Problems in functioning from the patient perspective using the International Classification of Functioning, Disability and Health (ICF) as a reference

We conducted a qualitative, multicenter study using a focus group design to explore the lived experiences of persons with any kind of primary sleep disorder with regard to functioning and contextual factors using six open-ended questions related to the International Classification of Functioning, Disability and Health (ICF) components. We classified the results using the ICF as a frame of reference. We identified the meaningful concepts within the transcribed data and then linked them to ICF categories according to established linking rules. The six focus groups with 27 participants yielded a total of 6986 relevant concepts, which were linked to a total of 168 different second-level ICF categories. From the patient perspective, the ICF components: (1) Body Functions; (2) Activities & Participation; and (3) Environmental Factors were equally represented; while (4) Body Structures appeared poignantly less frequently. Out of the total number of concepts, 1843 concepts (26%) were assigned to the ICF component Personal Factors, which is not yet classified but could indicate important aspects of resource management and strategy development of those who have a sleep disorder. Therefore, treatment of patients with sleep disorders must not be limited to anatomical and (patho-)physiological changes, but should also consider a more comprehensive view that includes patient's demands, strategies and resources in daily life and the contextual circumstances surrounding the individual.

Code-specific learning rules improve action selection by populations of spiking neurons

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Pathology: Classification and Immunoprofile

The classification of neuroendocrine neoplasms (NENs) has been evolving steadily over the last decades. Important prognostic factors of NENs are their proliferative activity and presence/absence of necrosis. These factors are reported in NENs of all body sites; however, the terminology as well as the exact rules of classification differ according to the location of the primary tumor. Only in gastroenteropancreatic (GEP) NENs a formal grading is performed. This grading is based on proliferation assessed by the mitotic count and/or Ki-67 proliferation index. In the lung, NEN grading is an intrinsic part of the tumor designation with typical carcinoids corresponding to neuroendocrine tumor (NET) G1 and atypical carcinoids to NET G2; however, the presence or absence of necrotic foci is as important as proliferation for the differentiation between typical and atypical carcinoids. Immunohistochemical markers can be used to demonstrate neuroendocrine differentiation. Synaptophysin and chromogranin A are, to date, the most reliable and most commonly used for this purpose. Beyond this, other markers can be helpful, for example in the situation of a NET metastasis of unknown primary, where a hormonal profile or a panel of transcription factors can give hints to the primary site. Many immunohistochemical markers have been shown to correlate with prognosis but are not used in clinical practice, for example cytokeratin 19 and KIT expression in pancreatic NETs. There is no predictive biomarker in use, with the exception of somatostatin receptor (SSTR) 2 expression for predicting the amenability of a tumor to in vivo SSTR targeting for imaging or therapy.