Pharmacokinetics of rituximab in a pediatric patient with therapy-resistant nephrotic syndrome

Background

Rituximab (RTX) has recently been introduced as a second-line therapy for nephrotic syndrome in children. Studies show that RTX given during the nephrotic state may be less effective than treatment during a non-nephrotic state, possibly due to loss of RTX in the urine.

Case-Diagnosis/Treatment

We describe a 10-year-old boy with steroid-resistant nephrotic syndrome (SRNS) treated with RTX during a phase of active non-selective proteinuria. The serum half-life of RTX in this patient was less than 1 day compared to 20 days in patients without protein losses. Urinary clearance was at least 25 %, compared to approximately 0 % in control patients. However, RTX loss in the urine, as well as in pleural effusion and ascites, only partly explains the rapid drop in the serum RTX concentration of this patient.

Conclusions

Serum half-life of RTX can be extremely short, partly due to excessive urinary losses in therapy-resistant nephrotic syndrome with non-selective proteinuria, as seen in our patient. These findings may help to explain the poor results of RTX treatment in patients with active proteinuria.

     

Women with a recent history of early-onset pre-eclampsia have a worse periodontal condition

OBJECTIVE: Pre-eclampsia is a complication of pregnancy characterized by systemic vascular dysfunction and pathological changes in placental arteries. Growing evidence of chronic infection as an aetiological factor in vascular diseases prompted us to study maternal periodontal disease in subjects with early-onset pre-eclampsia (<34 weeks). METHODS: A case-control study was carried out on 17 early-onset pre-eclamptic women and 35 controls with uncomplicated pregnancies in a period of 3-28 months postpartum. All were Caucasians. Full-mouth periodontal examinations were performed to determine the periodontal condition. Subgingival-plaque samples were analysed by anaerobic culture techniques for the presence of seven bacterial periodontal pathogens. Potential confounders as age, smoking, educational level and body mass index were determined. RESULTS: Severe periodontal disease was found in 82% of the pre-eclamptic and in 37% of the control group (p=0.009). After adjusting for age, smoking and educational level, the odds ratio was 7.9 (95% CI: 1.9-32.8). The periodontopathic microorganism Micromonas micros was more prevalent in the case group (p=0.040) while Campylobacter rectus was more prevalent in the control group (p=0.047). CONCLUSION: These results indicate that Caucasian women with a recent history of early-onset pre-eclampsia have a worse periodontal condition, as compared with women with uncomplicated deliveries.     

Simultaneous selection by object-based attention in visual and frontal cortex

Models of visual attention hold that top-down signals from frontal cortex influence information processing in visual cortex. It is unknown whether situations exist in which visual cortex actively participates in attentional selection. To investigate this question, we simultaneously recorded neuronal activity in the frontal eye fields (FEF) and primary visual cortex (V1) during a curve-tracing task in which attention shifts are object-based. We found that accurate performance was associated with similar latencies of attentional selection in both areas and that the latency in both areas increased if the task was made more difficult. The amplitude of the attentional signals in V1 saturated early during a trial, whereas these selection signals kept increasing for a longer time in FEF, until the moment of an eye movement, as if FEF integrated attentional signals present in early visual cortex. In erroneous trials, we observed an interareal latency difference because FEF selected the wrong curve before V1 and imposed its erroneous decision onto visual cortex. The neuronal activity in visual and frontal cortices was correlated across trials, and this trial-to-trial coupling was strongest for the attended curve. These results imply that selective attention relies on reciprocal interactions within a large network of areas that includes V1 and FEF.Visual scenes are usually too complex for all information to be analyzed at once. Selective attention selects a subset of the objects in the visual scene for detailed analysis at the expense of other items. Visual objects compete for selection, and the outcome of this competition depends on bottom-up cues such as saliency and perceptual organization and top-down cues that signal the objects’ behavioral relevance (1). It is not well understood how these different cues interact and which brain areas take the lead in visual selection.The top-down mechanisms for attentional selection are tightly linked to those for the selection of actions (2), and accordingly, cortical areas related to action planning influence the deployment of visual attention. The frontal eye fields (FEF) is one such area that is involved in visual processing, shifts of visual attention (2, 3), and also in the control of eye movements (4, 5). Area FEF contains different types of cells. Visual processing relies on visual and visuomovement cells, whereas the programming of eye movements relies on the activity of visuomovement and movement cells (6, 7). There are several lines of evidence that also implicate FEF in attentional control. First, FEF inactivation impairs attention shifts toward the contralateral visual field (8, 9). Second, subthreshold FEF microstimulation enhances neuronal activity in visual cortex in a manner that is reminiscent of selective attention (10, 11). Third, a role of FEF in the top-down guidance of attention is supported by studies on visual search. In search, selection signals in frontal cortex precede those in area V4 by 50 ms, suggesting that the frontal cortex determines selection and then provides feedback to visual cortex (12, 13). A comparable interareal delay in attentional effects was observed between the lateral intraparietal area and the motion sensitive middle temporal area (14). Thus, the parietal and frontal cortices appear to take the lead in attentional selection and to provide top-down signals to visual cortex. Within the visual cortex, such a reverse hierarchy (15) of attentional effects was observed in a task that required shifts of spatial attention (16) and also in a task demanding shifts between visual and auditory attention (17). Attentional signals in area V4 preceded signals in V2 by 50–250 ms, which in turn preceded attentional effects in the primary visual cortex (V1) by 50–400 ms.However, top-down factors are not the only ones that guide attention. Attention can be object-based, implying that the visual stimulus itself influences the distribution of attention too. If attention is directed to a feature, attention tends to coselect visually related features on the basis of perceptual grouping cues (18) so that entire objects rather than isolated features are attended (19, 20). The influence of perceptual grouping on attentional selection can be investigated with a curve-tracing task that requires grouping of the contour elements of a single curve (21, 22). Attention in this task is directed to the entire curve, implying that the curve’s shape itself influences the distribution of attention (22). Indeed, a traced curve evokes stronger activity in primary visual cortex than an irrelevant curve, revealing a neuronal correlate of object-based attention (23). However, it is not known if the coselection of all image elements of a single object is determined within early visual cortex or is guided by the frontal cortex, just as was shown for other tasks.Here we compare selection signals in areas FEF and V1 in the curve-tracing task with simultaneous recordings in the two areas. A priori, several possibilities exist for the interaction between V1 and FEF. First, the frontal cortex might select the relevant curve and then feed a guiding signal back to visual cortex (24, 25) as in the other tasks described above. If so, attentional selection signals in V1 might arise tens to hundreds of milliseconds later than in FEF. However, the chain of events in the curve-tracing task might differ because visual shape has a profound influence on the distribution of attention (26). Thus, a second possibility is that the visual cortex determines selection so that the attentional modulation in visual cortex precedes that in frontal cortex. A third possibility is that visual and frontal areas jointly determine what is relevant and what is not. In this situation, the selection signals are expected to occur in both areas at approximately the same time. It is also possible that the order of selection in different areas depends on the difficulty of the task. For example, the reverse hierarchy theory of visual perception (15) proposed that easy tasks are usually solved by higher visual areas, whereas lower visual areas are recruited when the picture has to be scrutinized. We therefore varied the difficulty of the curve-tracing task.     

Making portfolios work in practice

BACKGROUND: A portfolio captures learning from experience, enables an assessor to measure student learning, acts as a tool for reflective thinking, illustrates critical analytical skills and evidence of self-directed learning and provides a collection of detailed evidence of a person's competence. AIMS: This paper reports data on how assessors and nursing students match learning outcomes and/or competencies to their practice and then reconstruct those experiences into the format required by the portfolio documentation. The data were gathered as part of a larger study to evaluate the use of portfolios in the assessment of learning and competence in England. METHODS: This three phase stakeholder evaluation was designed to gain the views of those involved in designing, implementing and using portfolios in nurse education. In phase 2, 122 students and 58 nurse teachers were interviewed about their perceptions of portfolio use, and a further 32 students and 26 assessors were interviewed after they had been observed taking part in an assessment process. Thematic data analysis was used. FINDINGS: Assessors and students underwent a complex process of deconstructing learning outcomes/competences to fit these to their practice. These then had to be reconstructed through the written medium to fit the structure of the portfolio. Confirmation that this met teachers' expectations was essential to allay feelings of insecurity. CONCLUSIONS: To achieve maximum benefit from the portfolio as a learning tool to link theory and practice, there needs to be a clear fit between the model of portfolio and the professional practice that is to be assessed. Outcomes and competences, as well as the type of evidence required to demonstrate their achievement, and integration of the whole experience should match the students' stage of professional and academic development. Over-complex approaches to practice assessment, particularly in the early stages of students' careers, can detract from clinical learning in favour of learning how to complete the portfolio successfully.     

Comparing position and orientation accuracy of different electromagnetic sensors for tracking during interventions

Purpose

To compare the position and orientation accuracy between using one 6-degree of freedom (DOF) electromagnetic (EM) sensor, or the position information of three 5DOF sensors within the scope of tumor tracking.

Methods

The position accuracy of Northern Digital Inc Aurora 5DOF and 6DOF sensors was determined for a table-top field generator (TTFG) up to a distance of 52 cm. For each sensor 716 positions were measured for 10 s at 15 Hz. Orientation accuracy was determined for each of the orthogonal axis at the TTFG distances of 17, 27, 37 and 47 cm. For the 6DOF sensors, orientation was determined for sensors in-line with the orientation axis, and perpendicular. 5DOF orientation accuracy was determined for a theoretical 4 cm tumor. An optical tracking system was used as reference.

Results

Position RMSE and jitter were comparable between the sensors and increasing with distance. Jitter was within 0.1 cm SD within 45 cm distance to the TTFG. Position RMSE was approximately 0.1 cm up to 32 cm distance, increasing to 0.4 cm at 52 cm distance. Orientation accuracy of the 6DOF sensor was within 1\(^\circ \), except when the sensor was in-line with the rotation axis perpendicular to the TTFG plane (4\(^\circ \) errors at 47 cm). Orientation accuracy using 5DOF positions was within 1\(^\circ \) up to 37 cm and 2\(^\circ \) at 47 cm.

Conclusions

The position and orientation accuracy of a 6DOF sensor was comparable with a sensor configuration consisting of three 5DOF sensors. To achieve tracking accuracy within 1 mm and 1\(^\circ \), the distance to the TTFG should be limited to approximately 30 cm.