Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.

Multiplexed Serum Biomarkers to Discriminate Nonviable and Ectopic Pregnancy

OBJECTIVE

The use of multiplexed biomarkers may improve the diagnosis of normal and abnormal early pregnancies. In this study we assessed 24 markers with multiple machine learning-based methodologies to evaluate combinations of top candidates to develop a multiplexed prediction model for identification of 1) viability and 2) location of an early pregnancy.

DESIGN

A nested case-control design evaluating the predictive ability and discrimination of biomarkers in patients at risk of early pregnancy failure in the first trimester to classify viability and location SUBJECTS: 218 individuals with a symptomatic (pain and/or bleeding) early pregnancy: 75 with an ongoing intrauterine gestation, 68 ectopic pregnancies, and 75 miscarriages.

INTERVENTIONS

Serum values of 24 biomarkers were assessed in the same patients. Multiple machine learning-based methodologies to evaluate combinations of these top candidates to develop a multiplexed prediction model for identification of 1) a nonviable pregnancy (ongoing intrauterine pregnancy vs miscarriage or ectopic pregnancy) and 2) an ectopic pregnancy (ectopic pregnancy vs ongoing intrauterine pregnancy or miscarriage).

MAIN OUTCOME MEASURES

The predicted classification by each model was compared to actual diagnosis and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), conclusive classification, and accuracy were calculated.

RESULTS

Models using classification regression tree analysis using three markers (PSG3, CG-Alpha and PAPPA) were able to predict a maximum sensitivity 93.3%, a maximum specificity 98.6%. The model with the highest accuracy was 97.4% (with 70.2% receiving classification). Models using an overlapping group of three markers (sFLT, PSG3 and TFP12) achieved a maximum sensitivity of 98.5%. and a maximum specificity of 95.3%. The model with the highest accuracy was 94.4% (with 65.6% receiving classification). When the models were used simultaneously the conclusive classification increased to 72.7% with an accuracy 95.9%. The predictive ability of the biomarkers random forest produced similar test characteristics when using 11 predictive markers.

CONCLUSION

We have demonstrated a pool of biomarkers from divergent biological pathways that can be used to classify individuals with potential early pregnancy loss. The biomarkers CG-Alpha, PAPPA and PSG3 can be used to predict viability and sFLT, TPFI2 and PSG3 can be used to predict pregnancy location.

The genetic architecture of multimodal human brain age

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine .

Saturation of Fishbone Instability by Self-Generated Zonal Flows in Tokamak Plasmas

Gyrokinetic simulations of the fishbone instability in DIII-D tokamak plasmas find that self-generated zonal flows can dominate the nonlinear saturation by preventing coherent structures from persisting or drifting in the energetic particle phase space when the mode frequency down-chirps. Results from the simulation with zonal flows agree quantitatively, for the first time, with experimental measurements of the fishbone saturation amplitude and energetic particle transport. Moreover, the fishbone-induced zonal flows are likely responsible for the formation of an internal transport barrier that was observed after fishbone bursts in this DIII-D experiment. Finally, gyrokinetic simulations of a related ITER baseline scenario show that the fishbone induces insignificant energetic particle redistribution and may enable high performance scenarios in ITER burning plasma experiments.

Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis

We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals' cognitive status.

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.

Genomic loci influence patterns of structural covariance in the human brain

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

Homological landscape of human brain functional sub-circuits

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H 1 homological distance between rest and motor task were observed at both whole-brain and sub-circuit consolidated level which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task H 0 , ii) Motor task H 1 , and iii) Working memory task H 2 . At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject- domain which sheds light to subsequent Investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.

An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification

Introduction

Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors.

Methods

Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n  =  1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores.

Results

adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature.

Discussion

Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.

Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.

Preference matrix guided sparse canonical correlation analysis for mining brain imaging genetic associations in Alzheimer's disease

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetics-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlations as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

The Genetic Architecture of Multimodal Human Brain Age

The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.

Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline

Amyloid imaging has been widely used in Alzheimer's disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.

Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE

STREAMLINE is a simple, transparent, end-to-end automated machine learning (AutoML) pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The initial version is limited to binary classification. In this work, we extend STREAMLINE through implementing multiple regression-based ML models, including linear regression, elastic net, group lasso, and L21 norm. We demonstrate the effectiveness of the regression version of STREAMLINE by applying it to the prediction of Alzheimer's disease (AD) cognitive outcomes using multimodal brain imaging data. Our empirical results demonstrate the feasibility and effectiveness of the newly expanded STREAMLINE as an AutoML pipeline for evaluating AD regression models, and for discovering multimodal imaging biomarkers.

[The comparison of modified ESUR score and Mehralivand grade based on biparametric MRI for assessing extracapsulare extension in prostate cancer]

Objective: To compare the value of the modified European Society of Urogenital Radiology (ESUR) score and Mehralivand grade based on biparametric MRI (bpMRI) in the assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa). Methods: Data of 235 patients with postoperative pathology confirmed PCa, who underwent preoperative 3.0 T bpMRI examinations between March 2019 and March 2022 in the First Affiliated Hospital of Soochow University were retrospectively evaluated, including 107 ECE positive and 128 ECE negative cases, aged [M (Q₁, Q₃)] [71 (66, 75)] years. Reader 1 and 2 assessed the ECE using the modified ESUR score and Mehralivand grade, and the receiver operating characteristic curve and Delong test were used to evaluate the performance of the two scoring methods. Then, the statistically significant variables were included in multivariate binary logistics regression analysis to obtain the risk factors, which were combined with the scores of reader 1 to establish combined models. The assessment ability of the two combined models and the two scoring methods were compared subsequently. Results: The AUC of Mehralivand grade in reader 1 were higher than that of the modified ESUR score in reader 1 and 2 [0.746 (95%CI: 0.685-0.800) vs 0.696 (95%CI: 0.633-0.754) and 0.691 (95%CI: 0.627-0.749), both P<0.05]. The AUC of Mehralivand grade in reader 2 was higher than that of the modified ESUR score in reader 1 and 2 [0.753 (95%CI: 0.693-0.807) vs 0.696 (95%CI: 0.633-0.754) and 0.691 (95%CI: 0.627-0.749), both P<0.05]. The AUC of the combined model 1 based on the modified ESUR score and the combined model 2 based on Mehralivand grade were higher than that in the separate modified ESUR score [0.826 (95%CI: 0.773-0.879) and 0.841 (95%CI: 0.790-0.892) vs 0.696 (95%CI: 0.633-0.754), both P<0.001], and also higher than that in the separate Mehralivand grade [0.826 (95%CI: 0.773-0.879) and 0.841 (95%CI: 0.790-0.892) vs 0.746 (95%CI: 0.685-0.800), both P<0.05]. Conclusion: Based on bpMRI, the Mehralivand grade showed better diagnostic performance for assessing ECE preoperatively in patients with PCa than the modified ESUR score. The combination model of scoring methods and clinical variables can further enhance the diagnostic certainty in the assessment of ECE.

[The value of machine learning models based on biparametric MRI for diagnosis of prostate cancer and clinically significant prostate cancer]

Objective: To evaluate the value of machine learning (ML) models based on biparametric magnetic resonance imaging (bpMRI) for diagnosis of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). Methods: A total of 1 368 patients, aged from 30 to 92 (69.4±8.2) years, from 3 tertiary medical centers in Jiangsu Province were retrospectively collected from May 2015 to December 2020, including 412 cases of csPCa, 242 cases of clinically insignificant prostate cancer (ciPCa) and 714 cases of benign prostate lesions. The data of center 1 and center 2 were randomly divided into training cohort and internal testing cohort at a ratio of 7∶3 by random number sampling without replacement using Python Random package, and the data of center 3 were used as the independent external testing cohort. The training cohort includs 243 cases of csPCa, 135 cases of ciPCa and 384 cases of benign lesions, the internal testing cohort includs 104 cases of csPCa, 58 cases of ciPCa and 165 cases of benign lesions, and the external testing cohort includs 65 cases of csPCa, 49 cases of ciPCa and 165 cases of benign lesions. The radiomics features were extracted on T₂-weighted imaging, diffusion-weighted imaging and apparent diffusion coefficient map, and optimal radiomics features were selected by using Pearson correlation coefficient method and analysis of variance. The ML models were built using two ML algorithms, including support vector machine and random forest (RF) and were further tested in the internal testing cohort and external testing cohort. Finally, the PI-RADS scores evaluated by the radiologists were adjusted by the ML models which had superior diagnostic performance, namely adjusted PI-RADS. The receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the ML models and PI-RADS. DeLong test was used to compare the areas under curve (AUC) of models with those of PI-RADS. Results: For PCa diagnosis, in internal testing cohort, the AUC of ML model using RF algorithm and PI-RADS were 0.869 (95%CI: 0.830-0.908) and 0.874 (95%CI: 0.836-0.913), respectively, and the difference between the model and PI-RADS did not reach to the statistical significance (P=0.793). In the external testing cohort, the AUC of model and PI-RADS were 0.845 (95%CI: 0.794-0.897) and 0.915 (95%CI: 0.880-0.951), respectively, and the difference was statistically significant (P=0.01). For csPCa diagnosis, the AUC of ML model using RF algorithm and PI-RADS were 0.874 (95%CI: 0.834-0.914) and 0.892 (95%CI: 0.857-0.927), respectively, in internal testing cohort, and the difference between the model and PI-RADS was not statistically significant (P=0.341). In the external testing cohort, the AUC of model and PI-RADS were 0.876 (95%CI: 0.831-0.920) and 0.884 (95%CI: 0.841-0.926), respectively, and the difference between the model and PI-RADS was not statistically significant (P=0.704). When PI-RADS assessment was adjusted with the assistance of ML models, the specificities increased from 63.0% to 80.0% in the internal testing cohort and from 92.7% to 93.3% in the external test group in diagnosing PCa. In diagnosing csPCa, the specificities increased from 52.5% to 72.6% in the internal testing cohort and from 75.2% to 79.9% in the external testing cohort. Conclusions: The ML models based on bpMRI showed comparable diagnostic performance to PI-RADS assessed by senior radiologists and achieved good generalization ability in both diagnosing PCa and csPCa. The specificities of the PI-RADS were improved by ML models.

[Clinical features of IgG4-related lung disease]

Objective: To explore the clinical features of IgG4-related lung disease. Methods: The clinical data of 60 patients diagnosed with IgG4-related lung disease in Peking University People's Hospital from February 2012 to May 2021 were retrospectively collected. Analysis was made to explore the features of clinical manifestation, laboratory, imaging, prognosis and other characteristics of the disease. Results: A total of 60 patients were included, with 40 males, age of (58.2±12.9) years, an age of onset of (57.1±13.2) years, and 31.7% (19 cases) of the patients had a history of allergic disease. 36.7% (22 cases) of the patients had respiratory symptoms during the disease. 94.6% (53/56) of patients had serum IgG4>1.35 g/L, 24.1% (14/58) of patients had increased eosinophils, 79.2% (38/48) of patients had increased IgE level, and 53.7% (29/54) of patients had decreased C3 or C4. Common imaging findings included nodular changes (38 cases, 63.3%), mediastinal and/or hilar lymphadenopathy (34 cases, 56.7%), and ground glass opacities (31 cases, 51.7%). Fifty-three cases (88.3%) showed two or more imaging changes. The pathological examination of the patient was mainly characterized by lymphoplasmacytic infiltration and fibrosis, with only one case of phlebitis obliterans. Compared with the asymptomatic group (38 cases), patients with respiratory symptoms (22 cases) showed higher level of serum total IgG and eosinophils (43.2 vs 17.8 g/L, 0.30×10⁹/L vs 0.14×10⁹/L, P<0.05), lower proportion of allergic diseases, and higher proportion of consolidation shadows on chest CT (P<0.05). There were no significant differences in serum IgG4, IgE, complement levels, and imaging outcomes after treatment between the two groups (P>0.05). Conclusions: The clinical manifestations of IgG4-related lung disease are atypical, and asymptomatic patients account for a high proportion. The imaging of the disease is highly heterogeneous, and patients are prone to show coexisted multiple imaging changes. The main clinical features and imaging outcomes of patients with and without respiratory symptoms are not significantly different.

Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis

MOTIVATION

With the rapid development of modern technologies, massive data are available for the systematic study of Alzheimer's disease (AD). Though many existing AD studies mainly focus on single-modality omics data, multi-omics datasets can provide a more comprehensive understanding of AD. To bridge this gap, we proposed a novel structural Bayesian factor analysis framework (SBFA) to extract the information shared by multi-omics data through the aggregation of genotyping data, gene expression data, neuroimaging phenotypes and prior biological network knowledge. Our approach can extract common information shared by different modalities and encourage biologically related features to be selected, guiding future AD research in a biologically meaningful way.

METHOD

Our SBFA model decomposes the mean parameters of the data into a sparse factor loading matrix and a factor matrix, where the factor matrix represents the common information extracted from multi-omics and imaging data. Our framework is designed to incorporate prior biological network information. Our simulation study demonstrated that our proposed SBFA framework could achieve the best performance compared with the other state-of-the-art factor-analysis-based integrative analysis methods.

RESULTS

We apply our proposed SBFA model together with several state-of-the-art factor analysis models to extract the latent common information from genotyping, gene expression and brain imaging data simultaneously from the ADNI biobank database. The latent information is then used to predict the functional activities questionnaire score, an important measurement for diagnosis of AD quantifying subjects' abilities in daily life. Our SBFA model shows the best prediction performance compared with the other factor analysis models.

AVAILABILITY

Code are publicly available at https://github.com/JingxuanBao/SBFA.

CONTACT

qlong@upenn.edu.

[Analysis of clinical and pathological features of chronic hepatitis B combined with metabolic-associated fatty liver disease]

Objective: To analyze the clinical and histopathological features of patients with chronic hepatitis B (CHB) combined with metabolic-associated fatty liver disease (MAFLD). Methods: Clinical data of 529 cases who had liver biopsies at the First Affiliated Hospital of Zhengzhou University between January 2015 and October 2021 were collected. Among them were 290 cases with CHB, 155 cases with CHB combined with MAFLD, and 84 cases with MAFLD. Three groups of patients clinical data, including general information, biochemical indicators, FibroScan indicators, viral load, and histopathology, were analyzed. A binary logistic regression analysis was used to explore the factors influencing MAFLD in patients with CHB. Results: (1) Age, male status, proportion of hypertension and diabetes, body mass index, fasting blood glucose, γ-glutamyl transpeptidase, low-density lipoprotein, cholesterol, triglycerides, uric acid, creatinine, and the controlled attenuation parameter for hepatic steatosis were higher in CHB combined with MAFLD than in CHB patient groups. In contrast, the high-density lipoprotein, HBeAg positivity rate, viral load level, and liver fibrosis grade (S stage) were lower in CHB patients, and the differences were statistically significant (P < 0.05). (2) Alanine aminotransferase, aspartate aminotransferase, γ-glutamyl transpeptidase, triglycerides, uric acid, creatinine, and the controlled attenuation parameter for hepatic steatosis in CHB combined with the MAFLD were lower than those in MAFLD patient groups, while high-density lipoprotein was higher than that of MAFLD patients, and the difference was statistically significant (P < 0.05). There was no statistically significant difference in the grade of liver inflammation and fibrosis (GS stage) between the two groups (P > 0.05). Binary multivariate logistic regression analysis showed that overweight/obesity, triglycerides, low-density lipoprotein, the controlled attenuation parameter for hepatic steatosis, and HBeAg positivity were independent influencing factors for MAFLD in CHB patients. Conclusion: Patients with CHB combined with metabolic disorders are prone to developing MAFLD, and there is a certain correlation between HBV viral factors, the degree of liver fibrosis, and the fatty degeneration of hepatocytes.

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.

Emotional responses of piglets under long-term exposure to negative and positive auditory stimuli

The stress caused by sound is inevitable. The stress caused by noise and the positive effects of music can affect the endocrine of animals and their welfare. In this study, a total of 72 hybrid piglets (Large White × Duroc × Min pig) were randomly divided into 3 groups, including music (Mozart K.448, 60-70 dB), noise (recorded mechanical noise, 80-85 dB), and control (natural background sound, <40 dB) groups. S-IgA (secretory immunoglobulin A), IL-6 (interleukin-6), IL-8 (interleukin-8), and positive emotion-related behaviors were used as indicators to discuss whether noise induced stress and inflammation in piglets or whether music could have positive effects. Six hours of auditory exposure were given daily (10:00-16:00), which lasted for 56 days. Behavioral responses of the piglets were observed, and the concentrations of salivary S-IgA and serum IL-6 and IL-8 were measured. The results showed that the concentration of S-IgA increased in the noise and control groups on the 57th day (P < 0.05); S-IgA concentration in the music group was unchanged after long-term music exposure. The concentrations of IL-6 and IL-8 showed that long-term noise exposure might lead to stress and inflammation in piglets. Tail-wagging and play behaviors of the piglets in the music group were significantly greater than those in the noise and control groups, which implied that long-term music exposure improved the emotional state of the piglets in a restricted and barren environment.

Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer's Disease

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data

BACKGROUND

Brain volume has been widely studied in the neuroimaging field, since it is an important and heritable trait associated with brain development, aging and various neurological and psychiatric disorders. Genome-wide association studies (GWAS) have successfully identified numerous associations between genetic variants such as single nucleotide polymorphisms and complex traits like brain volume. However, it is unclear how these genetic variations influence regional gene expression levels, which may subsequently lead to phenotypic changes. S-PrediXcan is a tissue-specific transcriptomic data analysis method that can be applied to bridge this gap. In this work, we perform an S-PrediXcan analysis on GWAS summary data from two large imaging genetics initiatives, the UK Biobank and Enhancing Neuroimaging Genetics through Meta Analysis, to identify tissue-specific transcriptomic effects on two closely related brain volume measures: total brain volume (TBV) and intracranial volume (ICV).

RESULTS

As a result of the analysis, we identified 10 genes that are highly associated with both TBV and ICV. Nine out of 10 genes were found to be associated with TBV in another study using a different gene-based association analysis. Moreover, most of our discovered genes were also found to be correlated with multiple cognitive and behavioral traits. Further analyses revealed the protein-protein interactions, associated molecular pathways and biological functions that offer insight into how these genes function and interact with others.

CONCLUSIONS

These results confirm that S-PrediXcan can identify genes with tissue-specific transcriptomic effects on complex traits. The analysis also suggested novel genes whose expression levels are related to brain volumetric traits. This provides important insights into the genetic mechanisms of the human brain.

Semen parameters and sex hormones as affected by SARS-CoV-2 infection: A systematic review

Background

Impaired semen quality and reproductive hormone levels were observed in patients during and after recovery from coronavirus disease 2019 (COVID-19), which raised concerns about negative effects on male fertility. Therefore, this study systematically reviews available data on semen parameters and sex hormones in patients with COVID-19.

Methods

Systematic search was performed on PubMed and Google Scholar until July 18th, 2022. We identified relevant articles that discussed the effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on male fertility.

Results

A total number of 1,684 articles were identified by using a suitable keyword search strategy. After screening, 26 articles were considered eligible for inclusion in this study. These articles included a total of 1,960 controls and 2,106 patients. When all studies were considered, the results showed that the semen parameters and sex hormone levels of patients infected with SARS-CoV-2 exhibited some significant differences compared with controls. Fortunately, these differences gradually disappear as patients recover from COVID-19.

Conclusion

While present data show the negative effects of SARS-CoV-2 infection on male fertility, this does not appear to be long-term. Semen quality and hormone levels will gradually increase to normal as patients recover.

Mining High-Level Imaging Genetic Associations via Clustering AD Candidate Variants with Similar Brain Association Patterns

Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer's disease (AD). The individual level SNP-QT signals are high dimensional and typically have small effect sizes, making them hard to be detected and replicated. To overcome this limitation, this work proposes a new approach that identifies high-level imaging genetic associations through applying multigraph clustering to the SNP-QT association maps. Given an SNP set and a brain QT set, the association between each SNP and each QT is evaluated using a linear regression model. Based on the resulting SNP-QT association map, five SNP-SNP similarity networks (or graphs) are created using five different scoring functions, respectively. Multigraph clustering is applied to these networks to identify SNP clusters with similar association patterns with all the brain QTs. After that, functional annotation is performed for each identified SNP cluster and its corresponding brain association pattern. We applied this pipeline to an AD imaging genetic study, which yielded promising results. For example, in an association study between 54 AD SNPs and 116 amyloid QTs, we identified two SNP clusters with one responsible for amyloid beta clearances and the other regulating amyloid beta formation. These high-level findings have the potential to provide valuable insights into relevant genetic pathways and brain circuits, which can help form new hypotheses for more detailed imaging and genetics studies in independent cohorts.

Identifying Alzheimer's genes via brain transcriptome mapping

BACKGROUND

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders characterized by progressive decline in cognitive function. Targeted genetic analyses, genome-wide association studies, and imaging genetic analyses have been performed to detect AD risk and protective genes and have successfully identified dozens of AD susceptibility loci. Recently, brain imaging transcriptomics analyses have also been conducted to investigate the relationship between neuroimaging traits and gene expression measures to identify interesting gene-traits associations. These imaging transcriptomic studies typically do not involve the disease outcome in the analysis, and thus the identified brain or transcriptomic markers may not be related or specific to the disease outcome.

RESULTS

We propose an innovative two-stage approach to identify genes whose expression profiles are related to diagnosis phenotype via brain transcriptome mapping. Specifically, we first map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model. Then, the gene-diagnosis association is assessed by spatially correlating the brain transcriptome map with the diagnostic effect map on the brain-wide imaging traits. To demonstrate the promise of our approach, we apply it to the integrative analysis of the brain transcriptome data from the Allen Human Brain Atlas (AHBA) and the amyloid imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our method identifies 12 genes whose brain-wide transcriptome patterns are highly correlated with six different diagnostic effect maps on the amyloid imaging traits. These 12 genes include four confirmatory findings (i.e., AD genes reported in DisGeNET) and eight novel genes that have not be associated with AD in DisGeNET.

CONCLUSION

We have proposed a novel disease-related brain transcriptomic mapping method to identify genes whose expression profiles spatially correlated with regional diagnostic effects on a studied brain trait. Our empirical study on the AHBA and ADNI data shows the promise of the approach, and the resulting AD gene discoveries provide valuable information for better understanding biological pathways from transcriptomic signatures to intermediate brain traits and to phenotypic disease outcomes.

Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression

Importance

Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine.

Objective

To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles.

Design, Setting, and Participants

The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021.

Main Outcomes and Measures

Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics.

Results

A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets).

Conclusions and Relevance

This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.

Efficacy and safety of HDL/apoA-1 mimetics on human and mice with atherosclerosis: a systematic review and meta-analysis

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): the National Natural Science Foundation of China

Background

Low high-density lipoprotein cholesterol (HDL-C) level as a residual risk factor of cardiovascular disease (CVD) is still causing concern, although using chemical drugs for raising HDL-C level failed. The effect of high-density lipoprotein/ apolipiproteinA-1(HDL/apoA-1) mimetics on atherosclerosis is controversial.

Aim

In this meta-analysis we analyzed the effect of high-density lipoprotein/ apolipiproteinA-1(HDL/apoA-1) mimetics on atherosclerotic lesion both in human and mice.

Methods

We systematically searched PubMed, Cochrane, Web of Science and EMBASE databases up to June 6, 2020 for eligible studies using wide search terms and included all the publications meet the including criteria. The methodological quality of the human studies was assessed using Review Manager (RevMan) software (version 5.3.). The methodological quality of the mice studies was assessed by using stair list. WMD(SMD) with 95% CI was used as a measure of the association between HDL/apoA-1 mimetics and plaque regression in human (in mice), after pooling data across trials in a random effect model. Sensitivity and subgroup analyses were used to explore sources of heterogeneity and the effect of potential confounders. STATA (version 14.0) was used to conduct all statistical analyses.

Results

We identified 15 randomized controlled trials in which 6 trails including 754 ACS (HDL/apoA-1 mimetics = 414, placebo = 340) patients used for efficacy analysis and all of 15 trails used for safety analysis and 17 controlled trials for animal study. The pooled results showed that the use of HDL/apoA-1 mimetics did not significant decreased the percent atheroma volume(p = 0.494) and total atheroma volume(p = 0.560) in patients with acute coronary syndrome (ACS). However, HDL/apoA-1 mimetics (or gene transfection) was significant associated with all of final percent lesion area, final lesion area and changes in lesion area (SMD, -1.75; 95% CI: -2.21∼-1.29, p = 0.000; SMD, -0.78; 95% CI: -1.18∼-0.38, p = 0.000; SMD: -2.06; 95% CI, -3.92∼-0.2, p = 0.03) in mice.

Conclusions

In human, HDL/apoA-1 mimetics cannot significantly improve atheroma volume in artery, although it is safe. However, in animal, the results suggest HDL/apoA-1 mimetics (or gene transfection) can decrease lesion area. So additional studies are needed to further investigate and explain the different efficacy of HDL/apoA-1 mimetic peptides between human and animal. Abstract Figure. Forest plots of human studies

Identifying imaging genetic associations via regional morphometricity estimation

Brain imaging genetics is an emerging research field aiming to reveal the genetic basis of brain traits captured by imaging data. Inspired by heritability analysis, the concept of morphometricity was recently introduced to assess trait association with whole brain morphology. In this study, we extend the concept of morphometricity from its original definition at the whole brain level to a more focal level based on a region of interest (ROI). We propose a novel framework to identify the SNP-ROI association via regional morphometricity estimation of each studied single nucleotide polymorphism (SNP). We perform an empirical study on the structural MRI and genotyping data from a landmark Alzheimer's disease (AD) biobank; and yield promising results. Our findings indicate that the AD-related SNPs have higher overall regional morphometricity estimates than the SNPs not yet related to AD. This observation suggests that the variance of AD SNPs can be explained more by regional morphometric features than non-AD SNPs, supporting the value of imaging traits as targets in studying AD genetics. Also, we identified 11 ROIs, where the AD/non-AD SNPs and significant/insignificant morphometricity estimation of the corresponding SNPs in these ROIs show strong dependency. Supplementary motor area (SMA) and dorsolateral prefrontal cortex (DPC) are enriched by these ROIs. Our results also demonstrate that using all the detailed voxel-level measures within the ROI to incorporate morphometric information outperforms using only a single average ROI measure, and thus provides improved power to detect imaging genetic associations.

Identifying highly heritable brain amyloid phenotypes through mining Alzheimer's imaging and sequencing biobank data

Brain imaging genetics, an emerging and rapidly growing research field, studies the relationship between genetic variations and brain imaging quantitative traits (QTs) to gain new insights into the phenotypic characteristics and genetic mechanisms of the brain. Heritability is an important measurement to quantify the proportion of the observed variance in an imaging QT that is explained by genetic factors, and can often be used to prioritize brain QTs for subsequent imaging genetic association studies. Most existing studies define regional imaging QTs using predefined brain parcellation schemes such as the automated anatomical labeling (AAL) atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion could be negatively affected by heterogeneity within the regions in the partition. To bridge this gap, we propose a novel method to define highly heritable brain regions. Based on voxelwise heritability estimates, we extract brain regions containing spatially connected voxels with high heritability. We perform an empirical study on the amyloid imaging and whole genome sequencing data from a landmark Alzheimer's disease biobank; and demonstrate the regions defined by our method have much higher estimated heritabilities than the regions defined by the AAL atlas. Our proposed method refines the imaging endophenotype constructions in light of their genetic dissection, and yields more powerful imaging QTs for subsequent detection of genetic risk factors along with better interpretability.

Periodontitis Salivary Microbiota Worsens Colitis

Evidence suggests that periodontitis contributes to the pathogenesis of inflammatory bowel disease, including Crohn's disease and ulcerative colitis. However, few studies have examined the role of swallowing and saliva in the pathogenesis of gastrointestinal diseases. Saliva contains an enormous number of oral bacteria and is swallowed directly into the intestine. Here, we explored the influence of periodontitis salivary microbiota on colonic inflammation and possible mechanisms in dextran sulfate sodium (DSS)-induced colitis. The salivary microbiota was collected from healthy individuals and those with periodontitis and gavaged to C57BL/6 mice. Periodontitis colitis was induced by DSS for 5 d and ligature for 1 wk. The degree of colon inflammation was evaluated through hematoxylin and eosin staining, ELISA, and quantitative real-time polymerase chain reaction. Immune parameters were measured with quantitative real-time polymerase chain reaction, flow cytometry, and immunofluorescence. The gut microbiota and metabolome analyses were performed via 16S rRNA gene sequencing and liquid chromatography-mass spectrometry. Although no significant colitis-associated phenotypic changes were found under physiologic conditions, periodontitis salivary microbiota exacerbated colitis in a periodontitis colitis model after DSS induction. The immune response more closely resembled the pathology of ulcerative colitis, including aggravated macrophage M2 polarization and Th2 cell induction (T helper 2). Inflammatory bowel disease-associated microbiota, such as Blautia, Helicobacter, and Ruminococcus, were changed in DSS-induced colitis after periodontitis salivary microbiota gavage. Periodontitis salivary microbiota decreased unsaturated fatty acid levels and increased arachidonic acid metabolism in DSS-induced colitis, which was positively correlated with Aerococcus and Ruminococcus, suggesting the key role of these metabolic events and microbes in the exacerbating effect of periodontitis salivary microbiota on experimental colitis. Our study demonstrated that periodontitis contributes to the pathogenesis of colitis through the swallowing of salivary microbiota, confirming the role of periodontitis in systemic disease and providing new insights into the etiology of gastrointestinal inflammatory diseases.

Prognostic nutritional index as a prognostic factor in lung cancer patients receiving chemotherapy: a systematic review and meta-analysis

OBJECTIVE

Lung cancer is one of the leading causes of morbidity and mortality in the world. In the past decade, numerous studies focus on the prognostic nutritional index (i.e., a measure of serum albumin and lymphocyte in peripheral circulation) as a possible biomarker to predict the survival outcomes in cancer patients undergoing chemotherapy. Prognostic nutritional index can reliably predict the survivability outcomes by effectively quantifying the nutritional and immunological status of cancer patients. To date, only one review has attempted to evaluate the impact of the prognostic nutritional index on the survival outcomes in lung cancer patients with certain limitations. The goal of the present systematic review and meta-analysis is to bridge the gap in the literature and evaluate the capacity of the prognostic nutritional index for predicting the survivability outcomes in lung cancer patients undergoing chemotherapy. The aim of the study is to evaluate the impact of prognostic nutritional index scoring on survival outcomes in lung cancer patients undergoing chemotherapy.

MATERIALS AND METHODS

 A systematic academic literature search was performed based on the PRISMA guidelines across Web of Science, EMBASE, CENTRAL, Scopus, and MEDLINE databases. A random-effect meta-analysis was performed to evaluate the impact of prognostic nutritional index scoring (i.e., high/low) on survival outcomes (i.e., progression-free survival, overall survival) in lung cancer patients undergoing chemotherapy.

RESULTS

 From 963 studies, 16 eligible studies with 4250 lung cancer patients (62.32 ± 5.08 years) undergoing chemotherapy were included. Our meta-analysis revealed worse mortality outcomes in terms of progression-free survival (HR: 1.31) and overall survival (1.21) for the group with a low prognostic nutritional index score as compared to the group with a high prognostic nutritional index score in lung cancer patients undergoing chemotherapy. Subsequent subgroup analyses further demonstrated markedly worse outcomes for progression-free survival (1.32) and overall survival (1.52) in non-small lung cancer patients with lower prognostic nutritional index scores.

CONCLUSIONS

 We provide preliminary evidence suggesting that lower prognostic nutrition index scores are associated with worse survivability outcomes (progression-free survival and overall survival) in lung cancer patients undergoing chemotherapy. We also show that lower prognostic nutrition index scores correlate with even worse survival outcomes in patients with non-small lung cancer histological subtype of lung cancer. These findings should help clinicians to stratify the risks associated with the chemotherapeutic management of lung cancer patients.

A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits

Heritability analysis is an important research topic in brain imaging genetics. Its primary motivation is to identify highly heritable imaging quantitative traits (QTs) for subsequent in-depth imaging genetic analyses. Most existing studies perform heritability analyses on regional imaging QTs using predefined brain parcellation schemes such as the AAL atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion is largely deteriorate with inner partition noise and signal dilution. To bridge the gap, we propose a new semi-parametric Bayesian heritability estimation model to construct highly heritable imaging QTs. Our method leverages the aggregate of genetic signals to imaging QT construction by developing a new brain parcellation driven by voxel-level heritability. To ensure biological plausibility and clinical interpretability of the resulting brain heritability parcellations, hierarchical sparsity and smoothness, coupled with structural connectivity of the brain, are properly imposed on genetic effects to induce spatial contiguity of heritable imaging QTs. Using the ADNI imaging genetic data, we demonstrate the strength of our proposed method, in comparison with the standard GCTA method, in identifying highly heritable and biologically meaningful new imaging QTs.

Low dietary phosphorus impairs keel bone health and quality in laying hens

1. Phosphorus (P) is a necessary nutrient for egg production and bone quality in poultry diets. To investigate the effects of low dietary available P (avP) on keel bone, 180 laying hens were fed either a control (C, 0.3% avP) or low phosphorus (LP, 0.15% avP) diet from 20-36 weeks of age (WOA). Each diet was replicated in six cages with 15 birds per cage. Keel samples were collected at 24, 28, 32, and 36 WOA to measure indicators.2. The incidence of keel bone damage in the LP group was higher than C group and increased with age throughout the experiment period. Keel bone length from laying hens in the LP group was shorter than C group (P < 0.05) at 32 and 36 WOA.3. The mRNA expression of receptor activator of nuclear factor kappa-B ligand (RANKL) and ratio of RANKL to osteoprotegerin (OPG) were upregulated (P < 0.05), and that of sclerostin and OPG was downregulated (P < 0.05) in the LP group in comparison to hens in the C group. Meanwhile, mRNA expression of the integrin-binding sialoprotein was increased at 24 and 28 WOA (P < 0.05), and decreased at 32 and 38 WOA (P < 0.05) in the LP group.4. Laying hens in LP group had increased trabecular separation and bone surface fraction (P < 0.05), decreased bone volume, bone volume fraction, trabecular number and thickness, and bone mineral density (P < 0.05) at 32 WOA. The LP-fed hens had increased K, Ti, Mn, Fe, Zn, Se, Sr and Pb bone concentrations (P < 0.05), and decreased P and TI bone concentrations (P < 0.05) at 36 WOA.5. Feeding hens a P-deficient diet with 0.15% avP and 3.37% Ca during the laying period impaired keel bone quality, which could be related to the osteoporosis.

[High-fat diet promotes the impact of periodontitis on gut microbiota and glucose metabolism]

Objective: To study whether high-fat diet could aggravate the effect of periodontitis on gut microbiota and glucose metabolism. Methods: Twenty-four male SD rats were randomly and equally divided into four groups based on table of random numbers (n=6 in each group): control group, in which rats were given normal chow diet; periodontitis group, in which periodontitis was induced by ligating bilateral maxillary second molars with 5-0 silk thread; high-fat diet group, in which rats were given high-fat diet; high-fat diet+periodontitis group, in which rats were given high-fat diet and periodontitis was induced at the end of the 8th week. Fasting blood glucose and glucose tolerance were measured at the end of the 12th week. Then the rats were euthanized and the cecum content was collected. The microbial 16S rRNA gene sequencing was performed on the Illumina MiSeq platform. The taxonomy of the sequences was analyzed through RDP Classifier (http://rdp.cme.msu.edu/) against the SILVA (SSU123) 16S rRNA database. Pearson correlation analysis was performed to analyze the correlation between changes in gut microbiota and blood glucose. Results: After 4 weeks of periodontitis induction, the fasting blood glucose levels of the periodontitis group and the high-fat diet group were (4.93±0.28) and (5.25±0.24) mmol/L, respectively, which were significantly higher than that of the control group [(4.56±0.20) mmol/L] (P<0.05) with glucose intolerance. The fasting blood glucose level of high-fat diet+periodontitis group [(5.53±0.14) mmol/L] was significantly higher than that of periodontitis group and high-fat diet group, respectively (P<0.05), with the glucose tolerance curve higher than that of periodontitis group. The 16S rRNA gene analysis revealed that the Bacteroides/Firmicutes ratio in the periodontitis group is (0.37±0.23), which was significantly lower than that of the control group (0.68±0.05) (P<0.05). The relative abundance of Lachnospiraceae_NK4A136_group in the periodontitis group was (14.03±6.38)%, which was significantly lower than that of the control group [(28.21±4.82)%] (P<0.05). The relative abundance of Allobaculum [(4.27±2.67)%], Ruminococcaceae_UCG_005 [(3.70±0.90)%], Blautia [(0.63±0.45)%] in the periodontitis group were significantly higher than those of the control group [(0.60±0.72) %, (0.43±0.16) %, (0.13±0.13) %, respectively](P<0.05). Compared with periodontitis group, the relative abundance of Proteobacteria in high-fat diet+periodontitis group [(3.06±0.90)%] was significantly higher than that of the periodontitis group [(1.40±0.98)%] (P<0.05). The principal coordinate analysis and similarity analysis based on the Bray-Curtis distance showed that samples of the high-fat diet+periodontitis group clustered separately from the periodontitis group and the high-fat diet group. The results of correlation analysis showed that the abundance of Lachnospiraceae_NK4A136_group was negatively correlated with fasting blood glucose and glucose levels after loading for 60 and 120 minutes (r=-0.56, -0.50, -0.42, respectively) (P<0.05). The abundance of Allobaculum, [Eubacterium]_coprostanoligenes_group, Peptococcaceae_uncultured, [Ruminococcus]_torques_group, and several genera belonging to the Proteobacteria were positively correlated with glucose levels after loading for 120 minutes (P<0.05). Conclusions: Periodontitis might be closely related to impaired gut microbiota and glucose metabolism, and the effect could be aggravated by high-fat diet.

Metabolic syndrome-related SNPs in HLA and TNF7L2 may be risk factors for generalized pustular psoriasis in Chinese Han population

Background

Generalized pustular psoriasis (GPP) is a rare and severe type of psoriasis. Previous studies have reported that metabolic syndrome and its components have been associated with psoriasis.

Objective

To investigate the association of metabolic syndrome‐related single‐nucleotide polymorphisms (SNPs) and GPP in Chinese Han population.

Materials and Methods

One hundred and thirty‐six (136) GPP patients and 965 healthy controls were recruited in the study. Approximately, 4 ml peripheral venous blood was collected from each participant. After collection, second‐generation sequencing was used to detect genetic polymorphism of 15 SNPs. The plink 1.07 software package was used for statistical analysis.

Results

Rs805303 (p = 0.01, OR = 0.70) and rs3177928 (p = 3.18E−07, OR = 2.66) in HLA were significantly different between the two groups. Moreover, rs4506565 (p = 1.41E−03, OR = 2.72) and rs7901695 (p = 9.39E−04, OR = 2.82) in TCF7L2 were significantly associated with GPP in patients without a previous history of PsV. Genotype analysis of rs4506565 and rs7901695 showed that under the recessive model, genotype frequencies of rs4506565 (p = 0.00, OR = 18.52) and rs7901695 (p = 0.00, OR = 18.44) were significantly different between GPP patients and healthy controls.

Conclusion

Rs805303 and rs3177928 in HLA may increase the risk of GPP in the Chinese Han population. TCF7L2 may be a risk factor for GPP in patients without a previous history of PsV.

High-fat diet exacerbates periodontitis: is it because of dysbacteriosis or stem cell dysfunction?

Previous studies have shown that high-fat diet (HFD) may aggravate periodontitis, however the underlining mechanism remains to be further clarified. This study aims to explore whether HFD promotes periodontitis by inducing periodontal microbiota dysbiosis or stem cell dysfunction. A high-fat diet was given to four-week-old male Sprague-Dawley rats for 12 weeks. Periodontitis was induced during the latter 4 weeks. At the end of the 12th week, samples were collected after euthanasia. Maxillae were harvested for histological or microbial analysis. The microbial 16S rRNA gene sequencing was performed with the Illumina MiSeq platform. The data was analyzed through RDP Classifier against the SILVA database. The mandible molars were harvested for isolating periodontal ligament stem cells (PDLSCs). The protein level of p27, p21, and p16, which are negative regulators of the cell cycle, in PDLSCs were detected. Markers of osteogenic differentiation and pro-inflammatory mediators were detected by real-time polymerase chain reaction. Activation of pro-inflammatory signaling pathways was detected by Western blotting. We found that HFD significantly increased ligature-induced alveolar bone loss. HFD resulted in a less diverse periodontal microbiota, with increased proportions of Lactococcus, Bacillus, Alloprevotella, Carnobacterium, and Exiguobacterium and decreased proportion of Nitrospira. HFD increased the protein levels of p27, p16, and p21, and upregulated the expression of osteogenic biomarkers, IL-1β and IL-10 with the ERK1/2 signaling pathway activated in PDLSCs.

Gut Microbiota May Mediate the Influence of Periodontitis on Prediabetes

Mounting evidence has shown that periodontitis is associated with diabetes. However, a causal relationship remains to be determined. Recent studies reported that periodontitis may be associated with gut microbiota, which plays an important role in the development of diabetes. Therefore, we hypothesized that gut microbiota might mediate the link between periodontitis and diabetes. Periodontitis was induced by ligatures. Glycemic homeostasis was evaluated through fasting blood glucose (FBG), serum glycosylated hemoglobin (HbA1c), and intraperitoneal glucose tolerance test. Micro-computed tomography and hematoxylin and eosin staining were used to evaluate periodontal destruction. The gut microbiota was analyzed using 16S ribosomal RNA gene sequencing and bioinformatics. Serum endotoxin, interleukin (IL) 6, tumor necrosis factor α (TNF-α), and IL-1β were measured to evaluate the systemic inflammation burden. We found that the levels of FBG, HbA1c, and glucose intolerance were higher in the periodontitis (PD) group than in the control (Con) group (P < 0.05). When periodontitis was eliminated, the FBG significantly decreased (P < 0.05). Several butyrate-producing bacteria were decreased in the gut microbiota of the PD group, including Lachnospiraceae_NK4A136_group, Eubacterium_fissicatena_group, Eubacterium_coprostanoligenes_group, and Ruminococcaceae_UCG-014 (P < 0.05), which were negatively correlated with serum HbA1c (P < 0.05). Subsequently, the gut microbiota was depleted using antibiotics or transplanted through cohousing. Compared with the PD group, the levels of HbA1c and glucose intolerance were decreased in the gut microbiota-depleted mice with periodontitis (PD + Abx) (P < 0.05), as well as the serum levels of endotoxin and IL-6 (P < 0.05). The serum levels of IL-6, TNF-α, and IL-1β in the PD + Abx group were higher than those of the Con group (P < 0.05). Antibiotics exerted a limited impact on the periodontal microbiota. When the PD mice were cohoused with healthy ones, the elevated FBG and HbA1c significantly recovered (P < 0.05), as well as the aforementioned butyrate producers (P < 0.05). Thus, within the limitations of this study, our data indicated that the gut microbiota may mediate the influence of periodontitis on prediabetes.

A structural enriched functional network: An application to predict brain cognitive performance

The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models.

Keel-bone fractures are associated with bone quality differences in laying hens

This study aimed to investigate the relationship between bone quality in terms of metabolism, homeostasis of elements, bone mineral density (BMD), and microstructure and keel-bone fractures in laying hens (Gallusgallusdomesticus). One hundred and twenty 17 week old Lohmann White laying hens with normal keel bones were individually housed in furnished cages for 25 weeks. Birds were then euthanased and dissected to assess keel-bone status at 42 weeks. Serum and keel-bone samples from normal keel (NK) and fractured keel (FK) hens were collected to determine the previously mentioned bone quality parameters. The results showed FK hens to have higher levels of the components of osteocalcin, greater alkaline phosphatase activity in serum and keel bones, and greater tartrate-resistant acid phosphatase (TRAP) activity in keel bones, compared to NK hens. Additionally, FK hens also had higher concentrations of Li, B, K, Cu, As, Se, Sn, Hg, and Pb, but lower concentrations of Na, P, and Ca. Moreover, FK hens showed decreased bone microstructural parameters including bone volume/tissue volume, trabecular number, degree of anisotropy, connectivity density, and BMD, but increased trabecular separation. Meanwhile, no differences were detected in serum TRAP activity, trabecular thickness, bone surface, or bone surface/bone volume. Results showed laying hens with keel-bone fractures to have differences in bone metabolism, elements of homeostasis, bone microstructure parameters, and BMD. These results suggest that keel-bone fractures may be associated with bone quality.