Oscillatory stimuli differentiate adapting circuit topologies

This is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this record.Biology emerges from interactions between molecules, which are challenging to elucidate with current techniques. An orthogonal approach is to probe for 'response signatures' that identify specific circuit motifs. For example, bistability, hysteresis, or irreversibility are used to detect positive feedback loops. For adapting systems, such signatures are not known. Only two circuit motifs generate adaptation: negative feedback loops (NFLs) and incoherent feed-forward loops (IFFLs). On the basis of computational testing and mathematical proofs, we propose differential signatures: in response to oscillatory stimulation, NFLs but not IFFLs show refractory-period stabilization (robustness to changes in stimulus duration) or period skipping. Applying this approach to yeast, we identified the circuit dominating cell cycle timing. In Caenorhabditis elegans AWA neurons, which are crucial for chemotaxis, we uncovered a Ca2+ NFL leading to adaptation that would be difficult to find by other means. These response signatures allow direct access to the outlines of the wiring diagrams of adapting systems.The work was supported by US National Institutes of Health grant 5RO1-GM078153-07 (F.R.C.), NRSA Training Grant CA009673-36A1 (S.J.R.), a Merck Postdoctoral Fellowship at The Rockefeller University (S.J.R.), and the Simons Foundation (S.J.R.). J.L. was supported by a fellowship from the Boehringer Ingelheim Fonds. E.D.S. was partially supported by the US Office of Naval Research (ONR N00014-13-1-0074) and the US Air Force Office of Scientific Research (AFOSR FA9550-14-1-0060)

Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however.Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals.Results: The SPM yielded ROC-AUCs of 0.53–0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at <ext-link ext-link-type="uri" xlink:href="http://www.aicovid.org/" xmlns:xlink="http://www.w3.org/1999/xlink">www.aicovid.net</ext-link>.Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day Zero in the ICU

Rationale Given the expanding number of COVID-19 cases and the potential for upcoming waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objectives Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission.Methods We studied retrospectively 263 COVID-19 ICU patients. To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Logistic regression and random forest (RF) algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP).Results Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume, white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patients outcomes with a sensitivity of 70% and a specificity of 75%.Conclusions The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW along with gender and age. Complete blood count parameters were also crucial for some patients. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThe authors received no financial support for the research, authorship, and/or publication of this article.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The study was performed after approval by Iran University of Medical Sciences Ethics Committee (approval ID: IR.IUMS.REC.1399.595)All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe data that support the findings of this study are available from the corresponding authors upon request.ACE2Angiotensin-Converting Enzyme 2AIArtificial IntelligenceBUNBlood Urea NitrogenCOVID-19coronavirus disease of 2019CICclinical impact curveCrCreatinineCRPC reactive proteinDCdecision curveICUIntensive care unitINRInternational Normalized RatioIFNinterferonIL-6Interleukin 6IQRinterquartile rangeKSKolmogorov-SmirnovLRLogistics regressionLIMElocal interpretable model-agnostic explanationLIME-SPlocal interpretable model-agnostic explanation submodular-pickMLMachine learningMCHmean corpuscular hemoglobinMCVmean corpuscular volumeRFRandom forestRDWRed blood cell distribution widthROCreceiver operating characteristic curveRT-PCRreverse transcription-polymerase chain reactionWBCwhite blood cells coun

ERS international Congress 2023: highlights from the Basic and Translational Sciences Assembly

In this article, early career members of the Assembly 3: Basic and Translational Sciences of the European Respiratory Society summarise the key messages discussed during six selected sessions that took place at the ERS congress 2023 in Milan, Italy. Aligned with the theme of the congress, the first topic covered is “micro- and macro-environments and respiratory health”, followed by a summary of the “Scientific year in review” session. Next, recent advances in experimental methodologies and new technologies were highlighted within the “tissue modelling and remodelling” session, as well as summary of the translational science session, “what did you always want to know about omics analyses for clinical practice?”, organised as part of the ERS Translational Science Working group's aims. Details on how the next-generation sequencing can be integrated with laboratory methods were provided in the “lost in translation: new insights into cell-to-cell crosstalk in lung disease” session and a final summary of studies presented in the “from the transcriptome landscape to innovative preclinical models in lung diseases” session linking the transcriptome landscape with innovative preclinical models was included in this review. The wide range of topics covered in the selected sessions and the high quality of the research discussed highlight the strength of the basic and translational science being presented at the international respiratory conference organised by the ERS

Cytokine-armed dendritic cell progenitors for antigen-agnostic cancer immunotherapy

Dendritic cells (DCs) are antigen-presenting myeloid cells that regulate T cell activation, trafficking and function. Monocyte-derived DCs pulsed with tumor antigens have been tested extensively for therapeutic vaccination in cancer, with mixed clinical results. Here, we present a cell-therapy platform based on mouse or human DC progenitors (DCPs) engineered to produce two immunostimulatory cytokines, IL-12 and FLT3L. Cytokine-armed DCPs differentiated into conventional type-I DCs (cDC1) and suppressed tumor growth, including melanoma and autochthonous liver models, without the need for antigen loading or myeloablative host conditioning. Tumor response involved synergy between IL-12 and FLT3L and was associated with natural killer and T cell infiltration and activation, M1-like macrophage programming and ischemic tumor necrosis. Antitumor immunity was dependent on endogenous cDC1 expansion and interferon-γ signaling but did not require CD8+T cell cytotoxicity. Cytokine-armed DCPs synergized effectively with anti-GD2 chimeric-antigen receptor (CAR) T cells in eradicating intracranial gliomas in mice, illustrating their potential in combination therapies

Mycobacterial Disease And Impaired Ifn-Gamma Immunity In Humans With Inherited Isg15 Deficiency

ISG15 is an interferon (IFN)-alpha/beta-inducible, ubiquitin-like intracellular protein. Its conjugation to various proteins (ISGylation) contributes to antiviral immunity in mice. Here, we describe human patients with inherited ISG15 deficiency and mycobacterial, but not viral, diseases. The lack of intracellular ISG15 production and protein ISGylation was not associated with cellular susceptibility to any viruses that we tested, consistent with the lack of viral diseases in these patients. By contrast, the lack of mycobacterium-induced ISG15 secretion by leukocytes-granulocyte, in particular-reduced the production of IFN-gamma by lymphocytes, including natural killer cells, probably accounting for the enhanced susceptibility to mycobacterial disease. This experiment of nature shows that human ISGylation is largely redundant for antiviral immunity, but that ISG15 plays an essential role as an IFN-gamma-inducing secreted molecule for optimal antimycobacterial immunity.Wo

ERS International Congress 2022: highlights from the Basic and Translational Science Assembly

In this review, the Basic and Translational Science Assembly of the European Respiratory Society provides an overview of the 2022 International Congress highlights. We discuss the consequences of respiratory events from birth until old age regarding climate change related alterations in air quality due to pollution caused by increased ozone, pollen, wildfires and fuel combustion as well as the increasing presence of microplastic and microfibres. Early life events such as the effect of hyperoxia in the context of bronchopulmonary dysplasia and crucial effects of the intrauterine environment in the context of pre-eclampsia were discussed. The Human Lung Cell Atlas (HLCA) was put forward as a new point of reference for healthy human lungs. The combination of single-cell RNA sequencing and spatial data in the HLCA has enabled the discovery of new cell types/states and niches, and served as a platform that facilitates further investigation of mechanistic perturbations. The role of cell death modalities in regulating the onset and progression of chronic lung diseases and its potential as a therapeutic target was also discussed. Translational studies identified novel therapeutic targets and immunoregulatory mechanisms in asthma. Lastly, it was highlighted that the choice of regenerative therapy depends on disease severity, ranging from transplantation to cell therapies and regenerative pharmacology

ERS International Congress 2022: highlights from the Basic and Translational Science Assembly

In this review, the Basic and Translational Science Assembly of the European Respiratory Society provides an overview of the 2022 International Congress highlights. We discuss the consequences of respiratory events from birth until old age regarding climate change related alterations in air quality due to pollution caused by increased ozone, pollen, wildfires and fuel combustion as well as the increasing presence of microplastic and microfibres. Early life events such as the effect of hyperoxia in the context of bronchopulmonary dysplasia and crucial effects of the intrauterine environment in the context of pre-eclampsia were discussed. The Human Lung Cell Atlas (HLCA) was put forward as a new point of reference for healthy human lungs. The combination of single-cell RNA sequencing and spatial data in the HLCA has enabled the discovery of new cell types/states and niches, and served as a platform that facilitates further investigation of mechanistic perturbations. The role of cell death modalities in regulating the onset and progression of chronic lung diseases and its potential as a therapeutic target was also discussed. Translational studies identified novel therapeutic targets and immunoregulatory mechanisms in asthma. Lastly, it was highlighted that the choice of regenerative therapy depends on disease severity, ranging from transplantation to cell therapies and regenerative pharmacology.</p