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PURPOSE To explore trial-level and patient-level associations between response (complete remission [CR] and CR + CR with incomplete hematologic [CRi] or platelet [CRp] recovery), event-free survival (EFS), and overall survival (OS) in newly diagnosed acute myeloid leukemia (AML) trials of intensive chemotherapy. METHODS We identified data from eight randomized, active-controlled trials of intensive chemotherapy submitted to the US Food and Drug Administration for treatment of newly diagnosed AML (N = 4,482). Associations between trial-level odds ratios (ORs) for CR and CR + CRi or CRp, and hazard ratios (HRs) for EFS and OS were analyzed using weighted linear regression models. We performed patient-level responder analyses to compare OS by response using pooled data from all studies. RESULTS In trial-level analyses, association between HR for OS and OR for CR was moderate (R2 = 0.49; 95% CI, 0.05 to 0.86), as was the association with OR for CR + CRi or CRp (R2 = 0.48; 95% CI, 0.05 to 0.99). For OS versus EFS, a strong association was observed (R2 = 0.87; 95% CI, 0.47 to 0.98) when EFS definitions were harmonized across trials using raw data. In the patient-level responder analyses, patients who achieved CR had better OS compared with CRi or CRp responders (0.73; 95% CI, 0.64 to 0.84) and nonresponders (HR, 0.33; 95% CI, 0.31 to 0.37). CONCLUSION On a trial level, there is a moderate association between OS and CR rate. A strong association between EFS and OS was observed. However, CIs were wide, and results became moderate using alternative definitions for EFS. Patient-level analyses showed CR responders have better OS compared with CRi or CRp responders and nonresponders. A therapy in newly diagnosed AML with benefit in EFS or substantial benefit in CR rate would be likely to have an OS effect.

Objective: Longitudinal neuroimaging data have been widely used to predict clinical scores for automatic diagnosis of Alzheimer’s Disease (AD) in recent years. However, incomplete temporal neuroimaging records of the patients pose a major challenge to use these data for accurately diagnosing AD. In this paper, we propose a novel method to learn an enriched representation for imaging biomarkers, which simultaneously captures the information conveyed by both the baseline neuroimaging records of all the participants in a studied cohort and the progressive variations of the available follow-up records of every individual participant. Methods: Taking into account that different participants usually take different numbers of medical records at different time points, we develop a robust learning objective that minimizes the summations of a number of not-squared $\ell _2$ -norm distances, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative algorithm with rigorously proved convergence. Results: We have conducted extensive experiments using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Clear performance gains have been achieved when we predict different cognitive scores using the enriched biomarker representations learned by our new method. We further observe that the top selected biomarkers by our proposed method are in perfect accordance with the known knowledge in existing clinical AD studies. Conclusion: All these promising experimental results have demonstrated the effectiveness of our new method. Significance: We anticipate that our new method is of interest to biomedical engineering communities beyond AD research and have open-sourced the code of our method online. 1 1 The code package of this paper have been made publicly available online at https://github.com/lyujian/Improved-Prediction-of-Cognitive-Outcomes

Mendelian randomization is a versatile tool to identify the possible causal relationship between an omics biomarker and disease outcome using genetic variants as instrumental variables. A key theme is the prioritization of genes whose omics readouts can be used as predictors of the disease outcome through analyzing GWAS and QTL summary data. However, there is a dearth of study of the best practice in probing the effects of multiple -omics biomarkers annotated to the same gene of interest. To bridge this gap, we propose powerful combination tests that integrate multiple correlated $P$-values without assuming the dependence structure between the exposures. Our extensive simulation experiments demonstrate the superiority of our proposed approach compared with existing methods that are adapted to the setting of our interest. The top hits of the analyses of multi-omics Alzheimer’s disease datasets include genes ABCA7 and ATP1B1.

Background Sudden death (SD) and pump failure death (PFD) are the two leading causes of death in patients with heart failure and reduced ejection fraction (HFrEF). Objective Identifying patients at higher risk for mode-specific death would allow better targeting of individual patients for relevant device and other therapies. Methods We developed models in 7156 patients with HFrEF from the Prospective comparison of ARNI with ACEI to Determine Impact on Global Mortality and morbidity in Heart Failure (PARADIGM-HF) trial, using Fine-Gray regressions counting other deaths as competing risks. The derived models were externally validated in the Aliskiren Trial to Minimize Outcomes in Patients with Heart Failure (ATMOSPHERE) trial. Results NYHA class and NT-proBNP were independent predictors for both modes of death. The SD model additionally included male sex, Asian or Black race, prior CABG or PCI, cancer history, MI history, treatment with LCZ696 vs. enalapril, QRS duration and ECG left ventricular hypertrophy. While LVEF, ischemic etiology, systolic blood pressure, HF duration, ECG bundle branch block, and serum albumin, chloride and creatinine were included in the PFD model. Model discrimination was good for SD and excellent for PFD with Harrell’s C of 0.67 and 0.78 after correction for optimism, respectively. The observed and predicted incidences were similar in each quartile of risk scores at 3 years in each model. The performance of both models remained robust in ATMOSPHERE. Conclusion We developed and validated models which separately predict SD and PFD in patients with HFrEF. These models may help clinicians and patients consider therapies targeted at these modes of death. Trial registration number PARADIGM-HF: ClinicalTrials.gov NCT01035255, ATMOSPHERE: ClinicalTrials.gov NCT00853658. Graphics abstract

The COVID-19 pandemic has resulted in unprecedented burden on global health and economic systems, promoting worldwide efforts to understand, control, and fight the disease. Due to the wide spectrum of clinical severity, effective risk factors, biomarkers, and models for predicting disease severity and mortality in COVID-19 patients are urgently needed to provide guidance for clinical intervention and management. In this chapter, we first describe the infection features of different COVID-19 strains and the potential of clinical features, cytokine storm and biomarkers in predicting the severity of COVID-19 patients. We focus on how scoring systems, mathematical models and artificial intelligence (AI)-based models can promote the classification of COVID-19 severity at the population or individual level. Moreover, the development perspective of biomarkers and models for predicting the severity of COVID-19 is prospected. Therefore, this chapter highlights the clinical significance of biomarkers and models related to COVID-19 severity and provides important clues for improving the outcomes of COVID-19 patients, thereby facilitating timely disease assessment and precision medicine for individual COVID-19 patients.

Background Sudden death (SD) and pump failure death (PFD) are leading modes of death in heart failure and preserved ejection fraction (HFpEF). Risk stratification for mode-specific death may aid in patient enrichment for new device trials in HFpEF. Methods Models were derived in 4116 patients in the Irbesartan in Heart Failure with Preserved Ejection Fraction trial (I-Preserve), using competing risks regression analysis. A series of models were built in a stepwise manner, and were validated in the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved and Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trials. Results The clinical model for SD included older age, men, lower LVEF, higher heart rate, history of diabetes or myocardial infarction, and HF hospitalization within previous 6 months, all of which were associated with a higher SD risk. The clinical model predicting PFD included older age, men, lower LVEF or diastolic blood pressure, higher heart rate, and history of diabetes or atrial fibrillation, all for a higher PFD risk, and dyslipidaemia for a lower risk of PFD. In each model, the observed and predicted incidences were similar in each risk subgroup, suggesting good calibration. Model discrimination was good for SD and excellent for PFD with Harrell’s C of 0.71 (95% CI 0.68–0.75) and 0.78 (95% CI 0.75–0.82), respectively. Both models were robust in external validation. Adding ECG and biochemical parameters, model performance improved little in the derivation cohort but decreased in validation. Including NT-proBNP substantially increased discrimination of the SD model, and simplified the PFD model with marginal increase in discrimination. Conclusions The clinical models can predict risks for SD and PFD separately with good discrimination and calibration in HFpEF and are robust in external validation. Adding NT-proBNP further improved model performance. These models may help to identify high-risk individuals for device intervention in future trials. Clinical trial registration I-Preserve: ClinicalTrials.gov NCT00095238; TOPCAT: ClinicalTrials.gov NCT00094302; CHARM-Preserved: ClinicalTrials.gov NCT00634712. Graphic abstract

Background Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer’s Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method We adapt a newly developed learning-to-rank approach $${\mathtt {PLTR}}$$ PLTR to implement our paradigm. This method learns the latent scoring function that pushes the most effective cognitive assessments onto the top of the prioritization list. We also extend $${\mathtt {PLTR}}$$ PLTR to better separate the most effective cognitive assessments and the less effective ones. Results Our empirical study on the ADNI data shows that the proposed paradigm outperforms the state-of-the-art baselines on identifying and prioritizing individual-specific cognitive biomarkers. We conduct experiments in cross validation and level-out validation settings. In the two settings, our paradigm significantly outperforms the best baselines with improvement as much as 22.1% and 19.7%, respectively, on prioritizing cognitive features. Conclusions The proposed paradigm achieves superior performance on prioritizing cognitive biomarkers. The cognitive biomarkers prioritized on top have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.

Childhood exposure to violence has been consistently linked to externalizing behaviors like delinquency and aggression. Growing evidence indicates that physiological biomarkers from the parasympathetic and sympathetic nervous systems (PNS and SNS) and hypothalamic-pituitary-adrenal (HPA) axis may moderate or mediate the relation between childhood violence exposure and externalizing behaviors. We conducted a systematic review to synthesize recent findings on physiological biomarkers as mediators and/or moderators of this association across the life course, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search yielded 3878 articles, of which 44 met inclusion criteria (describing a total of 46 independent studies). We found consistent evidence for blunted HPA-axis reactivity as a mediator of the relation between childhood violence exposure and subsequent externalizing behaviors, and for non-reciprocal PNS/SNS activation as moderators exacerbating this relation. However, the results of the majority of included studies that demonstrated significant moderating effects of physiological biomarkers varied by participant sex, type of childhood violence exposure, and type of stimuli used to induce physiological reactivity. The observed mixed findings are consistent with some theories that emphasize that both high and low stress reactivity can be adaptive depending on one's early environment. These findings highlight the need for systematic explorations of heterogeneity, theory-driven research questions, and longitudinal studies that span multiple developmental periods and multiple biological systems. Clinical implications include the need to assess physiological biomarkers in treatment and intervention studies and the potential to target interventions based on both autonomic functioning and environmental contexts.

Background Platelet activation plays a crucial role in the pathogenesis of coronary artery disease (CAD). Platelet P-selectin (CD62P) is a classic platelet activation indicator on the platelet surface, and soluble TREM-like transcript-1 (sTLT-1) is a new indicator. However, the relationship between these two markers and CAD, especially in acute coronary syndrome (ACS), has not been elucidated. This study aimed to investigate CD62P expression on the platelet surface and sTLT-1 expression in serum, as well as to assess their relationship with CAD. Methods We measured the levels of CD62P and sTLT-1 in 83 patients with CAD compared to 49 controls. The association of these indicators with age, blood pressure, lipid profile, body mass index, and liver injury marker level were also examined. Results CD62P concentration was higher in CAD patients than in the control group (P P P Conclusions The consistency of sTLT-1 and CD62P expression levels in CAD patients indicates that sTLT-1 level, the same as CD62P, may be a new marker of platelet activation that is positively related to CAD.

The outbreak of the novel coronavirus in China (SARS‐CoV‐2) that began in December 2019 presents a significant and urgent threat to global health. This study was conducted to provide the international community with a deeper understanding of this new infectious disease. Epidemiological, clinical features, laboratory findings, radiological characteristics, treatment, and clinical outcomes of 135 patients in northeast Chongqing were collected and analyzed in this study. A total of 135 hospitalized patients with COVID‐19 were enrolled. The median age was 47 years (interquartile range, 36‐55), and there was no significant gender difference (53.3% men). The majority of patients had contact with people from the Wuhan area. Forty‐three (31.9%) patients had underlying disease, primarily hypertension (13 [9.6%]), diabetes (12 [8.9%]), cardiovascular disease (7 [5.2%]), and malignancy (4 [3.0%]). Common symptoms included fever (120 [88.9%]), cough (102 [76.5%]), and fatigue (44 [32.5%]). Chest computed tomography scans showed bilateral patchy shadows or ground glass opacity in the lungs of all the patients. All patients received antiviral therapy (135 [100%]) (Kaletra and interferon were both used), antibacterial therapy (59 [43.7%]), and corticosteroids (36 [26.7%]). In addition, many patients received traditional Chinese medicine (TCM) (124 [91.8%]). It is suggested that patients should receive Kaletra early and should be treated by a combination of Western and Chinese medicines. Compared to the mild cases, the severe ones had lower lymphocyte counts and higher plasma levels of Pt, APTT, d‐dimer, lactate dehydrogenase, PCT, ALB, C‐reactive protein, and aspartate aminotransferase. This study demonstrates the clinic features and therapies of 135 COVID‐19 patients. Kaletra and TCM played an important role in the treatment of the viral pneumonia. Further studies are required to explore the role of Kaletra and TCM in the treatment of COVID‐19., Research Highlights 83.7% of the patients had contact history in Wuhan or had been to Wuhan or had contact with people from Wuhan.Common symptoms included fever, cough, and fatigue. Other symptoms include myalgia, fatigue, dyspnea, anorexia, etc.Common complications of the patients include acute respiratory distress syndrome, acute cardiac injury, acute kidney injury, secondary infection and shock. ICU patients were more likely to have these complications than non‐ICU patients.Compared with non‐ICU patients, ICU patients had lower lymphocyte count, and higher plasma levels of the Pt, APTT, D‐dimer, LDH, PCT, ALB, CRP, AST.All patients received antiviral therapy (kaletra or interferon), antibacterial therapy and corticosteroid and many received traditional chinese medicine. It was suggested that patients should use kaletra early.

Background Genetic study of quantitative biomarkers in Alzheimer’s Disease (AD) is a promising method to identify novel genetic factors and relevant endophenotypes, which provides valuable information to deconvolute mechanistic complexity and better understand disease subtypes. Results Using the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we performed a genome-wide association study (GWAS) between 565,373 single nucleotide polymorphisms (SNPs) and 16 key AD biomarkers from 1,576 subjects at four visits. We identified a novel locus rs5011804 at 12p12.1 significantly associated with several AD biomarkers, including three cognitive traits (CDRSB, FAQ, ADAS13) and one imaging trait (fusiform volume). Additional mediation and interaction analyses investigated the relationships among this SNP, relevant biomarkers, and clinical diagnosis, confirming and further elaborating the genetic effects seen in the GWAS. Conclusion Our GWAS not only affirms key AD genes but also suggests the promising role of the SNP rs5011804 due to its associations with several AD cognitive and imaging outcomes. The SNP rs5011804 has a reported association with adult asthma and slightly affects intracranial volume but has not been associated with AD before. Our novel findings contribute to a more comprehensive view of the molecular mechanism behind AD. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-08269-8).

Major depressive disorder (MDD) is the leading cause of disability worldwide. There is an urgent need for objective biomarkers to diagnose this highly heterogeneous syndrome, assign treatment, and evaluate treatment response and prognosis. MicroRNAs (miRNAs) are short non-coding RNAs, which are detected in body fluids that have emerged as potential biomarkers of many disease conditions. The present study explored the potential use of miRNAs as biomarkers for MDD and its treatment. We profiled the expression levels of circulating blood miRNAs from mice that were collected before and after exposure to chronic social defeat stress (CSDS), an extensively validated mouse model used to study depression, as well as after either repeated imipramine or single-dose ketamine treatment. We observed robust differences in blood miRNA signatures between stress-resilient and stress-susceptible mice after an incubation period, but not immediately after exposure to the stress. Furthermore, ketamine treatment was more effective than imipramine at re-establishing baseline miRNA expression levels, but only in mice that responded behaviorally to the drug. We identified the red blood cell-specific miR-144-3p as a candidate biomarker to aid depression diagnosis and predict ketamine treatment response in stress-susceptible mice and MDD patients. Lastly, we demonstrate that systemic knockdown of miR-144-3p, via subcutaneous administration of a specific antagomir, is sufficient to reduce the depression-related phenotype in stress-susceptible mice. RNA-sequencing analysis of blood after such miR-144-3p knockdown revealed a blunted transcriptional stress signature as well. These findings identify miR-144-3p as a novel target for diagnosis of MDD as well as for antidepressant treatment, and enhance our understanding of epigenetic processes associated with depression.

Acute lung injury (ALI) is a life-threatening clinical syndrome in critically ill patients. The identification of novel biological markers for the early diagnosis of ALI and the development of more effective treatments are topics of current research. High mobility group box-1 protein (HMGB1) is a late inflammatory mediator associated with sepsis, malignancy, and immune disease. Levels of HMGB1 may reflect the severity of inflammation and tissue damage, indicating a potential role for HMGB1 as a prognostic biomarker in ALI, and a potential target for blocking inflammatory pathways. Several studies have shown that HMGB1 regulates autophagy. Autophagy, or type II programmed cell death, is an essential biological process that maintains cellular homeostasis. Studies have shown that HMGB1 and autophagy are involved in the pathogenesis of many lung diseases including ALI but the specific mechanisms underlying this association remain to be determined. This review aims to provide an update on the current status of the role of HMBG1 and autophagy in ALI.

CD44 is one of biomarkers of liver cancer stem cells (CSCs). The investigation of mechanism of CD44 translocation helps to uncover new molecular pathways participated in the regulation of various cellular processes in CSCs. In the present study, we observed the translocation of CD44 from cytoplasm to nuclear in the reprogramming process of C3A cells, full-length CD44 presented in the nucleus of liver iCSCs. CD44 was bound with importin β and transportin 1 in liver iCSCs. Inhibition of importin β transport leads to reduction of CD44 in the nucleus. Translocation of CD44 is also influenced by importin α. Besides, overexpression of naïve pluripotent genes, KLF2, KLF5, DNMT3L, GBX2, ZFP42, ESRRB and DPPA4 were found in liver iCSCs. Inhibition of CD44 leads to the reduction of these naïve genes. Luciferase and chromatin immunoprecipitation (ChIP) assays further identified nuclear CD44 bound to the promoter regions of naïve genes, KLF2, KLF5, and ESRRB functioned as transcriptional activators in liver iCSCs. Our present work provides new insight into the dynamic states and functions of CD44 in iCSCs.

The aim of this study was to investigate the role of B7-H3 in prognosis of congenital heart disease (CHD) children patients. A total of 65 CHD patients within age 3-12 years who went to our hospital were included during August 2011 to December 2012. Demographic data including age, sex, weight, clinical basic information such as New York Heart Association (NYHA) class, pathological type were collected. Blood samples were collected and serum levels of B7-H3, C-reactive protein (CRP), N-Terminal Pro-Brain Natriuretic Peptide (NT-pro-BNP), and High-sensitivity Troponin T (hsTnT) were determined by enzyme-linked immunosorbent assay (ELISA). Characteristics including age, gender, weight, pathological type, NYHA class, and serum levels of hsTnT and CRP showed no significant difference between deceased and survival patients. However, serum levels of B7-H3 and NT-pro-BNP were significantly higher in deceased patients compared survival patients. Patients with high expressed B7-H3 had higher risks for total major cardiovascular events (MACE) occurrence compared with the lower group. Among the MACE events, significant difference was observed in rates of death, new onset of arrhythmias, and surgical, but not in NYHA class worsening and percutaneous intervention. Patients with higher levels if B7-H3 had significantly higher risk for mortality in the 5-year follow-up compared with the lower group, logic analysis was also conducted and results showed that B7-H3 might be an independent risk factor for 5-year mortality for CHD patients. B7-H3 was up-regulated in dead CHD patients, and serum levels of B7-H3 were related to long-term MACE and 5-year mortality of CHD patients.

Although aflatoxin (AF) exposure has not been recognized as a major problem in the United States and other developed nations, recent global climate change may have a profound impact on distribution of toxigenic fungi growth and production of AFs in grain and groundnuts. Alterations in the contamination pattern can increase human dietary exposure, and further invoke public health concerns and associated disease risks. In this study, two populations from East and West Texas, known for their high risk of liver cancer, were examined for their AF exposure at three different time periods from 2004 to 2014. Serum samples (n=1124) were collected from participants recruited for various studies from Bexar County and Lubbock County, TX, over the span of 2004 through 2014. The exposure biomarker, serum AFB1-lysine adduct, was analyzed by HPLC-FLD and confirmed by LC-MS. Both populations showed a significant increase in detection rate, as well as median levels of serum AFB1-lysine adduct over time, from 2.35 to 4.34pg/mg albumin in East Texas (2007-2014), and 0.63-3.98pg/mg albumin in West Texas (2004-2010). This observed shift in exposure likely represents a shift in the AF contamination pattern in the State of Texas, and may warrant further studies on risk assessment for the potential etiological effects of such increased exposures.

Cardiovascular disease (CVD) is the most wide-spread disorder all over the world. The personalized and precision diagnosis, treatment and prevention of CVD is still a challenge. With the developing of metagenome sequencing technologies and the paradigm shifting to data-driven discovery in life science, the computer aided microbiota biomarker discovery for CVD is becoming reality. We here summarize the data resources, knowledgebases and computational models available for CVD microbiota biomarker discovery, and review the present status of the findings about the microbiota patterns associated with the therapeutic effects on CVD. The future challenges and opportunities of the translational informatics on the personalized drug usages in CVD diagnosis, prognosis and treatment are also discussed.

Increasing epidemic of ischemic stroke (IS) makes it urgent to understand the pathogenesis and regulatory mechanism, previous studies have described microRNAs (miRNAs) is part of the brain's response to ischemia. The aim of this study was to screen potential biomarkers for the prediction and novel treatment of IS. Differentially expressed miRNAs were screened from three newly diagnosed IS patients and three controls by RNA sequencing technology. Furthermore, target prediction databases were then used to analysis the target genes of different expressed miRNAs, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database were used to identify the functions and the main biochemical and signal pathways of differentially expressed target genes. Our results revealed that 27 miRNAs were differentially expressed in IS, among which, hsa-miR-659-5p was the most highly increased and was first found to be associated with IS. In addition, KEGG pathway analyses showed that differentially expressed miRNAs were mainly significantly enriched in lysosome pathway, cytokine-cytokine receptor interaction pathway, spliceosome pathway, base excision repair pathway. miRNAs were involved in IS pathogenesis, and hsa-miR-659-5p, hsa-miR-151a-3p and hsa-miR-29c-5p as the three highest |log2FoldChange| regulation in this study, which may be the biomarkers of IS and need further study.

Proprotein convertase subtilisin/kexin (PCSK) family member 3 (FURIN) has been suggested to be involved in the development of atherosclerosis. The aim of this study was to investigate the prognostic implication of FURIN in patients after acute myocardial infarction (AMI).This prospective study analyzed data from a total of 1312 consecutive patients hospitalized with ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction from August 2013 to June 2016. FURIN levels were analyzed in plasma obtained from AMI patients.The study included 1312 AMI patients. The patient population was predominantly male (63%) with a median age of 66 years (IQR: 19 years), and 59% were STEMI patients. During a follow-up of 2 years, 117 patients died, and 377 patients reached the combined endpoints of major adverse cardiac events (MACE). Patients with elevated FURIN levels had increased risk of MACE, all-cause mortality, recurrent MI and hospitalization for HF (log-rank test, p 0.0001). After adjusting for clinical risk factors and established markers, the association of FURIN concentrations with the risk of MACE and its individual components and cardiovascular death was statistically significant in the higher tertile of FURIN concentrations. After the addition of FURIN to the models, FURIN showed additive prognostic significance for 2-year clinical outcomes. Variable importance plots of the models showed that FURIN was of high importance to predict both occurrence of MACE and all-cause mortality.We found that FURIN was associated with all-cause mortality and recurrent cardiovascular events in AMI patients independent of conventional risk factors and established markers.

Alzheimer’s disease (AD) is a serious neurodegenerative condition that affects millions of individuals across the world. As the average age of individuals in the United States and the world increases, the prevalence of AD will continue to grow. To address this public health problem, the research community has developed computational approaches to sift through various aspects of clinical data and uncover their insights, among which one of the most challenging problem is to determine the biological mechanisms that cause AD to develop. To study this problem, in this paper we present a novel Joint Multi-Modal Longitudinal Regression and Classification method and show how it can be used to identify the cognitive status of the participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and the underlying biological mechanisms. By intelligently combining clinical data of various modalities ( i.e. , genetic information and brain scans) using a variety of regularizations that can identify AD-relevant biomarkers, we perform the regression and classification tasks simultaneously. Because the proposed objective is a non-smooth optimization problem that is difficult to solve in general, we derive an efficient iterative algorithm and rigorously prove its convergence. To validate our new method in predicting the cognitive scores of patients and their clinical diagnosis, we conduct comprehensive experiments on the ADNI cohort. Our promising results demonstrate the benefits and flexibility of the proposed method. We anticipate that our new method is of interest to clinical communities beyond AD research and have open-sourced the code of our method online. 1 1 The code package for the proposed Joint Multi-Modal Longitudinal Regression and Classification model have been made publicly available online at https://github.com/minds-mines/jmmlrc .

Diabetes is a risk factor for osteoporosis. Annatto-extracted tocotrienols (TT) have proven benefits in preserving bone matrix. Here, we evaluated the effects of dietary TT on glucose homeostasis, bone properties, and liver pro-inflammatory mRNA expression in high-fat diet (HFD)-induced type 2 diabetic (T2DM) mice. 58 male C57BL/6 J mice were divided into 5 groups: low-fat diet (LFD), HFD, HFD + 400 mgTT/kg diet (T400), HFD + 1600 mgTT/kg diet (T1600), and HFD + 200 mg metformin/kg (Met) for 14 weeks. Relative to the HFD group, both TT-supplemented groups (1) improved glucose homeostasis by lowering the area under the curve for both glucose tolerance and insulin tolerance tests, (2) increased serum procollagen I intact N-terminal propeptide (bone formation) level, trabecular bone volume/total volume, trabecular number, connectivity density, and cortical thickness, (3) decreased collagen type 1 cross-linked C-telopeptide (bone resorption) levels, trabecular separation, and structure model index, and (4) suppressed liver mRNA levels of inflammation markers including IL-2, IL-23, IFN-γ, MCP-1, TNF-α, ITGAX and F4/80. There were no differences in glucose homeostasis and liver mRNA expression among T400, T1600, and Met. The order of osteo-protective effects was LFD ≥T1600 ≥T400 = Met >HFD. Collectively, these data suggest that TT exerts osteo-protective effects in T2DM mice by regulating glucose homeostasis and suppressing inflammation.

Triglyceride (TG) abnormalities are the most prevalent type of dyslipidaemia in the people of northeast China. Many researchers have investigated the prevalence, treatment and control of TG abnormalities, but little is known about the associations between the TG values and the factors that influence TG levels. This study aims to reveal quantile-specific associations of TG with its risk factors. A sample of 16,340 participants in Jilin Province were included in this study. A quantile regression (QR) model was performed to identify the factors that affected TG levels in different quantiles. The distribution of TG levels was different between males and females (χ 2 = 155.77, P P70.6 to P95) only in males, while age had a positive association with TG levels in all quantiles only in females. The risk of WC on TG levels was higher with increasing TG levels, and smokers were more at risk for increasing TG levels, as well.

Summary Astrocytes, due to the proximity to neuronal lineage and capability to proliferate, are ideal starting cells to regenerate neurons. Human fetal astrocytes have been successfully converted into neuronal cells by small molecules, which offered a broader range of further applications than transcription factor-mediated neuronal reprogramming. Here we report that human adult astrocytes could also be converted into neuronal cells by a different set of small molecules. These induced cells exhibited typical neuronal morphologies, expressed neuronal markers, and displayed neuronal electrophysiological properties. Genome-wide RNA-sequencing analysis showed that the global gene expression profile of induced neuronal cells resembled that of human embryonic stem cell-differentiated neurons. When transplanted into post-natal mouse brains, these induced neuronal cells could survive and become electrophysiologically mature. Altogether, our study provides a strategy to directly generate transgene-free neuronal cells from human adult astrocytes by small molecules., Highlights • Human adult astrocytes can be converted into neuronal cells by small molecules • Induced neuronal cells possess neuronal markers and electrophysiological properties • The transcriptome of induced neuronal cells resembles that of hESC-derived neurons • Induced neuronal cells survive and become electrophysiologically mature in mice brain, Pei, Shen, and colleagues demonstrate that human adult astrocytes could be directly converted into neuronal cells by small molecules. The astrocyte-derived neuronal cells exhibited neuronal morphologies, expressed neuronal markers, displayed neuronal electrophysiological properties, shared a similar transcriptome profile with hESC-differentiated neurons, and could survive and become electrophysiologically mature in mice brain.

Paraben esters and their salts are widely used as preservatives in cosmetics, personal care products, pharmaceuticals, and foods. We and others have reported that parabens promote adipogenesis in vitro. Here, we investigated the effects of post-weaning exposure to parabens (methylparaben and butylparaben) on body weight, white adipose tissue mass, and obesity associated metabolic biomarkers in female obesity-prone C57BL/6J mice fed with a chow diet or a high fat diet. Methylparaben exposure by daily oral gavage (100 mg/kg/day) increased adiposity and serum leptin levels compared to the controls when fed the chow diet, but not the high fat diet. In contrast, butylparaben exposure did not induce such effects. Exposure to either paraben induced changes in gene expression related to adipocyte differentiation and lipogenesis in the white adipose tissue (WAT) and the liver, regardless of diet. Moreover, exposure to both parabens under the chow diet significantly decreased serum procollagen type 1 N-terminal propeptide (P1NP) but had no effects on C-terminal telopeptide of type I collagen (CTX-I) levels, suggesting that post-weaning exposure to paraben may negatively affect bone formation, but not bone resorption. Taken together, our results demonstrate that post-weaning exposure to paraben, methylparaben in particular, promotes adipogenesis but suppresses serum marker of bone formation in vivo. Our results add to the growing body of literature indicating potential negative health outcomes associated with paraben exposure. Further study of early life exposure to paraben on the development of fat and bone is warranted.

Background Genetic information is not routinely obtained in the management of most lipid disorders or in primary or secondary prevention of cardiovascular disease (CVD). We sought to determine the prevalence of pathogenic variants associated with lipoprotein metabolism or coronary artery disease (CAD) in a single lipid clinic and discuss the future use of genetic information in CVD prevention . Methods Genetic testing was offered to patients with hypertriglyceridemia (defined as pre-treatment fasting triglycerides ≥150 mg/dL), elevated LDL-C (defined as pre-treatment ≥190 mg/dL), low HDL-C (defined as ≤40 mg/dL), elevated lipoprotein (a) (defined as ≥50 mg/dL or 100 nmol/L) or premature CAD (defined as an acute coronary syndrome or revascularization before age 40 years in men and 50 years in women) using next-generation DNA sequencing of 327 exons and selected variants in 129 genes known or suspected to be associated with lipoprotein metabolism or CAD. Results 82 of 84 patients (97.6%) were found to have a variant associated with abnormal lipid metabolism or CAD. The most common pathogenic or likely pathogenic variants included those of the LDL receptor (15 patients) and lipoprotein lipase (9 patients). Other common variants included those of apolipoprotein A5 (14 patients) and variants associated with elevated lipoprotein (a) (25 patients). Conclusions The majority of patients presenting to a single lipid clinic were found to have at least one variant associated with abnormal lipoprotein metabolism or CAD. Incorporating genetic information, including the use of genetic risk scores, is anticipated in the future care of lipid disorders and CVD prevention.

Background Aerobic exercise, which has been shown to have beneficial effects on plasma lipids, has been recommended as an effective measure to improve the prognosis of individuals with coronary heart disease (CHD). Apolipoprotein C3 (apoC3) is associated with hypertriglyceridemia and is therefore closely related to CHD. Hypothesis We measured apoC3 concentration change in patients with CHD before and after long-term aerobic exercise. Methods Thirty-eight patients with coronary heart disease were randomly assigned to a non-exercise group (19 patients) or exercise group (19 patients). Both groups received essential drugs for CHD. The non-exercise group was kept sedentary while the exercise group performed moderate-intensive aerobic exercise for 8 weeks. Lipid levels and apoC3 levels were measured on the first day and 8 weeks later. Results Exercise for 8 weeks led to a significant decrease in concentration of triglyceride and apoC3 compared with the baseline. Triglyceride concentration changes were positively associated with apoC3 level changes. Conclusions Aerobic exercise can improve the lipid profile. It is effective in decreasing triglycerides by targeting apoC3 levels in patients with coronary heart disease.

Neuroimaging genetics has attracted growing attention and interest, which is thought to be a powerful strategy to examine the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on structures or functions of human brain. In recent studies, univariate or multivariate regression analysis methods are typically used to capture the effective associations between genetic variants and quantitative traits (QTs) such as brain imaging phenotypes. The identified imaging QTs, although associated with certain genetic markers, may not be all disease specific. A useful, but underexplored, scenario could be to discover only those QTs associated with both genetic markers and disease status for revealing the chain from genotype to phenotype to symptom. In addition, multimodal brain imaging phenotypes are extracted from different perspectives and imaging markers consistently showing up in multimodalities may provide more insights for mechanistic understanding of diseases (i.e., Alzheimer’s disease (AD)). In this work, we propose a general framework to exploit multi-modal brain imaging phenotypes as intermediate traits that bridge genetic risk factors and multi-class disease status. We applied our proposed method to explore the relation between the well-known AD risk SNP APOE rs429358 and three baseline brain imaging modalities (i.e., structural magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET) and F-18 florbetapir PET scans amyloid imaging (AV45)) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The empirical results demonstrate that our proposed method not only helps improve the performances of imaging genetic associations, but also discovers robust and consistent regions of interests (ROIs) across multi-modalities to guide the disease-induced interpretation.

IntroductionObesity is a major health concern in postmenopausal women, and chronic low-grade inflammation contributes to the development of obesity. Cellular studies and high-fat-diet-induced obese mouse model mimicking obesity show the antiobesity effect of annatto-extracted tocotrienols (TT) with antioxidant capability. We aim to assess the safety and efficacy of TT consumption for lipid-related parameters in obese postmenopausal women.Methods and analysisEligible obese postmenopausal women will be randomly assigned to placebo group (430 mg olive oil) and TT group (DeltaGold Tocotrienol 70%) for 24 weeks. In the present study, the primary outcome is total/regional fat mass and visceral adipose tissue. The secondary outcomes include lipid profile in serum, mRNA expression of fatty acid synthase and carnitine palmitoyltransferase 1A in fat tissue, oxylipins and endocannabinoids in plasma and adipose tissue, abundance and composition of intestinal microbiome in faeces, high-sensitivity C-reactive protein (hs-CRP) in serum and leptin in serum. Every participant will be evaluated at 0 (prior to starting intervention) and 24 weeks of intervention, except for serum lipid profile and hs-CRP at 0, 12 and 24 weeks. ‘Intent-to-treat’ principle is employed for data analysis. Hierarchical linear modelling is used to estimate the effects of dietary TT supplementation while properly accounting for dependency of data and identified covariates. To our knowledge, this is the first randomised, placebo-controlled, double-blinded study to determine dietary TT supplementation on an obese population. If successful, this study will guide the future efficacy TT interventions and TT can be implemented as an alternative for obese population in antiobesity management.Ethics and disseminationThis study has been approved by the Bioethics Committee of the Texas Tech University Health Sciences Center, Lubbock. An informed consent form will be signed by a participant before enrolling in the study. The results from this trial will be actively disseminated through academic conference presentation and peer-reviewed journals.Trial registration numberNCT03705845.

To address the unmet need for effective biomarker-driven targeted therapy for human papillomavirus (HPV)-associated head and neck squamous cell carcinoma (HNSCC) and cervical cancer, we conducted a high-throughput drug screen using 1122 compounds in 13 HPV-positive and 11 matched HPV-negative cell lines. The most effective drug classes were inhibitors of polo-like kinase, proteasomes, histone deacetylase, and Aurora kinases. Treatment with a pan-Aurora inhibitor, danusertib, led to G2M arrest and apoptosis in vitro. Furthermore, danusertib decreased tumor size compared with controls in patient derived xenograft models of HNSCC. To identify biomarkers predicting response, we determined associations between mutations and drug sensitivity. Our data and the Genomics of Drug Sensitivity in Cancer database showed that cancer cells with KMT2D mutations were more sensitive to Aurora kinase inhibitors than were cells without mutations. Knockdown of KMT2D in wild-type cells led to increased Aurora kinase inhibitor-induced apoptosis. We identified Aurora kinase inhibitors as effective and understudied drugs in HNSCC and CESC. This is the first published study to demonstrate that mutations in KMT2D, which are common in many cancers, correlate with drug sensitivity in two independent datasets.

IntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.MethodsFasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.DiscussionMetabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

Motivation Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. So far, most existing machine-learning approaches are widely used to detect the effective associations between genetic variants and brain imaging data at one time-point. However, those associations are based on static phenotypes and ignore the temporal dynamics of the phenotypical changes. The phenotypes across multiple time-points may exhibit temporal patterns that can be used to facilitate the understanding of the degenerative process. In this article, we propose a novel temporally constrained group sparse canonical correlation analysis (TGSCCA) framework to identify genetic associations with longitudinal phenotypic markers. Results The proposed TGSCCA method is able to capture the temporal changes in brain from longitudinal phenotypes by incorporating the fused penalty, which requires that the differences between two consecutive canonical weight vectors from adjacent time-points should be small. A new efficient optimization algorithm is designed to solve the objective function. Furthermore, we demonstrate the effectiveness of our algorithm on both synthetic and real data (i.e., the Alzheimer’s Disease Neuroimaging Initiative cohort, including progressive mild cognitive impairment, stable MCI and Normal Control participants). In comparison with conventional SCCA, our proposed method can achieve strong associations and discover phenotypic biomarkers across multiple time-points to guide disease-progressive interpretation. Availability and implementation The Matlab code is available at https://sourceforge.net/projects/ibrain-cn/files/.

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

Introduction Genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) have been crucial in advancing the understanding of Alzheimer's disease (AD) pathophysiology. Here, we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. Methods Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing data have been obtained and disseminated. Results ADNI genetic data have been downloaded thousands of times, and >300 publications have resulted, including reports of large-scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies used ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first whole exome and whole genome sequencing data sets and reports in healthy controls, mild cognitive impairment, and AD. Discussion Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multiomics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.

Introduction This study assessed apolipoprotein E ( APOE ) e4 carrier status effects on Alzheimer's disease imaging and cerebrospinal fluid (CSF) biomarkers in cognitively normal older adults with significant memory concerns (SMC). Methods Cognitively normal, SMC, and early mild cognitive impairment participants from Alzheimer's Disease Neuroimaging Initiative were divided by APOE e4 carrier status. Diagnostic and APOE effects were evaluated with emphasis on SMC. Additional analyses in SMC evaluated the effect of the interaction between APOE and [ 18 F]Florbetapir amyloid positivity on CSF biomarkers. Results SMC e4+ showed greater amyloid deposition than SMC e4−, but no hypometabolism or medial temporal lobe (MTL) atrophy. SMC e4+ showed lower amyloid beta 1–42 and higher tau/p-tau than e4−, which was most abnormal in APOE e4+ and cerebral amyloid positive SMC. Discussion SMC APOE e4+ show abnormal changes in amyloid and tau biomarkers, but no hypometabolism or MTL neurodegeneration, reflecting the at-risk nature of the SMC group and the importance of APOE in mediating this risk.

Regression models have been widely studied to investigate the prediction power of neuroimaging measures as biomarkers for inferring cognitive outcomes in the Alzheimer's disease study. Most of these models ignore the interrelated structures either within neuroimaging measures or between cognitive outcomes, and thus may have limited power to yield optimal solutions. To address this issue, we propose to use a new sparse multitask learning model called Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) and demonstrate its effectiveness by examining the predictive power of detailed cortical thickness measures toward 3 types of cognitive scores in a large cohort. G-SMuRFS proposes a group-level l2,1-norm strategy to group relevant features together in an anatomically meaningful manner and use this prior knowledge to guide the learning process. This approach also takes into account the correlation among cognitive outcomes for building a more appropriate predictive model. Compared with traditional methods, G-SMuRFS not only demonstrates a superior performance but also identifies a small set of surface markers that are biologically meaningful.

The relationships of dietary choline and folate intake with hepatic function have yet to be established in the Taiwanese population. We investigated the associations of choline and folate intake with hepatic inflammatory injury in Taiwanese adults.Blood samples and data on dietary choline components and folate intake from 548 Taiwanese adults without pathological liver disease were collected. Dietary intake was derived using a semiquantitative food-frequency questionnaire. Serum liver injury markers of alanine transaminase, aspartate transaminase, and hepatitis viral infection were measured.Elevated serum hepatic injury markers (40 U/L) were associated with low folate and free choline intake (p0.05). Folate intake was the most significant dietary determinant of serum aspartate transaminase concentration (beta=-0.05, p=0.04), followed by free choline intake (beta=-0.249, p=0.055). Folate intake exceeding the median level (268 μg/d) was correlated with a reduced rate of hepatitis viral infection (p=0.032) and with normalized serum aspartate transaminase (odds ratio [OR]=0.998, 95% confidence interval [CI]=0.996-1, p=0.042) and alanine transaminase (OR=0.998, 95% CI=0.007-1, p=0.019). Total choline intake exceeding the median level (233 mg/d) was associated with normalized serum aspartate transaminase (OR=0.518, 95% CI=0.360-0.745, p=0.018).The newly established relationships of dietary intake of total choline and folate with normalized hepatic inflammatory markers can guide the development of dietary choline and folate intake recommendations for Taiwanese adults.

Noninvasive and convenient tests to assess pulmonary fibrosis and disease progression in interstitial lung diseases (ILDs) are currently unavailable. The extracellular matrix molecules, laminin (LN), type IV collagen (IVC), procollagen III N-terminal peptide (PIIINP), and hyaluronic acid (HA) are involved in ILD development and progression. This study aims to investigate the association of disease progression and serum levels of LN, IVC, PIIINP, and HA in patients with ILD. This retrospective study included 323 patients (162 cases of idiopathic pulmonary fibrosis [IPF] and 161 cases of connective tissue diseases ILD [CTD-ILD]) treated in Shanghai Pulmonary Hospital between January 2013 and January 2015 and 160 healthy controls. Serum LN, IVC, PIIINP, and HA were analyzed by radioimmunoassay. Data of the percentage of forced vital capacity in the prediction value (FVC%pred), the percentage of diffusing capacity of the lung for carbon monoxide in the prediction value (DLCO%pred), high resolution computed tomography (HRCT) score, and patient mortality were collected. Serum LN, IVC, PIIINP, and HA were significantly increased in the patients with IPF or CTD-ILD compared with the healthy controls (all P

Little is known about the causes of heartburn in patients with gastro-esophageal reflux disease. Visible epithelial damage is seldom associated with symptom severity, evidenced by the significant symptom burden in patients with nonerosive reflux disease (NERD) compared with patients with erosive reflux disease (ERD) or Barrett's esophagus (BE). We studied the distribution of mucosal nerve fibers in patients with NERD, ERD, and BE, and compared the results with those of healthy subjects.We performed a prospective study of 13 patients with NERD, 11 patients with ERD, and 16 patients with BE undergoing endoscopic evaluation in the United Kingdom or Greece. Biopsies were obtained from the proximal and distal esophageal mucosa of patients with NERD, from the distal esophageal mucosa of patients with ERD, and the distal-most squamous epithelium of patients with BE. These were examined for the presence and location of nerve fibers that reacted with a labeled antibody against calcitonin gene-related peptide (CGRP), a marker of nociceptive sensory nerves. The results were compared with those from 10 healthy volunteers (controls).The distribution of CGRP-positive nerves did not differ significantly between the distal esophageal mucosa of controls (median, 25.5 cell layers to surface; interquartile range [IQR], 21.4-28.8) vs patients with ERD (median, 23 cell layers to surface; IQR, 16-27.5), or patients with BE (median, 21.5 cell layers to surface; IQR, 16.1-27.5). However, CGRP-positive nerves were significantly more superficial in mucosa from patients with NERD-both distal (median, 9.5 cell layers to surface; IQR, 1.5-13.3; P.0001 vs ERD, BE, and controls) and proximal (median, 5.0 cell layers to surface; IQR, 2.5-9.3 vs median 10.4 cell layers to surface; IQR, 8.0-16.9; P = .0098 vs controls).Proximal and distal esophageal mucosa of patients with NERD have more superficial afferent nerves compared with controls or patients with ERD or BE. Acid hypersensitivity in patients with NERD might be partially explained by the increased proximity of their afferent nerves to the esophageal lumen, and therefore greater exposure to noxious substances in refluxate.

Preeclampsia (PE) is initiated by abnormal placentation in the early stages of pregnancy, followed by systemic activation of endothelial cells of the maternal small arterioles in the late second or third trimester (TM) of pregnancy. During normal pregnancy, placental cytotrophoblasts (CTBs) invade the maternal uterine wall and spiral arteries, whereas this process is interrupted in PE. However, it is not known how the malformed placenta triggers maternal endothelial crisis and the associated manifestations. Here, we have focused on the association of CD81 with PE. CD81, a member of the tetraspanin superfamily, plays significant roles in cell growth, adhesion, and motility. The function of CD81 in human placentation and its association with pregnancy complications are currently unknown. In the present study, we have demonstrated that CD81 was preferentially expressed in normal first TM placentas and progressively down-regulated with gestation advance. In patients with early-onset severe PE (sPE), CD81 expression was significantly up-regulated in syncytiotrophoblasts (STBs), CTBs and the cells in the villous core. In addition, high levels of CD81 were observed in the maternal sera of patients with sPE. Overexpressing CD81 in CTBs significantly decreased CTB invasion, and culturing primary human umbilical vein endothelial cells (HUVECs) in the presence of a high dose of exogenous CD81 resulted in interrupted angiogenesis and endothelial cell activation in vitro. Importantly, the phenotype of human PE was mimicked in the CD81-induced rat model.

Background: Chronic inflammation is associated with increased bone resorption and is linked to osteopenia, or low bone mass. Obesity is also associated with low-grade chronic upregulation of inflammatory cytokines.Objective: This study investigated the effect of high-fat (HF) diet-induced obesity on bone structure changes in growing mice with existing systemic chronic inflammation induced by low-dose, slow-release lipopolysaccharide (LPS).Methods: Forty-eight 6-wk-old female C57BL/6 mice were randomly assigned to 4 treatment groups (n = 12/group) in a 2 × 2 factorial design-control (placebo) or LPS treatment (1.5 μg/d)-and consumed either a normal-fat (NF, 10% of energy as fat) or an HF (45% of energy as fat) diet ad libitum for 13 wk. Bone structure, serum biomarkers of bone turnover, and osteoclast differentiation were measured.Results: No alterations were observed in final body weights, fat mass, or lean mass in response to LPS treatment. LPS treatment increased serum concentration of tartrate-resistant acid phosphatase (TRAP, a bone resorption marker) and bone marrow osteoclast differentiation and decreased femoral and lumbar vertebral bone volume (BV):total volume (TV) by 25% and 24%, respectively, compared with the placebo. Mice fed the HF diet had greater body weight at the end of the study (P < 0.01) due to increased fat mass (P < 0.01) than did mice fed the NF diet. The HF diet increased serum TRAP concentration, bone marrow osteoclast differentiation, and expression of tumor necrosis factor α, interleukin 1β and interleukin 6 in adipose tissue. Compared with the NF diet, the HF diet decreased BV:TV by 10% and 8% at femur and lumbar vertebrae, respectively, and the HF diet was detrimental to femoral and lumbar vertebral bone structure with decreased trabecular number and increased trabecular separation and structure model index.Conclusion: Results suggest that HF diets and systemic chronic inflammation have independent negative effects on bone structure in mice.

Introduction Osteoporosis is a major health concern in postmenopausal women, and oxidative stress contributes to the development of bone loss. Cellular studies and ovariectomised rat model mimicking bone loss in postmenopausal women show the bone-protective effect of tocotrienols (TTs) with antioxidant capability. We aim to access the safety and efficacy of TT consumption for bone health in postmenopausal women. Methods and analysis In this 12-week randomised double-blinded placebo-controlled trial for the effects of dietary TT supplementation in postmenopausal women, postmenopausal women aged 45 years and older with at least 1 year after menopause and bone mineral density T-score at the spine and/or hip 2.5 or more below the reference values will be randomly assigned to 3 daily supplements: (1) placebo group receiving 860 mg olive oil, (2) low TT group receiving 430 mg of 70% pure TTs (containing 300 mg TT) and (3) high TT group receiving 860 mg of 70% pure TTs (600 mg TT). The primary outcome measure will be urinary N -terminal telopeptide. The secondary outcome measures will be serum bone-specific alkaline phosphatase, receptor activator of nuclear factor-κB ligand, osteoprotegerin, urinary 8-hydroxy-2’-deoxyguanosine and quality of life. At 0, 6 and 12 weeks, the following will be assessed: (1) primary and secondary outcome measures; (2) serum TT and tocopherol concentrations; (3) physical activity and food frequency questionnaires. Liver function will be monitored every 6 weeks for safety. ‘Intent-to-treat’ principle will be employed for data analysis. A model of repeated measurements with random effect error terms will be applied. Analysis of covariance, χ 2 analysis and regression will be used for comparisons. Ethics and dissemination This study was approved by the Bioethics Committee of the Texas Tech University Health Sciences Center. The findings of this trial will be submitted to a peer-reviewed journal in the areas of bone or nutrition and international conferences. Trial registration number NCT02058420; results.

There is considerable debate whether Alzheimer's disease (AD) originates in basal forebrain or entorhinal cortex. Here we examined whether longitudinal decreases in basal forebrain and entorhinal cortex grey matter volume were interdependent and sequential. In a large cohort of age-matched older adults ranging from cognitively normal to AD, we demonstrate that basal forebrain volume predicts longitudinal entorhinal degeneration. Models of parallel degeneration or entorhinal origin received negligible support. We then integrated volumetric measures with an amyloid biomarker sensitive to pre-symptomatic AD pathology. Comparison between cognitively matched normal adult subgroups, delineated according to the amyloid biomarker, revealed abnormal degeneration in basal forebrain, but not entorhinal cortex. Abnormal degeneration in both basal forebrain and entorhinal cortex was only observed among prodromal (mildly amnestic) individuals. We provide evidence that basal forebrain pathology precedes and predicts both entorhinal pathology and memory impairment, challenging the widely held belief that AD has a cortical origin., Whether Alzheimer's disease originates in basal forebrain or entorhinal cortex remains highly debated. Here the authors use structural magnetic resonance data from a longitudinal sample of participants stratified by cerebrospinal biomarker and clinical diagnosis to show that tissue volume changes appear earlier in the basal forebrain than in the entorhinal cortex.

Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes with expression levels that differed in ASD and the controls. We then constructed an autism-specific miRNA–mRNA network and inferred candidate autism biomarker miRNAs based on their regulatory modes and functions. We defined a novel parameter called the autism gene percentage as autism-specific knowledge to further facilitate the identification of autism-specific biomarker miRNAs. Finally, 11 miRNAs were screened as putative autism biomarkers, where eight miRNAs (72.7%) were significantly dysregulated in ASD samples according to previous reports. Functional enrichment results indicated that the targets of the identified miRNAs were enriched in autism-associated pathways, such as Wnt signaling (in KEGG and IPA), cell cycle (in KEGG), and glioblastoma multiforme signaling (in IPA), thereby supporting the predictive power of our model.