ABSTRACT
In Antarctic terrestrial ecosystems, dominant plant species (grasses and mosses) and soil physicochemical properties have a significant influence on soil microbial communities. However, the effects of dominant plants on bacterial antagonistic interactions in Antarctica remain unclear. We hypothesized that dominant plant species can affect bacterial antagonistic interactions directly and indirectly by inducing alterations in soil physicochemical properties and bacterial abundance. We collected soil samples from two typical dominant plant species; the Antarctic grass Deschampsia antarctica and the Antarctic moss Sanionia uncinata, as well as bulk soil sample, devoid of vegetation. We evaluated bacterial antagonistic interactions, focusing on species from the genera Actinomyces, Bacillus, and Pseudomonas. We also measured soil physicochemical properties and evaluated bacterial abundance and diversity using high-throughput sequencing. Our results suggested that Antarctic dominant plants significantly influenced bacterial antagonistic interactions compared to bulk soils. Using structural equation modelling (SEM), we compared and analyzed the direct effect of grasses and mosses on bacterial antagonistic interactions and the indirect effects through changes in edaphic properties and bacterial abundance. SEMs showed that (1) grasses and mosses had a significant direct influence on bacterial antagonistic interactions; (2) grasses had a strong influence on soil water content, pH, and abundances of Actinomyces and Pseudomonas and (3) mosses influenced bacterial antagonistic interactions by impacting abundances of Actinomyces, Bacillus, and Pseudomonas. This study highlights the role of dominant plants in modulating bacterial antagonistic interactions in Antarctic terrestrial ecosystems.
ABSTRACT
The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.1.1.529 also known as Omicron). The early detection of dangerous situations and forecasting techniques is important to prevent the spread of disease and restart economic activities quickly and safely. In this paper, we used weekly mobility data to analyze the current situation in countries worldwide. A methodology for the statistical analysis of the current situation as well as for forecasting future outbreaks is presented in this paper in terms of deaths caused by COVID-19. Our method is evaluated with a multi-layer perceptron neural network (MLPNN), which is a deep learning model, to develop a predictive framework. Furthermore, the Case Fatality Ratio (CFR), Cronbach's alpha, and other metrics were computed to analyze the performance of the forecasting. The MLPNN is shown to have the best outcomes in forecasting the statistics for infected patients and deaths in selected regions. This research also provides an in-depth analysis of the emerging COVID-19 variants, challenges, and issues that must be addressed in order to prevent future outbreaks.
ABSTRACT
Increased bacterial translocation in the gut and bloodstream infections are both major comorbidities of heart failure and myocardial infarction (MI). However, the alterations in the microbiome of the blood of patients with MI remain unclear. To test this hypothesis, we conducted this case-control study to explore the microbiota compositions in the blood of Chinese patients with MI. Using high-throughput Illumina HiSeq sequencing targeting the V3-V4 region of the 16S ribosomal RNA (rRNA) gene, the microbiota communities in the blood of 29 patients with MI and 29 healthy controls were examined. In addition, the relationship between the blood microbiome and clinical features of MI was investigated. This study revealed a significant reduction in alpha diversity (Shannon index) in the MI group compared with the healthy controls. Also, a significant difference was detected in the structure and richness between the patients with MI and healthy controls. The members of the phylum Actinobacteria, class Actinobacteria, order Bifdobacteriales, family Bifidobacteriaceae, and genus Bifidobacterium were significantly abundant in the MI group, while the members of the phylum Bacteroidetes, class Bacteroidia, and order Bacteroidales were significantly enriched in the healthy controls (p < 0.05). Moreover, the functional analysis revealed a significant variation between both groups. For instance, the enrichment of genes involved in the metabolism pathways of three amino acids decreased, that is, nucleotide transport and metabolism, coenzyme transport and metabolism, and lipid transport and metabolism, among others. Our study will contribute to a better knowledge of the microbiota of blood, which will further lead to improved MI diagnosis and therapy. Further study is needed to determine the role of the blood microbiota in human health and disease.
ABSTRACT
Background & Objectives: Traumatic Spinal cord injury (SCI) is a devastating condition that results in life long disability. Impairments associated with traumatic SCI such as sensory, motor, and autonomic dysfunctions lead to an array of secondary SCI-specific complications. Neuropathic pain is one of the most common medical complications of traumatic SCI which significantly affects motor function and activities of daily living (ADL) in people with traumatic SCI. Neuropathic pain is one of the main factors for dependency, decreased quality of life (QOL), poor rehabilitation outcomes, and depression in traumatic SCI individuals. The main aim of the current study was to determine the frequency of neuropathic pain and its effects on rehabilitation outcomes, balance function, and QOL in people with traumatic SCI. Methods: A cross-sectional survey was carried out at PCP from March to August 2020. Overall, 123 participants were added to the study using a non-probability convenience sampling technique. Information was collected using an adapted, validated questionnaire. Both male and female traumatic SCI patients with age between 18-60 years who received at least two weeks of rehabilitation, 42 days after diagnosis of traumatic SCI were included in current study while patients with Acute SCI, SCI patients with any other condition which can affect neuropathic pain such as traumatic brain injury, diabetic neuropathy, amputation, etc. and progressive neurological diseases such as multiple sclerosis and Guillain barre syndromes were excluded. Patients who have received at least two weeks of rehabilitation, 42 days after diagnosis of traumatic SCI. Patients with traumatic SCI. Results: Overall, 123 traumatic SCI patients were included in the study. The majority of the (n=101, 82%) participants were male and 83 (67.5%) were from urban areas. Eighty-Seven (70.73%) participants had neuropathic pain. Neuropathic pain was significantly associated (P-value <0.005) with rehabilitation outcomes, balance function, and quality of life. Conclusion: It can be concluded that more than two-third of SCI patients suffer from neuropathic pain. Moreover, neuropathic pain is significantly associated with rehabilitation outcomes, balance function, and quality of life.
ABSTRACT
The human gut microbiota has been proposed to serve as a multifunctional organ in host metabolism, contributing effects to nutrient acquisition, immune response, and digestive health. Fasting during Ramadan may alter the composition of gut microbiota through changes in dietary behavior, which ultimately affects the contents of various metabolites in the gut. Here, we used liquid chromatography-mass spectrometry-based metabolomics to investigate the composition of fecal metabolites in Chinese and Pakistani individuals before and after Ramadan fasting. Principal component analysis showed distinct separation of metabolite profiles among ethnic groups as well as between pre- and post-fasting samples. After Ramadan fasting, the Chinese and Pakistani groups showed significant differences in their respective contents of various fecal metabolites. In particular, L-histidine, lycofawcine, and cordycepin concentrations were higher after Ramadan fasting in the Chinese group, while brucine was enriched in the Pakistani group. The KEGG analysis suggested that metabolites related to purine metabolism, 2-oxocarboxylic acid metabolism, and lysine degradation were significantly enriched in the total subject population pre-fasting vs. post-fasting comparisons. Several bacterial taxa were significantly correlated with specific metabolites unique to each ethnic group, suggesting that changes in fecal metabolite profiles related to Ramadan fasting may be influenced by associated shifts in gut microbiota. The fasting-related differences in fecal metabolite profile, together with these group-specific correlations between taxa and metabolites, support our previous findings that ethnic differences in dietary composition also drive variation in gut microbial composition and diversity. This landscape view of interconnected dietary behaviors, microbiota, and metabolites contributes to the future development of personalized, diet-based therapeutic strategies for gut-related disorders.
ABSTRACT
The structure and diversity of human gut microbiota are directly related to diet, though less is known about the influences of ethnicity and diet-related behaviors, such as fasting (intermittent caloric restriction). In this study, we investigated whether fasting for Ramadan altered the microbiota in Chinese and Pakistani individuals. Using high-throughput 16S rRNA gene sequencing and self-reported dietary intake surveys, we determined that both the microbiota and dietary composition were significantly different with little overlap between ethnic groups. Principal Coordinate Analyses (PCoA) comparison of samples collected from both groups before and after fasting showed partial separation of microbiota related to fasting in the Pakistani group, but not in the Chinese group. Measurement of alpha diversity showed that Ramadan fasting significantly altered the coverage and ACE indices among Chinese subjects, but otherwise incurred no changes among either group. Specifically, Prevotella and Faecalibacterium drove predominance of Bacteroidetes and Firmicutes in the Pakistani group, while Bacteroides (phylum Bacteroidetes) were the most prevalent among Chinese participants both before and after fasting. We observed significant enrichment of some specific taxa and depletion of others in individuals of both populations, suggesting that fasting could affect beta diversity. Notably, Dorea, Klebsiella, and Faecalibacterium were more abundant in the Chinese group after fasting, while Sutterella, Parabacteroides, and Alistipes were significantly enriched after fasting in the Pakistani group. Evaluation of the combined groups showed that genera Coprococcus, Clostridium_XlV, and Lachnospiracea were all significantly decreased after fasting. Analysis of food intake and macronutrient energy sources showed that fat-derived energy was positively associated with Oscillibacter and Prevotella, but negatively associated with Bacteroides. In addition, the consumption of sweets was significantly positively correlated with the prevalence of Akkermansia. Our study indicated that diet was the most significant influence on microbiota, and correlated with ethnic groups, while fasting led to enrichment of specific bacterial taxa in some individuals. Given the dearth of understanding about the impacts of fasting on microbiota, our results provide valuable inroads for future study aimed at novel, personalized, behavior-based treatments targeting specific gut microbes for prevention or treatment of digestive disorders.
ABSTRACT
Deep learning, for image data processing, has been widely used to solve a variety of problems related to medical practices. However, researchers are constantly struggling to introduce ever efficient classification models. Recent studies show that deep learning can perform better and generalize well when trained using a large amount of data. Organizations such as hospitals, testing labs, research centers, etc. can share their data and collaboratively build a better learning model. Every organization wants to retain the privacy of their data, while on the other hand, these organizations want accurate and efficient learning models for various applications. The concern for privacy in medical data limits the sharing of data among multiple organizations due to some ethical and legal issues. To retain privacy and enable data sharing, we present a unique method that combines locally learned deep learning models over the blockchain to improve the prediction of lung cancer in health-care systems by filling the defined gap. There are several challenges involved in sharing that data while maintaining privacy. In this paper, we identify and address such challenges. The contribution of our work is four-fold: (i) We propose a method to secure medical data by only sharing the weights of the trained deep learning model via smart contract. (ii) To deal with different sized computed tomography (CT) images from various sources, we adopted the Bat algorithm and data augmentation to reduce the noise and overfitting for the global learning model. (iii) We distribute the local deep learning model wights to the blockchain decentralized network to train a global model. iv) We propose a recurrent convolutional neural network (RCNN) to estimate the region of interest (ROI) in theCT images. An extensive empirical study has been conducted to verify the significance of our proposed method for better prediction of cancer in the early stage. Experimental results of the proposed model can show that our proposed technique can detect the lung cancer nodules and also achieve better performance.
