Validation In Independent Samples Research Articles

Study on the relationship between body mass index and blood pressure indices in children aged 7-17 during COVID-19.

To explore the relationship between body mass index (BMI), age, sex, and blood pressure (systolic blood pressure, SBP; diastolic blood pressure, DBP) in children during COVID-19, providing reference for the prevention and screening of hypertension in children. This study adopted a large-scale cross-sectional design to investigate the association between BMI and blood pressure in 7-17-year-old students in City N, China, during COVID-19. Thirty-six primary and secondary schools in City N were sampled using a stratified cluster sampling method. A total of 11,433 students aged 7-17 years in City N, China, were selected for blood pressure (Diastolic blood pressure, DBP, Systolic blood pressure, SBP), height, and weight, Resting heart rate (RHR), chest circumference, measurements, and the study was written using the STROBE checklist. Data analysis was conducted using SPSS 26.0, calculating the mean and standard deviation of BMI and blood pressure for male and female students in different age groups. Regression analysis was employed to explore the impact of BMI, age, and sex on SBP and DBP, and predictive models were established. The model fit was evaluated using the model R2. The study included 11,287 primary and secondary school students, comprising 5,649 boys and 5,638 girls. It was found that with increasing age, BMI and blood pressure of boys and girls generally increased. There were significant differences in blood pressure levels between boys and girls in different age groups. In regression models, LC, Age, BMI, and chest circumference show significant positive linear relationships with SBP and DBP in adolescents, while RHR exhibits a negative linear relationship with SBP. These factors were individually incorporated into a stratified regression model, significantly enhancing the model's explanatory power. After including factors such as Age, Gender, and BMI, the adjusted R2 value showed a significant improvement, with Age and BMI identified as key predictive factors for SBP and DBP. The robustness and predictive accuracy of the model were further examined through K-fold cross-validation and independent sample validation methods. The validation results indicate that the model has a high accuracy and explanatory power in predicting blood pressure in children of different weight levels, especially among obese children, where the prediction accuracy is highest. During COVID-19, age, sex, and BMI significantly influence blood pressure in children aged 7-17 years, and predictive models for SBP and DBP were established. This model helps predict blood pressure in children and reduce the risk of cardiovascular diseases. Confirmation of factors such as sex, age, and BMI provide a basis for personalized health plans for children, especially during large-scale infectious diseases, providing guidance for addressing health challenges and promoting the health and well-being of children.

Measuring the Autistic Women's Experience (AWE).

We developed a Dutch questionnaire called the Autistic Women's Experience (AWE) and compared its psychometric properties to the Autism Spectrum Quotient (AQ). Whilst attenuated gender differences on the AQ have been widely replicated, this instrument may not fully capture the unique experience of autistic women. The AWE was co-developed with autistic women to include items that reflect autistic women's experience. We investigated the AWE (49 items) and compared it with the AQ (50 items) in Dutch autistic individuals (N = 153, n = 85 women) and in the general population (N = 489, n = 246 women) aged 16+. Both the AQ and AWE had excellent internal consistency and were highly and equally predictive of autism in both women and men. Whilst there was a gender difference on the AQ among non-autistic people (men > women), there was no gender difference among autistic people, confirming all earlier studies. No gender differences were detected on the AWE overall scale, yet subtle gender differences were observed on the subscales. We conclude that the AQ is valid for both genders, but the AWE provides an additional useful perspective on the characteristics of autistic women. The AWE needs further validation in independent samples using techniques that allow for testing gender biases, as well as a confirmatory factor analysis in a larger sample.

P071 Simple Vestibular-Occular Motor Assessment as a Predictor of Driving Performance Vulnerability following extended Wakefulness

Abstract Introduction Driver fatigue is a significant contributor to road crashes, but identifying individuals at driving risk is challenging. We examined the potential of simple baseline vestibular ocular motor system (VOMS) assessment via virtual reality goggles to predict subsequent vulnerability to driving simulator impairment following extended wakefulness. Methods 49 individuals (Mean±SD Age 32.6±12.9, 45% Males) underwent 9hr baseline sleep opportunity followed by approximately ~29hrs extended wakefulness with five 60min driving assessments. Cluster analysis classified drivers into vulnerable (n=17) or resistant (n=32) groups based on their worst steering deviation and number of crashes from driving tests. Baseline VOMS were performed ~10mins prior to the first three drives (1, 7 and 13hrs of wakefulness). XGBoost machine learning model was trained using baseline VOMs features to predict vulnerable vs resistant groups from driving tests 4 and 5 (19 and 25hrs of wakefulness) Model performance was evaluated using 5-fold cross-validation approach using ROC analysis. Results XGBoost machine learning ranked all 70 VOMS metrics on their importance in predicting vulnerable vs resistant groups. Top 10 VOMs metrics assessed during baseline non-sleep deprived tests demonstrated a strong ability to predict the driver's performance following extended wakefulness, differentiating between the vulnerable vs resistant groups (AUC 0.73 [95%CI 0.61-0.83, p<0.001]). Conclusion VOMs tests conducted at baseline holds promise for predicting future driving impairment. This approach has the potential to be highly valuable in determining an individual's fitness to drive. Future validation in independent samples, sleep disordered population and in-field on-road testing are needed to confirm these promising findings.

O072 Simple Vestibular-Occular Motor Assessment as a Predictor of Alertness State and Driving Impairment during Extended Wakefulness

Abstract Introduction Driver fatigue contributes to 2-16% of road crashes, highlighting the need for improved detection of at-risk drivers. We used a novel and brief test of vestibular ocular motor system (VOMS) assessed via virtual reality goggles to predict alertness state and driving simulator performance during 29hr extended wakefulness. Methods 49 individuals (Mean±SD Age, 32.6±12.9, 45% Males) undergone 9hr baseline sleep opportunity followed by ~29hrs extended wakefulness with five 60min driving assessments. Cluster analysis, combining steering deviation and number of crashes were used to split participants into groups of either poor vs good driving performance. VOMS assessment was conducted using virtual reality goggles approximately 10mins before and after each drive. Predictive importance of VOMs metrics were ranked using XGBoost machine learning model, which was then utilized to distinguish between poor vs good driving groups. Model performance was evaluated using a 5-fold cross-validation approach using ROC analysis. Results XGBoost machine learning ranked all 70 VOMS metrics on their importance in predicting driving performance group for each drive. Top 10 metrics from pre-drive VOMS test predicted both daytime driving (tests 1-3, AUC 0.8 [95%CI 0.64-0.93], p<0.001) and night-time driving (tests 4-5, AUC 0.78 [95%CI 0.6-0.95, p<0.001]). Post-driving VOMS assessments also predicted daytime (AUC 0.74 [95%CI 0.53-0.9, p<0.001] and night-time driving (AUC 0.76 [95%CI 0.52-0.94, p<0.001]). Conclusion VOMS assessment show promise as a short and effective assessment of sleepiness to predict driving failure. Future validation in independent samples, sleep disordered population and in-field on-road testing are needed to confirm these promising findings.

Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning.

Various research topics from the field of soil science or agriculture require digital maps of soil properties as input data. Such maps can be achieved by digital soil mapping (DSM) techniques which have developed consistently during the last decades. Our research focuses on the application of geostatistical methods (including ordinary kriging, regression-kriging and geographically weighted regression) and machine learning algorithms to produce high resolution digital maps of topsoil properties in Romania. Six continuous predictors were considered in our study (digital elevation model, topographic wetness index, normalized difference vegetation index, slope, latitude and longitude). A tolerance test was performed to ensure that all predictors can be used for the purpose of digital soil mapping. The input soil data was extracted from the LUCAS database and includes 7 chemical properties (pH, electrical conductivity, calcium carbonate, organic carbon, N, P, K) and the particle-size fractions (sand, silt, clay). The spatial autocorrelation is higher for pH, organic carbon and calcium carbonate, as indicated by the partial sill / nugget ratio of semivariograms, meaning that these properties are more predictable than the others by kriging interpolation. The optimal DSM method was selected by independent sample validation, using resampled statistics from 100 samples randomly extracted from the validation dataset. Also, an additional independent sample of soil profiles, comprising legacy soil data, and the 200k Romania soil map were used for a supplementary validation. The results show that machine learning and regression-kriging are the optimal methods in most cases. Among the machine learning tested algorithms, the best performance is associated with Support Vector Machines and Random Forests methods. The geographically weighted regression is also among the optimum methods for pH and calcium carbonates spatial prediction. Good predictions were achieved for pH (R2 of 0.417-0.469, depending on the method), organic carbon (R2 of 0.302-0.443), calcium carbonates (R2 of 0.300-0.330) and moderate predictions for electric conductivity, total nitrogen, silt and sand (R2 of 0.155-0.331), while the lowest prediction characterizes the phosphorous content (R2 of 0.015-0.044). LUCAS proved to be a reliable and useful soil database and the achieved spatial distributions of soil properties can be further used for national and regional soil studies.

490. CORRELATION BETWEEN HISTOLOGICAL AND MOLECULAR ALTERATIONS WITH POOR SURVIVAL IN ESOPHAGEAL ADENOCARCINOMA

Abstract The incidence of esophageal adenocarcinoma (EAC) has constantly risen in western countries over the past decades, often diagnosed at advanced stage and with a five-year survival around 20%1. In a previous study on EAC cases submitted to surgery (without neoadjuvant treatment) an algorithm (EACGSE classification)2 based on morphologic distinctions provided a significant prognostic impact. We aimed to evaluate the molecular basis underlying these differences, to improve patient management. The EAC cohort classified according to EACGSE2 was included in the study. Genomic DNA was available from formalin fixed paraffin embedded surgical specimens for 207 cases. The cases were sequenced for 26 cancer-related genes (panel #226722257; IDT) with high coverage on NextSeq500 (Illumina). Data analysis was performed using a dedicated pipeline3. 245 cases were analyzed for SMAD4 immunostaining (IHC). Loss of SMAD4 immunostaining was reported as % of negative tumor cells (at least 35% of neoplastic cells). TP53 was the most frequently altered gene (134/207 cases with at least one mutation). The presence of TP53 missense variants was associated to a poor cancer-specific survival (CSS) in the high-risk cases (glandular poorly differentiated, mucinous invasive, diffuse anaplastic, mixed) according to EACGSE2 (P=0.0005). A significant correlation was observed for TP53 truncative variants and loss of SMAD4 staining (P=0.008). Actually, SMAD4 loss was observed in 85/245 (35%) of all EAC cases analyzed via immunostaining. SMAD4 loss correlated with poor CCS (P=0.007) and disease-free survival (P=0.002) in EACGSE high-risk cases. TP53 missense mutations correlated to poor outcomes (CSS) in high-risk cases, TP53 truncative mutations were associated to SMAD4 loss. SMAD4 loss itself resulted a frequent event in EAC and correlated with lower CCS and disease-free survival in high-risk cases. Therefore, we were able to correlate EAC histological classification, clinical outcomes and molecular phenotypes. Validation in independent samples is warranted to corroborate these findings.

Construction of a short version of the Montreal Cognitive Assessment (MoCA) rating scale for the Thai population using Partial Least Squares analysis

Background The Montreal Cognitive Assessment (MoCA) rating scale is frequently used to assess cognitive impairments in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD). Objectives The aims of this study were to a) evaluate the construct validity of the MoCA and its subdomains or whether the MoCA can be improved by feature reduction, and b) develop a short version of the MoCA (MoCA-Brief) for the Thai population. Methods We recruited 181 participants, namely 60 healthy controls, 61 aMCI, and 60 AD patients. Results The construct reliability of the original MoCA was not optimal and could be improved by deleting one subdomain (Naming) and five items, namely Clock Circle, Lion, Digit Forward, Repeat 2nd Sentence, and Place, which showed inadequate loadings on their latent vectors. To construct the MoCA-Brief, the reduced model underwent further reduction and feature selection based on model quality data of the outer models. We produced a MoCA-Brief rating scale comprising five items, namely Clock Time, Subtract 7, Fluency, Month, and Year. The first latent vector extracted from these five indicators showed adequate construct validity with an Average Variance Extracted of 0.599, composite reliability of 0.822, Cronbach’s alpha of 0.832 and rho A of 0.833. The MoCA-Brief factor score showed a strong correlation with the total MoCA score (r = 0.98, p < 0.001) and shows adequate concurrent, test-retest, and inter-rater validity. Conclusion The construct validity of the MoCA may be improved by deleting five items. The new MoCA-Brief rating scale deserves validation in independent samples and especially in other countries.

A systematic analysis of diagnostic performance for Alzheimer's disease using structural MRI.

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders in the elderly. Although numerous structural magnetic resonance imaging (sMRI) studies have reported diagnostic models that could distinguish AD from normal controls (NCs) with 80-95% accuracy, limited efforts have been made regarding the clinically practical computer-aided diagnosis (CAD) system for AD. To explore the potential factors that hinder the clinical translation of the AD-related diagnostic models based on sMRI. To systematically review the diagnostic models for AD based on sMRI, we identified relevant studies published in the past 15 years on PubMed, Web of Science, Scopus, and Ovid. To evaluate the heterogeneity and publication bias among those studies, we performed subgroup analysis, meta-regression, Begg's test, and Egger's test. According to our screening criterion, 101 studies were included. Our results demonstrated that high diagnostic accuracy for distinguishing AD from NC was obtained in recently published studies, accompanied by significant heterogeneity. Meta-analysis showed that many factors contributed to the heterogeneity of high diagnostic accuracy of AD using sMRI, which included but was not limited to the following aspects: (i) different datasets; (ii) different machine learning models, e.g. traditional machine learning or deep learning model; (iii) different cross-validation methods, e.g. k-fold cross-validation leads to higher accuracies than leave-one-out cross-validation, but both overestimate the accuracy when compared to validation in independent samples; (iv) different sample sizes; and (v) the publication times. We speculate that these complicated variables might be the adverse factor for developing a clinically applicable system for the early diagnosis of AD. Our findings proved that previous studies reported promising results for classifying AD from NC with different models using sMRI. However, considering the many factors hindering clinical radiology practice, there would still be a long way to go to improve.

Rho GTPase gene expression and breast cancer risk: a Mendelian randomization analysis

The Rho GTPase family consists of 20 genes encoding intracellular signalling proteins that influence cytoskeletal dynamics, cell migration and cell cycle progression. They are implicated in breast cancer progression but their role in breast cancer aetiology is unknown. As aberrant Rho GTPase activity could be associated with breast cancer, we aimed to determine the potential for a causal role of Rho GTPase gene expression in breast cancer risk, using two-sample Mendelian randomization (MR). MR was undertaken in 122,977 breast cancer cases and 105,974 controls, including 69,501 estrogen receptor positive (ER+) cases and 105,974 controls, and 21,468 ER negative (ER−) cases and 105,974 controls. Single nucleotide polymorphisms (SNPs) underlying expression quantitative trait loci (eQTLs) obtained from normal breast tissue, breast cancer tissue and blood were used as genetic instruments for Rho GTPase expression. As a sensitivity analysis, we undertook co-localisation to examine whether findings reflected shared causal variants or genomic confounding. We identified genetic instruments for 14 of the 20 human Rho GTPases. Using eQTLs obtained from normal breast tissue and normal blood, we identified evidence of a causal role of RHOD in overall and ER+ breast cancers (overall breast cancer: odds ratio (OR) per standard deviation (SD) increase in expression level 1.06; (95% confidence interval (CI) 1.03, 1.09; P = 5.65 × 10–5) and OR 1.22 (95% CI 1.11, 1.35; P = 5.22 × 10–5) in normal breast tissue and blood respectively). There was a consistent direction of association for ER− breast cancer, although the effect-estimate was imprecisely estimated. Using eQTLs from breast cancer tissue and normal blood there was some evidence that CDC42 was negatively associated with overall and ER + breast cancer risk. The evidence from colocalization analyses strongly supported our MR results particularly for RHOD. Our study suggests a potential causal role of increased RHOD gene expression, and, although the evidence is weaker, a potential protective role for CDC42 gene expression, in overall and ER+ breast cancers. These finding warrant validation in independent samples and further biological investigation to assess whether they may be suitable targets for drug targeting.

Biomarker‐based polygenic risk scores for profiling genetic susceptibility in Alzheimer's disease

AbstractBackgroundPrecision medicine is designed to resolve heterogeneity in complex diseases such as Alzheimer’s (AD) to deliver timely and tailored treatment. Using ADNI data, we developed endophenotype‐specific polygenic risk scores (PRS) to assess the AD risk and to identify distinct genetic risk profiles among the converters.MethodWe used ADNI genetic, clinical and biomarker data for PRS development. We derived four ADRD endophenotype components including Amyloid (A), Tau (T), Neurodegeneration (N) and Vascular (V), by applying PCA on 11 phenotypic variables. Four individual biomarker PRSs (A, T, N, V) were obtained and combined into a single PRS (PRSATNV). Penalized regression (Lasso) applied for SNP selection and weight re‐estimation. Bootstrapping was used to minimize possible overfitting. On the ADNI test set, after removing participants with mild cognitive impairment (MCI), we estimated the odds and hazard of AD among the extreme PRSATNV quartiles (Q1,Q4). To identify biomarker‐specific genetic risk profiles among LMCI/AD participants, we performed K‐means clustering using the 4 biomarker PRSs (PRSA, PRST, PRSN, PRSV). Clustering was done based on APOE groups.ResultThe odds for AD individuals in the Q4 of PRSATNV was double those in Q1 (p=2.2e‐06, AUC=80%). On the same participants, we obtained a significant difference of 5 years in the AD onset between Q1 and Q4 (HR=2.2, p=6.9e‐03). Clustering restricted to LMCI/AD yielded 2 PRS clusters for ε3/ ε3 and ε3/ ε4 and 3 for ε4/ ε4 (Figure 1). In general, genetic risk profiles differed among the clusters for all APOE groups (PRSATNV in Table 1). The main difference was amyloid‐related for ε3/ ε3 carriers, neurodegeneration‐related for ε3/ ε4 and tau‐related for ε4/ ε4 (Table 1).ConclusionThe combined biomarker PRS we developed successfully identified individuals in high risk for AD. Using clusters derived from endophenotype‐specific genetic information we were able to identify distinct biomarker‐related genetic profiles among converters. Endophenotype‐based PRS may be useful for risk stratification and development of genetically tailored therapeutics as different mechanisms may contribute to AD onset for a given individual. Validation in independent samples is a critical next step.

Independent Sample Validation Of A Novel Sojourn-based Wrist Accelerometer Method In Free-living Settings

Independent Sample Validation Of A Novel Sojourn-based Wrist Accelerometer Method In Free-living Settings

A functional genomic approach to identify reference genes for human pancreatic beta cell real-time quantitative RT-PCR analysis

ABSTRACT Exposure of human pancreatic beta cells to pro-inflammatory cytokines or metabolic stressors is used to model events related to type 1 and type 2 diabetes, respectively. Quantitative real-time PCR is commonly used to quantify changes in gene expression. The selection of the most adequate reference gene(s) for gene expression normalization is an important pre-requisite to obtain accurate and reliable results. There are no universally applicable reference genes, and the human beta cell expression of commonly used reference genes can be altered by different stressors. Here we aimed to identify the most stably expressed genes in human beta cells to normalize quantitative real-time PCR gene expression. We used comprehensive RNA-sequencing data from the human pancreatic beta cell line EndoC-βH1, human islets exposed to cytokines or the free fatty acid palmitate in order to identify the most stably expressed genes. Genes were filtered based on their level of significance (adjusted P-value >0.05), fold-change (|fold-change| <1.5) and a coefficient of variation <10%. Candidate reference genes were validated by quantitative real-time PCR in independent samples. We identified a total of 264 genes stably expressed in EndoC-βH1 cells and human islets following cytokines – or palmitate-induced stress, displaying a low coefficient of variation. Validation by quantitative real-time PCR of the top five genes ARF1, CWC15, RAB7A, SIAH1 and VAPA corroborated their expression stability under most of the tested conditions. Further validation in independent samples indicated that the geometric mean of ACTB and VAPA expression can be used as a reliable normalizing factor in human beta cells.

Mapping landslide susceptibility at national scale by spatial multi-criteria evaluation

The representation of terrain propensity to generate landslides, meaning the mapping of landslide susceptibility, represents a first step in the assessment of the risk induced by these geomorphological hazards. Results of the numerous studies differ according to the available input data, the applied methodology, the level of detail or the scale of analysis. The main goal of the present study is to provide a new landslide susceptibility map for the national territory of Romania. The application of the European Landslide Susceptibility (ELSUS) mapping methodology, based on the analytical hierarchy process (AHP), using more accurate and higher spatial resolution input data, led to the achievement of a landslide susceptibility map within individual climate-physiographic zones across the country. The landslide susceptibility is computed by combining the information regarding landslide frequency with a Spatial Multi-Criteria Evaluation (SMCE) model based on three predictors (slope angle, lithology and land cover). The continuous landslide susceptibility model was validated by receiver operating characteristic (ROC) analysis, which indicated a better performance in the plateau, hilly and plain areas (AUC = 0.750) compared to the mountain area. Also, an independent sample validation was performed for testing the consistency of the landslide susceptibility classes. The classified susceptibility map shows patterns that are in agreement with terrain reality.

Endophenotype driven polygenic risk scores for Alzheimer’s disease

AbstractBackgroundPrecision medicine is designed to resolve heterogeneity in complex diseases such as Alzheimer’s (AD) to deliver timely and tailored treatment. Using ADNI data, we developed profiling based on genetically driven endophenotype‐specific polygenic risk scores (PRS) and assessed participant clustering on these profiles.MethodWe used ADNI genetic, clinical and biomarker data for PRS development. We performed cross‐sectional and longitudinal analysis in order to identify individual profiles at the baseline and to track disease progression. We derived four ADRD endophenotype components including Amyloid (A), Tau (T), Neurodegeneration (N) and Vascular (V), by applying PCA on 10 phenotypic variables. Bootstrapping was used for SNP selection and weight re‐estimation. APOE SNPs (rs429358, rs7412) and nearby genes were excluded. We performed K‐means clustering on the 4 derived A/T/N/V PRSs and cluster assignment was computed for 839 individuals of the validation set (excluding AD at the baseline). Baseline levels of cognition and biomarkers were estimated using linear regression adjusting for age, sex, education and (ICV when necessary) (Table 1, Figure 2). Longitudinal trajectories were estimated using linear mixed models (LMM) (Table 2, Figure 3). The performance of the LMM was assessed using marginal R2 (Table 3).ResultCluster assignment placed 306 individuals in Cluster1 and 533 in Cluster2. Both were balanced in terms of APOE e4 status, and demographics. Cluster1 showed elevated genetic risk (Figure 1) that was manifest at baseline by higher levels of amyloid (PET, CSF) and reduced temporal lobe thickness and memory (Table 1, Figure 2). Longitudinally, significant effect differences among the clusters were observed in angular gyrus, temporal lobe, white mater hyperintensity (WMHI), and clinical/cognitive measures (ADNI_EF and FAQ) (Table 2, Figure 3). Among APOE e4 non‐carriers, Cluster1 had significantly worse scores compared to Cluster2 (ADNI_EF and WMHI).ConclusionUsing clusters derived from endophenotype specific genetic information we were able to identify distinct progression profiles with predictive value beyond APOE alone. Endophenotype‐based PRS may be useful for therapeutic enrichment and targeting strategies in development of personalized therapeutics. Validation in independent samples is a critical next step.

Individualized prediction of 2-year risk of relapse as indexed by psychiatric hospitalization following psychosis onset: Model development in two first episode samples

BackgroundAlthough most patients with psychotic disorders experience relapse, it is not possible to predict whether or when an individual patient is going to relapse. We aimed to develop a multifactorial risk prediction algorithm for predicting risk of relapse in first episode psychosis (FEP). MethodsData from two prospectively collected cohorts of FEP patients (N = 1803) were used to develop three multiple logistic prediction models to predict risk of relapse (defined as hospitalization) within the first 2 years of onset of psychosis. Model 1 (M1S1) used data obtained from clinical notes (Sample 1) while model 2 (M2S2) applied the same set of predictors using data obtained from research interviews (Sample 2). The final model (Sample 2: M3S2) used the same predictors plus additional detailed information on predictors. Model performance was evaluated employing measures of overall accuracy, calibration, discrimination and internal validation. ResultsIn both samples, the 2-year probability of psychiatric hospitalization was 37%. Of all the models, discrimination accuracy was lowest when limited information (such as socio-demographic and clinical parameters) was included in the prediction model. Model M3S2 using additional information (descriptors of pattern of cannabis, nicotine, alcohol and other illicit drug use) obtained from research interview had the best discrimination accuracy (Harrell's C index 0.749). ConclusionsThe measures that contributed most to predicting hospitalization are readily accessible in routine clinical practice, suggesting that a risk prediction tool based on these models would be clinically practicable following validation in independent samples and permit a personalized approach to relapse prevention in psychosis.

MiRNAs responsive to the diabetic microenvironment in the human beta cell line EndoC-βH1 may target genes in the FOXO, HIPPO and Lysine degradation pathways

Altered expression of miRNAs is evident in the islets of diabetic human donors, but the effects of specific aspects of the diabetic microenvironment and identity of gene ontology pathways demonstrating target gene enrichment in response to each is understudied.We assessed changes in the miRNA milieu in response to high/low glucose, hypoxia, dyslipidaemia and inflammatory factors in a humanised EndoC-βH1 beta cell culture system and performed miRPath analysis for each treatment individually. The 10 miRNAs demonstrating the greatest dysregulation across treatments were then independently validated and Gene Set Enrichment Analysis to confirm targeted pathways undertaken.171 of 392 miRNAs displayed altered expression in response to one or more cellular stressors. miRNA changes were treatment specific, but their target genes were enriched in conserved pathways. 5 miRNAs (miR-136-5p, miR299-5p, miR-454-5p, miR-152 and miR-185) were dysregulated in response to multiple stressors and survived validation in independent samples (p = 0.008, 0.002, 0.012, 0.005 and 0.024 respectively). Target genes of dysregulated miRNAs were clustered into FOXO1, HIPPO and Lysine degradation pathways (p = 0.02, p = 5.84 × 10−5 and p = 3.00 × 10−3 respectively).We provide evidence that the diabetic microenvironment may induce changes to the expression of miRNAs targeting genes enriched in pathways involved in cell stress response and cell survival.

Candidate Predisposition Variants in Kaposi Sarcoma as Detected by Whole-Genome Sequencing.

Familial clustering of classic Kaposi sarcoma (CKS) is rare with, approximately 100 families reported to date. We studied 2 consanguineous families, 1 Iranian and 1 Israeli, with multiple cases of adult CKS and without overt underlying immunodeficiency. We performed genome-wide linkage analysis and whole-genome sequencing to discover the putative genetic cause for predisposition. A 9-kb homozygous intronic deletion in RP11-259O2.1 in the Iranian family and 2 homozygous variants, 1 in SCUBE2 and the other in CDHR5, in the Israeli family were identified as possible candidates. The presented variants provide a robust starting point for validation in independent samples.

BARD1 serum autoantibodies for the detection of lung cancer.

PurposeCurrently the screening for lung cancer for risk groups is based on Computed Tomography (CT) or low dose CT (LDCT); however, the lung cancer death rate has not decreased significantly with people undergoing LDCT. We aimed to develop a simple reliable blood test for early detection of all types of lung cancer based on the immunogenicity of aberrant forms of BARD1 that are specifically upregulated in lung cancer.MethodsELISA assays were performed with a panel of BARD1 epitopes to detect serum levels of antibodies against BARD1 epitopes. We tested 194 blood samples from healthy donors and lung cancer patients with a panel of 40 BARD1 antigens. Using fitted Lasso logistic regression we determined the optimal combination of BARD1 antigens to be used in ELISA for discriminating lung cancer from healthy controls. Random selection of samples for training sets or validations sets was applied to validate the accuracy of our test.ResultsFitted Lasso logistic regression models predict high accuracy of the BARD1 autoimmune antibody test with an AUC = 0.96. Validation in independent samples provided and AUC = 0.86 and identical AUCs were obtained for combined stages 1–3 and late stage 4 lung cancers. The BARD1 antibody test is highly specific for lung cancer and not breast or ovarian cancer.ConclusionThe BARD1 lung cancer test shows higher sensitivity and specificity than previously published blood tests for lung cancer detection and/or diagnosis or CT scans, and it could detect all types and all stages of lung cancer. This BARD1 lung cancer test could therefore be further developed as i) screening test for early detection of lung cancers in high-risk groups, and ii) diagnostic aid in complementing CT scan.

Prediction of fat globule particle size in homogenized milk using Fourier transform mid-infrared spectra.

Our objective was to develop partial least square models using data from Fourier transform mid-infrared (MIR) spectra to predict the particle size distributions d(0.5) and d(0.9), surface volume mean diameter D[3,2], and volume moment mean diameter D[4,3] of milk fat globules and validate the models. The goal of the study was to produce a method built into the MIR milk analyzer that could be used to warn the instrument operator that the homogenizer is near failure and needs to be replaced to ensure quality of results. Five homogenizers with different homogenization efficiency were used to homogenize pasteurized modified unhomogenized milks and farm raw bulk milks. Homogenized milks were collected from the homogenizer outlet and then run through an MIR milk analyzer without an in-line homogenizer to collect a MIR spectrum. A separate portion of each homogenized milk was analyzed with a laser light-scattering particle size analyzer to obtain reference values. The study was replicated 3 times with 3 independent sets of modified milks and bulk tank farm milks. Validation of the models was done with a set of 34 milks that were not used in the model development. Partial least square regression models were developed and validated for predicting the following milk fat globule particle size distribution parameters from MIR spectra: d(0.5) and d(0.9), surface volume mean diameter D[3,2], and volume moment mean diameter D[4,3]. The basis for the ability to model particle size distribution of milk fat emulsions was hypothesized to be the result of the partial least square modeling detecting absorbance shifts in MIR spectra of milk fat due to the Christiansen effect. The independent sample validation of particle size prediction methods found more variation in d(0.9) and D[4,3] predictions than the d(0.5) and D[3,2] predictions relative to laser light-scattering reference values, and this may be due to variation in particle size among different pump strokes. The accuracy of the d(0.9) prediction for routine quality assurance, to determine if a homogenizer within an MIR milk analyzer was near the failure level [i.e., d(0.9) >1.7µm] and needed to be replaced, is fit-for-purpose. The daily average particle size performance [i.e., d(0.9)] of a homogenizer based on the mean for the day could be used for monitoring homogenizer performance.

Methods for Improving the Prediction Skill of Summer Precipitation over East Asia–West Pacific

AbstractThe performance of summer precipitation prediction skill of the Beijing Climate Center Climate System Model (BCC_CSM1.1; hereafter CSM) over the East Asia–west Pacific (EA–WP) region indicates the need for further development in order to improve model prediction skill. Two methods, namely the statistical alone prediction and the statistical and model combined prediction, are proposed to improve prediction skill. For the former, more than 2000 combinations of precursory predictors are considered in the predictive model; both most similar and dissimilar information are considered in the prediction process. For the latter, both the statistical analog information and the CSM’s forecast are considered in the predictive model; this new prediction method is based on merging information from both a dynamical model and historical analogs. Cross validation of summer precipitation over EA–WP for the years 1991–2010 shows that the average spatial anomaly correlation coefficient (ACC) of the statistical alone prediction is 0.16 and the average spatial root-mean-standard error (RMSE) is 22.7, which are much improved compared to the systematic error correction with ACC and RMSE being 0.09 and 25.0 respectively. The ACC and RMSE results from the statistical and CSM combined prediction are 0.22 and 21.8, respectively, which also indicates an improvement compare to the statistical alone prediction. Independent sample validation for the years 2011–13 indicates that the average ACCs of the statistical alone prediction and the statistical and CSM combined prediction (0.25 and 0.24) are enhanced compared to the systematic error correction (−0.02). The average RMSE is also improved from 25.0 to 23.6 and 22.7. Therefore, the two new prediction methods proposed in this paper both demonstrate the possibilities for the operational prediction of summer precipitation over EA–WP.