Biology Methods and Protocols最新文献

{"title":"AllerTrans: a deep learning method for predicting the allergenicity of protein sequences.","authors":"Faezeh Sarlakifar, Hamed Malek, Najaf Allahyari Fard","doi":"10.1093/biomethods/bpaf040","DOIUrl":"10.1093/biomethods/bpaf040","url":null,"abstract":"<p><p>Allergens are a major concern in determining protein safety, especially with the growing use of recombinant proteins in new medical products. These proteins require a careful allergenicity assessment to guarantee their safety. However, traditional laboratory tests for allergenicity are expensive and time-consuming. To address this challenge, bioinformatics offers efficient and cost-effective alternatives for predicting protein allergenicity. Deep learning models offer a promising solution for this purpose. Recently, with the emergence of protein language models(pLMs), high-quality and impactful feature vectors can be extracted from protein sequences using these specialized language models. Although different computational methods can be effective individually, combining them could improve the prediction results. Given this hypothesis, can we develop a more powerful approach than existing methods to predict protein allergenicity? In this study, we developed an enhanced deep learning model to predict the potential allergenicity of proteins based on their primary structure represented as protein sequences. In simple terms, this model classifies protein sequences into allergenic or non-allergenic classes. Our approach utilizes two pLMs to extract distinct feature vectors for each sequence, which are then fed into a deep neural network (DNN) model for classification. Combining these feature vectors improves the results. Finally, we integrated our top-performing models using ensemble modeling techniques. This approach could balance the model's sensitivity and specificity. Our proposed model demonstrates an improvement compared to existing models, achieving a sensitivity of 97.91%, a specificity of 97.69%, an accuracy of 97.80%, and an area under the receiver operating characteristic curve of 99% using the standard 2-fold cross-validation. The AllerTrans model has been deployed as a web-based prediction tool and is publicly accessible at: https://huggingface.co/spaces/sfaezella/AllerTrans.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf040"},"PeriodicalIF":2.5,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12254128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-effective production of <i>Escherichia coli</i> \"GABase\" for spectrophotometric determination of γ-aminobutyrate (GABA) levels or glutamate decarboxylase activity.","authors":"Kirsten H Benidickson, Kyle F Symonds, Wayne A Snedden, William C Plaxton","doi":"10.1093/biomethods/bpaf050","DOIUrl":"10.1093/biomethods/bpaf050","url":null,"abstract":"<p><p>γ-aminobutyrate (GABA) is a non-proteinogenic amino acid produced by glutamate decarboxylase (GAD) that functions as a vital neurotransmitter in animals, and as an important metabolite and signaling molecule in plants and microbes. \"GABase\" consists of a mixture of recombinant GABA transaminase (GABA-T) and succinic semialdehyde dehydrogenase (SSDH) that is widely used for spectrophotometric quantification of glutamate decarboxylase (GAD) activity or GABA levels in tissue extracts. Both can be conveniently monitored at 340 nm owing to the sequential conversion of GABA into succinate by GABA-T and SSDH, and concomitant reduction of NADP⁺ into NADPH by SSDH. Currently, these assays rely on commercially available GABase from <i>Pseudomonas fluorescens</i>. However, the excessive cost of commercial GABase prompted us to develop an inexpensive and rapid \"DIY\" method for producing GABase by cloning, expressing and purifying His₆-tagged GABA-T and SSDH from <i>Escherichia coli</i>. We validated our in-house GABase preparation by comparing GAD activities and GABA levels of the model plant <i>Arabidopsis thaliana</i> with those obtained using commercial GABase. Both <i>pET30a</i> plasmids for expressing <i>E. coli</i> His₆-GABA-T and His₆-SSDH have been deposited into AddGene (www.addgene.com). Our protocols for producing and using recombinant <i>E. coli</i> GABase should be of interest to any researcher who studies eukaryotic or prokaryotic GABA and/or GAD activity.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf050"},"PeriodicalIF":2.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12255878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel reporter systems to detect cold and osmotic stress responses.","authors":"Kanon Maruyama, Hodaka Fujii","doi":"10.1093/biomethods/bpaf048","DOIUrl":"10.1093/biomethods/bpaf048","url":null,"abstract":"<p><p>Cells respond to environmental stresses such as cold and osmotic stresses. These stresses induce signal transduction pathways in cells. However, the molecular mechanisms activated by cold and osmotic stresses in higher eukaryotes remain elusive. Previously, we described a reporter system utilizing inducible translocation trap that detects nuclear translocation of 2-amino-3-ketobutyrate coenzyme A ligase (KBL) in response to cold and osmotic stresses. In the present study, we developed additional reporter systems to detect intracellular events induced by these stresses. These reporter systems will be instrumental to elucidate the intracellular signaling mechanisms activated by these stresses.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf048"},"PeriodicalIF":2.5,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing leptospirosis screening using a deep convolutional neural network with microscopic agglutination test images.","authors":"Murnihayati Hassan, Siti Nur Zawani Rosli, Natasya Amirah Mohamed Tahir, Nurul Azmawati Mohamed, Khairunnisa Mohd Sukri, Liyana Azmi, Norhasmira Mohammad","doi":"10.1093/biomethods/bpaf047","DOIUrl":"10.1093/biomethods/bpaf047","url":null,"abstract":"<p><p>Leptospirosis poses substantial challenges to global public health. In Malaysia, leptospirosis is endemic, with annual cases peaking during the monsoon season. The microscopic agglutination test (MAT) is the gold-standard serological method for confirmation of leptospirosis. However, it is labor-intensive and time-consuming, as it relies on the subjective interpretation of medical lab technicians. This study describes the development of a semiautomated workflow for <i>Leptospira</i> screening by integrating a TensorFlow and custom-designed Keras-based Deep Convolutional Neural Network (DCNN) with conventional MAT. We used a dataset of 442 positive and 442 negative MAT images, which consisted of a mixture of <i>Leptospira</i> serovars from Malaysia to train the model. The model was subjected to hyperparameter tuning, which modulated the number of convolutional layers, filters, kernel sizes, units in dense layers, activation functions, and learning rate. Verification of our tested model compared to the verified patient MAT results achieved the following metrics: a Precision score of 0.8125, a Recall of 0.9286, and an F1-Score of 0.8667. Combining our model with the current Malaysia <i>Leptospira</i> workflow can significantly speed up, reduce inaccuracies, and improve the management of leptospirosis. Furthermore, the application of this model is practical and adaptable, making it suitable for other labs that observe MAT as their <i>Leptospira</i> diagnosis. To our knowledge, this approach is Malaysia's first hybrid diagnostic approach for <i>Leptospira</i> diagnosis. Scaling up the dataset would enhance the model's accuracy, making it adaptable in other regions where leptospirosis is endemic.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf047"},"PeriodicalIF":2.5,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12188207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144498261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances and future directions of aptamer-functionalized nanoparticles for point-of-care diseases diagnosis.","authors":"Ilemobayo Victor Fasogbon, Erick Nyakundi Ondari, Deusdedit Tusubira, Tonny Kabuuka, Ibrahim Babangida Abubakar, Wusa Makena, Angela Mumbua Musyoka, Patrick Maduabuchi Aja","doi":"10.1093/biomethods/bpaf046","DOIUrl":"10.1093/biomethods/bpaf046","url":null,"abstract":"<p><p>Point-of-care (POC) diagnostics have revolutionized disease detection by enabling rapid, on-site testing without the need for centralized laboratory infrastructure. This review presents recent advances in aptamer-functionalized nanoparticles (AFNs) as promising tools for enhancing POC diagnostics, particularly in infectious diseases and cancer. Aptamers, with their high specificity, stability, and modifiability, offer significant advantages over antibodies, while nanoparticles contribute multifunctionality, including signal amplification and targeting capabilities. AFNs have demonstrated up to a 2-10 times increase in detection sensitivity and significant reductions in diagnostic timeframes. We discuss various nanoparticle types, conjugation strategies, real-world applications, and highlight innovative developments such as AI-assisted aptamer design, wearable diagnostic devices, and green nanoparticle synthesis. Challenges related to stability, manufacturing scalability, regulatory hurdles, and clinical translation are critically examined. By merging aptamer precision with nanoparticle versatility, AFNs hold transformative potential to deliver rapid, affordable, and decentralized healthcare solutions, especially in resource-limited settings. Future interdisciplinary research and sustainable practices will be pivotal in translating AFN-based diagnostics from promising prototypes to global healthcare standards.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf046"},"PeriodicalIF":2.5,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12212641/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144545181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rough microsomes isolated from snap-frozen canine pancreatic tissue retain their co-translational translocation functionality.","authors":"Marianne Croonenborghs, Marijke Verhaegen, Eva Pauwels, Becky Provinciael, Kurt Vermeire","doi":"10.1093/biomethods/bpaf044","DOIUrl":"10.1093/biomethods/bpaf044","url":null,"abstract":"<p><p>Proteins are essential for life in all organisms: they mediate cell signaling and cell division and provide structure/motility to cells and tissues. All proteins are synthesized on cytoplasmic ribosomes as unfolded precursors that need to find their correct location in the compartmentalized cell. In eukaryotes, ∼30% of the proteome is translocated across or integrated into the endoplasmic reticulum (ER) membrane, a process mostly mediated by the heterotrimeric Sec61 complex that spans the ER membrane. There is significant interest in identifying small-molecule inhibitors of the Sec61 translocon channel that hold great promise as putative anticancer, immunosuppressive, or antiviral drugs. Hence, representative models are needed to study Sec61-dependent protein import into the ER. Microsomal membranes (or microsomes) isolated from dog pancreatic tissue are the primary source of mammalian ER for cell-free <i>in vitro</i> protein translocation research. Here, we demonstrate that for the isolation of microsomal membranes, snap-frozen canine pancreatic tissue can serve as a valuable alternative to freshly isolated organ tissue from euthanized animals. For 17 out of 20 animals, a sufficient yield of microsomes was extracted from defrosted pancreatic tissue. The isolated microsomes contained the essential proteins of the translocation machinery, and proved to be intact as verified by the detection of ER lumenal chaperones. Importantly, 13 out of the 17 microsome samples retained their translocation competence, as reflected by successful <i>in vitro</i> co-translational translocation of wild-type bovine preprolactin. The microsomes supported post-translational modifications of the tested substrates such as signal peptide cleavage and N-linked glycosylation. Furthermore, the tested microsome samples responded well to the translocation inhibitor cyclotriazadisulfonamide in suppressing human CD4 protein translocation into the ER. In conclusion, microsomes isolated from frozen canine pancreatic tissue proved to retain their co-translational translocation functionality that can contribute to our research of Sec61-dependent protein translocation and selective inhibition thereof.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf044"},"PeriodicalIF":2.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convolutional-LSTM approach for temporal catch hotspots (CATCH): an AI-driven model for spatiotemporal forecasting of fisheries catch probability densities.","authors":"Altair Agmata, Svanur Guðmundsson","doi":"10.1093/biomethods/bpaf045","DOIUrl":"10.1093/biomethods/bpaf045","url":null,"abstract":"<p><p>Efficient fisheries management is crucial for sustaining both marine ecosystems and the economies that heavily depend on them, such as Iceland. Current fishing practices involve decisions informed by a combination of personal experience, current data on environmental and oceanographic conditions, reports from other captains, and target species within the constraints of the fishing quota. However, the intricate spatiotemporal dynamics of fish behaviour make it difficult to predict fish stock distributions. Despite technological breakthroughs in fishing vessel data collection, much of the decision-making still relies heavily on subjective judgement, highlighting the need for more robust, data-driven predictive methods. This paper presents CATCH, a convolutional long short-term memory neural network model that forecasts fish stock probability densities over time and space in Icelandic waters to support operational planning and adaptive strategy in fisheries. The framework represents the first utilization of large-scale Icelandic fishing fleet data integrating multidimensional inputs, particularly depth, bottom temperature, salinity, dissolved oxygen and catch data, to produce accurate, multivariate forecasts. The model achieves favourable performance with average RMSE, MAE, WD, and SSI of 4.71 × 10^-3, 1.16 × 10^-3, 0.94 × 10^-3, and 0.955, respectively, for cod, while 6.13 × 10^-3, 1.25 × 10^-3, 1.04 × 10^-3, and 0.949, respectively, across other target species (haddock, saithe, golden redfish, and Greenland halibut). Additionally, Syrjala's test yielded nonsignificant <i>P</i>-values (<i>P</i> > .05) in most cases across lags and forecast horizons, indicating that the predicted and observed distributions are statistically indistinguishable. Its promising results suggest deep learning models have the potential to optimize fisheries operations, enhance sustainability, and support data-driven decision-making.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf045"},"PeriodicalIF":2.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of computational ancestry inference for use in cancer cell lines.","authors":"Matthew S Chang, Katherine A Martinez, Chayil C Lattimore, Christina M Gobin, Kimberly J Newsom, Kristianna M Fredenburg","doi":"10.1093/biomethods/bpaf043","DOIUrl":"10.1093/biomethods/bpaf043","url":null,"abstract":"<p><p>Cancer cell lines have provided invaluable preclinical mechanistic data for cancer health disparities research. Although there are several studies that detail ancestry inference methods using microarray data, there are none that provide investigators with documentation of ancestry inference methods using sequencing data. Here, we describe our computational workflow for inferring genetic ancestry using either whole genome sequencing (WGS) or RNA-sequencing (RNA-seq) data from cancer cell lines. RNA-seq and WGS datasets were generated from four head and neck cancer cell lines with self-identified race/ethnicity (SIRE) as either White or Black. Our workflow included variant calling and genotype imputation via Illumina DRAGEN pipelines, merging genotyping datasets with the 1000 Genomes Project (1KGP), single nucleotide polymorphism (SNP) filtering via PLINK, and ancestry inference with ADMIXTURE. We encountered challenges in workflow development with SNP filtering and clustering of 1KGP superpopulations. Adjusting stringency of filtering parameters to a window size of 100 kb and <i>r</i> ² threshold of 0.8 resulted in 312,821 SNPs remaining for the RNA-seq dataset and 1,569,578 SNPs remaining for the WGS dataset. Clustering with 1KGP improved with a panel of 291 ancestry informative markers. To estimate proportions of genetic ancestry, we used all filtered SNPs. For the WGS dataset, both clustering and genetic ancestry proportions for each cancer cell line showed concurrence with SIRE. In conclusion, our optimized workflow offers investigators a robust approach for transforming cancer cell line sequencing data to infer genetic ancestry and suggests that WGS datasets are superior to RNA-seq datasets in clustering superpopulations and more accurately estimating genetic ancestry.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf043"},"PeriodicalIF":2.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DrugPipe: Generative artificial intelligence-assisted virtual screening pipeline for generalizable and efficient drug repurposing.","authors":"Phuc Pham, Viet Thanh Duy Nguyen, Kyu Hong Cho, Truong-Son Hy","doi":"10.1093/biomethods/bpaf038","DOIUrl":"10.1093/biomethods/bpaf038","url":null,"abstract":"<p><p>Drug repurposing presents a promising strategy to accelerate drug discovery by identifying new therapeutic uses for existing compounds, particularly for diseases with limited or no effective treatment options. We introduce <b>DrugPipe</b>, a 'Generative AI-Assisted Virtual Screening Pipeline' developed within the target-centric paradigm of drug repurposing, which aims to discover new indications by identifying compounds that interact with a specific protein target. 'DrugPipe' integrates generative modeling, binding pocket prediction, and similarity-based retrieval from drug databases to enable a scalable and generalizable <i>in silico</i> repurposing workflow. It supports blind virtual screening for any protein target without requiring prior structural or functional annotations, making it especially suited for novel or understudied targets and emerging health threats. By efficiently generating candidate ligands and rapidly retrieving structurally similar approved drugs, 'DrugPipe' accelerates the identification and prioritization of repurposable compounds. In comparative evaluations, it achieves hit rate performance comparable to QVina-W, a widely used blind docking tool, while significantly reducing computational time, highlighting its practical value for large-scale virtual screening and data-scarce repurposing scenarios. The full implementation and evaluation details are available at https://github.com/HySonLab/DrugPipe.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf038"},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tissue-specific DNA isolation from dissected millipedes for nanopore sequencing.","authors":"Elena Cruz, William Wittstock, Bruce A Snyder, Arnab Sengupta","doi":"10.1093/biomethods/bpaf042","DOIUrl":"10.1093/biomethods/bpaf042","url":null,"abstract":"<p><p>There are approximately 12,000 described species within the class Diplopoda. Only five species, falling within 4 of 16 described orders, have fully sequenced genomes. No whole genomes are available for incredibly diverse families like Xystodesmidae. Furthermore, genetic information attributed to key functions in these species is very limited. There is a growing interest in characterizing genomes of non-model organisms, however, extracting high-quality DNA for organisms with complex morphology can be challenging. Here we describe a detailed methodology for obtaining high-purity DNA from legs, head, and body tissues from wild-caught specimens of the millipede species <i>Cherokia georgiana</i>. Our dissection protocol separates the digestive tract minimizing microbial abundance in the extracted DNA sample. We describe sample homogenization steps that improve total DNA yield. To assess sample quality, concentration, and size we use spectrophotometry, fluorometry, and automated electrophoresis, respectively. We consistently obtain average DNA length upwards of 12-25 kb. We applied Oxford Nanopore Technologies MinION long-read sequencing, an affordable and accessible option with potential for field-based applications. Here we present tissue-specific DNA sequencing metrics, alignment and assembly of mitochondrial DNA consensus sequence, and phylogenetic analysis. While noting the limitations of our nanopore-based sequencing methodology, we provide a framework to process field specimens for PCR-free DNA sequencing data that can be used for gene-specific alignment and analysis.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf042"},"PeriodicalIF":2.5,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}