Search Results (60)

Background: Hearing loss results from diverse biological insults along the auditory pathway, including sensory hair cell death, neural degeneration, and central auditory processing deficits. Implantable auditory neuroprostheses, such as cochlear and brainstem implants, aim to restore hearing by directly stimulating neural structures. Advances in neurobiology and device technology underpin the development of more sophisticated implants tailored to the biological complexity of auditory dysfunction. Aim: This narrative review of reviews aims to map the integration of artificial intelligence (AI) in auditory neuroprosthetics, analyzing recent research trends, key thematic areas, and the opportunities and challenges of AI-enhanced devices. By synthesizing biological and computational perspectives, it seeks to guide future interdisciplinary efforts toward more adaptive and biologically informed hearing restoration solutions. Methods: This narrative review analyzed recent literature reviews from PubMed and Scopus (last 5 years), focusing on AI integration with auditory neuroprosthetics and related biological processes. Emphasis was placed on studies linking AI innovations to neural plasticity and device–nerve interactions, excluding purely computational works. The ANDJ (a standard narrative review checklist) checklist guided a transparent, rigorous narrative approach suited to this interdisciplinary, rapidly evolving field. Results and discussion: Eighteen recent review articles were analyzed, highlighting significant advancements in the integration of artificial intelligence with auditory neuroprosthetics, particularly cochlear implants. Established areas include predictive modeling, biologically inspired signal processing, and AI-assisted surgical planning, while emerging fields such as multisensory augmentation and remote care remain underexplored. Key limitations involve fragmented biological datasets, lack of standardized biomarkers, and regulatory challenges related to algorithm transparency and clinical application. This review emphasizes the urgent need for AI frameworks that deeply integrate biological and clinical insights, expanding focus beyond cochlear implants to other neuroprosthetic devices. To complement this overview, a targeted analysis of recent cutting-edge studies was also conducted, starting from the emerging gaps to capture the latest technological and biological innovations shaping the field. These findings guide future research toward more biologically meaningful, ethical, and clinically impactful solutions. Conclusions: This narrative review highlights progress in integrating AI with auditory neuroprosthetics, emphasizing the importance of biological foundations and interdisciplinary approaches. It also recognizes ongoing challenges such as data limitations and the need for clear ethical frameworks. Collaboration across fields is vital to foster innovation and improve patient care. Full article

Spinal cord injury (SCI) remains one of the toughest obstacles in neuroscience and regenerative medicine due to both severe functional loss and limited healing ability. This article aims to provide a key integrative, mechanism-focused review of the molecular landscape of SCI and the new disruptive therapy technologies that are now evolving in the SCI arena. Our goal is to unify a fundamental pathophysiology of neuroinflammation, ferroptosis, glial scarring, and oxidative stress with the translation of precision treatment approaches driven by artificial intelligence (AI), CRISPR-mediated gene editing, and regenerative bioengineering. Drawing upon advances in single-cell omics, systems biology, and smart biomaterials, we will discuss the potential for reprogramming the spinal cord at multiple levels, from transcriptional programming to biomechanical scaffolds, to change the course from an irreversible degeneration toward a directed regenerative pathway. We will place special emphasis on using AI to improve diagnostic/prognostic and inferred responses, gene and cell therapies enabled by genomic editing, and bioelectronics capable of rehabilitating functional connectivity. Although many of the technologies described below are still in development, they are becoming increasingly disruptive capabilities of what it may mean to recover from an SCI. Instead of prescribing a particular therapeutic fix, we provide a future-looking synthesis of interrelated biological, computational, and bioengineering approaches that conjointly chart a course toward adaptive, personalized neuroregeneration. Our intent is to inspire a paradigm shift to resolve paralysis through precision recovery and to be grounded in a spirit of humility, rigor, and an interdisciplinary approach. Full article

The process of obtaining Agave L. fibers dates back to pre-Hispanic times, and although humans have obtained different products from this crop, to date, the impact of humans (artificial selection, domestication and intensive cultivation) on these species is unknown. In this study, the expression of the CesA gene was evaluated in three species, namely, Agave L, A. sisalana Perrine and A. fourcroydes Lem. (Sac ki), both of which are used for fiber production, and Agave tequilana Weber. The results revealed that, compared with A. fourcroydes and A. tequilana, A. sisalana had a greater leaf area, a significantly greater cellulose content and a greater number of cellulose fibrils. In terms of cell organization, the number and size of sclerenchyma fibers were similar between A. sisalana and A. fourcroydes. However, the relative expression of the CesA gene was five times greater in A. fourcroydes than in A. sisalana and A. tequilana, in contrast with the number of copies in those genomes. In addition, the tertiary structure of the CESA protein in fiber-producing species was modeled, placing agaves in a group along with Populus, Linum, Corchorus and Boehmeria. The haplotype network analysis revealed that A. tequilana is closely grouped with species of the order Poales, unlike the rest of the fiber-producing agaves, which formed a unique cluster. These findings suggest that artificial selection by humans, for various purposes, has contributed to the specialization of genes associated with traits such as fiber production. Full article

The article describes a mathematical model of methanol steam reforming taking place at the anode of a molten carbonate fuel cell (MCFC). An artificial neural network with an appropriate structure was subjected to a learning process on the data obtained during an experiment on the laboratory stand for testing high-temperature fuel cells located at the Institute of Heat Engineering of the Warsaw University of Technology. The backpropagation of error method was used to train the neural network. The training data included the results of methanol steam reforming in the fuel cell for steam-to-carbon ratios of 2:1, 3:1, and 4:1. The artificial neural network was then asked to generate results for other steam-to-carbon ratios. As a result, the artificial neural network predicted that the highest power density for a molten carbonate fuel cell working on methanol would be obtained with a steam-to-carbon ratio of 2.8:1. The article’s key achievement is the application of artificial intelligence to calculate an unusual steam-to-carbon ratio for the methanol steam reforming process occurring directly at the anode of an MCFC fuel cell. The solution proposed in the article contributed to reducing the number of experimental studies. Full article

This paper will examine a methodology to enable the usage of Human Digital Twins (HDTs) for Quality Assurance in the aerospace manufacturing domain. Common-place hardware and infrastructure, including cloud-based facility security cameras, cloud-based commercial virtual environments, a virtual reality (VR) headset, and artificial intelligence (AI) detection algorithms, have been connected via application programming interfaces (API) to enable a 24-h surveillance and feedback capability for a representative aerospace manufacturing cell. Human operators who perform defined manufacturing assembly operations in real life in the cell can utilize this methodology to digitize their performance and provide objective evidence of conformity and safety messaging for their human-centric manufacturing operation in real time. The digitization of real human-centric performance using this methodology creates the foundation for a HDT. This paper will present the application of HDTs in a manner that can easily be scaled across manufacturing operations while utilizing technologies that are already commonly inserted into existing manufacturing operations, which facilitates the exploration of HDT concepts without the need for expensive capital purchases and emerging technologies. Full article

The present article focuses on the characterization of the new biocomposites of poly(butylene succinate) (PBS) with fillers of plant origin such as onion peels (OP) and durum wheat bran WB (Triricum durum) subjected to composting and artificial aging. The susceptibility to fungal growth, cytotoxicity and antibacterial properties were also examined. The biodegradation of the samples was investigated under normalized conditions simulating an intensive aerobic composting process. It was shown that the tested natural fillers significantly accelerate the biodegradation process of the composition (after 90 days mass loss of PBS 7%) and that the samples with WB degrade much faster (corresponding mass loss 86%) than those containing OP (corresponding mass loss 21%). The remains of the samples after composting were subjected to chemical structure analysis (FTIR), and their thermal properties were determined using differential scanning calorimetry (DSC). It was shown that the degree of crystallinity of PBS and composites increased with the increasing time of composting. In the case of pure PBS, this increase was a maximum of 31.5%, for biocomposite with OP 31.1% and for those containing WB 21.2%. FTIR results showed that cleavage of polymer chains by hydrolysis took place during composting. The tested samples were also subjected to artificial aging under conditions simulating solar radiation and were sprayed with water. After artificial aging, the significant changes in the color of the samples as well as the porosity of their surface was noted, which was mainly due to the effect of photodegradation of both the used OP and WB fillers. Additionally, FTIR analysis indicated that samples were degraded by photooxidation processes. The ability of fungi to grow on the surface of the samples was also tested. The results demonstrate the possibility of using the developed biocomposite materials as a carbon source for the growth of fungi. The antibacterial tests showed that samples containing OP exhibited strong antibacterial properties regardless of their wt.% content. Additionally, a cytotoxicity test was performed on a BJ cell line, demonstrating that none of the tested biocomposites were cytotoxic. Moreover, those with the addition of WB statistically significantly supported the viability of both fibroblast and bacteria cells, showing their biological safety but lack of antibacterial activity. Full article

The anterior cruciate ligament (ACL) is one of the most injured ligaments, with approximately 100,000 ACL reconstructions taking place annually in the United States. In order to successfully manage ACL rupture, it is of the utmost importance to understand the anatomy, unique physiology, and biomechanics of the ACL, as well as the injury mechanisms and healing capacity. Currently, the “gold standard” for the treatment of ACL ruptures is surgical reconstruction, particularly for young patients or athletes expecting to return to pivoting sports. Although ACL reconstruction boasts a high success rate, patients may face different, serious post-operative complications, depending on the type of graft and technique used in each one of them. Tissue engineering is a multidisciplinary field that could contribute to the formation of a tissue-engineered ACL graft manufactured by a combination of the appropriate stem-cell type, a suitable scaffold, and specific growth factors, combined with mechanical stimuli. In this review, we discuss the aspects that constitute the creation of a successful tissue-engineered graft while also underlining the current drawbacks that arise for each issue. Finally, we highlight the benefits of incorporating new technologies like artificial intelligence and machine learning that could revolutionize tissue engineering. Full article

This study demonstrates the significant advantages of integrating computational fluid dynamics (CFD) with artificial intelligence (AI)-based machine learning (ML) to optimize the pre-reforming process for reversible solid oxide cell (r-SOC) technologies. It places a distinct focus on the relationship between process variables, aiming to enhance the preparation of quality r-SOC-ready fuel, which is an indispensable element for successful operation. Evaluating the intricate thermochemistry of syngas-containing reforming processes involves employing an experimentally validated CFD model. The model serves as the foundation for gathering essential data, crucial for the development and training of AI-based machine learning models. The developed model forecasts and optimizes reforming processes across diverse fuel compositions, encompassing oxygen-containing syngas blends and controlled feedstock outlet process conditions. Impressively, the model’s predictions align closely with CFD outcomes with an error margin as low as 0.34%, underscoring its accuracy and reliability. This research significantly contributes to a deeper understanding and the qualitative enhancement of preparing high-quality syngas for SOC under improved process conditions. Enabling the early availability of valuable information drives forward sustainable research and ensures the safe, consistent operation assessment of r-SOC. Additionally, this strategic approach substantially reduces the need for resource-intensive experiments. Full article

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45$°$ sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel. Full article

Sepsis remains a significant contributor to neonatal mortality worldwide. However, the nonspecific nature of sepsis symptoms in neonates often leads to the necessity of empirical treatment, placing a burden of ineffective treatment on patients. Furthermore, the global challenge of antimicrobial resistance is exacerbating the situation. Artificial intelligence (AI) is transforming medical practice and in hospital settings. AI shows great potential for assessing sepsis risk and devising optimal treatment strategies. Background/Objectives: This review aims to investigate the application of AI in the detection and management of neonatal sepsis. Methods: A systematic literature review (SLR) evaluating AI methods in modeling and classifying sepsis between 1 January 2014, and 1 January 2024, was conducted. PubMed, Scopus, Cochrane, and Web of Science were systematically searched for English-language studies focusing on neonatal sepsis. Results: The analyzed studies predominantly utilized retrospective electronic medical record (EMR) data to develop, validate, and test AI models to predict sepsis occurrence and relevant parameters. Key predictors included low gestational age, low birth weight, high results of C-reactive protein and white blood cell counts, and tachycardia and respiratory failure. Machine learning models such as logistic regression, random forest, K-nearest neighbor (KNN), support vector machine (SVM), and XGBoost demonstrated effectiveness in this context. Conclusions: The summarized results of this review highlight the great promise of AI as a clinical decision support system for diagnostics, risk assessment, and personalized therapy selection in managing neonatal sepsis. Full article

The sliding wear of Ti-6Al-4V alloys coexisting with dental amalgam in a simulated temperature-controlled cell was evaluated. Disc-shaped samples of Ti-6Al-4V (n = 30) and spherical silver amalgam (n = 30) were prepared. Discs were subjected to wear while immersed in artificial and fluoridated saliva as follows: Ti-6Al-4V–Ti-6Al-4V (G1); amalgam–amalgam (G2), and Ti-6Al-4V–amalgam (G3). Samples were analyzed for mass variation, volume loss, and surface roughness. Wear tracks were characterized by scanning electron microscopy. Wearing induced significant mass loss for all groups except G3 in fluoridated saliva: Ti-6Al-4V (p = 0.045) and amalgam (p = 0.732). These samples presented an increase in mean surface roughness (p = 0.032 and 0.010, respectively). Overall, Ti-6Al-4V showed 0.07 mm³ (95% CI: [0.06–0.07]) higher wear track volume. Ti-6Al-4V has a higher mass loss when subjected to fluoridated media but no significant roughness variation. Fluor-containing substances should be avoided over Ti-6Al-4V alloys placed in areas of mechanical wear, especially if dental amalgam is also present. Full article

This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer. Full article

Background: The perception of tactile-stimulation locations is an important function of the human somatosensory system during body movements and its interactions with the surroundings. Previous psychophysical and neurophysiological studies have focused on spatial location perception of the upper body. In this study, we recorded single-trial electroencephalography (EEG) responses evoked by four vibrotactile stimulators placed on the buttocks and thighs while the human subject was sitting in a chair with a cushion. Methods: Briefly, 14 human subjects were instructed to sit in a chair for a duration of 1 h or 1 h and 45 min. Two types of cushions were tested with each subject: a foam cushion and an air-cell-based cushion dedicated for wheelchair users to alleviate tissue stress. Vibrotactile stimulations were applied to the sitting interface at the beginning and end of the sitting period. Somatosensory-evoked potentials were obtained using a 32-channel EEG. An artificial neural net was used to predict the tactile locations based on the evoked EEG power. Results: We found that single-trial beta (13–30 Hz) and gamma (30–50 Hz) waves can best predict the tactor locations with an accuracy of up to 65%. Female subjects showed the highest performances, while males’ sensitivity tended to degrade after the sitting period. A three-way ANOVA analysis indicated that the air-cell cushion maintained location sensitivity better than the foam cushion. Conclusion: Our finding shows that tactile location information is encoded in EEG responses and provides insights on the fundamental mechanisms of the tactile system, as well as applications in brain–computer interfaces that rely on tactile stimulation. Full article

There is currently growing interest in the creation of artificial microbial consortia, especially in the field of developing and applying various bioremediation processes. Heavy metals, dyes, synthetic polymers (microplastics), pesticides, polycyclic aromatic hydrocarbons and pharmaceutical agents are among the pollutants that have been mainly targeted by bioremediation based on various consortia containing fungi (mycelial types and yeasts). Such consortia can be designed both for the treatment of soil and water. This review is aimed at analyzing the recent achievements in the research of the artificial microbial consortia that are useful for environmental and bioremediation technologies, where various fungal cells are applied. The main tendencies in the formation of certain microbial combinations, and preferences in their forms for usage (suspended or immobilized), are evaluated using current publications, and the place of genetically modified cells in artificial consortia with fungi is assessed. The effect of multicomponence of the artificial consortia containing various fungal cells is estimated, as well as the influence of this factor on the functioning efficiency of the consortia and the pollutant removal efficacy. The conclusions of the review can be useful for the development of new mixed microbial biocatalysts and eco-compatible remediation processes that implement fungal cells. Full article

Fire blight, caused by Erwinia amylovora, is one of the most devastating apple diseases. The selection of cultivars of low susceptibility and the study of the genetic mechanisms of the disease play important roles in fire blight management. The susceptibility level to fire blight was evaluated in 102 accessions originating from Asturias, a cider-producing region located in the north of Spain with a wide apple germplasm. Evaluations took place under quarantine conditions using artificial inoculations of grafted plants. The results revealed wide variation in susceptibility responses and low-susceptible cultivars were identified. In addition, 91 cultivars were genotyped using the Affymetrix Axiom^® Apple 480 K SNP array to conduct genome-wide association studies (GWAS). A statistically significant signal was detected on chromosome 10 using the multi-locus mixed model (MLMM). Two genes were identified as major putative candidate genes: a TIR-NBS-LRR class disease protein and a protein containing a development and cell death (DCD) domain. The outcomes of this study provide a promising source of information, particularly in the context of cider apples, and set a starting point for future genetic and breeding approaches. Full article