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Perspectival content of visual experiences
No full textThe usual visual experiences possess a perspectival phenomenology as theyseem to present objects from a certain perspective. Nevertheless, it is notobvious how to characterise experiential content determining suchphenomenology. In particular, while there are many works investigatingperspectival properties of experienced objects, a question regarding howsubject is represented in visual perspectival experiences attracted lessattention. In order to address this problem, I consider four popularphenomenal intuitions regarding perspectival experiences and argue that themajor theories of perspectival experiences do not account for all of them.Relying on these observations, I show how a theory which accommodates allthese intuitions can be developed by (a) recognising that visual perspectivalexperiences are, in fact, multimodal visuo-bodily experiences and (b)distinguishing between egocentric and structural contents
Cyclic electron transfer in plants : potential molecular pathways of electron transfer to the cytochrome complex in the context of state transition
No full textCongenital temporomandibular joint ankylosis : investigating potential genetic etiologies with whole exome sequencing
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Phage endolysin–polyphosphate/alginate nanocomplexes inhibit staphylococcal biofilms for implant protection
No full textImplant-associated infections caused by Staphylococcus epidermidis and Staphylococcus aureus remain a major challenge in orthopedic surgery due to their robust biofilm formation and resistance to conventional antibiotics.
We aimed to develop an alginate-based delivery system for peptidoglycan hydrolase–polyphosphate (PGH-PP) nanoparticles to prevent staphylococcal biofilm formation on implant surfaces. These nanoparticles incorporated two chimeric phage-derived endolysins, GH15 and M23. Formulations were screened for antimicrobial and antibiofilm activity against staphylococcal species using low- and very low-viscosity alginate. The nanostructure of the delivery systems was analyzed via transmission electron microscopy and phosphate release assays. Biocompatibility with MC3T3 preosteoblasts and the formulation´s impact on bone development were evaluated. Efficacy in inhibiting biofilm formation by S. epidermidis 9142 and S. aureus ATCC 25923 was assessed both on orthopedic implant surrogates and in co-culture experiments with MC3T3 preosteoblasts.
Very low-viscosity alginate (0.625% w/v), in conjunction with a synergistic combination of M23-PP and GH15-PP, formed well-defined complex coacervates of approximately 350 nm (M23-PP/GH15-PP/VL alginate). The developed nanocomplexes effectively eradicated both bacterial species and further inhibited biofilm formation on implants, as confirmed by scanning electron microscopy. M23-PP/GH15-PP/VL alginate was biocompatible with MC3T3 preosteoblasts supporting its potential use in orthopedic applications. When MC3T3 cells were co-cultured with S. epidermidis or S. aureus, M23-PP/GH15-PP/VL alginate treatment led to an 8 log reduction in bacterial counts, with healthy mammalian cells proliferating in the absence of bacteria.
These results highlight the potential of M23-PP/GH15-PP/VL alginate as a biocompatible approach to preventing staphylococcal biofilm formation, mitigating the risk of implant-related bone infections
Spectral discrimination of pediatric SF188 and adult glioblastoma stem cells by deep learning–enhanced Raman profiling
No full textBackground Pediatric and adult glioblastomas (GBM) represent biologically distinct entities requiring age-tailored therapeutic strategies. However, rapid and non-invasive methods to distinguish these molecular subtypes remain an unmet clinical need. This study evaluates the potential of confocal Raman spectroscopy combined with deep learning as a label-free diagnostic tool to differentiate pediatric from adult GBM based on intrinsic biochemical fingerprints.MethodsWe acquired n=1,382 Raman spectra from a cohort of six patient-derived GBM neurosphere cell lines, comprising a pediatric model (SF188) and five adult-origin lines. A multilayer perceptron (MLP) neural network was trained to classify spectra by age group. To ensure rigorous validation and generalizability, performance was assessed on a strictly held-out external test set (20% of data), completely excluded from model optimization.Results The deep learning model successfully differentiated pediatric from adult GBM signatures with an overall accuracy of 83.6% and an ROC AUC of 0.855 on the independent test set. Spectral analysis revealed distinct vibrational modes, highlighting significant variations in lipid, protein, and nucleic acid content between age groups. Notably, the model achieved a high sensitivity for the pediatric phenotype (91.4% identification rate) . Conclusion This proof-of-concept study demonstrates that Raman spectroscopy, augmented by deep learning, can identify age-related molecular variations in GBM without extrinsic labeling. By capturing the unique biochemical landscape of pediatric versus adult tumors, this approach lays the foundation for rapid, automated, and objective diagnostic workflows in precision neuro-oncology.</p
Outcomes of high-risk pulmonary embolism stratified by clinical phenotype : results from two specialized pulmonary embolism centres
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