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> Anatomy > Body Part > Pancreas

Pancreas

The pancreas is a glandular organ located behind the stomach that plays a crucial role in the digestive and endocrine systems.
It produces enzymes that aid in the breakdown of food, as well as hormones such as insulin and glucagon that regulate blood sugar levels.
Pancreatic disorders can lead to conditions like pancreatitis, diabetes, and pancreatic cancer, making it an importamnt area of medical research.
Optimizing pancreas research through tools like PubCompare.ai can enhance reproducibility, accuracy, and the identification of effective protocols and methods.

Most cited protocols related to «Pancreas»

1
Standardized Single-Cell RNA-seq Normalization
We downloaded raw read or UMI matrices for all datasets, from their respective sources. The one exception was the 3pV1 dataset from the PBMC analysis. These data were originally quantified with the hg19 reference, while the other two PBMC datasets were quantified with GRCh38. Thus, we downloaded the fastq files from the 10X website (Supplementary Table 8). We quantified gene expression counts using Cell Ranger11 ,41 v2.1.0 with GRCh38. From the raw count matrices, we used a standard data normalization procedure, laid out below, for all analyses, unless otherwise specified. Except for the L2 normalization and within-batch variable gene detection, this procedure follows the standard guidelines of the Seurat single cell analysis platform.
We filtered cells with fewer than 500 genes or more than 20% mitochondrial reads. In the pancreas datasets, we filtered cells with the same thresholds used in Butler et al7 : 1750 genes for CelSeq, 2500 genes for CelSeq2, no filter for Fluidigm C1, 2500 genes for SmartSeq2, and 500 genes for inDrop. We then library normalized each cell to 10,000 reads, by multiplicative scaling, then log scaled the normalized data. We then identified the top 1000 variable genes, ranked by coefficient of variation, within in each dataset. We pooled these genes to form the variable gene set of the analysis. Using only the variable genes, we mean centered and variance 1 scaled the genes across the cells. Note that this was done in the aggregate matrix, with all cells, rather than within each dataset separately. With these values, we performed truncated SVD keeping the top 30 eigenvectors. Finally, we multiplied the cell embeddings by the eigenvalues to avoid giving eigenvectors equal variance.
Korsunsky I., Millard N., Fan J., Slowikowski K., Zhang F., Wei K., Baglaenko Y., Brenner M., Loh P.R, & Raychaudhuri S. (2019). Fast, sensitive, and accurate integration of single cell data with Harmony. Nature methods, 16(12), 1289-1296.
Publication 2019
DNA Library Gene Expression Genes Mitochondrial Inheritance Pancreas Single-Cell Analysis
2
Standardized Single-Cell RNA-seq Normalization
We downloaded raw read or UMI matrices for all datasets, from their respective sources. The one exception was the 3pV1 dataset from the PBMC analysis. These data were originally quantified with the hg19 reference, while the other two PBMC datasets were quantified with GRCh38. Thus, we downloaded the fastq files from the 10X website (Supplementary Table 8). We quantified gene expression counts using Cell Ranger11 ,41 v2.1.0 with GRCh38. From the raw count matrices, we used a standard data normalization procedure, laid out below, for all analyses, unless otherwise specified. Except for the L2 normalization and within-batch variable gene detection, this procedure follows the standard guidelines of the Seurat single cell analysis platform.
We filtered cells with fewer than 500 genes or more than 20% mitochondrial reads. In the pancreas datasets, we filtered cells with the same thresholds used in Butler et al7 : 1750 genes for CelSeq, 2500 genes for CelSeq2, no filter for Fluidigm C1, 2500 genes for SmartSeq2, and 500 genes for inDrop. We then library normalized each cell to 10,000 reads, by multiplicative scaling, then log scaled the normalized data. We then identified the top 1000 variable genes, ranked by coefficient of variation, within in each dataset. We pooled these genes to form the variable gene set of the analysis. Using only the variable genes, we mean centered and variance 1 scaled the genes across the cells. Note that this was done in the aggregate matrix, with all cells, rather than within each dataset separately. With these values, we performed truncated SVD keeping the top 30 eigenvectors. Finally, we multiplied the cell embeddings by the eigenvalues to avoid giving eigenvectors equal variance.
Korsunsky I., Millard N., Fan J., Slowikowski K., Zhang F., Wei K., Baglaenko Y., Brenner M., Loh P.R, & Raychaudhuri S. (2019). Fast, sensitive, and accurate integration of single cell data with Harmony. Nature methods, 16(12), 1289-1296.
Publication 2019
DNA Library Gene Expression Genes Mitochondrial Inheritance Pancreas Single-Cell Analysis
3
Integrating Single-Cell RNA-Seq Data with Seurat
The Seurat alignment procedure is designed to integrate single-cell RNA sequencing data (scRNA-seq) across distinct datasets. Following is an overview of the main steps comprising a typical workflow:

Data preprocessing and gene selection

Define a shared correlation space with canonical correlation analysis

Identify rare non-overlapping subpopulations

Align correlated subspaces using dynamic time warping

Integrated analysis across datasets (clustering, trajectory building, differential expression)

Below, we describe each of these steps in detail. Additionally, we provide full command lists for the integration of the stimulated and resting immune datasets and for the integration of the four scRNA-seq datasets of human pancreatic islet cells (produced with four different plate-based technologies CelSeq, CelSeq2, Fluidigm C1, SmartSeq2) as supplementary code.
Butler A., Hoffman P., Smibert P., Papalexi E, & Satija R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature biotechnology, 36(5), 411-420.
Publication 2018
Cells Genes Homo sapiens Islets of Langerhans Pancreas Single-Cell RNA-Seq
4
Canonical Marker-Based Cell Labeling
In the cell-line, PBMC, and Pancreas analyses, we labeled cells within individual datasets using canonical markers. We did this by using the standard pre-processing pipeline for each dataset, clustering (Louvain, as above), and identifying clusters specific for the canonical markers for that analysis. We used a similar strategy to identify fine-grained subpopulations of PBMCs and in the HCA 500,000 cell dataset. In these case, we clustered in the joint embedding produced by Harmony, then looked for clusters that specifically expressed expected canonical markers.
Korsunsky I., Millard N., Fan J., Slowikowski K., Zhang F., Wei K., Baglaenko Y., Brenner M., Loh P.R, & Raychaudhuri S. (2019). Fast, sensitive, and accurate integration of single cell data with Harmony. Nature methods, 16(12), 1289-1296.
Publication 2019
Cell Lines Cells Joints Pancreas Population Group
5
Batch Correction Comparison for Single-Cell Analysis
We compared our alignment method to both ComBat49 (link) and limma50 (link). For each pair of datasets, we first combined the UMI count matrices and scaled and normalized the combined expression matrix. For the ComBat comparisons, we performed batch correction on the scaled and normalized gene expression data using the ComBat function from the sva R package, treating the dataset as the batch. For the limma comparisons, we performed batch correction on the scaled and normalized gene expression data using the removeBatchEffect function from the limma R package, treating the dataset as the batch. All other default parameters were left unchanged for both methods. We then performed a principle component analysis to identify sources of variation that accounted for a majority of the variation in the corrected data. For the PBMC, hematopoietic progenitor, and pancreas datasets we used the first 19, 18 and 21 PCs respectively to visualize with tSNE and to calculate an alignment score. For the two multiple alignment examples of human pancreatic islet cells and PBMCs, we used the first 20 PCs.
Butler A., Hoffman P., Smibert P., Papalexi E, & Satija R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature biotechnology, 36(5), 411-420.
Publication 2018
Cells Gene Expression Hematopoietic System Homo sapiens Islets of Langerhans Pancreas

Most recents protocols related to «Pancreas»

1
Tucaresol Combination Therapy Evaluation
Not available on PMC !

Example 1

1) Tucaresol

Tucaresol (0-1200 μM) is exposed for 72 hours to a panel of human liquid, hematological, and solid tumors such as multiple myeloma, leukemia, colorectal, non-small cell lung cancer (squamous and adenocarcinoma), hepatocellular, renal, pancreatic and breast cancer cell lines, and human non-tumor such as HUVEC, PBMC, skin fibroblast cells lines. Tucaresol is studied either alone or in combination with standard-of-care agents (1-100 μM). All cell lines are grown in standard serum-containing media with an exposure time of 24-144 hours. Cell viability is measured using, for example, the Cell TiterGlo® Viability Assay. The potency (IC50) and efficacy (% cell kill) are determined from the percent cell growth of the vehicle control.

2) Tucaresol Plus PD-1 Antibody

Tucaresol (0-1200 μM) in the presence of a PD-1 antibody is exposed for 72 hours to a panel of human liquid, hematological, and solid tumor such as multiple myeloma, leukemia, colorectal, non-small cell lung cancer (squamous and adenocarcinoma), hepatocellular, renal, pancreatic and breast cancer cell lines, and human non-tumor such as HUVEC, PBMC, skin fibroblast cells lines, and the viability of the cell lines are measured as described above. The viability of the cell lines in the presence of tucaresol plus PD-1 antibody is compared to the viability of the cell lines in the presence of a CTLA-4 antibody plus the PD-1 antibody or PD-1 antibody alone.

3) CTLA-4 Antibody Plus PD-1 Antibody

CTLA-4 antibody in the presence of a PD-1 antibody is exposed for 72 hours to a panel of human liquid, hematological, and solid tumor such as multiple myeloma, leukemia, colorectal, non-small cell lung cancer (squamous and adenocarcinoma), hepatocellular, renal, pancreatic and breast cancer cell lines, and human non-tumor such as HUVEC, PBMC, skin fibroblast cells lines, and the viability of the cell lines are measured as described above.

4) Tucaresol Plus Plinabulin

Tucaresol (0-1200 μM) in the presence of Plinabulin is exposed for 72 hours to a panel of human liquid, hematological, and solid tumor such as multiple myeloma, leukemia, colorectal, non-small cell lung cancer (squamous and adenocarcinoma), hepatocellular, renal, pancreatic and breast cancer cell lines, and human non-tumor such as HUVEC, PBMC, skin fibroblast cells lines, and the viability of the cell lines are measured as described above.

The viability of the cell lines in the presence of tucaresol, tucaresol plus PD-1 antibody, CTLA-4 antibody plus the PD-1 antibody, and tucaresol plus plinabulin are compared.

US11857522B2. Compositions containing tucaresol or its analogs (2024-01-02). BeyondSpring Pharmaceuticals, Inc. [US]. Inventors: Ramon Mohanlal [US], Lan Huang [US], George Kenneth Lloyd [US].
Full text: Click here
Patent 2024
Adenocarcinoma Biological Assay Cell Lines Cells Cell Survival Cytotoxic T-Lymphocyte Antigen 4 Fibroblasts Homo sapiens Immunoglobulins Kidney Leukemia MCF-7 Cells Multiple Myeloma Neoplasms Non-Small Cell Lung Carcinoma Pancreas plinabulin Serum Skin tucaresol
2
Micropeptide SEQ ID NO: 1 Regulates Cancer Cell Proliferation
Not available on PMC !

Example 3

Lung cancer cell line A549 and squamous cell carcinoma cell line H10 expressing inducible SEQ ID NO: 1-HA vector were established as described previously. SEQ ID NO: 1 expression was detected by qPCR (FIG. 7A) and by Western Blot (FIG. 7B). Immunostaining using a custom-made antibody against SEQ ID NO: 1 reveals a predominant cytoplasmic localization with a filamentous pattern. This data demonstrates that the micropeptide can also be expressed and detectable in these cell lines.

To evaluate the effects of SEQ ID NO: 1 on proliferation, A549 and H10 cells transduced with SEQ ID NO: 1-HA vector or control vector were monitored for 14 days. Growth curves show that cells overexpressing micropeptide SEQ ID NO: 1 have a consistently lower growth rate compared to the control (FIG. 8A). This effect in proliferation is also accompanied by an increase in cells arrested in G1 phase (FIG. 8B). Collectively with the data shown before in the pancreatic cell line BxPC-3, there is a strong evidence of the role of the micropeptide of SEQ ID NO: 1 in decreasing cell proliferation in several cancer types (pancreas, lung and squamous cell carcinoma).

US11858972B2. Micropeptides and uses thereof (2024-01-02). FUNDACIÓ PRIVADA INSTITUT D'INVESTIGACIÓ ONCOLÒGICA DE VALL HEBRON [ES]. Inventors: Maria Abad Méndez [ES], Olga Boix Sánchez [ES], Emanuela Greco [ES], Iñaki Merino Valverde [ES].
Full text: Click here
Patent 2024
Adenocarcinoma of Lung Cell Cycle Arrest Cell Lines Cell Proliferation Cells Cloning Vectors Cytoplasmic Filaments G1 Phase Immunoglobulins Lung Lung Cancer Malignant Neoplasms Pancreas Squamous Cell Carcinoma Western Blot
3
Evaluating Tucaresol and Checkpoint Inhibitors in Xenograft Models
Not available on PMC !

Example 7

Five groups including tucaresol, tucaresol plus PD-1 or PD-L1 antibody, tucaresol plus CTLA-4 antibody, CTLA-4 antibody plus PD-1 or PD-L1 antibody, and tucaresol plus plinabulin are tested to determine their effect in an animal xenograft model.

The combined treatment with tucaresol and the checkpoint inhibitor(s) is tested in comparison with the treatment with tucaresol alone, the treatment with checkpoint inhibitor alone, or combination of checkpoint inhibitors. The tests are performed using seven to ten-week old athymic (nu/nu) mice that were injected subcutaneously with human tumor cell lines (of either solid or liquid tumor origin, for example of breast, lung, colon, brain, liver, leukemia, myeloma, lymphoma, sarcoma, pancreatic or renal origin). Six to ten testing groups are prepared, and each group includes 10 mice.

Each treatment starts at tumor size between 40-150 mm3 and continues until Day 24-56, when the animals are necropsied. To determine the efficacy of each treatment, the following data are collected: mortality; the body weight of the mice assessed twice weekly both prior to treatments; the rate of tumor growth as determined by the tumor size measurement (twice every week); the tumor growth index; overall survival rate; the tumor weight at necropsy; and the time required to increase tumor size 10 fold.

US11857522B2. Compositions containing tucaresol or its analogs (2024-01-02). BeyondSpring Pharmaceuticals, Inc. [US]. Inventors: Ramon Mohanlal [US], Lan Huang [US], George Kenneth Lloyd [US].
Full text: Click here
Patent 2024
Animal Model Animals Autopsy Body Weight Brain Breast CD274 protein, human Cell Cycle Checkpoints Cell Line, Tumor Colon Combined Modality Therapy CTLA4 protein, human Genes, Neoplasm GZMB protein, human Heterografts Homo sapiens Immunoglobulins inhibitors Kidney Leukemia Liver Lung Lymphoma Mice, Nude Multiple Myeloma Mus Neoplasms Pancreas plinabulin Sarcoma Thymic aplasia tucaresol
4
In Vivo PET/CT Imaging of Tumor Xenografts
Not available on PMC !

Example 9

In vivo PET/CT imaging was conducted in NCr nude mice bearing bxpc3 (human pancreatic adenocarcinoma cell line) and 4T1 (a murine breast cancer cell line that overexpresses integrin αvβ3 and CD13) tumor xenografts.

Mice were injected with bxpc3 cells (1 million cells in 150 μL PBS) into the subcutaneous flank of the right shoulder and 4T1 cells (1 million cells in 150 μL PBS) into the subcutaneous flank of the left shoulder. Either the CNGRC-(68Ga)NOTA-RGDyK heterodimer (“CNGRC” disclosed as SEQ ID NO: 1), (68Ga)NOTA(CNGRC) (“CNGRC” disclosed as SEQ ID NO: 1), or (68Ga)NOTA(RGDyK) were injected into the bloodstream via tail vein injection. Blocking studies were conducted for the heterodimer studies by co-injecting 100 times of cyclo(CNGRC) (“CNGRC” disclosed as SEQ ID NO: 1) and cyclo(RGDyK). Small animal PET/CT was performed at 1 hour post injection of tracers (FIG. 14). The heterodimer CNGRC-(68Ga)NOTA-RGDyK (“CNGRC” disclosed as SEQ ID NO: 1) showed improved enhanced in in vivo performance (such as longer blood retention, better tumor/non-tumor ratios).

US11857648B2. Dimerization strategies and compounds for molecular imaging and/or radioimmunotherapy (2024-01-02). UNIVERSITY OF PITTSBURGH—OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION [US]. Inventors: Dexing Zeng [US], Lingyi Sun [US], Yongkang Gai [US].
Full text: Click here
Patent 2024
1,4,7-triazacyclononane-N,N',N''-triacetic acid Adenocarcinoma Animals BLOOD Blood Circulation Cardiac Arrest Cell Lines Cells Homo sapiens Integrin alphaVbeta3 MCF-7 Cells Mice, Nude Mus Neoplasms Pancreas Retention (Psychology) Scan, CT PET Shoulder Tail Veins Xenografting
5
Preclinical Evaluation of MEK Inhibitor Compounds
Not available on PMC !

Example 16

The antitumor activity of exemplary MEK inhibitor compounds is evaluated in vivo using human cell line derived xenografts (CDX) grown in immunodeficient mice. For these studies, AsPC1 (pancreatic cell line with KRAS G12D mutation), NCI-H2122 (lung cell line with KRAS G12C mutation), and 5637 (bladder cell line with CRAF amplification) models are used. In addition, HCT-116 (colorectal cell line with KRAS G13D mutation), SKM-1 (AML cell line with KRAS K117N mutation), and OCI-AML-3 (AML cell line with NRAS Q61L mutation) models are used. The tumor cell lines (AsPC-1, NCI-H2122, 5637, and HCT-116 cells) are maintained in vitro as monolayer culture in medium at 37° C. in an atmosphere of 5% CO2 in air. The tumor cell lines (SKM-1 and OCI-AML-3 cells) are maintained in vitro as a suspension in medium at 37° C. in an atmosphere of 5% CO2 in air. The tumor cells are routinely sub-cultured before confluence by trypsin-EDTA treatment, not to exceed 4-5 passages. The cells growing in an exponential growth phase are harvested for tumor inoculation. AsPC1, NCI-H2122, and OCI-AML-3 tumors are implanted into Balb/c nude mice. HCT-116 tumors are implanted into Nu/Nu mice. 5637 and SKM-1 tumors are implanted into NOG mice. Each mouse is inoculated subcutaneously on the right flank with tumor cells in a 1:1 mixture with matrigel. Tumors are allowed to grow to approximately 150-200 mm3. At this time, mice are assigned to groups such that the mean tumor volume is the same for each treatment group. The MEK inhibitor compound treatments are administrated to the tumor-bearing mice via oral gavage. Throughout the study, mouse body weight and tumor volume are recorded. The measurement of tumor size is conducted twice weekly with a caliper and recorded. The tumor volume (mm3) is estimated using the formula: TV=a×b2/2, where “a” and “b” are long and short diameters of a tumor, respectively.

In the AsPC-1 model, exemplary MEK inhibitor I-2 was treated at 3 mg/kg QD and a percent TGI (tumor growth inhibition) on Day 21 of 83.4% was observed. The average body weight gain observed on Day 21 was 2.4%.

In the NCI-H2122 model, exemplary MEK inhibitor 1-2 was treated at 3 mg/kg QD and a percent TGI on Day 31 of 104% was observed. The average body weight loss observed on Day 31 was 1.5%.

In the 5637 model, exemplary MEK inhibitor I-2 was treated at 3 mg/kg QD and a percent TGI on Day 21 of 111% was observed. The average body weight loss observed on Day 21 was 6.8%.

In the HCT-116 model, exemplary MEK inhibitor I-2 was treated at 2 mg/kg QD, 3 mg/kg QOD or 6 mg/kg QOD and a percent TGIs on Day 20 of 102.9%, 98.1%, and 98%, respectively, were observed. The average body weight gain observed on Day 20 was 4%, 5.5%, and 12.1%, respectively.

In the SKM-1 model, exemplary MEK inhibitor I-2 was treated at 1 mg/kg QD, 3 mg/kg QD or 6 mg/kg QOD and venetoclax was treated at 100 mg/kg QD and a percent TGIs on Day 22 of 97.7%, 98.4%, 96.2%, and 46.6% respectively, were observed. The average body weight loss observed on Day 22 for the 3 mg/kg QD group was 1.2%, whereas weight gain was observed in 1 mg/kg QD, 6 mg/kg QOD and venetoclax groups (1.2%, 3.9, and 7.5%, respectively).

In the OCI-AML-3 model, exemplary MEK inhibitor I-2 was treated at 1 mg/kg QD, 3 mg/kg QD or 6 mg/kg QOD, and venetoclax was treated at 100 mg/kg QD and a percent TGIs on Day 15 of 94.8, 98.6, 95.2, and 13% respectively, were observed. The average body weight loss observed on Day 15 for the 1 and 3 mg/kg QD group was 2.9% and 7.8%, respectively, whereas weight gain was observed in 6 mg/kg QOD and venetoclax groups (3.3% and 8.3%, respectively).

US11878958B2. MEK inhibitors and uses thereof (2024-01-23). Ikena Oncology, Inc. [US]. Inventors: Alfredo C. Castro [US], Michael J. Burke [US], Thomas A. Wynn [US], Sabine K. Ruppel [US], Sergio L. Santillana Soto [US], Eric Haines [US], Lan Xu [US], Oksana Zavidij [US].
Full text: Click here
Patent 2024
Atmosphere Body Weight Cancer Vaccines Cell Line, Tumor Cell Lines Cells Edetic Acid HCT116 Cells Heterografts Homo sapiens Human Body Immunologic Deficiency Syndromes K-ras Genes Lung MAP2K2 protein, human matrigel MEK inhibitor I Mice, Inbred BALB C Mice, Nude Mus Mutation Neoplasms NRAS protein, human Pancreas Psychological Inhibition Raf1 protein, human Trypsin Tube Feeding Urinary Bladder venetoclax

Top products related to «Pancreas»

1
Fetal bovine serum (fbs) by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom, Germany, Australia, Japan, Canada, Italy, France, Switzerland, New Zealand, Brazil, Belgium, India, Spain, Israel, Austria, Poland, Ireland, Sweden, Macao, Netherlands, Denmark, Cameroon, Singapore, Portugal, Argentina, Holy See (Vatican City State), Morocco, Uruguay, Mexico, Thailand, Sao Tome and Principe, Hungary, Panama, Hong Kong, Norway, United Arab Emirates, Czechia, Russian Federation, Chile, Moldova, Republic of, Gabon, Palestine, State of, Saudi Arabia, Senegal
Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
2
Panc 1 by American Type Culture Collection
Sourced in United States, Japan, China, Germany, United Kingdom, Italy, Australia, France, Poland
PANC-1 is a cell line derived from a human pancreatic ductal adenocarcinoma. It is a commonly used model for in vitro studies of pancreatic cancer.
3
Dulbecco modified eagle medium (dmem) by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom, Germany, France, Australia, Canada, Japan, Italy, Switzerland, Belgium, Austria, Spain, Israel, New Zealand, Ireland, Denmark, India, Poland, Sweden, Argentina, Netherlands, Brazil, Macao, Singapore, Sao Tome and Principe, Cameroon, Hong Kong, Portugal, Morocco, Hungary, Finland, Puerto Rico, Holy See (Vatican City State), Gabon, Bulgaria, Norway, Jamaica
DMEM (Dulbecco's Modified Eagle's Medium) is a cell culture medium formulated to support the growth and maintenance of a variety of cell types, including mammalian cells. It provides essential nutrients, amino acids, vitamins, and other components necessary for cell proliferation and survival in an in vitro environment.
4
Bxpc 3 by American Type Culture Collection
Sourced in United States, Japan, Germany, China, Sweden, United Kingdom, Italy, France
BxPC-3 is a cell line derived from a human pancreatic adenocarcinoma. It is commonly used in research related to pancreatic cancer.
5
Penicillin streptomycin by Thermo Fisher Scientific
Sourced in United States, Germany, United Kingdom, China, Canada, France, Japan, Australia, Switzerland, Israel, Italy, Belgium, Austria, Spain, Gabon, Ireland, New Zealand, Sweden, Netherlands, Denmark, Brazil, Macao, India, Singapore, Poland, Argentina, Cameroon, Uruguay, Morocco, Panama, Colombia, Holy See (Vatican City State), Hungary, Norway, Portugal, Mexico, Thailand, Palestine, State of, Finland, Moldova, Republic of, Jamaica, Czechia
Penicillin/streptomycin is a commonly used antibiotic solution for cell culture applications. It contains a combination of penicillin and streptomycin, which are broad-spectrum antibiotics that inhibit the growth of both Gram-positive and Gram-negative bacteria.
6
Collagenase p by Roche
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Collagenase P is a laboratory reagent used for the enzymatic digestion of collagen, a structural protein found in the extracellular matrix of various tissues. It is commonly used in cell isolation and tissue dissociation protocols.
7
Streptomycin by Thermo Fisher Scientific
Sourced in United States, United Kingdom, Germany, China, France, Canada, Australia, Japan, Switzerland, Italy, Belgium, Israel, Austria, Spain, Netherlands, Poland, Brazil, Denmark, Argentina, Sweden, New Zealand, Ireland, India, Gabon, Macao, Portugal, Czechia, Singapore, Norway, Thailand, Uruguay, Moldova, Republic of, Finland, Panama
Streptomycin is a broad-spectrum antibiotic used in laboratory settings. It functions as a protein synthesis inhibitor, targeting the 30S subunit of bacterial ribosomes, which plays a crucial role in the translation of genetic information into proteins. Streptomycin is commonly used in microbiological research and applications that require selective inhibition of bacterial growth.
8
Penicillin by Thermo Fisher Scientific
Sourced in United States, United Kingdom, Germany, China, France, Canada, Japan, Australia, Switzerland, Italy, Israel, Belgium, Austria, Spain, Brazil, Netherlands, Gabon, Denmark, Poland, Ireland, New Zealand, Sweden, Argentina, India, Macao, Uruguay, Portugal, Holy See (Vatican City State), Czechia, Singapore, Panama, Thailand, Moldova, Republic of, Finland, Morocco
Penicillin is a type of antibiotic used in laboratory settings. It is a broad-spectrum antimicrobial agent effective against a variety of bacteria. Penicillin functions by disrupting the bacterial cell wall, leading to cell death.
9
Aspc 1 by American Type Culture Collection
Sourced in United States, Japan, China, Germany, United Kingdom, Italy
AsPC-1 is a cell line derived from a human pancreatic adenocarcinoma. It is a commonly used in vitro model for pancreatic cancer research.
10
Mia paca 2 by American Type Culture Collection
Sourced in United States, Germany, China, United Kingdom, Japan, Italy, Australia, France
MIA PaCa-2 is a human pancreatic carcinoma cell line derived from a primary tumor. It is a well-established model used in cancer research.

More about "Pancreas"

The pancreas is a vital organ that plays a crucial role in both the digestive and endocrine systems.
This glandular structure, situated behind the stomach, is responsible for producing essential enzymes that aid in the breakdown of food, as well as hormones like insulin and glucagon that regulate blood sugar levels.
Pancreatic disorders can lead to various health conditions, including pancreatitis, diabetes, and pancreatic cancer, making it a key area of medical research and investigation.
Optimizing pancreas research is essential, and tools like PubCompare.ai can help enhance reproducibility, accuracy, and the identification of effective protocols and methods.
PubCompare.ai's powerful platform allows researchers to easily locate the best protocols, pre-prints, and patents from the literature, streamlining the research process.
When studying the pancreas, researchers often utilize various cell lines, such as FBS, PANC-1, BxPC-3, AsPC-1, and MIA PaCa-2, which are derived from pancreatic cancer cells.
These cell lines are commonly cultured in DMEM media, supplemented with penicillin/streptomycin and other essential components, such as collagenase P, to maintain their growth and viability.
By leveraging the capabilities of PubCompare.ai, researchers can seamlessly navigate the vast array of pancreas-related information, identify the most effective methods and products, and enhance the overall efficiency and quality of their research.
The platform's user-friendly interface and cutting-edge technology ensure a streamlined and productive experience for scientists working to advance the understanding and treatment of pancreatic disorders.