Detection of periodontal microorganisms in coronary atheromatous plaque specimens of myocardial infarction patients: A systematic review and meta-analysis

BackgroundMicrobial translocation from inflamed periodontal pockets into coronary atheroma via systemic circulation is one of the proposed pathways that links periodontitis and myocardial infarction (MI). The purpose of this systematic review is to determine the reported prevalence of periodontal microorganisms in coronary atheroma and/or aspirated clot samples collected from MI patients with periodontal disease.MethodologyThe “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA) guidelines were followed. Six databases were systematically searched using Medical Subject Headings/Index and Entree terms. After a thorough screening, fourteen publications spanning over ten years (2007–2017) were eligible for this systematic review and meta-analysis.ResultsOut of 14 included studies, 12 reported presence of periodontal bacterial DNA in coronary atherosclerotic plaque specimens. Overall, Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans were the most frequently detected periodontal bacterial species. Meta-analysis revealed that the prevalence of P. gingivalis was significantly higher than A. actinomycetemcomitans in coronary atheromatous plaque samples. Apart from periodontal microbes, DNA from a variety of other microbes e.g. Pseudomonas fluorescens, Streptococcus species, Chlamydia pneumoniae were also recovered from the collected samples.ConclusionConsistent detection of periodontal bacterial DNA in coronary atheroma suggests their systemic dissemination from periodontal sites. It should further be investigated whether they are merely bystanders or induce any structural changes within coronary arterial walls.     

Identifying Areas for Operational Improvement and Growth in IR Workflow Using Workflow Modeling,Simulation, and Optimization Techniques

Identifying areas for workflow improvement and growth is essential for an interventional radiology (IR) department to stay competitive. Deployment of traditional methods such as Lean and Six Sigma helped in reducing the waste in workflows at a strategic level. However, achieving efficient workflow needs both strategic and tactical approaches. Uncertainties about patient arrivals, staff availability, and variability in procedure durations pose hindrances to efficient workflow and lead to delayed patient care and staff overtime. We present an alternative approach to address both tactical and strategic needs using discrete event simulation (DES) and simulation based optimization methods. A comprehensive digital model of the patient workflow in a hospital-based IR department was modeled based on expert interviews with the incumbent personnel and analysis of 192 days’ worth of electronic medical record (EMR) data. Patient arrival patterns and process times were derived from 4393 individual patient appointments. Exactly 196 unique procedures were modeled, each with its own process time distribution and rule-based procedure-room mapping. Dynamic staff schedules for interventional radiologists, technologists, and nurses were incorporated in the model. Stochastic model simulation runs revealed the resource “computed tomography (CT) suite” as the major workflow bottleneck during the morning hours. This insight compelled the radiology department leadership to re-assign time blocks on a diagnostic CT scanner to the IR group. Moreover, this approach helped identify opportunities for additional appointments at times of lower diagnostic scanner utilization. Demand for interventional service from Outpatients during late hours of the day required the facility to extend hours of operations. Simulation-based optimization methods were used to model a new staff schedule, stretching the existing pool of resources to support the additional 2.5 h of daily operation. In conclusion, this study illustrates that the combination of workflow modeling, stochastic simulations, and optimization techniques is a viable and effective approach for identifying workflow inefficiencies and discovering and validating improvement options through what-if scenario testing.     

Seizure Frequency Can Alter Brain Connectivity: Evidence from Resting-State fMRI

BACKGROUND AND PURPOSE:The frequency of seizures is an important factor that can alter functional brain connectivity. Analysis of this factor in patients with epilepsy is complex because of disease- and medication-induced confounders. Because patients with hot-water epilepsy generally are not on long-term drug therapy, we used seed-based connectivity analysis in these patients to assess connectivity changes associated with seizure frequency without confounding from antiepileptic drugs.MATERIALS AND METHODS:Resting-state fMRI data from 36 patients with hot-water epilepsy (18 with frequent seizures [>2 per month] and 18 with infrequent seizures [≤2 per month]) and 18 healthy age- and sex-matched controls were analyzed for seed-to-voxel connectivity by using 106 seeds. Voxel wise paired t-test analysis (P < .005, corrected for false-discovery rate) was used to identify significant intergroup differences between these groups.RESULTS:Connectivity analysis revealed significant differences between the 2 groups (P < .001). Patients in the frequent-seizure group had increased connectivity within the medial temporal structures and widespread areas of poor connectivity, even involving the default mode network, in comparison with those in the infrequent-seizure group. Patients in the infrequent-seizure group had focal abnormalities with increased default mode network connectivity and decreased left entorhinal cortex connectivity.CONCLUSIONS:The results of this study suggest that seizure frequency can alter functional brain connectivity, which can be visualized by using resting-state fMRI. Imaging features such as diffuse network abnormalities, involvement of the default mode network, and recruitment of medial temporal lobe structures were seen only in patients with frequent seizures. Future studies in more common epilepsy groups, however, will be required to further establish this finding.

Behind the unquestionable clinical and electroencephalographic manifestations of an epileptic seizure, there lie several molecular, metabolic, cellular, and hemodynamic events that alter the function of the brain in a complex manner. These alterations may be transient, but many such events can have a cumulative effect, resulting in psychological and memory deficits, personality changes, and reduced functioning in patients with epilepsy. Advances in neurophysiology, functional imaging, and computational neurosciences have made it possible to derive models mathematically to describe such complex diseases.Disease-state network analysis with resting-state fMRI is becoming increasingly popular because of its superior spatial resolution, nondependence on task, ease of acquisition, and ability to visualize whole-brain functional networks, which are amenable to long-term changes related to disease states.¹ Application of connectivity principles to these data has promoted research in various aspects of epileptic seizures, and there has been overwhelming report of decreased connectivity around the seizure-onset zone^2–6 and the default mode network (DMN) by several groups.^7,8 In 2012, Jehi⁷ and Morgan et al⁹ reported that connectivity patterns were different in patients with right and left mesial temporal sclerosis and that there was decreased connectivity between the regions of the DMN and the hippocampus and amygdala in patients with mesial temporal sclerosis. Similarly, hemispheric connectivity analysis in patients with unilateral mesial temporal sclerosis revealed decreased local and intrahemispheric connectivity and increased interhemispheric connectivity.¹⁰ In contradistinction to the aforementioned results, there have been reports on increased hippocampal connectivity that was presumed to be a compensatory mechanism because it linearly correlated with a disease duration of >10 years.^1–4,6 Graph-theory analysis of resting-state fMRI data from patients with epilepsy also revealed decreased functional nodal topologic properties of the DMN that were positively correlated with disease duration.^8,11–14 Regional homogeneity analysis of resting-state fMRI data was even used as a presurgical tool for seizure identification in patients with MR-negative focal epilepsy.¹⁵ Thus, various models of data analysis have helped in understanding epilepsy further, and now there is increasing interest in using these models to reclassify epilepsy as a focal epileptogenic area^10,15 or as a network of seizure-generating areas.^16–18Gower''s clinical observation that “seizures beget seizures” in 1881 triggered several studies, especially animal models that addressed the genesis of epilepsy. Kindling is a phenomenon in which the repetition of subconvulsive electrical stimuli results in a progressive epileptic state and an increased frequency of seizures.^19,20 Kindling is a continuous temporal process that remodels the mechanisms and circuits in the brain. Early changes, beginning with modulation of presynaptic and postsynaptic functioning in glutamate, N-methyl-d-aspartate, and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors, and late changes, including mossy fiber sprouting, synaptogenesis/neurogenesis, and neurotrophic factor regulation, are known to occur as a result of kindling.^20–22 The increased risk for seizure recurrence with increasing numbers of seizures was first demonstrated clinically by Hauser and Lee.²³ The effects of seizure frequency and chronic epilepsy have been studied sparsely by using imaging techniques.¹ For example, Widjaja et al²⁴ found no significant correlation with seizure frequency, age of seizure onset, or duration of epilepsy in a group of children with medically refractory epilepsy.Apart from other disease-related confounders such as type of seizure, duration of epilepsy, age of onset, family history of epilepsy, etc, each of which can induce connectivity differences independently, a major limitation of interpreting the results of functional connectivity in patients with epilepsy is the use of antiepileptic drugs. Patients with hot-water epilepsy (HWE) present with a history of complex partial seizures clinically suggestive of temporal lobe onset with or without secondary generalization. Most patients are drug naive at their first evaluation, which provided us an opportunity to study networks that were affected by seizure frequency and matched for other disease-related confounders but not antiepileptic drugs. On the basis of the phenomenon of kindling, we hypothesized that seizures can alter brain connectivity and that the frequency of seizures can influence the magnitude of this alteration. We predicted that patients with a higher seizure frequency would have widespread changes in comparison with patients with a lower seizure frequency. We used seed-to-voxel–based resting-state fMRI connectivity in 36 drug-naive patients with HWE who were comparable in terms of various factors such as age, sex, education, and epilepsy-related confounders. We hope that such quantifiable noninvasive in vivo evidence further enhances knowledge of the etiopathogenesis of HWE and, if applicable to refractory epilepsy, can identify such patients early to avoid time-consuming trial-and-error methods of pharmacotherapy and facilitate early intervention to minimize cognitive deficits.     

Mechanistic studies on the drug metabolism and toxicity originating from cytochromes P450

Abstract

Cytochromes P450 are oxidizing enzymes; a few families of cytochromes P450 are implicated in drug metabolism. These enzymatic reactions involve many processes including (i) prodrug to drug conversion, (ii) easy excretion of drug, (iii) generation of reactive metabolites, many of which cause toxicity. In this review, the fundamental biochemical mechanisms associated with the conversion of drugs into the useful or toxic metabolites have been discussed. The mechanisms can be established with the help of many experimental methods like mass spectral analysis, NMR and in vitro analysis etc. Computational methods provide detailed atomic level information, which is generally not available from experimental studies. Thus, the in silico efforts in elucidating the molecular mechanisms are complementary to the known experimental methods and are often clearer (especially in providing 3D information about the metabolites and their reactions). Quantum chemical methods and molecular docking become especially very useful. This review includes five case studies, which explain how the atomic level details were obtained to explore the reaction mechanisms of drug metabolism by cytochromes P450.     

Robotic total pelvic exenteration

Introduction

Pelvic exenteration is now becoming widely acceptable as a curative procedure rather than a palliative one. Performing these surgeries by minimally invasive techniques helps to improve the quality of life and decrease the morbidity of these extensive procedures.

Aims and objectives

To demonstrate the feasibility of performing a total pelvic exenteration robotically, and to study the morbidity associated with such extensive surgery.

Materials and methods

A 35-year-old female with advanced cervical cancer presented with a vesicovaginal fistula and a rectovaginal fistula. In view of these, we performed a total robotic pelvic exenteration with colo-anal anastomosis and uretero-sigmoidostomy. The patient refused an ileal-loop conduit for urinary tract diversion due to social reasons associated with a stoma.

Results

The total operative time was 240 min and the console time was 120 min. The estimated blood loss was 300 ml and the intensive care unit stay was 2 days. Post-operatively, the patient had good faecal and urinary continence and good quality of life.