scholarly journals Immunophenotyping and machine learning identify distinct immunotypes that predict COVID-19 clinical severity

2021 ◽  
Author(s):  
Yvonne M Mueller ◽  
Thijs J Schrama ◽  
Rik Ruijten ◽  
Marco W.J. Schreurs ◽  
Dwin G.B. Grashof ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Response ◽  
Antibody Response ◽  
Clinical Improvement ◽  
Hierarchical Clustering ◽  
Disease Duration ◽  
Tertiary Care ◽  
Low Risk ◽  
Clinical Severity ◽  
Kinetics Of

Quantitative or qualitative differences in immunity may drive and predict clinical severity in COVID-19. We therefore measured modules of serum pro-inflammatory, anti-inflammatory and anti-viral cytokines in combination with the anti-SARS-CoV-2 antibody response in COVID-19 patients admitted to tertiary care. Using machine learning and employing unsupervised hierarchical clustering, agnostic to severity, we identified three distinct immunotypes that were shown post-clustering to predict very different clinical courses such as clinical improvement or clinical deterioration. Immunotypes did not associate chronologically with disease duration but rather reflect variations in the nature and kinetics of individual patient's immune response. Here we demonstrate that immunophenotyping can stratify patients to high and low risk clinical subtypes, with distinct cytokine and antibody profiles, that can predict severity progression and guide personalized therapy.

scholarly journals Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement

2020 ◽  
pp. annrheumdis-2020-217840 ◽  
Author(s):  
Kimberly Showalter ◽  
Robert Spiera ◽  
Cynthia Magro ◽  
Phaedra Agius ◽  
Viktor Martyanov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Systemic Sclerosis ◽  
Clinical Improvement ◽  
Smooth Muscle Actin ◽  
Clinical Severity ◽  
Support Vector ◽  
Alpha Smooth Muscle Actin ◽  
Modified Rodnan Skin Score ◽  
Diffuse Cutaneous Systemic Sclerosis

ObjectiveWe sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma).MethodsFifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis).ResultsaSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts.ConclusionCD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.

scholarly journals The kinetic variations of anti-nucleocapsid antibody in SARS-CoV-2 infection

2021 ◽  
Author(s):  
Shoji Kawada ◽  
Atsushi Ogata ◽  
Yasuhiro Kato ◽  
Masashi Okamoto ◽  
Yuta Yamaguchi ◽  
...  
Keyword(s):  
Immune Response ◽  
Severe Acute Respiratory Syndrome ◽  
Antibody Response ◽  
Nucleocapsid Protein ◽  
Healthcare Workers ◽  
Humoral Immune ◽  
Antibody Persistence ◽  
Decay Pattern ◽  
Kinetics Of

AbstractThe humoral immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays a pivotal role in controlling coronavirus disease 2019 (COVID-19) infections. However, little is known about the persistence of the antibody response.We evaluated that the kinetics of anti-nucleocapsid protein antibody of SARS-CoV2 infected healthcare workers in COVID-19 cluster occurred hospital. The long-term kinetics of anti-N antibody was classified high and keep pattern, high and decay pattern, and low and keep pattern. COVID-19 contact and symptomaticity was not related to kinetic patterns.The reason of kinetic difference was still unclear. However natural anti-SARS-CoV-2 antibody persistence was not uniform, suggesting inter-individual difference of SARS-CoV2 vaccine efficacy.

Plasma Fibrin Stabilising Factor (F. S. F.) Activity in Normal Subjects and Patients with Chronic Liver Disease

1969 ◽  
Vol 21 (01) ◽  
pp. 134-143 ◽  
Author(s):  
W. D Walls ◽  
M. S Losowsky
Keyword(s):  
Liver Disease ◽  
Chronic Liver Disease ◽  
Clinical Improvement ◽  
Quantitative Estimation ◽  
Kinetic Method ◽  
Normal Subjects ◽  
Chronic Liver ◽  
Clinical Severity ◽  
Plasma Fibrinogen ◽  
Normal Range

SummaryA kinetic method for the quantitative estimation of plasma F.S.F. activity is described and discussed.This method was applied to normal subjects and to patients with chronic liver disease. The plasma F.S.F. activity was uninfluenced by either sex or age, and the normal range has been defined.A significant decrease in plasma F.S.F. activity was observed in patients with chronic liver disease. Subnormal levels of activity were found in 25% of such patients but were unrelated to episodes of abnormal haemorrhage. Plasma F.S.F. activity tended to be lower in patients with disease of greater clinical severity. In 2 patients showing clinical improvement there was an increase in plasma F. S. F. activity.It was confirmed that plasma fibrinogen levels increase with age.

scholarly journals Perceptions of partial gland ablation for prostate cancer among men on active surveillance: a qualitative study

2021 ◽  
Vol 3 (1) ◽  
pp. e000068
Author(s):  
Sonia Hur ◽  
Michael Tzeng ◽  
Eliza Cricco-Lizza ◽  
Spyridon Basourakos ◽  
Miko Yu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Qualitative Study ◽  
Active Surveillance ◽  
Treatment Modality ◽  
Tertiary Care ◽  
Low Risk ◽  
Intermediate Risk ◽  
Perceptions And Attitudes ◽  
Partial Gland Ablation

ObjectivesPartial gland ablation (PGA) therapy is an emerging treatment modality that targets specific areas of biopsy-proven prostate cancer (PCa) to minimize treatment-related morbidity by sparing benign prostate. This qualitative study aims to explore and characterize perceptions and attitudes toward PGA in men with very-low-risk, low-risk, and favorable intermediate-risk PCa on active surveillance (AS).Design92 men diagnosed with very-low-risk, low-risk, and favorable intermediate-risk PCa on AS were invited to participate in semistructured telephone interviews on PGA.SettingSingle tertiary care center located in New York City.Participants20 men with very-low-risk, low-risk, and favorable intermediate-risk PCa on AS participated in the interviews.Main outcome measuresEmerging themes on perceptions and attitudes toward PGA were developed from transcripts inductively coded and analyzed under standardized methodology.ResultsFour themes were derived from 20 interviews that represent the primary considerations in treatment decision-making: (1) the feeling of psychological safety associated with low-risk disease; (2) preference for minimally invasive treatments; (3) the central role of the physician; (4) and the pursuit of treatment options that align with disease severity. Eleven men (55%) expressed interest in pursuing PGA only if their cancer were to progress, while nine men (45%) expressed interest at the current moment.ConclusionsAlthough an emerging treatment modality, patients were broadly accepting of PGA for PCa, with men primarily debating the risks versus benefits of proactively treating low-risk disease. Additional research on men’s preferences and attitudes toward PGA will further guide counseling and shared decision-making for PGA.

scholarly journals New risk model is able to identify patients with a low risk of progression in systemic sclerosis

RMD Open ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e001524
Author(s):  
Nina Marijn van Leeuwen ◽  
Marc Maurits ◽  
Sophie Liem ◽  
Jacopo Ciaffi ◽  
Nina Ajmone Marsan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systemic Sclerosis ◽  
High Risk ◽  
Disease Progression ◽  
Risk Model ◽  
Immunosuppressive Drugs ◽  
Low Risk ◽  
Expert Assessment ◽  
Risk Of Progression

ObjectivesTo develop a prediction model to guide annual assessment of systemic sclerosis (SSc) patients tailored in accordance to disease activity.MethodsA machine learning approach was used to develop a model that can identify patients without disease progression. SSc patients included in the prospective Leiden SSc cohort and fulfilling the ACR/EULAR 2013 criteria were included. Disease progression was defined as progression in ≥1 organ system, and/or start of immunosuppression or death. Using elastic-net-regularisation, and including 90 independent clinical variables (100% complete), we trained the model on 75% and validated it on 25% of the patients, optimising on negative predictive value (NPV) to minimise the likelihood of missing progression. Probability cutoffs were identified for low and high risk for disease progression by expert assessment.ResultsOf the 492 SSc patients (follow-up range: 2–10 years), disease progression during follow-up was observed in 52% (median time 4.9 years). Performance of the model in the test set showed an AUC-ROC of 0.66. Probability score cutoffs were defined: low risk for disease progression (<0.197, NPV:1.0; 29% of patients), intermediate risk (0.197–0.223, NPV:0.82; 27%) and high risk (>0.223, NPV:0.78; 44%). The relevant variables for the model were: previous use of cyclophosphamide or corticosteroids, start with immunosuppressive drugs, previous gastrointestinal progression, previous cardiovascular event, pulmonary arterial hypertension, modified Rodnan Skin Score, creatine kinase and diffusing capacity for carbon monoxide.ConclusionOur machine-learning-assisted model for progression enabled us to classify 29% of SSc patients as ‘low risk’. In this group, annual assessment programmes could be less extensive than indicated by international guidelines.

Machine-learning algorithm as a prognostic tool in non-obstructive acute-on-chronic kidney disease in the cat

2021 ◽  
pp. 1098612X2110012
Author(s):  
Jade Renard ◽  
Mathieu R Faucher ◽  
Anaïs Combes ◽  
Didier Concordet ◽  
Brice S Reynolds
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Diagnostic Performance ◽  
Clinical Signs ◽  
Learning System ◽  
Clinical Severity ◽  
Medium Term ◽  
Term Survival

Objectives The aim of this study was to develop an algorithm capable of predicting short- and medium-term survival in cases of intrinsic acute-on-chronic kidney disease (ACKD) in cats. Methods The medical record database was searched to identify cats hospitalised for acute clinical signs and azotaemia of at least 48 h duration and diagnosed to have underlying chronic kidney disease based on ultrasonographic renal abnormalities or previously documented azotaemia. Cases with postrenal azotaemia, exposure to nephrotoxicants, feline infectious peritonitis or neoplasia were excluded. Clinical variables were combined in a clinical severity score (CSS). Clinicopathological and ultrasonographic variables were also collected. The following variables were tested as inputs in a machine learning system: age, body weight (BW), CSS, identification of small kidneys or nephroliths by ultrasonography, serum creatinine at 48 h (Crea48), spontaneous feeding at 48 h (SpF48) and aetiology. Outputs were outcomes at 7, 30, 90 and 180 days. The machine-learning system was trained to develop decision tree algorithms capable of predicting outputs from inputs. Finally, the diagnostic performance of the algorithms was calculated. Results Crea48 was the best predictor of survival at 7 days (threshold 1043 µmol/l, sensitivity 0.96, specificity 0.53), 30 days (threshold 566 µmol/l, sensitivity 0.70, specificity 0.89) and 90 days (threshold 566 µmol/l, sensitivity 0.76, specificity 0.80), with fewer cats still alive when their Crea48 was above these thresholds. A short decision tree, including age and Crea48, predicted the 180-day outcome best. When Crea48 was excluded from the analysis, the generated decision trees included CSS, age, BW, SpF48 and identification of small kidneys with an overall diagnostic performance similar to that using Crea48. Conclusions and relevance Crea48 helps predict short- and medium-term survival in cats with ACKD. Secondary variables that helped predict outcomes were age, CSS, BW, SpF48 and identification of small kidneys.

scholarly journals A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

2021 ◽  
Vol 22 (3) ◽  
pp. 1075
Author(s):  
Luca Bedon ◽  
Michele Dal Bo ◽  
Monica Mossenta ◽  
Davide Busato ◽  
Giuseppe Toffoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Progression Free Survival ◽  
Low Risk ◽  
Functional Enrichment ◽  
Free Survival ◽  
High Risk Patients ◽  
Risk Of Progression ◽  
Risk Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

scholarly journals Serum neurofilament light chain as a severity marker for spinocerebellar ataxia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hye-Rim Shin ◽  
Jangsup Moon ◽  
Woo-Jin Lee ◽  
Han Sang Lee ◽  
Eun Young Kim ◽  
...  
Keyword(s):  
Spinocerebellar Ataxia ◽  
Disease Duration ◽  
Trinucleotide Repeat ◽  
Neuronal Damage ◽  
Demographic Data ◽  
Brain Magnetic Resonance Imaging ◽  
Clinical Severity ◽  
P Value ◽  
Repeat Number ◽  
Neurofilament Light

AbstractSince the serum neurofilament light (NfL) chain is known as a promising biomarker in neurodegenerative diseases, we aimed to evaluate serum NfL as a biomarker indicating neuronal damage in autosomal-dominant (AD) spinocerebellar ataxia (SCA). We reviewed patients diagnosed with AD SCA in the outpatient clinic of Seoul National University Hospital’s (SNUH) Department of Neurology between May and August of 2019. We reviewed the demographic data, clinical characteristics, Scale for the Assessment and Rating of Ataxia (SARA) score, and brain magnetic resonance imaging (MRI) scans. The serum NfL was measured by electrochemiluminescence (ECL) immunoassay. Forty-nine patients with AD SCA were reviewed and their serum NfL level was determined. The median serum NfL level (109.5 pg/mL) was higher than control (41.1 pg/mL) (p-value < 0.001). Among the AD SCA patients, there was a positive correlation between the serum NfL level and the trinucleotide repeat number (r = 0.47, p-value = 0.001), disease duration (r = 0.35, p-value = 0.019), disease duration/age × trinucleotide repeat number (r = 0.330, p-value = 0.021), and SARA score (n = 33; r = 0.37, p-value = 0.033). This study shows that serum NfL is elevated in AD SCA patients and correlates with clinical severity.

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