Pacemaker Implantation following Heart Transplantation: Analysis of a Nation-Wide Database.

BACKGROUND: The 2018 United-Network-for-Organ-Sharing (UNOS) allocation-system changes resulted in greater recognition of mechanical circulatory support (MCS), leading to more heart transplantations (HTx) in patients with MCS. We aimed to investigate the effect of the new UNOS allocation system on the need for a permanent pacemaker and associated complications following HTx. METHODS: The UNOS Registry was questioned, to identify patients that received HTx in the US between 2000 and 2021. The primary objectives were to identify risk factors for the need for a pacemaker implantation following HTx. RESULTS: 49,529 HTx patients were identified, 1421 (2.9%) requiring a pacemaker post-HTx. Patients who required a pacemaker were older (53.9 ± 11.5 vs. 52.6 ± 12.8 years, p < 0.001), more frequently white (73% vs. 67%; p < 0.001) and less frequently black (18% vs. 20%; p < 0.001). In the pacemaker group, UNOS status 1A (46% vs. 41%; p < 0.001) and 1B (31% vs. 27%; p < 0.001) were more prevalent, and donor age was higher (34.4 ± 12.4 vs. 31.8 ± 11.5 years; p < 0.001). One-year survival was no different between the groups (HR: 1.08; 95% CI: 0.85, 1.37; p = 0.515). An era effect was observed (per year: OR: 0.97; 95% CI: 0.96, 0.98; p = 0.003), while ECMO pre-transplant was associated with lower risk of a pacemaker (OR: 0.41; 95% CI: 0.19, 0.86; p < 0.001). CONCLUSIONS: While associated with various patient and transplant characteristics, pacemaker implantation does not seem to impact one-year survival after HTx. The need for pacemaker implantation was lower in the more recent era and in patients who required ECMO pre-transplant, a finding explained by recent advances in perioperative care

Listen to genes : dealing with microarray data in the frequency domain

Background: We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes. Methodology: Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. The normalized gene expressions are then clustered in terms of their power spectrum density. The method of complex Granger causality is introduced to reveal interactions between sets of genes. Complex Granger causality along with partial Granger causality is applied in both time and frequency domains to selected as well as all the genes to reveal the interesting networks of interactions. The approach is successfully applied to Arabidopsis leaf microarray data generated from 31,000 genes observed over 22 time points over 22 days. Three circuits: a circadian gene circuit, an ethylene circuit and a new global circuit showing a hierarchical structure to determine the initiators of leaf senescence are analyzed in detail. Conclusions: We use a totally data-driven approach to form biological hypothesis. Clustering using the power-spectrum analysis helps us identify genes of potential interest. Their dynamics can be captured accurately in the time and frequency domain using the methods of complex and partial Granger causality. With the rise in availability of temporal microarray data, such methods can be useful tools in uncovering the hidden biological interactions. We show our method in a step by step manner with help of toy models as well as a real biological dataset. We also analyse three distinct gene circuits of potential interest to Arabidopsis researchers

A dose escalation and pharmacokinetic study of biweekly pegylated liposomal doxorubicin, paclitaxel and gemcitabine in patients with advanced solid tumours

To determine the maximum tolerated doses (MTDs) and dose-limiting toxicities (DLTs) of pegylated liposomal doxorubicin (PLD), paclitaxel (PCX) and gemcitabine (GEM) combination administered biweekly in patients with advanced solid tumours. Twenty-two patients with advanced-stage solid tumours were treated with escalated doses of PLD on day 1 and PCX plus GEM on day 2 (starting doses: 10, 100 and 800 mg m−2, respectively) every 2 weeks. DLTs and pharmacokinetic (PK) parameters of all drugs were determined during the first cycle of treatment. All but six (73%) patients had previously received at least one chemotherapy regimen. The DLT dose level was reached at PLD 12 mg m−2, PCX 110 mg m−2 and GEM 1000 mg m−2 with neutropaenia being the dose-limiting event. Of the 86 chemotherapy cycles delivered, grade 3 and 4 neutropaenia occurred in 20% with no cases of febrile neutropaenia. Non-haematological toxicities were mild. The recommended MTDs are PLD 12 mg m−2, PCX 100 mg m−2 and GEM 1000 mg m−2 administered every 2 weeks. The PK data revealed no obvious drug interactions. Biweekly administration of PLD, PCX and GEM is a well-tolerated chemotherapy regimen, which merits further evaluation in various types of solid tumours

A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

<p>Abstract</p> <p>Background</p> <p>The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from microarray experiments, is critical to advance our understanding of complex biological processes. In this context, biclustering algorithms have been recognized as an important tool for the discovery of local expression patterns, which are crucial to unravel potential regulatory mechanisms. Although most formulations of the biclustering problem are NP-hard, when working with time series expression data the interesting biclusters can be restricted to those with contiguous columns. This restriction leads to a tractable problem and enables the design of efficient biclustering algorithms able to identify all maximal contiguous column coherent biclusters.</p> <p>Methods</p> <p>In this work, we propose <it>e</it>-CCC-Biclustering, a biclustering algorithm that finds and reports all maximal contiguous column coherent biclusters with approximate expression patterns in time polynomial in the size of the time series gene expression matrix. This polynomial time complexity is achieved by manipulating a discretized version of the original matrix using efficient string processing techniques. We also propose extensions to deal with missing values, discover anticorrelated and scaled expression patterns, and different ways to compute the errors allowed in the expression patterns. We propose a scoring criterion combining the statistical significance of expression patterns with a similarity measure between overlapping biclusters.</p> <p>Results</p> <p>We present results in real data showing the effectiveness of <it>e</it>-CCC-Biclustering and its relevance in the discovery of regulatory modules describing the transcriptomic expression patterns occurring in <it>Saccharomyces cerevisiae </it>in response to heat stress. In particular, the results show the advantage of considering approximate patterns when compared to state of the art methods that require exact matching of gene expression time series.</p> <p>Discussion</p> <p>The identification of co-regulated genes, involved in specific biological processes, remains one of the main avenues open to researchers studying gene regulatory networks. The ability of the proposed methodology to efficiently identify sets of genes with similar expression patterns is shown to be instrumental in the discovery of relevant biological phenomena, leading to more convincing evidence of specific regulatory mechanisms.</p> <p>Availability</p> <p>A prototype implementation of the algorithm coded in Java together with the dataset and examples used in the paper is available in <url>http://kdbio.inesc-id.pt/software/e-ccc-biclustering</url>.</p

Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm

<p>Abstract</p> <p>Background</p> <p>Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space.</p> <p>Results</p> <p>We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (<it>Plasmodium chabaudi</it>), systemic acquired resistance in <it>Arabidopsis thaliana</it>, similarities and differences between inner and outer cotyledon in <it>Brassica napus </it>during seed development, and to <it>Brassica napus </it>whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples.</p> <p>Conclusions</p> <p>Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.</p

Time-series clustering of gene expression in irradiated and bystander fibroblasts: an application of FBPA clustering

<p>Abstract</p> <p>Background</p> <p>The radiation bystander effect is an important component of the overall biological response of tissues and organisms to ionizing radiation, but the signaling mechanisms between irradiated and non-irradiated bystander cells are not fully understood. In this study, we measured a time-series of gene expression after α-particle irradiation and applied the Feature Based Partitioning around medoids Algorithm (FBPA), a new clustering method suitable for sparse time series, to identify signaling modules that act in concert in the response to direct irradiation and bystander signaling. We compared our results with those of an alternate clustering method, Short Time series Expression Miner (STEM).</p> <p>Results</p> <p>While computational evaluations of both clustering results were similar, FBPA provided more biological insight. After irradiation, gene clusters were enriched for signal transduction, cell cycle/cell death and inflammation/immunity processes; but only FBPA separated clusters by function. In bystanders, gene clusters were enriched for cell communication/motility, signal transduction and inflammation processes; but biological functions did not separate as clearly with either clustering method as they did in irradiated samples. Network analysis confirmed p53 and NF-κB transcription factor-regulated gene clusters in irradiated and bystander cells and suggested novel regulators, such as KDM5B/JARID1B (lysine (K)-specific demethylase 5B) and HDACs (histone deacetylases), which could epigenetically coordinate gene expression after irradiation.</p> <p>Conclusions</p> <p>In this study, we have shown that a new time series clustering method, FBPA, can provide new leads to the mechanisms regulating the dynamic cellular response to radiation. The findings implicate epigenetic control of gene expression in addition to transcription factor networks.</p

Prognostic significance of p53, bax and bcl-2 gene expression in patients with laryngeal carcinoma

Aim: This study was designed to examine the prognostic significance of the coexpression of three genes (bax, bcl-2 and p53) which play a critical role in the apoptotic mechanisms in patients with squamous cell laryngeal carcinoma.(1-3) Materials and Methods: The immunohistochemical expression of bcl-2, bax and p53 genes was retrospectively examined in 38 patients with squarrous cell laryngeal carcinoma and in five controls (necrotomic tissue). Tissue specimens were obtained both during the diagnostic biopsy and at the time of surgery. Clinicopathological and survival data were correlated with the staining results. Results: Bcl-2 protein expression (P=0.0472), stage (P=0.0087) and lymph-node involvement (P=0.0488) were found to be independent prognostic factors. Increased bcl-2 protein expression correlated with a better 5-year survival (P=0.0472). Patients who were bcl-2(-)/p53(-) (n=25) or bax(+)/bcl-2(-) (n = 13) had a significantly worse overall survival (P=0.0305 andP=0.0482, respectively). Similarly, patients who were bax(+)/bcl-2(-)/p53(-) (n = 11) also had a worse 5-year survival compared with the rest of the group (P=0.0088). Changes that were noticed in bax and p53 protein expression from the time of biopsy until the time of surgery did not correlate with a significant increase in the overall survival. Conclusions: The expression of bcl-2 gene appears to be an independent prognostic factor for patients with laryngeal carcinoma. The coexpression of the genes studied can be used to determine aggressive clinical phenotypes. (C) 2001 Harcourt Publishers Ltd