The potential of microfluidic lung epithelial wounding: towards in vivo-like alveolar microinjuries

Idiopathic pulmonary fibrosis (IPF) remains a major clinical challenge to date. Repeated alveolar epithelial microinjuries are considered as the starting point and the key event in both the development and the progression of IPF. Various pro-fibrotic agents have been identified and shown to cause alveolar damage. In IPF, however, no leading cause of alveolar epithelial microinjuries can be identified and the exact etiology remains elusive. New results from epidemiologic studies suggest a causal relation between IPF and frequent episodes of gastric refluxes resulting in gastric microaspirations into the lung. The effect of gastric contents on the alveolar epithelium has not been investigated in detail. Here, we present a microfluidic lung epithelial wounding system that allows for the selective exposure of alveolar epithelial cells to gastric contents. The system is revealed to be robust and highly reproducible. The thereby created epithelial microwounds are of tiny dimensions and best possibly reproduce alveolar damage in the lung. We further demonstrate that exposure to gastric contents, namely hydrochloric acid (HCl) and pepsin, directly damages the alveolar epithelium. Together, this novel in vitro wounding system allows for the creation of in vivo-like alveolar microinjuries with the potential to study lung injury and alveolar wound repair in vitro.

A missense mutation of the Na+ channel αII subunit gene Nav1.2 in a patient with febrile and afebrile seizures causes channel dysfunction

Generalized epilepsy with febrile seizures plus (GEFS+), a clinical subset of febrile seizures (FS), is characterized by frequent episodes beyond 6 years of age (FS+) and various types of subsequent epilepsy. Mutations in β1 and αI-subunit genes of voltage-gated Na+ channels have been associated with GEFS+1 and 2, respectively. Here, we report a mutation resulting in an amino acid exchange (R187W) in the gene encoding the α-subunit of neuronal voltage-gated Na+ channel type II (Nav1.2) in a patient with FS associated with afebrile seizures. The mutation R187W occurring on Arg187, a highly conserved residue among voltage-gated Na+ channels, was not found in 224 alleles of unaffected individuals. Whole-cell patch clamp recordings on human embryonic kidney (HEK) cells expressing a rat wild-type (rNav1.2) and the corresponding mutant channels showed that the mutant channel inactivated more slowly than wild-type whereas the Na+ channel conductance was not affected. Prolonged residence in the open state of the R187W mutant channel may augment Na+ influx and thereby underlie the neuronal hyperexcitability that induces seizure activity. Even though a small pedigree could not show clear cosegregation with the disease phenotype, these findings strongly suggest the involvement of Nav1.2 in a human disease and propose the R187W mutation as the genetic defect responsible for febrile seizures associated with afebrile seizures.

A Framework for Fast Classification Algorithms

Today, due to globalization of the world the size of data set is increasing, it is necessary to discover the knowledge. The discovery of knowledge can be typically in the form of association rules, classification rules, clustering, discovery of frequent episodes and deviation detection. Fast and accurate classifiers for large databases are an important task in data mining. There is growing evidence that integrating classification and association rules mining, classification approaches based on heuristic, greedy search like decision tree induction. Emerging associative classification algorithms have shown good promises on producing accurate classifiers. In this paper we focus on performance of associative classification and present a parallel model for classifier building. For classifier building some parallel-distributed algorithms have been proposed for decision tree induction but so far no such work has been reported for associative classification.

A unified view of the Apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discoverymethods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

A unified view of the apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discovery methods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Pattern-growth based frequent serial episode discovery

Frequent episode discovery is a popular framework for pattern discovery from sequential data. It has found many applications in domains like alarm management in telecommunication networks, fault analysis in the manufacturing plants, predicting user behavior in web click streams and so on. In this paper, we address the discovery of serial episodes. In the episodes context, there have been multiple ways to quantify the frequency of an episode. Most of the current algorithms for episode discovery under various frequencies are apriori-based level-wise methods. These methods essentially perform a breadth-first search of the pattern space. However currently there are no depth-first based methods of pattern discovery in the frequent episode framework under many of the frequency definitions. In this paper, we try to bridge this gap. We provide new depth-first based algorithms for serial episode discovery under non-overlapped and total frequencies. Under non-overlapped frequency, we present algorithms that can take care of span constraint and gap constraint on episode occurrences. Under total frequency we present an algorithm that can handle span constraint. We provide proofs of correctness for the proposed algorithms. We demonstrate the effectiveness of the proposed algorithms by extensive simulations. We also give detailed run-time comparisons with the existing apriori-based methods and illustrate scenarios under which the proposed pattern-growth algorithms perform better than their apriori counterparts. (C) 2013 Elsevier B.V. All rights reserved.

Statistical significance of episodes with general partial orders

Frequent episode discovery is one of the methods used for temporal pattern discovery in sequential data. An episode is a partially ordered set of nodes with each node associated with an event type. For more than a decade, algorithms existed for episode discovery only when the associated partial order is total (serial episode) or trivial (parallel episode). Recently, the literature has seen algorithms for discovering episodes with general partial orders. In frequent pattern mining, the threshold beyond which a pattern is inferred to be interesting is typically user-defined and arbitrary. One way of addressing this issue in the pattern mining literature has been based on the framework of statistical hypothesis testing. This paper presents a method of assessing statistical significance of episode patterns with general partial orders. A method is proposed to calculate thresholds, on the non-overlapped frequency, beyond which an episode pattern would be inferred to be statistically significant. The method is first explained for the case of injective episodes with general partial orders. An injective episode is one where event-types are not allowed to repeat. Later it is pointed out how the method can be extended to the class of all episodes. The significance threshold calculations for general partial order episodes proposed here also generalize the existing significance results for serial episodes. Through simulations studies, the usefulness of these statistical thresholds in pruning uninteresting patterns is illustrated. (C) 2014 Elsevier Inc. All rights reserved.

Frequent Arousal from Hibernation Linked to Severity of Infection and Mortality in Bats with White-Nose Syndrome

White-nose syndrome (WNS), an emerging infectious disease that has killed over 5.5 million hibernating bats, is named for the causative agent, a white fungus (Geomyces destructans (Gd)) that invades the skin of torpid bats. During hibernation, arousals to warm (euthermic) body temperatures are normal but deplete fat stores. Temperature-sensitive dataloggers were attached to the backs of 504 free-ranging little brown bats (Myotis lucifugus) in hibernacula located throughout the northeastern USA. Dataloggers were retrieved at the end of the hibernation season and complete profiles of skin temperature data were available from 83 bats, which were categorized as: (1) unaffected, (2) WNS-affected but alive at time of datalogger removal, or (3) WNS-affected but found dead at time of datalogger removal. Histological confirmation of WNS severity (as indexed by degree of fungal infection) as well as confirmation of presence/absence of DNA from Gd by PCR was determined for 26 animals. We demonstrated that WNS-affected bats aroused to euthermic body temperatures more frequently than unaffected bats, likely contributing to subsequent mortality. Within the subset of WNS-affected bats that were found dead at the time of datalogger removal, the number of arousal bouts since datalogger attachment significantly predicted date of death. Additionally, the severity of cutaneous Gd infection correlated with the number of arousal episodes from torpor during hibernation. Thus, increased frequency of arousal from torpor likely contributes to WNS-associated mortality, but the question of how Gd infection induces increased arousals remains unanswered.

[Recurrent febrile episodes--normal, periodic fever syndrome or immunodeficiency?]

Fever is one of the main symptoms leading to medical evaluation. Not only infections cause fever but also inflammatory disorders. To distinguish one from another, a thorough medical history and clinical evaluation are needed. Sometimes, only the clinical course will reveal the diagnosis. PFAPA-Syndrome (periodic fever, aphthous stomatitis, pharyngitis, adenitis) is the most frequent periodic fever syndrome in Switzerland. No diagnostic test is available to support the diagnosis. Some important diseases have to be ruled out, such as Immunodeficiency, cyclic neutropenia, chronic viral infections and rheumatologic disorders. To know the diagnosis of the PFAPA-Syndrome can help avoiding antibiotic courses for febrile episodes in infants. There is a clinical overlap to hereditary periodic fever syndromes as familial Mediterranean fever (FMF), Hyper-IgD and fever syndrome (HIDS), Tumor-necrosis factor receptor associated periodic syndrome (TRAPS) and others, in which a genetic basis for the disease has already been found.

His-Purkinje system re-entry in patients with clustering ventricular tachycardia episodes

AIMS: Multiple arrhythmia re-inductions were recently shown in His-Purkinje system (HPS) ventricular tachycardia (VT). We hypothesized that HPS VT was a frequent mechanism of repetitive or incessant VT and assessed diagnostic criteria to select patients likely to have HPS VT. METHODS AND RESULTS: Consecutive patients with clustering VT episodes (>3 sustained monomorphic VT within 2 weeks) were included in the analysis. HPS VT was considered plausible in patients with (i) impaired left ventricular function associated with dilated cardiomyopathy or valvular heart disease; or (ii) ECG during VT similar to sinus rhythm QRS or to bundle-branch block QRS. HPS VT was plausible in 12 of 48 patients and HPS VT was demonstrated in 6 of 12 patients (50%, or 13% of the whole study group). Median VT cycle length was 318 ms (250-550). Catheter ablation was successful in all six patients. CONCLUSION: His-Purkinje system VT is found in a significant number of patients with repetitive or incessant VT episodes, and in a large proportion of patients with predefined clinical or electrocardiographic characteristics. Since it is easily amenable to catheter ablation, our data support the screening of all patients with repetitive VT in this regard and an invasive approach in a selected group of patients.

Frequent ventricular tachycardias: antiarrhythmic drug treatment or catheter ablation?

Antiarrhythmic drugs are used in at least 50% of patients who received an implantable cardioverter defibrillator (ICD). The potential indications for antiarrhythmic drug treatments in patients with an ICD are generally the following: reduction of the number of ventricular tachycardias (VTs) or episodes of ventricular fibrillation and therefore reduction of the number of ICD therapies, most importantly, the number of disabling ICD shocks. Accordingly, the quality of life should be improved and the battery life of the ICD extended. Moreover, antiarrhythmic drugs have the potential to increase the tachycardia cycle length to allow termination of VTs by antitachycardia pacing and reduction of the number of syncopes. In addition, supraventricular arrhythmias can be prevented or their rate controlled. Recently published or reported trials have shown the efficacy of amiodarone, sotalol and azimilide to significantly reduce the number of appropriate and inappropriate ICD shocks in patients with structural heart disease. However, the use of antiarrhythmic drugs may also have adverse effects: an increase in the defibrillation threshold, an excessive increase in the VT cycle length leading to detection failure. In this situation and when antiarrhythmic drugs are ineffective or have to be stopped because of serious side effects, catheter ablation of both monomorphic stable and pleomorphic and/or unstable VTs using modern electroanatomic mapping systems should be considered. The choice of antiarrhythmic drug treatment and the need for catheter ablation in ICD patients with frequent VTs should be individually tailored to specific clinical and electrophysiological features including the frequency, the rate, and the clinical presentation of the ventricular arrhythmia. Although VT mapping and ablation is becoming increasingly practical and efficacious, ablation of VT is mostly done as an adjunctive therapy in patients with structural heart disease and ICD experiencing multiple shocks, because the recurrence and especially the occurrence of "new" VTs after primarily successful ablation with time and disease progression have precluded a widespread use of catheter ablation as primary treatment.

History of Premenstrual Mood Change and Postpartum Episodes are Associated with Perimenopausal Episodes in Women with Bipolar Disorder

Background and Aims: Reproductive life events are potential triggers of mood episodes in women with bipolar disorder. We aimed to establish whether a history of premenstrual mood change and postpartum episodes are associated with perimenopausal episodes in women who have bipolar disorder. Methods: Participants were 339 post-menopausal women with DSM-IV bipolar disorder recruited into the Bipolar Disorder Research Network (www.bdrn.org). Women self-reported presence (N = 200) or absence (N = 139) of an illness episode during the perimenopausal period. History of premenstrual mood change was measured using the self-report Premenstrual Symptoms Screening Tool (PSST), and history of postpartum episodes was measured via semi-structured interview (Schedules for Clinical Assessment in Neuropsychiatry, SCAN) and inspection of case-notes. Results: History of a postpartum episode within 6 months of delivery (OR = 2.13, p = 0.03) and history of moderate/severe premenstrual syndrome (OR = 6.33, p < 0.001) were significant predictors of the presence of a perimenopausal episode, even after controlling for demographic factors. When we narrowed the definition of premenstrual mood change to premenstrual dysphoric disorder, it remained significant (OR = 2.68, p = 0.007). Conclusions: Some women who have bipolar disorder may be particularly sensitive to reproductive life events. Previous mood episodes in relation to the female reproductive lifecycle may help clinicians predict individual risk for women with bipolar disorder approaching the menopause. There is a need for prospective longitudinal studies of women with bipolar disorder providing frequent contemporaneous ratings of their mood to overcome the limitations of retrospective self-report data.

Identifying Risk Factors for Postpartum Mood Episodes in Bipolar Disorder – A UK Prospective Study

Background and Aims: Women with bipolar disorder are vulnerable to episodes postpartum, but risk factors are poorly understood. We are exploring risk factors for postpartum mood episodes in women with bipolar disorder using a prospective longitudinal design. Methods: Pregnant women with lifetime DSM-IV bipolar disorder are being recruited into the Bipolar Disorder Research Network (www.BDRN.org). Baseline assessments during late pregnancy include lifetime psychopathology and potential risk factors for perinatal episodes such as medication use, sleep, obstetric factors, and psychosocial factors. Blood samples are taken for genetic analysis. Perinatal psychopathology is assessed via follow-up interview at 12-weeks postpartum. Interview data are supplemented by clinician questionnaires and case-note review. Potential risk factors will be compared between women who experience perinatal episodes and those who remain well. Results: 80 participants have been recruited to date. 32/61 (52%) women had a perinatal recurrence by follow-up. 16 (26%) had onset in pregnancy. 21 (34%) had postpartum onset, 19 (90%) within 6-weeks of delivery: 11 (18%) postpartum psychosis, 5 (8%) postpartum hypomania, 5 (8%) postpartum depression. Postpartum relapse was more frequent in women with bipolar-I than bipolar-II disorder (45% vs 17%). 62% women with postpartum relapse took prophylactic medication peripartum and almost all received care from secondary psychiatric services (95%). Conclusions: Rate of postpartum relapse is high, despite most women receiving specialist care and medication perinatally. A larger sample size will allow us to examine potential risk factors for postpartum episodes, which will assist in providing accurate and personalised advice to women with bipolar disorder who are considering pregnancy.

Link-the-Wiki : performance evaluation based on frequent phrases

In this paper, we discuss our participation to the INEX 2008 Link-the-Wiki track. We utilized a sliding window based algorithm to extract the frequent terms and phrases. Using the extracted phrases and term as descriptive vectors, the anchors and relevant links (both incoming and outgoing) are recognized efficiently.