Risk of surgical site infection and efficacy of antibiotic prophylaxis: a cohort study of appendectomy patients in Thailand

BACKGROUND: No data currently exist about use of antibiotics to prevent surgical site infections (SSI) among patients undergoing appendectomy in Thailand. We therefore examined risk factors, use, and efficacy of prophylactic antibiotics for surgical site infection SSI among patients with uncomplicated open appendectomy. METHODS: From July 1, 2003 to June 30, 2004 we conducted a prospective cohort study in eight hospitals in Thailand. We used the National Nosocomial Infection Surveillance (NNIS) system criteria to identify SSI associated with appendectomy. We used logistic regression analysis to obtain relative risk estimates for predictors of SSI. RESULTS: Among 2139 appendectomy patients, we identified 26 SSIs, yielding a SSI rate of 1.2 infections/100 operations. Ninety-two percent of all patients (95% CI, 91.0–93.3) received antibiotic prophylaxis. Metronidazole and gentamicin were the two most common antibiotic agents, with a combined single dose administered in 39% of cases. In 54% of cases, antibiotic prophylaxis was administered for one day. We found that a prolonged duration of operation was significantly associated with an increased SSI risk. Antibiotic prophylaxis was significantly associated with a decreased risk of SSI regardless of whether the antibiotic was administered preoperatively or intraoperatively. Compared with no antibiotic prophylaxis, SSI relative risks for combined single-dose of metronidazole and gentamicin, one-day prophylaxis, and multiple-day antibiotic prophylaxis were 0.28 (0.09–0.90), 0.30 (0.11–0.88) and 0.32 (0.10–0.98), respectively. CONCLUSION: Single-dose combination of metronidazole and gentamicin seems sufficient to reduce SSIs in uncomplicated appendicitis patients despite whether the antibiotic was administered preoperatively or intraoperatively

The Impact of Multifunctional Genes on "Guilt by Association" Analysis

Many previous studies have shown that by using variants of “guilt-by-association”, gene function predictions can be made with very high statistical confidence. In these studies, it is assumed that the “associations” in the data (e.g., protein interaction partners) of a gene are necessary in establishing “guilt”. In this paper we show that multifunctionality, rather than association, is a primary driver of gene function prediction. We first show that knowledge of the degree of multifunctionality alone can produce astonishingly strong performance when used as a predictor of gene function. We then demonstrate how multifunctionality is encoded in gene interaction data (such as protein interactions and coexpression networks) and how this can feed forward into gene function prediction algorithms. We find that high-quality gene function predictions can be made using data that possesses no information on which gene interacts with which. By examining a wide range of networks from mouse, human and yeast, as well as multiple prediction methods and evaluation metrics, we provide evidence that this problem is pervasive and does not reflect the failings of any particular algorithm or data type. We propose computational controls that can be used to provide more meaningful control when estimating gene function prediction performance. We suggest that this source of bias due to multifunctionality is important to control for, with widespread implications for the interpretation of genomics studies

Association Analysis of 94 Candidate Genes and Schizophrenia-Related Endophenotypes

While it is clear that schizophrenia is highly heritable, the genetic basis of this heritability is complex. Human genetic, brain imaging, and model organism studies have met with only modest gains. A complementary research tactic is to evaluate the genetic substrates of quantitative endophenotypes with demonstrated deficits in schizophrenia patients. We used an Illumina custom 1,536-SNP array to interrogate 94 functionally relevant candidate genes for schizophrenia and evaluate association with both the qualitative diagnosis of schizophrenia and quantitative endophenotypes for schizophrenia. Subjects included 219 schizophrenia patients and normal comparison subjects of European ancestry and 76 schizophrenia patients and normal comparison subjects of African ancestry, all ascertained by the UCSD Schizophrenia Research Program. Six neurophysiological and neurocognitive endophenotype test paradigms were assessed: prepulse inhibition (PPI), P50 suppression, the antisaccade oculomotor task, the Letter-Number Span Test, the California Verbal Learning Test-II, and the Wisconsin Card Sorting Test-64 Card Version. These endophenotype test paradigms yielded six primary endophenotypes with prior evidence of heritability and demonstrated schizophrenia-related impairments, as well as eight secondary measures investigated as candidate endophenotypes. Schizophrenia patients showed significant deficits on ten of the endophenotypic measures, replicating prior studies and facilitating genetic analyses of these phenotypes. A total of 38 genes were found to be associated with at least one endophenotypic measure or schizophrenia with an empirical p-value<0.01. Many of these genes have been shown to interact on a molecular level, and eleven genes displayed evidence for pleiotropy, revealing associations with three or more endophenotypic measures. Among these genes were ERBB4 and NRG1, providing further support for a role of these genes in schizophrenia susceptibility. The observation of extensive pleiotropy for some genes and singular associations for others in our data may suggest both converging and independent genetic (and neural) pathways mediating schizophrenia risk and pathogenesis

Evolutionary origins of Brassicaceae specific genes in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>All sequenced genomes contain a proportion of lineage-specific genes, which exhibit no sequence similarity to any genes outside the lineage. Despite their prevalence, the origins and functions of most lineage-specific genes remain largely unknown. As more genomes are sequenced opportunities for understanding evolutionary origins and functions of lineage-specific genes are increasing.</p> <p>Results</p> <p>This study provides a comprehensive analysis of the origins of lineage-specific genes (LSGs) in <it>Arabidopsis thaliana </it>that are restricted to the Brassicaceae family. In this study, lineage-specific genes within the nuclear (1761 genes) and mitochondrial (28 genes) genomes are identified. The evolutionary origins of two thirds of the lineage-specific genes within the <it>Arabidopsis thaliana </it>genome are also identified. Almost a quarter of lineage-specific genes originate from non-lineage-specific paralogs, while the origins of ~10% of lineage-specific genes are partly derived from DNA exapted from transposable elements (twice the proportion observed for non-lineage-specific genes). Lineage-specific genes are also enriched in genes that have overlapping CDS, which is consistent with such novel genes arising from overprinting. Over half of the subset of the 958 lineage-specific genes found only in <it>Arabidopsis thaliana </it>have alignments to intergenic regions in <it>Arabidopsis lyrata</it>, consistent with either <it>de novo </it>origination or differential gene loss and retention, with both evolutionary scenarios explaining the lineage-specific status of these genes. A smaller number of lineage-specific genes with an incomplete open reading frame across different <it>Arabidopsis thaliana </it>accessions are further identified as accession-specific genes, most likely of recent origin in <it>Arabidopsis thaliana</it>. Putative <it>de novo </it>origination for two of the <it>Arabidopsis thaliana</it>-only genes is identified via additional sequencing across accessions of <it>Arabidopsis thaliana </it>and closely related sister species lineages. We demonstrate that lineage-specific genes have high tissue specificity and low expression levels across multiple tissues and developmental stages. Finally, stress responsiveness is identified as a distinct feature of Brassicaceae-specific genes; where these LSGs are enriched for genes responsive to a wide range of abiotic stresses.</p> <p>Conclusion</p> <p>Improving our understanding of the origins of lineage-specific genes is key to gaining insights regarding how novel genes can arise and acquire functionality in different lineages. This study comprehensively identifies all of the Brassicaceae-specific genes in <it>Arabidopsis thaliana </it>and identifies how the majority of such lineage-specific genes have arisen. The analysis allows the relative importance (and prevalence) of different evolutionary routes to the genesis of novel ORFs within lineages to be assessed. Insights regarding the functional roles of lineage-specific genes are further advanced through identification of enrichment for stress responsiveness in lineage-specific genes, highlighting their likely importance for environmental adaptation strategies.</p

Plasma and cellular fibronectin: distinct and independent functions during tissue repair

Fibronectin (FN) is a ubiquitous extracellular matrix (ECM) glycoprotein that plays vital roles during tissue repair. The plasma form of FN circulates in the blood, and upon tissue injury, is incorporated into fibrin clots to exert effects on platelet function and to mediate hemostasis. Cellular FN is then synthesized and assembled by cells as they migrate into the clot to reconstitute damaged tissue. The assembly of FN into a complex three-dimensional matrix during physiological repair plays a key role not only as a structural scaffold, but also as a regulator of cell function during this stage of tissue repair. FN fibrillogenesis is a complex, stepwise process that is strictly regulated by a multitude of factors. During fibrosis, there is excessive deposition of ECM, of which FN is one of the major components. Aberrant FN-matrix assembly is a major contributing factor to the switch from normal tissue repair to misregulated fibrosis. Understanding the mechanisms involved in FN assembly and how these interplay with cellular, fibrotic and immune responses may reveal targets for the future development of therapies to regulate aberrant tissue-repair processes

WSES guidelines for emergency repair of complicated abdominal wall hernias

Peer reviewe

A Review of Patient-Reported Outcome Measures in Childhood Cancer

Patient-reported outcomes (PROs) are used in clinical work and research to capture the subjective experiences of childhood cancer patients and survivors. PROs encompass content domains relevant and important to this population, including health-related quality-of-life (HRQOL), symptoms, and functional status. To inform future efforts in the application of PRO measures, this review describes the existing generic and cancer-specific PRO measures for pediatric cancer populations and summarizes their characteristics, available language translations, content coverage, and measurement properties into tables for clinicians and researchers to reference before choosing a PRO measure that suits their purpose. We have identified often unreported measurement properties that could provide evidence about the clinical utility of the PRO measures. Routine PRO assessment in pediatric cancer care offers opportunities to facilitate clinical decision-making and improve quality of care for these patients. However, we suggest that before implementing PRO measures into research or clinical care, the psychometric properties and content coverage of the PRO measures must be considered to ensure that PRO measures are appropriately assessing the intended construct in childhood cancer patients

Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review

Patient-reported outcomes (PROs; symptoms, functional status, quality-of-life) expressed in the ‘free-text’ or ‘unstructured’ format within clinical notes from electronic health records (EHRs) offer valuable insights beyond biological and clinical data for medical decision-making. However, a comprehensive assessment of utilizing natural language processing (NLP) coupled with machine learning (ML) methods to analyze unstructured PROs and their clinical implementation for individuals affected by cancer remains lacking. This study aimed to systematically review published studies that used NLP techniques to extract and analyze PROs in clinical narratives from EHRs for cancer populations. We examined the types of NLP (with and without ML) techniques and platforms for data processing, analysis, and clinical applications. Utilizing NLP methods offers a valuable approach for processing and analyzing unstructured PROs among cancer patients and survivors. These techniques encompass a broad range of applications, such as extracting or recognizing PROs, categorizing, characterizing, or grouping PROs, predicting or stratifying risk for unfavorable clinical results, and evaluating connections between PROs and adverse clinical outcomes. The employment of NLP techniques is advantageous in converting substantial volumes of unstructured PRO data within EHRs into practical clinical utilities for individuals with cancer.</p