Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent ...life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this article. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined.
We discovered odorous 16-androstenes (Androstenone and Androstenol) in endangered mouse deer during a captive breeding program. This study examined the molecular characteristics, their synthesis ...pathway, and the possible functional role of these compounds in the reproduction of mouse deer. CYP17A1 and CYB5 genes were cloned and expressed in HEK-293, COS-7 cell lines, and gonads of mouse deer to investigate the CYP17A1 gene’s andien-β-synthase activity towards the synthesis of 16-androstenes in mouse deer. An enzyme immunoassay was further developed and standardized to measure fecal androstenone during the reproductive cycles of mouse deer. Results showed that the mouse deer CYP17A1 gene possesses andien-β-synthase activity and could transform pregnenolone into 5,16-androstadien-3β-ol. The expression of the CYP17A1 gene upregulated in the testis and ovary compared to other tissues in mouse deer. Significantly elevated androstenone and estrogens were recorded prior to delivery and postpartum estrus/mating in mouse deer. Further, there were weak correlations between fecal androstenone and estrogens/androgens in mouse deer during the breeding season. These findings suggest that androstenone probably plays a role in the reproductive activities of mouse deer. This knowledge can be used for captive breeding programs of mouse deer in India and elsewhere.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Although the gut microbiome benefits the host in several ways, how anthropogenic forces impact the gut microbiome of mammals is not yet completely known. Recent studies have noted reduced gut ...microbiome diversity in captive mammals due to changes in diet and living environment. However, no studies have been carried out to understand how the gut microbiome of wild mammals responds to domestication. We analyzed the gut microbiome of wild and captive gaur and domestic mithun (domestic form of gaur) to understand whether the gut microbiome exhibits sequential changes from wild to captivity and after domestication. Both captive and domestic populations were characterized by reduced microbial diversity and abundance as compared to their wild counterparts. Notably, two beneficial bacterial families,
and
, which are known to play vital roles in herbivores' digestion, exhibited lower abundance in captive and domestic populations. Consequently, the predicted bacterial functional pathways especially related to metabolism and immune system showed lower abundance in captive and domestic populations compared to wild population. Therefore, we suggest that domestication can impact the gut microbiome more severely than captivity, which might lead to adverse effects on host health and fitness. However, further investigations are required across a wide range of domesticates in order to understand the general trend of microbiome shifts in domestic animals.
Until recently, the study of major histocompability complex (MHC) mediated immunity has focused on the direct link between MHC diversity and susceptibility to parasite infection. However, MHC genes ...can also influence host health indirectly through the sculpting of the bacterial community that in turn shape immune responses. We investigated the links between MHC class I and II gene diversity gut microbiome diversity and micro- (adenovirus, AdV) and macro- (helminth) parasite infection probabilities in a wild population of non-human primates, mouse lemurs of Madagascar. This setup encompasses a plethora of underlying interactions between parasites, microbes and adaptive immunity in natural populations. Both MHC classes explained shifts in microbiome composition and the effect was driven by a few select microbial taxa. Among them were three taxa (Odoribacter, Campylobacter and Prevotellaceae-UCG-001) which were in turn linked to AdV and helminth infection status, correlative evidence of the indirect effect of the MHC via the microbiome. Our study provides support for the coupled role of MHC diversity and microbial flora as contributing factors of parasite infection.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The ‘One Health’ framework emphasizes the ecological relationships between soil, plant, animal and human health. Microbiomes play important roles in these relationships, as they modify the health and ...performance of the different compartments and influence the transfer of energy, matter and chemicals between them. Standardized methods to characterize microbiomes along food chains are, however, currently lacking. To address this methodological gap, we evaluated the performance of DNA extraction kits and commonly recommended primer pairs targeting different hypervariable regions (V3‐V4, V4, V5‐V6, V5‐V6‐V7) of the 16S rRNA gene, on microbiome samples along a model food chain, including soils, maize roots, cattle rumen, and cattle and human faeces. We also included faeces from gnotobiotic mice colonized with defined bacterial taxa and mock communities to confirm the robustness of our molecular and bioinformatic approaches on these defined low microbial diversity samples. Based on Amplicon Sequence Variants, the primer pair 515F‐806R led to the highest estimates of species richness and diversity in all sample types and offered maximum diversity coverage of reference databases in in silico primer analysis. The influence of the DNA extraction kits was negligible compared to the influence of the choice of primer pairs. Comparing microbiomes using 515F‐806R revealed that soil and root samples have the highest estimates of species richness, while lowest richness was observed in human faeces. Primer pair choice directly influenced the estimation of community changes within and across compartments and may give rise to preferential detection of specific taxa. This work demonstrates why a standardized approach is necessary to analyse microbiomes within and between source compartments along food chains in the context of the One Health framework.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent ...life threatening cardiac circumstances. Cardiovascular diseases, listed as the underlying cause of death and have been reported to be the leading cause of mortality across the globe, accounted for approximately 836,546 deaths in the United States in 2018. Statistics show that almost one of every three deaths in the US is a result of heart disease. Nearly 2,300 Americans die of cardiovascular disease each day, an average of one death every 38 seconds. Among such diseases, Myocardial Infarction (MI), also known as "heart attack", is of main interest among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. This is while quick and immediate action at the onset of such heart conditions can save many lives. To this end, ample research has been reported in the literature on ECG signal analysis to determine arrhythmia and other cardiac conditions. We first present a detailed literature survey regarding ECG signal analysis has been conducted in this study and is presented as a stages based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. Traditional signal processing methods, machine learning and its sub branches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. However, more accurate and near real-time techniques are still under investigation. Where analyzing the ECG can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.This work introduces a classifier that will detect abnormalities of the ECG signal with its analysis as a 2-D image fed to a CNN classifier. The proposed method classifies the ECG signal as normal or abnormal by transforming the single-lead ECGsignal into images and then applying CNN classification.
Cardiovascular diseases have been reported to be the leading cause of mortality across the globe. Among such diseases, Myocardial Infarction (MI), also known as “heart attack”, is of main interest ...among researchers, as its early diagnosis can prevent life threatening cardiac conditions and potentially save human lives. Analyzing the Electrocardiogram (ECG) can provide valuable diagnostic information to detect different types of cardiac arrhythmia. Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user’s experience. However, advanced machine learning methods have put a burden on portable and wearable devices due to their high computing requirements. We present an improved, less complex Convolutional Neural Network (CNN)-based classifier model that identifies multiple arrhythmia types using the two-dimensional image of the ECG wave in real-time. The proposed model is presented as a three-layer ECG signal analysis model that can potentially be adopted in real-time portable and wearable monitoring devices. We have designed, implemented, and simulated the proposed CNN network using Matlab. We also present the hardware implementation of the proposed method to validate its adaptability in real-time wearable systems. The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions.
Although the significance of the gut microbiome for host health is well acknowledged, the impact of host traits and environmental factors on the interindividual variation of gut microbiomes of ...wildlife species is not well understood. Such information is essential; however, as changes in the composition of these microbial communities beyond the natural range might cause dysbiosis leading to increased susceptibility to infections. We examined the potential influence of sex, age, genetic relatedness, spatial tactics and the environment on the natural range of the gut microbiome diversity in free‐ranging Namibian cheetahs (Acinonyx jubatus). We further explored the impact of an altered diet and frequent contact with roaming dogs and cats on the occurrence of potential bacterial pathogens by comparing free‐ranging and captive individuals living under the same climatic conditions. Abundance patterns of particular bacterial genera differed between the sexes, and bacterial diversity and richness were higher in older (>3.5 years) than in younger individuals. In contrast, male spatial tactics, which probably influence host exposure to environmental bacteria, had no discernible effect on the gut microbiome. The profound resemblance of the gut microbiome of kin in contrast to nonkin suggests a predominant role of genetics in shaping bacterial community characteristics and functional similarities. We also detected various Operational Taxonomic Units (OTUs) assigned to potential pathogenic bacteria known to cause diseases in humans and wildlife species, such as Helicobacter spp., and Clostridium perfringens. Captive individuals did not differ in their microbial alpha diversity but exhibited higher abundances of OTUs related to potential pathogenic bacteria and shifts in disease‐associated functional pathways. Our study emphasizes the need to integrate ecological, genetic and pathogenic aspects to improve our comprehension of the main drivers of natural variation and shifts in gut microbial communities possibly affecting host health. This knowledge is essential for in situ and ex situ conservation management.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Evidence suggests obesity exerts a negative impact on cognition. Major Depressive Disorder (MDD) is also linked to problems in cognitive functioning. Obesity is highly prevalent in individuals with ...MDD and is linked to a failure to return to a full level of functioning. The study's objective was to investigate the effect of obesity on cognitive impairment in participants with MDD.
This study compared cognitive performance in obese individuals with MDD and two control populations (obese individuals without a psychiatric illness and non-obese controls). A standardized battery of neuropsychological tests specifically designed to assess performance in declarative memory, executive functioning, processing speed and attention was administered. Mood ratings, physical measurements, nutritional and health questionnaires were also completed.
We observed a consistent pattern across measures of memory, executive functioning, attention and processing speed. Whereas healthy controls performed better than both bariatric groups across the majority of measures administered, bariatric controls tended to outperform bariatric MDD patients.
The overall sample size of our study was small and thus largely explorative in nature. However, it provides compelling results (while controlling for extraneous variables such as medication load, nutritional status and common metabolic comordidities) that strongly urges for further investigation and study replication with larger sample sizes.
We found obesity has a subtle impact on cognition in obese individuals, and when obesity is present in individuals with MDD, this impact may be significant. It is important to minimize all modifiable variables that can add to cognitive burden in this population.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Increasing anthropogenic disturbances in Madagascar are exerting constrains on endemic Malagasy lemurs and their habitats, with possible effects on their health and survival. An important component ...of health is the gut microbiome, which might be disrupted by various stressors associated with environmental change. We have studied the gut microbiome of gray-brown mouse lemurs (
Microcebus griseorufus
), one of the smallest Malagasy primates and an important model of the convergent evolution of diseases. We sampled two sites: one situated in a national park and the other consisting of a more disturbed site around human settlement. We found that more intense anthropogenic disturbances indeed disrupted the gut microbiome of this lemur species marked by a reduction in bacterial diversity and a shift in microbial community composition. Interestingly, we noted a decrease in beneficial bacteria (i.e., members of the Bacteroidaceae family) together with a slight increase in disease-associated bacteria (i.e., members of the Veillonellaceae family), and alterations in microbial metabolic functions. Because of the crucial services provided by the microbiome to pathogen resistance and host health, such negative alterations in the gut microbiome of mouse lemurs inhabiting anthropogenically disturbed habitats might render them susceptible to diseases and ultimately affecting their survival in the shrinking biodiversity seen in Madagascar. Gut microbiome analyses might thus serve as an early warning signal for pending threats to lemur populations.
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