Haematogenous Pneumonia Caused by Kocuria Kristinae in a Patient with a Central Venous Catheter.

Kocuria kristinae is a Gram-positive commensal bacterium, rarely responsible for infection in immunocompromised patients. A 29-year-old woman affected by intestinal pseudo-obstruction and requiring home parenteral nutrition, was hospitalised for fever and shivering during the infusion through a long-term central venous catheter (CVC). Blood cultures were positive for K. kristinae infection. At a chest CT scan, two partially cavitated nodular lesions were evidenced. Meropenem antibiotic therapy was used locally and systemically, resulting in catheter use restoration. A chest CT scan two months later at follow-up showed two centimetric, fibrotic and disventilatory areas replacing the previous nodular thickenings. Kokuria kristinae was responsible for haematogenous pulmonary involvement with excavated nodules, requiring a differential diagnosis. Moreover, in the case of a CVC infection, in addition to the risk of right endocarditis, haematogenous pneumonia must also be considered. LEARNING POINTS: Kocuria kristinae is a Gram-positive commensal bacterium, potentially responsible for infection.In the case of central venous catheter infection, in addition to the risk of right endocarditis, haematogenous pneumonia must also be considered.A differential diagnosis between bacterial and fungal infection is necessary (less, but to be suspected, neoplastic metastases).

High Nutritional Risk Is Associated with Poor Functional Status and Prognostic Biomarkers in Stroke Patients at Admission to a Rehabilitation Unit.

Considering that malnutrition (undernutrition) is common in stroke patients and may negatively impact body function, the aim of this study was to determine the relationship between nutritional risk and functional status in stroke patients at admission to a rehabilitation unit. Nutritional risk was assessed using the Geriatric Nutritional Risk Index (GNRI), the Prognostic Nutritional Index (PNI) and the Controlling Nutritional Status (CONUT) score. Functional status was assessed using the Barthel Index, the modified Rankin Scale, the Trunk Control Test and the Sitting Balance Scale, and cognitive function was assessed using the Short Portable Mental Status Questionnaire. C-reactive protein, fibrinogen and D-dimer were also evaluated as established prognostic biomarkers. Stroke patients (n = 245; age 69.7 ± 12.8 years; 47%, women; 82% ischemic stroke) at admission to a rehabilitation unit were included in this study. A high prevalence of nutritional risk was detected with each tool and was found to be greater using the GNRI and in patients aged ≥75 years. Multiple logistic regression analysis showed that age and dysphagia were independent predictors of high nutritional risk. High risk groups performed worse on all functional tests compared to the low-risk groups (p < 0.05). Nutritional risk with each tool was associated with functional and cognitive statuses (with the highest correlation being with the Trunk Control Test). Significant associations were also found with C-reactive protein, fibrinogen and D-dimer. In conclusion, a high nutritional risk, as evaluated with the GNRI, the PNI and the CONUT score, was detected in stroke patients at admission to a rehabilitation unit. High nutritional risk was associated with functional status and with predictors of clinical outcomes (and specifically in older patients).

SIGMA: a set-cover-based inexact graph matching algorithm.

Network querying is a growing domain with vast applications ranging from screening compounds against a database of known molecules to matching sub-networks across species. Graph indexing is a powerful method for searching a large database of graphs. Most graph indexing methods to date tackle the exact matching (isomorphism) problem, limiting their applicability to specific instances in which such matches exist. Here we provide a novel graph indexing method to cope with the more general, inexact matching problem. Our method, SIGMA, builds on approximating a variant of the set-cover problem that concerns overlapping multi-sets. We extensively test our method and compare it to a baseline method and to the state-of-the-art Grafil. We show that SIGMA outperforms both, providing higher pruning power in all the tested scenarios.

SING: subgraph search in non-homogeneous graphs.

BACKGROUND: Finding the subgraphs of a graph database that are isomorphic to a given query graph has practical applications in several fields, from cheminformatics to image understanding. Since subgraph isomorphism is a computationally hard problem, indexing techniques have been intensively exploited to speed up the process. Such systems filter out those graphs which cannot contain the query, and apply a subgraph isomorphism algorithm to each residual candidate graph. The applicability of such systems is limited to databases of small graphs, because their filtering power degrades on large graphs. RESULTS: In this paper, SING (Subgraph search In Non-homogeneous Graphs), a novel indexing system able to cope with large graphs, is presented. The method uses the notion of feature, which can be a small subgraph, subtree or path. Each graph in the database is annotated with the set of all its features. The key point is to make use of feature locality information. This idea is used to both improve the filtering performance and speed up the subgraph isomorphism task. CONCLUSIONS: Extensive tests on chemical compounds, biological networks and synthetic graphs show that the proposed system outperforms the most popular systems in query time over databases of medium and large graphs. Other specific tests show that the proposed system is effective for single large graphs.