Panobacumab adjunctive immuno-therapy for Pseudomonas aeruginosa hospital-acquired pneumonia versus a cohort group

Objectives: To assess the efficacy of Panobacumab, a fully human IgM monoclonal antibody against P. aeruginosa serotype O11, by comparing a phase IIa trial with a standard care cohort trial both in hospital acquired pneumonia (HAP) caused by P. aeruginosa O11. Methods: Demographics, outcome and survival of HAP including Ventilator Associated Pneumonia (VAP) in patients either treated with standard antimicrobial therapy in a retrospective cohort trial (CT) or with adjunctive Panobacumab therapy during an open phase IIa trial were compared. Both trials applied the same inclusion exclusion criteria and the same trial period of 30 days. Results: 17 patients with VAP/HAP (14 / 3) caused by P. aeruginosa O11 were enrolled in a phase IIa trial (ITT population) and treated with Panobacumab, 13 of them received the full treatment course of 3 infusions (PP population, 12 VAP, 1 HAP) and 4 patients received only one infusion. In the cohort trial 14 patients (VAP/HAP: 12 / 2) treated with standard antibiotic therapy were included. The mean age and weight were 65.8 y (years) (SD 17.2) and 78.0 kg (SD 22.1) in the PP, 67.8 y (SD 15.4) and 77.1 kg (SD 20.2) in the ITT population and 51.8 y (SD 22.3) and 67.1 kg (SD 13.0) in the CT. At the time of suspicion of pneumonia a mean APACHE II and CPIS of 19.4 (13 - 33) and 8.7 (7 - 11) in the PP, 18.9 (13-33) and 8.5 (7 -11) in the ITT and 14.5 (2 - 24) and 7.5 (3 -12) in the CT population were observed. Tracheostomy was present in 53.8% and 52.9% in the PP and ITT populations and 38.4% in the CT. The pneumonia was polymicrobial in 69.2%, 70.6% and 85.7% in the PP, ITT and CT respectively. Stay at ICU and hospital before diagnosis of pneumonia were similar in the 3 groups. All 13 patients that received 3 doses of Panobacumab achieved resolution of pneumonia with only two relapsing during the study. Hence 85% achieved resolution and 15% recurrence at day 30. In the ITT group 64.7% of the pneumonia resolved 11.8% recurred and 23.5% continued while in the CT 57% resolved, 7% recurred and 34% continued. Resolution of pneumonia occurred markedly earlier in the Panobacumab trial (8.9 days, SD: 3.3) than in the cohort trial (15.3 days, SD: 9.5). The expected mortality derived from APACHE II score was 31% and 32% in the PP and ITT population and 22% in the cohort group. All patients who received 3 doses of Panobacumab survived, 18% died in the ITT group while in the CT 21% mortality matched the predicted mortality. Conclusions: Treatment of VAP/HAP caused by P. aeruginosa O11 with 3 doses of Panobacumab resulted in 100% survival, with highest pneumonia resolution (85%), and in a shorter time when compared with patients under standard therapy. The results indicate that Panobacumab may be effective in such life-threatening indication and warrants larger controlled trials.

Validation of a clinical prediction score for ruling out coronary heart disease in primary care patients with chest pain.

Background: A patient's chest pain raises concern for the possibility of coronary heart disease (CHD). An easy to use clinical prediction rule has been derived from the TOPIC study in Lausanne. Our objective is to validate this clinical score for ruling out CHD in primary care patients with chest pain. Methods: This secondary analysis used data collected from a oneyear follow-up cohort study attending 76 GPs in Germany. Patients attending their GP with chest pain were questioned on their age, gender, duration of chest pain (1-60 min), sternal pain location, pain increases with exertion, absence of tenderness point at palpation, cardiovascular risks factors, and personal history of cardiovascular disease. Area under the curve (ROC), sensitivity and specificity of the Lausanne CHD score were calculated for patients with full data. Results: 1190 patients were included. Full data was available for 509 patients (42.8%). Missing data was not related to having CHD (p = 0.397) or having a cardiovascular risk factor (p = 0.275). 76 (14.9%) were diagnosed with a CHD. Prevalence of CHD were respectively of 68/344 (19.8%), 2/62 (3.2%), 6/103 (5.8%) in the high, intermediate and low risk category. ROC was of 72.9 (CI95% 66.8; 78.9). Ruling out patients with low risk has a sensitivity of 92.1% (CI95% 83.0; 96.7) and a specificity of 22.4% (CI95% 18.6%; 26.7%). Conclusion: The Lausanne CHD score shows reasonably good sensitivity and can be used to rule out coronary events in patients with chest pain. Patients at risk of CHD for other rarer reasons should nevertheless also be investigated.

Ruling out coronary artery disease in primary care: development and validation of a simple prediction rule.

BACKGROUND: Chest pain can be caused by various conditions, with life-threatening cardiac disease being of greatest concern. Prediction scores to rule out coronary artery disease have been developed for use in emergency settings. We developed and validated a simple prediction rule for use in primary care. METHODS: We conducted a cross-sectional diagnostic study in 74 primary care practices in Germany. Primary care physicians recruited all consecutive patients who presented with chest pain (n = 1249) and recorded symptoms and findings for each patient (derivation cohort). An independent expert panel reviewed follow-up data obtained at six weeks and six months on symptoms, investigations, hospital admissions and medications to determine the presence or absence of coronary artery disease. Adjusted odds ratios of relevant variables were used to develop a prediction rule. We calculated measures of diagnostic accuracy for different cut-off values for the prediction scores using data derived from another prospective primary care study (validation cohort). RESULTS: The prediction rule contained five determinants (age/sex, known vascular disease, patient assumes pain is of cardiac origin, pain is worse during exercise, and pain is not reproducible by palpation), with the score ranging from 0 to 5 points. The area under the curve (receiver operating characteristic curve) was 0.87 (95% confidence interval [CI] 0.83-0.91) for the derivation cohort and 0.90 (95% CI 0.87-0.93) for the validation cohort. The best overall discrimination was with a cut-off value of 3 (positive result 3-5 points; negative result <or= 2 points), which had a sensitivity of 87.1% (95% CI 79.9%-94.2%) and a specificity of 80.8% (77.6%-83.9%). INTERPRETATION: The prediction rule for coronary artery disease in primary care proved to be robust in the validation cohort. It can help to rule out coronary artery disease in patients presenting with chest pain in primary care.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Outcome Prediction after Cardiac Arrest Treated with Hypothermia

Rationale: Clinical and electrophysiological prognostic markers of brain anoxia have been mostly evaluated in comatose survivors of out hospital cardiac arrest (OHCA) after standard resuscitation, but their predictive value in patients treated with mild induced hypothermia (IH) is unknown. The objective of this study was to identify a predictive score of independent clinical and electrophysiological variables in comatose OHCA survivors treated with IH, aiming at a maximal positive predictive value (PPV) and a high negative predictive value (NPV) for mortality. Methods: We prospectively studied consecutive adult comatose OHCA survivors from April 2006 to May 2009, treated with mild IH to 33-34_C for 24h at the intensive care unit of the Lausanne University Hospital, Switzerland. IH was applied using an external cooling method. As soon as subjects passively rewarmed (body temperature >35_C) they underwent EEG and SSEP recordings (off sedation), and were examined by experienced neurologists at least twice. Patients with status epilepticus were treated with AED for at least 24h. A multivariable logistic regression was performed to identify independent predictors of mortality at hospital discharge. These were used to formulate a predictive score. Results: 100 patients were studied; 61 died. Age, gender and OHCA etiology (cardiac vs. non-cardiac) did not differ among survivors and nonsurvivors. Cardiac arrest type (non-ventricular fibrillation vs. ventricular fibrillation), time to return of spontaneous circulation (ROSC) >25min, failure to recover all brainstem reflexes, extensor or no motor response to pain, myoclonus, presence of epileptiform discharges on EEG, EEG background unreactive to pain, and bilaterally absent N20 on SSEP, were all significantly associated with mortality. Absent N20 was the only variable showing no false positive results. Multivariable logistic regression identified four independent predictors (Table). These were used to construct the score, and its predictive values were calculated after a cut-off of 0-1 vs. 2-4 predictors. We found a PPV of 1.00 (95% CI: 0.93-1.00), a NPV of 0.81 (95% CI: 0.67-0.91) and an accuracy of 0.93 for mortality. Among 9 patients who were predicted to survive by the score but eventually died, only 1 had absent N20. Conclusions: Pending validation in a larger cohort, this simple score represents a promising tool to identify patients who will survive, and most subjects who will not, after OHCA and IH. Furthermore, while SSEP are 100% predictive of poor outcome but not available in most hospitals, this study identifies EEG background reactivity as an important predictor after OHCA. The score appears robust even without SSEP, suggesting that SSEP and other investigations (e.g., mismatch negativity, serum NSE) might be principally needed to enhance prognostication in the small subgroup of patients failing to improve despite a favorable score.

Surface Complexation Modeling in Variable Charge Soils: Prediction of Cadmium Adsorption

ABSTRACT Intrinsic equilibrium constants for 22 representative Brazilian Oxisols were estimated from a cadmium adsorption experiment. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. Intrinsic equilibrium constants were optimized by FITEQL and by hand calculation using Visual MINTEQ in sweep mode, and Excel spreadsheets. Data from both models were incorporated into Visual MINTEQ. Constants estimated by FITEQL and incorporated in Visual MINTEQ software failed to predict observed data accurately. However, FITEQL raw output data rendered good results when predicted values were directly compared with observed values, instead of incorporating the estimated constants into Visual MINTEQ. Intrinsic equilibrium constants optimized by hand calculation and incorporated in Visual MINTEQ reliably predicted Cd adsorption reactions on soil surfaces under changing environmental conditions.

Rgtsp: a generalized top scoring pairs package for class prediction.

SUMMARY: A top scoring pair (TSP) classifier consists of a pair of variables whose relative ordering can be used for accurately predicting the class label of a sample. This classification rule has the advantage of being easily interpretable and more robust against technical variations in data, as those due to different microarray platforms. Here we describe a parallel implementation of this classifier which significantly reduces the training time, and a number of extensions, including a multi-class approach, which has the potential of improving the classification performance. AVAILABILITY AND IMPLEMENTATION: Full C++ source code and R package Rgtsp are freely available from http://lausanne.isb-sib.ch/~vpopovic/research/. The implementation relies on existing OpenMP libraries.

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.

Pro-adrenomedullin usefulness in the management of children with community-acquired pneumonia, a preliminary prospective observational study

BackgroundIn adult population with community acquired pneumonia high levels of pro-adrenomedullin (pro-ADM) have been shown to be predictors of worse prognosis. The role of this biomarker in pediatric patients had not been analyzed to date. The objective of this study is to know the levels of pro-ADM in children with community acquired pneumonia (CAP) and analyze the relation between these levels and the patients¿ prognosis.FindingsProspective observational study including patients attended in the emergency service (January to October 2009) admitted to hospital with CAP and no complications at admission. The values for pro-ADM were analyzed in relation to: need for oxygen therapy, duration of oxygen therapy, fever and antibiotic therapy, complications, admission to the intensive care unit, and length of hospital stay. Fifty patients were included. Ten presented complications (7 pleural effusion). The median level of pro-ADM was 1.0065¿nmol/L (range 0.3715 to 7.2840¿nmol/L). The patients presenting complications had higher levels of pro-ADM (2.3190 vs. 1.1758¿nmol/L, p¿=¿0.013). Specifically, the presence of pleural effusion was associated with higher levels of pro-ADM (2.9440 vs. 1.1373¿nmol/L, p¿<¿0.001).ConclusionsIn our sample of patients admitted to hospital with CAP, pro-ADM levels are related to the development of complications during hospitalization.

The prediction of chemical reactivity from first principles: Collision and reaction dynamics

Aquest treball fa una revisió de mesures experimentals i càlculs teòrics sobre la dinàmica de col·lisions i reaccions moleculars. Els experiments se centren en col·lisions, a energies intermèdies, que involucren sistemes del tipus ió-àtom i iómolècula, per les quals es mesuren seccions eficaces totals, estat a estat, així com aquelles que discerneixen les diferents contribucions del moment angular d'espín. Els resultats obtinguts s'interpreten satisfactòriament en termes d'acoblaments no adiabàtics entre els diferents estats electrònics dels sistemes col·lisionants. Els càlculs teòrics utilitzen la metodologia quasiclàssica, així com metodologies mecanoquàntiques recentment desenvolupades, tant aproximades com exactes. S'han obtingut resultats totalment convergits per sistemes tipus, mentre que s'han analitzat, de manera detallada i extensiva, les característiques dinàmiques de sistemes triatòmic, tetraatòmic i pentaatòmic.

Integrative molecular bioinformatics study of human adrenocortical tumors : microRNA, tissue-specific target prediction, and pathway analysis.

MicroRNAs (miRs) are involved in the pathogenesis of several neoplasms; however, there are no data on their expression patterns and possible roles in adrenocortical tumors. Our objective was to study adrenocortical tumors by an integrative bioinformatics analysis involving miR and transcriptomics profiling, pathway analysis, and a novel, tissue-specific miR target prediction approach. Thirty-six tissue samples including normal adrenocortical tissues, benign adenomas, and adrenocortical carcinomas (ACC) were studied by simultaneous miR and mRNA profiling. A novel data-processing software was used to identify all predicted miR-mRNA interactions retrieved from PicTar, TargetScan, and miRBase. Tissue-specific target prediction was achieved by filtering out mRNAs with undetectable expression and searching for mRNA targets with inverse expression alterations as their regulatory miRs. Target sets and significant microarray data were subjected to Ingenuity Pathway Analysis. Six miRs with significantly different expression were found. miR-184 and miR-503 showed significantly higher, whereas miR-511 and miR-214 showed significantly lower expression in ACCs than in other groups. Expression of miR-210 was significantly lower in cortisol-secreting adenomas than in ACCs. By calculating the difference between dCT(miR-511) and dCT(miR-503) (delta cycle threshold), ACCs could be distinguished from benign adenomas with high sensitivity and specificity. Pathway analysis revealed the possible involvement of G2/M checkpoint damage in ACC pathogenesis. To our knowledge, this is the first report describing miR expression patterns and pathway analysis in sporadic adrenocortical tumors. miR biomarkers may be helpful for the diagnosis of adrenocortical malignancy. This tissue-specific target prediction approach may be used in other tumors too.