This study presents a biosensor utilizing electrospun SnO nanofiber films for real-time monitoring of C2C12 cells. The biosensor demonstrates sensitivity towards cellular behaviours, including adhesion, proliferation, and detachment. Alterations in semi-circle and dielectric properties are validated through Nyquist plot and an EEC model, highlighting the biosensor's potential for analyzing cellular dynamics.

Skin health monitoring focuses on identifying diseases through the assessment of the mechanical properties of the skin. These properties may degrade with time, which can alter the skin's natural frequencies and the form of the modes associated with those frequencies. Exploring the skin's mechanical properties can enhance our understanding of its dynamics, improving clinical trials and diagnostics. In this work, the dynamics of the skin were measured using a laser-based non-invasive optical sensor experiment. We measured the skin's mechanical properties over time by analyzing its resonant frequencies and mode shapes. A nanocarrier gel and ketoconazole cream were topically applied to keep the skin hydrated and facilitate deeper penetration of the additives in the skin. Time-based research was used to assess the effect of different formulations on skin elasticity. Experimental results for the modulus of elasticity were compared with those obtained using Finite Element Analysis (FEA) simulations. We observed a reduction in frequencies of cream and gel-treated skin by 29.98% and 44.029% respectively compared to normal skin (frequency: 263.3 ± 1.18 Hz and Modulus of elasticity: 7.56 ± 2.60 MPa). A decrease in stiffness (function of frequency) attributed to increased water content, was observed in cream- and nanocarrier gel-treated skin compared to normal skin. Experimental and numerical results are found to be consistent with one another. This optical sensor-based approach has the potential for studying diseased skin mechanics and its response to gel and cream treatments, aiming to reduce skin disorder morbidity and severity.

Radiotherapy (RT) is a widely used cancer treatment, and the use of metal-based nanoradiotherapy sensitizers has demonstrated promise in enhancing its efficacy. However, achieving effective accumulation of these sensitizers within tumors and overcoming resistance induced by the hypoxic tumor microenvironment remain challenging issues. In this study, we developed FePt/MnO@PEG nanoparticles with multiple radiosensitizing mechanisms, including high-atomic-number element-mediated radiation capture, catalase-mimicking oxygenation, and GSH depletion-induced ferroptosis. Both in vitro and in vivo experiments were conducted to validate the radiosensitizing mechanisms and therapeutic efficacy of FePt/MnO@PEG. In conclusion, this study presents a novel and clinically relevant strategy and establishes a safe and effective combination radiotherapy approach for cancer treatment. These findings hold significant potential for improving radiotherapy outcomes and advancing the field of nanomedicine in cancer therapy.

Presents corrections to the paper, Benchmarking Power Generation From 2 Multiple Wastewater Electrolytes in Microbial 3 Fuel Cells With 3D Printed Disk-Electrodes.

The purpose of this study was to investigate the effects of two different types of gold nanoparticles (AuNPs) delivered by intraperitoneal (IP) injection on blood and kidney tissue changes in a mouse model. Three groups of fifteen adult male BALB/c healthy mice, weighing approximately 25-30 g, were used for the experiment and designated G1, G2, and G3, respectively. G1 mice received vehicle, whereas G2 and G3 received an IP injection of 10 mg/kg body weight of methoxy poly ethylene glycol gold nanoparticles (PEG-AuNPs) and fluorescently dye labeled gold nanoparticles (Dye-AuNPs), respectively. Hematological parameters were measured based on the standard complete blood cell count (CBC) technique. The two nanoparticles, i.e., PEG-AuNPs and Dye-AuNPs, significantly reduced most red blood cell (RBC) parameters in the groups with the exception of a nonsignificant effect on hemoglobin (HBG) levels. Both gold nanoparticles, i.e., PEG-AuNPs and Dye-AuNPs, led to a reduced RBC count, mean corpuscular volume (MCV), and mean corpuscular hemoglobin (MCH) level when compared with the control. Notably, Dye-AuNPs and PEG-AuNPs resulted in a considerably higher RBC distribution RDW-(CV % and SD fL). Glomerular injury was suggested based on the development of hydropic degeneration and the presence of a protein-rich fluid inside the tubules. Renal tissue and blood indices changed significantly in response to the two nanoparticles, suggesting possible organ injury.

Bioinformatics is a rapidly evolving field that applies computational methods to analyze and interpret biological data. A key task in bioinformatics is identifying novel drug-target interactions (DTIs), which plays a crucial role in drug discovery. Most computational approaches treat DTI prediction as a binary classification problem, determining whether drug-target pairs interact. However, with the growing availability of drug-target binding affinity data, this binary task can be reframed as a regression problem focused on drug-target affinity (DTA). DTA quantifies the strength of drug-target binding, offering more detailed insights than DTI and serving as a valuable tool for virtual screening in drug discovery. Accurately predicting compound interactions with targets can accelerate the drug development process. In this study, we introduce a deep learning model named TC-DTA for DTA prediction, leveraging convolutional neural networks (CNN) and the encoder module of the transformer architecture. We begin by extracting raw drug SMILES strings and protein amino acid sequences from the dataset, which are then represented using various encoding methods. Subsequently, we employ CNN and the transformer's encoder module to extract features from the drug SMILES strings and protein sequences, respectively. Finally, the feature information is concatenated and input into a multi-layer perceptron to predict binding affinity scores. We evaluated our model on two benchmark DTA datasets, Davis and KIBA, comparing it with methods such as KronRLS, SimBoost, and DeepDTA. Our model, TC-DTA, outperformed these baseline methods based on evaluation metrics like Mean Squared Error (MSE), Concordance Index (CI), and Regression towards the Mean Index ( r ). These results highlight the effectiveness of the Transformer's encoder and CNN in extracting meaningful representations from sequences, thereby enhancing DTA prediction accuracy. This deep learning model can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, machine learning technology offers a more effective and efficient approach to drug discovery.

Pancreatic cancer is one of the most malignant cancers with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding new biomarkers for pancreatic cancer. Many network-based methods used to identify cancer biomarkers are proposed, among which the combination of network controllability appears. However, most of the existing methods do not study RNA, rely on priori and mutations information, or can only achieve classification tasks. In this study, we propose a method combined Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. Firstly, we construct a regulation network containing long non-coding RNA, microRNA, and messenger RNA. Secondly, Relational Graph Convolutional Network is used to learn the node representation. Finally, we use the idea of Deep Q-Network to build a model, which score and prioritize each RNA with the Popov-Belevitch-Hautus criterion. We train RDDriver on three small simulated networks, and calculate the average score after applying the model parameters to the regulation networks separately. To demonstrate the effectiveness of the method, we perform experiments for comparison between RDDriver and other eight methods based on the approximate benchmark of three types cancer drivers RNAs.

Detecting side effects of drugs is a fundamental task in drug development. With the expansion of publicly available biomedical data, researchers have proposed many computational methods for predicting drug-side effect associations (DSAs), among which network-based methods attract wide attention in the biomedical field. However, the problem of data scarcity poses a great challenge for existing DSAs prediction models. Although several data augmentation methods have been proposed to address this issue, most of existing methods employ a random way to manipulate the original networks, which ignores the causality of existence of DSAs, leading to the poor performance on the task of DSAs prediction. In this paper, we propose a counterfactual inference-based data augmentation method for improving the performance of the task. First, we construct a heterogeneous information network (HIN) by integrating multiple biomedical data. Based on the community detection on the HIN, a counterfactual inference-based method is designed to derive augmented links, and an augmented HIN is obtained accordingly. Then, a meta-path-based graph neural network is applied to learn high-quality representations of drugs and side effects, on which the predicted DSAs are obtained. Finally, comprehensive experiments are conducted, and the results demonstrate the effectiveness of the proposed counterfactual inference-based data augmentation for the task of DSAs prediction.

Given the persistent global challenge presented by rapidly spreading diseases, as evidenced notably by the widespread impact of the COVID-19 pandemic on both human health and economies worldwide, the necessity of developing effective infectious disease prediction models has become of utmost importance. In this context, the utilization of online social media platforms as valuable tools in healthcare settings has gained prominence, offering direct avenues for disseminating critical health information to the public in a timely and accessible manner. Propelled by the ubiquitous accessibility of the internet through computers and mobile devices, these platforms promise to revolutionize traditional detection methods, providing more immediate and reliable epidemiological insights. Leveraging this paradigm shift, our proposed framework harnesses Twitter data associated with infectious disease symptoms, employing ontology to identify and curate relevant tweets. Central to our methodology is a hybrid model that integrates XGBoost and Bidirectional Long Short-Term Memory (BiLSTM) architectures. The integration of XGBoost addresses the challenge of handling small dataset sizes, inherent during outbreaks due to limited time series data. XGBoost serves as a cornerstone for minimizing the loss function and identifying optimal features from our multivariate time series data. Subsequently, the combined dataset, comprising original features and predicted values by XGBoost, is channeled into the BiLSTM for further processing. Through extensive experimentation with a dataset spanning multiple infectious disease outbreaks, our hybrid model demonstrates superior predictive performance compared to state-of-the-art and baseline models. By enhancing forecasting accuracy and outbreak tracking capabilities, our model offers promising prospects for assisting health authorities in mitigating fatalities and proactively preparing for potential outbreaks.

The analysis and comprehension of multi-omics data has emerged as a prominent topic in the field of bioinformatics and data science. However, the sparsity characteristics and high dimensionality of omics data pose difficulties in terms of extracting meaningful information. Moreover, the heterogeneity inherent in multiple omics sources makes the effective integration of multi-omics data challenging To tackle these challenges, we propose MFCC-SAtt, a multi-level feature contrast clustering model based on self-attention to extract informative features from multi-omics data. MFCC-SAtt treats each omics type as a distinct modality and employs autoencoders with self-attention for each modality to integrate and compress their respective features into a shared feature space. By utilizing a multi-level feature extraction framework along with incorporating a semantic information extractor, we mitigate optimization conflicts arising from different learning objectives. Additionally, MFCC-SAtt guides deep clustering based on multi-level features which further enhances the quality of output labels. By conducting extensive experiments on multi-omics data, we have validated the exceptional performance of MFCC-SAtt. For instance, in a pan-cancer clustering task, MFCC-SAtt achieved an accuracy of over 80.38%.

Computational synthetic lethality (SL) method has become a promising strategy to identify SL gene pairs for targeted cancer therapy and cancer medicine development. Feature representation for integrating various biological networks is crutial to improve the identification performance. However, previous feature representation, such as matrix factorization and graph neural network, projects gene features onto latent variables by keeping a specific geometric metric. There is a lack of models of gene representational latent space with considerating multiple dimentionalities correlation and preserving latent geometric structures in both sample and feature spaces. Therefore, we propose a novel method to model gene Latent Space using matrix Tri-Factorization (LSTF) to obtain gene representation with embedding variables resulting from the potential interpretation of synthetic lethality. Meanwhile, manifold subspace regularization is applied to the tri-factorization to capture the geometrical manifold structure in the latent space with gene PPI functional and GO semantic embeddings. Then, SL gene pairs are identified by the reconstruction of the associations with gene representations in the latent space. The experimental results illustrate that LSTF is superior to other state-of-the-art methods. Case study demonstrate the effectiveness of the predicted SL associations.

Circular RNAs (circRNAs) play a crucial role in gene regulation and association with diseases because of their unique closed continuous loop structure, which is more stable and conserved than ordinary linear RNAs. As fundamental work to clarify their functions, a large number of computational approaches for identifying circRNA formation have been proposed. However, these methods fail to fully utilize the important characteristics of back-splicing events, i.e., the positional information of the splice sites and the interaction features of its flanking sequences, for predicting circRNAs. To this end, we hereby propose a novel approach called SIDE for predicting circRNA back-splicing events using only raw RNA sequences. Technically, SIDE employs a dual encoder to capture global and interactive features of the RNA sequence, and then a decoder designed by the contrastive learning to fuse out discriminative features improving the prediction of circRNAs formation. Empirical results on three real-world datasets show the effectiveness of SIDE. Further analysis also reveals that the effectiveness of SIDE.

DNA is considered one of the most attractive storage media because of its excellent reliability and durability. Early encoding schemes lacked flexibility and scalability. To address these limitations, we propose a combination of static mapping and dynamic encoding, named "Galaxy" encoding. This scheme uses both the "dual-rule interleaving" algorithm and the "twelve-element Huffman rotational encoding" algorithm. We tested it with "Shakespeare Sonnets" and other files, achieving an encoding information density of approximately 2.563 bits/nt. Additionally, the inclusion of Reed-Solomon error-correcting codes can correct nearly 5% of the errors. Our simulations show that it supports various file types (.gz, .tar, .exe, etc.). We also analyzed the cost and fault tolerance of "Galaxy" encoding, demonstrating its high coding efficiency and ability to fully recover original information while effectively reducing the costs of DNA synthesis and sequencing.

Esophageal cancer is a common malignant tumor, precisely predicting survival of esophageal cancer is crucial for personalized treatment. However, current region of interest (ROI) based methodologies not only necessitate prior medical knowledge for tumor delineation, but may also cause the model to be overly sensitive to ROI. To address these challenges, we develop an automated Hybrid Transformer based learning that integrates a Hybrid Transformer size-aware U-Net with a ranked survival prediction network to enable automatic survival prediction for esophageal cancer. Specifically, we first incorporate the Transformer with shifted windowing multi-head self-attention mechanism (SW-MSA) into the base of the U-Net encoder to capture the long-range dependency in CT images. Furthermore, to alleviate the imbalance between the ROI and the background in CT images, we devise a size-aware coefficient for the segmentation loss. Finally, we also design a ranked pair sorting loss to more comprehensively capture the ranked information inherent in CT images. We evaluate our proposed method on a dataset comprising 759 samples with esophageal cancer. Experimental results demonstrate the superior performance of our proposed method in survival prediction, even without ROI ground truth.

Parkinson's disease (PD) is a progressive neurodegenerative disease, and it is caused by the loss of dopaminergic neurons in the basal ganglia (BG). Currently, there is no definite cure for PD, and available treatments mainly aim to alleviate its symptoms. Due to impaired neurotransmitter-based information transmission in PD, molecular communication-based approaches can be employed as potential solutions to address this issue. Molecular Communications (MC) is a bio-inspired communication method utilizing molecules to carry information. This mode of communication stands out for developing biocompatible nanomachines for diagnosing and treating, particularly in addressing neurodegenerative diseases like PD, due to its compatibility with biological systems. This study presents a novel treatment method that introduces an Intelligent Dopamine Rate Modulator (IDRM), which is located in the synaptic gap between the substantia nigra pars compacta (SNc) and striatum to compensate for insufficiency dopamine release in BG caused by PD. For storing dopamine in the IDRM, dopamine compound (DAC) is swallowed and crossed through the digestive system, blood circulatory system, blood-brain barrier (BBB), and brain extracellular matrix uptakes with IDRMs. Here, the DAC concentration is calculated in these regions, revealing that the required exogenous dopamine consistently reaches IDRM. Therefore, the perpetual dopamine insufficiency in BG associated with PD can be compensated. This method reduces drug side effects because dopamine is not released in other brain regions. Unlike other treatments, this approach targets the root cause of PD rather than just reducing symptoms.

This paper presents a terahertz metasurface based sensor design incorporating graphene and other plasmonic materials for highly sensitive detection of different chemicals. The proposed sensor employs the combination of multiple resonator designs - including circular and square ring resonators - to attain enhanced sensitivity among other performance parameters. Machine learning techniques like Random Forest regression, are employed to enhance the sensor design and predict its performance. The optimized sensor demonstrates excellent sensitivity of 417 GHzRIU and a low detection limit of 0.264 RIU for ethanol and benzene detection. Furthermore, the integration of machine learning cuts down the simulation time and computational requirements by approximately 90% without compromising accuracy. The sensor's unique design and performance characteristics, including its high-quality factor of 14.476, position it as a promising candidate for environmental monitoring and chemical sensing applications. Moreover, it also demonstrates potential for 2-bit encoding applications through strategic modulation of graphene chemical potential values. On the other hand, it also shows prospects of 2-bit encoding applications via the modulation of graphene chemical. This work provides a major advancement to the terahertz sensing application by proposing new materials, structures, and methods in computation in order to develop a high-performance chemical sensor.

The features of the sublingual veins, including swelling, varicose patterns, and cyanosis, are pivotal in differentiating symptoms and selecting treatments in Traditional Chinese Medicine (TCM) tongue diagnosis. These features serve as a crucial reflection of the human blood circulation status. Nevertheless, the automatic and precise extraction of sublingual vein features remains a formidable challenge, constrained by the scarcity of datasets for sublingual images and the interference of noise from non-tongue and non-sublingual vein elements. In this paper, we present an innovative tongue feature extraction method that relies on focusing specifically on segmenting the sublingual vein rather than the entire tongue base. To achieve this, we have developed a sublingual vein segmentation framework utilizing a Polyp-PVT network, effectively eliminating noise from the surrounding regions of the sublingual vein. Furthermore, we pioneer the utilization of a transformer-based approach, such as the Swin-Transformer network, to extract sublingual vein features, leveraging the remarkable capabilities of transformer networks. To complement our methodology, we have constructed a comprehensive dataset of sublingual vein images, facilitating the segmentation and classification of sublingual veins. Experimental results have demonstrated that our tongue feature extraction method, coupled with sublingual vein segmentation, significantly outperforms existing tongue feature extraction techniques.

The article provides a comprehensive overview of the current and future advances of Photonic crystal fiber (PCF) based biosensors, the research explores the impact of structural parameter variations on phase matching conditions, by investigating pitch, air hole diameter, and gold layer thickness. Currently, these surface plasmon resonance (SPR) biosensors demonstrate the ability to detect a range of biological substances such as glucose, pH, serum proteins, and similar chemicals. They have the capacity to directly identify bio-components in urine, blood, and saliva, as well as pathogens, bacteria, and contaminants in food, water, and air. The study investigates by presenting the fundamental principles of PCF biosensors, highlighting their comparative benefits over conventional biosensors. Recent studies utilizing PCF biosensors for various application are reviewed, the findings of the review suggest that the integration of SPR enhances the sensing capabilities of these biosensors, making them promising tools for diverse applications in the field of biosensing.

Through this paper, a three-dimensional molecular communication (MC) inside a cuboid container is considered. Instead of normal diffusion phenomenon, the anomalous diffusion phenomenon is incorporated which enhances the practicability of the model. The Fick's law is re-defined for the considering rectangular coordinate system in which information carrying molecules (ICMs) diffuse anomalously in the environment. The impact of flow of the fluid along the +x direction in the environment is also considered. Moreover, considering free propagator phenomenon, the expressions of spatio-temporal probability density function (PDF) of the ICMs is derived for the considered model. Further, the novel closed-form expressions for first arrival time density (FATD) of the ICM, survival probability (SP) at any time, and its corresponding first arrival probability (FAP) are also derived. Furthermore, the considered MC model is also analyzed in terms of minimum bit-error-rate (BER) using log-likelihood ratio test (LLRT) optimal detector. The derived expressions are verified using MATLAB based particle-based and Monte-Carlo simulations.

Currently, DNA strand displacement (DSD) as the theoretical basis of DNA chemical reaction networks (CRNs) has promoted the development of chaotic synchronization technique. This paper introduces the synchronization technology of two isomorphic three-dimensional chaotic systems based on DNA strand displacement under state observer. By studying the theoretical knowledge of DNA molecules, multiple DSD reactions are used to construct three-dimensional chaotic system. Based on two isomorphic chaotic systems, the linear transformation system and the state observer system are designed according to the theory of state observer construction. In addition, in order to realize the synchronization of chaotic systems, a coupling controller is designed between the drive system and the linear transformation system, and a soft variable-structure controller is designed between the state observer system and the response system. Through multiple DSD reactions, the chemical reaction networks of four chaotic systems and two controllers are constructed, and they are cascaded to realize the synchronization of two isomorphic three-dimensional chaotic systems. Numerical simulations verify the effectiveness and robustness of the scheme. Our work will extend and provide a reference for new methods to achieve synchronization of chaotic systems using DSD.

Molecular communication via diffusion (MCvD) expects Brownian motions of the information molecules to transmit information. However, the signal propagation largely depends on the geometric characteristics of the assumed flow model, i.e., the characteristics of the environment, design, and position of the transmitter and receiver, respectively. These characteristics are assumed to be lucid in many ways by either consideration of one-dimensional diffusion, unbounded environment, or constant drift. In reality, diffusion often occurs in blood-vessel-like channels. To this aim, we try to study the effect of the biological environment on channel performance. The Red-Blood Cells (RBCs) found in blood vessels enforces a higher concentration of molecules towards the vessel walls, leading to better reception. Therefore, in this paper we derive an analytical expression of Channel Impulse Response (CIR) for a dispersion-advection-based regime, contemplating the influence of RBCs in the model and considering a point source transmitter and a realistic design of the receiver.