AIDS and the Ghana legal system: absolute ignorance or denial syndrome?

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KLIC-score for predicting early failure in prosthetic joint infections treated with debridement, implant retention and antibiotics

AbstractDebridement, irrigation and antibiotic treatment form the current approach in early prosthetic joint infection (PJI). Our aim was to design a score to predict patients with a higher risk of failure. From 1999 to 2014 early PJIs were prospectively collected and retrospectively reviewed. The primary end-point was early failure defined as: 1) the need for unscheduled surgery, 2) death-related infection within the first 60 days after debridement or 3) the need for suppressive antibiotic treatment. A score was built-up according to the logistic regression coefficients of variables available before debridement. A total of 222 patients met the inclusion criteria. The most frequently isolated microorganisms were coagulase-negative staphylococci (95 cases, 42.8%) and Staphylococcus aureus (81 cases, 36.5%). Treatment of 52 (23.4%) cases failed. Independent predictors of failure were: chronic renal failure (OR 5.92, 95% CI 1.47–23.85), liver cirrhosis (OR 4.46, 95% CI 1.15–17.24), revision surgery (OR 4.34, 95% CI 1.34–14.04) or femoral neck fracture (OR 4.39, 95% CI1.16–16.62) compared with primary arthroplasty, C reactive protein >11.5 mg/dL (OR 12.308, 95% CI 4.56–33.19), cemented prosthesis (OR 8.71, 95% CI 1.95–38.97) and when all intraoperative cultures were positive (OR 6.30, 95% CI 1.84–21.53). A score for predicting the risk of failure was designed using preoperative factors (KLIC-score: Kidney, Liver, Index surgery, Cemented prosthesis and C-reactive protein value) and it ranged between 0 and 9.5 points. Patients with scores of ≤2, >2–3.5, 4–5, >5–6.5 and ≥7 had failure rates of 4.5%, 19.4%, 55%, 71.4% and 100%, respectively. The KLIC-score was highly predictive of early failure after debridement. In the future, it would be necessary to validate our score using cohorts from other institutions

Biomarkers improve mortality prediction by prognostic scales in community-acquired pneumonia

Background: Prognostic scales provide a useful tool to predict mortality in community-acquired pneumonia (CAP). However, the inflammatory response of the host, crucial in resolution and outcome, is not included in the prognostic scales. Methods: The aim of this study was to investigate whether information about the initial inflammatory cytokine profile and markers increases the accuracy of prognostic scales to predict 30-day mortality. To this aim, a prospective cohort study in two tertiary care hospitals was designed. Procalcitonin (PCT), C-reactive protein (CRP) and the systemic cytokines tumour necrosis factor alpha (TNFalpha) and interleukins IL6, IL8 and IL10 were measured at admission. Initial severity was assessed by PSI (Pneumonia Severity Index), CURB65 (Confusion, Urea nitrogen, Respiratory rate, Blood pressure, > or = 65 years of age) and CRB65 (Confusion, Respiratory rate, Blood pressure, > or = 65 years of age) scales. A total of 453 hospitalised CAP patients were included. Results: The 36 patients who died (7.8%) had significantly increased levels of IL6, IL8, PCT and CRP. In regression logistic analyses, high levels of CRP and IL6 showed an independent predictive value for predicting 30-day mortality, after adjustment for prognostic scales. Adding CRP to PSI significantly increased the area under the receiver operating characteristic curve (AUC) from 0.80 to 0.85, that of CURB65 from 0.82 to 0.85 and that of CRB65 from 0.79 to 0.85. Adding IL6 or PCT values to CRP did not significantly increase the AUC of any scale. When using two scales (PSI and CURB65/CRB65) and CRP simultaneously the AUC was 0.88. Conclusions: Adding CRP levels to PSI, CURB65 and CRB65 scales improves the 30-day mortality prediction. The highest predictive value is reached with a combination of two scales and CRP. Further validation of that improvement is needed

Stability in community-acquired pneumonia: one step forward with markers?

Background: Biological markers as an expression of systemic inflammation have been recognised as useful for evaluating the host response in community-acquired pneumonia (CAP). The objective of this study was to evaluate whether the biological markers procalcitonin (PCT) and C-reactive protein (CRP) might reflect stability after 72 h of treatment and the absence of subsequent severe complications. Methods: A prospective cohort study was performed in 394 hospitalised patients with CAP. Clinical stability was evaluated using modified Halm's criteria: temperature (37.2uC; heart rate (100 beats/min; respiratory rate (24 breaths/min; systolic blood pressure >90 mm Hg; oxygen saturation >90%; or arterial oxygen tension >60 mm Hg. PCT and CRP levels were measured on day 1 and after 72 h. Severe complications were defined as mechanical ventilation, shock and/or intensive care unit (ICU) admission, or death after 72 h of treatment. Results: 220 patients achieved clinical stability at 72 h and had significantly lower levels of CRP (4.2 vs 7 mg/dl) and of PCT (0.33 vs 0.48 ng/ml). Regression logistic analyses were performed to calculate several areas under the ROC curve (AUC) to predict severe complications. The AUC for clinical stability was 0.77, 0.84 when CRP was added (p=0.059) and 0.77 when PCT was added (p=0.45). When clinical stability was achieved within 72 h and marker levels were below the cut-off points (0.25 ng/ml for PCT and 3 mg/dl for CRP), no severe complications occurred. Conclusions: Low levels of CRP and PCT at 72 h in addition to clinical criteria might improve the prediction of absence of severe complications

Differences in tetracycline resistance determinant carriage among Shigella flexneri and Shigella sonnei are not related to different plasmid Inc-type carriage

Objectives: The aim of this study was to establish the prevalence of the most common molecular mechanisms involved in tetracycline resistance as well as their relationship with plasmid incompatibility (Inc) groups in a collection of Shigella spp. causing traveller’s diarrhoea. Methods: Tetracycline susceptibility was established in 187 Shigella spp. (74 Shigella flexneri and 113 Shigella sonnei), of which 153 isolates were recovered as a confirmed cause of traveller’s diarrhoea. The prevalence of the tet(A), tet(B) and tet(G) genes was analysed by PCR. Eighteen plasmid Inc groups was determined in a subset of 59 isolates. Results: Among 154 tetracycline-resistant isolates, 122 (79.2%) harboured at least tet(A) or tet(B). The tet(B) gene was the most frequently detected, being present in 70 isolates (45.5%), whilst tet(A) was detected in 57 isolates (37.0%). The tet(G) gene was present in only 11 (7.2%) isolates. Moreover, the tet(A) gene was more frequent in S. sonnei (P = 0.0007), whilst the tet(B) gene was more frequent in S. flexneri (P < 0.0001). Plasmids belonging to Inc group B (P < 0.05) were significantly more frequent among S. flexneri, whilst those belonging to groups K, FIC and FIIA (P < 0.05) were preferentially detected among S. sonnei. Conclusion: The prevalence of the tet(A) and tet(B) genes differed between S. sonnei and S. flexneri. Moreover, the prevalence of plasmid Inc groups in S. flexneri and S. sonnei differed. However, no relationship was found between the two phenomena

Semantically Aware Text Categorisation for Metadata Annotation

In this paper we illustrate a system aimed at solving a longstanding and challenging problem: acquiring a classifier to automatically annotate bibliographic records by starting from a huge set of unbalanced and unlabelled data. We illustrate the main features of the dataset, the learning algorithm adopted, and how it was used to discriminate philosophical documents from documents of other disciplines. One strength of our approach lies in the novel combination of a standard learning approach with a semantic one: the results of the acquired classifier are improved by accessing a semantic network containing conceptual information. We illustrate the experimentation by describing the construction rationale of training and test set, we report and discuss the obtained results and conclude by drawing future work.</p