Find research datasets worth reusing

opencc-by-4.0Jan 2024View details →

Extant coccolithophore cell size measurements

This is a dataset of extant coccolithophore coccosphere and cell size measurements taken using light microscopy (LM) and measured using the image analysis progam Fiji. Samples come from the Atlantic Meridional Transect (AMT) 18 cruise (2008), cruise D366 around the NW European shelf (2011), and surface water samples taken in the Maldives during International Ocean Discovery Program (IODP) Expedition 359 (2015). Further information on the data collection and content of the dataset can be found in the dataset description word file. This dataset is currently related to two manuscripts in preparation: 1.     Rosie M. Sheward, Alex J. Poulton, Jeremy R. Young, Joost de Vries, Fanny M. Monteiro, Jens O. Herrle. “Cellular morphological trait dataset for extant coccolithophores from the Atlantic Ocean” to be submitted to Scientific Data. 2.     Joost de Vries, Rosie M. Sheward, Alex J. Poulton, Jeremy Young, Fanny Monteiro, Roberta Johnson, Kyoko Hagino, Patricia Zivera and Levi Wolf. “Dataset of extant coccolithophore size, carbon content and global distribution” to be submitted to Scientific Data.   Correspondence to Jeremy Young,  jeremy.young@ucl.ac.uk

dryad32/100

Dataset from: Changes in cell size and shape during 50,000 generations of experimental evolution with Escherichia coli

Bacteria adopt a wide variety of sizes and shapes, with many species exhibiting stereotypical morphologies. How morphology changes, and over what timescales, is less clear. Previous work examining cell morphology in an experiment with Escherichia coli showed that populations evolved larger cells and, in some cases, cells that were less rod-like. That experiment has now run for over two more decades. Meanwhile, genome sequence data are available for these populations, and new computational methods enable high-throughput microscopic analyses. In this study, we measured stationary-phase cell volumes for the ancestor and 12 populations at 2,000, 10,000, and 50,000 generations, including measurements during exponential growth at the last time point. We measured the distribution of cell volumes for each sample using a Coulter counter and microscopy, the latter of which also provided data on cell shape. Our data confirm the trend toward larger cells while also revealing substantial variation in size and shape across replicate populations. Most populations first evolved wider cells but later reverted to the ancestral length-to-width ratio. All but one population evolved mutations in rod shape maintenance genes. We also observed many ghost-like cells in the only population that evolved the novel ability to grow on citrate, supporting the hypothesis that this lineage struggles with maintaining balanced growth. Lastly, we show that cell size and fitness remain correlated across 50,000 generations. Our results suggest that larger cells are beneficial in the experimental environment, while the reversion toward ancestral length-to-width ratios suggests partial compensation for the less favorable surface area-to-volume ratios of the evolved cells.

opencc-zeroMar 2022View details →

FIGURE 8. Resulting areas from the endemicity analysis with a in Effect of cell size and thresholds in NDM/NVDM methods on recognizing areas of endemism

FIGURE 8. Resulting areas from the endemicity analysis with a progressive elimination of species. A. Areas obtained in the two runs after choosing the appropriate ei (ei minimum: 0.8, cell size 1 ° x 1 °). B. areas obtained in the analysis obtained after choosing the appropriate ei (ei minimum: 0.8, cell size 0.5 ° x 0.5 °). For the list of taxa that support each area, see Table S4.

FIGURE 7 in Effect of cell size and thresholds in NDM/NVDM methods on recognizing areas of endemism

FIGURE 7. Consensus areas supported by taxa with non-sympatric distribution, cell size 0.25° x 0.25° cell. The different letter indicates the species' minimum ei value that supports the areas of endemism in each analysis. A: no definition of ei minimum, B: ei minimum = 1, C: ei minimum = 0.9, D: ei minimum = 0.8, E: ei minimum = 0.7 and F: ei minimum = 0. On each map, we display the corresponding final area number. For the list of taxa that support each area, see Table S3.

FIGURE 3 in Effect of cell size and thresholds in NDM/NVDM methods on recognizing areas of endemism

FIGURE 3. Endemism's final areas resulted from the six analyses varying the minimum ei with 0.5 ° x 0.5 ° cells. The different letter indicates the species' ei minimum value that supports the areas in each run. A: no definition of ei minimum, B: ei minimum = 1, C: ei minimum = 0.9, D: ei minimum = 0.8, E: ei minimum = 0.7 and F: ei minimum = 0. On each map, we display the corresponding final area number. For the list of taxa that support each area, see Table S1.

FIGURE 2 in Effect of cell size and thresholds in NDM/NVDM methods on recognizing areas of endemism

FIGURE 2. Endemism's final areas resulted from the six analyses varying the minimum ei with 1 ° x 1 ° cells. The different letter indicates the species' minimum ei value supporting each run. A: no definition of ei minimum, B: ei minimum = 1, C: ei minimum = 0.9, D: ei minimum = 0.8, E: ei minimum = 0.7 and F: ei minimum = 0. On each map, we display the corresponding final area number. For the list of taxa that support each area, see Table S1.

FIGURE 5 in Effect of cell size and thresholds in NDM/NVDM methods on recognizing areas of endemism

FIGURE 5. Consensus areas supported by taxa with non-sympatric distribution, cell size 1° x 1° cell. The different letter indicates the species' ei minimum value that supports the areas of endemism in each analysis. A: no definition of ei minimum, B: ei minimum = 1, C: ei minimum = 0.9, D: ei minimum = 0.8, E: ei minimum = 0.7 and F: ei minimum = 0. On each map, we display the corresponding final area number. For the list of taxa that support each area, see Table S3.

FIGURE 6 in Effect of cell size and thresholds in NDM/NVDM methods on recognizing areas of endemism

FIGURE 6. Consensus areas supported by taxa with non-sympatric distribution, cell size 0.5°x0.5° cell. The different letter indicates the species' minimum ei value that supports the areas of endemism in each analysis. A: no definition of ei minimum, B: ei minimum = 1, C: ei minimum = 0.9, D: ei minimum = 0.8, E: ei minimum = 0.7 and F: ei minimum = 0. On each map, we display the corresponding final area number. For the list of taxa that support each area, see Table S3.

FIGURE 4 in Effect of cell size and thresholds in NDM/NVDM methods on recognizing areas of endemism

FIGURE 4. Final endemism areas resulted from the six analyses varying the minimum ei with 0.25 ° x 0.25 ° cells. The different letter indicates the species' minimum value that supports the areas in each run. A: no definition of ei minimum, B: ei minimum = 1, C: ei minimum = 0.9, D: ei minimum = 0.8, E: ei minimum = 0.7 and F: ei minimum = 0. On each map, we display the corresponding final area number. For the list of taxa that support each area, see Table S1.

opencc-by-4.0May 2024View details →

Data (2) with paper "Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells"

See also: https://zenodo.org/records/11401115 Data files related to manuscript: Wehrens M, Ershov D, Rozendaal R, Walker N, Schultz D, Kishony R, Levin PA, Tans SJ (2018). “Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells”. Current Biology. <a href="https://doi.org/10.1016/j.cub.2018.02.006">https://doi.org/10.1016/j.cub.2018.02.006</a>   Scripts are available at: <a href="https://github.com/TansLab/Tans_filamentation">https://github.com/TansLab/Tans_filamentation</a>   And you will also need the additional scripts from the repositories: <a href="https://github.com/TansLab/Common_libraries">https://github.com/TansLab/Common_libraries</a> <a href="https://github.com/TansLab/Tans_Schnitzcells">https://github.com/TansLab/Tans_Schnitzcells</a>   Script that generates figures: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a>   And more specifically, data is loaded, (partially analyzed,) and plotted here: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m</a>   Figure 3   See also <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a> <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m</a>   Related files: <table> <tbody> <tr> <td> Data files for cell morphology properties </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos2crop/data/pos2crop-skeletonData.mat </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos3crop/data/pos3crop-skeletonData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with fluorescence data </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos2crop/analysis/straightenedCells/2016-04-07pos2crop_straightFluorData.mat </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos3crop/analysis/straightenedCells/2016-04-07pos3crop_straightFluorData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with division and lineage data (also supplied above) </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos2crop/data/pos2crop-Schnitz.mat </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos3crop/data/pos3crop-Schnitz.mat </td> </tr> </tbody> </table>   Supplemental figure with nucleoid data   See also <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a> <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m</a>   Related files: <table> <tbody> <tr> <td> Data files for cell morphology properties </td> </tr> <tr> <td> 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/data/pos1cropd-skeletonData.mat </td> </tr> <tr> <td> 2017-10-12_FilaRecovery_hupA-mRuby2/pos1cropa2/data/pos1cropa2-skeletonData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with fluorescence data </td> </tr> <tr> <td> 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/analysis/straightenedCells/2017-11-08pos1cropd_straightFluorData.mat </td> </tr> <tr> <td> 2017-10-12_FilaRecovery_hupA-mRuby2/pos1cropa2/analysis/straightenedCells/2017-10-12pos1cropa2_straightFluorData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with division and lineage data (also supplied above) </td> </tr> <tr> <td> 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/data/pos1cropd-Schnitz.mat </td> </tr> <tr> <td> 2017-10-12_FilaRecovery_hupA-mRuby2/pos1cropa2/data/pos1cropa2-Schnitz.mat </td> </tr> </tbody> </table>   Lookup data file: 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/data/slookup.mat

opencc-by-4.0Mar 2018View details →

Data with paper "Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells"

See also the other Zenodo record: https://zenodo.org/records/11401470   Data files related to manuscript: Wehrens M, Ershov D, Rozendaal R, Walker N, Schultz D, Kishony R, Levin PA, Tans SJ (2018). “Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells”. Current Biology. <a href="https://doi.org/10.1016/j.cub.2018.02.006">https://doi.org/10.1016/j.cub.2018.02.006</a>   Currently, single cell experimental time trace data for figures 1, 2 and 4 is added. Additional data will follow.   Scripts are available at: <a href="https://github.com/TansLab/Tans_filamentation">https://github.com/TansLab/Tans_filamentation</a>   And you will also need the additional scripts from the repositories: <a href="https://github.com/TansLab/Common_libraries">https://github.com/TansLab/Common_libraries</a> <a href="https://github.com/TansLab/Tans_Schnitzcells">https://github.com/TansLab/Tans_Schnitzcells</a>   Script that generates figures: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a>   And more specifically, data is loaded, (partially analyzed,) and plotted here: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m</a>   Description of the .mat files   These .mat files contain the lineage information, which were used to generate figures 1 and 2.   Each row corresponds to a cell from birth to division.   Most important fields are: P is its parent cell (number referring to the row of the table). E is the daughter cell after this cell divides D is the other daughter cell after this cell divides frame_nrs is in which frame this cell lived areapx is its area in pixels   Different methods were used to determine the bacterias length. For the tetracycline experiments, often the field “length_fitNew” was used, which is a higher order polynomial fitted through the bacterium. For the other stress conditions, mostly the field “length_skeleton” was used, which is the length of the skeleton of the bacteria, extrapolated until it reaches the bacterial edge (this was done because in those experiments, these bacteria often had weird shapes that couldn’t be fitted by polynomials).   The growth rate of the bacteria is determined by fitting an exponential curve through length information of multiple frames. Given the fluctuation of growth rates, sometimes bacteria grow relatively slow over a certain amount of frames, or relatively fast. Therefor, sometimes the fit is done using more or less frames. The growth rate information can be found in e.g. the parameters 'muP15_fitNew_all','muP9_skeleton_all','muP5_skeleton_all', where mu refers to growth rate, PX indicates X frames around the frame of interest were used for the fit, and fitNew_all or skeleton_all refers respectively to which length parameter was used for the fit.   Note that birth sizes and interdivision times, as wel as added length, can be calculated from the above parameters using the data structure. (See also applicable scripts.)   The data also contains more fields with information about length and size, and also fields for fluorescence data. The latter is not really applicable here.    See also the methods section of the paper for more information.   INFORMATION ABOUT THE DATA   Files for the SulA condition: <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos1crop\data\pos1crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos2crop\data\pos2crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos3crop\data\pos3crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos7crop\data\pos7crop-Schnitz.mat </td> <td> 0 </td> </tr> </tbody> </table> If switch time is zero, recording started at the switch time.   For the temperature condition <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> 2016-03-23_FilaRecovery_asc777_42C\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 450 </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777\pos2crop\data\pos2crop-Schnitz.mat </td> <td> 329 </td> </tr> </tbody> </table>   Delta min tetracycline condition <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos1cropb\data\pos1cropb-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos2cropa\data\pos2cropa-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos2cropb\data\pos2cropb-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos2cropc\data\pos2cropc-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos3cropa\data\pos3cropa-Schnitz.mat </td> <td> 5 </td> </tr> </tbody> </table>   Tetracycline data Only data sets 1 to 5 where used here     <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos3_long.mat </td> <td> 890.9800  </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos4.mat </td> <td> 404.7500         </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos4_long.mat </td> <td> 0  </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos5.mat </td> <td> 529.7600  </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos5_long.mat </td> <td> 0 </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\2uM_pos2.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\2uM_pos4.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\2uM_pos6.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\10uM_pos1.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\10uM_pos3.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\10uM_pos6_long.mat </td> <td> NA </td> </tr> </tbody> </table>   Tetracycline data 2 (redundant with above)   <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> 2013-12-09\pos3crop\data\pos3crop-Schnitz.mat </td> <td> 890.9800  </td> </tr> <tr> <td> 2013-09-24\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 404.7500         </td> </tr> <tr> <td> 2013-12-16\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 0  </td> </tr> <tr> <td> 2013-09-24\pos5crop\data\pos5crop-Schnitz.mat </td> <td> 529.7600  </td> </tr> <tr> <td> 2013-12-16\pos5crop\data\pos5crop-Schnitz.mat </td> <td> 0 </td> </tr> </tbody> </table>

opennotspecifiedAug 2013View details →

FIGURE 4 in Recent changes in abundance and cell size of pelagic diatoms in the North American Great Lakes

FIGURE 4: Change in cell size (µm) for select diatom species from spring sample locations throughout the Great Lakes. Line lengths represent the difference in size between the average for pre-2000 years (1996, 1998, 1999) and post-2000 years (2007, 2008). Black, down-pointing lines indicate declines in average cell size at each sample location. Gray, up-pointing lines similarly indicate cell size increases over the sampling period. The presence of a circle at a sampling station indicates that the taxon was observed during both periods.

opennotspecifiedAug 2013View details →

FIGURE 3 in Recent changes in abundance and cell size of pelagic diatoms in the North American Great Lakes

FIGURE 3: Change in cell size (µm) for diatoms from sample locations throughout the Great Lakes. Line lengths represent the difference in size between the average for pre-2000 years (1996, 1998, 1999) and post-2000 years (2007, 2008). Sizes for the two periods were based on an average of all measured cells at each location. Seasonal data are presented for all, centric and pennate diatoms. Black, down-pointing lines indicate declines in average cell size at each sample location. Gray, up-pointing lines similarly indicate cell size increases over the sampling period.

opennotspecifiedAug 2013View details →

FIGURE 1 in Recent changes in abundance and cell size of pelagic diatoms in the North American Great Lakes

FIGURE 1: Average change in density and biovolume of total diatoms in spring and summer from sample locations throughout the Great Lakes. Lines represent the difference in diatom abundance (density or biovolume) between the average for pre-2000 years (1996, 1998, 1999) and post-2000 years (2007, 2008). Black, down-pointing lines indicate declines in diatom cell densities or biovolumes at each sample location. Gray, up-pointing lines similarly indicate density or biovolume increases over the sampling period.

dryad32/100

Cell size, chlorophyll fluorescence and cadmium bioaccumulation between wild-type and mutant strains of Chlamydomonas reinhardtii upon exposure to cadmium

Heavy metal contamination presents a constant threat to biological systems. Simultaneously, heavy metals have become one of the major contaminants in the aquatic ecosystem. In this regard, the investigation of heavy metal-tolerance genes in algae is relevant. Chlamydomonas reinhardtii is a unicellular green alga, and an excellent model organism used in heavy metal studies. In C. reinhardtii, a novel gene designated as Cia7, was hypothesized to play a role in heavy metal homeostasis due to CIA7's conserved cysteine-residue motif. This study compared two strains of C. reinhardtii, cc4425, the wild-type possessing the functional CIA7 protein and cc5013, the mutant strain possessing the disrupted cia7- gene. The hypothesis was that the expression of Cia7 contributes to an increased cadmium (Cd)-tolerance in C. reinhardtii. The Cd-tolerance would be described by physiological markers of microalgae health, and by intracellular accumulation of the metal. Methods: The objectives of this study were (1) to compare chlorophyll fluorescence and cell size in cc4425 and cc5013 exposed to Cd2+, and (2) to compare Cd2+ bioaccumulation in cc4425 and cc5013 strains in different growth media. Flow cytometry, and inductively coupled plasma optical emission spectrometry (ICP-OES) analysis were performed. Results: There was no significant statistical difference in Cd2+ bioaccumulation between the two strains, cc4425 and cc5013, regardless of growth media. However, a statistically significant difference in Cd2+ bioaccumulation (p<0.0001) was determined between the media (with acetate and without acetate). The cia7- mutant, cc5013 was found to be more susceptible to a Cd2+-induced decrease in chlorophyll fluorescence and had a reduced cell size compared to cc4425, the wild-type strain. Conclusions: These observed differences between the strains suggest that CIA7's biological activity could play a direct or indirect role in increasing Cd tolerance in C. reinhardtii.

opencc-zeroJul 2021View details →

opencc-by-4.0Aug 2023View details →

Bacterial cell-size changes resulting from altering the relative expression of Min proteins

E. coli single cell measurements of size changes, and FtsZ accumulation times in response to increasing minE expression.

Fat Cell Size and Overfeeding and Etopic Study

ClinicalTrials.gov study NCT01672632. IPD Sharing: Not stated. Countries: 1. Publications: 12.

The Method ISET (Insulation by Size of Epithelial Tumor Cells)

ClinicalTrials.gov study NCT00818558. IPD Sharing: Not stated. Countries: 1. Publications: 0.

The Combined Effect of Mesenchymal Stem Cell, HA-CaSO4,BMP-2, and Implant in Inducing The Healing of Critical-Sized Bone Defect

ClinicalTrials.gov study NCT01725698. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Study on the Correlation Between the Quantitative Parameters of Mr Mean Cell Size Imaging and the Histopathological Characteristics of Breast Cancer

ClinicalTrials.gov study NCT05373628. IPD Sharing: Not stated. Countries: 1. Publications: 1.