Purpose: The combination of oral derived stem cells and 3-D scaffolds is considered advantageous in bone repair. In particular, collagen membranes possess ideal biological properties and can support infiltration and proliferation of osteoblasts, promoting bone regeneration. Our study aimed to develop a new biocompatible osteogenic construct composed of a commercially available collagen membrane (Evolution [Evo]), human periodontal-ligament stem cells (hPDLSCs) enriched with extracellular vesicles (EVs), or polyethylenimine (PEI)-engineered EVs (PEI-EVs). Methods: Osteogenic ability and expression of osteogenic genes were evaluated in vitro in hPDLSCs cultured with or without Evo, with Evo and EVs, or PEI-EVs. In addition, the bone-regeneration capacity of Evo, Evo enriched with hPDLSCs, Evo enriched with hPDLSCs and EVs/PEI-EVs was investigated in rats subjected to calvarial defects. Results: Our results showed that Evo enriched with EVs and PEI-EVs showed high biocompatibility and osteogenic properties in vitro and in vivo. In addition, quantitative reverse-transcription polymerase chain reaction demonstrated the upregulation of osteogenic genes, such as TGFB1, MMP8, TUFT1, TFIP11, BMP2, and BMP4, in the presence of PEI-EVs. Upregulation of BMP2/4 was confirmed for Evo enriched with PEI-EVs and hPDLSCs both in vitro by Western blot and in vivo by immunofluorescence. Conclusion: Our results indicated that Evo enriched with hPDLSCs and PEI-EVs is able to promote a bone-regeneration process for the treatment of calvarium and ossification defects caused by accidental or surgery trauma. In particular, PEI-EVs had a significant role in activation of the osteogenic process.

Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors.

Arterial hypertension (AH) is a progressive issue that grows in importance with the increased average age of the world population. The potential role of artificial intelligence (AI) in its prevention and treatment is firmly recognized. Indeed, AI application allows personalized medicine and tailored treatment for each patient. Specifically, this article reviews the benefits of AI in AH management, pointing out diagnostic and therapeutic improvements without ignoring the limitations of this innovative scientific approach. Consequently, we conducted a detailed search on AI applications in AH: the articles (quantitative and qualitative) reviewed in this paper were obtained by searching journal databases such as PubMed and subject-specific professional websites, including Google Scholar. The search terms included artificial intelligence, artificial neural network, deep learning, machine learning, big data, arterial hypertension, blood pressure, blood pressure measurement, cardiovascular disease, and personalized medicine. Specifically, AI-based systems could help continuously monitor BP using wearable technologies; in particular, BP can be estimated from a photoplethysmograph (PPG) signal obtained from a smartphone or a smartwatch using DL. Furthermore, thanks to ML algorithms, it is possible to identify new hypertension genes for the early diagnosis of AH and the prevention of complications. Moreover, integrating AI with omics-based technologies will lead to the definition of the trajectory of the hypertensive patient and the use of the most appropriate drug. However, AI is not free from technical issues and biases, such as over/underfitting, the “black-box” nature of many ML algorithms, and patient data privacy. In conclusion, AI-based systems will change clinical practice for AH by identifying patient trajectories for new, personalized care plans and predicting patients’ risks and necessary therapy adjustments due to changes in disease progression and/or therapy response.

Background: Chronic low back pain (CLBP) is a leading cause of disability, impacting quality of life (QoL), function, and work productivity. Traditional rehabilitation faces challenges in accessibility and adherence. Remote rehabilitation and virtual reality (VR) interventions using motion sensors offer real-time movement tracking, biofeedback, and personalized exercises. This systematic review evaluates their effectiveness in pain reduction, functional improvement, adherence, and QoL. Methods: A systematic search was performed across PubMed, Scopus, Web of Science, and PEDro (2015–2025), including randomized controlled trials, observational, and feasibility studies on adults with CLBP undergoing sensor-based digital rehabilitation. The primary outcomes included pain, functional mobility, and movement biomechanics; secondary outcomes included adherence, QoL, and cost-effectiveness. Eight studies involving 7166 participants were included. Overall, sensor-based remote rehabilitation and VR interventions demonstrated positive effects on pain, function, and adherence. Pain reductions ranged from modest short-term decreases to over 60% in long-term programs (e.g., −68.5% in VAS). Functional improvements included lumbar ROM gains up to +9.9° and better movement control. Adherence was consistently high, with some programs reporting completion rates between 73% and 90%, particularly those incorporating gamification or real-time feedback. Selected studies also showed QoL improvements (e.g., +9.10 points on SF-36) and reductions in work impairment by over 60%. A few trials reported significant decreases in inflammatory markers (e.g., CRP −1.16 mg/L, TNF-α −8.9 pg/mL). Conclusions: Motion sensor-based remote rehabilitation and VR interventions show promising results in pain management, mobility, and adherence for individuals with CLBP. Gamification and biofeedback features enhance engagement, addressing a key challenge of conventional rehabilitation. However, more long-term RCTs and economic evaluations are needed to confirm their effectiveness and cost-efficiency.

Myotonic dystrophy type 1 (DM1) is the most prevalent adult muscular dystrophy, often accompanied by impairments in attention, memory, visuospatial and executive functions. Given that DM1 is a multi-system disorder, it requires a multi-disciplinary approach, including effective rehabilitation programs, focusing on the central nervous system neuroplasticity, in order to develop patient-tailored rehabilitative procedures for motor function recovery. Herein, we performed a transcranial magnetic stimulation (TMS) study aimed at investigating central motor conduction time, sensory-motor plasticity, and cortical excitability in 7 genetically defined DM1 patients. As compared to healthy individuals, DM1 patients showed a delayed central motor conduction time and an abnormal sensory-motor plasticity, with no alteration of cortical excitability. These findings may be useful to define patient-tailored motor rehabilitative programs.

Introduction The clinical relevance of the anatomy and variations of the anterior ethmoidal artery (AEA) is outstanding, considering its role as a landmark in endoscopic surgery, its importance in the therapy of epistaxis, and the high risks related to iatrogenic injuries. Objective To provide an anatomical description of the course and relationships of the AEA, based on direct computed-tomography (CT)-based 3D volume rendering. Methods Direct volume rendering was performed on 18 subjects who underwent (CT) with contrast medium for suspected cerebral aneurism. Results The topographical location of 36 AEAs was assessed as shown: 10 dehiscent (27.8%), 20 intracanal (55.5%), 6 incomplete canals (16.7%). Distances from important topographic landmarks are reported. Conclusion This work demonstrates that direct 3D volume rendering is a valid imaging technique for a detailed description of the anterior ethmoidal artery thus representing a useful tool for head pre-operatory assessments.