Quantitative and qualitative assessment of airborne microorganisms during gross anatomical class and the bacterial and fungal load on formalin-embalmed corpses.

Mold growth on body donations remains an underreported yet serious issue in anatomical teaching. Bacterial and fungal growth pose health risks to lecturers and students, alongside with ethical and aesthetic concerns. However, limited information exists on the presence of bacteria and fungi on body donations and their underlying causes. To investigate the potential impact of airborne germs on body donation contamination, we conducted indoor air measurements before, during, and after our anatomical dissection course, with outdoor measurements serving as a control. Tissue samples from the dissected body donations were collected to assess the germ load, with qualitative and quantitative microbiological analyses. Air samples from the dissection hall contained no fungi, but various fungal species were identified in the adjacent stairways and outdoors which implies that fungal occurrence in the dissection hall air was independent of lecturers' and students' presence. Moreover, our results indicate that adequate ventilation filters can effectively reduce indoor fungal germs during courses, while the bacterial load in room air appears to increase, likely due to the presence of lecturers and students. Additionally, the tissue samples revealed no bacterial or fungal germs which implies that our ethanol-formalin-based embalming solution demonstrates an effective long-term antimicrobial preservation of corpses.

Modeling and simulation for prediction of multiple sclerosis progression.

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

How to Use the Cuprizone Model to Study De- and Remyelination.

Multiple sclerosis (MS) is an autoimmune and inflammatory disorder affecting the central nervous system whose cause is still largely unknown. Oligodendrocyte degeneration results in demyelination of axons, which can eventually be repaired by a mechanism called remyelination. Prevention of demyelination and the pharmacological support of remyelination are two promising strategies to ameliorate disease progression in MS patients. The cuprizone model is commonly employed to investigate oligodendrocyte degeneration mechanisms or to explore remyelination pathways. During the last decades, several different protocols have been applied, and all have their pros and cons. This article intends to offer guidance for conducting pre-clinical trials using the cuprizone model in mice, focusing on discovering new treatment approaches to prevent oligodendrocyte degeneration or enhance remyelination.