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Cancer of Liver

Cancer of the Liver is a serious and complex disease characterized by the uncontrolled growth of malignant cells within the liver.
It can originate from primary liver cells or spread from other parts of the body.
Symptoms may include abdominal pain, weight loss, and jaundice.
Early detection and appropriate treatment are crucial for managing this condition.
Advancements in research, including the use of AI-powered tools like PubCompare.ai, are helping to optimize protocols and accelerate the discovery of promising therapies to improve outcomes for patients with liver cancer.

Most cited protocols related to «Cancer of Liver»

For most diseases and injuries, processed data are modelled using standardised tools to generate estimates of each quantity of interest by age, sex, location, and year. There are three main standardised tools: Cause of Death Ensemble model (CODEm), spatiotemporal Gaussian process regression (ST-GPR), and DisMod-MR. Previous publications2 (link), 3 (link), 12 and the appendix provide more details on these general GBD methods. Briefly, CODEm is a highly systematised tool to analyse cause of death data using an ensemble of different modelling methods for rates or cause fractions with varying choices of covariates that perform best with out-of-sample predictive validity testing. DisMod-MR is a Bayesian meta-regression tool that allows evaluation of all available data on incidence, prevalence, remission, and mortality for a disease, enforcing consistency between epidemiological parameters. ST-GPR is a set of regression methods that borrow strength between locations and over time for single metrics of interest, such as risk factor exposure or mortality rates. In addition, for select diseases, particularly for rarer outcomes, alternative modelling strategies have been developed, which are described in appendix 1 (section 3.2).
In GBD 2019, we designated a set of standard locations that included all countries and territories as well as the subnational locations for Brazil, China, India, and the USA. Coefficients of covariates in the three main modelling tools were estimated for these standard locations only—ie, we ignored data from subnational locations other than for Brazil, China, India, and the USA (appendix 1 section 1.1). Using this set of standard locations will prevent changes in regression coefficients from one GBD cycle to the next that are solely due to the addition of new subnational units in the analysis that might have lower quality data or small populations (appendix 1 section 1.1). Changes to CODEm for GBD 2019 included the addition of count models to the model ensemble for rarer causes. We also modified DisMod-MR priors to effectively increase the out-of-sample coverage of uncertainty intervals (UIs) as assessed in simulation testing (appendix 1 section 4.5).
For the cause Alzheimer's disease and other dementias, we changed the method of addressing large variations between locations and over time in the assignment of dementia as the underlying cause of death. Based on a systematic review of published cohort studies, we estimated the relative risk of death in individuals with dementia. We identified the proportion of excess deaths in patients with dementia where dementia is the underlying cause of death as opposed to a correlated risk factor (appendix 1 section 2.6.2). We changed the strategy of modelling deaths for acute hepatitis A, B, C, and E from a natural history model relying on inpatient case fatality rates to CODEm models after predicting type-specific acute hepatitis deaths from vital registration data with specified hepatitis type.
DisMod-MR was used to estimate deaths from three outcomes (dementia, Parkinson's, and atrial fibrillation), and to determine the proportions of deaths by underlying aetiologies of cirrhosis, liver cancer, and chronic kidney disease deaths.
Publication 2020
Alzheimer's Disease Atrial Fibrillation Cancer of Liver Chronic Kidney Diseases Dementia Hepatitis A Injuries Inpatient Liver Cirrhosis Patients Population Group Presenile Dementia
Cohort-1 hepatic tissues were obtained from the Liver Cancer Institute (LCI) with informed consent from patients who underwent radical resection between 2002 and 2003 at the Liver Cancer Institute and Zhongshan Hospital (Fudan University, Shanghai, China). The study was approved by the Institutional Review Board of the participating institutes. A total of 247 HCC patients were recruited. Cases were mainly from patients with a history of hepatitis B virus (HBV) infection or HBV-related liver cirrhosis; all were diagnosed with HCC by two independent pathologists, with detailed information on clinical presentation and pathological characteristics. For 242 patients, disease-free survival and overall survival as well as the cause of death were available.
The gene expression data of cohort-2 has been published earlier (20 (link), 21 (link)). Briefly, gene expression profiling of cohort-2 was performed by the Laboratory of Experimental Carcinogenesis (LEC) and analyzed using NCI’s Human Array-Ready Oligo Set microarray platform (GPL1528). The microarray data is publicly available at the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo) with accession numbers GSE1898 and GSE4024.
Publication 2010
B virus, Hepatitis Cancer of Liver Carcinogenesis Ethics Committees, Research Gene Expression Homo sapiens Liver Cirrhosis Microarray Analysis Oligonucleotides Pathologists Patients Tissues

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Publication 2016
Biological Processes Cancer of Liver CDH1 protein, human Cell Lines Cytotoxic T-Lymphocyte Antigen 4 E-Cadherin Gene Expression Genes Hypersensitivity Liver Malignant Neoplasms Melanoma Neoplasm Metastasis Neoplasms Patients Physiology, Cell Reproduction Selfish Genes SMAD6 protein, human TGFB1 protein, human Transcriptome Wounds
All tissue samples were uniformly homogenized using a TissueLyser LT from Qiagen (Germantown, MD, USA) in accordance with the manufacturer's instructions. RNA isolation was performed for all tissue lysates. Sample preparation and hybridization of labeled cRNA to the HumanHT‐12 v4 Expression BeadChip arrays (Illumina, Inc., San Diego, CA, USA) were conducted in accordance with the manufacturer's instructions. Data from 10 patients were excluded as a result of low RNA or microarray preparation quality (Kauffmann et al., 2009). Finally, we used 115 PT samples and 52 AT samples matched to 52 corresponding PT. After the completion of RNA and microarray quality control, the included samples were matched to corresponding clinical data and re‐identified before data analysis. The quantitative PCR experiments were conducted using a QuantStudio™ 6 Flex Real‐Time PCR system in accordance with the standard instructions for Power SYBR Green master mix from ABI systems (Applied Biosystems, Foster City, CA, USA). The qRT PCR signals were normalized with standard reference TBP and relative fold change abundances for desired genes were estimated.
The microarray data for the gene expression profiling of the HCC patient samples are publicly available at GEO: GSE76427. As a validation HCC cohort, we used publicly available data for the 206 HCC patients from the Liver Cancer Institute (the LCI HCC cohort, Fudan University, Shanghai, China, GSE14520) (Roessler et al., 2012) that passed the same microarray quality assessment (Kauffmann et al., 2009). We selected the LCI dataset for the Singapore data analysis validation because (a) the LCI group is a relatively large publicly available gene expression dataset (includes 206 paired PT–AT samples); (b) clinical data including patient survival data have been available for these 206 patients, allowing survival prediction analysis for the PT‐AT paired expression dataset to be carried out; (c) Singapore and LCI datasets include mostly Chinese/Asian patients; and (d) patients in both groups have a predominantly HBV‐driven ethology, which potentially could result in deregulated molecular pathways distinct from other available patients groups with predominantly HCV‐driven HCC.
Publication 2017
Asian Persons Cancer of Liver Chinese Complementary RNA Crossbreeding Gene Expression Genes isolation Microarray Analysis Patients SYBR Green I Tissues
The GBD study attributes each death to a single underlying cause that began the series of events leading to death, in accordance with ICD principles. The GBD study organises causes of death in a hierarchical list containing four levels (appendix 1 section 7). At the highest level (Level 1), all disease burden is divided among three mutually exclusive and collectively exhaustive categories: communicable, maternal, neonatal, and nutritional (CMNN) diseases; non-communicable diseases (NCDs); and injuries. Level 2 distinguishes these Level 1 categories into 21 cause groups, such as cardiovascular diseases; diarrhoeal diseases, lower respiratory infections (LRIs), and other common infectious diseases; or transport injuries. Level 3 disaggregates these causes further; in most cases this disaggregation represents the finest level of detail by cause, such as stroke, ischaemic heart disease, or road injuries. Where data are sufficiently available or specific policy relevance has been sought, selected causes are further disaggregated at Level 4, such as drug-susceptible tuberculosis, multidrug-resistant tuberculosis without extensive drug resistance, and extensively drug-resistant tuberculosis. For GBD 2017, the cause hierarchy was further refined to separately estimate causes with substantial policy interest or high levels of burden. Specific changes included separate estimation of non-rheumatic calcific aortic and degenerative mitral valve diseases, and myelodysplastic, myeloproliferative, and other haemopoietic neoplasms, resulting in a reduction in the estimates of some residual causes. Disaggregation of residual causes also allowed separate estimation of type 1 and type 2 diabetes, chronic kidney disease due to type 1 and type 2 diabetes, poisoning by carbon monoxide, liver cancer due to non-alcoholic steatohepatitis (NASH), subarachnoid haemorrhage, ectopic pregnancy, and invasive non-typhoidal salmonella. Maternal and neonatal disorders, previously estimated as separate cause groupings at Level 2 of the hierarchy, were estimated for GBD 2017 at Level 3 of the hierarchy, and then aggregated up to Level 2 to better capture the epidemiological connections and linked burden between them. The complete hierarchy of causes included in GBD 2017 and their corresponding ICD9 and ICD10 codes are described in appendix 1 (section 7).
Publication 2018
Aorta Cancer of Liver Carbon Monoxide Poisoning Cardiovascular Diseases Cerebrovascular Accident Chronic Kidney Diseases Communicable Diseases Diabetes Mellitus, Non-Insulin-Dependent Diarrhea Ectopic Pregnancy Extensively Drug-Resistant Tuberculosis Hematopoietic Neoplasms Infant, Newborn Injuries Mitral Valve Mothers Myocardial Ischemia Neonatal Diseases Nonalcoholic Steatohepatitis Noncommunicable Diseases Nutrition Disorders Pharmaceutical Preparations Resistance, Drug Respiratory Tract Infections Salmonella Subarachnoid Hemorrhage Tuberculosis Tuberculosis, Multidrug-Resistant Typhoid Fever

Most recents protocols related to «Cancer of Liver»

Example 1

In this example, the oligopeptide FTLE in chili pepper seeds was extracted as follows:

1) deseeding: fresh chili peppers were taken, and the flesh was separated from the seeds to obtain chili pepper seeds;

2) pulverizing: the chili pepper seeds were pulverized and sieved by an 80 mesh to obtain chili pepper seed powder ;

3) degreasing: the chili pepper seed powder was mixed with n-hexane at a ratio of 1:10 (g/ml); the mixture was stirred and degreased overnight; n-hexane was removed by suction filtration after the degreasing was completed to obtain a chili pepper seed meal;

4) protein extraction: the degreased chili pepper seed meal was dissolved in water at a ratio of 1:10 (w/v, g/mL); the pH value of the solution was adjusted to 9.5 with a NaOH solution to conduct dissolving for 4 h; then the pH value of the solution was adjusted to 4.5 with HCl to conduct precipitating for 2 h; the reaction solution was centrifuged at 8,000 rpm for 20 min, and the precipitate was collected as a crude protein extract;

5) ultra-high pressure assisted enzymolysis: the protein isolated was dissolved in water, and was subjected to an ultra-high pressure treatment at 300 MPa for 30 min; then the product obtained by the ultra-high pressure treatment was subjected to an enzymolysis treatment, in which the enzyme was Bacillus licheniformis, the mass ratio of the enzyme to the substrate was 1:20 (w/w, g/g), the temperature was 40° C., the pH value was adjusted to 8 with 1 mol/L NaOH, and the enzymolysis treatment was performed for 3 h;

6) enzyme inactivation: at the end of the enzymolysis, the enzyme was inactivated at 90° C. for 10 min to obtain a chili pepper seed zymolyte solution;

7) isolation and purification of zymolyte: the chili pepper seed zymolyte solution was passed through a DEAE anion chromatography column, where the mobile phase included deionized water and NaCl; the eluent in a periodfrom 35 min to 45 min was collected; then, isolation and purification were conducted by an ODS-A reverse phase C18 column (hydrophobic column), where the mobile phase included deionized water and 50% methanol, and the eluent in a periodfrom 75 min to 90 min was collected. The peptide fragments in the obtained eluate were subjected to mass spectrometry identification analysis, and information of multiple peptide sequences was obtained.

Example 2

Chemical systhesis was conducted in accordance with the peptide sequences obtained by mass spectrometry identification analysis of Example 1 to obtain synthetic peptides. The effect of each peptide on HepG2 cell proliferation was studied, and the specific steps were as follows:

1) HepG2 cell culture: hepG2 cells were obtained from the ATCC cell bank and were cultured in a DMEM medium containing 10% FBS at 37° C. in a 5% CO2 cell incubator. Cells were cultured in a 25 cm 2 flask, passaged when cells were grown to a density of 70% to 90%, and seeded in a 96-well plate.

2) Peptide fragment treatment: after 24 hours of cell culture in the 96-well plate, the original DMEM medium was aspirated from the wells. DMEM containing peptide fragments at concentrations of 0.1, 0.3, and 0.6 mM were added to each well to continue culturing for 24 hours.

3) Cell proliferation rate measured by MTT method: MTT at a concentration of 5 mg/mL was added to a 96-well plate in 20 μL per well. After incubation for 4 hours, the liquid was aspirated from each well. 150 μL DMSO was added to each well. The absorbance was measured after reacting for 20 min.

The results are shown in the figure. It can be seen that the oligopeptide FTLE has a better HepG2 cell inhibition rate than other oligopeptides, which is helpful for the prevention or treatment of liver cancer.

In the description of this specification, descriptions with reference to the terms “one embodiment”, “some embodiments”, “example”, “specific examples”, or “some examples”, etc. mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present disclosure. In this description, schematic representations of the terms above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The different embodiments or examples and the features of the different embodiments or examples described in this description can be integrated and combined by a person skilled in the art without contradicting each other.

While embodiments of the present disclosure have been shown and described, it will be understood that the above-described embodiments are illustrative and not restrictive and that changes, modifications, substitutions, and variations may be made to the embodiments by those skilled in the art without departing from the scope of the present disclosure.

Patent 2024
2-diethylaminoethanol Anions Bacillus licheniformis Cancer of Liver Cell Culture Techniques Cell Proliferation Cells Chromatography Enzymes Filtration Hep G2 Cells Hexanes isolation Malignant Neoplasms Mass Spectrometry Methanol n-hexane Oligopeptides Peppers, Chile Peptide Fragments Peptides Powder Pressure Proteins Psychological Inhibition Sodium Chloride Staphylococcal Protein A Suction Drainage Sulfoxide, Dimethyl Temporal epilepsy, familial Vision
Patients diagnosed with HCC and underwent TACE from January 2008 to December 2019 were included and analyzed. The inclusion criteria were as follows: (1) age greater than 18 years; (2) HCC diagnosis by imaging or histological findings according to the American Association for the Study of Liver Disease guidelines15 (link); (3) initial treatment with conventional TACE; (4) HCC with Barcelona Clinic Liver Cancer (BCLC) stage A, B, or C (subsegmental or segmental portal vein tumor thrombosis); (5) available medical records; and (6) Child–Pugh class A or B. The exclusion criteria were as follows: (1) absence of imaging data; (2) inability to measure the skeletal muscle mass; (3) concomitant malignancies; and (4) history of HCC rupture.
Publication 2023
ADAM17 protein, human Cancer of Liver Child Liver Function Tests Malignant Neoplasms Neoplasms Neoplasms, Liver Patients Skeletal Muscles Thrombosis Veins Veins, Portal
Descriptive statistics were used to describe the variables of participants’ characteristics. PA trajectories were identified based on the GBTM approach using PROC TRAJ in SAS 9.4 software. Cox proportional hazards regression was used to estimate the risk for cancer. In the multivariate model, we adjusted for age, income level, smoking status, alcohol consumption, BMI, and CCI score. We additionally adjusted for chronic viral hepatitis (i.e., B18 in ICD-10) in the multivariate model for liver cancer. For sensitivity analysis, we examined the association between PA trajectories and cancer incidence by smoking status and BMI groups. All statistical analyses were stratified by sex and performed using SAS 9.4 (SAS Institute, Inc., Cary, NC, USA).
Publication 2023
Cancer of Liver Hepatitis Viruses Hypersensitivity Malignant Neoplasms

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Publication 2023
Cancer of Liver Diabetes Mellitus Liver Transplantations Patients Shunt, Transjugular Intrahepatic Portosystemic Transplantation Transplant Recipients
A patient with liver cancer was recruited from the Department of Hepatobiliary Surgery, the First Affiliated Hospital of Hainan Medical College. The serum AFP concentration of the patient was >5000 ng/mL. 100 ml of blood was collected and the tumor-derived AFP(tAFP) was purified following a previously described procedure (31 (link), 32 (link)). M1-like macrophages derived from THP-1 and M1- or M2-like macrophages derived from monocytes were treated with tAFP(final concentration 20μg/mL) for 24h. The cells were resuspended in 1 mL of PBS, and 1 μL of 5-(and -6)-carboxyfluorescein diacetate succinimidyl ester (CFSE). Laser confocal microscopy and intelligent living-cell high-throughput imaging analyzer were used to observe macrophages phagocytizing polystyrene latex beads or liver cancer cells(HLE cells). The following describes the experimental operation of observing M1-like macrophages that phagocytize HCC cells using an intelligent living-cell high-throughput imaging analyzer.
Publication 2023
5-(6)-carboxyfluorescein diacetate succinimidyl ester BLOOD Cancer of Liver Cells Hepatocyte Laser Microscopy Macrophage Malignant Neoplasms Monocytes Neoplasms Patients Polystyrenes Serum

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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.
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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.
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HepG2 is a human liver cell line derived from the liver tissue of a 15-year-old Caucasian male with a well-differentiated hepatocellular carcinoma. It is a widely used in vitro model for the study of liver cell biology and function.
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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.
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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.
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Hep3B is a human cell line derived from a liver cancer (hepatocellular carcinoma) patient. It is a widely used in vitro model for liver disease research and drug development.
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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.
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RPMI 1640 medium is a commonly used cell culture medium developed at Roswell Park Memorial Institute. It is a balanced salt solution that provides essential nutrients, vitamins, and amino acids to support the growth and maintenance of a variety of cell types in vitro.
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Lipofectamine 2000 is a cationic lipid-based transfection reagent designed for efficient and reliable delivery of nucleic acids, such as plasmid DNA and small interfering RNA (siRNA), into a wide range of eukaryotic cell types. It facilitates the formation of complexes between the nucleic acid and the lipid components, which can then be introduced into cells to enable gene expression or gene silencing studies.
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HepG2 is a well-characterized human liver cancer cell line derived from a hepatocellular carcinoma. It is commonly used as a model system for in vitro studies related to liver cell biology and function.

More about "Cancer of Liver"

Hepatocellular Carcinoma (HCC), Primary Liver Cancer, Liver Malignancy, Liver Tumor.
Liver cancer is a serious and complex disease characterized by the uncontrolled growth of cancerous cells within the liver.
It can originate from primary liver cells (hepatocytes) or spread from other parts of the body (metastatic liver cancer).
Symptoms may include abdominal pain, weight loss, and jaundice.
Early detection and appropriate treatment are crucial for managing this condition.
Advancements in research, including the use of AI-powered tools like PubCompare.ai, are helping to optimize protocols and accelerate the discovery of promising therapies to improve outcomes for patients with liver cancer.
PubCompare.ai enables researchers to easily locate the best protocols from literature, pre-prints, and patents, facilitating the identification of the most promising products and accelerating liver cancer research.
Common in vitro models used in liver cancer research include HepG2 and Hep3B cell lines, which are derived from human hepatocellular carcinoma.
These cells are typically cultured in media such as DMEM or RPMI 1640, supplemented with fetal bovine serum (FBS) and antibiotics like penicillin and streptomycin.
Transfection reagents like Lipofectamine 2000 may be used to introduce genetic materials into these cell lines for various studies.