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Piezoelectric materials can provide in situ electrical stimulation without external chemical or physical support, opening new frontiers for future bioelectric therapies. Polyvinylidene fluoride (PVDF) possesses piezoelectricity and biocompatibility, making it an electroactive biomaterial capable of enhancing bioactivity through instantaneous electrical stimulation, which indicates significant potential in tissue engineering. In this study, we developed electroactive and biomimetic scaffolds made of electrospun PVDF and self-assembling peptides (SAPs) to enhance stem cell transplantation for spinal cord injury regeneration. We investigated the morphology and crystalline polymorphs of the electrospun scaffolds. Morphological studies demonstrated the benefit of using mixed sodium dodecyl sulfate (SDS) and SAPs as additives to form thinner, uniform, and defect-free fibers. Regarding electroactive phases, β and γ phases-evidence of electroactivity-were predominant in aligned scaffolds and scaffolds modified with SDS and SAPs. In vitro studies showed that neural stem cells (NSCs) seeded on electrospun PVDF with additives exhibited desirable proliferation and differentiation compared to the gold standard. Furthermore, the orientation of the fibers influenced scaffold topography, resulting in a higher degree of cell orientation in fiber-aligned scaffolds compared to randomly oriented ones., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Background: The cyclin D1-cyclin dependent kinases (CDK)4/6 inhibitor palbociclib in combination with endocrine therapy shows remarkable efficacy in the management of estrogen receptor (ER)-positive and HER2-negative advanced breast cancer (BC). Nevertheless, resistance to palbociclib frequently arises, highlighting the need to identify new targets toward more comprehensive therapeutic strategies in BC patients., Methods: BC cell lines resistant to palbociclib were generated and used as a model system. Gene silencing techniques and overexpression experiments, real-time PCR, immunoblotting and chromatin immunoprecipitation studies as well as cell viability, colony and 3D spheroid formation assays served to evaluate the involvement of the G protein-coupled estrogen receptor (GPER) in the resistance to palbociclib in BC cells. Molecular docking simulations were also performed to investigate the potential interaction of palbociclib with GPER. Furthermore, BC cells co-cultured with cancer-associated fibroblasts (CAFs) isolated from mammary carcinoma, were used to investigate whether GPER signaling may contribute to functional cell interactions within the tumor microenvironment toward palbociclib resistance. Finally, by bioinformatics analyses and k-means clustering on clinical and expression data of large cohorts of BC patients, the clinical significance of novel mediators of palbociclib resistance was explored., Results: Dissecting the molecular events that characterize ER-positive BC cells resistant to palbociclib, the down-regulation of ERα along with the up-regulation of GPER were found. To evaluate the molecular events involved in the up-regulation of GPER, we determined that the epidermal growth factor receptor (EGFR) interacts with the promoter region of GPER and stimulates its expression toward BC cells resistance to palbociclib treatment. Adding further cues to these data, we ascertained that palbociclib does induce pro-inflammatory transcriptional events via GPER signaling in CAFs. Of note, by performing co-culture assays we demonstrated that GPER contributes to the reduced sensitivity to palbociclib also facilitating the functional interaction between BC cells and main components of the tumor microenvironment named CAFs., Conclusions: Overall, our results provide novel insights on the molecular events through which GPER may contribute to palbociclib resistance in BC cells. Additional investigations are warranted in order to assess whether targeting the GPER-mediated interactions between BC cells and CAFs may be useful in more comprehensive therapeutic approaches of BC resistant to palbociclib., (© 2024. The Author(s).)

Background and Objectives: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention., Method: The AiCarePWP is a wearable electronics device designed to identify instances when a patient is on the brink of experiencing a freezing episode and subsequently deliver a brief electrical impulse to the patient's shank muscles to stimulate movement. The AiCarePWP wearable device aims to identify impending freezing episodes in PD patients and deliver brief electrical impulses to stimulate movement. The study validates this innovative approach using plantar insoles with a 3D accelerometer and electrical stimulator, analysing data from the inertial measuring unit and plantar-pressure foot data to detect and predict FoG., Results: Using a Convolutional Neural Network-based model, the study evaluated 47 gait features for their ability to differentiate resting, standing, and walking conditions. Variable selection was based on sensitivity, specificity, and overall accuracy, followed by Principal Component Analysis and Varimax rotation to extract and interpret factors. Factors with eigenvalues exceeding 1.0 were retained, and 37 features were retained., Conclusion: This study validates CNN's effectiveness in detecting FoG during various activities. It introduces a novel cueing method using electrical stimulation, which improves gait function and reduces FoG incidence in PD patients. Trustworthy wearable devices, based on Artificial Intelligence of Things (AIoT) and Artificial Intelligence of Medical Things (AIoMT), have been developed to support such interventions., Competing Interests: Declaration of competing interest We hereby acknowledge that disclosing any conflicts of interest promotes transparency and does not inherently suggest unethical conduct or bias in the execution of this study. Efforts have been made to effectively address any potential conflicts of interest encountered during the study process and to uphold the integrity and impartiality of our work. The study findings and conclusions outlined in this paper are derived from a meticulous application of scientific methodologies and substantiated by compelling empirical evidence., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This scoping review paper redefines the Artificial Intelligence-based Internet of Things (AIoT) driven Human Activity Recognition (HAR) field by systematically extrapolating from various application domains to deduce potential techniques and algorithms. We distill a general model with adaptive learning and optimization mechanisms by conducting a detailed analysis of human activity types and utilizing contact or non-contact devices. It presents various system integration mathematical paradigms driven by multimodal data fusion, covering predictions of complex behaviors and redefining valuable methods, devices, and systems for HAR. Additionally, this paper establishes benchmarks for behavior recognition across different application requirements, from simple localized actions to group activities. It summarizes open research directions, including data diversity and volume, computational limitations, interoperability, real-time recognition, data security, and privacy concerns. Finally, we aim to serve as a comprehensive and foundational resource for researchers delving into the complex and burgeoning realm of AIoT-enhanced HAR, providing insights and guidance for future innovations and developments.

The rapid advancement of modern communication technologies necessitates the development of generalized multi-access frameworks and the continuous implementation of rate splitting, augmented with semantic awareness. This trend, coupled with the mounting pressure on wireless services, underscores the need for intelligent approaches to radio signal propagation. In response to these challenges, intelligent reflecting surfaces (IRS) have garnered significant attention for their ability to control data transmission systems in a goal-oriented and dynamic manner. This innovation is largely attributed to equitable resource allocation and the dynamic enhancement of network performance. However, the integration of rate-splitting multi-access (RSMA) architecture with semantic considerations imposes stringent requirements on IRS platforms to ensure seamless connectivity and broad coverage for a diverse user base without interference. Semantic communications hinge on a knowledge base-a centralized repository of integrated information related to the transmitted data-which becomes critically important in multi-antenna scenarios. This article proposes a novel set of design strategies for RSMA-IRS systems, enabled by reconfigurable intelligent surface synergizing with semantic communication principles. An experimental analysis is presented, demonstrating the effectiveness of these design guidelines in the context of Beyond 5G/6G communication systems. The RSMA-IRS model, infused with semantic communication, offers a promising solution for future wireless networks. Performance evaluations of the proposed approach reveal that, despite an increase in the number of users, the delay in the RSMA-IRS framework incorporating semantics is 2.94% less than that of a RSMA-IRS system without semantic integration., (© 2024. The Author(s).)

Background and Objective: Chronic Obstructive Pulmonary Disease (COPD) is one of the world's worst diseases; its early diagnosis using existing methods like statistical machine learning techniques, medical diagnostic tools, conventional medical procedures, and other methods is challenging due to misclassification results of COPD diagnosis and takes a long time to perform accurate prediction. Due to the severe consequences of COPD, detection and accurate diagnosis of COPD at an early stage is essential. This paper aims to design and develop a multimodal framework for early diagnosis and accurate prediction of COPD patients based on prepared Computerized Tomography (CT) scan images and lung sound/cough (audio) samples using machine learning techniques, which are presented in this study., Method: The proposed multimodal framework extracts texture, histogram intensity, chroma, Mel-Frequency Cepstral Coefficients (MFCCs), and Gaussian scale space from the prepared CT images and lung sound/cough samples. Accurate data from All India Institute Medical Sciences (AIIMS), Raipur, India, and the open respiratory CT images and lung sound/cough (audio) sample dataset validate the proposed framework. The discriminatory features are selected from the extracted feature sets using unsupervised ML techniques, and customized ensemble learning techniques are applied to perform early classification and assess the severity levels of COPD patients., Results: The proposed framework provided 97.50%, 98%, and 95.30% accuracy for early diagnosis of COPD patients based on the fusion technique, CT diagnostic model, and cough sample model., Conclusion: Finally, we compare the performance of the proposed framework with existing methods, current approaches, and conventional benchmark techniques for early diagnosis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)