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653 results for “molecular profiling”
Molecular deciphering of primary liver neuroendocrine neoplasms confirms its distinct existence with foregut-like profile
<p>Isolated hepatic localizations of neuroendocrine tumors (NETs) are generally considered as metastatic NETs of unknown primary but could correspond to primary hepatic NETs (PHNETs), a poorly explored entity. We aimed to describe the clinicopathological and molecular features of PHNETs and compare them with other primary NETs. We assembled a retrospective cohort of patients managed for hepatic localization of NET without extra-hepatic primary tumor after exhaustive clinical, imaging and immunohistochemical characterization. We performed whole-exome sequencing with mutational and copy number analysis. Transcriptomic profiles were compared to pancreatic (n=31), small-bowel (n=22) and lung (n=15) NETs using principal component analysis, unsupervised clustering and gene-set enrichment analysis.Among 27 screened patients, 16 had PHNET (solitary tumor in 63%, median size 11 cm, G2 NETs in 81%) following clinical and pathological review. DNA analyses showed “foregut-like” genomic profiles with frequent alterations in pathways of Fanconi DNA repair (75%), histone modifiers (58%), adherens junctions (58%) and cell cycle control (50%). The most frequently involved genes were <em>KMT2A</em> (58%), <em>ATM</em> (42%), <em>CDH1</em>, <em>CDKN2C</em>, <em>FANCF</em> and <em>MEN1</em> (33% each). Transcriptomic analyses showed that PHNETs clustered closer to foregut (pancreatic, lung) NETs than to midgut (small-bowel) NETs, while remaining a distinct entity with specific profile. Assessment of potentially predictive biomarkers suggested efficacy of treatments usually active in foregut NETs. In conclusion, PHNETs display a foregut-like molecular profile distinct from other types of NETs, with recurrent molecular alterations. Upon exhaustive work-up to exclude unrecognized primary tumor, PHNETs should not be considered metastatic NETs from an unknown primary.</p>
Data supplementing the article "Boosting DNA metabarcoding for biomonitoring with phylogenetic estimation of OTUs' ecological profiles" F. Keck, V. Vasselon, F. Rimet, A. Bouchez, and M. Kahlert submitted to Molecular Ecology Resources journal
<p>These data supplement the article "Enhancing DNA metabarcoding for biomonitoring with phylogenetic estimation of OTUs' ecological profiles" F. Keck, V. Vasselon, F. Rimet, A. Bouchez, and M. Kahlert submitted to Molecular Ecology Resources journal</p> <p>The directory contains the following files:</p> <p><strong>278 (139 x 2 replicates) samples fastq files.rar </strong>- contains the 278 fastq files provided by the sequencing platform with demultiplexed and contig DNA reads corresponding to the 139 samples with 2 sequencing replicates (A and B).</p> <p><strong>Counts_diatoms.xlsx </strong>- contains the morphological inventories with species list (Omnidia code) and valve abundances for the 139 samples.</p> <p><strong>Sites_list.xlsx </strong>- contains information regarding the 139 samples, including: River name, GPS coordinates, code used for molecular analysis and corresponding to sequencing fastq names.</p>
Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets
<p>You can find here the datasets used in the publication: </p> <p><em><strong>Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong> </em></p> <p>This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: </p> <p><a href="https://github.com/alberto-valdeolivas/ST_CRC_CMS">https://github.com/alberto-valdeolivas/ST_CRC_CMS</a></p> <p>In particular, you will find here several zip compressed files with the following content: </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Intermediary_FileObjects.zip?versionId=989cd48d-45f6-46b9-9f90-1927af392a7e">Intermediary_FileObjects.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. </p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/IntermediaryFiles_ST_CRC_LiverMetastasis.zip">IntermediaryFiles_ST_CRC_LiverMetastasis.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Pathology_SpotAnnotations.zip?versionId=ce657a54-9fec-4633-9d89-31f1479b93b7">Pathology_SpotAnnotations.zip</a>: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) </p> <p>-<a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep1.zip?versionId=dbfaad0f-784b-44c9-91d1-713f063d64e3">SN048_A121573_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep2.zip?versionId=ae997080-ca69-44c3-86aa-65bc1d5ef120">SN048_A121573_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep1.zip?versionId=e453ed45-22d8-4d60-b7f3-4daa9212cc88">SN048_A416371_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep2.zip?versionId=be395926-eee8-4670-b355-125e72bf6281">SN048_A416371_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A551763_Rep1.zip?versionId=6b7fa01a-a0d9-43e7-8d1d-8c56cb422374">SN123_A551763_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A595688_Rep1.zip?versionId=f625a286-fbc7-48f6-a57d-7d0df67a0574">SN123_A595688_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A798015_Rep1.zip?versionId=3540f1e5-9cf4-412c-887c-b1d0cc4e03c5">SN123_A798015_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A938797_Rep1_X.zip?versionId=de59c354-fea2-4843-a5f9-5e7a8d863e51">SN123_A938797_Rep1_X.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A551763_Rep2.zip?versionId=9da50bec-8ba4-41b0-a29e-4fc778cf12b7">SN124_A551763_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A595688_Rep2.zip?versionId=29c3e99e-7db2-4c02-9004-dc9d8abf3c27">SN124_A595688_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A798015_Rep2.zip?versionId=a0cf2cca-f3c9-4c45-b311-1ddc81371e35">SN124_A798015_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A938797_Rep2.zip?versionId=e6e4e2bc-1593-4c0f-ac37-b00cc2fc1124">SN124_A938797_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep1.zip?versionId=ec31a69e-d0ce-4e4c-82dc-e7f2e617631a">SN84_A120838_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep2.zip?versionId=89c89532-7bb1-47e4-8900-b5c12a7c4ba0">SN84_A120838_Rep2.zip</a>: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study</p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_BelgianCohort.zip">DeconvolutionResults_ST_CRC_BelgianCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_KoreanCohort.zip">DeconvolutionResults_ST_CRC_KoreanCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_LiverMetastasis.zip">DeconvolutionResults_ST_CRC_LiverMetastasis.zip</a>: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)</p> <p> </p> <p>- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: </p> <p><br><a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V10B01-048_new%20CRC_2021_02_16.ndpi?versionId=d5c8cbd3-40de-43da-8370-329def9e4f14">Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ...</a> (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-084.ndpi?versionId=6d91b1f9-56e9-45c3-a2e6-4714975678fb">Visium Frozen_SN V19S23-084.ndpi</a> (samples A120838_Rep1 and A120838_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-123.ndpi?versionId=c535482c-0a3c-4ba5-a056-f96796c366b0">Visium Frozen_SN V19S23-123.ndpi</a> (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-124.ndpi?versionId=49ce857c-47bb-4930-bb0c-09213e4acf28">Visium Frozen_SN V19S23-124.ndpi</a> (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)</p> <p>- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: </p> <p><strong>IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: </strong></p> <ol> <li>Sample <a href="https://doi.org/10.5281/zenodo.13991781">S1_Cec</a> (A551763)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006187">S2_Col_R </a>(A595688)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13987002">S3_Col_R </a>(A416371) </li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13990328">S4_Col_Sig </a>(A120838)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13989699">S5_Rec </a>(A121573)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14008051">S6_Rec </a>(A938797)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006810">S7_Rec/Sig</a> (A798015)</li> </ol> <p> </p> <p> </p>
Molecular profiling of sponge deflation reveals an ancient relaxant-inflammatory response
<p>A hallmark of animals is the coordination of whole-body movement. Neurons and muscles are central to this, yet coordinated movements also exist in sponges that lack these cell types. Sponges are sessile animals with a complex canal system for filter-feeding. They undergo whole-body movements resembling “contractions'' that lead to canal closure and water expulsion. Here, we combine 3D optical coherence microscopy, pharmacology, and functional proteomics to elucidate anatomy, molecular physiology, and control of these movements. We show that they are driven by the relaxation of actomyosin stress fibers in epithelial canal cells, which leads to whole-body deflation via collapse of the incurrent and expansion of the excurrent system, controlled by an Akt/NO/PKG/A pathway. A concomitant increase in reactive oxygen species and secretion of proteinases and cytokines indicate an inflammation-like state reminiscent of vascular endothelial cells experiencing oscillatory shear stress. This suggests an ancient relaxant-inflammatory response of perturbed fluid-carrying systems in animals.</p> <p>File descriptions:</p> <ul> <li>pharma_*.avi: Exemplary videos for pharmacological treatments of Spongilla <em>lacustris</em>, related to Fig. 2</li> <li>OCM_tomographic_scan: 2D scan through an unsegmented sponge body. First a x-z scan, afterwards a x-y scan. </li> <li>ink_treatment_suppl_figS3.mov: Video of ink treated <em>Spongilla lacustris</em> specimen, related to Fig. S3</li> <li>S_lacustris_annotations_emapper: EggNOG mapper (v2.1.9) results used for GO term enrichment analysis</li> <li>S_lacustris_proteome: Proteome file used as database for proteomic searches</li> <li>S_lacustris_scRNAseq.h5ad: file containing scRNAseq and cell type information of <em>Spongilla lacustris.</em> </li> <li>Suppl_file_*.xlsx: Tables of proteomics (TPP, phophoproteomics, secretomics) results</li> </ul>
Molecular reshaping of phage-displayed Interleukin-2 at beta chain receptor interface to obtain potent super-agonists with improved developability profiles-primary dataset
<div class="c-message_kit__blocks c-message_kit__blocks--rich_text"> <div class="c-message__message_blocks c-message__message_blocks--rich_text"> <div class="p-block_kit_renderer"> <div class="p-block_kit_renderer__block_wrapper p-block_kit_renderer__block_wrapper--first"> </div> </div> </div> </div> <p>Interleukin-2 (IL-2) had been been engineered up to now by yeast display and in silico rational design. In this article we reshaped IL-2 interface with the IL-2 receptor beta subunit to increase binding affinity between both interacting partners, using phage display. Multiple IL-2 mutated variants were selected from large phage-displayed libraries, showing shared molecular patterns. An accumulation of negative charges in the segment 81-87 of IL-2 primary sequence was observed, as well as the strong preponderance of the replacement I92L. The first feature contributed to an optimized electrostatic complementarity between IL-2 and IL-2 receptor beta chain, resulting in higher affinity and faster association kinetics than the ones of previously reported H9 superkine retrieved from yeast display libraries. The presence of a Leu residue at position 92 was the key molecular determinant for a favourable biophysical profile characterized by high stability and production in mammalian-cell based recombinant systems, and decreased aggregation propensity. The new beta super-binders behaved as potent super agonists, both in vitro and in vivo. The latter scenario showed their better preformance when compared to both non-mutated IL-2 and H9. The current dataset contains source data for graphics showing frequency mutations and charge distribution among unselected variants contained in phage-displayed libraries and selected clones enriched after selection on immobilized beta chain. Data showing the direct comparison between different IL-2 mutated variants produced as Fc-fusion proteins are also presented. The comparison includes the results of beta chain binding assays (ELISA), proliferation and phosphorylation assays in vitro, in vivo expansion of lymphocyte populations and anti-tumor activity in animal models. Taken together, the above described data support the unique features of the new beta super-binders and their potential as immunostimulatory and anti-cancer agents.</p>
Pilot Study Using Molecular Profiling to Find Potential Targets & Select Treatments for Pts With Met br ca
ClinicalTrials.gov study NCT01074814. IPD Sharing: NO. Countries: 1. Publications: 1.
Pancreas Cancer: Molecular Profiling as a Guide to Therapy Before and After Surgery ("Personalized Medicine")
ClinicalTrials.gov study NCT01726582. IPD Sharing: NO. Countries: 1. Publications: 2.
Treatment Based on Molecular Profiling Diagnosis Carcinoma of Unknown Primary Site
ClinicalTrials.gov study NCT00737243. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Molecular and Clinical Profile of Diabetes Mellitus and Its Complications
ClinicalTrials.gov study NCT01105858. IPD Sharing: YES. Countries: 1. Publications: 3.
Molecular Profiling and Targeted Therapy for Advanced Non-Small Cell Lung Cancer, Small Cell Lung Cancer, and Thymic Malignancies
ClinicalTrials.gov study NCT01306045. IPD Sharing: YES. Countries: 1. Publications: 5.
Molecular reshaping of phage-displayed Interleukin-2 at beta chain receptor interface to obtain potent super-agonists with improved developability profiles-primary dataset
Open the record for dataset details and reuse information.
Data from: Molecular profiling of diatom assemblages in tropical lake sediments using taxon-specific PCR and Denaturing High-Performance Liquid Chromatography (PCR-DHPLC)
Here we present a protocol to genetically detect diatoms in sediments of the Kenyan tropical Lake Naivasha, based on taxon-specific PCR amplification of short fragments (approx. 100 bp) of the small subunit ribosomal (SSU) gene and subsequent separation of species-specific PCR products by PCR-based DHPLC. An evaluation of amplicons differing in primer specificity to diatoms and length of the fragments amplified demonstrated that the number of different diatom sequence types detected after cloning of the PCR products critically depended on the specificity of the primers to diatoms and the length of the amplified fragments whereby shorter fragments yielded more species of diatoms. The DHPLC was able to discriminate between very short amplicons based on the sequence difference, even if the fragments were of identical length. and if the amplicons differed only in a small number of nucleotides. Generally the method identified the dominant sequence types from mixed amplifications. A comparison with microscopic analysis of the sediment samples revealed that the sequence types identified in the molecular assessment corresponded well with the most dominant species. In summary, the PCR-based DHPLC protocol offers a fast, reliable, and cost-efficient possibility to study DNA from sediments and other environmental samples with unknown organismic content, even for very short DNA fragments.
Tri-modal single cell profiling reveals a distinct pediatric CD8αα T cell subset and broad age-related molecular reprogramming across the T cell compartment
<p>Age-associated changes in the T cell compartment are well-described. However, limitations of current single- or bi-modal single-cell assays, including flow cytometry, RNA-seq, and CITE-seq, have restricted our ability to deconvolve more complex cellular and molecular changes. Here, we profile more than 300,000 single T cells from healthy children (11–13 yrs) and older adults (55–65 yrs) using the trimodal assay, TEA-seq (protein, RNA, and chromatin accessibility), revealing that molecular programming of T cell subsets shifts toward a more activated basal state with age. Naive CD4 T cells, considered relatively resistant to aging, exhibited pronounced transcriptional and epigenetic reprogramming. Moreover, we discovered a novel CD8aa T cell subset lost with age that is epigenetically poised for rapid effector responses and displays distinct inhibitory, co-stimulatory and tissue homing properties. Together, these data reveal new insights into age-associated changes in the T cell compartment that may contribute to differential immune responses.</p>
Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics: fastq & bam files - Sample S5_Rec
<p>You can find here the fastq and bam files related to the datasets used in the publication: </p> <p>In this particular upload, you can find the fastq (version1) and bam (version2) files of the two replicates of sample S5_Rec (A121573)</p> <p><strong>Valdeolivas, A., Amberg, B., Giroud, N. <em>et al.</em> Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. <em>npj Precis. Onc.</em> 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong></p> <p> </p> <p> </p>
FIGURE 8 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests
FIGURE 8. Median lethal time (LT50) of Steinernema feltiae in the larvae of different insect pests at different time intervals and at different nematode concentrations, respectively.
FIGURE 6 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests
FIGURE 6. Maximum-likelihood phylogenetic tree between Steinernema feltiae and other species of Steinernema in the Feltiae-group based on nucleotide sequences of the D2–D3 expansion segments of large subunit (28S) of rRNA flanked by primers D2F and 536. Numbers at nodes represent bootstrap values based on 100 replications. Bars represent average nucleotide substitutions per sequence position. NCBI accession numbers of the nucleotide sequences used for the analyses are shown next to the species names. The scale bar shows the number of substitutions per site.
FIGURE 4 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests
FIGURE 4. Steinernema feltiae (light microscopy). A–C: First-generation female; A: Neck region (arrow pointing excretory pore); B: Vulva region; C: Posterior end (arrow pointing mucron). D,E: First-generation male; D: Neck region (arrow pointing excretory pore); E: Posterior end showing spicules and gubernaculum (arrow pointing mucron). F,G: Second-generation male; F: Neck region (arrow pointing excretory pore); G: Posterior end showing spicules and gubernaculum (arrow pointing mucron). H–J: Second-generation female; H: Neck region (arrow pointing excretory pore); I: Vulva region; J: Posterior end (arrow pointing mucron).
FIGURE 2 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests
FIGURE 2. Steinernema feltiae (line). A–D: First-generation female; A: Neck region; B: Posterior end; C: Vulva region; D: Whole female. E,G–K: Second-generation female; E: Whole female; G: Neck region; H–J: Posterior region showing variation in tail region; K: Vulva region. F: Whole infective juvenile.
FIGURE 3 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests
FIGURE 3. Steinernema feltiae (line). A–C: First-generation male; A: Whole male; B–C: Posterior region showing variations in spicule morphology. D,E: Second-generation male; D: Whole male; E: Posterior region.
FIGURE 7 in Morphological and molecular profiling of an entomopathogenic nematode Steinernema feltiae: Unlocking its biocontrol potential against vegetable insect pests
FIGURE 7. Median lethal concentration (LC50) of Steinernema feltiae in the larvae of different insect pests at different time intervals and at different nematode concentrations, respectively.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.