Atrial Fibrillation AF reported as the most occurring heart arrhythmia. Steadfast detection of AF in ECG monitoring systems is considerable for early treatment and health risks reduction. Various ECG mining and analysis efforts have addressed a wide variety of technical issues. However, the morphological descriptors are changing along the time within the different patients. As a result, the classification model constructed using old training data is not accurate enough to detect AF. This paper presents an outstanding dynamic learning method to achieve better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed 96.2%, 99.7%, and 99.4% for sensitivity, specificity, and overall accuracy, respectively. Accordingly, the proposed Cache learning method can be introduced to improve the performance of the AF intelligent detection systems.

Urocanic acid (UCA) is a major chromophore present abundantly in the epidermis. Upon UV exposure trans-UCA is isomerized to cis-UCA, which is known to cause immune suppression via various pathways, such as activation of 5-HT2A receptor and serotonin signaling. However, the effects of cis-UCA on the skin microbiome and innate immune system is not known. We recently showed that skin microbiome modulates the effects of UV on cellular response and immune function. Herein we investigated the effects of cis-UCA on skin microbiome and the innate immune system and its components such as antimicrobial peptides (AMPs) using mouse models. Our results show that UV-A, UV-B and PUVA significantly isomerized UCA compared to control groups. Interestingly, topical application of cis-UCA on shaved dorsal skin significantly modulated the skin microbial communities both at 8h and 24h. The prominent species affected by topical application of cis-UCA were Staphylococcus xylosus (at 8h) and Staphylococcus capitis/caprae and Propionibacterium acnes (at 24h). Additionally, gene expression of various AMPs and cytokines were also affected by cis-UCA. Intriguingly, mice that were disinfected prior to UV exposure showed reduced isomerization of UCA compared to non-disinfected control mice. Finally, using the contact allergy model we confirmed a dose-dependent increase in immune suppression to DNFB when mice were pre-treated with cis-UCA. Taken together, our results show significant effects of cis-UCA on skin microbiome (vice versa) and gene regulation of AMPs, which could play an important role in UV-induced immune suppression.

Die Kolonisationsresistenz bezeichnet die Fähigkeit einer mikrobiellen Gemeinschaft, die Ansiedlung von krankheitserregenden Bakterien zur verhindern. Mikrobielle Diversität und das Fehlen von Entzündung sind Indikatoren einer erhaltenen Kolonisationsresistenz. Das Ziel dieser Studie war es, Diversität und entzündliche Aktivität des Stuhlmikrobioms bei PatientInnen mit Leberzirrhose zu untersuchen.

Protonenpumpenhemmer (PPI) werden bei Leberzirrhose trotz aufkommender Sicherheitsbedenken häufig eingesetzt. Wie genau die Einnahme von PPI zu erhöhtem Infektions- und Mortalitätsrisiko führt, ist weitgehend unbekannt. In dieser Studie zeigen wir einen Zusammenhang zwischen PPI-Therapie, intestinaler Dysbiose, erhöhter Darmpermeabilität und Inflammation, die letztendlich zu erhöhter Mortalität führen.

Trans-urocanic acid (UCA) is a natural photoreceptor present in the stratum corneum of the skin. Upon exposure to ultraviolet-radiation (UV-R), trans-UCA is isomerized to cis-UCA. Several studies indicate that cis-UCA induces local and systemic immune suppression via various underlying mechanisms. However, microbes are established all over the surface of the skin and the interplay between cis-UCA and the skin microbiome is not completely understood. In this study, we investigated the effects of cis-UCA on the skin microbiome and antimicrobial peptides (AMPs) expression using mouse models. We employed HPLC to determine quantitative isomerization of trans-UCA to cis-UCA by UV-R. We further made use of the model of contact allergy to assess the percentage of immune suppression by UV-A, UV-B, PUVA and cis-UCA to the contact allergen DNFB. Next, we treated mice with UV-A, UV-B, PUVA and cis-UCA and performed 16S rRNA gene sequencing for microbiome analysis and qPCR for AMPs gene expression. We noted that UV-B (p=0.002) and PUVA (p=0.023) significantly increased the formation of cis-UCA, whereas UV-A exposure alone showed no significant formation of cis-UCA in the skin. Utilizing the contact allergy model, we observed a dose-dependent increase in immune suppression (by up to 100%) against the contact allergen DNFB, when mice were pretreated with cis-UCA. Furthermore, application of cis-UCA on the skin altered the microbial landscape of the skin both at 8h and 24h, correlating with a change in expression of various AMPs. Collectively our results suggest that cis-UCA alters the skin microbial landscape and AMP expression. This imbalance in the skin microbial landscape and altered AMP expression may be crucial in immune suppression upon UV-R exposure mediated through cis-UCA.

Background: An inflatable lunar/Mars analog habitat (ILMAH), simulated closed system isolated by HEPA filtration, mimics International Space Station (ISS) conditions and future human habitation on other planets except for the exchange of air between outdoor and indoor environments. The ILMAH was primarily commissioned to measure physiological, psychological, and immunological characteristics of human inhabiting in isolation, but it was also available for other studies such as examining its microbiological aspects. Characterizing and understanding possible changes and succession of fungal species is of high importance since fungi are not only hazardous to inhabitants but also deteriorate the habitats. Observing the mycobiome changes in the presence of human will enable developing appropriate countermeasures with reference to crew health in a future closed habitat.Results: Succession of fungi was characterized utilizing both traditional and state-of-the-art molecular techniques during the 30-day human occupation of the ILMAH. Surface samples were collected at various time points and locations to observe both the total and viable fungal populations of common environmental and opportunistic pathogenic species. To estimate the cultivable fungal population, potato dextrose agar plate counts method was utilized. The internal transcribed spacer region-based iTag Illumina sequencing was employed to measure the community structure and fluctuation of the mycobiome over time in various locations. Treatment of samples with propidium monoazide (PMA; a DNA intercalating dye for selective detection of viable microbial populations) had a significant effect on the microbial diversity compared to non-PMA-treated samples. Statistical analysis confirmed that viable fungal community structure changed (increase in diversity and decrease in fungal burden) over the occupation time. Samples collected at day 20 showed distinct fungal profiles from samples collected at any other time point (before or after). Viable fungal families like Davidiellaceae, Teratosphaeriaceae, Pleosporales, and Pleosporaceae were shown to increase during the occupation time.Conclusions: The results of this study revealed that the overall fungal diversity in the closed habitat changed during human presence; therefore, it is crucial to properly maintain a closed habitat to preserve it from deteriorating and keep it safe for its inhabitants. Differences in community profiles were observed when statistically treated, especially of the mycobiome of samples collected at day 20. On a genus level Epiccocum, Alternaria, Pleosporales, Davidiella, and Cryptococcus showed increased abundance over the occupation time.

Background: Metabolic syndrome (MetS) is associated with disturbances in gut microbiota. In animal models, modulation of gut microbiota by probiotic supplementation is possible. However, data in humans are scarce and controversial.

The knowledge discovery has been widely applied to mine significant knowledge from medical data. Nevertheless, previous studies have produced large numbers of imprecise patterns. To reduce the number of imprecise patterns, we need an approach that can discover interesting patterns that connote causality between antecedent and consequence in a pattern. In this paper, we propose association rule mining method that can discover interesting patterns that include medical knowledge in Korean acute myocardial infarction registry that consists of 1,247 young adults collected by 51 participating hospitals since 2005. Proposed method can remove imprecise patterns and discover target patterns that include associations between blood factors and disease history. The association that blood factors affect to disease history is defined as target pattern. In our experiments, the interestingness of a target pattern is evaluated in terms of statistical measures such as lift, leverage, and conviction. We discover medical knowledge that glucose, smoking, triglyceride total cholesterol, and creatinine are associated with diabetes and hypertension in Korean young adults with acute myocardial infarction.

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra-and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.

Reliable detection of atrial fibrillation (AF) in ECG monitoring systems is significant for early treatment and health risks reduction. Various ECG mining and analysis efforts have addressed a wide variety of clinical and technical issues. However, there is still scope for improvement mostly in the number and the types of ECG parameters necessity to detect AF arrhythmia with high quality that encounter a massive number of challenges in relation to computational efforts and time consuming. In this paper, we proposed a technique that caters these limitations. It select features related to the ECG parameters, so as to design a unique feature set that could be employed to describe AF in very sensitive manner. The performance of our proposed technique showed a sensitivity of 95% and a specificity of 99.6%, and overall accuracy of 99.2%.

A reliable detection of atrial fibrillation (AF) in Electrocardiogram (ECG) monitoring systems is significant for early treatment and health risk reduction. Various ECG mining and analysis studies have addressed a wide variety of clinical and technical issues. However, there is still room for improvement mostly in two areas. First, the morphological descriptors not only between different patients or patient clusters but also within the same patient are potentially changing. As a result, the model constructed using an old training data no longer needs to be adjusted in order to identify new concepts. Second, the number and types of ECG parameters necessary for detecting AF arrhythmia with high quality encounter a massive number of challenges in relation to computational effort and time consumption. We proposed a mixture technique that caters to these limitations. It includes an active learning method in conjunction with an ECG parameter customization technique to achieve a better AF arrhythmia detection in real-time applications. The performance of our proposed technique showed a sensitivity of 95.2%, a specificity of 99.6%, and an overall accuracy of 99.2%.

Coronary heart disease is being identified as the largest single cause of death along the world. The aim of a cardiac clinical information system is to achieve the best possible diagnosis of cardiac arrhythmias by electronic data processing. Cardiac information system that is designed to offer remote monitoring of patient who needed continues follow up is demanding. However, intra- and interpatient electrocardiogram (ECG) morphological descriptors are varying through the time as well as the computational limits pose significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is, therefore, a promising new intelligent diagnostic tool.

The Electrocardiogram (ECG) signal uses by Clinicians to extract very useful information about the functional status of the heart, accurate and computationally efficient means of classifying cardiac arrhythmias has been the subject of considerable research efforts in recent years. The contradicting considerations on the unique characteristics of patient's activities and the inherent requirements of real-time heart monitoring pose challenges for practical implementation. That is due to susceptibility to potentially changing morphology not only between different patients or patient cluster, but also within the same patient. As a result, the model constructed using an old training data no longer needs to be adapt with the new concepts. Consequently, developing one classifier model to satisfy all patients in different situation using static training datasets is unsuccessful. Our proposed methodology automatically trains the classifier model by up-to-date training data, so as to be identifying with the new concepts. The performance of the trigger method is evaluated using various approaches. The results demonstrate the effectiveness of our proposed technique, and they suggest that it can be used to enhance the performance of new intelligent assistance diagnosis systems.

The number and the types of ECG parameters necessity to detect different arrhythmias with high quality are counting a massive number of challenges in relation to computational efforts. Such computation is very complex to carry out by wireless sensors, since there are boundaries of power supply and problem of noise. Therefore, the current systems cannot detect the abnormalities accurately or detect them but afterward. We proposed a technique to tuning the ECG parameters for achieving better arrhythmias detection in real-time applications. Our proposed methodology selects the features related to the QRS complex plus those related to P or T waves, aiming to design a unique feature set that could be employed to describe specific arrhythmia in very sensitive manner. The performance of the tuning technique has been evaluated using various approaches. The results demonstrate the effectiveness of our proposed technique.

Electrocardiogram (ECG) signal utilized by Clinicians to extract very useful information about the functional status of the heart. Of particular interest systems designed for monitoring people outdoor and detecting abnormalities on the real time. However, there are far from achieving the ideal of being able to perform adequately real time remote cardiac health monitoring in practical life. That is due to problematical challenges. In this paper we discuss all these issues, furthermore our intimations and propositions to relief such concerns are stated.

Electrocardiogram (ECG) is a series of waves and deflections recording the cardiac’s (heart) electrical activity sensed by several electrodes. ECG signal utilized to extract very useful information about the functional status of the heart. Of particular interest are the systems designed for monitoring people outdoor and detecting abnormalities (arrhythmia) on the real time. In this paper, we propose a nested ensemble technique for real time arrhythmia classification. Its main components include manipulating the training dataset for learning the classifier by up-to-data training data, and manipulating the ECG features to select the proper adequate set to enhance the accuracy and excellence classification performance. Our experiment works demonstrated the necessity of including all the ECG features in cardiac health evaluation. Moreover prove the outstanding quality achieved by our model.