How holobionts get sick-toward a unifying scheme of disease.

All humans, animals, and plants are holobionts. Holobionts comprise the host and a myriad of interacting microorganisms-the microbiota. The hologenome encompasses the genome of the host plus the composite of all microbial genomes (the microbiome). In health, there is a fine-tuned and resilient equilibrium within the members of the microbiota and between them and the host. This relative stability is maintained by a high level of microbial diversity, a delicate bio-geographic distribution of microorganisms, and a sophisticated and intricate molecular crosstalk among the multiple components of the holobiont. Pathobionts are temporarily benign microbes with the potential, under modified ecosystem conditions, to become key players in disease. Pathobionts may be endogenous, living for prolonged periods of time inside or on the host, or exogenous, invading the host during opportunistic situations. In both cases, the end result is the transformation of the beneficial microbiome into a health-perturbing pathobiome. We hypothesize that probably all diseases of holobionts, acute or chronic, infectious or non-infectious, and regional or systemic, are characterized by a perturbation of the healthy microbiome into a diseased pathobiome.

Positron emission tomography/computed tomography for the diagnosis of endocarditis in patients with pulmonic stented valve/pulmonic stent.

Percutaneous pulmonic valve and pulmonic stent implantation have become a well-established treatment for recurrent pulmonic stenosis or insufficiency in patients with repaired congenital heart disease. Late endocarditis is seldom reported, but its diagnosis might be challenging due to the limited visualization of the stented valve or stent by transesophageal echocardiography. We present 2 young patients who were hospitalized for suspected endocarditis and in whom the diagnosis was made with the aid of positron emission tomography/computed tomography scan.

Risk factors and a clinical index for diagnosis of Pseudomonas aeruginosa bacteremia.

OBJECTIVE: To define risk factors significantly and independently associated with Pseudomonas aeruginosa bacteremia and to combine them in a diagnostic index which will define groups of septic patients at low or high risk of bloodstream infection caused by P. aeruginosa. METHODS: Logistic regression analysis was used to identify risk factors associated with pseudomonal bacteremia among all patients with bacteremia at St Thomas' Hospital, London, from 1969 to 1989. The coefficients of the final logistic model were used to compile a linear model for the diagnosis of pseudomonal bacteremia. The index was validated in another set of patients with bacteremia detected at Beilinson Medical Center, Petah Tiqva, Israel, from 1988 to 1991. RESULTS: Seven factors were significantly and independently predictive of pseudomonal bacteremia: 1) neutropenia; 2) previous or current treatment with antibiotics; 3) cytotoxic or corticosteroid treatment; 4) hospital acquisition of infection; 5) detection in the intensive care unit; 6) male gender; and 7) focus of infection. High-risk foci were the urinary tract with catheter or post-instrumentation, or unknown source. Low-risk foci were bone, joint, meninges, female genital tract and upper respiratory tract. The index score divided patients into three groups with increasing likelihood of P. aeruginosa bacteremia: 1%, 7% and 19%, respectively (p=0.0001). In the validation set, the percentage of P. aeruginosa bacteremia in the three respective groups defined by the index were 5%, 18% and 22% (p=0.0001). CONCLUSIONS: The use of simple clinical and laboratory data known within hours of detection of an infectious episode can define patients at high and low risk for P. aeruginosa bacteremia.