Interactive Web Tool Research Articles

Retraction Note: Immune landscape of periodontitis unveils alterations of infiltrating immunocytes and molecular networks-aggregating into an interactive web-tool for periodontitis related immune analysis and visualization

Retraction Note: Immune landscape of periodontitis unveils alterations of infiltrating immunocytes and molecular networks-aggregating into an interactive web-tool for periodontitis related immune analysis and visualization

The Identification of New Pharmacological Targets for the Treatment of Glaucoma: A Network Pharmacology Approach

Background: Glaucoma is a progressive optic neuropathy characterized by the neurodegeneration and death of retinal ganglion cells (RGCs), leading to blindness. Current glaucoma interventions reduce intraocular pressure but do not address retinal neurodegeneration. In this effort, to identify new pharmacological targets for glaucoma management, we employed a network pharmacology approach. Methods: We first retrieved transcriptomic data from GEO, an NCBI database, and carried out GEO2R (an interactive web tool aimed at comparing two or more groups of samples in a GEO dataset). The GEO2R statistical analysis aimed at identifying the top differentially expressed genes (DEGs) and used these as input of STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) app within Cytoscape software, which builds networks of proteins starting from input DEGs. Analyses of centrality metrics using Cytoscape were carried out to identify nodes (genes or proteins) involved in network stability. We also employed the web-server software MIRNET 2.0 to build miRNA–target interaction networks for a re-analysis of the GSE105269 dataset, which reports analyses of microRNA expressions. Results: The pharmacological targets, identified in silico through analyses of the centrality metrics carried out with Cytoscape, were rescored based on correlations with entries in the PubMed and clinicaltrials.gov databases. When there was no match (82 out of 135 identified central nodes, in 8 analyzed networks), targets were considered “potential innovative” targets for the treatment of glaucoma, after further validation studies. Conclusions: Several druggable targets, such as GPCRs (e.g., 5-hydroxytryptamine 5A (5-HT5A) and adenosine A2B receptors) and enzymes (e.g., lactate dehydrogenase A or monoamine oxidase B), were found to be rescored as “potential innovative” pharmacological targets for glaucoma treatment.

Mapping rights done right? – An interactive web tool with user at its heart

Mapping rights done right? – An interactive web tool with user at its heart

Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging

Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally non-quantitative or are low throughput due to technical limitations of ‘splitting hairs’. We developed a deep learning-based computer vision approach for the high throughput quantification of individual hair fibers at a high resolution. Our innovative computer vision tool can distinguish and extract overlapping fibers for quantification of multivariate features including length, width, and color to generate single-hair phenomes (shPhenome) of diverse conditions across the lifespan of mice. Using our tool, we explored the effects of hormone signaling, genetic modifications, and aging on hair follicle output. Our analyses revealed hair phenotypes resultant of endocrinological, developmental, and aging-related alterations in the fur coats of mice. These results demonstrate the efficacy of our deep hair phenomics tool for characterizing factors that modulate the hair follicle and developing new diagnostic methods for detecting disease through the hair fiber. Finally, we have generated a searchable, interactive web tool for the exploration of our hair fiber data at skinregeneration.org.

Comparative profiling of whole-cell and exosome samples reveals protein signatures that stratify breast cancer subtypes

Identifying novel breast cancer biomarkers will improve patient stratification, enhance therapeutic outcomes, and help develop non-invasive diagnostics. We compared the proteomic profiles of whole-cell and exosomal samples of representative breast cancer cell subtypes to evaluate the potential of extracellular vesicles as non-invasive disease biomarkers in liquid biopsies. Overall, differentially-expressed proteins in whole-cell and exosome samples (which included markers for invasion, metastasis, angiogenesis, and drug resistance) effectively discriminated subtypes; furthermore, our results confirmed that the proteomic profile of exosomes reflects breast cancer cell-of-origin, which underscores their potential as disease biomarkers. Our study will contribute to identifying biomarkers that support breast cancer patient stratification and developing novel therapeutic strategies. We include an open, interactive web tool to explore the data as a molecular resource that can explain the role of these protein signatures in breast cancer classification.Graphical (A) We quantified proteomic profiles of breast cancer cells (BCCs) and breast cancer-derived exosomes (BCDEs) samples from four breast cancer cell lines representative of common breast cancer subtypes – Her2-positive (Her2, MDA-MB-453), Luminal A (LA, MCF7), Luminal B (LB, ZR-75), and triple-negative (TN, MDA-MB-231). (B) We independently performed four comparisons for BCCs and BCDEs, comparing each cell line against the remaining three. (C) We identified differentially-expressed proteins (DEPs) for BCCs and BCDEs, defined protein signatures, and functionally analyzed resulting networks. Through pathway inference analysis (PIA), we identified Kyoto Encyclopedia of genes and genomes (KEGG) subpaths and biological Gene Ontology (GO) terms that displayed differential activation. (D) We validated our proteomic signature using the Cancer Genome Atlas (TCGA) and the Cancer Proteome Atlas (TCPA) databases, verified that differentially-activated pathways in BCDEs caused a corresponding response in receptor cells, and confirmed that the BCDE proteomic signature reflects their cell-of-origin and identifies candidate disease biomarkers in liquid biopsies

Exploring mechanobiology network of bone and dental tissue based on Natural Language Processing

Bone and cartilage tissues are physiologically dynamic organs that are systematically regulated by mechanical inputs. At cellular level, mechanical stimulation engages an intricate network where mechano-sensors and transmitters cooperate to manipulate downstream signaling. Despite accumulating evidence, there is a notable underutilization of available information, due to limited integration and analysis. In this context, we conceived an interactive web tool named MechanoBone to introduce a new avenue of literature-based discovery. Initially, we compiled a literature database by sourcing content from Pubmed and processing it through the Natural Language Toolkit project, Pubtator, and a custom library. We identified direct co-occurrence among entities based on existing evidence, archiving in a relational database via SQLite. Latent connections were then quantified by leveraging the Link Prediction algorithm. Secondly, mechanobiological pathway maps were generated, and an entity-pathway correlation scoring system was established through weighted algorithm based on our database, String, and KEGG, predicting potential functions of specific entities. Additionally, we established a mechanical circumstance-based exploration to sort genes by their relevance based on big data, revealing the potential mechanically sensitive factors in bone research and future clinical applications. In conclusion, MechanoBone enables: 1) interpreting mechanobiological processes; 2) identifying correlations and crosstalk among molecules and pathways under specific mechanical conditions; 3) connecting clinical applications with mechanobiological processes in bone research. It offers a literature mining tool with visualization and interactivity, facilitating targeted molecule navigation and prediction within the mechanobiological framework of bone-related cells, thereby enhancing knowledge sharing and big data analysis in the biomedical realm.

Hygrothermal risk assessment tool for brick walls in a changing climate

Due to the heritage value of historical buildings, external facades can often not be modified. Therefore, in heritage buildings interior insulation is often considered when undergoing an energy renovation. However, interior retrofitting drastically changes the hygrothermal behaviour of a wall and can potentially cause moisture-related problems. Besides an interior retrofit, a changing climate might also trigger some of these damage mechanisms as parameters such as temperature and precipitation will change over time. Hygrothermal models can provide relevant insights into the risk of deterioration associated with these damage phenomena. However, these Heat, Air and Moisture (HAM) tools are commercially available but rarely used in the building industry to study deterioration risks. Translating research into practical tools and guidelines is a challenge across the whole field of building renovation. This paper aims to tackle that challenge, by means of creating a hygrothermal risk assessment tool based on 48,384 HAM-simulations for the climate of Brussels, Belgium. Seven different performance criteria are addressed and discussed: freeze-thaw damage, mould growth, wood rot, corrosion, moisture accumulation, salt efflorescence and bio-colonisation. Subsequent to a sensitivity analysis, the study further explains how these results can be translated into practice, providing building practitioners the most suitable insights and recommendations. The development of an interactive web tool to assess hygrothermal risks is demonstrated and its use and benefits are further elaborated.

Comparative single-cell transcriptional and proteomic atlas of clinical-grade injectable mesenchymal source tissues.

Bone marrow aspirate concentrate (BMAC) and adipose-derived stromal vascular fraction (ADSVF) are the most marketed stem cell therapies to treat a variety of conditions in the general population and elite athletes. Both tissues have been used interchangeably clinically even though their detailed composition, heterogeneity, and mechanisms of action have neither been rigorously inventoried nor compared. This lack of information has prevented investigations into ideal dosages and has facilitated anecdata and misinformation. Here, we analyzed single-cell transcriptomes, proteomes, and flow cytometry profiles from paired clinical-grade BMAC and ADSVF. This comparative transcriptional atlas challenges the prevalent notion that there is one therapeutic cell type present in both tissues. We also provide data of surface markers that may enable isolation and investigation of cell (sub)populations. Furthermore, the proteome atlas highlights intertissue and interpatient heterogeneity of injected proteins with potentially regenerative or immunomodulatory capacities. An interactive webtool is available online.

Development of a Web-Based Interactive Tool for Visualizing Breast Cancer Clinical Trial Tolerability Data.

Longitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we developed an interactive web application to empower clinicians and researchers to explore and visualize patient tolerability data. We used adverse event (AE) data (Common Terminology Criteria for Adverse Events) and patient-reported outcomes (PROs) from the NSABP-B35 phase III clinical trial, which compared anastrozole with tamoxifen for breast cancer-free survival, to demonstrate the tools. An interactive web application was developed using R and the Shiny web application framework that generates Sankey diagrams to visualize AEs and PROs using four tools: AE Explorer, PRO Explorer, Cohort Explorer, and Custom Explorer. To illustrate how users can use the interactive tool, examples for each of the four applications are presented using data from the NSABP-B35 phase III trial and the NSABP-B30 trial for the Custom Explorer. In the AE and PRO explorers, users can select AEs or PROs to visualize within specified time periods and compare across treatments. In the cohort explorer, users can select a subset of patients with a specific symptom, severity, and treatment received to visualize the trajectory over time within a specified time interval. With the custom explorer, users can upload and visualize structured longitudinal toxicity and tolerability data. We have created an interactive web application and tool for clinicians and researchers to explore and visualize clinical trial tolerability data. This adaptable tool can be extended for other clinical trial data visualization and incorporated into future patient-clinician interactions regarding treatment decisions.

ProtoSpaceJAM: an open-source, customizable and web-accessible design platform for CRISPR/Cas insertional knock-in.

CRISPR/Cas-mediated knock-in of DNA sequences enables precise genome engineering for research and therapeutic applications. However, designing effective guide RNAs (gRNAs) and homology-directed repair (HDR) donors remains a bottleneck. Here, we present protoSpaceJAM, an open-source algorithm to automate and optimize gRNA and HDR donor design for CRISPR/Cas insertional knock-in experiments, currently supporting SpCas9, SpCas9-VQRand enAsCas12a Cas enzymes. protoSpaceJAM utilizes biological rules to rank gRNAs based on specificity, distance to insertion site, and position relative to regulatory regions. protoSpaceJAM can introduce 'recoding' mutations (silent mutations and mutations in non-coding sequences) in HDR donors to prevent re-cutting and increase knock-in efficiency. Users can customize parameters and design double-stranded or single-stranded donors. We validated protoSpaceJAM's design rules by demonstrating increased knock-in efficiency with recoding mutations and optimal strand selection for single-stranded donors. An additional module enables the design of genotyping primers for deep sequencing of edited alleles. Overall, protoSpaceJAM streamlines and optimizes CRISPR knock-in experimental design in a flexible and modular manner to benefit diverse research and therapeutic applications. protoSpaceJAM is available open-source as an interactive web tool at protospacejam.czbiohub.org or as a standalone Python package at github.com/czbiohub-sf/protoSpaceJAM.

Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.

Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combining data from multiple sources for online multimorbidity analysis. Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies (Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest similar structures of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Our findings demonstrate the robustness of large-scale EHR data for studying phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine. VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD.

Drugst.One-a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

From static snapshots to dynamic panoramas: the evolution and future vision of palliative care atlas in cross-national perspectives

Palliative care is essential to global health services as it improves the quality of life of patients, their families and caregivers. The ATLANTES Global Observatory of Palliative Care (University of Navarra) was created a decade ago to promote a positive attitude towards patients with advanced illness in society and medicine. To do so, and over the past 15 years, ATLANTES has mapped palliative care data worldwide using public health, macro and comparative perspectives in different atlases. These have enabled data to be contextualised and good examples to be identified concisely and graphically. Atlases have been widely employed as advocacy tools within international institutions and ministries of health. While the aim and the perspective have remained unalterable over time, the methods and design have evolved throughout the publications from sober cartography and static infographics to big interactive data visualisation web tools. By embracing technology, ATLANTES has developed an open-access web mapping tool reuniting information from regional atlases, favouring global access to data. In 2022, matching the increasingly recognised need for robust monitoring of palliative care worldwide, ATLANTES became a WHO Collaborating Centre for the Global Monitoring of Palliative Care Development. This attempt to bridge the gap and ensure equitable care information in countries with limited palliative care access has resulted today in more accessible, self-explanatory, and visually appealing palliative care data.

The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis.

Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines. An integrated atlas of scRNA-Seq expression constructed from multiple donors, developmental ages, and fertility states would be widely useful for the testis research community. To describe the generation and use of the human infertility single-cell testis atlas (HISTA), an interactive web tool for understanding human spermatogenesis through scRNA-Seq analysis. We obtained scRNA-Seq datasets derived from 12 donors, including healthy adult controls, juveniles, and several infertility cases, and reprocessed these data using methods to remove batch effects. Using Shiny, an open-source environment for data visualization, we created numerous interactive tools for exploring the data, some of which support simple statistical hypothesis testing. We used the resulting HISTA browser and its underlying data to demonstrate HISTA's value for testis researchers. A primary application of HISTA is to search by a single gene or a set of genes; thus, we present various analyses that quantify and visualize gene expression across the testis cells and pathology. HISTA also contains machine-learning-derived gene modules ("components") that capture the entire transcriptional landscape of the testis tissue. We show how the use of these components can simplify the highly complex data in HISTA and assist with the interpretation of genes with unknown functions. Finally, we demonstrate the diverse ways HISTA can be used for new data analysis, including hypothesis testing. HISTA is a research environment that can help scientists organize and understand the high-dimensional transcriptional landscape of the human testis. HISTA has already contributed to published testis research and can be updated as needed with input from the research community or downloaded and modified for individual needs.

GLADEnet: A progressive web app for multi-messenger cosmology and electromagnetic follow-ups of gravitational-wave sources

Multi-messenger astronomy is an emerging field of research aimed at unravelling the physics governing astrophysical transients. GW170817 stands out as the first multi-messenger observation of the coalescence of a binary system of neutron stars, detected by the LIGO and Virgo gravitational-wave interferometers, along with space- and ground-based electromagnetic telescopes. It is a striking example of how multi-messenger observations significantly enhance our understanding of the physics of compact objects, relativistic outflows, and nucleosynthesis. It shows a new way of making cosmology and has the potential to resolve the tension between different measurements of the expansion rate of the Universe. To optimise multi-messenger observational strategies, to evaluate the efficiency of the searches for counterparts, and to identify the host galaxy of the source in a large sky localisation, information about the volumes of galaxies within the gravitational-wave localisation is of paramount importance. This requires the use of galaxy catalogues and appropriate knowledge of their completeness. Here, we describe a new interactive web tool namedGLADEnetthat allows us to identify catalogued galaxies and to assess the incompleteness of the catalogue of galaxies in real time across the gravitational-wave sky localisation. This measure is of particular importance when using catalogues such as the GLADE catalogue (Galaxy List for the Advanced Detector Era), which includes a collection of various catalogues that make completeness differ across different regions of the sky. We discuss the analysis steps to defining a completeness coefficient and provide a comprehensive guide on how to use the web app, detailing its functionalities. The app is geared towards managing the vast collection of over 22 million objects in GLADE. The completeness coefficient and the GLADE galaxy list will be disseminated in real time viaGLADEnet, powered by the Virtual Observatory (VO) standard and tools.

A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes

Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.

SICURO+: A web platform to raise awareness on seismic risk in Italy

Italy is a country with high seismic risk. In the past many catastrophic earthquakes have occurred causing severe damage to buildings and infrastructures, serious economic impacts, and the loss of thousands of human lives. To mitigate the impact of seismic risk, as well as of other risks, many efforts have been made, implementing preventive structural measures and acting to raise awareness in the community. In Italy, this need was strengthened with the emanation of the new Civil Protection Code (D.Lgs January 2, 2018 n.1), with the purpose of encouraging the participation of citizens and spreading risk knowledge and the culture of civil protection, thus contributing to “increase the resilience of communities” (art. 31). As part of the agreement between the Italian Civil Protection Department (ICPD) and EUCENTRE (European Centre for training and research in Earthquake Engineering), an interactive web-tool was developed to allow citizens to display the most updated seismic risk maps. These maps were produced in 2018 by the ICPD, with the relevant support of the scientific community operating in the field of seismic risk, in particular of ReLUIS (The Laboratories University Network of seismic engineering) and EUCENTRE. The web-tool, named SICURO+ (Information System for Risk Communication, www.sicuropiu.it), was created to raise awareness in citizens on seismic risk in the place where they live, work, or go on holiday. Communication strategies and behavioural techniques were designed and tested experimentally with the support of behavioural scientists, as part of an agreement between the National School of Administration and the Prime Minister's Office.

Land Emissions and Removals Navigator (LEARN) Tool: Data Sources and Calculation Methods, Version 1.1

This technical note describes data and methods that enable communities in the United States to include forests and trees outside forests in their greenhouse gas (GHG) inventories using the Land Emissions and Removals Navigator (LEARN) tool. LEARN is an interactive web tool that provides automated analysis of the GHG impacts of forests and trees at the community scale. This is a living document that will be updated as improvements to the tool and its data sources are completed.

Evergene: an interactive webtool for large-scale gene-centric analysis of primary tumours.

The data sharing of large comprehensive cancer research projects, such as The Cancer Genome Atlas (TCGA), has improved the availability of high-quality data to research labs around the world. However, due to the volume and inherent complexity of high-throughput omics data, analysis of this is limited by the capacity for performing data processing through programming languages such as R or Python. Existing webtools lack functionality that supports large-scale analysis; typically, users can only input one gene, or a gene list condensed into a gene set, instead of individual gene-level analysis. Furthermore, analysis results are usually displayed without other sample-level molecular or clinical annotations. To address these gaps in the existing webtools, we have developed Evergene using R and Shiny. Evergene is a user-friendly webtool that utilizes RNA-sequencing data, alongside other sample and clinical annotation, for large-scale gene-centric analysis, including principal component analysis (PCA), survival analysis (SA), and correlation analysis (CA). Moreover, Evergene achieves in-depth analysis of cancer transcriptomic data which can be explored through dimensional reduction methods, relating gene expression with clinical events or other sample information, such as ethnicity, histological classification, and molecular indices. Lastly, users can upload custom data to Evergene for analysis. Evergene webtool is available at https://bshihlab.shinyapps.io/evergene/. The source code and example user input dataset are available at https://github.com/bshihlab/evergene.

Modeling delay of age at natural menopause with planned tissue cryopreservation and autologous transplantation

Ovarian tissue cryopreservation has been proven to preserve fertility against gonadotoxic treatments. It has not been clear how this procedure would perform if planned for slowing ovarian aging. This study aimed to determine the feasibility of cryopreserving ovarian tissue to extend reproductive life span and delay menopause by autotransplantation near menopause. Based on the existing biological data on follicle loss rates, a stochastic model of primordial follicle wastage was developed to determine the years of delay in menopause (denoted by D) by ovarian tissue cryopreservation and transplantation near menopause. Our model accounted for (1) age at ovarian tissue harvest (21-40 years), (2) the amount of ovarian cortex harvested, (3) transplantation of harvested tissues in single vs multiple procedures (fractionation), and (4) posttransplant follicle survival (40% [conservative] vs 80% [improved] vs 100% [ideal or hypothetical]). Our model predicted that, for most women aged <40 years, ovarian tissue cryopreservation and transplantation would result in a significant delay in menopause. The advantage is greater if the follicle loss after transplant can be minimized. As an example, the delay in menopause (D) for a woman with a median ovarian reserve who cryopreserves 25% of her ovarian cortex at the age of 25 years and for whom 40% of follicles survive after transplantation would be approximately 11.8 years, but this extends to 15.5 years if the survival is 80%. As another novel finding, spreading the same amount of tissue to repetitive transplants significantly extends the benefit. For example, for the same 25-year-old woman with a median ovarian reserve, 25% cortex removal, and 40% follicle survival, fractionating the transplants to 3 or 6 procedures would result in the corresponding delay in menopause (D) of 23 or 31 years. The same conditions (3 or 6 procedures) would delay menopause as much as 47 years if posttransplant follicle survival is improved to 80% with modern approaches. An interactive Web tool was created to test all variables and the feasibility of ovarian tissue freezing and transplantation to delay ovarian aging (here). Our model predicts that with harvesting at earlier adult ages and better transplant techniques, a significant menopause postponement and, potentially, fertile life span extension can be achieved by ovarian tissue cryopreservation and transplantation in healthy women.