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> Anatomy > Body Part > Larynx

Larynx

The larynx, also known as the voicebox, is a complex organ located at the top of the trachea.
It plays a crucial role in speech, swallowing, and respiration by regulating airflow and producing sound.
This hollow, tubular structure contains the vocal cords and is surrounded by cartilage, muscles, and ligaments that work together to control the movement and tension of the vocal cords.
The larynx is an important area of study for researchers investigating voice disorders, airway management, and related medical conditions.
PubCompare.ai's AI-driven platform can help optimize your larynx research protocols, ensuring reproducibility and accuracy by easily locating and comparing methods from literature, pre-prints, and patents to identify the best options for your studies.
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Most cited protocols related to «Larynx»

1
Fear-Potentiated Startle Protocol for Investigating Fear Learning and Extinction

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Publication 2010
Acoustics Attention Blinking Cedrus Electromyography Extinction, Psychological Fear Larynx Oculomotor Muscles Reflex, Startle Reinforcement, Psychological Response Elements Wounds and Injuries
2
Comprehensive Head and Neck Cancer Cell Panel
A panel of 122 human head and neck cell lines was assembled from a number of different researchers, institutions, and suppliers. This panel was chosen to represent each of the major HNSCC sites: oral cavity, oropharynx, hypopharynx, and larynx. Also chosen for study were anaplastic and papillary thyroid cancer, adenoid cystic carcinoma cell lines, and cell lines derived from lymph node metastases. In some cases isogenic cell line pairs were obtained, which included cells derived from both the primary tumor and lymph node metastases from the same patient. Also included were cell lines from cutaneous SCC, leukoplakia, immortalized primary keratinocytes, and normal epithelium.
Zhao M., Sano D., Pickering C.R., Jasser S.A., Henderson Y.C., Clayman G.L., Sturgis E.M., Ow T.J., Lotan R., Carey T.E., Sacks P.G., Grandis J.R., Sidransky D., Heldin N.E, & Myers J.N. (2011). Assembly And Initial Characterization Of A Panel Of 85 Genomically Validated Cell Lines From Diverse Head And Neck Tumor Sites. Clinical cancer research : an official journal of the American Association for Cancer Research, 17(23), 7248-7264.
Publication 2011
Adenoid Cystic Carcinoma Anaplasia Cell Lines Cells Epithelium Head Homo sapiens Hypopharynx Keratinocyte Larynx Leukoplakia Lymph Node Metastasis Neck Neoplasms Oral Cavity Oropharynxs Papillary Thyroid Carcinoma Patients Skin Squamous Cell Carcinoma of the Head and Neck
3
Optimal Neck Circumference Cutoff
In Beijing, 15 community health centers were selected by multistage random sampling approach. People with type 2 diabetes (aged 20–80 years) who had lived in the community over 5 years were recruited between August 2008 and July 2009. A total of 3,182 diabetic subjects with measurement of NC were available for analysis. People with severe disabilities, hepatic failure, renal failure, schizophrene, or goiter were excluded. Written informed consent was obtained from all participants.
Past medical history was determined with a standardized questionnaire. Blood pressure was measured twice after each subject had been seated for 10 min. The average was used for analysis. Waist circumference (WC) was measured at the level midway between the lower rib margin and the iliac crest. NC was measured with head erect and eyes facing forward, horizontally at the upper margin of the laryngeal prominence (Adam's apple). Fasting glucose and lipid profiles were determined using an autoanalyzer.
Overweight was defined as BMI ≥24 kg/m2, central obesity was defined as WC ≥85 cm for men and ≥80 cm for women (5 –6 ). MS was defined according to the Chinese Diabetes Society definition (7 ).
Receiver operating characteristic (ROC) curve analyses were performed using SPSS 11.5 software. The Youden index, defined as “sensitivity + specificity − 1,” was used to determine the optimal NC cutoff points.
Yang G.R., Yuan S.Y., Fu H.J., Wan G., Zhu L.X., Bu X.L., Zhang J.D., Du X.P., Li Y.L., Ji Y., Gu X.N, & Li Y. (2010). Neck Circumference Positively Related With Central Obesity, Overweight, and Metabolic Syndrome in Chinese Subjects With Type 2 Diabetes: Beijing Community Diabetes Study 4. Diabetes Care, 33(11), 2465-2467.
Publication 2010
Blood Pressure Chinese Costal Arch Diabetes Mellitus Diabetes Mellitus, Non-Insulin-Dependent Disabled Persons Eye Glucose Goiter Head Hepatic Insufficiency Iliac Crest Kidney Failure Larynx Lipids Schizophrenia Waist Circumference Woman
4
Cancer Burden Estimation: Integrating Diverse Data Sources
The GBD estimation process starts with cancer mortality. Data sources for cancer mortality include vital registration system (83% of data), cancer registry (14.4% of data), and verbal autopsy data (3% of data) (eTable 2 in the Supplement). Since cancer registries often exist in locations without cancer mortality data, cancer incidence data are used to model mortality by multiplying incidence with a separately modeled MIR. These mortality estimates are added to mortality data from the other sources and used in a cause of death ensemble model (CODEm).8 (link),13 (link) Each cancer type is estimated separately using covariates with a causal connection (eTable 8 in the Supplement). Final cancer-specific mortality estimates are divided by the MIR to estimate cancer incidence. Ten-year cancer prevalence is modeled using the MIR as a scalar to determine country-specific survival. Years lived with disability (YLDs) are estimated by dividing 10-year cancer prevalence into 4 sequelae: (1) diagnosis/treatment, (2) remission, (3) metastasic/disseminated, and (4) terminal phase. Each sequela prevalence is multiplied by a disability weight to estimate YLDs. For larynx, breast, colorectal, bladder, and prostate cancer, additional disability is estimated from procedures related to these cancers. Years of life lost (YLLs) are estimated by multiplying the estimated number of deaths by age with a standard life expectancy at that age.14 (link) Disability-adjusted life-years (DALYs) are calculated by summing YLDs and YLLs. As in GBD 2015, we estimate the contribution of population aging, population growth, and change in age-specific rates on the change in incident cases between 2006 and 2016.7 (link) We stratify results using Sociodemographic Index (SDI) quintiles. The SDI is a composite indicator including fertility, education, and income, and it has been shown to correlate well with health outcomes.7 (link)
Fitzmaurice C., Akinyemiju T.F., Al Lami F.H., Alam T., Alizadeh-Navaei R., Allen C., Alsharif U., Alvis-Guzman N., Amini E., Anderson B.O., Aremu O., Artaman A., Asgedom S.W., Assadi R., Atey T.M., Avila-Burgos L., Awasthi A., Ba Saleem H.O., Barac A., Bennett J.R., Bensenor I.M., Bhakta N., Brenner H., Cahuana-Hurtado L., Castañeda-Orjuela C.A., Catalá-López F., Choi J.Y., Christopher D.J., Chung S.C., Curado M.P., Dandona L., Dandona R., das Neves J., Dey S., Dharmaratne S.D., Doku D.T., Driscoll T.R., Dubey M., Ebrahimi H., Edessa D., El-Khatib Z., Endries A.Y., Fischer F., Force L.M., Foreman K.J., Gebrehiwot S.W., Gopalani S.V., Grosso G., Gupta R., Gyawali B., Hamadeh R.R., Hamidi S., Harvey J., Hassen H.Y., Hay R.J., Hay S.I., Heibati B., Hiluf M.K., Horita N., Hosgood H.D., Ilesanmi O.S., Innos K., Islami F., Jakovljevic M.B., Johnson S.C., Jonas J.B., Kasaeian A., Kassa T.D., Khader Y.S., Khan E.A., Khan G., Khang Y.H., Khosravi M.H., Khubchandani J., Kopec J.A., Kumar G.A., Kutz M., Lad D.P., Lafranconi A., Lan Q., Legesse Y., Leigh J., Linn S., Lunevicius R., Majeed A., Malekzadeh R., Malta D.C., Mantovani L.G., McMahon B.J., Meier T., Melaku Y.A., Melku M., Memiah P., Mendoza W., Meretoja T.J., Mezgebe H.B., Miller T.R., Mohammed S., Mokdad A.H., Moosazadeh M., Moraga P., Mousavi S.M., Nangia V., Nguyen C.T., Nong V.M., Ogbo F.A., Olagunju A.T., PA M., Park E.K., Patel T., Pereira D.M., Pishgar F., Postma M.J., Pourmalek F., Qorbani M., Rafay A., Rawaf S., Rawaf D.L., Roshandel G., Safiri S., Salimzadeh H., Sanabria J.R., Santric Milicevic M.M., Sartorius B., Satpathy M., Sepanlou S.G., Shackelford K.A., Shaikh M.A., Sharif-Alhoseini M., She J., Shin M.J., Shiue I., Shrime M.G., Sinke A.H., Sisay M., Sligar A., Sufiyan M.B., Sykes B.L., Tabarés-Seisdedos R., Tessema G.A., Topor-Madry R., Tran T.T., Tran B.X., Ukwaja K.N., Vlassov V.V., Vollset S.E., Weiderpass E., Williams H.C., Yimer N.B., Yonemoto N., Younis M.Z., Murray C.J, & Naghavi M. (2018). Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2016: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncology, 4(11).
Full text: Click here
Publication 2018
Autopsy Breast Diagnosis Dietary Supplements Disabled Persons Fertility Larynx Malignant Neoplasms Prostate Cancer sequels Urinary Bladder
5
Estimating Global Cancer Burden
The GBD cancer estimation process starts with mortality. Mortality estimates are made based on vital registration system (83% of data), cancer registry (16% of data) (eTable 3 in the Supplement), and verbal autopsy data (1% of data) using an ensemble model approach.9 (link),10 (link) Predictive covariates used in the model can be found in the eAppendix (eTable 8 in the Supplement). Single-cause mortality estimates are scaled into the separately estimated all-cause estimate.9 (link) To estimate cancer incidence, mortality estimates are divided by a separately estimated MIR for each cancer type, sex, 5-year age group, location, and year; additional information regarding incidence and MIR estimation can be found in the eAppendix and eFigure 2 in the Supplement. Data sources used for estimating MIRs are described in eTable 2 in the Supplement. MIRs allow for a uniform method to estimate incidence. Other cancer estimation frameworks11 (link),12 (link) have set a precedent for using MIRs for decades and have detailed its benefits, including greater representativeness, especially in settings that lack quality or complete population-based cancer registry systems. By determining incidence using mortality, we are able to account for uncaptured incident cases and, if mortality and incidence are determined correctly, estimating incidence based on MIRs should result in the similar results if using incidence directly. The correlation between survival data and the MIR is used to estimate 10-year cancer prevalence. Total prevalence is partitioned into 4 sequelae: (1) diagnosis/treatment, (2) remission, (3) metastatic/disseminated, and (4) terminal phase. Each sequela prevalence is multiplied by a disability weight to estimate YLDs. Lifetime prevalence of procedure-related disability is estimated for larynx, breast, colorectal, bladder, and prostate cancers. A standard life expectancy is used to estimate years of life lost (YLLs).9 (link) DALYs are the sum of YLDs and YLLs. To determine the contribution of population aging, population growth, and change in age-specific rates on the change in incident cases between 2007 and 2017, we use hypothetical demographic scenarios holding 2 of these 3 components constant. Results are stratified by quintiles of Socio-demographic Index (SDI), which is a composite indicator including fertility, education, and income.7 (link)
Fitzmaurice C., Abate D., Abbasi N., Abbastabar H., Abd-Allah F., Abdel-Rahman O., Abdelalim A., Abdoli A., Abdollahpour I., Abdulle A.S., Abebe N.D., Abraha H.N., Abu-Raddad L.J., Abualhasan A., Adedeji I.A., Advani S.M., Afarideh M., Afshari M., Aghaali M., Agius D., Agrawal S., Ahmadi A., Ahmadian E., Ahmadpour E., Ahmed M.B., Akbari M.E., Akinyemiju T., Al-Aly Z., AlAbdulKader A.M., Alahdab F., Alam T., Alamene G.M., Alemnew B.T., Alene K.A., Alinia C., Alipour V., Aljunid S.M., Bakeshei F.A., Almadi M.A., Almasi-Hashiani A., Alsharif U., Alsowaidi S., Alvis-Guzman N., Amini E., Amini S., Amoako Y.A., Anbari Z., Anber N.H., Andrei C.L., Anjomshoa M., Ansari F., Ansariadi A., Appiah S.C., Arab-Zozani M., Arabloo J., Arefi Z., Aremu O., Areri H.A., Artaman A., Asayesh H., Asfaw E.T., Ashagre A.F., Assadi R., Ataeinia B., Atalay H.T., Ataro Z., Atique S., Ausloos M., Avila-Burgos L., Avokpaho E.F., Awasthi A., Awoke N., Ayala Quintanilla B.P., Ayanore M.A., Ayele H.T., Babaee E., Bacha U., Badawi A., Bagherzadeh M., Bagli E., Balakrishnan S., Balouchi A., Bärnighausen T.W., Battista R.J., Behzadifar M., Behzadifar M., Bekele B.B., Belay Y.B., Belayneh Y.M., Berfield K.K., Berhane A., Bernabe E., Beuran M., Bhakta N., Bhattacharyya K., Biadgo B., Bijani A., Bin Sayeed M.S., Birungi C., Bisignano C., Bitew H., Bjørge T., Bleyer A., Bogale K.A., Bojia H.A., Borzì A.M., Bosetti C., Bou-Orm I.R., Brenner H., Brewer J.D., Briko A.N., Briko N.I., Bustamante-Teixeira M.T., Butt Z.A., Carreras G., Carrero J.J., Carvalho F., Castro C., Castro F., Catalá-López F., Cerin E., Chaiah Y., Chanie W.F., Chattu V.K., Chaturvedi P., Chauhan N.S., Chehrazi M., Chiang P.P., Chichiabellu T.Y., Chido-Amajuoyi O.G., Chimed-Ochir O., Choi J.Y., Christopher D.J., Chu D.T., Constantin M.M., Costa V.M., Crocetti E., Crowe C.S., Curado M.P., Dahlawi S.M., Damiani G., Darwish A.H., Daryani A., das Neves J., Demeke F.M., Demis A.B., Demissie B.W., Demoz G.T., Denova-Gutiérrez E., Derakhshani A., Deribe K.S., Desai R., Desalegn B.B., Desta M., Dey S., Dharmaratne S.D., Dhimal M., Diaz D., Dinberu M.T., Djalalinia S., Doku D.T., Drake T.M., Dubey M., Dubljanin E., Duken E.E., Ebrahimi H., Effiong A., Eftekhari A., El Sayed I., Zaki M.E., El-Jaafary S.I., El-Khatib Z., Elemineh D.A., Elkout H., Ellenbogen R.G., Elsharkawy A., Emamian M.H., Endalew D.A., Endries A.Y., Eshrati B., Fadhil I., Fallah V., Faramarzi M., Farhangi M.A., Farioli A., Farzadfar F., Fentahun N., Fernandes E., Feyissa G.T., Filip I., Fischer F., Fisher J.L., Force L.M., Foroutan M., Freitas M., Fukumoto T., Futran N.D., Gallus S., Gankpe F.G., Gayesa R.T., Gebrehiwot T.T., Gebremeskel G.G., Gedefaw G.A., Gelaw B.K., Geta B., Getachew S., Gezae K.E., Ghafourifard M., Ghajar A., Ghashghaee A., Gholamian A., Gill P.S., Ginindza T.T., Girmay A., Gizaw M., Gomez R.S., Gopalani S.V., Gorini G., Goulart B.N., Grada A., Ribeiro Guerra M., Guimaraes A.L., Gupta P.C., Gupta R., Hadkhale K., Haj-Mirzaian A., Haj-Mirzaian A., Hamadeh R.R., Hamidi S., Hanfore L.K., Haro J.M., Hasankhani M., Hasanzadeh A., Hassen H.Y., Hay R.J., Hay S.I., Henok A., Henry N.J., Herteliu C., Hidru H.D., Hoang C.L., Hole M.K., Hoogar P., Horita N., Hosgood H.D., Hosseini M., Hosseinzadeh M., Hostiuc M., Hostiuc S., Househ M., Hussen M.M., Ileanu B., Ilic M.D., Innos K., Irvani S.S., Iseh K.R., Islam S.M., Islami F., Jafari Balalami N., Jafarinia M., Jahangiry L., Jahani M.A., Jahanmehr N., Jakovljevic M., James S.L., Javanbakht M., Jayaraman S., Jee S.H., Jenabi E., Jha R.P., Jonas J.B., Jonnagaddala J., Joo T., Jungari S.B., Jürisson M., Kabir A., Kamangar F., Karch A., Karimi N., Karimian A., Kasaeian A., Kasahun G.G., Kassa B., Kassa T.D., Kassaw M.W., Kaul A., Keiyoro P.N., Kelbore A.G., Kerbo A.A., Khader Y.S., Khalilarjmandi M., Khan E.A., Khan G., Khang Y.H., Khatab K., Khater A., Khayamzadeh M., Khazaee-Pool M., Khazaei S., Khoja A.T., Khosravi M.H., Khubchandani J., Kianipour N., Kim D., Kim Y.J., Kisa A., Kisa S., Kissimova-Skarbek K., Komaki H., Koyanagi A., Krohn K.J., Bicer B.K., Kugbey N., Kumar V., Kuupiel D., La Vecchia C., Lad D.P., Lake E.A., Lakew A.M., Lal D.K., Lami F.H., Lan Q., Lasrado S., Lauriola P., Lazarus J.V., Leigh J., Leshargie C.T., Liao Y., Limenih M.A., Listl S., Lopez A.D., Lopukhov P.D., Lunevicius R., Madadin M., Magdeldin S., El Razek H.M., Majeed A., Maleki A., Malekzadeh R., Manafi A., Manafi N., Manamo W.A., Mansourian M., Mansournia M.A., Mantovani L.G., Maroufizadeh S., Martini S.M., Mashamba-Thompson T.P., Massenburg B.B., Maswabi M.T., Mathur M.R., McAlinden C., McKee M., Meheretu H.A., Mehrotra R., Mehta V., Meier T., Melaku Y.A., Meles G.G., Meles H.G., Melese A., Melku M., Memiah P.T., Mendoza W., Menezes R.G., Merat S., Meretoja T.J., Mestrovic T., Miazgowski B., Miazgowski T., Mihretie K.M., Miller T.R., Mills E.J., Mir S.M., Mirzaei H., Mirzaei H.R., Mishra R., Moazen B., Mohammad D.K., Mohammad K.A., Mohammad Y., Darwesh A.M., Mohammadbeigi A., Mohammadi H., Mohammadi M., Mohammadian M., Mohammadian-Hafshejani A., Mohammadoo-Khorasani M., Mohammadpourhodki R., Mohammed A.S., Mohammed J.A., Mohammed S., Mohebi F., Mokdad A.H., Monasta L., Moodley Y., Moosazadeh M., Moossavi M., Moradi G., Moradi-Joo M., Moradi-Lakeh M., Moradpour F., Morawska L., Morgado-da-Costa J., Morisaki N., Morrison S.D., Mosapour A., Mousavi S.M., Muche A.A., Muhammed O.S., Musa J., Nabhan A.R., Naderi M., Nagarajan A.J., Nagel G., Nahvijou A., Naik G., Najafi F., Naldi L., Nam H.S., Nasiri N., Nazari J., Negoi I., Neupane S., Newcomb P.A., Nggada H.A., Ngunjiri J.W., Nguyen C.T., Nikniaz L., Ningrum D.N., Nirayo Y.L., Nixon M.R., Nnaji C.A., Nojomi M., Nosratnejad S., Shiadeh M.N., Obsa M.S., Ofori-Asenso R., Ogbo F.A., Oh I.H., Olagunju A.T., Olagunju T.O., Oluwasanu M.M., Omonisi A.E., Onwujekwe O.E., Oommen A.M., Oren E., Ortega-Altamirano D.D., Ota E., Otstavnov S.S., Owolabi M.O., P A M., Padubidri J.R., Pakhale S., Pakpour A.H., Pana A., Park E.K., Parsian H., Pashaei T., Patel S., Patil S.T., Pennini A., Pereira D.M., Piccinelli C., Pillay J.D., Pirestani M., Pishgar F., Postma M.J., Pourjafar H., Pourmalek F., Pourshams A., Prakash S., Prasad N., Qorbani M., Rabiee M., Rabiee N., Radfar A., Rafiei A., Rahim F., Rahimi M., Rahman M.A., Rajati F., Rana S.M., Raoofi S., Rath G.K., Rawaf D.L., Rawaf S., Reiner R.C., Renzaho A.M., Rezaei N., Rezapour A., Ribeiro A.I., Ribeiro D., Ronfani L., Roro E.M., Roshandel G., Rostami A., Saad R.S., Sabbagh P., Sabour S., Saddik B., Safiri S., Sahebkar A., Salahshoor M.R., Salehi F., Salem H., Salem M.R., Salimzadeh H., Salomon J.A., Samy A.M., Sanabria J., Santric Milicevic M.M., Sartorius B., Sarveazad A., Sathian B., Satpathy M., Savic M., Sawhney M., Sayyah M., Schneider I.J., Schöttker B., Sekerija M., Sepanlou S.G., Sepehrimanesh M., Seyedmousavi S., Shaahmadi F., Shabaninejad H., Shahbaz M., Shaikh M.A., Shamshirian A., Shamsizadeh M., Sharafi H., Sharafi Z., Sharif M., Sharifi A., Sharifi H., Sharma R., Sheikh A., Shirkoohi R., Shukla S.R., Si S., Siabani S., Silva D.A., Silveira D.G., Singh A., Singh J.A., Sisay S., Sitas F., Sobngwi E., Soofi M., Soriano J.B., Stathopoulou V., Sufiyan M.B., Tabarés-Seisdedos R., Tabuchi T., Takahashi K., Tamtaji O.R., Tarawneh M.R., Tassew S.G., Taymoori P., Tehrani-Banihashemi A., Temsah M.H., Temsah O., Tesfay B.E., Tesfay F.H., Teshale M.Y., Tessema G.A., Thapa S., Tlaye K.G., Topor-Madry R., Tovani-Palone M.R., Traini E., Tran B.X., Tran K.B., Tsadik A.G., Ullah I., Uthman O.A., Vacante M., Vaezi M., Varona Pérez P., Veisani Y., Vidale S., Violante F.S., Vlassov V., Vollset S.E., Vos T., Vosoughi K., Vu G.T., Vujcic I.S., Wabinga H., Wachamo T.M., Wagnew F.S., Waheed Y., Weldegebreal F., Weldesamuel G.T., Wijeratne T., Wondafrash D.Z., Wonde T.E., Wondmieneh A.B., Workie H.M., Yadav R., Yadegar A., Yadollahpour A., Yaseri M., Yazdi-Feyzabadi V., Yeshaneh A., Yimam M.A., Yimer E.M., Yisma E., Yonemoto N., Younis M.Z., Yousefi B., Yousefifard M., Yu C., Zabeh E., Zadnik V., Moghadam T.Z., Zaidi Z., Zamani M., Zandian H., Zangeneh A., Zaki L., Zendehdel K., Zenebe Z.M., Zewale T.A., Ziapour A., Zodpey S, & Murray C.J. (2019). Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncology, 5(12), 1749-1768.
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Publication 2019
Age Groups Autopsy Breast Diagnosis Dietary Supplements Disabled Persons Fertility Larynx Malignant Neoplasms Prostate Cancer sequels Urinary Bladder

Most recents protocols related to «Larynx»

1
Evaluating EHEC O157:H7 Adherence to HEp-2 Cells
Not available on PMC !

Example 5

In order to compare levels of adherence to HEp-2 epithelial cells in culture, we used an established model for evaluating adherence of EHEC O157:H7 (27). HEp-2, human laryngeal carcinoma epithelial cells, were a kind gift from Dr. Carlton Gyles (Department of Pathobiology, University of Guelph). Briefly, HEp-2 cells grown in EMEM supplemented with 10% (v/v) FBS were plated onto 24-well tissue culture plates at 2×105 cells ml−1 and incubated for 24 h in the presence of 5% CO2. The cells were then maintained during the assay in serum and antibiotic-free EMEM. Before inoculation with bacteria, 10% (v/v) of L. acidophilus CFSM selected fractions were added in triplicate to treatment group wells. Wells containing the negative control groups were inoculated with 105 E. coli O157:H7 strain VS94 with or without supplementation with 100 μM propanolol (Sigma-Aldrich Canada Ltd.). Following inoculation of 105 EHEC O157 into treatment and control group wells, the plates were incubated for 3 h at 37° C. in the presence of 5% CO2. The cell monolayers were then washed three times with PBS to remove non-adhering bacteria and fresh medium was added. Cells were incubated for another 3 h and then washed six times with PBS. Washed cells were lysed with 0.1% Triton X-100. Released bacteria present in the suspension were collected and appropriate dilutions were plated on LB agar. To evaluate if the percentage of adherence in the treatment groups was significantly different from that of the control group, where the recovered counts from the control group (2.2×107 CFU ml−1) were considered to be 100%, the percentage of adherence in the negative control and treatment groups were calculated using the following equation.

% ⁢ ⁢ of ⁢ ⁢ Recovery ⁢ = Group _ 2.2 × 10 7 ⁢ ⁢ CFU ⁢ ⁢ ml - 1 ⁢ × 100 _

US11857581B2. Antiviral methods and compositions comprising probiotic bacterial molecules (2024-01-02). MicroSintesis Inc. [CA]. Inventors: Mansel Griffiths [CA].
Full text: Click here
Patent 2024
Agar Antibiotics Bacteria Bacterial Vaccines Biological Assay Carcinoma Cell Adhesion Cell Culture Techniques Cells Enterohemorrhagic Escherichia coli Epithelial Cells Escherichia coli O157 Homo sapiens Lactobacillus acidophilus Larynx Propranolol Serum Strains Technique, Dilution Tissues Triton X-100 Vaccination
2
Tracing Secondary Malignancies in HSCT
This study traced all the patients from the index date, date of transplantation for the HSCT group, and beginning of follow-up for controls until the commencement of secondary malignancies, dropout from the NHI program, or end of 2013. Secondary malignancies were identified by adopting the ICD-9-CM code of 135-154 for colorectal cancer, 155-157 for liver, gall bladder, and pancreatic cancer, 161-162 for laryngeal and lung cancer, 170 for bone cancer, 172 for skin melanoma, 173 for malignant neoplasm of the skin, 174-175 for female and male breast cancer, 176 for cutaneous Kaposi sarcoma, 179-180 for uterus and cervical uterine cancer, 182 for corpus uterine cancer, 185 for prostate cancer, 188-189 for bladder and kidney cancer, and 193 for thyroid cancer.
Liu H.L., Chen Y.H., Chung C.H., Wu G.J., Tsao C.H., Sun C.A., Chien W.C, & Hung C.T. (2023). Risk of Secondary Malignancies in Hematopoietic Stem Cell Transplantation Recipients: A Nationwide Population-Based Study in Taiwan. Balkan Medical Journal, 40(2), 131-138.
Publication 2023
Bone Cancer Cancer of Kidney Cancer of Skin Carcinoma, Male Breast Carcinoma, Thyroid Cervical Cancer Colorectal Carcinoma Familial Atypical Mole-Malignant Melanoma Syndrome Gallbladder Kaposi Sarcoma Larynx Liver Lung Cancer Neck Neoplasm Metastasis Pancreatic Cancer Patients Prostate Cancer Transplantation Urinary Bladder Uterine Cancer Uterus Woman
3
Nasal Airway Reconstruction and Adenoidectomy Simulation
In this study, a nasal airway of a 3-year-old patient who was diagnosed with adenoid hypertrophy (AH) was reconstructed from 269 CT images (DICOM format) with a resolution of 512 × 512 pixels and 0.5 mm intervals. The set of clinical images were taken at the end of inhalation processes and the patient was required to hold breath during scanning, which provides a clear vision of original nasal airway. The imaging data was collected by project members in Xi'an Jiaotong University, which was conducted with written informed consent from the parents and was approved by the Human Research Ethics Committee at the Second Affiliated Hospital of Xi'an Jiaotong University.To facilitate the computational simulation application, the set of medical imaging data was imported into 3D-slicer version 5.0.3 [open-source platform (available http://www.slicer.org)] for nasal geometry segmentation and registration purpose. Detailed reconstruction process has been reported in our previous work (32 (link)).
To better restore the flow patterns and particle transport behaviors which were jointly influenced by the external nares and surrounding environment, the realistic facial features around nares that enclosed by a spherical breathing zone has been attached to the nasal passage (33 (link)). The larynx region was preserved to obtain the fully developed outstream and better numerical convergence.
To facility visualized the influence of adenoidectomy on nasal flow patterns and aerosols transport, a virtual surgery was conducted on the adenoid region and removed the enlarged adenoid tissue artificially as shown in Figure 1. Except for the artificially restored adenoid region, the post-operative geometry keeps consistence with the pre-operative geometry.
Sun Q., Dong J., Zhang Y., Tian L, & Tu J. (2023). Numerical modelling of micron particle inhalation in a realistic nasal airway with pediatric adenoid hypertrophy: A virtual comparison between pre- and postoperative models. Frontiers in Pediatrics, 11, 1083699.
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Publication 2023
Adenoidectomy Adenoids Aerosols Ethics Committees, Research Face Homo sapiens Hypertrophy Larynx Nasal Cavity Nose Operative Surgical Procedures Parent Patients Reconstructive Surgical Procedures Tissues Vision
4
COVID-19 Intubation Outcomes Cohort Study
Recruitment for this prospective cohort study took place in the University Hospital in Krakow, Poland. Patients were offered the opportunity to participate in the study and asked for the signed consent form on admission to the hospital. During hospitalization, 56 of them qualified for intubation and were hospitalized in the temporary intensive care unit (ICU) for COVID-19 patients between 1st September and 31st January 2022.
The inclusion criteria were as follows:

SARS-CoV-2 infection confirmed by real-time reverse transcriptase-polymerase chain reaction (RT‒PCR) assay of nasal and pharyngeal swabs upon hospital admission

Signed consent to participate in the study

Patients admitted to the ICU

Intubation due to COVID-19-related pneumonia and acute respiratory distress syndrome (ARDS) within 36 h preceding study procedures

All medical procedures in the ICU were performed according to the local standards. To prevent leakage of fluids below the laryngeal/tracheal cuff, VBM medical manometers 21 [21 ] were used. The cuff pressure was measured by the nursing staff 3 times/day, with a normal range between 40 and 60 mmHg.
Demographic and clinical data were gathered from the hospital electronic medical records, including but not limited to age, sex, date of COVID-19 diagnosis, admission to the hospital and ICU, date of intubation, selected comorbidities, pre- and postintubation treatment, including systemic steroids and antibiotics, days of antibiotic treatment (DOT) before and after intubation (the number of Days a patient receives an antibiotic). Selected baseline and maximal laboratory results were also extracted.
Gregorczyk-Maga I., Pałka A., Fiema M., Kania M., Kujawska A., Maga P., Jachowicz-Matczak E., Romaniszyn D., Chmielarczyk A., Żółtowska B, & Wójkowska-Mach J. (2023). Impact of tooth brushing on oral bacteriota and health care-associated infections among ventilated COVID-19 patients: an intervention study. Antimicrobial Resistance and Infection Control, 12, 17.
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Publication 2023
Antibiotics Antibiotics, Antitubercular Biological Assay COVID 19 Diagnosis Hospitalization Intubation Larynx Manometry Nose Nursing Staff Patients Pharynx Pneumonia Pressure Real-Time Polymerase Chain Reaction Respiratory Distress Syndrome, Acute Reverse Transcriptase Polymerase Chain Reaction RNA-Directed DNA Polymerase Steroids Trachea
5
Hypofractionated vs. Conventional Radiotherapy Protocols
This longitudinal, prospective, observational study was conducted in the outpatient support clinic for patients undergoing cancer treatment at the Mario Penna Institute (Belo Horizonte, Minas Gerais, Brazil). All patients assisted between March to December 2021 were invited to participate in the study. This period covers the whole use of hypofractionated RT protocol in the institution.
Participants of both sexes, aged > 18 years with histopathological confirmation of squamous cell carcinoma in the oral cavity, larynx, pharynx, maxillary sinus, and salivary glands with an indication of conventional or hypofractionated RT were included in the study. Exclusion criteria were as follows: patients who continued to consume alcohol and tobacco during RT treatment, patients with an RT regimen different from the study protocols, patients who used a nasogastric tube before RT treatment, and patients who were clinically unable to open their mouths for intraoral evaluation.
A total of 109 patients were included in the study and stratified into two groups:

Conventional protocol group (GConv): Initially composed of 73 participants who underwent a conventional RT protocol consisting of the administration of 5 weekly fractions of 1.8 − 2.0 Gy, with a total of 66 − 70 Gy for 6 − 7 weeks.

Hypofractionated protocol group (GHipo): Initially composed of 36 patients who underwent an RT protocol consisting of the administration of 5 weekly fractions of 2.75 Gy, with a total of 55 Gy for 4 weeks.

For a short period of time, at the most critical moment of the COVID-19 pandemic in Brazil, the Institution implemented the Hypofractionated RT protocol following the recommendations of international guidelines or recent works[16 (link), 17 (link)]. Patients belonging to the GHIpo group were selected during this period. After the suspension of the indication of the hypofractionation radiotherapy protocol, a convenient sample was selected from patients submitted to the conventional RT protocol to create a control group (GConv).
Of the total number of participants included, 66 completed all stages of the study, totaling 23 participants for GHipo and 43 for GConv. The remaining participants were excluded due to death, interruption of treatment, refusal to continue in the study, or noncompliance with all stages proposed in the study. The sample calculation considered a prevalence of mucosistis of 61%[18 (link), 19 (link)], with 95% confidence. Therefore, the sample would have 92 subjects and considering the loss of up to 30%, the final sample of 66 patients is acceptable.
de Vasconcellos Ferreira P.M., Gomes M.D., Almeida A.C., Cornélio J.S., Arruda T.J., Mafra A., Nunes M.H., Salera R.B., Nogueira R.F., Sclauser J.M., Drummond-Lage A.P, & Rezende B.A. (2023). Evaluation of oral mucositis, candidiasis, and quality of life in patients with head and neck cancer treated with a hypofractionated or conventional radiotherapy protocol: a longitudinal, prospective, observational study. Head & Face Medicine, 19, 7.
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Publication 2023
COVID 19 Ethanol Gender Larynx Malignant Neoplasms Maxillary Sinus Oral Cavity Oral Examination Patients Pharynx Radiotherapy Salivary Glands Squamous Cell Carcinoma Tobacco Products Treatment Protocols

Top products related to «Larynx»

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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.
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Rpmi 1640 medium by Thermo Fisher Scientific
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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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Dulbecco modified eagle medium (dmem) by Thermo Fisher Scientific
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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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Hep 2 by American Type Culture Collection
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Penicillin by Thermo Fisher Scientific
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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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Streptomycin by Thermo Fisher Scientific
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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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Streptomycin by Merck Group
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Streptomycin is a laboratory product manufactured by Merck Group. It is an antibiotic used in research applications.
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Penicillin by Merck Group
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Penicillin is a type of antibacterial drug that is widely used in medical and laboratory settings. It is a naturally occurring substance produced by certain fungi, and it is effective against a variety of bacterial infections. Penicillin works by inhibiting the growth and reproduction of bacteria, making it a valuable tool for researchers and medical professionals.
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Fetal calf serum (fcs) by Thermo Fisher Scientific
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Fetal calf serum is a nutrient-rich cell culture supplement derived from the blood of bovine fetuses. It provides a complex mixture of proteins, growth factors, and other components that support the growth and proliferation of cells in in vitro cell culture systems.
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Penicillin streptomycin by Thermo Fisher Scientific
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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.

More about "Larynx"

The larynx, also known as the voice box or vocal apparatus, is a complex anatomical structure located at the top of the trachea.
This hollow, tubular organ plays a pivotal role in various physiological processes, including speech production, swallowing, and respiration.
The larynx is composed of a series of cartilages, muscles, and ligaments that work in harmony to control the movement and tension of the vocal cords.
These vocal folds, or vocal cords, are responsible for the generation of sound, which is then modulated by the resonating chambers of the pharynx and oral cavity to produce speech.
Researchers studying the larynx are often interested in investigating voice disorders, airway management, and related medical conditions.
These investigations may involve the use of various cell culture techniques and media, such as RPMI 1640 medium, DMEM, and fetal calf serum, as well as the use of antibiotics like penicillin and streptomycin to maintain cell culture integrity.
The Hep-2 cell line, a human laryngeal carcinoma cell line, is a commonly used model in laryngeal research, allowing for the study of cellular processes and responses within the laryngeal tissue.
PubCompare.ai's AI-driven platform can be a valuable tool in optimizing larynx research protocols, ensuring reproducibility and accuracy by enabling researchers to easily locate and compare methodologies from literature, pre-prints, and patents.
This can help identify the best approaches for their specific studies, enhancing the overall quality and impact of their larynx-related investigations.