Nĩ maũndũ marĩkũ mangĩtũma mũndũ agĩe na kansa ya mahũri?
Maũndũ marĩa mangĩtũma mũndũ agĩe na kansa ya mahũri nĩ:
1. Kũnyua thigara: Kũnyua thigara nĩkuo kũrehaga kansa ya mahũri.
Ũgwati wa kũnyua thigara nĩ wongererekaga o ũrĩa mũndũ aranyua thigara nyingĩ o mũthenya na o ũrĩa aranyua mĩaka mĩingĩ.
2. Kũhuhia ndogo ya thigara: Kũhuhia ndogo ya thigara no kuongerere ũgwati wa arĩa matanyuaga thigara kũgĩa na kansa ya mahũri.
3. Rĩera rĩa Radon: Kũhutio nĩ rĩera rĩa radon rĩingĩ mũno, rĩera rĩkoragwo rĩiyũrĩte mĩrimũ ya rĩera-inĩ, no kũingĩhĩrie ũgwati wa kũnyitwo nĩ kansa ya mahũri.
4. Asbestos na indo ingĩ ithũkagia mũndũ: Asbestos, arsenic, chromium, nickel, soot, tar, na indo ingĩ no itũme mũndũ agĩe na kansa ya mahũri.
5. Gũthũkio kwa rĩera: Kũhutio nĩ rĩera ihinda iraya, makĩria matũũra-inĩ, no kuongerere ũgwati wa mũndũ kũgĩa na kansa ya mahũri.
6. Andũ a famĩlĩ arĩa marĩ na mũrimũ ũcio: Angĩkorũo thĩinĩ wa famĩlĩ nĩ kũrĩ andũ marĩ na mũrimũ wa kansa ya mahũri, no ũkorũo ũrĩ ũgwati mũnene harĩ mũndũ ũcio.
7. Arĩa marĩ na mũrimũ wa mahũri: Arĩa marĩ na mũrimũ wa mahũri ta mũrimũ wa mahũri ũrĩa ũtangĩthira (COPD) kana TB no makorũo ũgwati-inĩ mũnene wa kũgĩa na kansa ya mahũri.
8. Ũkũrũ: Ũgwati wa kũnyitwo nĩ kansa ya mahũri wongererekaga o ũrĩa mũndũ arakũra, na kaingĩ arĩa marwaragwo nĩ mĩaka ĩyo nĩ arĩa makĩrĩte 65.
9. Mũthuri na mũtumia: Athuri nĩ makoragwo na ũhotekeku mũnene wa kũgĩa na kansa ya mahũri gũkĩra atumia.
10. Kũherithio na njĩra ya kũhũthĩra macini cia ũrugarĩ: Kũherithio na njĩra ya macini cia ũrugarĩ gĩthũri-inĩ nĩ ũndũ wa mĩthemba ĩngĩ ya kansa no kuongerere ũgwati wa kũgĩa na kansa ya mahũri.
11. Irio: Irio itarĩ na matunda na mboga nyingĩ no ciongerere ũgwati wa kũgĩa na kansa ya mahũri.
12. Kũnyua njohi: Kũnyua njohi mũno no kuongerere ũgwati wa kũnyitwo nĩ kansa ya mahũri.
13. Mũrimũ wa mwĩrĩ: Mũrimũ ũmwe wa mwĩrĩ no ũtũme mũndũ anyitwo nĩ kansa ya mahũri.
Nĩ ũndũ wa bata kũmenya atĩ mũndũ gũkorũo na ũndũ ũmwe kana maũndũ maingĩ marĩa monanagia atĩ mũndũ no agĩe na kansa ya mahũri ti kuuga atĩ no agĩe nayo, na andũ amwe arĩa magĩaga na kansa ya mahũri no makorũo matarĩ na maũndũ marĩa monanagia atĩ mũndũ no agĩe nayo.
O na kũrĩ ũguo, kũnyihanyihia kana gwĩthema maũndũ macio no kũgirĩrĩrie mũndũ kũgĩa na kansa ya mahũri.
Jin YJ, Tang W, Huang Y, Wang JW, Hou DH, Qi LL, Zhao SJ, Wu N: [Risk factors for lung cancer based on low-dose computed tomography screening]. Zhonghua Zhong Liu Za Zhi. 2020, 42 (3): 222-227.
Hosseini M, Naghan PA, Karimi S, SeyedAlinaghi S, Bahadori M, Khodadad K, Mohammadi F, Kaynama K, Keynama K, Masjedi MR: Environmental risk factors for lung cancer in Iran: a case-control study. Int J Epidemiol. 2009, 38 (4): 989-96.
Zhou X, Hu J, Zhang C, Zhan Y, Song Y, Fan W, Hu Z, Yang H, Yang Q, Wu D, Li F, Li D, Nie R: Clinical characteristics and risk factors for in-hospital mortality of lung cancer patients with COVID-19: A multicenter, retrospective, cohort study. Thorac Cancer. 2021, 12 (1): 57-65.
Liu X, Fan Y, Jiang Y, Xiang J, Wang J, Sun Z, Ren G, Yao S, Chang R, Zhao Y, Qiao Y, Zhou Q: [A cohort study on risk factors of lung cancer in Yunnan tin miners]. Zhongguo Fei Ai Za Zhi. 2013, 16 (4): 184-90.
Aoun J, Saleh N, Waked M, Salamé J, Salameh P: Lung cancer correlates in Lebanese adults: a pilot case--control study. J Epidemiol Glob Health. 2013, 3 (4): 235-44.
Chan-Yeung M, Koo LC, Ho JC, Tsang KW, Chau WS, Chiu SW, Ip MS, Lam WK: Risk factors associated with lung cancer in Hong Kong. Lung Cancer. 2003, 40 (2): 131-40.
['Ũkaana: thibitarĩ']
['Website ĩno ĩkoragwo ĩrĩ ya kũrutana na kũheana ũhoro tu na ti ya kũheana ũtaaro wa ũrigitani kana ũtungata wa kĩĩmwĩrĩ.']
['Ũhoro ũrĩa ũrĩ thĩinĩ wa broshua ĩyo ndwagĩrĩirũo kũhũthĩrũo gũthima kana kũrigita mũrimũ mũna, na arĩa marenda ũtaaro wa ũrigitani magĩrĩirũo gũcaria ũteithio wa ndagĩtarĩ.']
['No wone atĩ netiwaki ya neuron ĩrĩa ĩheanaga macokio ma ciũria icio, ndĩkoragwo na ũkinyanĩru mũno ũhoro-inĩ wĩgiĩ namba. Kwa ngerekano, mũigana wa andũ arĩa magwatĩtio mũrimũ mũna.']
['Hingo ciothe caria ũtaaro wa ndagĩtarĩ kana mũndũ ũngĩ wagĩrĩire ũgima-inĩ waku wa mwĩrĩ igũrũ rĩgiĩ mũrimũ. Ndũkaanahũthie ũtaaro wa ndagĩtarĩ kana ũcererũo kũũcaria nĩ ũndũ wa ũndũ ũthomete thĩinĩ wa website ĩno. Ũngĩkorũo ũrona ta wacemania na ũndũ mũhiũ, hũra thimũ 911 kana ũthiĩ thibitarĩ ya hakuhĩ na harĩa ũrĩ. Gũtirĩ ũrata wa ndagĩtarĩ na mũrwaru wonekaga nĩ ũndũ wa website ĩno kana kũhũthĩrũo kwayo. BioMedLib kana aruti ayo a wĩra, kana mũndũ ũngĩ wothe ũrutĩte wĩra thĩinĩ wa website ĩno, ndarĩ na ũira, wa ĩmwe kwa ĩmwe kana wa ĩmwe kwa ĩmwe, wĩgiĩ ũhoro ũrĩa ũheanĩtwo ho kana ũrĩa ũhũthĩrĩtwo.']
['Ũregani: wĩyathi wa kwandĩka']
['Watho wa Digital Millennium Copyright Act wa 1998, 17 U.S.C. § 512 (the DMCA) ũheanaga ũhoti wa ene a ihoto cia wandĩki arĩa metĩkĩtie atĩ ũhoro ũrĩa ũroneka intanetiinĩ nĩ ũtharĩtie ihooto ciao kũringana na watho wa U.S. wa ihoto cia wandĩki.']
['Ũngĩkorũo na wĩtĩkio atĩ ũhoro kana kĩndũ kĩna gĩtahingũrĩtwo ũhoro-inĩ wĩgiĩ website kana ũtungata witũ nĩ gĩgũthũkia ihooto ciaku, wee (kana mũndũ ũrĩa ũgũgũthondekera) no ũtũtũmĩre notithi ũkĩũria ũhoro kana kĩndũ kĩu kĩeherio, kana ũrigĩrĩrio ndũgacihũthĩre.']
["Marũa macio magĩrĩire gũtũmwo na njĩra ya kwandĩkwo na e-mail (rora ũhoro wa andirethi gĩcunjĩinĩ kĩa 'Maũndũ ma kwaranĩria')."]
['DMCA ĩbataraga atĩ notithi yaku ya kuuna ihooto cia wandĩki ĩkorwo na ũhoro ũyũ: (1) ũtaarĩria wa wĩra ũrĩa ũrĩ na ihooto cia wandĩki ũrĩa ũrarumwo; (2) ũtaarĩria wa ũhoro ũrĩa ũrarumwo na ũhoro mũiganu wa gũtũhotithia kũmenya kũrĩa ũhoro ũcio ũrĩ; (3) ũhoro waku wa kwaranĩria, hamwe na andirethi, namba ya thimũ na andirethi ya e-mail; (4) ndũmĩrĩri yaku atĩ wĩ na wĩtĩkio mwega atĩ ũhoro ũcio ũramenererio ndũrĩ na rũtha rwa mwene wa watho, kana mũrũgamĩrĩri, kana rwa watho o wothe; ']
['(5) nĩ mwandĩkanĩire, na nĩ mũkũheo mũkaana wa kũheenania, atĩ ũhoro ũrĩa ũrĩ kĩmenyithiainĩ kĩu nĩ wa ma na atĩ mũrĩ na ũhoti wa kũhingia ihooto iria mũreganĩte nacio;']
['na (6) kĩrore kĩa mwene kĩhoto kana kĩa mũndũ wĩtĩkĩritio gwĩtongoria handũ ha mwene kĩhoto.']
['Kwaga kwandĩka ũhoro ũcio wothe no gũtũme gũtangĩka gwaku kũhĩtũke.']
['Ũhoro wa Kwaranĩria']
['Tũma ndũmĩrĩri ya kũbucia kũgerera thimũ kana thimũ ya mohoro.']
What are the risk factors for lung cancer?
The risk factors for lung cancer include:
1. Smoking: Cigarette smoking is the leading cause of lung cancer.
The risk increases with the number of cigarettes smoked per day and the number of years a person has smoked.
2. Secondhand smoke: Exposure to secondhand smoke can increase the risk of lung cancer in non-smokers.
3. Radon gas: Exposure to high levels of radon gas, a naturally occurring radioactive gas, can increase the risk of lung cancer.
4. Asbestos and other carcinogens: Exposure to asbestos, arsenic, chromium, nickel, soot, tar, and other substances can cause lung cancer.
5. Air pollution: Long-term exposure to air pollution, particularly in urban areas, may slightly increase the risk of lung cancer.
6. Family history: A family history of lung cancer may increase a person's risk.
7. Personal history of lung disease: People with a history of lung diseases such as chronic obstructive pulmonary disease (COPD) or tuberculosis may have an increased risk of lung cancer.
8. Age: The risk of lung cancer increases with age, with most cases occurring in people over the age of 65.
9. Gender: Men are more likely to develop lung cancer than women.
10. Radiation therapy: Previous radiation therapy to the chest for other cancers can increase the risk of lung cancer.
11. Diet: A diet low in fruits and vegetables may increase the risk of lung cancer.
12. Alcohol consumption: Heavy alcohol consumption may increase the risk of lung cancer.
13. Genetics: Certain genetic mutations may increase the risk of lung cancer.
It is important to note that having one or more of these risk factors does not necessarily mean a person will develop lung cancer, and some people who develop lung cancer may not have any known risk factors.
However, reducing or avoiding exposure to these risk factors can help lower the chances of developing lung cancer.
Disclaimer: medical
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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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['Ũhoro wa']
['BioMedLib ĩhũthagĩra kombiuta cia gwĩthuurĩra (njĩra cia gũthoma macini) gũthondeka ciũria na macokio.']
['Twambĩrĩirie na mabuku milioni 35 megiĩ ũgima wa mwĩrĩ marĩa makoragwo thĩinĩ wa PubMed/Medline. Ningĩ, tũhũthagĩra ciarĩro cia RefinedWeb.']