Sporadic ALS is not associated with VAPB gene mutations in Southern Italy

Mutations in the Cu/Zn superoxide dismutase (Sod1) gene have been reported to cause adult-onset autosomal dominant Amyotrophic Lateral Sclerosis (FALS). In sporadic cases (SALS) de novo mutations in the Sod1 gene have occasionally been observed. The recent finding of a mutation in the VAMP/synaptobrevin-associated membrane protein B (VAPB) gene as the cause of amyotrophic lateral sclerosis (ALS8), prompted us to investigate the entire coding region of this gene in SALS patients. One hundred twenty-five unrelated patients with adult-onset ALS and 150 healthy sex-age-matched subjects with the same genetic background were analyzed. Genetic analysis for all exons of the VAPB gene by DHPLC revealed 5 variant profiles in 83 out of 125 SALS patients. Direct sequencing of these PCR products revealed 3 nucleotide substitutions. Two of these were found within intron 3 of the gene, harbouring 4 variant DHPLC profiles. The third nucleotide variation (Asp130Glu) was the only substitution present in the coding region of the VAPB gene, and it occurred within exon 4. It was found in three patients out of 125. The frequency of the detected exon variation in the VAPB gene was not significantly different between patients and controls. In conclusion, our study suggests that VAPB mutations are not a common cause of adult-onset SALS

A multidimensional evaluation framework for personal learning environments

Evaluating highly dynamic and heterogeneous Personal Learning Environments (PLEs) is extremely challenging. Components of PLEs are selected and configured by individual users based on their personal preferences, needs, and goals. Moreover, the systems usually evolve over time based on contextual opportunities and constraints. As such dynamic systems have no predefined configurations and user interfaces, traditional evaluation methods often fall short or are even inappropriate. Obviously, a host of factors influence the extent to which a PLE successfully supports a learner to achieve specific learning outcomes. We categorize such factors along four major dimensions: technological, organizational, psycho-pedagogical, and social. Each dimension is informed by relevant theoretical models (e.g., Information System Success Model, Community of Practice, self-regulated learning) and subsumes a set of metrics that can be assessed with a range of approaches. Among others, usability and user experience play an indispensable role in acceptance and diffusion of the innovative technologies exemplified by PLEs. Traditional quantitative and qualitative methods such as questionnaire and interview should be deployed alongside emergent ones such as learning analytics (e.g., context-aware metadata) and narrative-based methods. Crucial for maximal validity of the evaluation is the triangulation of empirical findings with multi-perspective (end-users, developers, and researchers), mixed-method (qualitative, quantitative) data sources. The framework utilizes a cyclic process to integrate findings across cases with a cross-case analysis in order to gain deeper insights into the intriguing questions of how and why PLEs work

Erythropoietin in the intensive care unit: beyond treatment of anemia

Erythropoietin (EPO) is the major hormone stimulating the production and differentiation of red blood cells. EPO is used widely for treating anemia of critical illness or anemia induced by chemotherapy. EPO at pharmacological doses is used in this setting to raise hemoglobin levels (by preventing the apoptosis of erythroid progenitor cells) and is designed to reduce patient exposure to allogenic blood through transfusions. Stroke, heart failure, and acute kidney injury are a frequently encountered clinical problem. Unfortunately, in the intensive care unit advances in supportive interventions have done little to reduce the high mortality associated with these conditions. Tissue protection with EPO at high, nonpharmacological doses after injury has been found in the brain, heart, and kidney of several animal models. It is now well known that EPO has anti-apoptotic effects in cells other than erythroid progenitor cells, which is considered to be independent of EPOs erythropoietic activities. This review article summarizes what is known in preclinical models of critical illness and discusses why this does not correlate with randomized, controlled clinical trials

124I-HuCC49deltaCH2 for TAG-72 antigen-directed positron emission tomography (PET) imaging of LS174T colon adenocarcinoma tumor implants in xenograft mice: preliminary results

<p>Abstract</p> <p>Background</p> <p>¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) is widely used in diagnostic cancer imaging. However, the use of ¹⁸F-FDG in PET-based imaging is limited by its specificity and sensitivity. In contrast, anti-TAG (tumor associated glycoprotein)-72 monoclonal antibodies are highly specific for binding to a variety of adenocarcinomas, including colorectal cancer. The aim of this preliminary study was to evaluate a complimentary determining region (CDR)-grafted humanized C_H2-domain-deleted anti-TAG-72 monoclonal antibody (HuCC49deltaC_H2), radiolabeled with iodine-124 (¹²⁴I), as an antigen-directed and cancer-specific targeting agent for PET-based imaging.</p> <p>Methods</p> <p>HuCC49deltaC_H2 was radiolabeled with ¹²⁴I. Subcutaneous tumor implants of LS174T colon adenocarcinoma cells, which express TAG-72 antigen, were grown on athymic Nu/Nu nude mice as the xenograft model. Intravascular (i.v.) and intraperitoneal (i.p.) administration of ¹²⁴I-HuCC49deltaC_H2 was then evaluated in this xenograft mouse model at various time points from approximately 1 hour to 24 hours after injection using microPET imaging. This was compared to i.v. injection of ¹⁸F-FDG in the same xenograft mouse model using microPET imaging at 50 minutes after injection.</p> <p>Results</p> <p>At approximately 1 hour after i.v. injection, ¹²⁴I-HuCC49deltaC_H2 was distributed within the systemic circulation, while at approximately 1 hour after i.p. injection, ¹²⁴I-HuCC49deltaC_H2 was distributed within the peritoneal cavity. At time points from 18 hours to 24 hours after i.v. and i.p. injection, ¹²⁴I-HuCC49deltaC_H2 demonstrated a significantly increased level of specific localization to LS174T tumor implants (p = 0.001) when compared to the 1 hour images. In contrast, approximately 50 minutes after i.v. injection, ¹⁸F-FDG failed to demonstrate any increased level of specific localization to a LS174T tumor implant, but showed the propensity toward more nonspecific uptake within the heart, Harderian glands of the bony orbits of the eyes, brown fat of the posterior neck, kidneys, and bladder.</p> <p>Conclusions</p> <p>On microPET imaging, ¹²⁴I-HuCC49deltaC_H2 demonstrates an increased level of specific localization to tumor implants of LS174T colon adenocarcinoma cells in the xenograft mouse model on delayed imaging, while ¹⁸F-FDG failed to demonstrate this. The antigen-directed and cancer-specific ¹²⁴I-radiolabled anti-TAG-72 monoclonal antibody conjugate, ¹²⁴I-HuCC49deltaC_H2, holds future potential for use in human clinical trials for preoperative, intraoperative, and postoperative PET-based imaging strategies, including fused-modality PET-based imaging platforms.</p

Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy

Background The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89.6 per cent) compared with that in countries with a middle (753 of 1242, 60.6 per cent; odds ratio (OR) 0.17, 95 per cent c.i. 0.14 to 0.21, P <0001) or low (363 of 860, 422 per cent; OR 008, 007 to 010, P <0.001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference -94 (95 per cent c.i. -11.9 to -6.9) per cent; P <0001), but the relationship was reversed in low-HDI countries (+121 (+7.0 to +173) per cent; P <0001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0.60, 0.50 to 073; P <0.001). The greatest absolute benefit was seen for emergency surgery in low- and middle-HDI countries. Conclusion Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries.Peer reviewe

Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies