Conectar
From previous studies, we have shown that viable colony forming units of bacteria and bacterial biofilms are reduced after sequential treatment with a surfactant-based dressing. Here, we sought to test the impact on visible bacterial pigments and the ultrastructural impact following the sequential treatment of the same surfactant-based dressing. Mature Pseudomonas aeruginosa biofilms were grown on ex vivo porcine skin explants, and an imaging-based analysis was used to compare the skin with and without a concentrated surfactant. In explants naturally tinted by bacterial chromophores, wiping alone had no effect, while the use of a surfactant-based dressing reduced coloration. Similarly, daily wiping led to increased immunohistochemical staining for P. aeruginosa antigens, but not in the surfactant group. Confocal immunofluorescent imaging revealed limited bacterial penetration and coating of the dermis and loose pieces of sloughing material. Ultrastructural analysis confirmed that the biofilms were masking the extracellular matrix (ECM), but the surfactant could remove them, re-exposing the ECM. The masking of the ECM may provide another non-inflammatory explanation for delayed healing, as the ECM is no longer accessible for wound cell locomotion. The use of a poloxamer-based surfactant appears to be an effective way to remove bacterial chromophores and the biofilm coating the ECM fibres.
Millions of people survive injuries to the central or peripheral nervous system for which neurorehabilitation is required. In addition to the physical and cognitive impairments, many neurorehabilitation patients experience pain, often not widely recognised and inadequately treated. This is particularly true for multiple sclerosis (MS) patients, for whom pain is one of the most common symptoms. In clinical practice, pain assessment is usually conducted based on a subjective estimate. This approach can lead to inaccurate evaluations due to the influence of numerous factors, including emotional or cognitive aspects. To date, no objective and simple to use clinical methods allow objective quantification of pain and the diagnostic differentiation between the two main types of pain (nociceptive vs neuropathic). Wearable technologies and artificial intelligence (AI) have the potential to bridge this gap by continuously monitoring patients’ health parameters and extracting meaningful information from them. Therefore, we propose to develop a new automatic AI-powered tool to assess pain and its characteristics during neurorehabilitation treatments using physiological signals collected by wearable sensors.
We aim to recruit 15 participants suffering from MS undergoing physiotherapy treatment. During the study, participants will wear a wristband for three consecutive days and be monitored before and after their physiotherapy sessions. Measurement of traditionally used pain assessment questionnaires and scales (ie, painDETECT, Doleur Neuropathique 4 Questions, EuroQoL-5-dimension-3-level) and physiological signals (photoplethysmography, electrodermal activity, skin temperature, accelerometer data) will be collected. Relevant parameters from physiological signals will be identified, and AI algorithms will be used to develop automatic classification methods.
The study has been approved by the local Ethical Committee (285-2022-SPER-AUSLBO). Participants are required to provide written informed consent. The results will be disseminated through contributions to international conferences and scientific journals, and they will also be included in a doctoral dissertation.
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