Search Results (9,484)

This paper outlines the inception, conceptualization and primary morphological selection of a robotic system that employs raw lunar regolith for constructing protective berms and shelters on the Moon’s surface. The lunar regolith is considered the most readily available material for in situ resource utilization on the Moon. The lunar environment is characterized, and the operational task is defined, informing the development of high-level system requirements and a functional analysis through the glass-box method. The key morphological areas are identified, and candidate concepts are evaluated using the Analytic Hierarchy Process (AHP). The evaluation process employs a new approach to aggregating expert data through the ZM${}_{II}$ method to establish priorities of the design criteria, which eliminates the need for pairwise comparisons in data collection. Each criterion is associated with a specific and quantifiable metric, which is then used to evaluate the morphologies during the AHP. The selected morphologies are determined as: a vibrating hopper for intake (normalized decision value of 27.5% out of 5 candidate solutions), a roller system for container deployment and filling (26.2% out of 7), a magnetic RCU interface (22.6% out of 7), and a 4-DoF manipulator to place the RCUs in the environment (23.6% out of 5). The final morphology is selected by combining the decision values across the primary morphological areas into a unified decision metric. This is followed by the preliminary selection of the system’s surrounding architecture. The design conceptualization is performed within a real-life operational scenario, namely, to create a blast berm for the landing pad using the lunar regolith provided by an existing excavator. The next phase of the work will include the system’s detailed design, as well as investigations on the requirements for a variety of construction tasks on the lunar surface. Full article

Flexible and wearable thermoelectric devices can convert body waste heat into electricity, showing a new direction to solve the long-lasting issue of energy supply on portable devices. However, thermoelectric fibers are prone to short circuits and failure due to sweat stains and washing practices. Therefore, it is quite necessary to solve this problem to realize the practical thermoelectric device. PDMS, with its excellent insulation and flexibility, can effectively address short-circuit issues by encapsulating the surface of thermoelectric fibers. In this work, hydrophilic nano-silica (H-SiO₂)-modified PDMS that insulates materials was prepared and coated on the surfaces of polyethyleneimine (PEI)- and hydrochloric acid (HCl)-treated dual-surface-modified thermoelectric fibers. The encapsulated fibers were then woven into spacer fabric to prepare thermoelectric textiles (TETs). After 50 water washing cycles, the fibers retained 97% of their conductivity, and the textiles continued to function normally underwater, indicating that the thermoelectric fibers are effectively protected under PDMS encapsulation. Full article

The rapid growth of generative artificial intelligence (AI) has enabled diverse applications but also introduced new attack techniques. Similar to deepfake media, generative AI can be exploited to create AI-generated traffic that evades existing intrusion detection systems (IDSs). This paper proposes a Dual Detection System to detect such synthetic network traffic in the Message Queuing Telemetry Transport (MQTT) protocol widely used in Internet of Things (IoT) environments. The system operates in two stages: (i) primary filtering with a Long Short-Term Memory (LSTM) model to detect malicious traffic, and (ii) secondary verification with a Transformer–MLP ensemble to identify AI-generated traffic. Experimental results show that the proposed method achieves an average accuracy of $99.1 \pm 0.6\%$ across different traffic types (normal, malicious, and AI-generated), with nearly 100% detection of synthetic traffic. These findings demonstrate that the proposed dual detection system effectively overcomes the limitations of single-model approaches and significantly enhances detection performance. Full article

The electrocardiogram (ECG) is a vital diagnostic tool used to monitor heart activity and detect cardiac abnormalities, such as arrhythmias. Accurate classification of normal and abnormal heartbeats is essential for effective diagnosis and treatment. Traditional deep learning methods for automated ECG classification primarily focus on reconstructing the original ECG signal and detecting anomalies based on reconstruction errors, which represent abnormal features. However, these approaches struggle with unseen or underrepresented abnormalities in the training data. In addition, other methods rely on manual feature extraction, which can introduce bias and limit their adaptability to new datasets. To overcome this problem, this study proposes an end-to-end model called ECG-CBA, which integrates the convolutional neural networks (CNNs), bidirectional long short-term memory networks (Bi-LSTM), and a multi-head Attention mechanism. ECG-CBA model learns discriminative features directly from the original dataset rather than relying on feature extraction or signal reconstruction. This enables higher accuracy and reliability in detecting and classifying anomalies. The CNN extracts local spatial features from raw ECG signals, while the Bi-LSTM captures the temporal dependencies in sequential data. An attention mechanism enables the model to primarily focus on critical segments of the ECG, thereby improving classification performance. The proposed model is trained on normal and abnormal ECG signals for binary classification. The ECG-CBA model demonstrates strong performance on the ECG5000 and MIT-BIH datasets, achieving accuracies of 99.60% and 98.80%, respectively. The model surpasses traditional methods across key metrics, including sensitivity, specificity, and overall classification accuracy. This offers a robust and interpretable solution for both ECG-based anomaly detection and cardiac abnormality classification. Full article

Background: Autosomal recessive polycystic kidney disease (ARPKD) is a rare form of PKD that leads to the development of multiple renal cysts and hepatic fibrosis. Aim: The first documented case of large hepatic cyst associated with dual PKHD1-PKD1 variants. Case report: We present the case of a 5-year-old boy with a kidney US image typical of ARPKD and numerous large cysts in the liver not typical for this disease. Genetic analysis revealed heterozygous missense mutations in the PKHD1 gene (maternally, c.107C>T/p.Thr36Met in exon 3; paternally, c.4870C>T/p.Arg1624Rrp in exon 32) and an additional new variant in PKD1 (maternally, c.5323G>A/p.Gly1775Ser in exon 32). Genetic tests excluded mutations in genes responsible for polycystic liver disease (PCLD). However, the presence of the PKD1 mutation is clinically not clear due to the normal abdominal US image in the mother; it seems to be the most likely explanation for unusual phenotype in our patient. Conclusions: This case may contribute to the understanding of the phenotypic variability in ARPKD and the potential modifying role of mutations in other PKD-related genes. Comprehensive genetic panels are crucial for explaining atypical phenotypes and prognosis in patients with PKD. Full article

Background: Aryl hydrocarbon receptor (AhR) is a transcription factor that is involved in the regulation of immunity. AhR inhibits T cell activation in tumors, which induces immune suppression in the blood and solid tumors. We identified effective small-molecule AhR antagonists for cancer immunotherapy. Methods: A new series of pyrazolopyrimidine derivatives was synthesized and evaluated for AhR antagonistic activity. Results: Compound 7k exhibited significant antagonistic activity against AhR in a transgenic zebrafish model. In addition, 7k exhibited good AhR antagonist activity, with a half-maximal inhibitory concentration (IC₅₀) of 13.72 nM. Compound 7k showed a good pharmacokinetic profile with an oral bioavailability of 71.0% and a reasonable half-life of 3.77 h. Compound 7k selectively exerted anti-proliferative effects on colorectal cancer cells without affecting normal cells, concurrently suppressing the expression of AhR-related genes and the PD-1/PD-L1 signaling pathway. Compound 7k exhibited potent antitumor activity in syngeneic colorectal cancer models. Importantly, the combination of anti-PD1 and compound 7k enhanced antitumor immunity by augmenting cytotoxic T lymphocyte (CTL)-mediated activity. Conclusions: Collectively, a new pyrazolopyrimidine derivative, 7k, shows promise as a potential therapeutic agent for treating colorectal cancer. Full article

Background/Objectives: Since its emergence, COVID-19—caused by the novel coronavirus SARS-CoV-2—has affected millions globally and led to over 1.2 million deaths in the United States alone. This global impact, coupled with the emergence of five new human coronaviruses over the past two decades, underscores the urgency of understanding its pathogenic mechanisms at the molecular level—not only for managing the current pandemic but also preparing for future outbreaks. Small non-coding RNAs (sncRNAs) critically regulate host and viral gene expression, including antiviral responses. Among the molecular regulators implicated in antiviral defense, the microRNA-processing enzyme Drosha has emerged as a particularly intriguing factor. In addition to its canonical role, Drosha also exerts a non-canonical, interferon-independent antiviral function against several RNA viruses. Methods: To investigate this, we employed q/RT-PCR, Western blot, and immunocytochemistry/immunofluorescence in an immortalized normal human lung/bronchial epithelial cell line (NuLi-1), as well as a human colorectal carcinoma Drosha CRISPR knockout cell line. Results: In this study, we observed a striking shift in Drosha isoform expression following infection with multiple SARS-CoV-2 variants. This shift was absent following treatment with the viral mimetic poly (I:C) or infection with other RNA viruses, including the non-severe coronaviruses HCoV-OC43 and HCoV-229E. We also identified a distinct alteration in Drosha’s cellular localization post SARS-CoV-2 infection. Moreover, Drosha ablation led to reduced expression of SARS-CoV-2 genomic and sub-genomic targets. Conclusions: Together, these observations not only elucidate a novel aspect of Drosha’s antiviral role but also advance our understanding of SARS-CoV-2 host–pathogen interactions, highlighting potential therapeutic avenues for future human coronavirus infections. Full article

Indocalamus tessellatus (Munro) P. C. Keng is a bamboo species with significant economic and ecological value, and exhibits considerable resistance to abiotic stresses. However, systematic evaluation of reference genes for gene expression analysis in this species is lacking. Analysis of multi-tissue transcriptomes yielded 3801 relatively stable genes; from these, we selected eleven new candidates along with nine widely adopted reference genes. We then evaluated these candidates under four conditions: drought (15% PEG-6000), salt (200 mM NaCl), waterlogging (root submergence in water), and a multi-tissue panel (leaf, leaf sheath, culm, shoot, and root). Under stress, early and sustained time points were sampled to capture dynamic transcriptional responses. Expression stability was assessed using geNorm, NormFinder, BestKeeper, and ΔCt, and results were integrated with RefFinder to generate comprehensive stability rankings for each condition. The most stable reference genes were condition-dependent: MD10B and PP2A under drought, eIF1A and Ite23725 under salt stress, PP2A and eIF4A under waterlogging, and 60S and UBP1 across different tissues. Notably, commonly used genes such as UBI and Actin7 were less stable. Peroxidase (POD) was used as a validation marker because it is a known stress-responsive gene, providing a sensitive readout of normalization accuracy. Validation confirmed that selecting suitable reference genes is essential for dependable expression quantification. These findings provide a robust set of reference genes for qRT-PCR studies in I. tessellatus, supporting future molecular and functional research in bamboo. Full article

Complex human diseases, including cancer, are linked to genetic factors. Genome-wide association studies (GWASs) are powerful for identifying genetic variants associated with cancer but are limited by their reliance on case–control data. We propose approaches to expanding GWAS by using tumor and paired normal tissues to investigate somatic mutations. We apply penalized maximum likelihood estimation for single-marker analysis and develop a Bayesian hierarchical model to integrate multiple markers, identifying SNP sets grouped by genes or pathways, improving detection of moderate-effect SNPs. Applied to breast cancer data from The Cancer Genome Atlas (TCGA), both single- and multiple-marker analyses identify associated genes, with multiple-marker analysis providing more consistent results with external resources. The Bayesian model significantly increases the chance of new discoveries. Full article

Precise stock price forecasting is essential for informed decision-making in financial markets. This study examines the combination of large language models (LLMs) with data augmentation approaches, utilizing improvements in cognitive computing to enhance stock price prediction. Traditional methods rely on structured data and basic time-series analysis. However, new research shows that deep learning and transformer-based architectures can effectively process unstructured financial data, such as news articles and social media sentiment. This study employs models, such as RNN, mBERT, RoBERTa, and GPT-4 based architectures, to illustrate the efficacy of our suggested method in forecasting stock movements. The research employs data augmentation techniques, including synthetic data creation using Generative Pre-trained Transformers, to rectify imbalances in training datasets. We assess metrics like accuracy, F1-score, recall, and precision to verify the models’ performance. We also investigate the influence of preprocessing methods like text normalization and feature engineering. Extensive tests show that transformer models are much better at predicting how stock prices will move than traditional methods. For example, the GPT-4 based model got an F1 score of 0.92 and an accuracy of 0.919, which shows that LLMs have a lot of potential in financial applications. Full article

Diabetic Macular Edema (DME) poses a significant threat to vision, often leading to permanent blindness if not detected and addressed swiftly. Existing manual diagnostic methods are arduous and inconsistent, highlighting the pressing necessity for automated, accurate, and personalized solutions. This study presents a novel methodology for diagnosing DME and categorizing choroidal neovascularization (CNV), drusen, and normal conditions from fundus images through the application of transfer learning models and bio-inspired optimization methodologies. The methodology utilizes advanced transfer learning architectures, including VGG16, VGG19, ResNet50, EfficientNetB7, EfficientNetV2-S, InceptionV3, and InceptionResNetV2, for analyzing both binary and multi-class Optical Coherence Tomography (OCT) datasets. We combined the OCT datasets OCT2017 and OCTC8 to create a new dataset for our study. The parameters, including learning rate, batch size, and dropout layer of the fully connected network, are further adjusted using the bio-inspired Particle Swarm Optimization (PSO) method, in conjunction with thorough preprocessing. Explainable AI approaches, especially Shapley additive explanations (SHAP), provide transparent insights into the model’s decision-making processes. Experimental findings demonstrate that our bio-inspired optimized transfer learning Inception V3 significantly surpasses conventional deep learning techniques for DME classification, as evidenced by enhanced metrics including the accuracy, precision, recall, F1-score, misclassification rate, Matthew’s correlation coefficient, intersection over union, and kappa coefficient for both binary and multi-class scenarios. The accuracy achieved is approximately 98% in binary classification and roughly 90% in multi-class classification with the Inception V3 model. The integration of contemporary transfer learning architectures with nature-inspired PSO enhances diagnostic precision to approximately 95% in multi-class classification, while also improving interpretability and reliability, which are crucial for clinical implementation. This research promotes the advancement of more precise, personalized, and timely diagnostic and therapeutic strategies for Diabetic Macular Edema, aiming to avert vision loss and improve patient outcomes. Full article

Antioxidant activity is a normal physiological function that is essential for healthy living, and it is maintained by antioxidant dietary nutrients. However, increases in free radical production and oxidative toxicity in many clinical conditions can cause serious and sometimes irreversible damage. Despite many investigations, including hundreds of clinical trials suggesting that there are health benefits obtained from the use of natural antioxidants, no antioxidant drugs have yet been developed for the treatment of any disease associated with free radical pathology. Millions of people choose to use nutraceutical and natural product antioxidants as therapeutics and also for chemoprevention against cancer and other diseases. New academic efforts and strategies are required for the development of antioxidant drugs in clinical practice in the absence of interest by the pharmaceutical and nutraceutical industries. One of the most effective antioxidant therapeutic strategies is inhibition by chelators of iron involved in the catalytic formation of free radical reactions and their associated damage. Hundreds of phytochelators have been shown to inhibit oxidative damage, similar to the iron-chelating drugs deferiprone and deferoxamine. In particular, several nutraceuticals and natural products such as ascorbic acid, quercetin, curcumin, fisetin, lipoic acid, and maltol have been shown to have high antioxidant activity and iron-binding capacity, as well as other effects on iron metabolism, in pre-clinical studies and clinical trials involving different categories of patients. For example, ascorbic acid and maltol–iron complexes are sold as pharmaceutical products for the treatment of iron deficiency. The development of nutraceuticals as antioxidant drugs may involve one or more applications, such as short- or long-term treatments, single-drug or combination therapies, and also different targets, such as the prevention, treatment, or post-treatment of diseases associated with free radical pathology as well as ferroptosis. The academic efforts surrounding the developments of iron-chelating nutraceuticals or natural products into antioxidant pharmaceuticals should fulfill all of the regulatory requirements and include clinical tests of antioxidants in rare or untreatable diseases, as well as the involvement of government translational research institutions and expert groups that specialize in regulatory drug affairs, among others. Full article

This study proposes a new approach to identify structural parameters under seismic excitation using classical and quantum-inspired algorithms. Traditional methods often struggle with complex effects, noise, and computing limits. A five-story building model with mass–spring–damper system was tested to find properties during earthquakes. The study used optimization methods including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and five quantum-inspired versions: Quantum Genetic Algorithm (QGA), Quantum Particle Swarm Optimization (QPSO), Quantum Non-Dominated Sorting Genetic Algorithm II (QNSGA-II), Quantum Differential Evolution (QDE), and Quantum Simulated Annealing (QSA). Additionally, statistical analysis used Shapiro–Wilk for normality, Levene and Bartlett for variance, ANOVA with Tukey–Bonferroni comparisons, Bootstrap model ranking, and Borda count. The results show that the quantum-inspired methods perform better than classical ones. QSA reduced mean squared error (MSE) by 15.3% compared to GA, and QNSGA-II reduced MSE by 8.6% and root mean squared error (RMSE) by 3.5%, with less variation and tighter rankings. The framework addresses computing cost and response time; quantum methods need significant computing power and their accuracy suits offline earthquake assessments and model updates. This balance helps monitor building health when real-time speed is less critical but accuracy matters. The method provides a scalable tool for checking civil structures and could enable digital twins. Full article

Background/Objectives: In this proof-of-concept study, the objective was to evaluate the phenotypic CarbaLux combination rapid test in terms of guiding the therapy of infections caused by multidrug-resistant Gram-negative bacteria with carbapenemase inhibitors and carbapenems, and to compare its results and practicability with standard diagnostic methods. Methods: In the classical CarbaLux test, a fluorescent carbapenem serves as a UV–visible diagnostic surrogate for clinically used carbapenem antibiotics. When exposed to extracted carbapenemases from bacterial colony growth on agar plates, fluorescence rapidly disappears, showing whether monotherapy with carbapenems is possible or must be rejected. It was expected that a specific inhibitor that protects imipenem or meropenem from enzymatic deactivation during antibacterial therapy would perform the same in vitro with fluorescent carbapenem and preserve its fluorescence. The new additional CarbaLux combination test is used if the classic test is positive for carbapenemases: a classic test tube pre-dosed with fluorescent carbapenem is spiked with cloxacillin; with recently launched carbapenemase inhibitors, e.g., avibactam, relebactam, zidebactam, nacubactam, or vaborbactam; or with picolinic acid. Fourteen Enterobacterales and six Acinetobacter baumannii isolates were analyzed. Results: At fixed concentrations, the new inhibitors protected fluorescent carbapenem from bacterial KPC-mediated inactivation and partially from AmpC beta-lactamase-mediated inactivation. In addition, avibactam also effectively inhibited OXA-48-like enzymes. Cloxacillin selectively inhibited AmpC beta-lactamases extracted from Enterobacter complex species. Non-therapeutic picolinic acid was specific for metallo-beta-lactamases and thus identified infections by pathogens that cannot be treated with carbapenems alone or in combination. Conclusions: Inhibitor/fluorescent carbapenem mixtures corresponding to therapeutic inhibitor/carbapenem combinations allow us to visualize the efficacy of carbapenemase inhibitors. The in vitro results are consistent with clinical experience regarding combination therapy. Enzymatic assays provide a rapid yes/no answer for carbapenem mono- or combination therapy and offer several advantages over current carbapenemase testing methods. In contrast to PCR and lateral flow tests, which only target a selection of carbapenemases, enzymatic assays work by employing a reproducible phenotypic mechanism. They are simpler, broader in scope, and more cost-effective; they can also detect antimicrobial heteroresistance or AmpC beta-lactamase hyperproduction, which is normally undetected when performing automated antibiotic susceptibility testing. The new tests are suitable for clinical diagnosis, public health purposes, and infection control. Full article

A functional blood vessel network is required to deliver oxygen and nutrients to the cancer cells for their growth. Angiogenesis, the formation of new blood vessels from pre-existing ones, is one of the major mechanisms to create this vascular network. Anti-angiogenic therapy was conceived as the inhibition of the cellular and molecular players involved in tumor angiogenesis such as vascular endothelial growth factor and its main receptors. Due to limitations of this therapy, different approaches of vessel modulation such as vascular normalization or vascular promotion have been studied showing benefits in different tumor models and clinical trials. In contrast to anti-angiogenic therapy, which inhibits the blood vessels that are being formed, vascular disruption therapy aims to destroy already formed tumor vessels. These malignant vascular structures differ from other blood vessels in terms of endothelial cell states, pericyte coverage and basement membrane development. The molecules used for vascular disruption are microtubule-binding molecules, flavonoids that induce endothelial cell apoptosis or molecules vectorized to endothelial receptors. Many vascular disruption agents have been tested in clinical trials showing some promising results, but with some limitations that include resistant rim cells or the development of hypoxia that induces cancer regrowth and poor delivery of the anti-tumor agents. The main objective of this review is to focus on vascular disruption agents therapy, novel molecules, new ways to overcome therapy resistance to them, current clinical status and, especially, the upcoming challenges and applications of these molecules. Full article