Measurements of changes in fluorescence signal is one of the most commonly applied methods for studying protein-ligand affinities. These measurements are generally carried out using cuvettes in spectrofluorometers, which can only measure one sample at a time. This makes screening procedures for multiple ligands and proteins extremely laborious, as each protein must be measured with multiple ligand concentrations, and usually in triplicate. Moreover, multiple equations exist to extract the affinity constants and other information from the data, and their underlying assumptions are often disregarded. In this study, the affinities of human, bovine and rat serum albumins for the mycotoxin zearalenone and five of its common derivatives were measured in 96-well microplates, allowing quick measurements of multiple samples using less reagent amounts. In comparison to measurements using a cuvette in a spectrofluorometer, the microplate method was shown to reproduce the affinity constants accurately. The results were discussed in terms of common pitfalls regarding experimental setup and available equations to analyze protein-ligand binding in fluorescence quenching assays. The commonly used Stern-Volmer equation was discussed in detail and the results used to show how inaccurate it is when a fluorescent protein-ligand complex is formed, and when other underlying approximations are ignored.

Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by α-glucosidase in the intestine into monosaccharides, rapidly raising blood sugar levels and contributing to type 2 diabetes mellitus (T2DM). Synthetic inhibitors of carbohydrate-digesting enzymes are used to manage T2DM but may harm organ function over time. Bioactive peptides offer a safer alternative, avoiding such adverse effects. Computational methods for predicting antidiabetic peptides (ADPs) can significantly reduce the time and cost of experimental testing. While machine learning (ML) has been applied to identify ADPs, advancements in data analysis and algorithms continue to drive progress in the field. To address this, we developed AntiT2DMP-Pred, the first ML-based tool specifically designed for predicting type 2 antidiabetic peptides (T2ADPs). This tool employs a feature fusion strategy, combining ten highly discriminative feature descriptors chosen from a pool of 32 descriptors and eight ML algorithms, tested across a range of baseline models. AntiT2DMP-Pred demonstrated excellent performance, surpassing both baseline and feature-optimized models, with an accuracy (ACC) and Matthews' correlation coefficient (MCC) of 0.976 and 0.953 on the training dataset, and an ACC and MCC of 0.957 and 0.851 on the independent dataset. The web server (https://balalab-skku.org/AntiT2DMP-Pred) is freely accessible, enabling researchers worldwide to utilize it in their experimental workflows and contribute to the discovery and understanding of T2ADPs, ultimately supporting peptide-based therapeutic development for diabetes management.

The PBMC-derived humanized mouse model (PBMC model) may serve as an excellent tool in the field of immunology for both preclinical research and personalized therapeutic strategy development. However, single transplantation of complete PBMCs without modifications prevents the identification of cell type-specific factors that are potentially involved in modulating cell-intrinsic functions for the immune response. Here, we establish an innovative strategy for PBMC model generation, where two-step transplantations coupled with cell type-specific gene manipulation were conducted to evaluate the potential role of CD8 T cell-intrinsic factors in regulating antitumor immunity toward PDX-based tumors. This method readily yields over 10 % of human CD45 cells within the PBMCs of humanized mice with high editing efficiency of gene expression in CD8 T cells that can be subsequently detected in the tumor microenvironment (TME). Our work provides a new method to generate a PBMC-derived humanized mouse model for investigating regulators of interest during antitumor immunity in a cell type-specific manner.

Compound-protein interaction (CPI) prediction is critical in the early stages of drug discovery, narrowing the search space for CPIs and reducing the cost and time required for traditional high-throughput screening. However, CPI-related data are usually distributed across different institutions and their sharing is restricted because of data privacy and intellectual property rights. Constructing a scheme that enhances multi-institutional collaboration to improve prediction accuracy while protecting data privacy is essential. To this end, we propose FedKD-CPI, the first framework based on federated knowledge distillation, to effectively facilitate multi-party CPI collaborative prediction and ensure data privacy and security. FedKD-CPI uses knowledge distillation technology to extract the updated knowledge of all client models and train the model on the server to achieve knowledge aggregation, which can effectively utilize the knowledge contained in public and private data. We evaluate FedKD-CPI on three benchmark datasets and compare it with four baselines. The results show that FedKD-CPI is very close to centralized learning and significantly better than localized learning. Furthermore, FedKD-CPI outperforms federated learning-based baselines on independent and identically distributed data and non-independent and identically distributed data. Overall, FedKD-CPI improves the CPI prediction while ensuring data security and promoting institutions' collaboration to accelerate drug discovery.

Diagnostics for neurodegenerative diseases lack non-invasive approaches suitable for early-stage biochemical screening and routine examination of neuropathology. Biomarkers of neurodegenerative diseases pass through the brain-nose interface (BNI) and accumulate in nasal secretion. Sample collection from the brain-nose interface presents a compelling prospect as basis for a non-invasive molecular diagnosis of neuropathologies. Here, we evaluated a novel medical device (nosecollect) that is tailored for the standardized collection of nasal secretion samples from BNI, focusing on its sample collection safety and efficiency.

In the field of medical science, skin segmentation has gained significant importance, particularly in dermatology and skin cancer research. This domain demands high precision in distinguishing critical regions (such as lesions or moles) from healthy skin in medical images. With growing technological advancements, deep learning models have emerged as indispensable tools in addressing these challenges. One of the state-of-the-art modules revealed in recent years, the 2D Selective Scan (SS2D), based on state-space models that have already seen great success in natural language processing, has been increasingly adopted and is gradually replacing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Leveraging the strength of this module, this paper introduces LiteMamba-Bound, a lightweight model with approximately 957K parameters, designed for skin image segmentation tasks. Notably, the Channel Attention Dual Mamba (CAD-Mamba) block is proposed within both the encoder and decoder alongside the Mix Convolution with Simple Attention bottleneck block to emphasize key features. Additionally, we propose the Reverse Attention Boundary Module to highlight challenging boundary features. Also, the Normalized Active Contour loss function presented in this paper significantly improves the model's performance compared to other loss functions. To validate performance, we conducted tests on two skin image datasets, ISIC2018 and PH2, with results consistently showing superior performance compared to other models. Our code will be made publicly available at: https://github.com/kwanghwi242/A-new-segmentation-model.

Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed which can learn genotype-phenotype associations and obtain relevant biomarkers. Specifically, a deep neural network is used first to explore the nonlinear relationships among samples. Secondly, self-representation learning based on hyper-Laplacian regularization is utilized to reconstruct the original data. In particular, the introduction of hyper-Laplacian regularization ensures the local structure of the high-dimensional spatial embedding and explores the higher-order relationships among the samples. Moreover, the structural regularization term in the association analysis uncovers chain relationships among SNPs and graphical relationships among imaging QTs, thus making the obtained markers more interpretable and enhancing the biological significance of the method. The performance of the proposed method is validated on real neuroimaging genetics data. Experimental results show that DHRSAA displays better canonical correlation coefficients and recognizes clearer canonical weight patterns compared to several state-of-the-art methods, which suggests that the proposed DHRSAA achieves better performance and identifies disease-related biomarkers.

Polyphosphate (polyP), a biopolymer composed of phosphates, impacts a wide range of biological functions and pathological conditions in all organisms. However, polyP's intricate physiology and structure in human cells have remained elusive, largely due to the lack of a reliable quantification method including its extraction. In this study, we assess critical points in the whole process: extraction, purification, and quantification polyP from human cell lines. We developed a highly efficient method that extracts between 3 and 100 times more polyP than previously achieved. Supported by Nuclear Magnetic Resonance (NMR), our approach confirms that mammalian polyP is primarily a linear unbranched polymer. We applied the optimized method to commonly used human cell lines, uncovering important variations of intracellular polyP that correlate with the expression levels of specific polyP converting enzymes. This study underscores the importance of employing several techniques for polyP characterization in parallel and provides a valuable and standardized tool for further exploration in this field.

Gap junction (GJ) channels, formed of connexin (Cx) protein, enable direct intercellular communication in most vertebrate tissues. One of the key biophysical characteristics of these channels is their unitary conductance, which can be affected by mutations in Cx genes and various biochemical factors, such as posttranslational modifications. Due to the unique intercellular configuration of GJ channels, recording single-channel currents is challenging, and precise data on unitary conductances of some Cx isoforms remain limited. In this study, we applied stationary noise analysis, a method successfully used for ion channels with very low unitary conductances, to GJ channels. We modified this technique to account for the residual conductance of GJ channels and present three strategies for estimating unitary conductance, including model-based evaluation of open-state probability and subtraction of residual conductance. To assess the validity, advantages, and limitations of these approaches, we performed mathematical analysis and simulation experiments. We also addressed practical issues such as the underestimation of sample variance in autocorrelated recordings and channel rundown, proposing solutions to these issues. Finally, we applied these strategies to electrophysiological data recorded from cells expressing Cx45. Our findings showed that noise-based estimates of Cx45 unitary conductance from macroscopic currents align well with those obtained from single-channel recordings.

The subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding the function of lncRNAs. Since the traditional biological experimental methods are time-consuming and some existing computational methods rely on high computing power, we are committed to finding a simple and easy-to-implement method to achieve more efficient prediction of the subcellular localization of lncRNAs. In this work, we proposed a model based on multi-source features and two-stage voting strategy for predicting the subcellular localization of lncRNAs (MVSLLnc). The multi-source features include k-mer frequency, features based on the coordinate values of Chaos Game Representation (CGR) and features based on physicochemical property (PhyChe). We feed the multi-source features into the traditional machine learning classifiers RF, SVM and XGBoost, respectively, and perform the final prediction task with two-stage voting strategy. Experimental results on three benchmark datasets show that the accuracy can reach 0.829, 0.793 and 0.968, respectively. The accuracy on three independent test sets is 0.642, 0.737 and 0.518, respectively, which are competitive with the existing methods. Our ablation analyses show that the two-stage voting strategy can make full use of the advantages of multi-source features and multiple classifiers, and obtain more robust results.

The electrocardiogram (ECG) is a ubiquitous medical diagnostic tool employed to localize myocardial infarction (MI) that is characterized by abnormal waveform patterns on the ECG. MI is a serious cardiovascular disease, and accurate, timely diagnosis is crucial for preventing severe outcomes. Current ECG analysis methods mainly rely on intra- and inter-lead feature extraction, but most models overlook the medical knowledge relevant to disease diagnosis. Moreover, existing models often fail to effectively utilize the global spatial relationships within multi-lead ECGs, limiting their ability to comprehensively understand and accurately localize the complex pathological mechanisms of MI. To address these issues, we propose a knowledge-driven overlapping lead grouping method. Based on clinical diagnostic knowledge, we group the 12 leads according to their relevance to MI localization while retaining the full set of 12 leads as a unified group. Additionally, we design a multi-stage learning network that first extracts basic features through initial convolutional layer and progressive convolutional block, followed by SE-enhanced multi-scale residual block and positional Transformer block to gradually learn deeper intra- and inter-lead features. Furthermore, we propose a branch-level weighted feature integration mechanism to effectively fuse the features extracted from each group. The proposed method was thoroughly evaluated on the publicly available multi-label PTB-XL dataset and achieved over 80% prediction accuracy for MI localization tasks. The results demonstrated significant improvements across several metrics compared to current state-of-the-art methods, confirming its exceptional performance.

Exploring the associations between microbes and drugs offers valuable insights into their underlying mechanisms. Traditional wet lab experiments, while reliable, are often time-consuming and labor-intensive, making computational approaches an attractive alternative. Existing similarity-based machine learning models for predicting microbe-drug associations typically rely on integrated similarities as input, neglecting the unique contributions of individual similarities, which can compromise predictive accuracy. To overcome these limitations, we develop MPEMDA, a novel method that pre-completes the microbe-drug association matrix using various similarity combinations and employs a label propagation algorithm with error correction to predict microbe-drug associations. Compared with existing methods, MPEMDA simultaneously utilizes the integrated and individual similarities obtained through the Similarity Network Fusion (SNF) method to pre-complete the known drug-microbe association matrix, followed by error correction to optimize the predictive scores generated by the label propagation algorithm. Experimental results on three benchmark datasets show that MPEMDA outperforms state-of-the-art methods in both the 5-fold cross-validation and de novo test. Additionally, case studies on drugs and microbes highlight the method's strong potential to identify novel microbe-drug associations. The MPEMDA code is available at https://github.com/lyx8527/MPEMDA.

The cornea is the primary refracting surface of the eye, requiring precise curvature to ensure optimal vision. Any distortion in its shape may result in significant visual impairment. Corneal ectasias, such as keratoconus (KC), is characterized by gradual thinning and protrusion of the thinned area, due to biomechanical weakening of the tissue, leading to astigmatism and vision loss. KC affects approximately 1 in 2000 individuals globally. While corneal transplantation is the main treatment, limited donor availability and potential immunogenic reactions have spurred the search for alternatives. Stromal lenticule implantation using decellularized porcine corneas offers a promising solution, with reduced immunogenicity and risk of rejection. Additionally, combining this approach with riboflavin and UV radiation treatment post-surgery enhances collagen fibril cross-linking, promoting tissue integration and organization. This study evaluated the efficacy of heterologous transplantation of decellularized porcine lenticules into the corneal stroma of rabbits, followed by riboflavin application and UV radiation. Results demonstrated increased stromal thickness and no signs of tissue rejection, indicating minimal immunogenicity of the lenticules. The cross-linking technique successfully improved tissue organization, suggesting that xenographic lenticule implantation, combined with riboflavin and UV light, is a promising alternative for treating corneal ectasias like KC. Further research is necessary to confirm the long-term efficacy and safety of this method in human subjects.

The EZH2 expression shows significantly associated with immunotherapeutic resistance in several tumors. A comprehensive analysis of the predictive values of EZH2 for immune checkpoint blockade (ICB) effectiveness in uveal melanoma (UM) remains unclear. We analyzed UM data from The Cancer Genome Atlas (TCGA) database, identified 888 differentially expressed genes (DEGs) associated with EZH2 expression, then conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to elucidate biological features of EZH2 in UM assays. The correlation of the expression of EZH2 with tumor immunity related factors such as immune-related pathways, infiltration of various immune cells, immune score and immune checkpoints were explored. The evaluation of EZH2's capability to predict immune therapy outcomes in UM was assessed by incorporating the Tumor Immune Dysfunction and Exclusion (TIDE) score. Lastly, programmed death-ligand 1 (PD-L1) expression was detected in an independent UM patient cohort by immunohistochemical analyses, the correlation of EZH2 with PD-L1 was evaluated. Results highlighted that the EZH2 expression was correlated with immune-related pathways, infiltration of various immune cells, immune score, the expression of immune checkpoints and immunotherapy sensitivity. Collectively, we suggested that EZH2 might be considered as predictor on the therapeutic effects of ICBs on UM patients, and a potential target for combined immunotherapy.

This paper proposes a detailed process for SV calling that permits a data-driven assessment of multiple SV callers that uses both genome assemblies and long-reads. The process is implemented as a software pipeline named Structural Variant - Jaccard Index Measure, or SVJIM, using the Snakemake [20] workflow management system. Like most state-of-the-art SV callers, SV-JIM detects the presence of variations between pairs of genomes, but it streamlines the numerous SV calling stages into a single process for user convenience and evaluates the multiple SV sets produced using the Jaccard index measure to identify those with the highest consistency among the included SV callers. SV-JIM then produces aggregated SV results based on how many callers supported the reported SVs. For validation, SV-JIM was assessed through three case studies on the Homo sapiens genome and two plant genomes - Brassica nigra and Arabidopsis thaliana. Executing SV-JIM identified a significant amount of inter-caller variance which varied by tens of thousands of results on the larger Brassica nigra and Homo sapiens genomes. Further, aggregating the SV sets helped simplify better retention of the less frequently occurring SV types by requiring a level of minimum support rather than from a specific SV caller combination. Finally, these case studies identified a potential for inflated precision reporting that can occur during evaluation. SV-JIM is available publicly under MIT license at https://github.com/USask-BINFO/SV-JIM.

The high prevalence of type 2 diabetes and atherosclerosis makes essential the availability of in vivo experimental models that accurately replicate the pathophysiological mechanisms of these diseases. Apolipoprotein E knockout mice (ApoE) have been used in atherosclerosis studies, and the db/db mice show hyperphagia and obesity. Mice harbouring both alterations (i.e., ApoE) are expected to develop combined features of type 2 diabetes, obesity and accelerated atherosclerosis. To deepen into their pathophysiological profile and further assess their potential as an experimental model, we studied their mortality and their pancreatic, cardiac, and renal phenotype. We analysed during 6 months the glycemic and lipid profile, pancreatic, cardiac and renal structure and function and atherosclerosis in ApoE mice. ApoE mice show increases in plasma glucose (although without statistical significance) and glucagon levels, total cholesterol, triglycerides and HDL-cholesterol and in both insulin-producing β and glucagon producing α cells, and in the tissue expression of both hormones with respect to control (C57BL/6) mice; they show a remarkably high degree of atherosclerosis, higher left ventricular ejection fraction. Although renal function is normal, glucose, sodium and albumin excretion and urinary flow are increased with respect to control mice. Summarizing, ApoE mice constitute a suitable experimental model for the study of type 2 diabetes associated with atherosclerosis.

Spatial transcriptomics has significantly advanced the measurement of spatial gene expression in the field of biology. However, the high cost of ST limits its application in large-scale studies. Using deep learning to predict spatial gene expression from H&E-stained histology images offers a more cost-effective alternative, but existing methods fail to fully leverage the multimodal information provided by Spatial transcriptomics and pathology images. In response, this paper proposes STMCL, a novel multimodal contrastive learning framework. STMCL integrates multimodal information, including histology images, gene expression features of spots, and their locations, to accurately infer spatial gene expression profiles. We tested four different types of multi-slice spatial transcriptomics datasets generated by the 10X Genomics platform. The results indicate that STMCL has advantages over baseline methods in predicting spatial gene expression profiles. Furthermore, STMCL is capable of capturing cancer-specific highly expressed genes and preserving gene expression patterns while maintaining the original spatial structure of gene expression. Our code is available at https://github.com/wenwenmin/STMCL.

Cancer is a complex and heterogeneous disease, and accurate cancer subtyping can significantly improve patient survival rates. The complexity of cancer spans multiple omics levels, and analyzing multi-omics data for cancer subtyping is a focus of considerable attention. However, extracting complementary information from different omics data sources and adaptively integrating them remains a major challenge. To address this, we proposed an adaptive approach integrating consensus and specific self-expressive coefficients for multi-omics cancer subtyping (CSSEC). First, independent self-expressive networks are applied to each omics to calculate coefficient matrices to measure patient similarity. Then, two feature graph convolutional network modules capture consensus and specific similarity features by the topK relevant features. Finally, the multi-omics self-expression coefficient matrix is constructed by consensus and specific similarity features. Furthermore, joint consistency and disparity constraints are applied to regularize the fusion process of the self-expressive coefficients. Experimental results demonstrate that CSSEC outperforms existing state-of-the-art methods in survival analysis. Moreover, case studies on Kidney cancer confirm that the cancer subtypes identified by CSSEC have significant biological relevance. The complete code can be available at https://github.com/ykxhs/CSSEC.