High Discrimination Accuracy Research Articles

Small Dense LDL Level and LDL/HDL Distribution in Acute Coronary Syndrome Patients

A total of 205 ACS patients and 100 healthy control subjects were enrolled. LDL particle size and LDL and HDL subclass distributions were measured using Quantimetric Lipoprint® linear polyacrylamide gel electrophoresis. Lipid ratios (total cholesterol, LDL cholesterol, non-HDL cholesterol, and HDL cholesterol) were determined to calculate the atherogenic index of plasma (AIP), the atherogenic coefficient (AC), Castelli's Risk-I (CR-I), and Castelli's Risk-II (CR-II). Receiver operating characteristic (ROC) curve analyses and area under the curve (AUC) were used to assess the predictive value of sdLDL as a marker for cardiovascular disease. The ACS patients, compared to the healthy control subjects, displayed an alteration of LDL particle distribution, with a significant increase in sdLDL serum concentrations (0.303 ± 0.478 mmol/L vs. 0.0225 ± 0.043 mmol/L, respectively, p < 0.001). The sdLDL levels had a high discrimination accuracy [AUC = 0.847 ± 0.0353 (95% CI 0.778 to 0.916, p < 0.0001)]. The best predictive cutoff value of ACS determined with the maximum Youden index (J) [(sensitivity + specificity) - 1 = 0.60] was 0.038 mmol/L. A Spearman correlation analysis showed that sdLDL levels were moderately but significantly and positively correlated with AC and CR-I (r = 0.37, p < 0.001) and weakly but significantly correlated with PAI and CR-II; r = 0.32 (p < 0.001) and r = 0.30 (p < 0.008), respectively. The subclass distribution of HDL particles from ACS patients was also altered, with a decrease in large HDL particles and an increase in small HDL particles compared to HDL from healthy control subjects. Due to their high atherogenicity, sdLDL levels could be used as a valuable marker for the prediction cardiovascular events.

Machine learning algorithms distinguish discrete digital emotional fingerprints for web pages related to back pain

Back pain is the leading cause of disability worldwide. Its emergence relates not only to the musculoskeletal degeneration biological substrate but also to psychosocial factors; emotional components play a pivotal role. In modern society, people are significantly informed by the Internet; in turn, they contribute social validation to a “successful” digital information subset in a dynamic interplay. The Affective component of medical pages has not been previously investigated, a significant gap in knowledge since they represent a critical biopsychosocial feature. We tested the hypothesis that successful pages related to spine pathology embed a consistent emotional pattern, allowing discrimination from a control group. The pool of web pages related to spine or hip/knee pathology was automatically selected by relevance and popularity and submitted to automated sentiment analysis to generate emotional patterns. Machine Learning (ML) algorithms were trained to predict page original topics from patterns with binary classification. ML showed high discrimination accuracy; disgust emerged as a discriminating emotion. The findings suggest that the digital affective “successful content” (collective consciousness) integrates patients’ biopsychosocial ecosystem, with potential implications for the emergence of chronic pain, and the endorsement of health-relevant specific behaviors. Awareness of such effects raises practical and ethical issues for health information providers.

Bottom-Up Signal Boosting with Fractal Nanostructuring and Primer Exchange Reaction for Ultrasensitive Detection of Cancerous Exosomes.

Exosomes are emerging as promising biomarkers for cancer diagnosis, yet sensitive and accurate quantification of tumor-derived exosomes remains a challenge. Here, we report an ultrasensitive and specific exosome sensor (NPExo) that initially leverages hierarchical nanostructuring array and primer exchange reaction (PER) for quantitation of cancerous exosomes. This NPExo uses a high-curvature nanostructuring array (bottom) fabricated by single-step electrodeposition to enhance capturing of the target exosomes. The immuno-captured exosome thus provides abundant membrane sites to insert numerous cholesterol-DNA probes with a density much higher than that by immune pairing, which further allows PER-based DNA extension to assemble enzyme concatemers (up) for signal amplification. Such a bottom-up signal-boosting design imparts NPExo with ultrahigh sensitivity up to 75 particles/mL (i.e., <1 exosome per 10 μL) and a broad dynamic range spanning 6 orders of magnitude. Furthermore, our sensor allows monitoring subtle exosomal phenotypic transition and shows high accuracy in discrimination of liver cancer patients from healthy donors via blood samples, suggesting the great potential of NPExo as a promising tool in clinical diagnostics.

Efficient pulse shape discrimination using scalogram image masking and decision tree

In radiation detectors, pulse shape discrimination (PSD) is essential for classifying radiation types. Designing efficient PSD method that has high discrimination accuracy and low time consumption is a challenging task. In this paper, an efficient approach is proposed to discriminate neutron and gamma pulses in radiation detectors, where high classification accuracy and low time consumption are presented. The proposed method is based on image processing through the Scalogram representation of the pulses, which is acquired using the continuous wavelet transform (CWT). From the grayscale Scalogram image, the higher energy density region is segmented, where lower effect of noise is located. Furthermore, a mask image is generated to select a portion from the segmented region, where the difference between the neutron and gamma pulses is significant. The discrimination factor is evaluated according to the number of white pixels in the masked image. Machine learning is utilized to provide a regression model for the designed method, where the discrimination time is significantly reduced. The decision tree regression model is used to predict the value of the discrimination factor according to the principal components of the pulse signal. To validate the effectiveness of the proposed method, a performance comparison is conducted with state-of-the art and traditional PSD techniques using synthetic data. Experimental results show that the proposed method outperforms other PSD methods in terms of the figure of merit and the classification accuracy. Moreover, the proposed method provides less time-consuming discrimination compared to recent methods.

E-Nose Technology for Mycotoxin Detection in Feed: Ready for a Real Context in Field Application or Still an Emerging Technology?

Mycotoxin risk in the feed supply chain poses a concern to animal and human health, economy, and international trade of agri-food commodities. Mycotoxin contamination in feed and food is unavoidable and unpredictable. Therefore, monitoring and control are the critical points. Effective and rapid methods for mycotoxin detection, at the levels set by the regulations, are needed for an efficient mycotoxin management. This review provides an overview of the use of the electronic nose (e-nose) as an effective tool for rapid mycotoxin detection and management of the mycotoxin risk at feed business level. E-nose has a high discrimination accuracy between non-contaminated and single-mycotoxin-contaminated grain. However, the predictive accuracy of e-nose is still limited and unsuitable for in-field application, where mycotoxin co-contamination occurs. Further research needs to be focused on the sensor materials, data analysis, pattern recognition systems, and a better understanding of the needs of the feed industry for a safety and quality management of the feed supply chain. A universal e-nose for mycotoxin detection is not realistic; a unique e-nose must be designed for each specific application. Robust and suitable e-nose method and advancements in signal processing algorithms must be validated for specific needs.

Comparison of phenomics and cfDNA in a large breast screening population: the Breast Screening and Monitoring Study (BSMS)

To assess their roles in breast cancer diagnostics, we aimed to compare plasma cell-free DNA (cfDNA) levels with the circulating metabolome in a large breast screening cohort of women recalled for mammography, including healthy women and women with mammographically detected breast diseases, ductal carcinoma in situ and invasive breast cancer: the Breast Screening and Monitoring Study (BSMS). In 999 women, plasma was analyzed by nuclear magnetic resonance (NMR) and Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) and then processed to isolate and quantify total cfDNA. NMR and UPLC-MS results were compared with data for 186 healthy women derived from the AIRWAVE cohort. Results showed no significant differences between groups for all metabolites, whereas invasive cancers had significantly higher plasma cfDNA levels than all other groups. When stratified the supervised OPLS-DA analysis and total cfDNA concentration showed high discrimination accuracy between invasive cancers and the disease/medication-free subjects. Furthermore, comparison of OPLS-DA data for invasive breast cancers with the AIRWAVE cohort showed similar discrimination between breast cancers and healthy controls. This is the first report of agreement between metabolomics and plasma cfDNA levels for discriminating breast cancer from healthy subjects in a true screening population. It also emphasizes the importance of sample standardization. Follow on studies will involve analysis of candidate features in a larger validation series as well as comparing results with serial plasma samples taken at the next routine screening mammography appointment. The findings here help establish the role of plasma analysis in the diagnosis of breast cancer in a large real-world cohort.

Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort.

Polygenic risk scores (PRS) which summarize individuals' genetic risk profile may enhance targeted colorectal cancer screening. A critical step towards clinical implementation is rigorous external validations in large community-based cohorts. This study externally validated a PRS-enhanced colorectal cancer risk model comprising 140 known colorectal cancer loci to provide a comprehensive assessment on prediction performance. The model was developed using 20,338 individuals and externally validated in a community-based cohort (n = 85,221). We validated predicted 5-year absolute colorectal cancer risk, including calibration using expected-to-observed case ratios (E/O) and calibration plots, and discriminatory accuracy using time-dependent AUC. The PRS-related improvement in AUC, sensitivity and specificity were assessed in individuals of age 45 to 74 years (screening-eligible age group) and 40 to 49 years with no endoscopy history (younger-age group). In European-ancestral individuals, the predicted 5-year risk calibrated well [E/O = 1.01; 95% confidence interval (CI), 0.91-1.13] and had high discriminatory accuracy (AUC = 0.73; 95% CI, 0.71-0.76). Adding the PRS to a model with age, sex, family and endoscopy history improved the 5-year AUC by 0.06 (P < 0.001) and 0.14 (P = 0.05) in the screening-eligible age and younger-age groups, respectively. Using a risk-threshold of 5-year SEER colorectal cancer incidence rate at age 50 years, adding the PRS had a similar sensitivity but improved the specificity by 11% (P < 0.001) in the screening-eligible age group. In the younger-age group it improved the sensitivity by 27% (P = 0.04) with similar specificity. The proposed PRS-enhanced model provides a well-calibrated 5-year colorectal cancer risk prediction and improves discriminatory accuracy in the external cohort. The proposed model has potential utility in risk-stratified colorectal cancer prevention.

Progressive structural and covariance connectivity abnormalities in patients with Alzheimer's disease.

Alzheimer's disease (AD) is one of most prevalent neurodegenerative diseases worldwide and characterized by cognitive decline and brain structure atrophy. While studies have reported substantial grey matter atrophy related to progression of AD, it remains unclear about brain regions with progressive grey matter atrophy, covariance connectivity, and the associations with cognitive decline in AD patients. This study aims to investigate the grey matter atrophy, structural covariance connectivity abnormalities, and the correlations between grey matter atrophy and cognitive decline during AD progression. We analyzed neuroimaging data of healthy controls (HC, n = 45) and AD patients (n = 40) at baseline (AD-T1) and one-year follow-up (AD-T2) obtained from the Alzheimer's Disease Neuroimaging Initiative. We investigated AD-related progressive changes of grey matter volume, covariance connectivity, and the clinical relevance to further understand the pathological progression of AD. The results showed clear patterns of grey matter atrophy in inferior frontal gyrus, prefrontal cortex, lateral temporal gyrus, posterior cingulate cortex, insula, hippocampus, caudate, and thalamus in AD patients. There was significant atrophy in bilateral superior temporal gyrus (STG) and left caudate in AD patients over a one-year period, and the grey matter volume decrease in right STG and left caudate was correlated with cognitive decline. Additionally, we found reduced structural covariance connectivity between right STG and left caudate in AD patients. Using AD-related grey matter atrophy as features, there was high discrimination accuracy of AD patients from HC, and AD patients at different time points.

Deep learning assisted contrast-enhanced CT–based diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases

ObjectivesLymph node (LN) metastasis is a common cause of recurrence in oral cancer; however, the accuracy of distinguishing positive and negative LNs is not ideal. Here, we aimed to develop a deep learning model that can identify, locate, and distinguish LNs in contrast-enhanced CT (CECT) images with a higher accuracy.MethodsThe preoperative CECT images and corresponding postoperative pathological diagnoses of 1466 patients with oral cancer from our hospital were retrospectively collected. In stage I, full-layer images (five common anatomical structures) were labeled; in stage II, negative and positive LNs were separately labeled. The stage I model was innovatively employed for stage II training to improve accuracy with the idea of transfer learning (TL). The Mask R-CNN instance segmentation framework was selected for model construction and training. The accuracy of the model was compared with that of human observers.ResultsA total of 5412 images and 5601 images were labeled in stage I and II, respectively. The stage I model achieved an excellent segmentation effect in the test set (AP50-0.7249). The positive LN accuracy of the stage II TL model was similar to that of the radiologist and much higher than that of the surgeons and students (0.7042 vs. 0.7647 (p = 0.243), 0.4216 (p < 0.001), and 0.3629 (p < 0.001)). The clinical accuracy of the model was highest (0.8509 vs. 0.8000, 0.5500, 0.4500, and 0.6658 of the Radiology Department).ConclusionsThe model was constructed using a deep neural network and had high accuracy in LN localization and metastasis discrimination, which could contribute to accurate diagnosis and customized treatment planning.Key Points• Lymph node metastasis is not well recognized with modern medical imaging tools.• Transfer learning can improve the accuracy of deep learning model prediction.• Deep learning can aid the accurate identification of lymph node metastasis.

Establishment of a nomogram model for the early diagnosis of childhood sepsis

To establish a nomogram model for the early diagnosis of sepsis in children. A total of 76 children with sepsis who were admitted to Sichuan Maternal and Child Health Hospital from January 2018 to June 2021 were retrospectively selected as the sepsis group. After matching for sex and age (±2 years) at a ratio of 1:1:1, 76 children with local infection who were hospitalized during the same period were enrolled as the local infection group, and 76 children with non-infectious diseases were enrolled as the control group. The three groups were compared in terms of laboratory markers and the results of quick Sequential Organ Failure Assessment (qSOFA) and Pediatric Critical Illness Score (PCIS). A multivariate logistic regression analysis was used to investigate the association between the above indicators and sepsis. R4.1.3 software was used to establish and validate the nomogram model for the early diagnosis of sepsis based on the results of the multivariate analysis. A receiver operating characteristic (ROC) curve analysis was used to evaluate the value of the nomogram model, and the Bootstrap method was used to perform the internal validation of the model. The multivariate logistic regression analysis showed that soluble triggering receptor expressed on myeloid cells-1, qSOFA score, PCIS score, C-reactive protein, interleukin-6, and interleukin-10 were independently associated with childhood sepsis (P<0.05). The above indicators were used to establish a nomogram for the early diagnosis of sepsis, with an area under the ROC curve of 0.837 (95%CI: 0.760-0.914), and the calibration curve results showed a mean absolute error of 0.024, suggesting that the performance of this model was basically consistent with that of the ideal model. The indicators soluble triggering receptor expressed on myeloid cells-1, qSOFA score, PCIS score, C-reactive protein, interleukin-6, and interleukin-10 are independently associated with childhood sepsis, and the nomogram model established based on these indicators has high discriminatory ability and accuracy in the early diagnosis of sepsis in children.

Rapid Evaporative Ionisation Mass Spectrometry fingerprinting can discriminate lamb meat due to different ageing methods and levels of dehydration

Dry-ageing is a technique for developing characteristic dry-aged flavour through the interplay of dehydration, lipid oxidation and microbial activities. In this study, we tested the hypothesis that the extent of drying influences the metabolite profile and the final flavour of lamb using an “Age-and-Dry” regime; and that Rapid Evaporative Ionisation Mass Spectrometry (REIMS) fingerprinting can be used to discriminate the metabolic fingerprints of lambs due to ageing methods and dehydration levels. Lamb loins (n = 60) were dehydrated with low (12%), medium (17%) and high (22%) weight losses and compared with the wet-aged equivalents using REIMS and evaluated by 12-member sensory panel. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) models based on 1400 tentatively identified m/z features were obtained for ageing methods (Q2 > 0.95) and dehydration levels (Q2 > 0.82) with high discrimination accuracy. Increased concentrations of dipeptides and metabolites associated with energy metabolism were observed in aged-and-dried lamb meat which supports the umami and savoury taste perceived by the sensory panel. A reduced concentration of polyunsaturated fatty acids with more aldehydes was observed in aged-and-dried lamb meat contributing to the nutty, roasted, and fatty flavour notes detected by the sensory panellists in these samples compared to the wet-aged. SignificanceOutcomes of this study demonstrated that REIMS can be used to accurately discriminate between different ageing methods and dehydration levels of meat, and the compounds that are associated with lamb flavour. The implications of this finding include: (1) the metabolite concentrating effect of dehydration and the associated effect on aged lamb flavour determined in this study could be used to tailor the processing of dry-aged lamb to deliver specific flavour outcomes in an industrial setting; (2) the ability of REIMS to rapidly detect differences in the aged lamb flavour due to dehydration effect would improve the quality of dry-aged meat and the efficiency with which it can be industrially produced. Thus, REIMS can be used as a rapid authentication and quality prediction tool for different ageing regimes and flavour for the meat industry.

Plasma and CSF biomarkers in a memory clinic: Head-to-head comparison of phosphorylated tau immunoassays.

Direct comparisons of the main blood phosphorylated tau immunoassays in memory clinic populations are needed to understand possible differences. In the BIODEGMAR study, 197 participants presenting with cognitive complaints were classified into an Alzheimer's disease (AD) or a non-AD cerebrospinal fluid (CSF) profile group, according to their amyloid beta 42/ phosphorylated tau (Aβ42/p-tau) ratio. We performed a head-to-head comparison of nine plasma and nine CSF tau immunoassays and determined their accuracy to discriminate abnormal CSF Aβ42/p-tau ratio. All studied plasma tau biomarkers were significantly higher in the AD CSF profile group compared to the non-AD CSF profile group and significantly discriminated abnormal CSF Aβ42/p-tau ratio. For plasma p-tau biomarkers, the higher discrimination accuracy was shown by Janssen p-tau217 (r=0.76; area under the curve [AUC]=0.96), ADx p-tau181 (r=0.73; AUC=0.94), and Lilly p-tau217 (r=0.73; AUC=0.94). Several plasma p-tau biomarkers can be used in a specialized memory clinic as a stand-alone biomarker to detect biologically-defined AD. Patients with an Alzheimer's disease cerebrospinal fluid (AD CSF) profile have higher plasma phosphorylated tau (p-tau) levels than the non-AD CSF profile group. All plasma p-tau biomarkers significantly discriminate patients with an AD CSF profile from the non-AD CSF profile group. Janssen p-tau217, ADx p-tau181, and Lilly p-tau217 in plasma show the highest accuracy to detect biologically defined AD. Janssen p-tau217, ADx p-tau181, Lilly p-tau217, Lilly p-tau181, and UGot p-tau231 in plasma show performances that are comparable to their CSF counterparts.

Development of a novel dementia risk prediction model in the general population: A large, longitudinal, population-based machine-learning study.

SummaryBackgroundThe existing dementia risk models are limited to known risk factors and traditional statistical methods. We aimed to employ machine learning (ML) to develop a novel dementia prediction model by leveraging a rich-phenotypic variable space of 366 features covering multiple domains of health-related data.MethodsIn this longitudinal population-based cohort of the UK Biobank (UKB), 425,159 non-demented participants were enrolled from 22 recruitment centres across the UK between March 1, 2006 and October 31, 2010. We implemented a data-driven strategy to identify predictors from 366 candidate variables covering a comprehensive range of genetic and environmental factors and developed the ML model to predict incident dementia and Alzheimer's Disease (AD) within five, ten, and much longer years (median 11.9 [Interquartile range 11.2–12.5] years).FindingsDuring a follow-up of 5,023,337 person-years, 5287 and 2416 participants developed dementia and AD, respectively. A novel UKB dementia risk prediction (UKB-DRP) model comprising ten predictors including age, ApoE ε4, pairs matching time, leg fat percentage, number of medications taken, reaction time, peak expiratory flow, mother's age at death, long-standing illness, and mean corpuscular volume was established. Our prediction model was internally evaluated based on five-fold cross-validation on discrimination and calibration, and it was further compared with existing prediction scales. The UKB-DRP model can achieve high discriminative accuracy in dementia (AUC 0.848 ± 0.007) and even better in AD (AUC 0.862 ± 0.015). The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.92), and the predictive power was solid in different incidence time groups. More importantly, our model presented an apparent superiority over existing models like Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score (AUC 0.705 ± 0.008), the Dementia Risk Score (AUC 0.752 ± 0.007), and the Australian National University Alzheimer's Disease Risk Index (AUC 0.584 ± 0.017). The model was internally validated in the general population of European ancestry and White ethnicity; thus, further validation with independent datasets is necessary to confirm these findings.InterpretationOur ML-based UKB-DRP model incorporated ten easily accessible predictors with solid predictive power for incident dementia and AD within five, ten, and much longer years, which can be used to identify individuals at high risk of dementia and AD in the general population.FundingThis study was funded by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600), National Key R&D Program of China (2018YFC1312904, 2019YFA070950), National Natural Science Foundation of China (282071201, 81971032, 82071997), Shanghai Municipal Science and Technology Major Project (2018SHZDZX01), Research Start-up Fund of Huashan Hospital (2022QD002), Excellence 2025 Talent Cultivation Program at Fudan University (3030277001), Shanghai Rising-Star Program (21QA1408700), Medical Engineering Fund of Fudan University (yg2021-013), and the 111 Project (No. B18015).

Are topsoil spectra or soil-environmental factors better indicators for discrimination of soil classes?

Visible and near-infrared (Vis-NIR) spectroscopy (350–2500 nm) is often used to discriminate different soil classes. Discrimination variables included generally reflectance, topographic factors, soil texture, and climate. In the present study, more soil properties, soil organic matter, soil pH, bulk density and thickness, were considered to build soil classes model, and the most important discrimination variables were identified. Reasons for misclassification and challenges in elaborating the soil class map were analyzed. The topsoil spectra of 1432 soil samples of nine soil classes in northeast China were measured and converted into continuum removal (CR) curves, and principal component analysis (PCA) was applied to extract spectral information for reducing data redundancy. Other discrimination variables, soil-environmental factors, were acquired, including topographic factors, soil texture, climate, and four soil properties of each soil sample. The importance of soil–environmental factors and CR spectra was calculated for building models. Random forest (RF) models were trained to discriminate nine soil classes based on five kinds of variables, and they were the principal components (PCs) of reflectance, PCs of CR spectra, soil–environmental factors, PCs of CR spectra + soil–environmental factors, and optimum combinations between CR spectra and soil–environmental factors according to the importance. Optimum discrimination variables of each soil class were selected to build random forest models, and then the sum accuracy was calculated. Our study showed that: (1) it was challenging to acquire a high discrimination accuracy using reflectance and CR spectra for large-scale soil data; (2) soil–environmental factors were more suitable to discriminate between soil classes than reflectance and CR spectra in northeast China; (3) the order of importance of discrimination variables was soil texture > climate > pH > thickness > other soil–environmental factors; and (4) the RF model with optimum combinations between CR spectra and soil–environmental factors led to an accuracy of 81.5 % and a kappa coefficient of 0.77. We validated soil-environmental factors were better than topsoil spectra for discrimination of soil classes, and we found soil constituents in soil-environmental factors had high importance, and we successfully discriminated nine soil classes with 1432 soil samples by optimum combinations between topsoil spectra and soil–environmental factors at a large scale.

Determining states of consciousness in the electroencephalogram based on spectral, complexity, and criticality features.

This study was based on the contemporary proposal that distinct states of consciousness are quantifiable by neural complexity and critical dynamics. To test this hypothesis, it was aimed at comparing the electrophysiological correlates of three meditation conditions using nonlinear techniques from the complexity and criticality framework as well as power spectral density. Thirty participants highly proficient in meditation were measured with 64-channel electroencephalography (EEG) during one session consisting of a task-free baseline resting (eyes closed and eyes open), a reading condition, and three meditation conditions (thoughtless emptiness, presence monitoring, and focused attention). The data were analyzed applying analytical tools from criticality theory (detrended fluctuation analysis, neuronal avalanche analysis), complexity measures (multiscale entropy, Higuchi’s fractal dimension), and power spectral density. Task conditions were contrasted, and effect sizes were compared. Partial least square regression and receiver operating characteristics analysis were applied to determine the discrimination accuracy of each measure. Compared to resting with eyes closed, the meditation categories emptiness and focused attention showed higher values of entropy and fractal dimension. Long-range temporal correlations were declined in all meditation conditions. The critical exponent yielded the lowest values for focused attention and reading. The highest discrimination accuracy was found for the gamma band (0.83–0.98), the global power spectral density (0.78–0.96), and the sample entropy (0.86–0.90). Electrophysiological correlates of distinct meditation states were identified and the relationship between nonlinear complexity, critical brain dynamics, and spectral features was determined. The meditation states could be discriminated with nonlinear measures and quantified by the degree of neuronal complexity, long-range temporal correlations, and power law distributions in neuronal avalanches.

In Vivo Coronary 18F-Sodium Fluoride Activity: Correlations With Coronary Plaque Histological Vulnerability and Physiological Environment

Background18F-sodium fluoride (18F-NaF) positron emission tomography (PET)/computed tomography (CT) is a promising new approach for assessing microcalcification in vascular plaque. ObjectivesThis prospective study aimed to evaluate the associations between in vivo coronary 18F-NaF PET/CT activity and ex vivo histological characteristics, to determine whether coronary 18F-NaF activity is a novel biomarker of plaque pathological vulnerability, and to explore the underlying physiological environment of 18F-NaF adsorption to vascular microcalcification. MethodsPatients with coronary artery disease (CAD) underwent coronary computed tomography angiography (CTA) and 18F-NaF PET/CT. Histological vulnerability and immunohistochemical characteristics were evaluated in coronary endarterectomy (CE) specimens from patients who underwent coronary artery bypass grafting with adjunctive CE. Correlations between in-vivo coronary 18F-NaF activity with coronary CTA adverse plaque features and with ex vivo CE specimen morphological features, CD68 expression, inflammatory cytokines expression (tumor necrosis factor-α, interleukin-1β), osteogenic differentiation cytokines expression (osteopontin, runt-related transcription factor 2, osteocalcin) were evaluated. High- and low- to medium-risk plaques were defined by standard pathological classification. ResultsA total of 55 specimens were obtained from 42 CAD patients. Coronary 18F-NaF activity of high-risk specimens was significantly higher than low- to medium-risk specimens (median [25th-75th percentile]: 1.88 [1.41-2.54] vs 1.12 [0.91-1.54]; P < 0.001). Coronary 18F-NaF activity showed high discriminatory accuracy in identifying high-risk plaque (AUC: 0.80). Coronary CTA adverse plaque features (positive remodeling, low-attenuation plaque, remodeling index), histologically vulnerable features (large necrotic core, thin-fibro cap, microcalcification), CD68 expression, tumor necrosis factor-α expression, and interleukin-1β expression correlated with coronary 18F-NaF activity (all P < 0.05). No significant association between coronary 18F-NaF activity and osteogenic differentiation cytokines was found (all P > 0.05). ConclusionsCoronary 18F-NaF activity was associated with histological vulnerability, CD68 expression, inflammatory cytokines expression, but not with osteogenic differentiation cytokines expression. 18F-NaF PET/CT imaging may provide a powerful tool for detecting high-risk coronary plaque and could improve the risk stratification of CAD patients.

Profiling metabolites and lipoproteins in COMETA, an Italian cohort of COVID-19 patients.

Metabolomics and lipidomics have been used in several studies to define the biochemical alterations induced by COVID-19 in comparison with healthy controls. Those studies highlighted the presence of a strong signature, attributable to both metabolites and lipoproteins/lipids. Here, 1H NMR spectra were acquired on EDTA-plasma from three groups of subjects: i) hospitalized COVID-19 positive patients (≤21 days from the first positive nasopharyngeal swab); ii) hospitalized COVID-19 positive patients (>21 days from the first positive nasopharyngeal swab); iii) subjects after 2–6 months from SARS-CoV-2 eradication. A Random Forest model built using the EDTA-plasma spectra of COVID-19 patients ≤21 days and Post COVID-19 subjects, provided a high discrimination accuracy (93.6%), indicating both the presence of a strong fingerprint of the acute infection and the substantial metabolic healing of Post COVID-19 subjects. The differences originate from significant alterations in the concentrations of 16 metabolites and 74 lipoprotein components. The model was then used to predict the spectra of COVID-19>21 days subjects. In this group, the metabolite levels are closer to those of the Post COVID-19 subjects than to those of the COVID-19≤21 days; the opposite occurs for the lipoproteins. Within the acute phase patients, characteristic trends in metabolite levels are observed as a function of the disease severity. The metabolites found altered in COVID-19≤21 days patients with respect to Post COVID-19 individuals overlap with acute infection biomarkers identified previously in comparison with healthy subjects. Along the trajectory towards healing, the metabolome reverts back to the “healthy” state faster than the lipoproteome.

Transcranial Doppler as a screening test to exclude intracranial hypertension in brain-injured patients: the IMPRESSIT-2 prospective multicenter international study

BackgroundAlternative noninvasive methods capable of excluding intracranial hypertension through use of transcranial Doppler (ICPtcd) in situations where invasive methods cannot be used or are not available would be useful during the management of acutely brain-injured patients. The objective of this study was to determine whether ICPtcd can be considered a reliable screening test compared to the reference standard method, invasive ICP monitoring (ICPi), in excluding the presence of intracranial hypertension.MethodsThis was a prospective, international, multicenter, unblinded, diagnostic accuracy study comparing the index test (ICPtcd) with a reference standard (ICPi), defined as the best available method for establishing the presence or absence of the condition of interest (i.e., intracranial hypertension). Acute brain-injured patients pertaining to one of four categories: traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), intracerebral hemorrhage (ICH) or ischemic stroke (IS) requiring ICPi monitoring, were enrolled in 16 international intensive care units. ICPi measurements (reference test) were compared to simultaneous ICPtcd measurements (index test) at three different timepoints: before, immediately after and 2 to 3 h following ICPi catheter insertion. Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) were calculated at three different ICPi thresholds (> 20, > 22 and > 25 mmHg) to assess ICPtcd as a bedside real-practice screening method. A receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC) was used to evaluate the discriminative accuracy and predictive capability of ICPtcd.ResultsTwo hundred and sixty-two patients were recruited for final analysis. Intracranial hypertension (> 22 mmHg) occurred in 87 patients (33.2%). The total number of paired comparisons between ICPtcd and ICPi was 687. The NPV was elevated (ICP > 20 mmHg = 91.3%, > 22 mmHg = 95.6%, > 25 mmHg = 98.6%), indicating high discriminant accuracy of ICPtcd in excluding intracranial hypertension. Concordance correlation between ICPtcd and ICPi was 33.3% (95% CI 25.6–40.5%), and Bland–Altman showed a mean bias of -3.3 mmHg. The optimal ICPtcd threshold for ruling out intracranial hypertension was 20.5 mmHg, corresponding to a sensitivity of 70% (95% CI 40.7–92.6%) and a specificity of 72% (95% CI 51.9–94.0%) with an AUC of 76% (95% CI 65.6–85.5%).Conclusions and relevanceICPtcd has a high NPV in ruling out intracranial hypertension and may be useful to clinicians in situations where invasive methods cannot be used or not available.Trial registration: NCT02322970.

Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics Approach.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.tb.). Our integrative analysis aims to identify the transcriptional profiling and gene expression signature that distinguish individuals with active TB (ATB) disease, and those with latent tuberculosis infection (LTBI). In the present study, we reanalyzed a microarray dataset (GSE37250) from GEO database and explored the data for differential gene expression analysis between those with ATB and LTBI derived from Malawi and South African cohorts. We used BRB array tool to distinguish DEGs (differentially expressed genes) between ATB and LTBI. Pathway enrichment analysis of DEGs was performed using DAVID bioinformatics tool. The protein–protein interaction (PPI) network of most upregulated genes was constructed using STRING analysis. We have identified 375 upregulated genes and 152 downregulated genes differentially expressed between ATB and LTBI samples commonly shared among Malawi and South African cohorts. The constructed PPI network was significantly enriched with 76 nodes connected to 151 edges. The enriched GO term/pathways were mainly related to expression of IFN stimulated genes, interleukin-1 production, and NOD-like receptor signaling pathway. Downregulated genes were significantly enriched in the Wnt signaling, B cell development, and B cell receptor signaling pathways. The short-listed DEGs were validated in a microarray data from an independent cohort (GSE19491). ROC curve analysis was done to assess the diagnostic accuracy of the gene signature in discrimination of active and latent tuberculosis. Thus, we have derived a seven-gene signature, which included five upregulated genes FCGR1B, ANKRD22, CARD17, IFITM3, TNFAIP6 and two downregulated genes FCGBP and KLF12, as a biomarker for discrimination of active and latent tuberculosis. The identified genes have a sensitivity of 80–100% and specificity of 80–95%. Area under the curve (AUC) value of the genes ranged from 0.84 to 1. This seven-gene signature has a high diagnostic accuracy in discrimination of active and latent tuberculosis.

A Data-Driven Algorithm to Recommend Initial Clinical Workup for Outpatient Specialty Referral: Algorithm Development and Validation Using Electronic Health Record Data and Expert Surveys.

BackgroundMillions of people have limited access to specialty care. The problem is exacerbated by ineffective specialty visits due to incomplete prereferral workup, leading to delays in diagnosis and treatment. Existing processes to guide prereferral diagnostic workup are labor-intensive (ie, building a consensus guideline between primary care doctors and specialists) and require the availability of the specialists (ie, electronic consultation).ObjectiveUsing pediatric endocrinology as an example, we develop a recommender algorithm to anticipate patients’ initial workup needs at the time of specialty referral and compare it to a reference benchmark using the most common workup orders. We also evaluate the clinical appropriateness of the algorithm recommendations.MethodsElectronic health record data were extracted from 3424 pediatric patients with new outpatient endocrinology referrals at an academic institution from 2015 to 2020. Using item co-occurrence statistics, we predicted the initial workup orders that would be entered by specialists and assessed the recommender’s performance in a holdout data set based on what the specialists actually ordered. We surveyed endocrinologists to assess the clinical appropriateness of the predicted orders and to understand the initial workup process.ResultsSpecialists (n=12) indicated that <50% of new patient referrals arrive with complete initial workup for common referral reasons. The algorithm achieved an area under the receiver operating characteristic curve of 0.95 (95% CI 0.95-0.96). Compared to a reference benchmark using the most common orders, precision and recall improved from 37% to 48% (P<.001) and from 27% to 39% (P<.001) for the top 4 recommendations, respectively. The top 4 recommendations generated for common referral conditions (abnormal thyroid studies, obesity, amenorrhea) were considered clinically appropriate the majority of the time by specialists surveyed and practice guidelines reviewed.Conclusions An item association–based recommender algorithm can predict appropriate specialists’ workup orders with high discriminatory accuracy. This could support future clinical decision support tools to increase effectiveness and access to specialty referrals. Our study demonstrates important first steps toward a data-driven paradigm for outpatient specialty consultation with a tier of automated recommendations that proactively enable initial workup that would otherwise be delayed by awaiting an in-person visit.