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Human bone marrow derived mesenchymal stem cells do not promote oral cancer cell growth in vitro and metastasis in vivo
Abstract Mesenchymal stem cells (MSCs), particularly those derived from bone marrow (BMMSCs), hold substantial promise for bone regeneration in the maxillofacial region, especially after surgical resections with bone involvement. However, their use in patients with resections undergoing oral cancer treatment poses potential risks due to the effects of MSCs in modulating cancer cell behavior. This study aimed to assess the effect of circulatory BMMSCs on proliferation, migration, invasion, tumor growth, and metastasis of oral squamous cell carcinoma (OSCC) cells. Human BMMSCs were isolated, characterized, and their conditioned medium (CM) was tested on OSCC cell lines (Ca1 and OSCC1). Further, BMMSCs were transduced with lentiviral particles to express firefly luciferase for live cell tracking in vivo to study their biodistribution and homing capability to xenograft tongue tumors. In vitro assays revealed that BMMSC-CM did not significantly alter OSCC proliferation or invasion in 3D organotypic assays, while significantly reducing their migration in 2D scratch wound assay. In vivo, bioluminescent imaging and histological analyses indicated that human BMMSCs predominantly localized to the lungs without homing to other organs or to the human xenograft tongue tumors. Moreover, circulatory BMMSCs did not influence tumor size, nor did they promote lung metastasis in xenografted mice under these conditions. These findings suggest that circulating BMMSCs did not exacerbate OSCC progression, supporting their potential use in regenerative applications for patients post-OSCC resection.
Effect of CaO content in Class C fly ash on the deformation properties of fully-graded concrete
Lethal effects of ivermectin structures on malaria vectors and in silico analysis of interactions with their glutamate-gated chloride ion channels
Abstract Ivermectin is lethal to Anopheles mosquitoes making it a possible malaria control intervention. The primary mode of action of ivermectin occurs when it binds to the glutamate-gated chloride channel (GluCl), allowing for continuous flow of chloride leading to flaccid paralysis and death of the mosquito. In Caenorhabditis elegans, ivermectin is thought to open the GluCl channel when the M2-M3 loop forms Van der Waals bonds with the first sugar ring and aglycone structure of ivermectin. Here we investigate in Anopheles dirus and Anopheles minimus the mosquito-lethal effect of ivermectin (both sugar rings), monosaccharide (one sugar ring), and aglycone (no sugar rings) demonstrating full, partial, and no effect, respectively. The Anopheles GluCl protein sequences were determined and used to a create 3-D structural docking models. The docking models identified new binding interactions with a hydrogen bond forming between the second sugar ring hydroxyl group (4″-OH) and THR304 of the Anopheles GluCl M2-M3 loop. This hydrogen bond is possible due to a single substitution in the M2-M3 loop from C. elegans ILE273 to Anopheles THR304. The work presented here improves our understanding of Anopheles GluCl-ivermectin interactions as well as how ivermectin resistance could arise in the future.
Novel convolutional neural network for bacterial identification of confocal microscopic datasets
Abstract Artificial intelligence (AI), complex mathematical algorithms, is currently employed across various fields to perform tasks quickly and effectively. In this study, a novel deep-learning algorithm named (CM-Net) was developed to classify biological data obtained as images from Confocal Microscopy. The images were collected for two types of bacterial species: ( Escherichia coli and Staphylococcus aureus ), where the number of images was 300 for each class. To enhance the dataset, we divided each image (using the augmentation method) into a small number of images with 224 × 224 dimensions, resulting in a total of 7066 images for both classes. These augmented images were fed to CM-Net to ensure accurate results and avoid bias in the developed algorithms. The algorithm was trained and tested 30 times with a 5-K cross-validation for each time. The algorithm’s performance was evaluated using seven metrics (accuracy, sensitivity, specificity, precision, NVA, F1-score, and MCC), where the respective results were 96.08%, 95.98%, 96.19%, 96.78%, 95.26%, 96.38%, and 92.11%, indicating the model’s high accuracy and reliability. CM-Net drastically reduces bacterial identification time by automating large-scale data analysis, processing results in 8.9 min. The automation provided by CM-Net simplifies workflows, enabling non-expert workers to perform microbial identification without extensive training. The significant outcomes of applying CM-Net for bacterial identification revolve around its transformative impact on data analysis’s speed, efficiency, and accuracy, making advanced analysis accessible to non-experts while minimizing human error.
Autoantibody and disease control stability following mRNA COVID-19 vaccination in rheumatoid arthritis: an observational cohort study
Groundwater quality assessment for agricultural utilizing indexical and machine learning techniques in Ouled Djellal Aquifer, Southern Algeria
Abstract Groundwater represents the main water resource for irrigation in the Ouled Djellal region (southeast of Algeria). Despite the importance of groundwater in this area, its quality and sustainability remain insufficiently studied. Therefore, this study aimed to introduce an integrated analytical framework by combining multivariate statistical techniques i.e., Principal Component Analysis (PCA) and Hierarchical Ascending Classification (HAC), irrigation indices (IWQI, SAR, Na%, SSP, PS, and RSC), and machine learning (ML) models such as Artificial Neural Network (ANN), Support Vector Machine (SVM), and Multiple Linear Regression (MLR) to assess and predict groundwater quality for irrigation. The main difference with previous studies is the fact that this work applied Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize irrigation indices derived from ML with higher precision. The approach enables cross-validation of model performance and captures complex nonlinear interactions among hydrochemical parameters. The attained results revealed that groundwater quality was varied from moderate to poor for irrigation, driven mainly by salinity and sodicity effects. In addition, the ANN model achieved the highest predictive accuracy (R² = 0.97, RMSE = 1.50), confirming its superiority in modelling complex hydrochemical behavior. The proposed modelling framework represents a methodological advancement for data-scarce arid regions, serving as a practical tool adaptable to groundwater monitoring and irrigation planning in similar regions.
Cell wall remodeling and inositol metabolism coexpression modules associated with nut size in Carya illinoinensis cvs. ‘Mahan’ and ‘Tiny Tim’
Skeleton motion topology-masked prediction and contrastive learning for self-supervised human action recognition
Metabolomic insights into residual Carrot biomass from a bioprospecting approach across Colombian microclimates
Techno economic and environmental evaluation of second life battery PV hybrid charging stations for sustainable e-mobility in tropical regions
Comparative performance analysis of quantum feature maps for quantum kernel-based machine learning
Abstract Quantum algorithms have become a popular research domain in recent times for discovering quantum-enhanced solutions in machine learning applications. Quantum kernels are one of the directions that establish such quantum-enhanced solutions to some extent. This work presents a detailed analysis of the quantum kernel approach leveraging feature maps and relevant hyperparameters to develop enhanced quantum kernels. The study includes a new high-order feature map and assesses five existing state-of-the-art feature maps for enhanced quantum kernel classifiers. Additionally, the significance of the rotational factor as a hyperparameter is highlighted for improving kernel performance. Also, it is analyzed whether different hyperparameter-tuned feature maps can lead to enhanced decision boundaries, demonstrating kernel expressivity. The analysis is undertaken on classification tasks using four different nonlinear datasets of distinct complexity. Comparative evaluations are also made with traditional machine learning models—Support Vector Machines (Linear and RBF), Naïve Bayes, Linear Discriminant Analysis, Decision Tree, Random Forest, Adaptive Boosting, and MLP. Overall, the study demonstrates that a well-tuned quantum feature map can enhance the generalization ability of quantum kernels, making them more effective for broader quantum-enhanced machine learning applications.
Development and validation of a deep learning model for identifying high-quality laryngoscopic images
Intraplanar percolation and interplanar bridge enables layered matrix for high-performance negative electrode
Three-dimensional reconstruction of a biliary system in a bioengineered liver using decellularized scaffold
MHC class II functions as a host-specific entry receptor for representative human and swine H3N2 influenza A viruses
Machine learning based power control in cellular and cell-free massive MIMO systems
Abstract Effective power control (PC) is essential for optimizing performance in large-scale multiple-input multiple-output (mMIMO) networks. Traditional methods such as the weighted minimum mean square error (WMMSE) algorithm offer reliable estimates but require substantial computational overhead This study examines PC in mMIMO systems, focusing on aggregate spectral efficiency (sum SE) and the per-user SE cumulative distribution function (CDF). This investigation explores the impact of factors such as the number of UEs, access points/base stations (APs/BSs), and deep neural network (DNN)-based PC implementations in both cellular (CL) and cell-free (CF) architectures. We introduce a new metric ( $$\:\varDelta\:\text{A}\text{U}\text{C}$$ ) the area between the per-user SE CDFs of the DNN-based PC and the WMMSE baseline - as a compact and interpretable measure of ML versus optimization performance under deployment scaling. To the best of our knowledge, this is the first paper to systematically apply this metric across both cellular and cell-free mMIMO architectures while varying AP/BS count, antenna count, user density, and dataset size. By combining this metric with RMSE, sum-rate change, and execution-time analysis (Figs. 1, 2, 3, 4, 5 and 6, Table 6), we provide prescriptive guidance on when DNN-based PC not only matches but also outperforms WMMSE in both performance and real-time latency, enabling practical deployment in dense and low-latency networks.
Diffusion models enable high-fidelity prediction of fuel cell impedance spectrum from short time-domain profiles
Transmitter-assisted joint data-aided channel estimation and PAPR reduction scheme in wireless fading channels
Abstract This paper presents a novel transmitter-assisted joint scheme that simultaneously addresses two critical challenges in modern wireless communication systems: peak-to-average power ratio (PAPR) reduction and accurate channel estimation. The proposed solution integrates modified gamma correction commanding (MGCC) with data-aided channel estimation (DACE) for both single-input single-output (SISO) and multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless systems. The key novelty lies in the unique dual-functionality approach, where high-peak power carriers, traditionally a source of distortion due to high PAPR, are repurposed as additional pilot signals at the receiver for improved channel estimation. This innovative use of MGCC not only optimizes the identification and utilization of these carriers but also ensures that the selection of reliable data carriers remains unaffected. By transforming the high PAPR problem into a performance advantage, the scheme significantly reduces the computational complexity typically associated with separate PAPR reduction and channel estimation processes, making it particularly suitable for low-complexity wireless devices. The proposed approach determines peak-powered subcarriers entirely from the transmitted signal, eliminating the need for receiver-to-transmitter feedback and thereby simplifying system design without compromising performance. Furthermore, the study identifies optimal companding parameters to achieve an effective balance between error performance and PAPR reduction. Extensive simulations under Rayleigh and Rician fading channels with varying tap configurations demonstrate the robustness and versatility of the proposed scheme. Performance evaluations, including mean square error and bit-error-rate analyses, confirm the superiority of the proposed approach when paired with least square and linear minimum mean square error channel estimators. The impact of receive antenna correlation on error performance is also analyzed, revealing a nonlinear trend where low correlation levels have minimal effect, while variation in correlation influences system behavior. The results highlight consistent and reliable performance across diverse fading environments, underscoring the potential of the proposed scheme to enhance the efficiency and reliability of next-generation wireless communication systems.
KidneyGenAfrica multi-cohort Genome-wide association study and polygenic prediction of kidney function in 110,000 Africans
Abstract Kidney disease disproportionately affects populations of African ancestry, yet most genetic studies have focused on Europeans. Here, we present a three-stage genome-wide association study meta-analysis of estimated glomerular filtration rate in ~26,000 individuals across Eastern, Western, and Southern Africa and ~81,000 African-ancestry individuals in the diaspora. Continental African meta-analysis identifies four independent genome-wide significant loci, including two previously unreported loci. Pan-African meta-analysis identifies 19 independent loci, including three previously unreported loci. Fine-mapping reveals four loci with high causality probability, and phenome-wide analyses demonstrate pleiotropic effects on cardiometabolic and immunological traits. Notably, APOL1 high-risk variants strongly associated with kidney disease in African Americans show markedly lower frequency and attenuated effects in continental Africa, indicating potential distinct genetic architectures. Polygenic scores from genetically similar populations significantly outperformed those from distant cohorts. These findings demonstrate the necessity of conducting genomic research across diverse African populations to enable equitable health outcomes.