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The unique characteristics of COVID-19 coagulopathy
Thrombotic complications and coagulopathy frequently occur in COVID-19. However, the characteristics of COVID-19-associated coagulopathy (CAC) are distinct from those seen with bacterial sepsis-induced coagulopathy (SIC) and disseminated intravascular coagulation (DIC), with CAC usually showing increased D-dimer and fibrinogen levels but initially minimal abnormalities in prothrombin time and platelet count. Venous thromboembolism and arterial thrombosis are more frequent in CAC compared to SIC/DIC. Clinical and laboratory features of CAC overlap somewhat with a hemophagocytic syndrome, antiphospholipid syndrome, and thrombotic microangiopathy. We summarize the key characteristics of representative coagulopathies, discussing similarities and differences so as to define the unique character of CAC.
Journal Article
Utility of circulating tumor DNA in cancer diagnostics with emphasis on early detection
Various recent studies have focused on analyzing tumor genetic material released into the blood stream, known as circulating tumor DNA (ctDNA). Herein, we describe current research on the application of ctDNA to cancer management, including prognosis determination, monitoring for treatment efficacy/relapse, treatment selection, and quantification of tumor size and disease burden. Specifically, we examine the utility of ctDNA for early cancer diagnostics focusing on the development of a blood test to detect cancer in asymptomatic individuals by sequencing and analyzing mutations in ctDNA. Next, we discuss the prospect of using ctDNA to test for cancer, and present our calculations based on previously published empirical findings in cancer and prenatal diagnostics. We show that very early stage (asymptomatic) tumors are not likely to release enough ctDNA to be detectable in a typical blood draw of 10 mL. Data are also presented showing that mutations in circulating free DNA can be found in healthy individuals and will likely be very difficult to distinguish from those associated with cancer.
We conclude that the ctDNA test, in addition to its high cost and complexity, will likely suffer from the same issues of low sensitivity and specificity as traditional biomarkers when applied to population screening and early (asymptomatic) cancer diagnosis.
Journal Article
Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification
Background
Blood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet.
Results
This paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified.
Conclusions
The results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values.
Journal Article
A machine learning prediction model for Cardiac Amyloidosis using routine blood tests in patients with left ventricular hypertrophy
Current approaches for cardiac amyloidosis (CA) identification are time-consuming, labor-intensive, and present challenges in sensitivity and accuracy, leading to limited treatment efficacy and poor prognosis for patients. In this retrospective study, we aimed to leverage machine learning (ML) to create a diagnostic model for CA using data from routine blood tests. Our dataset included 6,563 patients with left ventricular hypertrophy, 261 of whom had been diagnosed with CA. We divided the dataset into training and testing cohorts, applying ML algorithms such as logistic regression, random forest, and XGBoost for automated learning and prediction. Our model’s diagnostic accuracy was then evaluated against CA biomarkers, specifically serum-free light chains (FLCs). The model’s interpretability was elucidated by visualizing the feature importance through the gain map. XGBoost outperformed both random forest and logistic regression in internal validation on the testing cohort, achieving an area under the curve (AUC) of 0.95 (95%CI: 0.92–0.97), sensitivity of 0.92 (95%CI: 0.86–0.98), specificity of 0.95 (95%CI: 0.94–0.97), and an F1 score of 0.89 (95%CI: 0.85–0.92). Its performance was also superior to the serum FLC-kappa and FLC-lambda combination (AUC of 0.88). Furthermore, XGBoost identified unique biomarker signatures indicative of multisystem dysfunction in CA patients, with significant changes in eGFR, FT3, cTnI, ANC, and NT-proBNP. This study develops a highly sensitive and accurate ML model for CA detection using routine clinical laboratory data, effectively streamlining diagnostic procedures, and providing valuable clinical insights and guiding future research into disease mechanisms.
Journal Article
The role of mSEPT9 in screening, diagnosis, and recurrence monitoring of colorectal cancer
Background
The application of circulating, cell-free, methylated Septin9 (
m
SEPT9) DNA in screening and recurrence monitoring is highly promising. CpG island methylator phenotype (CIMP) is associated with microsatellite instability (MSI). The present study was performed to determine the diagnostic accuracy of
m
SEPT9 for colorectal cancer (CRC) and to evaluate its utility in CRC screening and recurrence monitoring.
Methods
For screening and diagnosis of CRC, peripheral
m
SEPT9 detection and fecal occult blood test (FOBT) were performed in 650 subjects, then the level of CEA, CA19–9 and CA724 was quantified in 173 subjects. Clinicopathological parameters and mismatch repair protein were detected among subjects with CRC. For recurrence monitoring of CRC, the sensitivity of
m
SEPT9 of 70 subjects was compared with tumor markers and contrast enhanced computed tomography (CECT).
Results
Seventy-three percent of CRC patients were
m
SEPT9-positive at 94.5% specificity, and 17.1% of patients with intestinal polyps and adenoma were
m
SEPT9-positive at 94.5% specificity, which were higher than FOBT for the screening of CRC. The sensitivity and specificity of
m
SEPT9 for diagnosis and recurrence monitoring were higher than that of CEA, CA19–9 and CA724. The combined detection of
m
SEPT9 and CECT enhanced the sensitivity for recurrence monitoring. Pre-therapeutic levels of
m
SEPT9 were strongly associated with TNM stage, Dukes stages and mismatch repair deficiency (dMMR).
Conclusions
m
SEPT9 analysis might be popularized as a routine biomarker for CRC screening. The combined detection of
m
SEPT9 and CECT can play an important role for recurrence monitoring. CIMP was highly associated with the pathological stage of CRC and dMMR.
Journal Article
Advances in blood biomarkers for Alzheimer disease (AD): A review
Alzheimer disease (AD) and Alzheimer Disease and Related Dementias (AD/ADRD) are growing public health challenges globally affecting millions of older adults, necessitating concerted efforts to advance our understanding and management of these conditions. AD is a progressive neurodegenerative disorder characterized pathologically by amyloid plaques and tau neurofibrillary tangles that are the primary cause of dementia in older individuals. Early and accurate diagnosis of AD dementia is crucial for effective intervention and treatment but has proven challenging to accomplish. Although testing for AD brain pathology with cerebrospinal fluid (CSF) or positron emission tomography (PET) has been available for over 2 decades, most patients never underwent this testing because of inaccessibility, high out‐of‐pocket costs, perceived risks, and the lack of AD‐specific treatments. However, in recent years, rapid progress has been made in developing blood biomarkers for AD/ADRD. Consequently, blood biomarkers have emerged as promising tools for non‐invasive and cost‐effective diagnosis, prognosis, and monitoring of AD progression. This review presents the evolving landscape of blood biomarkers in AD/ADRD and explores their potential applications in clinical practice for early detection, prognosis, and therapeutic interventions. It covers recent advances in blood biomarkers, including amyloid beta (Aβ) peptides, tau protein, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP). It also discusses their diagnostic and prognostic utility while addressing associated challenges and limitations. Future research directions in this rapidly evolving field are also proposed.
Journal Article
Next generation viscoelasticity assays in cardiothoracic surgery: Feasibility of the TEG6s system
Viscoelastic near-patient assays of global hemostasis have been found useful and cost-effective in perioperative settings. Shortcomings of current systems include substantial laboratory intensity, user-dependent reproducibility, relatively large sample volumes, sensitivity to ambient vibration and limited comparability between techniques and devices. The aim of this study was to assess feasibility of a new, resonance-based viscoelastic whole blood methodology (TEG6s) in cardiac surgery with cardiopulmonary bypass (CPB) and to compare the parameters this system produces with the ROTEM delta system and standard coagulation tests.
In a prospective evaluation study, twenty-three consecutive cardiac surgery patients underwent hemostasis management according to current guidelines, using the ROTEM delta system and standard coagulation tests. Blood samples were collected prior to CPB before anesthetic induction (pre-CPB), during CPB on rewarming (CPB), and 10 minutes after heparin reversal with protamine (post-CPB). ROTEM and standard coagulation test results were compared with TEG6s parameters, which were concurrently determined using its multi-channel microfluidic cartridge system.
TEG6s provided quantifiable results pre-CPB and post-CPB, but only R (clotting time) of CKH (kaolin with heparinase) was measurable during CPB (full heparinization). Spearman's correlation coefficient (rs) was 0.78 for fibrinogen levels and MA CFF (functional fibrinogen). Correlation of several TEG6s parameters was good (0.77 to 0.91) with MCF FIBTEM, and poor (<0.56) with prothrombin time and activated partial thromboplastin time (<0.44). Rs with platelet count was moderate (0.70, MA CK; 0.73, MA CRT). Accuracy of MA CFF for detection of fibrinogen deficiency < 1.5 g/L was high (ROC-AUC 0.93).
The TEG6s system, which is based on resonance viscoelastic methodology, appears to be feasible for POC hemostasis assessment in cardiac surgery. Its correlations with standard coagulation parameters are quite similar to those of ROTEM and there is good diagnostic accuracy for fibrinogen levels lower than 1.5 g/L. During full heparinization, TEG6s testing is limited to R measurement. Larger studies are needed to assess superiority over other POC systems.
Journal Article
Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests
Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
Journal Article
Point-of-care blood tests using a smartphone-based colorimetric analyzer for health check-up
A microscale colorimetric assay was designed and implemented for the simultaneous determination of clinical chemistry tests measuring six parameters, including glucose (GLU), total protein (TP), human serum albumin (HSA), uric acid (UA), total cholesterol (TC), and triglycerides (TGs) in plasma samples. The test kit was fabricated using chromogenic reagents, comprising specific enzymes and binding dyes. Multiple colors that appeared on the reaction well when it was exposed to each analyte were captured by a smartphone and processed by the homemade Check6 application, which was designed as a colorimetric analyzer and simultaneously generated a report that assessed test results against gender-dependent reference ranges. Six blood checkup parameters for four plasma samples were conducted within 12 min on one capture picture. The assay achieved wide working concentration ranges of 10.45–600 mg dL
−1
GLU, 1.39–10.0 g dL
−1
TP, 1.85–8.0 g dL
−1
HSA, 0.86–40.0 mg dL
−1
UA, 11.28–600 mg dL
−1
TC, and 11.93–400 mg dL
−1
TGs. The smartphone-based assay was accurate with recoveries of 93–108% GLU, 93–107% TP, 92–107% HSA, 93–107% UA, 92–107% TC, and 99–113% TGs. The coefficient of variation for intra-assay and inter-assay precision ranged from 3.2–5.2% GLU, 4.6–5.3% TP, 4.3–5.3% HSA, 2.8–6.6% UA, 2.7–6.5% TC, and 1.1–3.9% TGs. This assay demonstrated remarkable accuracy in quantifying the concentration-dependent color intensity of the plasma, even in the presence of other suspected interferences commonly present in serum. The results of the proposed method correlated well with results determined by the microplate spectrophotometer (
R
2
> 0.95). Measurement of these six clinical chemistry parameters in plasma using a microscale colorimetric test kit coupled with the Check6 smartphone application showed potential for real-time point-of-care analysis, providing cost-effective and rapid assays for health checkup testing.
Graphical abstract
Journal Article