Preoperative diagnosis of obscure gastrointestinal bleeding due to a GIST of the jejunum: a case report

Gastrointestinal stromal tumours (GISTs) are rare mesenchymal neoplasms affecting the digestive tract or nearby structures within the abdomen. We present a case of a 66-year-old female patient who presented with obscure anemia due to gastrointestinal bleeding and underwent exploratory laparotomy during which a large GIST of the small intestine was discovered. Examining the preoperative results of video capsule endoscopy, computed tomography, and angiography and comparing them with the operative findings we discuss which of these investigations plays the most important role in the detection and localization of GIST. A sort review of the literature is also conducted on these rare mesenchymal tumours

Dopamine Modulates Persistent Synaptic Activity and Enhances the Signal-to-Noise Ratio in the Prefrontal Cortex

The importance of dopamine (DA) for prefrontal cortical (PFC) cognitive functions is widely recognized, but its mechanisms of action remain controversial. DA is thought to increase signal gain in active networks according to an inverted U dose-response curve, and these effects may depend on both tonic and phasic release of DA from midbrain ventral tegmental area (VTA) neurons.We used patch-clamp recordings in organotypic co-cultures of the PFC, hippocampus and VTA to study DA modulation of spontaneous network activity in the form of Up-states and signals in the form of synchronous EPSP trains. These cultures possessed a tonic DA level and stimulation of the VTA evoked DA transients within the PFC. The addition of high (≥1 µM) concentrations of exogenous DA to the cultures reduced Up-states and diminished excitatory synaptic inputs (EPSPs) evoked during the Down-state. Increasing endogenous DA via bath application of cocaine also reduced Up-states. Lower concentrations of exogenous DA (0.1 µM) had no effect on the up-state itself, but they selectively increased the efficiency of a train of EPSPs to evoke spikes during the Up-state. When the background DA was eliminated by depleting DA with reserpine and alpha-methyl-p-tyrosine, or by preparing corticolimbic co-cultures without the VTA slice, Up-states could be enhanced by low concentrations (0.1–1 µM) of DA that had no effect in the VTA containing cultures. Finally, in spite of the concentration-dependent effects on Up-states, exogenous DA at all but the lowest concentrations increased intracellular current-pulse evoked firing in all cultures underlining the complexity of DA's effects in an active network.Taken together, these data show concentration-dependent effects of DA on global PFC network activity and they demonstrate a mechanism through which optimal levels of DA can modulate signal gain to support cognitive functioning

Weight change during chemotherapy changes the prognosis in non metastatic breast cancer for the worse

<p>Abstract</p> <p>Background</p> <p>Weight change during chemotherapy is reported to be associated with a worse prognosis in breast cancer patients, both with weight gain and weight loss. However, most studies were conducted prior to the common use of anthracycline-base chemotherapy and on North American populations with a mean BMI classified as overweight. Our study was aimed to evaluate the prognostic value of weight change during anthracycline-based chemotherapy on non metastatic breast cancer (European population) with a long term follow-up.</p> <p>Methods</p> <p>Patients included 111 women diagnosed with early stage breast cancer and locally advanced breast cancer who have been treated by anthracycline-based chemotherapy regimen between 1976 and 1989. The relative percent weight variation (WV) between baseline and postchemotherapy treatment was calculated and categorized into either weight change (WV > 5%) or stable (WV < 5%). The median follow-up was 20.4 years [19.4 - 27.6]. Cox proportional hazard models were used to evaluate any potential association of weight change and known prognostic factors with the time to recurrence and overall survival.</p> <p>Results</p> <p>Baseline BMI was 24.4 kg/m2 [17.1 - 40.5]. During chemotherapy treatment, 31% of patients presented a notable weight variation which was greater than 5% of their initial weight.</p> <p>In multivariate analyses, weight change (> 5%) was positively associated with an increased risk of both recurrence (RR 2.28; 95% CI: 1.29-4.03) and death (RR 2.11; 95% CI: 1.21-3.66).</p> <p>Conclusions</p> <p>Our results suggest that weight change during breast-cancer chemotherapy treatment may be related to poorer prognosis with higher reccurence and higher mortality in comparison to women who maintained their weight.</p

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

<p>Abstract</p> <p>Background</p> <p>The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.</p> <p>Results</p> <p>We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality.</p> <p>Conclusion</p> <p>We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality.</p

Integrated care pathways for cancer survivors - a role for patient-reported outcome measures and health informatics

Modern cancer treatments have improved survival rates and changed the nature of cancer care. The acute and long-term physical and psychosocial comorbidities associated with treatment place increasing demands on healthcare services to provide suitable models of follow-up care for the survivor population. Aim. We discuss the value and challenges of incorporating patient-reported outcome measures (PROMs) and eHealth interventions into routine follow-up care. We draw on our 15 years’ experience of developing electronic systems for capturing patient-reported data in oncology settings, with particular reference to eRAPID a new online symptom reporting system for cancer patients. The redesign of healthcare pathways. New stratified care pathways have been proposed for cancer survivors with an emphasis on supported self-management and shared care. The potential role of PROMs in survivorship care pathways. PROMs can be used to evaluate rehabilitation services, provide epidemiological ‘Big Data’ and screen patients for physical and psychological morbidities to determine the need for further support. In addition, electronic PROMs systems linked to electronic patient records (EPRs) have the capability to provide tailored self-management advice to individual patients. Integration of PROMs into clinical practice. The successful clinical utilisation of PROMs is dependent on a number of components including; choosing appropriate questionnaires, developing evidence-based scoring algorithms, the creation of robust electronic platforms for recording and transferring data into EPRs, and training staff and patients to engage effectively with PROMs. Discussion. There is increasingly positive evidence for using PROMs and eHealth approaches to support cancer patients’ care during treatment. Much of what has been learnt can be applied to cancer survivorship. PROMs integrated into eHealth platforms and with EPR have the potential to play a valuable role in the development of appropriate and sustainable long-term follow-up models for cancer survivors