Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing.

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

Polycomb-repressed genes have permissive enhancers that initiate reprogramming.

Key regulatory genes, suppressed by Polycomb and H3K27me3, become active during normal differentiation and induced reprogramming. Using the well-characterized enhancer/promoter pair of MYOD1 as a model, we have identified a critical role for enhancers in reprogramming. We observed an unexpected nucleosome-depleted region (NDR) at the H3K4me1-enriched enhancer at which transcriptional regulators initially bind, leading to subsequent changes in the chromatin at the cognate promoter. Exogenous Myod1 activates its own transcription by binding first at the enhancer, leading to an NDR and transcription-permissive chromatin at the associated MYOD1 promoter. Exogenous OCT4 also binds first to the permissive MYOD1 enhancer but has a different effect on the cognate promoter, where the monovalent H3K27me3 marks are converted to the bivalent state characteristic of stem cells. Genome-wide, a high percentage of Polycomb targets are associated with putative enhancers in permissive states, suggesting that they may provide a widespread avenue for the initiation of cell-fate reprogramming.

Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

Integrative genome modeling platform reveals essentiality of rare contact events in 3D genome organizations.

A multitude of sequencing-based and microscopy technologies provide the means to unravel the relationship between the three-dimensional organization of genomes and key regulatory processes of genome function. Here, we develop a multimodal data integration approach to produce populations of single-cell genome structures that are highly predictive for nuclear locations of genes and nuclear bodies, local chromatin compaction and spatial segregation of functionally related chromatin. We demonstrate that multimodal data integration can compensate for systematic errors in some of the data and can greatly increase accuracy and coverage of genome structure models. We also show that alternative combinations of different orthogonal data sources can converge to models with similar predictive power. Moreover, our study reveals the key contributions of low-frequency ('rare') interchromosomal contacts to accurately predicting the global nuclear architecture, including the positioning of genes and chromosomes. Overall, our results highlight the benefits of multimodal data integration for genome structure analysis, available through the Integrative Genome Modeling software package.

HCC EV ECG score: An extracellular vesicle-based protein assay for detection of early-stage hepatocellular carcinoma.

BACKGROUND AND AIMS: The sensitivity of current surveillance methods for detecting early-stage hepatocellular carcinoma (HCC) is suboptimal. Extracellular vesicles (EVs) are promising circulating biomarkers for early cancer detection. In this study, we aim to develop an HCC EV-based surface protein assay for early detection of HCC. APPROACH AND RESULTS: Tissue microarray was used to evaluate four potential HCC-associated protein markers. An HCC EV surface protein assay, composed of covalent chemistry-mediated HCC EV purification and real-time immuno-polymerase chain reaction readouts, was developed and optimized for quantifying subpopulations of EVs. An HCC EV ECG score, calculated from the readouts of three HCC EV subpopulations ( E pCAM + CD63 + , C D147 + CD63 + , and G PC3 + CD63 + HCC EVs), was established for detecting early-stage HCC. A phase 2 biomarker study was conducted to evaluate the performance of ECG score in a training cohort ( n  = 106) and an independent validation cohort ( n  = 72).Overall, 99.7% of tissue microarray stained positive for at least one of the four HCC-associated protein markers (EpCAM, CD147, GPC3, and ASGPR1) that were subsequently validated in HCC EVs. In the training cohort, HCC EV ECG score demonstrated an area under the receiver operating curve (AUROC) of 0.95 (95% confidence interval [CI], 0.90-0.99) for distinguishing early-stage HCC from cirrhosis with a sensitivity of 91% and a specificity of 90%. The AUROCs of the HCC EV ECG score remained excellent in the validation cohort (0.93; 95% CI, 0.87-0.99) and in the subgroups by etiology (viral: 0.95; 95% CI, 0.90-1.00; nonviral: 0.94; 95% CI, 0.88-0.99). CONCLUSION: HCC EV ECG score demonstrated great potential for detecting early-stage HCC. It could augment current surveillance methods and improve patients' outcomes.

Comparing efficacies of different exercises on androgen deprivation therapy adverse effects in prostate cancer patients: a network meta-analysis.

INTRODUCTION: Previous studies showed exercise have efficacies for androgen deprivation therapy (ADT) adverse effects. To compare the efficacies of different exercises on ADT adverse effects, we conducted the network meta-analysis (NMA). METHODS: Literature retrieval was performed in PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL). 19 studies (1184 participants) were included. All analyses were performed in R 4.1.2 or RevMan 5.4.1. RESULTS: NMA results showed that compared with the control group, both aerobic + resistance training (ART) (MD = 5.92, 95% CI [0.38; 11.46]) and resistance exercise (RE) (MD = 5.62, 95% CI [2.70; 8.55]) improved quality of life (QoL). ART (P score: 0.72) may have superiority over RE (P score: 0.7). ART (MD = -10.89, 95% CI [-17.67; -4.11]) significantly improved the performance of 400-m test. RE could significantly improve leg strength (MD = 118, 95% CI [78.75; 157.25]) and chest strength (MD = 13.30 [4.07; 22.53]). RE ranked first for strength improvements of leg and chest. CONCLUSION: ART showed better efficacy for the QoL, and significantly improved the performance of 400-m test. RE might be superior for the strengths of leg and chest. ART may be appropriate for patients with less significant muscle strength decline but also other adverse effects of ADT, such as decreased cardiopulmonary function.

Association between dietary inflammatory index and urinary flow rate: a nationwide study, NHANES 2009-2016.

Some evidence have suggested that various nutrients and inflammatory factors might influence the lower urinary tract function. However, the correlation between diet and urinary flow rate (UFR) is not clear. Our study aimed to evaluate the association between the dietary inflammatory index (DII) and UFR. We performed a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES) database from 2009-2016. The dependent and independent variables were UFR and DII score, respectively. Dietary information was collected by 24-hour dietary recall interviews method, and DII scores were computed based on it. Tertiles group was divided according to DII scores. The study included 17,114 participants for whom data on DII and UFR were available, with a mean age of 35.68 ± 20.96 years. Participants with higher DII score presented lower UFR levels (ß= -0.05; 95% CI: [-0.06-0.04]). In addition, the risk of UFR decline elevated significantly gradual across DII score tertiles (p for trend <0.001). Our findings revealed that increased intake of pro-inflammatory diet, as a higher DII score, is correlated with decreased UFR. These results might be useful for the public health system to provide primary prevention recommendations for lower urinary tract voiding problem, but further high-quality prospective research is needed.

Characterization of a novel marine microbial uricase from Priestia flexa and evaluation of the effects of CMCS conjugation on its enzymatic properties.

A novel uricase producing marine bacterium Priestia flexa alkaAU was isolated and identified. The 16S rDNA and the uricase coding gene were sequenced, analyzed and submitted to GenBank. The uricase from Priestia flexa alkaAU (PFU) was purified, determined to be 58.87 kDa, and conjugated with carboxymethyl chitosan (CMCS) by ionic gelation. CMCS conjugation had no effect on the optimum pH of PFU but decreased the optimum temperature by 10 °C. CMCS conjugation increased the specific activity of PFU by 53% at the human body temperature (37 °C) and small intestine's pH (pH 6.8). Uricase thermostabilizing ability of CMCS was significant in the range of 37-80 °C but not at lower temperatures. For improvement of the pH stability of PFU, CMCS was more effective at pHs 3-5 than pHs 6-11. CMCS increased the half-life of PFU against artificial intestinal fluid by 1.5 folds, which demonstrated the potential capability of CMCS-PFU for oral administration.

Associations of exposure to polycyclic aromatic hydrocarbons and kidney stones in U.S. general population: results from the National Health and Nutrition Examination Survey 2007-2016.

PURPOSE: It has been reported that polycyclic aromatic hydrocarbons (PAHs) exposure was associated with the increasing risk of various diseases. Utilizing the data from the general population of the U.S., we tried to assess the association between PAHs exposure and KS. METHODS: The dataset was extracted from National Health and Nutrition Examination Survey (NHANES) 2007-2016. The hydroxylated metabolites of polycyclic aromatic hydrocarbons (OH-PAHs) were detected as representative of urinary PAHs. Ranking-based PAHs score was used to evaluate the total PAHs exposure burden. Multivariable logistic regression analyses were performed to assess the association between PAHs exposure and KS after adjusting a series of confounding factors. RESULTS: 8975 eligible participants were included. In multivariable logistic regression analyses, after adjusting confounding variables, 2-hydroxynaphthalene (OR 1.38, 1.16-1.65; p = 0.038) and 9-hydroxyfluorene (OR 1.39, 1.06-1.84, p = 0.019) were still observed to have significant positive correlations with the prevalence of KS, respectively. The incidence of KS increased significantly with the increase of total PAHs burden (p for trend = 0.011). Significant interaction effects were observed in the subgroup of gender (p for interaction < 0.05). Among female participants, PAHs exposure was more significantly correlated with KS. Higher 2-hydroxynaphthalene (OR 1.94, 1.39-2.70; p < 0.001), 1-hydroxyphenanthrene (OR 1.57, 1.07-2.30; p = 0.022) and 2-hydroxyphenanthrene (OR 1.85, 1.11-3.06; p = 0.018) were significantly associated with the increased incidence of KS in women. CONCLUSIONS: There is a significant association between a high level of PAHs exposure and increased prevalence of KS. In particular, in the female population, the relationship between PAHs exposure and KS is especially significant.

Association between testosterone and serum soluble α-klotho in U.S. males: a cross-sectional study.

PURPOSE: Testosterone plays a crucial role in males, and the deficiency of testosterone leads to multiple adverse health conditions. Klotho is a recently discovered protein encoded by antiaging gene klotho. Both the levels of testosterone and klotho change with aging, so the relationship between them is worth exploring. The purpose of this study was to investigate whether total testosterone is associated with serum klotho levels in U.S. males aged 40-79 years. METHODS: Included in this study were 3750 male participants from the 2011 to 2016 National Health and Nutrition Examination Survey, aged 40-79 years with included information on klotho and sex hormones. The sex steroid hormone levels and klotho concentrations were assayed in laboratories using the recommended methods according to Nutrition Examination Survey guidelines. The association between sex hormones and klotho was calculated using multivariate linear regression models after adjustment for several possible confounding variables. RESULTS: Among the 3750 participants, the total testosterone concentration was 399.048 ± 184.780 ng/dL, and the testosterone deficiency prevalence was 1160 (30.942%). The geometric mean of serum klotho levels was 791.000 pg/mL. In the adjusted models, klotho increased 0.165 pg/mL for every 1 ng/dL increase of total testosterone (p = 0.004). In addition, estradiol (ß 2.232; 95% CI 0.588-3.876; p = 0.032) and sex hormone-binding globulin (ß 2.013; 95% CI 1.173-2.583; p = 0.002) were also positively associated with klotho concentrations. CONCLUSION: This study reported a significant association between klotho and sex hormones in the U.S. male population. The levels of klotho in men increased with total testosterone, estradiol and sex hormone-binding globulin levels, which may have implications for future research and clinical practice.

Association between sleep quality and urolithiasis among general population in Western China: a cross-sectional study.

BACKGROUND: Growing number of studies have evidently shown that sleep disorders are associated with the recently increased risk of various diseases in general human population. However, the relationship between sleep quality and urolithiasis condition in humans is still unclear. The present study explored the relationship between quality of sleep and urolithiasis in Chinese population of population, western China and hence investigated the effects of sleep quality on urolithiasis disease. METHODS: A cross-sectional analysis was performed using data from the West China Natural Population Cohort Study (WCNPCS). The data was collected between May 2019 and June 2021. This study evaluated the association between the sleep quality and urolithiasis. The sleep quality was assessed using the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) whereas urolithiasis, as the outcome was a binary variable. Multivariable logistic regression models that adjust the sociodemographic characteristics and health-related factors were used to assess the association between sleep quality and urolithiasis. Interaction was tested in prespecified subgroup of interest. RESULTS: After adjusting a series of confounding variables, the Pittsburgh Sleep Quality Index scores were found to have a significant positive correlation with the prevalence of urolithiasis (OR: 1.178; 95% CI = 1.083-1.282; p < 0.001). The risk of urolithiasis was significantly increased with an elevation of the component Pittsburgh Sleep Quality Index score in sleep latency, sleep duration, habitual sleep efficiency, and daytime dysfunction. CONCLUSIONS: It was evident that there is an association between sleep quality and prevalence of renal stones in natural population in western China regions. Poor sleep quality is related to urolithiasis. The findings of the current study hence highlighted the need for future public health guidelines to develop detailed strategies for improving sleep quality.

Crosstalk between Tumor-Associated Macrophages and MicroRNAs: A Key Role in Tumor Microenvironment.

As an in-depth understanding of immunotherapy continues to grow, current anticancer therapy research is increasingly focused on the tumor microenvironment (TME). MicroRNAs (miRNAs) play crucial roles in the regulation of genetic information and expression and mediate interactions between tumor cells and components in the TME, such as tumor-associated macrophages (macrophages). Macrophages are abundant in the TME, and their different polarization directions can promote or inhibit tumor growth and progression. By regulating biological behaviors, such as macrophage recruitment, infiltration, and polarization, miRNAs can affect various molecular pathways to regulate tumor progression and treatment response. In this review, we discuss in detail the effects of macrophages on tumors and the multifaceted effects of miRNAs on macrophages. We also discuss the potential clinical applications and prospects of targeted therapy based on miRNAs, novel clinical biomarkers, and drug delivery systems.

The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma is an aggressive liver carcinoma with increasing incidence and mortality. A good auxiliary prognostic prediction tool is desperately needed for the development of treatment strategies. The purpose of this study was to explore the prognostic value of the radiomics nomogram based on enhanced CT in intrahepatic cholangiocarcinoma. METHODS: In this retrospective study, 101 patients with pathological confirmation of intrahepatic cholangiocarcinoma were recruited. A radiomics nomogram was developed by radiomics score and independent clinical risk factors selecting from multivariate Cox regression. All patients were stratified as high risk and low risk by a nomogram. Model performance and clinical usefulness were assessed by calibration curve, ROC curve, and survival curve. RESULTS: A total of 101patients (mean age, 58.2 years old; range 36-79 years old) were included in the study. The 1-year, 3-year, and 5-year overall survival rates were 49.5%, 26.6%, and 14.4%, respectively, with a median survival time of 12.2 months in the whole set. The least absolute shrinkage and selection operator (LASSO) method selected 3 features. Multivariate Cox analysis found three independent prognostic factors. The radiomics nomogram showed a significant prognosis value with overall survival. There was a significant difference in the 1-year and 3-year survival rates of stratified high-risk and low-risk patients in the whole set (30.4% vs. 56.4% and 13.0% vs. 30.6%, respectively, p = 0.018). CONCLUSIONS: This radiomics nomogram has potential application value in the preoperative prognostic prediction of intrahepatic cholangiocarcinoma and may facilitate in clinical decision-making.

CMEP: a database for circulating microRNA expression profiling.

MOTIVATION: In recent years, several experimental studies have revealed that the microRNAs (miRNAs) in serum, plasma, exosome and whole blood are dysregulated in various types of diseases, indicating that the circulating miRNAs may serve as potential noninvasive biomarkers for disease diagnosis and prognosis. However, no database has been constructed to integrate the large-scale circulating miRNA profiles, explore the functional pathways involved and predict the potential biomarkers using feature selection between the disease conditions. Although there have been several studies attempting to generate a circulating miRNA database, they have not yet integrated the large-scale circulating miRNA profiles or provided the biomarker-selection function using machine learning methods. RESULTS: To fill this gap, we constructed the Circulating MicroRNA Expression Profiling (CMEP) database for integrating, analyzing and visualizing the large-scale expression profiles of phenotype-specific circulating miRNAs. The CMEP database contains massive datasets that were manually curated from NCBI GEO and the exRNA Atlas, including 66 datasets, 228 subsets and 10 419 samples. The CMEP provides the differential expression circulating miRNAs analysis and the KEGG functional pathway enrichment analysis. Furthermore, to provide the function of noninvasive biomarker discovery, we implemented several feature-selection methods, including ridge regression, lasso regression, support vector machine and random forests. Finally, we implemented a user-friendly web interface to improve the user experience and to visualize the data and results of CMEP. AVAILABILITY AND IMPLEMENTATION: CMEP is accessible at http://syslab5.nchu.edu.tw/CMEP.

Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA.

BACKGROUND: Sepsis remains a major challenge in intensive care units, causing unacceptably high mortality rates due to the lack of rapid diagnostic tools with sufficient sensitivity. Therefore, there is an urgent need to replace time-consuming blood cultures with a new method. Ideally, such a method also provides comprehensive profiling of pathogenic bacteria to facilitate the treatment decision. METHODS: We developed a Random Forest with balanced subsampling to screen for pathogenic bacteria and diagnose sepsis based on cell-free DNA (cfDNA) sequencing data in a small blood sample. In addition, we constructed a bacterial co-occurrence network, based on a set of normal and sepsis samples, to infer unobserved bacteria. RESULTS: Based solely on cfDNA sequencing information from three independent datasets of sepsis, we distinguish sepsis from healthy samples with a satisfactory performance. This strategy also provides comprehensive bacteria profiling, permitting doctors to choose the best treatment strategy for a sepsis case. CONCLUSIONS: The combination of sepsis identification and bacteria-inferring strategies is a success for noninvasive cfDNA-based diagnosis, which has the potential to greatly enhance efficiency in disease detection and provide a comprehensive understanding of pathogens. For comparison, where a culture-based analysis of pathogens takes up to 5 days and is effective for only a third to a half of patients, cfDNA sequencing can be completed in just 1 day and our method can identify the majority of pathogens in all patients.

Development and validation of a preoperative nomogram for predicting survival of patients with locally advanced prostate cancer after radical prostatectomy.

BACKGROUND: For selected locally advanced prostate cancer (PCa) patients, radical prostatectomy (RP) is one of the first-line treatments. We aimed to develop a preoperative nomogram to identify what kinds of patients can get the most survival benefits after RP. METHODS: We conducted analyses with data from the Surveillance, Epidemiology, and End Results (SEER) database. Covariates used for analyses included age at diagnosis, marital status, race, American Joint Committee on Cancer (AJCC) 7th TNM stage, Prostate specific antigen, Gleason biopsy score (GS), percent of positive cores. We estimated the cumulative incidence function for cause-specific death. The Fine and Gray's proportional subdistribution hazard approach was used to perform multivariable competing risk analyses and reveal prognostic factors. A nomogram was built by these factors (including GS, percent of positive cores and N stage) and validated by concordance index and calibration curves. Risk stratification was established based on the nomogram. RESULTS: We studied 14,185 patients. N stage, GS, and percent of positive cores were the independent prognostic factors used to construct the nomogram. For validating, in the training cohort, the C-index was 0.779 (95% CI 0.736-0.822), and in the validation cohort, the C-index was 0.773 (95% CI 0.710-0.836). Calibration curves showed that the predicted survival and actual survival were very close. The nomogram performed better over the AJCC staging system (C-index 0.779 versus 0.764 for training cohort, and 0.773 versus 0.744 for validation cohort). The new stratification of risk groups based on the nomogram also showed better discrimination than the AJCC staging system. CONCLUSIONS: The preoperative nomogram can provide favorable prognosis stratification ability to help clinicians identify patients who are suitable for surgery.

CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data.

The detection of tumor-derived cell-free DNA in plasma is one of the most promising directions in cancer diagnosis. The major challenge in such an approach is how to identify the tiny amount of tumor DNAs out of total cell-free DNAs in blood. Here we propose an ultrasensitive cancer detection method, termed 'CancerDetector', using the DNA methylation profiles of cell-free DNAs. The key of our method is to probabilistically model the joint methylation states of multiple adjacent CpG sites on an individual sequencing read, in order to exploit the pervasive nature of DNA methylation for signal amplification. Therefore, CancerDetector can sensitively identify a trace amount of tumor cfDNAs in plasma, at the level of individual reads. We evaluated CancerDetector on the simulated data, and showed a high concordance of the predicted and true tumor fraction. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor cfDNAs. In addition, the predicted tumor fraction showed great consistency with tumor size and survival outcome. Note that all of those testing were performed on sequencing data at low to medium coverage (1× to 10×). Therefore, CancerDetector holds the great potential to detect cancer early and cost-effectively.

Detecting early-warning signals of influenza outbreak based on dynamic network marker.

The seasonal outbreaks of influenza infection cause globally respiratory illness, or even death in all age groups. Given early-warning signals preceding the influenza outbreak, timely intervention such as vaccination and isolation management effectively decrease the morbidity. However, it is usually a difficult task to achieve the real-time prediction of influenza outbreak due to its complexity intertwining both biological systems and social systems. By exploring rich dynamical and high-dimensional information, our dynamic network marker/biomarker (DNM/DNB) method opens a new way to identify the tipping point prior to the catastrophic transition into an influenza pandemics. In order to detect the early-warning signals before the influenza outbreak by applying DNM method, the historical information of clinic hospitalization caused by influenza infection between years 2009 and 2016 were extracted and assembled from public records of Tokyo and Hokkaido, Japan. The early-warning signal, with an average of 4-week window lead prior to each seasonal outbreak of influenza, was provided by DNM-based on the hospitalization records, providing an opportunity to apply proactive strategies to prevent or delay the onset of influenza outbreak. Moreover, the study on the dynamical changes of hospitalization in local district networks unveils the influenza transmission dynamics or landscape in network level.

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization.

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.

The Role of Serum Tenascin-C in Predicting In-Hospital Death in Acute Aortic Dissection.

Tenascin-C (TNC) is involved in aortic disease pathophysiology. This study aims to evaluate TNC's value for predicting in-hospital death in acute aortic dissection (AD).We prospectively enrolled consecutive patients with suspected acute AD within 48 hours from symptom onset. Serum TNC and C-reactive protein (CRP) levels were examined on admission. Their baseline clinical characteristics and serum D-Dimer (DD) were collected. The endpoint was in-hospital death from AD.In the study cohort,78 survivors and 31 non-survivors with acute AD were enrolled. Compared to survivors, elevated median levels of serum TNC (141.10 pg/mL versus 75.30 pg/mL, P < 0.001), DD (8.74 µg/mL versus 4.58 µg/mL, P < 0.001), and CRP (19.20 mg/L versus 13.40 mg/L, P < 0.001) were found in non-survivors. Multiple logistic regressions revealed TNC, DD, and CRP were independent predictors of in-hospital death from acute AD. The OR and 95% CI were 1.038, 1.017-1.055; 1.084, 1.009-1.165 and 1.386, 1.107-1.643, respectively. Furthermore, TNC's sensitivity and specificity in predicting in-hospital death in acute AD were 83.87% and 83.33%. The combination of TNC and DD can improve the sensitivity and specificity to 90.30% and 88.46%.TNC is a valuable biomarker for predicting in-hospital death from acute AD. The combination of TNC and DD can improve predictions of in-hospital death from acute AD.