Using graph visualization to look at the trajectories of events that lead to readmission

Information on specific sequence of healthcare utilization events in heart failure patients may be useful for identifying distinct subpopulations of patients with HF. Knowledge of patient trajectories may help to improve prediction of future readmission which can be used to tailor management to the individual needs of the patient. This research introduces a new approach to mining administrative and clinical datasets by incorporating graph networks to identify & visualize the trajectories of sequences of events

Evaluation of Matrix Metalloproteinase 2 and 9 Activity in Patients with Prostate Cancer and Benign Prostate Hyperplasia Compared with Healthy Individuals

Background and Aim: Prostate cancer (PC) is one of the most prevalent cancers with high mortality and morbidity in men, which can be treated in different ways before the progression and metastasis to distant organs. Destruction of extracellular matrix by matrix metalloproteinase (MMP), particularly by the 2 and 9 subtypes, has an important role in the metastasis of PC. We aimed to assess the activity of MMP 2 and 9 and some related metalloproteinases in PC and with benign prostate hyperplasia (BPH) patients in comparison to healthy individuals. Methods: In this case-control study, 72 individuals referred to Imam Khomeini hospital (Tehran, Iran), have been divided into 3 groups, including PC, BPH, and healthy control. Age and body mass index (BMI) for all groups have been matched. Venous blood samples were used to assess the enzyme activity by the zymography technique. Results: The activity of MMP-2 and 9 was significantly higher in PC than BPH and control groups. But there was no difference in the activity of enzymes in patients with PC according to the Gleason score. Conclusion: The results suggested that MMPs activity can be considered a diagnostic marker for PC. However, further studies are required to establish this concept. *Corresponding Author: Abbas Khonakdar-Tarsi; Email: [email protected] Please cite this article as: Shojaee M, Mohammadi P, Jafarpour H, Pouriamehr S, Barmaki H, Khonakdar-Tarsi A. Evaluation of Matrix Metalloproteinase 2 and 9 Activity in Patients with Prostate Cancer and Benign Prostate Hyperplasia Compared with Healthy Individuals. Arch Med Lab Sci. 2020;6:1-6(e12). https://doi.org/10.22037/amls.v6.3237

Data archive: CICT for single cell RNA-seq network inference

<p>This archive contains benchmarking input data and results for using single cell gene expression data to infer gene regulatory networks (GRN) by the Causal Inference with Composition of Transactions (CICT) method and a selected set of published methods. This accompanies the manuscript "Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions" (Shojaee and Huang, Brief in Bioinform 2023. DOI: 10.1093/bib/bbad370). The CICT code is available at the GitHub repo (https://github.com/hlab1/scRNAseqWithCICT/).</p><p>The original CICT algorithm was described in Shojaee et al. (arXiv:1608.02658, 2016). The benchmarked methods were included in the BEELINE benchmarking pipeline (Pratapa et al., Nat Methods 2020), to which we added DEEPDRIM (Chen et al., Brief Bioinform 2021), SCENIC (Aibar et al., Nat Methods 2017), Inferelator 3.0 (Gibbs et al., Bioinformatics 2022), and CellOracle (Kamimoto et al., Nature 2023). The output directory names are (subdirectories within each dataset):</p><p>* CICT_ewMIshrink_RFmaxdepth10_RFntrees20/: CICT for simulated data<br>* CICT_v2/: CICT for experimental data<br>* CELLORACLEDB/: CellOracle for experimental data<br>* DEEPDRIM72_ewMIshrink_RFmaxdepth10_RFntrees20/: DEEPDRIM for simulated data<br>* DEEPDRIM72_v2/: DEEPDRIM for experimental data<br>* INFERELATOR38_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-Prior for simulated data<br>* INFERELATOR38_v2/: Inferelator-Prior for experimental data<br>* INFERELATOR34_ewMIshrink_RFmaxdepth10_RFntrees20/: Inferelator-NoPrior for experimental data<br>* INFERELATOR34_v2/: Inferelator-NoPrior for experimental data<br>* GENIE3/: GENIE3<br>* GRNBOOST2/: GRNBOST2<br>* LEAP/: LEAP<br>* PIDC/: PIDC<br>* PPCOR/: PPCOR<br>* SCENICDB/: SCENIC for experimental data<br>* SCNS/: SCNS<br>* SCODE/: SCODE<br>* SCRIBE/: SCRIBE<br>* SINCERITIES/: SINCERITIES<br>* SINGE/: SINGE<br>* RANDOM/: RANDOM</p><p>The methods were benchmarked against two kinds of scRNA-seq datasets:<br>* Simulated datasets produced by the SERGIO simulator from a synthetic network (Dibaeinia et al., Cell Systems 2020), including complete datasets and datasets with dropouts with shape parameter k=6.5 and rate parameter q=10, 30, 50, 70, 80. <br>* Experimental datasets compiled by the BEELINE pipeline, evaluated at three different levels L0, L1 and L2, with three types of ground truth networks.<br>    * Evaluation levels:<br>        * L0: 500 highly varying genes plus TFs<br>        * L1: 1000 highly varying genes plus TFs<br>        * L2: 500 highly varying genes, TFs and 500 genes randomly selected that excluded the 1000 highly varying genes from L1.<br>    * Types of ground truths:<br>        * Cell-type-specific ChIP-seq ground truth (L0, L1, L2)<br>        * Non-specific ChIP-seq ground truth (L0_ns, L1_ns, L2_ns)<br>        * Loss-of-function/gain-of-function ground truth (L0_lofgof, L1_lofgof, L2_lofgof)</p><p>The directory structure is organized in accordance with the BEELINE benchmarking pipeline. For complete details please please see the BEELINE documentation (https://murali-group.github.io/Beeline/) and Github repo (https://github.com/Murali-group/Beeline).</p><p> </p&gt

Simulation of flow through dam foundation by isogeometric method

This research introduces a numerical approach called IsoGeometric Analysis (IGA) to solve the Laplace equation. Non-Uniform Rational B-Splines (NURBS) basis function is applied for approximation of the anisotropic saturated porous media of dam foundation field, as for description of the geometry. The discretized form of the governing Laplace equation is obtained using the standard Galerkin method. The present results consist of uplift pressure, seepage discharge and exit gradient which are validated with existing experimental data based on a physical model. The obtained data are also compared with empirical data. The computed results show a satisfactory agreement with the experimental measurements in the wide ranges of upstream flow conditions. In addition, it was found that the mentioned numerical method improves the convergency and accuracy of parameters compared to traditional methods

Discovery of temporal and disease association patterns in condition-specific hospital utilization rates

<div><p>Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008–2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition’s seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes.</p></div

Pairwise Pearson correlation coefficients for indices of temporal variation for conditions with summer peaks included in k-means clustering.

<p>Pairwise Pearson correlation coefficients for indices of temporal variation for conditions with summer peaks included in k-means clustering.</p

Cumulative distribution of seasonal coefficient for 164 conditions with statistically significant seasonal variation.

<p>Displays the percentage of diseases (y-axis) that have less than or equal to the specified seasonal coefficient (x-axis). Fifty codes (30.5%) have a seasonal coefficient greater than 0.1 and 15 codes (9.1%) have a seasonal coefficient greater than 0.2.</p

Highest amplitude conditions with seasonal variation in hospital utilization by season.

<p>Highest amplitude conditions with seasonal variation in hospital utilization by season.</p