Familial hypercholesterolemia (FH) results in elevated levels of LDL-C, increasing the risk of developing cardiovascular disease. This study aims to identify genetic causes and examine the connection between genetic variants and the resulting genotype-protein phenotype in Saudi FH patients. Whole-exome sequencing (WES) and Sanger sequencing were employed to detect causative variants in affected Saudi FH families and their healthy relatives. Computational tools, including RNA stability analysis, molecular dynamics simulations, and molecular docking were used to assess the impact of these variants on mRNA stability and protein structure, particularly LDLR-LDLRAP1 interactions. WES identified two pathogenic variants in the LDLR gene in two Saudi FH families: c.103 C>T p.(Gln35Ter) and c.2416dup p.(Val806GlyfsTer11), both absent in healthy relatives and regional databases. The c.103 C>T variant alters the secondary RNA structure of LDLR, potentially affecting its stability and function. The c.2416dupG variant truncates the LDLR cytoplasmic tail, disrupting the NPXY–LDLRAP1 interaction and impairing receptor internalization. Molecular dynamics simulations using Desmond revealed increased structural flexibility and altered interaction dynamics in the LDLR protein due to the c.2416dup variant, suggesting further impacts on the protein’s functional integrity. In conclusion, this study identifies rare pathogenic variants c.2416dup and c.103 C>T in LDLR in extended Saudi Arabian families. It demonstrates the integration of bioinformatics methods with sequencing data to characterize and elucidate the pathogenic effects of genetic variants, providing comprehensive insights into the intricate interplay between LDLR genetic variants and their molecular impacts in FH patients.

In recent years, the application of deep convolutional neural networks (DCNNs) to medical image segmentation has shown significant promise in computer-aided detection and diagnosis (CAD). Leveraging features from different spaces (i.e. Euclidean, non-Euclidean, and spectrum spaces) and multi-modalities of data have the potential to improve the information available to the CAD system, enhancing both effectiveness and efficiency. However, directly acquiring data from different spaces across multi-modalities is often prohibitively expensive and time-consuming. Consequently, most current medical image segmentation techniques are confined to the spatial domain, which is limited to utilizing scanned images from MRI, CT, PET, etc. Here, we introduce an innovative Joint Spatial-Spectral Information Fusion method which requires no additional data collection for CAD. We translate existing single-modality data into a new domain to extract features from an alternative space. Specifically, we apply Discrete Cosine Transformation (DCT) to enter the spectrum domain, thereby accessing supplementary feature information from an alternate space. Recognizing that information from different spaces typically necessitates complex alignment modules, we introduce a contrastive loss function for achieving feature alignment before synchronizing information across different feature spaces. Our empirical results illustrate the greater effectiveness of our model in harnessing additional information from the spectrum-based space and affirm its superior performance against influential state-of-the-art segmentation baselines. The code is available at https://github.com/Auroradsy/SIN-Seg.

Cholangiocarcinoma (CCA) arises within the peritumoral bile microenvironment, yet microbial translocation from bile to intracholangiocarcinoma (IntraCCA) tissues remains poorly understood. Previous studies on bile microbiota alterations from biliary benign disease (BBD) to CCA have yielded inconsistent results, highlighting the need for cross-study analysis. We presented a comprehensive analysis of five cohorts (N = 266), including our newly established 16S rRNA gene profiling (n = 42), to elucidate these microbiota transitions. The concordance of bacteria between CCA bile and intraCCA tissue, represented by Enterococcus and Staphylococcus, suggested microbiota migration from bile to intratumoral tissues. A computational random forest machine learning model effectively distinguished intraCCA tissue from CCA bile, identifying Rhodococcus and Ralstonia as diagnostically significant. The model also excelled in differentiating CCA bile from BBD bile, achieving an AUC value of 0.931 in external validation. Using unsupervised hierarchical clustering, we established Biletypes based on microbial signatures in our cohort. A combination of 17 genera effectively stratified patients into Biletype A and Biletype B. Biletype B robustly discerned CCA from BBD, with Sub-Biletype B1 correlating with advanced TNM stage and poorer prognosis. Among the 17 genera, bacterial Cluster 1, composed of Sphingomonas, Staphylococcus, Massilia, Paenibacillus, Porphyrobacter, Lawsonella, and Aerococcus, was enriched in Biletype B1 and predicted CCA with an AUC of 0.96. Staphylococcus emerged as a promising single-genus predictor for CCA diagnosis and staging. In conclusion, this study delineates a potential microbiota transition pathway from the gut through CCA bile to intra-CCA tissue, proposing Biletypes and Staphylococcus as biomarkers for CCA prognosis.

The development of sustainable production of bio-based chemicals is targeted towards the use of lignocellulose. Overcoming its recalcitrance is a crucial step for biomass valorization. Here, we demonstrate a predictive system for reducing sugar yield from acid pretreatment and enzymatic hydrolysis processes of two types of biomass, rice straw and sugarcane leaves, which have different lignocellulosic compositions. A machine learning model based on the Decision Tree algorithm was employed to predict the amount of reducing sugars generated during enzymatic hydrolysis. The model demonstrated satisfactory accuracy, with an R² of 0.8910 for the training set and 0.8121 for the testing set, along with low error values (RMSE 0.1042 and MAE 0.0705). Scanning electron microscope (SEM) revealed that the biomass structure undergoes significant changes after enzymatic hydrolysis, as proven by the formation of surface pores. This morphological alteration reflects the enzymatic degradation of cellulose, resulting from the disruption of fiber bonds. The application of machine learning in this research shows great potential for enhancing biomass conversion efficiency, contributing to biomass valorization efforts.

High-throughput transcriptomic profiling (HTTr) enables scalable characterisation of transcriptional responses to chemical and genetic perturbations. While plate-based technologies such as MAC-Seq, TempO-seq and PLATE-seq have made HTTr more accessible, they pose unique computational challenges for data modelling and integration across modalities. We present macpie, an R package designed to streamline the analysis of HTTr data from plate-based screens. Built on the tidySeurat framework, macpie streamlines the entire analytical pipeline from preprocessing and quality control to pathway enrichment, chemical feature extraction, and multimodal data integration. The package incorporates multiple statistical frameworks and uses parallelisation for scalability. By leveraging Docker and Nextflow, macpie ensures reproducibility and ease of use for transcriptome-wide screening.Data availabilityAll code and example datasets used in this study are available in the macpie GitHub repository (https://github.com/PMCC-BioinformaticsCore/macpie). Additional data supporting the findings of this study are available from the corresponding author upon reasonable request.

The malignant transformation of odontogenic keratocysts (OKC) into cancerous odontogenic keratocysts (COKC) is exceedingly rare, and its mechanisms remain poorly understood. Studies exploring the cellular heterogeneity, molecular pathways, and clinical features of COKC are limited. In this study, we performed single-cell RNA sequencing (scRNA-seq) on three COKC samples and integrated the data with a public OKC dataset, identifying 22,509 single cells. Two COKC-specific epithelial subpopulations, Basal-C0-EXT1 and Basal-C3-HIST1H3B, were identified. These subpopulations exhibited enhanced stemness and invasive potential, respectively, suggesting their roles as key drivers of OKC carcinogenesis. Fibroblasts underwent phenotypic transitions, particularly from inflammation-associated fibroblasts (IFBs) to myofibroblasts (MFBs). Similarly, macrophage phenotypic transformation may also play a role in OKC carcinogenesis. Clinical observations of severe lesion-area pain in COKC patients suggest potential neuroinvasiveness, Supported by single-cell transcriptomic data, imaging findings, and histopathological evidence. A review of clinical data revealed that none of the COKC patients exhibited cervical lymph node metastasis. Single-cell transcriptomics suggests that this phenomenon may be associated with an active immune microenvironment in COKC, reduced epithelial-mesenchymal transition (EMT) activity, lower VEGFC expression, and upregulated MAST4 expression as a potential regulator of lymphatic metastasis. In conclusion, COKC exhibits distinct molecular, cellular, and clinical characteristics compared to OKC, featuring potent neuroinvasiveness and low lymph node metastatic potential. These findings provide important insights into the mechanisms underlying COKC development and may guide novel diagnostic and therapeutic strategies.

Activated carbon is a versatile material widely used in applications such as water and air purification, energy storage, and catalysis. Its unique structure and properties are derived from an intricate process of carbonization and activation, which creates a complex porous network with varied chemical functionality. This article reviews recent advances in the experimental characterization of activated carbons, highlighting techniques such as X-ray diffraction, infrared spectroscopy, and high-resolution electron microscopy, which have enabled a deeper understanding of their microstructure. Additionally, theoretical models, including Grand Canonical Monte Carlo (GCM) simulations and Molecular Dynamics (MD), are discussed as they complement experimental findings and allow for the prediction of properties such as adsorption and pore size distribution. Finally, emerging trends in the design of activated carbon materials are explored, emphasizing their potential for sustainable applications in environmental remediation and advanced energy technologies.

Post-transcriptional RNA modifications, such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), and pseudouridine (Ψ), are critical regulators of plant development, stress responses, and environmental adaptation. The roles of m6A, m5C, and Ψ in plant stress responses have been insufficiently explored. To investigate the epitranscriptomic landscape of these modifications, we employed direct RNA sequencing (DRS) to analyze native RNA landscape from the amphibious plant Riccia fluitans grown in both aquatic and terrestrial environments. Our study revealed the presence of Ψ, m5C, and m6A modifications and expression profiling in R. fluitans transcriptomes from diverse environments. Metabolomic profiling further demonstrated environment-dependent shifts in soluble sugars, aligning with transcriptional and epitranscriptomic changes in starch metabolism genes. Collectively, our findings highlight the dynamic regulation of RNA modifications as a crucial and previously underappreciated component of environmental adaptation in early land plants, providing new insights into the molecular basis of phenotypic plasticity and the evolutionary transition to terrestrial life.

Atrial fibrillation (AF) is a heart condition widely recognized as a significant risk factor for stroke. Left atrial (LA) volume variation has been identified as a key predictor of AF, and several researchers have proposed deep learning models capable of quickly providing this measurement by processing computed tomography (CT) or magnetic resonance images. In clinical imaging, time-varying ECG-gated CT offers precise information about LA anatomy and function, which could help in developing personalized treatment plans for AF patients. Furthermore, advancements in time-varying dataset acquisition indicate the potential for expanding the role of CT in the management of AF patients through specialized processing techniques. However, automatic segmentation of the LA across all cardiac phases remains challenging due to significant variations in both anatomical structures and image signals throughout the cardiac cycle. To overcome these challenges, this study presents a comprehensive AI-based framework designed to segment the LA across the entire cardiac cycle and classify patients with AF. Specifically, our framework employs a customized Residual 3D-UNet model to segment the LA from time-varying ECG-gated CT scans and utilizes a One-Class Support Vector Machine (OCSVM) to distinguish patients in sinus rhythm (SR) from those with AF. A dataset of 93 time-varying ECG-gated CT scans was retrospectively collected: 60 patients were used for the segmentation task, while 33 patients were used for the classification task. The Residual 3D-UNet model demonstrated high accuracy, achieving a mean Dice score of 0.94, with consistent precision (94.45%) and recall (94.83%) across ten cardiac phases. The OCSVM achieved 78.7% accuracy with high specificity (86.3%), effectively minimizing the risk of misclassifying AF as SR, although sensitivity was lower at 70%, demonstrating the potential of automated segmentation and rhythm classification, providing a potential valuable tool for AF diagnosis.