Biology Methods and Protocols最新文献

{"title":"A deep learning pipeline for morphological and viability assessment of 3D cancer cell spheroids.","authors":"Ajay K Mali, Sivasubramanian Murugappan, Jayashree Rajesh Prasad, Syed A M Tofail, Nanasaheb D Thorat","doi":"10.1093/biomethods/bpaf030","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf030","url":null,"abstract":"<p><p>Three-dimensional (3D) spheroid models have advanced cancer research by better mimicking the tumour microenvironment compared to traditional <b>two-</b>dimensional cell cultures. However, challenges persist in high-throughput analysis of morphological characteristics and cell viability, as traditional methods like manual fluorescence analysis are labour-intensive and inconsistent. Existing AI-based approaches often address segmentation or classification in isolation, lacking an integrated workflow. We propose a scalable, two-stage deep learning pipeline to address these gaps: (i) a U-Net model for precise detection and segmentation of 3D spheroids from microscopic images, achieving 95% prediction accuracy, and (ii) a CNN Regression Hybrid method for estimating live/dead cell percentages and classifying spheroids, with an <math> <mrow> <msup><mrow><mi>R</mi></mrow> <mrow><mn>2</mn></mrow> </msup> </mrow> </math> value of 98%. This end-to-end pipeline automates cell viability quantification and generates key morphological parameters for spheroid growth kinetics. By integrating segmentation and analysis, our method addresses environmental variability and morphological characterization challenges, offering a robust tool for drug discovery, toxicity screening, and clinical research. This approach significantly improves efficiency and scalability of 3D spheroid evaluations, paving the way for advancements in cancer therapeutics.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf030"},"PeriodicalIF":2.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12064216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015474","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":"Assessing nutritional pigment content of green and red leafy vegetables by image analysis: Catching the \"red herring\" of plant digital color processing via machine learning.","authors":"Avinash Agarwal, Filipe de Jesus Colwell, Viviana Andrea Correa Galvis, Tom R Hill, Neil Boonham, Ankush Prashar","doi":"10.1093/biomethods/bpaf027","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf027","url":null,"abstract":"<p><p>Estimating pigment content of leafy vegetables via digital image analysis is a reliable method for high-throughput assessment of their nutritional value. However, the current leaf color analysis models developed using green-leaved plants fail to perform reliably while analyzing images of anthocyanin (Anth)-rich red-leaved varieties due to misleading or \"red herring\" trends. Hence, the present study explores the potential for machine learning (ML)-based estimation of nutritional pigment content for green and red leafy vegetables simultaneously using digital color features. For this, images of <i>n </i>=<i> </i>320 samples from six types of leafy vegetables with varying pigment profiles were acquired using a smartphone camera, followed by extract-based estimation of chlorophyll (Chl), carotenoid (Car), and Anth. Subsequently, three ML methods, namely, Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were tested for predicting pigment contents using RGB (Red, Green, Blue), HSV (Hue, Saturation, Value), and <i>L*a*b*</i> (Lightness, Redness-greenness, Yellowness-blueness) datasets individually and in combination. Chl and Car contents were predicted most accurately using the combined colorimetric dataset via SVR (<i>R²</i> = 0.738) and RFR (<i>R²</i> = 0.573), respectively. Conversely, Anth content was predicted most accurately using SVR with HSV data (<i>R²</i> = 0.818). While Chl and Car could be predicted reliably for green-leaved and Anth-rich samples, Anth could be estimated accurately only for Anth-rich samples due to Anth masking by Chl in green-leaved samples. Thus, the present findings demonstrate the scope of implementing ML-based leaf color analysis for assessing the nutritional pigment content of red and green leafy vegetables in tandem.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf027"},"PeriodicalIF":2.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062736","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":"Quantitative tools for analyzing rhizosphere pH dynamics: localized and integrated approaches.","authors":"Poonam Kanwar, Stan Altmeisch, Petra Bauer","doi":"10.1093/biomethods/bpaf026","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf026","url":null,"abstract":"<p><p>The rhizosphere, the region surrounding plant roots, plays a critical role in nutrient acquisition, root development, and plant-soil interactions. Spatial variations in rhizosphere pH along the root axis are shaped by environmental cues, nutrient availability, microbial activity, and root growth patterns. Precise detection and quantification of these pH changes are essential for understanding plant plasticity and nutrient efficiency. Here, we present a refined methodology integrating pH indicator bromocresol purple with a rapid, non-destructive electrode-based system to visualize and quantify pH variations along the root axis, enabling high-resolution and scalable monitoring of root-induced pH changes in the rhizosphere. Using this approach, we investigated the impact of iron (Fe) availability on rhizosphere pH dynamics in wild-type (WT) and bHLH39-overexpressing (39Ox) seedlings. bHLH39, a key basic helix-loop-helix transcription factor in Fe uptake, enhances Fe acquisition when overexpressed, often leading to Fe toxicity and reduced root growth under Fe-sufficient conditions. However, its role in root-mediated acidification remains unclear. Our findings reveal that 39Ox plants exhibit enhanced rhizosphere acidification, whereas WT roots display zone-specific pH responses depending on Fe availability. To refine pH measurements, we developed two complementary electrode-based methodologies: localized rhizosphere pH change for region-specific assessment and integrated rhizosphere pH change for net root system variation. These techniques improve resolution, accuracy, and efficiency in large-scale experiments, providing robust tools for investigating natural and genetic variations in rhizosphere pH regulation and their role in nutrient mobilization and ecological adaptation.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf026"},"PeriodicalIF":2.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12036966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015478","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":"Digital pathology assessment of kidney glomerular filtration barrier ultrastructure in an animal model of podocytopathy.","authors":"Aksel Laudon, Zhaoze Wang, Anqi Zou, Richa Sharma, Jiayi Ji, Winston Tan, Connor Kim, Yingzhe Qian, Qin Ye, Hui Chen, Joel M Henderson, Chao Zhang, Vijaya B Kolachalama, Weining Lu","doi":"10.1093/biomethods/bpaf024","DOIUrl":"10.1093/biomethods/bpaf024","url":null,"abstract":"<p><p>Transmission electron microscopy (TEM) images can visualize kidney glomerular filtration barrier ultrastructure, including the glomerular basement membrane (GBM) and podocyte foot processes (PFP). Podocytopathy is associated with glomerular filtration barrier morphological changes observed experimentally and clinically by measuring GBM or PFP width. However, these measurements are currently performed manually. This limits research on podocytopathy disease mechanisms and therapeutics due to labor intensiveness and inter-operator variability. We developed a deep learning-based digital pathology computational method to measure GBM and PFP width in TEM images from the kidneys of Integrin-Linked Kinase (ILK) podocyte-specific conditional knockout (cKO) mouse, an animal model of podocytopathy, compared to wild-type (WT) control mouse. We obtained TEM images from WT and ILK cKO littermate mice at 4 weeks old. Our automated method was composed of two stages: a U-Net model for GBM segmentation, followed by an image processing algorithm for GBM and PFP width measurement. We evaluated its performance with a 4-fold cross-validation study on WT and ILK cKO mouse kidney pairs. Mean [95% confidence interval (CI)] GBM segmentation accuracy, calculated as Jaccard index, was 0.73 (0.70-0.76) for WT and 0.85 (0.83-0.87) for ILK cKO TEM images. Automated and manual GBM width measurements were similar for both WT (<i>P</i> = .49) and ILK cKO (<i>P</i> = .06) specimens. While automated and manual PFP width measurements were similar for WT (<i>P</i> = .89), they differed for ILK cKO (<i>P</i> < .05) specimens. WT and ILK cKO specimens were morphologically distinguishable by manual GBM (<i>P</i> < .05) and PFP (<i>P</i> < .05) width measurements. This phenotypic difference was reflected in the automated GBM (<i>P</i> < .05) more than PFP (<i>P</i> = .06) widths. Our deep learning-based digital pathology tool automated measurements in a mouse model of podocytopathy. This proposed method provides high-throughput, objective morphological analysis and could facilitate podocytopathy research.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf024"},"PeriodicalIF":2.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11992336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143986524","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":"Aptamers as innovative tools for malaria diagnosis and treatment: advances and future perspectives.","authors":"Wendy Yulieth Royero-Bermeo, Miryan Margot Sánchez-Jiménez, Juan David Ospina-Villa","doi":"10.1093/biomethods/bpaf025","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf025","url":null,"abstract":"<p><p>Malaria, caused by <i>Plasmodium</i> spp. parasites (<i>P. vivax</i>, P<i>. falciparum</i>, <i>P. ovale</i>, <i>P. malariae</i>, and <i>P. knowlesi</i>), remains a significant global health challenge, with 263 million cases and 567 000 deaths reported in 2023. Diagnosis in endemic regions relies on clinical symptoms, microscopy, and rapid diagnostic tests. Although widely used, microscopy suffers from variability in sensitivity due to operator expertise and low parasitemia. Rapid diagnostic tests, which are favored for their simplicity and speed, show high sensitivity for <i>P. vivax</i> but reduced accuracy (80%) for <i>P. falciparum</i>, which is attributed to deletions in histidine-rich protein 2/3 proteins caused by <i>Pfhrp2/3</i> gene mutations. Innovative diagnostic and therapeutic technologies, such as aptamers, are gaining attention. Aptamers are single-stranded oligonucleotides that bind specifically to target molecules with high affinity. They have shown promise in disease diagnosis, therapeutics, and environmental monitoring. In malaria, aptamers are being explored as highly sensitive and specific diagnostic tools capable of detecting <i>Plasmodium</i> proteins across all infection stages. Additionally, they offer potential for novel therapeutic strategies, enhancing disease control and treatment options. These advancements highlight the use of aptamers as versatile and innovative approaches for addressing malaria and other infectious diseases. A comprehensive literature search was conducted in the PubMed, ScienceDirect, and SCOPUS databases via the keywords \"Aptamers\" AND \"Malaria\" AND \"Aptamers\" AND \"Plasmodium.\" Additionally, patent searches were carried out in the LENS, WIPO, and LATIPAT databases via the same search terms. In total, 88 relevant articles were selected for this review, providing a comprehensive and evidence-based foundation to discuss emerging aptamer technologies for malaria diagnosis and treatment. The proteins commonly employed in rapid malaria diagnostic tests, such as histidine-rich protein 2, <i>P.</i> lactate dehydrogenase, and prostaglandin dehydrogenase, are highlighted. However, the identification of new targets, such as HMIGB1 and DRX1 (1-deoxy-d-xylulose-5-phosphate reductoisomerase), and the detection of whole cells have also been emphasized.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf025"},"PeriodicalIF":2.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11992340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053532","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":"Optimized immunofluorescence for liver structure analysis: Enhancing 3D resolution and minimizing tissue autofluorescence.","authors":"Elena Zoppolato, Hasse Mol, Carlos Estrella-García, Nicole Vizcaino-Rodríguez, Diana Sanchez, Nicole Procel, Isabel Baroja, Leticia Sansores-Garcia, Iván M Moya","doi":"10.1093/biomethods/bpaf023","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf023","url":null,"abstract":"<p><p>The study of liver biology and pathology through marker expression analysis and tissue structure visualization is constrained by the high autofluorescence caused by the presence of lipofuscins, vitamin A, and lipid droplets, which traditional staining methods do not effectively quench. This leads to low signal-to-noise ratios, obscured expression levels, and reduced structural resolution. We mitigated liver tissue autofluorescence using Sudan Black B staining, which effectively quenches background signals from lipid and lipofuscin accumulation. Additionally, these protocols typically use thin paraffin sections (5-7 µm), which limit the analysis of larger and more complex liver structures. Liver tissue is highly organized in three dimensions, with large hepatocytes (20-30 µm in diameter) arranged around sinusoids and bile canaliculi, which form intricate branching networks. Thin sections cannot capture this 3D organization, providing only a \"snapshot\" of the tissue at one plane. Here, we present an optimized immunofluorescence protocol using 100-200 µm vibratome-cut liver sections to enable a more comprehensive 3D-like analysis of liver architecture. Finally, our protocol includes antigen retrieval steps tailored to each antibody, maximizing epitope accessibility and signal clarity. Together, these improvements provide a robust method for detailed liver studies with enhanced specificity and structural resolution in immunofluorescent staining. This protocol is particularly suited for researchers focused on liver regeneration, cancer, chronic disease pathology, and structural analysis. However, other researchers interested in exploring complex tissue structures in other autofluorescent tissues, such as the kidney, brain, pancreas, spleen, and adipose tissue, will also find this method beneficial.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf023"},"PeriodicalIF":2.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040401","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":"Genome language modeling (GLM): a beginner's cheat sheet.","authors":"Navya Tyagi, Naima Vahab, Sonika Tyagi","doi":"10.1093/biomethods/bpaf022","DOIUrl":"https://doi.org/10.1093/biomethods/bpaf022","url":null,"abstract":"<p><p>Integrating genomics with diverse data modalities has the potential to revolutionize personalized medicine. However, this integration poses significant challenges due to the fundamental differences in data types and structures. The vast size of the genome necessitates transformation into a condensed representation containing key biomarkers and relevant features to ensure interoperability with other modalities. This commentary explores both conventional and state-of-the-art approaches to genome language modeling (GLM), with a focus on representing and extracting meaningful features from genomic sequences. We focus on the latest trends of applying language modeling techniques on genomics sequence data, treating it as a text modality. Effective feature extraction is essential in enabling machine learning models to effectively analyze large genomic datasets, particularly within multimodal frameworks. We first provide a step-by-step guide to various genomic sequence preprocessing and tokenization techniques. Then we explore feature extraction methods for the transformation of tokens using frequency, embedding, and neural network-based approaches. In the end, we discuss machine learning (ML) applications in genomics, focusing on classification, regression, language processing algorithms, and multimodal integration. Additionally, we explore the role of GLM in functional annotation, emphasizing how advanced ML models, such as Bidirectional encoder representations from transformers, enhance the interpretation of genomic data. To the best of our knowledge, we compile the first end-to-end analytic guide to convert complex genomic data into biologically interpretable information using GLM, thereby facilitating the development of novel data-driven hypotheses.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf022"},"PeriodicalIF":2.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12077296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080626","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":"Mass photometry as a fast, facile characterization tool for direct measurement of mRNA length.","authors":"Andrew Schmudlach, Saralynn Spear, Yimin Hua, Stephanie Fertier-Prizzon, Jianmei Kochling","doi":"10.1093/biomethods/bpaf021","DOIUrl":"10.1093/biomethods/bpaf021","url":null,"abstract":"<p><p>Oligonucleotide integrity is a critical quality attribute for many new therapeutic modalities. Current assays often measure attributes such as length using capillary electrophoresis or liquid chromatography. The length is then corroborated with sequencing data to ensure oligonucleotide quality. An orthogonal measure to these classical separations is to measure intact mass, which traditional mass spectrometry cannot. Herein, we report the use of mass photometry to directly measure RNA length using RNA ladders as a calibrant.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf021"},"PeriodicalIF":2.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754947","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":"A generalized protocol for the induction of M2-like macrophages from mouse and rat bone marrow mononuclear cells.","authors":"Ulugbek R Yakhshimurodov, Kizuku Yamashita, Kenji Miki, Takuji Kawamura, Shunsuke Saito, Shigeru Miyagawa","doi":"10.1093/biomethods/bpaf020","DOIUrl":"10.1093/biomethods/bpaf020","url":null,"abstract":"<p><p>Regardless of origin and localization, macrophages are the major immune cells that maintain homeostasis in both healthy and diseased states. However, there is no consensus on the phenotypes, functions and fates of macrophages. Existing studies clarify macrophage biology from different biomedical research perspectives, but the heterogeneity of induction methods hinders reproducibility and comparability. To address this problem, we validated a novel generalized <i>in vitro</i> protocol for the induction of M2-like macrophages from mice and rats bone marrow mononuclear cells. Our approach improves reliability and cross-species applicability, providing a valuable tool for macrophage research.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf020"},"PeriodicalIF":2.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11964487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774508","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":"Flow cytometric procedures for deep characterization of nanoparticles.","authors":"Valentina Tirelli, Felicia Grasso, Valeria Barreca, Deborah Polignano, Alessandra Gallinaro, Andrea Cara, Massimo Sargiacomo, Maria Luisa Fiani, Massimo Sanchez","doi":"10.1093/biomethods/bpaf019","DOIUrl":"10.1093/biomethods/bpaf019","url":null,"abstract":"<p><p>In recent years, there has been a notable increasing interest surrounding the identification and quantification of nano-sized particles, including extracellular vesicles (EVs) and viruses. The challenge posed by the nano-sized dimension of these particles makes precise examination a significant undertaking. Among the different techniques for the accurate study of EVs, flow cytometry stands out as the ideal method. It is characterized by high sensitivity, low time consumption, non-destructive sampling, and high throughput. In this article, we propose the optimization of flow cytometry procedures to identify, quantify, and purify EVs and virus-like particles. The protocol aims to reduce artefacts and errors in nano-sized particles counting, overall caused by the swarming effect. Different threshold strategies were compared to ensure result specificity. Additionally, the critical parameters to consider when using conventional flow cytometry outside of the common experimental context of use have also been identified. Finally, fluorescent-EVs sorting protocol was also developed with highly reliable results using a conventional cell sorter.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf019"},"PeriodicalIF":2.5,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11954549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754942","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}