Clinical Laser-Induced Thermotherapy (LITT) currently lacks precise control of tissue temperature increase during the procedure. This study presents a new method to automatically regulate the maximum temperature increase in vivo at different positions by adjusting LITT power delivered by multiple laser probes using real-time volumetric MR-thermometry.

Non-B DNA structures, or flipons, are important functional elements that regulate a large spectrum of cellular programs. Experimental technologies for flipon detection are limited to the subsets that are active at the time of an experiment and cannot capture whole-genome functional set. Thus, the task of generating reliable whole-genome annotations of non-B DNA structures is put on deep learning models, however their quality depends on the available experimental data for training. The data augmentation approach as the combination of synthetic and real data is widely used in various fields. Deep generative models demonstrated promising results in data augmentation improving classifiers' performance. Here we aimed at testing performance of diffusion models in comparison to other generative models in generating synthetic non-B DNA structures for data augmentation approach. We tested denoising diffusion probabilistic and implicit models (DDPM and DDIM), Wasserstein generative adversarial network (WGAN), vector quantised variational autoencoder (VQ-VAE) and showed that data augmentation improves the quality of classifiers. Diffusion models overall show the best results, but when considering three criteria of generative trilemma - quality of generated samples, diversity and sampling speed, we conclude that trade-off is possible between generative diffusion model and other architectures such as WGAN and VQ-VAE.

Bacillus anthracis, a gram-positive bacillus capable of forming spores, causes anthrax in mammals, including humans, and is recognized as a potential biological weapon agent. The diagnosis of anthrax is challenging due to variable symptoms resulting from exposure and infection severity. Despite the availability of a licensed vaccines, their limited long-term efficacy underscores the inadequacy of current human anthrax vaccines, highlighting the urgent need for next-generation alternatives. Our study aimed to identify molecular biomarkers and essential biological pathways for the early detection and accurate diagnosis of human anthrax infection. Using a comparative analysis of Bacillus anthracis gene expression data from the Gene Expression Omnibus (GEO) database, this cost-effective approach enables the identification of shared differentially expressed genes (DEGs) across separate microarray datasets without additional hybridization. Three microarray datasets (GSE34407, GSE14390, and GSE12131) of B. anthracis-infected human cell lines were analyzed via the GEO2R tool to identify shared DEGs. We identified 241 common DEGs (70 upregulated and 171 downregulated) from cell lines treated similarly to lethal toxins. Additionally, 10 common DEGs (5 upregulated and 5 downregulated) were identified across different treatments (lethal toxins and spores) and cell lines. Network meta-analysis identified JUN and GATAD2A as the top hub genes for overexpression, and NEDD4L and GULP1 for underexpression. Furthermore, prognostic analysis and SNP detection of the two identified upregulated hub genes were carried out in conjunction with machine learning classification models, with SVM yielding the best classification accuracy of 87.5 %. Our comparative analysis of Bacillus anthracis infection revealed striking similarities in gene expression 241 profiles across diverse datasets, despite variations in treatments and cell lines. These findings underscore how anthrax infection activates shared genes across different cell types, emphasizing this approach in the discovery of novel gene markers. These markers offer insights into pathogenesis and may lead to more effective therapeutic strategies. By identifying these genetic indicators, we can advance the development of precise immunotherapies, potentially enhancing vaccine efficacy and treatment outcomes.

The human ether-a-go-go-related gene (hERG) potassium channel is pivotal in drug discovery due to its susceptibility to blockage by drug candidate molecules, which can cause severe cardiotoxic effects. Consequently, identifying and excluding potential hERG channel blockers at the earliest stages of drug development is crucial. Most traditional machine learning models predict a molecule's cardiotoxicity or non-cardiotoxicity typically at 10 μM, which doesn't account for compounds with low IC values that are non-toxic at therapeutic levels due to their high effectiveness at lower concentrations. To address the need for more precise, quantitative predictions, we developed hERGBoost, a cutting-edge machine learning model employing a gradient-boosting algorithm. This model demonstrates superior accuracy in predicting the IC of drug candidates. Trained on a specially curated dataset for this study, hERGBoost not only exhibited excellent performance in external validation, achieving an R score of 0.394 and a low root mean square error of 0.616 but also significantly outstripped previous models in both qualitative and quantitative assessments. Representing a notable leap forward in the prediction of hERG channel blockers, the hERGBoost model and its datasets are freely available to the drug discovery community on our web server at. http://ssbio.cau.ac.kr/software/hergboost This resource promises to be invaluable in advancing safer pharmaceutical development.

Emotion recognition holds great promise in healthcare and in the development of affect-sensitive systems such as brain-computer interfaces (BCIs). However, the high cost of labeled data and significant differences in electroencephalogram (EEG) signals among individuals limit the cross-domain application of EEG-based emotion recognition models. Addressing cross-dataset scenarios poses greater challenges due to changes in subject demographics, recording devices, and stimuli presented. To tackle these challenges, we propose an improved method for classifying EEG-based emotions across domains with different distributions. We propose a Gradual Proximity-guided Target Data Selection (GPTDS) technique, which gradually selects reliable target domain samples for training based on their proximity to the source clusters and the model's confidence in predicting them. This approach avoids negative transfer caused by diverse and unreliable samples. Additionally, we introduce a cost-effective test-time augmentation (TTA) technique named Prediction Confidence-aware Test-Time Augmentation (PC-TTA). Traditional TTA methods often face substantial computational burden, limiting their practical utility. By applying TTA only when necessary, based on the model's predictive confidence, our approach improves the model's performance during inference while minimizing computational costs compared to traditional TTA approaches. Experiments on the DEAP and SEED datasets demonstrate that our method outperforms state-of-the-art approaches, achieving accuracies of 67.44% when trained on DEAP and tested on SEED, and 59.68% vice versa, with improvements of 7.09% and 6.07% over the baseline. It excels in detecting both positive and negative emotions, highlighting its effectiveness for practical emotion recognition in healthcare applications. Moreover, our proposed PC-TTA technique reduces computational time by a factor of 15 compared to traditional full TTA approaches.

This retrospective study aimed to kinematically classify mandibular movements collected during Temporomandibular Disorders (TMD) treatment, employing Fourier transformation (FT), Principal Component Analysis (PCA), and K-means clustering (k-means), and to investigate their correlation with symptoms of pain-related TMD. The study included five TMD participants diagnosed with myalgia (age: 39-86 years, with an SD of 18.96) and three healthy participants (age: 32-42 years, with an SD of 5.13) with no stomatognathic problems. TMD participants underwent tailored treatment for their symptoms, and their maximum unassisted mouth opening (MMO) was recorded randomly with a motion capture system (ARCUS digma II, Kavo, Biberach, Germany) at multiple time points. MMO for healthy participants served as a control. The dataset comprising 28 trials, was transferred to the AnyBody Modeling System (AnyBody Technology, Aalborg, Denmark) to extract joint angle time series, which were then transformed into Fourier series. Subsequently, PCA and k-means clustering were conducted. Two clusters were identified: Cluster 1, predominantly composed of symptomatic trials, and Cluster 2, mainly consisting of asymptomatic trials. Distinct transition pathways between the clusters were observed among participants, corresponding to the alleviation of pain-related symptoms during TMD treatment. These findings suggest that this approach has potential as an effective tool for diagnosing and assessing TMD by identifying symptomatic kinematic patterns and tracking temporal changes in mandibular movement. Despite the small dataset, these results suggest promise for a novel functional assessment method for TMD based on kinematic features.

N-palmitoylethanolamine (PEA) is an endogenous bioactive compound recognized for its anti-inflammatory effects and its role in tissue protection and repair. Despite the proposal of peroxisome proliferator-activated receptor alpha (PPARα) as a potential receptor for PEA, direct evidence of binding remains insufficient. This study offers a comprehensive analysis of human nuclear receptors (NRs) through structural bioinformatics and molecular docking, evaluating a total of 367 unique NR structures across 47 subfamilies. To explore the stability and binding affinity of PEA with selected nuclear receptors, we conducted molecular dynamics simulations following initial docking assessments. The results revealed Hepatocyte Nuclear Factor 4-alpha (HNF4α) as the highest-ranking receptor with a global score of 0.884, closely followed by Hepatocyte Nuclear Factor 4-gamma (HNF4γ) at 0.871 and Retinoic Acid Receptor gamma-1 (RARγ-1) at 0.829. Among these, HNF4γ demonstrated the strongest affinity for PEA, supported by consistent simulation results. In contrast, the PPARα receptor ranked 44th with a global score of 0.519, indicating that PEA may engage more effectively with other nuclear receptors. In conclusion, this study underscores PEA's potential as a multi-target therapeutic agent through its interactions with various nuclear receptors, particularly HNF4γ and the Mineralocorticoid Receptor (MR). The ability of PEA to influence multiple signaling pathways suggests its promise in addressing complex diseases associated with inflammation and metabolic disorders. Additionally, the integration of Root Mean Square Deviation (RMSD) and Gibbs free energy (ΔG) analyses further elucidates the stability and binding affinities of PEA, providing a foundation for future research into its therapeutic applications.

Eucommiae Cortex (ECO) is a traditional medicinal and edible plant endemic to China, highly prized for its numerous health benefits. It typically undergoes special processing before application. The efficacy of ECO is influenced by processing techniques, necessitating the assurance of stability and consistency in its effects. However, existing methods for identifying ECO are cumbersome, thus, there is an urgent need to develop an accurate, rapid, and non-invasive assessment method. Deep learning techniques employing ResNet and Vision Transformer (ViT) models were employed to classify ECO images at various processing levels. Concurrently, the anti-fatigue properties of ECO were assessed through swimming time, pole climbing experiments, and biochemical analyses including SDH, LDH, ATP content, Na-K-ATPase, and Ca-Mg-ATPase indices. We demonstrated the efficacy of using image analysis to automatically classify ECO with a high degree of accuracy. The results indicated that the Vision Transformer model performed exceptionally well, achieving an accuracy rate exceeding 95 % in grading ECO images. Additionally, our study revealed that mice treated with moderately processed ECO displayed enhanced fatigue mitigation compared to other processing levels, as evidenced by multiple assessments.

Carrion's disease, caused by the bacterium Bartonella bacilliformis, is a serious public health problem in Peru, Ecuador and Colombia. Currently there is no available vaccine against B. bacilliformis. While antibiotics are the standard treatment, resistant strains have been reported, and there is a potential spread of the vector that transmits the bacteria. This study aimed to design a multi-epitope vaccine candidate against the causative agent of Carrion's disease using immunoinformatics tools. Predictions of B-cell epitopes, as well as CD4 and CD8T cell epitopes, were performed from the entire proteome of B. bacilliformis KC583 using the most frequent alleles from Peru, Ecuador, Colombia, and worldwide. B-cell epitopes and T-cell nested epitopes from outer membrane and virulence-associated proteins were selected. Epitopes were filtered out based on promiscuity, non-allergenicity, conservation, non-homology and non-toxicity. Two vaccine constructs were assembled using linkers. The tertiary structure of the constructs was predicted, and their stability was evaluated through molecular dynamics simulations. The most stable construct was selected for molecular docking with the TLR4 receptor. This study proposes a vaccine construct evaluated in silico as a potential vaccine candidate against Bartonella bacilliformis.

Single-cell RNA sequencing (scRNA-seq) technology establishes a unique view for elucidating cellular heterogeneity in various biological systems. Yet the scRNA-seq data is compromised by a high dropout rate due to the technological limitation, and the substantial data loss poses computational challenges on subsequent analyses. This study introduces a novel graph clustering autoencoder (GCAE)-based imputation approach (GraCEImpute) to address the challenge of missing data in scRNA-seq data. Our comprehensive evaluation demonstrates that the GraCEImpute model outperforms existing approaches in accurately imputing dropout zeros within scRNA-seq data. The proposed GraCEImpute model also demonstrates the significantly enhanced quality of downstream scRNA-seq data analyses, including clustering, differential gene expression (DEG) analysis, and cell trajectory inference. These improvements underscore the GraCEImpute model's potential to facilitate a deeper understanding of cellular processes and heterogeneity through the scRNA-seq data analyses. The source code is released at https://www.healthinformaticslab.org/supp/.

Breast mass segmentation plays a crucial role in early breast cancer detection and diagnosis, and while Convolutional Neural Networks (CNN) have been widely used for this task, their reliance on local receptive fields limits ability to capture long-range dependencies. Vision Transformers (ViTs), on the other hand, excel in this area by leveraging multi-head self-attention mechanisms to generate attention maps that dynamically gather global spatial information, significantly outperforming CNN-based architectures in various tasks. However, traditional transformer-based models come with challenges, including high computational complexity due to the self-attention mechanism and inefficiency in the static MLP fusion process. To overcome these issues, the Hybrid Transformer U-Net (HTU-net) model is proposed for breast mass segmentation in mammography. Channel and spatial enhanced self-attention mechanisms are integrated with convolutions layers in HTU-Net, creating a hybrid architecture that combines the strengths of both CNNs and ViTs. The introduction of a multiscale attention mechanism further improves the model's ability to fuse information from different resolutions, enhancing the decoder's capacity to reconstruct fine details in the segmented output. By using both local texture-based features and global contextual information, HTU-Net excels in capturing essential features, thus improving segmentation performance. The experimental results across multiple datasets, including CBIS-DDSM and INbreast, demonstrate that HTU-Net outperforms several state-of-the-art methods, achieving superior accuracy, dice similarity coefficient, and intersection over union. This work highlights the potential of hybrid architectures in advancing computer-aided diagnosis systems, particularly in improving segmentation quality and reliability for breast cancer detection.

Gastric cancer (GC) is a common cancer worldwide. Therefore, searching for effective treatments is essential, and drug repositioning can be a promising strategy to find new potential drugs for GC therapy. For the first time, we sought to identify molecular alterations and validate new mechanisms related to Mebendazole (MBZ) treatment in GC cells through transcriptome analysis using microarray technology. Data revealed 1066 differentially expressed genes (DEGs), of which 345 (2.41 %) genes were upregulated, 721 (5.04 %) genes were downregulated, and 13,231 (92.54 %) genes remained unaltered after MBZ exposure. The overexpressed genes identified were CCL2, IL1A, and CDKN1A. In contrast, the H3C7, H3C11, and H1-5 were the top 3 underexpressed genes. Gene set enrichment analysis (GSEA) identified 8 pathways significantly overexpressed in the treated group (p < 0.05 and FDR<0.25). The validation of the expression of top desregulated genes by RT-qPCR confirmed the transcriptome results, where MBZ increased the CCL2, IL1A, and CDKN1A and reduced the H3C7, H3C11, and H1-5 transcript levels. Expression analysis in samples from TCGA databases correlated that the lower ILI1A and higher H3C11 and H1-5 gene expression are associated with decreased overall survival rates in patients with GC, indicating that MBZ treatment can improve the prognosis of patients. Thus, the data demonstrated that the drug MBZ alters the transcriptome of the AGP-01 lineage, mainly modulating the expression of histone proteins and inflammatory cytokines, indicating a possible epigenetic and immunological effect on tumor cells, these findings highlight new mechanisms of action related to MBZ treatment. Additional studies are still needed to better clarify the epigenetic and immune mechanism of MBZ in the therapy of GC.

The integration of Artificial Intelligence (AI) and Intelligent Learning Models (ILMs) in healthcare has transformed the field, offering precise diagnostics, remote monitoring, personalized treatment, and more. Cardioneurological disorders (CD), affecting the cardiovascular and neurological systems, present significant diagnostic and management challenges. Traditional testing methods often lack sensitivity and specificity, leading to delayed or inaccurate diagnoses. AI-driven ILMs trained on large datasets offer promise for accurate identification and prediction of CD by analyzing complex data patterns. However, there is a lack of comprehensive studies reviewing AI applications for the diagnosis of CD and inter related disorders. This paper comprehensively reviews existing integrated solutions involving AI and ILMs in CD, examining their clinical manifestations, epidemiology, diagnostic challenges, and therapeutic considerations. The study examines recent research on CD, reviews AI-driven models' landscape, evaluates existing models, addresses practical considerations, and outlines future research directions. Through this work, we aim to provide insights into the transformative potential of AI-driven ILMs in improving clinical practice and patient outcomes for CD.

In recent years, deep learning has become a popular tool to analyze and classify medical images. However, challenges such as limited data availability, high labeling costs, and privacy concerns remain significant obstacles. As such, generative models have been extensively explored as a solution to generate new images and overcome the stated challenges. In this paper, we augment a dataset of chest CT scans for Vertebral Compression Fractures (VCFs) collected from the American University of Beirut Medical Center (AUBMC), specifically targeting the detection of incidental fractures that are often overlooked in routine chest CTs, as these scans are not typically focused on spinal analysis. Our goal is to enhance AI systems to enable automated early detection of such incidental fractures, addressing a critical healthcare gap and leading to improved patient outcomes by catching fractures that might otherwise go undiagnosed. We first generate a synthetic dataset based on the segmented CTSpine1K dataset to simulate real grayscale data that aligns with our specific scenario. Then, we use this generated data to evaluate the generative capabilities of Deep Convolutional Generative Adverserial Networks (DCGANs), variational autoencoders (VAEs), and VAE-GAN models. The VAE-GAN model demonstrated the highest performance, achieving a Fréchet Inception Distance (FID) five times lower than the other architectures. To adapt this model to real-image scenarios, we perform transfer learning on the GAN, training it with the real dataset collected from AUBMC and generating additional samples. Finally, we train a CNN using augmented datasets that include both real and generated synthetic data and compare its performance to training on real data alone. We then evaluate the model exclusively on a test set composed of real images to assess the effect of the generated data on real-world performance. We find that training on augmented datasets significantly improves the classification accuracy on a test set composed of real images by 16 %, increasing it from 73 % to 89 %. This improvement demonstrates that the generated data is of high quality and enhances the model's ability to perform well against unseen, real data.

Cognitional heart disease is one of the leading causes of mortality among newborns. Tissue-engineered vascular grafts offer the potential to help treat cognitional heart disease through patient-specific vascular grafts. However, current methods often rely on non-personalized designs or involve significant human intervention. This paper presents a computational framework for the automatic shape optimization of patient-specific tissue-engineered vascular grafts for repairing the aortic arch, aimed at reducing the need for manual input and improving current treatment outcomes, which either use non-patient-specific geometry or require extensive human intervention to design the vascular graft. The paper's core innovation lies in an automatic shape optimization pipeline that combines Bayesian optimization techniques with the open-source finite volume solver, OpenFOAM, and a novel graft deformation algorithm. Specifically, our framework begins with Laplacian mode computation and the approximation of a computationally low-cost Gaussian process surrogate model to capture the minimum weighted combination of inlet-outlet pressure drop (PD) and maximum wall shear stress (WSS). Bayesian Optimization then performs a limited number of OpenFOAM simulations to identify the optimal patient-specific shape. We use imaging and flow data obtained from six patients diagnosed with cognitional heart disease to evaluate our approach. Our results showcase the potential of online training and hemodynamic surrogate model optimization for providing optimal graft shapes. These results show how our framework successfully reduces inlet-outlet PD and maximum WSS compared to pre-lofted models that include both the native geometry and human-designed grafts. Furthermore, we compare how the performance of each design optimized under steady-state simulation compares to that design's performance under transient simulation, and to what extent the optimal design remains similar under both conditions. Our findings underscore that the automated designs achieve at least a 16% reduction in blood flow pressure drop in comparison to geometries optimized by humans.

Axillary lymph node dissection (ALND) is the standard of care for breast cancer patients with positive sentinel lymph nodes (SLN), which are the first lymph nodes that drain the breast. However, many patients with positive SLNs may not have additional positive nodes, making the prediction of non-sentinel lymph node (NSLN) metastasis challenging. Reliable prognostic tools are essential for accurately assessing NSLN metastasis. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram has demonstrated effectiveness in this context, but it requires further evaluation within the Iranian breast cancer population. While ALND remains the gold standard, its unnecessary application in patients without evidence of additional positive nodes raises concerns due to potential complications such as lymphedema, nerve injury, and shoulder joint dysfunction. Furthermore, integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques presents an opportunity to enhance the precision of NSLN metastasis predictions.

Brain age, an emerging biomarker for brain diseases and aging, is typically predicted using single-modality T1-weighted structural MRI data. This study investigates the benefits of integrating structural MRI with diffusion MRI to enhance brain age prediction. We propose an attention-based deep learning model that fuses global-context information from structural MRI with local details from diffusion metrics. The model was evaluated using two large datasets: the Human Connectome Project (HCP, n = 1064, age 22-37) and the Cambridge Center for Aging and Neuroscience (Cam-CAN, n = 639, age 18-88). It was tested for generalizability and robustness on three independent datasets (n = 546, age 20-86), reproducibility on a test-retest dataset (n = 44, age 22-35), and longitudinal consistency (n = 129, age 46-92). We also examined the relationship between predicted brain age and behavioral measures. Results showed that the multimodal model improved prediction accuracy, achieving mean absolute errors (MAEs) of 2.44 years in the HCP dataset (sagittal plane) and 4.36 years in the Cam-CAN dataset (axial plane). The corresponding R values were 0.258 and 0.914, respectively, reflecting the model's ability to explain variance in the predictions across both datasets. Compared to single-modality models, the multimodal approach showed better generalization, reducing MAEs by 10-76 % and enhancing robustness by 22-82 %. While the multimodal model exhibited superior reproducibility, the sMRI model showed slightly better longitudinal consistency. Importantly, the multimodal model revealed unique associations between predicted brain age and behavioral measures, such as walking endurance and loneliness in the HCP dataset, which were not detected with chronological age alone. In the Cam-CAN dataset, brain age and chronological age exhibited similar correlations with behavioral measures. By integrating sMRI and dMRI through an attention-based model, our proposed approach enhances predictive accuracy and provides deeper insights into the relationship between brain aging and behavior.

This study aims to investigate the micro-hemodynamic effects of strut malapposition in patient-specific stented coronary bifurcations.

Multiple studies have aimed to consolidate drug-related data and predict drug effects. However, most of these studies have focused on integrating diverse data through correlation rather than representing them based on the pharmacodynamic mechanism of action (MOA). It is thus crucial to obtain interpretability to validate prediction results. In this study, we propose a novel framework to construct knowledge graphs that represent pharmacodynamic MOA, predict drug effects, and derive conceivable mechanistic pathways.

Serum metabolome analysis is essential for identifying disease biomarkers and predicting patient outcomes in precision medicine. Thus, this study aims to compare Ultra-High Performance Liquid Chromatography-High-Resolution Mass Spectrometry (UHPLC-HRMS) with Fourier Transform Infrared (FTIR) spectroscopy in acquiring the serum metabolome of critically ill patients, associated with invasive mechanical ventilation (IMV), and predicting death. Three groups of 8 patients were considered. Group A did not require IMV and survived hospitalization, while Groups B and C required IMV. Group C patients died a median of 5 days after sample harvest. Good prediction models were achieved when comparing groups A to B and B to C using both platforms' data, with UHPLC-HRMS showing 8-17 % higher accuracies (≥83 %). However, developing predictive models using metabolite sets was not feasible when comparing unbalanced populations, i.e., Groups A and B combined to Group C. Alternatively, FTIR-spectroscopy enabled the development of a model with 83 % accuracy. Overall, UHPLC-HRMS data yields more robust prediction models when comparing homogenous populations, potentially enhancing understanding of metabolic mechanisms and improving patient therapy adjustments. FTIR-spectroscopy is more suitable for unbalanced populations. Its simplicity, speed, cost-effectiveness, and high-throughput operation make it ideal for large-scale studies and clinical translation in complex populations.

Obesity is a chronic disease correlated with numerous risk factors that not only negatively affect all body functions but also increase the chances of developing chronic diseases and the associated morbidity and mortality rates. This study proposes a novel system that bridges the gap between healthcare providers and patients by offering both parties some tools for navigating the intricacies of dietary planning. In this system, machine learning techniques are used to determine the required calories before starting an obesity treatment. A hybrid precision model with minimal parameters is also developed to estimate the appropriate number of calories for losing weight and to formulate a healthy diet plan. A real dataset of 15 anthropometric measurements is analyzed using SVR, LR, and DTR regression models, and all the data are preprocessed before analysis to enhance model performance. Results show that the required calories can be estimated with a high correlation (R = 0.985) from independent measurements. The proposed model also calculates the healthy daily percentages of fats, proteins, and carbohydrates based on a knowledge base of medical rules and functions, thus facilitating the sequential treatment of obese patients. In sum, this study applies different models to design a practical, cost-effective approach for accurately determining the required calories and formulating valuable diet plans for obesity treatment and management.