This study aimed at investigating the fecal microbiota, and the fecal and urinary metabolome of non progressor (NP) and progressor (P) patients with immunoglobulin A nephropathy (IgAN). Three groups of volunteers were included in the study: (i) sixteen IgAN NP patients; (ii) sixteen IgAN P patients; and (iii) sixteen healthy control (HC) subjects, without known diseases. Selective media were used to determine the main cultivable bacterial groups. Bacterial tag-encoded FLX-titanium amplicon pyrosequencing of the 16S rDNA and 16S rRNA was carried out to determine total and metabolically active bacteria, respectively. Biochrom 30 series amino acid analyzer and gas-chromatography mass spectrometry/solid-phase microextraction (GC-MS/SPME) analyses were mainly carried out for metabolomic analyses. As estimated by rarefaction, Chao and Shannon diversity index, the lowest microbial diversity was found in P patients. Firmicutes increased in the fecal samples of NP and, especially, P patients due to the higher percentages of some genera/species of Ruminococcaceae, Lachnospiraceae, Eubacteriaceae and Streptococcaeae. With a few exceptions, species of Clostridium, Enterococcus and Lactobacillus genera were found at the highest levels in HC. Bacteroidaceae, Porphyromonadaceae, Prevotellaceae and Rikenellaceae families differed among NP, P and HC subjects. Sutterellaceae and Enterobacteriaceae species were almost the highest in the fecal samples of NP and/or P patients. Compared to HC subjects, Bifidobacterium species decreased in the fecal samples of NP and P. As shown by multivariate statistical analyses, the levels of metabolites (free amino acids and organic volatile compounds) from fecal and urinary samples markedly differentiated NP and, especially, P patients.
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Cureus
December 2024
Department of Biology, College of Science, Polytechnic University of the Philippines, Manila, PHL.
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Parasite Epidemiol Control
February 2025
Centre for Research on Health and Priority Pathologies, Institute of Medical Research and Medicinal Plants Studies, PO box 13033 Yaoundé, Cameroon.
Background: Urbanization coupled with poverty has promoted the exploitation of risk zones like flood-prone areas in the city of Yaoundé. The overcrowding and poor hygiene observed in these areas are responsible for the unsmiling variations in environmental cleanliness, exploitation of river water for domestic purposes thus putting them at risk for parasitic disease transmissions. This study was conducted in order to assess the risks of human helminthiases outbreaks in relation to water physico-chemical factors in the city of Yaoundé.
View Article and Find Full Text PDFWorld Allergy Organ J
January 2025
Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
Background: Childhood rhinitis and asthma are allergic respiratory diseases triggered by common allergens, but they affect different parts of the respiratory system, leading to distinct symptoms. However, a comprehensive multi-biofluid metabolomics-based approach to uncover valuable insights into childhood allergies and allergen sensitization remains unaddressed.
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BMC Vet Res
January 2025
College of Veterinary Medicine, Qingdao Agricultural University, Qingdao, 266109, China.
Background: Aleutian mink disease, mink viral enteritis and canine distemper are known as the three most serious diseases that cause great economic loss in the mink industry. In clinical practice, aleutian mink disease virus (AMDV), mink enteritis virus (MEV) and canine distemper virus (CDV) are common mixed infections, and they have similar clinical clinical signs, such as diarrhoea. Therefore, a rapid and accurate differential diagnosis method for use on mink ranches is essential for the control of these three pathogens.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Biological Sciences, Wellesley College, Wellesley, MA, USA.
Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution (±3 months) for the first 1.5 years of life.
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