Methods (San Diego, Calif.)[JOURNAL]
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200 papers
KRN-DTI: Towards accurate drug-target interaction prediction with Kolmogorov-Arnold and residual networks.
Li Z, Huang J, Liu X … +6 more , Xu P, Shen X, Pan C, Zhang W, Liu W, Han H
Methods · 2025 Aug · PMID 40287076 · Publisher ↗
Predicting drug-target interactions (DTIs) accurately is essential in the field of drug discovery. Recently, artificial intelligence (AI) technologies, especially graph convolutional networks (GCNs), have been developed...
Predicting drug-target interactions (DTIs) accurately is essential in the field of drug discovery. Recently, artificial intelligence (AI) technologies, especially graph convolutional networks (GCNs), have been developed to tackle this challenge. However, as the number of GCN layers increases, models may lose critical information due to excessive smoothing. Moreover, these methods often lack interpretability and are dependent on specific datasets, which limits their generalizability. Consequently, this study introduces a novel method, KRN-DTI, which employs interpretable GCN technology to predict DTIs based on a drug-target heterogeneous network. The method uses GCN technology to identify potential DTIs by leveraging known interactions and dynamically adjusting the weights, thereby enhancing the model's interpretability. Additionally, residual connection technology is employed to integrate GNN outputs, mitigating the over-smoothing issue. Furthermore, the model's interpretability is enhanced by adaptively adjusting weights using Kolmogorov-Arnold Networks (KAN) and attention mechanisms. Experimental results show that KRN-DTI outperforms several advanced computational methods on the benchmark dataset. Case studies further highlight the effectiveness of KRN-DTI in predicting potential DTIs, showcasing its potential for real-world applications in drug discovery. Our code and data are publicly accessible at: https://github.com/lizhen5000/KRN-DTI.git.
Ultra-sensitive graphene micro-ribbon integrated THz biosensor for breast cancer cell detection.
Selvaraj M, Sreeja BS
Methods · 2025 Aug · PMID 40280262 · Publisher ↗
In recent decades, the rising incidence of cancer has made early and rapid diagnosis, along with precise characterization of cancer cells, more crucial than ever. The paper presents a novel metasurface-assisted biosensor...
In recent decades, the rising incidence of cancer has made early and rapid diagnosis, along with precise characterization of cancer cells, more crucial than ever. The paper presents a novel metasurface-assisted biosensor operating in the THz regime, designed for non-invasive and rapid detection of breast cancer cells. The proposed biosensor incorporates graphene micro-ribbons to enhance THz wave interaction, boosting the biosensor's sensitivity and overall performance. When used for cancer cell sensing, the biosensor demonstrates three absorption peaks at 2.0012 THz, 2.8734 THz, and 3.2948 THz with the absorption of 99.18 %, 89.55 %, and 99.93 %, respectively. The biosensor achieves a maximum frequency shift of 49 GHz, a maximum theoretical sensitivity of 3.5 THz/RIU (Refractive Index Unit), and a figure of merit of 6.81 RIU. Additionally, the sensor offers an excellent detection limit of 0.26 RIU and a resolution of 0.91 THz. The ability of the proposed biosensor to detect small refractive index changes (as low as 0.26 RIU) adds to the sensor's versatility, allowing it to be used in a wide variety of clinical and laboratory settings. Given these features and performance, the proposed biosensor holds great promise for non-invasive cancer diagnostics, offering ultra-high sensitivity in a portable and miniaturized platform.
Exploring species taxonomic kingdom using information entropy and nucleotide compositional features of coding sequences based on machine learning methods.
Temesgen SA, Ahmad B, Grace-Mercure BK … +4 more , Liu M, Liu L, Lin H, Deng K
Methods · 2025 Aug · PMID 40280261 · Publisher ↗
The flow of genetic information from DNA to protein is governed by the central dogma of molecular biology. Genetic drift and mutations usually lead to changes in DNA composition, thereby affecting the coding sequences (C...
The flow of genetic information from DNA to protein is governed by the central dogma of molecular biology. Genetic drift and mutations usually lead to changes in DNA composition, thereby affecting the coding sequences (CDS) that encode functional proteins. Analyzing the nucleotide distribution in the coding regions of species is crucial for understanding their evolution. In this study, we applied Markov processes to analyze codon formation in 37,031,061 CDSs across 3,735 species genomes, spanning viruses, archaea, bacteria, and eukaryotes, to explore compositional changes. Our results revealed species preferences for different nucleotides. Information entropies and Markov information densities show that eukaryotes exhibit higher redundancy, followed by viruses, suggesting more gene duplication in eukaryotes and high mutation rates in viruses. Evolutionary trends showed an increase in information entropy and a decrease in Markov entropy, with negative correlations between first- and second-order Markov information densities. Furthermore, uniform manifold approximation and projection (UMAP) was used to reduce information redundancy for revealing unique evolutionary patterns in species classification. The machine learning methods demonstrated excellent performance in species classification accuracy, providing profound insights into CDS evolution and protein synthesis.
Development and optimization of an antibody-free nucleic acid lateral flow assay (AF-NALFA) as part of a molecular toolkit for visual readout of amplified Listeria monocytogenes DNA.
Lopes-Luz L, Sampaio GC, Alves LM … +8 more , Saavedra DP, da Mata LS, Schröder AL, Sucupira LC, Torres Fogaça MB, Neddermeyer PC, Stefani MMA, Bührer-Sékula S
Methods · 2025 Jul · PMID 40274035 · Publisher ↗
Listeria monocytogenes is a Gram-positive foodborne pathogen responsible for listeriosis, a severe disease with high mortality in immunocompromised individuals. Rapid and accurate detection in food samples is essential f...
Listeria monocytogenes is a Gram-positive foodborne pathogen responsible for listeriosis, a severe disease with high mortality in immunocompromised individuals. Rapid and accurate detection in food samples is essential for food safety. In this study, we developed and optimized an Antibody-Free Nucleic Acid Lateral Flow Assay (AF-NALFA) as part of a molecular detection toolkit for the visual readout of amplified L. monocytogenes hlyA gene, in combination with ultra-fast asymmetric PCR (aPCR) and oligonucleotide probe hybridization. Three critical parameters were optimized: oligonucleotide probe concentration on test and control lines, gold nanoparticle-probe conjugation ratio, and running buffer composition. In pure bacterial cultures, the limit of detection (LOD) of AF-NALFA was 12.62 copies for L. monocytogenes ATCC 7644, 8.68 copies for ATCC 19117, and 4.83 copies for ATCC 13932. These values were quantitatively assessed using qPCR, confirming the assay's consistency in detecting low DNA copy numbers. The prototype demonstrated 100% specificity against 13 other bacterial species. Furthermore, it was successfully tested in artificially contaminated UHT milk after 1 year of storage at room temperature, detecting L. monocytogenes at 1-30 CFU/mL without DNA purification or selective enrichment. The AF-NALFA enabled visual detection of target ssDNA hybridization within 20 min, offering a rapid, cost-effective alternative to DNA detection methods requiring expensive equipment, specialized expertise, and time-consuming procedures. These findings highlight AF-NALFA's potential as a complementary tool for L. monocytogenes surveillance, providing a practical solution for rapid screening in food safety laboratories and epidemiological monitoring.
ReLume: Enhancing DNA storage data reconstruction with flow network and graph partitioning.
Xie L, Cao B, Wen X … +4 more , Zheng Y, Wang B, Zhou S, Zheng P
Methods · 2025 Aug · PMID 40268154 · Publisher ↗
DNA storage is an ideal alternative to silicon-based storage, but focusing on data writing alone cannot address the inevitable errors and durability issues. Therefore, we propose ReLume, a DNA storage data reconstruction...
DNA storage is an ideal alternative to silicon-based storage, but focusing on data writing alone cannot address the inevitable errors and durability issues. Therefore, we propose ReLume, a DNA storage data reconstruction method based on flow networks and graph partitioning technology, which can accomplish the data reconstruction task of millions of reads on a laptop with 24 GB RAM. The results show that ReLume copes well with many types of errors, more than doubles sequence recovery rates, and reduces memory usage by about 60 %. ReLume is 10 times more durable than other representative methods, meaning that data can be read without loss after 100 years. Results from the wet lab DNA storage dataset show that ReLume's sequence recovery rates of 73 % and 93.2 %, respectively, significantly outperform existing methods. In summary, ReLume effectively overcomes the accuracy and hardware limitations and provides a feasible idea for the portability of DNA storage.
Computational models for prediction of m6A sites using deep learning.
Sheng N, Qiao J, Wei L … +3 more , Shi H, Guo H, Yang C
Methods · 2025 Aug · PMID 40268153 · Publisher ↗
RNA modifications play a crucial role in enhancing the structural and functional diversity of RNA molecules and regulating various stages of the RNA life cycle. Among these modifications, N6-Methyladenosine (m6A) is the...
RNA modifications play a crucial role in enhancing the structural and functional diversity of RNA molecules and regulating various stages of the RNA life cycle. Among these modifications, N6-Methyladenosine (m6A) is the most common internal modification in eukaryotic mRNAs and has been extensively studied over the past decade. Accurate identification of m6A modification sites is essential for understanding their function and underlying mechanisms. Traditional methods predominantly rely on machine learning techniques to recognize m6A sites, which often fail to capture the contextual features of these sites comprehensively. In this study, we comprehensively summarize previously published methods based on machine learning and deep learning. We also validate multiple deep learning approaches on benchmark dataset, including previously underutilized methods in m6A site prediction, pre-trained models specifically designed for biological sequence and other basic deep learning methods. Additionally, we further analyze the dataset features and interpret the model's predictions to enhance understanding. Our experimental results clearly demonstrate the effectiveness of the deep learning models, elucidating their strong potential in accurately recognizing m6A modification sites.
Non-invasive tools for analysis of plasma membrane protein topology in living cells.
Savenkova D, Bulatova L, Skripova V … +2 more , Kiyamova R, Bogdanov M
Methods · 2025 Jul · PMID 40262691 · Full text
Membrane protein topology studies offer guidance to membrane protein structure, folding, and function, serving as a credible scaffold for designing site-directed mutagenesis and biochemical experiments, helping to identi...
Membrane protein topology studies offer guidance to membrane protein structure, folding, and function, serving as a credible scaffold for designing site-directed mutagenesis and biochemical experiments, helping to identify functionally significant extracellular and intracellular regions, modeling three-dimensional structures, and building reliable mechanistic models. Membrane protein structure as a function of given lipid composition and physiological state of the cell is best probed in whole intact cells. A described simple and advanced immunofluorescence protocol applied to the transmembrane orientation of extramembrane domains permits a topology analysis of plasma membrane proteins in their native state in living unperturbed eucaryotic cells. The accessibility of native epitopes to corresponding antibodies is determined in intact and permeabilized cells to establish their extra- or intracellular or localization respectively. The ability of the given antibody to bind the epitope in intact live and permeabilized cells is then assessed routinely by intact and permeabilized cell immunofluorescent confocal microscopy or fluorescence flow cytometry parametric analyses in several hours. To ensure that the observed immunofluorescence is entirely a result of the binding of antibodies, cells are alive and the plasma membrane is intact, plasma membrane integrity is routinely monitored by co-incubating the cells with a cell membrane-impermeable probe, propidium iodide. Accordingly, plasma membrane side-specific immunostaining analysis was restricted to the propidium iodide-negative, non-permeabilized cell population. The strength of this technique is its simplicity since each native epitope is unique and there is no need to mutate any endogenous sites, introduce new epitopes, or engineer single, dual, or split colorimetric enzymatic reporters. Aside from its simplicity, the advantage of this approach is that the topology is documented in the context of full-length and fully biologically active membrane protein molecules, and topology mapping is carried out using whole live cells, thereby avoiding problems related to cell fixation or the conversion of cells into membrane vesicles with a uniform orientation. The protocol can be universally adapted to any cellular system to systematically map a uniform topology of target membrane protein.
EffiCOVID-net: A highly efficient convolutional neural network for COVID-19 diagnosis using chest X-ray imaging.
Kumar S, Bhowmik B
Methods · 2025 Aug · PMID 40252941 · Publisher ↗
The global COVID-19 pandemic has drastically affected daily life, emphasizing the urgent need for early and accurate detection to provide adequate medical treatment, especially with limited antiviral options. Chest X-ray...
The global COVID-19 pandemic has drastically affected daily life, emphasizing the urgent need for early and accurate detection to provide adequate medical treatment, especially with limited antiviral options. Chest X-ray imaging has proven crucial for distinguishing COVID-19 from other respiratory conditions, providing an essential diagnostic tool. Deep learning (DL)-based models have proven highly effective in image diagnostics in recent years. Many of these models are computationally intensive and prone to overfitting, especially when trained on limited datasets. Additionally, conventional models often fail to capture multi-scale features, reducing diagnostic accuracy. This paper proposed a highly efficient convolutional neural network (CNN) called EffiCOVID-Net, incorporating diverse feature learning units. The proposed model consists of a bunch of EffiCOVID blocks that incorporate several layers of convolution containing (3×3) filters and recurrent connections to extract complex features while preserving spatial integrity. The performance of EffiCOVID-Net is rigorously evaluated using standard performance metrics on two publicly available COVID-19 chest X-ray datasets. Experimental results demonstrate that EffiCOVID-Net outperforms existing models, achieving 98.68% accuracy on the COVID-19 radiography dataset (D), 98.55% on the curated chest X-ray dataset (D), and 98.87% on the mixed dataset (D) in multi-class classification (COVID-19 vs. Normal vs. Pneumonia). For binary classification (COVID-19 vs. Normal), the model attains 99.06%, 99.78%, and 99.07% accuracy, respectively. Integrating Grad-CAM-based visualizations further enhances interpretability by highlighting critical regions influencing model predictions. EffiCOVID-Net's lightweight architecture ensures low computational overhead, making it suitable for deployment in resource-constrained clinical settings. A comparative analysis with existing methods highlights its superior accuracy, efficiency, and robustness performance. However, while the model enhances diagnostic workflows, it is best utilized as an assistive tool rather than a standalone diagnostic method.
Methodologies for label free Raman microspectroscopic monitoring of viral replication processes in vitro.
Chaudary I, Barabas J, Power UF … +3 more , O'Neill L, Byrne HJ, Denning D
Methods · 2025 Aug · PMID 40252940 · Publisher ↗
This study demonstrates the use of Raman Spectroscopy, integrated with multivariate statistical analysis, to monitor the process of viral infection in cells, in-vitro, using the model example of Sendai Virus (SeV) infect...
This study demonstrates the use of Raman Spectroscopy, integrated with multivariate statistical analysis, to monitor the process of viral infection in cells, in-vitro, using the model example of Sendai Virus (SeV) infection in LLC-MK2 monkey kidney cells. A comprehensive methodology is described for determining a precise multiplicity of infection, 48 h post infection, tailored for analysis of viral-host interactions using Raman Microspectroscopy. SeV infected LLC-MK2 cells were fixed on a gold-coated glass slide for Raman spectroscopic analysis. 30-point spectra of uninfected control and 30-point spectra of SeV-infected cells were acquired, focusing randomly on the individual cells. Mean Raman spectra of the control and SeV-infected LLC-MK2 cells revealed spectral differences of peaks corresponding to nucleic acids (485 cm, 785 cm), lipids (1445 cm) and proteins (1600 cm, 1655 cm). These changes in the relative intensities of Raman peaks indicate modifications in the biochemical content, potentially due to viral entry and replication inside the cells. Principal Components Analysis distinguished between control and SeV-infected LLC-MK2 cells, indicating significant biochemical alterations in response to the SeV infection. Partial Least Squares Discriminant Analysis can be employed to quantify the differentiation of the spectral datasets of the infected/noninfected cells, classifying them with 100 % sensitivity and specificity. The detailed methodology described in the study is potentially a powerful tool for tracking viral replication and detecting viral infections and has the potential to impact future research on host-virus interactions and viral diagnostics. Further research on these spectral differences can contribute to developing more efficient viral screening techniques and a better understanding of viral infections.
Development and validation of novel RT-PCR assay for molecular diagnostic of viral variants using SARS-CoV-2 as a case study.
Singh P, Misra G, Mishra N … +2 more , Anvikar A, Potdar V
Methods · 2025 Aug · PMID 40228568 · Publisher ↗
Emerging viruses have long posed significant challenges to global public health, frequently leading to widespread morbidity and mortality. The ongoing evolution of viruses driven by genetic mutations is critical in the e...
Emerging viruses have long posed significant challenges to global public health, frequently leading to widespread morbidity and mortality. The ongoing evolution of viruses driven by genetic mutations is critical in the emergence of these novel pathogens. Among the numerous viruses that have demonstrated this capability the SARS-CoV-2 responsible for the COVID-19 pandemic is the prime example of how viral mutations can profoundly impact disease dynamics, transmission, and control measures. In this study, we present the development of a multiplex RT-PCR assay, using allele-specific primer-probe tailored for molecular diagnostic of viral variants using SARS-CoV-2 as a case study. We conducted a comprehensive evaluation to validate the assay performance using a diverse panel of leftover clinical samples, including a few coded reference samples from external providers. This multiplex PCR typing method detects seven unique mutations of Omicron and two unique mutations of Delta strain with allele-specific primers and probe sets against the spike protein's receptor-binding domain (RBD). The assay exhibits high analytical sensitivity, detecting about 1 x 10 copies/mL of SARS-CoV-2 RNA for each genetic variant tested, and possesses 100 % analytical specificity. Comparative analysis with existing commercial RT-PCR kits demonstrated better performance, particularly in detecting omicron and delta variants. This research highlights the translational potential of our approach in advancing diagnostic capabilities for emerging viral infections, enhancing public health responses to future outbreaks.
Novel multitarget LAMP and PCR assays for the detection of Bordetella species.
Koryukov MA, Oscorbin IP, Gordukova MA … +3 more , Turina IE, Aminova AI, Filipenko ML
Methods · 2025 Aug · PMID 40216281 · Publisher ↗
Whooping cough, or pertussis, is a highly contagious disease caused by several Bordetella species and continues to pose a significant global public health concern. The rising incidence of pertussis highlights the urgent...
Whooping cough, or pertussis, is a highly contagious disease caused by several Bordetella species and continues to pose a significant global public health concern. The rising incidence of pertussis highlights the urgent need for effective public health strategies to address Bordetella infections. Rapid and species-specific diagnostic tools are essential for preventing Bordetella transmission and are vital components of anti-infective measures. This study aimed to develop novel loop-mediated isothermal amplification (LAMP) and quantitative PCR (qPCR) assays for the detection of four Bordetella species responsible for human respiratory tract infections: B. pertussis, B. parapertussis, B. bronchiseptica, and B. holmesii. The qPCR assay demonstrated a low limit of detection (LoD), reliably identifying up to 5 copies of target DNA per reaction. The LAMP assays were approximately three times faster than qPCR (30 min) but had higher LoDs. Notably, qLAMP had a limit of detection of 25 copies per reaction for all four Bordetella species. In contrast, vLAMP had a LoD of 25 copies per reaction for B. pertussis and B. parapertussis; and a LoD of 50 copies per reaction for B. holmesii and B. bronchiseptica. We validated the assays using nasal swab samples from patients with respiratory tract infections, analyzing a total of 651 samples with qPCR and 145 samples with LAMP. Both assays exhibited no cross-reactivity with common viral and bacterial respiratory pathogens. The concordance rate between qPCR and LAMP was 94.5%, underscoring the reliability of both methods for clinical application. These findings suggest that the developed qPCR and LAMP tests can be successfully integrated into clinical practice for the detection and management of Bordetella infections.
Immobilized animal liver microsomes: A versatile tool for efficient ester hydrolysis in chemo-enzymatic synthesis.
Monnier C, Andrys R, Castellino I … +5 more , Mickova V, Haleckova A, Loskot J, Benek O, Zemanova L
Methods · 2025 Aug · PMID 40210104 · Publisher ↗
Animal liver microsomes are a rich source of carboxylesterases with potential for biocatalytic applications. However, their instability and difficulty in reuse limit their practical application. This study investigates t...
Animal liver microsomes are a rich source of carboxylesterases with potential for biocatalytic applications. However, their instability and difficulty in reuse limit their practical application. This study investigates the immobilization of animal liver microsomes from four species Mus musculus (house mouse), Sus scrofa (wild boar), Dama dama (fallow deer), and Capreolus capreolus (roe deer) on Perloza MG microparticles for enhanced stability and reusability. Immobilization significantly improved the stability and pH tolerance of the microsomes, particularly those from D. dama, maintaining esterase activity across a broad pH range (5-9) and enabling the reusability over ten consecutive cycles. The immobilized D. dama microsomes were successfully employed in a preparative-scale chemo-enzymatic synthesis of a cyclophilin D inhibitor, achieving a total reaction yield of 68% with 98% final product purity, demonstrating their potential for sustainable organic synthesis.
Quantitative mass spectrometric analysis of C-terminal 36 amino acid peptides of alpha-1 antitrypsin in plasma using survey spectra.
Tammen H, Pich A, Hess R … +3 more , Lechowicz U, Janciauskiene S, Chorostowska J
Methods · 2025 Aug · PMID 40210103 · Publisher ↗
C-terminal peptides of alpha-1 antitrypsin (AAT) may serve as biomarkers for diseases such as sepsis, chronic obstructive pulmonary disease, liver disease, and autoimmune disorders. In this study, we present a robust and...
C-terminal peptides of alpha-1 antitrypsin (AAT) may serve as biomarkers for diseases such as sepsis, chronic obstructive pulmonary disease, liver disease, and autoimmune disorders. In this study, we present a robust and straightforward MS (mass spectrometry)-based method for quantifying AAT peptides 388-418 (C36) and its polymorphic variant (E400D, C36D) in plasma samples. Absolute quantification was accomplished using MALDI-MS reflectron spectra and ESI-MS MS1 scans, implemented in two independent laboratories. Two plasma preparation methods, methanol precipitation and ultrafiltration, were evaluated, with methanol precipitation yielding significantly higher recovery rates. The impact of freeze-thaw cycles on C36 levels was also assessed, revealing a significant increase in C36 levels after each cycle. Comparisons between MALDI-MS and ESI-MS showed strong concordance in C36 and C36D measurements. Furthermore, C36 and C36D levels correlated strongly with post-precipitation protein content across both MS methods. Normalizing C36 levels to protein content effectively mitigated variability. This method should be straightforward to implement in other laboratories, facilitating clinical studies to evaluate the diagnostic and prognostic significance of C36 peptides across various diseases.
A high-throughput and time-efficient Nanopore full-length 16S rRNA gene sequencing protocol for synthetic microbial communities.
Zhou X, Faust K
Methods · 2025 Aug · PMID 40204203 · Publisher ↗
Next-generation sequencing (NGS) has transitioned from primarily research-focused applications to a mature technology. However, resolving microbial community composition on the species level based on the 16S rRNA gene is...
Next-generation sequencing (NGS) has transitioned from primarily research-focused applications to a mature technology. However, resolving microbial community composition on the species level based on the 16S rRNA gene is impeded by several critical bottlenecks that limit the efficiency and scalability of analyses. Specifically, standard MiSeq sequencing suffers from read-length limitation; library preparation requires multiple labour-intensive steps from DNA isolation to amplification and barcoding; and prolonged turnaround times delay results. These challenges underscore the need for improved methods, which our study aims to address. Recent advances in Oxford Nanopore long-read sequencing technology (ONT), including a smaller and cheaper benchtop instrument and support for diverse sample types, have enabled faster sequencing in-house with reduced costs. To address the need for standardized, reproducible workflows, we present an optimized and state-of-the-art protocol for full-length 16S rRNA gene sequencing using the ONT MinION sequencing device. Furthermore, we quantified the reproducibility and accuracy of our protocol and compared it with previous MiSeq results. The results showed that the accuracy of our sequencing pipeline for synthetic communities is significantly higher than for MiSeq pipeline. In summary, our protocol elucidates the composition of synthetic microbial communities in an easy, fast and accurate manner while ensuring reproducible results.
Transforming breast cancer diagnosis and treatment with large language Models: A comprehensive survey.
Ghorbian M, Ghobaei-Arani M, Ghorbian S
Methods · 2025 Jul · PMID 40199412 · Publisher ↗
Breast cancer (BrCa), being one of the most prevalent forms of cancer in women, poses many challenges in the field of treatment and diagnosis due to its complex biological mechanisms. Early and accurate diagnosis plays a...
Breast cancer (BrCa), being one of the most prevalent forms of cancer in women, poses many challenges in the field of treatment and diagnosis due to its complex biological mechanisms. Early and accurate diagnosis plays a fundamental role in improving survival rates, but the limitations of existing imaging methods and clinical data interpretation often prevent optimal results. Large Language Models (LLMs), which are developed based on advanced architectures such as transformers, have brought about a significant revolution in data processing and medical decision-making. By analyzing a large volume of medical and clinical data, these models enable early diagnosis by identifying patterns in images and medical records and provide personalized treatment strategies by integrating genetic markers and clinical guidelines. Despite the transformative potential of these models, their use in BrCa management faces challenges such as data sensitivity, algorithm transparency, ethical considerations, and model compatibility with the details of medical applications that need to be addressed to achieve reliable results. This review systematically reviews the impact of LLMs on BrCa treatment and diagnosis. This study's objectives include analyzing the role of LLM technology in diagnosing and treating this disease. The findings indicate that the application of LLMs has resulted in significant improvements in various aspects of BrCa management, such as a 35% increase in the Efficiency of Diagnosis and BrCa Treatment (EDBC), a 30% enhancement in the System's Clinical Trust and Reliability (SCTR), and a 20% improvement in the quality of patient education and information (IPEI). Ultimately, this study demonstrates the importance of LLMs in advancing precision medicine for BrCa and paves the way for effective patient-centered care solutions.
RNA aptamer-induced fluorescence enhancement for NADH monitoring in cellular environment.
Al Mazid MF, Eskasalam SR, Lee JS
Methods · 2025 Aug · PMID 40194718 · Publisher ↗
Cellular redox homeostasis is tightly regulated by the oxidation-reduction reactions of nicotinamide metabolites, including NAD(H) and NADP(H), which serve as essential cofactors in enzymatic processes related to energy...
Cellular redox homeostasis is tightly regulated by the oxidation-reduction reactions of nicotinamide metabolites, including NAD(H) and NADP(H), which serve as essential cofactors in enzymatic processes related to energy metabolism. Monitoring intracellular NADH levels is therefore of significant interest. Most chemosensor designs to date rely on fluorescence turn-on mechanisms triggered by NADH oxidation, but these reaction-based sensors are inherently limited by NADH concentration and reaction kinetics. While NADH exhibits intrinsic fluorescence, its low quantum yield has led to the development of redox-sensitive substrates that emit fluorescence upon NADH oxidation. Here, we report an alternative fluorescence enhancement strategy based on an NADH-binding RNA aptamer. The interaction between NADH and a 49-base-pair RNA aptamer induces a 1.4-fold increase in fluorescence emission in vitro and an 1.8-fold increase in live-cell imaging. This fluorescence enhancement arises from aptamer-induced structural rigidity, analogous to the mechanism by which 4-(p-hydroxybenzylidene)-5-imidazolidinone (HBI) enhances fluorescence in green fluorescent protein. Using our aptamer-based assay, we established a live-cell fluorescence emission assay for real-time monitoring of cellular NADH dynamics.
Predicting genes associated with ossification of the posterior longitudinal ligament using graph attention network.
Kong F, Liu H, Liu X … +1 more , Shi L
Methods · 2025 Aug · PMID 40188905 · Publisher ↗
Ossification of the posterior longitudinal ligament is a degenerative disease that severely impacts the spine, with a complex pathogenesis involving the interplay of multiple genes. This study utilizes a combination of g...
Ossification of the posterior longitudinal ligament is a degenerative disease that severely impacts the spine, with a complex pathogenesis involving the interplay of multiple genes. This study utilizes a combination of graph neural networks and deep neural networks to systematically analyze genes associated with OPLL, leveraging genomics and bioinformatics techniques. By integrating gene data from the DisGeNET and HumanNetV2 databases, we constructed a GNN model to identify potential pathogenic genes for OPLL and validated the expression characteristics and mechanisms of these genes in different cell types. The findings indicate that the GNN model achieves remarkable accuracy and reliability in predicting genes associated with OPLL. Additionally, cellular trajectory analysis and immune cell infiltration studies uncovered distinct cellular environments and immune features in OPLL patients, emphasizing the significant roles of fibroblasts and mesenchymal stem cells in the disease's progression. Drug sensitivity analysis also sheds light on future personalized treatment options. This study not only enhances the understanding of OPLL's molecular mechanisms but also suggests new avenues for diagnostic and targeted therapy development.
Optimized toolkit for the manipulation of immortalized axolotl fibroblasts.
Tajer BJ, Kalu G, Jay S … +25 more , Wynn E, Decaux A, Gilbert P, Singer HD, Kidd MD, Nelson JA, Harake N, Lopez NJ, Souchet NR, Luong AG, Savage AM, Min S, Karabacak A, Böhm S, Kim RT, Froitzheim T, Sousounis K, Courtemanche K, Han J, Payzin-Dogru D, Blair SJ, Roy S, Fei JF, Tanaka EM, Whited JL
Methods · 2025 Aug · PMID 40187387 · Full text
The axolotl salamander model has broad utility for regeneration studies, but this model is limited by a lack of efficient cell-culture-based tools. The Axolotl Limb-1 (AL-1) fibroblast line, the only available immortaliz...
The axolotl salamander model has broad utility for regeneration studies, but this model is limited by a lack of efficient cell-culture-based tools. The Axolotl Limb-1 (AL-1) fibroblast line, the only available immortalized axolotl cell line, was first published over 20 years ago, but many established molecular biology techniques, such as lipofectamine transfection, CRISPR-Cas9 mutagenesis, and antibiotic selection, work poorly or remain untested in AL-1 cells. Innovating technologies to manipulate AL-1 cells in culture and study their behavior following transplantation into the axolotl will complement in-vivo studies, decrease the number of animals used, and enable the faster, more streamlined investigation of regenerative biology questions. Here, we establish transfection, mutagenesis, antibiotic selection, and in-vivo transplantation techniques in axolotl AL-1 cells. These techniques will enable efficient culture with AL-1 cells and guide future tool development for the culture and manipulation of other salamander cell lines.
3D printing of calcium doped Isomalt via custom-made Extruder: Facile approach for creating blood vascular like networks within tissue mimicking hydrogel matrix.
Dhwaj A, Roy N, Prabhakar A … +1 more , Verma D
Methods · 2025 Jul · PMID 40185316 · Publisher ↗
3D printing domain has witnessed rapid advancements with immense applications in various fields ranging from aerospace to 3D printed organs. This study describes a facile biofabrication approach for creating an Artificia...
3D printing domain has witnessed rapid advancements with immense applications in various fields ranging from aerospace to 3D printed organs. This study describes a facile biofabrication approach for creating an Artificial blood vascular network inside the Hydrogel matrix by using Isomalt sugar (Sugar Alcohol) as a sacrificial component inside a composite-Hydrogel matrix. Conventional 3D-printers have extruder and hot-end assembly, whereas Bioprinters use pneumatic-piston, and piezoelectric-driven extrusion mechanisms. In this study, we describe the design and operation of a custom-made miniature precision lead screw-based syringe-pump extruder mechanism with integrated temperature-controlled heat-block. We are currently using this integrated setup for melt Isomalt-based 3D printing, which can be easily mounted over the Z-axis and is driven using a geared stepper motor with high torque, providing controlled extrusion of highly viscous polymers where sugar structures are used as sacrificial materials for making Artificial blood vascular like networks in the microfluidics domain within the composite Hydrogel matrix. Computational studies using COMSOL Multiphysics were performed to predict the diffusion pattern of the DMEM culture medium to estimate the rate of mass flow through a porous media. Furthermore, Cell based testing is performed using Human Adipose Derived Mesenchymal Stem Cells (HAD-MSC's) which were cultured over the vascular Hydrogel matrix perfused with culture media with defined flowrates to mimic the natural function of the Nutrient and gaseous exchange inside human tissues. The proposed can be used to produce equivalent Tissue models which could be potentially used in On-chip drug testing platforms, drug discovery and regenerative medicine domains.
Multi-Omics clustering by integrating clinical features from large language model.
Ye X, Shi T, Huang D … +1 more , Sakurai T
Methods · 2025 Jul · PMID 40180255 · Publisher ↗
Multi-omics clustering has emerged as a powerful approach for understanding complex biological systems and enabling cancer subtyping by integrating diverse omics data. Existing methods primarily focus on the integration...
Multi-omics clustering has emerged as a powerful approach for understanding complex biological systems and enabling cancer subtyping by integrating diverse omics data. Existing methods primarily focus on the integration of different types of omics data, often overlooking the value of clinical context. In this study, we propose a novel framework that incorporates clinical features extracted from large language model (LLM) to enhance multi-omics clustering. Leveraging clinical data extracted from pathology reports using a BERT-based model, our framework converts unstructured medical text into structured clinical features. These features are integrated with omics data through an autoencoder, enriching the information content of each omics layer to improve feature extraction. The extracted features are then projected into a latent subspace using singular value decomposition (SVD), followed by spectral clustering to obtain the final clustering result. We evaluate the proposed framework on six cancer datasets on three omics levels, comparing it with several state-of-the-art methods. The experimental results demonstrate that the proposed framework outperforms existing methods in multi-omics clustering for cancer subtyping. Moreover, the results highlight the efficacy of integrating clinical features derived from LLM, significantly enhancing clustering performance. This work underscores the importance of clinical context in multi-omics analysis and showcases the transformative potential of LLM in advancing precision medicine.
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