Progressive myelopathy, a consequence of intra‑thecal chemotherapy: Case report and review of the literature

Intra‑thecal chemotherapy is a recognized therapy for hematological malignancies such as acute lymphoblastic leukemia (ALL). Despite the advantage of these drugs in treating or preventing central nervous system disease, they are not without complications. The authors describe a 12‑year‑old girl with ALL, who developed progressive myelopathy following intra‑thecal administration of cytosine arabinoside. Initial presentation was urine and fecal retention that progressed to paraplegia, and finally encephalopathy. magnetic resonance imaging of the neuroaxis showed T2‑weighted foci of increased signal intensity within the substance of the cervical cord indicative of myelopathy. Physicians should be wary of this rare complication of intra‑thecal chemotherapy.Key words: Cytosine arabinoside, intra‑thecal, myelopath

Feeding behaviour, weight gain and blood sugar of male wistar rats fed on a high-calorie diet and vegetables

The transition in eating behaviour toward a diet rich in calories and low in vegetables is a major factor responsible for the rapid increase in the incidence of obesity and diabetes. The research aimed at investigating the effect of a high-calorie diet and vegetables on feeding behaviour, weight gain and blood sugar in male Wistar rats. The vegetables were dried, blended, and preserved in airtight containers. Thirty male Wistar rats weighing an average of 127.4 g were housed in 6 cages with 5 rats in each cage. There were six groups comprising the positive control which was fed standard rat feed and water, also the negative control which was given a high-calorie diet (high-fat feed and sugar water) and four treatment groups. The four treatment groups were fed on a high-calorie diet with a 5 % concentration of either Corchorus olitorius, Crassocephalum crepidiodes, Amaranthus hybridus or Solanecio biafrae respectively. Water (or sugar water) and feed intake of each group were measured and recorded daily. Weekly consumption of water and feed was computed for the entire 5 weeks of the experiment. The fasting blood sugar and weight of the test rats were recorded at baseline and weekly. Oral glucose tolerance test and serum insulin were determined at the end of the experiment using blood samples from the test rats. All results were analysed using ANOVA at p≤0.05 and means were separated with the use of Duncan’s multiple range tests (SPSS 20.0). The high-fat feed was significantly different from the standard rat feed in the composition of fat (26.79 g) and calories (422.67 kcal). The negative control and the treatment groups got adapted to feeding on the high-calorie diet before the end of the experimental period. Water and feed intakes of the positive control were only significantly higher during the first three and four weeks, respectively. At the end of the experiment, the positive control had the highest weight gain of 22 g which was significantly different at p≤0.05. C. crepidioides and S. biafrae significantly lowered the blood sugar (62.75 and 62.50 mg/dL) of the test rats. A. hybridus prevented insulin resistance by the attainment of peak level at 30 min alongside the positive control. There was a significant increase in the insulin level of the negative control while the vegetables prevented increased production of insulin

Incremental Support Vector Machine Framework for Visual Sensor Networks

Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication