Single-Cell Transcriptomics in Cancer Immunobiology: The Future of Precision Oncology.

Cancer is a heterogeneous and complex disease. Tumors are formed by cancer cells and a myriad of non-cancerous cell types that together with the extracellular matrix form the tumor microenvironment. These cancer-associated cells and components contribute to shape the progression of cancer and are deeply involved in patient outcome. The immune system is an essential part of the tumor microenvironment, and induction of cancer immunotolerance is a necessary step involved in tumor formation and growth. Immune mechanisms are intimately associated with cancer progression, invasion, and metastasis; as well as to tumor dormancy and modulation of sensitivity to drug therapy. Transcriptome analyses have been extensively used to understand the heterogeneity of tumors, classifying tumors into molecular subtypes and establishing signatures that predict response to therapy and patient outcomes. However, the classification of the tumor cell diversity and specially the identification of rare populations has been limited in these transcriptomic analyses of bulk tumor cell populations. Massively-parallel single-cell RNAseq analysis has emerged as a powerful method to unravel heterogeneity and to study rare cell populations in cancer, through unsupervised sampling and modeling of transcriptional states in single cells. In this context, the study of the role of the immune system in cancer would benefit from single cell approaches, as it will enable the characterization and/or discovery of the cell types and pathways involved in cancer immunotolerance otherwise missed in bulk transcriptomic information. Thus, the analysis of gene expression patterns at single cell resolution holds the potential to provide key information to develop precise and personalized cancer treatment including immunotherapy. This review is focused on the latest single-cell RNAseq methodologies able to agnostically study thousands of tumor cells as well as targeted single-cell RNAseq to study rare populations within tumors. In particular, we will discuss methods to study the immune system in cancer. We will also discuss the current challenges to the study of cancer at the single cell level and the potential solutions to the current approaches

A Technical Comparison of Digital Frequency-Lowering Algorithms Available in Two Current Hearing Aids

Background: Recently two major manufacturers of hearing aids introduced two distinct frequency-lowering techniques that were designed to compensate in part for the perceptual effects of high-frequency hearing impairments. The Widex ‘‘Audibility Extender’ ’ is a linear frequency transposition scheme, whereas the Phonak ‘‘SoundRecover’ ’ scheme employs nonlinear frequency compression. Although these schemes process sound signals in very different ways, studies investigating their use by both adults and children with hearing impairment have reported significant perceptual benefits. However, the modifications that these innovative schemes apply to sound signals have not previously been described or compared in detail. Methods: The main aim of the present study was to analyze these schemes’technical performance by measuring outputs from each type of hearing aid with the frequency-lowering functions enabled and disabled. The input signals included sinusoids, flute sounds, and speech material. Spectral analyses were carried out on the output signals produced by the hearing aids in each condition. Conclusions: The results of the analyses confirmed that each scheme was effective at lowering certain high-frequency acoustic signals, although both techniques also distorted some signals. Most importantly, the application of either frequency-lowering scheme would be expected to improve the audibility of many sounds having salient high-frequenc

Finding reliable subgraphs from large probabilistic graphs

Reliable subgraphs can be used, for example, to find and rank nontrivial links between given vertices, to concisely visualize large graphs, or to reduce the size of input for computationally demanding graph algorithms. We propose two new heuristics for solving the most reliable subgraph extraction problem on large, undirected probabilistic graphs. Such a problem is specified by a probabilistic graph G subject to random edge failures, a set of terminal vertices, and an integer K. The objective is to remove K edges from G such that the probability of connecting the terminals in the remaining subgraph is maximized. We provide some technical details and a rough analysis of the proposed algorithms. The practical performance of the methods is evaluated on real probabilistic graphs from the biological domain. The results indicate that the methods scale much better to large input graphs, both computationally and in terms of the quality of the result.Reliable subgraphs can be used, for example, to find and rank nontrivial links between given vertices, to concisely visualize large graphs, or to reduce the size of input for computationally demanding graph algorithms. We propose two new heuristics for solving the most reliable subgraph extraction problem on large, undirected probabilistic graphs. Such a problem is specified by a probabilistic graph G subject to random edge failures, a set of terminal vertices, and an integer K. The objective is to remove K edges from G such that the probability of connecting the terminals in the remaining subgraph is maximized. We provide some technical details and a rough analysis of the proposed algorithms. The practical performance of the methods is evaluated on real probabilistic graphs from the biological domain. The results indicate that the methods scale much better to large input graphs, both computationally and in terms of the quality of the result.Reliable subgraphs can be used, for example, to find and rank nontrivial links between given vertices, to concisely visualize large graphs, or to reduce the size of input for computationally demanding graph algorithms. We propose two new heuristics for solving the most reliable subgraph extraction problem on large, undirected probabilistic graphs. Such a problem is specified by a probabilistic graph G subject to random edge failures, a set of terminal vertices, and an integer K. The objective is to remove K edges from G such that the probability of connecting the terminals in the remaining subgraph is maximized. We provide some technical details and a rough analysis of the proposed algorithms. The practical performance of the methods is evaluated on real probabilistic graphs from the biological domain. The results indicate that the methods scale much better to large input graphs, both computationally and in terms of the quality of the result.Peer reviewe

Modest effects of dietary supplements during the COVID-19 pandemic: Insights from 445 850 users of the COVID-19 Symptom Study app

OBJECTIVE: Dietary supplements may ameliorate SARS-CoV-2 infection, although scientific evidence to support such a role is lacking. We investigated whether users of the COVID-19 Symptom Study app who regularly took dietary supplements were less likely to test positive for SARS-CoV-2 infection. DESIGN: App-based community survey. SETTING: 445 850 subscribers of an app that was launched to enable self-reported information related to SARS-CoV-2 infection for use in the general population in the UK (n=372 720), the USA (n=45 757) and Sweden (n=27 373). MAIN EXPOSURE: Self-reported regular dietary supplement usage (constant use during previous 3 months) in the first waves of the pandemic up to 31 July 2020. MAIN OUTCOMES MEASURES: SARS-CoV-2 infection confirmed by viral RNA reverse transcriptase PCR test or serology test before 31 July 2020. RESULTS: In 372 720 UK participants (175 652 supplement users and 197 068 non-users), those taking probiotics, omega-3 fatty acids, multivitamins or vitamin D had a lower risk of SARS-CoV-2 infection by 14% (95% CI (8% to 19%)), 12% (95% CI (8% to 16%)), 13% (95% CI (10% to 16%)) and 9% (95% CI (6% to 12%)), respectively, after adjusting for potential confounders. No effect was observed for those taking vitamin C, zinc or garlic supplements. On stratification by sex, age and body mass index (BMI), the protective associations in individuals taking probiotics, omega-3 fatty acids, multivitamins and vitamin D were observed in females across all ages and BMI groups, but were not seen in men. The same overall pattern of association was observed in both the US and Swedish cohorts. CONCLUSION: In women, we observed a modest but significant association between use of probiotics, omega-3 fatty acid, multivitamin or vitamin D supplements and lower risk of testing positive for SARS-CoV-2. We found no clear benefits for men nor any effect of vitamin C, garlic or zinc. Randomised controlled trials are required to confirm these observational findings before any therapeutic recommendations can be made

EEG-Based Functional Brain Networks: Does the Network Size Matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks – whose nodes can vary from tens to hundreds – are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies

Attention-dependent modulation of cortical taste circuits revealed by granger causality with signal-dependent noise

We show, for the first time, that in cortical areas, for example the insular, orbitofrontal, and lateral prefrontal cortex, there is signal-dependent noise in the fMRI blood-oxygen level dependent (BOLD) time series, with the variance of the noise increasing approximately linearly with the square of the signal. Classical Granger causal models are based on autoregressive models with time invariant covariance structure, and thus do not take this signal-dependent noise into account. To address this limitation, here we describe a Granger causal model with signal-dependent noise, and a novel, likelihood ratio test for causal inferences. We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing. The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis. In particular, there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness, and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity. In addition, there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity. These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging, and reveal some of the processes involved in a biased activation theory of selective attention

Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study.

BACKGROUND: The Pfizer-BioNTech (BNT162b2) and the Oxford-AstraZeneca (ChAdOx1 nCoV-19) COVID-19 vaccines have shown excellent safety and efficacy in phase 3 trials. We aimed to investigate the safety and effectiveness of these vaccines in a UK community setting. METHODS: In this prospective observational study, we examined the proportion and probability of self-reported systemic and local side-effects within 8 days of vaccination in individuals using the COVID Symptom Study app who received one or two doses of the BNT162b2 vaccine or one dose of the ChAdOx1 nCoV-19 vaccine. We also compared infection rates in a subset of vaccinated individuals subsequently tested for SARS-CoV-2 with PCR or lateral flow tests with infection rates in unvaccinated controls. All analyses were adjusted by age (≤55 years vs >55 years), sex, health-care worker status (binary variable), obesity (BMI <30 kg/m2vs ≥30 kg/m2), and comorbidities (binary variable, with or without comorbidities). FINDINGS: Between Dec 8, and March 10, 2021, 627 383 individuals reported being vaccinated with 655 590 doses: 282 103 received one dose of BNT162b2, of whom 28 207 received a second dose, and 345 280 received one dose of ChAdOx1 nCoV-19. Systemic side-effects were reported by 13·5% (38 155 of 282 103) of individuals after the first dose of BNT162b2, by 22·0% (6216 of 28 207) after the second dose of BNT162b2, and by 33·7% (116 473 of 345 280) after the first dose of ChAdOx1 nCoV-19. Local side-effects were reported by 71·9% (150 023 of 208 767) of individuals after the first dose of BNT162b2, by 68·5% (9025 of 13 179) after the second dose of BNT162b2, and by 58·7% (104 282 of 177 655) after the first dose of ChAdOx1 nCoV-19. Systemic side-effects were more common (1·6 times after the first dose of ChAdOx1 nCoV-19 and 2·9 times after the first dose of BNT162b2) among individuals with previous SARS-CoV-2 infection than among those without known past infection. Local effects were similarly higher in individuals previously infected than in those without known past infection (1·4 times after the first dose of ChAdOx1 nCoV-19 and 1·2 times after the first dose of BNT162b2). 3106 of 103 622 vaccinated individuals and 50 340 of 464 356 unvaccinated controls tested positive for SARS-CoV-2 infection. Significant reductions in infection risk were seen starting at 12 days after the first dose, reaching 60% (95% CI 49-68) for ChAdOx1 nCoV-19 and 69% (66-72) for BNT162b2 at 21-44 days and 72% (63-79) for BNT162b2 after 45-59 days. INTERPRETATION: Systemic and local side-effects after BNT162b2 and ChAdOx1 nCoV-19 vaccination occur at frequencies lower than reported in phase 3 trials. Both vaccines decrease the risk of SARS-CoV-2 infection after 12 days. FUNDING: ZOE Global, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, UK Medical Research Council, Wellcome Trust, UK Research and Innovation, American Gastroenterological Association

In-hospital outcome of patients with culture-confirmed tuberculous pleurisy: clinical impact of pulmonary involvement

<p>Abstract</p> <p>Background</p> <p>Outcomes for hospitalized patients with tuberculous pleurisy (TP) have rarely been reported, and whether or not pulmonary involvement affects outcomes is uncertain. This study aimed to analyze the in-hospital mortality rate of culture-confirmed TP with an emphasis on the clinical impact of pulmonary involvement.</p> <p>Methods</p> <p>Patients who were hospitalized for pleural effusion (PE) of unconfirmed diagnosis and finally diagnosed as TP were identified. We classified them according to the disease extent: isolated pleurisy (isolated pleurisy group) and pleurisy with pulmonary involvement (pleuro-pulmonary group).</p> <p>Results</p> <p>Among the 205 patients hospitalized before the diagnosis was established, 51 (24.9%) belonged to the isolated pleurisy group. Compared to the pleuro-pulmonary group, patients in the isolated pleurisy group were younger, had fewer underlying co-morbidities, and presented more frequently with fever and chest pain. Fewer patients in the isolated pleurisy group had hypoalbuminemia (< 3.5 g/dL) and anemia. The two groups were similar with regards to PE analysis, resistance pattern, and timing of anti-tuberculous treatment. Patients who had a typical pathology of TP on pleural biopsy received anti-tuberculous treatment earlier than those who did not, and were all alive at discharge. The isolated pleurisy group had a lower in-hospital mortality rate, a shorter length of hospital stay and better short-term survival. In addition, the presence of underlying comorbidities and not receiving anti-tuberculous treatment were associated with a higher in-hospital mortality rate.</p> <p>Conclusion</p> <p>In culture-confirmed tuberculous pleurisy, those with pulmonary involvement were associated with a higher in-hospital mortality rate. A typical pathology for TP on pleura biopsy was associated with a better outcome.</p

The Brain Matures with Stronger Functional Connectivity and Decreased Randomness of Its Network

We investigated the development of the brain's functional connectivity throughout the life span (ages 5 through 71 years) by measuring EEG activity in a large population-based sample. Connectivity was established with Synchronization Likelihood. Relative randomness of the connectivity patterns was established with Watts and Strogatz' (1998) graph parameters C (local clustering) and L (global path length) for alpha (∼10 Hz), beta (∼20 Hz), and theta (∼4 Hz) oscillation networks. From childhood to adolescence large increases in connectivity in alpha, theta and beta frequency bands were found that continued at a slower pace into adulthood (peaking at ∼50 yrs). Connectivity changes were accompanied by increases in L and C reflecting decreases in network randomness or increased order (peak levels reached at ∼18 yrs). Older age (55+) was associated with weakened connectivity. Semi-automatically segmented T1 weighted MRI images of 104 young adults revealed that connectivity was significantly correlated to cerebral white matter volume (alpha oscillations: r = 33, p<01; theta: r = 22, p<05), while path length was related to both white matter (alpha: max. r = 38, p<001) and gray matter (alpha: max. r = 36, p<001; theta: max. r = 36, p<001) volumes. In conclusion, EEG connectivity and graph theoretical network analysis may be used to trace structural and functional development of the brain

ELF5 Drives Lung Metastasis in Luminal Breast Cancer through Recruitment of Gr1+ CD11b+ Myeloid-Derived Suppressor Cells.

During pregnancy, the ETS transcription factor ELF5 establishes the milk-secreting alveolar cell lineage by driving a cell fate decision of the mammary luminal progenitor cell. In breast cancer, ELF5 is a key transcriptional determinant of tumor subtype and has been implicated in the development of insensitivity to anti-estrogen therapy. In the mouse mammary tumor virus-Polyoma Middle T (MMTV-PyMT) model of luminal breast cancer, induction of ELF5 levels increased leukocyte infiltration, angiogenesis, and blood vessel permeability in primary tumors and greatly increased the size and number of lung metastasis. Myeloid-derived suppressor cells, a group of immature neutrophils recently identified as mediators of vasculogenesis and metastasis, were recruited to the tumor in response to ELF5. Depletion of these cells using specific Ly6G antibodies prevented ELF5 from driving vasculogenesis and metastasis. Expression signatures in luminal A breast cancers indicated that increased myeloid cell invasion and inflammation were correlated with ELF5 expression, and increased ELF5 immunohistochemical staining predicted much shorter metastasis-free and overall survival of luminal A patients, defining a group who experienced unexpectedly early disease progression. Thus, in the MMTV-PyMT mouse mammary model, increased ELF5 levels drive metastasis by co-opting the innate immune system. As ELF5 has been previously implicated in the development of antiestrogen resistance, this finding implicates ELF5 as a defining factor in the acquisition of the key aspects of the lethal phenotype in luminal A breast cancer