Characterization of blood coagulation dynamics and oxygenation in ex‐vivo retinal vessels by fluorescence hyperspectral imaging

Blood coagulation mechanisms forming a blood clot and preventing hemorrhage have been extensively studied in the last decades. Knowing the mechanisms behind becomes very important particularly in the case of blood vessel diseases. Real‐time and accurate diagnostics accompanied by the therapy are particularly needed, for example, in diseases related to retinal vasculature. In our study, we employ for the first time fluorescence hyperspectral imaging (fHSI) combined with the spectral analysis algorithm concept to assess physical as well as functional information of blood coagulation in real‐time. By laser‐induced local disruption of retinal vessels to mimic blood leaking and subsequent coagulation and a proper fitting algorithm, we were able to reveal and quantify the extent of local blood coagulation through direct identification of the change of oxyhemoglobin concentration within few minutes. We confirmed and illuminated the spatio‐temporal evolution of the essential role of erythrocytes in the coagulation cascade as the suppliers of oxygenated hemoglobin. By additional optical tweezers force manipulation, we showed immediate aggregation of erythrocytes at the coagulation site. The presented fluorescence‐based imaging concept could become a valuable tool in various blood coagulation diagnostics as well as theranostic systems if coupled with the laser therapy.     

Target-dependent induction of secretory capabilities in an identified motoneuron during synaptogenesis

Cholinergic neurons isolated from the buccal ganglia of Helisoma were plated into cell culture with a variety of defined target cells to study the specificity of synaptogenesis. Motoneuron B19 selectively formed chemical connections with single dissociated muscle fibers derived from its appropriate target, the supralateral radular tensor (SLT) muscle. B19 did not form such connections with novel neuronal targets. In contrast to neuron B19, cholinergic neuron B5 nonselectively formed chemical connections with novel muscle and neuronal targets. Target cells were micromanipulated into contact with presynaptic neurons to examine the latent period until the onset of functional synaptic transmission. Neuron B5 formed chemical connections within the first minutes of contact with ACh-sensitive neurons and muscle while B19 required sustained periods of muscle-specific contact to induce the acquisition of a functional excitation-secretion coupling mechanism. These different latent periods from the onset of target contact suggest that neuron B5 acquires presynaptic secretory function before target contact, while B19 must receive a specific signal(s) from its appropriate target to induce the transformation of its terminal into a secretory state.     

Safe uses of Hill's model: an exact comparison with the Adair-Klotz model

Background  

The Hill function and the related Hill model are used frequently to study processes in the living cell. There are very few studies investigating the situations in which the model can be safely used. For example, it has been shown, at the mean field level, that the dose response curve obtained from a Hill model agrees well with the dose response curves obtained from a more complicated Adair-Klotz model, provided that the parameters of the Adair-Klotz model describe strongly cooperative binding. However, it has not been established whether such findings can be extended to other properties and non-mean field (stochastic) versions of the same, or other, models.     

A data-driven acute inflammation therapy

Acute inflammation is a severe medical condition defined as an inflammatory response of the body to an infection. Its rapid progression requires quick and accurate decisions from clinicians. Inadequate and delayed decisions makes acute inflammation the 10th leading cause of death overall in United States with the estimated cost of treatment about $17 billion annually. However, despite the need, there are limited number of methods that could assist clinicians to determine optimal therapies for acute inflammation. We developed a data-driven method for suggesting optimal therapy by using machine learning model that is learned on historical patients' behaviors. To reduce both the risk of failure and the expense for clinical trials, our method is evaluated on a virtual patients generated by a mathematical model that emulates inflammatory response. In conducted experiments, acute inflammation was handled with two complimentary pro- and anti-inflammatory medications which adequate timing and doses are crucial for the successful outcome. Our experiments show that the dosage regimen assigned with our data-driven method significantly improves the percentage of healthy patients when compared to results by other methods used in clinical practice and found in literature. Our method saved 88% of patients that would otherwise die within a week, while the best method found in literature saved only 73% of patients. At the same time, our method used lower doses of medications than alternatives. In addition, our method achieved better results than alternatives when only incomplete or noisy measurements were available over time as well as it was less affected by therapy delay. The presented results provide strong evidence that models from the artificial intelligence community have a potential for development of personalized treatment strategies for acute inflammation.     

Molecular analysis of Leptospira spp. isolated from humans by restriction fragment length polymorphism, real-time PCR and pulsed-field gel electrophoresis

A total of 17 Leptospira clinical strains isolated from humans in Croatia were serologically and genetically analysed. For serovar identification, the microscopic agglutination test (MAT) and pulsed-field gel electrophoresis (PFGE) were used. To identify isolates on genomic species level, PCR-based restriction fragment length polymorphism (RFLP) and real-time PCR were performed. MAT revealed the following serogroup affinities: Grippotyphosa (seven isolates), Icterohaemorrhagiae (eight isolates) and Javanica (two isolates). RFLP of PCR products from a 331-bp-long fragment of rrs (16S rRNA gene) digested with endonucleases MnlI and DdeI and real-time PCR revealed three Leptospira genomic species. Grippotyphosa isolates belonged to Leptospira kirschneri , Icterohaemorrhagiae isolates to Leptospira interrogans and Javanica isolates to Leptospira borgpetersenii . Genomic DNA from 17 leptospiral isolates was digested with NotI and SgrAI restriction enzymes and analysed by PFGE. Results showed that seven isolates have the same binding pattern to serovar Grippotyphosa, eight isolates to serovar Icterohaemorrhagiae and two isolates to serovar Poi. Results demonstrate the diversity of leptospires circulating in Croatia. We point out the usefulness of a combination of PFGE, RFLP and real-time PCR as appropriate molecular methods in molecular analysis of leptospires.