Search Results (48)

Tumor-associated macrophages (TAMs) are pivotal immune cells within the tumor stroma, whose dynamic alterations significantly impact tumor progression and therapeutic responses. Conventional methods for TAM detection, such as biopsy, are invasive and incapable of whole-body dynamic monitoring. In contrast, positron emission tomography (PET) and single-photon emission computed tomography (SPECT) offer a non-invasive imaging approach by targeting TAM-specific biomarkers like CD206, TSPO, and CCR2. This review comprehensively summarizes the advancements in TAM-targeted imaging probes, including cell surface markers, metabolic/functional markers, and multifunctional nanoprobe, while assessing their potential in tumor immune surveillance and tumor targeting therapeutic applications. While current probes, including ⁶⁸Ga-NOTA-anti-CD206 and ⁶⁴Cu-Macrin, have exhibited high specificity and theragnostic potential in preclinical and early clinical trials, challenges such as target heterogeneity, off-target effects, and clinical translation persist. Moving forward, the advancement of multi-target probes, optimization of pharmacokinetics, and incorporation of multimodal imaging technologies are anticipated to further enhance the impact of TAM-targeted imaging in precision medicine and tumor immunotherapy, fostering the refinement of personalized treatment strategies and improving patient outcomes. Full article

Digital competences (DC) are vital for improving employability, especially in tourism, where adapting to technology and communicating effectively are key. Proficiency in digital tools and a second language (SL) significantly enhances organizational performance and competitiveness, supporting sustainable development and innovation in dynamic business environments. This study explores the causal link between digital competences and employability dimensions, including second-language skills, in SMEs within the tourism sector in Quindío and Valle del Cauca, Colombia. Using a quantitative approach, data from 114 employees were collected through a semi-structured survey and analyzed via partial least squares structural equation modelling (PLS-SEM) to determine significant relationships. The results reveal that digital competences significantly enhance technological management, occupational experience (OE), anticipation and optimization (AO), and personal flexibility (PF). These skills contribute to sustainable tourism by promoting adaptability, innovation, and inclusive employability. Additionally, second-language proficiency demonstrates strong explanatory power in communication-related aspects. The findings highlight the need for tourism enterprises to prioritize digital upskilling, integrate research and innovation into job functions, strengthen adaptability to organizational changes, and view second-language development as a strategic resource. This study offers valuable insights for designing targeted training strategies aligned with the sector’s dynamic demands and advances the broader discourse on digital literacy in workforce development. Full article

RNA-based cancer vaccines have emerged as transformative immunotherapeutic platforms, leveraging advances in mRNA technology and personalized medicine approaches. Recent clinical breakthroughs, particularly the success of mRNA-4157 combined with pembrolizumab in melanoma patients, have demonstrated significant improvements in efficacy, with a 44% reduction in recurrence risk compared to checkpoint inhibitor monotherapy. Breakthrough results from pancreatic cancer vaccines and novel glioblastoma treatments using layered nanoparticle delivery systems mark 2024–2025 as a pivotal period for RNA cancer vaccine development. Current RNA vaccine platforms include conventional mRNA, self-amplifying RNA, trans-amplifying RNA, and emerging circular RNA technologies, with over 120 clinical trials currently underway across various malignancies. Critical advances in delivery optimization include next-generation lipid nanoparticles with tissue-specific targeting and novel nanoengineered systems achieving rapid immune system reprogramming. Manufacturing innovations focus on automated platforms, reducing production timelines from nine weeks to under four weeks for personalized vaccines, while costs remain challenging at over $ 100,000 per patient. Artificial intelligence integration is revolutionizing neoantigen selection through advanced algorithms and CRISPR-enhanced platforms, while regulatory frameworks are evolving with new FDA guidance for therapeutic cancer vaccines. Non-coding RNA applications, including microRNA and long non-coding RNA therapeutics, represent emerging frontiers with potential for enhanced immune modulation. With over 60 candidates in clinical development and the first commercial approvals anticipated by 2029, RNA cancer vaccines are positioned to become cornerstone therapeutics in personalized oncology, offering transformative hope for cancer patients worldwide. Full article

Personal Comfort Systems (PCS) have emerged as a flexible alternative to address the diversity of environmental perceptions in office environments. Unlike conventional HVAC systems, PCSs allow users to improve their satisfaction and comfort by exercising individualized control over their immediate environment without interfering with others around them. This study evaluated the use of machine learning models generated by H2O AutoML to predict the use of three PCSs in four office buildings with effective occupancy. These were a thermal wristband, a desk fan, and an adjustable lamp. Data collected through environmental sensors, perception surveys, and spatial and personal attributes were used. Synthetic data augmentation and automated variable selection were also used to optimize the models’ performance. The predictive models had a robust performance, with R² values in the test set of 0.86 for the wristband, 0.84 for the fan, and 0.52 for the lamp. The most influential variables included the BMI, CO₂ level, and thermal satisfaction, highlighting the importance of physiological and subjective factors. The results confirm that the models allow anticipating the use of PCS with high precision in most cases, laying the foundations for the future implementation of user-oriented adaptive systems. This preliminary approach contributes to the design of healthier, more personalized, and more energy-efficient work environments. Full article

An efficient solution strategy based on fluid network modeling, computational fluid dynamics (CFD) and discrete particle modeling (DPM) is presented in order to predict and improve air quality, specifically regarding breathing aerosol concentration, in a person-occupied mechanically ventilated room. The efficiency of the proposed workflow is evaluated for the specific case of a lecture hall. It is found that the actual vent system is imbalanced and inefficient in managing the aerosol concentration within the room. Despite a high volumetric exchange rate, aerosol residence times and local aerosol concentrations remain high over an extended period of time, without additional efforts to alter air flow circulation throughout the room. The proposed strategy illustrates how such changes can be efficiently implemented in the basic 1D/3D co-simulation workflow. Analysis of the lecture hall and vent system shows that the execution time for the overall process workflow can be optimized by the following: (1) CAD geometry generation of the room via 3D laser scanning, (2) mesh generation based on the anticipated air discharge behavior from the vent system and (3) by employing HPC resources. Additional simplifications such as the decoupling of vent air flow and room aerodynamics, as observed for the investigated test case, one-way coupling between air flow and aerosol dispersion at low aerosol concentrations and the successive solution of flow field equations can further reduce the problem’s complexity and processing times. Full article

Nuclear medicine has emerged as a critical modality in the diagnostic and therapeutic management of urological malignancies, particularly prostate cancer. Advances in single-photon emission computed tomography/computed tomography (CT) and positron emission tomography/CT (PET/CT) have enhanced tumor assessment across staging, treatment response, and recurrence settings. Molecular imaging, which offers insights beyond traditional anatomical imaging, is increasingly integral in specific clinical scenarios. Theranostic nuclear medicine, which combines diagnostic imaging with targeted therapy, has become a well-established treatment option, particularly for patients with metastatic castration-resistant prostate cancer (mCRPC). The development of the prostate-specific membrane antigen (PSMA) radioligands has revolutionized clinical management by enabling precise disease staging and delivering effective radioligand therapy (RLT). Ongoing research aims to refine the role of PSMA PET imaging in staging and treatment monitoring, while optimizing PSMA-targeted RLT for broader clinical use. Given that prostate cancer remains highly prevalent, the anticipated increase in the demand for RLT presents both challenges and opportunities for nuclear medicine services globally. Theranostic approaches exemplify personalized medicine by enabling the tailoring of treatments to individual tumor biology, thereby improving survival outcomes and maintaining patients’ quality of life with minimal toxicity. Although the current focus is on advanced disease, future research holds promise for expanding these strategies to earlier stages, potentially enhancing curative prospects. This evolving field not only signifies a paradigm shift in the care of prostate cancer patients but also underscores the growing importance of nuclear medicine in delivering precision oncology. Full article

The absence of standardized time zones has profound implications, influencing social, economic, and energy dynamics. It also disrupts personal aspects, such as sleep patterns and family routines. One specific dimension of this issue is the transition to daylight saving time (DST), which entails shifting between standard time (winter) and daylight saving time (summer). This practice has sparked global debates due to its varying impacts across regions and sectors. Although DST primarily aims to optimize energy consumption by leveraging natural daylight, much attention has focused on its broader societal effects. However, the energy performance of commercial buildings under DST remains an underexplored yet equally significant area. This article presents a literature review to critically evaluate the effects of the winter-to-summer time shift on commercial buildings, concentrating on three key factors: energy consumption, where seasonal variations in lighting, heating, and cooling demands may alter anticipated energy savings; occupant thermal comfort, as time changes can disrupt the circadian rhythms of building occupants, impacting productivity and well-being; and operational considerations, as building systems like HVAC and automated controls must adjust to shifting daylight schedules. Accordingly, this review seeks to offer a comprehensive understanding of how the winter–summer time transition affects commercial buildings by analyzing energy consumption patterns, occupant comfort levels, and operational challenges. In doing so, it contributes to optimizing building management practices under varying daylight conditions to enhance energy efficiency and occupant satisfaction. Full article

The optimal approach to managing motor neurone disease (MND) is through integrated, person-centred care (PCC), complemented by access to specialised MND multidisciplinary clinics (MDCs). However, in Australia, MND care is fragmented and uncoordinated. Objectives: To evaluate participant experiences of the implementation of a new regional MND MDC in New South Wales, Australia, and explore factors influencing its implementation. Methods: A qualitative evaluation was conducted. We used semi-structured interviews with people living with MND (plwMND) (n = 4), family carers (n = 2), healthcare providers (n = 6), and social care providers (n = 2). First, deductive analysis using the Theoretical Domains Framework and COM-B model was applied to identify factors influencing the adoption and sustainability of the MDC. Then, an inductive thematic analysis identified the impact of the MND MDC from participant perspectives. Results: The MND MDC was found to be appropriate and acceptable for providing equitable access to PCC MND care that was ‘closer to home’. The three main themes from the inductive analysis indicated that: 1. Implementing it was a ‘good idea’ [the MND-MDC]; 2. It ‘flushes out’ local service gaps and/or challenges; and 3. It results in positive outcomes. Key facilitators to implementation identified from the deductive analysis were staff expertise, strong trusting relationships with all clinic participants, and the belief that the MND MDC optimised care. Barriers to implementation included a lack of staff remuneration, organisational boundaries, limited representation of specialities, and anticipated difficulties in coordinating care with existing services. Conclusions: The commitment to providing equitable access to an MND MDC in a regional area is crucial to optimise care for plwMND and their families. However, overcoming complex organisational boundaries, creating local solutions, and building strong partnerships are key challenges to securing ongoing financial support and local health district ‘buy-in’ to support sustainability. Full article

Background/Objectives: Comprehensive cancer centers (CCCs) remain at the forefront of cancer control efforts. Limited clarity and variation exist around the models, scope, characteristics, and impacts of CCCs around the globe. This scoping review systematically searched and synthesized the international literature, describing core attributes and anticipated and realized impacts of CCCs, detailing changes over time. Methods: Searches for English language sources were conducted across PubMed, Cochrane CENTRAL, Epistemonikos, and the gray literature from January 2002 to April 2024. Data were extracted and appraised by two authors. Results were narratively synthesized. Results: Of 3895 database records and 843 gray literature sources screened, 81 sources were included. Papers were predominantly opinion-based, from the USA and Europe, and published between 2011 and 2020. Internationally, the interconnected attributes of CCCs included (1) clinical service provision; (2) research, data, and innovation; (3) education and clinical support; (4) networks and leadership; (5) health equity and inclusiveness; and (6) accountability and governance. Largely anticipated impacts were synergistic and included delivery of optimal, person-centered, complex care; development of a highly qualified cancer workforce; greater research activity and funding; effective, strategic alliances; and reduction in cancer-related inequalities. Limited evidence was found demonstrating measurable broad outcomes of CCCs. The early literature highlighted the establishment, development, and accreditation of CCCs. The ongoing literature has reflected the evolution of cancer care, key areas for growth, and limitations of CCCs. Recently, the CCC literature has increased exponentially and focused on the need for CCCs to drive networks and leadership to address health equity and inclusiveness. Conclusions: Results suggest that CCCs are yet to reach their full potential, with future efforts ideally focusing on accountability, effective networking, and health equity at a local, national, and international level. CCCs must generate evidence of impact, and continue to evolve in line with contemporary healthcare, to fulfil their role in cancer control efforts. Full article

Background: Interdialytic weight gain (IDWG), defined as the accumulation of salt and water intake between dialysis sessions, is a critical parameter of fluid management and a marker of adherence to dietary and fluid restrictions in hemodialysis patients. Excessive IDWG has been strongly associated with increased cardiovascular risk, including left ventricular hypertrophy, cardiac dysfunction, and cerebrovascular complications. Additionally, it necessitates more aggressive ultrafiltration, potentially compromising hemodynamic stability, impairing quality of life, and escalating healthcare costs. Despite international guidelines recommending an IDWG target of <4–4.5% of body weight, many patients struggle to achieve this due to barriers in adhering to dietary and fluid restrictions. This review explores the current state-of-the-art strategies to mitigate IDWG and evaluates emerging diagnostic and therapeutic perspectives to improve fluid management in dialysis patients. Methods: A literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science to identify studies on IDWG in hemodialysis. Keywords and MeSH terms were used to retrieve peer-reviewed articles, observational studies, RCTs, meta-analyses, and systematic reviews. Non-English articles, case reports, and conference abstracts were excluded. Study selection followed PRISMA guidelines, with independent screening of titles, abstracts, and full texts. Data extraction focused on IDWG definitions, risk factors, clinical outcomes, and management strategies. Due to study heterogeneity, a narrative synthesis was performed. Relevant data were synthesized thematically to evaluate both established strategies and emerging perspectives. Results: The current literature identifies three principal strategies for IDWG control: cognitive–behavioral interventions, dietary sodium restriction, and dialysis prescription adjustments. While educational programs and behavioral counseling improve adherence, their long-term effectiveness remains constrained by patient compliance and logistical challenges. Similarly, low-sodium diets, despite reducing thirst, face barriers to adherence and potential nutritional concerns. Adjustments in dialysate sodium concentration have yielded conflicting results, with concerns regarding hemodynamic instability and intradialytic hypotension. Given these limitations, alternative approaches are emerging. Thirst modulation strategies, including chewing gum to stimulate salivation and acupuncture for autonomic regulation, offer potential benefits in reducing excessive fluid intake. Additionally, technological innovations, such as mobile applications and telemonitoring, enhance self-management by providing real-time feedback on fluid intake. Biofeedback-driven dialysis systems enable dynamic ultrafiltration adjustments, improving fluid removal efficiency while minimizing hemodynamic instability. Artificial intelligence (AI) is advancing predictive analytics by integrating wearable bioimpedance sensors and dialysis data to anticipate fluid overload and refine individualized dialysis prescriptions, driving precision-based volume management. Finally, optimizing dialysis frequency and duration has shown promise in achieving better fluid balance and cardiovascular stability, suggesting that a personalized, multimodal approach is essential for effective IDWG management. Conclusions: Despite decades of research, IDWG remains a persistent challenge in hemodialysis, requiring a multifaceted, patient-centered approach. While traditional interventions provide partial solutions, integrating thirst modulation strategies, real-time monitoring, biofeedback dialysis adjustments, and AI-driven predictive tools represent the next frontier in fluid management. Future research should focus on long-term feasibility, patient adherence, and clinical efficacy, ensuring these innovations translate into tangible improvements in quality of life and cardiovascular health for dialysis patients. Full article

Multiple myeloma (MM), the second most common hematologic cancer, remains an incurable malignancy, characterized by an initial response to therapy followed by successive relapses. The upfront treatment typically involves induction therapy, autologous stem cell transplantation for eligible patients, and long-term maintenance therapy. It is important to note that the anticipated duration of myeloma response diminishes with each subsequent relapse. Therefore, the first relapse represents a critical juncture in treatment, where refractoriness to key drug classes emerges as a significant challenge. Addressing the optimal management in this setting requires careful consideration of disease biology, prior therapies, and patient-specific factors to optimize outcomes. Cilta-cel, a chimeric antigen receptor T-cell construct, has emerged as the most promising therapeutic option at first relapse, resulting in long-term remissions with a significant treatment-free interval. However, availability and accessibility are not universal and treatment logistics are complex. Triplet regimens based on carfilzomib, pomalidomide or selinexor, remain the cornerstone of treatment at first relapse, whereas the optimal combination is based on refractoriness to prior drugs, especially anti-CD38 monoclonal antibodies and lenalidomide, and patient comorbidities. With the rapidly expanding therapeutic landscape, clinicians face increasing complexity in selecting the most appropriate regimens for individual patients. This review aims to guide clinicians through these evolving options by consolidating evidence-based strategies and highlighting emerging therapies, ensuring a personalized approach to managing first-relapse MM. Full article

Artificial intelligence has increasingly taken over various aspects of daily life, resulting in the proliferation of smart devices and the development of smart living and working environments. One significant domain within this technological advancement is human activity recognition, which includes a broad spectrum of applications such as patient monitoring and supervision of children’s activities. In this research, we endeavor to design a human activity recognition system that effectively analyzes multi-user data through a machine learning framework centered on graphical plot images. The proposed methodology uses a PIR sensor-based system. This system uses a two-stage process; the first one involves generating new image datasets as density map images and graphical representations based on the Kyoto CASAS multi-user dataset. In the second stage, the generated data are provided to a sequential convolutional neural network, which predicts the 16 activities developed by two users. To generate the new datasets, we only used data from ambient sensors, which were organized in windows. We tested many types of window dimensions and extra features such as temporal aspect and the limitation of two activities in one window. The neural network was optimized by increasing the deconvolutional layers and adding the AdamW optimizer. The results demonstrate the viability of this method, evidencing an accuracy rate of 83% for multi-user activity and an accuracy rate of 99% for single-user activity. This study successfully achieved its objective of identifying an efficient activity recognition methodology and a data image representation. Furthermore, future enhancements are anticipated by integrating data sourced from PIR sensors, with information gathered from user-personal devices such as smartphones. This approach is also applicable to real-time recognition systems. Full article

Inflammatory bowel disease (IBD) management stands at the cusp of a transformative era, with recent breakthroughs heralding a paradigm shift in treatment strategies. Traditionally, IBD therapeutics revolved around immunosuppressants, but the landscape has evolved significantly. Recent approvals of etrasimod, upadacitinib, mirikizumab, and risankizumab have introduced novel mechanisms of action, offering renewed hope for IBD patients. These medications represent a departure from the status quo, breaking years of therapeutic stagnation. Precision medicine, involving Artificial Intelligence, is a pivotal aspect of this evolution, tailoring treatments based on genetic profiles, disease characteristics, and individual responses. This approach optimizes treatment efficacy, and paves the way for personalized care. Yet, the rising cost of IBD therapies, notably biologics, poses challenges, impacting healthcare budgets and patient access. Ongoing research strives to assess cost-effectiveness, guiding policy decisions to ensure equitable access to advanced treatments. Looking ahead, the future of IBD management holds great promise. Emerging therapies, precision medicine, and ongoing research into novel targets promise to reshape the IBD treatment landscape. As these advances continue to unfold, IBD patients can anticipate a brighter future, one marked by more effective, personalized, and accessible treatments. Full article

The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to AM. Advanced AI algorithms analyze patient-specific data such as body scans, movement patterns, and medical history to design customized assistive devices, orthoses, and prosthetics. This synergy enables the rapid prototyping and production of highly optimized solutions, improving comfort, functionality, and therapeutic outcomes. Machine learning (ML) models further streamline the process by anticipating biomechanical needs and adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative design and topology optimization to create lightweight yet durable structures that are ideally suited to the patient’s anatomy and rehabilitation goals .AI-based AM also facilitates the production of multi-material devices that combine flexibility, strength, and sensory capabilities, enabling improved monitoring and support during physical therapy. New perspectives include integrating smart sensors with printed devices, enabling real-time data collection and feedback loops for adaptive therapy. Additionally, these solutions are becoming increasingly accessible as AM technology lowers costs and improves, democratizing personalized healthcare. Future advances could lead to the widespread use of digital twins for the real-time simulation and customization of rehabilitation devices before production. AI-based virtual reality (VR) and augmented reality (AR) tools are also expected to combine with AM to provide immersive, patient-specific training environments along with physical aids. Collaborative platforms based on federated learning can enable healthcare providers and researchers to securely share AI insights, accelerating innovation. However, challenges such as regulatory approval, data security, and ensuring equity in access to these technologies must be addressed to fully realize their potential. One of the major gaps is the lack of large, diverse datasets to train AI models, which limits their ability to design solutions that span different demographics and conditions. Integration of AI–AM systems into personalized rehabilitation and physical therapy should focus on improving data collection and processing techniques. Full article

The ability to automate and personalize the recommendation of multimedia contents to consumers has been gaining significant attention recently. The burgeoning demand for digitization and automation of formerly analog communication processes has caught the attention of researchers and professionals alike. In light of the recent interest and anticipated transition to fully autonomous vehicles, this study proposes a text–image embedding method recommender system for the optimization of personalized multimedia content for in-vehicle infotainment. This study leverages existing pre-trained text embedding models and pre-trained image feature extraction methods. Previous research to date has focused mainly on textual-only or image-only analyses. By employing similarity measurements, this study demonstrates how recommendation of the most relevant multimedia content to consumers is enhanced through text–image embedding. Full article