Independent Validation Sample Research Articles

Validation of the German long and short versions of the UPPS-P Impulsive Behavior Scale.

The UPPS-P Impulsive Behavior Scale is a widely used self-report measure of impulsivity, but there is currently no validated German version that includes the Positive Urgency scale. We combined existing German translations of UPPS scales and included the Positive Urgency dimension to validate the UPPS-P in a sample of 399 participants. In addition, we developed a revised short version of the UPPS-P (SUPPS-P) with 20 items and conducted a confirmatory factor analysis (CFA) to verify the structure in an independent validation sample with 349 participants. To determine evidence of convergent and discriminant validity, we used measures of impulsivity, depression, anxiety, stress, problematic alcohol and substance use. CFA of the five factorial structure of the UPPS-P demonstrated acceptable fits and evidence of validity and reliability for the subscales. Psychometric characteristics of the SUPPS-P using the original item configuration were not satisfactory. As a result, we developed a revised German version of the SUPPS-P and confirmed the five-factor structure using a CFA in the validation sample. For the revised version, model fits and evidence of validity and internal consistencies were good. Associations with other constructs were as expected. For example, whereas Sensation Seeking was associated moderately with problematic alcohol use, lacking associations of Lack of Premeditation to internalizing symptoms showed evidence of discriminant validity. The German translations of both UPPS-P and SUPPS-P are valid tools for measuring impulsive behaviors. They are well-suited for exploring the associations between different facets of impulsivity and psychopathological phenomena.

Vis-NIR spectroscopic discriminant analysis of aflatoxin B1 excessive standard in peanut meal as feedstuff materials

A fast, simple and reagent-free detection method for aflatoxin B1 (AFB1) is of great significance to food safety and human health. Visible and near-infrared (Vis-NIR) spectroscopy was applied to the discriminant analysis of AFB1 excessive standard of peanut meal as feedstuff materials. Two types of excessive standard discriminant models based on spectral quantitative analysis with partial least squares (PLS) and direct pattern recognition with partial least squares-discrimination analysis (PLS-DA) were established, respectively. Multi-parameter optimization of Norris derivative filtering (NDF) was used for spectral preprocessing; the two-stage wavelength screening method based on equidistant combination-wavelength step-by-step phase-out (EC-WSP) was used for wavelength optimization. A rigorous sample experimental design of calibration-prediction-validation was utilized. The calibration and prediction samples were used for modeling and parameter optimization, and the selected model was validated using the independent validation samples. For quantitative analysis-based, the positive, negative and total recognition-accuracy rates in validation (RARV+, RARV-, and RARV) were 84.8 %, 74.6 % and 79.8 %, respectively; but, the relative root mean square error of prediction was as high as 51.0 %. For pattern recognition-based, the RARV+, RARV-, and RARV were 93.3 %, 90.5 % and 91.9 %, respectively. Moreover, the number of wavelengths N was drastically reduced to 17, and the discrete wavelength combination was in NIR overtone frequency region. The results indicated that, the EC-WSP-PLS-DA model achieved significantly better discrimination effect. Thus demonstrated that Vis-NIR spectroscopy has feasibility for the excessive standard discrimination of aflatoxin B1 in feedstuff materials.

Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.

Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinations are equivocal. Such individuals can benefit from novel accurate assessments that complement clinical diagnostics. We introduce a Bayesian machine learning classifier to identify concussion through cortico-cortical connectome mapping from magnetic resonance imaging in persons with quasi-normal cognition and without neuroradiological findings. Classifier features are generated from connectivity matrices specifying the mean fractional anisotropy of white matter connections linking brain structures. Each connection's saliency to classification was quantified by training individual classifier instantiations using a single feature type. The classifier was tested on a discovery sample of 92 healthy controls (HCs; 26 females, age μ ± σ: 39.8 ± 15.5 years) and 471 adult mTBI patients (158 females, age μ ± σ: 38.4 ± 5.9 years). Results were replicated in an independent validation sample of 256 HCs (149 females, age μ ± σ: 55.3 ± 12.1 years) and 126 patients with concussion (46 females, age μ ± σ: 39.0 ± 17.7 years). Classifier accuracy exceeds 99% in both samples, suggesting robust generalizability to new samples. Notably, 13 bilateral cortico-cortical connection pairs predict diagnostic status with accuracy exceeding 99% in both discovery and validation samples. Many such connection pairs are between prefrontal cortex structures, fronto-limbic and fronto-subcortical structures, and occipito-temporal structures in the ventral ("what") visual stream. This and related connectivity form a highly salient network of brain connections that is particularly vulnerable to concussion. Because these connections are important in mediating cognitive control, memory, and attention, our findings explain the high frequency of cognitive disturbances after concussion. Our classifier was trained and validated on concussed participants with cognitive profiles very similar to those of HCs. This suggests that the classifier can complement current diagnostics by providing independent information in clinical contexts where patients have quasi-normal cognition but where concussion diagnosis stands to benefit from additional evidence.

A prediction model for lung metastases in patients with indeterminate pulmonary nodules in newly diagnosed colorectal cancer

IntroductionMultidisciplinary teams treating patients with newly diagnosed Colorectal Cancer (CRC) often encounter the appearance of Indeterminate Pulmonary Nodules (IPNs) that warrants follow–up with repetitive medical imaging and anxiety for patients. We determined the incidence of IPNs in patients with newly diagnosed CRC and developed and validated a model for individualized risk prediction of IPNs being lung metastases. Material and methodsNewly diagnosed CRC who underwent surgery between November 2011 to June 2014 were included to create the risk model, developed using both clinical experience and statistical selection. Discrimination and calibration slopes of the risk score were evaluated in an independent temporal validation sample. A nomogram is presented to assist clinicians in estimating an individual risk score. ResultsOut of 2111 CRC patients staged with chest CT, 204 (9.6%) had IPNs and 54/204 (26%) had lung metastases. We identified 4 predictors: “location of primary tumour”, “pathological nodal stage”, “size of the largest nodule” and “extrapulmonary synchronous metastases at diagnosis”. Discrimination of the final model in the validation sample was demonstrated by the difference in mean predicted risk between progressed cases en non-progressed cases (49% versus 21%, p = <0.001). ConclusionA prediction model with 4 clinical risk factors can be used to assist multidisciplinary teams in the prediction of individualized risk of lung metastases and imaging strategy in patients with IPNs and newly diagnosed colorectal cancer. The model performed well in new patients not included in the model development.

Algorithm Training and Testing for a Nonendoscopic Barrett’s Esophagus Detection Test in Prospective Multicenter Cohorts

Endoscopic Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC) detection is invasive and expensive. Nonendoscopic BE/EAC detection tools are guideline-endorsed alternatives. We previously described a 5-methylated DNA marker (MDM) panel assayed on encapsulated sponge cell collection device (CCD) specimens. We aimed to train a new algorithm using a 3-MDM panel and test its performance in an independent cohort. Algorithm training and test samples were from 2 prospective multicenter cohorts. All BE cases had esophageal intestinal metaplasia (with or without dysplasia/EAC); control subjects had no endoscopic evidence of BE. The CCD procedure was followed by endoscopy. From CCD cell lysates, DNA was extracted, bisulfite treated, and MDMs were blindly assayed. The algorithm was set and locked using cross-validated logistic regression (training set) and its performance was assessed in an independent test set. Training (N= 352) and test (N= 125) set clinical characteristics were comparable. The final panel included 3 MDMs (NDRG4, VAV3, ZNF682). Overall sensitivity was 82% (95% CI, 68%-94%) at 90% (79%-98%) specificity and 88% (78%-94%) sensitivity at 84% (70%-93%) specificity in training and test sets, respectively. Sensitivity was 90% and 68% for all long- and short-segment BE, respectively. Sensitivity for BE with high-grade dysplasia and EAC was 100% in training and test sets. Overall sensitivity for nondysplastic BE was 82%. Areas under the receiver operating characteristic curves for BE detection were 0.92 and 0.94 in the training and test sets, respectively. A locked 3-MDM panel algorithm for BE/EAC detection using a nonendoscopic CCD demonstrated excellent sensitivity for high-risk BE cases in independent validation samples. (Clinical trials.gov: NCT02560623, NCT03060642.).

Identification of important modules and biomarkers in tuberculosis based on WGCNA.

Tuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited. We performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers. Based on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs. This study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.

Social frailty and the incidence of motoric cognitive risk syndrome in older adults.

Various associations between social factors and motoric cognitive risk syndrome (MCR) have been reported. However, whether social frailty (integrated from multiple social factors) is associated with MCR is still unclear. We included 4657 individuals without MCR at Round 1 of the NHATS as the discovery sample, and 3075 newly recruited individuals from Round 5 of the NHATS as the independent validation sample. Social frailty was assessed by five social items. MCR was defined as the presence of both subjective cognitive complaints and slow gait speed in individuals without dementia or mobility disability. Compared with normal individuals, those with social frailty had higher risk of incident MCR (hazard ratio [HR]: 1.57, 95% confidence interval [CI]: 1.34-1.84). Each additional unfavorable social item was associated with an increased risk of MCR (HR: 1.32, 95% CI: 1.22-1.43). Social frailty was associated with an increased risk of incident MCR in older adults. Various associations between social factors and motoric cognitive risk syndrome (MCR) have been reported. Social frailty that integrated from multiple social factors was associated with an increased risk of incident MCR. Social frailty should be included in the early screening of individuals to identify those at higher risk of MCR.

Polygenic Risk Score Reveals Genetic Heterogeneity of Alzheimer's Disease between the Chinese and European Populations.

The polygenic risk score (PRS) aggregates the effects of numerous genetic variants associated with a condition across the human genome and may help to predict late-onset Alzheimer's disease (LOAD). Most of the current PRS studies on Alzheimer's disease (AD) have been conducted in Caucasian ancestry populations, while it is less studied in Chinese. To establish and examine the validity of Chinese PRS, and explore its racial heterogeneity. We constructed a PRS using both discovery (N = 2012) and independent validation samples (N = 1008) from Chinese population. The associations between PRS and age at onset of LOAD or cerebrospinal fluid (CSF) biomarkers were assessed. We also replicated the PRS in an independent replication cohort with CSF data and constructed an alternative PRS using European weights. Multi-center genetics study. A total of 3020 subjects were included in the study. PRS was calculated using genome-wide association studies data and evaluated the performance alone (PRSnoAPOE) and with other predictors (full model: LOAD ~ PRSnoAPOE + APOE+ sex + age) by measuring the area under the receiver operating curve (AUC). PRS of the full model achieved the highest AUC of 84.0% (95% CI = 81.4-86.5) with pT< 0.5, compared with the model containing APOE alone (61.0%). The AUC of PRS with pT<5e-8 was 77.8% in the PRSnoAPOE model, 81.5% in the full model, and only ranged from 67.5% to 75.1% in the PRS with the European weights model. A higher PRS was significantly associated with an earlier age at onset (P <0.001). The PRS also performed well in the replication cohort of the full model (AUC=83.1%, 95% CI = 74.3-92.0). The CSF biomarkers of Aβ42 and the ratio of Aβ42/Aβ40 were significantly inversely associated with the PRS, while p-Tau181 showed a positive association. This finding suggests that PRS reveal genetic heterogeneity and higher prediction accuracy of the PRS for AD can be achieved using a base dataset and validation within the same ethnicity. The effective PRS model has the clinical potential to predict individuals at risk of developing LOAD at a given age and with abnormal levels of CSF biomarkers in the Chinese population.

Looking for the Key to Winning: Psychophysiological Predicting Factors in Healthy University Students.

Performance in competitive situations has been linked to various psychobiological factors such as personality traits (e.g., competitiveness), situational appraisal (e.g., motivation), and cardiovascular response (e.g., heart rate). However, it remains unclear whether these factors can predict competitive success. This paper aims to assess, through discriminant analysis, the predictive capacity of these psychobiological variables regarding the likelihood of winning, ultimately delineating a psychophysiological profile associated with success. Across three distinct studies, a total of 154 participants (66 men) engaged in a face-to-face laboratory competition. Prior to the competition, assessments of competitiveness traits, anxiety, self-efficacy, and motivation were conducted, and heart rate reactivity during the competition was measured. These variables collectively formed the basis for constructing the predictive model. The results of the initial study demonstrated that our model accurately classified 68.8% of the cases. Specifically, high levels of competitiveness, self-efficacy, motivation, and heart rate reactivity, coupled with low anxiety, were predictive of winning. These findings were subsequently replicated in two independent validation samples involving both men and women (studies 2 and 3), thereby reinforcing the robustness of the earlier results. In conclusion, our findings suggest that the psychological state preceding competition, along with cardiovascular reactivity, may serve as predictors for the probability of winning.

A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis.

Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker. In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design. The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems. This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.

Multivariate prediction of cognitive performance from the sleep electroencephalogram

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5–10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

Forensic age estimation in adults by pubic bone mineral density using multidetector computed tomography.

Radiology plays a crucial role in forensic anthropology for age estimation. However, most studies rely on morphological methods. This study aims to investigate the feasibility of using pubic bone mineral density (BMD) as a new age estimation method in the Chinese population. 468 pubic bone CT scans from living individuals in a Chinese hospital aged 18 to 87years old were used to measure pubic BMD. The BMD of the bilateral pubic bone was measured using the Mimics software on cross-sectional CT images and the mean BMD of the bilateral pubic bone was also calculated. Regression analysis was performed to assess the correlation between pubic BMD and chronological age and to develop mathematical models for age estimation. We evaluated the accuracy of the best regression model using an independent validation sample by calculating the mean absolute error (MAE). Among all established models, the cubic regression model had the highest R2 value in both genders, with R2 = 0.550 for males and R2 = 0.634 for females. The results of the best model test showed that the MAE for predicting age using pubic BMD was 8.66years in males and 7.69years in females. This study highlights the potential of pubic BMD as a useful objective indicator for adult age estimation and could be used as an alternative in forensic practice when other better indicators are lacking.

Markers of imminent myocardial infarction

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): AFA Insurance and the European Commission Seventh Framework Programme. Background and Purpose Myocardial infarction is a leading cause of death globally, but is notoriously difficult to predict. While most studies to date have considered long timeframes for etiological and prediction models, we aimed to identify biomarkers of an imminent first myocardial infarction and design relevant prediction models. Methods We used a novel case–cohort consortium of 2018 persons without prior cardiovascular disease (420 cases, 1598 subcohort representatives) from six European cohorts. We investigated associations of 817 proteins, 1025 metabolites, and 16 clinical variables at baseline with subsequent risk of a first myocardial infarction within 6 months after baseline. Associations in weighted, stratified Cox proportional hazards models that passed multiple testing bounds in the discovery sample were verified in an independent validation sample. We also developed a prediction model for clinical use. Results Forty-eight proteins, 43 metabolites, age, sex, and systolic blood pressure were associated with risk of an imminent first myocardial infarction in models adjusted for technical covariates only (Figure 1). Brain natriuretic peptide (BNP) was most consistently associated with imminent myocardial infarction risk, in models adjusting for additional covariates including age and sex. Using clinically readily available variables, we devised a prediction model (Figure 2) with good ability to discriminate between subsequent cases and non-cases (cross-validated C-index 0.78) to which the addition of BNP did not improve performance. Conclusion We found 94 biomarkers associated with risk of a first myocardial infarction within 6 months in persons without prior cardiovascular disease, and devised a prediction model with good discriminative ability for an imminent first myocardial infarction in the general population, with potential for motivating primary prevention efforts.

Improved Object-Based Mapping of Aboveground Biomass Using Geographic Stratification with GEDI Data and Multi-Sensor Imagery

Aboveground biomass (AGB) mapping using spaceborne LiDAR data and multi-sensor images is essential for efficient carbon monitoring and climate change mitigation actions in heterogeneous forests. The optimal predictors of remote sensing-based AGB vary greatly with geographic stratification, such as topography and forest type, while the way in which geographic stratification influences the contributions of predictor variables in object-based AGB mapping is insufficiently studied. To address the improvement of mapping forest AGB by geographic stratification in heterogeneous forests, satellite multisensory data from global ecosystem dynamics investigation (GEDI) and series of advanced land observing satellite (ALOS) and Sentinel were integrated. Multi-sensor predictors for the AGB modeling of different types of forests were selected using a correlation analysis of variables calculated from topographically stratified objects. Random forests models were built with GEDI-based AGB and geographically stratified predictors to acquire wall-to-wall biomass values. It was illustrated that the mapped biomass had a similar distribution and was approximate to the sampled forest AGB. Through an accuracy comparison using independent validation samples, it was determined that the geographic stratification approach improved the accuracy by 34.79% compared to the unstratified process. Stratification of forest type further increased the mapped AGB accuracy compared to that of topography. Topographical stratification greatly influenced the predictors’ contributions to AGB mapping in mixed broadleaf–conifer and broad-leaved forests, but only slightly impacted coniferous forests. Optical variables were predominant for deciduous forests, while for evergreen forests, SAR indices outweighed the other predictors. As a pioneering estimation of forest AGB with geographic stratification using satellite multisensory data, this study offers optimal predictors and an advanced method for obtaining carbon maps in heterogeneous regional landscapes.

Characterization of brain resilience in Alzheimer's disease using polygenic risk scores and further improvement by integrating mitochondria-associated loci

IntroductionIdentification of high-risk people for Alzheimer's disease (AD) is critical for prognosis and early management. Longitudinal epidemiologic studies have observed heterogeneity in the brain and cognitive aging. Brain resilience was described as above-expected cognitive function. The “resilience” framework has been shown to correlate with individual characteristics such as genetic factors and age. Besides, accumulative evidence has confirmed the association of mitochondria with the pathogenesis of AD. However, it is challenging to assess resilience through genetic metrics, in particular incorporating mitochondria-associated loci. ObjectivesIn this paper, we first demonstrated that polygenic risk scores (PRS) could characterize individuals' resilience levels. Then, we indicated that mitochondria-associated loci could improve the performance of PRSs, providing more reliable measurements for the prevention and diagnosis of AD. MethodsThe discovery (N = 1,550) and independent validation samples (N = 2,090) were used to construct nine types of PRSs containing mitochondria-related loci (PRSMT) from both biological and statistical aspects and combined them with known AD risk loci derived from genome-wide association studies (GWAS).Individuals' levels of brain resilience were comprehensively measured by linear regression models using eight pathological characteristics. ResultsIt was found that PRSs could characterize brain resilience levels (e.g., Pearson correlation test Pmin = 7.96×10-9). Moreover, the performance of PRS models could be efficiently improved by incorporating a small number of mitochondria-related loci (e.g., Pearson correlation test P improved from 1.41×10-3 to 6.09×10-6). PRSs' ability to characterize brain resilience was validated. More importantly, by incorporating some mitochondria-related loci, the performance of PRSs in measuring brain resilience could be significantly improved. ConclusionOur findings imply that mitochondria may play an important role in brain resilience, and targeting mitochondria may open a new door to AD prevention and therapy.

Nomogram Models for Distinguishing Intraductal Carcinoma of the Prostate From Prostatic Acinar Adenocarcinoma Based on Multiparametric Magnetic Resonance Imaging.

To compare multiparametric magnetic resonance imaging (MRI) features of intraductal carcinoma of the prostate (IDC-P) with those of prostatic acinar adenocarcinoma (PAC) and develop prediction models to distinguish IDC-P from PAC and IDC-P with a high proportion (IDC ≥ 10%, hpIDC-P) from IDC-P with a low proportion (IDC < 10%, lpIDC-P) and PAC. One hundred and six patients with hpIDC-P, 105 with lpIDC-P and 168 with PAC, who underwent pretreatment multiparametric MRI between January 2015 and December 2020 were included in this study. Imaging parameters, including invasiveness and metastasis, were evaluated and compared between the PAC and IDC-P groups as well as between the hpIDC-P and lpIDC-P subgroups. Nomograms for distinguishing IDC-P from PAC, and hpIDC-P from lpIDC-P and PAC, were made using multivariable logistic regression analysis. The discrimination performance of the models was assessed using the receiver operating characteristic area under the curve (ROC-AUC) in the sample, where the models were derived from without an independent validation sample. The tumor diameter was larger and invasive and metastatic features were more common in the IDC-P than in the PAC group (P < 0.001). The distribution of extraprostatic extension (EPE) and pelvic lymphadenopathy was even greater, and the apparent diffusion coefficient (ADC) ratio was lower in the hpIDC-P than in the lpIDC-P group (P < 0.05). The ROC-AUCs of the stepwise models based solely on imaging features for distinguishing IDC-P from PAC and hpIDC-P from lpIDC-P and PAC were 0.797 (95% confidence interval, 0.750-0.843) and 0.777 (0.727-0.827), respectively. IDC-P was more likely to be larger, more invasive, and more metastatic, with obviously restricted diffusion. EPE, pelvic lymphadenopathy, and a lower ADC ratio were more likely to occur in hpIDC-P, and were also the most useful variables in both nomograms for predicting IDC-P and hpIDC-P.

Fine-grained age-matching improves atrophy-based detection of mild cognitive impairment more than amyloid-negative reference subjects

IntroductionIn clinical practice, differentiating between age-related gray matter (GM) atrophy and neurodegeneration-related atrophy at early disease stages, such as mild cognitive impairment (MCI), remains challenging. We hypothesized that fined-grained adjustment for age effects and using amyloid-negative reference subjects could increase classification accuracy. MethodsT1-weighted magnetic resonance imaging (MRI) data of 131 cognitively normal (CN) individuals and 91 patients with MCI from the Alzheimer’s disease neuroimaging initiative (ADNI) characterized concerning amyloid status, as well as 19 CN individuals and 19 MCI patients from an independent validation sample were segmented, spatially normalized and analyzed in the framework of voxel-based morphometry (VBM). For each participant, statistical maps of GM atrophy were computed as the deviation from the GM of CN reference groups at the voxel level. CN reference groups composed with different degrees of age-matching, and mixed and strictly amyloid-negative CN reference groups were examined regarding their effect on the accuracy in distinguishing between CN and MCI. Furthermore, the effects of spatial smoothing and atrophy threshold were assessed. ResultsApproaches with a specific reference group for each age significantly outperformed all other age-adjustment strategies with a maximum area under the curve of 1.0 in the ADNI sample and 0.985 in the validation sample. Accounting for age in a regression-based approach improved classification accuracy over that of a single CN reference group in the age range of the patient sample. Using strictly amyloid-negative reference groups improved classification accuracy only when age was not considered. ConclusionOur results demonstrate that VBM can differentiate between age-related and MCI-associated atrophy with high accuracy. Crucially, age-specific reference groups significantly increased accuracy, more so than regression-based approaches and using amyloid-negative reference groups.

Landscape Pattern Consistency Assessment of 10 m Land Cover Products in Different Ecological Zoning Contexts of Sichuan Province, China

The research on land cover and its changing value to the ecology environment and ecosystem service is of great importance. Understanding the landscape patterns and accuracy in remote sensing land cover data from multiple sources under eco-regionalization is important for relevant research under eco-regionalization. Land cover and land use in different ecological regionalization contexts influence massive ecosystem processes on a global scale, and many ecosystem models are dependent on accurate land cover information. It is, therefore, important to assess the available global land cover products based on different ecological zoning contexts and to understand the differences between them so that different researchers can apply them in a rational way. This study took Sichuan Province as an experimental case. Based on the three methods of spatial superposition, landscape index, and validation sample, we discussed and then analyzed the consistency of landscape patterns for the three 10 m global land cover data under different ecogeographic zones. The results showed that the spatial consistency of FROM-GLC, ESA, and ESRI land cover data were high under the ecological zoning of Palearctic0518 (PA0518) and PA1017, while the spatial pattern was less consistent for the three land cover data under the ecological zoning of PA0509 and PA0437. The fully consistent areas for the three data were 44,420.9 km2 and 53,368.9 km2, respectively. The results of the quantitative analysis of the landscape index showed significant differences in the degree of landscape fragmentation, landscape shape complexity, and the connectivity among landscape patches of several land cover data were significantly different under different ecological zones. Based on the three kinds of independent validation samples to achieve the absolute accuracy of evaluation, the overall accuracy of the FROM-GLC, ESA, and ESRI land cover data was less than 60%, and future drawing still needs to further improve the regional land cover data mapping accuracy under different ecological zones in order to carry out ecological environment monitoring, land ecological security evaluation, and related research to provide a reference.

Plasma GFAP:NfL discriminates FTLD‐tau from FTLD‐TDP

AbstractBackgroundBiomarkers are lacking that can discriminate frontotemporal lobar degeneration (FTLD) with tau (FTLD‐tau) from TDP‐43 (FTLD‐TDP). In FTLD patients with autopsy‐ or mutation‐confirmed pathology, we tested the glial fibrillary acidic protein (GFAP) to neurofilament light chain (NfL) ratio (GFAP/NfL) in plasma. We validated application of GFAP/NfL ratio in an independent sample of frontotemporal dementia (FTD).MethodsTraining sample was composed of 105 FTLD‐TDP and 44 FTLD‐tau participants, with pathology confirmed by either autopsy or pathogenic mutations (MAPT, C9orf72, GRN, TARDBP). Average accumulations of postmortem tau, TDP‐43, and amyloid across neocortical, limbic and brainstem regions were scored on a 0‐3 scale. Regression tested differences in analytes (log transformed) across FTLD‐tau and FTLD‐TDP, covarying for age, interval to death, and sex. Regression tested association of analytes (log transformed) with postmortem tau and TDP‐43 accumulation, covarying for postmortem amyloid‐β and plasma‐to‐death interval. Receiver operating characteristic (ROC) analyses and area under the curve (AUC) analyses were performed in patients with negligible secondary pathology. We defined an optimal cut‐point to discriminate FTLD‐tau from FTLD‐TDP using plasma GFAP/NfL. We tested the defined cut‐point in an independent validation sample of FTD patients with a clinical diagnosis of progressive supranuclear palsy (PSP) with likely tau (FTD‐tau; n=33) or diagnosis of amytrophic lateral sclerosis (ALS) with likely TDP‐43 (FTD‐TDP; n=35).ResultsPlasma GFAP was lower in FTLD‐TDP than FTLD‐tau (β=‐0.24, p=0.021) with small effect size (partial η2=0.082). Plasma NfL was higher in FTLD‐TDP than FTLD‐tau (β=0.57, p=1.0e‐05) with medium effect size (partial η2=0.24). GFAP/NfL was significantly lower in FTLD‐TDP than FTLD‐tau (β=‐0.82, SE=0.13, p=2.2e‐09) with large effect size (partial η2=0.39). Higher plasma GFAP was associated with higher postmortem tau accumulation (β=0.15, p=0.032). Higher levels of plasma NfL were associated with increased TDP‐43 burden (β=0.2, p=0.024). ROC revealed excellent discrimination by plasma GFAP/NfL in the autopsy/test sample (AUC=0.89). In the validation sample, plasma GFAP/NfL had good overall discrimination accuracy (AUC=0.85); there was 0.79 sensitivity and 0.77 specificity when applying the training‐derived GFAP/NfL threshold.ConclusionPlasma GFAP and NfL relate to pathological accumulation of tau and TDP‐43, respectively. The plasma ratio of GFAP/NfL is a promising candidate to discriminate FTLD‐tau from FTLD‐TDP.

Vulnerability of the Neonatal Connectome following Postnatal Stress.

Stress following preterm birth can disrupt the emerging foundation of the neonatal brain. The current study examined how structural brain development is affected by a stressful early environment and whether changes in topological architecture at term-equivalent age could explain the increased vulnerability for behavioral symptoms during early childhood. Longitudinal changes in structural brain connectivity were quantified using diffusion-weighted imaging (DWI) and tractography in preterm born infants (gestational age <28 weeks), imaged at 30 and/or 40 weeks of gestation (N = 145, 43.5% female). A global index of postnatal stress was determined based on the number of invasive procedures during hospitalization (e.g., heel lance). Higher stress levels impaired structural connectivity growth in a subnetwork of 48 connections (p = 0.003), including the amygdala, insula, hippocampus, and posterior cingulate cortex. Findings were replicated in an independent validation sample (N = 123, 39.8% female, n = 91 with follow-up). Classifying infants into vulnerable and resilient based on having more or less internalizing symptoms at two to five years of age (n = 71) revealed lower connectivity in the hippocampus and amygdala for vulnerable relative to resilient infants (p < 0.001). Our findings suggest that higher stress exposure during hospital admission is associated with slower growth of structural connectivity. The preservation of global connectivity of the amygdala and hippocampus might reflect a stress-buffering or resilience-enhancing factor against a stressful early environment and early-childhood internalizing symptoms.SIGNIFICANCE STATEMENT The preterm brain is exposed to various external stimuli following birth. The effects of early chronic stress on neonatal brain networks and the remarkable degree of resilience are not well understood. The current study aims to provide an increased understanding of the impact of postnatal stress on third-trimester brain development and describe the topological architecture of a resilient brain. We observed a sparser neonatal brain network in infants exposed to higher postnatal stress. Limbic regulatory regions, including the hippocampus and amygdala, may play a key role as crucial convergence sites of protective factors. Understanding how stress-induced alterations in early brain development might lead to brain (re)organization may provide essential insights into resilient functioning.