Neurodegenerative disorders are frequent, incurable diseases characterised by abnormal protein accumulation and progressive neuronal loss. Despite their growing prevalence, the underlying pathomechanism remains unclear. Lemur tyrosine kinase 2 (LMTK2) is a member of a transmembrane serine/threonine-protein kinase family. Although it was described more than a decade ago, our knowledge on LMTK2’s biological functions is still insufficient. Recent evidence has suggested that LMTK2 is implicated in neurodegeneration. After reviewing the literature, we identified three LMTK2-mediated mechanisms which may contribute to neurodegenerative processes: disrupted axonal transport, tau hyperphosphorylation and enhanced apoptosis. Moreover, LMTK2 gene expression is decreased in an Alzheimer’s disease mouse model. According to these features, LMTK2 might be a promising therapeutic target in near future. However, further investigations are required to clarify the exact biological functions of this unique protein.

Twelve compounds (1–12) were isolated from the methanol extract of brick cap mushroom (Hypholoma lateritium (Schaeff.) P. Kumm.). The structures of the compounds were elucidated using extensive spectroscopic analyses, including NMR and MS measurements. Lanosta-7,9(11)-diene-12β,21α-epoxy-2α,3β,24β,25-tetraol (1) and 8-hydroxy-13-oxo-9E,11E-octa-decadienoic acid (2) were identified as new natural products, together with ten known compounds, from which 3β-hydroxyergosta-7,22-diene (4), demethylincisterol A2 (5), cerevisterol (6), 3β-O-glucopyranosyl-5,8-epidioxyergosta-6,22-diene (7), fasciculol E (9), and uridine (12) were identified in this species for the first time. The isolated triterpenes (1, 3–11) were investigated for their toxicity in vivo using bdelloid rotifer assays. Most of the examined steroids in general showed low toxicity, although the effects of the compounds varied in a wider range from the non-toxic lanosta-7,9(11)-diene-12β,21α-epoxy-2α,3β,24β,25-tetraol (1) to the significantly toxic cerevisterol (6), with substantial dependence in some cases on the presence of nutrient in the experimental environment.

The chemical ecology of rotifers has been little studied. A yet unknown property is presented within some monogonant rotifers, namely the ability to produce an exogenic filamentous biopolymer, named 'Rotimer'. This rotifer-specific viscoelastic fiber was observed in six different freshwater monogonants (Euchlanis dilatata, Lecane bulla, Lepadella patella, Itura aurita, Colurella adriatica and Trichocerca iernis) in exception of four species. Induction of Rotimer secretion can only be achieved by mechanically irritating rotifer ciliate with administering different types (yeast cell skeleton, denatured BSA, epoxy, Carmine or urea crystals and micro-cellulose) and sizes (approx. from 2.5 to 50 µm diameter) of inert particles, as inductors or visualization by adhering particles. The thickness of this Rotimer is 33 ± 3 nm, detected by scanning electron microscope. This material has two structural formations (fiber or gluelike) in nano dimension. The existence of the novel adherent natural product becomes visible by forming a 'Rotimer-Inductor Conglomerate' (RIC) web structure within a few minutes. The RIC-producing capacity of animals, depends on viability, is significantly modified according to physiological- (depletion), drug- (toxin or stimulator) and environmental (temperature, salt content and pH) effects. The E. dilatata-produced RIC is affected by protein disruptors but is resistant to several chemical influences and its Rotimer component has an overwhelming cell (algae, yeast and human neuroblastoma) motility inhibitory effect, associated with low toxicity. This biopolymer-secretion-capacity is protective of rotifers against human-type beta-amyloid aggregates.

In this paper, we seek to automatically identify Hungarian patients suffering from mild cognitive impairment (MCI) or mild Alzheimer’s Disease (mAD) based on their speech transcripts, focusing only on linguistic features. In addition to the features examined in our earlier study, we introduce syntactic, semantic and pragmatic features of spontaneous speech that might affect the detection of dementia. In order to ascertain the most useful features for distinguishing healthy controls, MCI patients and mAD patients, we will carry out a statistical analysis of the data and investigate the significance level of the extracted features among various speaker group pairs and for various speaking tasks. In the second part of the paper, we use this rich feature set as a basis for an effective discrimination among the three speaker groups. In our machine learning experiments, we will analyze the efficacy of each feature group separately. Our model which uses all the features achieves competitive scores, either with or without demographic information (3-class accuracy values: 68–70%, 2-class accuracy values: 77.3–80%). We also analyze how different data recording scenarios affect linguistic features and how they can be productively used when distinguishing MCI patients from healthy controls.