Immunological isolation and characterization of neuronal progenitors from human dental pulp: A laboratory-based investigation.

AIMS: Dental pulp stem cells (DPSCs) contain a population of stem cells with a broad range of differentiation potentials, as well as more lineage-committed progenitors. Such heterogeneity is a significant obstacle to experimental and clinical applications. The aim of this study is to isolate and characterize a homogenous neuronal progenitor cell population from human DPSCs. METHODOLOGY: Polysialylated-neural cell adhesion molecule (PSA-NCAM+) neural progenitors were isolated from the dental pulp of three independent donors using magnetic-activated cell sorting (MACS) technology. Immunofluorescent staining with a panel of neural and non-neural markers was used to characterize the magnetically isolated PSA-NCAM+ fraction. PSA-NCAM+ cells were then cultured in Neurobasal A supplemented with neurotrophic factors: dibutyryl cyclic-AMP, neurotrophin-3, B27 and N2 supplements to induce neuronal differentiation. Both PSA-NCAM+ and differentiated PSA-NCAM+ cells were used in Ca2+ imaging studies to assess the functionality of P2X3 receptors as well as membrane depolarization. RESULTS: PSA-NCAM+ neural progenitors were isolated from a heterogeneous population of hDPSCs using magnetic-activated cell sorting and anti-PSA-NCAM MicroBeads. Flow cytometry analysis demonstrated that immunomagnetic sorting significantly increased the purity of PSA-NCAM+ cells. Immunofluorescent staining revealed expression of pan-neuronal and mature neuronal markers, PGP9.5 and MAP2, respectively, as well as weak expression of the mature sensory markers, peripherin and islet1. ATP-induced response was mediated predominately by P2X3 receptors in both undifferentiated and differentiated cells, with a greater magnitude observed in the latter. In addition, membrane depolarizations were also detected in cells before and after differentiation when loaded with fast-voltage-responding fluorescent molecule, FluoVolt™ in response to potassium chloride. Interestingly, only differentiated PSA-NCAM+ cells were capable of spontaneous membrane oscillations. CONCLUSIONS: In summary, DPSCs contain a population of neuronal progenitors with enhanced neural differentiation and functional neural-like properties that can be effectively isolated with magnetic-activated cell sorting (MACS).

Transformer-Based Network for Accurate Classification of Lung Auscultation Sounds.

Respiratory diseases are a major cause of death worldwide, affecting a significant proportion of the population with lung function abnormalities that can lead to respiratory illnesses. Early detection and prevention are critical to effective management of these disorders. Deep learning algorithms offer a promising approach for analyzing complex medical data and aiding in early disease detection. While transformer-based models for sequence classification have proven effective for tasks like sentiment analysis, topic classification, etc., their potential for respiratory disease classification remains largely unexplored. This paper proposes a classifier utilizing the transformer-encoder block, which can capture complex patterns and dependencies in medical data. The proposed model is trained and evaluated on a large dataset from the International Conference on Biomedical Health Informatics 2017, achieving state-of-the-art results with a mean sensitivity of 70.53%, mean specificity of 84.10%, mean average score of 77.32%, and mean harmonic score of 76.10%. These results demonstrate the model's effectiveness in diagnosing respiratory diseases while taking up minimal computational resources.