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Endocervix

Q: What are some common conditions that affect the endocervix?

Endocervical conditions can include cervicitis (inflammation of the cervix), cervical polyps, and cervical dysplasia (precancerous changes in the cells). Research focusing on the endocervix is crucial for developing effective diagnostic and therapeutic interventions for these and other conditions affecting the cervix and uterus.

Most cited protocols related to «Endocervix»

Pan-Cancer Transcriptomic and Proteomic Analysis

Results are based in part upon data generated by TCGA Research Network (http://cancergenome.nih.gov/). We aggregated TCGA transcriptomic and RPPA data from public repositories, listed in the “Data availability” section. RNA-seq expression data were processed by TCGA at the gene level, rather than at the transcript level. Tumors spanned 32 different TCGA projects, each project representing a specific cancer type, listed as follows: LAML, acute myeloid leukemia; ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; LGG, lower grade glioma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; CRC, colorectal adenocarcinoma (combining COAD and READ projects); ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THYM, thymoma; THCA, thyroid carcinoma; UCS, uterine carcinosarcoma; UCEC, uterine corpus endometrial carcinoma; UVM, uveal melanoma. Cancer molecular profiling data were generated through informed consent as part of previously published studies and analyzed per each original study’s data use guidelines and restrictions.

Chen F., Chandrashekar D.S., Varambally S, & Creighton C.J. (2019). Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers. Nature Communications, 10, 5679.

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Publication 2019

4-carboxyphenylglyoxal Adenocarcinoma Adenocarcinoma of Lung Adrenocortical Carcinoma Breast Carcinoma Carcinoma, Thyroid Carcinoma, Transitional Cell Carcinosarcoma Cells Cholangiocarcinoma Chromophobia Chronic Obstructive Airway Disease Diffuse Large B-Cell Lymphoma Endocervix Endometrial Carcinoma Esophageal Cancer Familial Atypical Mole-Malignant Melanoma Syndrome Gene Expression Profiling Genes Glioblastoma Multiforme Glioma Hepatocellular Carcinomas Hypernephroid Carcinomas Kidney Leukemia, Myelocytic, Acute Lung Lymph Malignant Neoplasms Mesothelioma Neck Neoplasms Ovary Pancreas Paraganglioma Pheochromocytoma Prostate Renal Cell Carcinoma RNA-Seq Sarcoma Serous Cystadenocarcinoma Squamous Cell Carcinoma Squamous Cell Carcinoma of the Head and Neck Stomach Testicular Germ Cell Tumor Thymoma Urinary Bladder Uterus Uveal melanoma X-Ray Photoelectron Spectroscopy

Comprehensive Pan-cancer RNA-Seq and Protein Expression Analysis

The pancan normalized gene-level RNA-Seq data for the TCGA cohorts were downloaded from the UC Santa Cruz Cancer Genomics Browser [67 (link)] (https://genome-cancer.ucsc.edu/). These cohorts consisted of adrenocortical cancer (ACC, N_tumor = 79, N_normal
= 0), bladder urothelial carcinoma (BLCA, N_tumor = 407, N_normal
= 19), lower grade glioma (LGG, N_tumor = 530, N_normal
= 0), breast invasive carcinoma (BRCA, N_tumor = 1097, N_normal
= 113), cervical and endocervical cancer (CESC, N_tumor = 305, N_normal
= 3), colon and rectum adenocarcinoma (COADREAD, N_tumor = 383, N_normal
= 50), glioblastoma multiforme (GBM, N_tumor = 167, N_normal
= 5), head and neck squamous cell carcinoma (HNSC N_tumor = 521, N_normal
= 43), kidney chromophobe (KICH, N_tumor = 66, N_normal
= 25), kidney clear cell carcinoma (KIRC, N_tumor = 530, N_normal
= 72), kidney papillary cell carcinoma (KIRP, N_tumor = 291, N_normal
= 32), liver hepatocellular carcinoma (LIHC, N_tumor = 373, N_normal
= 50), lung adenocarcinoma (LUAD, N_tumor = 510, N_normal
= 58), lung squamous cell carcinoma (LUSC, N_tumor = 502, N_normal
= 51), ovarian serous cystadenocarcinoma (OVCA, N_tumor = 266, N_normal
= 0), prostate adenocarcinoma (PRAD, N_tumor = 498, N_normal
= 52), skin cutaneous melanoma (SKCM, N_tumor = 472, N_normal
= 1), thyroid carcinoma (THCA, N_tumor = 513, N_normal
= 59), and uterine carcinosarcoma (UCS, N_tumor = 57, N_normal
= 0).
TCGA ccRCC-specific analyses were performed with the KIRC datasets downloaded from Firebrowse (http://firebrowse.org). RSEM-normalized gene level data and reverse phase protein array (RPPA) data were used for gene and protein expression analyses, respectively. Samples that had RNA-Seq, mutation and clinical data (n = 415) were included in the discovery phase of the immune infiltration clusters.
The Sato et al. [29 (link)] Agilent microarray gene expression dataset was downloaded from ArrayExpress (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1980/) and all samples (n = 101) were included in the analysis. The probe identifiers in the Agilent platform were mapped to HGNC gene symbols and the arithmetic mean across identifiers was used for cases where multiple Agilent identifiers mapped to a single HGNC symbol.
The Gerlinger et al. [57 (link)] Affymetrix Human Gene 1.0 ST microarray gene expression dataset was obtained via personal communication with the authors on 10 November 2014. This dataset includes 56 tumor and six normal samples from nine ccRCC patients. All samples were included in our analysis. The probe sets in this Affymetrix platform were mapped to HGNC gene symbols and the geometric mean across probe sets was used for cases where multiple probe sets mapped to a single HGNC symbol.

Şenbabaoğlu Y., Gejman R.S., Winer A.G., Liu M., Van Allen E.M., de Velasco G., Miao D., Ostrovnaya I., Drill E., Luna A., Weinhold N., Lee W., Manley B.J., Khalil D.N., Kaffenberger S.D., Chen Y., Danilova L., Voss M.H., Coleman J.A., Russo P., Reuter V.E., Chan T.A., Cheng E.H., Scheinberg D.A., Li M.O., Choueiri T.K., Hsieh J.J., Sander C, & Hakimi A.A. (2016). Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biology, 17, 231.

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Publication 2016

Adenocarcinoma Adenocarcinoma of Lung Breast Carcinoma Cancer of Adrenal Cortex Carcinoma, Thyroid Carcinoma, Transitional Cell Carcinosarcoma Chromophobia Colon Endocervix Familial Atypical Mole-Malignant Melanoma Syndrome Gene Expression Genes Genome Glioblastoma Multiforme Glioma Hepatocellular Carcinomas Homo sapiens Hypernephroid Carcinomas Kidney Lung Malignant Neoplasms Microarray Analysis Mutation Neck Neoplasms Ovary Patients Prostate Protein Arrays Proteins Rectum RNA-Seq Serous Cystadenocarcinoma Squamous Cell Carcinoma Squamous Cell Carcinoma of the Head and Neck Urinary Bladder Uterus

Comprehensive Cancer Transcriptome Atlas

We downloaded RNA-seq BAM files of 6,309 patient samples (including 6,083 primary tumor samples and 226 metastasis samples) across 20 TCGA cancer types and their related 564 non-tumor tissue samples (if available)(10 (link)) from the UCSC Cancer Genomics Hub (CGHub, https://cghub.ucsc.edu/). Included were bladder urothelial carcinoma (BLCA), brain lower grade glioma (LGG), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma (COAD), cutaneous melanoma (SKCM), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrioid carcinoma (UCEC). We also downloaded 739 BAM files of Cancer Cell Line Encyclopedia (CCLE) cell lines (12 (link)) from CGHub. In addition, we obtained the RNA-seq files of 531 samples from another three independent studies, including lung adenocarcinoma (13 (link)), clear-cell renal cell carcinoma (14 (link)), and glioblastomas (15 ). In total, the current TANRIC release includes RNA-seq data from 8,143 samples (1,142 billion reads).

Li J., Han L., Roebuck P., Diao L., Liu L., Yuan Y., Weinstein J.N, & Liang H. (2015). TANRIC: An interactive open platform to explore the function of lncRNAs in cancer. Cancer research, 75(18), 3728-3737.

Publication 2015

Adenocarcinoma Adenocarcinoma of Lung Brain Breast Carcinoma Carcinoma, Thyroid Carcinoma, Transitional Cell Cell Lines Cells Chromophobia Colon Adenocarcinomas Endocervix Endometrial Carcinoma Glioblastoma Glioblastoma Multiforme Glioma Hepatocellular Carcinomas Hypernephroid Carcinomas Kidney Lung Malignant Neoplasms Melanoma Neck Neoplasm Metastasis Neoplasms Ovary Patients Prostate Rectum Renal Cell Carcinoma RNA-Seq Serous Cystadenocarcinoma Squamous Cell Carcinoma Squamous Cell Carcinoma of the Head and Neck Stomach Tissues Urinary Bladder

Developing OncomiR Cancer Database (OMCD)

To create OMCD, we used the LAMP software bundle (Linux, Apache 2, MySQL 5.0, and PHP) and Hypertext Markup Language (HTML), as described previously [8 (link)] and made the resulting website accessible to researchers across the globe. To host OMCD’s web application, we chose an Apache web server. To generate the user interface and enable communication with the MySQL database at the back end, we chose PHP, given its database-driven architecture that was designed for incorporation of additional information. Normalized expression data, statistical results, and annotation data are all stored in OMCD. To facilitate data retrieval and selection of different criteria for analysis, we designed a user-friendly graphic interface.
To construct the content of OMCD, we downloaded from TCGA the miRNA expression data of 9656 patients (represented by 8993 tumor samples and 663 control samples of normal tissue with 33 different cancer types (https://gdc.nci.nih.gov; Table 1). We used a 2-group t test to determine which miRNAs were differentially expressed between 1) control and tumor samples, for a given cancer type, 2) a cancer patient’s control sample, as compared with all other patients’ available control samples, and 3) a cancer patient’s tumor sample, as compared with all other patients’ available tumor samples. It can be noted that each of our 3 analyses had a different statistical power, which may account for the absence of a given miRNA from a specific dataset.Table 1

Number of patients in the OncomiR Cancer Database (OMCD), by cancer type

Cancer Type (TCGA Code)	Total number of samples	Tumor	Normal
Breast invasive carcinoma [BRCA]	869	782	87
Brain Lower Grade Glioma [LGG]	530	530	0
Thyroid carcinoma [THCA]	573	514	59
Prostate adenocarcinoma [PRAD]	551	499	52
Ovarian serous cystadenocarcinoma [OV]	495	495	0
Head and Neck squamous cell carcinoma [HNSC]	532	488	44
Lung adenocarcinoma [LUAD]	504	458	46
Skin Cutaneous Melanoma [SKCM]	453	451	2
Uterine Carcinosarcoma [UCS]	450	418	32
Bladder Urothelial Carcinoma [BLCA]	436	417	19
Stomach adenocarcinoma [STAD]	450	404	46
Liver hepatocellular carcinoma [LIHC]	426	375	51
Lung squamous cell carcinoma [LUSC]	388	343	45
Cervical squamous cell carcinoma and endocervical adenocarcinoma [CESC]	313	310	3
Kidney renal papillary cell carcinoma [KIRP]	326	292	34
Colon adenocarcinoma [COAD]	280	272	8
Sarcoma [SARC]	263	263	0
Kidney renal clear cell carcinoma [KIRC]	332	261	71
Esophageal carcinoma [ESCA]	200	187	13
Pheochromocytoma and Paraganglioma [PCPG]	187	184	3
Pancreatic adenocarcinoma [PAAD]	183	179	4
Testicular Germ Cell Tumors [TGCT]	156	156	0
Thymoma [THYM]	126	124	2
Rectum adenocarcinoma [READ]	97	94	3
Mesothelioma [MESO]	87	87	0
Uveal Melanoma [UVM]	80	80	0
Adrenocortical carcinoma [ACC]	79	79	0
Kidney Chromophobe [KICH]	91	66	25
Uterine Corpus Endometrial Carcinoma [UCEC]	57	57	0
Diffuse Large B-cell Lymphoma [DLBC]	47	47	0
FFPE Pilot Phase II [FPPP]	45	45	0
Cholangiocarcinoma [CHOL]	45	36	9
Glioblastoma multiforme [GBM]	5	0	5
Total	9656	8993	663

Sarver A.L., Sarver A.E., Yuan C, & Subramanian S. (2018). OMCD: OncomiR Cancer Database. BMC Cancer, 18, 1223.

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Publication 2018

4-carboxyphenylglyoxal Adenocarcinoma Carcinoma Carcinoma, Transitional Cell Carcinosarcoma Chromophobia Chronic Obstructive Airway Disease Endocervix Endometrial Carcinoma Eye Glioma Hepatocellular Carcinomas Hypernephroid Carcinomas Malignant Neoplasms Melanoma MicroRNAs Neck Neoplasms Paraganglioma Patients Renal Cell Carcinoma Reticulosarcoma Serous Cystadenocarcinoma Squamous Cell Carcinoma Testicular Germ Cell Tumor Tissues Tumor, Germ Cell X-Ray Photoelectron Spectroscopy

Comprehensive Cancer Genomics Analysis

Aligned RNA-seq bam files were analyzed using the ISB Cancer Genomics Cloud (https://isb-cgc.appspot.com/). These 33 cancer types included in this study are adrenocortical carcinoma [ACC], bladder urothelial carcinoma [BLCA], brain lower grade glioma [LGG], breast invasive carcinoma [BRCA], cervical squamous cell carcinoma and endocervical adenocarcinoma [CESC], cholangiocarcinoma [CHOL], colon adenocarcinoma [COAD], esophageal carcinoma [ESCA], glioblastoma multiforme [GBM], head and neck squamous cell carcinoma [HNSC], kidney chromophobe [KICH], kidney renal clear cell carcinoma [KIRC], kidney renal papillary cell carcinoma [KIRP], acute myeloid leukemia [LAML], liver hepatocellular carcinoma [LIHC], lung adenocarcinoma [LUAD], lung squamous cell carcinoma [LUSC], lymphoid neoplasm diffuse large B cell lymphoma [DLBC], mesothelioma [MESO], ovarian serous cystadenocarcinoma [OV], pancreatic adenocarcinoma [PAAD], pheochromocytoma and paraganglioma [PCPG], prostate adenocarcinoma [PRAD], rectum adenocarcinoma [READ], sarcoma [SARC], skin cutaneous melanoma [SKCM], stomach adenocarcinoma [STAD], testicular germ cell tumors [TGCT], thymoma [THYM], thyroid carcinoma [THCA], uterine carcinosarcoma [UCS], uterine corpus endometrial carcinoma [UCEC], and uveal melanoma [UVM]. The sample set consists of 10,337 total TCGA samples, 9,624 tumor samples, and 713 normal samples.
Level-3 gene expression (RSEM) and segment-based copy number data were downloaded from Broad GDAC firehose (https://gdac.broadinstitute.org) (version: 2016_01_28). Gene-based copy number data were obtained by intersecting with RefSeq gene annotation bed file (version: 2013-07-27). Mutation calls were provided by the Multi-Center Mutation Calling in Multiple Cancers (MC3) working group within TCGA (Key Resources Table; Ellrott et al., 2018 ).

Gao Q., Liang W.W., Foltz S.M., Mutharasu G., Jayasinghe R.G., Cao S., Liao W.W., Reynolds S.M., Wyczalkowski M.A., Yao L., Yu L., Sun S.Q., Chen K., Lazar A.J., Fields R.C., Wendl M.C., Van Tine B.A., Vij R., Chen F., Nykter M., Shmulevich I, & Ding L. (2018). Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell reports, 23(1), 227-238.e3.

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Publication 2018

Adenocarcinoma Adenocarcinoma of Lung Adrenocortical Carcinoma Brain Breast Carcinoma Carcinoma, Thyroid Carcinoma, Transitional Cell Carcinosarcoma Cells Cholangiocarcinoma Chromophobia Colon Adenocarcinomas Diffuse Large B-Cell Lymphoma Endocervix Endometrial Carcinoma Esophageal Cancer Familial Atypical Mole-Malignant Melanoma Syndrome Gene Annotation Gene Expression Genes Glioblastoma Multiforme Glioma Hepatocellular Carcinomas Hypernephroid Carcinomas Kidney Leukemia, Myelocytic, Acute Lung Lymph Malignant Neoplasms Mesothelioma Mutation Neck Neoplasms Ovary Pancreas Paraganglioma Pheochromocytoma Prostate Rectum Renal Cell Carcinoma RNA-Seq Sarcoma Serous Cystadenocarcinoma Squamous Cell Carcinoma Squamous Cell Carcinoma of the Head and Neck Stomach Testicular Germ Cell Tumor Thymoma Urinary Bladder Uterus Uveal melanoma

Most recents protocols related to «Endocervix»

Longitudinal Sexual Health Monitoring

Research nurses collected data at 3-monthly study visits using interviewer-administered questionnaires. Data were collected on sociodemographic characteristics, sexual behavior, contraceptive use, pregnancy, sexually transmitted infections (STIs), and substance use. Volunteers who became pregnant were linked to antenatal care services and followed up for a pregnancy outcome.
Laboratory tests included pregnancy tests for beta-human chorionic gonadotrophin (β-hcg) hormone performed on urine samples using QuickVue (Quidel Corporation, San Diego, CA, United States) and HIV tests following the national testing algorithm, which involves a screening test (Alere Medical Co. Ltd., Chuba, Japan), the StatPak rapid confirmatory kit (Chembo Diagnostics System Inc., Medford, NY, United States), and SD Bioline as a tiebreaker (Standard Diagnostics, Inc., South Korea). STIs (Neisseria gonorrhoeae and Chlamydia trachomatis) were tested on endocervical swabs using the GeneXpert platform (Cepheid AB, Rontgenvagen 5, Soina, Sweden).

Namukisa M., Kamacooko O., Lunkuse J.F., Ruzagira E., Price M.A, & Mayanja Y. (2023). Incidence of unintended pregnancy and associated factors among adolescent girls and young women at risk of HIV infection in Kampala, Uganda. Frontiers in Reproductive Health, 5, 1089104.

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Publication 2023

Care, Prenatal Chlamydia trachomatis Chorionic Gonadotropin, beta Subunit, Human Contraceptive Agents Diagnosis Endocervix Homo sapiens Hormones Interviewers Neisseria gonorrhoeae Nurses Pregnancy Pregnancy Tests Sexually Transmitted Diseases Substance Use Testing, AIDS Urine Voluntary Workers

Transcriptome Analysis of Cervical Cancer

RNAseq data in the transcripts per million (TPM) format from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and The Genotype-Tissue Expression (GTEx) (https://gtexportal.org/) were uniformly processed using the Toil process (20 (link)). Through extraction of the cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) data in TCGA and corresponding normal tissue data in GTEx, the 306 cervical tumor samples were classified as the malignant group and the three samples adjacent to cancer from TCGA together with the 10 normal cervix tissues from GTEx were classified as the non-malignant group. The RNAseq data in TPM format were log2 transformed for expression comparison between samples.

Yang X, & Zhu W. (2023). ERBB3 mediates the PI3K/AKT/mTOR pathway to alter the epithelial‑mesenchymal transition in cervical cancer and predict immunity filtration outcome. Experimental and Therapeutic Medicine, 25(4), 146.

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Publication 2023

Adenocarcinoma Cervix Uteri Endocervix Genome Genotype Malignant Neoplasms Neck Squamous Cell Carcinoma Tissues Uterine Cervical Neoplasm

TCGA pan-cancer gene expression profiling

Manifests containing fragments per kilobase per million (FPKM) normalized RNA-seq data from 34 TCGA cohorts – Acute Myeloid Leukemia - (TCGA-LAML), Adrenocortical carcinoma (TCGA-ACC), Bladder Urothelial Carcinoma (TCGA-BLCA), Glioblastoma multiforme and Brain Lower Grade Glioma and (TCGA-GBMLGG), Breast Invasive Carcinomas (TCGA-BRCA), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC), Cholangiocarcinoma (TCGA-CHOL), Chronic Myelogenous Leukemia (TCGA-LCML), Colon Adenocarcinoma (TCGA-COAD), Esophageal Carcinoma (TCGA-ESCA), Head and Neck Squamous Cell Carcinoma (TCGA-HNSC), pan-Kidney Cancer (TCGA-KIPAN), Liver Hepatocellular Carcinoma (TCGA-LIHC), Lung Adenocarcinoma (TCGA-LUAD), Lung Squamous Cell Carcinoma (TCGA-LUSC), Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (TCGA-DLBC), Mesothelioma (TCGA-MESO), Ovarian Serous Cystadenocarcinoma (TCGA-OV), Pancreatic Adenocarcinoma (TCGA-PAAD), Pheochromocytoma and Paraganglioma (TCGA-PCPG), Prostate Adenocarcinoma (TCGA-PRAD), Rectum Adenocarcinoma (TCGA-READ), Sarcoma (TCGA-SARC), Skin Cutaneous Melanoma (TCGA-SKCM), Stomach Adenocarcinoma (TCGA-STAD), Testicular Germ Cell Tumors (TCGA-TGCT), Thymoma (TCGA-TGCT), Thyroid Carcinoma (TCGA-THCA), Uterine Carcinosarcoma (TCGA-USC), Uterine Corpus Endometrial Carcinoma (TCGA-UCES), Uveal Melanoma (TCGA-UVM) were downloaded from the Broad Institute GDAC (TCGA data version 20150601). Patients were then sorted by increasing B7-H3 expression in each cohort.

Liu H.J., Du H., Khabibullin D., Zarei M., Wei K., Freeman G.J., Kwiatkowski D.J, & Henske E.P. (2023). mTORC1 upregulates B7-H3/CD276 to inhibit antitumor T cells and drive tumor immune evasion. Nature Communications, 14, 1214.

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Publication 2023

4-carboxyphenylglyoxal Adenocarcinoma Adenocarcinoma of Lung Adrenocortical Carcinoma Brain Breast Carcinoma Cancer of Kidney Carcinoma, Thyroid Carcinoma, Transitional Cell Carcinosarcoma Cholangiocarcinoma Chronic Obstructive Airway Disease Colon Adenocarcinomas Diffuse Large B-Cell Lymphoma Endocervix Endometrial Carcinoma Esophageal Cancer Familial Atypical Mole-Malignant Melanoma Syndrome Glioblastoma Multiforme Glioma Hepatocellular Carcinomas Leukemia, Myelocytic, Acute Leukemias, Chronic Granulocytic Lung Lymph Mesothelioma Neck Neoplasms Ovary Pancreas Paraganglioma Patients Pheochromocytoma Prostate Rectum RNA-Seq Sarcoma Serous Cystadenocarcinoma Squamous Cell Carcinoma Squamous Cell Carcinoma of the Head and Neck Stomach Testicular Germ Cell Tumor Thymoma Urinary Bladder Uterus Uveal melanoma X-Ray Photoelectron Spectroscopy

Colposcopy and Biopsy for Cervical Cytology

ASC-US with positive HR-HPV or negative HR-HPV and more than twice ASC-US cytology were referred for colposcopy for biopsy. Colposcopy and biopsy were performed by experienced gynecological specialists from TMUGH. The duration between cytological sampling and colposcopy did not exceed 60 days. During the colposcopy, all the visually abnormal areas were biopsied. Quadrants with normal colposcopic impressions were biopsied at the squamocolumnar junction (“random biopsy”). Endocervical curettage(ECC)is performed when the TZ is not visible or fully visible. All histological slides were reviewed by two gynecological pathologists who were blinded to the cytology results at TMUGH. Immunohistochemistry was used to adjudicate difficult or equivocal diagnoses.

Li B., Dong L., Wang C., Li J., Zhao X., Dong M., Li H., Ma X., Dong Y., Wu M., Yan Y., Fan A, & Xue F. (2023). Analysis of the related factors of atypical squamous cells of undetermined significance (ASC-US) in cervical cytology of post-menopausal women. Frontiers in Cellular and Infection Microbiology, 13, 1123260.

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Publication 2023

Atypical Squamous Cells of Undetermined Significance Biopsy Colposcopy Curettage Cytological Techniques Diagnosis Endocervix Immunohistochemistry Pathologists Specialists

Diagnosis of Cesarean Scar Pregnancy

The diagnosis of CSP was based on the following transvaginal ultrasound scan findings: (1) an empty uterine cavity; (2) a clearly visible empty endocervical canal without direct contact with the gestational sac; (3) the presence of a gestation sac, with or without a fetal pole, with or without fetal cardiac activity (depending on the gestational age), in the anterior part of the uterine isthmus on the prior cesarean section scar; and (4) the absence of or a defect in the myometrial tissue between the bladder and the gestational sac (15 (link), 16 (link)). All transvaginal ultrasound reports were written and reviewed by the same group of doctors experienced in diagnostic medical sonography, and the diagnosis of CSP was stratified into three types, as mentioned above (13 (link)).

Yang X., Zheng W., Wei X., Ma J., Yan J., Poon L.C, & Yang H. (2023). Management of cesarean scar pregnancy: Importance of gestational age at diagnosis and disease type—A single center’s 5 years of experience involving 223 cases. Frontiers in Surgery, 10, 1055245.

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Publication 2023

Care, Prenatal Cesarean Section Cicatrix Dental Caries Diagnosis Endocervix Fetal Heart Gestational Age Gestational Sac Myometrium Physicians Tissues Ultrasonography Urinary Bladder Uterus

Top products related to «Endocervix»

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The Xpert® CT/NG Vaginal/Endocervical Specimen Collection kits are laboratory equipment used to collect and preserve vaginal or endocervical samples for the detection of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) infections. The kits provide the necessary materials and instructions for proper specimen collection and handling.

Preservcyt solution by Hologic

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PreservCyt solution is a liquid-based cytology preservative used for the collection, transportation, and processing of cervical cell samples. It is designed to maintain the integrity and morphology of the collected cells, enabling effective cytological examination.

Fetal bovine serum (fbs) by Thermo Fisher Scientific

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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.

Thinprep by Hologic

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The ThinPrep system is a laboratory equipment product that prepares patient samples for cytological examination. It is designed to process and prepare cellular samples in a standardized manner to improve the quality and consistency of specimen slides for analysis.

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What is the purpose of the endocervix?

What are some common conditions that affect the endocervix?

How can PubCompare.ai assist with endocervical research?

PubCompare.ai is an AI-driven tool that can help optimize your endocervical research. The platform allows you to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. PubCompare.ai can help researchers identify the most effective protocols related to the endocervix for their specific research goals. The platform's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy in your endocervical studies.

Are there different types or variations of the endocervix?

While the endocervix is generally composed of columnar epithelial cells and mucus-producing glands, there can be some variatin in its appearance and characteristics. For example, the endocervix may appear more pink or red in color depending on the individual's hormonal status or stage of the menstrual cycle. Additionally, the amount and consistency of the mucus produced by the endocervix can vary throughout the cycle, playing a role in fertility and other reproductive processes.

More about "Endocervix"

The endocervix is the inner lining of the cervix, the lower, narrow portion of the uterus.
It is composed of columnar epithelial cells and contains mucus-producing glands.
The endocervix plays a crucial role in the female reproductive system, facilitating the passage of sperm and menstrual flow.
Research focusing on the endocervix is essential for understanding female physiology and developing effective diagnostic and therapeutic interventions for conditions affecting the cervix and uterus.
Endocervical specimens are commonly collected using Xpert® CT/NG Vaginal/Endocervical Specimen Collection kits, which help ensure accurate sample collection.
The collected samples are often preserved in PreservCyt solution, a liquid-based cytology medium, for further analysis.
Fetal bovine serum (FBS) and RPMI 1640 media are frequently used to culture endocervical cells, such as the End1/E6E7 cell line, for research purposes.
The ThinPrep system, which utilizes the PreservCyt solution, is a widely used method for preparing endocervical samples for cytological examination, including the detection of sexually transmitted infections like Chlamydia and Gonorrhea using the Xpert® TV assay.
Optimizing endocervical research can be achieved by utilizing AI-driven tools like PubCompare.ai.
This tool helps researchers locate the best protocols and products from literature, preprints, and patents, enhancing reproducibility and accuracy through seamless comparison features.
By discovering the most reliable and effective methods for endocervical research, scientists can advance our understanding of female reproductive health and develop more effective diagnostic and therapeutic interventions.