Machine learning for exploring microbial inter-kingdom associations in Cystic Fibrosis and Bronchiectasis

Background: Cystic fibrosis (CF) and non-CF bronchiectasis (BX) are lung diseases characterised by severe chronic infections. Fungal and bacterial components of infection are both recognized. Recent molecular investigation of sputum from patients with CF and BX has revealed a complex mycobiome. However, little is known about how fungal and bacterial organisms interact or whether the interactions impact on disease outcomes. Methods: Quantitative PCR and next generation sequencing of ITS2 and 16S rRNA gene was carried out on 107 patients with CF and BX and defined clinical fungal infection status. Fungal and bacterial communities were explored using supervised and unsupervised machine learning to understand associations between fungal and bacterial communities and their relationship to disease. Results: Fungal and bacterial communities both had significantly higher biomass and lower diversity in CF compared to BX patients. Random forest modelling demonstrated that the fungal and bacterial communities were distinct between CF and BX patients. Within the CF group, bacterial communities contained no predictive signal for fungal disease status. Neither bacterial nor fungal community composition were predictive of the presence of CF pulmonary exacerbation (CFPE). Intra-kingdom correlations were far stronger than those between the two kingdoms. Dirichlet mixture components analysis identified two distinct clusters of bacteria related to the relative abundance of Pseudomonas. Fungal community composition contained no predictive signal for bacterial clusters. Conclusions: Clear changes in diversity were observed between patients with different clinical disease status. Although our results demonstrate that bacterial community composition differs in the presence of fungal disease, no direct relationship between bacterial and fungal OTUs was found.

Immunoprotection against Cryptococcosis Offered by Znf2 Depends on Capsule and the Hyphal Morphology

Cryptococcosis claims close to 200,000 lives annually. There is no vaccine clinically available for this fungal disease.

Clinical features of invasive fungal disease in children with no underlying disease

There is limited research into Invasive fungal disease (IFD) in children with no underlying disease. We undertook a retrospective study of children with IFD who did not suffer from another underlying disease, from June 2010 to March 2018 in Changsha, China. Nine children were identified. Eosinophil counts were elevated in six cases. The level of procalcitonin (PCT) was elevated in six cases. Fungal culture was positive in all patients, including eight cases of Cryptococcus neoformans and one case of Candida parapsilosis. 8.33 days following antifungal treatment, the body temperature of the eight patients affected by cryptococcal disease had returned to normal. Our study indicates that the primary pathogen in IFD was Cryptococcus neoformans in children who had no other underlying disease. Eosinophils can be considered to be indicators of cryptococcal infection. IFD in children with no other underlying disease has a satisfactory prognosis.

Prevalence and Healthcare Burden of Fungal Infections in the United States, 2018

Background Fungal infections are responsible for >1.5 million deaths globally per year, primarily in those with compromised immune function. This is concerning as the number of immunocompromised patients, especially in those without human immunodeficiency virus (HIV), has risen in the past decade. The purpose of this analysis was to provide the current prevalence and impact of fungal disease in the United States. Methods We analyzed hospital discharge data from the most recent (2018) Healthcare Cost and Utilization Project National Inpatient Sample, and outpatient visit data from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey. Costs are presented in 2018 United States (US) dollars. Results In the 35.5 million inpatient visits documented in 2018 in the US, approximately 666 235 fungal infections were diagnosed, with an estimated attributable cost of $6.7 billion. Aspergillus, Pneumocystis, and Candida infections accounted for 76.3% of fungal infections diagnosed, and 81.1% of associated costs. Most fungal disease occurred in patients with elevated risk of infection. The visit costs, lengths of stay, and risks of mortality in this population were more than twice that of those without fungal diagnoses. A further 6.6 million fungal infections were diagnosed during outpatient visits. Conclusions Fungal disease is a serious clinical concern with substantial healthcare costs and significant increases in morbidity and mortality, particularly among predisposed patients. Increased surveillance, standardized treatment guidelines, and improvement in diagnostics and therapeutics are needed to support the rising numbers of at-risk patients.

Inhibitory Effect and Mechanism of Chitosan–Ag Complex Hydrogel on Fungal Disease in Grape

Hydrogel antibacterial agent is an ideal antibacterial material because of it could diffuses antibacterial molecules into the decayed area by providing a suitable microenvironment and the hydrogel acts as a protective barrier on the decay interface. The biocompatibility and biodegradation make the removal process easily which were widely used in medical fields. However, there have been few reports on its application for controlling postharvest diseases in fruit. In this study, the Chitosan-Ag (CS-Ag) complex hydrogels were prepared using the physical crosslinking method, which used for controlling postharvest diseases in grape. The prepared hydrogels were stable for a long period at room temperature. The structure and surface morphology of CS-Ag composite hydrogels were characterized by UV-Vis, FTIR, SEM, and XRD. The inhibitory effects of CS-Ag hydrogel on disease in grape caused by P. expansum, A. niger and B. cinerea were investigated both in vivo and in vitro. The remarkable antibacterial activity of CS-Ag hydrogels was mainly due to the synergistic antibacterial and antioxidant effects of CS and Ag. Preservation test showed that the CS-Ag hydrogel had positive fresh-keeping effect. This revealed CS-Ag hydrogels plays a critical role in controlling fungal disease in grape.

Effect of pruning and mechanical fruit thinning on crop load and berry and wine composition of Tempranillo in Texas

Tempranillo is the second most planted variety in Texas. However, over-cropping can be an issue. Crop load can be managed by pruning and mechanical fruit thinning. Mechanizing fruit thinning provides three benefits: yield reduction, berry thinning to decrease cluster compactness and reduce fungal disease and lower production costs than fruit thinning by hand (Tardaguila et al., 2008). In this study, crop load was manipulated by pruning and mechanical fruit thinning and its effect was determined on berry and wine quality.

An Inference System for Classifying Oil Palm Fungal Diseases

The oil palm plant is one of the major important cash crops of the Nigerian economy and a significant contributor to the world market for vegetable oils. Unfortunately, infection with fungi has caused a decline in the productivity of oil palms and subsequently the palm oil industry. Hence the need to detect oil palm plant disease earlier before it affects it informed this research to develop a fuzzy inference model to predict the influence of fungal disease on the oil plant plant. Following extensive review of related works, the factors associated with the severity of fungal diseases in the oil palm plant were identified following validation by Botanist. Fuzzy triangular membership functions were used to formulate the input factors identified alongside the target variables for identifying the severity of fungal diseases affecting the oil palm plant. The rule base was formulated using IF-THEN statements to combine the values of the input factors with the respective values of the target severity of oil palm plant disease. The classification model for oil palm plant disease severity was simulated using the Fuzzy Logic Toolbox available in the MATLAB R2015b Software. The results showed that the developed inference system for oil palm plant was capable of classifying and predicting the degree of the fungal disease infection into four groups; no severity, low severity, moderate severity and high severity.