Granulocyte colony stimulating factor supports liver regeneration in a surgical small for size liver remnant mouse model

ntense liver regeneration and almost 100% survival follows partial hepatectomy of up to 70% of liver mass in rodents. More extensive resections of 70 to 80% have an increased mortality and partial hepatectomies of >80% constantly lead to acute hepatic failure and death in mice. The aim of the study was to determine the effect of systemically administered granulocyte colony stimulating factor (G-CSF) on animal survival and liver regeneration in a small for size liver remnant mouse model after 83% partial hepatectomy (liver weight <0.8% of mouse body weight). Methods: Male Balb C mice (n=80, 20-24g) were preconditioned daily for five days with 5μg G-CSF subcutaneously or sham injected (aqua ad inj). Subsequently 83% hepatic resection was performed and daily sham or G-CSF injection continued. Survival was determined in both groups (G-CSF n=35; Sham: n=33). In a second series BrdU was injected (50mg/kg Body weight) two hours prior to tissue harvest and animals euthanized 36 and 48 hours after 83% liver resection (n=3 each group). To measure hepatic regeneration the BrdU labeling index and Ki67 expression were determined by immunohistochemistry by two independent observers. Harvested liver tissue was dried to constant weight at 65 deg C for 48 hours. Results: Survival was 0% in the sham group on day 3 postoperatively and significantly better (26.2% on day 7 and thereafter) in the G-CSF group (Log rank test: p<0.0001). Dry liver weight was increased in the G-CSF group (T-test: p<0.05) 36 hours after 83% partial hepatectomy. Ki67 expression was elevated in the G-CSF group at 36 hours (2.8±2.6% (Standard deviation) vs 0.03±0.2%; Rank sum test: p<0.0001) and at 48 hours (45.1±34.6% vs 0.7±1.0%; Rank sum test: p<0.0001) after 83% liver resection. BrdU labeling at 48 hours was 0.1±0.3% in the sham and 35.2±34.2% in the G-CSF group (Rank sum test: p<0.0001) Conclusions: The surgical 83% resection mouse model is suitable to test hepatic supportive regimens in the setting of small for size liver remnants. Administration of G-CSF supports hepatic regeneration after microsurgical 83% partial hepatectomy and leads to improved long-term survival in the mouse. G-CSF might prove to be a clinically valuable supportive substance in small for size liver remnants in humans after major hepatic resections due to primary or secondary liver tumors or in the setting of living related liver donation.

Learning Spike-Based Population Codes by Reward and Population Feedback

We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to n-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.

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.

Use of a modified Delphi panel to identify and weight criteria for prioritization of zoonotic diseases in Switzerland

Zoonotic diseases have a significant impact on public health globally. To prevent or reduce future zoonotic outbreaks, there is a constant need to invest in research and surveillance programs while updating risk management strategies. However, given the limited resources available, disease prioritization based on the need for their control and surveillance is important. This study was performed to identify and weight disease criteria for the prioritization of zoonotic diseases in Switzerland using a semi-quantitative research method based on expert opinion. Twenty-eight criteria relevant for disease control and surveillance, classified under five domains, were selected following a thorough literature review, and these were evaluated and weighted by seven experts from the Swiss Federal Veterinary Office using a modified Delphi panel. The median scores assigned to each criterion were then used to rank 16 notifiable and/or emerging zoonoses in Switzerland. The experts weighted the majority of the criteria similarly, and the top three criteria were Severity of disease in humans, incidence and prevalence of the disease in humans and treatment in humans. Based on these weightings, the three highest ranked diseases were Avian Influenza, Bovine Spongiform Encephalitis, and Bovine Tuberculosis. Overall, this study provided a preliminary list of criteria relevant for disease prioritization in Switzerland. These were further evaluated in a companion study which involved a quantitative prioritization method and multiple stakeholders.