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Although it is well known dietary factors are closely correlated with bone health, the association between macronutrients intake distribution and bone mineral density (BMD) is still unclear. The aims of this study were to investigate how macronutrients distribution was correlated with BMD, and to evaluate how the substitution between macronutrients could be associated with BMD.We conducted a cross-sectional study based on data from National Health and Nutrition Examination Survey. Dietary recall method was used to assessed the intake of macronutrients. Macronutrient intake distribution including carbohydrate, protein and fat was calculated as percentages of energy intake from total energy. BMD was converted to T-score and low BMD was defined as T-score less than -1.0. The association between the percentages of energy intake from carbohydrate, protein and fat with T-score and risk of low BMD was evaluated using multivariate regression models. Isocaloric substitution analysis was conducted using the multivariate nutrient density method.Data form 4447 adults aged 20 years and older who underwent BMD examination were included in this study. Higher percentage of energy intake from carbohydrate was associated with lower T-score (-0.03 [95%CI, -0.05 to -0.01]; P = 0.001) and higher risk of low BMD (1.05 [95%CI, 1.02-1.08]; P = 0.003), while higher percentage of energy intake from protein was associated with higher T-score (0.05 [95%CI, 0.01-0.08]; P = 0.009) and lower odds of low BMD (0.92 [95%CI, 0.87-0.98]; P = 0.007). The percentage of energy intake from fat seemed to be positively correlated with T-score, but the correlation became insignificant after adjusting for metabolism related confounders. Isocaloric substitution analysis showed that only the substitution between carbohydrate and protein was significantly and independently associated with T-score (-0.05 [95%CI, -0.08 to -0.01]; P = 0.01) and the risk of low BMD (1.08 [95%CI, 1.02-1.15]; P = 0.008).Based on the results from this study, we hypothesized that a high-protein diet coupled with low carbohydrate intake would be beneficiary for prevention of bone loss in adults. However, randomized clinical trials or longitudinal studies are needed to further assessed our findings.

We studied the spectral signature of different components of the Diffuse X-ray Background (DXB), including Local Hot Bubble (LHB), Solar Wind Charge Exchange (SWCX), Galactic Halo, and typically unresolved point sources (galaxies and AGN), in the direction of the Chandra Deep Field South (CDFS) using the 4 Ms XMM-Newton survey and Chandra 4 Ms Source Catalog. In this paper, we present our results showing how the different components contribute to the DXB below 1 keV. In particular, we have found that ~6% of the emission at 3/4 keV (all-sky average value ~ 3$\times10^{-3}$ cm$^{-6}$pc), which is typically associated with Galactic Halo (GH) and Circum-galactic medium (CGM) is, in fact, due to emission from typically unresolved galaxies. We will discuss the effect that this has on our understanding of GH and CGM, and to our understanding of the missing CGM baryons., 18 pages, 13 figures, 5 tables

Background. The prognosis is poor for hepatocellular carcinoma (HCC), a tumor and cancer associated with inflammation that is common. New data showed that significant levels of KIAA1522 were expressed in HCC tissues and cell lines, suggesting that KIAA1522 may be a highly useful prognostic marker for HCC. However, its biochemical processes and impacts on the immune system go deeper. Objective. To verify the significance of KIAA1522 in HCC and investigate its related carcinogenic mechanisms. Methods. Studies examining the relationship between KIAA1522 expression and clinical-pathologic characteristics in HCC have been checked in the Cancer Genome Atlas (TCGA) database. A receiver operating characteristic (ROC) curve was used to assess the diagnostic efficacy of KIAA1522 in HCC. Western blot analysis was used to find the presence of the KIAA1522 protein in the tumor and paraneoplastic tissues of eight randomly chosen HCC patients. The GSVA program in R language was used to evaluate the relationship between KIAA1522 and immune cell infiltration in HCC. We searched the Search Tool for the Retrieval of Interacting Genes (STRING) database for interacting proteins connected to the expression of KIAA1522. Pathways were involved in the enrichment analysis of KIAA1522 to anticipate potential mechanisms through which KIAA1522 may affect immunological infiltration. Results. Our study found that KIAA1522 was commonly elevated in HCC tumor tissues and that it also signaled a bad outcome. We found an inverse link between KIAA1522 and cytotoxic cells and an inverse relationship between KIAA1522 and Th2 cell infiltration. In STRING analysis, the top 5 coexpressed proteins of KIAA1522 were BAIAP2, NCK2, TSNAXIP1, POGK, and KLHL31. The effect of KIAA1522 on HCC may entail cell cycle alteration, an immunological response, and suppression of the PPAR signaling pathway. Conclusion. High expression of KIAA1522 was linked to HCC immune cell infiltration, disease progression, and a poor prognosis.

Herein, a nanocomposite comprising polyvinyl pyrrolidone (PVP) and cobalt(II) hydroxide (Co(OH)2) nanoparticles (PVP–Co(OH)2) was synthesized via a hydrothermal method and used for detection of Cu(II) ions. Using PVP grafted nanosized Co(OH)2 as a surfactant could decrease the aggregation and reduce the particle size. The conducted morphology investigations showed that PVP–Co(OH)2 nanocomposite was composed of small spherical particles with a diameter of 40–80 nm that could accelerate mass diffusion and favor the deposition-stripping process of Cu(II) ions. High catalytic activity of Co(OH)2 and good affinity of PVP to Cu(II) ions synergistically enhances sensing performance of the PVP–Co(OH)2 nanocomposite-modified electrode than Co(OH)2- and PVP-modified electrodes. A linear response was obtained for Cu(II) ion concentration over a range of 1.0–90.0 μg/L, with a detection limit of 0.26 μg/L (S/N = 3). The proposed electrode exhibited good performance in terms of selectivity, stability, and reproducibility. It was also employed to detect Cu(II) ions in water and food samples, and the results were in good agreement with those obtained by graphite furnace atomic absorption spectrometry.

Oxaliplatin (OXA) is a first-line chemotherapeutic agent for treating colorectal cancer (CC). However, the chemotherapeutic effect of OXA on CC is limited by the M2-like polarization of tumor-associated macrophages (TAMs) in the tumor microenvironment (TME) and protective autophagy of tumor cells. Here, a cationic polymer APEG-PAsp(PEI) (PAPEI) was prepared to deliver small-interfering RNA (siRNA) to silence the lactate dehydrogenase A (LDHA) gene (LDHA-siRNA) to enhance the chemotherapeutic effect of OXA on CC. The PAPEI/LDHA-siRNA nanocomplex effectively silenced the LDHA gene to inhibit the secretion of lactic acid from tumor cells, resulting in inhibition of the M2-like polarization of TAMs. In addition, the nanocomplex also amplified OXA-induced autophagy and transformed protective autophagy into autophagic death. Consequently, the combination treatment of OXA and PAPEI/LDHA-siRNA showed a dramatically increased chemotherapeutic effect on CC compared with the OXA-alone treatment, which also suggested its attractive potential for treating CC-like immune "cold" tumors.

Objective We aimed to determine the real-world prevalence and investigate risk factors for rheumatoid arthritis (RA)-related lung disease on chest computed tomography (CT) imaging. We also investigated the impact of RA-related lung disease on mortality. Methods We studied chest CT imaging abnormalities among RA patients. We determined the presence and type of abnormalities using the chest CT imaging radiologic report. RA-related lung disease was defined as interstitial lung disease (ILD), bronchiectasis, or pleural disease. We examined whether demographics and RA characteristics were associated with RA-related lung disease using logistic regression. RA-related lung disease and mortality was described using survival curves and Cox regression. Results We analyzed 190 patients who had chest CT imaging performed for clinical indications. Mean age was 64.2 years (SD 11.8), 80.0% were female, and 75.3% were seropositive. RA-related lung disease was detected in 54 patients (28.4%); 30 (15.8%) had ILD, 27 (14.2%) had bronchiectasis, and 18 (9.5%) had pleural disease. RA-related lung disease was reported in both seropositive and seronegative RA (28.7% vs. 27.7%, p = 1.00). Male sex (OR 2.62, 95%CI 1.17–5.88) and current methotrexate use (OR 2.73, 95%CI 1.27–5.61 vs. not current) were associated with RA-related lung disease. Twenty-four (44.4%) patients with RA-related lung disease died during mean 7.0 years of follow-up. RA-related lung disease had HR of 5.35 (95%CI 0.72–39.9) for mortality compared to normal chest CT. Conclusions In this real-world study, RA-related lung disease was commonly detected on chest CT imaging regardless of RA serostatus. RA-related lung disease had high mortality, emphasizing the importance in close monitoring of these patients.

Rationale: Maintaining iron homeostasis is essential for proper cardiac function. Both iron deficiency and iron overload are associated with cardiomyopathy and heart failure via complex mechanisms. Although ferritin plays a central role in iron metabolism by storing excess cellular iron, the molecular function of ferritin in cardiomyocytes remains unknown. Objective: To characterize the functional role of Fth (ferritin H) in mediating cardiac iron homeostasis and heart disease. Methods and Results: Mice expressing a conditional Fth knockout allele were crossed with 2 distinct Cre recombinase-expressing mouse lines, resulting in offspring that lack Fth expression specifically in myocytes (MCK-Cre) or cardiomyocytes (Myh6-Cre). Mice lacking Fth in cardiomyocytes had decreased cardiac iron levels and increased oxidative stress, resulting in mild cardiac injury upon aging. However, feeding these mice a high-iron diet caused severe cardiac injury and hypertrophic cardiomyopathy, with molecular features typical of ferroptosis, including reduced glutathione (GSH) levels and increased lipid peroxidation. Ferrostatin-1, a specific inhibitor of ferroptosis, rescued this phenotype, supporting the notion that ferroptosis plays a pathophysiological role in the heart. Finally, we found that Fth-deficient cardiomyocytes have reduced expression of the ferroptosis regulator Slc7a11, and overexpressing Slc7a11 selectively in cardiomyocytes increased GSH levels and prevented cardiac ferroptosis. Conclusions: Our findings provide compelling evidence that ferritin plays a major role in protecting against cardiac ferroptosis and subsequent heart failure, thereby providing a possible new therapeutic target for patients at risk of developing cardiomyopathy.

Background. Infantile Hemangiomas (IHs) are common benign vascular tumors of infancy that may have serious consequences. The research on diagnostic markers for IHs is scarce. Methods. The “limma” R package was applied to identify differentially expressed genes (DEGs) in developing IHs. Plugin ClueGO in Cytoscape software performed functional enrichment of DEGs. The Search Tool for Retrieving Interacting Genes (STRING) database was utilized to construct the PPI network. The least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were used to identify diagnostic genes for IHs. The receiver operating characteristic (ROC) curve evaluated diagnostic genes’ discriminatory ability. Single-gene based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted by Gene Set Enrichment Analysis (GSEA). The chemicals related to the diagnostic genes were excavated by the Comparative Toxicogenomics Database (CTD). Finally, the online website Network Analyst was used to predict the transcription factors targeting the diagnostic genes. Results. A total of 205 DEGs were singled out from IHs samples of 6-, 12-, and 24-month-old infants. These genes principally participated in vasculogenesis and development-related, endothelial cell-related biological processes. Then we mined 127 interacting proteins and created a network with 127 nodes and 251 edges. Furthermore, LASSO and SVM-RRF algorithms identified five diagnostic genes, namely, TMEM2, GUCY1A2, ISL1, WARS, and STEAP4. ROC curve analysis results indicated that the diagnostic genes had a powerful ability to distinguish IHs samples from normal samples. Next, the results of GSEA for a single gene illustrated that all five diagnostic genes inhibited the “valine, leucine, and isoleucine degradation” pathway in the development of IHs. WARS, TMEM2, and STEAP4 activated the “blood vessel development” and “vasculature development” in IHs. Subsequently, inhibitors targeting TMEM2, GUCY1A2, ISL1, and STEAP4 were mined. Finally, 14 transcription factors regulating GUCY1A2, 14 transcription factors regulating STEAP4, and 26 transcription factors regulating ISL1 were predicted. Conclusion. This study identified five diagnostic markers for IHs and further explored the mechanisms and targeting drugs, providing a basis for diagnosing and treating IHs.

While there exist numerous methods to identify binary phenotypes (i.e. COPD) using electronic health record (EHR) data, few exist to ascertain the timings of phenotype events (i.e. COPD onset or exacerbations). Estimating event times could enable more powerful use of EHR data for longitudinal risk modeling, including survival analysis. Here we introduce Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP), a semi-supervised machine learning algorithm to estimate phenotype event times using EHR data with limited observed labels, which require resource-intensive chart review to obtain. SAMGEP models latent phenotype states as a binary Markov process, and it employs an adaptive weighting strategy to map timestamped EHR features to an embedding function that it models as a state-dependent Gaussian process. SAMGEP’s feature weighting achieves meaningful feature selection, and its predictions significantly improve AUCs and F1 scores over existing approaches in diverse simulations and real-world settings. It is particularly adept at predicting cumulative risk and event counting process functions, and is robust to diverse generative model parameters. Moreover, it achieves high accuracy with few (50-100) labels, efficiently leveraging unlabeled EHR data to maximize information gain from costly-to-obtain event time labels. SAMGEP can be used to estimate accurate phenotype state functions for risk modeling research.

In rheumatoid arthritis (RA), there are limited data on risk factors for the clinical heart failure (HF) subtypes of HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). This study examined the association between inflammation and incident HF subtypes in RA. Because inflammation changes over time with disease activity, we hypothesized that the effect of inflammation may be stronger at the 5-year follow-up than at the standard 10-year follow-up from general population studies of cardiovascular risk.We studied an electronic health record (EHR)-based RA cohort with data pre- and post-RA incidence. We applied a validated approach to identify HF and extract ejection fraction to classify HFrEF and HFpEF. Follow-up started from the RA incidence date (index date) to the earliest occurrence of incident HF, death, last EHR encounter, or 10 years. Baseline inflammation was assessed using erythrocyte sedimentation rate or C-reactive protein values. Covariates included demographic characteristics, established HF risk factors, and RA-related factors. We tested the association between baseline inflammation with incident HF and its subtypes using Cox proportional hazards models.We studied 9,087 patients with RA; 8.2% developed HF during 10 years of follow-up. Elevated inflammation was associated with increased risk for HF at both 5- and 10-year follow-ups (hazard ratio [HR] 1.66, 95% confidence interval [95% CI] 1.12-2.46 and HR 1.46, 95% CI 1.13-1.90, respectively), which is also seen for HFpEF at 5 years (HR 1.72, 95% CI 1.09-2.70) and 10 years (HR 1.45, 95% CI 1.07-1.94). HFrEF was not associated with inflammation for either follow-up time.Elevated inflammation early in RA diagnosis was associated with HF; this association was driven by HFpEF and not HFrEF, suggesting a window of opportunity for prevention of HFpEF in RA.

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1–2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no). PheCAP takes structured data and narrative notes from electronic medical records and enables patients with a particular clinical phenotype to be identified.

Long non-coding RNAs (lncRNAs) represent a group of ncRNAs with more than 200 nucleotides. These RNAs can specifically regulate gene expression at both the transcriptional and the post-transcriptional levels, and increasing evidence indicates that they play vital roles in a variety of disease-related cellular processes. The lncRNA GAS8 antisense RNA 1 (GAS8-AS1, also known as C16orf3) is located in the second intron of GAS8 and has been reported to be both abnormally expressed in several diseases and closely correlated with many clinical characteristics. GAS8-AS1 has been shown to affect many biological functions, including cell proliferation, migration, invasiveness, and autophagy using several signaling pathways. In this review, we have summarized current studies on GAS8-AS1 roles in disease and discuss its potential clinical utility. GAS8-AS1 may be a promising biomarker for both diagnoses and prognoses, and a novel target for many disease therapies.

Summary Leveraging large-scale electronic health record (EHR) data to estimate survival curves for clinical events can enable more powerful risk estimation and comparative effectiveness research. However, use of EHR data is hindered by a lack of direct event time observations. Occurrence times of relevant diagnostic codes or target disease mentions in clinical notes are at best a good approximation of the true disease onset time. On the other hand, extracting precise information on the exact event time requires laborious manual chart review and is sometimes altogether infeasible due to a lack of detailed documentation. Current status labels—binary indicators of phenotype status during follow-up—are significantly more efficient and feasible to compile, enabling more precise survival curve estimation given limited resources. Existing survival analysis methods using current status labels focus almost entirely on supervised estimation, and naive incorporation of unlabeled data into these methods may lead to biased estimates. In this article, we propose Semisupervised Calibration of Risk with Noisy Event Times (SCORNET), which yields a consistent and efficient survival function estimator by leveraging a small set of current status labels and a large set of informative features. In addition to providing theoretical justification of SCORNET, we demonstrate in both simulation and real-world EHR settings that SCORNET achieves efficiency akin to the parametric Weibull regression model, while also exhibiting semi-nonparametric flexibility and relatively low empirical bias in a variety of generative settings.