E levu na ka e rawa ni vakavuna na kenisa ni tauvi kenisa ni tauvi kenisa ni tauvi kenisa, wili kina na kena ivakatagedegede, yabaki ni bula kei na ituvaki ni nona bula, kei na kaukaua ni sela ni kenisa.
E dau vinaka na mate ni kenisa ni prostate, vakabibi ke kilai totolo.
Na kena rawa ni bula tiko e dua na tamata ni tauvi koya na kenisa ni tauvi koya tiko ga na prostate ena loma ni lima na yabaki e voleka ni 100%.
E bibi mo drau veivosakitaka kei na dua na vuniwai na veika ena yaco ena ituvaki ni nomu bula, ni duidui na ituvaki ni tamata yadua.
Yang X, Wang R, Xu K, Yang F, Chen Y, Zuo Z, Song B: [Knockdown of myeloid cell leukemia 1 gene inhibits the proliferation and promotes apoptosis of prostate cancer cells]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 2017, 33 (1): 62-66.
Kubota Y, Ito K, Imai K, Yamanaka H: Effectiveness of mass screening for the prognosis of prostate cancer patients in Japanese communities. Prostate. 2002, 50 (4): 262-9.
Li K, Zhou H, Wang F, Zhao C, Fan C, Wang J: ZBTB38 suppresses prostate cancer cell proliferation and migration via directly promoting DKK1 expression. Cell Death Dis. 2021, 12 (11): 998.
Sun HF, Wang WD, Feng L: Effect of SPAG9 on migration, invasion and prognosis of prostate cancer. Int J Clin Exp Pathol. 2017, 10 (9): 9468-9474.
Xia Q, Li J, Yang Z, Zhang D, Tian J, Gu B: Long non-coding RNA small nucleolar RNA host gene 7 expression level in prostate cancer tissues predicts the prognosis of patients with prostate cancer. Medicine (Baltimore). 2020, 99 (7): e18993.
Dong L, Ding H, Li Y, Xue D, Li Z, Liu Y, Zhang T, Zhou J, Wang P: TRIP13 is a predictor for poor prognosis and regulates cell proliferation, migration and invasion in prostate cancer. Int J Biol Macromol. 2019, 121 (): 200-206.
Rucci N, Angelucci A: Prostate cancer and bone: the elective affinities. Biomed Res Int. 2014, 2014 (): 167035.
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['Disclaimer: dodonu ni lavelavetaki']
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['Ke o vakabauta ni dua na ka e tabaki ena neimami website se na neimami veiqaravi e beci kina na nomu dodonu ni kena maroroi na itukutuku, o iko (se nomu mata) e rawa ni o vakauta mai e dua na notisi mo kerea me kau laivi se me tarovi na kena vakayagataki. ']
['Me vakau na itukutuku ena i-meli (raica na "Veitaratara" ena tabana e baleta na itikotiko ni imeli). ']
['Na DMCA e gadrevi kina na nomu itukutuku ni kena beitaki ni beci na dodonu ni lavelavetaki e oka kina na itukutuku oqo: (1) ivakamacala ni cakacaka ni taqomaki ni dodonu ni lavelavetaki e tukuni ni sa beci; (2) ivakamacala ni lewena e beitaki ni beci kei na itukutuku e veirauti me rawa kina ni da kunea na lewena; (3) itukutuku ni veitaratara me baleti iko, oka kina na nomu itikotiko, naba ni talevoni kei na itikotiko ni imeli; (4) e dua na itukutuku mai vei iko ni o vakabauta ena vakabauta vinaka ni lewena ena kena ivakarau e kudruvaka e sega ni vakadonui mai vua na itaukei ni dodonu ni lavelavetaki, se na kena mata, se ena cakacaka ni dua na lawa; ']
['(5) e dua na nomu ivakamacala, e sainitaki ena itotogi ni bubului lasu, ni dina na itukutuku e volai ena notisi, ni tu tale ga vei iko na dodonu mo valataka na dodonu ni kena taqomaki na ka e volai; ']
['E sega ni dodonu me dua na ka e caka ena kena vakayagataki na Internet.']
['Ke o sega ni vola na itukutuku kece qori, ena rawa ni berabera na kena qaravi na nomu kudru.']
['Veitaratara']
['Yalovinaka vakauta mai na imeli ena dua na taro se vakatutu.']
What is prognosis of prostate cancer?
The prognosis of prostate cancer depends on several factors, including the stage of the cancer, the patient's age and overall health, and the aggressiveness of the cancer cells.
In general, prostate cancer has a good prognosis, especially when caught early.
The 5-year survival rate for localized prostate cancer (cancer that has not spread beyond the prostate) is nearly 100%. However, the prognosis worsens as the cancer progresses and spreads to other parts of the body.
It is important to discuss the specific prognosis with a healthcare professional, as it can vary greatly from person to person.
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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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['Rauta na']
['Na BioMedLib e vakayagataka na kompiuta (machine-learning algorithms) me bulia na taro kei na kena isau.']
['Eda tekivu ena 35 na milioni na ivola vakavuniwai e tabaki ena PubMed/Medline, kei na tabana ni RefinedWeb.']
