Search Results (34,483)

Hair loss disorders, particularly androgenetic alopecia (AGA), are common conditions that carry significant psychosocial impact. Current standard therapies, including minoxidil, finasteride, and hair transplantation, primarily slow progression or re-distribute existing follicles and do not regenerate lost follicular structures. In recent years, regenerative medicine has been associated with a gradual shift toward approaches that aim to restore follicular function and architecture. Stem cell-derived conditioned media and exosomes have shown the ability to activate Wnt/β-catenin signaling, enhance angiogenesis, modulate inflammation, and promote dermal papilla cell survival, resulting in improved hair density and shaft thickness with favorable safety profiles. Autologous cell-based therapies, including adipose-derived stem cells and dermal sheath cup cells, have demonstrated the potential to rescue miniaturized follicles, although durability and standardization remain challenges. Adjunctive interventions such as microneedling and platelet-rich plasma (PRP) further augment follicular regeneration by inducing controlled micro-injury and releasing growth and neurotrophic factors. In parallel, machine learning-based diagnostic tools and deep hair phenotyping offer improved severity scoring, treatment monitoring, and personalized therapeutic planning, while robotic Follicular Unit Excision (FUE) platforms enhance surgical precision and graft preservation. Advances in tissue engineering and 3D follicle organoid culture suggest progress toward producing transplantable follicle units, though large-scale clinical translation is still in early development. Collectively, these emerging biological and technological strategies indicate movement beyond symptomatic management toward more targeted, multimodal approaches. Future progress will depend on standardized protocols, regulatory clarity, and long-term clinical trials to define which regenerative approaches can reliably achieve sustainable follicle renewal in routine cosmetic dermatology practice. Full article

As the global transition toward carbon neutrality accelerates, the sustainability of power electronics has received growing attention from both academia and industry. Nevertheless, standardized methodologies for evaluating the sustainability of power electronic systems—particularly traction inverters—remain limited, largely due to the absence of comprehensive databases and unified assessment frameworks. Leveraging industrial extensive design experience, this paper presents an enhanced methodology for sustainability evaluation of traction inverters. The proposed framework combines advanced component-level modelling with multi-physics-based analysis to more accurately quantify the environmental impacts associated with different power semiconductor technologies. A Random Forest (RF)-based algorithm is employed for junction temperature (T_J) estimation, offering reliable thermal data crucial for sustainability assessment. Experimental validation on a prototype automotive inverter confirms the accuracy and robustness of the RF-based T_J estimation approach, ensuring realistic thermal–environmental coupling within the evaluation workflow. From a thermal perspective, the sizing of power electronics key components (PEKCs) is performed with high precision, enabling a more accurate estimation of power electronics-related material (PERM) usage. Combined with a preliminary CO₂-equivalent (CO₂e) emissions database, this allows sustainability assessment to be integrated directly into the design stage of the traction inverter. The effectiveness of the proposed approach is demonstrated through a comparative evaluation of three representative inverter topologies. Full article

Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally with diverse objects in dynamic environments. Building on this foundation, the paper presents a vision-guided grasping algorithm for a prosthetic hand, integrating perception, planning, and control for dexterous manipulation. A camera mounted on the set up captures the scene, and a Bounding Volume Hierarchy (BVH)-based vision algorithm is employed to segment an object for grasping and define its bounding box. Grasp contact points are then computed by generating candidate trajectories using Rapidly-exploring Random Tree Star (RRT*) algorithm, and selecting fingertip end poses based on the minimum Euclidean distance between these trajectories and the object’s point cloud. Each finger’s grasp pose is determined independently, enabling adaptive, object-specific configurations. Damped Least Square (DLS) based Inverse kinematics solver is used to compute the corresponding joint angles, which are subsequently transmitted to the finger actuators for execution. Our intention in this work was to present a proof-of-concept pipeline demonstrating that fingertip poses derived from a simple, computationally lightweight geometric representation, specifically an AABB-based segmentation can be successfully propagated through per-finger planning and executed in real time on the Linker Hand O7 platform. The proposed method is validated in simulation, and experimental integration on a Linker Hand O7 platform. Full article

Late-onset sporadic Alzheimer’s disease (LOAD) is the most common form of dementia. The disease is characterized by progressive loss of memory and behavioral changes followed by neurodegeneration of all cortical areas. While the contribution of genetic and environmental factors is important, advanced aging remains the most important disease risk factor. Because LOAD does not naturally occur in most animal species, except humans, studies have traditionally relied on the use of transgenic mouse models recapitulating early-onset familial Alzheimer’s disease (EOAD). Hence, the development of more representative LOAD models through reprograming of patient-derived cells into neuronal, glial, and immune cells became a necessity to better understand the disease’s origin and pathophysiology. Herein, and focusing on neurons, we review current work in the field and compare results obtained with two different reprograming methods to generate LOAD patient’s neuronal cells: the induced pluripotent stem cell and induced neuron technologies. We also evaluate if these models can faithfully mimic cellular and molecular pathologies observed in LOAD patients’ brains. Full article

The discovery and development of new drugs is a lengthy, complex, and costly process, often requiring 10–20 years to progress from initial concept to market approval, with clinical trials representing the most resource-intensive stage. In recent years, Artificial Intelligence (AI) has emerged as a transformative technology capable of reshaping the entire pharmaceutical research and development (R&D) pipeline. The purpose of this narrative review is to examine the role of AI in drug discovery and development, highlighting its contributions, challenges, and future implications for pharmaceutical sciences and global public health. A comprehensive review of the scientific literature was conducted, focusing on published studies, reviews, and reports addressing the application of AI across the stages of drug discovery, preclinical development, clinical trials, and post-marketing surveillance. Key themes were identified, including AI-driven target identification, molecular screening, de novo drug design, predictive toxicity modelling, and clinical monitoring. The reviewed evidence indicates that AI has significantly accelerated drug discovery and development by reducing timeframes, costs, and failure rates. AI-based approaches have enhanced the efficiency of target identification, optimized lead compound selection, improved safety predictions, and supported adaptive clinical trial designs. Collectively, these advances position AI as a catalyst for innovation, particularly in promoting accessible, efficient, and sustainable healthcare solutions. However, substantial challenges remain, including reliance on high-quality and representative biomedical data, limited algorithmic transparency, high implementation costs, regulatory uncertainty, and ethical and legal concerns related to data privacy, bias, and equitable access. In conclusion, AI represents a paradigm shift in pharmaceutical research and drug development, offering unprecedented opportunities to improve efficiency and innovation. Addressing its technical, ethical, and regulatory limitations will be essential to fully realize its potential as a sustainable and globally impactful tool for therapeutic innovation. Full article

Near-infrared (NIR) spectroscopy has emerged as a pivotal non-destructive analytical technique within the cheese industry, offering rapid and precise insights into the chemical composition and quality attributes of various cheese types. This review explores the evolution of NIR spectral sensors, highlighting key technological advancements and their integration into cheese production processes as well as final products already in markets. In addition, the review discusses challenges such as calibration complexities, the influence of sample heterogeneity and the need for robust data and interpretation models through spectroscopy coupled with AI methods. The future potential of NIR spectral sensors, including real-time in-line monitoring and the development of portable devices for on-site analysis, is also examined. This review aims to provide a critical assessment of current NIR spectral sensors and their impact on the cheese industry, offering insights for researchers and industry professionals aiming to enhance quality control and innovation in cheese production, as well as authenticity and fraud studies. The review concludes that the integration of advanced NIR spectroscopy with AI represents a transformative approach for the cheese industry, enabling more accurate, efficient and sustainable quality assessment practices that can strengthen both production consistency and consumer trust. Full article

Driven by the energy transition within the framework of the United Nations Framework Convention on Climate Change, second-generation (2G) ethanol stands out as a technical and sustainable alternative to fossil fuels. Although first-generation ethanol, produced from saccharine and starchy feedstocks, represents an advance in mitigating emissions, its expansion is limited by competition with areas destined for food production. In this context, 2G ethanol, obtained from residual lignocellulosic biomass, emerges as a strategic route for diversifying and expanding the renewable energy matrix. Thus, this work discusses the current state of 2G ethanol technology based on the gradual growth in production and the consolidation of this route over the last few years. Industrial second-generation ethanol plants operating around the world demonstrate the high potential of agricultural waste as a raw material, particularly corn straw in the United States, which offers a lower cost and significant yield in the production of this biofuel. Similarly, in Brazil, sugarcane by-products, especially bagasse and straw, are consolidating as the main sources for 2G ethanol, integrated into the biorefinery concept and the valorization of by-products obtained during the 2G ethanol production process. However, despite the wide availability of lignocellulosic biomass and its high productive potential, the consolidation of 2G ethanol is still conditioned by technical and economic challenges, especially the high costs associated with pretreatment stages and enzymatic cocktails, as well as the formation of inhibitory compounds that compromise the efficiency of the process. Genetic engineering plays a particularly important role in the development of microorganisms to produce more efficient enzymatic cocktails and to ferment hexoses and pentoses (C₆ and C₅ sugars) into ethanol. In this scenario, not only are technological limitations important but also public policies and tax incentives, combined with the integration of the biorefinery concept and the valorization of (by)products, which prove fundamental to reducing costs, increasing process efficiency, and ensuring the economic viability and sustainability of second-generation ethanol. Full article

This study presents a systematic literature review exploring the challenges and solutions for scaling 3D printing in affordable residential construction in Kuwait. This review explores the urgent need to alleviate housing shortages through faster, cost-effective, and sustainable building approaches, highlighting the potential of additive manufacturing. Guided by the PRISMA framework, this review synthesizes findings from 20 key sources selected from an initial pool of 141 studies. The analysis identifies major scalability challenges—high material costs, limited supply chain readiness, complex regulatory frameworks, environmental constraints, and technical limitations—and evaluates proposed solutions such as geopolymer concrete, advanced printing technologies, and policy reforms. While this study does not include empirical data, it offers a comprehensive synthesis of the existing literature to inform policymakers and industry leaders about the potential of 3D printing to address Kuwait’s housing crisis. Full article

Background: Mutations in the KRAS gene play a pivotal role in lung cancer development and progression and are becoming increasingly important in therapeutic decision-making. The detection of these mutations in circulating tumor DNA (ctDNA) has attracted attention as a minimally invasive diagnostic approach. However, the accuracy reported in different studies varies widely. Methods: We conducted a systematic review and meta-analysis in accordance with the PRISMA-DTA guidelines. Eligible studies evaluated the detection of KRAS mutations in ctDNA in plasma or serum for lung cancer diagnosis and reported sufficient data to construct 2 × 2 contingency tables. Primary pooled estimates of sensitivity, specificity and likelihood ratios were calculated using aggregated 2 × 2 contingency tables. Additionally, a bivariate random-effects model was applied in a secondary analysis to investigate between-study heterogeneity. Results: Nine diagnostic study arms comprising 691 patients met the inclusion criteria. Across all datasets, there were 255 true positives, 19 false positives, 136 false negatives, and 281 true negatives. The pooled sensitivity was 65.2%, while the pooled specificity was 93.7%. The positive likelihood ratio was 10.35, and the negative likelihood ratio was 0.37, resulting in a diagnostic odds ratio of 28.0, which indicates strong rule-in capability. Sensitivity showed moderate heterogeneity across studies. In contrast, specificity demonstrated minimal heterogeneity. Conclusions: ctDNA-based detection of KRAS mutations demonstrates high specificity but moderate sensitivity for diagnosing lung cancer. These findings suggest that a KRAS liquid biopsy could be a valuable complementary diagnostic tool, particularly when a tissue biopsy is not possible or is inadequate, and it could support more personalized decision-making as analytical technologies continue to advance. Full article

In the rapidly evolving digital economy, the expansion of business-to-business e-commerce ecosystems has compelled traditional industries to integrate into digital supply chains to achieve sustainable development. Industrial e-commerce is no longer limited to online transactions but extends to the digital transformation of backend operations. Drawing upon the perspective of the digital business ecosystem, this study investigates how digital supply chain integration, manifested through digital transformation, impacts energy efficiency. By utilizing a panel fixed effects model and advanced text mining techniques on a dataset of 721 listed firms in the resource-intensive sectors of China spanning from 2011 to 2023, this research constructs a novel index to quantify corporate digital maturity based on semantic analysis. The empirical results demonstrate that digital transformation significantly enhances energy efficiency by facilitating optimized resource allocation and data-driven decision making required by modern digital markets. Mechanism analysis reveals that green innovation functions as a pivotal mediator that bridges the gap between digital investments and environmental performance. Furthermore, this relationship is found to be contingent upon corporate social responsibility strategies, ownership structures, and the scale of the firm. This study contributes to the electronic commerce literature by elucidating how traditional manufacturers can leverage digital technologies and green innovation to navigate the twin transition of digitalization and sustainability, offering theoretical implications for platform governance in industrial sectors. Full article

Pseudouridine (Ψ), the most abundant RNA modification, plays essential roles in shaping RNA structure, stability, and translational output. Beyond cancer, Ψ is dynamically regulated across numerous physiological and pathological contexts—including immune activation, metabolic disorders, stress responses, and pregnancy-related conditions such as preeclampsia—where elevated Ψ levels reflect intensified RNA turnover and modification activity. These broad functional roles highlight pseudouridylation as a central regulator of cellular homeostasis. Emerging evidence demonstrates that Ψ dysregulation contributes directly to the development and progression of several women’s cancers, including breast, ovarian, endometrial, and cervical malignancies. Elevated Ψ levels in tissues, blood, and urine correlate with tumor burden, metastatic potential, and therapeutic responsiveness. Aberrant activity of Ψ synthases such as PUS1, PUS7, and the H/ACA ribonucleoprotein component dyskerin alters pseudouridylation patterns across multiple RNA substrates, including rRNA, tRNA, mRNA, snoRNAs, and ncRNAs. These widespread modifications reshape ribosome function, modify transcript stability and translational efficiency, reprogram RNA–protein interactions, and activate oncogenic signaling programs. Advances in high-resolution, site-specific Ψ mapping technologies have further revealed mechanistic links between pseudouridylation and malignant transformation, highlighting how modification of distinct RNA classes contributes to altered cellular identity and tumor progression. Collectively, Ψ and its modifying enzymes represent promising biomarkers and therapeutic targets across women’s cancers, while also serving as sensitive indicators of diverse non-cancer physiological and disease states. Full article

Background and Objectives: Given the non-specificity of symptoms and complex methods for diagnosing heart failure, which are not applicable in screening, it is of great importance to develop a simple screening method for identifying systolic dysfunction of the heart based on available biosignals, one of which is a single-channel electrocardiogram (ECG). The method does not require the participation of medical staff. Aim: To create a screening model for detecting left ventricular systolic dysfunction in a complex analysis of single-channel ECG parameters using machine learning algorithms Methods: We included 624 patients aged 18 to 90 years. All patients underwent echocardiography and single-channel I-lead ECG recording using a portable electrocardiograph. The left ventricle ejection fraction (LV EF) was determined in the apical 2-chamber and 4-chamber view using the BIPLANE Simpson method and confirmed by two independent experts. Single-channel ECG analysis was performed using advanced signal processing and machine learning techniques. Results: For identifying LV EF below 52% in men and below 54% in women, the best result was demonstrated by “Lasso regression”: sensitivity 79.2%, specificity 81.7%, AUC = 0.849. For detection of LVEF below 40%, the “Extra Trees” model was the best, with a sensitivity of 83.1% and a specificity of 82.7%, AUC = 0.972. External testing of the algorithm was conducted on a sample of 600 patients. The accuracy was 98%, specificity 98.4%, and sensitivity 93.5%. Conclusions: The results indicate quite high diagnostic accuracy of screening for left ventricular systolic dysfunction when analyzing single-channel ECG parameters using modern signal processing and machine learning technologies. Full article

This study investigates how emotionally structured online interaction networks shape consumer perception of new energy vehicle (NEV) technology. Drawing on discussion forum data from two leading NEV brands, Brand_T and Brand_B, we focus on how users respond to brand technological narratives and how these responses translate into distinct patterns of peer-to-peer interaction. Using a behavioral network analysis framework, we integrate sentiment analysis, topic modeling, and Exponential Random Graph Modeling (ERGM) to uncover the psychological and structural mechanisms underlying consumer engagement. Three main findings emerge. First, users display brand-specific emotional-cognitive profiles: Brand_T communities show broader technological engagement but more heterogeneous emotional responses, whereas Brand_B communities exhibit more emotionally aligned discussions. Second, emotional homophily is a robust driver of interaction ties, particularly in Brand_B forums, where positive sentiment clusters into dense and supportive discussion subnetworks. Third, perceived technological benefits, rather than risk sensitivity, are consistently associated with higher interaction intensity, underscoring the motivational salience of anticipated gains over cautionary concerns in shaping engagement behavior. The study contributes to behavioral science and transportation behavior research by linking consumer sentiment, cognition, and social interaction dynamics in digital environments, offering an integrated theoretical account that bridges the Elaboration Likelihood Model, social identity processes, and behavioral network formation. This advances the understanding of technology perception from static individual evaluations to dynamic, group-structured outcomes. It highlights how emotionally patterned interaction networks can reinforce or recalibrate technology-related perceptions, offering practical implications for NEV manufacturers and policymakers seeking to design psychologically informed communication strategies that support sustainable technology adoption. Full article

As the core technology of the new energy revolution, lithium-ion batteries have broad development prospects and significant strategic importance. With continuous improvements in energy density, enhanced safety, and breakthroughs in fast-charging technology, lithium-ion batteries will play a more substantial role in fields such as new energy vehicles and energy storage. Nevertheless, the development of the lithium-ion battery industry still faces safety issues related to thermal runaway risks. The intense exothermic reactions during thermal runaway can release flammable gases, potentially leading to uncontrolled combustion or explosions, thereby posing major safety threats. This paper reviews the analysis of gas composition and patterns during lithium-ion battery thermal runaway under different conditions, as well as research on gas explosion characteristics. It introduces advanced methods for gas detection and suppression during thermal runaway and summarizes studies on the chemical kinetic mechanisms and predictive models of gas generation during thermal runaway. These studies provide a scientific basis for improving the reliability of renewable energy storage systems and formulating and refining battery safety standards. Full article

The entrepreneurial ecosystem theory posits that regional development emerges from synergistic interactions among entrepreneurs, institutions, and markets. This study positions entrepreneurial vitality as the core catalyst synchronizing digital inclusive finance (DIF) with rural revitalization—two systems often advancing in isolation, leading to unbalanced rural development. Using a coupling coordination degree model and provincial panel data from China (2011–2020), we demonstrate that entrepreneurial vitality significantly strengthens DIF–rural revitalization coupling coordination, following a nonlinear threshold pattern. Coordination gains accelerate only after vitality passes empirically identified critical levels, explaining persistent regional disparities in coupling coordination. Furthermore, the vitality–coordination link is moderated by technological infrastructure, organizational electronic commerce (e-commerce) engagement, and regional economic development, as outlined by the Technology–Organization–Environment framework. Framing DIF as an e-commerce-related ICT input, this paper advances the entrepreneurial ecosystem, e-commerce, and ICT-for-development (ICT4D) literature by revealing the threshold-driven nature of resource coordination in rural contexts. The findings offer a contextualized framework for catalyzing balanced and inclusive rural development in emerging economies. Full article