Role and mechanism of PVN-sympathetic-adipose circuit in depression and insulin resistance induced by chronic stress.

Chronic stress induces depression and insulin resistance, between which there is a bidirectional relationship. However, the mechanisms underlying this comorbidity remain unclear. White adipose tissue (WAT), innervated by sympathetic nerves, serves as a central node in the interorgan crosstalk through adipokines. Abnormal secretion of adipokines is involved in mood disorders and metabolic morbidities. We describe here a brain-sympathetic nerve-adipose circuit originating in the hypothalamic paraventricular nucleus (PVN) with a role in depression and insulin resistance induced by chronic stress. PVN neurons are labelled after inoculation of pseudorabies virus (PRV) into WAT and are activated under restraint stress. Chemogenetic manipulations suggest a role for the PVN in depression and insulin resistance. Chronic stress increases the sympathetic innervation of WAT and downregulates several antidepressant and insulin-sensitizing adipokines, including leptin, adiponectin, Angptl4 and Sfrp5. Chronic activation of the PVN has similar effects. ß-adrenergic receptors translate sympathetic tone into an adipose response, inducing downregulation of those adipokines and depressive-like behaviours and insulin resistance. We finally show that AP-1 has a role in the regulation of adipokine expression under chronic stress.

The Eotaxin-1/CCR3 Axis and Matrix Metalloproteinase-9 Are Critical in Anti-NC16A IgE-Induced Bullous Pemphigoid.

Bullous pemphigoid (BP) is the most common autoimmune bullous skin disease of humans and is characterized by eosinophilic inflammation and circulating and tissue-bound IgG and IgE autoantibodies directed against two hemidesmosomal proteins: BP180 and BP230. The noncollagenous 16A domain (NC16A) of BP180 has been found to contain major epitopes recognized by autoantibodies in BP. We recently established the pathogenicity of anti-NC16A IgE through passive transfer of patient-derived autoantibodies to double-humanized mice that express the human high-affinity IgE receptor, FcεRI, and human NC16A domain (FcεRI/NC16A). In this model, anti-NC16A IgEs recruit eosinophils to mediate tissue injury and clinical disease in FcεRI/NC16A mice. The objective of this study was to characterize the molecular and cellular events that underlie eosinophil recruitment and eosinophil-dependent tissue injury in anti-NC16A IgE-induced BP. We show that anti-NC16A IgEs significantly increase levels of key eosinophil chemoattractants, eotaxin-1 and eotaxin-2, as well as the proteolytic enzyme matrix metalloproteinase-9 (MMP-9) in the lesional skin of FcεRI/NC16A mice. Importantly, neutralization of eotaxin-1, but not eotaxin-2, and blockade of the main eotaxin receptor, CCR3, drastically reduce anti-NC16A IgE-induced disease activity. We further show that anti-NC16A IgE/NC16A immune complexes induce the release of MMP-9 from eosinophils, and that MMP-9-deficient mice are resistant to anti-NC16A IgE-induced BP. Lastly, we find significantly increased levels of eotaxin-1, eotaxin-2, and MMP-9 in blister fluids of BP patients. Taken together, this study establishes the eotaxin-1/CCR3 axis and MMP-9 as key players in anti-NC16A IgE-induced BP and candidate therapeutic targets for future drug development and testing.

Two-phase stratified sampling and analysis for predicting binary outcomes.

The two-phase study design is a cost-efficient sampling strategy when certain data elements are expensive and, thus, can only be collected on a sub-sample of subjects. To date guidance on how best to allocate resources within the design has assumed that primary interest lies in estimating association parameters. When primary interest lies in the development and evaluation of a risk prediction tool, however, such guidance may, in fact, be detrimental. To resolve this, we propose a novel strategy for resource allocation based on oversampling cases and subjects who have more extreme risk estimates according to a preliminary model developed using fully observed predictors. Key to the proposed strategy is that it focuses on enhancing efficiency regarding estimation of measures of predictive accuracy, rather than on efficiency regarding association parameters which is the standard paradigm. Towards valid estimation and inference for accuracy measures using the resultant data, we extend an existing semiparametric maximum likelihood ethod for estimating odds ratio association parameters to accommodate the biased sampling scheme and data incompleteness. Motivated by our sampling design, we additionally propose a general post-stratification scheme for analyzing general two-phase data for estimating predictive accuracy measures. Through theoretical calculations and simulation studies, we show that the proposed sampling strategy and post-stratification scheme achieve the promised efficiency improvement. Finally, we apply the proposed methods to develop and evaluate a preliminary model for predicting the risk of hospital readmission after cardiac surgery using data from the Pennsylvania Health Care Cost Containment Council.

Feasibility of risk assessment for breast cancer molecular subtypes.

PURPOSE: Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination. METHODS: Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype. RESULTS: There were 3,073 ER/PR+ HER2 - , 340 ER/PR +HER2 + , 126 ER/PR-ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR-HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI. CONCLUSION: Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes.

Effective alleviation of depressive and anxious symptoms and sleep disorders in benzodiazepine-dependent patients through repetitive transcranial magnetic stimulation.

Benzodiazepine (BZD) dependence poses a significant challenge in mental health, prompting the exploration of treatments like repetitive transcranial magnetic stimulation (rTMS). This research aims to assess the impact of rTMS on alleviating symptoms of BZD dependence. A randomized control trial was employed to study 40 BZD-dependent inpatients. Their symptoms were quantified using the Hamilton Anxiety Rating Scale (HAMA), Montgomery-Åsberg Depression Rating Scale (MADRS) and Pittsburgh Sleep Quality Index (PSQI). Participants were divided into a conventional treatment group (daily diazepam with gradual tapering) with supportive psychotherapy and another group receiving the same treatment supplemented with rTMS (five weekly sessions for 2 weeks). Significant improvements were observed in both groups over baseline in MADRS, HAMA and PSQI scores at the 2nd, 4th, 8th and 12th week assessments (p < 0.05). The group receiving rTMS in addition to conventional treatment exhibited superior improvements in all measures at the 8th and 12th weeks. The addition of rTMS to conventional treatment methods for BZD dependence significantly betters the recovery in terms of depression, anxiety and sleep quality, highlighting the role of rTMS as an effective adjunct therapy.

Methods and Software to Analyze Gene-Environment Interactions under a Case-Mother-Control-Mother Design with Partially Missing Child Genotype.

INTRODUCTION: The case-mother-control-mother design allows to study fetal and maternal genetic factors together with environmental exposures on early life outcomes. Mendelian constraints and conditional independence between child genotype and environmental factors enabled semiparametric likelihood methods to estimate logistic models with greater efficiency than standard logistic regression. Difficulties in child genotype collection require methods handling missing child genotype. METHODS: We review a stratified retrospective likelihood and two semiparametric likelihood approaches: a prospective one and a modified retrospective one, the latter either modeling the maternal genotype as a function of covariates or leaving their joint distribution unspecified (robust version). We also review software implementing these modeling alternatives, compare their statistical properties in a simulation study, and illustrate their application, focusing on gene-environment interactions and partially missing child genotype. RESULTS: The robust retrospective likelihood provides generally unbiased estimates, with standard errors only slightly larger than when modeling maternal genotype based on exposure. The prospective likelihood encounters maximization problems. In the application to the association of small-for-gestational-age babies with CYP2E1 and drinking water disinfection by-products, the retrospective likelihood allowed a full array of covariates, while the prospective likelihood was limited to few covariates. CONCLUSION: We recommend the robust version of the modified retrospective likelihood.

Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial.

BACKGROUND: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. OBJECTIVE: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. METHODS: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. RESULTS: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. CONCLUSIONS: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-054675.

Efficient inference of parent-of-origin effect using case-control mother-child genotype data.

Parent-of-origin effect plays an important role in mammal development and disorder. Case-control mother-child pair genotype data can be used to detect parent-of-origin effect and is often convenient to collect in practice. Most existing methods for assessing parent-of-origin effect do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parent-of-origin effect through a logistic regression model, with predictors including maternal and child genotypes, parental origins, and covariates. The parental origins may not be fully inferred from genotypes of a target genetic marker, so we propose to use genotypes of markers tightly linked to the target marker to increase inference efficiency. A robust statistical inference procedure is developed based on a modified profile log-likelihood in a retrospective way. A computationally feasible expectation-maximization algorithm is devised to estimate all unknown parameters involved in the modified profile log-likelihood. This algorithm differs from the conventional expectation-maximization algorithm in the sense that it is based on a modified instead of the original profile log-likelihood function. The convergence of the algorithm is established under some mild regularity conditions. This expectation-maximization algorithm also allows convenient handling of missing child genotypes. Large sample properties, including weak consistency, asymptotic normality, and asymptotic efficiency, are established for the proposed estimator under some mild regularity conditions. Finite sample properties are evaluated through extensive simulation studies and the application to a real dataset.

A constrained maximum likelihood approach to developing well-calibrated models for predicting binary outcomes.

The added value of candidate predictors for risk modeling is routinely evaluated by comparing the performance of models with or without including candidate predictors. Such comparison is most meaningful when the estimated risk by the two models are both unbiased in the target population. Very often data for candidate predictors are sourced from nonrepresentative convenience samples. Updating the base model using the study data without acknowledging the discrepancy between the underlying distribution of the study data and that in the target population can lead to biased risk estimates and therefore an unfair evaluation of candidate predictors. To address this issue assuming access to a well-calibrated base model, we propose a semiparametric method for model fitting that enforces good calibration. The central idea is to calibrate the fitted model against the base model by enforcing suitable constraints in maximizing the likelihood function. This approach enables unbiased assessment of model improvement offered by candidate predictors without requiring a representative sample from the target population, thus overcoming a significant practical challenge. We study theoretical properties for model parameter estimates, and demonstrate improvement in model calibration via extensive simulation studies. Finally, we apply the proposed method to data extracted from Penn Medicine Biobank to inform the added value of breast density for breast cancer risk assessment in the Caucasian woman population.

Testing calibration of phenotyping models using positive-only electronic health record data.

Validation of phenotyping models using Electronic Health Records (EHRs) data conventionally requires gold-standard case and control labels. The labeling process requires clinical experts to retrospectively review patients' medical charts, therefore is labor intensive and time consuming. For some disease conditions, it is prohibitive to identify the gold-standard controls because routine clinical assessments are performed for selective patients who are deemed to possibly have the condition. To build a model for phenotyping patients in EHRs, the most readily accessible data are often for a cohort consisting of a set of gold-standard cases and a large number of unlabeled patients. Hereby, we propose methods for assessing model calibration and discrimination using such "positive-only" EHR data that does not require gold-standard controls, provided that the labeled cases are representative of all cases. For model calibration, we propose a novel statistic that aggregates differences between model-free and model-based estimated numbers of cases across risk subgroups, which asymptotically follows a Chi-squared distribution. We additionally demonstrate that the calibration slope can also be estimated using such "positive-only" data. We propose consistent estimators for discrimination measures and derive their large sample properties. We demonstrate performances of the proposed methods through extensive simulation studies and apply them to Penn Medicine EHRs to validate two preliminary models for predicting the risk of primary aldosteronism.

Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening.

PURPOSE: Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort. MATERIALS AND METHODS: We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman. RESULTS: PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical. CONCLUSIONS: Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.

Time of Clinic Appointment and Serious Illness Communication in Oncology.

INTRODUCTION: Serious illness communication in oncology increases goal concordant care. Factors associated with the frequency of serious illness conversations are not well understood. Given prior evidence of the association between suboptimal decision-making and clinic time, we aimed to investigate the relationship between appointment time and the likelihood of serious illness conversations in oncology. METHODS: We conducted a retrospective study of electronic health record data from 55 367 patient encounters between June 2019 to April 2020, using generalized estimating equations to model the likelihood of a serious illness conversation across clinic time. RESULTS: Documentation rate decreased from 2.1 to 1.5% in the morning clinic session (8am-12pm) and from 1.2% to .9% in the afternoon clinic session (1pm-4pm). Adjusted odds ratios for Serious illness conversations documentation rates were significantly lower for all hours of each session after the earliest hour (adjusted odds ratios .91 [95% CI, .84-.97], P = .006 for overall linear trend). CONCLUSIONS: Serious illness conversations between oncologists and patients decrease considerably through the clinic day, and proactive strategies to avoid missed conversations should be investigated.

A robust approach for electronic health record-based case-control studies with contaminated case pools.

We consider analyses of case-control studies assembled from electronic health records (EHRs) where the pool of cases is contaminated by patients who are ineligible for the study. These ineligible patients, referred to as "false cases," should be excluded from the analyses if known. However, the true outcome status of a patient in the case pool is unknown except in a subset whose size may be arbitrarily small compared to the entire pool. To effectively remove the influence of the false cases on estimating odds ratio parameters defined by a working association model of the logistic form, we propose a general strategy to adaptively impute the unknown case status without requiring a correct phenotyping model to help discern the true and false case statuses. Our method estimates the target parameters as the solution to a set of unbiased estimating equations constructed using all available data. It outperforms existing methods by achieving robustness to mismodeling the relationship between the outcome status and covariates of interest, as well as improved estimation efficiency. We further show that our estimator is root-n-consistent and asymptotically normal. Through extensive simulation studies and analysis of real EHR data, we demonstrate that our method has desirable robustness to possible misspecification of both the association and phenotyping models, along with statistical efficiency superior to the competitors.

Optimal sampling for positive only electronic health record data.

Identifying a patient's disease/health status from electronic medical records is a frequently encountered task in electronic health records (EHR) related research, and estimation of a classification model often requires a benchmark training data with patients' known phenotype statuses. However, assessing a patient's phenotype is costly and labor intensive, hence a proper selection of EHR records as a training set is desired. We propose a procedure to tailor the best training subsample with limited sample size for a classification model, minimizing its mean-squared phenotyping/classification error (MSE). Our approach incorporates "positive only" information, an approximation of the true disease status without false alarm, when it is available. In addition, our sampling procedure is applicable for training a chosen classification model which can be misspecified. We provide theoretical justification on its optimality in terms of MSE. The performance gain from our method is illustrated through simulation and a real-data example, and is found often satisfactory under criteria beyond MSE.

Goodness-of-fit two-phase sampling designs for time-to-event outcomes: a simulation study based on New York University Women's Health Study for breast cancer.

BACKGROUND: Sub-cohort sampling designs such as a case-cohort study play a key role in studying biomarker-disease associations due to their cost effectiveness. Time-to-event outcome is often the focus in cohort studies, and the research goal is to assess the association between the event risk and risk factors. In this paper, we propose a novel goodness-of-fit two-phase sampling design for time-to-event outcomes when some covariates (e.g., biomarkers) can only be measured on a subgroup of study subjects. METHODS: Assuming that an external model, which can be the well-established risk models such as the Gail model for breast cancer, Gleason score for prostate cancer, and Framingham risk models for heart diseases, or built from preliminary data, is available to relate the outcome and complete covariates, we propose to oversample subjects with worse goodness-of-fit (GOF) based on an external survival model and time-to-event. With the cases and controls sampled using the GOF two-phase design, the inverse sampling probability weighting method is used to estimate the log hazard ratio of both incomplete and complete covariates. We conducted extensive simulations to evaluate the efficiency gain of our proposed GOF two-phase sampling designs over case-cohort study designs. RESULTS: Through extensive simulations based on a dataset from the New York University Women's Health Study, we showed that the proposed GOF two-phase sampling designs were unbiased and generally had higher efficiency compared to the standard case-cohort study designs. CONCLUSION: In cohort studies with rare outcomes, an important design question is how to select informative subjects to reduce sampling costs while maintaining statistical efficiency. Our proposed goodness-of-fit two-phase design provides efficient alternatives to standard case-cohort designs for assessing the association between time-to-event outcome and risk factors. This method is conveniently implemented in standard software.

Giant anterior scleral staphyloma caused by blunt ocular trauma: a case report.

BACKGROUND: Anterior scleral staphyloma is a relatively rare disease characterized by thinning and expansion of sclera. We described the clinical presentation, diagnosis and treatment of a case with giant anterior scleral staphyloma caused by blunt ocular trauma. CASE PRESENTATION: A 24-years-old male, presented with a black cyst-like mass protruding from the right eyeball for 9 years after a history of glass crush contusion. The ultrasound biomicroscopy examination showed two cysts in the right eyeball. The larger one was about 5.92 mm*4.69 mm in size and the scleral lacerations were connected to the posterior chamber below the cyst. For treatment, resection of the anterior scleral staphyloma and the scleral patch graft transplantation was performed. The vision of the patient was improved compared with that before surgery. There were no obvious complications. CONCLUSION: The clinical presentation, diagnosis, and treatment of the case with giant anterior scleral staphyloma can provide a reference for the management of anterior scleral staphyloma. Surgical resection and scleral patch graft should be a good option for the treatment of giant anterior scleral staphyloma.

Short-wavelength artificial light affects visual neural pathway development in mice.

Nearly all modern life depends on artificial light; however, it does cause health problems. With certain restrictions of artificial light emitting technology, the influence of the light spectrum is inevitable. The most remarkable problem is its overload in the short wavelength component. Short wavelength artificial light has a wide range of influences from ocular development to mental problems. The visual neuronal pathway, as the primary light-sensing structure, may contain the fundamental mechanism of all light-induced abnormalities. However, how the artificial light spectrum shapes the visual neuronal pathway during development in mammals is poorly understood. We placed C57BL/6 mice in three different spectrum environments (full-spectrum white light: 400-750 nm; violet light: 400 ± 20 nm; green light: 510 ± 20 nm) beginning at eye opening, with a fixed light time of 7:00-19:00. During development, we assessed the ocular axial dimension, visual function and retinal neurons. After two weeks under short wavelength conditions, the ocular axial length (AL), anterior chamber depth (ACD) and length of lens thickness, real vitreous chamber depth and retinal thickness (LLVR) were shorter, visual acuity (VA) decreased, and retinal electrical activity was impaired. The density of S-cones in the dorsal and ventral retinas both decreased after one week under short wavelength conditions. In the ventral retina, it increased after three weeks. Retinal ganglion cell (RGC) density and axon thickness were not influenced; however, the axonal terminals in the lateral geniculate nucleus (LGN) were less clustered and sparse. Amacrine cells (ACs) were significantly more activated. Green light has few effects. The KEGG and GO enrichment analyses showed that many genes related to neural circuitry, synaptic formation and neurotransmitter function were differentially expressed in the short wavelength light group. In conclusion, exposure to short wavelength artificial light in the early stage of vision-dependent development in mice delayed the development of the visual pathway. The axon terminus structure and neurotransmitter function may be the major suffering.

A systematic review of the natural history and biomarkers of primary lecithin:cholesterol acyltransferase deficiency.

Syndromes associated with LCAT deficiency, a rare autosomal recessive condition, include fish-eye disease (FED) and familial LCAT deficiency (FLD). FLD is more severe and characterized by early and progressive chronic kidney disease (CKD). No treatment is currently available for FLD, but novel therapeutics are under development. Furthermore, although biomarkers of LCAT deficiency have been identified, their suitability to monitor disease progression and therapeutic efficacy is unclear, as little data exist on the rate of progression of renal disease. Here, we systematically review observational studies of FLD, FED, and heterozygous subjects, which summarize available evidence on the natural history and biomarkers of LCAT deficiency, in order to guide the development of novel therapeutics. We identified 146 FLD and 53 FED patients from 219 publications, showing that both syndromes are characterized by early corneal opacity and markedly reduced HDL-C levels. Proteinuria/hematuria were the first signs of renal impairment in FLD, followed by rapid decline of renal function. Furthermore, LCAT activity toward endogenous substrates and the percentage of circulating esterified cholesterol (EC%) were the best discriminators between these two syndromes. In FLD, higher levels of total, non-HDL, and unesterified cholesterol were associated with severe CKD. We reveal a nonlinear association between LCAT activity and EC% levels, in which subnormal levels of LCAT activity were associated with normal EC%. This review provides the first step toward the identification of disease biomarkers to be used in clinical trials and suggests that restoring LCAT activity to subnormal levels may be sufficient to prevent renal disease progression.

Covariate adjusted inference of parent-of-origin effects using case-control mother-child paired multilocus genotype data.

It is of great interest to identify parent-of-origin effects (POEs) since POEs play an important role in many human heritable disorders and human early life growth and development. POE is sometimes referred to as imprinting effect in the literature. Compared with the standard logistic regression analyses, retrospective likelihood-based statistical methods are more powerful in identifying POEs when data are collected from related individuals retrospectively. However, none of existing retrospective-based methods can appropriately incorporate covariates that should be adjusted for if they are confounding factors. In this paper, a novel semiparametric statistical method, M-HAP, is developed to detect POEs by fully exploring available information from multilocus genotypes of case-control mother-child pairs and covariates. Some large sample properties are established for M-HAP. Finite sample properties of M-HAP are illustrated by extensive simulation studies and real data applications to the Jerusalem Perinatal Study and the Danish National Birth Cohort study, which confirm the desired superiority of M-HAP over some existing methods. M-HAP has been implemented in the updated R package CCMO.

OLMALINC/OCT4/BMP2 axis enhances osteogenic-like phenotype of renal interstitial fibroblasts to participate in Randall's plaque formation.

BACKGROUND: Randall's plaques (RP) are identified as anchored sites for kidney calcium oxalate stones, but the mechanism remains unclear. Given the importance of osteogenic-like cells in RP formation and OCT4 in reprogramming differentiated cells to osteoblasts, the current study explored the potential role of OCT4 in RP formation. METHODS: OCT4 and biomineralization were evaluated in RP, and immunofluorescence co-staining was performed to identify these cells with alteration of OCT4 and osteogenic markers. Based on the analysis of tissue, we further investigated the mechanism of OCT4 in regulating osteogenic-like differentiation of primary human renal interstitial fibroblasts (hRIFs) in vitro and vivo. RESULTS: We identified the upregulated OCT4 in RP, with a positive correlation to osteogenic markers. Interestingly, fibroblast marker Vimentin was partially co-localized with upregulated OCT4 and osteogenic markers in RP. Further investigations revealed that OCT4 significantly enhanced the osteogenic-like phenotype of hRIFs in vitro and in vivo. Mechanically, OCT4 directly bound to BMP2 promoter and facilitated its CpG island demethylation to transcriptionally promote BMP2 expression. Furthermore, combination of RIP and RNA profiling uncovered that lncRNA OLMALINC physically interacted with OCT4 to promote its stabilization via disrupting the ubiquitination. Additionally, OLMALINC was upregulated in fibroblasts in RP visualized by FISH, and a positive correlation was revealed between OLMALINC and OCT4 in RP. CONCLUSIONS: The upregulation of OCT4 in hRIFs was a pathological feature of RP formation, and OLMALINC/OCT4/BMP2 axis facilitated hRIFs to acquire osteogenic-like phenotype under osteogenic conditions, through which the pathway might participate in RP formation. Our findings opened up a new avenue to better understand RP formation in which osteogenic-like process was partially triggered by lncRNAs and pluripotency maintenance related genes.