A prospective study was performed to evaluate the impact of surgical decompression (SD) and instrumented fusion within 8 h versus 8-24 h after injury on neurological recovery after cervical traumatic spinal cord injury (tSCI) in patients operated on in the UMC Ljubljana, Slovenia. Only patients with the American Spinal Injury Association (ASIA) Impairment Scale (AIS) grades of A through C and with MRI-confirmed spinal cord compression were enrolled. The primary outcome was the change in AIS grade at the 6-month follow-up. Of the 48 enrolled patients, 22 patients who underwent surgery within 8 h (group 8 h) and 20 patients who underwent surgery between 8 and 24 h (Group 8-24 h) after injury concluded the study. At admission, there was no statistically significant difference in AIS grade between the study groups. At the 6-month follow-up, an improvement of at least two AIS grades was found in 45.5% of patients in group 8 h and in 10% of patients in group 8-24 h (p=0.017). The median improvement in the ASIA motor score was 38.5 (10.0-61.0) motor points in group 8 h and 15.0 (8.8-34.0) motor points in group 8-24 h (p=0.0468). In a multivariate analysis, adjusted for the preoperative AIS grade and the degree of spinal canal compromise, the odds of an at least two-grade AIS improvement were at least 106% higher for patients in group 8 h than for patients in group 8-24 h (odds ratio=11.08, p=0.004). No statistically significant difference was found in the rate of perioperative complications, pneumonia, and the number of ventilator-dependent days or the mortality between the groups. Our results suggest that the patients with tSCI who undergo SD within 8 h after injury have superior neurological outcomes than patients who undergo SD 8-24 h after injury, without any increase in the rate of adverse effects.

It is generally considered that at least 1/3 of resuscitated patients sustain rib fractures and at least 1/5 sustains sternum fractures. However, our study showed that these injuries are much more frequent and that increased compression rate and depth cause more SCI. Since in the period 2011-2013 accompanying severe injuries occurred in only 1.85% of cases, the resuscitation technique has not yet jeopardised patient's safety, but further close monitoring is needed.

Haemorrhagic fever with renal syndrome (HFRS) in Slovenia can be caused by infection with either Dobrava (DOBV) or Puumala (PUUV) virus, but a clear difference in disease severity is observed. We hypothesized that the wide spectrum of disease observed among HFRS patients might be related to differing immune responses and viral load kinetics. To test this hypothesis we analysed sequential blood samples from 29 HFRS patients hospitalized in Slovenia. Measuring viral RNA in patient samples revealed that viraemia lasts for longer than previously believed, with DOBV or PUUV-infected patients having viraemias lasting on average 30 days or 16 days, respectively. DOBV-infected patients were found to have a higher viral load than the PUUV-infected patients (10(7) vs. 10(5) RNA copies/mL). Both DOBV and PUUV-infected patients had IgM at the time of hospital admission, but there was a difference in IgG antibody dynamics, with only a minority of DOBV-infected patients having IgG antibodies. In our study, elevated levels of IL-10, TNF-α and IFN-γ were detected in all of the samples regardless of the causative agent. In DOBV-infected patients the decrease in cytokine secretion level appeared around day 20 post-infection, while in PUUV-infected patients the change was earlier. In general, our findings point toward notable differences between PUUV and DOBV infections, in terms of viral load and antibody and cytokine response dynamics, all of which may be reflected in differing disease severities and clinical outcomes.

We propose a model for network formation and study some of its statistical properties. The motivation for the model comes from the growth of several kinds of real networks (i.e., kinship and trading networks, networks of corporate alliances, networks of autocatalytic chemical reactions). These networks grow either by establishing closer connections by adding links in the existing network or by adding new nodes. A node in these networks lacks the information of the entire network. In order to establish a closer connection to other nodes it starts a search in the neighboring part of the network and waits for a possible feedback from a distant node that received the "searching signal." Our model imitates this behavior by growing the network via the addition of a link that creates a cycle in the network or via the addition of a new node with a link to the network. The forming of a cycle creates feedback between the two ending nodes. After choosing a starting node, a search is made for another node at a suitable distance; if such a node is found, a link is established between this and the starting node, otherwise (such a node cannot be found) a new node is added and is linked to the starting node. We simulate this algorithm and find that we cannot reject the hypothesis that the empirical degree distribution is a q-exponential function, which has been used to model long-range processes in nonequilibrium statistical mechanics.

In the paper, we propose a semiparametric framework for modeling the COVID-19 pandemic. The stochastic part of the framework is based on Bayesian inference. The model is informed by the actual COVID-19 data and the current epidemiological findings about the disease. The framework combines many available data sources (number of positive cases, number of patients in hospitals and in intensive care, etc.) to make outputs as accurate as possible and incorporates the times of non-pharmaceutical governmental interventions which were adopted worldwide to slow-down the pandemic. The model estimates the reproduction number of SARS-CoV-2, the number of infected individuals and the number of patients in different disease progression states in time. It can be used for estimating current infection fatality rate, proportion of individuals not detected and short term forecasting of important indicators for monitoring the state of the healthcare system. With the prediction of the number of patients in hospitals and intensive care units, policy makers could make data driven decisions to potentially avoid overloading the capacities of the healthcare system. The model is applied to Slovene COVID-19 data showing the effectiveness of the adopted interventions for controlling the epidemic by reducing the reproduction number of SARS-CoV-2. It is estimated that the proportion of infected people in Slovenia was among the lowest in Europe (0.350%, 90% CI [0.245–0.573]%), that infection fatality rate in Slovenia until the end of first wave was 1.56% (90% CI [0.94–2.21]%) and the proportion of unidentified cases was 88% (90% CI [83–93]%). The proposed framework can be extended to more countries/regions, thus allowing for comparison between them. One such modification is exhibited on data for Slovene hospitals.