The full understanding of molecular mechanisms of cell differentiation requires a holistic view. Here we combine label-free FTIR and Raman hyperspectral imaging with data mining to detect the molecular cell composition enabling noninvasive monitoring of cell differentiation and identifying biochemical heterogeneity. Mouse adipose-derived mesenchymal stem cells (AD-MSCs) undergoing adipogenesis were followed by Raman and FT-IR imaging, Oil Red, and immunofluorescence. A workflow of the data analysis (IRRSmetrics4stem) was designed to identify spectral predictors of adipogenesis and test machine-learning (ML) methods (hierarchical clustering, PCA, PLSR) for the control of the AD-MSCs differentiation degree. IRRSmetrics4stem provided insights into the chemism of adipogenesis. With single-cell tracking, we established IRRS metrics for lipids, proteins, and DNA variations during AD-MSCs differentiation. The over 90% predictive efficiency of the selected ML methods proved the high sensitivity of the IRRS metrics. Importantly, the IRRS metrics unequivocally recognize a switch from proliferation to differentiation. This study introduced a new bioassay identifying molecular markers indicating molecular transformations and delivering rapid and machine learning-based monitoring of adipogenesis that can be relevant to other differentiation processes. Thus, we introduce a novel, rapid, machine learning-based bioassay to identify molecular markers of adipogenesis. It can be relevant to identification of differentiation-related molecular processes in other cell types, and beyond the cell differentiation including progression of different cellular pathophysiologies reconstituted in vitro.

The astA gene encoding an alternative sulfate transporter was originally cloned from the genome of the Japanese Aspergillus nidulans isolate as a suppressor of sulfate permease-deficient strains. Expression of the astA gene is under the control of the sulfur metabolite repression system. The encoded protein transports sulfate across the cell membrane. In this study we show that AstA, having orthologs in numerous pathogenic or endophytic fungi, has a second function and, depending on growth conditions, can be translocated into mitochondria. This effect is especially pronounced when an astA-overexpressing strain grows on solid medium at 37 °C. AstA is also recruited to the mitochondria in the presence of mitochondria-affecting compounds such as menadione or antimycin A, which are also detrimental to the growth of the astA-overexpressing strain. Disruption of the Hsp70–Porin1 mitochondrial import system either by methylene blue, an Hsp70 inhibitor, or by deletion of the porin1-encoding gene abolishes AstA translocation into the mitochondria. Furthermore, we observed altered ATP levels and sulfite oxidase activity in the astA-overexpressing strain in a manner dependent on sulfur sources. The presented data indicate that AstA is also involved in the mitochondrial sulfur metabolism in some fungi, and thereby indirectly manages redox potential and energy state.

This study was carried out on six adult red kangaroos of both sexes. To determine the location of the oligodendrocytes (OLGs) of the hippocampus (Hip) and corpus callosum (CC), the method of impregnation of the neuroglia with silver salts was applied. The iron distribution in the OLGs was determined by the histochemical method. The Nissl method was used to determine the location of the brain structure and to analyze the number of OLGs. In the Hip, these cells are located one beside another, mainly in blood vessels and neurons; in the neocortex (NC), they are located in layers I–VI; and in the CC, they are arranged in characteristic rows and accompany both nerve fibers and blood vessels. The analysis of the results obtained by the chosen methods in the Hip, NC, and CC in males and females did not show statistically significant differences in the distribution and location of the red kangaroo OLGs. The involvement of these cells is a physiological process that proceeds in a similar manner throughout the life of individuals and actively influences the metabolism of neurons and myelin.

Introduction: Early-Onset (EOAD) and Late-Onset Alzheimer’s Disease (LOAD) are often described as presenting distinct cognitive phenotypes. However, reported differences are inconsistent and may reflect demographic and severity-related confounding rather than stable etiological distinctions. Materials and Methods: A retrospective analysis was conducted in 776 patients with clinically diagnosed Alzheimer’s disease (EOAD, n = 175; LOAD, n = 601). Cognitive performance was assessed using the Addenbrooke’s Cognitive Examination III (ACE-III) and the Mini-Mental State Examination (MMSE). Dementia severity was staged using the Functional Assessment Staging Tool (FAST). Cognitive outcomes were analysed using age- and severity-adjusted ANCOVA models including a diagnosis × age interaction. Severity-stratified and domain-level analyses were also performed. results: Unadjusted analyses revealed limited EOAD–LOAD differences in the Attention and Visuospatial domains, with small effect sizes. After adjustment for demographic factors and dementia severity, no cognitive measure reliably differentiated EOAD from LOAD. Cognitive decline followed parallel trajectories in both groups and was primarily driven by disease severity. Results: Unadjusted analyses revealed limited EOAD-LOAD differences in the Attention and Visuospatial domains, with small effect sizes. After adjustment for demographic variables and dementia severity, no cognitive measure reliably differentiated EOAD from LOAD. Cognitive performance showed similar cross-sectional patterns across groups and was primarily determined by dementia severity. Discussion: Small, stage-dependent differences observed at mild and moderate stages did not persist in more advanced dementia and did not indicate stable cognitive dissociation between EOAD and LOAD. Conclusions: After adjustment for demographic factors and dementia severity, EOAD and LOAD do not exhibit distinct cognitive profiles on ACE-III. Cognitive performance reflects disease stage rather than age at onset, supporting the use of ACE-III for staging cognitive impairment rather than for differentiating EOAD from LOAD in typical amnestic presentations.