Background: The prevalence of fungi in cystic fibrosis (CF) lung infections is poorly understood and studies have focused on adult patients. We investigated the fungal diversity in children with CF using bronchoalveolar lavage (BAL) and induced sputum (IS) samples to capture multiple lung niches.
Methods: Sequencing of the fungal ITS2 region and molecular mycobiota diversity analysis was performed on 25 matched sets of BAL-IS samples from 23 children collected as part of the CF-SpIT study (UKCRN14615; ISRCTNR12473810).
Results: Aspergillus and Candida were detected in all samples and were the most abundant and prevalent genera, followed by Dipodascus, Lecanicillium and Simplicillium. The presumptive CF pathogens Exophiala, Lomentospora and Scedosporium were identified at variable abundances in 100 %, 64 %, and 24 % of sample sets, respectively. Fungal pathogens observed at high relative abundance (≥40 %) were not accurately diagnosed by routine culture microbiology in over 50 % of the cohort. The fungal communities captured by BAL and IS samples were similar in diversity and composition, with exception to C. albicans being significantly increased in IS samples. The respiratory mycobiota varied greatly between individuals, with only 13 of 25 sample sets containing a dominant fungal taxon. In 11/25 BAL sample sets, airway compartmentalisation was observed with diverse mycobiota detected from different lobes of the lung.
Conclusions: The paediatric mycobiota is diverse, complex and inadequately diagnosed by conventional microbiology. Overlapping fungal communities were identified in BAL and IS samples, showing that IS can capture fungal genera associated with the lower airway. Compartmentalisation of the lower airway presents difficulties for consistent mycobiota sampling.
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http://dx.doi.org/10.1016/j.jcf.2024.07.011 | DOI Listing |
Brief Bioinform
November 2024
School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.
The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.
View Article and Find Full Text PDFBMC Med Imaging
December 2024
Department of Radiology, Peking University First Hospital, 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
Background: The apparent diffusion coefficient (ADC) has been reported as a quantitative biomarker for assessing the aggressiveness of upper urinary tract urothelial carcinoma (UTUC), but it has typically been used only with mean ADC values. This study aims to develop a radiomics model using ADC maps to differentiate UTUC grades by incorporating texture features and to compare its performance with that of mean ADC values.
Methods: A total of 215 patients with histopathologically confirmed UTUC were enrolled retrospectively and divided into training and test sets.
Pol J Vet Sci
December 2024
School of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang, China.
Pseudorabies virus (PRV) is one of the most important infectious diseases which leads to significant economic losses in the global swine industry. The gE-deleted vaccine is widely used to prevent susceptible pigs from PRV infection. There is no report of the differentiation of PRV wild strain and vaccine strain by recombinase polymerase amplification (RPA) coupled with a lateral flow dipstick (LFD) method.
View Article and Find Full Text PDFFront Immunol
December 2024
Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Objective: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast cancer (BC). The objective is to provide guidance for developing scientifically individualized treatment plans, assessing prognosis, and planning preoperative interventions.
Methods: A retrospective analysis was conducted on clinical and imaging data from 270 patients with BC confirmed by surgical pathology at the Third Hospital of Shanxi Medical University between November 2022 and April 2024.
Netw Neurosci
December 2024
Department of Physics, Indiana University, Bloomington, IN, USA.
Most of the recent work in psychedelic neuroscience has been done using noninvasive neuroimaging, with data recorded from the brains of adult volunteers under the influence of a variety of drugs. While these data provide holistic insights into the effects of psychedelics on whole-brain dynamics, the effects of psychedelics on the mesoscale dynamics of neuronal circuits remain much less explored. Here, we report the effects of the serotonergic psychedelic N,N-diproptyltryptamine (DPT) on information-processing dynamics in a sample of in vitro organotypic cultures of cortical tissue from postnatal rats.
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