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5,016 results for “THERAPEUTICS”
Semaphorin 3E as a novel therapeutic target for neuropathic pain
GEO Series GSE290407. Mus musculus. 3 samples. Type: Expression profiling by array.
Partitioning of cancer therapeutics in nuclear condensates
GEO Series GSE149085. Homo sapiens. 5 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Gene expression profiling to identify potential therapeutic targets and improve prognostication of advanced penile squamous cell carcinoma
GEO Series GSE85730. Homo sapiens. 33 samples. Type: Expression profiling by array.
Therapeutic use of microRNA-181a in the adoptive immunotherapy of cancer
GEO Series GSE9240. Homo sapiens; Mus musculus. 18 samples. Type: Expression profiling by array.
Comprehensive transcriptome profiling of ulcerative colitis mouse model suggests biomarkers and therapeutic targets for human colitis
GEO Series GSE155303. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
KDM5 inhibition is a novel therapeutic strategy for the treatment of KMT2D mutant lymphomas
GEO Series GSE153671. Homo sapiens. 105 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Pathway based integrative analysis reveals a key role for the hexosamine biosynthetic pathway in prostate cancer progression with therapeutic implications
GEO Series GSE50661. Homo sapiens. 16 samples. Type: Expression profiling by array.
Site-specific lambda-integrase-mediated seamless vector transgenesis in human LINE-1 elements for therapeutic protein expression
GEO Series GSE99883. Homo sapiens. 1 samples. Type: Other.
Poly(I:C) offers a therapeutic opportunity to redirect immunosuppressed colorectal tumour landscapes towards a proinflammatory and CMS1-like regenerative state
GEO Series GSE280541. Homo sapiens. 16 samples. Type: Expression profiling by array.
Apoa1, a safe harbor locus for therapeutic genome editing in the liver
GEO Series GSE152993. Mus musculus. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Length of hospital stay, Clinical and Therapeutic characteristics of patients with COVID-19
<p>Background: The Covid-19 (SARS-CoV-2) pandemic marked unprecedented on healthcare sector globally with increase demand of hospital beds, intensive care facilities, lifesaving supplies and equipments. This retrospective study of two healthcare centers focuses on length of hospital stay, clinical and therapeutic characteristics of patients with COVID-19. These parameters help to identify the available resources and to determine the optimum requirements of healthcare units in pandemics.</p> <p>Materials and Methods: The retrospective data was collected between 12<sup>th</sup> of March and 30<sup>th</sup> of Jun 2020 of polymerase chain reaction (PCR) confirming SARS-COV-2 patients and categorized into mild, moderate, and severe diseased groups based on symptoms and its severity of COVID 19.</p> <p>Results: A total of 843 SARS-COV-2 positive patients were identified for this study, 132 were mild symptomatic cases<em>, </em>168<em>, </em>moderate symptomatic, and 17 were severe symptomatic with mean age of 34.95 ± 15.9 years. The mean length of hospital stay was 16.38, 13.18, 13.72, 9.30, 6.96, 10.86, 5.77 and 7.37 from 1 to 8 treatments consecutively, with 156.327 Chi-square, and 0.000* p-value. Kruskal-Wallis test shows there were significant differences seen between the length of hospital stay among different treatment groups with chi-square (7) = 156.327, with p-value of 0.000(p-value<0.005). Treatment group 1 who were on antiviral had the highest mean no. of hospital stay while treatment group 7 patients who were on no treatments had the lowest stay with mean duration of 5.7days.</p> <p>Conclusion: Length of hospital stay, clinical and therapeutic characteristics of patients with COVID-19 are crucial indicators of pandemic management. The lower length of hospital stay is a positive outcome of better Covid-19 management.</p> <p>Background: The Covid-19 (SARS-CoV-2) pandemic marked unprecedented on healthcare sector globally with increase demand of hospital beds, intensive care facilities, lifesaving supplies and equipments. This retrospective study of two healthcare centers focuses on length of hospital stay, clinical and therapeutic characteristics of patients with COVID-19. These parameters help to identify the available resources and to determine the optimum requirements of healthcare units in pandemics.</p> <p>Materials and Methods: The retrospective data was collected between 12<sup>th</sup> of March and 30<sup>th</sup> of Jun 2020 of polymerase chain reaction (PCR) confirming SARS-COV-2 patients and categorized into mild, moderate, and severe diseased groups based on symptoms and its severity of COVID 19.</p> <p>Results: A total of 843 SARS-COV-2 positive patients were identified for this study, 132 were mild symptomatic cases<em>, </em>168<em>, </em>moderate symptomatic, and 17 were severe symptomatic with mean age of 34.95 ± 15.9 years. The mean length of hospital stay was 16.38, 13.18, 13.72, 9.30, 6.96, 10.86, 5.77 and 7.37 from 1 to 8 treatments consecutively, with 156.327 Chi-square, and 0.000* p-value. Kruskal-Wallis test shows there were significant differences seen between the length of hospital stay among different treatment groups with chi-square (7) = 156.327, with p-value of 0.000(p-value<0.005). Treatment group 1 who were on antiviral had the highest mean no. of hospital stay while treatment group 7 patients who were on no treatments had the lowest stay with mean duration of 5.7days.</p> <p>Conclusion: Length of hospital stay, clinical and therapeutic characteristics of patients with COVID-19 are crucial indicators of pandemic management. The lower length of hospital stay is a positive outcome of better Covid-19 management.</p> <p> </p>
Dataset for the project "Dissecting molecular mechanisms triggered by progressive loss of progranulin and C9orf72 in frontotemporal dementia: novel therapeutic targets? (RF-2016-02361492)
<p><strong>Dataset for the project “Dissecting molecular mechanisms triggered by progressive loss of progranulin and C9orf72 in frontotemporal dementia: novel therapeutic targets?” Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2016-02361492)</strong></p> <p><strong>Specific Aim 1</strong></p> <p>To further collect and generate human cellular disease models (skin fibroblast and LCL) of progressive progranulin and C9orf72 loss.</p> <p><strong>Specific Aim 2</strong></p> <p>To test the hypothesis that EVs secreted from microglia, in which GRN or C9orf72 are downregulated, contain and deliver excessive C1q and C3 complements factors to the synapses, thereby causing aberrant synaptic pruning. Results of this aim will clarify whether microglia-derived EVs have a pathogenic role in the disease, thus indicating a novel therapeutic target to limit synaptic loss and disease progression.</p> <p><strong>Specific Aim 3</strong></p> <p>To identify sensitive biomarkers for patient stratification and treatment monitoring by a) characterization of exosomal and lysosomal pathways in human cellular disease models with progressive loss of progranulin and C9orf72; b) studying lysosomal and immune proteins in EVs circulating in body fluids of subjects with progressive loss of progranulin and C9orf72; c) studying the impact of variants in lysosomal genes on age of disease onset in subjects carrying GRN and C9orf72 mutations.</p> <p>The present dataset contains the following folders:</p> <ul> <li><strong>RawData_Aim2 [Unpublished Data]:</strong> <ul> <li>Materials and Methods_RawData_Aim2</li> <li>RawData_Aim2: a dataset (excel file) containing the following sheets: <ul> <li>Fig.1: Number of EVs from microglia (analysis with Tunable Resistive Pulse Sensing-TRPS technique, using a qNano instrument)</li> <li>Fig.2: Genetic expression levels of homeostatic and inflammatory genes in microglia</li> <li>Fig.3: WB analysis to confirm GRN silencing in microglia</li> <li>Fig.4A: Quantification of immunofluorescence analysis for Lamp-1, CD68, and Cathepsin D</li> <li>Fig.4B: WB analysis for Lamp-1, CD68, TMEM119 and Cathepsin D in microglia</li> <li>Fig.5A: WB analysis for C3, C1q, and TMEM119 in microglia</li> <li>Fig.5B: Quantification of immunofluorescence analysis for C1q and Cd11b in microglia</li> <li>Fig.5C: WB analysis for C3, C1q, Alix, Flotillin, Annexin-A2, and TOM20 in EVs from microglia</li> <li>Fig.6A-B: WB analysis for C3, C1q, Lamp1, CD68, Cathepsin D, Alix, and Annexin-A2 in EVs</li> <li>Fig.8: densitometric analysis of presynaptic terminals (Basson) and postsynaptic terminals (Shank2) in neuron-microglia cocultures (no treatment)</li> <li>Fig.9: densitometric analysis of presynaptic terminals (Basson) and postsynaptic terminals (Shank2) in neuron-microglia cocultures (treatments GW4869)</li> <li>Fig.10: densitometric analysis of presynaptic terminals (Basson) and postsynaptic terminals (Shank2) in neuron-microglia cocultures (treatments with EVs)</li> <li>Fig.11: Phagocytosis assay</li> </ul> </li> <li>Fig.4A: a folder containing original immunofluorescence pictures for Lamp-1, CD68, and Cathepsin D in microglia</li> <li>Fig.5B: a folder containing original immunofluorescence pictures for C1q and Cd11b in microglia</li> <li>Fig.11: a folder containing original pictures from phagocytosis assay</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>RawData_Aim3a_Fibroblast [Unpublished Data]</strong>: <ul> <li>Materials and Methods_Aim3a_Fibroblasts</li> <li>RawData_Aim3a_Fibroblasts_NTA_Complement_LC3_CathD: a folder containing: <ul> <li>RawData_Aim3a_Fibroblasts_NTA_Complement_LC3_CathD: a dataset (excel file) containing the following variables referred to EVs from fibroblasts: <ul> <li>Sample (Col. A) and its Category (Col. B)</li> <li>Raw NTA File name (Col. C)</li> <li>Raw Values_C1q EVs from Bioplex assay (Col. D-F)</li> <li>Raw Values_C3 EVs from Bioplex assay (Col. G-I)</li> <li>Raw Values_C4 EVs from Bioplex assay (Col. J-L)</li> <li>Raw Values_LC3 EVs from ELISA assay (Col. M-P)</li> <li>Raw Values_Cathepsin D EVs from ELISA assay (Col. Q-T)</li> </ul> </li> <li>RawData_Aim3a_Fibroblasts_NTA_Files: a folder containing Raw NTA file for each sample (from these files we extracted EV concentration and size)</li> </ul> </li> <li>RawData_Aim3a_Fibroblasts_EVMarkers_PGRN_C9orf72_pTDP43: a folder containing: <ul> <li>WB_Aim3a_Fibroblasts_EVMarkers: a folder containing original WB pictures for EV biomarkers TSG101, Alix, CD63, Flotillin-1</li> <li>ELISA_C9orf72_Fibroblasts: a dataset containing raw C9orf72 data from ELISA assay on fibroblasts lysates and EVs lysates</li> <li>ELISA_pTDP43_Fibroblasts: a dataset containing raw pTDP43 data from ELISA assay on fibroblasts lysates and EVs lysates</li> <li>WB_Analyses_Fibroblasts_PGRN: a dataset containing densitometric analysis from WB on fibroblasts lysates and EVs lysates for PGRN and Actin/TSG101 (for cells/EVs normalization respectively)</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>3.a RawData_Aim3a_LCL [Unpublished Data]</strong>: <ul> <li>Materials and Methods_Aim3a _LCL</li> <li>RawData_Aim3a_LCL_NTA_Complement_LC3_CathD: a folder containing: <ul> <li>RawData_Aim3a_LCL_NTA_Complement_LC3_CathD: a dataset (excel file) containing the following variables referred to EVs from LCL: <ul> <li>Sample (Col. A)</li> <li>Category (Col. B)</li> <li>Raw NTA File name (Col. C)</li> <li>Raw Values_C1q EVs from Bioplex assay (Col. D-F)</li> <li>Raw Values_C3 EVs from Bioplex assay (Col. G-I)</li> <li>Raw Values_C4 EVs from Bioplex assay (Col. J-L)</li> <li>Raw Values_LC3 EVs from ELISA assay (Col. M-P)</li> <li>Raw Values_Cathepsin D EVs from ELISA assay (Col. Q-T)</li> </ul> </li> <li>RawData_Aim3a_LCL_NTA_Files: a folder containing Raw NTA file for each sample (from these files we extracted EV concentration and size)</li> </ul> </li> <li>WB_Analyses_Aim3a_LCL_PGRN_C9orf72_TDP43_pTDP43_Lamp1: a dataset containing densitometric analysis from WB on LCL lysates and EVs lysates for PGRN, C9orf72, p-TDP43, TDP43, Lamp-1, Actin/TSG101 (for cells/EVs normalization respectively)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>3.b </strong><strong>RawData_Aim3b_Plasma_1 [Published] </strong><br> Results (NTA data) from aim 3.b are published in Bellini&Saraceno et al. Cells 2022 (doi:10.3390/cells11030488) and the raw data supporting the results are published in the Mendeley Data Repository at doi:10.17632/kds9sb4z6t.2 <ul> <li>Materials and Methods_Aim3b_Plasma</li> <li>RawData_Aim3b_Plasma: a folder containing: <ul> <li>RawData_Aim3b_Plasma: a dataset (excel file) containing the following variables: <ul> <li>Sample (Col. A)</li> <li>Category (Col. B)</li> <li>Age (Col. C)</li> <li>Corresponding Raw NTA File name (Col. D)</li> </ul> </li> <li>RawData_Aim3b_NTA_Files: a folder containing Raw NTA file for each sample (from these files we extracted EV concentration and size)</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>3.b </strong><strong>RawData_Aim3b_Plasma_2 [<em>in press</em>]</strong><br> Results (NTA and Cathepsin D data) from aim 3.b have been included in an article submitted to IJMS (Manuscript ID: ijms-1899683; <em>in press</em>) and the raw data supporting the results are published in the Zenodo Data Repository at doi: 10.5281/zenodo.6958338. <ul> <li>Materials and Methods_Aim3b_Plasma</li> <li>RawData_Aim3b_Plasma: a folder containing: <ul> <li>RawData_Aim3b_Plasma: a dataset (excel file) containing the following variables: <ul> <li>Sample (Col. A)</li> <li>Category (Col. B)</li> <li>Age (Col. C)</li> <li>Onset (Col. D)</li> <li>Corresponding Raw NTA File name (Col. E)</li> <li>Raw Values_Cathepsin D plasma EVs from ELISA assay (Col. F-J)</li> <li>Raw Values_Cathepsin D plasma from ELISA assay (Col. K-O)</li> </ul> </li> <li>RawData_Aim3b_NTA_Files: a folder containing Raw NTA file for each sample (from these files we extracted EV concentration and size)</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>3.c </strong><strong>RawData_Aim3c_NGS [Submitted]</strong><br> Results from aim 3.c have been included in an article submitted to IJMS (Manuscript ID: ijms-1922450; Under review) and the raw data supporting the results are openly available in the Zenodo Data Repository at doi: 10.5281/zenodo.7040532. <ul> <li>Materials and Methods_Aim3c_NGS</li> <li>RawData­_ Aim3c_NGS: a folder containing: <ul> <li>Correspondence_RawData_NGS: a dataset (excel file) containing the following variables: <ul> <li>Sample (Col. A)</li> <li>Group (Col. B)</li> <li>Corresponding g.vcf File name (Col. C)</li> </ul> </li> </ul> </li> <li>Gene_Selection: a file with the selection of the 98 genes related to the lysosomal pathway included in the NGS panel</li> </ul> </li> </ul> <p> </p>
EWS-WT1 fusion isoforms establish oncogenic programs and therapeutic vulnerabilities in Desmoplastic Small Round Cell Tumors
<p>EWS fusion oncoproteins underlie the pathogenesis of several human malignancies including Desmoplastic Small Round Cell Tumor (DSRCT), an aggressive mesenchymal tumor driven by fusions between the disordered domain of EWS and the developmental transcription factor WT1. Here we combined chromatin occupancy and long-range interaction profiles to identify EWS-WT1-dependent gene regulation networks and directly controlled target genes. We show that EWS-WT1 operates primarily as a powerful activator of distal regulatory elements and controls an oncogenic gene expression program that characterize primary DSRCTs. Moreover, EWS-WT1 has two isoforms that differ by three amino acids in their DNA binding domain (+/- KTS), as observed for wild type WT1, and we show that each fusion isoform has a specific DNA binding profile that is distinct from its wild type counterparts and requires a functional EWSR1 prion like domain. Remarkably, xenograft experiments using human mesothelial cells, candidate cells of origin of DSRCT, reveal that both isoforms are required to generate viable tumors that resemble DSRCT. Finally, we identify new candidate EWS-WT1 target genes with potential therapeutic implications, including <em>CCND1</em>, whose inhibition by the clinically-approved drug Palbociclib leads to marked tumor burden decrease in DSRCT PDXs <em>in vivo</em>. Taken together, our studies identify gene regulation programs and therapeutic vulnerabilities in DSRCT and provide a mechanistic understanding of the complex isoform-dependent oncogenic activity of EWS-WT1.</p>
Evaluation of therapeutic efficacy of Sodium thiosulfate in combating I/R injury in rat brain: Exploring its efficiency further in vascular calcified brain slice model
<p>Cerebral ischemia reperfusion injury (CIR) is one of the clinical manifestations encountered during the management of stroke. High prevalence of intracranial arterial calcification is reported in stroke patients. However, the impact of vascular calcification (VC) in the outcome of CIR and therapeutic interventions remains unclear. Two different experimental models namely carotid artery occlusion and brain slice models were used to evaluate the efficacy of STS in male Wistar rats. IR was inflicted in rat by occluding carotid artery for 30 minutes followed by 24 hr reperfusion after STS (100 mg/kg) administration. Brain slice model was used to reconfirm the results to account blood brain barrier permeability. Further, brain slice tissue was utilised to evaluate the efficacy of STS in VC rat brain by measuring the histological alterations and biochemical parameters.</p> <p> </p>
DIAPASON Project: Therapeutic alliance and gender: an analysis of DIAPASON data
<p>Dataset of the paper entitled <em>Therapeutic alliance and gender: an analysis of DIAPASON data.</em> of the project "DAILY TIME USE, PHYSICAL ACTIVITY, QUALITY OF CARE AND INTERPERSONAL RELATIONSHIPS IN PSYCHIATRIC RESIDENTIAL FACILITIES: AN ITALIAN MULTICENTRE STUDY" Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-<a>2018-12365514</a>) (https://www.diapason-study.eu/)</p>
Growth differentiation factor 1-induced tumour plasticity provides a therapeutic window for immunotherapy in Hepatocellular Carcinoma
<p>Tumour lineage plasticity is an emerging hallmark of aggressive tumours. Tumour cells usually hijack developmental signaling pathways to gain cellular plasticity and evade therapeutic targeting. In the present study, the secreted protein growth and differentiation factor 1 (GDF1) was found to be closely associated with poor tumour differentiation. Overexpression of GDF1 suppressed cell proliferation but strongly enhanced tumour dissemination and metastasis. Ectopic expression of GDF1 induced the dedifferentiation of hepatocellular carcinoma (HCC) cells into their ancestral lineages and reactivated a broad panel of cancer testis antigens (CTAs), which further stimulated the immunogenicity of HCC cells to immune-based therapies. Mechanistic studies revealed that GDF1 functioned through the Anaplastic lymphoma kinase 7 (ALK7)-Mothers against decapentaplegic homolog 2/3 (SMAD2/3) signaling cascade and suppressed the epigenetic regulator Lysine specific demethylase 1 (LSD1) to boost CTA expression. GDF1-induced tumour lineage plasticity might be an Achilles heel for HCC immunotherapy. Inhibition of LSD1 based on GDF1 biomarker prescreening might widen the therapeutic window for immune checkpoint inhibitors in the clinic.</p>
ScienceDex guides
Understand access before you commit
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.