scholarly journals Prediction of occult tumor progression via platelet RNAs in a mouse melanoma model: a potential new platform of cancer screening for early detection of cancer

2021 ◽  
Author(s):  
Yue Yin ◽  
Ruilan Jiang ◽  
Mingwang Shen ◽  
Zhaofang Li ◽  
Ni Yan ◽  
...  
Keyword(s):  
Cancer Screening ◽  
Early Detection ◽  
Liquid Biopsy ◽  
Prediction Models ◽  
Validation Cohort ◽  
Blood Platelets ◽  
Gene Signature ◽  
Training Data ◽  
Occult Tumor ◽  
Testing Data

AbstractCancer screening provides the opportunity to detect cancer early, ideally before symptom onset and metastasis, and offers an increased opportunity for a better prognosis. The ideal biomarkers for cancer screening should discriminate individuals who have not developed invasive cancer yet but are destined to do so from healthy subjects1,2. However, most cancers lack effective screening recommendations. Therefore, further studies on novel screening strategies are urgently required. Here, our proof-of-concept study shows blood platelets could be a platform for liquid biopsy-based early cancer detection. By using a simple suboptimal inoculation melanoma mouse model, we identified differentially expressed RNAs in platelet signatures of mice injected with a suboptimal number of cancer cells (eDEGs) compared with mice with macroscopic melanomas and negative controls. These RNAs were strongly enriched in pathways related to immune response and regulation. Moreover, 36 genes selected from the eDEGs via bioinformatics analyses were verified in a mouse validation cohort via quantitative real-time PCR. LASSO regression was employed to generate the prediction models with gene expression signatures as the best predictors for occult tumor progression in mice. The prediction models showed great diagnostic efficacy and predictive value in our murine validation cohort, and could discriminate mice with occult tumors from control group (area under curve (AUC) of 0.935 (training data) and 0.912 (testing data)) (gene signature including Cd19, Cdkn1a, S100a9, Tap1, and Tnfrsf1b) and also from macroscopic tumor group (AUC of 0.920 (training data) and 0.936 (testing data)) (gene signature including Ccr7, Cd4, Kmt2d, and Ly6e). Our study provides evidence for potential clinical relevance of blood platelets as a platform for liquid biopsy-based early detection of cancer. Furthermore, the eDEGs are mostly immune-related, not tumor-specific. Hence it is possible platelets-based liquid biopsy could enable simultaneous early detection of cancers from multiple organs of origin3. It is also feasible to determine the origin of cancer since platelet profiles are influenced by tumor type3.

scholarly journals A machine learning framework to determine geolocations from metagenomic profiling

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Lihong Huang ◽  
Canqiang Xu ◽  
Wenxian Yang ◽  
Rongshan Yu
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Geographic Origin ◽  
Training Data ◽  
Metagenomic Data ◽  
Training Dataset ◽  
Kriging Interpolation ◽  
Learning Framework ◽  
Testing Data ◽  
Microbial Samples

Abstract Background Studies on metagenomic data of environmental microbial samples found that microbial communities seem to be geolocation-specific, and the microbiome abundance profile can be a differentiating feature to identify samples’ geolocations. In this paper, we present a machine learning framework to determine the geolocations from metagenomics profiling of microbial samples. Results Our method was applied to the multi-source microbiome data from MetaSUB (The Metagenomics and Metadesign of Subways and Urban Biomes) International Consortium for the CAMDA 2019 Metagenomic Forensics Challenge (the Challenge). The goal of the Challenge is to predict the geographical origins of mystery samples by constructing microbiome fingerprints.First, we extracted features from metagenomic abundance profiles. We then randomly split the training data into training and validation sets and trained the prediction models on the training set. Prediction performance was evaluated on the validation set. By using logistic regression with L2 normalization, the prediction accuracy of the model reaches 86%, averaged over 100 random splits of training and validation datasets.The testing data consists of samples from cities that do not occur in the training data. To predict the “mystery” cities that are not sampled before for the testing data, we first defined biological coordinates for sampled cities based on the similarity of microbial samples from them. Then we performed affine transform on the map such that the distance between cities measures their biological difference rather than geographical distance. After that, we derived the probabilities of a given testing sample from unsampled cities based on its predicted probabilities on sampled cities using Kriging interpolation. Results show that this method can successfully assign high probabilities to the true cities-of-origin of testing samples. Conclusion Our framework shows good performance in predicting the geographic origin of metagenomic samples for cities where training data are available. Furthermore, we demonstrate the potential of the proposed method to predict metagenomic samples’ geolocations for samples from locations that are not in the training dataset.

scholarly journals Early detection of cancer in Nepal: Role of liquid biopsy

2018 ◽  
Vol 8 (2) ◽  
pp. 1399-1407
Author(s):  
Sameer Chhetri Aryal ◽  
Gopi Aryal
Keyword(s):  
Cancer Screening ◽  
Early Detection ◽  
Liquid Biopsy ◽  
Early Detection Of Cancer ◽  
Cancer Control ◽  
Target Population ◽  
Population Based ◽  
Warning Signs ◽  
Screening Programs

Cancers of the uterine cervix, breast, lung and stomach are four of the most common cancers in Nepal. Lack of knowledge and awareness about cancer, its risk factors and negligence of the early warning signs play crucial role in raising the incidence of the cancer. Curative therapies are most successful when cancer is diagnosed and treated at an early stage.Organized cancer screening programmes provide screening to target population and use multidisciplinary delivery teams, coordinated clinical oversight committees, and regular review by a multidisciplinary evaluation board. For population-based screening programs, decision- making and governance structures, tasks and procedures need to be defined.In this paper, we review population-based cancer screening programmes of different countries and share recommendations and relevant evidence for screening and early detection of common cancers in Nepal. The evidence-based recommendations provided in this Review are intended to act as a guide for policy makers, clinicians, and public health practitioners who are developing and implementing strategies in cancer control.  We also discuss the role of liquid biopsy in early detection, diagnosis and monitoring of cancers using circulating biomarkers. Despite challenges, time has come to include cell free circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs), as a parameters for early detection of cancer in the days to come.

On the Early Detection of Intravascular Coagulation

1972 ◽  
Vol 27 (03) ◽  
pp. 365-376 ◽  
Author(s):  
G Fedder ◽  
Elisabeth M. Prakke ◽  
J Vreeken
Keyword(s):  
Early Detection ◽  
Clinical Medicine ◽  
Blood Platelets ◽  
Factor V ◽  
Intravascular Coagulation ◽  
Fibrin Monomer ◽  
Gelation Test

SummarySince the conception of intravascular coagulation has been introduced in clinical medicine, the interest of clinicians in the early detection of this syndrome is continuously increasing. Therefore small amounts of thrombin and thromboplastin were infused into rabbits and special parameters, such as presence of an activated form of factor V and occurrence of a positive fibrin monomer test, were checked. As it turned out, activation of factor V (proaccelerin, accelerator globulin or AcG) was an earlier sign of intravascular coagulation than the appearance of a positive gelation test, which may occur without changes in fibrinogen or the number of blood platelets. These experiments could be of value for the early detection of intravascular coagulation in man.

Systematic review of cervical cancer prevalence and determinants of early detection in Sub-Saharan Africa

2018 ◽  
Vol 1 (1) ◽  
pp. 32-36
Author(s):  
Eleazar Ndabarora ◽  
Dariya Mukamusoni ◽  
Clarte Ndikumasabo ◽  
Védaste Ngirinshuti
Keyword(s):  
Cervical Cancer ◽  
Cancer Screening ◽  
Early Detection ◽  
Cervical Cancer Screening ◽  
Sub Saharan Africa ◽  
Extensive Literature ◽  
Early Management ◽  
Screening Programs ◽  
Mesh Terms ◽  
Sub Saharan

Cervical cancer is one of the leading causes of morbidity and mortality globally and in Sub-Saharan Africa in particular. There is evidence that early detection and early management of cases are the best strategies to prevent and control this health threat, since treatment of the later stages of the diseases are very expensive. The objectives of the review were: (1) to identify and review studies on the prevalence of cervical cancer and determinants of early detection in Sub-Saharan Africa, and (2) to recommend further studies and interventions based on the findings of this review. Extensive literature search was conducted using the MeSH terms. Articles on cervical cancer and/or determinants of early detection which fulfilled inclusion criteria were reviewed independently by three reviewers. The prevalence of cervical cancer in Sub-Saharan Africa is increasing. Although there are evidences that cervical cancer screening programs are practical and feasible even in resource-limited settings in Sub-Saharan Africa, there is a very low uptake of cervical cancer screening and there are key factors that need to be addressed in order to make these programs established and effective.

An Analog Circuit Fault Diagnosis Approach Based on Wavelet-based fractal analysis and Multiple Kernel SVM

2020 ◽  
Vol 13 ◽  
Author(s):  
Jianfeng Jiang
Keyword(s):  
Fault Diagnosis ◽  
Fractal Analysis ◽  
Analog Circuit ◽  
Training Data ◽  
Support Vector ◽  
Pass Filter ◽  
Multiple Kernel ◽  
Testing Data ◽  
Circuit Fault Diagnosis ◽  
Diagnosis Approach

Objective: In order to diagnose the analog circuit fault correctly, an analog circuit fault diagnosis approach on basis of wavelet-based fractal analysis and multiple kernel support vector machine (MKSVM) is presented in the paper. Methods: Time responses of the circuit under different faults are measured, and then wavelet-based fractal analysis is used to process the collected time responses for the purpose of generating features for the signals. Kernel principal component analysis (KPCA) is applied to reduce the features’ dimensionality. Afterwards, features are divided into training data and testing data. MKSVM with its multiple parameters optimized by chaos particle swarm optimization (CPSO) algorithm is utilized to construct an analog circuit fault diagnosis model based on the testing data. Results: The proposed analog diagnosis approach is revealed by a four opamp biquad high-pass filter fault diagnosis simulation. Conclusion: The approach outperforms other commonly used methods in the comparisons.

scholarly journals Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sanna Iivanainen ◽  
Jussi Ekstrom ◽  
Henri Virtanen ◽  
Vesa V. Kataja ◽  
Jussi P. Koivunen
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Adverse Events ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Patient Reported Outcomes ◽  
Prediction Models ◽  
Accuracy Score ◽  
Patient Reported ◽  
Auc Value

Abstract Background Immune-checkpoint inhibitors (ICIs) have introduced novel immune-related adverse events (irAEs), arising from various organ systems without strong timely dependency on therapy dosing. Early detection of irAEs could result in improved toxicity profile and quality of life. Symptom data collected by electronic (e) patient-reported outcomes (PRO) could be used as an input for machine learning (ML) based prediction models for the early detection of irAEs. Methods The utilized dataset consisted of two data sources. The first dataset consisted of 820 completed symptom questionnaires from 34 ICI treated advanced cancer patients, including 18 monitored symptoms collected using the Kaiku Health digital platform. The second dataset included prospectively collected irAE data, Common Terminology Criteria for Adverse Events (CTCAE) class, and the severity of 26 irAEs. The ML models were built using extreme gradient boosting algorithms. The first model was trained to detect the presence and the second the onset of irAEs. Results The model trained to predict the presence of irAEs had an excellent performance based on four metrics: accuracy score 0.97, Area Under the Curve (AUC) value 0.99, F1-score 0.94 and Matthew’s correlation coefficient (MCC) 0.92. The prediction of the irAE onset was more difficult with accuracy score 0.96, AUC value 0.93, F1-score 0.66 and MCC 0.64 but the model performance was still at a good level. Conclusion The current study suggests that ML based prediction models, using ePRO data as an input, can predict the presence and onset of irAEs with a high accuracy, indicating that ePRO follow-up with ML algorithms could facilitate the detection of irAEs in ICI-treated cancer patients. The results should be validated with a larger dataset. Trial registration Clinical Trials Register (NCT3928938), registration date the 26th of April, 2019

scholarly journals A simple nomogram to predict contrast-induced acute kidney injury in patients with congestive heart failure undergoing coronary angiography

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
L Lei ◽  
Y He ◽  
Z Guo ◽  
B Liu ◽  
J Liu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Acute Kidney Injury ◽  
Coronary Angiography ◽  
Prediction Models ◽  
Validation Cohort ◽  
Kidney Injury ◽  
Class Ii ◽  
Functional Class ◽  
Killip Class

Abstract Background Patients with congestive heart failure (CHF) are vulnerable to contrast-induced acute kidney injury (CI-AKI), but few prediction models are currently available. Objectives We aimed to establish a simple nomogram for CI-AKI risk assessment for patients with CHF undergoing coronary angiography. Methods A total of 1876 consecutive patients with CHF (defined as New York Heart Association functional class II-IV or Killip class II-IV) were enrolled and randomly (2:1) assigned to a development cohort and a validation cohort. The endpoint was CI-AKI defined as serum creatinine elevation of ≥0.3 mg/dL or 50% from baseline within the first 48–72 hours following the procedure. Predictors for the nomogram were selected by multivariable logistic regression with a stepwise approach. The discriminative power was assessed using the area under the receiver operating characteristic (ROC) curve and was compared with the classic Mehran score in the validation cohort. Calibration was assessed using the Hosmer–Lemeshow test and 1000 bootstrap samples. Results The incidence of CI-AKI was 9.06% (n=170) in the total sample, 8.64% (n=109) in the development cohort and 9.92% (n=61) in the validation cohort (p=0.367). The simple nomogram including four predictors (age, intra-aortic balloon pump, acute myocardial infarction and chronic kidney disease) demonstrated a similar predictive power as the Mehran score (area under the curve: 0.80 vs 0.75, p=0.061), as well as a well-fitted calibration curve. Conclusions The present simple nomogram including four predictors is a simple and reliable tool to identify CHF patients at risk of CI-AKI, whereas further external validations are needed. Figure 1 Funding Acknowledgement Type of funding source: None

scholarly journals Risk Factors for Pneumonia and Death in Adult Patients With Seasonal Influenza and Establishment of Prediction Scores: A Population-Based Study

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Koichi Miyashita ◽  
Eiji Nakatani ◽  
Hironao Hozumi ◽  
Yoko Sato ◽  
Yoshiki Miyachi ◽  
...  
Keyword(s):  
High Risk ◽  
Seasonal Influenza ◽  
Prediction Models ◽  
Validation Cohort ◽  
Risk Groups ◽  
Population Based ◽  
Limited Data ◽  
Population Based Study ◽  
Relative Risks ◽  
Primary Care Settings

Abstract Background Seasonal influenza remains a global health problem; however, there are limited data on the specific relative risks for pneumonia and death among outpatients considered to be at high risk for influenza complications. This population-based study aimed to develop prediction models for determining the risk of influenza-related pneumonia and death. Methods We included patients diagnosed with laboratory-confirmed influenza between 2016 and 2017 (main cohort, n = 25 659), those diagnosed between 2015 and 2016 (validation cohort 1, n = 16 727), and those diagnosed between 2017 and 2018 (validation cohort 2, n = 34 219). Prediction scores were developed based on the incidence and independent predictors of pneumonia and death identified using multivariate analyses, and patients were categorized into low-, medium-, and high-risk groups based on total scores. Results In the main cohort, age, gender, and certain comorbidities (dementia, congestive heart failure, diabetes, and others) were independent predictors of pneumonia and death. The 28-day pneumonia incidence was 0.5%, 4.1%, and 10.8% in the low-, medium-, and high-risk groups, respectively (c-index, 0.75); the 28-day mortality was 0.05%, 0.7%, and 3.3% in the low-, medium-, and high-risk groups, respectively (c-index, 0.85). In validation cohort 1, c-indices for the models for pneumonia and death were 0.75 and 0.87, respectively. In validation cohort 2, c-indices for the models were 0.74 and 0.87, respectively. Conclusions We successfully developed and validated simple-to-use risk prediction models, which would promptly provide useful information for treatment decisions in primary care settings.

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