What is pathophysiology of Lung cancer?

Teerera peji iri

Chii chinonzi pathophysiology yekenza yemapapu?

Pathophysiology yekenza yemapapu inoreva shanduko mumashandiro akajairika epanyama uye mashandiro anoitika mukukura uye kufambira mberi kwekenza yemapapu.

Kenza yemapapu chirwere chakaoma kunzwisisa chinokonzerwa nokukura kusingadzorwi uye kupatsanuka kwemasero asina kujairika mumapapu.

Masero aya anogona kuumba tumors uye kupararira kune dzimwe nzvimbo dzomuviri, zvichitungamirira kuzviratidzo zvakasiyana-siyana nezvinetso.

Pathophysiology yekenza yemapapu inosanganisira zvinhu zvinoverengeka, kusanganisira shanduko dzezvibereko, zvinhu zvezvakatipoteredza, uye mararamiro.

Shanduko dzedzinza dzinogona kuitika muDNA yemasero emapapu, zvichikonzera kukura kwemasero uye kupatsanuka kusingadzorwi.

Shanduko idzi dzinogona kupfuudzwa kana kuti kuwanikwa, uye dzinogona kukonzerwa nokubatwa nemakenza, akadai seutsi hwefodya, radon, asbestos, uye kusvibiswa kwemhepo.

Kenza yemapapu inogona kukamurwa kuva mhando mbiri huru: kenza yemapapu ine masero maduku (SCLC) uye kenza yemapapu isina masero maduku (NSCLC). NSCLC yakakamurwazve kuva mhando nhatu: adenocarcinoma, squamous cell carcinoma, uye large cell carcinoma.

Pathophysiology yemarudzi aya ekenza yemapapu inogona kusiyana, sezvo aine shanduko dzakasiyana dzegene uye achipindura zvakasiyana pakurapa.

Pathophysiology yekenza yemapapu inobatanidzawo kudyidzana pakati pemasero ekenza uye tishu dzakapoteredza, kusanganisira immune system.

Masero ekenza anogona kunzvenga immune system, achivabvumira kukura uye kupararira asingadziviswi.

Mukuwedzera, microenvironment yechirwere chinogona kukurudzira kukura kwechirwere uye metastasis nokupa nharaunda inotsigira masero ekenza.

Pathophysiology yekenza yemapapu inzira yakaoma kunzwisisa uye ine simba, uye vatsvakurudzi vari kuramba vachishanda kuti vanzwisise zviri nani nzira dziri pasi payo kuti vagadzire marapirwo anoshanda zvikuru uye kuvandudza migumisiro yevarwere.

Mashoko okufananidzira

PubMed/Medline https://www.nlm.nih.gov/databases/download/pubmed_medline.html

RefinedWeb https://arxiv.org/abs/2306.01116

Li Z, Qian Y, Li W, Liu L, Yu L, Liu X, Wu G, Wang Y, Luo W, Fang F, Liu Y, Song F, Cai Z, Chen W, Huang W: Human Lung Adenocarcinoma-Derived Organoid Models for Drug Screening. iScience. 2020, 23 (8): 101411.

Nurwidya F, Syahruddin E, Yunus F: Pain management in lung cancer. Adv Respir Med. 2016, 84 (6): 331-336.

Khan KA, Kennedy MP, Moore E, Crush L, Prendeville S, Maher MM, Burke L, Henry MT: Radiological characteristics, histological features and clinical outcomes of lung cancer patients with coexistent idiopathic pulmonary fibrosis. Lung. 2015, 193 (1): 71-7.

Paramanantham A, Asfiya R, Das S, McCully G, Srivastava A: Extracellular Vesicle (EVs) Associated Non-Coding RNAs in Lung Cancer and Therapeutics. Int J Mol Sci. 2022, 23 (21): .

Lee D, Kim Y, Chung C: Scientific Validation and Clinical Application of Lung Cancer Organoids. Cells. 2021, 10 (11): .

Mucchietto V, Crespi A, Fasoli F, Clementi F, Gotti C: Neuronal Acetylcholine Nicotinic Receptors as New Targets for Lung Cancer Treatment. Curr Pharm Des. 2016, 22 (14): 2160-9.

Kurambidzwa kwebasa: zvokurapa

Nzvimbo iyi yepaIndaneti inopiwa nokuda kwezvinangwa zvedzidzo uye zvemashoko chete uye haisi kupa mazano ezvokurapa kana kuti mabasa ehunyanzvi.

Mashoko anopiwa haafaniri kushandiswa kuongorora kana kuti kurapa chinetso cheutano kana kuti chirwere, uye avo vanotsvaka mazano ezvokurapa vanofanira kubvunza chiremba ane rezinesi.

Ndapota cherechedzai kuti neural net iyo inogadzira mhinduro dzemibvunzo, haina kururama zvikuru kana iri nyaya yenhamba. Somuenzaniso, nhamba yevanhu vanoonekwa vaine chirwere chakati.

Nguva dzose tsvaka zano rachiremba wako kana mumwe mupi wezvoutano akakodzera pamusoro pemamiriro ezvinhu ezvokurapa. Usambofa wakaregeredza zano rezvokurapa rehunyanzvi kana kunonoka kuritsvaka nemhaka yechinhu chipi nechipi chawakaverenga pawebsite ino. Kana uchifunga kuti unogona kuva nenjodzi yezvokurapa, fonera 911 kana kuenda kukamuri rokukurumidzira riri pedyo nokukurumidza. Hapana ukama hwechiremba nemurwere hunogadzirwa newebhusaiti ino kana kushandiswa kwayo. NeBioMedLib kana vashandi vayo, kana chero mupiro kune iyi webhusaiti, haitauri chero zvirevo, zvakataurwa kana kuti zvataurwa, maererano neruzivo rwunopihwa pano kana kushandiswa kwayo.

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What is pathophysiology of lung cancer?

The pathophysiology of lung cancer refers to the changes in the normal physiological processes and mechanisms that occur in the development and progression of lung cancer.

Lung cancer is a complex disease that arises from the uncontrolled growth and division of abnormal cells in the lungs.

These cells can form tumors and spread to other parts of the body, leading to various symptoms and complications.

The pathophysiology of lung cancer involves several factors, including genetic mutations, environmental factors, and lifestyle choices.

Genetic mutations can occur in the DNA of lung cells, leading to uncontrolled cell growth and division.

These mutations can be inherited or acquired, and they can be caused by exposure to carcinogens, such as tobacco smoke, radon, asbestos, and air pollution.

Lung cancer can be classified into two main types: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). NSCLC is further divided into three subtypes: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma.

The pathophysiology of these types of lung cancer can differ, as they have different genetic mutations and respond differently to treatment.

The pathophysiology of lung cancer also involves the interaction between cancer cells and the surrounding tissue, including the immune system.

Cancer cells can evade the immune system, allowing them to grow and spread unchecked.

Additionally, the tumor microenvironment can promote tumor growth and metastasis by providing a supportive environment for cancer cells.

The pathophysiology of lung cancer is a complex and dynamic process, and researchers are continuously working to better understand the underlying mechanisms to develop more effective treatments and improve patient outcomes.

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Please note the neural net that generates answers to the questions, is specially inaccurate when it comes to numeric content. For example, the number of people diagnosed with a specific disease.

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