Given a passage with a question and an answer to that question, classify if the answer actually answers the question into 'yes' or 'no'. Output 'yes' if the answer answers the question and output 'no' if the answer does not answer the question.

Example Input: Question: Is cerebral inflammation an underlying mechanism of early death in Alzheimer 's disease : a 13-year cause-specific multivariate mortality study?
Answer: This is the first study to link neuroinflammation independently to early death in AD and, hence, a rapidly progressing disease. Frontal and medial temporal atrophies and low cognition were also significant predictors. These are probably downstream biomarkers that reflect neuronal degeneration and late-stage disease. Our results suggest that inflammation, and not amyloid or tau pathology, is an independent underlying mechanism in the malignancy of AD.
Example Output: yes

Example Input: Question: Does pTEN loss in biopsy tissue predict poor clinical outcomes in prostate cancer?
Answer: PTEN loss at the time of biopsy seems to predict time to development of metastasis, prostate cancer-specific mortality and, for the first time, castration-resistant prostate cancer and response to androgen deprivation therapy after radical prostatectomy. If confirmed by larger studies, this would support the use of PTEN loss as an early marker of aggressive prostate cancer.
Example Output: yes

Example Input: Question: Is additional reverse transcription-polymerase chain reaction of peripheral slices superior to analysis of the central slice in sentinel lymph nodes from melanoma patients?
Answer: Molecular analysis of peripheral slices yielded six additional patients (10%) positive by RT-PCR who were initially negative by standard RT-PCR of the central slice. However, three of these six patients were found to express only MelanA but not tyrosinase. As MelanA expression was also found in 13% of control lymph nodes, positive MelanA expression alone in SLNs of melanoma patients requires cautious interpretation in order to avoid false-positive findings. Thus, additional molecular processing of peripheral slices did not significantly increase the number of patients with RT-PCR-positive SLNs.
Example Output:
no