Fungal and bacterial gut microbiota differ between Clostridioides difficile colonization and infection

Aim: The bacterial microbiota is well-recognized for its role in Clostridioides difficile colonization and infection, while fungi and yeasts remain understudied. The aim of this study was to analyze the predictive value of the mycobiota and its interactions with the bacterial microbiota in light of C. difficile colonization and infection. Methods: The mycobiota was profiled by ITS2 sequencing of fecal DNA from C. difficile infection (CDI) patients (n = 29), asymptomatically C. difficile colonization (CDC) patients (n = 38), and hospitalized controls with C. difficile negative stool culture (controls; n = 38). Previously published 16S rRNA gene sequencing data of the same cohort were used additionally for machine learning and fungal-bacterial network analysis. Results: CDI patients were characterized by a significantly higher abundance of Candida spp. (MD 0.270 ± 0.089, P = 0.002) and Candida albicans (MD 0.165 ± 0.082, P = 0.023) compared to controls. Additionally, they were deprived of Aspergillus spp. (MD -0.067 ± 0.026, P = 0.000) and Penicillium spp. (MD -0.118 ± 0.043, P = 0.000) compared to CDC patients. Network analysis revealed a positive association between several fungi and bacteria in CDI and CDC, although the analysis did not reveal a direct association between Clostridioides spp. and fungi. Furthermore, the microbiota machine learning model outperformed the models based on the mycobiota and the joint microbiota-mycobiota model. The microbiota classifier successfully distinguished CDI from CDC [Area Under the Receiver Operating Characteristic (AUROC) = 0.884] and CDI from controls (AUROC = 0.905). Blautia and Bifidobacterium were marker genera associated with CDC patients and controls. Conclusion: The gut mycobiota differs between CDI, CDC, and controls and may affect Clostridioides spp. through indirect interactions. The mycobiota data alone could not successfully discriminate CDC from controls or CDI patients and did not have additional predictive value to the bacterial microbiota data. The identification of bacterial marker genera associated with CDC and controls warrants further investigation.

The diagnosis of allergic rhinitis: how to combine the medical history with the results of radioallergosorbent tests and skin prick tests

OBJECTIVE

To identify the most useful combinations of symptoms and the results of radioallergosorbent tests (RASTs) and skin prick tests (SPTs) for the diagnosis of allergic rhinitis.

DESIGN

A prospective comparison was made of symptoms and the results of RASTs and SPTs with 7 different nasal allergies; the references used were the "consensus diagnoses" provided by 3 experts.

SETTING

Nineteen general practices in The Netherlands.

PATIENTS

365 consecutive patients aged 12 or over who visited their general practitioner because of chronic or recurrent nasal symptoms between 1 March 1990 and 1 March 1991.

MAIN OUTCOME MEASURES

The most useful combinations of items from the history, RASTs, and SPTs, for the diagnosis of 7 different nasal allergies; the predictive probabilities of these combinations.

RESULTS

Diagnostic criteria could be drawn up resulting in a near-perfect discrimination between patients diagnosed as having allergic rhinitis and patients diagnosed as not having allergic rhinitis. Most of these criteria combined only a single item from the history with either RAST or SPT. For nearly all nasal allergies, both the negative predictive probabilities and the positive predictive probabilities were 97% or more.

CONCLUSIONS

The common nasal allergies can be diagnosed with a very high certainty with the aid of simple diagnostic criteria. Data from a strictly limited case history combined with either RAST or SPT are sufficient.

The Phadiatop test compared with RAST, with the CAP system; proposal for a third Phadiatop outcome: "inconclusive"

In 19 general practices, blood samples were obtained from 361 patients aged 12 years or older with chronic nasal symptoms. The Phadiatop test and a panel of RASTs to common inhalant allergens were performed on all sera with the recently introduced Pharmacia CAP system. The RAST panel was accepted as the standard. The sensitivity of the Phadiatop was 94% (95% confidence interval (CI): 89-97%), the specificity 98% (95% CI: 95-99%), the positive predictive value 97% (95% CI: 94-99%), and the negative predictive value 95% (95% CI: 91-98%). It is noteworthy that these values are very similar to those found in hospital outpatient departments. It was possible to reduce further the small percentage of false outcomes by replacing the cutoff point of the Phadiatop ratio of 1.00 by the two cutoff points 0.75 and 1.15. This resulted in three possible outcomes: a highly predictive positive outcome, a highly predictive negative outcome, and an "inconclusive" outcome. Alternatively, the cutoff point of 1.00 may be maintained while attaching the annotation "borderline" to all positive or negative Phadiatop outcomes where the Phadiatop ratio is between 0.75 and 1.15. By this simple method, physicians are alerted to the possibility of a false outcome; on the basis of the case history and other clinical findings, they can then decide whether further testing should be done.

The physical properties of a jet-nebulizer and their relevance for the histamine provocation test

We have examined the physical properties of eight specimens of a frequently used jet-nebulizer (Wiesbadener Doppelinhalator). For each nebulizer, under various conditions we measured driving pressure, solution output, solute output, particle concentration and size distribution in the aerosol, and the change of solute concentration in the storage vessel. Using histamine acid phosphate solutions in distilled water, there was considerable variability between the nebulizers in solute output (range about a factor 3). Solute output was strongly related to driving pressure (r2 = 0.89), to solution output (r2 = 0.96) and to particle concentration (r2 = 0.87). The particle size distributions of the eight nebulizers were very similar. The volume median aerodynamic diameter (VMAD) of the stable evaporated particles was on average 0.33 micrograms (geometric standard deviation of 1.78), which implies a VMAD of the primary droplets of about 3.2 micrograms. In fifteen subjects with bronchial hyperreactivity, a twofold increase of histamine output of the nebulizer without a change in particle size resulted on average in a twofold decrease of the histamine concentration that caused a 20 per cent fall (PC20) in FEV1. At constant histamine output the administration of dry particles instead of direct nebulization of (partly) saturated droplets into the mouth increased PC20 by a factor 2. This difference could not be explained by a changed deposition in the hypopharynx. There were large interindividual differences in the response to the altered aerosol characteristics. We conclude that the physical properties of the administered aerosols should be carefully controlled since they influence the biological response to inhaled histamine. The solute output of the investigated jet-nebulizer can satisfactorily be validated by weighing, for which an equation is given. In bronchial provocation testing, administration of dry particles is recommended.