracted with 4 techniques from 6 different sources. Definition of 5 intimate partner psychological violence levels (0-Low Risk, 1-Emotional Blackmail, 2-Jealousy/Justification, 3-Insults/Humiliations, and 4-Threats/Possessiveness) and the data labeling were supervised by a psychologist expert on the problem. Techniques TF-IDF and Word2Vec were used to get the vectors and were tested five Machine Learning algorithms (SVM, MLP, Random Forest, Logistic Regression, and Naive Bayes) with various combinations of parameters. Too were tested pad sequences with LSTM and Bidirectional LSTM. The best result was 93.45% accuracy and 0.2476 categorical cross-entropy loss, obtained with extensive preprocessing, pad sequences, and LSTM. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Case Report: Severe Acute Pulmonary COVID-19 in a Teenager Post Autologous Hematopoietic Stem Cell Transplant.

Pulmonary severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children is generally described as mild, and SARS-CoV-2 infection in immunocompromised children are observed as generally mild as well. A small proportion of pediatric patients will become critically ill due to (cardio)respiratory failure and require intensive care treatment. We report the case of a teenager with Hodgkin's lymphoma who acquired SARS-CoV-2 (detected by PCR) on the day of her autologous stem cell transplant and developed acute respiratory distress syndrome, successfully treated with a combination of antivirals, immunomodulation with steroids and biologicals, and ECMO.

The potential of virtual global mobility: implications for practice and future research

PurposeThe COVID-19 pandemic has forced global organizations to adopt technology-driven virtual solutions involving faster, less costly and more effective ways to work worldwide even after the pandemic. One potential outcome may be through virtual global mobility (VGM), defined as the replacement of personal physical international interactions for work purposes with electronic personal online interactions. The purpose of this article is to establish VGM as a theoretical concept and explore to what extent it can replace or complement physical global work assignments.Design/methodology/approachThis perspectives article first explores advantages and disadvantages of global virtual work and then discusses the implementation of VGM and analyses to what extent and how VGM can replace and complement physical global mobility.FindingsRepresenting a change of trend, long-term corporate expatriates could become necessary core players in VGM activities while the increase of the number of global travelers may be halted or reversed. VGM activities will grow and further develop due to a continued rapid development of communication and coordination technologies. Consequently, VGM is here to stay!Originality/valueThe authors have witnessed a massive trend of increasing physical global mobility where individuals have crossed international borders to conduct work. The authors are now observing the emergence of a counter-trend: instead of moving people to their work the authors often see organizations moving work to people. This article has explored some of the advantages, disadvantages, facilitators and barriers of such global virtual work. Given the various purposes of global work the authors chart the suitability of VGM to fulfill these organizational objectives.

The MiR-320 Family Is Strongly Downregulated in Patients with COVID-19 Induced Severe Respiratory Failure.

A high incidence of thromboembolic events associated with high mortality has been reported in severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infections with respiratory failure. The present study characterized post-transcriptional gene regulation by global microRNA (miRNA) expression in relation to activated coagulation and inflammation in 21 critically ill SARS-CoV-2 patients. The cohort consisted of patients with moderate respiratory failure (n = 11) and severe respiratory failure (n = 10) at an acute stage (day 0-3) and in the later course of the disease (>7 days). All patients needed supplemental oxygen and severe patients were defined by the requirement of positive pressure ventilation (intubation). Levels of D-dimers, activated partial thromboplastin time (aPTT), C-reactive protein (CRP), and interleukin (IL)-6 were significantly higher in patients with severe compared with moderate respiratory failure. Concurrently, next generation sequencing (NGS) analysis demonstrated increased dysregulation of miRNA expression with progression of disease severity connected to extreme downregulation of miR-320a, miR-320b and miR-320c. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis revealed involvement in the Hippo signaling pathway, the transforming growth factor (TGF)-ß signaling pathway and in the regulation of adherens junctions. The expression of all miR-320 family members was significantly correlated with CRP, IL-6, and D-dimer levels. In conclusion, our analysis underlines the importance of thromboembolic processes in patients with respiratory failure and emphasizes miRNA-320s as potential biomarkers for severe progressive SARS-CoV-2 infection.

Group Testing for SARS-CoV-2 Allows for Up to 10-Fold Efficiency Increase Across Realistic Scenarios and Testing Strategies.

Background: Due to the ongoing COVID-19 pandemic, demand for diagnostic testing has increased drastically, resulting in shortages of necessary materials to conduct the tests and overwhelming the capacity of testing laboratories. The supply scarcity and capacity limits affect test administration: priority must be given to hospitalized patients and symptomatic individuals, which can prevent the identification of asymptomatic and presymptomatic individuals and hence effective tracking and tracing policies. We describe optimized group testing strategies applicable to SARS-CoV-2 tests in scenarios tailored to the current COVID-19 pandemic and assess significant gains compared to individual testing. Methods: We account for biochemically realistic scenarios in the context of dilution effects on SARS-CoV-2 samples and consider evidence on specificity and sensitivity of PCR-based tests for the novel coronavirus. Because of the current uncertainty and the temporal and spatial changes in the prevalence regime, we provide analysis for several realistic scenarios and propose fast and reliable strategies for massive testing procedures. Key Findings: We find significant efficiency gaps between different group testing strategies in realistic scenarios for SARS-CoV-2 testing, highlighting the need for an informed decision of the pooling protocol depending on estimated prevalence, target specificity, and high- vs. low-risk population. For example, using one of the presented methods, all 1.47 million inhabitants of Munich, Germany, could be tested using only around 141 thousand tests if the infection rate is below 0.4% is assumed. Using 1 million tests, the 6.69 million inhabitants from the city of Rio de Janeiro, Brazil, could be tested as long as the infection rate does not exceed 1%. Moreover, we provide an interactive web application, available at www.grouptexting.com, for visualizing the different strategies and designing pooling schemes according to specific prevalence scenarios and test configurations. Interpretation: Altogether, this work may help provide a basis for an efficient upscaling of current testing procedures, which takes the population heterogeneity into account and is fine-grained towards the desired study populations, e.g., mild/asymptomatic individuals vs. symptomatic ones but also mixtures thereof. Funding: German Science Foundation (DFG), German Federal Ministry of Education and Research (BMBF), Chan Zuckerberg Initiative DAF, and Austrian Science Fund (FWF).

Efficacy and safety of venetoclax Combinations in t(11;14) multiple myeloma: Real World data of 50 patients collected from 7 Hungarian centers

Background: Venetoclax, a selective bcl-2 inhibitor first approved for CLL has been investigated for the treatment of relapsed myeloma patients, and although it failed to show benefit for myeloma patients as a whole, t(11;14) patients demonstrated exceptional responses in the Bellini trial (Lancet Oncol 2020;21:1630), thus paving the way towards the first genetically targeted treatment in myeloma. Aims: Off label use of venetoclax is on the rise, even though clinicians have to face unanswered questions regarding the right dosage and length of therapy, as well as the potential for adverse events (AEs), especially infections. Real world data could help to elucidate its optimal use, but is very limited as of yet. Methods: We addressed all Hungarian centers treating myeloma to evaluate the efficacy and safety of venetoclax in their practice treating t(11;14) myeloma, collecting data about the treatment duration, AEs, dose modifications and treatment discontinuations, and analyzed response rates as well as progression free and overall survivals (PFS, OS). Results: 50 patients were reported from 7 Hungarian sites. 33 patients were relapsed and heavily pretreated with an average of 4.8 prior lines, whereas 17 patients received venetoclax after a suboptimal initial response to their first line treatment (8 PR, 7 SD, 2 PD) as planned pre-transplantation salvage. Most patients had venetoclax in combination with a proteasome inhibitor and dexamethasone. The response rate was remarkably high given the refractoriness of this cohort: all but two patients responded, with 28% CRs, 38% VGPRs and 30% PRs. The median PFS and OS calculated from initiation of venetoclax dosing were 15.5 and 24 months, respectively, Figure 1. shows the PFS and OS curves of the 2nd line and late line cohorts. The most common AEs were cytopenias, gastrointestinal toxicities and infections, reported in 8, 9 and 10 patients with 1 fatal infection. Two patients had COVID-19 related hospitalization, both recovered. An important point to emphasize is the very high risk nature of this cohort. 13 out of the 47 patients had deletion 17p before venetoclax dosing was initiated, explaining their refractoriness to standard treatments. Notably, of these patients 2 reached CR, 9 VGPR, 1 PR, and only one progressed while on venetoclax treatment. The median PFS and OS were 9.6 and 10 months, respectively. Five patients had plasma cell leukaemia and 1 CNS involvement, the PFS and OS of these ultra-high risk patients were 10 and 12 months. Another important aspect of our analysis was the question of venetoclax dosing, as the appropriate dose in this indication is not yet clear. Reflecting this uncertainty, as well as funding difficulties with this off-label drug, only 2 patients received the 800 mg dose as seen in the Bellini trial;one received 600 mg daily, with all others taking 400 mg or less. To counteract this many centers employed a combination with either clarithromycin or fluconazole, CYP3A inhibitors known to increase venetoclax serum levels two- to threefold. Where available, serum venetoclax levels were monitored to ensure serum levels comparable to regular dosing.Summary/Conclusion: Our results highlight the importance of targeted treatments in multiple myeloma. We experienced lasting responses in quadruple-refractory patients, many with other high risk features. In the newly diagnosed group, where the depth of pre-ASCT response has great impact on PFS, venetoclax may have a role converting suboptimal responses into CRs by eliminating residual disease.

Mapping single-cell data to reference atlases by transfer learning.

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

Single-cell RNA sequencing reveals ex vivo signatures of SARS-CoV-2-reactive T cells through 'reverse phenotyping'.

The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we use single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induce transcriptional shifts by antigenic stimulation in vitro and take advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for 'reverse phenotyping'. This allows identification of SARS-CoV-2-reactive TCRs and reveals phenotypic effects introduced by antigen-specific stimulation. We characterize transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and show correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

Analysis of Humoral Immune Responses in Patients With Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has caused a pandemic with tens of millions of cases and hundreds of thousands of deaths. The infection causes coronavirus disease 2019 (COVID-19), a disease of the respiratory system of divergent severity. In the current study, humoral immune responses were characterized in a cohort of 143 patients with COVID-19 from the University Hospital Frankfurt am Main, Germany. METHODS: SARS-CoV-2-specific-antibodies were detected by enzyme-linked immunosorbent assay (ELISA). SARS-CoV-2 and human coronavirus NL63 neutralization activity was analyzed with pseudotyped lentiviral vectors. RESULTS: The severity of COVID-19 increased with age, and male patients encountered more serious symptoms than female patients. Disease severity was correlated with the amount of SARS-CoV-2-specific immunoglobulin (Ig) G and IgA and the neutralization activity of the antibodies. The amount of SARS-CoV-2-specific IgG antibodies decreased with time after polymerase chain reaction conformation of the infection, and antibodies directed against the nucleoprotein waned faster than spike protein-directed antibodies. In contrast, for the common flu coronavirus NL63, COVID-19 disease severity seemed to be correlated with low NL63-neutralizing activities, suggesting the possibility of cross-reactive protection. CONCLUSION: The results describe the humoral immune responses against SARS-CoV-2 and might aid the identification of correlates of protection needed for vaccine development.

Single-cell RNA sequencing reveals in vivo signatures of SARS-CoV-2-reactive T cells through 'reverse phenotyping' (preprint)

The in vivo phenotypic profile of T cells reactive to severe acute respiratory syndrome (SARS)-CoV-2 antigens remains poorly understood. Conventional methods to detect antigen-reactive T cells require in vitro antigenic re-stimulation or highly individualized peptide-human leukocyte antigen (pHLA) multimers. Here, we used single-cell RNA sequencing to identify and profile SARS-CoV-2-reactive T cells from Coronavirus Disease 2019 (COVID-19) patients. To do so, we induced transcriptional shifts by antigenic stimulation in vitro and took advantage of natural T cell receptor (TCR) sequences of clonally expanded T cells as barcodes for reverse phenotyping. This allowed identification of SARS-CoV-2-reactive TCRs and revealed phenotypic effects introduced by antigen-specific stimulation. We characterized transcriptional signatures of currently and previously activated SARS-CoV-2-reactive T cells, and showed correspondence with phenotypes of T cells from the respiratory tract of patients with severe disease in the presence or absence of virus in independent cohorts. Reverse phenotyping is a powerful tool to provide an integrated insight into cellular states of SARS-CoV-2-reactive T cells across tissues and activation states.

Distant Residues Modulate the Conformational Opening in SARS-CoV-2 Spike Protein (preprint)

Infection by SARS-CoV-2 involves the attachment of the receptor binding domain (RBD) of its spike proteins to the ACE2 receptors on the peripheral membrane of host cells. Binding is initiated by a down to up conformational change in the spike protein, an opening which presents the RBD to the receptor. To date, computational and experimental studies for therapeutics have concentrated, for good reason, on the RBD. However, the RBD region is highly prone to mutations, and therefore will possibly arise drug resistance. In contrast, we here focus on the correlations between the RBD and residues distant to it in the spike protein. We thereby provide a deeper understanding of the role of distant residues in the molecular mechanism of infection. Predictions of key mutations in distant allosteric binding sites are provided, with implications for therapeutics. Identifying these emerging mutants can also go a long way towards pre-designing vaccines for future outbreaks. The model we use, based on time-independent component analysis (tICA) and protein graph connectivity network, is able to identify multiple residues that exhibit long-distance coupling with the RBD opening. Mutation on these residues can lead to new strains of coronavirus with different degrees of transmissibility and virulence. The most ubiquitous D614G mutation and the A570D mutation of the highly contageous UK SARS-CoV-2 variant are predicted ab-initio from our model. Conversely, broad spectrum therapeutics like drugs and monoclonal antibodies can be generated targeting these key distant but conserved regions of the spike protein. Significance statementThe novel coronavirus SARS-CoV-2 has created the largest pandemic of recent times, resulting in economic and public health crises. Significant research effort to design drugs against COVID-19 is focused on the receptor binding domain of the spike protein, although this region is prone to mutations that can cause resistance against therapeutics. We applied deep data analysis methods on all-atom molecular dynamics simulations of the spike protein to identify key non-RBD residues that play a crucial role in spike-receptor binding and infection of human cells. These residues can not only be targeted by broad spectrum antibodies and drugs, but can also offer predictive insights into the mutations with the potential to generate new strains that might appear during future epidemics.

Extended in vitro inactivation of SARS-CoV-2 by titanium dioxide surface coating (preprint)

SARS-CoV-2 transmission occurs via airborne droplets and surface contamination. We show tiles coated with TiO2 120 days previously can inactivate SARS-CoV-2 under ambient indoor lighting with 87% reduction in titres at 1h and complete loss by 5h exposure. TiO2 coatings could be an important tool in containing SARS-CoV-2.

Engineered receptor binding domain immunogens elicit pan-coronavirus neutralizing antibodies (preprint)

Effective countermeasures are needed against emerging coronaviruses of pandemic potential, similar to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Designing immunogens that elicit broadly neutralizing antibodies to conserved viral epitopes on the major surface glycoprotein, spike, such as the receptor binding domain (RBD) is one potential approach. Here, we report the generation of homotrimeric RBD immunogens from different sarbecoviruses using a stabilized, immune-silent trimerization tag. We find that that a cocktail of homotrimeric sarbecovirus RBDs can elicit a neutralizing response to all components even in context of prior SARS-CoV-2 imprinting. Importantly, the cross-neutralizing antibody responses are focused towards conserved RBD epitopes outside of the ACE-2 receptor-binding motif. This may be an effective strategy for eliciting broadly neutralizing responses leading to a pan-sarbecovirus vaccine.