Decreased NMIIA heavy chain phosphorylation at S1943 promotes mitoxantrone resistance by upregulating BCRP and N-cadherin expression in breast cancer cells.

Mitoxantrone (MX) is an effective treatment for breast cancer; however, high efflux of MX that is accomplished by breast cancer resistance protein (BCRP) leads to acquired multidrug resistance (MDR), reducing MX's therapeutic efficacy in breast cancer. Non-muscle myosin IIA (NMIIA) and its heavy phosphorylation at S1943 have been revealed to play key roles in tumor metastasis and progression, including in breast cancer; however, their molecular function in BCRP-mediated MDR in breast cancer remains unknown. In this study, we revealed that the expression of NMIIA heavy chain phosphorylation at S1943 was downregulated in BCRP-overexpressing breast cancer MCF-7/MX cells, and stable expression of NMIIA-S1943A mutant increased BCRP expression and promoted the resistance of MCF-7/MX cells to MX. Meanwhile, NMIIA S1943 phosphorylation induced by epidermal growth factor (EGF) was accompanied by the downregulation of BCRP in MCF-7/MX cells. Furthermore, stable expression of NMIIA-S1943A in MCF-7/MX cells resulted in upregulation of N-cadherin and the accumulation of ß-catenin on the cell surface, which inhibited the nucleus translocation of ß-catenin and Wnt/ß-catenin-based proliferative signaling. EGF stimulation of MCF-7/MX cells showed the downregulation of N-cadherin and ß-catenin. Our results suggest that decreased NMIIA heavy phosphorylation at S1943 increases BCRP expression and promotes MX resistance in breast cancer cells via upregulating N-cadherin expression.

PEcnv: accurate and efficient detection of copy number variations of various lengths.

Copy number variation (CNV) is a class of key biomarkers in many complex traits and diseases. Detecting CNV from sequencing data is a substantial bioinformatics problem and a standard requirement in clinical practice. Although many proposed CNV detection approaches exist, the core statistical model at their foundation is weakened by two critical computational issues: (i) identifying the optimal setting on the sliding window and (ii) correcting for bias and noise. We designed a statistical process model to overcome these limitations by calculating regional read depths via an exponentially weighted moving average strategy. A one-run detection of CNVs of various lengths is then achieved by a dynamic sliding window, whose size is self-adopted according to the weighted averages. We also designed a novel bias/noise reduction model, accompanied by the moving average, which can handle complicated patterns and extend training data. This model, called PEcnv, accurately detects CNVs ranging from kb-scale to chromosome-arm level. The model performance was validated with simulation samples and real samples. Comparative analysis showed that PEcnv outperforms current popular approaches. Notably, PEcnv provided considerable advantages in detecting small CNVs (1 kb-1 Mb) in panel sequencing data. Thus, PEcnv fills the gap left by existing methods focusing on large CNVs. PEcnv may have broad applications in clinical testing where panel sequencing is the dominant strategy. Availability and implementation: Source code is freely available at https://github.com/Sherwin-xjtu/PEcnv.

Efficacy and safety of tai chi exercise on bone health: An umbrella review.

PURPOSE: To critically evaluate systematic reviews (SRs) of the Tai Chi (TC) exercise on bone health and provide more recently available evidence. METHODS: SRs with or without meta-analysis (MA) of TC on bone health were comprehensively searched in eight electronic databases (PubMed, EMBASE, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang Database, Chinese Biomedical Literature Database, and Chinese Scientific Journals Database) and in the international prospective register of systematic reviews of (PROSPERO) from initiation to March 2023. Descriptive analyses of SRs were performed, and reporting and methodological quality of the included SRs were evaluated using the updated version of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist and A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR-2). The certainty of the synthesized evidence was assessed with the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. RESULTS: Eighteen SRs, 15 with MAs, were included. Forty-nine RCTs and 16 NRSIs with 3956 and 1157 participants, respectively, were included in these SRs. The reporting quality of the included SRs ranged from high to low, but most received critically low AMSTAR-2 scores. Efficacy of TC on nine bone health biomarkers has been explored, covering bone mineral density (BMD) and serum biomarkers. The results showed that compare to non-intervention, perimenopausal and postmenopausal participants who practiced TC may benefit in BMD of the lumbar spine [MD = 0.04, 95% CI (0.02, 0.07)], and femoral neck [MD = 0.04, 95% CI (0.02, 0.06)], but not BMD of the femoral proximal trochanter [MD = 0.02, 95% CI (0.00, 0.03)], ward's triangle [MD = 0.02, 95% CI (-0.01, 0.04)], and femoral shaft [SMD = 0.16, 95% CI (-0.11, 0.44)]. Elders practicing TC may benefit in BMD of the femoral neck [SMD = 0.28, 95% CI (0.10, 0.45)], femoral proximal trochanter [SMD = 0.39, 95% CI (0.05, 0.73)], and ward's triangle [SMD = 0.21, 95% CI (0.05,0.37)], but may not in BMD of lumbar spine [SMD = 0.03, 95% CI (-0.22, 0.27)]. CONCLUSION: We have low certainty that for perimenopausal and postmenopausal women, compare to those with no exercise, TC could improve BMD of the lumbar spine, femoral neck. We also have low certainty that in elder population, TC practitioners may benefit in BMD of femoral neck, and Ward's triangle. REGISTRATION: PROSPERO (CRD42020173543).

Metformin Alleviates Sepsis-Associated Myocardial Injury by Enhancing AMP-Activated Protein Kinase/Mammalian Target of Rapamycin Signaling Pathway-Mediated Autophagy.

ABSTRACT: Sepsis-associated myocardial injury is one of the main causes of death in intensive care units, and current clinical treatments have not been satisfactory. Therefore, finding an effective intervention is an urgent requirement. Metformin, an anti-type 2 diabetes drug, has been reported to be an autophagic activator agent that confers protection in some diseases. However, it is unclear whether it can provide defense against sepsis-associated myocardial injury. In this study, we investigated the cardioprotective effects of metformin pretreatment against lipopolysaccharide (LPS)-induced myocardial injury in C57BL/6J mice or H9c2 cells and the possible underlying mechanisms. Metformin was administered at a dose of 100 mg/kg for a week before LPS intraperitoneal injection. Twenty-four hours after LPS intervention, echocardiographic evaluation, reactive oxygen species measurement, Hoechst staining, western blotting, hematoxylin and eosin staining, and enzyme-linked immunosorbent assay were performed. Inhibitors of autophagy and AMP-activated protein kinase (AMPK) were used to further clarify the mechanisms involved. Metformin pretreatment effectively attenuated cardiac dysfunction, reduced the levels of myocardial enzymes, and alleviated cardiac hydroncus in LPS-treated mice. In addition, metformin restored the LPS-disrupted antioxidant defense and activated LPS-reduced autophagy by modulating the AMPK/mammalian target of rapamycin (AMPK/mTOR) pathway both in vivo and in vitro. The antioxidant effects of metformin on cardiomyocytes were abolished by the autophagy inhibitor 3-methyladenine (3-MA). Treatment with compound C, an AMPK inhibitor, reversed the metformin-induced autophagy in LPS-treated H9c2 cells. In conclusion, metformin pretreatment alleviates LPS-induced myocardial injury by activating AMPK/mTOR pathway-mediated autophagy.

State-level metabolic comorbidity prevalence and control among adults age 50-plus with diabetes: estimates from electronic health records and survey data in five states.

BACKGROUND: Although treatment and control of diabetes can prevent complications and reduce morbidity, few data sources exist at the state level for surveillance of diabetes comorbidities and control. Surveys and electronic health records (EHRs) offer different strengths and weaknesses for surveillance of diabetes and major metabolic comorbidities. Data from self-report surveys suffer from cognitive and recall biases, and generally cannot be used for surveillance of undiagnosed cases. EHR data are becoming more readily available, but pose particular challenges for population estimation since patients are not randomly selected, not everyone has the relevant biomarker measurements, and those included tend to cluster geographically. METHODS: We analyzed data from the National Health and Nutritional Examination Survey, the Health and Retirement Study, and EHR data from the DARTNet Institute to create state-level adjusted estimates of the prevalence and control of diabetes, and the prevalence and control of hypertension and high cholesterol in the diabetes population, age 50 and over for five states: Alabama, California, Florida, Louisiana, and Massachusetts. RESULTS: The estimates from the two surveys generally aligned well. The EHR data were consistent with the surveys for many measures, but yielded consistently lower estimates of undiagnosed diabetes prevalence, and identified somewhat fewer comorbidities in most states. CONCLUSIONS: Despite these limitations, EHRs may be a promising source for diabetes surveillance and assessment of control as the datasets are large and created during the routine delivery of health care. TRIAL REGISTRATION: Not applicable.

Chrysin prevents lipopolysaccharide-induced acute lung injury in mice by suppressing the IRE1α/TXNIP/NLRP3 pathway.

Acute lung injury (ALI) remains a serious challenge in the intensive care unit. Inflammation plays a key role in the progression of ALI. Chrysin (CHR) is a natural flavonoid with anti-inflammatory functions. We investigated the anti-inflammatory effects in a mouse model of ALI induced by lipopolysaccharide (LPS), and identified the underlying mechanisms of its action. Following CHR administration, mice were challenged with LPS intratracheally for 6 h to induce ALI. Compared to mice challenged with LPS alone, the presence of CHR showed a reduction in the development of lung injuries, as confirmed by histopathological observation. Pre-treatment with CHR attenuated inflammation by reducing the production of myeloperosidase (MPO), and pro-inflammatory cytokine levels in the lung and bronchoalveolar lavage fluid (BALF). Furthermore, CHR improved lung edema by reducing the vascular permeability, as demonstrated by less evans blue staining in the lung tissue and low levels of protein in BALF. In addition, our results proved that CHR improved the antioxidant capacity by increasing the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in the lung tissue. Results of western blot assays suggested that CHR suppressed the LPS-induced expression of glucose-regulated protein 78 (GRP78) and phosphorylated inositol-requiring enzyme 1α (p-IRE1α). We also found that CHR suppressed the expression of thioredoxin interaction protein (TXNIP), nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) and cleaved caspase-1. In conclusion, CHR improved vascular permeability and mitigated the inflammatory response of lung tissue by suppressing the IRE1α/TXNIP/NLRP3 pathway, thereby alleviating LPS-induced ALI in the lungs of mice.

Epithelial cell adhesion molecule promotes breast cancer resistance protein-mediated multidrug resistance in breast cancer by inducing partial epithelial-mesenchymal transition.

Overexpression of breast cancer resistance protein (BCRP) plays a crucial role in the acquired multidrug resistance (MDR) in breast cancer. The elucidation of molecular events that confer BCRP-mediated MDR is of major therapeutic importance in breast cancer. Epithelial cell adhesion molecule (EpCAM) has been implicated in tumor progression and drug resistance in various types of cancers, including breast cancer. However, the role of EpCAM in BCRP-mediated MDR in breast cancer remains unknown. In the present study, we revealed that EpCAM expression was upregulated in BCRP-overexpressing breast cancer MCF-7/MX cells, and EpCAM knockdown using siRNA reduced BCRP expression and increased the sensitivity of MCF-7/MX cells to mitoxantrone (MX). The epithelial-mesenchymal transition (EMT) promoted BCRP-mediated MDR in breast cancer cells, and EpCAM knockdown partially suppressed EMT progression in MCF-7/MX cells. In addition, Wnt/ß-catenin signaling was activated in MCF-7/MX cells, and the inhibition of this signaling attenuated EpCAM and BCRP expression and partially reversed EMT. Together, this study illustrates that EpCAM upregulation by Wnt/ß-catenin signaling induces partial EMT to promote BCRP-mediated MDR resistance in breast cancer cells. EpCAM may be a potential therapeutic target for overcoming BCRP-mediated resistance in human breast cancer.

A non-linear ensemble model-based surgical risk calculator for mixed data from multiple surgical fields.

BACKGROUND: The misestimation of surgical risk is a serious threat to the lives of patients when implementing surgical risk calculator. Improving the accuracy of postoperative risk prediction has received much attention and many methods have been proposed to cope with this problem in the past decades. However, those linear approaches are inable to capture the non-linear interactions between risk factors, which have been proved to play an important role in the complex physiology of the human body, and thus may attenuate the performance of surgical risk calculators. METHODS: In this paper, we presented a new surgical risk calculator based on a non-linear ensemble algorithm named Gradient Boosting Decision Tree (GBDT) model, and explored the corresponding pipeline to support it. In order to improve the practicability of our approach, we designed three different modes to deal with different data situations. Meanwhile, considering that one of the obstacles to clinical acceptance of surgical risk calculators was that the model was too complex to be used in practice, we reduced the number of input risk factors according to the importance of them in GBDT. In addition, we also built some baseline models and similar models to compare with our approach. RESULTS: The data we used was three-year clinical data from Surgical Outcome Monitoring and Improvement Program (SOMIP) launched by the Hospital Authority of Hong Kong. In all experiments our approach shows excellent performance, among which the best result of area under curve (AUC), Hosmer-Lemeshow test ([Formula: see text]) and brier score (BS) can reach 0.902, 7.398 and 0.047 respectively. After feature reduction, the best result of AUC, [Formula: see text] and BS of our approach can still be maintained at 0.894, 7.638 and 0.060, respectively. In addition, we also performed multiple groups of comparative experiments. The results show that our approach has a stable advantage in each evaluation indicator. CONCLUSIONS: The experimental results demonstrate that NL-SRC can not only improve the accuracy of predicting the surgical risk of patients, but also effectively capture important risk factors and their interactions. Meanwhile, it also has excellent performance on the mixed data from multiple surgical fields.

Risk-adjusted zero-inflated Poisson CUSUM charts for monitoring influenza surveillance data.

BACKGROUND: The influenza surveillance has been received much attention in public health area. For the cases with excessive zeroes, the zero-inflated Poisson process is widely used. However, the traditional control charts based on zero-inflated Poisson model, ignore the association between influenza cases and risk factors, and thus may lead to unexpected mistakes when implementing monitoring charts. METHOD: In this paper, we proposed risk-adjusted zero-inflated Poisson cumulative sum control charts, in which the risk factors were put to adjust the risk of influenza and the adjustment was made by zero-inflated Poisson regression. We respectively proposed the control chart monitoring the parameters individually and simultaneously. RESULTS: The performance of our proposed risk-adjusted zero-inflated Poisson cumulative sum control chart was evaluated and compared with the unadjusted standard cumulative sum control charts in simulation studies. The results show that for different distribution of impact factors and different coefficients, the risk-adjusted cumulative sum charts can generate much less false alarm than the standard ones. Finally, the influenza surveillance data from Hong Kong is used to illustrate the application of the proposed chart. CONCLUSIONS: Our results suggest that the adjusted cumulative sum control chart we proposed is more accurate and credible than the unadjusted standard control charts because of the lower false alarm rate of the adjusted ones. Even the unadjusted control charts may signal a little faster than the adjusted ones, the alarm they raise may have low credibility since they also raise alarm frequently even the processes are in control. Thus we suggest using the risk-adjusted cumulative sum control charts to monitor the influenza surveillance data to alert accurately, credibly and relatively quickly.

OCRDetector: Accurately Detecting Open Chromatin Regions via Plasma Cell-Free DNA Sequencing Data.

Open chromatin regions (OCRs) are special regions of the human genome that can be accessed by DNA regulatory elements. Several studies have reported that a series of OCRs are associated with mechanisms involved in human diseases, such as cancers. Identifying OCRs using ATAC-seq or DNase-seq is often expensive. It has become popular to detect OCRs from plasma cell-free DNA (cfDNA) sequencing data, because both the fragmentation modes of cfDNA and the sequencing coverage in OCRs are significantly different from those in other regions. However, it is a challenging computational problem to accurately detect OCRs from plasma cfDNA-seq data, as multiple factors-e.g., sequencing and mapping bias, insufficient read depth, etc.-often mislead the computational model. In this paper, we propose a novel bioinformatics pipeline, OCRDetector, for detecting OCRs from whole-genome cfDNA sequencing data. The pipeline calculates the window protection score (WPS) waveform and the cfDNA sequencing coverage. To validate the proposed pipeline, we compared the percentage overlap of our OCRs with those obtained by other methods. The experimental results show that 81% of the TSS regions of housekeeping genes are detected, and our results have obvious tissue specificity. In addition, the overlap percentage between our OCRs and the high-confidence OCRs obtained by ATAC-seq or DNase-seq is greater than 70%.

Accurately estimating the length distributions of genomic micro-satellites by tumor purity deconvolution.

BACKGROUND: Genomic micro-satellites are the genomic regions that consist of short and repetitive DNA motifs. Estimating the length distribution and state of a micro-satellite region is an important computational step in cancer sequencing data pipelines, which is suggested to facilitate the downstream analysis and clinical decision supporting. Although several state-of-the-art approaches have been proposed to identify micro-satellite instability (MSI) events, they are limited in dealing with regions longer than one read length. Moreover, based on our best knowledge, all of these approaches imply a hypothesis that the tumor purity of the sequenced samples is sufficiently high, which is inconsistent with the reality, leading the inferred length distribution to dilute the data signal and introducing the false positive errors. RESULTS: In this article, we proposed a computational approach, named ELMSI, which detected MSI events based on the next generation sequencing technology. ELMSI can estimate the specific length distributions and states of micro-satellite regions from a mixed tumor sample paired with a control one. It first estimated the purity of the tumor sample based on the read counts of the filtered SNVs loci. Then, the algorithm identified the length distributions and the states of short micro-satellites by adding the Maximum Likelihood Estimation (MLE) step to the existing algorithm. After that, ELMSI continued to infer the length distributions of long micro-satellites by incorporating a simplified Expectation Maximization (EM) algorithm with central limit theorem, and then used statistical tests to output the states of these micro-satellites. Based on our experimental results, ELMSI was able to handle micro-satellites with lengths ranging from shorter than one read length to 10kbps. CONCLUSIONS: To verify the reliability of our algorithm, we first compared the ability of classifying the shorter micro-satellites from the mixed samples with the existing algorithm MSIsensor. Meanwhile, we varied the number of micro-satellite regions, the read length and the sequencing coverage to separately test the performance of ELMSI on estimating the longer ones from the mixed samples. ELMSI performed well on mixed samples, and thus ELMSI was of great value for improving the recognition effect of micro-satellite regions and supporting clinical decision supporting. The source codes have been uploaded and maintained at https://github.com/YixuanWang1120/ELMSI for academic use only.

A hybrid correcting method considering heterozygous variations by a comprehensive probabilistic model.

BACKGROUND: The emergence of the third generation sequencing technology, featuring longer read lengths, has demonstrated great advancement compared to the next generation sequencing technology and greatly promoted the biological research. However, the third generation sequencing data has a high level of the sequencing error rates, which inevitably affects the downstream analysis. Although the issue of sequencing error has been improving these years, large amounts of data were produced at high sequencing errors, and huge waste will be caused if they are discarded. Thus, the error correction for the third generation sequencing data is especially important. The existing error correction methods have poor performances at heterozygous sites, which are ubiquitous in diploid and polyploidy organisms. Therefore, it is a lack of error correction algorithms for the heterozygous loci, especially at low coverages. RESULTS: In this article, we propose a error correction method, named QIHC. QIHC is a hybrid correction method, which needs both the next generation and third generation sequencing data. QIHC greatly enhances the sensitivity of identifying the heterozygous sites from sequencing errors, which leads to a high accuracy on error correction. To achieve this, QIHC established a set of probabilistic models based on Bayesian classifier, to estimate the heterozygosity of a site and makes a judgment by calculating the posterior probabilities. The proposed method is consisted of three modules, which respectively generates a pseudo reference sequence, obtains the read alignments, estimates the heterozygosity the sites and corrects the read harboring them. The last module is the core module of QIHC, which is designed to fit for the calculations of multiple cases at a heterozygous site. The other two modules enable the reads mapping to the pseudo reference sequence which somehow overcomes the inefficiency of multiple mappings that adopt by the existing error correction methods. CONCLUSIONS: To verify the performance of our method, we selected Canu and Jabba to compare with QIHC in several aspects. As a hybrid correction method, we first conducted a groups of experiments under different coverages of the next-generation sequencing data. QIHC is far ahead of Jabba on accuracy. Meanwhile, we varied the coverages of the third generation sequencing data and compared performances again among Canu, Jabba and QIHC. QIHC outperforms the other two methods on accuracy of both correcting the sequencing errors and identifying the heterozygous sites, especially at low coverage. We carried out a comparison analysis between Canu and QIHC on the different error rates of the third generation sequencing data. QIHC still performs better. Therefore, QIHC is superior to the existing error correction methods when heterozygous sites exist.

A prognostic index based on a fourteen long non-coding RNA signature to predict the recurrence-free survival for muscle-invasive bladder cancer patients.

BACKGROUND: Bladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a prognosis predicting signature based on long-noncoding RNA (lncRNA) for the invasive BC patients. METHODS: The lncRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) database, along with the correlated clinicopathological information. The univariate Cox regression test was employed to screen out the recurrence-free survival (RFS)-related lncRNAs. Then, the LASSO method was conducted to construct the signature based on these RFS-related lncRNA candidates. Genes correlated with these fourteen lncRNAs were extracted from the mRNA expression profile, with the Pearson correlation coefficient > 0.60 or < - 0.40. Subsequently, the Proteomap pathway enrichment analyses were conducted to classify the function of these correlated genes. Furthermore, the multivariate analyses were executed to reveal the independent role of the proposed signature with the clinicopathological features. RESULTS: We established an lncRNA-based RFS predicting signature by the LASSO Cox regression test, and proved its usage and stability on both the training and validation cohorts by the Kaplan-Meier and receiver operating characteristic (ROC) curves. Notably, the multivariate Cox regression analysis found that our classifier was an independent indicator for muscle-invasive BC patients rather than sex, age and tumor grade, with higher predictive value than the existing ones. Besides, we did the pathway analyses for these genes that highly correlated with the proposed fourteen lncRNAs, as well as the differentially expressed genes (DEGs) derived from the high-risk vs. low-risk groups, and the recurrence vs. non-recurrence groups, respectively. Notably, these results were consistent, and these genes were mostly enriched in the transcription factors, G protein-coupled receptors, MAPK signaling pathways, which were proved significantly associated with tumor progression and drug resistance. CONCLUSIONS: Our results suggested that the fourteen-lncRNA-based RFS predicting signature is an independent indicator for BC patients. Further prospective studies with more samples are needed to verify our findings.

A machine learning framework for accurately recognizing circular RNAs for clinical decision-supporting.

BACKGROUND: Circular RNAs (circRNAs) are those RNA molecules that lack the poly (A) tails, which present the closed-loop structure. Recent studies emphasized that some circRNAs imply different functions from canonical transcripts, and further associated with complex diseases. Several computational methods have been developed for detecting circRNAs from RNA-seq data. However, the existing methods prefer to high sensitivity strategies, which always introduce many false positives. Thus, in clinical decision-supporting system, a comprehensive filtering approach is needed for accurately recognizing real circRNAs for decision models. METHODS: In this paper, we first reviewed the detection strategies of the existing methods. According to the features from RNA-seq data, we showed that any single feature (data signal) selected by the existing strategies cannot accurately distinguish a circRNA. However, we found that some combinations of those features (data signals) could be used as signatures for recognizing circRNAs. To avoid the high computational complexity of the combinational optimization problem, we present CIRCPlus2, which adopts a machine learning framework to recognize real circRNAs according to multiple data signals captured from RNA-seq data. By comparing multiple machine learning frameworks, CIRCPlus2 adopts a Gradient Boosting Decision Tree (GBDT) framework. RESULTS: Given a set of candidate circRNAs, reported by any existing detection tool(s), the features of each candidate are extracted from the aligned reads. The GBDT framework can be trained by a training dataset. By applying the selected features on the framework, the predictions on true/false positives are reported. To verify the performance of the proposed approach, we conducted several groups of experiments on both real RNA-seq datasets and a series of simulation datasets with different preset configurations. The results demonstrated that CIRCPlus2 clearly improved the specificities, while it also maintained high levels of sensitivities. CONCLUSIONS: Filtering false positives is quite important in RNA-seq data analysis pipeline. Machine learning framework is suitable for solving this filtering problem. CIRCPlus2 is an efficient approach to identify the false positive circRNAs from the real ones.

The Importance of Addressing Depression and Diabetes Distress in Adults with Type 2 Diabetes.

People with type 2 diabetes often experience two common mental health conditions: depression and diabetes distress. Both increase a patient's risk for mortality, poor disease management, diabetes-related complications, and poor quality of life. The American Diabetes Association and the U.S. Preventive Services Task Force recommend routine evaluations for these conditions in adults for optimal disease management and prevention of life-threatening complications. However, barriers exist within primary care and specialty settings that make screening for depression and diabetes distress challenging. Depression and diabetes distress influence diabetes self-care and diabetes control and barriers in clinical care practice that can hinder detection and management of psychosocial issues in diabetes care. This paper highlights opportunities to increase mental health screenings and provides strategies to help providers address depression and diabetes distress in patients with type 2 diabetes.

State-level estimation of diabetes and prediabetes prevalence: Combining national and local survey data and clinical data.

Many statisticians and policy researchers are interested in using data generated through the normal delivery of health care services, rather than carefully designed and implemented population-representative surveys, to estimate disease prevalence. These larger databases allow for the estimation of smaller geographies, for example, states, at potentially lower expense. However, these health care records frequently do not cover all of the population of interest and may not collect some covariates that are important for accurate estimation. In a recent paper, the authors have described how to adjust for the incomplete coverage of administrative claims data and electronic health records at the state or local level. This article illustrates how to adjust and combine multiple data sets, namely, national surveys, state-level surveys, claims data, and electronic health record data, to improve estimates of diabetes and prediabetes prevalence, along with the estimates of the method's accuracy. We demonstrate and validate the method using data from three jurisdictions (Alabama, California, and New York City). This method can be applied more generally to other areas and other data sources.

An Efficient Algorithm for Sensitively Detecting Circular RNA from RNA-seq Data.

Circular RNA (circRNA) is an important member of non-coding RNA family. Numerous computational methods for detecting circRNAs from RNA-seq data have been developed in the past few years, but there are dramatic differences among the algorithms regarding the balancing of the sensitivity and precision of the detection and filtering strategies. To further improve the sensitivity, while maintaining an acceptable precision of circRNA detection, a novel and efficient de novo detection algorithm, CIRCPlus, is proposed in this paper. CIRCPlus accurately locates circRNA candidates by identifying a set of back-spliced junction reads by comparing the local similar sequence of each pair of spanning junction reads. This strategy, thus, utilizes the important information provided by unbalanced spanning reads, which facilitates the detection especially when the expression levels of circRNA are unapparent. The performance of CIRCPlus was tested and compared to the existing de novo methods on the real datasets as well as a series of simulation datasets with different configurations. The experiment results demonstrated that the sensitivities of CIRCPlus were able to reach 90% in common simulation settings, while CIRCPlus held balanced sensitivity and reliability on the real datasets according to an objective assessment criteria based on RNase R-treated samples. The software tool is available for academic uses only.

Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures.

Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings.

An improved burden-test pipeline for identifying associations from rare germline and somatic variants.

BACKGROUND: Identifying rare germline and somatic variants associated with cancer progression is an important research topic in cancer genomics. Although many approaches are proposed for rare variant association study, they are not fit for cancer sequencing data due to multiple issues, such as overly relying on pre-selection, losing sight of interacting hotspots, etc. RESULTS: In this article, we propose an improved pipeline to identify germline variant and somatic mutation interactions influencing cancer susceptibility from pair-wise cancer sequencing data. The proposed pipeline, RareProb-C performs an algorithmic selection on the given variants by incorporating the variant allelic frequencies. The interactions among the variants are considered within the regions which are limited by a four-gamete test. Then it filters singular cases according to the posterior probability at each site. Finally, it outputs the selected candidates that pass a collapse test. CONCLUSIONS: We apply RareProb-C on a series of carefully constructed simulation cases and it outperforms six existing genetic model-free approaches. We also test RareProb-C on 429 TCGA ovarian cancer cases, and RareProb-C successfully identifies the known highlighted variants which are considered increasing disease susceptibilities.

Novel Methods and Data Sources for Surveillance of State-Level Diabetes and Prediabetes Prevalence.

States bear substantial responsibility for addressing the rising rates of diabetes and prediabetes in the United States. However, accurate state-level estimates of diabetes and prediabetes prevalence that include undiagnosed cases have been impossible to produce with traditional sources of state-level data. Various new and nontraditional sources for estimating state-level prevalence are now available. These include surveys with expanded samples that can support state-level estimation in some states and administrative and clinical data from insurance claims and electronic health records. These sources pose methodologic challenges because they typically cover partial, sometimes nonrandom subpopulations; they do not always use the same measurements for all individuals; and they use different and limited sets of variables for case finding and adjustment. We present an approach for adjusting new and nontraditional data sources for diabetes surveillance that addresses these limitations, and we present the results of our proposed approach for 2 states (Alabama and California) as a proof of concept. The method reweights surveys and other data sources with population undercoverage to make them more representative of state populations, and it adjusts for nonrandom use of laboratory testing in clinically generated data sets. These enhanced diabetes and prediabetes prevalence estimates can be used to better understand the total burden of diabetes and prediabetes at the state level and to guide policies and programs designed to prevent and control these chronic diseases.